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data/part_2/0009547581.md

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+# 2014 Nutrition country profile: Hungary
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/6ce02e5d-3b66-42b0-92d8-7b4a3f8d0a54/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2014  
+**Rights:** N/A  
+**GARDIAN ID:** 103fea2e1671877fbc7e7351e28dc8f2  
+**DataNODE ID:** 35623cb0e45ac4dff545e89599a923c6  
+**Siever ID:** 33c390df-6e4c-46b2-b59f-3a74923df3e7  
+**Token Count:** 992  
+**Page Count:** 0
+
+## Keywords
+
+birth weight, anaemia, nutrition policies, indicators, stunting, malnutrition, nutrition, trace elements, food supply, children, mortality, poverty, breastfeeding
+
+## Geography
+
+- **Regions:** Eastern Europe, Europe, World
+- **Countries:** Hungary
+
+## Content
+
+FINANCIAL RESOURCES AND POLICY, LEGISLATION, AND INSTITUTIONAL ARRANGEMENTS
+www.globalnutritionreport.org2014 Nutrition Country Profile
+ECONOMICS AND DEMOGRAPHY
+CHILD ANTHROPOMETRY
+ADOLESCENT AND ADULT NUTRITION STATUS
+Hungary
+WORLD HEALTH ASSEMBLY INDICATORS: PROGRESS AGAINST GLOBAL WHA TARGETS
+Under-5 stunting Under-5 wasting Under-5 overweight WRA anemia, 2011
+NA NA NA Currently off course
+Source: WHO 2014.
+Notes: Currently it is only possible to determine whether a country is on or off course for four of the six WHA targets. The year refers to the most recent data available; on/off-course calculation is based on trend data. 
+WRA = women of reproductive age. NA = not available.
+INCOME INEQUALITY
+Gini index, 2000 27
+Source: World Bank 2014.
+Note: 0 = perfect equality, 100 = perfect inequality.
+POPULATION
+Population (000) 9,976 2012
+Under-5 population (000) 491 2012
+Urban (%) 69 2010
+> 65 years (%) 17 2012
+Source: UNPD 2013.
+CHILD ANTHROPOMETRY
+Number of children under 5 affected (000)
+Stunting a NA NA
+Wasting a NA NA
+Overweight a NA NA
+Percentage of children under 5 affected
+Wasting a NA NA
+Severe wasting a NA NA
+Overweight a NA NA
+Low birth weight b 9 2001
+Sources: a UNICEF/WHO/WB 2014; b UNICEF 2014.
+Note: NA = not available.
+ADOLESCENT AND ADULT ANTHROPOMETRY (% POPULATION)
+Adolescent overweight a NA NA
+Adolescent obesity a NA NA
+Women of reproductive age, thinness b NA NA
+Women of reproductive age, short stature b NA NA
+Sources: a WHO 2014; b DHS 2014.
+Note: NA = not available.
+MICRONUTRIENT STATUS OF POPULATION
+Women of reproductive age with anemia a
+Total population affected (000) 560 2011
+Total population affected (%) 24 2011
+Vitamin A deficiency in preschool-age children (%) b 7 NA
+Population classification of iodine nutrition 
+(age group 6–12) c
+NA NA
+Sources: a Stevens et al. 2013; b WHO 2009; c WHO 2004.
+Note: NA = not available.
+CHANGES IN STUNTING PREVALENCE OVER TIME, BY WEALTH QUINTILE
+Data not available
+Source: DHS surveys 1990−2011 adapted from Bredenkamp et al. 2014.
+PREVALENCE OF 
+UNDER-5 STUNTING (%)
+Data not available
+Source: UNICEF/WHO/WB 2014.
+POVERTY RATES AND GDP
+1990 2000 2010 2013
+US$1.25/day (%) US$2/day (%) GDP per capita 
+PPP ($)
+0.2
+0.4
+17,018 17,737
+21,998
+22,146
+Source: World Bank 2014.
+Note: PPP = purchasing power parity.
+UNDER-5 MORTALITY RATE
+Deaths per 1,000 live births
+19
+11
+7 6
+2012201020001990
+Source: UN Inter-agency Group for Child Mortality Estimation 2013.
+METABOLIC RISK FACTORS FOR DIET-RELATED 
+NONCOMMUNICABLE DISEASES, 2008 (%)
+Raised blood pressure Raised blood glucose Raised blood cholesterol
+Both sexes Male Female
+54
+9
+41
+55
+11
+50
+55
+10
+46
+Source: WHO 2014.
+PREVALENCE OF ADULT OVERWEIGHT AND OBESITY, 2008 (%)
+Both sexes
+Male
+Female
+Obesity (BMI ≥ 30)Overweight (BMI ≥ 25)
+49
+23
+66
+26
+58
+25
+Source: WHO 2014.
+Note: BMI = body mass index.
+1
+INTERVENTION COVERAGE AND CHILD-FEEDING PRACTICES
+UNDERLYING DETERMINANTS
+FINANCIAL RESOURCES AND POLICY, LEGISLATION, AND INSTITUTIONAL ARRANGEMENTS
+ECONOMICS AND DEMOGRAPHY
+CHILD ANTHROPOMETRY
+ADOLESCENT AND ADULT NUTRITION STATUS
+2014 Nutrition Country Profile
+For complete source information: www.Globalnutritionreport.org/about/technical-notes. © 2014 International Food Policy Research Institute
+Hungary
+INTERVENTION COVERAGE  (%)
+Severe acute malnutrition, geographic coverage a NA NA
+Vitamin A supplementation, full coverage b NA NA
+Children under 5 with diarrhea receiving ORS b NA NA
+Immunization coverage, DTP3 b 99 2012
+Iodized salt consumption b NA NA
+Sources: a UNICEF/Coverage Monitoring Network/ACF International 2012; b UNICEF 2014.
+Notes: ORS = oral rehydration salts; DTP3 = 3 doses of combined diphtheria/tetanus/
+pertussis vaccine. NA = not available.
+INFANT AND YOUNG-CHILD FEEDING PRACTICES (% 6–23 MONTHS)
+Minimum acceptable diet NA NA
+Minimum dietary diversity NA NA
+Source: DHS.
+Note: NA = not available.
+GENDER-RELATED DETERMINANTS
+Early childbearing: births by age 18 (%) a NA NA
+Gender Inequality Index (score*) b 0.247 2013
+Gender Inequality Index (country rank) b 45 2013
+Sources: a UNICEF 2014; b UNDP 2014.
+Notes: *0 = low inequality, 1 = high inequality. NA = not available.
+POPULATION DENSITY OF HEALTH WORKERS 
+PER 1,000 PEOPLE
+Physicians 2.960 2011
+Nurses and midwives 6.388 2011
+Community health workers NA NA
+Source: WHO 2014.
+Note: NA = not available.
+POLICY AND LEGISLATIVE PROVISIONS
+National implementation of the International 
+Code of Marketing of Breast-milk Substitutes a
+Many provisions 
+law
+2014
+Extent of constitutional right to food b Medium-low 2003
+Maternity protection (Convention 183) c Yes 2011
+Wheat fortification legislation d No fortification NA
+Undernutrition mentioned in national develop-
+ment plans and economic growth strategies e
+NA NA
+Sources: a UNICEF 2014; b FAO 2003; c ILO 2013; d FFI 2014; e IDS 2014.
+Note: NA = not available.
+AVAILABILITY AND STAGE OF 
+IMPLEMENTATION OF GUIDELINES/
+PROTOCOLS/STANDARDS FOR THE 
+MANAGEMENT OF NCDs
+Diabetes
+Available, fully 
+implemented
+2010
+Hypertension
+Available, fully 
+implemented
+2010
+Source: WHO 2014.
+Note: NCDs = noncommunicable diseases.
+CONTINUUM OF CARE (%)
+99
+7
+Unmet need for family planning, 1992-1993b
+Continued breastfeeding at 1 yeara
+Initiation of breastfeeding within 1 hour after birtha
+Skilled attendant at birth, 2010a
+Antenatal care (4+ visits)a
+Sources: a UNICEF 2014; b UNPD 2014.
+RATE OF EXCLUSIVE BREASTFEEDING 
+ OF INFANTS UNDER 6 MONTHS (%)
+Data not available
+Source: UNICEF 2014.
+FOOD SUPPLY
+Undernourishment (%): 
+data for 1991, 2000, 
+2010, 2014
+Available calories 
+from nonstaples (%): 
+data for 1991, 2000, 
+2009
+Availability of fruits and 
+vegetables (grams): 
+data for 1990, 2000, 
+2010, 2011
+1991 2000 2010 2014
+69
+70
+71
+437
+496
+364
+437
+Source: FAOSTAT 2014.
+FEMALE SECONDARY 
+EDUCATION ENROLLMENT (%)
+88
+97 99 101
+2012201020001990
+Source: UNESCO Institute for Statistics 2014.
+GOVERNMENT EXPENDITURES (%)
+1990 2000 2010 2012
+Health
+Education
+Social protection
+Agriculture
+2.7
+10.4 10.45.4
+11.0 11.3
+35.9
+31.2 35.9
+7.0
+3.3
+0.9
+Source: IFPRI 2014.
+SCALING UP NUTRITION (SUN) COUNTRY 
+INSTITUTIONAL TRANSFORMATIONS, 2014 (%)
+Data not available
+Source: SUN 2014.
+IMPROVED DRINKING WATER COVERAGE (%)
+87 92
+9 74 1
+1990 2000 2012
+Piped on premises
+Other improved
+Unimproved
+Surface water
+Source: WHO/UNICEF JMP 2014.
+IMPROVED SANITATION COVERAGE (%)
+100 100 100
+1990 2000 2012
+Improved facilities
+Shared facilities
+Unimproved facilities
+Open defecation
+Source: WHO/UNICEF JMP 2014.
+2
+

data/part_2/0022585250.md

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+# World food trends and future food security: meeting tomorrow's food needs without exploiting the environment
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/7aa6c365-4b6b-46c7-b90d-bca23dbc2966/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 1994  
+**Rights:** N/A  
+**GARDIAN ID:** e5159d21fc134511be4bbeb3fbb25045  
+**DataNODE ID:** f4ee00a054875ed4a6cf16b45f795cc8  
+**Siever ID:** 391df0e3-93ed-4063-9d3e-fc9d8e30af5c  
+**Token Count:** 1459  
+**Page Count:** 0
+
+## Keywords
+
+food supply, forecasting, food security, world, food, trends, needs, environment, production, population, prediction, hunger
+
+## Description
+
+In World Food Trends and Future Food Security, an IFPRI Food Policy Statement, Director General Per Pinstrup-Andersen looks at recent world food trends and asks if the positive production trends of the past 30 years are likely to continue. Or, as 100 million new people are added to the world's population each year, will Malthus' prediction of increasing food scarcity come true? Will food scarcity, hunger, and disease related to malnutrition become even more widespread in the next 20-30 years?
+
+## Content
+
+INTERNATIONAL 
+FOOD 
+POLICY 
+RESEARCH 
+IN&ITUTE 
+NUMBER 18 MARCH 1994 
+WORLD FOOD TRENDS AND FUTURE FOOD SECURITY 
+MEETING TOMORROW'S FOOD NEEDS 
+WITHOUT EXPLOITING THE ENVIRONMENT Per Pinstrup-Andersen 
+Malthus never fully anticipated the mlr­
+ades of technological innovation, which, 
+despite population doubling and re­
+doubling, have so far kept at bay the 
+threat of food supplies falling below the 
+level where life can be sustained. In­
+stead, the 1980s saw a near balance in 
+growth of population and per capita 
+food production in many regions: in 
+fact, for the world as a whole, per cap­
+ita food production increased by 5 per­
+cent. Yields of major cereals have more 
+than doubled in the past few decades. 
+Today more than 700 million 
+people in developing 
+countries do not have access 
+to sufficient food to lead 
+healthy, productive lives. 
+As a consequence, marveling at 
+the miracle of the "green revolution" 
+has given way to a complacent assur­
+ance that technological innovation will 
+always be able to conjure up adequate 
+supplies to feed a growing population. 
+Many countries and institutions have 
+even begun to cut back their invest­
+ment in the very agricultural research 
+that has made it possible to feed the 
+world in the past. 
+In World Food Trends and Future 
+Food Security, an IFPRI Food Policy 
+Report, Director General Per Pinstrup­
+Andersen looks at recent world food 
+trends and asks if the positive produc­
+tion trends of the past 30 years are 
+likely to continue. Or, as 1 00 million 
+new people are added io the world's 
+population each year, will Malthus' pre­
+diction of increasing food scarcity 
+come true? Will food scarcity, hunger, 
+and disease related to malnutrition be­
+come even more widespread in the 
+next 20-30 years? 
+Overall positive trends disguise 
+wide disparities in production and dis­
+tribution of food among regions. Today 
+more than 700 million people in devel­
+oping countries do not have access to 
+sufficient food to lead healthy, produc­
+tive lives. More than 180 million chil­
+dren are underweight. As many as 
+500,000 preschool children go blind 
+each year as a result of vitamin A defi­
+ciency. Lack of micronutrients such as 
+vitamin A and iron not only causes suf­
+fering and death but also cuts deeply 
+into productivity. Through research 
+and policy, diets could be changed to 
+eliminate much of this suffering. 
+Although enough food is now be­
+ing produced to feed everyone if it 
+were evenly distributed, access to ade­
+quate food is largely governed by in­
+come. Of the 1.1 billion poor people in 
+developing countries in 1990, 50 per­
+cent were in South Asia, 19 percent in 
+Sub-Saharan Africa, 15 percent in East 
+Asia, and 1 0 percent in Latin America. 
+In South Asia and Africa, 50 percent of 
+the regions' populations live in poverty. 
+While significant reductions are ex­
+pected in both South and East Asia, 
+the poor in Africa are expected to in­
+crease by 40 percent by the year 2000. 
+YIELD GAINS ARE KEY 
+Although food production increases of 
+30 percent in the 1 980s seem impres­
+sive, they are less so in the light of 
+population growth. On a per capita ba­
+sis, 75 developing countries produced 
+less food per person at the end of the 
+1980s than at the beginning. Except in 
+Africa, 80 percent of the production 
+gains came from increased yields in 
+major cereal crops. The area cultivated 
+has actually begun to decline in some 
+regions. From now on, however, even 
+Africa, which has always relied on cul­
+tivation of new land for produ9tion in­
+creases, will have to count on yield 
+gains or pay high financial and ecologi­
+cal costs for expansion into areas not 
+yet cultivated. 
+On a per capita basis, 75 
+developing countries 
+produced less food per 
+person at the end of the 
+1980s than at the beginning. 
+Yield trends have climbed steadily 
+upward in all major cereals since the 
+1960s, but some experts detect a ta­
+pering off. In China, for example, rice 
+yield growth rates have slowed from 
+more than 4 percent a year in the late 
+1970s to about 1.6 percent a year dur­
+ing the 1980s. Stagnation between 
+1980 and 1993 in per capita grain pro­
+duction in developing countries is 
+causing concern because factors in 
+1200 SEVENTEENTH STREET, N.W. • WASHINGTON, D.C. 20036-3097 • U.S.A. 1-202/862-5600 • FAX 1-202/467-4439 • E-MAIL IFPRI@CGNET.COM 
+addition to population growth are push­
+ing up demand. Expected growth in 
+world feedgrain demand is more than 
+twice the expected population growth. 
+Failure to invest in agricultural 
+research today will show up in 
+production shortfalls 1 0-20 
+years from now. 
+For more than 50 years food sup­
+plies have been sufficient to assure 
+that international food prices increased 
+less than other prices. Recent projec­
+tions indicate that real food prices are 
+unlikely to increase significantly during 
+the remainder of the 1990s. Low food 
+prices in the world market do not nec­
+essarily mean that more people will be 
+fed, however. Poor people cannot ex­
+press their demand for food because 
+they cannot afford to buy it. More than 
+1 billion people live in households that 
+earn less than a dollar a day per per­
+son. Clearly, they are not in a position 
+to convert their food needs to effective 
+market demand. Since price is a prod­
+uct of both food supplies and economic 
+demand, low prices indicate the per­
+sistence of poverty and a lack of suffi­
+cient purchasing power as well as in­
+creasing food production. 
+If a sustainable balance between 
+world food production and food needs 
+(as opposed to food demand) is to be 
+achieved in the coming years, four 
+conditions must be met: (1) Economic 
+growth must resume in the developing 
+world, especially in Sub-Saharan Africa; 
+(2) effective policies to reduce popula-
+tion growth and to slow rural-to-urban 
+migration must be adopted; (3) re­
+sources must be committed to devel­
+opment of rural infrastructure, to con­
+tinuation of international and national 
+agricultural research, and to provision 
+of credit and technical assistance to 
+give farmers access to modern inputs; 
+and (4) measures must be .developed 
+to manage natural resources and to 
+prevent environmental degradation. 
+NO TIME FOR 
+COMPLACENCY 
+At this point, international real food 
+prices are low, food surpluses exist in 
+developed countries, and there is rea­
+son to believe that former Soviet Union 
+countries will increase their food pro­
+duction in the decade to come. Yields 
+of wheat, rice, and maize are still in­
+creasing in Asia and parts of Latin 
+America although at a lower rate than 
+before. All of these positive signs have 
+caused developed countries to reduce 
+their support for developing-country 
+agriculture, including investment in re­
+search and technology. 
+. . . large areas of land are 
+being degraded and 
+deforested, largely due to 
+poverty, population growth, 
+and limited access to 
+appropriate technology. 
+Although the overall picture is 
+bright, about 700 million people are 
+food insecure today, and tomorrow 
+does not look so promising. Population 
+in Sub-Saharan Africa is expected to 
+grow at 3 percent a year and food 
+production at less than 2 percent. If 
+current trends in population growth 
+and food production continue, by the 
+year 2020, the World Bank estimates 
+that Africa alone will have a food short­
+age of 250 million tons. And poverty 
+and the numbers of underfed children 
+will grow accordingly. Though less se­
+vere, shortages are also likely in South 
+Asia. At the same time, large areas of 
+land are being degraded and defor­
+ested, largely due to poverty, popula­
+tion growth, and limited access to ap­
+propriate technology. 
+Now is not the time for compla­
+cency. Malthus' predictions have failed 
+to materialize so far because science 
+has been used to expand food produc­
+tion. Failure to invest in agricultural re­
+search today will show up in production 
+shortfalls 1 0-20 years from now. If en­
+vironmental degradation continues un­
+checked, shortfalls could occur much 
+sooner. But even if food supplies con­
+tinue to be adequate to meet global 
+demand at low prices, complacency is 
+not in order. Unless more food is pro­
+duced by the poor in the developing 
+countries where large increases in 
+population and poverty are expected, 
+food insecurity and its toll in human 
+misery will continue to increase. To 
+avoid future food crises, adequate in­
+vestments in the components of agri­
+cultural development such as rural in­
+frastructure, research, and technology 
+must be made today. 
+Please send me the Food Policy Report, World Food Trends and Future Food Security, by Per Pinstrup-Andersen. 
+Organization--------------------------------------
+Add~ss ________________________________________________________________________ ___ 
+Publications are sent free of charge by surface airlift; allow three to four weeks for delivery. If you wish to receive a copy outside the United States within 
+two weeks, please enclose a check for US$3.00 to cover airmail/first class postage. 
+

data/part_2/0038929716.md

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+# Agricultural trade and trade integration in the East African community
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/38958584-cc47-46eb-b05c-4531bc83636e/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2023  
+**Rights:** CC-BY  
+**GARDIAN ID:** 0d5f910133e7fa1f80643e5ac33fe8ea  
+**DataNODE ID:** 947107654e8c8e79d7d4f079d2eda662  
+**Siever ID:** b7af948a-2f2c-4f63-bd56-096cae81a494  
+**Token Count:** 311  
+**Page Count:** 0
+
+## Keywords
+
+income, economic aspects, production, policies, investment, employment, regional planning, agriculture, trade, developing countries, trade agreements, democratic republic of the congo
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World, Northern Africa, Southern Africa, Western Africa
+- **Countries:** Uganda, Tanzania, Sudan, Rwanda, Kenya, Burundi
+
+## Description
+
+The East African Community (EAC) is a regional intergovernmental organization of seven partner states, comprising Burundi, Democratic Republic of the Congo (DRC), Kenya, Rwanda, South Sudan, Tanzania, and Uganda, with its headquarters in Arusha, Tanzania. Like other regional trade agreements (RTAs), the EAC pursues economic and political objectives through regional integration. As Eken (1979) explains, there are two main justifications for regional integration, especially for developing countries. First, for economic reasons, regional integration may provide an important instrument of economic growth. Removing barriers to the free movement of goods, labor, and capital between countries leads to the expansion of trade, and therefore of incomes and employment. Large economic entities with their larger markets (people and space) should permit economies of scale in production, leading to an efficient allocation of resources (capital and labor) and attracting substantial foreign direct investment. Second, for political purposes, establishing regional economic communities (RECs) strengthens collective self-reliance and is therefore expected to reinforce the political independence of groups of countries and enlarge their economic and political role in international relations, a point especially important for developing countries. This chapter addresses five main issues concerning the EAC. It first presents the EAC’s origin and main achievements, and then highlights the EAC’s agricultural trade performance relative to other RECs in Africa by comparing agricultural trade indicators, assessing the composition of trade, and identifying the main destinations/origins of agricultural exports/imports across RECs. This section also compares the level of trade integration in the EAC to that of other African RECs to determine its main agricultural comparative advantages. The same analysis is then repeated at the country level. The following section assesses the magnitude of formal (registered) and informal cross-border agricultural trade within the EAC, discusses the factors of trade integration, and highlights the role of tariff and nontariff measures (NTMs), logistic performance, and exchange rates. The final section offers conclusions.
+
+## Content
+
+The East African Community (EAC) is a regional intergovernmental organization of seven partner states, comprising Burundi, Democratic Republic of the Congo (DRC), Kenya, Rwanda, South Sudan, Tanzania, and Uganda, with its headquarters in Arusha, Tanzania. Like other regional trade agreements (RTAs), the EAC pursues economic and political objectives through regional integration. As Eken (1979) explains, there are two main justifications for regional integration, especially for developing countries. First, for economic reasons, regional integration may provide an important instrument of economic growth. Removing barriers to the free movement of goods, labor, and capital between countries leads to the expansion of trade, and therefore of incomes and employment. Large economic entities with their larger markets (people and space) should permit economies of scale in production, leading to an efficient allocation of resources (capital and labor) and attracting substantial foreign direct investment. Second, for political purposes, establishing regional economic communities (RECs) strengthens collective self-reliance and is therefore expected to reinforce the political independence of groups of countries and enlarge their economic and political role in international relations, a point especially important for developing countries. This chapter addresses five main issues concerning the EAC. It first presents the EAC’s origin and main achievements, and then highlights the EAC’s agricultural trade performance relative to other RECs in Africa by comparing agricultural trade indicators, assessing the composition of trade, and identifying the main destinations/origins of agricultural exports/imports across RECs. This section also compares the level of trade integration in the EAC to that of other African RECs to determine its main agricultural comparative advantages. The same analysis is then repeated at the country level. The following section assesses the magnitude of formal (registered) and informal cross-border agricultural trade within the EAC, discusses the factors of trade integration, and highlights the role of tariff and nontariff measures (NTMs), logistic performance, and exchange rates. The final section offers conclusions.

data/part_2/0046272056.md

@@ -0,0 +1,1346 @@
+# Guatemala, Strengthening and Evaluation of the Hogares Comunitarios Program in Guatemala City, 1999
+
+**Source:** gardian_index  
+**URL:** https://dataverse.harvard.edu/api/access/datafile/:persistentId/?persistentId=doi:10.7910/DVN/GWWWEU/27A8VZ  
+**Language:** English  
+**Resource Type:** Dataset / Tabular  
+**Release Year:** 2001  
+**Rights:** CC-BY-NC  
+**GARDIAN ID:** dae842606311f84f5dfa16cf9138d025  
+**DataNODE ID:** 034fd15f1f68794cce1bbac6a75508c0  
+**Siever ID:** dd251863-c15f-420e-90b8-e1b9aa1f3244  
+**Token Count:** 2589  
+**Page Count:** 0
+
+## Keywords
+
+impact assessment, anthropometry, gender, women, guatemala, central america, social capital, child care, household income, development policies, evaluation, assessment
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World, Central America, Latin America and the Caribbean, Americas, Northern America
+- **Countries:** United States of America, Guatemala, Bangladesh
+
+## Description
+
+<br>This survey was designed to provide a qualitative and quantitative assessment of the operations and impact of the Hogares Comunitarios program, a day care program under the auspices of the office of the First Lady of Guatemala. Two surveys were carried out: a random sample of 1340 households in one municipio of Guatemala City; and an impact evaluation sample of 550 households divided into participating and control households. Topics in the random sample survey include mother's income and employment status and history; assets; social capital; literacy and schooling; children under seven and mother's anthropometry; household composition, child care arrangements; family history; and hygiene spot check. Topics in the impact evaluation sample survey include household income; employment; household value of consumed goods; assets; social capital; literacy and schooling; morbidity of children 2-5 years old; anthropometry of 2-5 year olds and mother; household composition; child care arrangements; hamily history; hygiene spot check; house construction material; availability of water, sanitation, garbage-removal services; and child's diet.
+</br>
+ <br>This dataset, along with the KwaZulu-Natal Income Dynamics Survey dataset and the Bangladesh Commercial Vegetable and Fish Polyculture Production dataset, were collected with the objective of examining to what extent intrahousehold allocation processes would affect the outcome of development policies, with particular emphasis on gender as a determinant of intrahousehold allocation. Because the data were designed to make some analyses comparable across countries, several modules are si
+milar for all three datasets. The common modules include: information on assets at marriage of husband and wife, family background information, individual education and anthropometric data. Other modules are different because each country study has a different emphasis. See Table 1 (PDF 65K) for a comparison of these datasets.
+</br>
+
+## Content
+
+Formulario 10a.  Preparaciones en el hogar comunitario ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ ___ 1 
+INCAP/IFPRI/Programa de Hogares Comunitarios 1999 Hogar Niño Niño Niño Niño Niño B/C 
+  
+Página __ __ de __ __ 
+        Nombre de la cuidadora _________________________________________________________  Nombre de la madre _________________________________________________________ 
+ 
+ 
+Fecha de la observación (d-m-a)    
+ 
+            /             /             
+ 
+Identificación de la Encuestadora 
+ 
+                  
+ 
+No. 
+ 
+5. No. de 
+orden de la 
+prep. 
+ 
+6. Código de la 
+preparación 
+ 
+Nombre de la 
+Preparación 
+ 
+ 
+7. Peso del 
+recipiente  
+ 
+8. Peso prep. 
+lista para comer 
+c/recip. 
+ 
+9. Código de Ingrediente 
+        Ingrediente 
+ 
+10. Peso 
+bruto 
+ 
+11. Peso 
+neto 
+ 
+1 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+2 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+3 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+4 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+5 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+6 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+7 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+8 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+9 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+10 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+11 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+12 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+13 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+14 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+Abreviaturas= Libra: lb.  Onza: oz.  Gramos: gr.  Cuch. sopera: cda.  Cucharita: cdta.  Pequeño: p.  Mediano: m.  Grande: g.  Manojo: mjo.  Pedazo: pzo.  Paquete:paq.    
+9 enero 99 
+ 
+Formulario 10b.  Preparaciones en el lugar de cuidado del niño control ___ ___ ___ ___ ___ ___ 2 
+INCAP/IFPRI/Programa de Hogares Comunitarios 1999 Hogar Niño B/C 
+  
+Página __ __ de __ __  
+        Nombre de la cuidadora _________________________________________________________  Nombre de la madre _________________________________________________________ 
+ 
+ 
+Fecha de la observación (d-m-a)    
+ 
+            /             /             
+ 
+Identificación de la Encuestadora 
+ 
+                  
+ 
+No. 
+ 
+5. No. de 
+orden de la 
+prep. 
+ 
+6. Código de la 
+preparación 
+ 
+Nombre de la 
+Preparación 
+ 
+ 
+7. Peso del 
+recipiente  
+ 
+8. Peso prep. 
+lista para comer 
+c/recip. 
+ 
+9. Código de Ingrediente 
+        Ingrediente 
+ 
+10. Peso 
+bruto 
+ 
+11. Peso 
+neto 
+ 
+1 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+2 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+3 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+4 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+5 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+6 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+7 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+8 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+9 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+10 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+11 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+12 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+13 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+14 
+ 
+ 
+ 
+__ __ __ __ __ 
+ 
+  
+ 
+__ __ __ __ 
+ 
+__ __ __ __ __ 
+ 
+__ __ __ __ __  
+  
+__ __ __ __ 
+ 
+__ __ __ __ 
+ 
+Abreviaturas= Libra: lb.  Onza: oz.  Gramos: gr.  Cuch. sopera: cda.  Cucharita: cdta.  Pequeño: p.  Mediano: m.  Grande: g.  Manojo: mjo.  Pedazo: pzo.  Paquete:paq.    
+9 enero 99 
+ 
+Formulario 11.  Alimentos y/o preparaciones consumidos  por el niño ___ ___ ___ ___ ___ ___ ___ 
+INCAP/IFPRI/Programa de Hogares Comunitarios 1999 Hogar Niño B/C 
+ 
+Nombre de la madre cuidadora (hogar) o de la cuidadora (control): ________________________________    Colonia donde esta el hogar (o cuidadora) _________ Pagina __ de __ 
+Nombre de la madre del niño _____________________________________  Dirección __________________________________________   Dias que NO TRABAJA  (especificar a.m. p.m.) _________________ 
+Asistencia (Si=1; No=0)                      Hora que sale el niño del hogar _______________ p.m     Preguntar al niño si le dieron algo de comer o de tomar antes de venir al hogar (1=si;  0=No) _____  
+             
+    
+ 
+Nombre del niño _________________________________________________________ 
+Fechas de  Solo niños beneficiarios 
+ID del niño ___ ___ ___ ___ / ___ ___ / ___      SEXO NIÑO  (1=M: 2=F)    _____ 1.Nacimiento:  ___ ___ / ___ ___ / 9 ___ 3.Fecha de ingreso:  ___ ___ / ___ ___ / 9 ___ 
+ID de encuestadora ___ ___ 2. Observación: ___ ___ / ___ ___ / 9 9 4. Verificación: 1.  Verificada  2.  De memoria   ____ 
+No. 5.  
+Hora del 
+consumo 
+ 6. 
+No. orden 
+de la prep. 
+(form. 10) 
+7.  
+Código de 
+la prep. o 
+alimento 
+ 
+Nombre de la 
+preparación o 
+alimentos 
+8.  
+Tipo de  
+preparación
+  
+9.  
+Peso del 
+recipiente 
+10.  
+Peso de la ración 
+c/recipiente 
+(1 ALIMENTO) 
+11.  
+Peso restos 
+o desperdicios 
+(MAS DE 1 ALIMENTO) 
+        Peso plato  Plato + resto 
+1  
+         :         
+ 
+             
+ 
+                    
+ 
+  
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+2 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+3 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+4 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+5 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+6 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+7 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                    
+ 
+                        __ __ __ __ __ __ __ __ 
+ 
+8 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+9 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+10 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+11 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+ 
+12 
+ 
+         :         
+ 
+             
+ 
+                    
+ 
+ 
+ 
+        
+ 
+                        
+ 
+                      __ __ __ __ __ __ __ __ 
+Tipo de preparación: 1) Preparación del hogar 2) Preparación del hogar adaptada al niño 3) Alimento simple 4) Alimento o preparación que le niño trajo de su casa 
+ 
+ANTROPOMETRIA DEL NIÑO:  101. TALLA/LONGITUD (CM)  102. PESO (KG)  103. ROPA: 1) Liviana; 2) Mediana; 3) Gruesa       
+ 1era     ___ ___.___    ___ ___ ___.___  ____  
+            2nda     ___ ___.___    ___ ___ ___.___       
+            3ra     ___ ___.___    ___ ___ ___.___  
+Formulario 13.  Recordatorio de noche y mañana ___ ___ ___ ___ ___ ___  ___ 
+INCAP/IFPRI/Programa de Hogares Comunitarios 1999 Hogar Niño B/C 
+           Página 1 
+ 
+Unidad de la medida 
+1. gramos 
+totales 
+4. unidades peq. 7.  tortilla peq. delgada 12. tortilla grande gruesa 15. Cuchara 15 rasa 18. Cuchara 17 llena 21. Cuchara 21 rasa 33. Medida peq. Nan* 
+llena 
+2. onzas 5. unidades med. 8.  tortilla peq. gruesa 13. Cuchara , level 16. Cuchara 15 llena 19. Cuchara 19 rasa 22. Cuchara 21 llena 34. Medida gde. Nan* 
+rasa 
+3. gr / unidad 6. unidades grand. 11.  tortilla grande delgada 14. Cuchara 13 llena 17. Cuchara 17 rasa 20. Cuchara 19 llena 32. Medida peq. Nan* rasa 35. Medida gde. Nan* 
+llena 
+* ‘Nan’ spoon pertains to the spoon that comes with a brand of powdered milk called “Nan”. 
+23 de enero de 99 
+Nombre de la madre del niño o de la informante:                                                                                 Dirección: ______________________________________________________________________ 
+Nombre del niño _________________________________________________________ 
+Fechas de  Solo niños beneficiarios 
+ID del niño ___ ___ ___ ___ / ___ ___ / ___ 1.Nacimiento:  ___ ___ / ___ ___ / 9 ___ 3.Fecha de ingreso:  ___ ___ / ___ ___ / 9 ___ 
+ID de encuestadora ___ ___ 2. Observación: ___ ___ / ___ ___ / 9 9 4. Verificación: 1.  Verificada  2.  De memoria   ____ 
+ 
+5. a.Niños beneficiarios: Habitualmente,  antes de llegar al hogar comunitario,  usted le da algo de comer o tomar a su niño? (No= 0;Si= 1; 9=no aplica)   _____ 
+    b.Niños controles: Antes de las 6:00 de la mañana, usted le da algo de comer o tomar a su niño? (No= 0;Si= 1; 9=no aplica)     _____ 
+6. a.Niños beneficiarios: Habitualmente, después de haber salido del hogar comunitario, hasta antes de ponerlo a irse a dormir,  le da algo de comer o tomar a  su niño? (No= 0;Si= 1; 9=no 
+aplica)      _____   
+   b. Niños controles:      Habitualmente, después de las 4 de la tarde, hasta antes de dormirse le da algo de comer o de tomar a su niño? (No= 0;Si= 1; 9=no aplica)  _____ 
+ 
+Si acostumbra comer o tomar algo el niño antes de llegar al hogar comunitario (o a su lugar de cuidado), anotar lo que come o toma habitualmente:      
+      
+ 
+ 
+No. 
+ 
+ 7. Código de 
+ la prep. o 
+ alimento 
+ 
+Nombre de la 
+preparación o 
+alimentos 
+ 
+ 8. Unidad de la 
+ medida 
+ utilizada 
+ 
+ 9. Cantidad 
+ servida 
+ 
+ 10. Sobras 
+ 
+Observación 
+ 
+1 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+2 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+3 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+4 
+ 
+                            
+  
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+5 
+ 
+                            
+  
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+6 
+ 
+                            
+  
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+7 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+8 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+Si acostumbra comer o tomar algo después de volver del hogar (o de su lugar de cuidado), hasta antes de dormirse anotar lo que come o toma habitualmente: 
+Formulario 13.  Recordatorio de noche y mañana ___ ___ ___ ___ ___ ___  ___ 
+INCAP/IFPRI/Programa de Hogares Comunitarios 1999 Hogar Niño B/C 
+           Página 2 
+ 
+Unidad de la medida 
+1. gramos 
+totales 
+4. unidades peq. 7.  tortilla peq. delgada 12. tortilla grande gruesa 15. Cuchara 15 rasa 18. Cuchara 17 llena 21. Cuchara 21 rasa 33. Medida peq. Nan* 
+llena 
+2. onzas 5. unidades med. 8.  tortilla peq. gruesa 13. Cuchara , level 16. Cuchara 15 llena 19. Cuchara 19 rasa 22. Cuchara 21 llena 34. Medida gde. Nan* 
+rasa 
+3. gr / unidad 6. unidades grand. 11.  tortilla grande delgada 14. Cuchara 13 llena 17. Cuchara 17 rasa 20. Cuchara 19 llena 32. Medida peq. Nan* rasa 35. Medida gde. Nan* 
+llena 
+* ‘Nan’ spoon pertains to the spoon that comes with a brand of powdered milk called “Nan”. 
+23 de enero de 99 
+ 
+ 
+No. 
+ 
+ 11. Código de 
+ la prep. o 
+ alimento 
+ 
+Nombre de la 
+preparación o 
+alimentos 
+ 
+ 12. Unidad de la 
+ medida 
+ utilizada 
+ 
+ 13. Cantidad 
+ servida 
+ 
+ 14. Sobras 
+ 
+Observaciones 
+ 
+1 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+2 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+3 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+4 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+5 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+6 
+ 
+                            
+  
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+7 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+8 
+ 
+                            
+ 
+  
+ 
+            _  
+ 
+             .             
+ 
+             .             
+ 
+ 
+Solamente para niños beneficiarios: 
+ 
+Si la madre manda al niño al hogar con algún tipo de alimento o preparación: 
+ 
+15.  ¿ Usted da algo de comida a  su niño para que lo lleve al hogar (cada día o cada semana)?   0.  No   1. Sí        ______ 
+ 
+16.  Si dice que Sí, ¿ qué le da? (marca, tipo):  
+ 
+Nombre de la preparación o alimentos 
+ 
+  
+ 
+  
+ 
+  
+ 
+Formulario 20a.  Menús del fín de semana ___ ___  ___ ___ ___ ___ ___ 
+INCAP/Programa de Hogares Comunitarios / IFPRI 1999 Hogar  Niño B/C 
+ID encuestadora:  ___ ___ Nombre de la madre: __________________ 
+Fecha de la entrevista: __ __ / __ __/ 9 __  
+Nombre del niño: ______________________ Fecha de nacimiento: __ __ / __ __ / 9 __ 
+ 
+  C:/Formularios/Dieta/20a. Menus 12 de enero 99 
+  Sábado Domingo 
+1a. ¿Durante el fín de semana pasado, cómo estuvo su niño? 
+1.  Sano   2.  Enfermo 
+ 
+_____ 
+ 
+_____ 
+1.b. Si estuvo enfermo, ¿comió lo normal?      0.  No   1. Si _____ _____ 
+ 
+2. ¿Estuvo el niño en la casa durante los tiempos de comida  (1.  Sí   o  0.  No) 
+ Desayuno Refacción  mañana Almuerzo Refacción tarde Cena 
+Sábado      
+Domingo      
+ 
+3.a. ¿Tomó pachas su niño?        0.  No   1. Si _____ _____ 
+  Sábado Sábado Domingo Domingo 
+3.b. Si tomó pachas ¿A qué 
+horas? 
+9 am=09:00, 3:30pm=15:30 
+__ __ : __ __ 
+__  __ : __ __ 
+__ __  : __ __ 
+__ __ : __ __ 
+__  __ : __ __ 
+__ __  : __ __ 
+__ __ : __ __ 
+__  __ : __ __ 
+__ __  : __ __ 
+__ __ : __ __ 
+__  __ : __ __ 
+__ __  : __ __ 
+ 
+4.  ¿Qué comieron el día SABADO? 
+Desayuno Almuerzo Cena 
+   
+   
+   
+   
+   
+   
+   
+   
+Refacción de la mañana Refacción de la tarde Después de cenar Antes del desayuno 
+    
+    
+    
+    
+ 
+5. ¿Qué comieron el día DOMINGO? 
+Desayuno Almuerzo Cena 
+   
+   
+   
+   
+   
+   
+   
+   
+Refacción de la mañana Refacción de la tarde Después de cenar Antes del desayuno 
+    
+    
+    
+    
+ 
+Formulario 20b.  Recordatorio de fin de semana ___ ___ ___ ___ ___ ___ ___  
+INCAP/Programa de Hogares Comunitarios/ IFPRI 1999 Hogar Niño B/C 
+ 
+DIA RECORDADO:  Sábado ____   Domingo ____           HOJA # _______ DE _______      
+Nombre del niño   _______________________________________________ FECHA DE NACIMIENTO DEL NINO (DD/MM/AA)   _____ / _____  /_____ Error! Bookmark not defined.ID  
+ENCUESTADORA:         ____ _____ 
+Nombre del informante: __________________________________________ FECHA DE LA ENTREVISTA: __ __ /__ __ /__ __  
+ 
+PREPARACION ALIMENTOS USADOS DIETA FAMILIAR DIETA NINO 
+1 
+No / 
+Cód. 
+de la 
+prep. 
+2 
+Nombre de la preparación 
+3 
+Tie
+m 
+ 
+ 
+4 
+Ori
+g 
+ 
+ 
+5 
+Código 
+ 
+ 
+ 
+5a 5
+b 
+P
+N 
+6 
+Nombre, tipo, color, precio 
+7 
+Cantidad 
+usada 
+8 
+Cantidad 
+preparada 
+ 
+9 
+Peso / vol 
+de la 
+cantidad 
+preparada 
+10 
+Unidad 
+de  la 
+medida 
+11 
+No. total  
+de raciones 
+ 
+12 
+Ración  
+NO 
+consumida 
+ 
+13 
+Cantidad 
+Servida 
+ 
+ 
+14 
+Sobras 
+ 
+ 
+ 
+XX  X X XXXXX  X   X X . X X XXX. X XX XX.XX XX.XX XX.XX XX.XX 
+     1           
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+OBSERVACIONES: 
+ 
+ 
+C:/formularios/dieta/20b. Recordatorio FS  9 enero. 99Error! Bookmark not defined. 
+

data/part_2/0048107139.md

@@ -0,0 +1,166 @@
+# Climate Change and Food Security in Southeast Asia: Issues and Policy Options
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/383894d6-1923-4125-80e9-4d5c94ba2fcb/retrieve  
+**Language:** English  
+**Resource Type:** Poster / Presentation  
+**Release Year:** 2011  
+**Rights:** N/A  
+**GARDIAN ID:** 5f1bf09b72303eedd6b27f3e700a7733  
+**DataNODE ID:** a8f66040da3b4fbfe61c9ceefe6b9409  
+**Siever ID:** 78d6fc9f-4196-4496-b531-f70fe867a2fc  
+**Token Count:** 649  
+**Page Count:** 0
+
+## Keywords
+
+climate change, food security, southeast asia, development
+
+## Geography
+
+- **Regions:** Asia, World
+
+## Description
+
+Ancha Srinivasan, Ph.D. Asian Development Bank
+
+## Content
+
+Climate Change and Food 
+Security in Southeast Asia: 
+Issues and Policy Options 
+Ancha Srinivasan, Ph.D.
+Asian Development Bank
+Key Issues
+ High vulnerability
+ Physical and economic impacts
+ Food insecurity in SEA
+ Impacts of climate change on food security
+ Water stress and food security
+ Saltwater intrusion and food security
+ Impacts on the private sector & urban food 
+security
+ Non-climate stresses and food security
+Southeast Asia is highly vulnerable to 
+climate change
+• Highly exposed areas 
+(islands, deltas, coastal regions, steep slopes) 
+ High concentration of population and economic 
+activity in coastal areas
+ High reliance on climate-sensitive sectors –
+agriculture, water, energy (hydro), tourism
+ Millions of poor with low adaptive capacity 
+ Rapid urbanization and high urban population in 
+vulnerable areas
+Countries Identified as Vulnerable to 
+Climate Change in Southeast Asia
+High Exposure
+(temp >1.5oC; rainfall +/- 20%)
+High 
+Sensitivity
+(dependence on agriculture/fisheries)
+Low Adaptive 
+Capacity
+(income-related poverty)
+Cambodia
+Lao PDR
+Indonesia
+Malaysia
+Myanmar
+Philippines
+Singapore
+Thailand
+Viet Nam
+Cambodia
+Indonesia
+Lao PDR 
+Myanmar
+Thailand
+Timor-Leste
+Viet Nam
+Cambodia
+Lao PDR
+Myanmar
+Timor-Leste
+Current Vulnerability to Climate Change
+Source: EEP 2009
+Observed Physical Impacts 
+ Indonesia: Wet season rainfall increased; dry 
+season rainfall decreased; Number of 
+floods/storms increased; Number of hot days 
+and warm nights increased; Intensity and 
+frequency of heat waves and forest fires 
+increased 
+ Malaysia: Number of rainy days declined
+ Philippines: Increasing intensity and frequency 
+of events associated with El-Nino and La-Nina; 
+Annual frequency of cyclones increased by 4.2 
+ Thailand: Decreasing rainfall; growing intensity 
+of storms
+ Viet Nam: Decrease in monthly rainfall (July-
+Aug); rapid increase in extreme events
+Observed Economic Impacts 
+ Flood-related damages in Asia increased by 8 times 
+in the 1990s than in 1970s;
+ Direct damage costs from tropical cyclones in Asia 
+in the 1990s increased by 35 times more than in 
+1970s;
+ Increased food prices and civil unrest in several 
+countries
+ Philippines:  Typhoons in 2009 alone cost ~3% of 
+GDP
+ Thailand:  2008 - >200,000 people affected by water 
+borne diseases following storms
+ Viet Nam:  Typhoon 2007 - $725m loss
+Countries in Asia and the Pacific with Cultivated Crop Areas Lost in Excess of 100,000 
+Hectares, 1-Meter Sea-Level Rise
+Climate Change Projections
+Suggest that the worst is yet to come.
+ 
+    Without urgent action, mean temperature may increase by 
+4.8oC and sea level by up to 70 cm by 2100 from the 1990 levels
+Source: ADB 2009
+Potential Economic Impact could be equivalent 
+to losing 6.7% of GDP each year by 2100
+More than twice the global average loss
+Source: ADB 2009
+Three Dimensions of 
+Sustainable Food Security
+Impact on Rice Production 
+Page 12
+Climate induced percentage change in production in 
+2050:  Irrigated Rice in Asia
+Change in production = -27% NCAR A2, no CF
+Climate induced percentage change in production in 
+2050:  Rainfed Rice in Asia
+Change in production = -12% NCAR A2, no CF
+Estimated % Declines in Crop Yields 
+due to Climate Change by 2080
+Adapted from Cline 2007
+Impact on Calorie Availability 
+Page 16
+18% decline in calorie availability in Asian developing countries 
+due to climate change
+Impacts on Food and Beverage 
+Sectors
+ Declining crop/animal yields & High prices for 
+agricultural inputs (water, chemical inputs)  High 
+agricultural commodity prices/price volatility
+ Increasing water scarcity  Adverse impacts on 
+operating efficiency/processing costs
+ Growing concerns on food safety and community 
+relations  Increasing reputational and legal risks
+◦ Shrimp farming in Thailand – Growing conflicts among sectors
+◦ Oil palm industry in Malaysia – New regulations on expansion
+◦ Vinamilk (Viet Nam Dairy) – Increasing variability in milk supply
+Impacts of climate change on Food and Beverage sub-sectors
+Policy Options
+ Mainstreaming climate concerns
+ Research on climate change and food sec.
+ Food-water-energy nexus
+ Adaptation technologies and investments
+ REDD+ strategies
+ Mitigation-Adaptation synergies
+ Regional cooperation & Trade liberalization
+Thank you.
+

data/part_2/0065491817.md

@@ -0,0 +1,165 @@
+# Rural finance and poverty alleviation
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/f7eb483a-61d1-47fb-94cf-b73a46e2a8e0/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 1998  
+**Rights:** N/A  
+**GARDIAN ID:** 2659090bf40e8dec5ee4d3015808c10d  
+**DataNODE ID:** 1ca0ec1a0be1ae2245a9d23d731caee7  
+**Siever ID:** 60abe183-1f54-492f-8716-71eb1967b2e8  
+**Token Count:** 1014  
+**Page Count:** 0
+
+## Keywords
+
+poverty, agricultural credit, developing countries, rural finance, rural development, finance, poverty alleviation, informal sector (economics), financial institutions, household surveys, banks, cooperatives, lending
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World, Middle Africa, Sub-Saharan Africa, Africa, Eastern Asia, Northern Africa, Western Africa, Eastern Africa, Oceania
+- **Countries:** Pakistan, Nepal, Malawi, Madagascar, Ghana, Egypt, China, Cameroon, Bangladesh
+
+## Description
+
+The authors examine the lessons that rural financial institutions such as banks and cooperatives can learn from the informal lending sector. They also considers the roles government should play in the provision of financial services. The report’s findings are gleaned from a series of detailed household surveys conducted in nine countries of Asia and Africa: Bangladesh, Cameroon, China, Egypt, Ghana, Madagascar, Malawi, Nepal, and Pakistan. Most of the poor in these countries could benefit from credit, savings, and insurance services, but what is available varies greatly from country to country.
+
+## Content
+
+FOOD 
+POLICY 
+STATEMENT 
+NUMBEilll, SEPTEMBER 1998 
+2013 K STRffT, N.W. 
+WASHINGTON, D.C . .200()6.1002 
+USA. 
+PHONE: 1·202-862·5600 
+FAX: 1·202..f67-Hl9 
+E·MAIL: II'PRI@CCNET.COM 
+WEB: "WWw.CGIAR.ORC/ IFPRJ 
+~~~=CEAND POVERJtm~MV 
+MANFRED ZELLER AND MANOHAR SHARMA 
+SEP 2 2 1998 
+INT. FG0v POUCY 
+n many developing countries, poor rural households face se~&~· cf>d~itJiliifQ)ten 
+they seek credit from formal lending institutions. Formal financial services such as 
+those offered by banks are often not available to those below the poverty line because 
+of restrictions requiring that loans be backed by collateral. Nor do banks welcome 
+the small amounts the poor want to save. As a result, the poor usually turn first to informal 
+sources such as friends, relatives, or moneylenders, who loan small amounts for short peri­
+ods, or to informal, indigenous institutions such as savings clubs and lending networks to 
+borrow enough to purchase food and other basic necessities. These informal networks are 
+frequently successful in tiding the poor over difficult times, such as a bad harvest, and they 
+enable poor households to build up savings for investments that can help lift them out of 
+poverty. 
+A recent Food Policy Report, Rural Finance and Poverty Alleviation, by Manfred Zeller 
+and Manohar Sharma, examines the lessons that rural financial institutions such as banks 
+and cooperatives can learn from the informal lending sector. It also considers the roles gov­
+ernment should play in the provision of financial services. The report's findings are gleaned 
+from a series of detailed household surveys conducted in nine countries of Asia and Africa: 
+Bangladesh, Cameroon, China, Egypt, Ghana, Madagascar, Malawi, Nepal, and Pakistan. 
+Most of the poor in these countries could benefit from credit, savings, and insurance serv­
+ices, but what is available varies greatly from country to country. 
+LESSONS TO BE LFARNED FROM INFORJ"AI I NOFRS 
+n looking at the lessons that can be learned from studying the relationship between infor­
+mal lenders and their poor clients, the report finds the following: ( 1) A credible long­
+term relationship is the key to enforcing loan repayment: the borrower will repay the loan if 
+he or she expects to be able to borrow again in the future. (2) Financial services should be 
+tailored to the demand patterns of the borrowers. For example, farm loans that can only be 
+used for seeds or fertilizer reduce the flexibility of the household to make the best use of the 
+loan. (3) Decisionmaking on loans granted should be made at the local level. ( 4) Institutions 
+ought to have clear plans for loan recovery before lending begins. (5) Group-based transac­
+tions hold promise, but more research is needed to compare group lending and saving ac­
+tivities with other member-based institutions such as credit unions and village banks. 
+(6) Saving services should be provided. (7) Incentives for managers of rural financial pro­
+grams should be built into the programs. 
+INNOVATIONS KEY Tn FlNANCIAl SERVICES OR THE POOR 
+n recent years, micro finance institutions designed to serve the poor, such as the Gramecn 
+Bank in Bangladesh, have received wide attention, but these institutions depend on sub­
+sidies from national governments and international donors. Zeller and Sharma argue that 
+these subsidies represent good investments of public funds on two counts: they enable serv­
+ices to be offered that the marketplace is not willing to provide on its own, and they have 
+been proven to alleviate poverty. 
+Although excessive government 
+interference and rigid regulations have 
+suppressed innovation in financial 
+services, liberalization of financial 
+markets alone has not been able to trig­
+ger the kinds of innovation that reduce 
+transaction costs for the poor. Rural fi­
+nancial markets in developing coun­
+tries have inherent problems that make 
+investments risky and costly: clients 
+ACCESS TO CREDIT OR 
+PARTICIPATION IN A CREDIT 
+PROGRAM POSITIVELY 
+AFFECTED HOUSEHOLD 
+INCOME IN FOUR OUT OF FIVE 
+COUNTRIES ASSESSED 
+are too scattered, rural clients all want 
+to borrow at the same time (in the pre­
+harvest season) and to save immedi­
+ately after the harvest, and the poor 
+own few assets to secure loans. 
+Private-sector financial institutions 
+are reluctant to take on these risks. In 
+the long run, however, innovations 
+that improve the usefulness of these 
+institutions to the rural poor will also 
+improve the efficiency and sustain­
+ability of rural financial programs. 
+PUBLIC INVESTMENT PAYS OFF 
+illce the market, by itself, has not 
+U been able to stimulate much in­
+stitutional research and experimentation 
+in rural areas, public support in the 
+development phase is critical. "Once vi­
+able prototypes are identified, they will 
+eventually be adopted by the private 
+sector," the report says. "Well-directed 
+support to promising microfmance insti­
+tutions is likely to have payoffs in both 
+services to the poor and reduced costs of 
+services in the long run." 
+Access to credit or participation 
+in a credit program positively affected 
+household income in four out of five 
+countries assessed, the report finds. 
+Households with improved access to 
+credit were also better able to adopt 
+technology; they spent more on food 
+and, in some cases, had higher calorie 
+intakes. Access to financial services 
+POOR HOUSEHOLDS STRIVE TO 
+REPAY LOANS SO THAT THEY 
+WILL BE ABLE TO BORROW 
+ANOTHER TIME. 
+improves the incomes of and opportu­
+nities for the rural poor, and provides 
+support to tide families over difficult 
+times. And poor households strive to 
+repay loans so that they will be able to 
+borrow another time. 
+But, for the poorest of the poor, 
+the report indicates that financia l serv­
+ices must be offered in combination 
+with other programs such as training in 
+basic literacy, enterprise management, 
+and education in nutrition, health, and 
+family planning. 
+The report makes a strong case for 
+strengthening rural financial markets 
+through appropriate public interven­
+tion. In the long run, public investment 
+in institutional innovations will pay 
+off in efficient microfinance institu­
+tions that offer full-fledged savings 
+and credit services to the rural poor. 
+__________________ ._,_,__ ___________ _ 
+Please send the Food Policy Report Rural Finance and Poverty Alleviation, by Manfred Zeller and Manohar Sharma. 
+Name --------------------------------------------------------------------------------
+Organization·---------------------------------------------------------------------------
+Address -------------------------------------------------------------------------------
+The report will be sent free of charge by surface airlift. Please allow 3-4 weeks for delivery. 
+

data/part_2/0086795411.md

@@ -0,0 +1,34 @@
+# Food as the “silent weapon”: Russia’s gains and Ukraine’s losses
+
+**Source:** gardian_index  
+**URL:**   
+**Language:** English  
+**Resource Type:** Report  
+**Release Year:** 2024  
+**Rights:** CP  
+**GARDIAN ID:** a1f47401ccea189b5649c7a37ae91d34  
+**DataNODE ID:** 00ff62d6431d61fb7f5292b6da6be78a  
+**Siever ID:** 8a6aa85c-3697-40f6-965b-38db3f2097f5  
+**Token Count:** 258  
+**Page Count:** 0
+
+## Keywords
+
+agriculture, armed conflicts, infrastructure, gross national product, food insecurity, war, gender equality, youth and social inclusion, nutrition, health and food security, systems transformation, food and agriculture organization, agricultural sector, food prices, agricultural production
+
+## Geography
+
+- **Regions:** Eastern Europe, Europe, World, Northern Europe
+- **Countries:** Ukraine, United Kingdom, Russia
+
+## Description
+
+Russia’s war in Ukraine has caused the greatest military-related disruption to global agricultural markets in at least a century. Ukraine’s agricultural sector has been a major front in Russia’s war since February 2022, and the primary purpose of Russia’s targeting of Ukraine’s agricultural infrastructure is likely to undercut a main source of Ukraine’s income. Ukraine’s GDP contracted by more than 29 percent in 2022 compared to 2021, and the value of agriculture as a proportion of Ukraine’s GDP was 39 percent lower in 2022 than 2021.
+
+The global disruptions to the agricultural market due to Ukraine’s diminished production and exports have been stark: world food prices reached all-time nominal highs in March 2022, according to the UN Food and Agriculture Organization Food Price Index. In 2022, 258 million people suffered from acute food insecurity, an all-time high, according to the Global Report on Food Crises. At the same time, the cost of addressing these challenges also soared due to concurrent shocks in the global energy and fertilizer markets brought on by Russia’s war. For example, the cost of the delivery of humanitarian assistance also peaked due to the increased cost of food and fuel for operations. At the same time, for countries hoping to address domestic food insecurity with domestic agricultural production, the increased cost of fertilizer became a limiting factor. Likewise, countries dealing with the high price of food imports, high prices of agricultural inputs, and high levels of food insecurity also had less fiscal space for social programs following the Covid-19 pandemic, which drained national budgets.
+
+## Content
+
+Russia’s war in Ukraine has caused the greatest military-related disruption to global agricultural markets in at least a century. Ukraine’s agricultural sector has been a major front in Russia’s war since February 2022, and the primary purpose of Russia’s targeting of Ukraine’s agricultural infrastructure is likely to undercut a main source of Ukraine’s income. Ukraine’s GDP contracted by more than 29 percent in 2022 compared to 2021, and the value of agriculture as a proportion of Ukraine’s GDP was 39 percent lower in 2022 than 2021.
+
+The global disruptions to the agricultural market due to Ukraine’s diminished production and exports have been stark: world food prices reached all-time nominal highs in March 2022, according to the UN Food and Agriculture Organization Food Price Index. In 2022, 258 million people suffered from acute food insecurity, an all-time high, according to the Global Report on Food Crises. At the same time, the cost of addressing these challenges also soared due to concurrent shocks in the global energy and fertilizer markets brought on by Russia’s war. For example, the cost of the delivery of humanitarian assistance also peaked due to the increased cost of food and fuel for operations. At the same time, for countries hoping to address domestic food insecurity with domestic agricultural production, the increased cost of fertilizer became a limiting factor. Likewise, countries dealing with the high price of food imports, high prices of agricultural inputs, and high levels of food insecurity also had less fiscal space for social programs following the Covid-19 pandemic, which drained national budgets.

data/part_2/0088483240.md

@@ -0,0 +1,29 @@
+# Langt ude på landet i Latinamerika
+
+**Source:** gardian_index  
+**URL:**   
+**Language:** Danish  
+**Resource Type:** Book / Monograph  
+**Release Year:** 2001  
+**Rights:** N/A  
+**GARDIAN ID:** 05f366e8aac303f3c1b2543b8b3328cc  
+**DataNODE ID:** 8ddbe29199f32b5a3944e5eb35a6c3ff  
+**Siever ID:** 47f9f330-901e-4c86-8817-90c3af51d7e4  
+**Token Count:** 136  
+**Page Count:** 0
+
+## Keywords
+
+biological diversity, agricultural innovation, agricultural research, cgiar, agriculture, environmental factors, latin america, agricultural products, food crops, sweet potatoes, world, potatoes
+
+## Geography
+
+- **Regions:** Latin America and the Caribbean, Americas, World
+
+## Description
+
+Latin America has given the world many of its key agricultural products. Food crops like potatoes, sweet potatoes, maize, beans, and many, many others have traveled far and wide....[It] is one of the richest concentrations of biodiversity on the face of the globe.....Today, however, many people in rural Latin America live in extreme poverty....At the same time, the rich mountain and lowland ecosystems of the American tropics are threatened by degradation and loss of species.....Ebbe Schiøler -- an intrepid traveler and untiring observer of the CGIAR and of the work of the Future Harvest centers it supports -- has journeyed to seven countries to gather, first-hand, the stories of the men and women who benefit on a day-to-day basis from the research of the centers." (From Foreword by Hubert Zandstra, Chairman of the Board, Future Harvest)
+
+## Content
+
+Latin America has given the world many of its key agricultural products. Food crops like potatoes, sweet potatoes, maize, beans, and many, many others have traveled far and wide....[It] is one of the richest concentrations of biodiversity on the face of the globe.....Today, however, many people in rural Latin America live in extreme poverty....At the same time, the rich mountain and lowland ecosystems of the American tropics are threatened by degradation and loss of species.....Ebbe Schiøler -- an intrepid traveler and untiring observer of the CGIAR and of the work of the Future Harvest centers it supports -- has journeyed to seven countries to gather, first-hand, the stories of the men and women who benefit on a day-to-day basis from the research of the centers." (From Foreword by Hubert Zandstra, Chairman of the Board, Future Harvest)

data/part_2/0100744003.md

@@ -0,0 +1,25 @@
+# Sustainable intensification for food and nutrition security
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/560bd74f-f760-4739-b0d2-6812d6d93e57/retrieve  
+**Language:** English  
+**Resource Type:** Poster / Presentation  
+**Release Year:** 2013  
+**Rights:** N/A  
+**GARDIAN ID:** e43a4008e193df9732f301318d3719ed  
+**DataNODE ID:** 5d0df115aba0642b55ffbbcc4cf4ca29  
+**Siever ID:** 9abe08f9-125d-4a8f-b361-9fdecb44dede  
+**Token Count:** 25  
+**Page Count:** 0
+
+## Keywords
+
+sustainable intensification, nutrition security, food
+
+## Description
+
+CIFOR-IFPRI Policy Seminar "Food, Forests, and Landscapes - Solutions for Sustainable Development" with Shenggen Fan, IFPRI, Peter Holmgren, CIFOR, and Geeta Sethi, The World Bank.
+
+## Content
+
+CIFOR-IFPRI Policy Seminar "Food, Forests, and Landscapes - Solutions for Sustainable Development" with Shenggen Fan, IFPRI, Peter Holmgren, CIFOR, and Geeta Sethi, The World Bank.

data/part_2/0105230449.md

@@ -0,0 +1,183 @@
+# Short-term impacts of COVID-19 in rural Guatemala: Call for a closer, continuous look at the food security and nutritional patterns of vulnerable families
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/7f3b7a83-ac3a-46b0-9744-7bfc2370c119/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2022  
+**Rights:** CC-BY  
+**GARDIAN ID:** 809c4977ed4aef3e409b09c39a69ee86  
+**DataNODE ID:** 93512b52a5f8ae23e5aa13140528cf95  
+**Siever ID:** 4f7c171f-80c9-45d7-95be-593a86943c62  
+**Token Count:** 1518  
+**Page Count:** 0
+
+## Keywords
+
+value chains, income, agricultural products, policies, covid-19, health, social protection, nutrition, food security, poverty, rural areas, household consumption
+
+## Geography
+
+- **Regions:** Central America, Latin America and the Caribbean, Americas, World, Northern America
+- **Countries:** Guatemala
+
+## Description
+
+In early 2020, Guatemala reacted swiftly to the unfolding COVID-19 pandemic. It was one of the first countries in Latin America to impose strict measures to contain the spread of infection, including travel restrictions and a six-month nationwide lockdown beginning March 21 (eight days after its first reported case), comprising a temporary halt of activities in the private and public sectors, suspension of public transportation, and mobility restrictions, with a strict curfew from 6 p.m. to 5 a.m. According to the Oxford COVID-19 Government Response Tracker (OxCGRT), the country’s measures were among the top five in Latin America in terms of stringency.
+
+## Content
+
+From IFPRI’s COVID-19 Blog
+8. Short-term impacts of COVID-19 
+in rural Guatemala: Call for a closer, 
+continuous look at the food security and 
+nutritional patterns of vulnerable families
+Francisco Ceballos, Manuel Hernandez, and Cynthia Paz
+In early 2020, Guatemala reacted swiftly to the unfolding COVID-19 pandemic. It was one of the first 
+countries in Latin America to impose strict measures to contain the spread of infection, including 
+travel restrictions and a six-month nationwide lockdown beginning March 21 (eight days after its first 
+reported case), comprising a temporary halt of activities in the private and public sectors, suspension 
+of public transportation, and mobility restrictions, with a strict curfew from 6 p.m. to 5 a.m. According 
+to the Oxford COVID-19 Government Response Tracker (OxCGRT), the country’s measures were 
+among the top five in Latin America in terms of stringency.
+In a country where, according to pre-pandemic statistics, nearly 6 out of 10 people live in poverty 
+and half of children under 5 are stunted, the economic and social consequences of COVID-19 and 
+corresponding control measures deserve close attention. Moreover, Guatemala’s existing structural 
+inequalities along cultural and geographic lines, institutional and public service deficiencies, and vul-
+nerability to climate shocks (as shown by the devastating Eta and Iota hurricanes in November 2020), 
+all fan the flames of this crisis and call for continuous monitoring and rapid and innovative responses.
+Our recent study closely examines the short-term effects of the COVID-19 lockdown on food secu-
+rity and nutrition among rural households in Guatemala’s Western Highlands — possibly the country’s 
+most vulnerable region, with the highest poverty and stunting rates and characterized by smallholder 
+farming, low agricultural productivity, and reduced market access. The results indicate that incomes 
+fell, food insecurity doubled, and dietary diversity declined.
+The analysis relies on a comprehensive panel dataset of 1,824 small agricultural households located 
+in the departments of Huehuetenango, Quiché, and San Marcos, collected pre- and post-lockdown 
+during November–December 2019 and May–June 2020. Post-lockdown data gathering was con-
+ducted exclusively by phone, using numbers collected during the first round, and relying on commu-
+nity leaders to contact households that did not answer repeated phone calls, as some of them had 
+lost or changed their numbers (a common practice in rural Guatemala).
+Key findings
+The lockdown’s direct economic consequences are evident at first glance: Almost two-thirds of the 
+interviewed households reported a decrease in agricultural and non-agricultural income (the latter 
+54 Food Security & Poverty
+EMBARGOED UNTIL MARCH 7, 2022
+being sharper), while the large majority (94 percent) reported decreased receipt of remittances, con-
+sistent with national reports during the first months after the outbreak. In aggregate, roughly three 
+out of every four households reported an unambiguous decrease in income (Figure 1).
+Despite the relatively quick rollout of government support programs, the study finds that poor 
+households mostly relied on limited coping mechanisms to deal with these income reductions. 
+This, together with reported reduced food availability and higher food prices in local markets (a 
+result of disruptions in trade and logistics and labor shortages, despite the agriculture sector’s offi-
+cial exemption from lockdown restrictions), appears to have reduced households’ food security and 
+dietary diversity.
+The prevalence of food insecurity roughly doubled between the end of 2019 and mid-2020, the sur-
+vey indicates. This pattern was observed consistently across all forms of food insecurity: mild (hav-
+ing eaten only a few kinds of foods because of a lack of money or other resources), moderate (having 
+eaten less than they thought they should), and severe (not having eaten despite feeling hungry).
+In addition, households’ dietary diversity fell overall, as indicated by a small but statistically significant 
+decrease from 6.9 to 6.4 in the Household Dietary Diversity Score (HDSS), defined as the number of 
+food groups consumed — ranging from 0 to 12 — in the 24 hours preceding the interview. Households 
+seemed to switch away from consumption of animal-source foods toward greater consumption of 
+fruits and vegetables, with no significant changes observed in other food groups, such as cereals 
+and grains or legumes and nuts (Figure 2). Unfortunately, the data did not permit us to determine net 
+changes in nutrient intake brought about by this dietary switch, as quantities consumed were not col-
+lected during the surveys.
+FIGure 1 Reported changes in income sources in Guatemala’s 
+Western Highlands
+Reported changes in income sources 
+in Guatemala’s Western Highlands
+77%
+53%
+35%
+17%
+12%
+27%
+4%
+31%
+30%
+2%
+4%
+8%
+Remittances
+Non-agricultural 
+income
+Agricultural 
+income
+Decreased a lot Decreased a little Remains the same Increased
+55Food Security & Poverty
+EMBARGOED UNTIL MARCH 7, 2022
+At the individual level, dietary diversity among women ages 15–49 remained unchanged at around 
+4.5 (on a range of 0–9 food groups) and increased among children ages 6–23 months from 3.3 to 3.9 
+(on a range of 0–7 food groups). This points toward potential changes in intrahousehold allocation of 
+foods in response to a shock, where young children may have been prioritized.
+Interestingly, the study indicates that higher-income households reduced their dietary diversity more 
+than lower-income ones, and were also more prone to report a decrease in income. The lockdown 
+may thus have had relatively greater impacts on higher-income versus lower-income households, 
+which tend to depend more on subsistence farming and other small-scale, locally oriented activi-
+ties less affected by the restrictions. Nonetheless, the latter could still have been worse off in abso-
+lute terms, and exhibit additional vulnerabilities along several dimensions — acute malnutrition, for 
+example, more than doubled in Guatemala over the months after the start of the pandemic compared 
+to same period in 2019. Households located in communities that imposed additional access restric-
+tions during the lockdown (over 75 percent of those sampled) also showed a larger decrease in their 
+dietary diversity compared with those in communities that did not.
+FIGure 2 Household consumption before and after COVID-19
+Household consumption changes in Guatemala’s 
+Western Highlands before and after COVID-19
+0% 10% 20% 30% 40%
+Percentage of households
+50% 60% 70%
+Other vegetables
+Green leafy vegetables
+Vegetables rich in vitamin A
+Other fruits
+Fruits rich in vitamin A
+Dairy products
+Eggs
+Poultry
+Beef and pork meat
+Households’ animal- 
+source food consumption
+Households’ fruits and 
+vegetables consumption
+Sausages and cold meats
+November – December 2019
+May – June 2020
+56 Food Security & Poverty
+EMBARGOED UNTIL MARCH 7, 2022
+Policy responses and looking forward
+Starting in April 2020, the government of Guatemala scaled up programs to contain the negative 
+effects of the crisis on livelihoods and food security. These included greater support for micro, small, 
+and medium enterprises, subsidies for public services, and price controls on foods included in the 
+basic food basket. Two COVID-19 programs provide direct assistance to vulnerable rural and urban 
+families: the Programa de Apoyo Alimentario (Food Support Program) distributes rations, prioritiz-
+ing the procurement of basic grains from smallholder farmers; the Bono Familia provides an emer-
+gency supplementary monthly income of around US$130. Despite these efforts, the study shows the 
+assistance may not be reaching many of its intended recipients. While 6 out of every 10 communities 
+received some form of public or private aid (as reported by community leaders), only 2 out of every 
+10 households reported receiving aid. This suggests the need to intensify efforts to reach a larger 
+share of rural households affected by COVID-19.
+Overall, the study suggests a complex array of impacts from the COVID-19 pandemic and related 
+control measures in the nutritionally compromised context of Guatemala’s Western Highlands — 
+including decreases in household food security and overall dietary diversity following reported 
+reductions in income, price increases, and lower food availability at local markets. While the pan-
+demic impacts continue to evolve and present ongoing challenges, our findings call for a closer and 
+continuous look at the conditions rural families in the region face, together with their responses. A 
+second follow-up survey implemented in May–June 2021, which is part of an ongoing study, indicates 
+that the pandemic has continued to affect the incomes, food security, and dietary patterns of sur-
+veyed households. Despite slight improvements across most dimensions compared to the 2020 sur-
+vey, study households still report lower agricultural and non-agricultural income and remittances, 
+more food insecurity experiences, and a decrease in dietary diversity compared to pre-pandemic lev-
+els. Similarly, given the prolonged nature of the COVID-19 pandemic, households reporting an unam-
+biguous income decrease have shifted to more costly coping mechanisms (toward borrowing and 
+away from using savings or relying on friends and relatives). Moreover, households that reported a 
+decrease in income and dietary diversity in 2020 were found to be more prone to report a decrease 
+in 2021, pointing to persisting economic and nutritional effects of the pandemic on specific popula-
+tion groups. A third, follow-up survey in 2022 will permit us to assess longer-term variations on food 
+security and nutritional patterns.
+The paper discussed here is part of a COVID-19 special issue of Agricultural Economics edited by IFPRI’s Johan Swinnen and Rob Vos. 
+The study was funded by the U.S. Agency for International Development (USAID).
+Originally published May 13, 2021, and updated January 4, 2022.
+57Food Security & Poverty
+EMBARGOED UNTIL MARCH 7, 2022
+

data/part_2/0114228201.md

@@ -0,0 +1,1285 @@
+# A&T Bangladesh Maternal Nutrition Endline Survey 2016: Shasthya Shebika (SS)
+
+**Source:** gardian_index  
+**URL:** https://dataverse.harvard.edu/api/access/datafile/:persistentId/?persistentId=doi:10.7910/DVN/JQNLHJ/K1MQZB  
+**Language:** English  
+**Resource Type:** Dataset / Tabular  
+**Release Year:** 2016  
+**Rights:** CC-BY  
+**GARDIAN ID:** ecc062a5b5041aff523b53c2fca64c36  
+**DataNODE ID:** d7fdb97f343655b199d2c85ab5704b2e  
+**Siever ID:** 45545e84-03ae-4f7e-81b6-f3f6e1154fa3  
+**Token Count:** 13650  
+**Page Count:** 0
+
+## Keywords
+
+endline surveys, frontline health workers, shasthya shebika, households, health, health communication, nutrition, nutrition education, supplements, anemia, diet, breast feeding, infant feeding, child feeding, mass media, hygiene, training, work satisfaction, motivation, developing countries, bangladesh, south asia, asia
+
+## Geography
+
+- **Regions:** South-eastern Asia, Asia, World, Southern Asia
+- **Countries:** Bangladesh, Indonesia
+
+## Description
+
+This dataset is the result of the frontline health worker (FHW) survey conducted to gather data for the Maternal Nutrition Endline as a part of an impact evaluation study of Alive & Thrive (A&T) interventions delivered through Building Resources Across Communities' (BRAC) Essential Health Care (EHC) Program in Bangladesh. </p>
+
+A&T is a global initiative that supports the scaling up of nutrition interventions to save lives, prevent illnesses, and contribute to healthy growth and development through improved maternal nutrition, breastfeeding and complementary feeding practices.</p>
+
+In setting its country program goal for Bangladesh in this phase of its study, A&T decided to focus on demonstrating the feasibility of integrating a package of maternal nutrition interventions in a large-scale Maternal, Newborn, and Child Health (MNCH) program. Maternal nutrition should receive equal priority as child nutrition and the A&T program of BRAC already have developed an effective strategy through improving IYCF practices.</p>
+
+The objective of this impact evaluation is to assess the impact of integrating nutrition-focused behavior change communication (BCC- interpersonal counselling and mass communication) and community mobilization into BRAC's rural MNCH program on: 1) coverage and utilization of key maternal nutrition interventions; 2) consumption of diversified and adequate amount of foods and micronutrients by pregnant and postpartum women; and 3) early breastfeeding practices. In addition, factors affecting integration of nutrition interventions into a well-established community-based MNCH program platform through frontline health workers and social mobilization were examined.</p>
+The study used a cluster-randomized design with repeated cross-sectional surveys at baseline and endline. As with the baseline, the endline survey used the same ten subdistricts from four districts (Mymensingh, Rangpur, Kurigram, and Lalmonirhat) in which BRAC's existing rural MNCH project is in place have been selected randomly to provide intensified maternal nutrition interventions. Another 10 subdisctricts/upazilas from the same four districts have been selected as comparison for the evaluation. It was conducted between July–August 2016 by the team from International Food Policy Research Institute (IFPRI), in collaboration with the survey firm, Data Analysis and Technical Assistance, Ltd. (DATA). </p>
+
+The endline survey had three components: 1) Household survey for recently delivered women (RDW) and their husbands, 2) Household survey for pregnant women (PW) (with detailed dietary recall), and 3) a Frontline health workers survey (Shasthya Shebika (SS) and Shasthya Kormi (SK)).</p>
+The frontline health worker (FHW) survey gathered data on service provision by BRAC frontline health workers and other healthcare providers. Data were also gathered on health workers’ time commitment, knowledge and attitude and training related to maternal nutrition, and their job motivation, satisfaction, and supervision. In addition, questions on household assets and mass media habits were included. Two questionnaires were developed for frontline health workers survey—(i) Shasthya Shebika (SS) questionnaire, and (ii) Shasthya Kormi (SK) questionnaire.</p>
+The data included here are from the survey of Shasthya Shebika (SS).
+
+## Content
+
+Variable name Type Variable label
+A01 double A1. SS
+A03 int A3 Village (sample village)
+A03_1 double A3_1 Is it respondent own village?:
+A06 int A6 Union / Ward No
+A07 byte A7 Thana/Upazila
+A08 byte A8 District
+A10 double A10 Religion
+A14 double A14. Have taking interview in this SS at 2015?
+A15 double A15 If yes, write ID of SS
+B01Y double B01Y. How long have you been working as a BRAC SS in this community? (year)
+B01M double B01M How long have you been working as a BRAC SS in this community? (month)
+B02_01 byte main activities - SS - HH visits
+B02_02 byte main activities - SS - Conduct health education forum
+B02_03 byte main activities - SS - Check on immunization of children
+B02_04 byte main activities - SS - Check TT for pregnant women
+B02_05 byte main activities - SS - Provide ANC
+B02_06 byte main activities - SS - Visit HH and providing pregnancy-related advice
+B02_07 byte main activities - SS - Supervise the work of SS
+B02_08 byte main activities - SS - Attend monthly refresher trainings for SS
+B02_09 byte main activities - SS - Maintain coordination
+B02_10 byte main activities - SS - Provide advice on water and sanitation
+B02_11 byte main activities - SS - Provide advice on maternal nutrition
+B02_12 byte main activities - SS - Demonstrate daily diet chart
+B02_13 byte main activities - SS - Help with childbirth
+B02_14 byte main activities - SS - Ensuring EBF
+B02_15 byte main activities - SS - Ensuring EIBF
+B02_16 byte main activities - SS - Provide free IFA tablets
+B02_17 byte main activities - SS - provide free Calcium tablets
+B02_18 byte main activities - SS - Sale IFA tablets
+B02_19 byte main activities - SS - Sale Calcium tablets
+B02_20 byte main activities - SS - other
+B03 double B03 How many days of the month do you USUALLY work as a BRAC SS?
+B04 double B04 How many days of the month do you USUALLY make home visits for your work as a BRAC SS? 
+B05 double B05 How many home visits do you USUALLY make each day on days that you make home visits?
+B06 double B06 How long do you usually spend in each home that you visit (on average)
+B07_01 byte main activities - visit a home - Supervise SS
+B07_02 byte main activities - visit a home - Register new birth
+B07_03 byte main activities - visit a home - Register pregnant women
+B07_04 byte main activities - visit a home - Provide pregnancy-related advice
+B07_05 byte main activities - visit a home - Provide advice about family planning
+B07_06 byte main activities - visit a home - Take weight and height when pregnant
+B07_07 byte main activities - visit a home - Check blood pressure
+B07_08 byte main activities - visit a home - Advice on maternal nutrition
+B07_09 byte main activities - visit a home - Provide advice on water and sanitation
+B07_10 byte main activities - visit a home - Provide advice on hand washing
+B07_11 byte main activities - visit a home - Provide free IFA tablets
+B07_12 byte main activities - visit a home - provide free Calcium tablets
+B07_13 byte main activities - visit a home - Sale IFA tablets
+B07_14 byte main activities - visit a home - Sale Calcium tablets
+B07_15 byte main activities - visit a home - other
+B08 double
+B08 Do you spend any time discussing maternal and child nutrition when you visit homes with 
+pregnant and lactating women?
+B09 double
+B09 On average, how long do you usually spend discussing maternal and child nutrition during your 
+home visits?
+B10_HH double B10_HH How much time do you USUALLY spend on preparing reports each month?
+B10_MM double B10_MM How much time do you USUALLY spend on preparing reports each month?
+B11_HH double
+B11_HH How long do you USUALLY spend when you attend the monthly SS refresher training 
+meetings?
+B11_MM double
+B11_MM How long do you USUALLY spend when you attend the monthly SS refresher training 
+meetings?
+B12 double
+B12 How many days in a month do you meet your supervisor (PO) who supervises you to discuss 
+your work? 
+B15 double B15 How satisfied are you overall with the volunteer work you do?
+B17_YY double B17_YY When did you last receive training on maternal nutrition from BRAC?(year)
+B17_MM double B17_MM When did you last receive training on maternal nutrition from BRAC?(month)
+B18_01 byte topics discussed - last training - Objectives and overview of project
+B18_02 byte topics discussed - last training - maternal health/nutrition,breastfeeding
+B18_03 byte topics discussed - last training - Roles of SK, SS, PO, UM, Monitors
+B18_04 byte topics discussed - last training - Importance of maternal nutrition & BF
+B18_05 byte topics discussed - last training - priority interventions
+B18_06 byte topics discussed - last training - Counselling PW and RDW
+B18_07 byte topics discussed - last training - Preparing diet chart&calculating food budget
+B18_08 byte topics discussed - last training - How to measure and record weight of the PW
+B18_09 byte topics discussed - last training - Counting and recording of IFA& Ca consumption
+B18_10 byte topics discussed - last training - How to engage husbands & other family members
+B18_11 byte topics discussed - last training - Technique of counselling BF issue
+B18_12 byte topics discussed - last training - How to express breastmilk
+B18_13 byte topics discussed - last training - Early initiation of BF
+B18_14 byte topics discussed - last training - How to prepare registers
+B18_15 byte topics discussed - last training - Others
+B19 double B19 Do you usually attend monthly refresher trainings from BRAC?
+B20_DD double B20_YY When did you last attend monthly refresher training?
+B20_MM double B20_MM When did you last attend monthly refresher training?
+B21_01 byte topics discussed - refresher training - Objectives and overview of project
+B21_02 byte topics discussed - refresher training - maternal health/nutrition,BF
+B21_03 byte topics discussed - refresher training - Roles of SK, SS, PO, UM, Monitors
+B21_04 byte topics discussed - refresher training - Importance of maternal nutrition & BF
+B21_05 byte topics discussed - refresher training - priority interventions
+B21_06 byte topics discussed - refresher training - Counselling PW and RDW
+B21_07 byte topics discussed-refresher training-Preparing diet chart&calculating food budget
+B21_08 byte topics discussed-refresher training-How to measure and record weight of the PW
+B21_09 byte topics discussed-refresher training-Counting and recording of IFA & Ca consumption
+B21_10 byte topics discussed-refresher training-How to engage husbands&other family members
+B21_11 byte topics discussed - refresher training - Technique of counselling BF issue
+B21_12 byte topics discussed - refresher training - How to express breastmilk
+B21_13 byte topics discussed - refresher training - Early initiation of BF
+B21_14 byte topics discussed - refresher training - How to prepare registers
+B21_15 byte topics discussed - refresher training - Others
+B22 double B22 Who is your direct supervisor?
+B23 double B23 How many times in the last 30 days have you had contact with PO/SK?
+B24 double
+B24 Whom do you usually contact when you face a problem with your job as SS including problems 
+related to breast feeding and maternal nutrition and health of mothers?
+B25_1 byte reason for contacting - Pregnancy related problems
+B25_2 byte reason for contacting - Supplementation related problems
+B25_3 byte reason for contacting - Breast problem
+B25_4 byte reason for contacting - Breastfeeding related problems
+B25_5 byte reason for contacting - others
+B26A double B26A My supervisor keeps me informed about the follow-up of my concerns/worries
+B26B double B26B My supervisor informs me about upcoming trainings/meetings, etc., in a timely fashion
+B26C double B26C My supervisor respects my fixed monthly activities when planning other meetings
+B26D double B26D My supervisor consults with me before making changes to the activities that I am involved in
+B26E double B26E When I make a mistake on the job, my supervisor scolds me
+B26F double B26F My supervisor praises me when I do something really well
+B26G double B26G My supervisor helps me to organize my time and activities in an efficient manner
+B27A double B27A My supervisor ensures that I have enough of the supplies that I need to do my daily work
+B27B double B27B When I disagree with my supervisor I feel safe to express my opinion
+B27C double B27C The way the supervisor provides feedback on my performance to the upper man
+B27D double B27D My supervisor takes into account/considers my suggestions to improve things
+B27E double B27E My supervisor works with me to identify solutions to program activity related problems
+B27F double B27F I feel that my supervisor is sympathetic to my problems/cares about my problems
+B27G double B27G My supervisor gives me enough guidance and structure to help me do my job
+B27H double
+B27H My supervisor uses times when I make mistakes or don?t perform well as opportunities to 
+help me improve my skills
+C1_1 double C1_1 How many children between 0-6 months are there in your catchment area?
+C1_2 double C1_2 How many pregnant women are there in your catchment area?
+C1_3 double C1_3 How many recently delivered women are there in your area?
+C1_4_1A double C1_4_1A Households with pregnant women in first trimester (0-3 months): Times/day
+C1_4_1B double C1_4_1B Households with pregnant women in first trimester (0-3 months): Times/week
+C1_4_1C double C1_4_1C Households with pregnant women in first trimester (0-3 months): Times/month
+C1_4_2A double C1_4_2A Households with pregnant women in their second trimester (4-6 months): Times/day
+C1_4_2B double C1_4_2B Households with pregnant women in their second trimester (4-6 months): Times/week
+C1_4_2C double C1_4_2C Households with pregnant women in their second trimester (4-6 months): Times/month
+C1_4_3A double C1_4_3A Households with pregnant women in their third trimester: Times/day
+C1_4_3B double C1_4_3B Households with pregnant women in their third trimester: Times/week
+C1_4_3C double C1_4_3C Households with pregnant women in their third trimester: Times/month
+C1_4_4A double C1_4_4A Households with women who delivered within last 42 days: Times/day
+C1_4_4B double C1_4_4B Households with women who delivered within last 42 days: Times/week
+C1_4_4C double C1_4_4C Households with women who delivered within last 42 days: Times/months
+C2_5_1 byte HH visits, whom do you talk to ab maternal nutrition, health -PW/RDW
+C2_5_2 byte HH visits, whom do you talk to ab maternal nutrition, health -PW/RDW husband
+C2_5_3 byte HH visits, whom do you talk to ab maternal nutrition, health -PW/RDW mother-in-law
+C2_5_4 byte HH visits, whom do you talk to ab maternal nutrition, health -PW/RDW father-in-law
+C2_5_5 byte HH visits, whom do you talk to ab maternal nutrition, health -relatives
+C2_5_6 byte HH visits, whom do you talk to ab maternal nutrition, health -whoever is available
+C2_5_7 byte HH visits, whom do you talk to ab maternal nutrition, health -No one
+C2_5_8 byte C2_5 During your household visits, whom do you talk to about maternal nutrition and health?
+C2_6_01 byte
+message give when visit HH-Proper diet ensures weight gain & adequate growth of baby inside the 
+womb
+C2_6_02 byte message give when visit HH-Proper diet ensures quick recovery of mothers
+C2_6_03 byte message give when visit HH-Proper diet save costs on doctor and medicine
+C2_6_04 byte message give when visit HH-Nutritious food is not always expensive
+C2_6_05 byte message give when visit HH-Daily intake of 5 varieties of food and daal, rice
+C2_6_06 byte message give when visit HH-PW/RDW have to eat fish/meat everyday
+C2_6_07 byte message give when visit HH-PW/RDW have to eat an egg everyday
+C2_6_08 byte message give when visit HH-PW/RDW have to eat milk/milk products everyday
+C2_6_09 byte message give when visit HH-PW/RDW have to eat DGLV everyday
+C2_6_10 byte message give when visit HH-PW/RDW have to eat yellow/orange fruits or vegetables
+C2_6_11 byte message give when visit HH-PW/RDW have to eat thick daal everyday
+C2_6_12 byte message give when visit HH-Take one IFA tablet everyday
+C2_6_13 byte message give when visit HH-Take one calcium tablet every day
+C2_6_14 byte message give when visit HH-Use iodized salt while cooking
+C2_6_15 byte message give when visit HH-Take at least two hours of rest every afternoon
+C2_6_16 byte message give when visit HH-PW have to weighed every month
+C2_6_17 byte message give when visit HH-PW gain 10-12 kgs of weight during pregnancy
+C2_6_18 byte message give when visit HH-PW avoid doing heavy work or lifting anything heavy
+C2_6_19 byte message give when visit HH-Avoid tea-coffee during pregnancy
+C2_6_20 byte message give when visit HH-Ensure EIBF
+C2_6_21 byte message give when visit HH-Ensure EBF
+C2_6_22 byte message give when visit HH-Daily consumption of fruits is essential
+C2_6_23 byte message give when visit HH-Daily consumption of fish/meat/egg is essential
+C2_6_24 byte message give when visit HH-Take anti-helminth tablet
+C2_6_25 byte message give when visit HH-Drink at least 8 glasses of water everyday
+C2_6_26 byte message give when visit HH-other
+C2_7_1 byte message give to the members of the family when visit HH-To help the women with her work
+C2_7_2 byte message give to the members of the family when visit HH-To feed her program prescribed diet
+C2_7_3 byte message give to the members of the family when visit HH-To make her rest 2h everyday
+C2_7_4 byte message give to the members of the family when visit HH-other
+C2_8_01 byte
+message give when visit HH-Proper diet ensures weight gain & adequate growth of baby inside the 
+womb
+C2_8_02 byte message give when visit HH-Proper diet ensures quick recovery of mothers
+C2_8_03 byte message give when visit HH-Proper diet save costs on doctor and medicine
+C2_8_04 byte message give when visit HH-Nutritious food is not always expensive
+C2_8_05 byte message give when visit HH-Daily intake of 5 varieties of food and daal, rice
+C2_8_06 byte message give when visit HH-PW/RDW have to eat fish/meat everyday
+C2_8_07 byte message give when visit HH-PW/RDW have to eat an egg everyday
+C2_8_08 byte message give when visit HH-PW/RDW have to eat milk/milk products everyday
+C2_8_09 byte message give when visit HH-PW/RDW have to eat DGLV everyday
+C2_8_10 byte message give when visit HH-PW/RDW have to eat yellow/orange fruits or vegetables
+C2_8_11 byte message give when visit HH-PW/RDW have to eat thick daal everyday
+C2_8_12 byte message give when visit HH-Take one IFA tablet everyday
+C2_8_13 byte message give when visit HH-Take one calcium tablet every day
+C2_8_14 byte message give when visit HH-Use iodized salt while cooking
+C2_8_15 byte message give when visit HH-Take at least two hours of rest every afternoon
+C2_8_16 byte message give when visit HH-PW have to weighed every month
+C2_8_17 byte message give when visit HH-PW gain 10-12 kgs of weight during pregnancy
+C2_8_18 byte message give when visit HH-PW avoid doing heavy work or lifting anything heavy
+C2_8_19 byte message give when visit HH-Avoid tea-coffee during pregnancy
+C2_8_20 byte message give when visit HH-Ensure EIBF
+C2_8_21 byte message give when visit HH-Ensure EBF
+C2_8_22 byte message give when visit HH-Proper diet results in sufficient amount of milk for the baby
+C2_8_23 byte message give when visit HH-Daily consumption of fruits is essential
+C2_8_24 byte message give when visit HH-Daily consumption of fish/meat/egg
+C2_8_25 byte message give when visit HH-Drink at least 8 glasses of water everyday
+C2_8_26 byte message give when visit HH-Take vitamin A capsule within first 14 days after delivery
+C2_8_27 byte message give when visit HH-other
+C2_10 double
+C2_10 In the last 30 days during home visits how many times have you demonstrated food intake 
+according to the diet chart?
+C3_11_1 byte C3_11 common food related problems-Nausea or vomiting
+C3_11_2 byte C3_11 common food related problems-Constipation/dark stool
+C3_11_3 byte C3_11 common food related problems-Metallic taste
+C3_11_4 byte C3_11 common food related problems-not feeling hungry/food tastes stale
+C3_11_5 byte C3_11 common food related problems-Others
+C3_12_1 byte C3_12 advice give when hear about nausea/vomiting-Take small, frequent meals
+C3_12_2 byte C3_12 advice give when hear about nausea/vomiting-Drink a lot of water
+C3_12_3 byte C3_12 advice give when hear about nausea/vomiting-Others
+C3_13_1 byte C3_13 advice give when hear about constipation/dark stool-Eat a lot of fruits, vegetables
+C3_13_2 byte C3_13 advice give when hear about constipation/dark stool-Drink a lot of water
+C3_13_3 byte C3_13 advice give when hear about constipation/dark stool-Others
+C3_13A double C3_13a Do you notice complication in neonate?s health in your catchment area?
+C3_13B_01 byte kind of problem notice in neonate health-Child has difficulty in sucking milk
+C3_13B_02 byte kind of problem notice in neonate health-Child has difficulty in swallowing milk
+C3_13B_03 byte kind of problem notice in neonate health-Child has difficulty with breathing
+C3_13B_04 byte kind of problem notice in neonate health-Child suffers from seizure
+C3_13B_05 byte kind of problem notice in neonate health-Child is lethargic
+C3_13B_06 byte kind of problem notice in neonate health-Child went in coma
+C3_13B_07 byte kind of problem notice in neonate health-Child suffers from regular occurrence of fever
+C3_13B_08 byte kind of problem notice in neonate health-Child suffers from hypothermia
+C3_13B_09 byte kind of problem notice in neonate health-Child suffered from umbilical cord infection
+C3_13B_10 byte kind of problem notice in neonate health-Child suffered from skin infection
+C3_13B_11 byte kind of problem notice in neonate health-Child suffers from bleeding
+C3_13B_12 byte kind of problem notice in neonate health-Child suffered from jaundice
+C3_13B_13 byte kind of problem notice in neonate health-Child has a tendency to vomit
+C3_13B_14 byte kind of problem notice in neonate health-Child suffers from frequent diarrhea
+C3_13B_15 byte kind of problem notice in neonate health-OTHER
+C3_14 double C3_14 Do you distribute free IFA tablets in your catchment area?
+C3_15A double
+C3_15A On average, how many IFA tablets do you provide to a postnatal mother over the whole 
+course of pregnancy and lactation?/number of tablets for RDW
+C3_15B double
+C3_15B On average, how many IFA tablets do you provide to a pregnant mother over the whole 
+course of pregnancy and lactation?/number of tablets for PW
+C3_16 double C3_16 Do you distribute free Calcium tablets in your catchment area?
+C3_17RDW double C3_17A On average, how many calcium tablets do you provide to a postnatal mother
+C3_17PW double C3_17B On average, how many calcium tablets do you provide to a pregnant mother
+C3_17A double C17a Do you provide anti-helminth tablet (medicine to remove worm) to pregnant women?
+C3_17B double
+C17b In which month of pregnancy do you provide anti-helminth tablet (medicine to remove worm) 
+to pregnant women?
+C3_18 double C3_18 Do you provide breastfeeding related messages to pregnant women?
+C3_19 double C3_19 Do recently delivered women tell you about problems regarding breastfeeding?
+C3_20 double C3_20 Is there any baby with low weight in your catchment area?
+C3_21_1 byte C3_21 advice give to RDW whose baby is low-weight-Feed the child frequently
+C3_21_2 byte C3_21 advice give to RDW whose baby is low-weight-Keep the baby close to mother
+C3_21_3 byte C3_21 advice give to RDW whose baby is low-weight-Keep the baby worm
+C3_21_4 byte C3_21 advice give to RDW whose baby is low-weight-use express milk
+C3_21_5 byte C3_21 advice give to RDW whose baby is low-weight-Use cup & spoon,avoid using bottle
+C3_21_6 byte C3_21 advice give to RDW whose baby is low-weight-other
+C4_25 double C4_25 Have you ever received incentive from BRAC?
+C4_26 double C4_26 Did you ever received incentive from BRAC last month?
+C4_27 double C4_27 How much incentive did you receive from BRAC last month (record in TAKA)?
+C4_28_1 byte Why get incentive last month-ensure PW/RDW taking/eating at least 5 group of foods
+C4_28_2 byte Why get incentive last month-ensure PW/RDW taking/eating recommended amount of food
+C4_28_3 byte Why get incentive last month-ensure PW/RDW compliance of 30 IFA tablet and 30 Calcium tablet
+C4_28_4 byte Why get incentive last month-took weight of all PW within her catchment area
+C4_28_5 byte Why get incentive last month-ensure initiation of BF and no pre-lacteals
+C4_28_6 byte Why get incentive last month-other
+C4_29 double C4_29 How important is incentives in your decision to continue providing SS services?
+D01_1 byte D01 Why is proper nutrition important-For adequate weight gain of PW
+D01_2 byte D01 Why is proper nutrition important-For child inside the womb grows adequately/ healthy
+D01_3 byte D01 Why is proper nutrition important-For a brainy child with bright future
+D01_4 byte D01 Why is proper nutrition important-Quicker recovery after delivery
+D01_5 byte D01 Why is proper nutrition important-Extra costs due to doctors and medicine will be saved
+D01_6 byte D01 Why is proper nutrition important-It is a good investment in future
+D01_7 byte D01 Why is proper nutrition important-To produce adequate breastmilk
+D01_8 byte D01 Why is proper nutrition important-Others
+D01_9 byte D01 Why is proper nutrition important-Dont know
+D02A_01 byte How should PW/RDW eat to provide good nutrition-Eat more at each meal
+D02A_02 byte How should PW/RDW eat to provide good nutrition-Eat more frequently
+D02A_03 byte How should PW/RDW eat to provide good nutrition-Eat more animal foods
+D02A_04 byte How should PW/RDW eat to provide good nutrition-Eat more eggs
+D02A_05 byte How should PW/RDW eat to provide good nutrition-Eat more Yellow/orange vegetable
+D02A_06 byte How should PW/RDW eat to provide good nutrition-Eat more Dark green leafy vegetable
+D02A_07 byte How should PW/RDW eat to provide good nutrition-Eat more Any other vegetable
+D02A_08 byte How should PW/RDW eat to provide good nutrition-Eat more vegetables in general
+D02A_09 byte How should PW/RDW eat to provide good nutrition-Eat more Yellow/orange fruits
+D02A_10 byte How should PW/RDW eat to provide good nutrition-Eat more Citreous/sour fruits
+D02A_11 byte How should PW/RDW eat to provide good nutrition-Eat more Any other fruits
+D02A_12 byte How should PW/RDW eat to provide good nutrition-Eat more fruits in general
+D02A_13 byte How should PW/RDW eat to provide good nutrition-Eat more Milk/milk products
+D02A_14 byte How should PW/RDW eat to provide good nutrition-Take one IFA tablet every day
+D02A_15 byte How should PW/RDW eat to provide good nutrition-Take one Calcium tablet every day
+D02A_16 byte How should PW/RDW eat to provide good nutrition-Others
+D02A_17 byte How should PW/RDW eat to provide good nutrition-DK
+D03 double D03 Have you heard about anemia?
+D04_1 byte D04 how to recognize who has anemia-Less energy/weakness
+D04_2 byte D04 how to recognize who has anemia-Paleness/pallor
+D04_3 byte D04 how to recognize who has anemia-More likely to become sick
+D04_4 byte D04 how to recognize who has anemia-Other
+D04_5 byte D04 how to recognize who has anemia-Dont know
+D07_1 byte Health risks for PW of a lack of iron-Develop anemia/less iron in blood
+D07_2 byte Health risks for PW of a lack of iron-Difficult delivery
+D07_3 byte Health risks for PW of a lack of iron-Risk of dying during or after pregnancy
+D07_4 byte Health risks for PW of a lack of iron-Other
+D07_5 byte Health risks for PW of a lack of iron-Dont know
+D05_1 byte what causes anemia-Lack of iron in the diet/eat too little, not much
+D05_2 byte what causes anemia-Sickness/infection
+D05_3 byte what causes anemia-Heavy bleeding during menstruation
+D05_4 byte what causes anemia-Other
+D05_5 byte what causes anemia-Dont know
+D06_1 byte How can anemia be prevented-Eat/feed iron-rich foods
+D06_2 byte How can anemia be prevented-Eat/give vitamin-C-rich foods
+D06_3 byte How can anemia be prevented-Take/give iron supplements
+D06_4 byte How can anemia be prevented-Treat other causes of anemia
+D06_5 byte How can anemia be prevented-Other
+D06_6 byte How can anemia be prevented-Dont know
+D08_1 byte food groups rich in iron-Organ meat
+D08_2 byte food groups rich in iron-Flesh meat
+D08_3 byte food groups rich in iron-Fish and seafood
+D08_4 byte food groups rich in iron-Dark green vegetables
+D08_5 byte food groups rich in iron-Other
+D08_6 byte food groups rich in iron-Dont know
+D09 double
+D09 When taken during meals, certain foods help the body absorb and use iron. What are those 
+foods?
+D10_1 byte D10 beverages decrease iron absorption-Coffee
+D10_2 byte D10 beverages decrease iron absorption-Tea
+D10_3 byte D10 beverages decrease iron absorption-Milk
+D10_4 byte D10 beverages decrease iron absorption-Other
+D10_5 byte D10 beverages decrease iron absorption-Dont know
+D11 double D11 Have you ever heard about iron-folic acid (IFA) tablets?
+D12 double D12 How many IFA tablets do you think a pregnant woman should take in one month?
+D13 double D13 For how many months a pregnant woman should take IFA tablets?
+D14_1 byte Why PW should take IFA-reduce the risk of anemia for PW
+D14_2 byte Why PW should take IFA-reduce the risk of anemia for child
+D14_3 byte Why PW should take IFA-reduce the risk of LBW
+D14_4 byte Why PW should take IFA-help improve child intelligence
+D14_5 byte Why PW should take IFA-reduce the risk of excessive blood loss during delivery
+D14_6 byte Why PW should take IFA-reduce risk of excessive blood loss after delivery
+D14_7 byte Why PW should take IFA-make mother healthy/strong
+D14_8 byte Why PW should take IFA-Dont know
+D15 double D15 Have you ever heard about calcium tablets?
+D16 double D16 How many calcium tables do you think a pregnant woman should take in one month?
+D17 double D17 For how many months a pregnant woman should take Calcium tablets?
+D18_1 byte D18 Why PW should take Ca-recover the loss in PW
+D18_2 byte D18 Why PW should take Ca-ensure adequate growth of child bones and teeth
+D18_3 byte D18 Why PW should take Ca-reduce the risk of pre-eclampsia/ eclampsia
+D18_4 byte D18 Why PW should take Ca-reduce the risk of hypertension
+D18_5 byte D18 Why PW should take Ca-Dont know
+D19_HH double D19_HH How much rest should a pregnant woman take every day?
+D19_MM double D19_MM How much rest should a pregnant woman take every day?
+D19A double D19a When should a pregnant woman take anti-helminth tablet?
+D20 double D20 When should a woman take one vitamin A capsule?
+D21 double D21 How much weight should a pregnant women gain during pregnancy?
+D23_11 double kind of food women should eat every day during pregnancy-Rice-YN
+D23_21 double quantity each day-Rice
+D23_12 double kind of food women should eat every day during pregnancy-Bread-YN
+D23_22 double quantity each day-Bread
+D23_13 double kind of food women should eat every day during pregnancy-Puffed rice-YN
+D23_23 double quantity each day-Puffed rice
+D23_14 double kind of food women should eat every day during pregnancy-Thick daal-YN
+D23_24 double quantity each day-Thick daal
+D23_15 double kind of food women should eat every day during pregnancy-Yellow/orange vegetable-Yn
+D23_25 double quantity each day-Yellow/orange vegetable
+D23_16 double kind of food women should eat every day during pregnancy-Dark green leafy vegetable-YN
+D23_26 double quantity each day-Dark green leafy vegetable
+D23_17 double kind of food women should eat every day during pregnancy-Other vegetable-YN
+D23_27 double quantity each day-Other vegetable
+D23_18 double kind of food women should eat every day during pregnancy-Orange/yellow fruits-YN
+D23_28 double quantity each day-Orange/yellow fruits
+D23_19 double kind of food women should eat every day during pregnancy-Citreous/sour fruits-YN
+D23_29 double quantity each day-Citreous/sour fruits
+D23_110 double kind of food women should eat every day during pregnancy-Other fruits-YN
+D23_210 double quantity each day-Other fruits
+D23_111 double kind of food women should eat every day during pregnancy-Egg-YN
+D23_211 double quantity each day-Egg
+D23_112 double kind of food women should eat every day during pregnancy-Milk/ Milk products-YN
+D23_212 double quantity each day-Milk/ Milk products
+D23_113 double kind of food women should eat every day during pregnancy-Fish/ sea foods-YN
+D23_213 double quantity each day-Fish/ sea foods
+D23_114 double kind of food women should eat every day during pregnancy-Meat-YN
+D23_214 double quantity each day-Meat
+D23_115 double kind of food women should eat every day during pregnancy-Oil-YN
+D23_215 double quantity each day-Oil
+D23_116 double kind of food women should eat every day during pregnancy-Chips and chanachur-YN
+D23_216 double quantity each day-Chips and chanachur
+D23_117 double kind of food women should eat every day during pregnancy-Nutritious snacks-YN
+D23_217 double quantity each day-Nutritious snacks
+D23_118 double kind of food women should eat every day during pregnancy-Coke-YN
+D23_218 double quantity each day-Coke
+D25_2_1 double Proper diet every day during pregnancy ensures weight gain-ever heard
+D25_3_1_01 byte Proper diet every day during pregnancy ensures weight gain-Hospital/UHC
+D25_3_1_02 byte Proper diet every day during pregnancy ensures weight gain-Doctor
+D25_3_1_03 byte Proper diet every day during pregnancy ensures weight gain-Nurse/Midwife
+D25_3_1_04 byte Proper diet every day during pregnancy ensures weight gain-FWA/HA
+D25_3_1_05 byte Proper diet every day during pregnancy ensures weight gain-FWV
+D25_3_1_06 byte Proper diet every day during pregnancy ensures weight gain-CHCP
+D25_3_1_07 byte Proper diet every day during pregnancy ensures weight gain-SS
+D25_3_1_08 byte Proper diet every day during pregnancy ensures weight gain-SK
+D25_3_1_09 byte Proper diet every day during pregnancy ensures weight gain-NGO workers
+D25_3_1_10 byte Proper diet every day during pregnancy ensures weight gain-TTBA
+D25_3_1_11 byte Proper diet every day during pregnancy ensures weight gain-TBA
+D25_3_1_12 byte Proper diet every day during pregnancy ensures weight gain-Village Doctor
+D25_3_1_13 byte Proper diet every day during pregnancy ensures weight gain-Homeopath doctor
+D25_3_1_14 byte Proper diet every day during pregnancy ensures weight gain-Kabiraj/Herbal healer
+D25_3_1_15 byte Proper diet every day during pregnancy ensures weight gain-Spiritual healer
+D25_3_1_16 byte Proper diet every day during pregnancy ensures weight gain-Pharmacy
+D25_3_1_17 byte Proper diet every day during pregnancy ensures weight gain-Husband
+D25_3_1_18 byte Proper diet every day during pregnancy ensures weight gain-Mother/Mother-in-law
+D25_3_1_19 byte Proper diet every day during pregnancy ensures weight gain-Other HH members
+D25_3_1_20 byte Proper diet every day during pregnancy ensures weight gain-Neighbor/friends
+D25_3_1_21 byte Proper diet every day during pregnancy ensures weight gain-Private clinic
+D25_3_1_22 byte Proper diet every day during pregnancy ensures weight gain-Community clinic
+D25_3_1_23 byte Proper diet every day during pregnancy ensures weight gain-EPI
+D25_3_1_24 byte Proper diet every day during pregnancy ensures weight gain-No one/never needed advice
+D25_3_1_25 byte Proper diet every day during pregnancy ensures weight gain-Radio/TV
+D25_3_1_26 byte Proper diet every day during pregnancy ensures weight gain-Books/Newspaper/Poster/Billboard
+D25_3_1_27 byte Proper diet every day during pregnancy ensures weight gain-Internet/website
+D25_3_1_28 byte Proper diet every day during pregnancy ensures weight gain-Jatra/Pala/Cinema
+D25_3_1_29 byte Proper diet every day during pregnancy ensures weight gain-BRAC training course
+D25_3_1_30 byte Proper diet every day during pregnancy ensures weight gain-other
+D25_2_2 double Proper diet every day during pregnancy ensures growth of baby-ever heard
+D25_3_2_01 byte Proper diet every day during pregnancy ensures growth of baby-Hospital/UHC
+D25_3_2_02 byte Proper diet every day during pregnancy ensures growth of baby-Doctor
+D25_3_2_03 byte Proper diet every day during pregnancy ensures growth of baby-Nurse/Midwife
+D25_3_2_04 byte Proper diet every day during pregnancy ensures growth of baby-FWA/HA
+D25_3_2_05 byte Proper diet every day during pregnancy ensures growth of baby-FWV
+D25_3_2_06 byte Proper diet every day during pregnancy ensures growth of baby-CHCP
+D25_3_2_07 byte Proper diet every day during pregnancy ensures growth of baby-SS
+D25_3_2_08 byte Proper diet every day during pregnancy ensures growth of baby-SK
+D25_3_2_09 byte Proper diet every day during pregnancy ensures growth of baby-NGO workers
+D25_3_2_10 byte Proper diet every day during pregnancy ensures growth of baby-TTBA
+D25_3_2_11 byte Proper diet every day during pregnancy ensures growth of baby-TBA
+D25_3_2_12 byte Proper diet every day during pregnancy ensures growth of baby-Village Doctor
+D25_3_2_13 byte Proper diet every day during pregnancy ensures growth of baby-Homeopath doctor
+D25_3_2_14 byte Proper diet every day during pregnancy ensures growth of baby-Kabiraj/Herbal healer
+D25_3_2_15 byte Proper diet every day during pregnancy ensures growth of baby-Spiritual healer
+D25_3_2_16 byte Proper diet every day during pregnancy ensures growth of baby-Pharmacy
+D25_3_2_17 byte Proper diet every day during pregnancy ensures growth of baby-Husband
+D25_3_2_18 byte Proper diet every day during pregnancy ensures growth of baby-Mother/Mother-in-law
+D25_3_2_19 byte Proper diet every day during pregnancy ensures growth of baby-Other HH members
+D25_3_2_20 byte Proper diet every day during pregnancy ensures growth of baby-Neighbor/friends
+D25_3_2_21 byte Proper diet every day during pregnancy ensures growth of baby-Private clinic
+D25_3_2_22 byte Proper diet every day during pregnancy ensures growth of baby-Community clinic
+D25_3_2_23 byte Proper diet every day during pregnancy ensures growth of baby-EPI
+D25_3_2_24 byte Proper diet every day during pregnancy ensures growth of baby-No one/never needed advice
+D25_3_2_25 byte Proper diet every day during pregnancy ensures growth of baby-Radio/TV
+D25_3_2_26 byte
+Proper diet every day during pregnancy ensures growth of baby-
+Books/Newspaper/Pooster/Billboard
+D25_3_2_27 byte Proper diet every day during pregnancy ensures growth of baby-Internet/website
+D25_3_2_28 byte Proper diet every day during pregnancy ensures growth of baby-Jatra/Pala/Cinema
+D25_3_2_29 byte Proper diet every day during pregnancy ensures growth of baby-BRAC training course
+D25_3_2_30 byte Proper diet every day during pregnancy ensures growth of baby-other
+D25_2_3 double Proper diet every day ensures quick recovery of mothers-ever heard
+D25_3_3_01 byte Proper diet every day ensures quick recovery of mothers-Hospital/UHC
+D25_3_3_02 byte Proper diet every day ensures quick recovery of mothers-Doctor
+D25_3_3_03 byte Proper diet every day ensures quick recovery of mothers-Nurse/Midwife
+D25_3_3_04 byte Proper diet every day ensures quick recovery of mothers-FWA/HA
+D25_3_3_05 byte Proper diet every day ensures quick recovery of mothers-FWV
+D25_3_3_06 byte Proper diet every day ensures quick recovery of mothers-CHCP
+D25_3_3_07 byte Proper diet every day ensures quick recovery of mothers-SS
+D25_3_3_08 byte Proper diet every day ensures quick recovery of mothers-SK
+D25_3_3_09 byte Proper diet every day ensures quick recovery of mothers-NGO workers
+D25_3_3_10 byte Proper diet every day ensures quick recovery of mothers-TTBA
+D25_3_3_11 byte Proper diet every day ensures quick recovery of mothers-TBA
+D25_3_3_12 byte Proper diet every day ensures quick recovery of mothers-Village Doctor
+D25_3_3_13 byte Proper diet every day ensures quick recovery of mothers-Homeopath doctor
+D25_3_3_14 byte Proper diet every day ensures quick recovery of mothers-Kabiraj/Herbal healer
+D25_3_3_15 byte Proper diet every day ensures quick recovery of mothers-Spiritual healer
+D25_3_3_16 byte Proper diet every day ensures quick recovery of mothers-Pharmacy
+D25_3_3_17 byte Proper diet every day ensures quick recovery of mothers-Husband
+D25_3_3_18 byte Proper diet every day ensures quick recovery of mothers-Mother/Mother-in-law
+D25_3_3_19 byte Proper diet every day ensures quick recovery of mothers-Other HH members
+D25_3_3_20 byte Proper diet every day ensures quick recovery of mothers-Neighbor/friends
+D25_3_3_21 byte Proper diet every day ensures quick recovery of mothers-Private clinic
+D25_3_3_22 byte Proper diet every day ensures quick recovery of mothers-Community clinic
+D25_3_3_23 byte Proper diet every day ensures quick recovery of mothers-EPI
+D25_3_3_24 byte Proper diet every day ensures quick recovery of mothers-No one/never needed advice
+D25_3_3_25 byte Proper diet every day ensures quick recovery of mothers-Radio/TV
+D25_3_3_26 byte Proper diet every day ensures quick recovery of mothers-Books/Newspaper/Poster/Bilboard
+D25_3_3_27 byte Proper diet every day ensures quick recovery of mothers-Internet/website
+D25_3_3_28 byte Proper diet every day ensures quick recovery of mothers-Jatra/Pala/Cinema
+D25_3_3_29 byte Proper diet every day ensures quick recovery of mothers-BRAC training course
+D25_3_3_30 byte Proper diet every day ensures quick recovery of mothers-other
+D25_2_4 double Proper diet every day save costs on doctor and medicine -ever heard
+D25_3_4_01 byte Proper diet every day save costs on doctor and medicine -Hospital/UHC
+D25_3_4_02 byte Proper diet every day save costs on doctor and medicine -Doctor
+D25_3_4_03 byte Proper diet every day save costs on doctor and medicine -Nurse/Midwife
+D25_3_4_04 byte Proper diet every day save costs on doctor and medicine -FWA/HA
+D25_3_4_05 byte Proper diet every day save costs on doctor and medicine -FWV
+D25_3_4_06 byte Proper diet every day save costs on doctor and medicine -CHCP
+D25_3_4_07 byte Proper diet every day save costs on doctor and medicine -SS
+D25_3_4_08 byte Proper diet every day save costs on doctor and medicine -SK
+D25_3_4_09 byte Proper diet every day save costs on doctor and medicine -NGO workers
+D25_3_4_10 byte Proper diet every day save costs on doctor and medicine -TTBA
+D25_3_4_11 byte Proper diet every day save costs on doctor and medicine -TBA
+D25_3_4_12 byte Proper diet every day save costs on doctor and medicine -Village Doctor
+D25_3_4_13 byte Proper diet every day save costs on doctor and medicine -Homeopath doctor
+D25_3_4_14 byte Proper diet every day save costs on doctor and medicine -Kabiraj/Herbal healer
+D25_3_4_15 byte Proper diet every day save costs on doctor and medicine -Spiritual healer
+D25_3_4_16 byte Proper diet every day save costs on doctor and medicine -Pharmacy
+D25_3_4_17 byte Proper diet every day save costs on doctor and medicine -Husband
+D25_3_4_18 byte Proper diet every day save costs on doctor and medicine -Mother/Mother-in-law
+D25_3_4_19 byte Proper diet every day save costs on doctor and medicine -Other HH members
+D25_3_4_20 byte Proper diet every day save costs on doctor and medicine -Neighbor/friends
+D25_3_4_21 byte Proper diet every day save costs on doctor and medicine -Private clinic
+D25_3_4_22 byte Proper diet every day save costs on doctor and medicine -Community clinic
+D25_3_4_23 byte Proper diet every day save costs on doctor and medicine -EPI
+D25_3_4_24 byte Proper diet every day save costs on doctor and medicine -No one/never needed advice
+D25_3_4_25 byte Proper diet every day save costs on doctor and medicine -Radio/TV
+D25_3_4_26 byte Proper diet every day save costs on doctor and medicine -Books/Newspaper/Poster/Billboard
+D25_3_4_27 byte Proper diet every day save costs on doctor and medicine -Internet/website
+D25_3_4_28 byte Proper diet every day save costs on doctor and medicine -Jatra/Pala/Cinema
+D25_3_4_29 byte Proper diet every day save costs on doctor and medicine -BRAC training course
+D25_3_4_30 byte Proper diet every day save costs on doctor and medicine -other
+D25_2_5 double Nutritious food is not always expensive -ever heard
+D25_3_5_01 byte Nutritious food is not always expensive -Hospital/UHC
+D25_3_5_02 byte Nutritious food is not always expensive -Doctor
+D25_3_5_03 byte Nutritious food is not always expensive -Nurse/Midwife
+D25_3_5_04 byte Nutritious food is not always expensive -FWA/HA
+D25_3_5_05 byte Nutritious food is not always expensive -FWV
+D25_3_5_06 byte Nutritious food is not always expensive -CHCP
+D25_3_5_07 byte Nutritious food is not always expensive -SS
+D25_3_5_08 byte Nutritious food is not always expensive -SK
+D25_3_5_09 byte Nutritious food is not always expensive -NGO workers
+D25_3_5_10 byte Nutritious food is not always expensive -TTBA
+D25_3_5_11 byte Nutritious food is not always expensive -TBA
+D25_3_5_12 byte Nutritious food is not always expensive -Village Doctor
+D25_3_5_13 byte Nutritious food is not always expensive -Homeopath doctor
+D25_3_5_14 byte Nutritious food is not always expensive -Kabiraj/Herbal healer
+D25_3_5_15 byte Nutritious food is not always expensive -Spiritual healer
+D25_3_5_16 byte Nutritious food is not always expensive -Pharmacy
+D25_3_5_17 byte Nutritious food is not always expensive -Husband
+D25_3_5_18 byte Nutritious food is not always expensive -Mother/Mother-in-law
+D25_3_5_19 byte Nutritious food is not always expensive -Other HH members
+D25_3_5_20 byte Nutritious food is not always expensive -Neighbor/friends
+D25_3_5_21 byte Nutritious food is not always expensive -Private clinic
+D25_3_5_22 byte Nutritious food is not always expensive -Community clinic
+D25_3_5_23 byte Nutritious food is not always expensive -EPI
+D25_3_5_24 byte Nutritious food is not always expensive -No one/never needed advice
+D25_3_5_25 byte Nutritious food is not always expensive -Radio/TV
+D25_3_5_26 byte Nutritious food is not always expensive -Books/Newspaper/Poster/ Billboard
+D25_3_5_27 byte Nutritious food is not always expensive -Internet/website
+D25_3_5_28 byte Nutritious food is not always expensive -Jatra/Pala/Cinema
+D25_3_5_29 byte Nutritious food is not always expensive -BRAC training course
+D25_3_5_30 byte Nutritious food is not always expensive -other
+D25_2_6 double Avoid hot foods -ever heard
+D25_3_6_01 byte Avoid hot foods -Hospital/UHC
+D25_3_6_02 byte Avoid hot foods -Doctor
+D25_3_6_03 byte Avoid hot foods -Nurse/Midwife
+D25_3_6_04 byte Avoid hot foods -FWA/HA
+D25_3_6_05 byte Avoid hot foods -FWV
+D25_3_6_06 byte Avoid hot foods -CHCP
+D25_3_6_07 byte Avoid hot foods -SS
+D25_3_6_08 byte Avoid hot foods -SK
+D25_3_6_09 byte Avoid hot foods -NGO workers
+D25_3_6_10 byte Avoid hot foods -TTBA
+D25_3_6_11 byte Avoid hot foods -TBA
+D25_3_6_12 byte Avoid hot foods -Village Doctor
+D25_3_6_13 byte Avoid hot foods -Homeopath doctor
+D25_3_6_14 byte Avoid hot foods -Kabiraj/Herbal healer
+D25_3_6_15 byte Avoid hot foods -Spiritual healer
+D25_3_6_16 byte Avoid hot foods -Pharmacy
+D25_3_6_17 byte Avoid hot foods -Husband
+D25_3_6_18 byte Avoid hot foods -Mother/Mother-in-law
+D25_3_6_19 byte Avoid hot foods -Other HH members
+D25_3_6_20 byte Avoid hot foods -Neighbor/friends
+D25_3_6_21 byte Avoid hot foods -Private clinic
+D25_3_6_22 byte Avoid hot foods -Community clinic
+D25_3_6_23 byte Avoid hot foods -EPI
+D25_3_6_24 byte Avoid hot foods -No one/never needed advice
+D25_3_6_25 byte Avoid hot foods -Radio/TV
+D25_3_6_26 byte Avoid hot foods -Books/Newspaper/Poster/ Billboard
+D25_3_6_27 byte Avoid hot foods -Internet/website
+D25_3_6_28 byte Avoid hot foods -Jatra/Pala/Cinema
+D25_3_6_29 byte Avoid hot foods -BRAC training course
+D25_3_6_30 byte Avoid hot foods -other
+D25_2_6A double During pregnancy, women should eat less than usual -ever heard
+D25_3_6A_01 byte During pregnancy, women should eat less than usual -Hospital/UHC
+D25_3_6A_02 byte During pregnancy, women should eat less than usual -Doctor
+D25_3_6A_03 byte During pregnancy, women should eat less than usual -Nurse/Midwife
+D25_3_6A_04 byte During pregnancy, women should eat less than usual -FWA/HA
+D25_3_6A_05 byte During pregnancy, women should eat less than usual -FWV
+D25_3_6A_06 byte During pregnancy, women should eat less than usual -CHCP
+D25_3_6A_07 byte During pregnancy, women should eat less than usual -SS
+D25_3_6A_08 byte During pregnancy, women should eat less than usual -SK
+D25_3_6A_09 byte During pregnancy, women should eat less than usual -NGO workers
+D25_3_6A_10 byte During pregnancy, women should eat less than usual -TTBA
+D25_3_6A_11 byte During pregnancy, women should eat less than usual -TBA
+D25_3_6A_12 byte During pregnancy, women should eat less than usual -Village Doctor
+D25_3_6A_13 byte During pregnancy, women should eat less than usual -Homeopath doctor
+D25_3_6A_14 byte During pregnancy, women should eat less than usual -Kabiraj/Herbal healer
+D25_3_6A_15 byte During pregnancy, women should eat less than usual -Spiritual healer
+D25_3_6A_16 byte During pregnancy, women should eat less than usual -Pharmacy
+D25_3_6A_17 byte During pregnancy, women should eat less than usual -Husband
+D25_3_6A_18 byte During pregnancy, women should eat less than usual -Mother/Mother-in-law
+D25_3_6A_19 byte During pregnancy, women should eat less than usual -Other HH members
+D25_3_6A_20 byte During pregnancy, women should eat less than usual -Neighbor/friends
+D25_3_6A_21 byte During pregnancy, women should eat less than usual -Private clinic
+D25_3_6A_22 byte During pregnancy, women should eat less than usual -Community clinic
+D25_3_6A_23 byte During pregnancy, women should eat less than usual -EPI
+D25_3_6A_24 byte During pregnancy, women should eat less than usual -No one/never needed advice
+D25_3_6A_25 byte During pregnancy, women should eat less than usual -Radio/TV
+D25_3_6A_26 byte During pregnancy, women should eat less than usual -Books/Newspaper/Poster/ Billboard
+D25_3_6A_27 byte During pregnancy, women should eat less than usual -Internet/website
+D25_3_6A_28 byte During pregnancy, women should eat less than usual -Jatra/Pala/Cinema
+D25_3_6A_29 byte During pregnancy, women should eat less than usual -BRAC training course
+D25_3_6A_30 byte During pregnancy, women should eat less than usual -other
+D25_2_7 double Daily consumption of fruits during pregnancy  -ever heard
+D25_3_7_01 byte Daily consumption of fruits during pregnancy -Hospital/UHC
+D25_3_7_02 byte Daily consumption of fruits during pregnancy -Doctor
+D25_3_7_03 byte Daily consumption of fruits during pregnancy -Nurse/Midwife
+D25_3_7_04 byte Daily consumption of fruits during pregnancy -FWA/HA
+D25_3_7_05 byte Daily consumption of fruits during pregnancy -FWV
+D25_3_7_06 byte Daily consumption of fruits during pregnancy -CHCP
+D25_3_7_07 byte Daily consumption of fruits during pregnancy -SS
+D25_3_7_08 byte Daily consumption of fruits during pregnancy -SK
+D25_3_7_09 byte Daily consumption of fruits during pregnancy -NGO workers
+D25_3_7_10 byte Daily consumption of fruits during pregnancy -TTBA
+D25_3_7_11 byte Daily onsumption of fruits during pregnancy -TBA
+D25_3_7_12 byte Daily consumption of fruits during pregnancy -Village Doctor
+D25_3_7_13 byte Daily consumption of fruits during pregnancy -Homeopath doctor
+D25_3_7_14 byte Daily consumption of fruits during pregnancy -Kabiraj/Herbal healer
+D25_3_7_15 byte Daily consumption of fruits during pregnancy -Spiritual healer
+D25_3_7_16 byte Daily consumption of fruits during pregnancy -Pharmacy
+D25_3_7_17 byte Daily consumption of fruits during pregnancy -Husband
+D25_3_7_18 byte Daily consumption of fruits during pregnancy -Mother/Mother-in-law
+D25_3_7_19 byte Daily consumption of fruits during pregnancy -Other HH members
+D25_3_7_20 byte Daily consumption of fruits during pregnancy -Neighbor/friends
+D25_3_7_21 byte Daily consumption of fruits during pregnancy -Private clinic
+D25_3_7_22 byte Daily consumption of fruits during pregnancy -Community clinic
+D25_3_7_23 byte Daily consumption of fruits during pregnancy -EPI
+D25_3_7_24 byte Daily consumption of fruits during pregnancy -No one/never needed advice
+D25_3_7_25 byte Daily consumption of fruits during pregnancy -Radio/TV
+D25_3_7_26 byte Daily consumption of fruits during pregnancy -Books/Newspaper/Poster/ Billboard
+D25_3_7_27 byte Daily consumption of fruits during pregnancy -Internet/website
+D25_3_7_28 byte Daily consumption of fruits during pregnancy -Jatra/Pala/Cinema
+D25_3_7_29 byte Daily consumption of fruits during pregnancy -BRAC training course
+D25_3_7_30 byte Daily consumption of fruits during pregnancy -other
+D25_2_8 double Daily consumption of fish/meat/egg  -ever heard
+D25_3_8_01 byte Daily consumption of fish/meat/egg -Hospital/UHC
+D25_3_8_02 byte Daily consumption of fish/meat/egg -Doctor
+D25_3_8_03 byte Daily consumption of fish/meat/egg -Nurse/Midwife
+D25_3_8_04 byte Daily consumption of fish/meat/egg -FWA/HA
+D25_3_8_05 byte Daily consumption of fish/meat/egg -FWV
+D25_3_8_06 byte Daily consumption of fish/meat/egg -CHCP
+D25_3_8_07 byte Daily consumption of fish/meat/egg -SS
+D25_3_8_08 byte Daily consumption of fish/meat/egg -SK
+D25_3_8_09 byte Daily consumption of fish/meat/egg -NGO workers
+D25_3_8_10 byte Daily consumption of fish/meat/egg -TTBA
+D25_3_8_11 byte Daily consumption of fish/meat/egg -TBA
+D25_3_8_12 byte Daily consumption of fish/meat/egg -Village Doctor
+D25_3_8_13 byte Daily consumption of fish/meat/egg -Homeopath doctor
+D25_3_8_14 byte Daily consumption of fish/meat/egg -Kabiraj/Herbal healer
+D25_3_8_15 byte Daily consumption of fish/meat/egg -Spiritual healer
+D25_3_8_16 byte Daily consumption of fish/meat/egg -Pharmacy
+D25_3_8_17 byte Daily consumption of fish/meat/egg -Husband
+D25_3_8_18 byte Daily consumption of fish/meat/egg -Mother/Mother-in-law
+D25_3_8_19 byte Daily consumption of fish/meat/egg -Other HH members
+D25_3_8_20 byte Daily consumption of fish/meat/egg -Neighbor/friends
+D25_3_8_21 byte Daily consumption of fish/meat/egg -Private clinic
+D25_3_8_22 byte Daily consumption of fish/meat/egg -Community clinic
+D25_3_8_23 byte Daily consumption of fish/meat/egg -EPI
+D25_3_8_24 byte Daily consumption of fish/meat/egg -No one/never needed advice
+D25_3_8_25 byte Daily consumption of fish/meat/egg -Radio/TV
+D25_3_8_26 byte Daily consumption of fish/meat/egg -Books/Newspaper/Poster/ Billboard
+D25_3_8_27 byte Daily consumption of fish/meat/egg -Internet/website
+D25_3_8_28 byte Daily consumption of fish/meat/egg -Jatra/Pala/Cinema
+D25_3_8_29 byte Daily consumption of fish/meat/egg -BRAC training course
+D25_3_8_30 byte Daily consumption of fish/meat/egg -other
+D25_2_9 double Avoid some kinds of fish -ever heard
+D25_3_9_01 byte Avoid some kinds of fish -Hospital/UHC
+D25_3_9_02 byte Avoid some kinds of fish -Doctor
+D25_3_9_03 byte Avoid some kinds of fish -Nurse/Midwife
+D25_3_9_04 byte Avoid some kinds of fish -FWA/HA
+D25_3_9_05 byte Avoid some kinds of fish -FWV
+D25_3_9_06 byte Avoid some kinds of fish -CHCP
+D25_3_9_07 byte Avoid some kinds of fish -SS
+D25_3_9_08 byte Avoid some kinds of fish -SK
+D25_3_9_09 byte Avoid some kinds of fish -NGO workers
+D25_3_9_10 byte Avoid some kinds of fish -TTBA
+D25_3_9_11 byte Avoid some kinds of fish -TBA
+D25_3_9_12 byte Avoid some kinds of fish -Village Doctor
+D25_3_9_13 byte Avoid some kinds of fish -Homeopath doctor
+D25_3_9_14 byte Avoid some kinds of fish -Kabiraj/Herbal healer
+D25_3_9_15 byte Avoid some kinds of fish -Spiritual healer
+D25_3_9_16 byte Avoid some kinds of fish -Pharmacy
+D25_3_9_17 byte Avoid some kinds of fish -Husband
+D25_3_9_18 byte Avoid some kinds of fish -Mother/Mother-in-law
+D25_3_9_19 byte Avoid some kinds of fish -Other HH members
+D25_3_9_20 byte Avoid some kinds of fish -Neighbor/friends
+D25_3_9_21 byte Avoid some kinds of fish -Private clinic
+D25_3_9_22 byte Avoid some kinds of fish -Community clinic
+D25_3_9_23 byte Avoid some kinds of fish -EPI
+D25_3_9_24 byte Avoid some kinds of fish -No one/never needed advice
+D25_3_9_25 byte Avoid some kinds of fish -Radio/TV
+D25_3_9_26 byte Avoid some kinds of fish -Books/Newspaper/Poster/ Billboard
+D25_3_9_27 byte Avoid some kinds of fish -Internet/website
+D25_3_9_28 byte Avoid some kinds of fish -Jatra/Pala/Cinema
+D25_3_9_29 byte Avoid some kinds of fish -BRAC training course
+D25_3_9_30 byte Avoid some kinds of fish -other
+D25_2_10 double During pregnancy, take one IFA tablet everyday -ever heard
+D25_3_10_01 byte During pregnancy, take one IFA tablet everyday -Hospital/UHC
+D25_3_10_02 byte During pregnancy, take one IFA tablet everyday -Doctor
+D25_3_10_03 byte During pregnancy, take one IFA tablet everyday -Nurse/Midwife
+D25_3_10_04 byte During pregnancy, take one IFA tablet everyday -FWA/HA
+D25_3_10_05 byte During pregnancy, take one IFA tablet everyday -FWV
+D25_3_10_06 byte During pregnancy, take one IFA tablet everyday -CHCP
+D25_3_10_07 byte During pregnancy, take one IFA tablet everyday -SS
+D25_3_10_08 byte During pregnancy, take one IFA tablet everyday -SK
+D25_3_10_09 byte During pregnancy, take one IFA tablet everyday -NGO workers
+D25_3_10_10 byte During pregnancy, take one IFA tablet everyday -TTBA
+D25_3_10_11 byte During pregnancy, take one IFA tablet everyday -TBA
+D25_3_10_12 byte During pregnancy, take one IFA tablet everyday -Village Doctor
+D25_3_10_13 byte During pregnancy, take one IFA tablet everyday -Homeopath doctor
+D25_3_10_14 byte During pregnancy, take one IFA tablet everyday -Kabiraj/Herbal healer
+D25_3_10_15 byte During pregnancy, take one IFA tablet everyday -Spiritual healer
+D25_3_10_16 byte During pregnancy, take one IFA tablet everyday -Pharmacy
+D25_3_10_17 byte During pregnancy, take one IFA tablet everyday -Husband
+D25_3_10_18 byte During pregnancy, take one IFA tablet everyday -Mother/Mother-in-law
+D25_3_10_19 byte During pregnancy, take one IFA tablet everyday -Other HH members
+D25_3_10_20 byte During pregnancy, take one IFA tablet everyday -Neighbor/friends
+D25_3_10_21 byte During pregnancy, take one IFA tablet everyday -Private clinic
+D25_3_10_22 byte During pregnancy, take one IFA tablet everyday -Community clinic
+D25_3_10_23 byte During pregnancy, take one IFA tablet everyday -EPI
+D25_3_10_24 byte During pregnancy, take one IFA tablet everyday -No one/never needed advice
+D25_3_10_25 byte During pregnancy, take one IFA tablet everyday -Radio/TV
+D25_3_10_26 byte During pregnancy, take one IFA tablet everyday -Books/Newspaper/Poster/ Billboard
+D25_3_10_27 byte During pregnancy, take one IFA tablet everyday -Internet/website
+D25_3_10_28 byte During pregnancy, take one IFA tablet everyday -Jatra/Pala/Cinema
+D25_3_10_29 byte During pregnancy, take one IFA tablet everyday -BRAC training course
+D25_3_10_30 byte During pregnancy, take one IFA tablet everyday -other
+D25_2_11 double During pregnancy, take one Ca tablet everyday -ever heard
+D25_3_11_01 byte During pregnancy, take one Ca tablet everyday -Hospital/UHC
+D25_3_11_02 byte During pregnancy, take one Ca tablet everyday -Doctor
+D25_3_11_03 byte During pregnancy, take one Ca tablet everyday -Nurse/Midwife
+D25_3_11_04 byte During pregnancy, take one Ca tablet everyday -FWA/HA
+D25_3_11_05 byte During pregnancy, take one Ca tablet everyday -FWV
+D25_3_11_06 byte During pregnancy, take one Ca tablet everyday -CHCP
+D25_3_11_07 byte During pregnancy, take one Ca tablet everyday -SS
+D25_3_11_08 byte During pregnancy, take one Ca tablet everyday -SK
+D25_3_11_09 byte During pregnancy, take one Ca tablet everyday -NGO workers
+D25_3_11_10 byte During pregnancy, take one Ca tablet everyday -TTBA
+D25_3_11_11 byte During pregnancy, take one Ca tablet everyday -TBA
+D25_3_11_12 byte During pregnancy, take one Ca tablet everyday -Village Doctor
+D25_3_11_13 byte During pregnancy, take one Ca tablet everyday -Homeopath doctor
+D25_3_11_14 byte During pregnancy, take one Ca tablet everyday -Kabiraj/Herbal healer
+D25_3_11_15 byte During pregnancy, take one Ca tablet everyday -Spiritual healer
+D25_3_11_16 byte During pregnancy, take one Ca tablet everyday -Pharmacy
+D25_3_11_17 byte During pregnancy, take one Ca tablet everyday -Husband
+D25_3_11_18 byte During pregnancy, take one Ca tablet everyday -Mother/Mother-in-law
+D25_3_11_19 byte During pregnancy, take one Ca tablet everyday -Other HH members
+D25_3_11_20 byte During pregnancy, take one Ca tablet everyday -Neighbor/friends
+D25_3_11_21 byte During pregnancy, take one Ca tablet everyday -Private clinic
+D25_3_11_22 byte During pregnancy, take one Ca tablet everyday -Community clinic
+D25_3_11_23 byte During pregnancy, take one Ca tablet everyday -EPI
+D25_3_11_24 byte During pregnancy, take one Ca tablet everyday -No one/never needed advice
+D25_3_11_25 byte During pregnancy, take one Ca tablet everyday -Radio/TV
+D25_3_11_26 byte During pregnancy, take one Ca tablet everyday -Books/Newspaper/Poster/ Billboard
+D25_3_11_27 byte During pregnancy, take one Ca tablet everyday -Internet/website
+D25_3_11_28 byte During pregnancy, take one Ca tablet everyday -Jatra/Pala/Cinema
+D25_3_11_29 byte During pregnancy, take one Ca tablet everyday -BRAC training course
+D25_3_11_30 byte During pregnancy, take one Ca tablet everyday -other
+D25_2_12 double During pregnancy, take at least two hours of rest -ever heard
+D25_3_12_01 byte During pregnancy, take at least two hours of rest -Hospital/UHC
+D25_3_12_02 byte During pregnancy, take at least two hours of rest -Doctor
+D25_3_12_03 byte During pregnancy, take at least two hours of rest -Nurse/Midwife
+D25_3_12_04 byte During pregnancy, take at least two hours of rest -FWA/HA
+D25_3_12_05 byte During pregnancy, take at least two hours of rest -FWV
+D25_3_12_06 byte During pregnancy, take at least two hours of rest -CHCP
+D25_3_12_07 byte During pregnancy, take at least two hours of rest -SS
+D25_3_12_08 byte During pregnancy, take at least two hours of rest -SK
+D25_3_12_09 byte During pregnancy, take at least two hours of rest -NGO workers
+D25_3_12_10 byte During pregnancy, take at least two hours of rest -TTBA
+D25_3_12_11 byte During pregnancy, take at least two hours of rest -TBA
+D25_3_12_12 byte During pregnancy, take at least two hours of rest -Village Doctor
+D25_3_12_13 byte During pregnancy, take at least two hours of rest -Homeopath doctor
+D25_3_12_14 byte During pregnancy, take at least two hours of rest -Kabiraj/Herbal healer
+D25_3_12_15 byte During pregnancy, take at least two hours of rest -Spiritual healer
+D25_3_12_16 byte During pregnancy, take at least two hours of rest -Pharmacy
+D25_3_12_17 byte During pregnancy, take at least two hours of rest -Husband
+D25_3_12_18 byte During pregnancy, take at least two hours of rest -Mother/Mother-in-law
+D25_3_12_19 byte During pregnancy, take at least two hours of rest -Other HH members
+D25_3_12_20 byte During pregnancy, take at least two hours of rest -Neighbor/friends
+D25_3_12_21 byte During pregnancy, take at least two hours of rest -Private clinic
+D25_3_12_22 byte During pregnancy, take at least two hours of rest -Community clinic
+D25_3_12_23 byte During pregnancy, take at least two hours of rest -EPI
+D25_3_12_24 byte During pregnancy, take at least two hours of rest -No one/never needed advice
+D25_3_12_25 byte During pregnancy, take at least two hours of rest -Radio/TV
+D25_3_12_26 byte During pregnancy, take at least two hours of rest -Books/Newspaper/Poster/ Billboard
+D25_3_12_27 byte During pregnancy, take at least two hours of rest -Internet/website
+D25_3_12_28 byte During pregnancy, take at least two hours of rest -Jatra/Pala/Cinema
+D25_3_12_29 byte During pregnancy, take at least two hours of rest -BRAC training course
+D25_3_12_30 byte During pregnancy, take at least two hours of rest -other
+D25_2_13 double Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -ever heard
+D25_3_13_01 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Hospital/UHC
+D25_3_13_02 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Doctor
+D25_3_13_03 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Nurse/Midwife
+D25_3_13_04 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -FWA/HA
+D25_3_13_05 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -FWV
+D25_3_13_06 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -CHCP
+D25_3_13_07 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -SS
+D25_3_13_08 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -SK
+D25_3_13_09 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -NGO worker
+D25_3_13_10 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -TTBA
+D25_3_13_11 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -TBA
+D25_3_13_12 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Village Doctor
+D25_3_13_13 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Homeopath doctor
+D25_3_13_14 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Kabiraj/Herbal healer
+D25_3_13_15 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Spiritual healer
+D25_3_13_16 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Pharmacy
+D25_3_13_17 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Husband
+D25_3_13_18 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Mother/Mother-in-law
+D25_3_13_19 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Other HH members
+D25_3_13_20 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Neighbor/friends
+D25_3_13_21 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Private clinic
+D25_3_13_22 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Community clinic
+D25_3_13_23 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -EPI
+D25_3_13_24 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -No one/never needed advice
+D25_3_13_25 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Radio/TV
+D25_3_13_26 byte
+Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -
+Books/Newspaper/Poster/Billboard
+D25_3_13_27 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Internet/website
+D25_3_13_28 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -Jatra/Pala/Cinema
+D25_3_13_29 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -BRAC training course
+D25_3_13_30 byte Do not lay down on the bed, eat, cook during a lunar/ solar eclipses -other
+D25_2_14 double consume at least 1 food item from 5 different food groups -ever heard
+D25_3_14_01 byte consume at least 1 food item from 5 different food groups -Hospital/UHC
+D25_3_14_02 byte consume at least 1 food item from 5 different food groups -Doctor
+D25_3_14_03 byte consume at least 1 food item from 5 different food groups -Nurse/Midwife
+D25_3_14_04 byte consume at least 1 food item from 5 different food groups -FWA/HA
+D25_3_14_05 byte consume at least 1 food item from 5 different food groups -FWV
+D25_3_14_06 byte consume at least 1 food item from 5 different food groups -CHCP
+D25_3_14_07 byte consume at least 1 food item from 5 different food groups -SS
+D25_3_14_08 byte consume at least 1 food item from 5 different food groups -SK
+D25_3_14_09 byte consume at least 1 food item from 5 different food groups -NGO workers
+D25_3_14_10 byte consume at least 1 food item from 5 different food groups -TTBA
+D25_3_14_11 byte consume at least 1 food item from 5 different food groups -TBA
+D25_3_14_12 byte consume at least 1 food item from 5 different food groups -Village Doctor
+D25_3_14_13 byte consume at least 1 food item from 5 different food groups -Homeopath doctor
+D25_3_14_14 byte consume at least 1 food item from 5 different food groups -Kabiraj/Herbal healer
+D25_3_14_15 byte consume at least 1 food item from 5 different food groups -Spiritual healer
+D25_3_14_16 byte consume at least 1 food item from 5 different food groups -Pharmacy
+D25_3_14_17 byte consume at least 1 food item from 5 different food groups -Husband
+D25_3_14_18 byte consume at least 1 food item from 5 different food groups -Mother/Mother-in-law
+D25_3_14_19 byte consume at least 1 food item from 5 different food groups -Other HH members
+D25_3_14_20 byte consume at least 1 food item from 5 different food groups -Neighbor/friends
+D25_3_14_21 byte consume at least 1 food item from 5 different food groups -Private clinic
+D25_3_14_22 byte consume at least 1 food item from 5 different food groups -Community clinic
+D25_3_14_23 byte consume at least 1 food item from 5 different food groups -EPI
+D25_3_14_24 byte consume at least 1 food item from 5 different food groups -No one/never needed advice
+D25_3_14_25 byte consume at least 1 food item from 5 different food groups -Radio/TV
+D25_3_14_26 byte consume at least 1 food item from 5 different food groups -Books/Newspaper/Poster/Billboard
+D25_3_14_27 byte consume at least 1 food item from 5 different food groups -Internet/website
+D25_3_14_28 byte consume at least 1 food item from 5 different food groups -Jatra/Pala/Cinema
+D25_3_14_29 byte consume at least 1 food item from 5 different food groups -BRAC training course
+D25_3_14_30 byte consume at least 1 food item from 5 different food groups -other
+D25_2_15 double Proper diet ensure that the child will be brainy -ever heard
+D25_3_15_01 byte Proper diet ensure that the child will be brainy -Hospital/UHC
+D25_3_15_02 byte Proper diet ensure that the child will be brainy -Doctor
+D25_3_15_03 byte Proper diet ensure that the child will be brainy -Nurse/Midwife
+D25_3_15_04 byte Proper diet ensure that the child will be brainy -FWA/HA
+D25_3_15_05 byte Proper diet ensure that the child will be brainy -FWV
+D25_3_15_06 byte Proper diet ensure that the child will be brainy -CHCP
+D25_3_15_07 byte Proper diet ensure that the child will be brainy -SS
+D25_3_15_08 byte Proper diet ensure that the child will be brainy -SK
+D25_3_15_09 byte Proper diet ensure that the child will be brainy -NGO workers
+D25_3_15_10 byte Proper diet ensure that the child will be brainy -TTBA
+D25_3_15_11 byte Proper diet ensure that the child will be brainy -TBA
+D25_3_15_12 byte Proper diet ensure that the child will be brainy -Village Doctor
+D25_3_15_13 byte Proper diet ensure that the child will be brainy -Homeopath doctor
+D25_3_15_14 byte Proper diet ensure that the child will be brainy -Kabiraj/Herbal healer
+D25_3_15_15 byte Proper diet ensure that the child will be brainy -Spiritual healer
+D25_3_15_16 byte Proper diet ensure that the child will be brainy -Pharmacy
+D25_3_15_17 byte Proper diet ensure that the child will be brainy -Husband
+D25_3_15_18 byte Proper diet ensure that the child will be brainy -Mother/Mother-in-law
+D25_3_15_19 byte Proper diet ensure that the child will be brainy -Other HH members
+D25_3_15_20 byte Proper diet ensure that the child will be brainy -Neighbor/friends
+D25_3_15_21 byte Proper diet ensure that the child will be brainy -Private clinic
+D25_3_15_22 byte Proper diet ensure that the child will be brainy -Community clinic
+D25_3_15_23 byte Proper diet ensure that the child will be brainy -EPI
+D25_3_15_24 byte Proper diet ensure that the child will be brainy -No one/never needed advice
+D25_3_15_25 byte Proper diet ensure that the child will be brainy -Radio/TV
+D25_3_15_26 byte Proper diet ensure that the child will be brainy -Books/Newspaper/Poster/ Billboard
+D25_3_15_27 byte Proper diet ensure that the child will be brainy -Internet/website
+D25_3_15_28 byte Proper diet ensure that the child will be brainy -Jatra/Pala/Cinema
+D25_3_15_29 byte Proper diet ensure that the child will be brainy -BRAC training course
+D25_3_15_30 byte Proper diet ensure that the child will be brainy -other
+D25_2_16 double Avoid tea/coffee -ever heard
+D25_3_16_01 byte Avoid tea/coffee -Hospital/UHC
+D25_3_16_02 byte Avoid tea/coffee -Doctor
+D25_3_16_03 byte Avoid tea/coffee -Nurse/Midwife
+D25_3_16_04 byte Avoid tea/coffee -FWA/HA
+D25_3_16_05 byte Avoid tea/coffee -FWV
+D25_3_16_06 byte Avoid tea/coffee -CHCP
+D25_3_16_07 byte Avoid tea/coffee -SS
+D25_3_16_08 byte Avoid tea/coffee -SK
+D25_3_16_09 byte Avoid tea/coffee -NGO workers
+D25_3_16_10 byte Avoid tea/coffee -TTBA
+D25_3_16_11 byte Avoid tea/coffee -TBA
+D25_3_16_12 byte Avoid tea/coffee -Village Doctor
+D25_3_16_13 byte Avoid tea/coffee -Homeopath doctor
+D25_3_16_14 byte Avoid tea/coffee -Kabiraj/Herbal healer
+D25_3_16_15 byte Avoid tea/coffee -Spiritual healer
+D25_3_16_16 byte Avoid tea/coffee -Pharmacy
+D25_3_16_17 byte Avoid tea/coffee -Husband
+D25_3_16_18 byte Avoid tea/coffee -Mother/Mother-in-law
+D25_3_16_19 byte Avoid tea/coffee -Other HH members
+D25_3_16_20 byte Avoid tea/coffee -Neighbor/friends
+D25_3_16_21 byte Avoid tea/coffee -Private clinic
+D25_3_16_22 byte Avoid tea/coffee -Community clinic
+D25_3_16_23 byte Avoid tea/coffee -EPI
+D25_3_16_24 byte Avoid tea/coffee -No one/never needed advice
+D25_3_16_25 byte Avoid tea/coffee -Radio/TV
+D25_3_16_26 byte Avoid tea/coffee -Books/Newspaper/Poster/ Billboard
+D25_3_16_27 byte Avoid tea/coffee -Internet/website
+D25_3_16_28 byte Avoid tea/coffee -Jatra/Pala/Cinema
+D25_3_16_29 byte Avoid tea/coffee -BRAC training course
+D25_3_16_30 byte Avoid tea/coffee -other
+D25_2_17 double Avoid alcohol/tobacco/betel leaf/betel nut -ever heard
+D25_3_17_01 byte Avoid alcohol/tobacco/betel leaf/betel nut -Hospital/UHC
+D25_3_17_02 byte Avoid alcohol/tobacco/betel leaf/betel nut -Doctor
+D25_3_17_03 byte Avoid alcohol/tobacco/betel leaf/betel nut -Nurse/Midwife
+D25_3_17_04 byte Avoid alcohol/tobacco/betel leaf/betel nut -FWA/HA
+D25_3_17_05 byte Avoid alcohol/tobacco/betel leaf/betel nut -FWV
+D25_3_17_06 byte Avoid alcohol/tobacco/betel leaf/betel nut -CHCP
+D25_3_17_07 byte Avoid alcohol/tobacco/betel leaf/betel nut -SS
+D25_3_17_08 byte Avoid alcohol/tobacco/betel leaf/betel nut -SK
+D25_3_17_09 byte Avoid alcohol/tobacco/betel leaf/betel nut -NGO workers
+D25_3_17_10 byte Avoid alcohol/tobacco/betel leaf/betel nut -TTBA
+D25_3_17_11 byte Avoid alcohol/tobacco/betel leaf/betel nut -TBA
+D25_3_17_12 byte Avoid alcohol/tobacco/betel leaf/betel nut -Village Doctor
+D25_3_17_13 byte Avoid alcohol/tobacco/betel leaf/betel nut -Homeopath doctor
+D25_3_17_14 byte Avoid alcohol/tobacco/betel leaf/betel nut -Kabiraj/Herbal healer
+D25_3_17_15 byte Avoid alcohol/tobacco/betel leaf/betel nut -Spiritual healer
+D25_3_17_16 byte Avoid alcohol/tobacco/betel leaf/betel nut -Pharmacy
+D25_3_17_17 byte Avoid alcohol/tobacco/betel leaf/betel nut -Husband
+D25_3_17_18 byte Avoid alcohol/tobacco/betel leaf/betel nut -Mother/Mother-in-law
+D25_3_17_19 byte Avoid alcohol/tobacco/betel leaf/betel nut -Other HH members
+D25_3_17_20 byte Avoid alcohol/tobacco/betel leaf/betel nut -Neighbor/friends
+D25_3_17_21 byte Avoid alcohol/tobacco/betel leaf/betel nut -Private clinic
+D25_3_17_22 byte Avoid alcohol/tobacco/betel leaf/betel nut -Community clinic
+D25_3_17_23 byte Avoid alcohol/tobacco/betel leaf/betel nut -EPI
+D25_3_17_24 byte Avoid alcohol/tobacco/betel leaf/betel nut -No one/never needed advice
+D25_3_17_25 byte Avoid alcohol/tobacco/betel leaf/betel nut -Radio/TV
+D25_3_17_26 byte Avoid alcohol/tobacco/betel leaf/betel nut -Books/Newspaper/Poster/ Billboard
+D25_3_17_27 byte Avoid alcohol/tobacco/betel leaf/betel nut -Internet/website
+D25_3_17_28 byte Avoid alcohol/tobacco/betel leaf/betel nut -Jatra/Pala/Cinema
+D25_3_17_29 byte Avoid alcohol/tobacco/betel leaf/betel nut -BRAC training course
+D25_3_17_30 byte Avoid alcohol/tobacco/betel leaf/betel nut -other
+D25_2_18 double NB babies should be placed on breast immediately after delivery -ever heard
+D25_3_18_01 byte NB babies should be placed on breast immediately after delivery -Hospital/UHC
+D25_3_18_02 byte NB babies should be placed on breast immediately after delivery -Doctor
+D25_3_18_03 byte NB babies should be placed on breast immediately after delivery -Nurse/Midwife
+D25_3_18_04 byte NB babies should be placed on breast immediately after delivery -FWA/HA
+D25_3_18_05 byte NB babies should be placed on breast immediately after delivery -FWV
+D25_3_18_06 byte NB babies should be placed on breast immediately after delivery -CHCP
+D25_3_18_07 byte NB babies should be placed on breast immediately after delivery -SS
+D25_3_18_08 byte NB babies should be placed on breast immediately after delivery -SK
+D25_3_18_09 byte NB babies should be placed on breast immediately after delivery -NGO workers
+D25_3_18_10 byte NB babies should be placed on breast immediately after delivery -TTBA
+D25_3_18_11 byte NB babies should be placed on breast immediately after delivery -TBA
+D25_3_18_12 byte NB babies should be placed on breast immediately after delivery -Village Doctor
+D25_3_18_13 byte NB babies should be placed on breast immediately after delivery -Homeopath doctor
+D25_3_18_14 byte NB babies should be placed on breast immediately after delivery -Kabiraj/Herbal healer
+D25_3_18_15 byte NB babies should be placed on breast immediately after delivery -Spiritual healer
+D25_3_18_16 byte NB babies should be placed on breast immediately after delivery -Pharmacy
+D25_3_18_17 byte NB babies should be placed on breast immediately after delivery -Husband
+D25_3_18_18 byte NB babies should be placed on breast immediately after delivery -Mother/Mother-in-law
+D25_3_18_19 byte NB babies should be placed on breast immediately after delivery -Other HH member
+D25_3_18_20 byte NB babies should be placed on breast immediately after delivery -Neighbor/friend
+D25_3_18_21 byte NB babies should be placed on breast immediately after delivery -Private clinic
+D25_3_18_22 byte NB babies should be placed on breast immediately after delivery -Community clinic
+D25_3_18_23 byte NB babies should be placed on breast immediately after delivery -EPI
+D25_3_18_24 byte NB babies should be placed on breast immediately after delivery -No one/never needed advice
+D25_3_18_25 byte NB babies should be placed on breast immediately after delivery -Radio/TV
+D25_3_18_26 byte
+NB babies should be placed on breast immediately after delivery -
+Books/Newspaper/Poster/Billboard
+D25_3_18_27 byte NB babies should be placed on breast immediately after delivery -Internet/website
+D25_3_18_28 byte NB babies should be placed on breast immediately after delivery -Jatra/Pala/Cinema
+D25_3_18_29 byte NB babies should be placed on breast immediately after delivery -BRAC training course
+D25_3_18_30 byte NB babies should be placed on breast immediately after delivery -other
+D25_2_19 double No water, honey or sugar water -ever heard
+D25_3_19_01 byte No water, honey or sugar water -Hospital/UHC
+D25_3_19_02 byte No water, honey or sugar water -Doctor
+D25_3_19_03 byte No water, honey or sugar water -Nurse/Midwife
+D25_3_19_04 byte No water, honey or sugar water -FWA/HA
+D25_3_19_05 byte No water, honey or sugar water -FWV
+D25_3_19_06 byte No water, honey or sugar water -CHCP
+D25_3_19_07 byte No water, honey or sugar water -SS
+D25_3_19_08 byte No water, honey or sugar water -SK
+D25_3_19_09 byte No water, honey or sugar water -NGO workers
+D25_3_19_10 byte No water, honey or sugar water -TTBA
+D25_3_19_11 byte No water, honey or sugar water -TBA
+D25_3_19_12 byte No water, honey or sugar water -Village Doctor
+D25_3_19_13 byte No water, honey or sugar water -Homeopath doctor
+D25_3_19_14 byte No water, honey or sugar water -Kabiraj/Herbal healer
+D25_3_19_15 byte No water, honey or sugar water -Spiritual healer
+D25_3_19_16 byte No water, honey or sugar water -Pharmacy
+D25_3_19_17 byte No water, honey or sugar water -Husband
+D25_3_19_18 byte No water, honey or sugar water -Mother/Mother-in-law
+D25_3_19_19 byte No water, honey or sugar water -Other HH members
+D25_3_19_20 byte No water, honey or sugar water -Neighbor/friends
+D25_3_19_21 byte No water, honey or sugar water -Private clinic
+D25_3_19_22 byte No water, honey or sugar water -Community clinic
+D25_3_19_23 byte No water, honey or sugar water -EPI
+D25_3_19_24 byte No water, honey or sugar water -No one/never needed advice
+D25_3_19_25 byte No water, honey or sugar water -Radio/TV
+D25_3_19_26 byte No water, honey or sugar water -Books/Newspaper/Poster/ Billboard
+D25_3_19_27 byte No water, honey or sugar water -Internet/website
+D25_3_19_28 byte No water, honey or sugar water -Jatra/Pala/Cinema
+D25_3_19_29 byte No water, honey or sugar water -BRAC training course
+D25_3_19_30 byte No water, honey or sugar water -other
+D25_2_20 double only breastmilk for the first six months -ever heard
+D25_3_20_01 byte only breastmilk for the first six months -Hospital/UHC
+D25_3_20_02 byte only breastmilk for the first six months -Doctor
+D25_3_20_03 byte only breastmilk for the first six months -Nurse/Midwife
+D25_3_20_04 byte only breastmilk for the first six months -FWA/HA
+D25_3_20_05 byte only breastmilk for the first six months -FWV
+D25_3_20_06 byte only breastmilk for the first six months -CHCP
+D25_3_20_07 byte only breastmilk for the first six months -SS
+D25_3_20_08 byte only breastmilk for the first six months -SK
+D25_3_20_09 byte only breastmilk for the first six months -NGO workers
+D25_3_20_10 byte only breastmilk for the first six months -TTBA
+D25_3_20_11 byte only breastmilk for the first six months -TBA
+D25_3_20_12 byte only breastmilk for the first six months -Village Doctor
+D25_3_20_13 byte only breastmilk for the first six months -Homeopath doctor
+D25_3_20_14 byte only breastmilk for the first six months -Kabiraj/Herbal healer
+D25_3_20_15 byte only breastmilk for the first six months -Spiritual healer
+D25_3_20_16 byte only breastmilk for the first six months -Pharmacy
+D25_3_20_17 byte only breastmilk for the first six months -Husband
+D25_3_20_18 byte only breastmilk for the first six months -Mother/Mother-in-law
+D25_3_20_19 byte only breastmilk for the first six months -Other HH members
+D25_3_20_20 byte only breastmilk for the first six months -Neighbor/friends
+D25_3_20_21 byte only breastmilk for the first six months -Private clinic
+D25_3_20_22 byte only breastmilk for the first six months -Community clinic
+D25_3_20_23 byte only breastmilk for the first six months -EPI
+D25_3_20_24 byte only breastmilk for the first six months -No one/never needed advice
+D25_3_20_25 byte only breastmilk for the first six months -Radio/TV
+D25_3_20_26 byte only breastmilk for the first six months -Books/Newspaper/Poster/ Billboard
+D25_3_20_27 byte only breastmilk for the first six months -Internet/website
+D25_3_20_28 byte only breastmilk for the first six months -Jatra/Pala/Cinema
+D25_3_20_29 byte only breastmilk for the first six months -BRAC training course
+D25_3_20_30 byte only breastmilk for the first six months -other
+D25_2_21 double PW should not leave their houses in the evening -ever heard
+D25_3_21_01 byte PW should not leave their houses in the evening -Hospital/UHC
+D25_3_21_02 byte PW should not leave their houses in the evening -Doctor
+D25_3_21_03 byte PW should not leave their houses in the evening -Nurse/Midwife
+D25_3_21_04 byte PW should not leave their houses in the evening -FWA/HA
+D25_3_21_05 byte PW should not leave their houses in the evening -FWV
+D25_3_21_06 byte PW should not leave their houses in the evening -CHCP
+D25_3_21_07 byte PW should not leave their houses in the evening -SS
+D25_3_21_08 byte PW should not leave their houses in the evening -SK
+D25_3_21_09 byte PW should not leave their houses in the evening -NGO workers
+D25_3_21_10 byte PW should not leave their houses in the evening -TTBA
+D25_3_21_11 byte PW should not leave their houses in the evening -TBA
+D25_3_21_12 byte PW should not leave their houses in the evening -Village Doctor
+D25_3_21_13 byte PW should not leave their houses in the evening -Homeopath doctor
+D25_3_21_14 byte PW should not leave their houses in the evening -Kabiraj/Herbal healer
+D25_3_21_15 byte PW should not leave their houses in the evening -Spiritual healer
+D25_3_21_16 byte PW should not leave their houses in the evening -Pharmacy
+D25_3_21_17 byte PW should not leave their houses in the evening -Husband
+D25_3_21_18 byte PW should not leave their houses in the evening -Mother/Mother-in-law
+D25_3_21_19 byte PW should not leave their houses in the evening -Other HH members
+D25_3_21_20 byte PW should not leave their houses in the evening -Neighbor/friends
+D25_3_21_21 byte PW should not leave their houses in the evening -Private clinic
+D25_3_21_22 byte PW should not leave their houses in the evening -Community clinic
+D25_3_21_23 byte PW should not leave their houses in the evening -EPI
+D25_3_21_24 byte PW should not leave their houses in the evening -No one/never needed advice
+D25_3_21_25 byte PW should not leave their houses in the evening -Radio/TV
+D25_3_21_26 byte PW should not leave their houses in the evening -Books/Newspaper/Poster/ Billboard
+D25_3_21_27 byte PW should not leave their houses in the evening -Internet/website
+D25_3_21_28 byte PW should not leave their houses in the evening -Jatra/Pala/Cinema
+D25_3_21_29 byte PW should not leave their houses in the evening -BRAC training course
+D25_3_21_30 byte PW should not leave their houses in the evening -other
+E1_2 double E1_2 Do you have any other work outside the home (work other than being a BRAC SK)?
+E1_3 double E1_3 Is that work voluntary or you are paid for that?
+E1_4 double E1_4 How many hours do you spend on this other work (on AVERAGE PER DAY)?
+E1_5 double E1_5 How do you get compensated for this work?
+E1_6 double E1_6 Do you own a mobile phone?
+E1_7 double E1_7 Do you have a microcredit loan from BRAC?
+E1_8_YY double E1_8_YY If yes, how long have you had the loan?
+E1_8_MM double E1_8_MM If yes, how long have you had the loan?
+E1_9 double E1_9 Do you own the house you live in?
+E1_10 double E1_10 Main floor maternial
+E1_11 double E1_11 Main exterior wall material
+E1_12 double E1_12 Main roof material
+E1_13 double E1_13 Do you have a garden where you grow vegetables and/or fruits?
+E1_14 double E1_14 Does your household have any electricity?
+E1_15 double E1_15 Do you have any other kind of electric power? If yes, which type?
+E1_16 double E1_16 What type of fuel does your household mainly use for cooking?
+E1_18 double E1_18 What is the main source of drinking water for members of your household?
+E1_19 double E1_19 What is the main source of water used by your household for cooking?
+E1_20 double E1_20 What is the main source of water used by your household for bathing?
+E1_21 double E1_21 What is the main source of water used by your household for washing utensils?
+E1_22 double E1_22 What kind of toilet facility do members of your household usually use?
+E1_23 double E1_23 Do you share this toilet facility with other households?
+E17_21 double How many are in usable Condition - Metal cooking pots/pans
+E17_22 double How many are in usable Condition - Bucket
+E17_24 double How many are in usable Condition - Plates/Pans
+E17_25 double How many are in usable Condition - Cup/mug
+E17_26 double How many are in usable Condition - Bed/Khat/Chowki
+E17_27 double How many are in usable Condition - Mattress/blanket
+E17_28 double How many are in usable Condition - Table/ Chair
+E17_29 double How many are in usable Condition - Almirah
+E17_210 double How many are in usable Condition - Trunk / Suitcase
+E17_211 double How many are in usable Condition - Electric fan (Ceiling/Table)
+E17_212 double How many are in usable Condition - Table lamp
+E17_213 double How many are in usable Condition - Electric iron
+E17_214 double How many are in usable Condition - Radio
+E17_215 double How many are in usable Condition - Audio cassette/CD player
+E17_216 double How many are in usable Condition - TV
+E17_217 double How many are in usable Condition - Refrigerator
+E17_219 double How many are in usable Condition - Sewing machine
+E17_220 double How many are in usable Condition - Wall clock/wrist watch
+E17_221 double How many are in usable Condition - Camera
+E17_222 double How many are in usable Condition - Bicycle
+E17_223 double How many are in usable Condition - Motorcycle
+E17_225 double How many are in usable Condition - Rickshaw/Van
+E17_226 double How many are in usable Condition - Bullock cart/Push cart
+E17_229 double How many are in usable Condition - Phone/mobile phone
+E17_230 double How many are in usable Condition - Cow/buffalo
+E17_231 double How many are in usable Condition - Goat/sheep
+E17_232 double How many are in usable Condition - Chicken/duck
+E17_233 double How many are in usable Condition - other1
+E17_234 double How many are in usable Condition - other2
+E17_235 double How many are in usable Condition - other3
+F01 double F1 Do you ever watch TV?
+F02 double F2 How often do you watch TV?
+F03_1 byte What time of the day do you watch TV-6AM/ 12PM
+F03_2 byte What time of the day do you watch TV-12PM/ 6PM
+F03_3 byte What time of the day do you watch TV-6PM/ 12AM
+F03_4 byte What time of the day do you watch TV-12AM/ 6AM
+F04_1 byte Which programmes do you watch commonly-News
+F04_2 byte Which programmes do you watch commonly-Music
+F04_3 byte Which programmes do you watch commonly-Children’s program
+F04_4 byte Which programmes do you watch commonly-Sports
+F04_5 byte Which programmes do you watch commonly-Soap opera
+F04_6 byte Which programmes do you watch commonly-Movie
+F04_7 byte Which programmes do you watch commonly-Health/disease programs
+F04_8 byte Which programmes do you watch commonly-Religious program
+F04_9 byte Which programmes do you watch commonly-Other
+F05 double F5 Do you ever listen to the Radio?
+F06 double F6 How often do you listen to the Radio?
+F07_1 byte What time of the day do you listen to the Radio-6 AM/ 12 PM
+F07_2 byte What time of the day do you listen to the Radio-12 PM/ 6 PM
+F07_3 byte What time of the day do you listen to the Radio-6 PM/ 12 AM
+F07_4 byte What time of the day do you listen to the Radio-12 AM/ 6 AM
+F08_1 byte Which programmes do you listen commonly-News
+F08_2 byte Which programmes do you listen commonly-Music
+F08_3 byte Which programmes do you listen commonly-Children program
+F08_4 byte Which programmes do you listen commonly-Sports
+F08_5 byte Which programmes do you listen commonly-Soap opera
+F08_6 byte Which programmes do you listen commonly-Movie
+F08_7 byte Which programmes do you listen commonly-Health/disease programs
+F08_8 byte Which programmes do you listen commonly-Religious program
+F08_9 byte Which programmes do you listen commonly-Other
+F09 double F9 Have you ever seen this advertisement?
+F10 double F10 Have you seen this TV spot in the last 3 months?
+F11 double F11 Where have you seen it?
+F12_01 byte Key messages of TVC-saving money to buy fish
+F12_02 byte Key messages of TVC-grandchild healthy and intelligent
+F12_03 byte Key messages of TVC-PW eating properly & taking rest
+F12_04 byte Key messages of TVC-PW dont do hard work
+F12_05 byte Key messages of TVC-PW should eat five food groups
+F12_06 byte Key messages of TVC-PW should take one IFA tablet
+F12_07 byte Key messages of TVC-PW should take one Calcium tablet
+F12_08 byte Key messages of TVC-Do not take both IFA and calcium together
+F12_09 byte Key messages of TVC-Other
+F12_10 byte Key messages of TVC-DK
+F13 double F13 Have you ever seen this advertisement?
+F14 double F14 Have you seen this TV spot in the last 3 months?
+F15 double F15 Where have you seen it?
+F16_01 byte Key messages of TVC-to gain weight during pregnancy
+F16_02 byte Key messages of TVC-check weight regularly
+F16_03 byte Key messages of TVC-Proper nutrition ensure proper weight gain
+F16_04 byte Key messages of TVC-eat 5 types of nutritious food
+F16_05 byte Key messages of TVC-take 180 IFA tablets
+F16_06 byte Key messages of TVC-take 180 Calcium tablets
+F16_07 byte Key messages of TVC-IFA reduce the risk of anemia
+F16_08 byte Key messages of TVC-IFA reduce the risk of LBW
+F16_09 byte Key messages of TVC-IFA improve child intelligence
+F16_10 byte Key messages of TVC-IFA reduce the risk of excessive blood loss
+F16_11 byte Key messages of TVC-Calcium prevent high blood pressure and eclampsia
+F16_12 byte Key messages of TVC-Calcium help have strong bones and teeth
+F16_13 byte Key messages of TVC-Other
+F16_14 byte Key messages of TVC-DK
+F17 double F17 Have you ever seen this advertisement?
+F18 double F18 Have you seen this TV spot in the last 3 months?
+F19 double F19 Where have you seen it?
+F20_01 byte Key messages of TVC-eat 5 types of nutritious food
+F20_02 byte Key messages of TVC-Increase quantity of foods
+F20_03 byte Key messages of TVC-take iron folic acid everyday
+F20_04 byte Key messages of TVC-take calcium everyday
+F20_05 byte Key messages of TVC-IFA will prevent excessive blood loss
+F20_06 byte Key messages of TVC-Taking IFA will increase child development
+F20_07 byte Key messages of TVC-Taking calcium will prevent high BP
+F20_08 byte Key messages of TVC-Taking calcium make baby have strong bones and teeth
+F20_09 byte Key messages of TVC-Nutritious food not cost too much
+F20_10 byte Key messages of TVC-Husband should save money to buy food for his pregnant wife
+F20_11 byte Key messages of TVC-Using saving to buy nutritious foods for PW
+F20_12 byte Key messages of TVC-Nutritious foods can be produced at home
+F20_13 byte Key messages of TVC-Eat proper nutrition will have healthchild
+F20_14 byte Key messages of TVC-If child grows well, she will have education and earn enough money
+F20_15 byte Key messages of TVC-Other
+F20_16 byte Key messages of TVC-DK
+F21 double F21 Have you ever seen this advertisement?
+F22 double F22 Have you seen this TV spot in the last 3 months?
+F23 double F23 Where have you seen it?
+F24_1 byte Key messages of TVC-fed breast milk within an hour of birth
+F24_2 byte Key messages of TVC-fed BM immediately to protect from sicknesses
+F24_3 byte Key messages of TVC-Do not feed the baby anything exept breast milk
+F24_4 byte Key messages of TVC-Do not feed baby honey or sugar water
+F24_5 byte Key messages of TVC-BF immediately after birth keeps the baby healthy
+F24_6 byte Key messages of TVC-BF immediately after birth helps milk production
+F24_7 byte Key messages of TVC-Other
+F24_8 byte Key messages of TVC-DK
+F25 double F25 Have you ever seen this advertisement?
+F26 double F26 Have you seen this TV spot in the last 3 months?
+F27 double F27 Where have you seen it?
+F28_1 byte Key messages of TVC-Feeding foods in first six months can be harmful
+F28_2 byte Key messages of TVC-only breast milk is sufficient for the baby in 1st 6m
+F28_3 byte Key messages of TVC-Not to feed the baby anything
+F28_4 byte Key messages of TVC-Malnourished mothers can also sufficiently BF their child for 6moths
+F28_5 byte Key messages of TVC-Other
+F28_6 byte Key messages of TVC-DK
+agegr double SS age groups
+edu double SS education
+AT float Intervention
+tcode float cluster
+ATpaird float paird
+

data/part_2/0127680614.md

@@ -0,0 +1,30 @@
+# Constraints for small-scale private irrigation systems in the North Central zone of Nigeria: Insights from a typology analysis and a case study
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/6f80e44b-e0d8-4527-974a-ffdc53f5bc40/retrieve  
+**Language:** English  
+**Resource Type:** Working Paper  
+**Release Year:** 2017  
+**Rights:** N/A  
+**GARDIAN ID:** feffd3176b4bec4bc434004c47664353  
+**DataNODE ID:** a5da436eb56875e9332db12490f9803c  
+**Siever ID:** a7232c0b-f650-425d-b880-4e5720790794  
+**Token Count:** 114  
+**Page Count:** 0
+
+## Keywords
+
+irrigation systems, farm budgets, labour costs, typology, private sector, agricultural productivity, agricultural transformation, agricultural sector, irrigation schemes, constraints, nigeria, analysis
+
+## Geography
+
+- **Regions:** Western Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Nigeria
+
+## Description
+
+Agricultural transformation has been slow in Nigeria despite relatively fast growth in the non-agricultural sector of the economy. The limited contributions of irrigation in the agricultural sector have been considered to be one of the causes of slow agricultural transformation in Nigeria. Irrigation is used in both public-sector and private-sector irrigation schemes. Information is, however, often limited regarding small-scale private irrigation systems and their expansion potential and constraints, as compared to information on public irrigation schemes. This paper aims to provide various qualitative indicators which can shed light on irrigation system diversity and its recent evolution in Nigeria, as well as key economic characteristics of a selected private irrigation system as a case study.
+
+## Content
+
+Agricultural transformation has been slow in Nigeria despite relatively fast growth in the non-agricultural sector of the economy. The limited contributions of irrigation in the agricultural sector have been considered to be one of the causes of slow agricultural transformation in Nigeria. Irrigation is used in both public-sector and private-sector irrigation schemes. Information is, however, often limited regarding small-scale private irrigation systems and their expansion potential and constraints, as compared to information on public irrigation schemes. This paper aims to provide various qualitative indicators which can shed light on irrigation system diversity and its recent evolution in Nigeria, as well as key economic characteristics of a selected private irrigation system as a case study.

data/part_2/0139672039.md

@@ -0,0 +1,34 @@
+# Building pathways out of poverty in Baidoa: Evidence from a randomized controlled trial at endline
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/32a3a189-2255-46e4-9a71-5a8fa3821c46/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2024  
+**Rights:** CC-BY  
+**GARDIAN ID:** 3e78e7139425f18bf14d7bd11d5e7c82  
+**DataNODE ID:** bee51b9f746139af1d12892f78bc5a6b  
+**Siever ID:** 4c5c786f-2dfa-4c08-bae5-41dba70304b1  
+**Token Count:** 201  
+**Page Count:** 0
+
+## Keywords
+
+poverty, conflicts, natural disasters, displacement, women, unemployment, gender, nutrition, health and food security, poverty reduction, livelihoods and jobs, systems transformation, rural areas, sustainable livelihoods
+
+## Geography
+
+- **Regions:** Western Africa, Sub-Saharan Africa, Africa, World, Eastern Africa
+- **Countries:** Somalia, Mali
+
+## Description
+
+Somalia is one of the poorest countries in the world, and severe poverty, ongoing armed conflict, and recurring droughts and floods have created a humanitarian crisis characterized by a high level of inter nal displacement. Baidoa city—the site of this evaluation—hosts 517 sites for internally displaced per sons (IDP), with almost 600,000 households, and 64 percent of the individuals living in these sites are women and girls. According to the second Somali High Frequency Survey (Pape and Karamba 2019), IDP settlements (along with rural areas) face a particularly high level of poverty, exacerbated by high unemployment rates and the absence of income-generating opportunities. 
+
+This brief reports on endline findings from a randomized controlled trial (RCT) evaluating the project Building Pathways Out of Poverty for Ultra-poor IDPs and Vulnerable Host Communities in Baidoa, an ultra-poor graduation (UPG) intervention implemented by World Vision and funded by the United States Agency for International Development’s Bureau for Humanitarian Assistance (BHA). The project seeks to enable ultra-poor internally displaced households to graduate from extreme poverty and begin the upward trajectory to self-reliance for displacement-affected communities by enabling gender-sensitive, context-appropriate, and sustainable livelihoods in an urban setting. IFPRI is collaborating with World Vision to conduct the trial.
+
+## Content
+
+Somalia is one of the poorest countries in the world, and severe poverty, ongoing armed conflict, and recurring droughts and floods have created a humanitarian crisis characterized by a high level of inter nal displacement. Baidoa city—the site of this evaluation—hosts 517 sites for internally displaced per sons (IDP), with almost 600,000 households, and 64 percent of the individuals living in these sites are women and girls. According to the second Somali High Frequency Survey (Pape and Karamba 2019), IDP settlements (along with rural areas) face a particularly high level of poverty, exacerbated by high unemployment rates and the absence of income-generating opportunities. 
+
+This brief reports on endline findings from a randomized controlled trial (RCT) evaluating the project Building Pathways Out of Poverty for Ultra-poor IDPs and Vulnerable Host Communities in Baidoa, an ultra-poor graduation (UPG) intervention implemented by World Vision and funded by the United States Agency for International Development’s Bureau for Humanitarian Assistance (BHA). The project seeks to enable ultra-poor internally displaced households to graduate from extreme poverty and begin the upward trajectory to self-reliance for displacement-affected communities by enabling gender-sensitive, context-appropriate, and sustainable livelihoods in an urban setting. IFPRI is collaborating with World Vision to conduct the trial.

data/part_2/0146982198.md

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+# Malaria control and elimination in Kenya: Economy-wide benefits and regional disparities
+
+**Source:** gardian_index  
+**URL:**   
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2023  
+**Rights:** CC-BY  
+**GARDIAN ID:** 683546923a4d3f2fffb3e21f3cd697b7  
+**DataNODE ID:** c64c1d877d438da0e433e9f9db019ea1  
+**Siever ID:** 1e4446f3-51c6-4e13-8ef2-39842b6d82d6  
+**Token Count:** 352  
+**Page Count:** 0
+
+## Keywords
+
+research methods, policy innovation, households, human diseases, malaria, capacity development, economics, government, prices, public health, labor, sustainable development goals
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Kenya
+
+## Description
+
+Background Malaria remains a public health problem in Kenya despite several concerted control efforts. Empirical evidence regarding malaria effects in Kenya suggests that the disease imposes substantial economic costs, jeopardizing the achievement of sustainable development goals. The Kenya Malaria Strategy (2019–2023), which is currently being implemented, is one of several sequential malaria control and elimination strategies. The strategy targets reducing malaria incidences and deaths by 75% of the 2016 levels by 2023 through spending around Kenyan Shillings 61.9 billion over 5 years. This paper assesses the economy-wide implications of implementing this strategy. Methods An economy-wide simulation model is calibrated to a comprehensive 2019 database for Kenya, considering different epidemiological zones. Two scenarios are simulated with the model. The first scenario (GOVT) simulates the annual costs of implementing the Kenya Malaria Strategy by increasing government expenditure on malaria control and elimination programmes. The second scenario (LABOR) reduces malaria incidences by 75% in all epidemiological malaria zones without accounting for the changes in government expenditure, which translates into rising the household labour endowment (benefits of the strategy). Results Implementing the Kenya Malaria Strategy (2019–2023) enhances gross domestic product at the end of the strategy implementation period due to more available labour. In the short term, government health expenditure (direct malaria costs) increases significantly, which is critical in controlling and eliminating malaria. Expanding the health sector raises the demand for production factors, such as labour and capital. The prices for these factors rise, boosting producer and consumer prices of non-health-related products. Consequently, household welfare decreases during the strategy implementation period. In the long run, household labour endowment increases due to reduced malaria incidences and deaths (indirect malaria costs). However, the size of the effects varies across malaria epidemiological and agroecological zones depending on malaria prevalence and factor ownership. Conclusions This paper provides policymakers with an ex-ante assessment of the implications of malaria control and elimination on household welfare across various malaria epidemiological zones. These insights assist in developing and implementing related policy measures that reduce the undesirable effects in the short run. Besides, the paper supports an economically beneficial long-term malaria control and elimination effect.
+
+## Content
+
+Background Malaria remains a public health problem in Kenya despite several concerted control efforts. Empirical evidence regarding malaria effects in Kenya suggests that the disease imposes substantial economic costs, jeopardizing the achievement of sustainable development goals. The Kenya Malaria Strategy (2019–2023), which is currently being implemented, is one of several sequential malaria control and elimination strategies. The strategy targets reducing malaria incidences and deaths by 75% of the 2016 levels by 2023 through spending around Kenyan Shillings 61.9 billion over 5 years. This paper assesses the economy-wide implications of implementing this strategy. Methods An economy-wide simulation model is calibrated to a comprehensive 2019 database for Kenya, considering different epidemiological zones. Two scenarios are simulated with the model. The first scenario (GOVT) simulates the annual costs of implementing the Kenya Malaria Strategy by increasing government expenditure on malaria control and elimination programmes. The second scenario (LABOR) reduces malaria incidences by 75% in all epidemiological malaria zones without accounting for the changes in government expenditure, which translates into rising the household labour endowment (benefits of the strategy). Results Implementing the Kenya Malaria Strategy (2019–2023) enhances gross domestic product at the end of the strategy implementation period due to more available labour. In the short term, government health expenditure (direct malaria costs) increases significantly, which is critical in controlling and eliminating malaria. Expanding the health sector raises the demand for production factors, such as labour and capital. The prices for these factors rise, boosting producer and consumer prices of non-health-related products. Consequently, household welfare decreases during the strategy implementation period. In the long run, household labour endowment increases due to reduced malaria incidences and deaths (indirect malaria costs). However, the size of the effects varies across malaria epidemiological and agroecological zones depending on malaria prevalence and factor ownership. Conclusions This paper provides policymakers with an ex-ante assessment of the implications of malaria control and elimination on household welfare across various malaria epidemiological zones. These insights assist in developing and implementing related policy measures that reduce the undesirable effects in the short run. Besides, the paper supports an economically beneficial long-term malaria control and elimination effect.

data/part_2/0161329380.md

@@ -0,0 +1,226 @@
+# Agro-processing, food prices, and COVID-19: The case of rice mills in Myanmar
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/fc282bb8-4460-48ec-9d57-366e68cb4361/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2021  
+**Rights:** CC-BY  
+**GARDIAN ID:** 8bd72c4a2431d233bbadb4688a83c6ae  
+**DataNODE ID:** c164295149859ec8c03033f4e52adcd9  
+**Siever ID:** 74d417be-9f4c-410c-8c06-78ebfcfac5e9  
+**Token Count:** 1899  
+**Page Count:** 0
+
+## Keywords
+
+agro-processing, surveys, covid-19, rice, food prices, byproducts, prices, resilience, milling, mills, myanmar, impacts, processing, accounts
+
+## Geography
+
+- **Regions:** South-eastern Asia, Asia, World
+- **Countries:** Myanmar
+
+## Description
+
+This assesses the impacts of COVID-19 on the processing sector of Myanmar’s agri-food system. We focus on the milling of rice, Myanmar’s most important staple, which accounts for more than half of calories consumed and serves as one of the country’s leading export commodities. Using unique data collected from telephone surveys with more than 400 medium- and large-scale rice mils, we highlight the major disruptions caused by the pandemic.
+
+## Content
+
+MYANMAR
+Agro-processing, food prices, and 
+COVID-19 
+The case of rice mills in Myanmar 
+STRATEGY SUPPORT PROGRAM POLICY NOTE 46 MARCH 2021 
+This note assesses the impacts of COVID-19 on the processing sector of Myanmar’s agri-food 
+system. We focus on the milling of rice, Myanmar’s most important staple, which accounts for 
+more than half of calories consumed and serves as one of the country’s leading export 
+commodities. Using unique data collected from telephone surveys with more than 400 
+medium- and large-scale rice mils, we highlight the major disruptions caused by the pandemic. 
+Key findings 
+• The COVID-19 pandemic caused significant disruptions for medium- and large-scale rice
+mills, including lower milling throughput, employee layoffs, and lower credit availability.
+• Despite these issues, we find significant resilience in the sector. COVID-19 has been
+associated with relatively small changes in processing margins. Any changes in rice prices
+during the pandemic were mostly transmitted to rice farmers.
+• Modern mills pay higher prices to their suppliers and sell rice more expensively due to
+extra processing. Modern and traditional mills were similarly affected by the COVID-19
+crisis, as seen in similar changes in the prices each paid to buy paddy and the prices they
+received for their milled rice.
+• Byproducts are very important for milling margins both before and during the COVID-19
+pandemic. Without byproduct sales there would be a need for much higher margins
+between paddy producer and rice consumer prices to assure the profitability of the mills.
+Recommended actions 
+• Access to international markets has seemingly contributed to price stability in local
+markets, indicating the importance of continued trade, albeit in a safe way, during shocks.
+• Monitoring crucial processing nodes in agricultural value chains through high-frequency
+inexpensive telephone interviews has allowed us to track a large sector of Myanmar’s
+economy that has strong and wide links to producers and consumers. Similar survey set-
+ups should be pursued in other sectors.
+• Modernization of mills is associated with higher prices for farmers and, therefore, should
+be encouraged. Further relaxation of restrictions on investments in agro-processing and
+on international trade in the sector will foster increased modernization.
+2 
+Introduction 
+In the rice supply chain, mills are the most important actor and add significant value, which benefits 
+both consumers and producers. Mills process raw paddy into rice, which is the single most important 
+processed food in Myanmar by a wide margin, with average per capita consumption at 170 kg per 
+year.1 Rice is also an important export commodity and Myanmar was the sixth biggest rice exporter 
+worldwide in 2018, with about 2.7 million tons of milled rice exported. From a production perspective, 
+rice is Myanmar’s most important crop, accounting for more than 30 percent of all crop value.2 The 
+far-reaching upstream and downstream influences of rice milling highlight the importance of 
+understanding COVID-19’s impacts on the sector.  
+To learn about the effects of COVID-19 on Myanmar’s rice processing, we conducted phone 
+interviews with a sample of rice millers starting in July 2020 and continuing monthly through 
+November 2020. The sample covers six townships in three regions–Ayeyarwady, Bago, and 
+Yangon–which collectively account for 45 percent of the monsoon rice produced in Myanmar. The 
+phone surveys were designed as a panel across the five monthly interview rounds. Six-hundred and 
+fifty-seven mills were randomly selected as our sample. Each mill was called for the five rounds. 
+However, the number of interviews fluctuated across rounds due to mill closures, unavailable or 
+unreachable phone numbers, and interview refusals. The number of completed interviews for each 
+of the five rounds was approximately 400 per round. 
+Significant disruptions to the rice milling sector due to COVID-19 
+Mills reported large business disruptions caused by COVID-19 and the corresponding policy 
+responses implemented to mitigate its tremendous health burdens. In the August survey round, 
+44 percent of the millers interviewed reported disruptions to buying paddy caused by transportation 
+restrictions. However, the downstream effects of these restrictions in selling rice were less 
+pronounced, with only 26 percent reporting such disruptions (Table 1). This likely reflects the 
+localized implementation of transport restrictions in Myanmar. Millers had more difficulty with 
+transport in the upstream sections of rice supply chains in obtaining paddy in production regions than 
+they did in downstream sections in supplying commodity exchange centers and wholesale markets 
+with milled rice. Moreover, 38 percent of the mills reduced the number of employees.  
+Table 1. Share of rice mills reporting operations changes and disruptions due to COVID-19 
+Share of mills reporting operations changes, August 2020 compared with August 2019 (%) 
+ Decrease Same Increase Mean change 
+Rice throughput 51 46 3 -18 
+Demand for credit from farmers 1 86 13 4 
+Expected annual revenue 79 17 4 -28 
+Share of mills reporting business disruptions in August 2020 (%) 
+Transport restrictions in selling rice 26    
+Transport restrictions in buying paddy 44    
+Applied for COVID-19 relief loan 38    
+Reduced the number of employees 38    
+Reduced mill operating days 46    
+Closed for at least one week 19    
+Source: Mill survey 
+ 
+1 USDA (United States Department of Agriculture). 2020. “Burma: Grain and Feed.” Annual report no. BM 2020-0003.  
+2 CSO (Central Statistical Organization). 2019. “Myanmar agricultural statistics (2008-2019 to 2017-2018).” Ministry of Planning and 
+Finance. Nay Pyi Taw. 
+3 
+The net effects of these challenges for the mills appear to be lower rice throughput and decreased 
+revenues. Only 3 percent reported higher daily throughput of rice in August 2020 compared to 2019, 
+while 51 percent of mills reported a year-on-year decline. Only 4 percent of millers expected a 
+revenue increase in 2020 compared to 2019. There were also increases in demand for credit, both 
+by mills–38 percent of millers applied for a government COVID-19 relief loan in August, the first 
+month that loans were made available to agribusinesses–and by farmers–13 percent of mills 
+reported increased demand for credit provision from the farmers supplying them paddy.  
+Resilience in the rice sector during the pandemic 
+Rice exports were almost at similar levels during the pandemic period as a year earlier.3 Rice prices 
+were generally higher in 2020 compared to 2019. Overall, these increases were passed through to 
+farmers as the prices paid for paddy were also higher (Figure 1). Varietal differences are shown to 
+matter tremendously in price setting. Pawsan, which is a variety mostly destined for local markets, 
+receives significantly higher prices than Emata, a variety mostly destined for international markets. 
+Gross margins for both Emata and Pawsan were slightly higher in 2020 than in 2019, but not by very 
+much, suggesting that challenges presented by the COVID-19 crisis have not had substantial 
+negative effects on milling margins. We further note that margins for the more expensive Pawsan 
+variety are higher, as noted in other settings for higher quality rice.4 Modern mills can achieve higher 
+rice quality, controlling for variety, through the use of polishers and color sorters, which translates to 
+higher margins of about 10 MMK per pound over traditional mills. A substantial portion–about 
+50 percent–of the higher prices modern mills receive for head (whole grain) rice is passed through 
+to farmers in higher prices for paddy. In terms of price changes during the COVID-19 crisis, both 
+modern and traditional mills show similar patterns. 
+Figure 1. Rice prices before and during the COVID-19 pandemic (September 2019 versus 
+September 2020), by rice variety and type of rice mill 
+September 2019 September 2020 
+  
+Source: Mill survey 
+Milling byproducts matter enormously for mill profits, including during the 
+pandemic 
+Figure 2 presents the average output revenues and paddy costs for Emata and Pawsan by year. 
+The importance of byproduct sales is evident in both years. The revenue from rice sales alone is 
+ 
+3 USDA (United States Department of Agriculture). 2021. “Burma: Rice Trade – Monthly.” Report no. BM 2021-0004. 
+4 Minten, B., K.A.S. Murshid, and T. Reardon. 2013. “Food quality changes and implications: Evidence from the rice value chain of 
+Bangladesh.” World Development, 42, February, 100-113. 
+0
+100
+200
+300
+400
+Before During Before During
+Emata variety Pawsan variety
+M
+M
+K
+/l
+b
+Paddy Rice
+0
+100
+200
+300
+400
+Before During Before During
+Modern mill Traditional mill
+M
+M
+K
+/l
+b
+Paddy Rice
+4 
+less than the paddy cost in each case. Thus, without the ability to market byproducts, milling paddy-
+to-rice margins would need to increase for mills to remain profitable, putting downward pressure on 
+paddy prices paid to farmers and upward pressure on milled rice prices to consumers.  
+Figure 2. Average milling paddy costs, revenues, and margins in MMK per 100 baskets of 
+paddy, Pawsan and Emata varieties for 2020 and 2019 
+  
+Source: Mill survey 
+After head rice, broken rice is the main contributor to miller’s margins. Pawsan revenues from 
+broken rice are slightly higher than those for Emata. However, this is not because Pawsan broken 
+rice prices are higher, but because more broken rice is recovered from Pawsan varieties, as the final 
+consumer head rice is sold with a lower percentage of broken rice in it.  
+Bran is the third leading contributor to milling margins, but with total values of about one-third of 
+that of broken rice for Emata and one-quarter of broken rice for Pawsan. The value of husks is 
+negligible.  
+The overall expansion of marketing opportunities for byproducts, such as, for instance, for feed 
+in the rapidly growing aquaculture and poultry sectors in the country, and their stable or increasing 
+prices during the pandemic might have had important spillover effects and contributed to lower 
+paddy-to-rice processing margins. Overall, this resulted in reduced rice prices for consumers and 
+higher paddy prices for farmers.5 
+  
+ 
+5 Fang, P., B. Belton, X. Zhang, and H.E. Win. 2020. “Impacts of COVID-19 on Myanmar’s poultry sector: Implications for achieving the 
+sustainable development goals.” Myanmar SSP Discussion Paper 05. Washington DC: IFPRI (International Food Policy Research 
+Institute). 
+5 
+ACKNOWLEDGMENTS 
+This work was undertaken as part of the Myanmar Agricultural Policy Support Activity (MAPSA) led 
+by the International Food Policy Research Institute (IFPRI) and in partnership with Michigan State 
+university (MSU). Funding support for this study was provided by the CGIAR Research Program on 
+Policies, Institutions, and Markets (PIM), the United States Agency of International Development 
+(USAID), and the Livelihoods and Food Security Fund (LIFT). This Policy Note has not gone through 
+IFPRI’s standard peer-review procedure. The opinions expressed here belong to the authors, and 
+do not necessarily reflect those of IFPRI, MSU, USAID, LIFT, or CGIAR.  
+INTERNATIONAL FOOD POLICY RESEARCH 
+INSTITUTE 
+1201 Eye St, NW | Washington, DC 20005 USA 
+T. +1-202-862-5600 |  F. +1-202-862-5606
+ifpri@cgiar.org
+www.ifpri.org | www.ifpri.info
+IFPRI-MYANMAR 
+No. 99-E6 U Aung Kein Lane 
+Than Lwin Road, Bahan Township 
+Yangon, Myanmar 
+IFPRI-Myanmar@cgiar.org 
+www.myanmar.ifpri.info 
+The Myanmar Strategy Support Program (Myanmar SSP) is led by the International Food Policy Research Institute (IFPRI) in partnership 
+with Michigan State University (MSU). Funding support for Myanmar SSP is provided by the CGIAR Research Program on Policies, 
+Institutions, and Markets; the Livelihoods and Food Security Fund (LIFT); and the United States Agency for International Development 
+(USAID). This publication has been prepared as an output of Myanmar SSP. It has not been independently peer reviewed. Any opinions 
+expressed here belong to the author(s) and do not necessarily reflect those of IFPRI, MSU, LIFT, USAID, or CGIAR. 
+© 2021, Copyright remains with the author(s). This publication is licensed for use under a Creative Commons Attribution 4.0 International 
+License (CC BY 4.0). To view this license, visit https://creativecommons.org/licenses/by/4.0. 
+IFPRI is a CGIAR Research Center | A world free of hunger and malnutrition 
+

data/part_2/0168979666.md

@@ -0,0 +1,33 @@
+# Opening data with trust: Reflections on a holistic approach to data governance at IFPRI
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/5de552c8-ee3e-4052-8c93-aa8298cdfa22/retrieve  
+**Language:** English  
+**Resource Type:** Poster / Presentation  
+**Release Year:** 2020  
+**Rights:** N/A  
+**GARDIAN ID:** c10172c12be227b2007e2faa6010c1b5  
+**DataNODE ID:** f02b13236f1aae3beb02b25199af59ba  
+**Siever ID:** fbf50a97-6a0f-4d46-a6f3-a642474845c3  
+**Token Count:** 305  
+**Page Count:** 0
+
+## Keywords
+
+open data, governance, research data management, data repositories, data management, developing countries, household surveys, research projects, data, research, policies, protocols, processes, components
+
+## Description
+
+IFPRI’s experience building overall data governance for Research Data Management (RDM) has helped us to build trust among IFPRI staff for opening their data. We will highlight how policies, protocols and processes are equally important components for opening data in addition to infrastructure and digital repositories.
+
+The mandate of IFPRI is to provide research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries. In pursuit of that mandate, IFPRI generates data ranging from household surveys to process-produced data and makes those data available and easily accessible. IFPRI has moved progressively toward full FAIR compliance beginning with publicly available datasets hosted on the website in 1999, a published data policy in 2000, establishing a data repository through Dataverse in 2007, and a Data Governance Team (DGT) in 2017. Revisions to the data policy in 2005 required researchers to publish the datasets generated during research projects within two years of data collection/finalization. However, the intention of the institute did not lead to automatic compliance by research staff. The data policy was not observed consistently, and publishing datasets remained voluntary. Researchers repeatedly expressed hesitations for opening data widely.
+
+To overcome resistance from research staff, respond to open data movements, regulations, and policies from donors and governments, and identify gaps in RDM within the institute, IFPRI established a term-limited DGT in 2017. The first undertaking of this team was launching a review of the data management practices by an external consultant. Based on the recommendations from the review, IFPRI established a permanent data governance body, Data Governance and Management Committee (DGMC). DGMC facilitates the implementation of other recommendations from the review and address staff concerns. IFPRI has established new policies, processes, incentives and systems for RDM. As a result, IFPRI has 420 well-documented open datasets, and researchers are more confident and trust the system.
+
+## Content
+
+IFPRI’s experience building overall data governance for Research Data Management (RDM) has helped us to build trust among IFPRI staff for opening their data. We will highlight how policies, protocols and processes are equally important components for opening data in addition to infrastructure and digital repositories.
+
+The mandate of IFPRI is to provide research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries. In pursuit of that mandate, IFPRI generates data ranging from household surveys to process-produced data and makes those data available and easily accessible. IFPRI has moved progressively toward full FAIR compliance beginning with publicly available datasets hosted on the website in 1999, a published data policy in 2000, establishing a data repository through Dataverse in 2007, and a Data Governance Team (DGT) in 2017. Revisions to the data policy in 2005 required researchers to publish the datasets generated during research projects within two years of data collection/finalization. However, the intention of the institute did not lead to automatic compliance by research staff. The data policy was not observed consistently, and publishing datasets remained voluntary. Researchers repeatedly expressed hesitations for opening data widely.
+
+To overcome resistance from research staff, respond to open data movements, regulations, and policies from donors and governments, and identify gaps in RDM within the institute, IFPRI established a term-limited DGT in 2017. The first undertaking of this team was launching a review of the data management practices by an external consultant. Based on the recommendations from the review, IFPRI established a permanent data governance body, Data Governance and Management Committee (DGMC). DGMC facilitates the implementation of other recommendations from the review and address staff concerns. IFPRI has established new policies, processes, incentives and systems for RDM. As a result, IFPRI has 420 well-documented open datasets, and researchers are more confident and trust the system.

data/part_2/0182251128.md

@@ -0,0 +1,32 @@
+# Ethiopia Feed the Future Innovation Lab for Small-Scale Irrigation (ILSSI) Baseline Survey, 2014
+
+**Source:** gardian_index  
+**URL:**   
+**Language:** English  
+**Resource Type:** Dataset / Tabular  
+**Release Year:** 2017  
+**Rights:** CC-BY  
+**GARDIAN ID:** 2edade5b0b4e28ffa8698b4087646477  
+**DataNODE ID:** 1ca7f57c5f4a0822b254ad043f08b352  
+**Siever ID:** c2a449b5-4361-4fba-b6bf-084fceaa71de  
+**Token Count:** 190  
+**Page Count:** 0
+
+## Keywords
+
+irrigation, households, agricultural production, nutrition, dietary diversity, anthropometry, health, gender, women's empowerment, decision making, ethiopia, east africa, africa south of sahara, africa
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World, Western Africa, Northern America, Americas
+- **Countries:** United States of America, Tanzania, Ghana, Ethiopia
+
+## Description
+
+The Feed the Future Innovation Lab on Small-Scale Irrigation (FTF-ILSSI) is a cooperative agreement funded by USAID under the Feed the Future program to undertake research aimed to increase food production, improve nutrition, accelerate economic development and contribute to the protection of the environment. The project seeks these objectives through identifying, testing and demonstrating technological options in small-scale irrigation and irrigated fodder, supported by a continual dialogue approach with stakeholders and capacity development toward sustained use of research approaches and evidence. Collaborators on this project include Texas A&M University, the International Water Management Institute (IWMI), the International Food Policy Research Institute (IFPRI), the International Livestock Research Institute (ILRI), North Carolina A&T State University (NCAT) and Texas A&M AgriLife Research (TAMUS). As part of this project, IFPRI is undertaking a study of irrigating and non-irrigating households in Ethiopia, Tanzania and Ghana to investigate the connections between irrigation, gender, nutrition and health. The survey explores these linkages through an in-depth household questionnaire with questions on agricultural production, nutrition and health, a WEAI module and a community questionnaire. 
+<p>This work forms part of the CGIAR Research Program on Water, Land and Ecosystems (WLE).
+
+## Content
+
+The Feed the Future Innovation Lab on Small-Scale Irrigation (FTF-ILSSI) is a cooperative agreement funded by USAID under the Feed the Future program to undertake research aimed to increase food production, improve nutrition, accelerate economic development and contribute to the protection of the environment. The project seeks these objectives through identifying, testing and demonstrating technological options in small-scale irrigation and irrigated fodder, supported by a continual dialogue approach with stakeholders and capacity development toward sustained use of research approaches and evidence. Collaborators on this project include Texas A&M University, the International Water Management Institute (IWMI), the International Food Policy Research Institute (IFPRI), the International Livestock Research Institute (ILRI), North Carolina A&T State University (NCAT) and Texas A&M AgriLife Research (TAMUS). As part of this project, IFPRI is undertaking a study of irrigating and non-irrigating households in Ethiopia, Tanzania and Ghana to investigate the connections between irrigation, gender, nutrition and health. The survey explores these linkages through an in-depth household questionnaire with questions on agricultural production, nutrition and health, a WEAI module and a community questionnaire. 
+<p>This work forms part of the CGIAR Research Program on Water, Land and Ecosystems (WLE).

data/part_2/0194256883.md

@@ -0,0 +1,798 @@
+# POSHAN's abstract digest on maternal and child nutrition research - Issue 35
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/e40826e7-248b-43b5-97b9-fdcc36069712/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2020  
+**Rights:** N/A  
+**GARDIAN ID:** 315a8f2f973a4061e9cab18d40bebc35  
+**DataNODE ID:** 074de625a1fb401861c7fa3b82c16bed  
+**Siever ID:** d63df223-adc7-4f28-bfb2-ddf8a0cb529c  
+**Token Count:** 7223  
+**Page Count:** 0
+
+## Keywords
+
+child nutrition, health, covid-19, nutrition, maternal nutrition, data quality, research, stunting, countries, chhattisgarh, assessment, components, leaders
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World
+- **Countries:** India
+
+## Description
+
+This issue of Abstract Digest brings to you a set of interesting articles on stunting burden, drivers, and learnings from countries that have been successful in reducing stunting, including a case study on Chhattisgarh. In addition, there are studies on anthropometric data quality assessment, and a study describing the health system components required for the delivery of nutrition-specific interventions. This issue also includes studies on COVID-19 and its implications for child nutrition. In this edition, we have included a Call for Action issued by leaders of four UN agencies to protect children's right to nutrition in the face of the COVID-19 pandemic. In India, a diverse group of nutrition stakeholders have pledged their renewed Commitment to Action for supporting efforts by the government and all of society.
+
+## Content
+
+1 
+  
+NO. 35 | AUGUST 2020 
+ 
+ 
+ 
+ 
+ 
+ 
+EDITOR’S NOTE 
+ 
+This issue of Abstract Digest brings to you a set of interesting articles on stunting burden, drivers, 
+and learnings from countries that have been successful in reducing stunting, including a case study 
+on Chhattisgarh.  In addition, there are studies on anthropometric data quality assessment, and a 
+study describing the health system components required for the delivery of nutrition-specific 
+interventions. This issue also includes studies on COVID-19 and its implications for child nutrition. 
+ 
+In this edition, we have included a Call for Action issued by leaders of four UN agencies to protect 
+children's right to nutrition in the face of the COVID-19 pandemic. In India, a diverse group of 
+nutrition stakeholders have pledged their renewed Commitment to Action  for supporting efforts by 
+the government and all of society.  
+ 
+We would like to  highlight that  we, along with 19 partners, are preparing to co-host the third India-
+focused implementation research conference on “Delivering for Nutrition in India: Insights from 
+Implementation Research”. This virtual event will include theme-based sessions convene academics, 
+implementers, development partners, and policy makers from multiple institutes on a common 
+platform to deliberate on selected research studies and implementation experiences focused on the 
+core pillars of POSHAN Abhiyaan, India’s National Nutrition Mission, and platforms supporting 
+actions for nutrition. The conference program features 3 outstanding plenary lectures with global 
+and local experts, and 12 thematic sessions based on selected and poster presentations, social 
+hangouts and panels with policymakers and research funders.  We invite our readers to REGISTER 
+and participate in this event. 
+ 
+Given below is the list of peer-reviewed articles. Please click on the title if you wish to go straight to 
+the article or scroll down to explore the abstract in the pages that follow. 
+ 
+Stay safe and enjoy reading! 
+ 
+ 
+List of articles in this issue 
+ 
+Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence   
+Wang et al. 2020. International Journal of Antimicrobial Agents 55(6): 105948. 
+ 
+Impacts of COVID-19 on childhood malnutrition and nutrition-related mortality  
+Headey et al. 2020. The Lancet.  
+ 
+Child malnutrition and COVID-19: the time to act is now  
+Fore et al. 2020. The Lancet.  
+ 
+Stunting among Preschool Children in India: Temporal Analysis of Age-Specific Wealth Inequalities  
+Rajpal et al. 2020. International Journal of Environmental Research and Public Health 17(13): 4702.  
+ 
+Stunting in childhood: an overview of global burden, trends, determinants, and drivers of decline 
+Vaivada et al. 2020. The American Journal of Clinical Nutrition.  
+Abstract Digest 
+ISSUE 35 | AUGUST 2020 
+2 
+ 
+ABSTRACT DIGEST 
+ 
+How countries can reduce child stunting at scale: lessons from exemplar countries     
+Bhutta et al. 2020. The American Journal of Clinical Nutrition. nqaa153. 
+ 
+The role of the state government, civil society and programmes across sectors in stunting 
+reduction in Chhattisgarh, India, 2006–2016   
+Kohli et al. 2020. BMJ Global Health 5(7). 
+ 
+Antenatal Iron-Folic Acid Supplementation Is Associated with Improved Linear Growth and 
+Reduced Risk of Stunting or Severe Stunting in South Asian Children Less than Two Years of Age: A 
+Pooled Analysis from Seven Countries  
+Nisar et al. 2020. Nutrients 12 (9): 10.3390/nu12092632.   
+ 
+The Impact of Nutrition-Specific and Nutrition-Sensitive Interventions on Hemoglobin 
+Concentrations and Anemia: A Meta-review of Systematic Reviews 
+Moorthy et al. 2020. Advances in Nutrition. 
+ 
+Dietary Variation among Children Meeting and Not Meeting Minimum Dietary Diversity: An 
+Empirical Investigation of Food Group Consumption Patterns among 73,036 Children in India  
+Beckerman-Hsu et al. 2020. The Journal of Nutrition nxaa223.  
+ 
+High Coverage and Low Utilization of the Double Fortified Salt Program in Uttar Pradesh, India: 
+Implications for Program Implementation and Evaluation    
+Cyriac et al. 2020. Current Developments in Nutrition: nzaa133.  
+ 
+Making the health system work for the delivery of nutrition interventions   
+King et al. 2020. Maternal & Child Nutrition. 
+ 
+Anthropometric data quality assessment in multisurvey studies of child growth 
+Perumal et al. 2020. The American Journal of Clinical Nutrition nqaa162.  
+ 
+Anthropometric data quality assessment in multisurvey studies of child growth: A comparison of 
+the Indian diet with the EAT-Lancet reference diet 
+Sharma et al. 2020.  BMC Public Health 20: 812. 
+ 
+Building Implementation Science in Nutrition  
+Warren et al. 2020. Advances in Nutrition nmaa066. 
+ 
+Identifying spatial variation in the burden of diabetes among women across 640 districts in India: a 
+cross-sectional study  
+Singh et al. 2020. Journal of Diabetes & Metabolic Disorders.   
+ 
+ 
+ 
+ 
+ 
+3 
+  
+NO. 35 | AUGUST 2020 
+PEER-REVIEWED 
+Comment 
+Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence   
+Wang, L., Y. Wang, D.Ye, and Q. Liu. 2020. “Review of the 2019 novel coronavirus (SARS-CoV-2) 
+based on current evidence”. International Journal of Antimicrobial Agents 55(6): 105948. 
+https://doi.org/10.1016/j.ijantimicag.2020.105948 
+ 
+COVID-19, the disease caused by SARS-CoV-2, is a highly contagious disease. The World Health 
+Organization has declared the ongoing outbreak to be a global public health emergency. Currently, 
+the research on SARS-CoV-2 is in its primary stages. Based on current published evidence, this review 
+systematically summarizes the epidemiology, clinical characteristics, diagnosis, treatment and 
+prevention of COVID-19. It is hoped that this review will help the public to recognize and deal with 
+SARS-CoV-2, and provide a reference for future studies. 
+ 
+Comment 
+Impacts of COVID-19 on childhood malnutrition and nutrition-related mortality  
+Headey, D., R. Heidkamp, S. Osendarp, M. Ruel, N. Scott, R. Black, M. Shekar, H. Bouis, A. Flory, L. 
+Haddad, and N. Walker on behalf of the Standing Together for Nutrition consortium. 2020. “Impacts 
+of COVID-19 on childhood malnutrition and nutrition-related mortality”. The Lancet. 
+https://doi.org/10.1016/S0140-6736(20)31647-0 
+ 
+The Standing Together for Nutrition consortium, a multidisciplinary consortium of nutrition, 
+economics, food, and health systems researchers, is working to estimate the scale and reach of 
+nutrition challenges related to COVID-19. These efforts link three approaches to model the 
+combined economic and health systems impacts from COVID-19 on malnutrition and mortality: 
+MIRAGRODEP's macroeconomic projections of impacts on per capita gross national income (GNI);4 
+microeconomic estimates of how predicted GNI shocks impact child wasting using data on 1·26 
+million children from 177 Demographic Health Surveys (DHS) conducted in 52 LMICs between 1990–
+2018; and the Lives Saved Tool (LiST), which links country-specific health services disruptions and 
+predicted increases in wasting to child mortality. 
+ 
+Comment 
+Child malnutrition and COVID-19: the time to act is now  
+Fore, H.H., Q. Dongyu, D.M. Beasley, and T.A. Ghebreyesus. 2020. “Child malnutrition and COVID-19: 
+the time to act is now”. The Lancet. https://doi.org/10.1016/S0140-6736(20)31648-2 
+ 
+The leaders of four UN agencies have issued a call for action to protect children's right to nutrition in 
+the face of the COVID-19 pandemic. This requires a swift response and investments from 
+governments, donors, the private sector, and the UN. 
+ 
+ 
+Stunting among Preschool Children in India: Temporal Analysis of Age-Specific Wealth Inequalities  
+Rajpal, S., R. Kim, W. Joe, and S.V. Subramanian. 2020. “Stunting among preschool children in India: 
+Temporal analysis of age-specific wealth inequalities”. International Journal of Environmental 
+Research and Public Health 17(13): 4702. https://doi.org/10.3390/ijerph17134702 
+ 
+Adequate nutritional intake for mothers during pregnancy and for children in the first two years of 
+life is known to be crucial for a child’s lifelong physical and neurodevelopment. In this regard, the 
+global nutrition community has focused on strategies for improving nutritional intake during the first 
+1,000 day period. This is largely justified by the observed steep decline in children’s height-for-age z 
+scores from birth to 23 months and presumed growth faltering at later ages as a reflection of earlier 
+4 
+ 
+ABSTRACT DIGEST 
+deprivation that is accumulated and irreversible. Empirical evidence on the age-stratified burden of 
+child undernutrition is needed to re-evaluate the appropriate age for nutrition interventions to 
+target among children. Using data from two successive rounds of National Family Health Surveys 
+conducted in 2006 and 2016, the objective of this paper was to analyze intertemporal changes in the 
+age-stratified burden of child stunting across socioeconomic groups in India. We found that child 
+stunting in India was significantly concentrated among children entering preschool age (24 or above 
+months). Further, the temporal reduction in stunting was relatively higher among children aged 36–
+47 months compared to younger groups (below 12 and 12–23 months). Greater socioeconomic 
+inequalities persisted in stunting among children from 24 months or above age-groups, and these 
+inequalities have increased over time. Children of preschool age (24 or above months) from 
+economically vulnerable households experienced larger reductions in the prevalence of stunting 
+between 2006 and 2016, suggesting that policy research and strategies beyond the first 1000 days 
+could be critical for accelerating the pace of improvement of child nutrition in India. 
+ 
+ 
+Stunting in childhood: an overview of global burden, trends, determinants, and drivers of decline  
+ Vaivada, T., N. Akseer, S. Akseer, A. Somaskandan, M. Stefopulos, and Z. A. Bhutta. 2020. “Stunting 
+in childhood: an overview of global burden, trends, determinants, and drivers of decline.” The 
+American Journal of Clinical Nutrition. https://doi.org/10.1093/ajcn/nqaa159   
+ 
+Background: Progress has been made worldwide in reducing chronic undernutrition and rates of 
+linear growth stunting in children under 5 y of age, although rates still remain high in many regions. 
+Policies, programs, and interventions supporting maternal and child health and nutrition have the 
+potential to improve child growth and development. Objective: This article synthesizes the available 
+global evidence on the drivers of national declines in stunting prevalence and compares the relative 
+effect of major drivers of stunting decline between countries. Methods: We conducted a systematic 
+review of published peer-reviewed and gray literature analyzing the relation between changes in key 
+determinants of child linear growth and contemporaneous changes in linear growth outcomes over 
+time. Results: Among the basic determinants of stunting assessed within regression-decomposition 
+analyses, improvement in asset index score was a consistent and strong driver of improved linear 
+growth outcomes. Increased parental education was also a strong predictor of improved child 
+growth. Of the underlying determinants of stunting, reduced rates of open defecation, improved 
+sanitation infrastructure, and improved access to key maternal health services, including optimal 
+antenatal care and delivery in a health facility or with a skilled birth attendant, all accounted for 
+substantially improved child growth, although the magnitude of variation explained by each differed 
+substantially between countries. At the immediate level, changes in several maternal characteristics 
+predicted modest stunting reductions, including parity, interpregnancy interval, and maternal 
+height. Conclusions: Unique sets of stunting determinants predicted stunting reduction within 
+countries that have reduced stunting. Several common drivers emerge at the basic, underlying, and 
+immediate levels, including improvements in maternal and paternal education, household 
+socioeconomic status, sanitation conditions, maternal health services access, and family planning. 
+Further data collection and in-depth mixed-methods research are required to strengthen 
+recommendations for those countries where the stunting burden remains unacceptably high. 
+ 
+ 
+How countries can reduce child stunting at scale: lessons from exemplar countries     
+Bhutta, Z. A., N. Akseer, E. C. Keats, T. Vaivada, S. Baker, S. E. Horton, J. Katz, P. Menon, E. Piwoz, M. 
+Shekar, C. Victora, and R. Black. 2020. “How countries can reduce child stunting at scale: lessons 
+from exemplar countries”. The American Journal of Clinical Nutrition nqaa153. 
+https://doi.org/10.1093/ajcn/nqaa153 
+ 
+5 
+  
+NO. 35 | AUGUST 2020 
+Background: Child stunting and linear growth faltering have declined over the past few decades and 
+several countries have made exemplary progress. Objectives: To synthesize findings from mixed 
+methods studies of exemplar countries to provide guidance on how to accelerate reduction in child 
+stunting. Methods: We did a qualitative and quantitative synthesis of findings from existing 
+literature and 5 exemplar country studies (Nepal, Ethiopia, Peru, Kyrgyz Republic, Senegal). 
+Methodology included 4 broad research activities: 1) a series of descriptive analyses of cross-
+sectional data from demographic and health surveys and multiple indicator cluster surveys; 2) 
+multivariable analysis of quantitative drivers of change in linear growth; 3) interviews and focus 
+groups with national experts and community stakeholders and mothers; and 4) a review of policy 
+and program evolution related to nutrition. Results: Several countries have dramatically reduced 
+child stunting prevalence, with or without closing geographical, economic, and other population 
+inequalities. Countries made progress through interventions from within and outside the health 
+sector, and despite significant heterogeneity and differences in context, contributions were 
+comparable from health and nutrition sectors (40% of change) and other sectors (50%), previously 
+called nutrition-specific and -sensitive strategies. Improvements in maternal education, maternal 
+nutrition, maternal and newborn care, and reductions in fertility/reduced interpregnancy intervals 
+were strong contributors to change. A roadmap to reducing child stunting at scale includes several 
+steps related to diagnostics, stakeholder consultations, and implementing direct and indirect 
+nutrition interventions related to the health sector and nonhealth sector. Conclusions: Our results 
+show that child stunting reduction is possible even in diverse and challenging contexts. We propose 
+that our framework of organizing nutrition interventions as direct/indirect and inside/outside the 
+health sector should be considered when mapping causal pathways of child stunting and planning 
+interventions and strategies to accelerate stunting reduction to achieve the 2030 Sustainable 
+Development Goals.  
+ 
+ 
+The role of the state government, civil society and programmes across sectors in stunting 
+reduction in Chhattisgarh, India, 2006–2016   
+Kohli, N., P.H. Nguyen, R. Avula, and P. Menon. 2020. “The role of the state government, civil society 
+and programmes across sectors in stunting reduction in Chhattisgarh, India, 2006–2016”. BMJ Global 
+Health 5(7). http://dx.doi.org/10.1136/bmjgh-2019-002274 
+ 
+Introduction: Childhood stunting has declined in India between 2006 and 2016, but not uniformly 
+across all states. Little is known about what helped some states accelerate progress while others did 
+not. Insights on subnational drivers of progress are useful not just for India but for other 
+decentralised policy contexts. Thus, we aimed to identify the factors that contributed to declines in 
+childhood stunting (from 52.9% to 37.6%) between 2006 and 2016 in the state of Chhattisgarh, a 
+subnational success story in stunting reduction in India. Methods: We examined time trends in 
+determinants of stunting using descriptive and regression decomposition analysis of National Family 
+Health Survey data from 2005 to 2006 and 2015–2016. We reviewed nutrition-relevant policies and 
+programmes associated with the drivers of change to construct a policy timeline. Finally, we 
+interviewed multiple stakeholders in the state to understand the changes in the drivers of 
+undernutrition. Results: The regression decomposition analysis shows that multiple factors explain 
+66% of the change in stunting between 2006 and 2016. Improvements in three key drivers—health 
+and nutrition services, household assets, and sanitation and hygiene—explained 47% of the change 
+in stunting. A shared vision for impact, political stability and capable bureaucracy, state-level 
+innovations, support from development partners and civil society, and community mobilisation were 
+found to contribute to improvements in programmes for health, poverty and sanitation. Conclusion: 
+Change in multiple sectors is important for stunting reduction and can be achieved in subnational 
+contexts. More work lies ahead to close gaps in various determinants of stunting. 
+ 
+6 
+ 
+ABSTRACT DIGEST 
+ 
+Antenatal Iron-Folic Acid Supplementation Is Associated with Improved Linear Growth and 
+Reduced Risk of Stunting or Severe Stunting in South Asian Children Less than Two Years of Age: A 
+Pooled Analysis from Seven Countries  
+Nisar, Y. B., V. M. Aguayo, S. M. Billah, and M. J. Dibley. 2020. “Antenatal Iron-Folic Acid 
+Supplementation Is Associated with Improved Linear Growth and Reduced Risk of Stunting or Severe 
+Stunting in South Asian Children Less than Two Years of Age: A Pooled Analysis from Seven 
+Countries”. Nutrients 12(9): E2632. https://doi.org/10.3390/nu12092632 
+ 
+In South Asia, an estimated 38% of preschool-age children have stunted growth. We aimed to assess 
+the effect of WHO-recommended antenatal iron, and folic acid (IFA) supplements on smaller than 
+average birth size and stunting in South Asian children <2 years old. The sample was 96,512 mothers 
+with their most recent birth within two years, from nationally representative surveys between 2005 
+and 2016 in seven South Asian countries. Primary outcomes were stunting [length-for-age Z-score 
+(LAZ) < -2], severe stunting [length-for-age Z-score (LAZ) < -3], length-for-age Z score, and perceived 
+smaller than average birth size. Exposure was the use of IFA supplements. We conducted analyses 
+with Poisson, linear and logistic multivariate regression adjusted for the cluster survey design, and 
+14 potential confounders covering the country of the survey, socio-demographic factors, household 
+economic status, maternal characteristics, and duration of respondent recall. The prevalence of 
+stunting was 33%, severe stunting was 14%, and perceived smaller than average birth size was 22%. 
+Use of antenatal IFA was associated with a reduced adjusted risk of being stunted by 8% (aRR 0.92, 
+95% CI 0.89, 0.95), of being severely stunted by 9% (aRR 0.91, 95% CI 0.86, 0.96) and of being smaller 
+than average birth size by 14% (aRR 0.86, 95% CI 0.80, 0.91). The adjusted mean LAZ was 
+significantly higher in children whose mothers used IFA supplements. Maternal use of IFA in the first 
+four months gestation and consuming 120 or more supplements throughout pregnancy was 
+associated with the largest reduction in risk of child stunting. Antenatal IFA supplementation was 
+associated with a significantly reduced risk of stunting, severe stunting, and smaller than average 
+perceived birth size and improved LAZ in young South Asian children. The early and sustained use of 
+antenatal IFA has the potential to improve child growth outcomes in South Asia and other low-and-
+middle-income countries with high levels of iron deficiency in pregnancy.  
+ 
+ 
+The Impact of Nutrition-Specific and Nutrition-Sensitive Interventions on Hemoglobin 
+Concentrations and Anemia: A Meta-review of Systematic Reviews  
+Moorthy, D., R. Merrill, S. Namaste, and L. Iannotti. 2020. “The Impact of Nutrition-Specific and 
+Nutrition-Sensitive Interventions on Hemoglobin Concentrations and Anemia: A Meta-review of 
+Systematic Reviews”. Advances in Nutrition: nmaa070. https://doi.org/10.1093/advances/nmaa070 
+ 
+Anemia is a multifactorial condition arising from inadequate nutrition, infection, chronic disease, and 
+genetic-related etiologies. Our aim was to assess the impact of nutrition-sensitive and nutrition-
+specific interventions on hemoglobin (Hb) concentrations and anemia to inform the prioritization 
+and scale-up of interventions to address the multiple causes of anemia. We performed a meta-
+review synthesis of information by searching multiple databases for reviews published between 
+1990 and 2017 and used standard methods for conducting a meta-review of reviews, including 
+double independent screening, extraction, and quality assessment. Quantitative pooling and 
+narrative syntheses were used to summarize information. Hb concentration and anemia outcomes 
+were pooled in specific population groups (children aged <5 y, school-age children, and pregnant 
+women). Methodological quality of the systematic reviews was assessed using Assessing the 
+Methodological Quality of Systematic Reviews (AMSTAR) criteria. Of the 15,444 records screened, 
+we identified 118 systematic reviews that met inclusion criteria. Reviews focused on nutrition-
+specific interventions (96%). Daily and intermittent iron supplementation, micronutrient powders, 
+7 
+  
+NO. 35 | AUGUST 2020 
+malaria treatment, use of insecticide-treated nets (ITNs), and delayed cord clamping were associated 
+with increased Hb concentration in children aged <5 y. Among children older than 5 y, daily and 
+intermittent iron supplementation and deworming, and in pregnant women, daily iron-folic acid 
+supplementation, use of ITNs, and delayed cord clamping, were associated with increased Hb 
+concentration. Similar results were obtained for the reduced risk of anemia outcome. This meta-
+review suggests the importance of nutrition-specific interventions for anemia and highlights the lack 
+of evidence to understand the influence of nutrition-sensitive and multifaceted interventions on the 
+condition. 
+ 
+ 
+Dietary Variation among Children Meeting and Not Meeting Minimum Dietary Diversity: An 
+Empirical Investigation of Food Group Consumption Patterns among 73,036 Children in India  
+Beckerman-Hsu, J. P., R. Kim, S. Sharma, and S. V. Subramanian. 2020. “Dietary Variation among 
+Children Meeting and Not Meeting Minimum Dietary Diversity: An Empirical Investigation of Food 
+Group Consumption Patterns among 73,036 Children in India”. The Journal of Nutrition nxaa223. 
+https://doi.org/10.1093/jn/nxaa223 
+ 
+Background: Minimum Dietary Diversity (MDD) is a widely used indicator of adequate dietary 
+micronutrient density for children 6–23 mo old. MDD food-group data remain underutilized, despite 
+their potential for further informing nutrition programs and policies. Objectives: We aimed to 
+describe the diets of children meeting MDD and not meeting MDD in India using food group data, 
+nationally and subnationally. Methods: Food group data for children 6–23 mo old (n = 73,036) from 
+the 2015–16 National Family Health Survey in India were analyzed. Per WHO standards, children 
+consuming ≥5 of the following food groups in the past day or night met MDD: breast milk; grains, 
+roots, or tubers; legumes or nuts; dairy; flesh foods; eggs; vitamin A–rich fruits and vegetables; and 
+other fruits and vegetables. Children not meeting MDD consumed <5 food groups. We analyzed the 
+number and types of foods consumed by children meeting MDD and not meeting MDD at the 
+national and subnational geographic levels. Results: Nationally, children not meeting MDD most 
+often consumed breast milk (84.5%), grains, roots, and tubers (62.0%), and/or dairy (42.9%). 
+Children meeting MDD most often consumed grains, roots, and tubers (97.6%), vitamin A–rich fruits 
+and vegetables (93.8%), breast milk (84.1%), dairy (82.1%), other fruits and vegetables (79.5%), 
+and/or eggs (56.5%). For children not meeting MDD, district-level dairy consumption varied the most 
+(6.4%–79.9%), whereas flesh foods consumption varied the least (0.0%–43.8%). For children meeting 
+MDD, district-level egg consumption varied the most (0.0%–100.0%), whereas grains, roots, and 
+tubers consumption varied the least (66.8%–100.0%). Conclusions: Children not meeting MDD had 
+low fruit, vegetable, and protein-rich food consumption. Many children meeting MDD also had low 
+protein-rich food consumption. Examining the number and types of foods consumed highlights 
+priorities for children experiencing the greatest dietary deprivation, providing valuable 
+complementary information to MDD. 
+ 
+ 
+High Coverage and Low Utilization of the Double Fortified Salt Program in Uttar Pradesh, India: 
+Implications for Program Implementation and Evaluation    
+Cyriac, S., R. Haardörfer, L. M. Neufeld, A. W. Girard, U. Ramakrishnan, R. Martorell, and M. N. N. 
+Mbuya. 2020. “High Coverage and Low Utilization of the Double Fortified Salt Program in Uttar 
+Pradesh, India: Implications for Program Implementation and Evaluation”. Current Developments in 
+Nutrition: nzaa133. https://doi.org/10.1093/cdn/nzaa133 
+ 
+Background: Double Fortified Salt (DFS) is efficacious in addressing iron deficiency, but evidence of 
+its effectiveness is limited. The few published evaluations do not include details on program 
+implementation, limiting their utility for programmatic decisions. Objective: We sought to 
+8 
+ 
+ABSTRACT DIGEST 
+characterize the coverage of a DFS program implemented through the Public Distribution System 
+(PDS) in Uttar Pradesh (UP), India, and understand the drivers of DFS adherence. Methods: After 
+eight months of implementation, we surveyed 1202 households in five districts and collected data 
+on sociodemographic characteristics, asset ownership, food security and regular PDS utilization. We 
+defined ‘DFS program coverage’ as the proportion of PDS beneficiaries who had heard of and 
+purchased DFS, and ‘DFS adherence’ as DFS use reported by households. We used principal 
+components analysis to create an asset-based index of relative wealth, and categorized households 
+into higher/lower relative wealth quintiles. We conducted path analyses to examine the drivers of 
+DFS adherence, particularly the mediated influence of household wealth on DFS adherence. The 
+evaluation is registered at RIDIE‐STUDY‐ID‐58f6eeb45c050. Results: The DFS program had good 
+coverage – 83% respondents had heard of DFS, 74% had purchased it at least once and yet, only 23% 
+exclusively used DFS. Respondents had low awareness about DFS benefits and considered DFS 
+quality as poor. Being in a lower household wealth quintile and being food insecure were significant 
+drivers of DFS adherence and regular PDS utilization acted as a mediator. Adherence was lower in 
+urban areas. Conclusions: We observed significant heterogeneity in DFS implementation as reflected 
+by high coverage and low adherence. Learnings from this process evaluation informed the design of 
+an adaptive impact evaluation, and provided generalizable insights for ensuring the potential for 
+impact is realized. Efforts are needed to increase awareness, improve product quality as well as 
+mitigate against the sensory challenges identified. 
+ 
+ 
+Making the health system work for the delivery of nutrition interventions     
+King, S. E., T. Sawadogo‐Lewis, R. E. Black, and T. Roberton. 2020. “Making the health system work 
+for the delivery of nutrition interventions”. Maternal & Child Nutrition. 
+https://doi.org/10.1111/mcn.13056. 
+ 
+Addressing malnutrition requires strategies that are comprehensive and multi‐sectoral. Within a 
+multi‐sectoral approach, the health system is essential to deliver 10 nutrition‐specific interventions, 
+which, if scaled up, could substantially reduce under‐5 deaths in high‐burden countries through 
+improving maternal and child undernutrition. This study identifies the health system components 
+required for the effective delivery of these interventions, highlighting opportunities and challenges 
+for nutrition programmes and policies. We reviewed implementation guidance for each nutrition‐
+specific intervention, mapping the delivery process for each intervention and determining the health 
+system components required for their delivery. We integrated the components into a single health 
+systems framework for nutrition, illustrating the pathways by which health system components 
+influence household‐level determinants of nutrition and individual‐level health outcomes. Nutrition‐
+specific interventions are typically delivered in one of four ways: (i) when nutrition interventions are 
+intentionally sought out, (ii) when care is sought for other, unrelated interventions, (iii) at a health 
+facility after active community case finding and referral, and (iv) in the community after active 
+community case finding. A health system enables these processes by providing health services and 
+facilitating care seeking for services, which together require a skilled and motivated health 
+workforce, an effective supply chain, demand for services and access to services. The nutrition 
+community should consider the processes by which nutrition‐specific interventions are delivered 
+and the health system components required for their success. Programmes should encourage the 
+delivery of nutrition interventions at every client–provider interaction and should actively generate 
+demand for services—in general, and for nutrition services specifically. 
+ 
+Anthropometric data quality assessment in multisurvey studies of child growth 
+Perumal, N., S. Namaste, H. Qamar, A. Aimone, D.G. Bassani, and D.E. Roth. 2020. “Anthropometric 
+data quality assessment in multisurvey studies of child growth”. The American Journal of Clinical 
+Nutrition. Doi: nqaa162. https://doi.org/10.1093/ajcn/nqaa162 
+9 
+  
+NO. 35 | AUGUST 2020 
+ 
+Background: Population-based surveys collect crucial data on anthropometric measures to track 
+trends in stunting [height-for-age z score (HAZ) < −2SD] and wasting [weight-for-height z score 
+(WHZ) < −2SD] prevalence among young children globally. However, the quality of the 
+anthropometric data varies between surveys, which may affect population-based estimates of 
+malnutrition. Objectives: We aimed to develop composite indices of anthropometric data quality for 
+use in multisurvey analysis of child health and nutritional status. Methods: We used anthropometric 
+data for children 0–59 mo of age from all publicly available Demographic and Health Surveys (DHS) 
+from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, 
+including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit 
+preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of 
+biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component 
+factor analysis was used to generate a composite index of anthropometric data quality for HAZ and 
+WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in 
+anthropometric quality across countries and over time. Results: Of the 145 DHS included, the 
+majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest 
+to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. 
+Although slightly higher values in recent DHS suggest potential improvements in anthropometric 
+data quality over time, there continues to be substantial heterogeneity in the quality of 
+anthropometric data across surveys. Results were similar for the WHZ data quality index. 
+Conclusions: A composite index of anthropometric data quality using a parsimonious set of 
+individual indicators can effectively discriminate among surveys with excellent and poor data quality. 
+These index can be used to account for variations in anthropometric data quality in multisurvey 
+epidemiologic analyses of child health. 
+ 
+ 
+Anthropometric data quality assessment in multisurvey studies of child growth: A comparison of 
+the Indian diet with the EAT-Lancet reference diet 
+Sharma, M., A. Kishore, D. Roy, and K. Joshi. 2020. “A comparison of the Indian diet with the EAT-
+Lancet reference diet”. BMC Public Health 20: 812. https://doi.org/10.1186/s12889-020-08951-8 
+ 
+Background: The 2019 EAT-Lancet Commission report recommends healthy diets that can feed 10 
+billion people by 2050 from environmentally sustainable food systems. This study compares food 
+consumption patterns in India, from different income groups, regions and sectors (rural/urban), with 
+the EAT-Lancet reference diet and highlights the deviations. Methods: The analysis was done using 
+data from the Consumption Expenditure Survey (CES) of a nationally representative sample of 0.102 
+million households from 7469 villages and 5268 urban blocks of India conducted by the National 
+Sample Survey Organization (NSSO) in 2011–12. This is the most recent nationally representative 
+data on household consumption in India. Calorie consumption (kcal/capita/day) of each food group 
+was calculated using the quantity of consumption from the data and nutritional values of food items 
+provided by NSSO. Diets for rural and urban, poor and rich households across different regions were 
+compared with EAT-Lancet reference diet. Results: The average daily calorie consumption in India is 
+below the recommended 2503 kcal/capita/day across all groups compared, except for the richest 5% 
+of the population. Calorie share of whole grains is significantly higher than the EAT-Lancet 
+recommendations while those of fruits, vegetables, legumes, meat, fish and eggs are significantly 
+lower. The share of calories from protein sources is only 6–8% in India compared to 29% in the 
+reference diet. The imbalance is highest for the households in the lowest decile of consumption 
+expenditure, but even the richest households in India do not consume adequate amounts of fruits, 
+vegetables and non-cereal proteins in their diets. An average Indian household consumes more 
+calories from processed foods than fruits. Conclusions: Indian diets, across states and income 
+groups, are unhealthy. Indians also consume excess amounts of cereals and not enough proteins, 
+10 
+ 
+ABSTRACT DIGEST 
+fruits, and vegetables. Importantly, unlike many countries, excess consumption of animal protein is 
+not a problem in India. Indian policymakers need to accelerate food-system-wide efforts to make 
+healthier and sustainable diets more affordable, accessible and acceptable. 
+ 
+ 
+Building Implementation Science in Nutrition  
+Warren, A.M., E.A. Frongillo, and R. Rawat. 2020. “Building Implementation Science in Nutrition”. 
+Advances in Nutrition. Doi: nmaa066. https://doi.org/10.1093/advances/nmaa066 
+ 
+The field of nutrition has been investing in the development of many nutrition-specific and -sensitive 
+policies and programs aimed at improving population-level malnutrition in all its forms. When there 
+is a need to learn about a new system, programmatic context, or target population to understand 
+how to effectively deploy an intervention to help improve nutrition, it is important to be able to ask 
+a broad range of questions, both in topic and in scope. Our aim is to provide a simple and 
+conceptually clear definition and principles to elaborate the science of implementation for nutrition 
+to distinguish it from other ways of knowing and learning and to serve as a guide to the articulation 
+of implementation science questions and methods. Implementation science is a body of 
+systematized knowledge about how to improve implementation that 1) is distinguished by its aims 
+to learn about the process of implementation, 2) uses methods that derive from and fit with the 
+aims, and 3) is built with tacit (as well as expert) knowledge and experiential learning. 
+Implementation science aims to generate the learning needed to improve implementation through 
+facilitating collaboration among stakeholders to articulate and pursue the aims; capturing and using 
+tacit knowledge and experiential learning from stakeholders, systems, providers, and recipients; and 
+applying a mix of methods suited to the aims. This elaboration of the science provides a simple way 
+to help those who already do, or want to do, implementation science understand and communicate 
+how this science is unique and the value that it adds to the current landscape of nutrition priorities, 
+innovations, and the attendant complex learning needs that follow. Implementation science 
+encompasses both discovery- and mission-oriented research, and centers implementation as the 
+object of study for the purposes of broad-based learning. 
+ 
+ 
+Identifying spatial variation in the burden of diabetes among women across 640 districts in India: a 
+cross-sectional study  
+Singh, S., P. Puri, and S.V. Subramanian. 2020. “Identifying spatial variation in the burden of diabetes 
+among women across 640 districts in India- a cross-sectional study”. Journal of Diabetes & Metabolic 
+Disorders. https://doi.org/10.1007/s40200-020-00545-w 
+ 
+Purpose: Diabetes is one of the leading causes of mortality and morbidity among women in India. 
+The burden of diabetes among women was found to increase with age and exposure to the post-
+partum period. The present study examines the spatial variation in the prevalence of diabetes 
+among women in the late reproductive age-group of 35–49 years across 640 districts in India. 
+Methods: The study utilized data from the recent round of the National Family Health Survey, 2015–
+16. Age-standardized prevalence rates were calculated, followed by an examination of economic 
+inequality using the poor-rich-ratio (PRR) and Wagstaff’s concentration index. Spatial variation in the 
+prevalence of diabetes was explored with a series of quantile maps, univariate, and bivariate LISA 
+cluster maps. Further, to explore the district-level diabetes prevalence among women in the 
+country, Ordinary Least Square and Spatial Autoregressive (SAR) models were used. Results: The 
+study findings affirm the presence of spatial clustering in the burden of diabetes among women. The 
+burden is relatively higher among women from the Southern and Eastern parts of the country. 
+Findings establish obesity, hypertension, and living in urban areas as major correlates of diabetes. 
+Conclusion: Program with an aim to lower the intensity of community-based prevalence of diabetes, 
+11 
+  
+NO. 35 | AUGUST 2020 
+especially among women in their late reproductive ages, should adopt differential approaches across 
+different states/districts in the context of their lifestyle, dietary pattern, working pattern, and other 
+socio-cultural practices.  
+ 
+NON-PEER REVIEWED  
+ 
+Visit POSHAN website to explore issues of our COVID-19 Nutrition Digest – a collection of recently 
+published peer- and non-peer-reviewed resources, including research articles blogposts, opinion 
+pieces etc. These are collated from various sources, and analyze the impacts of COVID-19 pandemic 
+on the outcomes, determinants and coverage of interventions related to maternal and child 
+nutrition. 
+• COVID-19 Nutrition Digest (Aug 2020) - http://poshan.ifpri.info/2020/08/11/covid-19-
+nutrition-digest-august-2020/ 
+• COVID-19 Nutrition Digest (July 2020) - http://poshan.ifpri.info/2020/07/20/covid-19-
+nutrition-digest-july-2020/ 
+• COVID-19 Nutrition Digest (June 2020) - http://poshan.ifpri.info/2020/06/05/covid-19-
+nutrition-digest-june-2020/ 
+• COVID-19 Nutrition Digest (May 2020) - http://poshan.ifpri.info/2020/05/21/covid-19-
+nutrition-digest-may-2020/ 
+ 
+ 
+POSHAN COVID-19 Monitoring Report  
+UNICEF, IIT-B, IFPRI, World Food Programme and the World Bank. 2020. POSHAN COVID-19 
+Monitoring Report. New Delhi, UNICEF India. https://poshancovid19.in/Monitoring.html 
+ 
+The POSHAN COVID-19 Monitoring Report presents all relevant data to monitor the effects of 
+COVID-19 on nutrition/food security across both the most populous states affected by both the 
+pandemic and those with the largest burden of malnutrition. The purpose is to present available 
+data to policy makers and programme managers to strengthen the public health nutrition response 
+during the COVID-19 crisis. It is informed by various development partners working in the area of 
+food and nutrition security and compiled by UNICEF, IIT-B, IFPRI, World Food Programme and the 
+World Bank. 
+ 
+ 
+POSHAN COVID-19 Resources  
+UNICEF India. 2020. POSHAN COVID-19 Resources. New Delhi, UNICEF India. 
+https://poshancovid19.in/Resources.html 
+ 
+It is an online repository of government circulars, national and international guidelines and technical 
+documents on programming during the COVID-19 Pandemic. This compilation focuses on nutrition, 
+food security, early childhood development and related issues. The central and state-level policies 
+issued since the start of the COVID-19 outbreak, including those on the continuity of essential 
+services are provided. 
+ 
+ 
+Prevention, Early Detection and Treatment of Wasting in Children 0-59 Months through National 
+Health Systems in the Context of COVID-19 
+Prevention, Early Detection and Treatment of Wasting in Children 0-59 Months 
+through National Health Systems in the Context of COVID-19. United Nations Children’s Fund and 
+World Health Organization, New York, 2020. 
+12 
+ 
+ABSTRACT DIGEST 
+https://aa9276f9-f487-45a2-a3e7-
+8f4a61a0745d.usrfiles.com/ugd/aa9276_f8ae809af929450780f08c98793badf5.pdf 
+ 
+This document serves as a tool for implementing the recommendations reflected in existing WHO 
+and UNICEF guidance on the delivery of services through national health systems for the prevention, 
+early detection and treatment of child wasting in the context of COVID-19. This note reflects broad 
+guidance for all levels of the health system, including community health services that offer 
+prevention, early detection and treatment services for child wasting. WHO and UNICEF recognize 
+that context-specific adaptations to these recommendations will be necessary depending on 
+transmission levels, population mobility restrictions, resources, and other national public health 
+measures to respond to and mitigate the effects of the pandemic across different countries. This 
+note therefore offers specific examples of programmatic changes or adaptations that may be 
+temporarily introduced to ensure the continuity and safety of prevention and treatment services. 
+ 
+ 
+The State of Food Security and Nutrition in the World 2020: Transforming food systems for 
+affordable healthy diets 
+FAO, IFAD, UNICEF, WFP and WHO. 2020. The State of Food Security and Nutrition in the World 2020. 
+Transforming food systems for affordable healthy diets. Rome, FAO. 
+https://doi.org/10.4060/ca9692en 
+ 
+Updates for many countries have made it possible to estimate hunger in the world with greater 
+accuracy this year. In particular, newly accessible data enabled the revision of the entire series of 
+undernourishment estimates for China back to 2000, resulting in a substantial downward shift of the 
+series of the number of undernourished in the world. Nevertheless, the revision confirms the trend 
+reported in past editions: the number of people affected by hunger globally has been slowly on the 
+rise since 2014. The report also shows that the burden of malnutrition in all its forms continues to be 
+a challenge. There has been some progress for child stunting, low birthweight and exclusive 
+breastfeeding, but at a pace that is still too slow. Childhood overweight is not improving and adult 
+obesity is on the rise in all regions. 
+The report complements the usual assessment of food security and nutrition with projections of 
+what the world may look like in 2030, if trends of the last decade continue. Projections show that 
+the world is not on track to achieve Zero Hunger by 2030 and, despite some progress, most 
+indicators are also not on track to meet global nutrition targets. The food security and nutritional 
+status of the most vulnerable population groups is likely to deteriorate further due to the health and 
+socio economic impacts of the COVID-19 pandemic. 
+ 
+ 
+Improving Young Children’s Diets (June 2020) 
+Nutrition Exchange (NEX) South Asia. 2020. Improving Young Children’s Diets. ENN & UNICEF. 
+https://mcusercontent.com/fb1d9aabd6c823bef179830e9/files/15066773-a82a-4c88-963c-
+719f647c1b7f/2020_ENN_and_UNICEF_NEX_South_Asia_on_improving_young_children_s_diets.pdf 
+ 
+The South Asia region continues to bear the highest burden of child malnutrition in the world, with 
+significant implications for global progress. As with the first issue, this issue follows on from a 
+regional conference, convened by SAARC (the South Asian Association for Regional Cooperation) and 
+UNICEF (United Nations Children’s Fund) on ‘Stop Stunting: Improving Young Children’s Diets in 
+South Asia’ in 2019 in Nepal. Poor complementary feeding practices are associated with high rates of 
+child malnutrition in the South Asia region and it is vital therefore to understand how related 
+national policies and programmes are being designed and implemented and share the lessons 
+learned. Through a partnership with UNICEF’s Regional Office for South Asia (ROSA), we have 
+13 
+  
+NO. 35 | AUGUST 2020 
+worked closely with a range of authors to support the development of nine articles from six 
+countries – Afghanistan, Bangladesh, Bhutan, India, Nepal and Pakistan – as well as an overview 
+from UNICEF ROSA and a regional perspective on tackling the double burden of malnutrition.  
+ 
+ 
+Marketing of breast-milk substitutes: National implementation of the International Code 
+(Status report 2020) 
+Marketing of breast-milk substitutes: national implementation of the international code, status 
+report 2020. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO. 
+https://www.unicef.org/sites/default/files/2020-05/Marketing-of-breast-milk-substitutes-status-
+report-2020.pdf 
+ 
+Despite efforts to stop the harmful promotion of breast-milk substitutes, countries are still falling 
+short in protecting parents from misleading information. The report, produced by WHO, UNICEF and 
+the International Baby Food Action Network (IBFAN), provides an update on the status of 
+implementing the International Code of Marketing of Breast-milk Substitutes and subsequent 
+relevant World Health Assembly (WHA) resolutions (“the Code'”) in countries. 
+Of the 194 countries analysed, 136 have in place some form of legal measure related to "the Code". 
+However, the legal restrictions in most counties do not fully cover marketing that occurs in health 
+facilities. Only 79 countries prohibit the promotion of breast-milk substitutes in health facilities. 
+Given the important role of health workers in protecting pregnant women, mothers and their infants 
+from inappropriate promotion of breast-milk substitutes, the 2020 report provides an extensive 
+analysis of legal measures taken to prohibit promotion of breast-milk substitutes.  
+ 
+ 
+Cost-Benefit Analyses of Nutrition Interventions in India’s Policy Framework 
+Kumar, A., and W. Joe. 2020. “Cost-Benefit Analyses of Nutrition Interventions in India’s Policy 
+Framework”. 2020. IEG working paper no. 406. Population Research Centre, Institute of Economic 
+Growth, Delhi. 
+http://www.iegindia.org/upload/profile_publication/doc-190820_174628wp406.pdf 
+ 
+The Government of India has launched several important nutrition and health programmes and 
+interventions. This study is an attempt to estimate the costs and benefits accruing from the 
+implementation of the national interventions. The benefits are measured in terms of the number of 
+years of life saved due to decreased child mortality and valued at 3 times the value of GDP/capita. 
+Benefits also include the value of avoiding a brief period of life spent living with the disability arising 
+from nutrition related illness. Productivity benefits for those who avoid stunting have also been 
+estimated. Three alternate scenarios have been created on the basis of specific nutrition based 
+interventions which include counselling for behaviour change, supplementary food and an overall 
+package consisting of both the interventions. Estimated benefits for India from the overall package 
+at 3 times the value of per capita GDP and discounted at 3% are approximately $3070 and estimated 
+costs are approximately $159, resulting in a benefit/cost ratio of approximately 19.4. Every dollar 
+spent on nutrition can yield benefits of more than 19 dollars. To conclude, the coverage of nutrition-
+based interventions for mothers is not the problem, but the low utilization poses a challenge. On the 
+other hand, the interventions for children need to be scaled up. Promotion and provision of timely 
+and appropriate complementary feeding practices can improve the health outcomes among 
+both women and children. 
+ 
+ 
+ 
+ 
+14 
+ 
+ABSTRACT DIGEST 
+UPCOMING EVENTS & DEADLINES 
+ 
+Delivering for Nutrition in India: Insights from Implementation Research   
+When: September 14-18, 2020 
+Where: Virtual event 
+Registration open 
+For more information: http://poshan.ifpri.info/delivering-for-nutrition-in-india-insights-from-
+implementation-research/ 
+ 
+ 
+European Society Of Clinical Nutrition And Metabolism 42nd Congress 2020 (ESPEN 2020)  
+When: September 19-21, 2020 
+Where: Virtual event 
+For more information: https://espencongress.com/ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+Issue 19 December 2017 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ABOUT POSHAN  
+Partnerships and Opportunities to Strengthen and Harmonize Actions for Nutrition in India (POSHAN) is a multi-year initiative that aims to build evidence on effective 
+actions for nutrition and support the use of evidence in decision-making. It is supported by the Bill & Melinda Gates Foundation and led by IFPRI in India. 
+ 
+ABOUT ABSTRACT DIGEST  
+In each issue, the POSHAN Abstract Digest brings you some of the new and noteworthy studies on maternal and child nutrition. It focuses on India-specific studies and 
+also brings to you other relevant global or regional literature with broader implications for maternal and child nutrition. The Abstract Digest is based on literature searches 
+to identify selected studies that we think are most relevant to nutrition issues in India and to Indian programs and policies. We share with you a collection of abstracts 
+from articles published in peer-reviewed journals, as well as selected non-peer-reviewed articles by researchers in reputed academic and/or research institutions and 
+which demonstrated rigor in their research objectives, methodology, and analysis. The abstracts in this document are reproduced in their original form from their source, 
+and without editorial commentary about specific articles. 
+ 
+CONTACT US  
+Email us at IFPRI-POSHAN@cgiar.org 
+IFPRI-NEW DELHI  
+INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE  
+NASC Complex, CG Block, Dev Prakash Shastri Road, Pusa, New Delhi 110012, India  
+T +91.11.66166565; F +91.11.66781699 
+http://poshan.ifpri.info/ 
+IFPRI-HEADQUARTERS  
+INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE  
+2033 K Street, NW, Washington, DC 20006-1002 USA  
+T. +1.202.862.5600 F. +1.202.467.4439  
+Skype: IFPRIhomeoffice; Email: ifpri@cgiar.org   
+www.ifpri.org 
+ 
+This publication has been prepared by POSHAN with research assistance from Pratima Mathews, IFPRI, and has not been peer reviewed. Any opinions stated herein are 
+those of the author(s) and do not necessarily reflect the policies of the International Food Policy Research Institute. Please contact Dr. Rasmi Avula for any questions. 
+ 
+Copyright © 2020 International Food Policy Research Institute. All rights reserved. For permission to republish, contact ifpri-copyright@cgiar.org. 
+

data/part_2/0198361654.md

@@ -0,0 +1,25 @@
+# 2015 Global Nutrition Report
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/252290be-7796-4ea4-a647-b169baeebef9/retrieve  
+**Language:** English  
+**Resource Type:** Poster / Presentation  
+**Release Year:** 2015  
+**Rights:** N/A  
+**GARDIAN ID:** 7f2372a9d3c8ab0c96c58db2a947f754  
+**DataNODE ID:** 91c4e1c8c7b34c6c8293e6cc2ded2d53  
+**Siever ID:** 1e8ee609-eb04-452d-b08d-a59cf042a538  
+**Token Count:** 23  
+**Page Count:** 0
+
+## Keywords
+
+nutrition, report, new york
+
+## Description
+
+Presentation by IFPRI senior researcher Lawrence Haddad at 2015 Global Nutrition Report launch event held in New York City on September 22, 2015
+
+## Content
+
+Presentation by IFPRI senior researcher Lawrence Haddad at 2015 Global Nutrition Report launch event held in New York City on September 22, 2015

data/part_2/0204770465.md

@@ -0,0 +1,42 @@
+# ASTI Zimbabwe database
+
+**Source:** gardian_index  
+**URL:**   
+**Language:** English  
+**Resource Type:** Dataset / Tabular  
+**Release Year:** 2014  
+**Rights:** CC0  
+**GARDIAN ID:** 7e5f0b70681501d27b21a39344ff7174  
+**DataNODE ID:** 4b2cb370d02aa179c0d17828ee6ba906  
+**Siever ID:** 77634499-169a-43ad-8465-4d98d4387deb  
+**Token Count:** 146  
+**Page Count:** 0
+
+## Keywords
+
+zimbabwe, southern africa, africa south of sahara, africa, agricultural research, research and development, higher education, data collection, data, resources, information, research
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Zimbabwe
+
+## Description
+
+This dataset contains national-level data on financial, human, and institutional resources for agricultural research and development (R&D).
+</br>
+</br>
+Accurate, reliable, and internationally comparable quantitative information on investments, human capacity, and the institutional structure of agricultural R&D  is fundamental to understanding the contribution of research to agricultural growth in low- and middle-income countries. Providing such data is the mission of IFPRI’s Agricultural Science and Technology Indicators (ASTI) program. Through its large network of national, regional, and international partners, ASTI collects and analyzes data from government, higher education, nonprofit, and (where possible) private-sector agencies involved in agricultural R&D. The program conducts ongoing analysis of these datasets; disseminates the results of this analysis to promote advocacy and support policymaking; and builds national and regional capacity for data collection and analysis.<b><i>This data was collected in 2014 and made it to Dataverse in 2015.</i></b>
+</br>
+</br>
+<Strong><u>Data File</u></strong>: <a href="http://www.asti.cgiar.org/zimbabwe">http://www.asti.cgiar.org/zimbabwe</a>
+
+## Content
+
+This dataset contains national-level data on financial, human, and institutional resources for agricultural research and development (R&D).
+</br>
+</br>
+Accurate, reliable, and internationally comparable quantitative information on investments, human capacity, and the institutional structure of agricultural R&D  is fundamental to understanding the contribution of research to agricultural growth in low- and middle-income countries. Providing such data is the mission of IFPRI’s Agricultural Science and Technology Indicators (ASTI) program. Through its large network of national, regional, and international partners, ASTI collects and analyzes data from government, higher education, nonprofit, and (where possible) private-sector agencies involved in agricultural R&D. The program conducts ongoing analysis of these datasets; disseminates the results of this analysis to promote advocacy and support policymaking; and builds national and regional capacity for data collection and analysis.<b><i>This data was collected in 2014 and made it to Dataverse in 2015.</i></b>
+</br>
+</br>
+<Strong><u>Data File</u></strong>: <a href="http://www.asti.cgiar.org/zimbabwe">http://www.asti.cgiar.org/zimbabwe</a>

data/part_2/0219925334.md

@@ -0,0 +1,320 @@
+# Using participatory learning and action to empower women’s groups to improve feeding practices in Madhya Pradesh
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/58e35d2b-d8da-4164-8360-2fed3d42a02e/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2015  
+**Rights:** N/A  
+**GARDIAN ID:** ce41ac3754a0713eceda8822616a6262  
+**DataNODE ID:** ed76fce69d17955aae996ac91627643f  
+**Siever ID:** 50b8d282-696c-4955-9b4d-cda2b3f3e70d  
+**Token Count:** 1748  
+**Page Count:** 0
+
+## Keywords
+
+gender, undernutrition, nutrition policies, malnutrition, nutrition, children, food security, capacity building, madhya pradesh, nongovernmental organizations, child development, public health
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World
+- **Countries:** India
+
+## Description
+
+Participatory learning and action (PLA) is a promising approach to promote infant and young children feeding (IYCF) and other health, nutrition, water sanitation, and hygiene (HNWASH) practices. Since February 2014, PLA has been implemented in 14 blocks in eight districts of Madhya Pradesh through the Sanjhi Sehat program, which is led by the Government of Madhya Pradesh’s State Rural Livelihood Mission (SLRM). The mission implements Sanjhi Sehat in five districts, and district- level nongovernmental organizations (NGOs) implement it in three additional districts. Technical assistance is provided by the Madhya Pradesh Technical Assistance and Support Team (MPTAST) under the Department for International Development (DIFD)-supported Madhya Pradesh Health Systems Reforms program. Other partners include the Department of Health and Family Welfare and the Department of Women & Child Development, whose staff ensure that all services of the program are accessible and accountable to the community. The Public Health Engineering Department is responsible for the safe water and sanitation (toilet construction) infrastructure and services, which forms an important component of the HNWASH interventions.
+
+## Content
+
+No. 18  |  NOVEMBER 2015 
+Partnerships and Opportunities to Strengthen 
+and Harmonize Actions for Nutrition in India
+Implementation Note
+Using Participatory Learning and Action 
+to Empower Women’s Groups to Improve 
+Feeding Practices in Madhya Pradesh
+BACKGROUND
+Participatory learning and action (PLA) is a 
+promising approach to promote infant and young 
+children feeding (IYCF) and other health, nutrition, 
+water sanitation, and hygiene (HNWASH) practices. 
+Since February 2014, PLA has been implemented 
+in 14 blocks in eight districts of Madhya Pradesh 
+through the Sanjhi Sehat program, which is led by 
+the Government of Madhya Pradesh’s State Rural 
+Livelihood Mission (SLRM). The mission implements 
+Sanjhi Sehat in five districts, and district- level 
+nongovernmental organizations (NGOs) implement 
+it in three additional districts. Technical assistance 
+is provided by the Madhya Pradesh Technical 
+Assistance and Support Team (MPTAST) under 
+the Department for International Development 
+(DIFD)-supported Madhya Pradesh Health Systems 
+Reforms program. Other partners include the 
+Department of Health and Family Welfare and 
+the Department of Women & Child Development, 
+whose staff ensure that all services of the program 
+are accessible and accountable to the community. 
+The Public Health Engineering Department is 
+responsible for the safe water and sanitation (toilet 
+construction) infrastructure and services, which 
+forms an important component of the HNWASH 
+interventions.
+APPROACHES AND METHODS FOR 
+IMPLEMENTATION
+PLA is an approach for learning about and engaging 
+with communities. It combines participatory and 
+visual methods with engaging techniques, and is 
+intended to facilitate a process of collective analysis 
+and learning. The approach can be used to identify 
+needs and to plan, monitor, or evaluate projects 
+and programs. PLA goes beyond mere consultation 
+by promoting active participation of communities 
+in understanding issues, and prioritizing and 
+developing solutions that shape their lives. It has 
+been found to be effective in helping the rural 
+poor to unlock their ideas on the nature and 
+causes of the issues that affect them, prioritize, 
+and collectively arrive at realistic solutions based 
+on their knowledge of local conditions. It offers an 
+opportunity to the marginalized and excluded to be 
+heard and become partners in executing solutions 
+that benefit them.
+The design of Sanjhi Sehat is based on previous 
+successful experiences using PLA by the NGO 
+Ekjutin Jharkhand and by the DFID-supported 
+Health Systems Strengthening program in Bihar and 
+Odisha. The design is based on evidence that PLA is 
+effective in improving health outcomes (Prost et al. 
+2013). 
+The PLA approach involves a series of 20–22 
+meetings conducted fortnightly using interactive 
+modules developed by MPTAST. Each module is 
+devoted to a particular HNWASH topic, including 
+four devoted to IYCF practices. One module is 
+presented at each PLA meeting held with target 
+groups (for example, pregnant or lactating women, 
+mothers of preschool children, adolescent girls). 
+PLA meetings are conducted using the platform of 
+existing self-help groups (SHGs) of the SLRM. At 
+each meeting, a trained PLA facilitator takes the 
+group through a process of collective reflection 
+in which the group assesses current behaviors 
+and related social, cultural, and psychological 
+barriers related to the HNWASH topic; household 
+and community actions needed to address 
+the barriers; and creating demand for relevant 
+services. Participatory activities at the meetings 
+involve showing picture cards, playing games, 
+displaying age-appropriate quantities and varieties 
+of complementary foods, and discussing ways of 
+making meals richer in nutrients and calories. The 
+meetings also involve an annaprasan ceremony, 
+at which a group of six- to eight-month-olds are 
+fed an appropriate starter semi-solid food by their 
+mothers under the guidance of an anganwadi 
+worker (AWW). 
+Midway through and at the end of the series of 
+meetings (after 10 and 22 meetings, respectively), 
+a larger community meeting is held, where other 
+stakeholders and community leaders receive key 
+messages. At the end of the series, there is a session 
+on participatory evaluation, at which the community 
+evaluates the program’s implementation. Depending 
+on the results of the evaluation, additional meetings 
+are held as needed.
+MPTAST trains, implements, monitors, and 
+evaluates the project. Trained facilitators from the 
+implementing NGOs and the SLRM (one facilitator 
+per 11–14 village clusters) plan and conduct the 
+meetings for each topic, maintain attendance data, 
+record meeting notes, and carry out follow-up 
+action, when needed. Community mobilizers 
+(one per village) assist facilitators with mobilizing 
+women and SHG members to attend the PLA 
+meetings. Local government health and Integrated 
+Child Development Services (ICDS) functionaries 
+actively participate in the PLA meetings. One cluster 
+coordinator is appointed for every 5–10 facilitators 
+to maintain quality and monitor the activity of 
+the meetings. Each cluster coordinator mentors 
+the facilitators as needed and resolves local issues 
+needing attention. A district PLA coordinator oversees 
+the functions of the PLA facilitators and cluster 
+coordinator by conducting monthly reviews of the 
+meetings, checking the management information 
+system, and serving as the link between state 
+MPTASTs and the field staff. The cluster coordinators 
+2
+Photo © UNICEF
+IMPLEMENTATION NOTE
+3No. 18  |  NOVEMBER 2015
+and facilitators are contractual staff employed by 
+SLRM or NGOs in their respective districts.
+With a view to ensuring sustainability, the program is 
+strategically led by an existing government program—
+the SLRM— and builds upon an existing network 
+of SHGs. The intent is that essential elements of the 
+program can be carried forward by these SHGs after 
+the project ends, even if the form and frequency of 
+the structured PLA meetings change.
+KEY FINDINGS
+As of March 2015, 16,824 PLA meetings have been 
+held in the eight intervention districts. Some of 
+the key emerging findings given below are based 
+on routine monitoring data collected at each 
+meeting, where women are questioned about their 
+understanding, recall, and practice of the messages 
+of the previous meeting; a complied report of data 
+provided by seven cluster coordinators representing 
+100 PLA facilitators (MPTAST and MPHSRP 
+2015a); and a quantitative report summarizing 
+trends in ICDS service utilization before and after 
+the program based on monthly data collected 
+from AWWs in 14 blocks where the program is 
+implemented (MPTAST and MPHSRP 2015b). 
+ ▶ Approximately 80 percent of PLA meetings 
+are being held with marginalized rural-tribal 
+communities, whose women have shown a high 
+receptivity to the PLA approach. 
+ ▶ On average, 25–40 women attend meetings and 
+show high recall of the messages.
+ ▶ More than 80 percent of women attending IYCF 
+sessions report increased knowledge of how 
+to enrich complementary feeding and feed the 
+required quantity to the child.
+ ▶ There has been an increased uptake of ICDS 
+services, such as take-home rations and baby 
+weighing.
+ ▶ Groups practice local solutions, for example, 
+bringing their own complementary feeding 
+recipes from home and feeding their children.
+ ▶ Attendance of Department of Health-ICDS 
+functionaries in the PLA meetings (for example, 
+AWWs participate in more than half of the 
+meetings) has reduced social distance between 
+the families and the functionaries and enhances 
+the use of services. 
+ ▶ Over time, men’s interest in PLA meetings and 
+their support to their women to attend these 
+meetings has increased.
+One challenge in implementing the program has 
+been reaching those who are located far from the 
+meeting site in remote tribal hamlets or are unable 
+to attend the meetings because of seasonal heavy 
+workload in the harvesting or sowing periods. In 
+response, implementers have changed the meeting 
+venues as needed, have held the meetings early in 
+the morning or evening, and for migrant women, 
+have made home visits.
+Another challenge has been the resistance on the 
+part of mothers-in-law to changing some of the 
+IYCF practices, such as increasing the quantity or 
+frequency of foods given to the child. In response, 
+implementers have encouraged these women 
+to attend the meetings, and their resistance has 
+decreased over time.
+CONCLUSION
+An endline evaluation report of Sanjhi Sehat will 
+be available in December 2015. However, experi-
+ential learning from the field suggests that the PLA 
+approach appears to be effective in Madhya Pradesh, 
+especially in reaching marginalized communities and 
+facilitating group learning to change practices and 
+generate demand for services. The endline evalua-
+tion will provide insights regarding the processes and 
+impact of this program on the vulnerable groups.
+REFERENCES
+MPTAST (Madhya Pradesh Technical Assistance 
+and Support Team) MPHSRP (Madhya Pradesh 
+Health Sector Reform Programme). 2015a. 
+What Is the Acceptance and Practice of IYCF 
+Messages in Modules 12-13 of Sanjhi Sehat 
+Program by Women—A Feedback from 100 
+PLA Facilitators in Seven Districts. Bhopal.
+———. 2015b. Analysis of ICDS MIS Data for 
+Sanjhi Sehat Blocks. Bhopal.
+Prost, A., T. Colbourn, N. Seward, K. Azad, 
+A.Coomarasamy, A. Copas, T. A. J. Houweling, 
+et al. 2013. “Women’s Groups Practicing 
+Participatory Learning and Action to Improve 
+Maternal and Newborn Health in Low-Resource 
+Settings: A Systematic Review and Meta-
+analysis.” Lancet 381 (9879): 1736–1746.
+ABOUT POSHAN 
+Partnerships and Opportunities to 
+Strengthen and Harmonize Actions for 
+Nutrition in India (POSHAN) is a 4-year 
+initiative that aims to build evidence on 
+effective actions for nutrition and support 
+the use of evidence in decisionmaking. It 
+is supported by the Bill & Melinda Gates 
+Foundation and led by IFPRI in India.
+ABOUT  
+IMPLEMENTATION NOTES
+Implementation Notes summarize 
+experiences related to how specific 
+interventions or programs are delivered. 
+They are intended to share information 
+on innovations in delivery and are not 
+research products.
+CONTACT US
+Email us at IFPRI-POSHAN@cgiar.org
+IFPRI-NEW DELHI
+INTERNATIONAL FOOD POLICY 
+RESEARCH INSTITUTE
+NASC Complex, CG Block,  
+Dev Prakash Shastri Road,  
+Pusa, New Delhi 110012, India 
+T+91.11.2584.6565 to 6567  
+F+91.11.2584.8008
+IFPRI-HEADQUARTERS
+INTERNATIONAL FOOD POLICY 
+RESEARCH INSTITUTE
+2033 K Street, NW,  
+Washington, DC 20006-1002 USA 
+T. +1.202.862.5600  
+F. +1.202.467.4439  
+Skype: IFPRIhomeoffice  
+ifpri@cgiar.org  
+www.ifpri.org
+This publication has been prepared by 
+POSHAN. It has not been peer reviewed. 
+Any opinions stated herein are those of 
+the author(s) and do not necessarily reflect 
+the policies of the International Food 
+Policy Research Institute.
+Copyright © 2015 International Food 
+Policy Research Institute. All rights 
+reserved. For permission to republish, 
+contact ifpri-copyright@cgiar.org.
+Partnership members:
+Institute of Development Studies (IDS)
+Public Health Foundation of India  (PHFI)
+One World South Asia
+Vikas Samvad
+Coalition for Sustainable Nutrition  Security in India
+Save the Children, India
+Public Health Resource Network (PHRN)
+Vatsalya
+Centre for Equity Studies
+WRITTEN BY
+Shubhada Kanani, FHI360–Madhya Pradesh Technical Assistance 
+and Support team
+Rachna Singh, FHI360–Madhya Pradesh Technical Assistance and 
+Support team
+Syed Baqar, FHI360–Madhya Pradesh Technical Assistance and 
+Support team
+Uma Mahajan, FHI360–Madhya Pradesh Technical Assistance and 
+Support team
+L. M. Belwal, State Rural Livelihood Mission, Government of 
+Madhya Pradesh
+ 
+SUGGESTED CITATION
+Kanani, S., R. Singh, S. Baqar, U. Mahajan, and L.M. Belwal. 2015. 
+Using Participatory Learning and Action to Empower Women’s 
+Groups to Improve Feeding Practices in Madhya Pradesh. POSHAN 
+Implementation Note No. 18. New Delhi: International Food Policy 
+Research Institute.
+

data/part_2/0240558205.md

@@ -0,0 +1,594 @@
+# Regional analysis of communal river diversion: Potential for expansion in Sub-Saharan Africa
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/176d1f19-be58-4dda-b21e-e0f17a20ec00/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2012  
+**Rights:** N/A  
+**GARDIAN ID:** 928200f6b4ac2d3dee76f4175c28fc69  
+**DataNODE ID:** 99aac7167c566ad8560dc8a9396aabd0  
+**Siever ID:** c44b0488-8913-4b09-9e30-2c20f7240882  
+**Token Count:** 3241  
+**Page Count:** 0
+
+## Keywords
+
+water management, irrigation, water use, climate change, models, soil quality, smallholders, rivers, geographical information systems, soil analysis, water analysis, dynamic models, river basins
+
+## Description
+
+Sub-Saharan Africa (SSA) faces great challenges in development, including the highest poverty rate in the world, food insecurity, and malnutrition. Given that agriculture is the single most important source of rural livelihood in Africa, an agricultural growth strategy will go a long way to reducing hunger and poverty on the subcontinent. Among the numerous challenges to enhancing agricultural production in SSA is the large spatial and temporal variability and availability of water resources. Currently, agriculture in SSA is predominantly rainfed. The limited access to water in arid areas or during dry seasons and drought spells often presents restrictions to farming and to improving agricultural productivity. Therefore, enhanced agricultural water management has been regarded as a promising solution to boost levels of agricultural productivity in SSA.
+
+## Content
+
+ 
+ 
+Agricultural Water Management Regional Analysis Document 
+REGIONAL ANALYSIS OF  
+COMMUNAL RIVER DIVERSION 
+Potential for expansion in  
+Sub-Saharan Africa 
+ awm-solutions.iwmi.org 
+JULY 2012 
+ 
+ 
+ 
+Introduction 
+Sub-Saharan Africa (SSA) faces great challenges in 
+development, including the highest poverty rate in the world, 
+food insecurity, and malnutrition. Given that agriculture is the 
+single most important source of rural livelihood in Africa, an 
+agricultural growth strategy will go a long way to reducing 
+hunger and poverty on the subcontinent. Among the 
+numerous challenges to enhancing agricultural production in 
+SSA is the large spatial and temporal variability and 
+availability of water resources. Currently, agriculture in SSA is 
+predominantly rainfed. The limited access to water in arid 
+areas or during dry seasons and drought spells often presents 
+restrictions to farming and to improving agricultural 
+productivity.  Therefore, enhanced agricultural water 
+management has been regarded as a promising solution to 
+boost levels of agricultural productivity in SSA.  
+ 
+Communal river diversion is a traditional irrigation method 
+that could potentially be improved and expanded 
+throughout the region. Communal river diversion refers to 
+di"erent schemes for diverting surface water from rivers 
+through traditional furrows or canals to farmers’ $elds. 
+Traditional communal river diversion schemes are initiated 
+and operated by farmers, without any external intervention. 
+They are often characterized by poor infrastructure, poor 
+water management, and low yields. Improved river diversion 
+schemes sometimes bene$t from external interventions like 
+construction of new canals, but they are still managed by the 
+communities.  
+ 
+Methodology 
+This brief is based on a study that uses an integrated 
+modeling system that combines geographic (GIS) data 
+analysis, biophysical and economic predictive modeling, and 
+crop mix optimization tools to assess the regional potential 
+for smallholder agricultural water management in SSA and 
+South Asia (SA). It focuses on the potential for the expansion 
+of communal river diversions throughout SSA.  
+ 
+ 
+The assessment process includes two components: ex-ante 
+GIS and predictive modeling analyses. The ex-ante analysis 
+uses a set of suitability criteria to identify areas where the 
+technology could potentially be applied, pixel by pixel, across 
+the region. The formulation of assessment criteria and the 
+scoring scheme were developed through expert 
+consultations and validation and re)ect the best available 
+expert knowledge.  For communal diversions, the 
+environmental suitability criteria for ex-ante GIS analysis are 
+shown in Table 1. 
+ 
+A pixel with a score greater than 57 is considered to have 
+irrigation potential. The application areas derived from the 
+suitability analysis were also compared with the labor-
+constrained application areas obtained from rural population 
+analysis at the basin level; the minimum of the two 
+application areas in a river basin was selected as the $nal ex-
+ante estimates for the areas with irrigation potential in the 
+river basin. 
+ 
+The results derived from ex-ante GIS analysis are further 
+re$ned in an analysis that involves the application of two 
+biophysical and economic predictive modeling tools: the Soil 
+and Water Assessment Tool (SWAT) and the model of 
+Dynamic Research Evaluation for Management (DREAM). 
+Currently, agriculture in SSA is dominantly rainfed and 
+farming activities concentrate in the rainy seasons. This 
+analysis assumes that communal diversions would enable 
+producers to extend crop production into the dry season, 
+when the irrigation demand is highest. Under this 
+assumption, the SWAT and DREAM models were run to 
+simulate the hydrology, estimate crop water demand and 
+agricultural productivity in the added dry growing season, 
+and forecast price shifts in agricultural commodities as a 
+result of increased supply. The results produced from the 
+SWAT–DREAM predictive analysis allow for quantitative water 
+balance and cost–bene$t analysis of irrigation activities. This 
+further constrains the potential for irrigation expansion 
+compared to the ex-ante analysis, based on physical scarcity 
+and economic viability. 
+ 
+ awm-solutions.iwmi.org 
+Table 1.  Ex-ante GIS analysis criteria for communal river diversions  
+Criteria for motor pumps Scoring scheme 
+FAO Fluvisols  False = 0, 1 - 15 % = 6, 16 - 50 % = 11, 51 - 100 % = 17  
+Market access 5 km = 10 minutes = 17, 10 km = 20 minutes = 11, 20 km = 40 minutes = 6, 30 km = 60 
+minutes = 0, 60 km = 120 minutes = 0  
+Distance to surface water  < 5 km = 16, >5 km =  excluded  
+Topography  0-4% = 16, 4-10% = 8, 10% < =  0  
+Runo"  1 - 25 mm =  0, 25 - 45 mm = 4, 45 - 75 mm = 9, 75 - 110 mm = 13, > 110 mm = 17  
+Population density  0 - 5 = excluded, > 5 = 17  
+ 
+ 
+ 
+ 
+Other key assumptions in the predictive modeling 
+assessment include the following: 
+ 
+• Water Availability. No associated runo" storage capacity 
+is assumed for communal river diversions. Moreover, 20 
+percent of runo" is reserved for environmental )ows. 
+ 
+• Cultivation of  Particular Crops. The assessment assumes 
+that communal diversions are used for the cultivation of 
+a series of crops based on evidence from $eld studies 
+including: tomatoes, onions, peppers, cabbages, beans, 
+peas, potatoes, sweet potatoes, sugarcane, ground nuts, 
+maize, wheat, and rice. 
+ 
+• Fertilizer Input. Agricultural production in SSA is 
+characterized by the wide presence of low-input farming 
+systems. However, because there exists strong synergy 
+between water and nutrient management—that is, 
+farmers need to provide an appropriate amount of 
+nutrients to the soil, especially nitrogen, to ensure 
+irrigation is e"ective in improving crop yields—medium 
+rates of nitrogen fertilizer applications were assumed in 
+the crop simulation. The assumed amount of nitrogen 
+fertilizer applied to each crop type is shown in Table 2. 
+The estimated yields of selected crops cultivated under 
+irrigation and assumed nitrogen fertilizer applications (as 
+opposed to the estimated yields in low-input farming 
+systems in SSA) are shown in Table 3. 
+ 
+• Production and Irrigation Costs. Assumed costs of 
+production for the selected crops are shown in Table 2.  
+A cost for irrigation of US$640 per hectare per year was 
+also assumed, with average amortized capital 
+investment costs of $440/ha-yr (original capital 
+investment: $3,500 and reinvestment timeframe of 35 
+years) and labor and operating costs of $200/ha-yr. The 
+cost–bene$t results are very sensitive to these cost 
+assumptions. A sensitivity analysis in which irrigation 
+costs were increased or decreased by 50 percent was, 
+therefore, conducted. 
+It is expected that irrigation will boost agricultural 
+productivity and increase the supply of agricultural 
+commodities, while also lowering their prices. To account for 
+the e"ect of price changes on the economic pro$tability of 
+irrigation development, the DREAM model is used to forecast 
+price shifts. Baseline data for the model were obtained from 
+FAOSTAT Food Balance sheets, FAO PriceSTAT, and the IFPRI 
+IMPACT model.  
+ 
+It was found that the estimated irrigation potential is also 
+sensitive to changes in initial crop prices. A 30 percent 
+increase and a 30 percent decrease in initial crop prices were 
+implemented as additional sensitivity analyses. 
+ 
+Potential for expansion of communal river 
+diversions in SSA 
+The ex-ante assessment shows that communal river 
+diversions could be expanded to 82 million ha, potentially 
+reaching a rural population of 457 million people. The 
+potential for expansion of communal river diversions is 
+highest in the Gulf of Guinea region, with potential 
+expansion of over 25 million ha reaching 132 million people, 
+driven largely by huge potential in Nigeria. The Eastern and 
+Central regions also show considerable potential for 
+expansion of the technology, with 122 and 85 million people 
+potentially reached in these regions, respectively. After 
+Nigeria, the potential is greatest in the Democratic Republic 
+of the Congo and Ethiopia (Table 4).  
+ 
+ awm-solutions.iwmi.org 
+Table 2.  Nitrogen fertilizer application rates and 
+nonirrigation production costs assumed in the crop 
+simulation and crop mix optimization 
+Crops 
+N fertilizer 
+(KG/ha) 
+Costs  
+(US$/ha-yr) 
+Tomatoes 100 3,500 
+Onions 100 3,500 
+Peppers 100 3,000 
+Cabbage 100 4,000 
+Beans 0 1,000 
+Peas 0 500 
+Potatoes 80 3,000 
+Sweet potatoes 60 2,500 
+Groundnuts 0 1,000 
+Sugarcane 80 1,500 
+Wheat 50 700 
+Maize 60 600 
+Rice (paddy) 80 1,000 
+Source: IFPRI Team based on project inputs and secondary sources 
+ 
+Figure 1: Suitable area for expansion of communal river diversions,  
+ex-ante results 
+Source: IFPRI Team 
+ 
+ 
+ 
+ awm-solutions.iwmi.org 
+Table 3.  Yield improvement of estimated high-input (HI) yields of selected crops cultivated under irrigation and assumed nitrogen 
+                     fertilizer applications compared to low-input (LI) rainfed yields 
+Country  
+Sweet potato Green bean Maize Paddy rice Groundnut 
+LI HI LI HI LI HI LI HI LI HI 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Central Africa  
+Angola 2.2 547 0.2 385 0.3 1,195 - - 0.1 2,432 
+Cameroon 8.1 68 0.5 -62 1.3 215 1.0 327 0.3 647 
+Central African    
+Republic 
+6.8 36 - - 1.1 274 1.2 218 0.8 155 
+Republic of Congo - - 0.5 -55 0.8 378 0.7 348 0.4 410 
+Democratic Republic 
+of Congo 
+- 
+- 
+0.4 59 0.9 349 0.7 441 0.8 153 
+Equatorial Guinea 2.6 309 - - - - - - 0.8 137 
+Gabon 6.8 58 - - 0.9 329 1.0 223 0.7 142 
+Eastern and Indian Ocean countries  
+Burundi 6.4 157 0.4 102 1.0 349 - - 0.7 289 
+Ethiopia - - 0.6 40 1.1 262 - - 0.5 446 
+Kenya 6.8 155 0.5 88 1.0 352 - - 1.4 72 
+Madagascar 6.0 171 0.5 63 1.1 298 1.0 496 - - 
+Rwanda 5.9 202 0.4 138 0.7 615 1.3 277 1.1 151 
+Tanzania 1.5 990 0.5 66 1.8 140 1.8 184 0.6 308 
+Uganda 4.5 215 0.5 15 1.8 128 1.4 263 0.7 288 
+Gulf of Guinea 
+Benin 7.3 56 0.4 -81 1.2 226 1.3 200 0.6 202 
+Côte d'Ivoire  4.9 153 - - 1.0 302 1.6 159 0.7 195 
+Ghana 6.5 73 - - 1.1 268 1.5 163 0.7 185 
+Guinea 7.6 49 - - 1.1 254 1.3 220 0.7 173 
+Guinea-Bissau - - - - 1.0 284 1.2 177 1.2 47 
+Liberia - - - - - - 1.2 243 0.6 271 
+Nigeria 14.2 -15 - - 1.5 167 1.4 191 1.7 30 
+Sierra Leone 2.8 327 - - 1.1 270 1.1 282 0.7 186 
+Togo 6.1 81 0.3 -72 1.2 246 1.1 240 0.5 275 
+Botswana - - 0.01 1,466 0.1 4,322 - - 1.0 163 
+Lesotho - - 0.4 291 1.8 148 - - - - 
+Malawi 11.8 23 0.3 148 1.5 183 1.2 309 0.7 326 
+Mozambique 30.9 -57 0.2 98 1.1 277 0.3 1,384 0.4 524 
+Namibia - - 0.2 329 0.8 429 - - 0.4 529 
+South Africa 9.1 88 1.4 -23 1.7 176 - - 1.1 189 
+Swaziland 1.8 956 0.2 405 0.9 410 3.4 61 - - 
+Zambia 8.1 76 - - 1.1 301 0.9 448 0.4 602 
+Southern Africa  
+ 
+ 
+ 
+ 
+Table 3.  Yield improvement of estimated high-input (HI) yields of selected crops cultivated under irrigation and assumed nitrogen 
+                     fertilizer applications compared to low-input (LI) rainfed yields (cont’d) 
+Country  
+Sweet potato Green beans Maize Paddy rice Groundnut 
+LI HI LI HI LI HI LI HI LI HI 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfe
+d  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Rainfed  
+yield  
+(t/ha) 
+Irrigated 
+yield  
+(% increase) 
+Zimbabwe 2.2 546 0.4 49 1.6 163 2.2 129 0.7 270 
+Sudano-Sahelian region  
+Burkina Faso 9.8 9 - - 1.3 221 1.4 179 0.5 292 
+Chad 6.2 57 0.3 -68 0.8 434 0.8 326 0.6 272 
+Eritrea - - 0.3 20 0.7 507 - - - - 
+Gambia - - - - 1.2 246 1.4 126 0.7 140 
+Mali 14.3 -21 - - 0.8 433 0.8 396 0.7 213 
+Mauritania 1.9 284 0.9 -94 0.9 396 - - 0.6 225 
+Niger 13.9 -9 0.4 -42 0.5 904 1.8 147 0.2 791 
+Senegal 5.3 68 - - 1.1 287 0.7 394 0.7 149 
+Somalia - - 0.3 -77 0.7 525 - - 0.7 179 
+Sudan 2.6 251 1.9 -86 0.8 448 1.6 124 0.5 376 
+Source: IFPRI Team                                                                                               
+Note: LI rainfed yields are derived from the Spatial Production Allocation Model (SPAM).  
+ 
+Taking river basin hydrology, environmental constraints, 
+yield improvements, costs of the investment, and price 
+impacts of expanding crop production into account results 
+in considerably lower potential for adoption of communal 
+river diversions in the region compared to the ex-ante 
+assessment (Figure 2). The results of the SWAT–DREAM 
+assessment for communal river diversions are summarized in 
+Table 5 for the baseline scenario.  
+The results indicate a potential area expansion of 20 million 
+ha, reaching 113 million people, with the greatest potential 
+found in the Gulf of Guinea region.    
+This represents about one quarter of the area potential 
+shown in the ex-ante analysis, suggesting that there are 
+considerable environmental and economic constraints to 
+the expansion of communal river diversion schemes 
+throughout the region.  
+ 
+Total net revenues as a result of the expansion of communal 
+river diversions throughout the region would be US$14 
+billion per year, with revenues highest in the Eastern and 
+Southern regions. The total increase in water consumption 
+as a result of the expansion of communal river diversions in 
+SSA is estimated at 61 billion m3/yr, representing an increase 
+of 89 percent over current water consumption.   
+ 
+Country Name 
+Potential 
+application 
+area (1000 ha) 
+Rural population 
+reached  
+(thousand people) 
+Central 15,005 84,618 
+Eastern and Indian 
+Ocean Countries 
+21,821 122,280 
+Gulf of Guinea 25,050 132,470 
+Southern Africa 9,848 49,383 
+Sudano-Sahelian 9,997 68,731 
+All SSA 81,721 457,481 
+Table 4.  Ex-ante potential for the expansion of 
+communal river diversions  in SSA,  assuming 100 
+percent adoption  
+Source: IFPRI team    
+Figure 2: Suitable Area for expansion of communal river 
+diversions, SWAT-DREAM results                                                            
+Source: IFPRI Team. 
+ awm-solutions.iwmi.org 
+ 
+ 
+ 
+ awm-solutions.iwmi.org 
+Table 5.  Predictive modeling  results for the potential expansion of communal river diversions, baseline scenario results 
+Country  
+Application area  
+(thousand ha) 
+Net revenue 
+(US$ billion/yr) 
+Rural population 
+reached 
+(thousand people) 
+Water consumption 
+(billion m3/yr) 
+Water consumption  
+Increase 
+% 
+Angola 876 0.40 4,965 3.89 494.4 
+Cameroon 97 0.03 536 0.16 17.3 
+Central African Republic 405 0.07 1,982 0.94 798.9 
+Congo 531 0.29 2,302 0.53 108.3 
+Congo, DRC 2,278 0.75 13,163 6.26 1,798.9 
+Equatorial Guinea 11 0.002 106 0.01 10.7 
+Gabon 81 0.01 424 0.07 133.2 
+Central Africa 4,280 1.55 23,479 11.85 421.5 
+Burundi 48 0.07 271 0.18 49.2 
+Ethiopia 2,398 2.71 13,858 8.40 472.1 
+Kenya 512 0.62 2,619 1.31 78.5 
+Madagascar 76 0.01 406 0.26 8.3 
+Rwanda 56 0.10 281 0.17 129.6 
+Tanzania 923 0.77 5,332 3.50 199.2 
+Uganda 1,399 0.68 7,928 2.39 355.5 
+Eastern and Indian 
+Ocean Countries 
+5,413 4.94 30,695 16.21 171.4 
+Benin 99 0.02 585 0.20 83.9 
+Côte d'Ivoire  680 0.11 4,307 1.59 298.6 
+Ghana 1,690 0.69 7,512 4.16  1,461.5 
+Guinea 198 0.05 1,321 0.55 121.7 
+Guinea-Bissau 41 0.17 289 0.05 58.2 
+Liberia 317 0.09 2,007 0.71 3,231.1 
+Nigeria 2,416 0.41 12,350 4.44 34.4 
+Sierra Leone 271 0.08 1,808 0.92 101.1 
+Togo 180 0.04 1,120 0.32 290.3 
+Gulf of Guinea 5,893 1.67 31,299 12.93 83.2 
+Botswana 7 0.002 32 0.02 15.0 
+Lesotho 139 0.07 650 0.06 147.7 
+Malawi 1,196 3.14 6,243 5.54 937.0 
+Mozambique 290 0.54 1,451 1.14 116.0 
+Namibia 2 0.001 13 0.01 4.9 
+South Africa 503 0.59 2,234 1.36 22.3 
+Swaziland 16 0.01 94 0.05 7.7 
+Zambia 1,480 0.68 7,892 7.98 715.0 
+ 
+ 
+ 
+ 
+ awm-solutions.iwmi.org 
+ 
+Table 5.  Predictive modeling results for the potential expansion of communal river diversions, baseline scenario 
+                    Results (cont’d) 
+Country  
+Application 
+area  
+(thousand ha) 
+Net revenue 
+(US$ billion/yr) 
+Rural population 
+reached 
+(thousand people) 
+Water consumption 
+(billion m3/yr) 
+Water consumption  
+Increase 
+% 
+Burkina Faso 632 0.12 4,706 1.82 148.8 
+Chad 169 0.02 994 0.24 33.8 
+Eritrea 7 0.003 38 0.04 39.7 
+Mali 21 0.002 130 0.03 0.4 
+Mauritania 3 0.001 22 0.005 0.5 
+Niger 6 0.004 43 0.02 0.9 
+Senegal 57 0.03 653 0.10 7.4 
+Somalia 25 0.01 157 0.07 2.7 
+Sudan 145 0.23 1,.000 0.45 3.2 
+The Gambia 9 0.001 155 0.01 20.4 
+Sudano-Sahelian region 1,074 0.42 7,897 2.77 9.5 
+All SSA 20,442 13.70 112,743 60.65 89.0 
+Source: IFPRI Team 
+Zimbabwe 149 0.07 763 0.74 49.7 
+Southern Africa 3,782 5.12 19,372 16.89 149.5 
+Table 6.  Predictive modeling results for the potential expansion of  communal river diversions, scenario results 
+ Baseline 
+-50% irrigation 
+cost 
++ 50% irrigation 
+costs 
+-30% initial crop 
+price 
++ 30% initial crop 
+price 
+Area (thousand ha.) 20,442 26,097 10,929 6,151 25,926 
+Rural population reached (thousand people) 112,743 143,636 59,210 33,227 142,629 
+Net revenue (US$ billion) 13.70 21.39 8.66 3.81 28.71 
+Water consumption  (billion m3 /yr) 60.65 71.36 39.24 23.18 73.92 
+Irrigation water consumption increase (%) 88.98 104.69 57.57 34.01 108.46 
+Source: IFPRI Team                                                                                                                                                                                           
+ Note: Results shown are for all of  SSA 
+The results of the sensitivity analysis (Table 6) show that the 
+estimated application areas, net revenues, and rural 
+population reached increase with decreasing irrigation costs 
+and higher food prices, and vice versa. With a 50 percent 
+reduction in the cost of irrigation, the application area 
+would increase by 6 million ha, net revenues would increase 
+by $8 billion per year, and rural population reached would 
+increase by 31 million.  
+ 
+Conversely, application area decreases by 10 million ha, net 
+revenues decline by $5 billion, and the number of people 
+reached decreases by 54 million when irrigation costs 
+increase by 50 percent.  
+ 
+Under the di"erent crop price scenarios, a 30 percent 
+increase in initial crop price results in an additional potential 
+application area of 5 million ha, an increase in net revenues 
+of $15 billion annually, and an additional 30 million people 
+reached; while a decrease in the initial crop price results in a 
+lower application area (by 14 million ha), a reduction in net 
+revenues (by $10 billion), and fewer people reached (by 80 
+million), compared to the baseline  
+ 
+ 
+ awm-solutions.iwmi.org 
+ 
+Table 7.  Ex-ante and predictive modeling results for the potential expansion of communal river diversions under 
+climate change 
+ SWAT+DREAM 
+ Baseline Csia Cnra Baseline Csia Cnra 
+Area (thousand ha) 81,720 81,553 81,671 20,442 17,993 21,471 
+Rural population reached (thousand people) 457,481 455,615 456,057 112,743 99,703 118,175 
+Net revenue (billion dollars) - - - 13.70 12.52 12,75 
+Water consumption (billion m3/year) - - - 60.65 58.65 69.47 
+Irrigation water consumption increase (%) - - - 88.98 86.06 101.92 
+Source IFPRI Team. 
+Note: Results shown are for all of SSA.  
+Ex-Ante 
+Water consumption increases signi$cantly under scenarios 
+resulting in an expansion of communal river diversions. A 50 
+percent decrease in irrigation costs or a 30 percent increase in 
+initial crop price would increase water use by an additional 11 
+billion or 13 billion m3/yr, respectively, compared to the 
+baseline. 
+ 
+The impacts of climate change on the application potential of 
+communal river diversions across SSA were also estimated 
+under two climate scenarios projected by the CSIRO-Mk3.0 
+model (Csia) and the CNRM-CM3 model (Cnra) (Table 7). In a 
+preliminary analysis, the two scenarios were identi$ed as the 
+“driest” and “wettest” scenarios, respectively, among 12 future 
+climate change scenarios projected by general circulation 
+models for SSA. Both scenarios use the SRES A2 emissions 
+scenario, which is considered moderate.  
+ 
+The results in Table 7 show that changes in the estimated 
+application area due to climate change range from -12 percent 
+to +5 percent.  
+ 
+ 
+Conclusions 
+The ex-ante analysis reveals large expansion potential for 
+communal river diversions in SSA in terms of application area 
+and rural population reached. However, when additional 
+constraints are introduced, the potential is signi$cantly 
+reduced—from 82 million ha to 20 million ha. 
+ 
+The main constraint to the expansion of communal river 
+diversions is the limited availability of runo", as no associated 
+storage capacity is assumed and 20 percent of runo" is reserved 
+for environmental )ows. Moreover, investment costs are also 
+signi$cant.  
+ 
+Creating a river diversion need not be sophisticated. Here, a  
+simple channel for diverting river water has been dug in the mud. 
+

data/part_2/0263754002.md

@@ -0,0 +1,60 @@
+# Ghana: Local Public Finance and Decentralization, 1994-2004
+
+**Source:** gardian_index  
+**URL:** https://dataverse.harvard.edu/api/access/datafile/:persistentId/?persistentId=doi:10.7910/DVN/HBHIP2/EHUJCP  
+**Language:** English  
+**Resource Type:** Dataset / Tabular  
+**Release Year:** 2012  
+**Rights:** CC-BY-NC  
+**GARDIAN ID:** d4fb0e15be47733fec5a38bc4357d995  
+**DataNODE ID:** 98bedae6cee65d72a1fdcfff78f842f0  
+**Siever ID:** 968578d2-27c1-44fe-a6a8-79d21e4f1d29  
+**Token Count:** 292  
+**Page Count:** 0
+
+## Keywords
+
+decentralization, inter-governmental transfers, local government, internally generated revenues, ghana, public finance, data compilation, public investment, rural development, data management, data, research, environment, researchers
+
+## Geography
+
+- **Regions:** Western Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Ghana
+
+## Description
+
+<br>The purpose of this data compilation effort was to use the data for research on fiscal decentralization and public investment in Ghana. The dataset, which consists of district-level observations, covers all of Ghana’s 110 districts in existence during the period 1994 to 2004. (Since then, the number of districts in Ghana has grown as the result of administrative rearrangements.) The dataset was compiled from administrative data sources of the Ministry of Local Government, Rural Development,
+and Environment (MLGRDE) of Ghana, Inspectorate Division (under the then-leadership of Mr. Johnson Alifo). Some of the data were obtained directly from the Ministry, while other parts of the data had been previously obtained from the Ministry by researchers at the Institute for Statistical Social and Economic Research (ISSER) in the University of Ghana, Accra. These data are secondary, “raw” data, however were compiled and organized by IFPRI staff from both electronic sources, as well as from hardcopy
+sources available only in Ghanaian government physical archival records. Additional data management activities undertaken were the compilation of the data into a consistent format (e.g. each row represents a district), the application of the same spelling of districts across files, and the use of a consistent variable name across files.
+</br>  <br>The values in the dataset were denoted in ‘old’ cedi, not the new Ghana cedi (GHC), which was introduced in July 2007. One new GHC is equivalent to 10,000 ‘old’ cedi. The dataset retains the denomination used in the original dataset (which pertains to the years 1994-2004, before the introduction of the GHC). The values are nominal (not adjusted for inflation). No questionnaires or other survey instruments have been used, since this was a collection of secondary administrative data.</b
+r>
+
+## Content
+
+Ghana: Local Public Finance and Decentralization, 1994-2004 
+ 
+Acknowledgements 
+IFPRI requests that users of the data acknowledge the source of the Local Public Finance and 
+Decentralization in Ghana dataset in all publications, conference papers, and manuscripts. The 
+dataset was compiled from administrative data of the Ministry of Local Government, Rural 
+Development, and Environment (MLGRDE) of Ghana, Inspectorate Division, from Ghana 
+government physical archives, and from data previously collected from MLGRDE by researchers 
+at the Institute for Statistical Social and Economic Research (ISSER) in the University of Ghana, 
+Accra. The funding for the survey was provided by IFPRI’s Ghana Strategy Support Program 
+(GSSP), which is financially supported by the United States Agency for International 
+Development (USAID). 
+ 
+Disclaimer 
+The International Food Policy Research Institute (IFPRI) encourages the use of the Local Public 
+Finance and Decentralization in Ghana dataset, but emphasizes that the attached data files are 
+secondary, “raw” data. There is no information that would allow individuals to be identified; all 
+other information remains in the data files. The decision not to alter the contents of the data files 
+means that the user of these files will need to take care in handling missing observations, outlier 
+values, and violations of logical consistency. The authorized use of these data is limited to 
+government, academic, research or other institutions (or individuals associated with these 
+institutions) to be used for informing and improving government policy or for educational 
+purposes. The data is not authorized to be used for commercial purposes. The data are provided 
+‘as is’ and in no event shall IFPRI be liable for any damages resulting from use of the data. While 
+great effort was taken to obtain high quality data, the accuracy or reliability of the data is not 
+guaranteed or warranted in any way. 
+

data/part_2/0275663682.md

@@ -0,0 +1,30 @@
+# Rural institutions and producer organizations in imperfect markets: experiences from producer marketing groups in semi-arid eastern Kenya
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/bec70f66-6d72-4b65-86ee-eb448c884c87/retrieve  
+**Language:** English  
+**Resource Type:** Working Paper  
+**Release Year:** 2006  
+**Rights:** N/A  
+**GARDIAN ID:** a8d26270f7d872a9906bc5b38632dcc5  
+**DataNODE ID:** 294139274df33ed9762ccd50bebae74a  
+**Siever ID:** 58cde15e-8b0c-4d3a-a5d4-4ba965ff7a84  
+**Token Count:** 247  
+**Page Count:** 0
+
+## Keywords
+
+markets, imperfect competition, transaction costs, institutions, semiarid zones, marketing, governance, collective action, producer organizations, marketing groups, sub-saharan africa, smallholder farmers
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Kenya
+
+## Description
+
+Many countries in sub-Saharan Africa have liberalized markets to improve efficiency and enhance market linkages for smallholder farmers. The expected positive response by the private sector in areas with limited market infrastructure has however been disappointing. The functioning of markets is constrained by high transaction costs and coordination problems along the production-to-consumption value chain. New kinds of institutional arrangements are needed to reduce these costs and fill the vacuum left when governments withdrew from markets in the era of structural adjustments. One of these institutional innovations has been the strengthening of producer organizations and formation of collective marketing groups as instruments to remedy pervasive market failures in rural economies. The analysis presented here with a case study from eastern Kenya has shown that while collective action – embodied in Producer Marketing Groups (PMGs) – is feasible and useful, external shocks and structural constraints that limit the volume of trade and access to capital and information require investments in complementary institutions and coordination mechanisms to exploit scale economies. The effectiveness of PMGs was determined by the level of collective action in the form of increased participatory decision making, member contributions and initial start-up capital. Failure to pay on delivery, resulting from lack of capital credit, is a major constraint that stifles PMG competitiveness relative to other buyers. These findings call for interventions that improve governance and participation; mechanisms for improving access to operating capital; and effective strategies for risk management and enhancing the business skills of the PMGs.
+
+## Content
+
+Many countries in sub-Saharan Africa have liberalized markets to improve efficiency and enhance market linkages for smallholder farmers. The expected positive response by the private sector in areas with limited market infrastructure has however been disappointing. The functioning of markets is constrained by high transaction costs and coordination problems along the production-to-consumption value chain. New kinds of institutional arrangements are needed to reduce these costs and fill the vacuum left when governments withdrew from markets in the era of structural adjustments. One of these institutional innovations has been the strengthening of producer organizations and formation of collective marketing groups as instruments to remedy pervasive market failures in rural economies. The analysis presented here with a case study from eastern Kenya has shown that while collective action – embodied in Producer Marketing Groups (PMGs) – is feasible and useful, external shocks and structural constraints that limit the volume of trade and access to capital and information require investments in complementary institutions and coordination mechanisms to exploit scale economies. The effectiveness of PMGs was determined by the level of collective action in the form of increased participatory decision making, member contributions and initial start-up capital. Failure to pay on delivery, resulting from lack of capital credit, is a major constraint that stifles PMG competitiveness relative to other buyers. These findings call for interventions that improve governance and participation; mechanisms for improving access to operating capital; and effective strategies for risk management and enhancing the business skills of the PMGs.

data/part_2/0279421338.md

@@ -0,0 +1,1204 @@
+# Determinants of household water and energy access and their impacts on food security and health outcomes in Sudan
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/39c7539a-e85b-44f7-8004-5911025af516/retrieve  
+**Language:** English  
+**Resource Type:** Working Paper  
+**Release Year:** 2025  
+**Rights:** N/A  
+**GARDIAN ID:** 6c2b823b09e21e2d8f229d8bd098b3a3  
+**DataNODE ID:** 93e84db0e0b77f995b9fb91ff013436e  
+**Siever ID:** 7b381162-5ae5-4856-8468-a4013dd4f532  
+**Token Count:** 11042  
+**Page Count:** 0
+
+## Keywords
+
+energy policies, food security, health, households, socioeconomics, water, water policies, environmental health and biodiversity, climate adaptation and mitigation, nutrition, health and food security, systems transformation, rural areas, energy
+
+## Geography
+
+- **Regions:** Northern Africa, Africa, World
+- **Countries:** Sudan
+
+## Description
+
+This study investigates the determinants of access to safe water and reliable energy for households in Sudan using nationally representative data from a recent labor market survey. The results show that urbanization, education, and wealth significantly enhance the access households have to these essential services, while rural areas and less developed regions, particularly in the Darfur and Kordofan regions, face substantial challenges. Access to reliable energy correlates with better food security and health outcomes within households, and improved access to safe water significantly enhances the health of household members. Policy recommendations supported by these research results include targeted rural infrastructure investments, educational improvements, and regional interventions to address disparities in household access to safe water and reliable energy across Sudan.
+
+## Content
+
+IFPRI Discussion Paper 02338 
+May 2025 
+Determinants of Household Water and Energy Access and Their Impacts on 
+Food Security and Health Outcomes in Sudan 
+Oliver Kiptoo Kirui 
+Mosab Ahmed 
+Mariam Raouf 
+Hala Abushama 
+Khalid Siddig 
+Development Strategy and Governance Unit 
+INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 
+The International Food Policy Research Institute (IFPRI), established in 1975, provides research-based 
+policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI’s strategic research 
+aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; 
+build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural 
+economies; and strengthen institutions and governance. Gender is integrated in all the Institute’s work. 
+Partnerships, communications, capacity strengthening, and data and knowledge management are essential 
+components to translate IFPRI’s research from action to impact. The Institute’s regional and country 
+programs play a critical role in responding to demand for food policy research and in delivering holistic 
+support for country-led development. IFPRI collaborates with partners around the world.  
+AUTHORS 
+Oliver Kiptoo Kirui (o.k.kirui@cgiar.org) is a Research Fellow in the Development Strategy and 
+Governance (DSG) Unit of the International Food Policy Research Institute (IFPRI) and Country Program 
+Leader for IFPRI Nigeria, Abuja, Nigeria. 
+Mosab Ahmed is a Social Policy Specialist at UNICEF, Portsudan, Sudan. 
+Mariam Raouf (mariam.raouf234@gmail.com) is an Assistant Professor of Economics, International 
+Economic Relations Center, Institute of National Planning, Cairo, Egypt.  
+Hala Abushama (h.abushama@cgiar.org) is a Research Analyst in IFPRI’s DSG Unit, Cairo, Egypt. 
+Khalid Siddig (k.siddig@cgiar.org) is a Senior Research Fellow in IFPRI’s DSG Unit and the Leader of 
+IFPRI’s Sudan Strategy Support Program, Nairobi, Kenya, and Associate Professor at the University of 
+Khartoum, Sudan. 
+Notices 
+1 IFPRI Discussion Papers contain preliminary material and research results and are circulated in order to stimulate discussion 
+and critical comment. They have not been subject to a formal external review via IFPRI’s Publications Review Committee. Any 
+opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by IFPRI. 
+2 The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or 
+acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors. The opinions expressed 
+are fully those of the authors and do not necessarily reflect those of IFPRI. 
+3 Copyright remains with the authors. The authors are free to proceed, without further IFPRI permission, to publish this paper, or 
+any revised version of it, in outlets such as journals, books, and other publications.
+iii 
+CONTENTS 
+1 INTRODUCTION ..................................................................................................................................... 1 
+2 LITERATURE REVIEW .......................................................................................................................... 2 
+2.1 Determinants of clean water and reliable energy access .................................................................... 2 
+2.2 Effects of poor access to clean water and reliable energy on household well-being ......................... 3 
+2.3 Gender dimensions to household water and energy access ............................................................... 4 
+2.4 Methods of analysis of water and energy access and their effects on household well-being ............ 5 
+3 ACCESS TO CLEAN WATER AND RELIABLE ENERGY IN SUDAN .............................................. 7 
+4 METHODOLOGY .................................................................................................................................... 9 
+4.1 Data sources and sample characteristics ............................................................................................ 9 
+4.2 Determinants of access to clean water and reliable energy ............................................................. 12 
+4.3 Effect of access to clean water and reliable energy on selected household outcomes ..................... 12 
+5 RESULTS AND DISCUSSIONS ............................................................................................................ 14 
+5.1 Summary statistics and t-tests .......................................................................................................... 14 
+5.2 Regression analysis on determinants of household access to clean water and reliable energy ....... 17 
+5.2.1 Access to clean water ......................................................................................................... 19 
+5.2.2 Access to reliable energy .................................................................................................... 20 
+5.2.3 Combined access to clean water and energy ...................................................................... 21 
+5.2.4 Summary of the analyses of the determinants of household access to clean water and 
+reliable energy in Sudan .................................................................................................... 21 
+5.3 Effects on household welfare of access to clean water and reliable energy .................................... 22 
+6 CONCLUSIONS AND POLICY RECOMMENDATIONS ................................................................... 25 
+REFERENCES ........................................................................................................................................... 27 
+TABLES 
+Table 3.1 Access to water in Sudan, Egypt, Ethiopia, and South Africa, in 2015 and 2022 ........................ 7 
+Table 3.2 Access to energy in Sudan, Egypt, Ethiopia, and South Africa in 2015 and 2022 ....................... 8 
+Table 4.1 Analytical variables used, descriptive statistics .......................................................................... 11 
+Table 5.1 Clean water access—explanatory variables t-tests ..................................................................... 15 
+Table 5.2 Reliable energy access—explanatory variables t-tests ............................................................... 16 
+Table 5.3 Both clean water and reliable energy access—explanatory variables t-tests .............................. 17 
+Table 5.4 Determinants of improved water and improved energy access .................................................. 18 
+Table 5.6 Effects of improved water and improved energy on household food security and health .......... 24 
+FIGURES 
+Figure 5.1 Overlap plots for covariate distributions of households with access and those without access to 
+clean water and energy sources ........................................................................................................ 23 
+  
+ 
+ABSTRACT 
+This study investigates the determinants of access to safe water and reliable energy for households in 
+Sudan using nationally representative data from a recent labor market survey. The results show that 
+urbanization, education, and wealth significantly enhance the access households have to these essential 
+services, while rural areas and less developed regions, particularly in the Darfur and Kordofan regions, 
+face substantial challenges. Access to reliable energy correlates with better food security and health 
+outcomes within households, and improved access to safe water significantly enhances the health of 
+household members. Policy recommendations supported by these research results include targeted rural 
+infrastructure investments, educational improvements, and regional interventions to address disparities in 
+household access to safe water and reliable energy across Sudan. 
+Keywords: Water-energy access, Sudan, socioeconomic disparities, rural-urban divide, food security, 
+health 
+ 
+ 
+ 
+  
+ 
+ 
+ACKNOWLEDGMENTS 
+This work was carried out under the CGIAR Initiative on NEXUS Gains, which is grateful for the support 
+of CGIAR Trust Fund contributors: www.cgiar.org/funders. Funding for the SSSP is graciously provided 
+by the United States Agency for International Development (USAID). 
+1 
+1 INTRODUCTION 
+Water, energy, and food are all interrelated, with numerous connections between them. The water-energy-
+food nexus is the term used to describe this idea (Ringler et al. 2013; Hlahla 2022). Natural resources are 
+becoming scarce. This could impede efforts to achieve goals related to human well-being and economic 
+progress. Nexus thinking has emerged from these considerations. Shortages of water, energy, and food 
+could result from strains on available resources, particularly for weaker members of society. This may 
+negatively affect economic development, social cohesion, and the environment (Ringler et al. 2013). 
+The water-food linkage is particularly important, given that irrigated agriculture remains a significant 
+global user of freshwater, making up over 70 percent of global withdrawals, 90 percent of consumptive 
+usage, and 40 percent of total grain output (Ringler et al. 2013). Water is essential to produce food and 
+energy, particularly hydroelectricity, which is a significant energy source, as well as for maintaining the 
+ecosystems that have an impact on agriculture and other economic sectors vital for ensuring food security. 
+Food production and the provision of water, including its extraction, purification, and delivery, depend on 
+energy (Hlahla 2022). Electricity is a key driver of economic growth and poverty reduction in developing 
+countries. Electricity can drive economic and social development by increasing productivity, enabling 
+new types of job-creating enterprises, and reducing household workloads, thereby freeing up time for paid 
+work (Pueyo and Maestre 2019). 
+The energy-water nexus is increasingly being studied, as both resources are becoming scarcer in many 
+contexts. Changes in the availability of one are influencing the availability of the other. Looking at a 
+developing country context, this study investigates household access to water and energy in Sudan and the 
+impact of differing levels of access on household food security and health status.  
+In section 2, we provide a review of relevant literature related to access to water and energy, the 
+determinants of such access, the impact of restricted access to water and energy on household food 
+security and health, any gender dimensions to these associations, and the methods used to assess them. 
+Section 3 provides an overview of access to clean water and reliable energy in Sudan. Section 4 covers 
+the data and methods used in this research. Section 5 presents the results of the research, while Section 6 
+concludes and draws some policy recommendations. 
+2 
+2 LITERATURE REVIEW 
+While the water-energy-food nexus is increasingly recognized as vital for sustainable development, 
+research findings that incorporate the concept still needs to be expanded, particularly in the context of 
+monitoring progress towards the SDGs (Nkiaka et al. 2022). The water-energy-food nexus is complex, 
+involving multiple scales across several dimensions—spatial (local, national, regional, or global), 
+temporal (present or future), and institutional (transboundary river basin, sub-regional, or other). This 
+complexity makes it challenging to assess and address the interdependencies between water, energy, and 
+food comprehensively. 
+Nkiaka et al. (2022) developed a multidimensional index to investigate the determinants of access to 
+water, energy, and food, incorporating indicators of water security, energy security, and food security. 
+Their analysis identified seven key socioeconomic variables as potential drivers of water, energy, and 
+food security—GDP per capita, government effectiveness as proxied by the Government Effectiveness 
+Index (World Bank 2024), human development as proxied by the Human Development Index (UNDP 
+2024), the urban population as a share of the total population, infrastructural development, foreign direct 
+investment levels, and official development assistance directed to water supply and sanitation, agriculture, 
+and energy. 
+2.1 Determinants of clean water and reliable energy access 
+Access to water is universally recognized as a human right, as emphasized in the global Sustainable 
+Development Goals (SDG)—SDG-6 focuses on the provision of universal and equitable access to safe 
+and affordable drinking water. This goal is particularly pertinent in low- and middle-income countries 
+where marginalized groups, such as women and those living in remote rural communities, often face 
+additional challenges in accessing water. Poor water, sanitation, and hygiene services and practices 
+disproportionately affect these populations (UNICEF and WHO 2023). 
+Several studies have explored the determinants of access to clean and safe water at household level, 
+particularly in developing and least-developed countries. Adil et al. (2021) identified key factors 
+influencing access to safe drinking water and improved sanitation in Punjab. Their analysis revealed that 
+media exposure, education level of the household head, household wealth, and ethnic background 
+significantly impact the degree of access households have to safe drinking water. Notably, the study 
+found that household wealth plays a critical role, with wealthier households enjoying better access to safe 
+drinking water. 
+3 
+In Nepal, Behera, Rahut, and Sethi (2020) observed a significant decline in the proportion of 
+households using piped water in urban areas. Their study concluded that education level, economic status, 
+and location are key determinants of household access to improved drinking water, sanitation, and waste 
+disposal services. 
+Antunes and Martins (2020) focused on countries with national water supply service coverage below 
+95 percent. The study found that investments in infrastructure, particularly in urban areas, education, and 
+encouraging paid female employment positively impact water access. They also noted that countries with 
+a large agricultural share in their GDP tend to have lower water access, highlighting the need for targeted 
+efforts to achieve universal water access. 
+Energy access, defined as the ability to reliably use energy, is crucial for socioeconomic development. 
+Saputri, Setyonugroho, and Hartono (2024) investigated household-level energy poverty in Indonesia. 
+They found that energy prices and household economic conditions and demographic characteristics are 
+closely linked to energy poverty. Specifically, household wealth and higher income significantly reduce 
+the likelihood of energy poverty. Larger household sizes and higher education levels also play important 
+roles in improving household access to energy. The study also noted that households with male heads are 
+more likely to experience energy poverty, while women's involvement in energy-related decisions 
+decreases this likelihood. 
+In a study of energy poverty in Ethiopia, Alema and Demekeb (2020) emphasized the impact of rising 
+kerosene prices, a primary cooking fuel in urban areas, on energy poverty. A 10 percent  increase in 
+kerosene prices was found to have resulted in a 1.8 percent increase in energy poverty, underscoring the 
+vulnerability of low-income Ethiopian households to energy price fluctuations. 
+2.2 Effects of poor access to clean water and reliable energy on household well-being 
+Inadequate access to clean water and reliable energy, as well as to several other basic services, can lead to 
+significant health and economic costs, particularly for low-income households. Lack of access to safe 
+drinking water can result in waterborne diseases, increasing medical expenses for the households and 
+reducing members’ ability to work, thereby reducing household well-being. Poor sanitation further 
+compounds these challenges, posing serious threats to public health and environmental sustainability 
+(Behera, Rahut, and Sethi 2020). 
+Similarly, energy poverty—characterized by the inability to reliably obtain affordable energy—has 
+substantial implications for human development, affecting the economic productivity, health, and 
+education of household members. Improved access to cleaner burning fuels can reduce indoor air 
+pollution, a leading cause of premature death in developing countries. In 2012, household air pollution 
+4 
+from biomass-based fuels accounted for 4.3 million deaths, mostly among women and children, 
+representing 7.7 percent  of global mortality (Smith et al. 2005). In sub-Saharan Africa, the death toll 
+from indoor air pollution surpasses that of tuberculosis and is comparable to malaria. 
+2.3 Gender dimensions to household water and energy access 
+Women in many countries are responsible for providing food for their households, gathering fuelwood for 
+cooking, and fetching potable water, all of which are unpaid productive activities (Villamor et al. 2018). 
+Therefore, there are significant gender dimensions to exploring the interlinkages between water and 
+energy access and household food security and health. Disparities are seen not only across regions but 
+also between the sexes. The challenges that women face in obtaining reliable access to clean water align 
+with two of the SDGs—SDG-5 on gender equality and SDG-6 on clean water and sanitation. The water 
+and sanitation sector has the potential to contribute to redressing inequality and can greatly improve the 
+social, political, and economic position of women (WSP 2010). 
+Despite their primary responsibility for managing the water supply and sanitation for the household 
+and for safeguarding the health of household members, women face significant inequities in access to 
+water resources. In Malawi and Ethiopia, for example, schoolgirls often lack access to adequate sanitation 
+and hygiene facilities, such as clean water supplies and sufficient latrines (Hlahla 2022). Additionally, 
+women and girls in rural areas are frequently compelled to walk long distances to fetch water, a task that 
+consumes time and energy—the average distance for a water collection trip in sub-Saharan Africa is 
+estimated at between 4 and 5 kilometers, taking approximately 33 minutes each way (Connell 2017). For 
+women, inadequate access to water is particularly detrimental, as it increases their vulnerability to 
+waterborne illnesses and reduces their time for other productive activities. Proximity to water sources can 
+enhance household and personal cleanliness and improve health outcomes, particularly in resource-
+limited regions like eastern Zimbabwe, where gathering water can take over 10 hours per week (Connell 
+2017). The connections between gender, water, and health are well-documented. Poor access to water and 
+sanitation for women generally results in increased health costs (Kayser et al. 2019). 
+In addition, rural women bear the primary responsibility within the household for energy-related tasks 
+(Hlahla 2022). The time spent by women in Benin in collecting fuelwood was shown to be four times 
+greater than it was for men (Köhlin et al. 2011). In rural Gujarat, India, women spend up to 40 percent of 
+their day engaged either in fuel collection or cooking (WLPGA 2014). Studies have also highlighted the 
+benefits for women of investing in clean fuels and improved cookstoves, which can enhance efficiency, 
+reduce pollution, and yield significant health and economic benefits (World Bank 2017). 
+5 
+These daily burdens significantly limit the opportunities of women and girls to engage in other activities, 
+such as education. These gendered challenges to household well-being highlight the need for targeted 
+interventions to improve water and energy access, particularly for women and girls. 
+ 
+2.4 Methods of analysis of water and energy access and their effects on household well-
+being 
+The determinants of water and energy access, and their combined effects on household well-being, 
+have been widely studied using diverse methodological approaches. These methods focus on 
+understanding access disparities and their implications for food security and health, particularly among 
+vulnerable populations. 
+Several studies have employed regression-based methods to explore access patterns. For instance, Adil 
+et al. (2021) used binomial logistic regression to examine access to safe drinking water and improved 
+sanitation in Punjab, emphasizing the role of socio-economic and regional disparities. Similarly, Behera, 
+Rahut, and Sethi (2020) applied multinomial logistic regression to analyze factors influencing access to 
+water, sanitation, and waste management in Nepal over three decades, providing insights into how access 
+evolves over time and across socio-economic groups. 
+Other studies have incorporated techniques to address causality and initial conditions. Antunes and 
+Martins (2020) used a linear multivariate regression model with lagged independent variables to study 
+global water access, highlighting the impact of historical conditions and policy interventions on current 
+access levels. Saputri, Setyonugroho, and Hartono (2024) analyzed energy poverty in Indonesia using 
+logistic regression with district-level fixed effects, accounting for regional heterogeneity and emphasizing 
+the role of energy pricing and socio-economic factors. 
+Innovative thresholds for defining energy poverty have also been explored. For instance, Alema and 
+Demekeb (2020) employed the Minimum Energy Consumption Threshold Approach, defining energy 
+poverty as consumption below 50 kilograms of oil equivalent annually for cooking and lighting. This 
+method, although debated, offers a benchmark for identifying households lacking basic energy needs. 
+This study builds on existing literature by applying a doubly robust estimation method to examine the 
+effects of access to clean water and reliable energy on household well-being, specifically food security 
+and health outcomes. The doubly robust approach combines propensity score weighting and regression 
+adjustment, ensuring unbiased estimates even if one of the two models is mis-specified. This method 
+addresses potential endogeneity and selection bias, which are common challenges in analyzing access to 
+6 
+essential services. By using this advanced technique, our study extends prior work by providing more 
+accurate and policy-relevant insights into how water and energy access influences key well-being 
+indicators. Furthermore, the focus on combined access to both resources offer a holistic perspective on 
+their synergistic effects, an area often overlooked in earlier research. 
+ 
+7 
+3 ACCESS TO CLEAN WATER AND RELIABLE ENERGY IN SUDAN 
+This study focuses on Sudan. Millions of Sudanese do not have access to reliable energy, safe drinking 
+water, or sanitation facilities. In part as a result, one-fourth of Sudan's population is malnourished. Sudan 
+has a huge discrepancies within its population in access to water, energy, and food nexus. The country’s 
+population is projected to continue to expand rapidly, further increasing demand for water, energy, and 
+food. 
+In Sudan, 71.4 percent of the population has access to basic improved water (Table 3.1). However, 
+rural households face much greater challenges in obtaining safe water—only 64 percent of rural 
+households have access to basic improved water, compared to 78 percent of urban households. There also 
+are sharp disparities between states—only about one-third of households have access to safe water in Red 
+Sea, White Nile, and Gedaref, compared to 90 percent in Khartoum and Northern. Insufficient funding 
+and inadequate management underlies the poor supply of safe drinking water to Sudanese households.  
+Table 3.1 Access to water in Sudan, Egypt, Ethiopia, and South Africa, in 2015 and 2022 
+ Sudan Egypt Ethiopia South Africa 
+Indicators 2015 2022 2015 2022 2015 2022 2015 2022 
+Access to drinking water, at least basic, % 91.8 94.5 98.7 98.8 41.5 51.5 57.1 64.9 
+Access to drinking water, limited (more than 30 
+mins), % 2.8 2.7 <1.0 <1.0 21.4 28.0 25.3 28.9 
+Access to drinking water, unimproved, % 2.0 1.1 1.0 <1.0 24.3 16.0 12.1 3.9 
+Access to drinking water, surface water, % 3.4 1.6 <1.0 <1.0 12.8 4.5 5.5 2.3 
+Access to drinking water, annual rate of change in 
+at least basic, %/year nd 0.5 nd 0.0 nd 1.5 nd 0.9 
+Drinking water, share of population using improved 
+water supplies, accessible on premises, % 74.5 79.3 96.8 97.7 14.8 21.7 36.3 41.2 
+Drinking water, share of population using improved 
+water supplies, available when needed, % 73.6 71.4 79.7 82.4 51.7 66.9 45.8 55.1 
+Drinking water, share of population using improved 
+water supplies, piped, % nd 90.5 97.0 98.7 34.9 44.1 46.5 nd 
+Drinking water, share of population using improved 
+water supplies, non-piped, % nd 6.8 2.0 <1.0 28.0 35.4 35.9 nd 
+Basic drinking water services, share of population 
+using at least, % nd 64.9 98.7 98.8 41.5 51.5 91.8 nd 
+Source: UNICEF and WHO (2023). 
+Note: nd = “no data”. 
+Thirteen million Sudanese are estimated to still use unimproved and unsafe sources of drinking water 
+(UNICEF 2017). These sources include surface water and groundwater from open or damaged 
+groundwater wells. Chemical or bacterial contamination often reduces the quality of the water sources. 
+These contaminants are mostly derived from industrial, commercial, and domestic waste, including 
+8 
+excreta, urine, and grey water, which is washed into surface water bodies or injected into groundwater 
+aquifers. National and state-level acts to prevent harmful pollutants have been legislated in Sudan, but 
+they are rarely activated and enforced. Access to improved water sources has increased over the last 30 
+years in Sudan. However, this access is gradually being eroded due to the country’s high population 
+growth, limited investment in housing, water, and sanitation infrastructure, and climate change 
+compromising existing water supplies. The ongoing conflict which started in April 2023 has also reduced 
+access to safe water supplies for many Sudanese households (IFPRI and UNDP 2024) 
+Only 53 percent of rural households have improved drinking water sources within a 30-minute walk, 
+while 28 percent have even less access and must travel farther to safe water sources. The rest of the rural 
+population uses dirty water from shallow waterholes (O’Brien 2023). This lack of access to safe water, 
+combined with poor sanitation and inadequate hygiene practices, poses serious risks to children and has a 
+considerable impact on rising malnutrition rates, illness outbreaks, and needless deaths. In 2022, over 
+3 million children under five years of age in Sudan suffered from acute malnutrition, half of which was 
+associated with recurring diarrhea or worm infections caused by poor water, sanitation, and hygiene 
+conditions (UNICEF 2023). 
+In parallel, Sudan’s energy sector is underdeveloped, making household access to energy one of the 
+country’s greatest obstacles to social and economic development. Many people in Sudan rely largely on 
+traditional biomass fuels, like wood, charcoal, dung, and agricultural waste, for cooking and heating. Only 
+around 60 percent of the population has access to electricity or other clean cooking fuels (Table 3.2). 
+Moreover, there are significant variations across Sudan in per capita energy consumption, particularly 
+between urban and rural households.  
+Table 3.2 Access to energy in Sudan, Egypt, Ethiopia, and South Africa in 2015 and 2022 
+ Sudan Egypt Ethiopia South Africa 
+Indicators 2015 2022 2015 2022 2015 2022 2015 2022 
+Modern renewable sources, share in total 
+final energy consumption, % 
+23.3 22.9 
+(2020) 
+2.2 3.3 
+(2020) 
+2.5 3.0 
+(2020) 
+2.4 3.9 
+(2020) 
+         
+Access to clean cooking fuel, share of 
+population, % 47.2 61.9 99.8 99.7 4.2 7.6 83.8 89.1 
+Access to clean fuels and technologies for 
+cooking, share of population, % 48.0 65.6 99.9 99.9 4.3 8.8 83.8 89.4 
+Total electricity access rate, share of 
+population % 48.0 63.2 99.3 100.0 29.0 55.0 85.3 86.5 
+Source: IEA, IRENA, UNSD, World Bank, and WHO (2023). 
+9 
+4 METHODOLOGY 
+The objectives of this study are twofold. First, we investigate the status and determinants of clean water 
+and reliable energy access for Sudanese households. Second, the analysis is then extended to estimate the 
+effect of access to clean water and reliable energy sources on selected household welfare outcomes, 
+particularly food security and general health and well-being. 
+In this study, access to improved water refers to the use of "at least basic" drinking water services, as 
+defined by UNICEF and WHO (2023). This includes improved sources such as piped water, boreholes, or 
+protected wells that are accessible within no more than 30 minutes round trip, including queuing time. 
+Households that require more than 30 minutes to collect water are categorized as having limited access, 
+while those relying on unimproved sources or surface water are considered to have no access to improved 
+water sources. Access to improved energy sources is defined as a household’s ability to use energy from a 
+reliable grid or off-grid source (e.g., electricity, solar, or diesel generator) for lighting and basic domestic 
+needs. This measure reflects functional access – that is, energy that is both available and usable – rather 
+than mere connection to a supply network. 
+4.1 Data sources and sample characteristics 
+The data used for this study is the 2022 Sudan Labor Market Panel Survey (SLMPS), which was 
+implemented by the Economic Research Forum in collaboration with Sudan's Central Bureau of Statistics. 
+SLMPS is a comprehensive, national household survey designed to capture a wide range of socio-
+economic variables pertinent to the Sudanese labor market (Krafft, Assaad, and Cheung 2023). It is the 
+first wave of a planned longitudinal study aimed at understanding human resource development and 
+deployment in Sudan. The survey is modeled after similar labor market surveys conducted in Egypt, 
+Jordan, and Tunisia. The survey questionnaire includes modules from the Living Standards Measurement 
+Study Plus household survey program to collect gender-disaggregated information on asset ownership, 
+employment, and entrepreneurship activities, including household enterprises. The survey is designed to 
+be nationally representative, providing estimates at the national level, for urban and rural areas, and across 
+all states of Sudan. The SLMPS sample is made up of approximately 5,000 households. Information is 
+collected on all household members aged five years and above. 
+The SLMPS 2022 survey instruments encompass a broad range of topics essential for understanding 
+the socio-economic landscape of Sudan. These include information on parental background, education 
+attainment of household members, housing and access to services for the household, residential mobility 
+and migration, and the time use of household members of working age. Questions are asked in the survey 
+on the food security of the household and the health status of its members. The survey also explores 
+10 
+marriage patterns, fertility, and women’s empowerment, and provides detailed data on employment, 
+unemployment, and earnings of household members of working age. Additionally, it examines the 
+operation of household enterprises and farms, community infrastructure, and the use of social safety nets. 
+The survey also captures household vulnerability and the strategies members use to cope with shocks to 
+the household. Notably, it includes a significant number of retrospective questions about major life 
+events, including residential moves, employment changes, marriage, and fertility. This allows for a 
+comprehensive understanding of the timing and context of these events. Overall, the survey aims to 
+provide a detailed and nationally representative picture of Sudan's labor market and related socio-
+economic processes, covering a diverse range of issues that affect the Sudanese population. 
+A random stratified cluster sampling technique was used to create the sample for SLMPS 2022. The 
+survey is representative across several strata, including refugee camps, internally displaced persons (IDP) 
+camps, and urban and rural areas. Two hundred fifty primary sampling units (PSU) were initially selected, 
+with backup PSUs also selected for use if logistical challenges in surveying any of the initially selected 
+PSUs arose during fieldwork. Data collection was conducted through face-to-face interviews from June to 
+November 2022. Multiple visits were made to each household to ensure that information was collected 
+directly from all members aged five years and older. The survey instruments included a household 
+questionnaire and an individual questionnaire, both of which contained multiple modules to capture 
+detailed socio-economic data. 
+Sample weights were generated to ensure national representativeness. The weights accounted for the 
+probability of PSU selection and household inclusion within the selected PSUs. Detailed efforts were 
+made to address potential biases due to non-response or logistical challenges during fieldwork. 
+Table 1 presents descriptive statistics on the sample households. Most of the households are headed by 
+middle-aged males with very limited  formal education. Table 1 also shows that most household heads in 
+Sudan are either not employed or are self-employed.  
+11 
+Table 4.1 Analytical variables used, descriptive statistics 
+Variable  Mean 
+Dependent variables  
+Access to clean water and reliable energy (1= yes, 0 =otherwise) 0.386 
+Access to improved energy sources (1= yes, 0 =otherwise)  0.322 
+Access to both improved water and improved energy sources (1= yes, 0 =otherwise) 0.250 
+Independent variables  
+Urban household (0/1)  0.50 
+Female head of household (0/1) 0.23 
+Age of household head (years) 47.07 
+THousehold members (adult equivalent units)  5.22 
+Dependents in household (number) 2.49 
+Females in household (number) 2.65 
+Education level of household head (share of all household heads)  
+Did not complete primary school, cannot read or write 0.43 
+Did not complete primary school, but can read and write 0.19 
+Completed primary school 0.19 
+Completed secondary school 0.11 
+Completed post-secondary schooling (not university) 0.01 
+Completed university 0.06 
+Completed postgraduate education 0.01 
+Employment of household head   
+Wage worker 0.61 
+Employer 0.07 
+Self-employed 0.26 
+Unpaid family worker 0.05 
+Has no employment 0.02 
+Sector of employment of household head   
+Agricultural sector 0.19 
+Manufacturing sector 0.04 
+Services sector 0.44 
+Not employed  0.32 
+Wealth index (5 = wealthiest; based on principal component analysis)1 3.22 
+Travel time to work (minutes) 32.53 
+Number of Observations  4,741 
+Source: Authors’ weighted analysis of the 2022 Sudan Labor Market Panel Survey. 
+Note: In the analysis, state-level dummy variables were included for all 18 states of Sudan. 
+ 
+ 
+1 The wealth index used to rank households was constructed using Principal Component Analysis (PCA), a common method for 
+creating composite indices based on household asset ownership, housing characteristics, and access to services. PCA assigns 
+weights to various indicators, creating a standardized index to rank households into wealth quintiles. For details on this 
+methodology, see Filmer and Pritchett (2001) and Vyas and Kumaranayake (2006). 
+12 
+4.2 Determinants of access to clean water and reliable energy  
+To assess the determinants of access to clean water sources and reliable energy sources in Sudan, we 
+estimated a binary logistic regression model using cross-sectional data from the SLMPS for 2022. The 
+binary logistic model is appropriate for our analysis as it is designed to predict the probability of a binary 
+outcome, i.e., access to clean water, access to reliable energy, or both, based on one or more predictor 
+variables.  
+The model estimates the probability that a given observation falls into one of these categories. The 
+reduced form of the binary logistic model applied in our study is given by: 
+logit(P(Y=1)) = β0 + β1X1 + β2X2 + ⋯ + βnXn + ϵi (1) 
+where Y is the dependent variable (access to clean water; access to reliable energy; or both); β0 is the 
+intercept; β1, β2, ⋯ βn are the coefficients for the independent variables; and ϵi is the error term. The 
+independent variables for the analyses include the range of demographic, socio-economic, and regional 
+factors described in Table 4.1. 
+4.3 Effect of access to clean water and reliable energy on selected household outcomes  
+The second objective of this study is to estimate the effects of access to clean water and reliable energy on 
+household food security and the overall health status of the household. The model used to estimate these 
+effects can be expressed as:  
+Yi =β0 + β1Ti + β2Xi + ϵi (2) 
+Yi represents the outcome variable for household i. We use two different outcome variables. The food 
+security status of the household is based on the Food Insecurity Experience Scale (FIES) (Cafiero, 
+Viviani, and Nord 2018, FAO 2021). The second outcome, the overall health status of the household, is 
+based on a multinomial variable measured across five levels in ascending order from “very poor” to 
+“excellent.” It is expected that households with access to clean water and reliable energy sources will 
+have better health outcomes because of reduced incidences of waterborne diseases and respiratory-related 
+illnesses.  
+The main explanatory variable of interest is Ti, which is measured as a binary treatment variable taking 
+the value of one if a household accessed clean water or reliable energy sources, or both, and zero 
+otherwise. Xi denotes a vector of covariates, including a range of demographic, socio-economic, and 
+regional factors as described in Table 4.1. ϵi is a random error term, and the βs are the parameters to be 
+estimated.  
+13 
+We recognize that there is a potential selection bias problem when estimating the effects of access to 
+clean water, reliable energy, or both (equation (2)), given that access to water or energy is not based on 
+random assignment. To reduce this potential bias, we apply the inverse-probability regression adjustment 
+method, which is also known as the doubly robust estimation technique (Wooldridge 2010). While this 
+method can correct for selection bias due to observable characteristics, it cannot control unobserved 
+heterogeneity. Unfortunately, the access to clean water and reliable energy being assessed here is not 
+based on experimental design, which can properly address unobserved heterogeneity bias. Furthermore, 
+we are unable to rely on an instrumental variable (IV) approach to correct for selection bias problems 
+because of the difficulty in identifying valid instruments based on the secondary nature of our data. Thus, 
+we refer to the estimated relationships between Ti and Yi in equation (2) as correlations rather than causal 
+impacts.  
+The doubly robust method follows three steps.  
+• First, logit regression models are used to estimate the probability of a household accessing 
+clean water, reliable energy, or both, i.e., the treatment model.  
+• Second, using inverse-probability weights obtained from the first step, weighted outcome 
+models are fitted to obtain the predicted outcomes for the households with access and those 
+without access (outcome models). Note that the outcome models are fitted using linear, probit, 
+and Poisson regression models for continuous, binary, and count outcome variables, 
+respectively.  
+• Finally, the means of the predicted outcomes are then used to estimate the average treatment 
+effect on the treated (ATET), which quantifies the effects on household food security or 
+general health status of access to clean water or reliable energy sources.  
+These three steps of the doubly robust method are jointly estimated using the teffects ipwra command 
+in the Stata statistical software package.  
+In this instance, the doubly robust method is superior to other commonly used selection-on-observable 
+estimators, such as propensity score matching (PSM). This is because the doubly robust property ensures 
+that if either one of the treatment or outcome models is misspecified, the ATET estimates will still be 
+consistent (Imbens and Wooldridge 2009).  
+14 
+5 RESULTS AND DISCUSSIONS 
+5.1 Summary statistics and t-tests 
+We constructed three different binary outcome variables for this study—access to clean water sources, 
+access to reliable energy sources, and access to both clean water and reliable energy sources. We 
+conducted t-tests to compare the means of the explanatory variables across the access and no-access 
+groups for the three different outcome variables. 
+For access to clean water, the t-tests show significant differences between households with and 
+without clean water access for several explanatory variables (Table 5.1). Households with clean water 
+access are more likely to be urban and to have heads that are male and relatively older. Additionally, 
+households with lower ratios of dependents in the household lower are more likely to be able to access 
+clean water. Higher educational attainment of the household head is significantly associated with clean 
+water access across all education levels—households with heads that have not completed primary 
+education are more likely not to have access to clean water. Households with wage workers and 
+employers are significantly likely to have access to clean water, as are households with workers employed 
+in the services sector. Wealthier households are more likely to have access to clean water. 
+15 
+Table 5.1 Clean water access—explanatory variables t-tests 
+Variable 
+Overall 
+mean 
+Clean water access 
+Difference p-value 
+Clean 
+water 
+Unclean 
+water 
+Urban household 0.503 0.707 0.378 -0.328 0.000 
+Female head of household 0.226 0.167 0.262 0.095 0.000 
+Age of household head 47.070 49.750 45.830 -3.920 0.000 
+Household members 5.224 5.195 5.241 0.046 0.260 
+Dependents in household 2.487 2.184 2.674 0.489 0.000 
+Females in household 2.653 2.597 2.687 0.090 0.021 
+Cannot read write 0.425 0.224 0.549 0.324 0.000 
+No ed–can read write 0.194 0.176 0.205 0.030 0.005 
+Completed primary 0.191 0.275 0.139 -0.136 0.000 
+Completed secondary 0.106 0.172 0.066 -0.106 0.000 
+Completed post-sec 0.011 0.023 0.004 -0.020 0.000 
+Completed university 0.055 0.101 0.027 -0.074 0.000 
+Completed postgrad 0.008 0.020 0.000 -0.020 0.000 
+Wage worker 0.186 0.280 0.128 -0.152 0.000 
+Employer 0.045 0.064 0.033 -0.031 0.000 
+Self-employed 0.283 0.302 0.271 -0.032 0.010 
+Unpaid family worker 0.047 0.022 0.062 0.040 1.000 
+Has no employment 0.286 0.223 0.325 0.102 1.000 
+Agricultural sector employ 0.188 0.102 0.242 0.140 1.000 
+Manufacturing sector employ 0.035 0.047 0.028 -0.019 0.001 
+Services sector employ 0.432 0.579 0.342 -0.237 0.000 
+Wealth quintile  3.216 4.204 2.607 -1.597 0.000 
+Travel time to work 32.529 26.613 36.170 9.558 1.000 
+No. of observations 4,741 1,806 2,935   
+Source: Authors’ analysis. 
+The t-tests for access to reliable energy similarly reveal significant differences for the explanatory 
+variables for households with and without such access (Table 5.2). Households with access to reliable 
+energy are significantly more likely to be urban, with older or more educated household heads. Wealthier 
+households are more likely to have access to reliable energy. 
+16 
+Table 5.2 Reliable energy access—explanatory variables t-tests 
+Variable 
+Overall 
+mean 
+Energy access 
+Difference p-value 
+Reliable 
+sources 
+Unreliable 
+sources 
+Urban household 0.503 0.706 0.409 -0.296 0.000 
+Female head of household 0.226 0.154 0.259 0.105 1.000 
+Age of household head 47.019 49.737 45.758 -3.979 0.000 
+Household members 5.224 5.190 5.239 0.050 0.749 
+Dependents in household 2.487 2.136 2.650 0.514 1.000 
+Females in household 2.653 2.599 2.678 0.079 0.959 
+Cannot read write 0.425 0.160 0.548 0.387 1.000 
+No ed–can read write 0.194 0.171 0.205 0.034 0.997 
+Completed primary 0.106 0.194 0.066 -0.128 0.000 
+Completed secondary 0.011 0.024 0.005 -0.019 0.000 
+Completed post-sec 0.055 0.123 0.024 -0.098 0.000 
+Completed university 0.008 0.023 0.001 -0.022 0.000 
+Completed postgrad 0.186 0.278 0.144 -0.134 0.000 
+Wage worker 0.045 0.075 0.031 -0.044 0.000 
+Employer 0.000 0.312 0.269 -0.042 0.002 
+Self-employed 0.047 0.022 0.059 0.037 1.000 
+Unpaid family worker 0.286 0.218 0.318 0.100 1.000 
+Has no employment 0.188 0.098 0.230 0.132 1.000 
+Agricultural sector employ 0.035 0.051 0.028 -0.023 0.000 
+Manufacturing sector employ 0.432 0.605 0.352 -0.252 0.000 
+Services sector employ 0.432 0.605 0.352 -0.252 1.000 
+Wealth quintile  3.216 4.487 2.626 -1.860 0.000 
+Rooms per adult 0.636 0.787 0.566 -0.221 0.000 
+Travel time to work 32.529 29.196 34.075 4.880 1.000 
+No. of observations 4,741 1,502 3,239   
+Source: Authors’ analysis. 
+For access to both clean water and reliable energy (Table 5.3), significant differences are observed for 
+multiple variables. Urban households are significantly more likely to have combined access. Households 
+with older household heads and with lower dependents ratios are both associated with combined access. 
+Households with heads with relatively higher educational attainment are significantly more likely to have 
+access to both clean water and reliable energy. Wealthier households are significantly more likely to have 
+combined access. 
+17 
+Table 5.3 Both clean water and reliable energy access—explanatory variables t-tests 
+Variable Overall mean 
+Both clean water and reliable 
+energy access 
+Difference p-value 
+Access  
+to both 
+Does not have 
+access to both 
+Urban household 0.503 0.726 0.431 -0.295 0.000 
+Female head of household 0.226 0.148 0.251 0.104 0.000 
+Age of household head 47.019 50.559 45.866 -4.693 0.000 
+Household members 5.146 5.146 5.249 0.103 0.096 
+Dependents in household 2.487 2.069 2.623 0.555 0.000 
+Females in household 2.653 2.568 2.680 0.112 0.011 
+Cannot read write 0.425 0.126 0.522 0.396 0.000 
+No ed–can read write 0.194 0.153 0.207 0.055 0.000 
+Completed primary 0.191 0.311 0.152 -0.159 1.000 
+Completed secondary 0.106 0.207 0.074 -0.134 1.000 
+Completed post-sec 0.011 0.029 0.005 -0.024 1.000 
+Completed university 0.055 0.137 0.029 -0.108 1.000 
+Completed postgrad 0.008 0.029 0.001 -0.028 1.000 
+Wage worker 0.186 0.296 0.150 -0.146 1.000 
+Employer 0.045 0.075 0.036 -0.039 1.000 
+Self-employed 0.283 0.326 0.269 -0.057 1.000 
+Unpaid family worker 0.047 0.021 0.055 0.034 0.000 
+Has no employment 0.286 0.193 0.316 0.123 0.000 
+Agricultural sector employ 0.188 0.097 0.218 0.121 0.000 
+Manufacturing sector employ 0.035 0.050 0.030 -0.019 0.997 
+Services sector employ 0.432 0.614 0.373 -0.241 1.000 
+Wealth quintile  3.216 4.573 2.774 -1.799 1.000 
+Travel time to work 32.529 26.145 34.607 8.462 0.000 
+No. of observations  4,741 1,164 3,557   
+Source: Authors’ analysis. 
+5.2 Regression analysis on determinants of household access to clean water and reliable 
+energy 
+We estimated three logistic regression models to examine the determinants of the access that Sudanese 
+households have to clean water, to reliable energy, and to both clean water and reliable energy. The 
+results (odds-ratios) are presented in Table 5.4. 
+18 
+Table 5.4 Determinants of improved water and improved energy access 
+ 
+(a) 
+ 
+Clean water access 
+(b) 
+ 
+Reliable energy access 
+(c) 
+Access to both clean 
+water and reliable 
+energy  
+Coefficient Stnd. 
+error 
+Coefficient Stnd. 
+error 
+Coefficient Stnd. 
+error 
+Urban household 1.633*** 0.121 0.483*** 0.135 1.067*** 0.142 
+Female head of household 0.160 0.137 -0.266 0.167 -0.223 0.182 
+Age of household head 0.016*** 0.004 0.000 0.004 0.007 0.005 
+Household members -0.057 0.042 0.020 0.048 -0.027 0.050 
+Dependents in household -0.056 0.042 -0.026 0.049 -0.050 0.051 
+Females in household -0.008 0.051 -0.062 0.060 -0.033 0.063 
+No ed–can read write 0.381*** 0.144 0.450*** 0.170 0.420** 0.190 
+Completed primary 0.697*** 0.142 0.893*** 0.158 1.152*** 0.173 
+Completed secondary 0.777*** 0.177 0.967*** 0.192 1.238*** 0.204 
+Completed post-sec 1.559*** 0.534 1.653*** 0.560 1.734*** 0.541 
+Completed university 0.574** 0.232 1.430*** 0.262 1.315*** 0.255 
+Completed postgrad 3.333*** 1.098 4.218*** 1.211 4.506*** 1.174 
+Employer -0.189 0.232 -0.004 0.247 -0.295 0.255 
+Self-employed 0.005 0.126 0.036 0.144 -0.103 0.148 
+Unpaid family worker -0.212 0.303 -0.115 0.384 -0.280 0.428 
+Employer 0.075 0.141 0.351** 0.164 0.297 0.182 
+Manuf. sector employ -0.173 0.255 -0.123 0.296 -0.562* 0.296 
+Services sector employ 0.225** 0.110 -0.045 0.129 0.068 0.140 
+Wealth quintile  0.870*** 0.054 1.634*** 0.088 1.786*** 0.105 
+Rooms per adult -0.138 0.124 0.414** 0.163 -0.004 0.147 
+Travel time to work -0.012*** 0.002 -0.004* 0.002 -0.013*** 0.003 
+Central Darfur -0.780*** 0.299 0.000 a 0.000 0.000 a 0.000 
+East Darfur -2.363*** 0.235 -3.900*** 0.324 -3.379*** 0.362 
+North Darfur -2.410*** 0.246 -3.703*** 0.344 -2.904*** 0.372 
+South Darfur -2.377*** 0.229 -3.821*** 0.320 -3.294*** 0.360 
+West Darfur -3.494*** 0.378 -3.819*** 0.490 -3.559*** 0.637 
+North Kordofan -1.097*** 0.227 -3.430*** 0.290 -3.066*** 0.314 
+South Kordofan -5.318*** 1.021 0.000b 0.000 0.000 b 0.000 
+West Kordofan -3.668*** 0.406 -5.510*** 0.661 -4.386*** 0.676 
+Sennar 1.309*** 0.261 -1.511*** 0.237 -0.415* 0.241 
+Gedaref -1.923*** 0.256 -2.242*** 0.286 -2.564*** 0.334 
+Blue Nile -0.769*** 0.231 -3.638*** 0.332 -2.942*** 0.356 
+White Nile -2.905*** 0.279 -2.818*** 0.292 -4.421*** 0.621 
+Northern 0.957*** 0.290 0.783** 0.377 0.742*** 0.281 
+River Nile 1.650*** 0.304 2.306*** 0.429 2.867*** 0.385 
+Aj Jazirah 1.508*** 0.226 -1.878*** 0.208 -0.924*** 0.205 
+Kassala -0.017 0.229 -1.837*** 0.252 -1.057*** 0.260 
+Red Sea -5.390*** 0.398 -1.017*** 0.256 -5.291*** 0.418 
+Constant -3.758*** 0.340 -5.937*** 0.445 -7.797*** 0.530 
+No. of observations 4,741 
+ 
+4,365 
+ 
+4,365 
+ 
+Chi-squared 3547.8 
+ 
+3573.1 
+ 
+3234.8 
+ 
+Adjusted R-squared 0.563 
+ 
+0.636 
+ 
+0.639 
+ 
+p-value 0.000 
+ 
+0.000 
+ 
+0.000 
+ 
+Source: Authors’ analysis. 
+Note: The analytical variables ‘Cannot read write’, ‘Wage worker’, ‘Agricultural sector employ’, and ‘Khartoum’ were dropped 
+from the regression analyses to avoid overspecification. 
+a, b: Central Darfur and South Kordofan have zeros in the reliable energy and combined access models due to insufficient data or 
+lack of variation in access levels in these regions. 
+ 
+19 
+5.2.1 Access to clean water 
+The logistic regression results for household access to clean water identify several significant predictors 
+(Table 5.4, panel (a)). Urban households are notably more likely to have access to sources of clean water 
+compared to rural households, highlighting the disparity in infrastructure and resource allocation between 
+urban and rural areas. Urban centers typically benefit from higher investments and better governance, 
+which improve access to essential services and infrastructure for urban households. This is consistent with 
+findings by Antunes and Martins (2020), who emphasize the importance of infrastructure investments and 
+governance in urban areas for improving water access. 
+Examining specific determinants of household access to clean water, the age of the head of the 
+household shows a positive and significant effect on access to clean water—households with older heads 
+are more likely to have such access, possibly due to their generally greater financial stability. The 
+educational attainment of the head emerges as another crucial determinant of water access, aligning with 
+findings by Bamou Tankoua (2021) and Adams et al. (2016). It is particularly households with heads that 
+cannot read and write that are likely not to have access to clean water. All of the variables on educational 
+attainment used in the regression show positive coefficients relative to the base case of households with 
+heads that cannot read and write. These results underscore the role of education in enhancing household 
+welfare and access to essential services, as educated individuals are more likely to understand the benefits 
+of clean water sources and navigate the systems to obtain them. Furthermore, there is a noticeable trend 
+that higher education levels correlate with better access to water.  
+Wealth, as indicated by the household's wealth quintile, is another significant positive predictor of 
+clean water access. Households with greater wealth have the financial capacity to invest in better water 
+sources, either through direct infrastructure investments or by residing in areas with superior public 
+services. 
+The “Travel time to work" variable is used as a proxy for the quality of local infrastructure. This 
+variable has a significant negative effect on access to clean water access. Longer travel times suggest poor 
+local infrastructure, which hinders the access of households to clean water sources.  
+Spatially across the states of Sudan, several exhibit significantly lower odds of households having 
+access to clean water compared to the reference category of Khartoum—East Darfur, North Darfur, South 
+Darfur, West Darfur, South Kordofan, and West Kordofan states are particularly disadvantaged in their 
+access to clean water. These regional disparities highlight the need for targeted interventions to improve 
+access to clean water in order to resolve underlying spatial inequalities. Conversely, certain regions show 
+significantly higher odds of households having access to clean water relative to Khartoum—households in 
+Sennar, Aj Jazirah, River Nile, and Northern states all have relatively good access to clean water sources. 
+20 
+A common factor among these states is their location within the Nile Basin, with the river providing a 
+reliable water source. Additionally, these states generally have smaller populations compared to the 
+densely populated capital, Khartoum, which may reduce the strain in each on the provision and 
+continuing maintenance of clean water infrastructure. 
+5.2.2 Access to reliable energy 
+The findings for access to reliable energy are detailed in Table 5.4, panel (b). The results indicate that 
+urban households are significantly more likely to have access to improved energy sources than their rural 
+counterparts. This mirrors the pattern observed in access to clean water, further emphasizing the urban–
+rural divide in the availability of essential services. Unlike other factors, the sex of the household head 
+does not significantly impact energy access, suggesting it is not a determining factor for households in 
+securing access to reliable energy sources. This pattern was also seen in the analysis of the determinants 
+of household access to clean water. 
+As with access to clean water, education is also a key predictor of household access to reliable energy, 
+with a pattern similar to that seen for clean water—households that are headed by individuals who can 
+read or write or have completed some education are significantly more likely to have access to reliable 
+energy. These findings highlight the pivotal role of education in household welfare and the accessibility 
+of modern energy services, as educated individuals tend to be more knowledgeable about the benefits and 
+methods of obtaining improved and reliable energy. 
+Wealth is another significant positive determinant of access by households to reliable energy. 
+Households with greater financial resources are better able to afford the higher costs associated with 
+accessing reliable energy sources. Additionally, the number of rooms in a household serves as an 
+indicator of better living conditions, so the variable is positively correlated with a household having 
+access to reliable energy. As with access to clean water, poor local infrastructure quality, as measured by 
+the "Travel time to work" variable, negatively affects the access of local households to reliable energy. 
+Longer travel times to work suggest poorer infrastructure, which, in turn, hampers the ability of 
+households to access sources of reliable energy.  
+As with access to clean water, households in most of the states in the Darfur and Kordofan regions 
+face challenges in accessing reliable energy sources. However, households in several other states also are 
+found to face significant difficulties in accessing reliable energy, even though their access to clean water 
+is relatively good—notably, Gedaref, Blue Nile, and White Nile. This pattern should motivate efforts to 
+enhance energy infrastructure across Sudan and address the inequalities in access to reliable energy. 
+However, several states stand out for households in them having relatively good access to reliable 
+21 
+energy—River Nile, particularly, but also Northern state exhibit significantly higher odds of securing 
+better energy sources compared to Khartoum. Even after accounting for other factors, these states appear 
+to have better access to reliable energy.  
+5.2.3 Combined access to clean water and energy 
+The results for combined access to clean water and energy are presented in Table 5.4, panel (c). Urban 
+households exhibit higher odds of having combined access to clean water and reliable energy compared to 
+rural households. This result, consistent with the previous models, highlights the substantial advantages 
+urban households have over rural ones in accessing essential services. 
+Education remains a consistent and significant predictor across all levels. Households where the head 
+can read and write or has received any education are more likely to have combined access to clean water 
+and energy than households in which the head cannot read or write. This underscores the overarching 
+importance of education in securing better living conditions and access to services. 
+Wealth significantly increases the likelihood of a Sudanese household having combined access to 
+clean water and reliable energy—wealthier households are better able than poorer ones to afford both 
+clean water and reliable energy. Infrastructure development, as indicated by the time spent commuting to 
+work, shows a significant negative effect on combined access, suggesting that that households resident in 
+areas with poorer local infrastructure face greater barriers in obtaining both clean water and reliable 
+energy. 
+The regional disparities seen with access to clean water and reliable energy, respectively, persist when 
+considering combined access to both. Again, households in most of the states in the Darfur and Kordofan 
+regions have poor access. However, households in many of the other states also face challenges in 
+accessing both clean water and reliable energy. Only households in Northern and River Nile states are 
+more likely than households in Khartoum to have access to both clean water and reliable energy. These 
+results highlight the compounded disadvantage faced by households in most states in Sudan, necessitating 
+comprehensive regional development strategies to address the multifaceted nature of deprivation in access 
+to essential services, including clean water and reliable energy. 
+5.2.4 Summary of the analyses of the determinants of household access to clean water and 
+reliable energy in Sudan 
+The logistic regression analyses provide critical insights into the determinants of access to clean water 
+and reliable energy for the population of Sudan. Urbanization, education, and wealth emerge as 
+significant positive predictors, highlighting the disparities between urban and rural areas and the 
+importance of socioeconomic status in securing basic services. Urban households consistently show 
+22 
+higher access to both clean water and reliable energy. This underscores the need for rural development 
+programs to bridge the gap in service provision between urban and rural areas. Investment in rural 
+infrastructure and services is essential to ensure equitable access to essential services across different 
+geographic locations. 
+Education is a powerful determinant of access to clean water and energy. Higher levels of education 
+achieved by household heads are associated with significantly better access for their households to both. 
+This indicates that educational attainment enhances the ability of individuals and their households to 
+navigate and benefit from available resources and services, including clean water and reliable energy. 
+These findings suggest that policies aimed at improving educational outcomes could have far-reaching 
+effects on access to essential services and overall household welfare. 
+Wealth also plays a crucial role in determining access to clean water and reliable energy. Wealthier 
+households can better afford the costs associated with accessing and maintaining these services. This 
+finding highlights the need for economic policies that promote income growth and reduce poverty to 
+enhance access to such essential services. 
+The significant regional disparities in access to clean water and reliable energy, particularly in the 
+Darfur and Kordofan regions, point to the need for targeted regional interventions. The states in these 
+regions face compounded disadvantages that require comprehensive development strategies addressing 
+infrastructure and public services.  
+5.3 Effects on household welfare of access to clean water and reliable energy 
+Before examining the doubly robust estimation results on the effects of access to clean water and energy 
+on household food security and health, we first ensure that the estimated models pass essential diagnostic 
+tests. Figure 5.1 shows sufficient overlap in the covariate distributions of households with access and 
+those without access to clean water and reliable energy sources. This suggests a non-violation of the 
+overlap or common support condition (Imbens 2004). Furthermore, following Imai and Ratkovic (2014), 
+we present corroborating balance diagnostic test results in Table 5.5. Those results show insignificant 
+Chi-squared statistics, which confirm that the first step of the doubly robust model successfully balanced 
+the covariates by weighting.  
+  
+23 
+Figure 5.1 Overlap plots for covariate distributions of households with access and those without 
+access to clean water and energy sources 
+Clean water access Reliable energy access 
+Clean water and  
+reliable energy access 
+   
+Source: Authors’ analysis. 
+Table 5.5 Covariate balancing test results 
+Treatment variable  Chi-squared p-value  
+Food security    
+Access to clean water sources 68.74 0.7865 
+Access to reliable energy sources 46.48 0.1130 
+Access to clean water and reliable energy sources 8.66 0.9997 
+Household health status    
+Access to clean water sources 71.09 0.7848 
+Access to reliable energy sources 46.52 0.1127 
+Access to clean water and reliable energy sources 8.65 0.9997 
+Source: Authors’ analysis. 
+The results in Table 5.6 show that access to clean water is positively correlated with food security and 
+health. However, the treatment effect estimate is only significant for the health status of the household—
+households with access to clean water have a 54 percent  higher likelihood of having better health status 
+than households without access. On the other hand, access to reliable energy is positively and 
+significantly correlated with both the food security and the health status of households. Specifically, the 
+analysis shows that households using reliable energy sources have a 19 percent and 72 percent higher 
+likelihood of having better food security and health status, respectively, than households without access to 
+reliable energy. Furthermore, households with access to both clean water and reliable energy sources 
+show positive correlations with both food security and health status, though only the effect on health 
+status is marginally statistically significant, with a 46 percent higher likelihood of having better health 
+status than households with access to neither clean water nor reliable energy. 
+0
+2
+4
+6
+8
+D
+en
+si
+ty
+0 .2 .4 .6 .8 1
+Propensity score for water access
+Not Improved Improved 
+0
+10
+20
+30
+40
+D
+en
+si
+ty
+0 .2 .4 .6 .8 1
+Propensity score for improved energy access
+Not Improved Improved
+0
+10
+20
+30
+40
+50
+D
+en
+si
+ty
+0 .2 .4 .6 .8 1
+Propensity score for improved water and energy access
+Not Improved Improved
+24 
+Table 5.6 Effects of improved water and improved energy on household food security and health 
+Outcome 
+Variable 
+Clean 
+water access 
+Reliable  
+energy access 
+Both clean water and 
+reliable energy access 
+ATET 
+Robust 
+SE 
+ATET in 
+% ATET 
+Robust 
+SE 
+ATET in 
+% ATET 
+Robust 
+SE 
+ATET in 
+% 
+Food security (1/0) 0.082 0.024 118.5 0.446*** 0.052 19.0 0.054 0.050 23.5 
+Health status (1/0) 0.025* 0.018 54.4 0.047*** 0.021 72.4 0. 060* 0.032 46.2 
+Source: Authors’ analysis. 
+Notes: ***p < 0.01, **p < 0.05, *p < 0.1. 
+ATET = “average treatment effect on the treated”, SE = “standard error”. 
+These findings align with the research literature indicating that access to clean water and reliable 
+energy sources is crucial for enhancing household well-being. For instance, Hutton and Haller (2004) 
+found that access to clean water and sanitation reduces the incidence of waterborne diseases, thereby 
+improving overall health. Similarly, the World Health Organization (WHO 2006) reported that improved 
+and more reliable energy sources, such as electricity and clean cooking fuels, reduce indoor air pollution 
+and related health risks, contributing to better health outcomes. Additionally, studies by Guarcello, Lyon, 
+and Rosati (2008) show that clean water and reliable energy infrastructure can enhance food security by 
+increasing agricultural productivity and reducing the time burden on household members, particularly 
+women, allowing them more time for food production and other income-generating activities. 
+25 
+6 CONCLUSIONS AND POLICY RECOMMENDATIONS 
+This study explored the determinants of household access to clean water and reliable energy in Sudan, 
+drawing on data from the 2022 Sudan Labor Market Panel Survey. By employing binary logistic 
+regression models and the doubly robust estimation technique, the analysis revealed critical insights into 
+the disparities in household access to these essential resources and their impact on food security and 
+health outcomes. 
+The analysis identified several key determinants of the access of households to clean water and 
+reliable energy in Sudan. Urban households are significantly more likely to have access to these resources 
+than rural ones, highlighting the urban-rural divide. Education also emerged as a crucial determinant, with 
+higher educational attainment by the head of the household being strongly associated with better access 
+by the household to both water and energy. Wealthier households were found to have a greater ability to 
+secure clean water and reliable energy services, underscoring the importance of economic resources in 
+obtaining these essential services. Additionally, regional disparities were evident, particularly in the 
+Darfur and Kordofan states, where access to clean water and reliable energy is notably lower compared to 
+other states. These findings underscore the urgent need for targeted interventions to ensure equitable 
+access to essential services across Sudan. 
+The impact of access to clean water and reliable energy on food security and health outcomes was also 
+a key focus of this study. The results indicated that households with access to improved energy were more 
+likely to experience better food security and health outcomes. In contrast, access to improved water was 
+strongly associated with better health outcomes, but a less important driver of improved household food 
+security. These findings align with the broader literature, emphasizing the importance of clean water and 
+reliable energy access in enhancing household well-being and reducing vulnerability to health risks. 
+Given the disparities and the critical role of water and energy access in improving livelihoods, several 
+policy recommendations emerge from these findings. There is a pressing need for investments in rural 
+infrastructure, particularly in the water and energy sectors. Expanding access to these services in rural 
+areas can significantly enhance living standards and economic opportunities, helping to bridge the urban-
+rural divide. Strengthening educational systems, particularly in rural areas, should be a priority. Education 
+is a powerful tool in improving access to essential services, as higher educational attainment is closely 
+linked to better access to water and energy. Policies aimed at improving educational outcomes could have 
+a multiplier effect on household welfare and access to resources. 
+Enhancing household incomes through economic growth and poverty reduction strategies is vital. 
+Wealth is a significant determinant of access to clean water and reliable energy. Policies that promote 
+26 
+economic development and reduce poverty can help more households secure these essential services. 
+Significant regional disparities in the access households have to clean water and reliable energy require 
+targeted interventions to eliminate. These should include investments in infrastructure, improved 
+governance, and tailored public services that address the unique challenges faced by disadvantaged 
+regions. Such comprehensive development strategies are necessary to mitigate the compounded 
+deprivations experienced by households in these areas. 
+In conclusion, addressing the multifaceted challenges of water and energy access by households in 
+Sudan is critical for improving the quality of life for all its citizens. By implementing the recommended 
+policy measures, Sudan can make significant strides toward achieving equitable access to essential 
+services, thereby fostering greater social and economic development across the country. 
+  
+27 
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+Sudan.” Khartoum: UNICEF–Sudan. https://www.unicef.org/sudan/press-releases/reality-check-which-
+ignites-foreverychild-striving-universal-access-water-sudan  
+Pueyo, A. and M. Maestre. 2019. “Linking Energy Access, Gender and Poverty: A Review of the Literature on 
+Productive Uses of Energy.” Energy Research and Social Science 53: 170–181. 
+https://doi.org/10.1016/j.erss.2019.02.019  
+Ringler, C., A. Bhaduri, and R. Lawford. 2013. “The Nexus across Water, Energy, Land and Food (WELF): 
+Potential for Improved Resource Use Efficiency?” Current Opinion in Environmental Sustainability, 5 (6): 
+617–624. https://doi.org/10.1016/j.cosust.2013.11.002  
+Saputri, N.K., L.D. Setyonugroho, and D. Hartono. 2024. “Exploring the Determinants of Energy Poverty in 
+Indonesia’s Households: Empirical Evidence from the 2015–2019 SUSENAS.” Humanities and Social 
+Sciences Communications, 11 (60). https://doi.org/10.1057/s41599-023-02514-z  
+Smith, K., J. Rogers, and S.C. Cowlin. 2005. Household Fuels and Ill-Health in Developing Countries: What 
+Improvements Can Be Brought by LP Gas. Paris: World LP Gas Association and Intermediate Technology 
+Development Group. 
+UNDP (United Nations Development Programme). 2024. “Human Development Reports | Human 
+Development Index (HDI)”. New York: UNDP. https://hdr.undp.org/data-center/human-development-
+index#/indicies/HDI 
+UNICEF (United Nations Children’s Fund). 2017. “World Water Day: 68 Percent of Sudan’s Population With 
+Access to Basic Improved Drinking Water.” Khartoum: UNICEF Sudan. 
+https://reliefweb.int/report/sudan/world-water-day-68-percent-sudan-s-population-access-basic-improved-
+drinking-water-enar 
+UNICEF (United Nations Children’s Fund). 2023. Sudan—Humanitarian Action for Children, 2023. 
+Khartoum: UNICEF Sudan. https://www.unicef.org/media/131721/file/2023-HAC-Sudan(1).pdf 
+UNICEF and WHO (United Nations Children’s Fund and World Health Organization). 2023. Progress on 
+Household Drinking Water, Sanitation and Hygiene 2000–2022: Special Focus on Gender. New York: 
+UNICEF and WHO. 
+Villamor, G.B., G. Dawit, D. Utkur, and M. Alisher. 2018. Gender Specific Perspectives Among Smallholder 
+Farm Households on Water-Energy-Food Security Nexus Issues in Ethiopia. ZEF-Discussion Papers on 
+Development Policy No. 258. Bonn: Center for Development Research (ZEF), University of Bonn. 
+https://www.zef.de/uploads/tx_zefnews/zef_dp_258.pdf  
+29 
+Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal 
+component analysis. Health Policy and Planning, 21(6), 459-468. 
+WHO (World Health Organization). 2006. Fuel for Life: Household Energy and Health. Geneva: WHO Press. 
+WLPGA (World Liquefied Petroleum Gas Association). 2014. World LPG Association—Annual Report 2014. 
+Neuilly-sur-Seine, France:WLPGA. https://www.worldliquidgas.org/wp-content/uploads/2015/01/wlpga-
+ar2014.pdf 
+Wooldridge, J.M. 2010. Econometric Analysis of Cross Section and Panel Data. second ed. Cambridge, MA: 
+MIT Press. 
+World Bank. 2017. “Putting Clean Cooking on the Front Burner.” Washington, DC: World Bank. 
+https://www.worldbank.org/en/news/feature/2017/12/21/putting-clean-cooking-on-the-front-burner  
+World Bank. 2024. “DataBank | Metadata Glossary | Government Effectiveness: Estimate” Washington, DC: 
+World Bank. https://databank.worldbank.org/metadataglossary/worldwide-governance-
+indicators/series/GE.EST  
+WSP (Water and Sanitation Program). 2010. Gender in Water and Sanitation. Nairobi: WSP, World Bank. 
+https://resources.peopleinneed.net/documents/301-wsp-2010-mainstreaming-gender-in-water-and-
+sanitation.pdf  
+ 
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data/part_2/0279944426.md

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+# Stuck in the middle? Structural change and productivity growth in Botswana
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/34db90d8-0bf9-4d98-b6e1-d38ca8e3537f/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2017  
+**Rights:** CC-BY-NC-ND  
+**GARDIAN ID:** d2d5bb906946a2d6d75fc6150618837c  
+**DataNODE ID:** 9d0220e91da0e2b3f0e43747cb762c72  
+**Siever ID:** 9d638e8a-0058-4334-938b-68091ebc8e4d  
+**Token Count:** 153  
+**Page Count:** 0
+
+## Keywords
+
+structural adjustment, education, trade liberalization, economic growth, economic development, health, institutions, trade policies, productivity, structural change, world bank, growth
+
+## Geography
+
+- **Regions:** Southern Africa, Sub-Saharan Africa, Africa, World, Eastern Asia, Asia, Northern America, Americas
+- **Countries:** United States of America, China, Botswana
+
+## Description
+
+In 1966 when Botswana gained independence, it was one of the poorest countries in the world. But by 1986, Botswana had achieved middle-income status, and in 2005, the World Bank classified it as an upper-middle-income country. The only other country to enjoy such rapid economic growth over such a long period is China—an average of 9 percent between 1968 and 2010. Botswana has also maintained democracy throughout its recent history, and this combination of economic and political success has earned it the reputation of an “African success story” (Acemoglu, Johnson, and Robinson 2002). Botswana’s rapid economic growth has nonetheless left many individuals behind. Unemployment is a major issue, particularly among the young. Income inequality is extremely high, as is poverty. As such, it is important to understand the sources of Botswana’s economic growth to better appreciate where it may come from in the future and what prospects it has for being more inclusive.
+
+## Content
+
+In 1966 when Botswana gained independence, it was one of the poorest countries in the world. But by 1986, Botswana had achieved middle-income status, and in 2005, the World Bank classified it as an upper-middle-income country. The only other country to enjoy such rapid economic growth over such a long period is China—an average of 9 percent between 1968 and 2010. Botswana has also maintained democracy throughout its recent history, and this combination of economic and political success has earned it the reputation of an “African success story” (Acemoglu, Johnson, and Robinson 2002). Botswana’s rapid economic growth has nonetheless left many individuals behind. Unemployment is a major issue, particularly among the young. Income inequality is extremely high, as is poverty. As such, it is important to understand the sources of Botswana’s economic growth to better appreciate where it may come from in the future and what prospects it has for being more inclusive.

data/part_2/0283579097.md

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+# Biofortification: harnessing agricultural technology to improve the health of the poor
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/103d768d-c720-4d2e-870f-41653a8d9b82/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2002  
+**Rights:** N/A  
+**GARDIAN ID:** fd46af38ef9d5ea5fa814415c47d180e  
+**DataNODE ID:** 015a3f5f2cf345c882586ee487ad2611  
+**Siever ID:** bb8dadaa-a0c1-4dcb-a8d3-5bdd14ff11c9  
+**Token Count:** 1912  
+**Page Count:** 0
+
+## Keywords
+
+crops, nutrition, genetic engineering, plant breeding, innovation, nutritional disorders, malnutrition, rice, maize, wheat, vitamin deficiencies, iron deficiency chlorosis, trace elements, developing countries
+
+## Description
+
+This brief discusses a new breed of ultra-nourishing crops capable of alleviating malnutrition in even the most hard-to-reach populations—crops such as rice loaded with iron, maize packed with zinc, and wheat strengthened with vitamin A. These staples would need no commercial fortification, and could be grown on family plots throughout the developing world. It is now possible to breed plants for increased vitamin and mineral content, making “biofortified™” crops one of the most promising new tools in the fight to end malnutrition and save lives. The authors conclude that biofortification makes sense as part of an integrated food systems approach to reducing malnutrition. It addresses the root causes of micronutrient malnutrition, targets the poorest people, uses built-in delivery mechanisms, is scientifically feasible and cost-effective, and complements other on-going methods of dealing with micronutrient deficiencies.
+
+## Content
+
+S
+uch crops are no longer imaginary. 
+It is now possible to breed plants for
+increased vitamin and mineral content,
+making “biofortified™” crops one of the
+most promising new tools in the fight to end
+malnutrition and save lives. 
+Micronutrient Malnutrition: 
+The Hidden Hunger
+More than 840 million people do not have
+enough food to meet their basic daily energy
+needs. Far more—an estimated 3 billion—suffer
+the insidious effects of micronutrient deficien-
+cies because they lack money to buy enough
+meat, fish, fruits, lentils, and vegetables.
+Women and children in Sub-
+Saharan Africa, South and
+Southeast Asia, Latin America
+and the Caribbean are
+especially at risk of
+disease, premature
+death, and
+impaired cognitive
+abilities because
+of diets poor in
+crucial
+nutrients,
+particularly
+iron, vitamin
+A, iodine, and
+zinc.
+Current efforts to combat micronutrient
+malnutrition in the developing world focus on
+providing vitamin and mineral supplements for
+pregnant women and young children, and on
+fortifying foods through post-production pro-
+cessing. These approaches have accomplished
+much. In regions with adequate infrastructure
+and well-established markets for food process-
+ing and delivery, food fortification has greatly
+improved the micronutrient intake of vulnerable
+populations, particularly the urban poor. 
+Unfortunately, there are limits to commercial
+fortification and supplementation. In the
+poorest countries, consumption of commercially
+fortified foods is minimal.
+Furthermore, the recurrent
+costs associated with supple-
+mentation and commercial
+fortification are signifi-
+cant.
+By conservative
+estimates, providing
+vitamin A supple-
+ments and iron-
+fortified foods to one
+half of those in
+need would cost
+$100 million every
+year in South Asia
+alone.
+BIOFORTIFICATIONHARNESSING AGRICULTURAL TECHNOLOGY
+TO IMPROVE THE HEALTH OF THE POOR
+Plant Breeding to Combat Micronutrient Deficiency
+Imagine a new breed of ultra-nourishing crops capable of alleviating malnutrition
+in even the most hard-to-reach populations—crops such as rice loaded with iron,
+maize packed with zinc, and wheat strengthened with vitamin A. These staples
+would need no commercial fortification, and could be grown on family plots
+throughout the developing world. 
+BIOFORTIFICATION:
+A New Paradigm for Agriculture and 
+a Tool for Improved Human Health
+The introduction of biofortified crops—varieties
+bred for increased mineral and vitamin
+content—would complement existing nutrition
+approaches by offering a sustainable and low-
+cost way to reach people with poor access to
+formal markets or health care systems.
+Biofortification can provide ongoing benefits
+throughout the developing world at a fraction
+of the recurring cost of either supplementation
+or post-production fortification.
+The biofortification approach is backed by
+sound science. Research funded by Danish
+International Development Assistance (Danida)
+and coordinated by the International Food
+Policy Research Institute (IFPRI) has examined
+the feasibility of a plant breeding approach for
+improving the micronutrient content of staple
+crops and found that:
+• substantial useful genetic variation exists in
+key staple crops; 
+• breeding programs can readily manage
+nutritional quality traits, which for some
+crops are highly heritable and simple to
+screen for;
+• desired traits are sufficiently stable across a
+wide range of growing environments; and 
+• traits for high nutrient content can be
+combined with superior agronomic charac-
+teristics and high yields. 
+The ability of crop research to screen for and
+improve the nutrient content of staple crops
+has also been amply demonstrated by the
+Future Harvest international agricultural
+research institutes and their partners. Ability
+exists today to further improve and more
+widely disseminate these crucial varieties: 
+• Iron-rich rice (International Rice Research
+Institute, Philippines)
+• Quality protein maize (International Maize
+and Wheat Improvement Center, Mexico)
+• High-carotene sweet potato (International
+Potato Center, Peru)
+• High-carotene cassava (International Center
+for Tropical Agriculture, Colombia)
+Biofortified Crops for
+Improved Human Nutrition
+It is time to move forward with a strong
+program to develop nutrient-rich crop
+varieties, demonstrate their impact on
+human nutrition, and distribute them to the
+people who need them most.
+These tasks will be accomplished by a new
+international coalition bringing together an
+extraordinary range of knowledge and ability,
+including expertise in plant breeding, plant genomics,
+human nutrition, social behavior, and policy analysis. CIAT and
+IFPRI will coordinate the plant breeding, nutrition, crop dissemi-
+nation, and policy analysis activities, which will be carried out at
+eight international agricultural research centers, numerous
+national agricultural research and extension institutions, and
+departments of plant science and human nutrition at universities
+in developing and developed countries. Nongovernmental organi-
+zations (NGOs) in developed and developing countries, farmer
+organizations, and private sector partnerships will strengthen the
+alliance and provide linkages to consumers.
+Initial biofortification efforts will focus on six staple crops for
+which prebreeding feasibility studies have been completed: beans,
+cassava, maize, rice, sweet potatoes, and wheat.The project will
+also examine the potential for nutrient enhancement in 11
+additional crops important in the diets of those suffering from
+micronutrient deficiencies: bananas, barley, cowpeas, groundnuts,
+lentils, millet, pigeon peas, plantains, potatoes, sorghum, and yams.
+The objectives of the biofortification project are bold but realistic:
+SHORT TERM (1-4 years)
+• Determine nutritionally optimal breeding objectives.
+• Screen CGIAR germplasm for high iron, zinc, and beta-carotene
+levels. Initiate crosses of high-yielding adapted germplasm for
+selected crops. Clarify genotype-by-environment interactions
+and cultural and food processing practices, and their effect on
+micronutrient content and bioavailability.
+• Discern the genetics of high micronutrient levels, and identify
+markers available to facilitate the transfer of traits through
+conventional or novel means or both. Undertake in vitro and
+animal studies of the bioavailability of enhanced micronutrients
+in promising lines.
+• Begin bioefficacy studies to determine biofortified crops’ effect
+on micronutrient status of human subjects.
+• Initiate study of trends in the dietary quality of poor people
+and the underlying factors driving these trends.
+• Conduct benefit-cost analysis of plant breeding and of other
+food-based interventions to reduce micronutrient malnutrition.
+The Future HarvestSM centers, located around the
+scientists, and policymakers to help alleviate pover
+resource base. The Future HarvestSM centers are pri
+tions, and regional and international organization
+Agricultural Research (CGIAR).
+Once the genes controlling nutrient levels have
+been identified, marker-assisted selection can
+be used to transfer genes for high content of
+desired micronutrients into new varieties.
+Meanwhile, researchers will begin to measure
+the impacts of micronutrient-rich varieties on
+human nutrition. 
+Winning Acceptance of Biofortified Crops
+A major advantage of biofortification is that this
+strategy does not require a change in behavior
+by farmers or consumers. The crops are
+already widely produced and consumed by poor
+households in the developing world. Changes in
+mineral content will not necessarily alter their
+appearance, taste, texture, or cooking qualities. 
+In cases where scientists can combine high
+micronutrient content with high yield, farmer
+adoption and market success of nutritionally
+improved varieties is virtually guaranteed. In
+fact, research showing that high levels of trace
+minerals in seeds also aid plant nutrition has
+fueled expectations of increased productivity in
+biofortified strains. 
+MEDIUM TERM (5-7 years)
+• Continue bioefficacy studies to determine how improved
+varieties affect the micronutrient status of human subjects in
+test sites in Africa,Asia, and Latin America.
+• Initiate farmer participatory breeding.
+• Adapt high-yielding, conventionally bred, micronutrient-dense
+lines for South Asia, East Africa, Central America, and Brazil.
+• Release new conventionally biofortified varieties to farmers.
+• Identify gene systems with potential for increasing nutritional
+value beyond traditional breeding methods.
+• Produce transgenic lines at experimental level and screen for
+micronutrients.Test for compliance with biosafety regulations.
+• Implement effective social marketing and communications to
+promote nutritionally improved varieties.
+• Begin production and distribution of improved varieties.
+LONG TERM (8-10 years)
+• Scale up production and distribution of improved varieties.
+• Undertake nutritional impact studies to identify factors affecting
+the adoption of biofortified crops, the impact on household
+resources, and the health effects on individuals.
+Through the deployment of just six micronutrient-enhanced
+staple crops the biofortification approach could reach roughly 90
+percent of the population at risk from micronutrient malnutrition
+in the developing world.Achieving these objectives will require a
+one-time investment of about $8 million per major staple crop
+over 10 years.These investments not only will generate returns
+in the billions of dollars, but also will improve the health and lives
+of billions of people.
+AN INTERNATIONAL CONSORTIUM OF COLLABORATIVE PARTNERS
+Collaborating Future HarvestSM Research Centers:
+International Center for Tropical Agriculture (CIAT), International Maize and
+Wheat Improvement Center (CIMMYT), International Potato Center (CIP),
+International Center for Agricultural Research in the Dry Areas (ICARDA),
+International Crops Research Institute for the Semi-Arid Tropics (ICRISAT),
+International Food Policy Research Institute (IFPRI®), International Institute
+of Tropical Agriculture (IITA), International Rice Research Institute (IRRI).
+Partner Collaborating Institutions:
+National agricultural research systems (NARS) in developing countries;
+departments of human nutrition in developing- and developed-country
+universities; NGOs; University of Adelaide; University of Freiburg; Michigan
+State University; Plant, Soil, and Nutrition Laboratory, U.S. Department of
+Agriculture, Agricultural Research Service (USDA-ARS); Childrens' Nutrition
+Research Center, USDA-ARS.
+Research Generously Supported By:
+Asian Development Bank (ADB), Australian Center for International
+Agricultural Research (ACIAR), Danish International Development
+Assistance (DANIDA), Micronutrients Initiative (MI), and the U.S. Agency
+for International Development (USAID).
+e world, conduct research in partnership with farmers,
+rty and increase food security while protecting the natural
+incipally funded through the 58 countries, private founda-
+ns that make up the Consultative Group on International CGIAR
+One way to ensure that farmers will like the
+new varieties is to give them a say in what
+traits are bred into the plants. Experience
+suggests that “participatory plant breeding,” 
+in which scientists take farmers’ perspectives
+and preferences into account during the
+breeding process, can sometimes be more 
+cost-effective than confining breeding to
+research stations. 
+Distributing the New Varieties
+A common problem faced by supplementation
+and fortification programs is the lack of
+delivery systems to get products to the poorest
+people. This constraint is being met through
+seed-based technologies inherent in the biofor-
+tification approach. When households grow
+micronutrient-rich crops, the delivery system
+is built into the existing food
+production and marketing
+process. Little intervention or
+investment is needed once
+farmers have adopted the new
+seed. And micronutrient-
+rich seed can easily be
+saved and shared by
+even the poorest
+households.
+Through their ongoing
+work with seed systems
+and their contributions to
+disaster-response, Future
+Harvest centers have gained valuable experi-
+ence in building and promoting local seed
+distribution systems. These established
+systems offer a natural route for disseminat-
+ing biofortified seed. Local agricultural
+research committees and small farmer seed
+enterprises, in particular, will play a crucial
+role in getting micronutrient-rich varieties into
+the hands of growers. 
+It Makes Sense
+The ultimate solution to eradicating malnutri-
+tion in developing countries, of course, is to
+substantially increase the consumption of
+meat, fish, fruits, legumes, and vegetables
+among the poor. Achieving this will take many
+decades and untold billions of dollars. 
+Meanwhile, biofortification makes sense as
+part of an integrated food systems approach to
+reducing malnutrition. It addresses the root
+causes of micronutrient malnutrition, targets
+the poorest people, uses built-in delivery
+mechanisms, is scientifically feasible and cost-
+effective, and complements other on-
+going methods of dealing with
+micronutrient deficiencies.
+It is an obvious first
+step in enabling rural
+households to improve
+family health and
+nutrition in sustain-
+able ways.
+Contact: Bonnie McClafferty or Nathan Russell  •  Email: b.mcclafferty@cgiar.org or n.russell@cgiar.org
+Copyright © 2002 International Food Policy Research Institute. All rights reserved. This brief may be reproduced without the express
+permission of but with acknowledgment to the International Food Policy Research Institute.
+A.A. 6713,Cali, Colombia
+Phone: 57-2-445-0000 (direct)
+1-650-833-6625 (via USA)    
+Fax: 57-2-455-0073 (direct)
+1-650-833-6626 (via USA)
+E-mail: ciat@cgiar.org    Web: www.ciat.cgiar.org
+INTERNATIONAL FOOD 
+POLICY RESEARCH INSTITUTE
+sustainable options for ending hunger and poverty
+2033 K Street, NW, Washington, DC 20006-1002 USA
+Phone: 1-202-862-5600    Fax: 1-202-467-4439
+E-mail: ifpri@cgiar.org    Web: www.ifpri.org
+Revised October 2002
+

data/part_2/0288822151.md

@@ -0,0 +1,1309 @@
+# Land management, crop production, and household income in the highlands of Tigray, northern Ethiopia: An econometric analysis
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/223184cf-a07c-4467-8d2b-ca16dfb18d3c/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2006  
+**Rights:** N/A  
+**GARDIAN ID:** 66c601bf47c2e3297ed9c8366ae9836d  
+**DataNODE ID:** 0a67e1fff4a998a16331b1ba21e6b52a  
+**Siever ID:** 641ce93b-de5c-4531-a87e-68ff3a8ee09e  
+**Token Count:** 12830  
+**Page Count:** 0
+
+## Keywords
+
+economic analysis, land management, plant production, socioeconomic environment, econometric models, households, income, highlands, nrm, research, crop production, household income
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Ethiopia
+
+## Description
+
+This paper is divided in sections. The first section deals with empirical model, methods, and hypotheses and looks into methods, data Sources, econometric approach, and predicted Impacts of selected variables. The second one discusses agriculture and land management in the highlands of Tigray with particular emphasis on biophysical and socioeconomic conditions, results of econometric analysis, and direct and indirect effects on production and income. The third section, key findings and implications examines population pressure, access to roads and markets, income strategies, irrigation, agricultural extension and credit, and endowments of physical, human, and social capital. The study is based on econometric analysis of household and plot-level surveys conducted in 100 villages in 50 tabias (the lowest administrative unit in Tigray, usually comprising four or five villages) in the highlands of Tigray during 1999-2000. It builds on a prior study based on tabia- and village-level surveys in the same communities in 1998-99 (Pender et al. 2001), which were used in the empirical work Reported in Chapter 4. This broad sample and the information collected at different levels enable investigation of the impacts of community-level factors such as population density, investments in irrigation and roads, as well as household and plot-level factors such as household wealth, education., education, land tenure, and other factors on land management and the implications for agricultural productivity and land degradation.
+
+## Content
+
+C h a p t e r  5
+Land Management, Crop Production,
+and Household Income in the Highlands
+of Tigray, Northern Ethiopia:
+An Econometric Analysis
+John Pender and Berhanu Gebremedhin
+L ow agricultural productivity, poverty, and land degradation are critical and
+closely related problems in the Ethiopian highlands. These problems are
+particularly severe in the highlands of Tigray in northern Ethiopia. Cereal
+yields average less than 1 ton per hectare in this region, and over half of the area of
+the Tigray highlands has been characterized as severely degraded, according to one
+study (Hurni 1988).1 The average farm size is only 1 hectare, and most households
+subsist on incomes of less than $1 per day (based on results of the survey discussed
+in this chapter).
+In recognition of these problems, the regional government of Tigray has under-
+taken a massive program of investment and resource conservation since the fall of
+the Derg regime in 1991. The regional development strategy of conservation-based
+agricultural development–led industrialization has focused on promoting conser-
+vation of natural resources and improvement of agricultural productivity and wel-
+fare through a broad program of rehabilitation of natural resources, investment in
+infrastructure, agricultural extension, education, and other services. These efforts
+built on the philosophy of self-reliance and strategies of local democratic participa-
+tion and community mobilization for local conservation and development efforts
+that were initiated during the struggle of the Tigray People’s Liberation Front (TPLF)
+against the Derg regime (Young 1996; Hagos, Pender, and Gebreselassie 1999; Hailu
+and Haile 2001) and have been given high priority as a result of the recurrent famines
+in the region.
+Empirical evidence of the impacts of these policies and identification of spe-
+cific areas where problems need to be addressed are needed. Addressing this infor-
+mation need is the primary objective of this study.
+This study is based on econometric analysis of household and plot-level sur-
+veys conducted in 100 villages in 50 tabias (the lowest administrative unit in Tigray,
+usually comprising four or five villages) in the highlands of Tigray during 1999–
+2000.2 It builds on a prior study based on tabia- and village-level surveys in the
+same communities in 1998–99 (Pender et al. 2001a), which were used in the empir-
+ical work reported in Chapter 4. This broad sample and the information collected
+at different levels enable investigation of the impacts of community-level factors
+such as population density, investments in irrigation and roads, as well as house-
+hold and plot-level factors such as household wealth, education, land tenure, and
+other factors on land management and the implications for agricultural productivity
+and land degradation.
+Empirical Model, Methods, and Hypotheses
+Empirical Model
+The key outcomes of interest in this study are agricultural production and per
+capita income.3 We consider the proximate causes of each of these, including house-
+hold choices regarding income strategies, land management, and other decisions,
+and the underlying determinants of these choices.
+Crop production. For agricultural production, we focus on the value of crop
+production per hectare. We assume that the value of crop production by household
+h on plot p ( yhp) is determined by the amount of inputs (labor, ox power, fertilizer,
+seeds) used (INhp);4 the land management practices (manure or compost, burning,
+contour plowing, reduced tillage, intercropping) used (LMhp); the “natural capital”
+of the plot (NChp) (biophysical characteristics and presence of land investments);
+the tenure characteristics of the plot (Thp) (how plot was acquired, i.e., whether
+allocated in prior land distribution, inherited, leased [sharecropped in almost all
+cases], received as gift, or borrowed); the household’s endowments of physical cap-
+ital (PCh) (land, livestock, radio [reflecting access to information as well as wealth],
+human capital (HCh) (education, age, and gender of household head, size of house-
+hold), financial capital (use of credit and accumulation of savings), and “social cap-
+ital” (SCh) (assets in form of relationships, indicated by participation in programs
+108 JOHN PENDER AND BERHANU GEBREMEDHIN
+and organizations); the household’s income strategy (ISh) (primary and secondary
+income sources); village-level factors that determine local comparative advantages
+(Xv) (agro-ecological conditions, access to markets and infrastructure, and popula-
+tion density); and random factors (uyhp):
+yhp = y(INhp, LMhp, NChp, Thp, PCh, HCh, FCh, SCh, ISh, Xv, uyhp) (5.1)
+Equation (5.1) is not a production function but rather a gross revenue func-
+tion. As such, it aggregates the value of production per hectare of different crops
+and depends on the farm-level prices of the crops produced.5 Because different
+crops are produced by different households in different locations in Ethiopia, we
+do not explicitly include crop prices as determinants of crop revenue per hectare;
+this would result in many missing observations for farm level prices. Instead, we
+assume that farm-level prices are determined by village-level factors determining
+local supply, demand, and transportation costs of commodities (Xv) and household-
+level factors affecting households’ transactions costs and marketing abilities (HCh,
+FCh, SCh, ISh). Land tenure (Thp) can affect productivity, for example, by affect-
+ing incentives to apply labor effort and other inputs to sharecropped land compared
+to owner-operated land (Shaban 1987).6 Household endowments of physical cap-
+ital (PCh) can also affect crop production if there are imperfect factor markets. For
+example, ownership of oxen may influence crop production even after controlling
+for the amount of ox labor used because owners of oxen have preferential access to
+ox power at times of peak demand. In addition, agro-ecological conditions, house-
+holds’ human and social capital, and their farming experience may also influence
+agricultural productivity, even if these factors have no impact on prices.
+Input use and land management. In equation (5.1), input use and land man-
+agement are choices in the current year, determined by the natural capital and tenure
+of the plot; the household’s endowments of physical, human, social, and finan-
+cial capital at the beginning of the year; the household’s income strategy; agro-
+ecological conditions, access to markets and infrastructure, and population density
+(Xv); and unobservable factors (uINhp and uLMhp):
+INhp = IN(NChp, Thp, PCh, HCh, SCh, FCh, ISh, Xv, uINhp) (5.2)
+LMhp = LM(NChp, Thp, PCh, HCh, SCh, FCh, ISh, Xv, uLMhp) (5.3)
+Most of the determinant factors in equations (5.2) and (5.3) are either exoge-
+nous to the household (e.g., Xv) or state variables that are predetermined at the
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 109
+beginning of each year (e.g., NChp, PCh, HCh, and FCh). Income strategies may
+change from year to year but are usually slow to change because of irreversible
+investments in human and social capital (required for such changes as development
+of new skills and investments in developing market connections are needed to
+shift from subsistence to cash crop production).7 Thus, we assume that households’
+current income strategies are determined by fixed or slowly changing factors and
+therefore are predetermined in equations (5.1)–(5.3).
+Participation in programs and organizations (SCh) and use of credit (FCh)
+may be partly or wholly determined in the current year and hence potentially
+affected by current decisions about input use and land management. In the econo-
+metric analysis (discussed in more detail below), we use predicted participation in
+programs and organizations and predicted use of credit as instrumental variables to
+address this potential endogeneity concern. We predict participation in programs
+and organizations and use of credit using village-level factors affecting local com-
+parative advantages and placement of programs (Xv), household endowments of
+land (NCh), and human capital (HCh).8 For example, membership in an agricul-
+tural cadre requires literacy and some experience in modern agricultural practices,
+access to credit may depend on the household’s endowment of land, and placement
+of programs may depend on local comparative advantages.
+SCh = SC(HCh, NCh, Xv) (5.4)
+FCh = FC(HCh, NCh, Xv) (5.5)
+The determinants of value of crop production will be estimated using the struc-
+tural model (accounting for potential endogeneity bias, as discussed below) repre-
+sented by equation (5.1) as well as in reduced form. The reduced form is obtained
+by substituting equations (5.2)–(5.5) into equation (5.1):
+yhp = y ′(NChp, Thp, PCh, HCh, ISh, Xv, u′yhp) (5.6)
+Per capita income. We assume that household per capita income is determined
+by the same endowments that determine land management and input use deci-
+sions, except that plot-level factors are aggregated to the household level:9
+Ih = I(NCh, Th, PCh, HCh, SCh, FCh, ISh, Xv, uIh) (5.7)
+Equation (5.7) is a reduced-form equation because we do not include endoge-
+nous decisions that affect income such as input use and land management practices,
+110 JOHN PENDER AND BERHANU GEBREMEDHIN
+as in equation (5.1). It is not a fully reduced form, however, because it includes FCh
+and SCh, which are potentially endogenous variables as noted above. These vari-
+ables are included in this specification because we want to investigate the impacts
+of these factors on household income. Substituting equations (5.4) and (5.5) into
+equation (5.6), we also can derive the fully reduced form version of equation (5.7):
+Ih = I ′(NCh, Th, PCh, HCh, ISh, Xv, u′Ih) (5.8)
+Equations (5.1)–(5.8) are the basis for the econometric estimations.
+Methods
+Data Sources
+This study is based on a survey of 500 households in 100 villages in 50 commu-
+nities (tabias) in the highlands of Tigray conducted in 1999 and 2000. Tabias less
+than 1,500 meters above sea level elevation were excluded from the sample frame.
+A random sample of tabias was used, stratified by distance to the woreda (district)
+town and whether an irrigation project was present in the tabia. Two villages were
+randomly selected within each sample tabia, and five households were randomly
+selected from each village. In addition to household-level information, information
+was collected on all plots owned or operated by the respondent households. The
+survey data were supplemented by data from tabia and village surveys on prices and
+other factors, secondary data from the 1994 Population Census on the population
+of each tabia, and maps of the boundaries of each tabia (used to calculate popula-
+tion density).
+Econometric Approach
+The dependent variables analyzed in this study include the amounts of inputs used
+on each plot in 1998 (labor, draft animal power, and seeds), adoption of the most
+common crop and land management practices in 1998 (use of fertilizer, improved
+seeds, manure or compost, burning to clear the plot, contour plowing, reduced
+tillage, and intercropping or mixed cropping), the value of crop production on the
+plot, per capita income of the household, and whether the household head partici-
+pated in the extension program, used formal or informal credit, or participated as a
+member in certain community organizations (tabia council, village council, mar-
+keting cooperative, or agricultural cadre).10 The econometric model used depends
+on the nature of the dependent variable. For use of labor, ox-power, and seeds, the
+value of crop production, and per capita income, least-squares regressions were used.
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 111
+For explaining whether various land management practices were used, whether
+the household participated in agricultural extension, various organizations, or used
+credit, probit models were used.
+The explanatory variables include indicators of agricultural potential (average
+rainfall and altitude); population density; access to roads and markets (walking time
+to nearest all-weather road and to the woreda [district] town); wealth (land and
+livestock owned); human capital (gender, age, and education of household head,
+and household size, a proxy for family labor endowment); income strategy (pri-
+mary and secondary income source); ownership of a radio (a determinant of access
+to information); availability of cash savings; household social capital (membership in
+various organizations); use of formal or informal credit; contact of the household
+with the agricultural extension program; and various plot-level factors, including
+land use of cultivated plots11 (whether homestead, rain-fed, or irrigated), land
+tenure (how plot acquired), presence of investments on the plot (stone terrace, soil
+bund, fence), and several indicators of different aspects of quality of the plot (size
+of plot, distance of the plot to the farmer’s residence, plot slope, position on slope,
+soil depth, color, texture, and presence of gullies).
+In the crop production regression and the input use regressions, we used a log-
+arithmic Cobb-Douglas specification. We included interaction terms between fer-
+tilizer use and presence of a stone terrace, a soil bund, or irrigation to test whether
+there is complementarity between fertilizer use and these investments, because
+of the expected impact of these investments on soil moisture availability. Because
+inputs and land management practices are endogenous choice variables in the crop
+production regression, and participation in programs and organizations and use of
+credit may be endogenous, we use instrumental variables (IV) estimation, using
+instruments for input use, land management practices, participation in programs
+and organizations, and use of credit. We also estimate the full model using ordinary
+least squares (OLS) and test for endogeneity bias using a Hausman (1978) test.
+Predicted values of the endogenous discrete explanatory variables from probit regres-
+sions (equations [5.2]–[5.5]) were used as instrumental variables. Exclusion restric-
+tions for other instrumental variables excluded from the regression were based on
+joint statistical Wald tests (only variables that were jointly statistically insignificant
+at the 20 percent level or greater in both OLS and IV models were excluded). We
+also estimate the reduced form (RF) specified in equation (5.6) and report the
+robustness of our results across specifications.12 The reduced form gives an indi-
+cation of the total effect of underlying explanatory variables on crop production,
+allowing for change in input use, land management practices, participation in pro-
+grams and organizations, and use of credit. We also investigate indirect effects using
+simulations as discussed below.
+112 JOHN PENDER AND BERHANU GEBREMEDHIN
+We also use IV estimation in the input use and income per capita regressions
+to account for possible endogeneity of participation in programs and organizations
+and use of credit, as noted above, and report the robustness of our results. In the
+land management (probit) regressions, IV estimation could not be used.13 We used
+predicted values of the potentially endogenous variables as explanatory variables.
+Because of space limitations, we do not report the results of the probit regressions
+used to predict participation in programs and organizations and use of credit.14 As
+for the value of crop production, we also estimate the determinants of per capita
+income in reduced form (equation [5.8]).
+We tested the regression specifications for problems of multicollinearity but
+found this not to be a serious problem in any of the specifications.15 Various regres-
+sion diagnostics were used to identify outliers and influential observations and to find
+and correct data errors. All models used the Huber-White estimator of the covari-
+ance matrix, which is robust to heteroskedasticity, accounted for clustering of the
+data by household (i.e., estimated standard errors are robust to nonindependence
+of observations from the same household), and accounted for the stratification and
+probability of sampling each village and household in the sample frame (StataCorp
+2003). The results are thus robust to potential problems of heteroskedasticity and
+nonindependence and are statistically representative of the highlands of Tigray.
+Predicted Impacts of Selected Variables
+In a complex structural model, such as estimated in this study, a change in a partic-
+ular causal factor may have impacts on outcomes of interest through many different
+channels, given the many intervening response variables that may be affected. For
+example, improvements in education may affect agricultural productivity directly
+by affecting farmers’ ability to use technologies that affect productivity. But it may
+also influence productivity indirectly, for example, by affecting households’ choice
+of land management practices or participation in extension. Such indirect effects
+must be accounted for if we are to understand the full effect of causal factors on
+agricultural production and income.
+In studies in which the empirical relationships are linear and involve continu-
+ous variables, the predicted total impacts of changes in explanatory variables can
+be determined using total differentiation of the system (Fan, Hazell, and Thorat
+1999). In this study, this approach is not practical because of the nonlinear limited
+dependent variable models estimated. To address this issue, we simulate the pre-
+dicted responses implied by the estimated econometric relationships under alterna-
+tive assumptions about the values of the explanatory variables for the entire sample
+and carry these predicted responses forward to determine their influence on subse-
+quent relationships in the system.16
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 113
+Agriculture and Land Management in the
+Highlands of Tigray
+Biophysical and Socioeconomic Conditions
+The average annual rainfall is generally less than 1,000 millimeters in the semiarid
+highlands of Tigray and averages about 650 millimeters for all sample households
+(Table 5.1). Altitude in the highlands averages 2,174 meters above sea level and
+ranges from 1,500 to well over 3,000 meters above sea level.
+The rural population is growing rapidly at more than 3 percent per annum,
+and population pressure is high in the Tigray highlands, with average population
+density of 137 persons per square kilometer in the sample communities. As a result,
+the average farm size in the Tigray highlands is only 1 hectare. Land is relatively
+equally distributed in the Ethiopian highlands because of the radical land reform
+program begun in 1975 by the Derg regime (Rahmato 1984; Bruce, Hoben, and
+Rahmato 1994; Abate 1995; Amare 1995) and the continued prohibition of land
+sales and mortgages under the current government of the Ethiopian Peoples Revo-
+lutionary Democratic Front (EPRDF), a policy enshrined in the new Ethiopian
+constitution.17 Hence, the maximum farm size in our sample was only about 4
+hectares. Almost all households own livestock, with cattle most important (in value
+terms), followed by sheep and goats. The average household size is 5.4.
+Access to roads, transportation, and other services has improved substantially
+in Tigray since 1991. Nevertheless, most households are still far from roads, trans-
+portation services, and markets. In 1998, the average walking time (the dominant
+mode of transport) to the nearest all-weather road was more than 2 hours, while
+walking time to the nearest woreda town averaged 3.5 hours.
+Education has improved dramatically in Tigray since 1991 as a result of the
+greatly increased number of schools and literacy campaigns. Still, only about 15
+percent of household heads had formal schooling by 1998 (only 6 percent had
+more than 2 years), and 7 percent had participated in a literacy campaign.
+The availability of agricultural extension and credit services has also greatly
+expanded. Nearly three-fifths of households had access to credit from formal sources
+in 1998. Development agents of the extension service were involved in virtually
+every community, though only about 11 percent of sample households had direct
+contact with an extension agent.
+About 6 percent of households have members in a marketing cooperative that
+is involved in marketing agricultural (mainly crop) outputs and providing inputs.
+About 2 percent of households have a member in an agricultural cadre that focuses
+on improving agricultural production. A similar small proportion of households
+are involved as community leaders in the local tabia or village council.
+114 JOHN PENDER AND BERHANU GEBREMEDHIN
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 115
+Table 5.1 Descriptive statistics of households in Tigray highlands survey, 1998
+Variable Number of observations Mean (standard error)
+Annual rainfall (millimeters) 480 652 (5)
+Altitude (meters above sea level) 500 2,174 (22)
+Population density (persons/km2) 490 136.8 (4.4)
+Female head of household 500 21.8 (2.2)
+Age of household head (years) 500 46.0 (0.7)
+Household size (number) 500 5.4 (0.1)
+Education of household head (percentage of households)
+1–2 years 500 9.2 (1.6)
+3+ years 500 6.1 (1.3)
+Literacy campaign 500 7.3 (1.5)
+Walking time to nearest (hours)
+All-weather road 496 2.33 (0.l3)
+Woreda town 497 3.54 (0.17)
+Ownership of assets
+Land (hectares) 477 0.98 (0.04)
+Oxen (number) 496 1.12 (0.05)
+Other cattle (number) 496 2.71 (0.16)
+Small ruminants (number) 496 4.95 (0.54)
+Pack animals (number) 496 0.71 (0.06)
+Radio (percentage owning) 500 13.2 (1.9)
+Cash savings (percentage having) 500 54.4 (2.7)
+Secondary income source (percentage of households)
+No secondary source 496 20.6 (2.2)
+Cereals 496 2.9 (0.9)
+Perishable annuals 496 3.3 (1.1)
+Perennial crops 496 3.6 (1.2)
+Cattle 496 35.0 (0.9)
+Small ruminants 496 2.2 (0.9)
+Beekeeping 496 0.4 (0.3)
+Food-for-work 496 6.1 (1.1)
+Salary employment 496 1.6 (0.6)
+Farm employment 496 1.4 (0.6)
+Trading 496 6.5 (1.3)
+Food/other assistance 496 6.5 (1.3)
+Other nonfarm 496 10.0 (1.7)
+Membership in organizations (percentage of households)
+Tabia council 500 1.6 (0.7)
+Village council 500 2.0 (0.9)
+Marketing cooperative 500 6.4 (1.2)
+Agricultural cadre 500 1.6 (0.7)
+Use of credit (percentage of households)
+Formal credit 500 57.7 (2.7)
+Informal credit 500 18.9 (2.1)
+Contact with extension (percentage of households) 500 11.4 (1.7)
+Household income (birr) 477 1,924 (120)
+Per capita income (birr) 477 388 (22)
+Poverty is severe in the highlands of Tigray. Average per capita income among
+the sample households was only 388 EB in 1998 (less than $60).18 Per capita income
+is even lower among female-headed households and larger households.
+Income strategies. For at least 2,000 years, the predominant farming system and
+income strategy in the northern Ethiopian highlands has been cereal cultivation sup-
+ported by ox-plow tillage (McCann 1995). Not surprisingly, the dominant source
+of income in the highlands of Tigray is still cereal crop production, which is the
+primary source of income for 97 percent of sample households. Different income
+strategies are thus distinguished more by differences in the secondary source of
+income. One-fifth of households have no secondary source of income; cereal
+crop production is their sole income source. In about one-third of households, the
+secondary source of income is cattle production. Nonfarm activities—including
+trading activities, food-for-work, salary employment, and other nonfarm activities—
+are the secondary income source of about one-fourth of households. Other less
+common secondary sources of income include production of perishable annual or
+perennial crops, small ruminants, beekeeping, farm wage labor, and food aid and
+other forms of assistance.19
+Land management. Preharvest labor use in crop production averaged 86 per-
+son-days per hectare, most of this for plowing, planting, and weeding (Table 5.2).
+Draft animal use (mainly oxen) averaged 25 animal days per hectare. Seed use
+averages 118 kilograms per hectare. Fertilizer was used on 27 percent of plots, and
+manure or compost on about 20 percent of plots in 1998. Improved seeds were
+used on only about 2 percent of plots.
+The most common investments in land improvement in Tigray are stone ter-
+races and soil bunds. Stone terraces existed on nearly 37 percent of cultivated plots
+in 1998, while soil bunds existed on about 8 percent. These investments have been
+widely promoted in Tigray during the past few decades through food-for-work pro-
+grams and community labor mass mobilization campaigns,20 as well as resulting
+from farmers’ own private investment initiatives (Hagos, Pender, and Gebreselassie
+1999; Kinfe 2002; Hagos and Holden 2005). Although public conservation invest-
+ments are most common, private soil and water conservation investments are also
+relatively common, and the intensity of such investment is greater where private
+investment is involved (Hagos and Holden 2005). Other less common investments
+included constructing a fence or planting a live fence, and planting trees.
+Several land management practices are commonly used in Tigray, including con-
+tour plowing, burning to prepare fields, reduced tillage, and intercropping or mixed
+cropping. Contour plowing is very common, practiced on nearly 90 percent of plots.
+116 JOHN PENDER AND BERHANU GEBREMEDHIN
+Table 5.2 Descriptive statistics of plots in Tigray highlands survey, 1998
+Variable Number of observations Mean (standard error)
+Land investments (percentage of plots)
+Stone terrace 1,785 36.5 (1.9)
+Soil bund 1,785 8.3 (1.0)
+Constructed fence 1,785 3.8 (0.7)
+Live fence or barrier 1,785 3.1 (0.6)
+Land use (percentage of plots)
+Homestead 1,785 19.7 (1.0)
+Rainfed cultivated 1,785 73.7 (1.1)
+Irrigated cultivated 1,785 6.6 (1.0)
+Use of inputs
+Labor (person-days/hectare) 1,785 86.4 (9.5)
+Oxen power (animal-days/hectare) 1,785 25.3 (1.9)
+Seed (kilogram/hectare) 1,785 118.1 (7.7)
+Improved seed (percentage of plots) 1,785 2.4 (0.5)
+Fertilizer (percentage of plots) 1,785 27.0 (1.6)
+Use of land management practices (percentage of plots)
+Burning to prepare field 1,785 11.0 (1.3)
+Contour plowing 1,785 87.5 (1.6)
+Reduced tillage 1,785 12.3 (1.2)
+Intercropping/mixed cropping 1,785 11.4 (1.2)
+Manure or compost 1,785 22.8 (1.3)
+Value of crop production (EB/hectare) 1,593 1816 (176)
+How plot acquired (percentage of plots)
+Leased in 1,785 13.7 (1.3)
+Allocated by tabia 1,785 84.0 (1.4)
+Inherited 1,785 1.4 (0.5)
+Received as gift/other 1,785 0.9 (0.3)
+Plot area (hectares) 1,508 0.30 (0.01)
+Walking time to residence (hours) 1,780 0.39 (0.02)
+Plot slope (percentage of plots)
+Flat 1,779 57.8 (2.0)
+Gentle 1,779 32.3 (2.0)
+Steep 1,779 9.9 (1.4)
+Position on slope (percentage of plots)
+Top 1,785 13.1 (1.4)
+Middle 1,785 21.1 (1.6)
+Bottom 1,785 28.0 (2.2)
+Not on slope 1,785 37.9 (2.2)
+Soil depth (percentage of plots)
+Deep 1,767 21.9 (1.4)
+Medium 1,767 38.1 (1.7)
+Shallow 1,767 40.0 (1.8)
+Soil color (percentage of plots)
+Black 1,767 27.8 (2.2)
+Brown 1,767 12.4 (1.1)
+Grey 1,767 22.9 (1.8)
+Red 1,767 36.8 (2.0)
+Soil texture (percentage of plots)
+Clay 1,767 27.6 (2.2)
+Loam 1,767 31.7 (2.0)
+Sand 1,767 29.7 (1.8)
+Silt 1,767 11.1 (1.3)
+Gullies on plot (percentage of plots) 1,785 5.1 (0.7)
+Crop production. The average estimated value of crop production on surveyed
+plots was 1,815 EB per hectare in 1998.21 The average value of production was
+higher on plots where inorganic fertilizer was applied (2,184 EB/hectare) than
+where no fertilizer was applied (1,684 EB/hectare). The average value of produc-
+tion was substantially higher on irrigated plots (6,726 EB/hectare) than on non-
+irrigated homestead plots (1,838 EB/hectare) or rain-fed field plots (1,428 EB/
+hectare). These figures are the total value of production during the year, including
+multiple crops, which is why the irrigated production value was so much higher.
+These differences may also result from other factors besides fertilizer use or irriga-
+tion (such as differences in cropland quality); multivariate analysis is needed to
+control for such factors.
+Results of Econometric Analysis
+Input use and land management practices. Population pressure is associated with higher
+use of labor and animal draft power per hectare and with a higher probability of use
+of fertilizer and intercropping (Table 5.3). We also find that households that own
+more land are less likely to apply fertilizer to a particular plot and more likely to use
+118 JOHN PENDER AND BERHANU GEBREMEDHIN
+Table 5.3 Determinants of input use and land management practices in crop production, 1998
+Labor Oxen
+ln(person ln(animal- Seeds
+Variablea days/ha)b days/ha)b ln(kg/ha)b Fertilizerc
+ln(Population density/km2) 0.122**+ 0.154***+++ 0.079 0.076**++
+Female head of household –0.415***––– –0.207***–– 0.241** –0.050
+ln(Age of household head) (years) 0.224**++ –0.045 0.216 0.071
+ln(Household size) (number) 0.123* 0.061 0.152* –0.019
+Education of household head
+3+ years 0.319***++ –0.001 0.201 –0.009
+Literacy campaign 0.047 0.119 –0.005 –0.012
+Walking time to (hours)
+All-weather road –0.081***––– –0.016 –0.001 –0.048***–––
+Woreda town 0.016 –0.044***––– –0.009 0.006
+Plot from residence –0.125 –0.026 0.077 –0.095**––
+Ownership of assets
+Land (tsimad) 0.015 –0.005 –0.025 –0.059**–
+Oxen (number) 0.087**+ 0.071**++ 0.039 0.013
+Other cattle (number) 0.012 0.007 0.022*+++ 0.011**++
+Small ruminants (number) –0.008**–– –0.006***–– –0.014***––– –0.002
+Pack animals (number) –0.004 –0.009 0.026 –0.015––
+Radio (yes/no) 0.028 –0.127**–– 0.096 0.052+
+Cash savings (yes/no) 0.080+ –0.008 0.125 0.058*+++
+reduced tillage. These findings support the Boserup (1965) hypothesis that popu-
+lation pressure causes farmers to intensify use of labor and other inputs and to
+adopt more intensive land management practices, and are consistent with the find-
+ings of Kruseman, Ruben, and Tesfay in Chapter 4.22
+Access to roads, markets, and farmers’ fields also affects the intensity of land
+management. Households closer to an all-weather road use more labor per hectare
+and are more likely to use fertilizer, burning, and contour plowing, also consistent
+with findings in Chapter 4.23 Households closer to a woreda town use more draft
+animal power per hectare but are less likely to contour plow. Farmers are more
+likely to use fertilizer, improved seeds, and manure or compost on plots closer to
+their residence, probably because of the difficulty of transporting inputs to distant
+plots. This is consistent with the findings of Gebremedhin and Swinton (2003a),
+who found that farmers in central Tigray were more likely to use stone terraces on
+plots nearer to the homestead, in the sense that more intensive land management is
+used on plots closer to the residence.
+Income strategies affect land management. Households for which cereals are
+a secondary income source use less ox power per hectare and are more likely to use
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 119
+Intercropping/
+Improved Manuring/ Burning to Contour Reduced mixed
+seedc composting prepare fieldc plowingc tillagec croppingc
+0.0002 0.0491* –0.0025 –0.0048 –0.0160 0.0605***+++
+–0.0018 –0.0871***––– 0.0245 –0.1112*** 0.0018 –0.0199
+0.0024*+++ –0.0464 0.0347*+++ 0.0466*++ –0.0325 0.0182+++
+–0.0006 –0.0138 –0.0043++ 0.0049 –0.0472* 0.0199++
+0.0014+++ 0.0171 0.0220+++ 0.0278++ –0.0126 –0.0148++
+0.0020 0.0489 0.0442+++ –0.0075 –0.0118 –0.0063
+0.0009 –0.0097 0.0156***––– –0.0143**– 0.0069 0.0049
+–0.0012* 0.0061 0.0054+++ 0.0114**++ –0.0036 –0.0110**
+0.0082***––– –0.3178***––– 0.0106 –0.0317*– 0.0381 –0.0280–
+–0.0009 –0.0136 0.0072 0.0276** 0.0551***++ –0.0082
+0.0008+ 0.0388***++ 0.0097 0.0378***+++ –0.0359***–– 0.0153*+
+0.0000 0.0043 0.0046**+ 0.0000 0.0021 0.0024
+–0.0001 0.0017 –0.0028***––– 0.0009 0.0018 –0.0019**–
+–0.0014***––– –0.0143 –0.0056 0.0064 0.0026 –0.0195***––
+–0.0014 0.0051 –0.0150 –0.0030 0.0402 –0.0362*––
+0.0003 –0.0659**–– 0.0058 –0.0231 –0.0063 –0.0050
+(continued )
+120 JOHN PENDER AND BERHANU GEBREMEDHIN
+Table 5.3 (continued)
+Labor Oxen
+ln(person ln(animal- Seeds
+Variablea days/ha)b days/ha)b ln(kg/ha)b Fertilizerc
+Secondary income source
+Cereals –0.112 –0.404**–– 0.003 –0.009
+Perishable annuals 0.534** –0.108 0.004 –0.012
+Perennial crops 0.247*+ 0.039 0.001 –0.009
+Cattle –0.188**–– –0.211***–– 0.145 –0.012
+Small ruminants/beekeeping –0.272*– –0.215 0.163 –0.009
+Food-for-work/farm work –0.438***––– –0.368***––– 0.248* –0.012
+Salary employment 0.129 –0.067 0.192 –0.009
+Trading 0.019 –0.119 –0.189 –0.012*
+Food/other assistance –0.310* –0.351***––– 0.321* –0.009
+Other nonfarm 0.006 –0.099 0.083 –0.012
+Contact with extension –0.095 –0.120*– –0.061 –0.046
+Membership in organizations
+Tabia council –0.041 –0.036 –0.263 –0.057
+Village council 0.644***+++ 0.178 0.243 0.119
+Marketing cooperative –0.111 0.007 0.250*+ 0.013
+Agricultural cadre –0.301** 0.079 –0.778*** 0.191
+Use of credit
+Formal credit 0.023 –0.006 0.179** 0.191***
+Informal credit 0.048 –0.064 –0.066 0.038
+Land use (cf. rain-fed plot)
+Homestead plot 0.426***+++ 0.160***+++ 0.236***+++ 0.006
+Irrigated plot 0.875***+++ 0.308**++ –0.025 0.160*+
+Initial investment on plot
+Stone terrace 0.023 –0.028 0.068 0.091***+++
+Soil bund 0.064 0.090++ 0.024 0.052
+Fence (live or constructed) 0.367***+++ 0.002 0.053 0.016
+Intercept 7.215***+++ 6.652***+++ 3.394 NR
+Number of observations 1,402 1,353 1,435 1,607
+Mean of dependent variable 3.932 3.184 4.229 0.2698
+Mean predicted dependent variable 3.932 3.184 4.229 0.2685
+R2 or pseudo-R2 0.5314 0.3357 0.4913 0.2125
+Note: NR means that the intercept is not reported by the Stata procedure showing marginal effects in probit models.
+*, **, *** mean coefficient is statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively.
++, ++, +++ and –, – –, – – – mean coefficient is positive (negative) and statistically significant at 10 percent, 5 percent, and 
+1 percent levels, respectively in IV regressions and probit models using predicted values of participation in extension, credit,
+and organizations.
+aCoefficients of biophysical variables (annual rainfall, altitude, plot slope, position on slope, soil depth, soil color, soil texture,
+and presence of gullies), plot area, and how plot acquired not reported to save space. Full results available upon request.
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 121
+Intercropping/
+Improved Manuring/ Burning to Contour Reduced mixed
+seedc composting prepare fieldc plowingc tillagec croppingc
+–0.0005 –0.1002 –0.0192 –0.0439 0.1922**++ –0.0356*
+0.1522***+++ 0.0180 0.0123 –0.1398* 0.1838**++ 0.0631
+0.0592***+++ –0.0790 –0.0377*–– –0.0248 –0.0255 d
+0.0044*+ –0.0092 –0.0681***–– –0.0367 0.0295 –0.0419**
+d –0.0935**–– 0.0271 0.0578 0.0501 –0.0493***–––
+0.0163**+ –0.0520 –0.0337*– –0.0711* 0.0715++ 0.0015
+0.0041 –0.0981*– –0.0291 –0.0043 –0.0445 –0.0112
+0.0079 –0.0193 –0.0277 0.0245 0.1383**+++ 0.0025
+0.0087+ –0.0852**–– –0.0448**––– –0.0561 0.0407 –0.0430**––
+0.0088 –0.0667 –0.0142 –0.0391 –0.0011 0.0238
+0.0013––– –0.0207 –0.0190––– 0.0171– –0.0039–– 0.0185–
+d –0.0713– 0.0349 e 0.0312 d
+0.2415***++ 0.2785***+++ d e –0.0476 0.2535**+
+0.0001 0.0174 –0.0326*–– –0.0110 0.0389 0.0189
+–0.0017* –0.0755– 0.0548 –0.0036 –0.0265 0.0007––
+0.0024**+ 0.0060 0.0183– 0.0305* 0.0001 –0.0249
+–0.0011+++ –0.0304 0.0238++ 0.0143+ 0.0292+ –0.0224+
+–0.0028***––– 0.4402***+++ 0.0092 0.0365*+ 0.0371 0.0228
+0.0156**++ 0.1125 0.0380 –0.0171 0.125***+++ –0.0382
+–0.0004 0.0274 0.0137 0.0361**++ 0.0193 0.0068
+–0.0015– –0.0188 0.0773***+++ 0.0387 0.0196 0.0154
+0.0020 0.2783***+++ 0.0096 0.0375 –0.0259 0.0015
+NR NR NR NR NR NR
+1,528 1,607 1,559 1,524 1,588 1,528
+0.0236 0.2429 0.1078 0.8803 0.1181 0.1176
+0.0233 0.2437 0.1078 0.8674 0.1182 0.1171
+0.3512 0.4241 0.2717 0.2951 0.1748 0.2747
+bLeast squares regression. Coefficients and standard errors adjusted for sampling weights, clustering, and stratification.
+Hausman test failed to reject OLS model in all cases (P = 1.000).
+cProbit regression. Reported coefficients represent effect of a unit change in explanatory variable on probability of use at the
+mean of the explanatory variables.
+dNo positive values of dependent variable for positive values of the explanatory variable. Observations with positive values
+of the explanatory variable dropped from the regression.
+eOnly positive values of dependent variable for positive values of the explanatory variable. Observations with positive values
+of the explanatory variable dropped from the regression.
+reduced tillage. Producers of perishable annuals and perennial crops are more likely
+to use improved seed than cereals-only producers. Producers of perishable annuals
+also are more likely to use reduced tillage. Cattle producers use less labor and draft
+power in crop production than cereals-only farmers and are less likely to use burn-
+ing, suggesting that cattle producers are less focused on intensive crop production
+than cereals-only producers. Similarly, small ruminant producers are less likely to
+apply manure or compost or to use intercropping. Households dependent on food-
+for-work or farm employment use less labor and draft power than cereals-only pro-
+ducers but are more likely to use improved seeds. Households involved in trading are
+more likely to use reduced tillage, probably because of labor and capital constraints.
+Households dependent on food aid or other assistance use less draft power and are
+less likely to apply manure or compost, to use burning, or to use intercropping.
+Such households apparently lack the ability to farm as intensively as others.
+As expected, irrigation increases use of labor, ox power, improved seeds, and
+fertilizer (impact on fertilizer weakly significant at the 10 percent level) because of
+the production of multiple crops per year.24 Irrigation also promotes reduced tillage.
+Fertilizer use and contour plowing are more likely on plots with a stone terrace,
+suggesting complementarity of such soil and water conservation investments with
+use of inputs and contour plowing. Labor use and use of manure and compost are
+greater on plots that have a fence, suggesting that fences help to promote labor-
+intensive practices. Burning is more common on plots with soil bunds; perhaps
+such bunds contribute to problems with weeds (Herweg 1993b).
+Not surprisingly, use of formal-sector credit is strongly associated with greater
+use of fertilizer and improved seeds. This is because this credit is used primarily to
+purchase such crop inputs.25 Informal credit is not significantly associated with use
+of any crop inputs or land management practices, perhaps because informal credit
+is used for other purposes than agricultural production. Surprisingly, contact with
+the extension program is not significantly associated with use of inputs or land
+management practices. It appears that it is not the extension program per se that is
+leading to significant increases in use of fertilizer in Tigray but, rather, availability
+of credit and other factors.
+Ownership of livestock and other assets affects land management. Households
+that own more oxen use more labor and ox draft power per hectare, suggesting
+that oxen and labor are complements and that imperfect markets for hiring oxen
+constrain households that own fewer oxen. Greater ox ownership also increases use
+of manure and compost and contour plowing but decreases use of reduced tillage.
+Greater ownership of other types of cattle is associated with greater use of seeds and
+fertilizer, probably because income generated from cattle products helps farmers
+afford to buy these inputs. Consistent with this explanation, households with cash
+122 JOHN PENDER AND BERHANU GEBREMEDHIN
+savings are more likely to use fertilizer and less likely to use manure and compost,
+suggesting that cash constraints limit use of fertilizer. By contrast, greater owner-
+ship of small ruminants is associated with less use of labor, draft power, seeds, and
+burning. This suggests that small ruminant producers focus less of their effort on
+crop production.
+Human capital affects land management. Female-headed households use sig-
+nificantly less labor and draft power, probably because of labor constraints and a
+cultural taboo against women plowing and threshing in Tigray.26 Consistent with
+this, female-headed households also are less likely to apply manure or compost and
+less likely to use contour plowing. Older household heads use more labor, probably
+because of greater availability of family labor old enough to be involved in crop
+production. Farmers who have completed three years of education use more labor
+than uneducated heads.
+Social capital also affects land management. Households with members of a
+village council use more labor per hectare and are more likely to use improved
+seeds, manure or compost, and intercropping. Such households appear to be more
+oriented toward intensive crop production than other households.
+Crop production. The amounts of seed and ox power used have relatively large
+and statistically significant positive impacts on production (elasticities of 0.27 and
+0.20 in the OLS model) (Table 5.4). By contrast, the impact of human labor is
+quantitatively small (elasticity = 0.04) and statistically insignificant. This suggests
+that surplus labor exists in crop production in Tigray, with additional labor yield-
+ing little positive impact, although capital and seed inputs are key constraints. This
+is not surprising, given the very small farm sizes and marginal agricultural condi-
+tions in Tigray, and it implies that population growth can have very negative con-
+sequences for human welfare because the additional labor may not be productively
+used in agriculture (Lewis 1954). Of course, as we have seen, population pressure
+and small farm sizes contribute to adoption of more intensive practices such as use
+of oxen and fertilizer, which can increase yields. Thus, the negative consequences of
+population pressure can be mitigated to some extent by such Boserupian responses.
+We investigate the extent of this mitigation below.
+Several land investments and land management practices have large and statis-
+tically significant influences on the value of crop production. The predicted value
+of production is 23 percent higher on plots with stone terraces controlling for labor
+use, land management practices, and other factors.27 Use of burning to prepare
+the field is associated with 29 percent lower yields, and reduced tillage with 45 per-
+cent higher yields. Use of fertilizer is associated with 14 percent higher yields, and
+manure or compost with 13 percent higher yields (both effects statistically significant
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 123
+124 JOHN PENDER AND BERHANU GEBREMEDHIN
+Table 5.4 Determinants of value of crop production per hectare, 1998
+ln(Value of crop production/hectare)
+Variablea OLSb IVb RF
+ln(Population density/km2) –0.013 –0.154 0.016
+Female head of household –0.551*** –0.478** –0.481***
+ln(Age of household head) (years) –0.148 c –0.090
+ln(Household size) (number) –0.145* –0.212* –0.090
+Education of household head
+3+ years 0.139 c 0.235*
+Literacy campaign 0.003 c 0.066
+Walking time to (hours)
+All-weather road 0.017 c 0.014
+Woreda town –0.056** –0.028 –0.069***
+Plot from residence 0.066 c 0.052
+Ownership of assets
+Land (hectares) –0.009 c –0.019
+Oxen (number) –0.043 c –0.035
+Other cattle (number) 0.052*** 0.014 0.063***
+Small ruminants (number) 0.006 c 0.005
+Pack animals (number) –0.046* c –0.032
+Radio (yes/no) –0.147* c –0.078
+Cash savings (yes/no) 0.098 c 0.027
+Secondary income source (cf. none)
+Cereals 0.263 c 0.121
+Perishable annuals –0.250 –0.413 –0.337
+Perennial crops 0.269* d 0.199
+Cattle –0.226* –0.245* –0.223*
+Small ruminants/beekeeping 0.049 c 0.080
+Food-for-work/farm employment 0.007 c 0.133
+Salary employment –0.197 c –0.190
+Trading 0.106 c 0.193
+Food/other assistance 0.403** 0.319 0.604***
+Other nonfarm –0.016 c –0.009
+Contact with extension –0.142* 0.104 e
+Membership in organizations
+Tabia council 0.435** d e
+Village council 0.227 c e
+Marketing cooperative 0.342*** 0.073 e
+Agricultural cadre –0.130 c e
+Use of credit
+Formal credit 0.067 c e
+Informal credit –0.067 c e
+Land use (cf. rain-fed plot)
+Homestead plot 0.147** –0.359 0.425***
+Irrigated plot –0.173 –0.714 0.134
+Initial investment on plot
+Stone terrace 0.206*** 0.397*** 0.163**
+Soil bund 0.153 –0.458 –0.100
+Fence (live or constructed) 0.083 0.086 0.068
+only at the 10 percent level). Presence of a soil bund reduces the predicted return
+to fertilizer. This may be because of weed or pest problems caused by the combina-
+tion of these technologies.
+Almost all of these impacts are robust to the regression specification. The im-
+pacts of stone terraces, fertilizer, seed, ox labor, and reduced tillage are still statistically
+and quantitatively significant in the IV model.28 Stone terraces also have a significant
+positive impact on crop production in the reduced form specification.
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 125
+Table 5.4 (continued)
+ln(Value of crop production/hectare)
+Variablea OLSb IVb RF
+Use of inputs
+Fertilizer (1 = yes) 0.130* 0.799* e
+Fertilizer × stone terrace –0.076 –0.804** e
+Fertilizer × soil bund –0.455*** 0.369 e
+Fertilizer × irrigation 0.131 0.663 e
+ln(Seed/hectare) (kilograms/hectare) 0.268*** 0.617*** e
+Improved seed (1 = yes) 0.162 0.352 e
+ln(Labor/hectare) (days/hectare) 0.040 –0.124 e
+ln(Oxen labor/hectare) (days/hectare) 0.199*** 0.819* e
+Use of land management practices
+Burning to prepare field –0.336*** –0.728 e
+Contour plowing 0.099 0.276 e
+Reduced tillage 0.375*** 1.571*** e
+Intercropping/mixed cropping –0.043 –0.048 e
+Manure or compost 0.125* 0.628 e
+Intercept 18.159*** 11.231** 23.124***
+Number of observations 1,160 1,020 1,340
+R2 0.4948 0.0735 0.3758
+Note: Least-squares regressions. Coefficients and standard errors adjusted for sampling weights, clustering, and
+stratification.
+*, **, *** mean coefficient is statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively.
+aCoefficients of biophysical variables (annual rainfall, altitude, plot slope, position on slope, soil depth, soil color, soil
+texture, and presence of gullies), plot area and how plot acquired not reported to save space. Full results available on
+request.
+bHausman test failed to reject OLS model (P = 1.000).
+cVariables jointly statistically insignificant in full version of both OLS and IV models dropped from reported version of
+IV model.
+dVariable coefficient not estimable due to multicollinearity. Variable dropped in IV estimation.
+eEndogenous variable excluded from reduced form.
+Population pressure and farm sizes have a small and statistically insignificant
+impact on crop production per hectare in all regressions, even though we found
+that higher population density and smaller farm size promote greater use of some
+inputs. Larger households attain lower crop yields (significant at the 10 percent
+level). These findings do not support the Boserupian optimistic perspective about
+the responses of households to population pressure leading to increased yields and
+suggest that food production per capita will not keep pace with increasing popula-
+tion as farm sizes decline because there is very little possibility to expand area under
+crop production in the densely populated highlands of Tigray. Unless households
+are able to depend on alternative livelihoods, food insecurity is thus likely to worsen
+as population continues to grow.
+Households with better access to a woreda town had higher values of crop pro-
+duction, probably because of greater production of high-value products closer to
+towns. For example, teff (the highest value cereal produced in Tigray) production is
+negatively correlated with distance to the nearest woreda town (correlation = 0.12,
+0.6 percent significance level).29
+Most income strategies have an insignificant impact on crop production. One
+exception is households dependent on food aid or other assistance, whose yields
+are surprisingly significantly higher than those of other households. We also find
+that households dependent on food aid and other assistance have higher incomes
+per capita than cereals-only households (results for income discussed in the next
+subsection). These findings may be related to a lack of targeting of food aid in
+Ethiopia, as has been observed by other authors (Clay, Molla, and Habtewold
+1999; Jayne, Strauss, and Yamano 2002; Barrett and Clay 2003; Gebremedhin and
+Swinton 2003b).
+We do not find a statistically significant effect of irrigation on the value of crop
+production, other factors being equal. However, irrigation increases crop produc-
+tion indirectly by increasing the use of inputs, including labor, oxen, fertilizer, and
+improved seeds. Below, we estimate the impacts of these indirect effects of irriga-
+tion and other factors.
+Use of credit (formal or informal) is not associated with significant increases in
+crop production, even though we found that formal credit promotes use of fertil-
+izer. This is consistent with the fact that our evidence shows only limited impacts of
+fertilizer on crop production. Contact with the agricultural extension program also
+has insignificant impact on crop production.
+Ownership of cattle other than oxen is associated with higher crop productivity.
+This may be related to greater deposition of manure on plots operated by house-
+holds owning more livestock (especially homestead plots).
+126 JOHN PENDER AND BERHANU GEBREMEDHIN
+Female-headed households achieve 42 percent lower crop yields than male-
+headed households with similar use of labor, ox power, and other inputs. Thus, not
+only are female-headed households disadvantaged in terms of their ability to apply
+inputs, but their productivity in using inputs is lower.
+Households with members of a marketing cooperative attain substantially
+higher output value per hectare, probably because they focus on higher-value crops
+and have more timely availability of inputs. For example, members of marketing
+cooperatives produce nearly three times as much teff (the highest value cereal in
+Tigray), on average, as nonmembers.
+Income. Many of the same factors that affect the value of crop production also
+affect per capita income (Table 5.5). Households with better access to a woreda
+town earn higher income (significant only in the IV regression), consistent with the
+result that value of crop production is higher closer to towns. This result is consis-
+tent with the findings of Kruseman, Ruben, and Tesfay in Chapter 4 that housing
+quality (as measured by proportion of households with a metal roof) is better in areas
+closer to markets. Households with more cattle (other than oxen) earn higher in-
+come, whereas female-headed households earn significantly lower income per capita.
+Members of a marketing cooperative earn significantly higher income than other
+households (significant only in the OLS regression). Larger households earn less
+income per capita. Population density, farm size, other assets, and access to credit
+and extension have statistically insignificant impacts on per capita income.30
+Households pursuing many types of income strategies earn higher incomes
+than cereals-only producers. This includes households for whom cereals are a sec-
+ondary income source and households whose secondary income source is cattle,
+food-for-work or farm employment, salary employment, trading, other nonfarm
+activities, and food aid or other assistance. In general, households with secondary
+income sources earn higher income per capita than those solely dependent on cereal
+production. The fact that households dependent on food aid or other assistance
+earn higher incomes (excluding such aid as income) is consistent with the finding
+discussed above that these households have higher crop yields and with the argu-
+ment that food aid is not well targeted.
+Direct and Indirect Effects on Production and Income
+The predicted direct and indirect effects of changes in selected policy-relevant fac-
+tors on crop production and per capita income are shown in Table 5.6. The factors
+considered include increase in population density, improved access to an all-weather
+road or to a woreda town, increased education, increased access to extension or formal
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 127
+128 JOHN PENDER AND BERHANU GEBREMEDHIN
+Table 5.5 Determinants of per capita income, 1998 (birr)
+Variablea OLS IV RF
+Population density (persons/km2) –0.36 –0.36 –0.36
+Female head of household –108.76* –110.82** –106.23*
+Age of household head (years) –0.02 b 0.58
+Household size (number) –74.27*** –71.14*** –73.78***
+Education of household head (cf. <3 years)
+3+ years 101.15 b 93.70
+Literacy campaign 185.11* 173.50 175.5*
+Walking time to (hours)
+All weather road –7.96 b –9.41
+Woreda town –20.53 –25.83** –19.76
+Plot from residence 59.00 b 66.42
+Ownership of assets
+Land (hectares) 24.58 b 21.86
+Oxen (number) 6.55 b 3.18
+Other cattle (number) 12.48* 18.63** 15.15**
+Small ruminants (number) –0.39 b –0.50
+Pack animals (number) 27.30 b 24.49
+Radio (yes/no) 49.55 b 47.38
+Cash savings (yes/no) 30.45 b 19.12
+Secondary income source (cf. none)
+Cereals 213.10** 272.38*** 256.72***
+Perishable annuals –93.50 –99.15 –90.61
+Perennial crops 84.36 103.78 85.10
+Cattle 127.20** 148.49** 135.16**
+Small ruminants/beekeeping 372.22 425.59* 411.00
+Food-for-work/farm employment 200.80*** 191.54*** 202.78***
+Salary employment 251.63*** 241.93*** 267.01***
+Trading 149.20* 243.32* 206.37**
+Food/other assistance 313.93** 344.18*** 327.76**
+Other nonfarm 152.63** 178.41*** 159.38**
+Contact with extension 32.00 b c
+Membership in organizations
+Tabia council –223.03 b c
+Village council –64.58 b c
+Marketing cooperative 195.92** –137.19 c
+Agricultural cadre 14.31 b c
+Use of credit
+Formal credit –13.32 b c
+Informal credit –38.48 b c
+Land use (cf. rain-fed plots) (proportion of area)
+Homestead plots –28.15 –57.74 –33.39
+Irrigated plots 151.72 229.14 194.61
+Initial investment on plot in 1998 (proportion of area)
+Stone terrace 93.10 89.32 93.72
+Soil bund 73.16 76.09 89.23
+Fence (live or constructed) –107.32 –39.94 –94.29
+Intercept 662.32* 851.47*** 658.51**
+credit, increased participation in marketing cooperatives, investment in irrigation
+or stone terraces, or increased ox or cattle ownership.
+Participation in marketing cooperatives has the largest predicted impacts on
+both crop production and income, increasing both by more than 40 percent.
+Investment in stone terraces also has relatively large and positive predicted impacts
+on both crop production and income (around 14 percent), though the impacts on
+income are statistically insignificant. Improved access to a woreda town (by up to
+one hour walking time) is predicted to increase both the value of crop production
+and income by about 5–6 percent. Increased ownership of cattle (by one cow) also
+is predicted to increase crop production and income moderately. All of these
+scenarios represent possible “win-win” outcomes, increasing both productivity and
+incomes.
+Many of the changes considered have relatively small (less than 5 percent
+change) and statistically insignificant predicted quantitative effects on crop pro-
+duction and income. This includes the influences of population growth (10 per-
+sons/km2 increase), improved access to an all-weather road (up to one hour closer),
+universal access to formal credit, and increased ox ownership (by one ox). Some of
+the changes have quantitatively large but statistically insignificant effects, including
+investment in primary education (large positive influences on both crop production
+and income), extension (negative effect on crop production but positive effect on
+income), and irrigation (small effect on crop production but large influence on in-
+come). However, given the statistical insignificance of the coefficients on which these
+predicted influences are based, not too much should be made of their magnitudes.
+These results suggest that the most promising investments for increasing agri-
+cultural productivity and incomes in the highlands of rural Tigray are in marketing
+institutions, improved access to markets, in soil and water conservation measures
+such as stone terraces, and in cattle (other than oxen). Investments in roads, extension,
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 129
+Table 5.5 (continued)
+Variablea OLS IV RF
+Number of observations 436 425 436
+R2 0.2604 0.2129 0.2445
+Note: Least squares regressions. Coefficients and standard errors adjusted for sampling weights, clustering, and strat-
+ification. The Hausman test result was inconclusive (negative test statistic).
+*, **, *** mean coefficient is statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively.
+aCoefficients of biophysical variables (annual rainfall, altitude, plot slope, position on slope, soil depth, soil color, soil
+texture, and presence of gullies); how plot acquired not reported to save space. Full results available upon request.
+bVariables jointly statistically insignificant in full version of both OLS and IV models dropped from reported IV model.
+cEndogenous variable excluded from reduced form.
+Table 5.6 Simulated impacts of changes in selected variables on value of crop production and per capita income
+Value of crop production Per capita income 
+Mean of selected variable (plot level) (percentage) (percentage)
+Variable Scenario Before change After change Direct effects Total effects Direct effects Total effects
+Population density (persons/km2) 10 persons/km2 increase 137 147 –0.1 +0.4 –1.0 –1.1
+Access to all-weather road (hours walking) Maximum 1 hour closer 2.3 1.3 –1.4 –1.2 +1.7 +2.8
+Access to market town (hours walking) Maximum 1 hour closer 3.5 2.5 +5.5** +6.8R +5.3++ +5.6
+Primary education (proportion of household heads) Minimum 3 years for household 0.06 0.92 +12.0 +17.6 +23.4 +23.4
+heads with less
+Access to extension (proportion of household) Universal access 0.11 1.00 –11.1* –14.0 +7.6 +7.6
+Access to formal credit (proportion of household) Universal access 0.58 1.00 +2.0 +3.9 –1.5 –1.5
+Participation in marketing cooperative (proportion Universal participation 0.06 1.00 +33.7*** +45.5 +48.7** +48.7
+of household)
+Irrigation (proportion of plots) All rain-fed plots irrigated 0.07 0.80 –10.7 –1.2 +25.8 +19.0
+Stone terraces (proportion of plots) All plots terraced 0.37 1.00 +13.6***+++ +13.8R +14.5 +14.5
+Oxen ownership (number owned) 1 additional ox owned 1.1 2.1 –4.2 –2.4 +1.8 +1.8
+Other cattle ownership (number owned) 1 additional animal owned 2.7 3.7 +5.4***+ +6.2R +3.3*++ +3.3R
+Note: Values are percentage change in mean predicted values. Simulation results for direct effects based on predictions from OLS model regressions reported in Tables 5.4 and 5.5. Results of OLS
+and probit regressions predicting input use and land management practices were used to predict indirect effects on crop production. Results of probit regressions for determinants of use of credit,
+participation in extension, and organizations used to predict indirect effects on income.
+*, **, *** mean direct effect is based on a coefficient that is statistically significant in the OLS regression at 10 percent, 5 percent, or 1 percent level, respectively.
++, ++, +++ and –, – –, – – – mean direct effect is of the sign shown and statistically significant in the IV regression at 10 percent, 5 percent, or 1 percent level, respectively.
+RCoefficient is of the same sign and statistically significant at 5 percent level in the reduced form regression.
+and credit are of less clear benefit. The effects of education may be large and positive,
+though we cannot be confident of its influence based on our results.
+Key Findings and Implications
+Here we summarize key findings with regard to our hypotheses and their implica-
+tions. The qualitative findings are summarized in Table 5.7.
+Population Pressure
+Population pressure, as reflected by higher population density, is associated with
+more intensive use of labor, ox power, fertilizer, and intercropping. Smaller farms
+are also more likely to use fertilizer on a given plot and less likely to use reduced
+tillage. These findings are consistent with the predictions of population-induced
+intensification, as hypothesized by Boserup (1965) and her followers. However, in-
+creased farming intensity in more densely populated areas was not found to lead to
+significantly higher crop yields. In addition, population pressure at the household
+level, in terms of larger household size, is associated with lower yields and lower
+income per capita. These findings suggest that population growth, larger house-
+holds, and smaller farm sizes will lead to reduced food production and income
+per capita because options for expanding crop production onto new land are very
+limited in the highlands of Tigray. The negative implications of population pres-
+sure are consistent with findings of other recent studies in the Ethiopian highlands
+(Grepperud 1996; Pender et al. 2001a).
+Access to Roads and Markets
+Better access to an all-weather road contributes to more intensive use of labor, fer-
+tilizer, burning, and contour plowing. However, we find little impact of better road
+access on the value of crop production or income. This probably is because even in
+areas with relatively better road access, most households are still quite far from
+roads and rely primarily on walking and donkeys to transport commodities and
+inputs. The impacts of improved road access are quite limited in such a setting.
+Households with better access to a woreda town use more ox draft power
+but less contour plowing and obtain higher values of crop production and higher
+per capita income than households in more remote locations (though impact on per
+capita income was significant only in IV regression). In contrast to road access,
+access to even small urban markets makes a difference for rural livelihoods.
+Income Strategies
+As expected, different income strategies are associated with differences in input use
+and land management practices. For example, households having several types of
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 131
+Table 5.7 Summary of qualitative empirical results
+Labor Capital intensity Value of crop Per capita
+Factor intensity (oxen, purchased inputs) Land management practices production income
+Population pressure
+Population density + + oxen, fertilizer + intercropping 0 0
+Smaller farm size 0 + fertilizer – reduced tillage 0 0
+Household size 0 0 0 0 –
+Access to roads + + fertilizer + burning, contour plowing 0 0
+Access to markets 0 + oxen – contour plowing + 0
+Income strategies
+Cattle – – oxen – burning 0 +
+Nonfarm 0 – oxen + reduced tillage 0 +
+High-value crops 0 + improved seeds + reduced tillage 0 0
+Food-for-work/farm work – – oxen, + improved seeds 0 0 +
+Food/other aid 0 – oxen – manure, burning, intercropping + +
+Irrigation + + oxen, improved seed + reduced tillage 0 0
+Credit 0 + improved seed 0 0 0
+Extension 0 0 0 0 0
+Physical capital 0
+Oxen + + oxen + manure, contour plowing, – reduced tillage 0 0
+Other cattle 0 + seeds, fertilizer + burning + +
+Small ruminants – – oxen, seeds –burning, intercropping 0 0
+Human capital
+Primary education + 0 0 0 0
+Female head – – oxen – manure – –
+Financial capital (savings) 0 + fertilizer – manure 0 0
+Natural capital
+Stone terrace 0 + fertilizer + contour plowing + 0
+Soil bund 0 0 + burning 0 0
+Social capital
+Village council + + improved seed + manure, intercropping 0 0
+Marketing cooperative 0 0 – burning + 0
+Note: + Positive (– negative) and statistically significant impact (5 percent level) in at least one specification and significant at 10 percent level in two specifications. 0 impact not statistically
+significant and robust.
+noncrop income use labor and ox draft power less intensively than cereals-only pro-
+ducers, whereas producers of perennials and perishable annuals are more likely to
+use improved seeds. Despite such differences in cropping practices, we find no sig-
+nificant difference among most income strategies in value of crop production,
+except (surprisingly) households dependent on food aid and other assistance, which
+have higher crop production. These aid-dependent households also earn higher
+income per capita than cereals-only households (suggesting lack of targeting of
+food aid and other assistance), as do households pursuing many other income
+strategies. In general, households with more diversified income sources have higher
+incomes per capita.
+Irrigation
+As expected, irrigation increases the intensity of input use in crop production,
+including labor, ox power, fertilizer, and improved seeds. Surprisingly, however,
+we do not find that irrigation contributes to higher value of crop production or
+income, even after accounting for the indirect effects of increased intensity of pro-
+duction. There are many problems affecting the performance of small-scale irriga-
+tion in Tigray, including problems of inadequate access to irrigation water when
+needed, salinity buildup as a result of seepage and poor drainage, lack of experience
+in using irrigation, and other factors (Tesfay et al. 2000). These problems are cer-
+tainly limiting the potential of small-scale irrigation in Tigray. But our inability to
+identify an independent effect of irrigation may also be caused by multicollinearity
+and an inadequate sample of irrigated plots.31 We have a relatively small sample of
+irrigated plots in our sample (91 plots), and irrigation is correlated with other plot
+quality factors, especially plot size. Further research on the influence of small-scale
+irrigation in Tigray and the policy, institutional, and technical factors affecting its
+effectiveness, is needed.
+Agricultural Extension and Credit
+The agricultural extension and credit program has sought to boost productivity
+largely by promoting use of fertilizer and improved seeds. The evidence presented
+shows some influence of fertilizer use on crop production (though the impact is
+statistically weak and not robust), increasing predicted value of production by 250
+EB/hectare on average, other factors remaining constant. This yield increase is
+insufficient to cover the average costs of fertilizer (about 280 EB/hectare in 1998),
+indicating that fertilizer use was unprofitable on average and explaining why farmers
+are reluctant to adopt it, despite substantial efforts to promote its use. In a semiarid
+environment as in the highlands of Tigray, use of fertilizer can be risky as well as
+unprofitable if adequate soil moisture cannot be assured. Given the heavy emphasis
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 133
+of the agricultural extension and credit program on promoting fertilizer use at the
+time of the study, it is not surprising that these programs were found to have little
+influence on crop production and income.
+Although the return to fertilizer is low, there are indigenous technologies with
+potential to substantially increase crop yields. Stone terraces increase crop produc-
+tivity by an estimated 23 percent. Because stone terraces help to conserve soil mois-
+ture, they also increase the benefit of using fertilizer, which is probably why we find
+more fertilizer adoption on plots that have stone terraces. The estimated average
+rate of return to stone terraces is 46 percent, based on the predicted increase in
+annual value of crop production and our data on costs of constructing these ter-
+races. This is comparable to the estimated rate of return to stone terraces in south
+central Tigray by Gebremedhin, Swinton, and Tilahun (1999), who estimated a 50
+percent rate of return to stone terraces, and shows that investment in stone terraces
+is fairly profitable in Tigray. Several other low external input land management
+practices, including application of manure and compost, reduced tillage, and no
+burning also could have substantial impacts on crop productivity. Promotion of such
+technologies by the extension program could yield greater benefits than the emphasis
+on fertilizer and improved seeds.
+Endowments of Physical, Human, and Social Capital
+Livestock ownership significantly influences land management. Households that
+own more oxen use more ox draft power, are more likely to use contour plowing
+and to apply manure, and are less likely to use reduced tillage. Despite these dif-
+ferences, we find no significant differences in crop production or income per capita
+resulting from differences in ox ownership, suggesting that informal arrangements
+to share or lease oxen work relatively well in Tigray. Thus, although ox draft power
+is a critical component of the farming system in northern Ethiopia (some argue
+it is the most critical component), and most households are not able to own as
+many oxen as desired (Bauer 1977; Amare 1995, 2003; McCann 1995), many
+households are able to overcome this constraint through ox-sharing arrangements,
+especially between households owning only one ox (e.g., Amare 1995), and house-
+holds without any oxen will often sharecrop out their land. Ownership of other
+cattle is associated with greater use of seed and fertilizer, perhaps because this helps
+to relax financial constraints. Households with more cattle (other than oxen)
+obtain higher yields and incomes, supporting Aune’s (Chapter 12 of this volume)
+argument that milking animals are more profitable than oxen in the highlands of
+Ethiopia. Ownership of small ruminants appears to reduce intensity of crop pro-
+duction; small ruminants are associated with less use of labor, ox power, burning,
+and intercropping.
+134 JOHN PENDER AND BERHANU GEBREMEDHIN
+Primary education is associated with more intensive use of labor, though the
+reason for this is not clear. We find generally insignificant impacts of education on
+other aspects of land management, crop yields, and income, probably because of the
+generally low levels of education among all households in the sample. By contrast,
+gender is very important in affecting land management and outcomes. Female-
+headed households use much less labor and ox power, are less likely to apply
+manure, and obtain substantially lower crop yields and incomes than male-headed
+households. A cultural taboo against women using oxen for plowing is one factor
+disadvantaging female-headed households. Moreover, women are not usually in-
+cluded in agricultural extension programs. Priority should be given to promoting
+changes in such attitudes as well as assisting female-headed households to pursue
+alternative livelihoods.
+Some forms of social capital, as measured by involvement in local organiza-
+tions, have a significant influence on crop production. Members of a village council
+farm with greater labor intensity and are more likely to use improved seeds, manure,
+and intercropping than other households. Members of a marketing cooperative use
+less burning and attain substantially higher value of crop production per hectare,
+probably because they focus on producing higher-value crops and/or have better
+access to input and output markets than other farmers.
+Conclusions
+We have investigated the impacts of many factors commonly hypothesized to affect
+land management and agricultural productivity in the highlands of Tigray. Some of
+these factors, including population pressure, small landholdings, access to roads,
+irrigation, and extension and credit programs, have weaker influences on agricul-
+tural production and incomes than often hypothesized. Most of these factors do
+affect the intensity of agricultural production and adoption of various land man-
+agement practices. However, these effects on intensity do not add up to much
+influence on total crop production, in part because of the low marginal product of
+labor in crop production and limited productivity effect of inputs such as fertilizer
+that have been promoted by some of these factors.
+Some land management practices were found to substantially increase crop
+production, including construction of stone terraces, reduced burning, and reduced
+tillage. These practices possibly contribute to productivity by helping to conserve
+soil moisture and organic matter. Greater ownership of cattle (other than oxen)
+is also strongly associated with increased crop productivity, probably as a result of
+increased manure availability, and higher income. Promotion of such conservation
+practices and exploitation of complementary livestock production show more
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 135
+promise to boost crop production and incomes than large application of modern
+inputs such as inorganic fertilizer and improved seeds. However, there do appear to
+be opportunities to exploit complementarities between use of such inputs (espe-
+cially fertilizer) and investment in stone terraces.
+Livelihood diversification is a key to reducing poverty in the highlands of Tigray
+because of population pressure and the low productivity of land. Households that
+focus only on cereal production earn significantly lower incomes than households
+having more diversified income sources, including livestock, off-farm employment,
+and nonfarm activities.
+Special attention to the problems of female-headed households is needed. Efforts
+to change attitudes about women plowing, enhance their farming skills, and to
+promote alternative livelihoods for women are needed to address the low levels of
+agricultural productivity and income of this vulnerable group.
+Overall, the findings of this study show that profitable opportunities exist to
+increase agricultural production and incomes and to achieve more sustainable land
+management in the highlands of Tigray. These opportunities include improvement
+of crop production using low-external-input investments and practices such as ter-
+races, reduced tillage, and reduced burning and improved livestock management.
+The comparative advantage of people in the Tigray highlands appears not to be in
+input-intensive cereal crop production but more in low-external-input technologies
+and alternative livelihood activities, such as livestock raising and nonfarm activities.
+As a result, greater emphasis on developing these alternatives in agricultural exten-
+sion and other development programs is needed. Food crop production should not
+be ignored in the development strategy, especially if more drought-resistant vari-
+eties can be developed, but more prudent use of external inputs such as fertilizer
+and improved seeds, and greater emphasis on low-external-input sustainable land
+management practices would be helpful.
+Notes
+1. There is considerable variation in estimates of the size and impacts of soil erosion and
+other forms of land degradation in the Ethiopian highlands, causing controversy about the exact
+magnitude of these impacts (FAO 1986; Hurni 1988; Hurni and Perich 1992; Sutcliffe 1993; Böjo
+and Cassells 1995; Kappel 1996; Sonneveld 2002). For example, the Ethiopian Highlands Recla-
+mation Study (FAO 1986) estimated an average rate of soil loss of 35 tons per hectare per year in the
+highlands, with much higher rates on cultivated land (130 tons/hectare per year), leading to a pre-
+dicted loss of 7.6 million hectares of productive cropland and a loss of 2.6 million tons of annual
+crop production by the year 2010. Hurni (1988) estimated much lower rates of soil erosion, averag-
+ing 42 tons/hectare per year on cropland but reaching as high as 300 tons/hectare per year on some
+steeply sloping lands, based on measurements of soil erosion taken at several sites throughout the
+highlands under the Soil Conservation Research Project (SCRP). Studies in specific locations in
+136 JOHN PENDER AND BERHANU GEBREMEDHIN
+Tigray have also estimated high average but widely varying rates of erosion (Eweg, van Lammeren,
+and Yifter 1997; Hengsdijk, Meijerink, and Mosugu 2005) and soil nutrient depletion (Abegaz
+2005). Hurni and Perich (1992) estimated that Tigray’s soils have lost 30–50 perecnt of their origi-
+nal productive capacity as a result of land degradation. Subsequent studies have argued that both
+FAO’s and Hurni’s estimates overstate the impact of soil erosion because they do not account for
+deposition of eroded soils elsewhere in the landscape (Sutcliffe 1993; Böjo and Cassells 1995).
+Based on assumptions about the amount of soil deposition and the influence of net soil loss on pro-
+ductivity, Böjo and Cassells estimated the cumulative gross discounted economic losses caused by
+soil erosion in the Ethiopian highlands to be between EB 3 billion and EB 7 billion, and Kappel
+(1996) estimated these losses to be somewhat larger ($1.25 billion). Considering the value of soil
+nutrients lost via burning of dung and crop residues, Böjo and Cassells estimated that the dis-
+counted economic losses through nutrient depletion were even greater than those from erosion (EB
+8 billion). Regardless of the variation in estimates, there seems little dispute that land degradation
+and its costs are severe in many locations in the Ethiopian highlands, though these vary greatly
+across locations and land uses (Keeley and Scoones 2004; Nyssen et al. 2004).
+2. Highlands were defined to include areas at or above 1,500 meters above sea level.
+3. This empirical model is based on a theoretical dynamic household model that is presented
+in Nkonya et al. (2004).
+4. In the econometric work, we use dummy variables for whether the household applied fer-
+tilizer or improved seeds, rather than the quantities of these inputs, because of zero values of these
+inputs for many households, making it difficult to account for the amount of use in a logarithmic
+production function as estimated in this chapter.
+5. We did not estimate separate production functions for each crop produced in order to
+simplify the analysis because that would result in much smaller sample sizes for each crop (hence
+reduced statistical power) and because intercropping or mixed cropping cannot be modeled with
+single-output production functions. The revenue function is an aggregation of production and local
+price functions and hence depends on the variables that influence both production and prices. That
+is, if production of crop i on plot p depends on a vector of inputs and biophysical conditions (Xp)
+according to the production function fi(Xp), and the farm level price of crop i depends on a vector
+of conditions related to market access and household-level transaction costs and marketing abilities
+(Zh) according to the relationship pi(Zh), then revenue from plot p of household h is equal to
+Σpi(Zh)fi(Xp), which we define as the revenue function y (Zh, Xp).
+6. If labor effort and other inputs are measured perfectly, these effects would be reflected in
+the effects of these inputs on production. However, if they are measured imperfectly, tenure may
+have a greater influence on productivity.
+7. Fewer than 3 perecnt of households in our sample changed their primary source of
+income between 1991 and 1998, and only one-fifth changed their secondary income source.
+8. We do not include other, more variable factors such as ownership of physical assets as
+determinants of participation in programs or use of credit because these may not be predetermined
+relative to decisions about participation or credit use, which may have occurred before the current
+year.
+9. For example, in the income regression, we use share of farmland of different tenure, slope,
+and soil type classes.
+10. Members of agricultural cadres are supposed to be innovative farmers who are contact
+farmers for technical assistance programs.
+11. Pasture, woodlots, and fallow plots were excluded from the analysis.
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 137
+12. Except where noted, the results discussed below are statistically significant at the 5 percent
+level in at least two of the specifications.
+13. Normal IV estimation requires a continuous uncensored dependent variable, which we do
+not have in the case of the land management regressions. Instrumental variables estimation
+approaches have been developed for probit models (e.g., Smith and Blundell 1986; Blundell and
+Smith 1989), but these approaches assume that the endogenous explanatory variables are continu-
+ous uncensored variables, or that they are continuous, uncensored latent variables (Maddala 1983).
+Neither assumption holds in our models.
+14. These regression results are available on request.
+15. The maximum variance inflation factor was less than five in all cases (except when pre-
+dicted values of explanatory variables were used, in which case multicollinearity was a problem).
+16. The method used to predict direct and indirect influences is explained fully in Nkonya et al.
+(2004).
+17. As a result of the predominant risti land tenure system that existed in northern Ethiopia
+before the 1975 land reform (which also involved periodic land redistribution and prohibited land
+sales and mortgages), land ownership was not greatly unequal in most places even before 1975 (Bruce,
+Hoben, and Rahmato 1994).
+18. The official exchange rate averaged about 7 EB per U.S. dollar in 1998.
+19. Remittance income from family members residing elsewhere accounted for less than
+1 percent of household income of our sample households. This is consistent with Bauer’s (1977)
+description of a high degree of individualism in Tigray society.
+20. In Tigray, adults are required to contribute 20 days per year to community labor mass-
+mobilization campaigns, which are used to construct conservation measures, plant trees, build
+roads, and for other activities (Hagos, Pender, and Gebreselassie 1999). During the 1980s up to four
+months of such labor contribution was expected, but this was reduced to 20 days in 1992 (Hagos,
+Pender, and Gebreselassie 1999).
+21. To compute the value of production, we used average prices in Tigray based on community-
+and household-level surveys. We were not able to compute value of production using local prices
+because of a limited number of observations for many crops. Thus, the data represent a weighted
+production index, where regional prices are used to weight production of different crops and do not
+reflect local variation in prices.
+22. In Chapter 4, Kruseman, Ruben, and Tesfay found that higher population density is asso-
+ciated with a greater proportion of households who use fertilizer and pesticides and a smaller propor-
+tion who use fallow.
+23. Kruseman, Ruben, and Tesfay found a positive impact of road access on fertilizer use in
+Chapter 4.
+24. This contrasts with the results in Chapter 4, in which the presence of irrigation institutions
+was positively but not significantly correlated with the proportion of households using inputs such
+as fertilizer and improved seeds; this may reflect weaker statistical power of community survey results
+to explain household- and plot-level technology adoption.
+25. Again, the relationship between formal credit and use of fertilizer and improved seed was
+positive but not statistically significant in Chapter 4.
+26. The prohibition against women plowing and threshing is a long-standing one that, accord-
+ing to Bauer (1977), is based on “an indigenous theory that their participation in these activities
+would decrease the amount of crops produced” (Bauer 1977, p. 98). These attitudes may be chang-
+ing in Tigray as some female-headed households have had the need and courage to challenge such
+138 JOHN PENDER AND BERHANU GEBREMEDHIN
+norms, though this can be difficult, and such women may be subject to ridicule or intimidation
+(Abay et al. 2001).
+27. Because of the logarithmic specification for the dependent variable, the predicted impact
+of stone terraces using the OLS specification is exp(0.206) = 1.229, or a 23 percent increase.
+28. The Hausman test failed to reject the OLS model (P = 1.000), so the OLS model is the
+preferred model.
+29. In Chapter 4, Kruseman, Ruben, and Tesfay also found greater production of teff closer to
+markets.
+30. These results contrast with the findings of Kruseman, Ruben, and Tesfay in Chapter 4
+that higher population density is associated with better housing quality. That finding may reflect the
+influence of household-level variables that are correlated with income and wealth and may also be
+correlated with population density, such as education, which are included as explanatory variables
+in our analysis but were not controlled for in their analysis. Consistent with this explanation, Kruse-
+man, Ruben, and Tesfay found that indicators of education were greater in more densely populated
+communities.
+31. Recall that the value of crop production per hectare was much higher on irrigated plots
+than on rain-fed or homestead plots in our descriptive analysis. Such differences are not found in
+the econometric analysis when plot size and land quality indicators are included.
+AN ECONOMETRIC ANALYSIS IN THE HIGHLANDS OF TIGRAY 139
+
+

data/part_2/0289790596.md

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+# Leveraging the role of MSMEs for healthier diets and nutrition: Insights from fruit and vegetable value chain studies across five countries
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/39e71b57-38f5-4bc5-9a0e-ccce275c1766/retrieve  
+**Language:** English  
+**Resource Type:** Working Paper  
+**Release Year:** 2024  
+**Rights:** CC-BY  
+**GARDIAN ID:** 474c65413c1975b6dde1db5b4a442e74  
+**DataNODE ID:** 02d706d0402c7343b5c166ed6c78192c  
+**Siever ID:** 09b9c952-3fc7-49b4-ba57-7b5fcda5a1a0  
+**Token Count:** 224  
+**Page Count:** 0
+
+## Keywords
+
+food systems, small and medium enterprises, sustainability, healthy diets, value chains, fruits, vegetables, food environment, markets, nutrition, health and food security, systems transformation, informal sector, rural areas
+
+## Geography
+
+- **Regions:** Western Africa, Sub-Saharan Africa, Africa, World, Eastern Africa, South-eastern Asia, Asia
+- **Countries:** Viet Nam, Tanzania, Philippines, Ethiopia, Benin
+
+## Description
+
+In most low and middle-income countries (LMICs) the food system falls short in providing sufficient amounts of healthy foods to a burgeoning population. The growing awareness of how food systems are stressing planetary boundaries and failing to provide sustainable healthy diets and livelihoods has prompted the widespread call to transform the global food system (Béné 2022; FAO et al. 2020, 2024; Webb et al. 2020). Transforming food systems requires engaging various groups of actors with diverse perspectives and challenges (Leeuwis et al. 2021), including setting up alliances with the informal sector (Brouwer & Ruben 2021) and a strengthened focus on the role of micro-, small- and medium-sized enterprises (MSMEs).
+
+Globally, MSMEs represent about 90 percent of all businesses and account for 60 to 70 percent of employment and 50 percent of GDP. In the current food system, by being present at all value chain stages and better linking small-scale farmers to markets, MSMEs can offer affordable food to both urban and rural areas, create jobs and opportunities for young and female entrepreneurs, and support sustainable, circular food practices (IFAD 2021). These promises can be fulfilled if certain barriers that can hinder their contributions, such as high rates of food loss and waste (FLW), food safety concerns, and the uncertain informal context in which the majority of them operate are addressed (Termeer et al. 2024).
+
+## Content
+
+In most low and middle-income countries (LMICs) the food system falls short in providing sufficient amounts of healthy foods to a burgeoning population. The growing awareness of how food systems are stressing planetary boundaries and failing to provide sustainable healthy diets and livelihoods has prompted the widespread call to transform the global food system (Béné 2022; FAO et al. 2020, 2024; Webb et al. 2020). Transforming food systems requires engaging various groups of actors with diverse perspectives and challenges (Leeuwis et al. 2021), including setting up alliances with the informal sector (Brouwer & Ruben 2021) and a strengthened focus on the role of micro-, small- and medium-sized enterprises (MSMEs).
+
+Globally, MSMEs represent about 90 percent of all businesses and account for 60 to 70 percent of employment and 50 percent of GDP. In the current food system, by being present at all value chain stages and better linking small-scale farmers to markets, MSMEs can offer affordable food to both urban and rural areas, create jobs and opportunities for young and female entrepreneurs, and support sustainable, circular food practices (IFAD 2021). These promises can be fulfilled if certain barriers that can hinder their contributions, such as high rates of food loss and waste (FLW), food safety concerns, and the uncertain informal context in which the majority of them operate are addressed (Termeer et al. 2024).

data/part_2/0303733455.md

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+# The diffusion of agricultural technologies within social networks: Evidence from composting in Mali
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/04cc70eb-c7da-4dda-ae17-b78c30697ae9/retrieve  
+**Language:** French  
+**Resource Type:** Brief  
+**Release Year:** 2014  
+**Rights:** CP  
+**GARDIAN ID:** 825f3ef96560758a042e875f592395d6  
+**DataNODE ID:** e0a923c54ec7aa20c9053bc908ea331f  
+**Siever ID:** 7ccee9de-b4dc-41eb-8bc6-5a5bf11f6050  
+**Token Count:** 1919  
+**Page Count:** 0
+
+## Keywords
+
+gender, environmental factors, social networks, assets, climate change adaptation, resilience, women, climate change, diffusion of information, diffusion, composting, mali
+
+## Geography
+
+- **Regions:** Western Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Mali
+
+## Description
+
+This policy note summarizes research on the effect of social network characteristics and gender on the diffusion of information about an agricultural technology.
+
+## Content
+
+POLICY NOTE | SEPTEMBER 2014
+CL IMATE CHANGE ,
+& WOMEN’S ASSETS
+COLLECTIVE ACTION,
+IFPRI
+Eight Agricultural Development Interventions 
+in Africa and South Asia
+Diffusion des technologies agricoles  
+via les réseaux sociaux :  
+l’exemple du compostage au Mali
+Lori Beaman et Andrew Dillon
+EN L’ABSENCE D’INSTITUTIONS FORMELLES, LES RÉSEAUX SOCIAUX SONT UN VÉHICULE 
+primordial de l’information, notamment pour les agriculteurs des pays en développement. 
+La difficulté majeure de la promotion de nouvelles technologies réside dans l’effort coûteux et à forte 
+intensité de main-d’œuvre nécessaire pour sensibiliser les agriculteurs à ces technologies. L’approche la 
+plus courante consiste à mettre en œuvre des programmes de formation basés sur le groupe à l’échelle 
+des villages par le biais d’associations ou de coopératives agricoles. En effet, ces programmes considèrent 
+implicitement que les réseaux sociaux des agriculteurs renforcent les messages de vulgarisation tout en 
+promouvant l’adoption de nouvelles technologies. Ils sont parfois plus rentables que des visites directes aux 
+agriculteurs dans leurs champs, bien que les membres vulnérables ou moins influents de la communauté 
+puissent ne pas en bénéficier au vu de l’impact des normes sociales ou de la composition hommes/femmes 
+des groupes ciblés. Nous en savons encore très peu sur la manière dont les réseaux sociaux diffusent 
+des informations sur les technologies agricoles, mais les implications politiques de cette question sont 
+essentielles pour déterminer si les approches basées sur le groupe peuvent promouvoir l’adoption de 
+technologies d’atténuation des risques climatiques favorables à l’égalité des sexes. Cette note d’orientation 
+propose une synthèse des recherches sur l’impact des caractéristiques des réseaux sociaux et du genre sur 
+la diffusion d’informations relatives à une technologie agricole.
+CONTEXTE DE L’ÉTUDE
+Cette étude vise à vérifier l’impact des structures 
+des réseaux sociaux sur la diffusion de l’information 
+relative au compostage chez les agriculteurs maliens. 
+L’adoption de pratiques améliorées de gestion des 
+sols, notamment le compostage, est importante non 
+seulement pour la fertilité et la productivité à long 
+terme des sols, mais aussi parce qu’elle atténue les 
+risques à long terme du changement climatique. Les 
+avantages tirés de l’application de compost dans le 
+sol, comme il est le cas de nombreuses pratiques 
+agricoles, restent encore incertains. Des intrants 
+complémentaires ainsi qu’une connaissance de la 
+pratique en question par les agriculteurs sont encore 
+nécessaires. Le compostage présente l’avantage 
+NOTE D’ORIENTATION RE
+HANGEMENT CLIMATIQUE,
+ACTIFS DES FEMMES
+ACTION COLLECTIVE &
+IFPRI
+2
+d’augmenter la stabilité de la matière organique 
+dans le sol, ce qui peut modifier le pH et le taux 
+d’humidité du sol, augmenter la biomasse et réduire 
+le ruissellement des eaux. Ces avantages dépendent 
+des caractéristiques du sol avant application du 
+compost, des composants mêmes du compost et 
+de la qualité de ce dernier avant application. À 
+titre d’exemple, les composts à base de résidus 
+de récolte libèrent des nutriments dans le sol sur 
+une durée plus longue que ceux produits avec 
+des déchets animaux. Ainsi, les avantages à long 
+terme sont potentiellement plus importants, mais ils 
+s’accumulent à un rythme plus lent.
+Pour mieux comprendre l’impact de la structure 
+du réseau social sur la diffusion des informations 
+agricoles, des calendriers expliquant les techniques 
+de compostage et de fabrication des engrais 
+organiques ont été distribués de façon aléatoire aux 
+agriculteurs afin d’observer leur propagation via les 
+réseaux domestiques. Ces calendriers ont été choisis 
+parce que les Maliens les considèrent souvent comme 
+des objets décoratifs et aiment à les conserver chez 
+eux pendant plusieurs années.
+MÉTHODOLOGIE EXPÉRIMENTALE
+Cette recherche expérimentale visait principalement 
+à randomiser la probabilité qu’un ménage reçoive 
+des informations soit directement soit via un 
+réseau social ; les données ont permis de vérifier 
+si les nœuds les plus influents (à savoir les points 
+de contact au sein du réseau social) pouvaient 
+augmenter la probabilité de diffusion.
+La première étape de l’étude réalisée en 2008 a 
+consisté dans la collecte de données de référence 
+sur les réseaux sociaux. Dans chaque village, tous 
+les ménages et chacun de leurs membres ont été 
+recensés au cours d’une première visite. Les maris 
+et leurs épouses ont été invités à recenser tous leurs 
+liens sociaux dans le village, à savoir les personnes 
+avec lesquelles ils discutent fréquemment de 
+questions agricoles ou ont conclu des transactions 
+financières, ou encore des parents proches ou des 
+organisations auxquelles ils sont affiliés. Par ailleurs, 
+les caractéristiques démographiques et relatives au 
+bien-être des deux nœuds de chaque lien social ont 
+été prises en compte.
+Chaque village s’est vu attribuer de façon aléatoire 
+un des trois traitements suivants. Deux traitements 
+se sont basés sur les caractéristiques du réseau social 
+pour déterminer les bénéficiaires des calendriers, 
+tandis que le troisième s’est basé sur une distribution 
+aléatoire des informations dans le village. Deux 
+caractéristiques du réseau social ont été retenues : 
+le degré – le nombre de liens auxquels le nœud est 
+relié –, et l’intermédiarité, à savoir la part des chemins 
+les plus courts provenant de toutes les paires de 
+nœuds du réseau qui sont reliées à ce ménage. En 
+bref, le degré a mesuré la connectivité potentielle des 
+ménages, et l’intermédiarité l’influence potentielle 
+du réseau. Dans le premier traitement, les deux 
+femmes et les deux hommes ayant le degré le plus 
+élevé dans le village ont été choisis pour recevoir 
+des calendriers ; dans le deuxième traitement, les 
+ménages avec la mesure d’intermédiarité la plus 
+élevée ont été choisis, sans savoir si le bénéficiaire 
+du calendrier au sein du ménage était l’homme ou la 
+femme. Dans le traitement aléatoire, la moitié de tous 
+les bénéficiaires des calendriers étaient des femmes.
+Vingt-trois villages « aléatoires », 15 villages 
+de « degré » et 15 « d’intermédiarité » ont été 
+dénombrés. L’expérience a été menée dans 
+30 villages en 2010 (15 aléatoires et 15 de 
+degré) et 23 villages en 2011 (8 aléatoires et 
+15 d’intermédiarité). Des tests d’équilibrage ont 
+également été réalisés afin de déterminer si les 
+caractéristiques observables des bénéficiaires du 
+calendrier différaient par genre – indiquant que 
+3
+Pa
+n
+o
+s/
+R
+. 
+Jo
+n
+es
+cela pouvait être seulement un des mécanismes qui 
+influence la diffusion des informations. Les tests 
+d’équilibrage hommes-femmes ont montré que les 
+actifs, la taille du ménage et l’expérience des cultures 
+primaires cultivées dans ces villages n’étaient pas 
+statistiquement différents par genre, un résultat 
+compatible avec ceux des villages aléatoires et des 
+villages de degré et d’intermédiarité.
+Le protocole a été uniformisé dans tous les 
+villages. Après la première distribution aléatoire 
+de calendriers, les nœuds initiaux ont reçu trois 
+nouveaux calendriers à distribuer à d’autres villageois 
+après leur formation initiale sur le calendrier des 
+pratiques de compostage. Tous les ménages d’un 
+même village ont reçu une nouvelle visite un 
+mois plus tard ; ils ont également subi un test de 
+connaissances sur le compostage pour déterminer 
+si des informations justes avaient été propagées et 
+suivre la distribution des calendriers.
+IMPLICATIONS POLITIQUES
+Bien que la stratégie empirique du ciblage des 
+réseaux sociaux puisse différer des modalités 
+de mise en œuvre des approches basées sur le 
+groupe dans les programmes de développement (ils 
+peuvent utiliser des groupes plus petits), l’analyse 
+menée pour cette étude montre que les liens et 
+leurs caractéristiques de genre sont des facteurs 
+déterminants dans la propagation des connaissances. 
+Sans une compréhension des caractéristiques des 
+réseaux sociaux, au niveau du village ou dans un 
+groupe plus petit, l’inégalité des connaissances peut 
+affecter l’efficacité de la stratégie d’adaptation ou 
+l’intervention générale du programme.
+Les résultats montrent que la propagation des 
+connaissances était fonction de la distance entre 
+le ménage et le nœud initial, mais que les femmes 
+étaient moins susceptibles de recevoir un calendrier 
+que les hommes. Toutefois, dans le sous-échantillon 
+des femmes, la probabilité que les femmes reçoivent 
+un calendrier était beaucoup plus égale à travers les 
+distances du réseau social que pour les hommes. 
+Concernant la connaissance du compostage, la 
+distance du réseau social des femmes par rapport au 
+nœud initial a eu un effet beaucoup plus important 
+que pour les hommes. Les femmes positionnées 
+à quatre liens d’un nœud initial avaient 79 % de 
+connaissances en moins par rapport à la situation 
+contrefactuelle, alors que les hommes positionnés 
+à quatre liens d’un nœud initial n’avaient que 
+35 % de connaissances en moins par rapport à 
+leur situation contrefactuelle. En outre, les femmes 
+ciblées en fonction de l’influence du réseau social des 
+membres de leur village avaient nettement moins de 
+connaissances que les femmes ciblées dans les villages 
+sur la base du nombre de contacts des villageois.
+Les résultats donnent des indications importantes sur 
+l’efficacité potentielle d’un ciblage de l’information 
+sur l’efficience allocative des biens publics et sur les 
+technologies agricoles. Alors que les réseaux sociaux, 
+ou les approches basées sur le groupe reposant 
+sur des réseaux à l’intérieur des groupes offrent la 
+Ce projet bénéficie de l’appui du ministère fédéral allemand de la Coopération économique et du Développement. Il s’inscrit dans le cadre du 
+Programme de recherche du CGIAR sur les Politiques, Institutions et Marchés (PIM).
+Lori Beaman (l-beaman@northwestern.edu) est maître de conférences au Department of Economics du Northwestern University, Evanston, 
+IL, États-Unis. Andrew Dillon (dillona6@msu.edu) est maître de conférences au Agricultural, Food, and Resource Economics Department du 
+Michigan State University, East Lansing, États-Unis.
+possibilité de propager des informations à moindre 
+coût et de façon efficace, la diffusion de l’information 
+basée sur le réseau peut creuser les inégalités dans 
+la zone ou dans la population ciblée si les nœuds 
+visés sont influents, mais reliés seulement à un sous-
+ensemble de villageois, ou si l’information circule 
+inégalement entre hommes et femmes. Cela peut se 
+produire lorsque des informations ou des ressources 
+ne se propagent pas équitablement au sein d’un 
+réseau ou si des membres de la communauté – en 
+l’occurrence les femmes – sont socialement exclus 
+et de ce fait privés des liens sociaux nécessaires 
+pour bénéficier de l’intervention. Des recherches 
+supplémentaires sur l’effet de la structure du réseau 
+social sur la diffusion de technologies seront cruciales 
+pour mieux comprendre les stratégies d’adaptation 
+au changement climatique et la conception des 
+politiques potentielles, sachant que les éventuelles 
+inégalités sociales et entre hommes et femmes auront 
+une incidence sur la diffusion.
+LECTURES COMPLÉMENTAIRES
+T. Conley et C. Udry, « The Adoption of New 
+Agricultural Technologies in Ghana », American 
+Journal of Agricultural Economics 83 (3), pp. 668-
+673, 2004.
+M. Fafchamps et F. Gubert, « The Formation of 
+Risk Sharing Networks », Journal of Development 
+Economics 83 (2), pp. 326-350, 2007.
+M. Jackson et L. Yariv, « The Diffusion of Behavior 
+and Equilibrium Structure on Social Networks », 
+American Economic Review, Papers and Proceedings 
+Issue 97 (2), pp. 92-98, 2007.
+INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
+Un monde sans faim ni malnutrition
+2033 K Street, NW, Washington, DC 20006-1002 USA | T. +1.202.862.5600 | F. +1.202.467.4439 | Skype: IFPRIhomeoffice | ifpri@cgiar.org | www.ifpri.org
+Cette publication a été préparée dans le cadre du projet Enhancing Women’s Assets to Manage Risk under Climate Change: Potential for Group-Based Approaches 
+(Renforcement des actifs des femmes pour gérer les risques liés au changement climatique : le potentiel des approches basées sur le groupe). Elle n’a pas fait l’objet 
+d’un examen collégial. Les opinions exprimées ici sont celles des auteurs ; elles ne représentent pas nécessairement l’opinion ni la position de l’Institut international 
+de recherche sur les politiques alimentaires.
+Cet ouvrage est une traduction d’un texte original publié en anglais par l’IFPRI. En cas de divergence entre le texte original et la traduction, la version originale fait 
+foi. Référence anglaise exacte : Beaman, L., and A. Dillon. 2014. The Diffusion of Agricultural Technologies within Social Networks: Evidence from Composting in Mali. 
+Washington, DC: International Food Policy Research Institute.
+Copyright © 2014 International Food Policy Research Institute. Tous droits réservés. Pour solliciter une autorisation de reproduction, veuillez contacter ifpri-copyright@cgiar.org.
+

data/part_2/0333923335.md

@@ -0,0 +1,586 @@
+# Food crisis and export taxation: Revisiting the adverse effects of noncooperative aspect of trade policies
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/6e8cd3c6-d35d-4090-ad2c-575bd71eddcc/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2016  
+**Rights:** CC-BY-NC  
+**GARDIAN ID:** ce9ac911d0a724efc60c2aec5bc562fb  
+**DataNODE ID:** b0aa257244757daf0c4fb832e19761cb  
+**Siever ID:** aa86b3e1-479a-4061-9f8d-88f165501b89  
+**Token Count:** 6327  
+**Page Count:** 0
+
+## Keywords
+
+models, computable general equilibrium models, wto, trade agreements, food security, trade policies, food, taxation, effects
+
+## Content
+
+ 
+ 
+ 
+ 
+ 
+ 
+ Food crisis and export taxation: Revisiting the adverse 
+effects of noncooperative aspect of trade policies 
+ 
+Citation Bouët, Antoine; and Laborde Debucquet, David. 2016. Food crisis and 
+export taxation: Revisiting the adverse effects of noncooperative aspect 
+of trade policies. In Food price volatility and its implications for food 
+security and policy, eds. Matthias Kalkuhl, Joachim von Braun, and 
+Maximo Torero. Chapter 8, pp. 167 - 179. http://dx.doi.org/10.1007/978-
+3-319-28201-5_8 
+ 
+DOI 
+ 
+http://dx.doi.org/10.1007/978-3-319-28201-5_8  
+Access 
+through 
+IFPRI e-
+brary 
+ 
+http://ebrary.ifpri.org/cdm/ref/collection/p15738coll5/id/5342  
+ 
+Terms of 
+Use 
+IFPRI uploaded the final published version to the institutional repository 
+and is made available under the copyright law of the United States 
+(Title 17, United States Code) which governs "fair use" or the making of 
+photocopies or other reproductions of copyrighted material. Photocopy 
+or reproduction is not to be “used for any purpose other than private 
+study, scholarship, or research.” 
+Permitted 
+Re-Use 
+Permitted re-use of this open access article is determined by the 
+author’s choice of user license. 
+e-Access to IFPRI Research 
+IFPRI has made this chapter openly available. If this access benefits you or your community 
+share your story with ifpri-km@cgiar.org. 
+8Food Crisis and Export Taxation: Revisiting
+the Adverse Effects of Noncooperative Aspect
+of Trade Policies
+Antoine Bouët and David Laborde Debucquet
+8.1 Introduction
+Export restrictions are a common practice in the current world trading system.
+For instance, some developing countries implemented export taxes and export
+restrictions during the recent food crisis (2006–2008). But beyond crisis periods,
+export restrictions are, in fact, trade measures that are permanently adopted by some
+countries: export taxes implemented by Indonesia on palm oil; by Madagascar on
+vanilla, coffee, pepper, and cloves; by Pakistan on raw cotton; by the Philippines on
+copra and coconut oil; and by Argentina on crops and meat.
+At a first glance, from a mercantilist point of view, it might be difficult to under-
+stand why countries implement so many export restrictions. Indeed, policymakers
+tend to favor exports and discourage imports. However, a more thorough analysis
+revealed several justifications.
+In this chapter, we consider these justifications and study how export taxation
+may worsen a food crisis. It is important to keep in mind that reducing import duties
+may also amplify food crisis and that these policy options form the basis of an
+asymmetric game.
+A. Bouët • D. Laborde Debucquet ()
+International Food Policy Research Institute (IFPRI), Washington, DC, USA
+University of Bordeaux, Bordeaux, France
+e-mail: a.bouet@cgiar.org
+© The Author(s) 2016
+M. Kalkuhl et al. (eds.), Food Price Volatility and Its Implications for Food Security
+and Policy, DOI 10.1007/978-3-319-28201-5_8
+167
+168 A. Bouët and D. Laborde Debucquet
+We also focus on institutional aspects and, in particular, why export taxes can
+be so easily raised. It appears that countries have a considerably large degree
+of freedom when implementing such taxes as the WTO does not prohibit export
+taxes and other forms of export restrictions. As stated by Crosby (2008), “general
+WTO rules do not discipline Members’ application of export taxes,” but “they can
+agree—and several recently acceded countries, including China, have agreed—to
+legally binding commitments in this regard.” The Uruguay Round Agreement on
+Agriculture only stipulates that, when implementing a new export restriction, a
+WTO member must (1) consider the implications of these policies on food security
+in importing countries, (2) give notice to the Committee on Agriculture, and (3)
+consult with WTO members that have an interest. The agreement does not institute
+any penalty for countries ignoring the rules. Restrictive export policies do not
+receive much attention from the public or the academic establishment.
+Section 8.2 provides the various justifications for export restrictions. Section 8.3
+investigates the role of export taxes in worsening a food crisis. Section 8.4 focuses
+on the limited institutional role of WTO in the topic of restrictive export policies.
+Section 8.5 concludes this chapter.
+8.2 Why Do Countries Implement Export Restrictions?
+Before discussing the policy justifications for export restrictions, it is noteworthy
+that, from a theoretical point of view, export taxes and export quotas are equivalent:
+quotas could raise revenue if quota allocations are not issued for free but auctioned
+under competitive conditions. However, in the real world, export licenses are given
+to domestic producers and do not generate public revenue. Therefore, export taxes
+and export quotas are not equivalent in the real world.1
+The first justification is the terms-of-trade argument and the desire to increase
+export prices. This is perhaps the most important justification from a theoretical
+point of view. By restricting its exports, a country that supplies a significant share
+of a commodity to the world market may raise the world price of that commodity.
+This implies an improvement in that country’s terms of trade. The reasoning behind
+this argument is very similar to the optimum tariff argument, which states that, by
+implementing a tariff on its imports, a “large” country can significantly decrease
+the demand for a commodity that it imports; this therefore leads to a decrease in
+the commodity’s world price, which is again an improvement in the terms of trade
+(Bickerdike 1906; Johnson 1953).
+When considering the final consumption of food products, the second justifi-
+cation is food security: export taxes reduce domestic prices. When considering a
+food product which is an important commodity in a country’s national consumption
+1Let us mention that export quota and export taxes are also not equivalent under retaliation, that
+is to say if implemented during a trade war between large countries (see Rodriguez 1974; Tower
+1975).
+8 Food Crisis and Export Taxation: Revisiting the Adverse Effects. . . 169
+structure and is also exported, by imposing an export tax, a government creates
+a wedge between the world price and the country’s domestic price. This can
+lower the final domestic consumption price by reorienting domestic supply toward
+the domestic market. Piermartini (2004) cited the Indonesian government as an
+example. The Indonesian government frequently imposes export taxes on palm oil
+products, in particular on palm cooking oil, as it considers cooking oil an “essential
+commodity” for local households. This rationale was often used by governments
+during the food crisis of 2006–2008 to justify implementing export taxes and other
+forms of export restrictions. Some examples of which are as follows: Bangladesh,
+Brazil, Cambodia, China, Egypt, and India implemented restrictive policies on rice
+and Argentina, India, and Kazakhstan on wheat. Export restrictions are anticyclical
+trade policy instruments: when international prices are high, local consumers are
+hurt by high domestic prices; implementing export restrictions decreases local prices
+but contributes to the rise of international prices.
+The third justification takes into account the existence of intermediate consumers
+(firms) of the taxed products in a country. If a raw commodity is exported
+and is also used by the local processing industry, imposing export taxes on
+this primary commodity indirectly subsidizes the local processing industry by
+lowering the domestic price of inputs compared to the commodity’s world price,
+which is nondistorted. It has the same mechanism as the previous reason: export
+taxation gives local producers more incentive to sell their product domestically.
+For example, in Indonesia, an export tax on lumber promoted the development
+of the domestic wood-processing industry; the development was judged to be
+excessive for environmental reasons as it contributed to the depletion of forests
+(World Bank 1998). In 1988, Pakistan imposed an export tax on raw cotton in
+order to stimulate the development of the yarn cotton industry. Export taxes on
+palm oil are imposed in Indonesia and Malaysia to support the development of
+downstream industries (biodiesel and cooking oil; see Amiruddin 2003). According
+to this line of reasoning, export taxes may also be applied to a whole value chain
+by decreasing the level of taxation along the value chain. This is called differential
+export tax (DET) rates: the policy of imposing high export taxes on raw commodities
+and low export taxes on processed goods. This policy generates public revenues
+and promotes production at the later stages of a value chain. Bouët et al. (2014)
+studied the theoretical justification of this trade policy, and then they developed a
+partial equilibrium model of the global oilseed value chain and simulated the total
+elimination of DETs in Argentina and Indonesia and the independent removal of
+export taxes at various stages of production in the two countries. Their estimations
+showed that removing export taxes along the entire value chain in Argentina and
+Indonesia reduced the local biofuel production; they also point out that the DETs
+were implemented to raise public revenues.
+The fourth justification is also a “raison d’être” for export taxes. Export taxes
+provide a source of revenue to developing countries that have limited capacity to
+rely on domestic taxation. This is a second-best argument because the imposition
+of lump-sum taxes is a first-best policy (Ramsey 1927; Diamond 1975). It is
+noteworthy that only export taxes (and not export quotas) serve this objective.
+170 A. Bouët and D. Laborde Debucquet
+As with all trade policy, export taxes may serve the purpose of redistributing
+income. This is the fifth justification of this policy instrument combining different
+aspects from the three previous arguments. Like import tariffs, export taxes are
+measures that imply distribution of income. Here, this is detrimental to domestic
+producers of the taxed commodity but benefits domestic consumers and public
+revenues.
+So we arrive at the first conclusion: export taxes are attractive policy instruments
+since they may serve different positive purposes for a government.
+This is the reason why export taxes are relatively common in the current global
+trading system. Some studies have estimated their importance. Laborde et al. (2013)
+used a new detailed global data set on export taxes at the HS6 level and the MIRAGE
+global CGE model to assess the impact of export taxes on the world economy.
+They found that the average export tax on global merchandise trade was 0.48 %
+in 2007, with the bulk of these taxes imposed on energy products. Moreover, the
+removal of these taxes would increase global welfare by 0.23 %, a larger figure than
+the gains projected by the Doha Round. Both developed and emerging economies,
+such as China and India, would gain from removing export taxes. Medium and
+small food-importing countries without market power (such as the least-developed
+countries) would also benefit from the elimination of export restrictions. The export
+taxes implemented by the countries in the Commonwealth of Independent States
+on their energy sector appear to play a critical role in the overall economic impact
+of the removal of these taxes. However, some countries, such as Argentina, would
+experience income losses.
+In the next section, we focus on using food security as a justification for export
+taxation. We show how implementing this policy instrument is a noncooperative
+trade policy when food prices are high. During a food crisis, governments of food-
+exporting countries are tempted to alleviate high food prices by restricting exports to
+encourage local producers to sell food items domestically and decrease local prices.
+But in doing so, these countries decrease the food supply on the world markets,
+causing world food prices to increase. This worsens the food crisis and is typically
+a “beggar-thy-neighbor” policy.
+But in times of food crisis, restricting exports is not the only noncooperative
+trade policy. Food-importing countries are, at the same time, tempted to decrease
+domestic food prices by decreasing import duties. In doing so, they increase their
+national demand on the world market, reinforcing the upward pressure on world
+food prices. This is another noncooperative aspect of trade policies in periods of
+food crisis.
+The combination of export taxes and reduced import duties increases the upward
+pressure on world prices when food prices are high. On the contrary, when world
+agricultural prices are low, food-exporting countries may be tempted to decrease
+export taxes and food-importing countries to increase import duties. This increases
+food supply and reduces food demand on world markets and therefore once again
+increases the downward pressure on world prices. It may appear that trade policies
+make world markets structurally more volatile.
+8 Food Crisis and Export Taxation: Revisiting the Adverse Effects. . . 171
+8.3 ToWhat Extent Does Export Taxation Amplify Food Price
+Volatility?
+Economic literature helps to explain why large food-exporting countries implement
+export taxes and large food-importing countries implement import duties. The first
+reason is terms of trade. Bouët and Laborde (2012) designed a general equilibrium
+model of international trade between four countries—two large (1 and 2) and two
+small (3 and 4)—which trade the two commodities A (agricultural commodity) and
+I (industrial good). Countries 1 and 4 have a comparative advantage in A, while
+countries 2 and 3 have a comparative advantage in I. Import duties on the industrial
+good are assumed to be bound at 0, which implies that countries 1 and 4 will not
+use this policy instrument.
+Using this simple framework, it is easy to show that if governments’ objective
+is to maximize real income (welfare), the Nash equilibrium is a combination of a
+positive import duty in country 2 (the large food-importing country) and a positive
+export tax in country 1 (the large food-exporting country), while free trade is the
+best policy for both small countries. The results point out that large countries may
+manipulate world prices by imposing import duties or export taxes, depending on
+their export status. This Nash equilibrium implies a reduction in world real income,
+but large countries may benefit by having augmented real income. It is important to
+note that an import duty in the large food-importing country tends to decrease the
+world price of the agricultural commodity, while an export tax in the large food-
+exporting country tends to increase it. If at the Nash equilibrium, the world price
+of this commodity is increased, the small food-importing country’s real income is
+reduced, while the small food-exporting country’s real income is augmented. This
+teaches us that (1) export taxes on agricultural commodity improves terms of trade
+of large food-exporting countries and (2) when combined with import duties in large
+food-importing countries, world trade is drastically reduced and world real income
+is hurt with no policy option for small countries.
+Bouët and Laborde (2012) also showed that if a government’s objective is to
+achieve stable domestic agricultural goods prices during a food crisis, the best
+response is to decrease import taxes for a large food-importing country and to
+increase export taxes for a large food-exporting country. Both policies increase the
+world price of agricultural goods, thereby hurting a small food-importing country
+while increasing a small food-exporting country’s real income.
+Consequently, a collective action problem emerges from this simple theoretical
+framework: in case of a food price spike, governments which are concerned with
+establishing domestic food security and stabilizing domestic food prices are tempted
+to reduce import duties on food items if they are food importers and to increase
+export taxes on food items if they are food exporters. Both policy reactions tend
+to reinforce the increase in food world prices. Martin and Anderson (2012) also
+pointed out this inefficiency. Gouel (2014) designed a simple stochastic partial
+equilibrium model and concluded that countercyclical trade policies are inefficient
+172 A. Bouët and D. Laborde Debucquet
+at the global level: these trade policies increase world prices when the prices are
+relatively high, while they reduce world prices when the prices are relatively low.2
+How much these trade policies amplify world price spikes remains to be known.
+In the same paper, Bouët and Laborde (2012) used the MIRAGE model of the world
+economy to evaluate this point. The study uses the static version of MIRAGE under
+perfect competition with 27 regions and 25 sectors.3 They simulated a demand
+shock which led to a 10 % increase of the world wheat price. In the first policy
+scenario, countries that are net wheat exporters implement export taxes such that
+the real domestic price of wheat is constant. This led to additional export taxes in
+the range of 16–25 %. This policy reaction also caused the world wheat price to
+increase by 16.8 % rather than 10 %. In the second scenario, countries that are
+net wheat importers implemented import taxes (import subsidies are forbidden)
+such that the real domestic wheat price remained constant (the domestic price is
+not constant if the strategic rigidity—i.e., no import subsidies—is binding). Import
+duties are decreased by between 13 and 30 % age points, and the world price of
+wheat increased by 12.6 %. If both policy reactions are allowed (increasing export
+taxes and reducing import duties without implementing import subsidies), additional
+export taxes between 19 and 50 % were implemented, and the world price of wheat
+increased by 20.6 %: implementing these trade policies caused the world price to
+more than double.
+Concerning countries’ national real income, net wheat exporters’ economic wel-
+fare is positively affected by the initial shock and their policy response (increasing
+export taxes), while that of net wheat importers’ welfare is negatively affected. The
+economic welfare of Argentina as well as those of Australia, Canada, and Ukraine
+significantly increased under all shocks, in particular under the shock that combines
+endogenous export taxes and import tariffs. On the other hand, net wheat importers,
+such as Egypt and Eastern Africa, are significantly hurt by these shocks in terms of
+real income.
+This collective action problem necessitates an institutional response: the next
+section examines to what extent the WTO may provide a framework adapted to
+discipline these inefficient trade policies.
+2In case of food glut on world markets, world prices are relatively low: in the model designed by
+Gouel (2014), import duties may be increased in the large food-importing country and export taxes
+may be decreased in the large food-exporting country since governments have also an objective of
+domestic price smoothing.
+3The use of a dynamic version of MIRAGE could open the door for new analyses and new
+policy conclusions. In the long term, export restrictions diminish sector profitability and, as such,
+may decrease investment in these sectors. This means less supply in following periods of time
+with a potentially higher risk of increased domestic price which could lead local governments to
+implement new export restrictions. This increases the long-term cost of these policies with the
+extreme situation where a net-exporting country turns into a net-importing country.
+8 Food Crisis and Export Taxation: Revisiting the Adverse Effects. . . 173
+8.4 Can Export Restrictions Be Disciplined in theWTO
+Framework?
+There is a clear trade-off between import duties and export taxes with a double
+asymmetry. First, in times of food crisis, export taxes are raised while import
+duties are reduced. Second, while increasing export taxes is clearly identified as a
+noncooperative policy, it is much more difficult to criticize a country when it reduces
+its import duties. However, both policy reactions have the same impact on world
+prices, and both policies hurt poor food-importing countries. While reducing import
+duties cannot be opposed from an institutional point of view, the policy reaction may
+be considered as a “beggar-thy-neighbor” policy when analyzed from an economic
+perspective.
+The literature clearly reflects this dilemma. While Martin and Anderson (2012)
+and Bouët and Laborde (2012) underlined that reducing import duties also affects
+world price variability, Josling (2014) noted that “such impact : : : [is] : : : likely
+minor compared to the positive benefits for domestic consumers. Exporters : : : [are]
+also benefiting from the reduction in protection levels and it would therefore not
+: : : [make] sense to develop rules that : : : [inhibit] countries from making increased
+use of imports when domestic prices are high” (Josling 2014, p. 6). On the contrary,
+Gouel (2014) concluded that “export restrictions do not play a more important
+role : : : [in recent food price spikes] than tariffs. : : : they both contribute to shift
+volatility to partners’ markets” (Gouel 2014, p. 18).4
+While the WTO gives its members total freedom to decrease import duties
+(even import subsidies are tolerated), the institution forbids the implementation of
+quantitative export restrictions (Article X1:1). However, international law makes an
+exception for temporary export quotas in times of critical shortages of food items
+(Article XI:2). Export taxes are not prohibited, but the WTO requires its members to
+consider how their export taxes will affect their trading partners and to notify when
+implementing export taxes.
+Anania (2014) considered that the provisions concerning export restrictions,
+which was included in the agricultural “modalities” issued in December 2008,
+reflected a broad agreement on this issue and are not ambitious. He proposed
+modifying Article XI.2 by limiting the export prohibitions and restrictions which
+are allowed under Article XI to a certain time frame. He wrote: “Existing export
+prohibitions and restrictions in foodstuffs and feeds under Article XI.2 (a) of GATT
+1994 shall be eliminated by the end of the first year of implementation” and “any
+new export prohibitions or restrictions under Article XI.2 (a) of GATT 1994 should
+not normally be longer than 12 months, and shall only be longer than 18 months with
+the agreement of the affected importing Members.” He also highlighted the need to
+4However, Gouel (2014) also concludes that export restrictions may be more damaging in the
+real world because of the asymmetry of world price distribution (commodity prices are positively
+skewed).
+174 A. Bouët and D. Laborde Debucquet
+strengthen the consultation and notification procedures so that they are performed
+within 90 days of introducing a new restrictive export measure.
+Anania (2014) recommended two options, which he deemed realistic and can
+potentially be included in a low-ambition Doha Agreement. First, as proposed
+by many other observers, the commitment to shelter noncommercial interventions
+from export restrictions made by the G20 at the 2011 Cannes Summit5 needs to
+be transformed into a legal commitment at the WTO. Unfortunately, at the 2011
+WTO Ministerial Conference in Geneva, the proposal6 to adopt this approach at a
+multilateral level was opposed by key countries including Argentina, Brazil, China,
+India, and South Africa7, which are all G20 members. And without a consensus, the
+proposal was not adopted. Even though it is not legally binding, a statement made
+during a Ministerial Conference would have been the first step toward the inclusion
+of this basic requirement in the final Doha package—avoiding export restrictions
+because they adversely affect food aid. Indeed, food purchases by international
+organizations concern mainly key staple products and a few processed products for
+emergency reasons.8 They represent a limited amount of total worldwide traded
+quantities of these food items. Second, making existing disciplines enforceable
+essentially involves clarifying the definition of the conditions under which export
+quantitative restrictions are allowed. The exact wording of Article XI is imprecise:
+“temporarily applied to prevent or relieve critical shortages of foodstuffs or other
+products essential to the exporting contracting party” (Article XI:2a of GATT 1994).
+In particular, the words “temporarily” and “critical” need to be clearly defined.
+However bringing discipline into the area of export restrictions is a complex issue.
+Cardwell and Kerr (2014) adopted a pessimistic view on this issue. They opined
+that any disciplinary measures to deal with export taxes would neither be effective
+nor have any deterrent effects. Trade disputes, including export restrictions, occur
+over a different time frame than the other disputes. Any disputes arising from export
+restrictions during a period of high food prices are unlikely to be resolved before the
+prohibited restriction is lifted. Moreover, the authors also believed that retaliatory
+5“According to the Action Plan, we agree to remove food export restrictions or extraordinary
+taxes for food purchased for noncommercial humanitarian purposes by the World Food Program
+and agree not to impose them in the future.” G20 Cannes Summit, 3–4 November 2011. This
+commitment was based on the G20 Action Plan defined on 23 June 2011 and was based on Rec-
+ommendation #5 from the international organizations report for the G20 on “Price volatility in food
+and agricultural markets: policy responses.” Available at http://www.amis-outlook.org/fileadmin/
+templates/AMIS/documents/Interagency_Report_to_the_G20_on_Food_Price_Volatility.pdf.
+6The proposal was supported by Australia, Canada, Chile, Costa Rica, the European Union, Korea,
+Indonesia, Japan, Mexico, Norway, Saudi Arabia, Singapore, Switzerland, and Turkey.
+7See Bridges, Volume 15-number 37. Available at http://ictsd.org/i/news/bridgesweekly/117348.
+8For instance, the World Food Program, in 2013, procured mainly rice, maize, wheat,
+wheat flour, pulses, vegetable oil, sorghum, maize meal, sugar, and blended food. The lat-
+ter includes pasta, high-energy biscuits, emergency rations, and ready-to-use supplementary
+foods (breast milk supplement)(see http://documents.wfp.org/stellent/groups/public/documents/
+communications/wfp264134.pdf).
+8 Food Crisis and Export Taxation: Revisiting the Adverse Effects. . . 175
+measures are difficult to design; retaliation for an export restriction in a particular
+sector should be carried out in another sector, and the retaliation should amount to
+the same value as the lost exports. This is likely difficult to implement when there is
+great disparity between the countries concerned, such as in the case of trade between
+poor net food-importing countries and countries having imposed export restrictions.
+8.5 Concluding Remarks: Looking for a Solution
+As discussed in Sect. 8.2, export restrictions play an important role in increasing
+price volatility and magnifying the impact of natural weather variability on agricul-
+tural markets. It greatly contributes to policy uncertainty and therefore undermines
+private investments in domestic agricultural supply, and in trade-related infrastruc-
+ture and network. The binding process of import tariffs at the WTO was particularly
+aimed at reducing this policy instability, creating a more secure environment for the
+private sector and fostering investments. At the same time, it limits the possibility
+of a retaliation and prevents noncooperative outcomes and the so-called trade wars
+from emerging.9 However, the current system is quite asymmetric at the WTO, as
+mentioned in Sect. 8.3, while import restrictions are severely dealt with by a set
+of disciplinary measures, export restrictions do not face the same constraints. On
+the import side, a clear framework is provided by the binding of tariffs (100 % in
+agriculture); tariffication and elimination of quantitative import restrictions (GATT
+article XI), exceptional conditions notwithstanding; and stringent rules framing the
+use of contingent protection (antidumping duties in GATT article 6, safeguards
+GATT article 19, etc.). On the export side, only quantitative export restrictions are
+currently disciplined, and the policy space to use them remains large, especially
+for food products. Because supplier countries do not face similar disciplines, this
+asymmetry undermines the pursuit of global integration of agricultural markets,
+and it strengthens the arguments of countries that do not want to reduce their
+tariffs and increase their reliance on world markets. Indeed, the current framework
+provides an unbalanced distribution of risks between importers and exporters, and
+it also lets suppliers increase their market power. It could potentially even have
+worse consequences: the overall price instability and the asymmetry in disciplinary
+measures could lead to the relaxation of disciplinary actions against contingent
+9In fact, applying the game theory to trade policy leads to the conclusion that to facilitate the
+emergence of cooperation, there is a choice of either institutionalizing a discipline that forbids
+noncooperation (a world institution that forbids countries to implement beggar-thy-neighbor trade
+policies) or allowing countries to use retaliatory measures to prevent other countries from being
+noncooperative. The threat of retaliation is viewed as a powerful means of encouraging cooperation
+(see Axelrod 1981; Bouët 1992). The reality of the trading system today lies somewhere between
+these two options since the WTO forbids the use of some policy instruments (import duties) but
+authorizes the use of others (export restrictions). Moreover, a global institution is necessary since
+trading partners differ in size and capacity to hurt other countries.
+176 A. Bouët and D. Laborde Debucquet
+import measures, as with the special safeguard mechanism introduced by the G-33,
+instead of strengthening regulations on contingent export restrictions.
+In this context, it is important to discuss potential solutions by means of new
+WTO regulations or experimenting with new concepts found in some bilateral
+agreements. Indeed, the elimination of export restrictions can be seen as a first-
+best solution, but domestic political economy will make it unrealistic to attain such
+outcome in the short run, especially for countries with weak institutions. This is
+because these countries will need time to reform their tax system to replace export
+taxes by production taxes.
+If not at the multilateral level, a solution may be reached at least on a plurilateral
+basis.10 Looking at recent bilateral agreements reveals that some of these features
+are already included in both North–North and North–South deals. As an example of
+a North–North deal, the Comprehensive Trade and Economic Agreement (CETA)
+between the EU and Canada states its position on restrictive trade policies in certain
+terms; Article 7 of the agreement eliminates duties and taxes on exports: “Neither
+Party may maintain or institute any duties, taxes or other fees and charges imposed
+on, or in connection with, the exportation of goods to the other Party, or any internal
+taxes or fees and charges on goods exported to the other Party, that are in excess
+of those that would be imposed on those goods when destined for internal sale.”
+The Dominican Republic–Central America Free Trade Agreement (CAFTA-DR) is
+a free trade agreement between the USA, five Central American countries, and the
+Dominican Republic. The agreement’s key principle is to bind existing measures,
+granting them a “grandfathering” clause, and ban new export taxes (export bans are
+still subject to Article XI of the GATT); Article 3.8 of the agreement states: “[ : : : ]no
+Party may adopt or maintain any prohibition or restriction on [ : : : ] the exportation
+or sale for export of any good destined for the territory of another Party, except in
+accordance with Article XI of the GATT 1994.” Article 3.11 indicates clearly that
+discriminatory practices are banned: “Export Taxes Except as provided in Annex
+3.11, no Party may adopt or maintain any duty, tax, or other charge on the export of
+any good to the territory of another Party, unless such duty, tax, or charge is adopted
+or maintained on any such good: (a) when exported to the territories of all other
+Parties; and (b) when destined for domestic consumption.”
+The Economic Partnership Agreement, negotiated between the EU and some
+members of the Southern African Development Community (2015), also expresses
+its position in firm language while still maintaining some flexibility for the less-
+advanced economies. Article 26.1 follows the binding approach: “No new customs
+duties or taxes imposed on or in connection with the exportation of goods shall be
+introduced, nor shall those already applied be increased, in the trade between the
+10If a plurilateral approach on all commodities is not achievable, a commodity-by-commodity
+approach following the sectoral initiatives could be considered. The main limit is that for most
+of the key staple commodities, one of the major exporters is very defensive regarding export taxes
+regulations (e.g., Russia, Argentina, and India on wheat).
+8 Food Crisis and Export Taxation: Revisiting the Adverse Effects. . . 177
+Parties from the date of entry into force of this Agreement, except as otherwise
+provided for in this Article.” Article 26:2 recognizes that “In exceptional circum-
+stances, [ : : : ] where essential for the prevention or relief of critical general or local
+shortages of foodstuffs or other products essential to ensure food security Botswana,
+Lesotho, Namibia, Mozambique and Swaziland may introduce, after consultation
+with the EU, temporary customs duties or taxes imposed on or in connection with
+the exportation of goods, on a limited number of additional products.” So, in this
+agreement, the largest economies (South Africa, the EU) have strong commitments
+to fulfill, while the others benefit from a special and differentiated treatment.
+Sections 6–10 of Article 26 provide an interesting framework for how to prevent
+products exempted from export taxes from being reexported to third parties on a
+bilateral basis.
+So, what can be done, especially in the context of restricting contingent,
+short-term export restrictions? As previously discussed, humanitarian interventions
+should be shielded from these measures in any basic WTO decisions, but attempts to
+change international laws have faced strong opposition. In this context, the first basic
+step is to enforce a strong monitoring and notifications process,11 aimed at reducing
+asymmetry of information. To keep both private and public agents informed, there
+are ongoing efforts to create agricultural market information systems aimed at
+providing updated policy changes for key agricultural commodities not only at the
+WTO but also at the G20, with its AMIS initiative.12 However, the lack of automatic
+sanctions when countries fail to notify, which is a larger issue facing the WTO
+than export restrictions, is still a major problem. The second step is to develop a
+system that focuses on protecting small and vulnerable economies (SVEs). SVEs
+are generally more open and have lower income, poorer consumers, and no capacity
+to retaliate. Also, their demand, even when aggregated, cannot be considered as a
+major driver of global price increase. To ensure healthy global trade, protecting these
+countries and limiting negative externalities coming from other larger countries
+should be prioritized.
+A natural way to address this issue is the “reversed” tariff quota approach. For
+normal import levels (e.g., the average bilateral import volume in the last 3 years),
+SVEs should be able to import food products without quantitative restrictions and
+additional export taxes. This would guarantee normal market access conditions
+even when world market turmoil causes major traders to change their policies.
+Beyond the “historical” level of imports, exporters would be free to apply short-
+term restrictions.
+11This issue was emphasized in the WTO agricultural committee meeting on 21 June 2011: “These
+require the restricting country to take into account the impact on importing countries’ food security,
+to notify the WTO as soon as possible, and as far in advance as possible, to be prepared to discuss
+the restriction with importing countries and to supply them with detailed information when asked
+for it.”
+12http://www.amis-outlook.org/home/en/
+178 A. Bouët and D. Laborde Debucquet
+Another solution is to replace rigid legislation by a price mechanism and to
+apply a Pigouvian tax on the negative externalities of short-term surges in export
+restrictions. When a country, at least a G20 country, implements a new export
+restriction on food products, it would have to pay a fee. If more sophisticated pricing
+rules can be developed, a first approximation could be the historical amount of taxes
+collected from goods imported by an SVE from this exporter. The automaticity
+of the payment is ensured by the effective revenue collected by the exporting
+countries13 and will address the key problems of (1) a lengthy dispute settlement at
+the WTO and (2) the lack of retaliation capacity by the SVE. The income generated
+through collecting this fee could be directly channeled toward helping SVEs pay
+their surging food import bills and fund their emergency safety nets. Alternatively,
+the income could also be used to provide the World Food Program with extra
+resources so that the program can cope with an increase in world food prices
+and develop targeted interventions. Similarly, a market for authorizing quantitative
+restrictions (like the “permits to pollute”) can allow exporters to restrict their export
+quantities, while SVEs would have “importing rights” calculated based on historical
+import levels and could sell these licenses to exporters, thereby generating income
+to cover their import bills. These different measures are designed to provide an
+international insurance mechanism against harmful policies by reducing incentives
+to implement them (additional costs to exporters) and providing remedies for the
+most vulnerable countries.
+Open Access This chapter is distributed under the terms of the Creative Commons Attribution-
+Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any
+noncommercial use, distribution, and reproduction in any medium, provided the original author(s)
+and source are credited.
+The images or other third party material in this chapter are included in the work’s Creative
+Commons license, unless indicated otherwise in the credit line; if such material is not included
+in the work’s Creative Commons license and the respective action is not permitted by statutory
+regulation, users will need to obtain permission from the license holder to duplicate, adapt or
+reproduce the material.
+References
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+13For short-run export taxes during an episode of high price volatility, tax revenue is rarely the
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+8 Food Crisis and Export Taxation: Revisiting the Adverse Effects. . . 179
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+

data/part_2/0359075530.md

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+# The impact of cash and food transfers: Evidence from a randomized intervention in Niger
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/65d2aa8b-ff84-44b8-ae98-29311fd92b11/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2013  
+**Rights:** N/A  
+**GARDIAN ID:** 85385b8a41c97682a7537f36940a9786  
+**DataNODE ID:** ee7e38304c9faaaa0ce6fa6e3ca63582  
+**Siever ID:** 98f1fc7a-7ca9-411f-b20b-72c64538e5c6  
+**Token Count:** 7933  
+**Page Count:** 0
+
+## Keywords
+
+cash transfers, food aid, food security, experimental design, food, niger
+
+## Geography
+
+- **Regions:** Western Africa, Sub-Saharan Africa, Africa, World
+- **Countries:** Niger
+
+## Content
+
+ 
+ 
+ 
+ 
+ 
+ 
+The impact of cash and food transfers: Evidence from a randomized intervention in 
+Niger  
+ 
+ 
+John Hoddinott 
+International Food Policy Research Institute 
+ 
+Susanna Sandström 
+World Food Programme and Abo Akademi University, Turku, Finland 
+ 
+Joanna Upton 
+Cornell University 
+ 
+ 
+ 
+Selected Paper prepared for presentation at the Agricultural & Applied 
+Economics Association’s 2013 AAEA & CAES Joint Annual Meeting, Washington, 
+DC, August 4-6, 2013. 
+ 
+ 
+ 
+ 
+ 
+ 
+Acknowledgements: 
+We are grateful to Kountche Boubacar Idrissa for supervising the survey team, to Lynn Brown, Gianluca 
+Ferrera, Giorgi Dolidze, Marco Sanguineti and other staff at the World Food Programme for valuable support 
+and conversations and seminar participants at Cornell University for comments on an earlier draft. We 
+gratefully acknowledge funding from the Government of Spain received through the World Food Programme. 
+Errors are ours.   
+ 
+Corresponding author: John Hoddinott, 2033 K St. N.W., Washington D.C. 20006. J.Hoddinott@cgiar.org 
+ 
+Key words: cash and food transfers; food security; Niger; randomized intervention 
+ 
+Copyright 2013 by John Hoddinott, Susanna Sandström and Joanna Upton. All rights reserved. Readers may 
+make verbatim copies of this document for non-commercial purposes by any means, provided that this 
+copyright notice appears on all such copies. 
+  
+Abstract 
+ 
+We assess the relative impacts of receiving cash versus food transfers using a 
+randomized design. Drawing on data collected in eastern Niger, we find that 
+households randomized to receive a food basket experienced larger, positive 
+impact on measures of food consumption and diet quality than those 
+receiving the cash transfer. Other outcomes showed greater variation by 
+season. Receiving food reduced the use of a number of coping strategies but 
+this effect was more pronounced during the height of the lean season. 
+Households receiving cash spent more money repairing their dwellings prior 
+to the start of the rainy season and spent more on agricultural inputs during 
+the growing season. Less than five percent of food was sold or exchanged for 
+other goods. Food and cash were delivered with the same degree of 
+frequency and timeliness but the food transfers cost 15 percent more to 
+implement. 
+ 
+ 
+JEL classification: D04, I38, O12 
+  
+1. Introduction 
+Interest in providing cash transfers for food assistance has been increasing in recent years. 
+Cash transfers have known advantages relative to food transfers with respect to timeliness 
+of delivery (Gentilini 2007; Lentz et al forthcoming).The other potential benefits and 
+drawbacks of each form of transfer, across a range of criteria, depend on the context and 
+objectives of the program (Upton and Lentz 2011). It is widely supposed that--as predicted 
+by economic theory--recipients would prefer to receive cash; provided that cash transfers 
+integrate the transaction costs involved in obtaining a comparable food transfer, recipients 
+can better meet their diverse needs with a cash transfer. However, there is little rigorous 
+evidence on the comparative impacts of cash and food transfers on food security and food 
+related outcomes. There are numerous studies on the impact of cash transfers (see 
+summaries in Fiszbein et al 2009 and DfID 2011) and numerous studies on the impact of 
+food transfers (see Margolies and Hoddinott 2011).  However, as Hidrobo et al (2012) note 
+comparisons of these impacts is confounded by differences in program design, the 
+magnitude of the transfer, and the frequency of the transfer.1  
+This paper contributes to our understanding of the impact of cash and food transfers 
+on household food security. It uses a randomized design implemented by the World Food 
+Programme (WFP) in the Zinder region of Niger. Niger is an appropriate venue for such a 
+study. Following a famine in 2005, it has become a significant recipient of food assistance 
+(WFP 2012). There are sharp seasonal dimensions to food insecurity in Niger and our 
+evaluation design allows us to assess whether the impact of food and cash transfers varies 
+by season.  
+We find that food and cash have different impacts on measures of food security. 
+Households in villages randomized to receive the food basket experienced larger, positive 
+impact on measures of food consumption and diet quality than those receiving the cash 
+transfer. The likelihood of attaining an acceptable food consumption score was 10.9 
+percentage points higher for food households in July and 12.1 in percentage points higher in 
+October. By contrast, households randomized to receive cash were more likely to make bulk 
+purchases of grains. Other outcomes, however, showed greater variation by season. 
+Receiving food reduced the use of a number of coping strategies but this effect was more 
+                                                     
+1
+ See Hidrobo et al (2012) for a review of recent studies including work by Sharma (2006) and Cunha, De 
+Giorgi, & Jayachandran (2011).  
+pronounced during the lean season. Households receiving cash spent more money repairing 
+their dwellings prior to the start of the rainy season and spent more on agricultural inputs 
+during the growing season. Less than five percent of food was sold or exchanged for other 
+goods. Both food and cash were delivered with the same degree of frequency and 
+timeliness but the food transfers cost 15 percent more to implement.  
+ 
+2. Contexts 
+Zinder region, Niger 
+Niger is one of the poorest countries in the world. It is the fifth poorest when ranked by 
+gross national income per capita (PPP dollars), 172 of 187 when ranked on life expectancy 
+and 186 of 187 on the Human Development Index (UNDP 2012). Poverty in Niger is 
+endemic; 65 percent of the population falls under the national poverty line of $1.65 PPP per 
+day, and the Human Development Report headcount index ranks nearly 93 percent of the 
+population as suffering from deprivation (UNDP 2012).  Only about 11 percent of Niger’s 
+land is considered arable, and crops suffer from volatility in rainfall and frequent drought. 
+Even when food is available, there are systemic and periodic problems with access and use. 
+Severe food crises affected parts of Niger in 2005-2006, 2010, and again in 2012.  
+The Zinder region is by Nigerian standards relatively well off.2  It is in the southern 
+part of the country that receives more rain than the arid north.  Approximately 40 percent 
+of Niger’s millet production comes from Zinder and the nearby region of Maradi, and Zinder 
+is a surplus production zone for millet and cowpeas, two key staples (FEWS 2010).  Many 
+inhabitants are agro-pastoralists, mixing agriculture with the raising of livestock primarily 
+small ruminants. It is also a key commercial hub, in part due to its close proximity and close 
+cultural ties to Nigeria (Eilerts 2006).  Yet the region has frequently been among the hardest 
+hit by food crises, and chronically suffers some of the highest rates of malnutrition (Grobler-
+Tanner 2006).  During the 2005 famine, daily mortality rates were higher in Zinder than in 
+any other region, and an estimated 65% of the population had to resort to ‘irreversible’ 
+coping strategies such as selling large livestock or production tools (Reza et al 2008).  These 
+challenging conditions are embedded in a complex cultural landscape. Zinder is culturally 
+dominated by the Hausa people, a traditionally agricultural people who speak the Hausa 
+                                                     
+2
+ Outside of the capital, Niamey, Niger is divided into seven regions which in turn are divided into 36 
+departements which are further divided into communes. 
+language.  They share Zinder with several smaller ethnic groups including the agro-pastoral 
+Kanuri and the pastoral Peulh, Touareg, and Toubou.   
+ 
+Experimental design 
+In late 2010, the Government of Niger’s (GoN) famine early warning system identified the 
+Mirriah departement in Zinder as a place where humanitarian assistance would be required 
+during the six month period before the September 2011 harvest. Given the availability of 
+grains in local markets, WFP determined that it would be feasible to provide both food and 
+cash to beneficiaries in this area.3  
+Within Mirrah, WFP in cooperation with the GoN identified 126 villages both in need 
+of assistance and suitable for the public works envisaged as part of this intervention. Some 
+villages were subsequently dropped because another organization was planning to provide 
+food assistance to them or because the villages themselves declined to participate. Further 
+investigation indicated that 13 villages had such poor market access that it was 
+inappropriate to provide them with cash. These villages received transfers but were not 
+included in the surveys leaving 79 villages that were both suitable for the project and that 
+could receive either food or cash transfers. Implementing parties deemed that it would be 
+too complicated and/or lead to tension if proximate villages—especially that shared a 
+worksite during the public works phase—received different forms of transfer. Hence 
+randomization was done at the worksite level. This led to 52 village or village cluster 
+randomization units. Randomization was done through a procedure that assured an 
+approximately equal distribution of villages/worksites by zone and size receiving each 
+transfer.  
+The project was implemented in two phases over a six month period, from April 
+through September 2011. Phase 1 involved public works activities that took place from April 
+to June. Every household in participating villages was guaranteed 75 day’s work on these 
+projects.4 Most worksites were located near the targeted villages. While participation in 
+public works was voluntary, almost all households in these villages too part in work activities 
+                                                     
+3
+ A market assessment in May 2011 confirmed that most traders in Zinder were still purchasing grain from 
+local sources. Unlike the northern and western parts of Niger, Zinder is relatively secure which meant that 
+heavily armed escorts would not be needed for cash disbursements. 
+4
+ A small number of households such as those with a young mother and young children were exempted from 
+the work requirement and given an unconditional payment. 
+(98 percent in the food transfer zone and 95 percent in the cash transfer zone). The 
+registered beneficiary, who was usually the household head, was paid twice-monthly. In 
+cash villages, they received 1000 FCFA (roughly 2 USD) per day worked to a maximum of 
+25000 FCFA per month. Food payments were provided in the form of a food basket of 
+commodities similar to those typically eaten in the region. A day payment provided a full 
+ration of food for the average household size of seven people, including 3.5 kg of grain 
+(primarily maize in the first transfer period and sorghum in the second), 0.72 kg of pulses 
+(cowpeas, red beans, or lentils), 0.14 kg of vegetable oil, and 0.035 kg of salt. Based on the 
+average monthly prices of these commodities between April and September 2010, the 
+average monthly cost of this food basket to recipients was 24000 FCFA. During the design 
+phase, respondents told project staff that it would cost approximately 800 FCFA to make 
+four trips per month to markets to buy food. Subtracting these transport costs made the 
+value of the food basket and the cash transfer equivalent.5 The transport, storage and 
+distribution of food and cash payments were contracted out to several Nigerian non-
+governmental organizations. For the cash transfers, they charged WFP a fixed percentage of 
+the total amount of cash distributed. For food transfers, they charged a monetary fee based 
+on the quantity of food delivered. These transport, storage and distribution costs were 15.4 
+per cent higher for food relative to the cash payments.6 
+During the second phase, from July through September, 50 percent of households in 
+each village were selected to continue to receive the same transfer without having to fulfill 
+a work requirement; this was dropped out of concern that public works activities would 
+interfere with the planting and weeding of crops during the agricultural season. Targeting of 
+unconditional transfer recipients was undertaken using a combination of demographic 
+                                                     
+5
+ Respondents at the community level indicated that on average it cost 480 FCFA (roughly 1 USD) to transport 
+100kg of cereals from the market to home, or otherwise1920 FCFA for the transfer period (four trips). This 
+figure, however, does not take into account households pooling transport costs, which could significantly 
+reduce the per-household cost. The average cost for obtaining the food transfers by beneficiaries was reported 
+to be only 60 CFA per trip.  
+6
+ These calculations abstract from a number of fixed costs associated with setting up these payments. For 
+example each smart card used for the cash payments cost $6.00 and there were additional costs associated 
+with writing the computer programs needed to dispense payments through the mobile ATMs. Costs such as 
+these are not included in the calculations reported here. We exclude costs that were common to both the food 
+and cash payments such as costs associated with implementing the public works, identifying the beneficiaries, 
+program sensitization, identification of implementing partners and contract negotiations with MFIs selected to 
+implement this intervention. 
+targeting and a participatory approach.7 A locality selected to receive cash(food) used 
+cash(food) for both public works and unconditional transfer payments. 
+ 
+3. Data 
+The first survey was implemented in July, at the conclusion of the public works but before 
+the roll-out of the unconditional transfer.8 All households in all villages were administered a 
+basic questionnaire. A randomly selected sample of 2268 households who had been 
+targeted for the unconditional transfers was interviewed in greater depth. A follow up 
+survey was then administered to the sampled households at the conclusion of the 
+unconditional transfers, with 2209 being successfully traced and interviewed, an attrition 
+rate of 2.6 percent.  
+In both rounds household and community surveys were administered. The 
+household survey instruments included questions on demographic characteristics, 
+livelihoods, assets, livestock, agricultural production, and public works participation. Pre-
+intervention characteristics (ie as of April 2011) including household composition, asset 
+ownership and indebtedness were retrospectively assessed as part of July survey. Food 
+security impacts and intra-household sharing were captured in modules on food 
+consumption, coping strategies and children’s food consumption. The survey instrument 
+also included questions on non-food expenditures, debt, inter-household transfers, 
+migration, and labor force participation. The community survey instrument collected 
+information on access to services, proximity and distance of markets, prices on key staples 
+and livestock, and criteria for selection of beneficiaries for the unconditional transfers.  
+Table 1 provides pre-intervention descriptive statistics of households using 
+information found in the retrospective components of the survey instrument.9 These data 
+are reported as worksite means, disaggregated by whether the locality was randomized to 
+                                                     
+7
+ The implementing agencies made the selection in partnership with village leadership committees, with 
+reference to a set of categorical indicators such as households with children under the age of 24 months, 
+single parent household, etc. 
+8
+ Impact evaluations usually have baseline surveys prior to the start of the intervention, though as McKenzie 
+(2012) notes, this is not always necessary. In our case, several factors prevented us from doing so. The security 
+situation in Niger at the start of 2012 was poor and this severely limited access by the research team to the 
+study sites. There was considerable uncertainty regarding take-up if public works and the targeting of 
+unconditional recipients had not been fully completed prior to first payments being made in April 2012.  
+9
+ We provide unweighted statistics. Using sampling weights that reflect the inclusion probability of the 
+households in the sample have a minor impact on the results. 
+receive food or cash. Households are relatively large. About third are either polygamous or 
+female headed. They are poor. Fewer than 10 percent of heads have any formal schooling.10 
+While nearly all households own or rent farmland, and average operating sizes look large, 
+this is land of very low quality. Housing quality is poor and the vast majority of households 
+own little in the way of productive assets or consumer durables. We summarize these in the 
+form of an asset index. Around 30 per cent of households report that they own no livestock 
+and another 12 percent own only chickens or one ruminant. We convert data on livestock 
+holdings to Tropical Livestock Units (TLU). Households own, on average, one TLU. There are 
+no statistically significant differences across treatment arms when we look at a wide range 
+of household demographic, asset, or livelihood characteristics.   
+Table 1 also provides information on locality characteristics aggregated at the 
+worksite level. About two-thirds of villages are accessible by road. It typically takes just 
+under one hour to reach a road and about the same time to access a market. There are 
+relatively few food markets in these villages. Nearly all have cell phone coverage. There are 
+no statistically significant differences across treatment arms in infrastructure. 
+The survey module on household food security identified which foods were 
+consumed and the frequency of their consumption over the previous seven days. The 
+specific items selected were based on previous survey work in this area as well as 
+discussions with key informants. While the survey instrument did not collect information on 
+quantities consumed, it distinguished between foods that are served as a separate item and 
+foods that are used only as a sauce or condiment. We use these data to construct two 
+measures of household food security: the Dietary Diversity Index (DDI) and the Food 
+Consumption Score (FCS). DDI is calculated by simply summing the number of distinct food 
+categories consumed by the household in the previous seven days. The household 
+questionnaire covers 25 such food categories, and thus the DDI in this survey ranges from 0 
+to 25. Hoddinott and Yohannes (2002) show that the DDI correlates well with both 
+household dietary quantity and quality. Next, we aggregate these 25 food categories into 
+eight groups: staples, pulses, vegetables, fruit, meat/fish, milk/dairies, sugar/honey, 
+oils/fats. The FCS is calculated by summing the number of days each food group was 
+                                                     
+10
+ Formal education refers to the completion of at least one year of primary schooling. We exclude attendance 
+at Koranic schools because individuals attending these do not necessarily learn to read and write.  
+consumed then multiplying those frequencies by a predetermined set of weights designed 
+to reflect the heterogeneous dietary quality of each food group (Weismann et al 2009).11  
+Three considerations motivate our use of these outcome variables. First, the FCS is 
+considered a “core” indicator by WFP (WFP 2008) and the success of interventions such as 
+the one evaluated here is measured by improvements in this outcome. Second, validation 
+studies show that the FCS is highly correlated with measures of food security that draw on 
+more detailed food consumption data such as per capita caloric availability derived from 
+seven day recall of food quantities consumed (Wiesmann et al 2009). Third, logistical 
+constraints meant that we needed to keep the survey instrument as simple as possible. It 
+was simply impractical to include a more detailed consumption module.  
+Table 2 describes these outcomes variables by both round and modality. The DDI 
+shows us that in July 2011 households consumed on average 8.2 foods out of the list of 25 
+items, and in October (following the 2011 harvest) on average 9.2. When we compare 
+individual food groups over time, we see increases of five to 15 percentage points in the 
+proportion of households consuming vegetables, oils, pulses, dairy, sugars, tubers and 
+meats. There is no meaningful change in the proportion of households consuming fruit, fish 
+or eggs.  
+WFP classifies households as having poor food security when the FCS falls below 21, 
+borderline when it lies between 21 and 35, and acceptable if over 35. Loosely, a cut-off of 35 
+corresponds to daily per capita caloric availability of around 1950 kcal. Food insecurity is 
+widespread in this sample in July 2011; while the full sample average is 40.8, 33 percent of 
+households have borderline food insecurity and 24.7 percent have poor food insecurity. 
+These figures improve significantly in October, with the full sample average FCS up to 47.3, 
+those with borderline down four percentage points to 29 per cent and those with poor 
+down to only 9 per cent. Figure1 shows the density of FCS by transfer modality in July and 
+October, with the rightward shifts in October indicating improvement for both cash and 
+food households. Table 1 shows that households in localities that were randomized to 
+receive food have higher mean levels of DDI and FCS. 
+We also consider a second measure of food security, household coping strategies. 
+These actions taken by individuals or households who, under stress, restrict expenditures or 
+                                                     
+11
+ Weights are: staples, 2; pulses, 3; vegetables, 1; fruit, 1; meat, poultry, fish and eggs, 4; dairy 4; sugars, 0.5; 
+oils and fats, 0.5 
+generate additional resources so as to acquire basic consumption goods (food, shelter) 
+while protecting existing asset holdings. As Devereux and others have stressed (e.g. 
+Devereux, 1993), these exist along a continuum from those that involve relatively modest 
+shifts in consumption patterns to more extreme behaviors such as going without food for a 
+full day. The household survey instrument contained a set of questions on household coping 
+strategies. We look in turn at a range of food-related coping strategies, such as not having to 
+borrow or beg for the means to purchase food, consuming undesirable foods, or reducing 
+portion sizes or the number of meals. We then construct a Coping Strategies Index (CSI) 
+following Maxwell and Caldwell (2008), as an aggregate measure of food security. Each 
+strategy is given a frequency score depending on the number of times it was used and a 
+weight reflecting its severity. There is significant improvement in the coping strategies index 
+over the course of the second round of intervention between July and October, from an 
+average of 5.4 to an average of only 0.8. There are significant differences in both periods 
+between cash and food households, but this gap closes between July and October. 
+We hypothesized that beneficiaries might use their transfers to buy food in bulk. 
+Since the notion of a “bulk” purchase is somewhat subjective, in both survey rounds we 
+asked this in an open ended fashion. For example, in the July survey this was phrased as 
+“Depuis avril 2011, avez-vous acheté des graines en plus grande quantité que vos achats de 
+grains habituels? (“Since April 2011, have you purchased grains in larger quantities than you 
+usually purchase?”) In July, 504 out of 2,263 households (22.2 percent) indicated that they 
+had made such a purchase, 85 percent of whom were households in villages randomly 
+assigned to receive cash. We then asked the cash value of such purchases. We also 
+examined non-food expenditures across a range of categories. There are some differences 
+between cash and food households, as well as between periods, but most are small in 
+magnitude (Table 3). Cash households spend more for example on wages, veterinary 
+products, and seeds, in both July and October, while food households spend somewhat 
+more on a few other items. Cash households do however spend significantly more on 
+average on bulk grains; they are nearly 30 percentage points more likely to invest in ‘larger 
+quantities of grain than usual,’ and spend larger sums, in both periods. 
+ 
+4. Methods 
+We begin with a single difference model of the form  
+ 
+                                (1) 
+ 
+where     is the outcome of interest for household   at worksite  after the intervention 
+and                 is a dummy variable equal to one if a household lives in a village 
+receiving food (and 0 otherwise). The parameter   is the parameter of primary interest. It 
+tells us the impact on outcomes of being randomized into a village receiving food relative to 
+being randomized into a village receiving cash. We allow for the error terms to be correlated 
+by clustering at the worksite (randomization) level. The randomization of the modality 
+ensures that E(food villagei εiw) equals 0 and thus that δ is an unbiased estimate of impact. 
+Because we do not observe pre-intervention food security outcomes, we cannot 
+estimate a double difference model. McKenzie (2012) argues that difference-in-difference 
+estimators are preferable to a post-intervention estimator only when the autocorrelation of 
+the outcome variables is relatively high. He notes that this will not be the case for outcomes 
+such as consumption that fluctuate over time. Further, he notes that conditioning on 
+variables that are correlated with the dependent variable can reduce the variance of the 
+treatment estimator. Accordingly, we estimate the following model 
+ 
+                                   (2) 
+ 
+where    is a vector of household baseline covariates and village characteristics. These 
+include household demographics such as size and head characteristics such as sex, age and 
+level of education. We control for ethnicity and for ownership of durables which acts as a 
+proxy for household wealth. We control for livelihood zone (agricultural, agro-pastoral), 
+whether or not there is a market and a cereal bank in the village, the price of millet at the 
+end of the transfer period and the change in the price of millet over the transfer period. We 
+also control for cattle prices (milk cows and a goats) as reported in our community surveys. 
+We control for the distance to a main highway and whether or not the village has mobile 
+network coverage and for commune fixed effects.12  We estimate (2) separately for 
+outcomes measured in July and in October. We use OLS for outcomes that are continuous, 
+                                                     
+12
+ For brevity, we only report δ in our tables. Full results are available on request. 
+probits where they are dichotomous, Poisson regressions where we have count data and 
+tobits where the outcome is continuous but also censored at zero. Estimates of   are 
+transformed into marginal effects where the estimator is non-linear. Standard errors are 
+calculated accounting for clustering at the unit of randomization. 
+ 
+5. Results 
+a. Food security 
+Table 4 shows the impact of residing in a village whose worksite was randomized to receive 
+food transfers on the DDI, FCS and whether the FCS was above the WFP cut-off for a 
+minimally acceptable diet.  
+We begin with the DDI. There is a small, positive impact of being in a village receiving 
+food on the DDI, an additional 0.36 food items in July and 0.54 items in October. But these 
+magnitudes are relatively small, corresponding to increases of 4.9 and 6.7 percent 
+respectively. By contrast, there are large, positive and statistically significant impacts of the 
+receipt of food on the FCS. After controlling for household and village characteristics, 
+households in localities receiving food have an FCS on average 3.9 points higher than cash 
+households in July and 4.6 points higher in October, relative to an over-all mean FCS in July 
+of 40.8. The likelihood of having an acceptable food consumption score is 10.9 percentage 
+points higher for food households in July and 12.1 percentage points higher in October.  
+Table 5 reports the impact of access to food transfers on the likelihood and 
+frequency of consumption of selected food groups in the seven days prior to the survey. We 
+find that relative to households receiving cash, households in villages randomly assigned to 
+receive food consumed more of the items given to them in the food basket: cereals, pulses 
+and oil. They also increased the frequency of their consumption of these items: increasing 
+their consumption of oils by one day and pulses by 0.6 days. By contrast, their consumption 
+of cheap, starchy calories from tubers declines. There is no differential effect on the 
+frequency of consumption of meat, dairy, fruit or vegetables. This is consistent with 
+information food recipients provided to us. Only 5 percent of food recipients reported that 
+they sold some of the food, and 13 percent that they exchanged some of the payment for 
+other food or non-food items.  Just 1.2 percent of all food received was sold and only 3.7 
+percent exchanged.    
+Table 6 shows the results of estimating our single difference equations for the July 
+and October survey rounds where the dependent variables are the likelihood of making a 
+large grain purchase and the value of this purchase. In the three months prior to the 
+July(October) survey, households in food localities were 27(40) percentage points less likely 
+to make these purchases relative to households in cash localities. The marginal impact was a 
+reduction in the value of such purchases of 14,289 FCFA in July and 25,015 FCFA in October. 
+In other words, it appears that relative to households in food localities, households receiving 
+cash used a significant proportion of their transfers to purchase the cheapest form of 
+calories available.  
+One reason lies in the sharply seasonal nature of grain prices in this region. 
+Agriculture production is characterized by volatile conditions and one fairly short growing 
+season. The climate is hot and dry year round, but hottest in May, right before the brief but 
+at times intense rainy season of June to August. Field preparation may start as early as April 
+but peaks between July and September, the pre-harvest period known as the soudure or 
+hungry season. Millet, the dominant food produced and consumed throughout Niger, is 
+surplus in production throughout much of the southern part of the country, especially 
+Zinder, where millet is sourced for much of the country. Niger often produces a deficit, 
+however, and imports millet from Nigeria, Benin, and Burkina Faso during the hungry 
+season. The seasonality of production patterns and trade flows leads to inter-seasonal 
+fluctuations in the prices of key staple commodities in Zinder.  
+This seasonality, as is shown in Figures 2a and 2b, provides clues as to why we may 
+be observing these bulk purchases of grains by households in cash villages. Figure 2a shows 
+that historically grain prices in the survey area, both millet and maize, rise between January 
+and August. They fall sharply during the harvest period before starting to rise again in 
+November. Figure 2a also shows that this pattern was somewhat different in the year prior 
+to the intervention. Not only were grain prices significantly above historical averages, millet 
+prices rose faster than the historical average. Figure 2b shows that in the four months prior 
+to the start of the intervention, both millet and maize prices were again rising, with April 
+2011 prices already equal to or higher than the highest price typically observed during the 
+peak of the hungry season. Given this historical experience, it is understandable that may 
+cash households may have felt compelled to buy large grain quantities rather than risk 
+exposure to uncertain food price changes. 
+ 
+b. Coping strategies and non-food expenditures 
+Table 7 examines the coping strategy index (CSI) and individual coping strategies used by 
+households to acquire food. Recall that the higher the CSI, the more severe the coping 
+strategies used. Households in food localities have a lower CSI than cash households in July 
+and October. In July, food households were less likely to report that they consumed less 
+preferred foods, were less likely to report that they reduce portion sizes served to children 
+or that household members went to be hungry. While the marginal effects look small, they 
+are relatively large compared to the mean values reported in Table 3. However, these 
+effects on more severe coping strategies are less marked in October as the harvest period 
+begins.  
+ We considered whether households in food and cash villages had different patterns 
+of expenditures on non-food items (Table 8). Across all items, the marginal impact of being 
+in a food village is to raise monthly expenditures on all non-food items by 1874 FCFA in July. 
+This is equivalent to about eight percent of the value of the monthly transfer. There is no 
+statistically significant impact on all non-food items in October. Across the individual items, 
+it is difficult to discern consistent patterns. Only eight of the 18 coefficients are statistically 
+significant at the 10 percent level or higher and the magnitude of two of these (firewood 
+and other fuels; soap, perfumes and hair products) is small, less than 500 FCFA. The most 
+noteworthy finding is that households in cash villages spent more on agricultural inputs in 
+both the lead up and during the main cropping season and the magnitude of this effect 
+especially in October (5819 FCFA or just over 20 percent of the monthly transfer) was large. 
+Also, households in cash villages spent some of their transfers on repairing their dwellings in 
+the three months prior to the July survey, in advance of the rains. 
+ 
+c. Additional results 
+We considered whether there were larger changes over time in households residing in 
+localities assigned to receive food. To do so, we also estimated models of the following 
+form: 
+ 
+                                                  (3) 
+ 
+Generally, across the outcomes we consider,   is not statistically significant when we 
+estimate (3), that is, we do not reject the null hypothesis that changes in outcomes over 
+time are different in food and cash villages. The exception to this are the results for specific 
+coping strategies where   is negative and significant for a number of the more severe 
+coping strategies such as reducing children’s portion sizes and going a whole day without 
+eating.  
+We looked for evidence of heterogeneous impacts along two dimensions, household 
+wealth and the gender of the household head. Across all outcomes we consider and across 
+both survey rounds, we do not find any evidence that the interaction terms between gender 
+of head and residing in a village receiving food are statistically significant. Across all 
+outcomes measured in the July round, the interaction terms between wealth (measured in 
+tertiles, quartiles or quintiles) and residing in a village receiving food are not statistically 
+significant. In the October round, households in the lowest quartile and in food villages 
+obtained greater improvements in the FCS measure and were less likely to have poor food 
+security status. Overall, however, we found little evidence of heterogeneous impacts across 
+wealth categories and gender of head. 
+ 
+6. Conclusions 
+In this paper, we have used a randomized design to inform debates regarding the use of 
+cash and in-kind transfers as a means of improving household food security. With respect to 
+the short term food security objectives of this intervention, the food basket had clear 
+advantages. Households in localities randomized to receive the food basket experienced 
+larger, positive impacts on measures of food security and dietary diversity than those 
+receiving the cash transfer. One reason that the cash recipients had less diverse diets lies in 
+their choice of purchasing grains in bulk, a reflection we perceive of both the extreme 
+poverty found in this area and uncertainty regarding future food prices. While these 
+differences held in both periods, other outcomes showed greater variation by season. 
+Households receiving food resorted to fewer coping strategies, and this effect was more 
+pronounced during the height of the lean season than during the growing season. Food 
+recipients did not trade their transfers to any large extent; less than five percent of food was 
+sold or exchanged for other goods. Households receiving cash spent more money repairing 
+their dwellings during the lean season, prior to the start of the rains, but spent more on 
+agricultural inputs during the growing season. Both food and cash were delivered with the 
+same degree of frequency and timeliness, but the food transfers cost 15 percent more to 
+implement.  
+While food recipients experienced greater food security benefits in the short term, 
+we cannot assess the relative benefits in the long term; the fact that beneficiaries receiving 
+cash spent more on agricultural inputs may mean that these households have higher 
+incomes in the future. Finally, the specific context of this study is important. Our results are 
+informative about the relative impacts of food and cash transfers in an extremely poor, rural 
+setting, but caution should be exercised in extrapolating them to settings much different 
+than those found in rural Niger.  
+  
+8. References 
+Cunha, J. M., G. De Giorgi, and S. Jayachandran (2011). The Price Effects of Cash Versus In-
+Kind Transfers. NBER Working Paper No. 17456. 
+Department for International Development (2011). Cash Transfers – Literature Review. 
+Policy Division, Department for International Development. 
+Devereux, S. (1993). Goats before Ploughs: Dilemmas of Household Response Sequencing 
+During Food Shortages. IDS Bulletin, 24(4): 52-59. 
+Eilerts, G. (2006). Niger 2005: Not a Famine, But Something Much Worse. Humanitarian 
+Exchange Magazine, Issue 33, April. 
+Famine Early Warning System (2010). Rapport Spécial : Fonctionnement des marchés et 
+sécurité alimentaire en 2010 dans le département de Zinder.  
+Fiszbein, A., N. Shady, F.H.G. Ferreira, M. Grosh, N. Keleher, P. Olinto and E. Skoufias (2009). 
+Conditional Cash Transfers: Reducing Present and Future Poverty. Washington, DC: 
+World Bank. 
+Gentilini, U. (2007). Cash and Food Transfers: A Primer. Occasional Papers No. 18. World 
+Food Programme. 
+Grober-Tanner, C. (2006). Understanding Nutrition Data and the Causes of Malnutrition in 
+Niger: A Special Report by the Famine Early Warning Systems Network. USAID/FEWS. 
+Harvey, P. (2007). Cash-based Responses in Emergencies. HPG Report 24. Overseas 
+Development Institute. 
+Hidrobo, M., J. Hoddinott, A. Peterman, A. Margolies, and V. Moreira (2012). Cash, Food, or 
+Vouchers? Evidence from a Randomized Experiment in Northern Ecuador. Mimeo. 
+International Food Policy Research Institute. 
+Hoddinott, J. and Y. Yohannes (2002). Dietary Diversity as a Food Security Indicator. FCND 
+Discussion Paper No. 136. Food Consumption and Nutrition Division, International 
+Food Policy Research Institute.  
+Kennedy, G., T. Ballard and M.C. Dop (2011). Guidelines for Measuring Household and 
+Individual Dietary Diversity. Nutritional and Consumer Protection Division, Food and 
+Agriculture Organization. 
+Lentz, E.C., S. Passarelli, and C.B. Barrett, (forthcoming). The Timeliness and Cost 
+Effectiveness of the Local and Regional Procurement of Food Aid. World 
+Development. 
+Margolies, A. and J. Hoddinott (2011). Mapping the Impacts of Food Aid – Current 
+Knowledge and Future Directions. WIDER Working Paper 2012/34. World Institute 
+for Development Economics Research.  
+Maxwell, D. and R. Caldwell (2008). The Coping Strategies Index: Field Methods Manual. 
+Second Edition.  
+McKenzie, D. (2012). Beyond Baseline and Follow-up: The Case for More T in Experiments. 
+Journal of Development Economics 99 (2012): 210-221. 
+Reza, A., B. Tomczyk, V. Aguayo, N. Zagré, K. Goumbi, C. Blanton, and L. Talley (2008). 
+Retrospective Determination of Whether Famine Existed in Niger, 2005: Two State 
+Cluster Survey. BMJ 337:a1622. 
+Sharma, M. (2006). An Assessment of the Effects of the Cash Transfer Pilot project on 
+Household Consumption Patterns in Tsunami-affected Areas of Sri Lanka. 
+International Food Policy Research Institute. 
+United Nations Development Program (2012). Human Development Index. Available on line 
+at: http://hdr.undp.org/en/statistics/. 
+Upton, J. and E. Lentz (2011).  Expanding the Food Assistance Toolbox. Chapter 5 in Barrett, 
+C.B., A. Binder and J. Steets (Eds). Uniting on Food Assistance: The Case for 
+Transatlantic Policy Convergence.  London: Routledge. 
+Wiesmann, D. L. Bassett., T. Benson, and J. Hoddinott (2009). Validation of the World Food 
+Programme's Food Consumption Score and Alternative Indicators of Household Food 
+Security. IFPRI Discussion Paper 00870. International Food Policy Research Institute.  
+World Food Programme (2012). Integrated Food Aid Information System. Available on line 
+at www.wfp\interfais.org. 
+World Food Programme (2010). Draft Terms of Reference: EMOP 200170: Saving Lives and 
+Improving Nutrition in Niger.  Evaluation and Quality Assurance System, WFP, 5 
+November 2010. 
+World Food Programme (2008). Calculation and Use of the Food Consumption Score in Food 
+Security Analysis. Vulnerability Analysis and Mapping Branch, World Food 
+Programme.  
+ 
+  
+Figure 1: Density function of FCS by transfer modality 
+ 
+ 
+ 
+ 
+  
+0
+.0
+1
+.0
+2
+.0
+3
+0 20 40 60 80 100
+Food Consumption Score
+Cash, July Food, July
+Cash, October Food, October
+Poor Borderline
+Figure 2a 
+ 
+ 
+Figure 2b 
+ 
+ 
+ 
+  
+0.20
+0.25
+0.30
+0.35
+0.40
+0.45
+0.50
+Ja
+n
+u
+ar
+y
+Fe
+b
+ru
+ar
+y
+M
+ar
+ch
+A
+p
+ri
+l
+M
+ay
+Ju
+n
+e
+Ju
+ly
+A
+u
+gu
+st
+Se
+p
+te
+m
+b
+e
+r
+O
+ct
+o
+b
+e
+r
+N
+o
+ve
+m
+b
+e
+r
+D
+e
+ce
+m
+b
+er
+P
+ri
+ce
+, U
+SD
+/K
+G
+ 
+Average Grain Prices 
+Retail, Zinder 
+Millet-Avg 2000-2009
+Millet-2010
+Maize - Avg 2000-2009
+Maize - 2010
+0.20
+0.25
+0.30
+0.35
+0.40
+0.45
+0.50
+P
+ri
+ce
+s,
+ U
+SD
+/K
+G
+ 
+Average Grain Prices in Zinder 
+2010 and 2011 
+Maize - 2010
+Maize - 2011
+Millet-2010
+Millet - 2011
+Table 1: Pre-intervention characteristics by transfer modality 
+ 
+ 
+Demographic characteristics 
+CASH work 
+sites 
+FOOD work 
+sites 
+P-value 
+Household size (average) 7.0 6.9 0.55 
+Polygamous household (percentage) 13.2 15.7 0.24 
+Households belonging to ethnic majority (percentage) 90.4 87.7 0.49 
+Female household heads (percentage) 18.3 17.5 0.80 
+Age of head (average) 44.6 45.1 0.60 
+Heads with formal education (percentage) 7.2 6.1 0.56 
+ 
+ Livelihoods and assets 
+   
+Percentage households growing crops (percentage) 96.9 97.2 0.76 
+Area cultivated (ha) 4.6 5.3 0.34 
+Tropical Livestock Units 0.9 1.0 0.65 
+Asset Score -0.1 0.2 0.22 
+ 
+Land allocation to crops 
+   
+Millet Allocation (percentage) 64.4 62.0 0.49 
+Sorghum Allocation (percentage) 17.5 16.9 0.80 
+Cowpeas Allocation (percentage) 11.7 14.1 0.19 
+Peanuts Allocation (percentage) 4.5 4.8 0.86 
+ 
+Infrastructure 
+   
+Road Accessible in All Seasons (percentage) 68.6 64.4 0.74 
+Distance to Main Road (minutes) 57.5 53.0 0.65 
+Market in Village (percentage) 11.1 8.7 0.77 
+Time to Reach Market if NOT in village (minutes) 62.5 72.3 0.47 
+Cell Phone Service in Village (percentage) 86.6 96.0 0.23 
+Notes:  P values are from t tests where the null hypothesis is that the work site means are equal. There are 27 
+worksites that received food and 25 that received cash.  
+ 
+ 
+ 
+ 
+ 
+ 
+Table 2: Food security measures and coping strategies by survey round and transfer modality 
+ 
+ July October 
+  Cash villages Food villages P-value of 
+t-test 
+Cash villages Food villages P-value of 
+t-test 
+HDDI 7.8 8.7 0.00 8.9 9.6 0.00 
+FCS (average) 37.6 44.4 0.00 44.4 50.6 0.00 
+FCS categories (percentage of households)             
+   Poor 31.4 17.1 0.00 11.4 6.6 0.00 
+   Borderline 34.9 31.6 0.09 34.7 23.4 0.00 
+   Acceptable 33.6 51.3 0.00 53.9 70 0.00 
+Food Groups Consumed (percentage of HHs)             
+Cereals 100.0 100.0 . 100.0 100.0 . 
+Tubers 30.7 20.9 0.00 32.7 28.3 0.03 
+Vegetables 94.2 94.3 0.95 99.8 100 0.19 
+Fruit 8.6 14.2 0.00 5.9 11.0 0.00 
+Meat 22.7 30.4 0.00 28.8 34.5 0.00 
+Eggs 2.5 2.3 0.79 1.3 1.2 0.82 
+Fish 2.8 4.9 0.01 3.9 5.2 0.13 
+Pulses 76.5 85.3 0.00 96.0 99.1 0.00 
+Dairy 55.8 61.1 0.01 73.8 68.9 0.01 
+Oils 80.3 94.5 0.00 87.3 96.6 0.00 
+Sugars 48.0 54.5 0.00 60.2 60.2 0.99 
+Coping strategy index (Average) 7.3 3.1 0.00 1.0 0.6 0.02 
+Individual Coping Strategies (percentage of HHs)             
+Relied on less preferred foods (w=1)* 28.8 18.6 0.00 6.7 6.0 0.51 
+Borrowed food from relatives, neighbors or friends (w=2) 18.9 8.5 0.00 6.3 5.4 0.40 
+Purchased food on credit (w=2) 17.4 8.5 0.00 5.1 3.2 0.03 
+Consumed more than usual of shortage food (w=4) 9.8 3.2 0.00 0.4 0.0 0.04 
+Consumed seed stock (w=3) 11.0 7.1 0.00 1.5 0.5 0.02 
+Had to beg (w=4) 1.8 0.7 0.03 0.1 0.3 0.25 
+Reduced portion sizes for adults (w=2) 16.7 6.6 0.00 2.5 0.6 0.00 
+Reduced portion sizes for children (w=1) 10.5 3.9 0.00 1.4 1.1 0.54 
+Had to reduce number of meals per day (w=2) 14.3 5.9 0.00 2.2 0.6 0.00 
+Had entire days without eating (w=4)) 6.2 1.7 0.00 0.4 0.3 0.60 
+Had to cancel debt repayments to buy food 13.4 6.4 0.00 1.9 1.3 0.25 
+Number of households 1198 1070   1179 1030   
+*w refers to severity weight used for calculating the CSI (if strategy included in the index)     
+ 
+  
+Table 3: Household expenditures by survey round and transfer modality  
+ 
+ July October 
+  Cash 
+villages 
+Food 
+villages 
+P-value of 
+t-test 
+Cash 
+villages 
+Food 
+villages 
+P-value of 
+t-test 
+Bulk Grain Purchases             
+Household has purchased larger quantities of grain than usual, prior 3 
+mos. (percentage) 
+36.0 7 0 32 2 0 
+Average monthly purchase of lumpy grain, Apr-Jun / Jul-Sep (FCFA) 3419 644 0 3434 219 0 
+Non-Food Purchases (FCFA)             
+Total spending, past 3 months (all households) 27349 30742 0.07 25981 27372 0.39 
+Firewood, charcoal/ Oil, gas, batteries/ Fuel, lubricants 518 707 0.00 746 948 0.24 
+Bodycare (soap, perfumes, braids) 1807 1926 0.13 1818 1899 0.30 
+Communication/transports 2525 3294 0.27 2576 3153 0.24 
+Wages, veterinary products and seeds 4413 3534 0.01 3635 2553 0.02 
+Health 5272 5185 0.89 5242 5595 0.51 
+Education 1329 975 0.05 333 234 0.20 
+Clothing, footwear 5346 6762 0.00 7757 8466 0.06 
+Ceremonials, funerals, festivities 6591 9454 0.00 5819 7007 0.07 
+Construction, repair, housing 2289 2000 0.39 1013 860 0.45 
+Number of households 1198 1070   1179 1030   
+ 
+24 | P a g e  
+ 
+ 
+Table 4: Impact of food transfers, relative to cash, on food security outcomes by survey round  
+ 
+ 
+Food Security Outcome July October 
+Dietary Diversity Index (DDI) 0.356* 0.544** 
+ (0.207) (0.229) 
+Food Consumption Score (FCS) 3.923*** 4.647*** 
+ (1.424) (1.139) 
+Household has FCS above WFP cut-off 0.109** 0.121*** 
+ (0.043) (0.041) 
+Notes: Controls included but not reported are: age, sex, education and ethnicity of household head; household size; asset score; 
+whether household is located in pastoral zone; infrastructure, whether village has market, health clinic, mobile phone coverage; 
+distance to main road; livestock prices; change in millet price during period; millet price at end of period; and commune fixed 
+effects. Standard errors, shown in parentheses, are calculated accounting for clustering at the worksite level. *, significant at the 
+10% level; **, significant at the 5% level; ***, significant at the 1% level. Sample sizes are 2256 for July round and 2187 for 
+October round. Marginal effects are reported where the outcome is dichotomous. 
+ 
+ 
+  
+25 | P a g e  
+ 
+Table 5: Marginal effects of food transfers, relative to cash, on consumption of selected food groups by survey 
+round 
+ 
+ In the last seven days 
+ Were items in this food group 
+consumed 
+Number of days items in this food 
+group were consumed 
+Food Group July October July October 
+Cereals - - 0.093* 0.109*** 
+   (0.051) (0.035) 
+Pulses 0.064** 0.021 0.638** 0.820*** 
+ (0.032) (0.013) (0.314) (0.168) 
+Oils 0.106*** 0.042** 0.959*** 1.010*** 
+ (0.033) (0.017) (0.258) (0.186) 
+Tubers -0.080*** -0.040 -0.301*** -0.106 
+ (0.026) (0.030) (0.082) (0.069) 
+Meat 0.036 -0.012 0.072 0.001 
+ (0.031) (0.030) (0.098) (0.073) 
+Dairy 0.013 -0.067** 0.015 -0.005 
+ (0.035) (0.027) (0.207) (0.175) 
+Vegetables - - 0.051 0.018 
+   (0.112) (0.048) 
+Fruits -0.034 0.046 -0.052 0.055 
+ (0.037) (0.030) (0.107) (0.042) 
+Sugar 0.030 0.006 0.008 0.197 
+ (0.031) (0.026) (0.176) (0.138) 
+Notes: Consumption of items estimated using a probit. Number of days consumed estimated using a Poission model. Results are 
+reported as marginal effects. Also see Table 4 notes. 
+ 
+Table 6: Marginal effects of food transfers, relative to cash, on purchase of large quantities of grain  
+ 
+ Did household make purchase Expenditure on this item 
+ July October July Oct 
+Purchase of grains in bulk -0.273*** -0.400*** -14289.4*** -25015.1*** 
+ (0.020) (0.034) (1570.8) (432.0) 
+Notes: Purchase of items estimated using a probit. Expenditures estimated using a tobit. Results are reported as marginal 
+effects. Also see Table 4 notes. 
+  
+26 | P a g e  
+ 
+ 
+Table 7: Impact of food transfers, relative to cash, on coping strategies by survey round  
+ 
+  July     October    
+Coping Strategies Index  -3.708*   -3.168*** 
+  (1.916)    (0.411)    
+Selected coping strategies   
+Relied on less preferred foods   -0.039*   0.024    
+  (0.022)    (0.020)    
+Borrowed food from relatives, neighbors or friends   -0.082*** -0.022    
+  (0.024)    (0.021)    
+Purchased food on credit  -0.058***  -0.027    
+  (0.018)    (0.019)    
+Had to rely on aid from outside the household  0.003     0.030    
+ (0.015)    (0.020)    
+Had to cancel debt repayments  -0.038**   0.057*** 
+ (0.017)    (0.009)    
+Consumed seed stock  -0.006     0.052    
+ (0.020)    (0.036)    
+Had to ask other households for food to feed the children  -0.007     0.002    
+  (0.017)    (0.011)    
+Reduced portion sizes for adults  -0.025     -0.046*** 
+ (0.025)    (0.014)    
+Reduced portion sizes for children  -0.038**   -0.023    
+  (0.018)    (0.016)    
+Had to reduce number of meals per day  -0.025     -0.036**  
+ (0.024)    (0.015)    
+Had entire days without eating  -0.030*    0.007    
+ (0.016)    (0.010)    
+Had to go to bed hungry  -0.023*    0.005    
+  (0.013)    (0.010)    
+ Notes: See Table 4. 
+ 
+  
+27 | P a g e  
+ 
+ 
+ 
+Table 8: Marginal effects of food transfers, relative to cash, on non-food expenditures  
+ 
+ Did household make purchase Expenditure on this item 
+ July October July Oct 
+Total monthly non-food expenditures - - 1874.7*** -592.0 
+   (502.0) (1010.7) 
+Firewood, charcoal, gas, batteries,  lubricants 0.035 -0.087*** 245.28 -223.2 
+ (0.032) (0.021) (129.2) (288.5) 
+Bodycare (soap, perfumes, braids) -0.010 -0.002 257.7*** 80.2 
+ (0.008) (0.008) (87.4) (125.2) 
+Communication and transport -0.059** -0.036 -1909.8 -1140.1 
+ (0.025) (0.028) (1818.3) (1576.6) 
+Wages, veterinary products and seeds -0.105*** -0.090*** -1778.8** -5819.3** 
+ (0.035) (0.029) (816.2) (2604.0) 
+Health -0.056** -0.049* 547.2 -957.6 
+ (0.022) (0.027) (920.2) (852.6) 
+Education 0.081** -0.025* 3642.1*** -3253.0* 
+ (0.033) (0.014) (253.7) (1953.9) 
+Clothing, footwear -0.025 -0.006 738.2 48.5 
+ (0.021) (0.018) (665.1) (616.9) 
+Ceremonials, funerals, feasts 0.028 -0.013 3424.3** 68.3 
+ (0.028) (0.026) (1551.1) (1125.2) 
+Construction, repair, housing -0.034* 0.002 -2870.8* 495.2 
+ (0.021) (0.016) (1686.3) (403.9) 
+ Notes: Purchase of items estimated using a probit. Expenditures estimated using a tobit. Results are reported as marginal 
+effects. Also see Table 4 notes. 
+ 
+ 
+ 
+

data/part_2/0363187075.md

@@ -0,0 +1,977 @@
+# The rapid rise in domestic value chains of nutrient-dense foods (fruits, vegetables, and animal products) in Sub-Saharan Africa: Policy implications
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/8f885757-9f53-44b4-9c3c-709247fae503/retrieve  
+**Language:** English  
+**Resource Type:** Working Paper  
+**Release Year:** 2023  
+**Rights:** N/A  
+**GARDIAN ID:** ca70d819bb35fb0db7e4e11bb3fd310d  
+**DataNODE ID:** 9a816422fec5af58d9dc952165777de5  
+**Siever ID:** f94d83c8-43e7-453d-81ab-fd9079a99f81  
+**Token Count:** 11269  
+**Page Count:** 0
+
+## Keywords
+
+supply chains, foods, agricultural production, vegetables, nutrients, fruits, infrastructure, consumers, farm production, food, nutrition, health and food security, systems transformation, value chains
+
+## Geography
+
+- **Regions:** Africa, World
+
+## Description
+
+Despite African consumers under-consuming nutrient dense fruits and vegetables (FV) and animal products (AP), and the farm production and supply chains of these products are fraught with constraints that keep them from operating optimally, we find abundant recent evidence of dynamism in these sectors. To wit: (1) consumption of these products in levels and shares is already substantial and growing rapidly; (2) supply of these products is growing rapidly, just not yet much faster than population growth; (3) supply growth is manifested in a number of countries by dynamic “meso booms” with diffusion of farming and growth in midstream ("Hidden Middle") value chain segments; these booms are “grassroots” driven, without subsidy or management by government or NGOs or large companies. We reviewed recent survey-based evidence of these booms and discussed the drivers. The policy implications are the need for governments to invest in the conditions we found to be enabling these booms, that is, roads and wholesale markets and electrification and other infrastructure hard and soft.
+
+## Content
+
+The rapid rise in domestic value chains of 
+nutrient-dense foods (fruits, vegetables, 
+and animal products) in Sub-Saharan 
+Africa: Policy implications 
+Thomas Reardon, Saweda Liverpool-Tasie, Ben Belton, Michael Dolislager, 
+Bart Minten, Barry Popkin, and Rob Vos 
+  
+                 
+                   
+INITIATIVE TECHNICAL PAPER 1 AUGUST 2023 
+1 
+Abstract 
+Despite African consumers under-consuming nutrient dense fruits and vegetables (FV) and animal 
+products (AP), and the farm production and supply chains of these products are fraught with constraints 
+that keep them from operating optimally, we find abundant recent evidence of dynamism in these sec-
+tors. To wit: (1) consumption of these products in levels and shares is already substantial and growing 
+rapidly; (2) supply of these products is growing rapidly, just not yet much faster than population growth; 
+(3) supply growth is manifested in a number of countries by dynamic “meso booms” with diffusion of 
+farming and growth in midstream ("Hidden Middle") value chain segments; these booms are “grass 
+roots” driven, without subsidy or management by government or NGOs or large companies. We re-
+viewed recent survey-based evidence of these booms and discussed the drivers. The policy implica-
+tions are the need for governments to invest in the conditions we found to be enabling these booms, 
+that is, roads and wholesale markets and electrification and other infrastructure hard and soft. 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+2 
+CONTENTS (TOC Heading, 14pt) 
+Abstract............................................................................................................................................ 1 
+1. Introduction ...................................................................................................................................... 3 
+2. Macro View of supply of FV and AP: lingering per capita inadequacy but Asia-matching total 
+growth .................................................................................................................................................. 4 
+3. Substantial consumption of fruits/vegetables (FV) and animal products (AP) in SSA: view 
+from household data ........................................................................................................................... 5 
+3.1 Urban SSA shares of AP+FV already exceed those of starchy staples – and are similar to 
+developing Asia ................................................................................................................................ 5 
+3.2 Rural SSA shares of AP+FV are still below those of starchy staples – and are just a bit below 
+those of developing Asia .................................................................................................................. 6 
+3.3 Focus on FV: SSA countries have substantial (and in some zones or countries growing) shares 
+and levels of FV in household consumption ..................................................................................... 6 
+3.4 Focus on AP: SSA countries have substantial (and in some zones or countries growing) shares 
+and levels of AP in household consumption ..................................................................................... 8 
+4. Meso booms in Animal Product clusters and domestic VCs ........................................................ 9 
+4.1 Fish in Nigeria ............................................................................................................................ 9 
+4.2 Dairy in Ethiopia ....................................................................................................................... 10 
+5. Meso booms in vegetable clusters and domestic value chains: Tanzania, Zambia, Ethiopia, 
+Zambia ................................................................................................................................................ 11 
+5.1 Vegetables in Tanzania ............................................................................................................ 11 
+5.2 Vegetables in Zambia .............................................................................................................. 13 
+5.3 Vegetables in Ethiopia ............................................................................................................. 15 
+6. Conclusions and policy implications ........................................................................................... 17 
+About the Authors ............................................................................................................................. 19 
+Acknowledgments ............................................................................................................................. 19 
+References ......................................................................................................................................... 20 
+ 
+ 
+ 
+ 
+3 
+1. INTRODUCTION 
+The supply and demand of nutrient-dense foods, such as fruits and vegetables (FV) and animal prod-
+ucts (AP), have been found to be inadequate and too expensive for most consumers in Sub-Saharan 
+Africa (SSA) (FAO, IFAD, UNICEF, WFP, WHO, 2023). The international debate has mainly focused on 
+the constraints and problems fueling this inadequacy. While we acknowledge the challenges and inade-
+quacies, we believe that the debate’s focus on them has led to inadequate attention to the rapid growth 
+of consumption and supply of these products, and to a widespread reference to a “missing middle”, the 
+idea that there has been little to no growth in the midstream segments of domestic value chains (VCs) 
+of the products in SSA.  
+By contrast, we find substantial levels and rapid growth of both demand and domestic supply of these 
+products, and “meso booms” including rapid growth in farming of these products and dynamism in the 
+growth of the midstream of their VCs. We contend that rather than a “missing middle” there is a “hidden 
+middle” (Reardon 2015; Reardon et al. 2021), as the dynamism of the midstream, and the rural produc-
+tion that fuels it, has been “hidden” from the debate. We believe that the debate’s focus on the con-
+straints has caused a relative neglect of the evidence of this growth. That neglect limits the international 
+debate’s ability to learn policy lessons from these booms and better support the growth of these value 
+chains in a way that can improve the per capita consumption of these products.  
+In this paper we lay out evidence of substantial (but still inadequate) and growing consumption and 
+supply of these products. We use a mix of macro supply data, micro consumption and enterprise sur-
+vey findings, and “meso” level analysis of findings from survey data on spontaneous (as opposed to 
+government or NGO managed) clusters of farms and midstream firms, supplying inputs and agricultural 
+services, wholesaling and processing output, and providing third-party logistics or 3PLS.  
+Our case illustrations focus on “meso booms” that feature “endogenous growth”, that is, spontaneous 
+and “grass roots” rapid development in situations where enabling conditions were present. We chose 
+this focus for three reasons. First, we want to show that when enabling conditions exist, in particular 
+where there is a demand pull from urban growth and governments invested in roads, electricity and 
+wholesale markets, rapid spontaneous growth in local SMEs occurred. That challenges the myth that 
+domestic SMEs are stymied and not demand responsive. That further opens the debate about how to 
+get governments to make more of these crucial investments. Second, we want to show that SME farms 
+and midstream firms made their own investments when the enabling conditions were in place. This 
+counters what we think is a skewed focus in international debate on investments made “for them” by 
+external actors (like big companies or agroparks or NGOs or government subsidy projects). 
+We pointedly do not discuss cases where NGOs or governments or large companies set up and/or sub-
+sidized growth, because we think these cases are already very visible in the debate and literature, and 
+these programs form a small share of supply. We seek to show what the market actors are doing in 
+non-artificial (non-subsidized) situations where only the enabling environment was in place. We focus 
+on domestic markets, not export markets. This is again because we want to consider the most common 
+situations; exports are less than 1% of output of FV and AP in SSA (Awokuse et al. 2019). We also fo-
+cus on cases of proliferation and growth of small and medium enterprise (SME) farms and midstream 
+firms. This is because the great majority of the SSA food economy is in the “transition stage” of VC 
+transformation where SMEs dominate. The “modern stage” is still small and just emerging in Africa 
+(Reardon et al. 2019), although there is evidence of its emergence spurring inclusive development in 
+some situations (Maertens and Swinnen, 2009).  
+4 
+We proceed as follows. Section 2 shows macro data concerning the growth in supply (and consump-
+tion) of FV and AP foods in SSA. Section 3 reviews micro data showing substantial levels (though still 
+inadequate) and growth in consumption of FV and AP. Sections 4 and 5 present recent survey-based 
+studies of “meso booms” of farms and firms in value chains (VCs) of FV and AP in various countries in 
+SSA: fish in Nigeria, dairy in Ethiopia, and vegetables in Tanzania, Zambia, and Ethiopia. Section 6 
+concludes with policy implications and an agenda for further research.  
+ 
+2. MACRO VIEW OF SUPPLY OF FV AND AP: LINGERING 
+PER CAPITA INADEQUACY BUT ASIA-MATCHING TOTAL 
+GROWTH 
+Dolislager et al. (forthcoming) shows macro data adapted from FAO Food Balance Sheets for domestic 
+output and imports and the degree of adequacy (relative to requirements for a healthy diet drawn from 
+Harris et al. 2022). They cover 10 years (2020 versus 2010). Several points stand out.  
+First, the great majority of supply (and consumption) of animal products, fruit, and vegetables in SSA is 
+from domestic supply: 90% of animal products, 98% of fruit, and 96% of vegetables. Imports (in ton-
+nage terms) form only 10%, 2%, and 4% of the consumption by disappearance of these three product 
+categories in SSA. The low shares of imports for FV and AP are at odds with what we believe to be the 
+widespread view that SSA is strongly import dependent for these products. SSA’s import shares are 
+close to those in Asia and both are below the average import shares globally.  
+Second, SSA’s per capita supply of AP and FV is starkly below (about half) that of Asia and the world. It 
+is also well below (33%, 40%, and 55%) the healthy-diet adequacy level for AP, fruits, and vegetables, 
+respectively. Asia by contrast is above adequacy by 18% and 9% for AP and vegetables, but in fruit in-
+adequate in a degree similar to SSA. SSA’s degree of adequacy in these products barely changed from 
+2010 to 2020.  
+Third, in contrast to the problems of adequacy and stagnancy per capita over a decade, total output of 
+these products soared over the decade in SSA: 29% for animal products (versus 31% in Asia), 43% for 
+fruits (versus 26% in Asia), and 35% in vegetables (versus 25% in Asia).  
+We next explore the trends with a longer time lens, focusing on AP. Delgado (2003) noted that there 
+had been a “livestock revolution” - a rapid growth in AP consumption per capita in developing countries 
+from the 1970s to the mid-1990s, driven by increases in population, urbanization, and incomes. The in-
+crease in meat and milk consumption was more than twice the market value of the increase in cereals 
+consumption that occurred in the “Green Revolution.” While the growth was rapid, by the late 1990s, 
+consumption per capita was still only one third the meat and one fifth the milk consumed in developed 
+countries.   
+In sharp contrast to Asia, SSA’s meat and milk consumption stagnated per capita in the 1980s-1990s 
+(Delgado 2003). We analyzed FAOSTAT FBS (food budget sheet) data comparing 2000 to 2020 to see 
+if the situation had improved.  Keep in mind that SSA population increased nearly 2-fold over those two 
+decades.  
+We found that in absolute terms output grew rapidly but did not exceed population growth in  red meat 
+(whose output increased 2-fold) and fish and seafood (whose output grew 1.7-fold). Thus though both 
+5 
+sets grew fast they just kept up with population growth so per capita output did not grow. By contrast, 
+dairy output grew 2.6-fold and poultry and eggs, 3.8 fold, both faster than population growth, so output 
+per capita grew.  
+This points to an important paradox. While SSA is experiencing major success in increasing total sup-
+ply (still largely domestic), SSA still has limitations in the adequacy of these nutrient-dense foods. This 
+inadequacy is because supply (though increasing) is not yet outpacing population growth except in fruit 
+and dairy and poultry. The good news is that there is rapid growth in these foods and this is reflected in 
+a number of booms at the meso level in the supply of these products. The challenge is that the growth 
+of these products in the aggregate is not yet sufficient to remove the macro level inadequacy – and thus 
+the need for domestic supply to grow even faster. 
+ 
+3. SUBSTANTIAL CONSUMPTION OF 
+FRUITS/VEGETABLES (FV) AND ANIMAL PRODUCTS (AP) 
+IN SSA: VIEW FROM HOUSEHOLD DATA 
+3.1 Urban SSA shares of AP+FV already exceed those of starchy staples – 
+and are similar to developing Asia 
+Dolislager et al. (forthcoming) analyzed LSMS consumption data for 11 SSA countries and compared 
+“high-food-budget countries” (in relative terms), including Côte d’Ivoire, Ethiopia, Mali, Nigeria, and 
+Senegal with “low-food-budget countries” (including Benin, Burkina Faso, Guinea-Bissau, Malawi, Ni-
+ger, and Togo). Note that these are not higher income and lower income countries, just higher food 
+budget and lower food budget. Here we examine their urban findings. 
+Consumption patterns in urban SSA are of special interest for several reasons: (1) urban areas con-
+sume around 50-60% of all food consumed in SSA and 60-70% of marketed food (Liverpool-Tasie et al. 
+2021); (2) urban areas form the main market for farmers and thus are crucial in creating the incentives 
+for farmers to produce AP & FV; (3) while consumption patterns in urban areas usually have higher 
+rates of consumption of AP and FV than rural areas (in part because of Bennett’s Law; Popkin and Bis-
+grove, 1988), historically it is common for the rural consumption patterns to eventually shift toward the 
+urban ones.  
+For urban areas of high-food-budget SSA countries, Dolislager et al. (forthcoming) found that the con-
+sumption share of AP+FV 1 exceeds that of starchy staples (grains and roots and tubers): 34% (AP+FV) 
+versus 30% (starchy staples). Urban areas of low-food-budget SSA countries show a surprising similar-
+ity with the high-food-budget countries. Again, the consumption share of AP+FV exceeds that of 
+starchy staples: 40% versus 28% (starchy staples).  
+These urban patterns are similar to those of developing Asia. For example, Indonesia urban food con-
+sumption is 37% AP+FV versus 26% for starchy staples; Nepal urban food consumption is 39% AP+FV 
+versus 28% for starchy staples (Reardon et al. 2014). 
+ 
+ 
+1 For at-home consumption, as food-away-from-home is a category unto itself without a product breakdown. 
+6 
+3.2 Rural SSA shares of AP+FV are still below those of starchy staples – 
+and are just a bit below those of developing Asia 
+In upper-food-budget SSA countries the share of AP+FV is 26% versus 42% for starchy staples. Again 
+surprisingly, in lower-food-budget countries the shares are close, with 29% of AP+FV versus 43% for 
+starchy staples. If one can call a diet where starchy staples dominate diversification foods a “traditional 
+diet”, the rural areas are still in that mode, but arguably more diversified than decades ago.  
+As with urban areas, there is a similarity, but somewhat lower in AP+FV of rural SSA compared with 
+developing Asia. For example, in Indonesia in rural areas AP+FV form 37% and starchy staples, 34%. 
+In Nepal, the shares are 35% for AP+FV versus 36% for starchy staples (Reardon et al. 2014). 
+ 
+3.3 Focus on FV: SSA countries have substantial (and in some zones or 
+countries growing) shares and levels of FV in household consumption 
+Normal First, there are several studies that show substantial shares of FV in food consumption.  Using 
+LSMS data, Dolislager et al. (forthcoming) show for high-food-budget countries that 15% of urban, 13% 
+of peri-urban, and 12% rural of rural food consumption (in value terms) is in FV. Low-food-budget coun-
+tries show 17%, 16%, and 15% for the three areas, respectively. This is interesting for several reasons: 
+(1) the shares are similar to developing Asia (as discussed below); (2) contrary to the conventional 
+view, the shares are similar over urban and rural areas and over high and low food-budget countries. 
+There is thus evidence of convergence in patterns of behavior. 
+There is of course substantial variation over countries. For instance, in Ethiopia, Minten et al. (2020) 
+show, using HCES data for 2016, a share of FV of 9% overall (but 19% in the capital city, Addis Ab-
+aba). By contrast, for Senegal, Faye et al. (2023) show 27% for urban areas and 17% for rural areas; 
+interestingly, they show there is little variation in shares over zones of the rural area, with rural peri-ur-
+ban at 17%, intermediate zones at 16%, and hinterland (far from towns) at 16%, suggesting penetration 
+of FV supply chains deep into rural areas. Amfo et al. (2019) found 34% for urban Ghana. Smale et al. 
+(2020) found 13% in rural and 20% in urban areas in Mali. In earlier work, Ruel et al. (2005) found (we 
+round)14% for FV for rural and urban together in Mozambique, 12% in Tanzania, 10% in Kenya and 
+11% in Ghana. Ayieko et al. (2005) found 26% for Nairobi. 
+Again, we note that these FV shares in Africa, despite variation over countries and zones, are substan-
+tial and roughly similar to findings from developing Asia. Examples include in Nepal where 15% of food 
+consumption in value terms is in FV in urban areas and 14% in rural areas, and in Indonesia, 15% ur-
+ban, and 17% rural.  
+Second, Bennett’s Law and the few regression studies of FV shares (e.g., Faye et al. 2023) show that 
+the share of non-staples in the diet rise with income. Yet survey evidence also shows that this increase 
+starts even among households with incomes below the poverty level (Dolislager et al. 2022).  
+Moreover, lumping fruits and vegetables masks differences between fruit (usually a luxury) and vegeta-
+bles (usually a necessity) in the few studies that break down these by income groups or income elastici-
+ties. For example, in Nigeria, Parkhi et al. (2023) find most vegetables to be income inelastic while fruit 
+is highly income elastic. Dolislager et al. (forthcoming) found for "high food-budget countries” in Africa 
+that vegetables had a steady 11% over income terciles, while the share of fruit went from 2% for the 
+lower and middle tercile to 3% for the upper. In low-food-budget countries, the share of vegetables in 
+7 
+food consumption dropped with income tercile, from 14% to 13% to 13%. This makes sense when one 
+thinks of fruit mainly as a dessert in those food cultures while vegetables are a basic element of sauces 
+for lunch and dinner.  
+Third, as expected from Bennett’s Law, macro data show that FV consumption is growing much faster 
+than cereals consumption in SSA. For Senegal, cereal consumption grew 2.6-fold and FV consumption 
+grew 4.4-fold over 1990 to 2018 (Faye et al. 2023). The few household survey studies that show FV 
+shares over time have often shown a rise. For Ethiopia, Minten et al. (2020) show the share rose from 
+4.5% to 9% over 2000-2016; Hassen et al. (2017) show the rise in the share FV was partly at the ex-
+pense of the cereals share; the latter dropped from 46% in 1996 to 36% in 2011.  
+The evolution of FV shares can differ by the level of economic development of a country’s region and 
+by fruits versus vegetables. Parkhi et al. (2023) for Nigeria show over 2010-2019 that the share of 
+households consuming fruits (a relative luxury) jumped from 32% to 63% in the poorer North, versus 58 
+to 83% in the richer South (while the share of households consuming vegetables stayed near 100% 
+over the period in both regions). The share of FV in overall food consumption stayed at around 12-13% 
+over the decade in the combined urban plus rural North  versus growing from 13 to 16% in the richer 
+south.     
+Fourth, levels of FV consumption have risen, even in per capita terms in some countries and zones (but 
+with levels still inadequate when with the WHO-recommended minimum consumption of 146kg/capita 
+of FV per year; Harris et al. 2022). For example, for Ethiopia, Bachewe and Minten (2023) show that 
+consumption of FV per capita grew 1.6-fold in urban and 1.3-fold in rural areas from 2011 to 2016. This 
+is rapid growth, but it should be noted that it starts from a low base and reaches a still-inadequate con-
+sumption level of 59 kg/capita overall in 2016 (with urban at 72 and rural at 56kg).  
+For Senegal, consumption per capita is 80kg/year (similar to Ruel et al. (2005) finding for Ghana of 
+75kg/year), with urban Senegal at 128kg and rural Senegal at 63kg by 2018. Note that in Dakar the 
+consumption is at 137/kg, near to adequacy levels (although it is below the 177kg/capita found by 
+Ayieko et al. (2005) for Nairobi, a higher average income city than Dakar).  
+For Nigeria, Parkhi et al. (2023) found for 2019 FV consumption at 89kg/capita in the richer South, and 
+54 in the poorer North (as low as rural Ethiopia). Moreover, they show that the North had even declined 
+over the decade from 58 to 54, while the South had increased from 65 to 89, a 1.4-fold increase in a 
+decade. 
+Fifth, another indicator of rapid transformation is that the composition of FV consumption in Africa has 
+changed over several decades. It has undergone what can be called “Westernization” with a shift from 
+a focus on traditional vegetables (such as African eggplant, okra, and indigenous green leafy vegeta-
+bles) to non-indigenous vegetables and fruits especially tomatoes, onions, and chili peppers, now the 
+dominant vegetables in the diet (e.g., in Senegal, Faye et al. (2023), and Nigeria, Parkhi et al. (2023)).  
+An important driver of the rise of tomatoes/onions/chili peppers is that they are versatile to adapt to tra-
+ditional dish forms as well as relatively new (over a half century) dishes such as rice jollof in Nigeria.  
+Sixth, purchases now form a high, even majority share of rural FV consumption in SSA (for Senegal, 
+Faye et al. 2023; Mali, Smale et al. 2020; Nigeria, Parkhi et al. 2023). This jibes with Sibhatu and Qaim 
+(2018) finding that there is little correlation in SSA between diversity of diet (such as in FV) and own-
+farming of FV. For example, in Senegal, in rural peri urban areas, 75% of FV (in value terms) is pur-
+chased; that share is 78% in intermediate rural areas and 75% in hinterland rural (Faye et al. 2023). 
+8 
+Much of SSA’s FV production takes place in a few commercial zones (focused on domestic markets) 
+supplying via medium to long VCs the urban areas (and other rural areas). Examples include vegeta-
+bles from the Rift Valley three hours to Addis Ababa (and other cities) (Minten et al. 2020), tomatoes 
+from a few main irrigated tomato zones to consumers all around Tanzania (Ijumba et al. 2023), and to-
+matoes mainly from a few areas in Northern Nigeria to Southern Nigeria (Liverpool-Tasie et al. 2023b).  
+ 
+3.4 Focus on AP: SSA countries have substantial (and in some zones or 
+countries growing) shares and levels of AP in household consumption 
+First, there are several studies that show substantial shares of AP in food consumption in SSA.  Using 
+LSMS data, Dolislager et al. (forthcoming) shows for high-food-budget countries that 19% of urban, 
+14% of peri-urban, and 14% rural of rural food consumption (in value terms) is in AP. Low-food-budget 
+countries show 23%, 16%, and 14% for the three areas, respectively. As with FV, these findings for AP 
+are interesting because contrary to the conventional view, the shares are similar over urban and rural 
+areas and over high and low food-budget countries.  
+Keep in mind that the above shares of AP are underestimated. This is because food-away-from-home 
+is an important share of food consumption in high-food-budget countries (averaging 11%) and 5% in 
+low-budget countries, and that many “food service” dishes at street vendors have animal products in 
+them (such as dairy with grain porridge; meat in traditional sauces; and the popular “chicken with chips” 
+found in many SSA cities). LSMS data in SSA generally do not show the composition of food away from 
+home. 
+Shares of AP in food consumption in SSA are only (we say "only" because we feel the conventional 
+wisdom is that there are sharp differences with Asia) a bit below findings from developing Asia. Rear-
+don et al. (2014) shows the AP shares in food consumption in Indonesia as 22% in urban and 20% in 
+rural; in Nepal, 24% in urban and 21% in rural.   
+Second, Dolislager et al. (forthcoming) found for "high food-budget countries” in SSA that AP in food 
+consumption was 10% for low, 14% for middle, and 19% for upper tercile households. In low-food-
+budget countries, the pattern was similar: 11%, 15%, and 23% over the three terciles. The finding of AP 
+as luxury foods is expected (and similar to other findings in SSA, such as for Ethiopia (Abegaz et al. 
+2018), and in developing Asia, Reardon et al. 2014).   
+Third, as expected from Bennett’s Law, macro data show that AP consumption is growing much faster 
+than cereals consumption in SSA. Moreover, the few household survey studies that show AP shares 
+over time have shown a rise in the share. For example, for Ethiopia, Minten et al. (2020) show the 
+share rose from 8% to 13% over the period 2000-2016.  
+Fourth, levels of AP consumption have risen over 2000-2020, during which dairy macro data showed 
+for overall SSA a 2.4-fold rise in consumption per capita. Some micro studies reflect this. For Ethiopia 
+dairy, Minten et al. (2020) show for Addis Ababa that annual intake per adult equivalent increased by 
+31% in only 10 years (2005-2016).  For fish in Nigeria, Liverpool-Tasie et al. (2021b) show that the 
+share of households consuming fish rose from 59 to 72% over just 5 years, 2010-2015. That masks 
+sharp regional differences: in the poorer North the share only went from 46 to 49%; while in the south, 
+from 71 to 90% in those 5 years. In the North, the kg/capita stayed at about 6.3kg, while in the richer 
+South, from 17 to 18.7 kg.    
+9 
+For chicken and eggs, in Ethiopia, Abegaz et al. (2018) show that intake doubled from 3.8 birr/capita 
+per year to 7.1 in 15 years (1996-2011). In Ghana, Knößlsdorfer and Qaim (2023) show that intake of 
+chicken rose from 40,000 tons in 1999 to 260,000 tons in 2018, although 75% is supplied by imports. 
+The latter is far higher than the all-SSA share of imports in chicken/egg consumption of 22%, with an 
+even lower rate (15%) in Nigeria (Ogunleye et al. 2016). 
+ 
+4. MESO BOOMS IN ANIMAL PRODUCT CLUSTERS AND 
+DOMESTIC VCS 
+4.1 Fish in Nigeria 
+Research is emerging on fish-capture and aquaculture clusters and rapid development (“booms”) in do-
+mestic supply in SSA, such as in Kenya (Naziri et al. 2023) and in Nigeria, which we illustrate here. Ni-
+gerian domestic fish output in tons (per FAOSTAT data) grew 4.1-fold (twice the pan-SSA rate noted 
+above). Imports into Nigeria rose only 2-fold. These data point to a boom in domestic fish supply. By 
+2020 this supply was 75% by (equal parts) marine capture and inland capture and 25% by aquaculture 
+(which was nearly 0% in 2000) (Liverpool-Tasie et al. 2023). 
+a) Fish Production clusters supplying short and long supply chains in Nigeria 
+ 
+There are several important aquaculture and capture fishery clusters feeding the fish supply 
+boom in Nigeria. We focus here on three, in the Southwest in Oyo State (near Ibadan and La-
+gos), the Southeast in Ebonyi, and in the North in Kebbi State, drawing on a rapid reconnais-
+sance study of hundreds of supply chain actors (Liverpool-Tasie et al. 2023); and Gona et al. 
+(2018) based on a “meso inventory” with a 10-year recall of supply chain actors in the four main 
+fishing/fish farming clusters in Kebbi State.  
+ 
+All three of these cluster-sets: (1) are based in areas with good enabling conditions for fish pro-
+duction (well-watered); (2) due to government investments are well-connected by highways to 
+major cities near and far and well served by wholesale markets; (3) have displayed dynamic 
+transformation of both the structure and the conduct of the value chains/clusters; (4) are charac-
+terized by dominance of SMEs who responded to increasing demand and favorable conditions; 
+(5) supply in their large majority domestic markets in general and urban markets in particular.  
+ 
+b) Diffusion of capture fishers and fish farms 
+ 
+To illustrate the size and growth of primary producers in these clusters, we focus on findings 
+from the Kebbi State clusters (one big cluster and a few smaller ones), by 2018 around 21,000 
+fishers and fish farmers. (Small scale farms form 61% and medium farms 28% of the total). 
+Over the prior 10 years there had been a 182% increase in fishers and a 200% increase in fish 
+farmers (Gona et al. 2018). This growth rate was even greater than a boom qualified as a “Quiet 
+Revolution” in aquaculture in Bangladesh (Hernandez et al. 2018).  
+ 
+There has been transformation of the conduct of primary producers in the Kebbi as well as the 
+Oyo and Ebonyi clusters, in particular intensification of aquaculture (Liverpool-Tasie et al. 2023). 
+10 
+Examples include: (1) diffusion of mobile fiber and tarpaulin tanks to adapt to small landholdings 
+and high pond construction costs; (2) and diffusion of antibiotics and commercial fish feed use 
+(at all scales of farms). 
+ 
+c) Growth in the midstream of farm inputs VC 
+ 
+There has been rapid growth in the fish farm inputs value chains (Liverpool-Tasie et al. 2023): 
+(1) emergence of long-distance (cross state) trade in fish seed from clusters of hatcheries in ar-
+eas with good environmental conditions and transport (similar to what happened in Bangladesh, 
+see Hernandez et al. 2018); (2) emergence of markets for broodstock for hatcheries; (3) emer-
+gence of specialized long-distance fingerling traders; (4) spillovers from poultry feed sector (pro-
+cessing and marketing) development to supplying fish farms, again, similar to what has hap-
+pened in Asia; (5) emergence of “rural-hub one-stop-shops” such as Chi Farms in Oyo that sells 
+and distributes juveniles, live catfish, frozen catfish, fillet, and fish feed to fish farmers and pro-
+vides training to farmers. 
+ 
+d) Growth in the midstream and downstream of the fish VCs 
+ 
+There were nearly 9,000 midstream actors (wholesalers, processors, and transport logistics) in 
+the Kebbi clusters by 2018. Growth in these segments was dynamic. For example, the number 
+of rural and urban wholesalers in the clusters grew 1.3-fold over the decade (as fish producers 
+increased 1.9-fold, this implies an increase in trader scale over the decade). Urban fish retailers 
+in the state jumped 2.5-fold. These midstream intermediaries were in urban and rural retail mar-
+kets, rural and urban wholesale markets, farmgate markets, and trader collection points totaling 
+around 255 over the period (Gona et al. 2018).  
+ 
+The conduct of midstream actors transformed in the Kebbi as well as the Oyo and Ebonyi clus-
+ters (Liverpool-Tasie et al. 2023). Examples include: (1) indigenous innovation in processing, 
+such as locally manufactured kilns and adoption of gas burners for fish frying (reducing wood 
+use); (2) lengthening of value chains of smoked fish to markets around Nigeria and to neighbor-
+ing countries; (3) improvements in cold storage infrastructure by private and public investments; 
+(4) rapid development of third party logistics (3PLS) in private and public transport (again, simi-
+lar to what occurred in Asia, for Myanmar see Belton et al. (2018)).  
+ 
+4.2 Dairy in Ethiopia 
+Above we noted that in one decade (2005-2016), dairy consumption per capita in Addis Ababa (a city of 
+4.5 million) grew 31%. The city was expanding and household incomes were rising quickly in that dec-
+ade and it became the demand, and partly the supply, center of a boom in dairy. To supply this in-
+crease there was a rise in dairy farming and processing. 
+a) Dairy farming grew quickly in one decade – SME-dominated but with increasing concen-
+tration 
+ 
+Milk farming has developed quickly both inside and around the city. Minten et al. (2020) found 
+that 31% of the city’s supply comes from 29,000 dairy cows inside the city: 26% from suburban 
+11 
+areas, and 37% from rural areas. 89% of the milk supplied to Addis comes from small farms 
+(below 25 cows).  
+ 
+Medium farms with more than 25 cows supply 11% of the milk supply but are growing much 
+more quickly than small farmers: they increased 8-fold from 2007-17 (mainly in the suburban 
+areas). Moreover, medium farms have nearly 5 times greater productivity per cow and 15 times 
+higher per worker than small farms. Productivity is also correlated with proximity to Addis: milk 
+yields are 5 times higher for farms close to Addis versus those far out. Milk yields among me-
+dium farms grew substantially over the decade while those of small farms stagnated. The me-
+dium farms tend to be urban or peri-urban; this phenomenon has also been noted in India (Bur-
+kitbayeva et al. 2023). Medium farms are the “change agents” driving the boom in the farm sec-
+tor of the dairy supply chain to Addis. Minten et al. (2020) show that the medium farms are 
+much more likely to undertake capital-led intensification: (1) cross-bred cows rather than tradi-
+tional breeds; (2) use artificial insemination; (3) commercial feed; (4) access to animal health 
+and dairy-related extension services. These in turn were supplied by growing input and services 
+supply chains, and the proliferation of commercial feed mills.  
+ 
+ 
+b) Midstream boom – with increasing concentration 
+ 
+Minten et al. (2020) noted that milk processing firms tripled (from 8 to 25) in only 10 years; this 
+rapid growth is similar to what Minten et al. (2016) documented for teff processors, transporters, 
+and wholesalers in and to the Addis market in the same decade. Dairy processing reached 
+200,000 liters of milk per day.  
+ 
+As expected, concentration in processing has proceeded faster and further than in the farming 
+sector: the four largest processors produced three-quarters of the pasteurized milk. The concen-
+tration is far less among processors producing unpasteurized milk as expected. Cooperatives 
+only have a 5% share in processing.  
+ 
+5. MESO BOOMS IN VEGETABLE CLUSTERS AND 
+DOMESTIC VALUE CHAINS: TANZANIA, ZAMBIA, 
+ETHIOPIA, ZAMBIA 
+5.1 Vegetables in Tanzania 
+Aggregate supply of domestic FV grew very rapidly over the past several decades: Tanzanian FV out-
+put in tons (per FAOSTAT data) increased 4.1-fold from 1990 to 2020; vegetable output grew 2.3 times 
+and fruit output, 7-fold. This rapid growth kept up with population growth (2.4-fold) in for vegetables and 
+well exceeded it for fruit. Fruit supply responded to growth in fruit demand which is income-elastic while 
+for vegetables is inelastic. Tanzanian income per capita increased 6-fold in constant dollars over the 3 
+decades (per World Bank data).  
+12 
+This domestic supply growth translated nearly fully into domestic consumption growth: less than 1% of 
+SSA agricultural output is exported and less than 1% of consumption is imported. The composition of 
+vegetable output changed, with tomatoes shifting from 9% to 17%, and onions from 3% to 7%, mirror-
+ing a consumption shift toward tomatoes and onions in Tanzania as in other SSA countries (as dis-
+cussed above). Tomato output leapt 4.4-fold in those three decades. These macro trends were re-
+flected in evidence of meso booms as follows. 
+a) Rapid ingress of farmers into FV at the national level 
+ 
+Farmers were responsive to the growth in domestic demand. There was a rapid diffusion of FV 
+farming per agricultural census data (NBS 2021). In 2008, 9.5% of Tanzania farms grew FV; just 
+12 years later (2020) the share doubled to 21%. The fastest shift was among small farmers, 
+from 8% of farms to 20%. For medium farms, the shift was from 24% to 38%, and large farms, 
+from 16% to 26%. Overall, area under FV jumped 130% - adding 240,000 hectares in that dec-
+ade. Half of that increase in area was a jump in area under tomatoes. By contrast, cereal area 
+expanded only 27%. 
+ 
+b) Rapid rise of zone-specific clusters of FV production linked by long supply chains to 
+consumption centers 
+While green leafy vegetables are grown throughout Tanzania in small plots in rural areas or 
+near cities, most of the other main vegetables and fruits are grown on farms clustered in specific 
+zones with favorable climates and soils and water. Examples are citrus and bananas in the hot 
+areas of the coast; and tomatoes in well-watered areas mainly in the interior and near highways. 
+The combination of similar FV consumption patterns all over Tanzania (Ijumba 2021) combined 
+with FV-growing farms (apart from green leafy vegetables) producing in specific “lead zones” 
+has meant that these commercial FV zones send FV all over the country in long supply chains. 
+An example is the clusters of irrigated tomato farms, such as in the center of the country 
+(Morogoro-Dodoma), in the Southern Highlands (such as Iringa), and in the eastern region of 
+Dar es Salaam, send out tomatoes to cities and rural areas all over the country. These findings 
+are similar to what we show below for Zambia and Ethiopia, and what was found in Nigeria for 
+the case of tomato; Liverpool-Tasie et al. 2023b). 
+ 
+This reality is in sharp contrast to the traditional view of FV produced in backyard gardens (an 
+image dating from decades ago when most FV consumption was subsistence and most of the 
+population rural and little purchased) or small bands of “peri-urban horticulture” around the few 
+cities such as in the 1990s and 2000s in Tanzania.  
+ 
+c) Rapid growth in domestic value chains with a proliferation of wholesalers & public in-
+vestment in wholesale markets 
+ 
+The long supply chains noted above feed an urban population that has been growing rapidly: 
+from 1990 to 2020 the urban population grew 4.5-fold and went from 19 to 35% of the popula-
+tion (World Bank). The urban share of consumption of FV reached 60% by 2012 and nearly 
+100% of urban consumption of FVs comes from purchases from supply chains (Ijumba 2021). 
+Long supply chains are not aimed only at cities. Ijumba (2021) found that nearly 60% of rural 
+consumption of FV is from purchases and most of those purchases of a given zone are of FV 
+(like tomatoes and onions) that are not grown in that rural zone.  
+13 
+ 
+Urban and rural consumers are supplied via retailers who are in turn supplied via urban and 
+peri-urban FV wholesale markets. These markets have spread very quickly in a short time, 
+keeping pace with rapid urbanization and income increases. The first multiple city survey of 
+these markets was undertaken in 2023 (Ijumba et al. 2023). They found 55 FV wholesale mar-
+kets in 8 cities in Tanzania, of which 31 wholesale tomatoes. Nearly all started in the past 3 dec-
+ades and two-thirds of them in only the past 20 years: about 10 of those markets were started in 
+each of the past 3 decades. 84% of the markets were started by municipal/district governments 
+and represent important public investments in the “enabling environment” over time. Moreover, 
+the number of wholesalers in these 31 markets nearly doubled in just the past 10 years. 
+ 
+5.2 Vegetables in Zambia 
+a) Vegetable farming and commercialization boom over a decade 
+ 
+First, there has been a rapid ingress of SME farmers into horticulture as well as commercial hor-
+ticulture per se. Kabwe et al. (2023) shows that the share of SME farms growing fruits and vege-
+tables jumped from 38% in 2007 to 79% in 2018; the share of SME commercial farms in the total 
+of SME farms (growing all crops) jumped from 18 to 30% over those 10 years.  
+ 
+The latter implies a 1.6-fold leap in a decade in the number of commercialized farms. In absolute 
+numbers, 664,000 started horticulture, of which 188,000 farmers entered commercial horticulture. 
+The result was that by 2018, 1.3 million farmers produced fruits and vegetables, and 486 thousand 
+sold them. This boom in SME commercial horticulture can be compared with the only 1.1-fold 
+increase in maize farmers, and 1.4-fold increase in maize sellers, to 1.4 million maize farmers 
+and 489 thousand maize sellers. Moreover, the commercial horticulture farmers are 3.8 times 
+more numerous than cotton sellers (although cotton commercial farming dominates the debate 
+on “cash cropping”).  
+ 
+Second, the farming boom has involved in some cases shifting from grain farming into vegetables 
+for some plots while staying in the traditional communal farming areas, and in other cases starting 
+vegetable farms outside communal areas, in peri-urban areas, along rivers, and near roads to 
+access water and transport to urban markets. These new areas became “spontaneous clusters” 
+of vegetable farms with complementary services such as input retailers, rural traders, and truck-
+ers. They were not formed by or coordinated by large firms, any NGOs, or the government.  
+ 
+Third, vegetable commercial farmers early in the decade were mainly small scale; over the dec-
+ade many scaled up into medium and even some large commercial farms. While there are many 
+small farms participating, the bulk of the vegetable marketed volume is formed by medium farms. 
+Many have sunk boreholes for irrigation both in communal and non-communal lands.  
+ 
+Fourth, vegetable farm output composition diversified and “climbed the value ladder” over the 
+decade, from half tomatoes, a quarter leafy greens (“low entry costs” basic greens, cabbage and 
+rape), and a tenth other (high value) vegetables, to two-fifths tomatoes, a quarter greens, and a 
+quarter, other vegetables. The other tenth is fruit.  
+ 
+14 
+Fifth, vegetable farming has intensified. Compared with traditional off-season vegetable farming 
+in communal villages, which relies little on inputs other than labor, the vegetable boom clusters 
+grow vegetables with irrigation (from rivers or from the ground via boreholes and pumps) and with 
+external inputs (seeds, including hybrid tomato seeds, fertilizer, fungicides, and insecticides to 
+control the heavy disease pressure during the growing season).  
+ 
+b) Growth of midstream of the VC 
+ 
+Vegetable farming has commercialized with a concomitant growth in the VC midstream. There 
+has been a shift of over time from the bulk of vegetables being home produced and consumed in 
+rural households decades ago to the commercialized SME producers mainly selling their produce. 
+Most is sold to urban areas such as Lusaka, a city with a metro area of 3 million, or Kitwe, a city 
+of nearly a million, as well as a dozen other large and medium cities and to Kasumbalesa the 
+border town in the Democratic Republic of Congo.  
+ 
+The great bulk of vegetables are sold through wholesale markets by wholesalers, with the great 
+majority going to the domestic market (Tschirley and Hichaambwa 2010). The wholesale markets 
+are crucial public goods in the rapidly expanding and already huge volume of marketed vegeta-
+bles. But one should keep in mind what an achievement the markets and the wholesalers working 
+them have made: the total sales volume jumped 4-fold in just 10 years, a massive influx that the 
+markets handled to feed cities. Moreover, the government has upgraded the infrastructure of sev-
+eral large markets such as the Soweto Wholesale Market in Lusaka in the past decade.  
+ 
+ 
+c) Non-government drivers of the boom 
+ 
+Kabwe et al. (2023) outline non-governmental drivers of the boom. First has been the massive 
+and growing demand from urban areas, and overall income growth. The urban population of Zam-
+bia tripled in 20 years from 3.5 million to 9 million. Average per capita GDP nearly doubled in 2 
+decades from 774 (constant USD) in 2000 to 1274 in 2021. 
+ 
+Second, rural nonfarm employment and migration remittances funded at least in part the start and 
+development of SME vegetable farmers. Formal credit sources played little to no role. After start-
+ing, own savings were important to funding the farming development, such as for purchase of 
+pumps. 
+ 
+Third, private input suppliers such as sellers of pumps and seeds and chemicals, and seedling 
+producers played an important role. Government and NGO sources of these were minor or nil. 
+ 
+Fourth, there was little role played either by NGOs or donor schemes, in terms of the share of 
+total vegetable output affected, and no role by multinational firms, except for supermarket chains 
+buying a small share of the vegetables. It was a “grass roots”, SME-driven spontaneous under-
+taking. 
+ 
+ 
+ 
+ 
+15 
+d) Government investment and policy drivers of the boom 
+ 
+Kabwe et al. (2023) outline several public sector drivers of the above boom. First, clusters of 
+farms, seedling nurseries, wholesalers, and truckers spontaneously formed in the main vegetable 
+areas near cities and rivers and main roads, there was no “management” of those clusters by 
+government. Moreover, there were no government subsidies of any kind for the vegetable boom, 
+not for pumps, nor inputs, nor land. 
+ 
+Second, however, government investment in infrastructure was crucial, in particular in rural elec-
+trification (important for pumps), wholesale markets, and roads. But the government National Ag-
+ricultural Research System (NARS) has so far played little role in the vegetable boom. The hybrid 
+tomatoes were the main varietal innovation and those were adopted from foreign seed sources. 
+There was also little role of public extension services as the latter are focused on foodgrains in 
+Zambia.    
+ 
+5.3 Vegetables in Ethiopia 
+ 
+a) Ingress of small and medium farmers and boom structure 
+ 
+Minten et al.  (2020) present survey findings regarding a vegetable farming boom in the Rift Valley 
+of Ethiopia. They showed rapid entry of SME farmers and growth of output and commercialization. 
+A “spontaneous cluster” developed of vegetable farms, wholesalers, input retailers, and outsource 
+agricultural services. This cluster mainly supplies the huge Addis Ababa market,  with none of the 
+products exported; moreover, the Addis vegetable market is mainly supplied by this cluster.  The 
+main findings are as follows.   
+ 
+First, there was a rapid increase in vegetable production in the 2010s in the Rift Valley. The boom 
+was driven on the supply side by private (farmer) investment in pump irrigation. The irrigated area 
+in the cluster more than doubled over a 10-year period.  
+ 
+Second, while 40% of the area is operated by small farms, almost 60% of the vegetable area is 
+cultivated by medium-scale tenant (land renting) farmers who produce 70% of the vegetable out-
+put of the cluster. The medium farms cultivate on average almost 5 hectares of vegetables. The 
+area operated by the medium farmers tripled over the decade, pointing to concentration in farm-
+ing.  
+ 
+b) Conduct: value ladder and technological change (intensification) 
+ 
+Minten et al. (2020) found that the small farms in this cluster, as shown for Zambia above, spe-
+cialize in “easy entry” (low investment) green leafy vegetables such as Ethiopian kale. The me-
+dium farms specialize in vegetables that require more investment, such as (high return) tomatoes. 
+Tomatoes require more labor and spraying than do other vegetables like onions. But tomatoes 
+and onions are both higher value and have higher production costs than leafy greens, so medium 
+farmers grow the two former items.   
+ 
+16 
+Second, the farms in the cluster have undertaken rapid intensification of production, with large 
+investments in pump irrigation, purchase of seedlings, fertilizers, and pesticides. Farming costs 
+for the medium farmers were twice those for small farmers of vegetables. But these costs are 
+about 10 times more than costs for producing staple grains and thus there are significant entry 
+costs. The great majority of seeds, including hybrid seeds, as well as pesticides, are from private 
+agro-dealers (with the government supported cooperatives playing a small role). Fertilizer is 
+bought mainly from the cooperatives.  
+ 
+Pump ownership a decade before was about half by the farmers themselves; a decade later three-
+quarters were owned by the farmers themselves. The pumps are mainly imported from China. 
+Minten et al. (2020) cite Hossain (2009) who noted that the availability of cheap pumps (imported) 
+was crucial to the intensification and yield increase of rice in Bangladesh. Minten et al. note the 
+similarity with the drivers of the vegetable boom in Ethiopia. 
+ 
+c) Booms in the midstream of the agricultural services and output value chains 
+ 
+First, driven by the medium farmers requiring heavy inputs of labor and inputs, combined with 
+their assets constraints (equipment and skills), SME outsource services have rapidly developed 
+and sell the farmers the following: (1) equipment and labor teams (managing migrant labor) for 
+digging wells and ponds; (2) mechanized plowing; (3) planting seedlings; (4) applying chemicals; 
+(5) harvesting; (6) loading of trucks; (7) marketing. This is similar to “one-stop-shop” outsource 
+services in mango areas in Asia (e.g., for Indonesia, Qanti et al., 2017). 
+ 
+Second, the commercialized SME farmers in the cluster market the great majority of their vege-
+tables to urban areas, especially to Addis Ababa, a city of 5 million.  In Ethiopia, the great majority 
+of vegetables are sold through wholesale markets by wholesalers. The wholesale markets are 
+crucial public goods in the rapidly expanding and already huge volume of marketed vegetables.   
+ 
+d) Non-government drivers 
+ 
+First, of fundamental importance to the vegetable boom in the Rift Valley has been the large and 
+growing demand from urban areas, and overall income growth. The urban population of Ethiopia 
+nearly tripled in 20 years from 10 million to 26 million. Average per capita income in Ethiopia has 
+nearly doubled in 2 decades: Ethiopian GDP/capita more than tripled from 262 (constant USD) in 
+2000 to 852 in 2021.  
+ 
+As one can predict from Bennett’s Law, with such rapid increase in income/capita, the FV share 
+in consumption baskets doubled over 10 years, from 4.5% in 2006 to 9% in 2016. FV consumption 
+per capita increased 1.3-fold in urban areas and 1.4-fold in rural areas between 2011 and 2016. 
+In the case of Addis Ababa, 19% of the food basket was going towards vegetables in 2020, sig-
+nificantly higher than national levels. In 2020, per the survey in Minten et al. (2020), almost three-
+quarters of the four main vegetables in Addis Ababa were procured from the Rift Valley vegetable 
+cluster.  
+ 
+Second, there was a major role by private input suppliers such as sellers of pumps and imported 
+seeds and chemicals, and seedling producers. The outsource services noted above were major 
+facilitators of the boom.  
+17 
+ 
+Third, factor markets have played major roles. On the one hand, there has been a major influx of 
+intra-Ethiopian migrant labor into the vegetable cluster. About 4300 workers are employed in the 
+cluster. On the other hand, land markets especially for rental land have been crucial as many 
+medium farmers entered by renting land.  
+ 
+Fourth, there was very little role played either by NGOs or donor schemes, nor by microcredit 
+institutions or banks.  It was a “grass roots”, SME-driven spontaneous undertaking.  
+ 
+Fifth, the natural and constructed context was favorable to the rise of the cluster. The Central Rift 
+Valley is near lakes and crossed by rivers and endowed with shallow water tables, all good for 
+irrigation. The area is on a good road and a three-hour drive to Addis Ababa. It is close to three 
+major secondary cities. The area’s climate zone (sub-tropical semi-arid) is good for vegetable 
+farming when irrigation water is available. 
+ 
+e) Government’s role 
+ 
+First, there was no “management” of that cluster by government or any entity. Moreover, there 
+were no government subsidies of any kind for the vegetable boom, not for pumps, nor inputs, nor 
+land. 
+ 
+Second, government investment in infrastructure has been crucial, in particular in rural electrifi-
+cation (important for pumps), ICT infrastructure, wholesale markets, and roads (Dorosh and 
+Minten, 2020).  
+ 
+Third, the government National Agricultural Research System (NARS) played little role in the veg-
+etable boom. Hybrid tomatoes were the main varietal innovation and those were adopted from 
+foreign seed sources. There was also little role of public extension services as the latter are fo-
+cused on foodgrains in Ethiopia.     
+ 
+6. CONCLUSIONS AND POLICY IMPLICATIONS 
+This paper showed that despite the incontrovertible facts that SSAs under-consume nutrient dense 
+fruits and vegetables (FV) and animal products (AP), and the farm production and supply chains of 
+these products are fraught with constraints that keep them from operating optimally, there is abundant 
+recent evidence of dynamism in these sectors. To wit: (1) consumption of these products in levels and 
+shares is already substantial and growing rapidly; (2) supply of these products is growing rapidly, just 
+not yet much faster than population growth; (3)  supply growth is manifested in a number of countries 
+by dynamic “meso booms” with diffusion of farming and growth in midstream VC segments. We re-
+viewed recent survey-based evidence of these booms and discussed the drivers of them. That leads to 
+policy implications as follows.  
+First, the policy debate itself has to “refresh” and take into account the dynamism that already exists 
+along value chains across Africa where the enabling conditions are present. The policy debate also 
+needs to recognize that this dynamism is “grass roots”, that is, it is a product mainly of domestic SME 
+farms and midstream enterprises investing vigorously and spreading and growing. There is little need to 
+18 
+“reinvent the wheel” based on what we contend is a myth that there is a missing middle, that growth is 
+absent. Rather, transformation is afoot and rapid, spontaneous clusters are emerging over the past 
+several decades, and the middle is not missing but rather is a “hidden middle.” (Reardon 2015).  
+Second, the SME farms and midstream firms discussed in the cases presented were responding to the 
+enormous pull of  both urban and rural demand of consumers to purchase FVs and APs. We contend 
+that demand is the builder of both incentives and eventually capacity for this supply response and we 
+emphasize the massive domestic market opportunity in SSA fueling these booms.  
+Third, there was a fascinating consistency over the five case studies (and more that we did not have 
+space to include) in the elements of the enabling environment of the meso booms. Important is what 
+was consistently absent; i.e., the direct “hand” of the government actually starting and managing the 
+clusters, or the hand of NGO microcredit actions or contracts and help from large companies, or gov-
+ernment subsidies, or special economic zones or “agroparks”.  
+Rather, government investment was always an important context and foundation of the booms: govern-
+ment investment usually at the district and municipal level (not national level) in wholesale markets; na-
+tional government investment in roads and in some cases electrification; in some cases government 
+investment in NARS providing adapted breeds of cows and varieties of tomatoes. Not directly noted 
+was government provision of a modicum of security at least for the immediate clusters. We did not dis-
+cuss policies of certification and registration because the great majority of the actors in all segments of 
+these chains were informal and small to medium.  
+There is much left out of this paper and much still to do in research; the most glaring and promising 
+ones follow. First, there is more research to do to study these spontaneous clusters in the meso booms 
+from the perspective of “empirical industrial organization”, understanding their effects on the efficiency 
+of value chains bringing food to consumers, on consumer pricing, on sector restructuring, and on the 
+development of “relational contracts” between these SME clusters and small farms (Macchiavello et al. 
+2022; Liverpool-Tasie et al. 2020) as well as their resilience to climate shocks and violent conflict 
+(Reardon and Zilberman, 2018; Vargas et al. 2023).  
+Second, there is research to do on what might be constraining the further growth and proliferation of 
+these meso supply booms such as input costs and transaction costs. That would also help to point the 
+way to further development of policies and public investments.  
+Third, there is more research to do on the implications for employment of youth and women and allevia-
+tion of poverty by these growth cases, including how “inclusive” they are of the asset poor in these ar-
+eas, and what conditions their degree of inclusion.  
+Fourth, there is more research to do on food safety and hygiene, as well as environment issues facing 
+these clusters and the existing and potential institutional arrangements that could improve on the ability 
+of these clusters to be an environment where affordable, safe and nutritious foods could be accessed 
+by consumers. These crucial issues could imply important trade-offs between food quality, environ-
+ment-friendly practices and affordability of and access to food, that requires further research to assess 
+the dynamic growth seen on merits against these multiple food system objectives. 
+ 
+ 
+19 
+ 
+ABOUT THE AUTHORS  
+Thomas Reardon is a University Distinguished Professor in the Department of Agricultural, Food and 
+Resource Economics at Michigan State University (MSU). He is also a non-resident Senior Research 
+Fellow with the International Food Policy Research Institute (IFPRI)’s Markets, Trade, and Institutions 
+(MTI) Unit. 
+Saweda Liverpool-Tasie is an MSU Foundation Professor in the Department of Agricultural, Food, and 
+Resource Economics. She is also Senior Researcher at the International Institute of Tropical Agricul-
+ture (IITA). 
+Ben Belton is an Associate Professor with MSU’s Department of Agricultural, Food, and Resource Eco-
+nomics. He is also a Research Fellow with IFPRI’s Development Strategies and Governance (DSG) Di-
+vision.  
+Michael Dolislager is an Associate Professor of Economics and Economic Development at Messiah 
+University. 
+Bart Minton is a Senior Research Fellow with IFPRI’s DSG Division. 
+Barry Popkin is a Distinguished Professor of nutrition University of North Carolina’s Gillings School of 
+Global Public Health. 
+Rob Vos is the lead for CGIAR research initiative on Rethinking Food Markets. He also serves as the 
+Director for MTI Unit within IFPRI.  
+ACKNOWLEDGMENTS  
+This work is part of the CGIAR Research Initiative on Rethinking Food Markets and Value Chains for 
+Inclusion and Sustainability. Launched in January 2022, the Rethinking Food Markets initiative is a col-
+laborative effort of seven CGIAR centers, including the International Food Policy Research Institute 
+(IFPRI), the Alliance of Bioversity International and the International Center for Tropical Agriculture (Alli-
+ance Bioversity-CIAT), the International Institute of Tropical Agriculture (IITA), the International Maize 
+and Wheat Improvement Center (CIMMYT), the International Center for Agricultural Research in the Dry 
+Areas (ICARDA), International Water Management Institute (IWMI), and WorldFish. The initiative further 
+collaborates with national and international partners to leverage innovations and policies that improve 
+the functioning of food markets and value chains in order to address food insecurity and malnutrition, 
+reduce poverty and income inequality, and minimize food systems’ ecological footprint.  
+The initiative is currently undertaking research testing the effectiveness and scalability of market and 
+value chain innovations in seven countries in Africa, Asia, and Latin America. In partnership with the 
+ISEAL Alliance, the initiative has further launched the Knowledge Platform for Inclusive and Sustainable 
+Food Markets and Value Chains (KISM) to help farmer organizations, food businesses, governments, 
+and practitioners make better-informed investment and policy decisions on inclusive and sustainable food 
+value chains. The Initiative’s leadership thanks all funders for supporting this research through their con-
+tributions to the CGIAR Trust Fund, and in particular also the Bill and Melinda Gates Foundation for 
+20 
+designated funds received. Barry Popkin is grateful to the Global Food Research Program at the Univer-
+sity of North Carolina Chapel Hill for financial support. 
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+Galiè, A. Celestin. 2020. A scoping review of market links between value chain actors and small-scale producers in developing regions. 
+Nature Sustainability. October. https://doi.org/10.1038/s41893-020-00621-2  
+Liverpool-Tasie, LSO., R.I. Yau, A. Ibrahim, A.Y. Bashir, V. Olunmogun, O. Oyediji, I.E. Martins; R. Onyeneke, M. Amadi, C. Emenekwe, A. 
+Wineman, O. Tasie, T. Reardon. 2023. Transformation in vegetable value chains  in Nigeria: rapid reconnaissance. RSM2SNF Project. 
+Powerpoint. May 23. 
+Liverpool-Tasie, L.S.O, T. Reardon, B. Belton. 2021. “Essential non-essentials”: COVID-19 policy missteps in Nigeria rooted in persistent 
+myths about African food value chains. Applied Economic Perspectives and Policy. 43(1), March: 205-224. 
+https://doi.org/10.1002/aepp.13139   
+Liverpool-Tasie, LSO, A. Sanou, T. Reardon, B. Belton. 2021b. Demand for Imported versus Domestic Fish in Nigeria: Panel Data Evidence. 
+Journal of Agricultural Economics. 1-23. https://onlinelibrary.wiley.com/doi/full/10.1111/1477-9552.12423 
+Liverpool-Tasie, LSO, A. Wineman; M. Amadi, A. Gona, C. Emenekwe, S. Norbert, O. Olunuga, R. Onyeneke, M. Taiwo, T. Reardon, B. Bel-
+ton. 2023. Transformation in the fish value chain in Nigeria. Powerpoint Presentation. RSM2SNF Project, Ibadan, Nigeria. May 23.  
+Macchiavello, R., T. Reardon, T. Richards. 2022. Empirical Industrial Organization Economics to Analyze Developing Country Food Value 
+Chains. Annual Review of Resource Economics. 14, October: 193-220. https://doi.org/10.1146/annurev-resource-101721-023554  
+Maertens, M., JFM Swinnen. 2009. Trade, standards, and poverty: Evidence from Senegal. World Development. 37(1): January: 161-178. 
+https://doi.org/10.1016/j.worlddev.2008.04.006  
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+https://doi.org/10.1371/journal.pone.0237456  
+Minten, B., B. Mohammed, S. Tamru. 2020. Emerging Medium-Scale Tenant Farming, Gig Economies, and the COVID-19 Disruption: The 
+Case of Commercial Vegetable Clusters in Ethiopia. The European Journal of Development Research. 32: 1402-1429. 
+Minten, B., Tamru, E., Engida, E., Tadesse, K. 2016. Feeding Africa's cities: The case of the supply chain of teff to Addis Ababa, Economic 
+Development and Cultural Change, 64(2): 265-297 
+Naziri, D., B. Belton, S.A. Loison, T. Reardon, K.M. Shikuku, W. Kaguongo, K. Maina, E. Ogello, K. Obiero. 2023. COVID-19 disruptions and 
+pivoting in SMEs in the hidden middle of Kenya’s potato and fish value chains. International Food and Agribusiness Management Re-
+view. published in press. http://doi.org/10.22434/IFAMR2022.0120  
+National Bureau of Statistics. 2021. National Sample Census of Agriculture 2019/2020. Tanzanian National Bureau of Statistics. 
+https://www.nbs.go.tz/index.php/en/census-surveys/agriculture-statistics/661-2019-20-national-sample-census-of-agriculture-main-report  
+Ogunleye, W.O., A. Sanou, LSO Liverpool-Tasie, T. Reardon. 2016. Contrary to Conventional Wisdom, Smuggled Chicken Imports are not 
+Holding Back Rapid Development of the Chicken Value Chain in Nigeria. Feed the Future Innovation Lab for Food Security Policy Re-
+search Brief 19. East Lansing: Michigan State University. November. 
+Parkhi, C.M., LSO Liverpool-Tasie, T. Reardon, M. Dolislager. Heterogeneous consumption patterns of fruits and vegetables in Nigeria: A 
+panel data analysis. Policy Research Brief 2. Feed the Future Nigeria Agricultural Policy Activity. East Lansing: Michigan State University. 
+July. https://www.canr.msu.edu/fsg/publications/Nigeria_Policy%20Brief_Fruits_Vegetable%20Consumption%201.pdf  
+Popkin, B.M., Bisgrove, E.Z., 1988. Urbanization and nutrition in low-income countries. Food Nutr. Bull. 10(1), 3–23. 
+Qanti, S.R., T. Reardon, A. Iswariyadi. 2017. Triangle of linkages among modernizing markets, sprayer traders, and mango-farming intensifi-
+cation in Indonesia. Bulletin of Indonesian Economic Studies. 53(2): 187-208.  http://dx.doi.org/10.1080/00074918.2017.1299923     
+Reardon, T. 2015. The Hidden Middle: The Quiet Revolution in the Midstream of Agrifood Value Chains in Developing Countries. Oxford Re-
+view of Economic Policy.31(1), Spring: 45-63. https://doi.org/10.1093/oxrep/grv011 
+Reardon, T., R. Echeverría, J. Berdegué, B. Minten, S. Liverpool-Tasie, D. Tschirley, D. Zilberman. 2019. Rapid transformation of Food Sys-
+tems in Developing Regions: Highlighting the role of agricultural research & innovations. Agricultural Systems. 172 (June): 47-59. 
+https://doi.org/10.1016/j.agsy.2018.01.022  
+Reardon, T., L.S.O. Liverpool-Tasie, B. Minten. 2021. Quiet Revolution by SMEs in the midstream of value chains in developing regions: 
+wholesale markets, wholesalers, logistics, and processing. Food Security. https://doi.org/10.1007/s12571-021-01224-1 
+Reardon, T., D. Tschirley, M. Dolislager, J. Snyder, C. Hu, and S. White. 2014. Urbanization, Diet Change, and Transformation of Food Supply 
+Chains in Asia, White Paper, Michigan State University Project of the Global Center for Food Systems Innovation and the USAID Food 
+Security Policy Innovation Lab. 
+Reardon, T., D. Zilberman. 2018. Climate smart food supply chains in developing countries in an era of rapid dual change in agrifood systems 
+and the climate. In Lipper, L., McCarthy, N., Zilberman, D., Asfaw, S., Branca, G. (Eds.), Climate Smart Agriculture: Building resilience to 
+climate change. FAO and Springer: Series on Natural Resource Management and Policy, volume 52: pages 335-351. 
+https://link.springer.com/chapter/10.1007/978-3-319-61194-5_15  
+Ruel, M.T., Minot, N. and Smith, L. (2005), Patterns and Determinants of Fruit and Vegetable Consumption in Sub-Saharan Africa: A Multi-
+country Comparison, WHO, Geneva. 
+Sibhatu K.T., Qaim, M. (2018) Meta-analysis of the association between production diversity, diets, and nutrition in smallholder farm house-
+holds. Food Policy. 77, May: 1-18. https://doi.org/10.1016/j.foodpol.2018.04.013  
+Smale, M., Theriault, V. and Vroegindewey, R. 2020. Nutritional implications of dietary patterns in Mali. African Journal of Agricultural and 
+Resource Economics, 15(3): 177-193. doi: 10.22004/ag.econ.307628. 
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+Security Research Project.  http://dx.doi.org/10.22004/ag.econ.93006  
+Vargas, C.M., LSO Liverpool-Tasie, T. Reardon. 2023. Violence incidence in Food Supply Chains: the case of Nigerian Maize Traders. Policy 
+Research Brief 1. Feed the Future Nigeria Agricultural Policy Activity. East Lansing: Michigan State University. June. 
+22 
+https://www.canr.msu.edu/fsg/projects/Nigeria%20-%20Policy%20Brief%20Violence%20in%20the%20mid-
+stream%20of%20value%20chains%20June%202023.pdf  
+ 
+ 
+© Copyright of this publication remains with the authors and IFPRI.  
+This publication has been prepared as an output of the CGIAR Research Initiative on Rethinking Food Markets and has not been inde-
+pendently peer reviewed. Any opinions expressed here belong to the author(s) and are not necessarily representative of or endorsed by IFPRI 
+or CGIAR.  
+INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 
+A world free of hunger and malnutrition 
+IFPRI is a CGIAR Research Center 
+1201 Eye Street, NW, Washington, DC 20005 USA | T. +1-202-862-5600 | F. +1-202-862-5606 | Email: ifpri@cgiar.org | www.ifpri.org | www.ifpri.info 
+© 2023, copyright remains with the author(s). All rights reserved. 
+

data/part_2/0365367704.md

@@ -0,0 +1,25 @@
+# The Water-Energy-Food Nexus: Opportunities for the Eastern Nile
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/1086e9bc-51f9-41b4-a3f6-dc1c732d428d/retrieve  
+**Language:** English  
+**Resource Type:** Poster / Presentation  
+**Release Year:** 2017  
+**Rights:** N/A  
+**GARDIAN ID:** 4c34b34a77c3355a5851850364d096dd  
+**DataNODE ID:** 1a3dbd69ef707b4cafbc570a2f13b2b0  
+**Siever ID:** b95d901e-7531-4ff3-a032-e19496c521d4  
+**Token Count:** 19  
+**Page Count:** 0
+
+## Keywords
+
+agriculture, water, irrigation, food, africa, energy, nexus, development
+
+## Description
+
+Presented by Claudia Ringler, IFPRI, and Helen Berga, ZEF, at the Nile Basin Development Forum on October 24, 2017.
+
+## Content
+
+Presented by Claudia Ringler, IFPRI, and Helen Berga, ZEF, at the Nile Basin Development Forum on October 24, 2017.

data/part_2/0369169362.md

@@ -0,0 +1,30 @@
+# Does rural non-farm employment relieve or exacerbate the agricultural diversification-farm efficiency tradeoff: The case of aquaculture in Bangladesh
+
+**Source:** gardian_index  
+**URL:** https://www.tandfonline.com/doi/pdf/10.1080/13657305.2024.2446142  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2025  
+**Rights:** CC-BY-NC-ND  
+**GARDIAN ID:** f41d593b901ac9e54fcd38c15ae91664  
+**DataNODE ID:** b94e95bf7047c390afecfa7f0b3b8d57  
+**Siever ID:** 7caa0de8-824d-44ee-a5e1-4c9b4acfc509  
+**Token Count:** 116  
+**Page Count:** 0
+
+## Keywords
+
+rural employment, agriculture, diversification, aquaculture, efficiency, labour allocation, poverty reduction, livelihoods and jobs, administrative management, fish, fish production, income, labor, off-farm employment, bangladesh, allocative efficiency, productivity, rnfe, technical efficiency, tradeoff
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World
+- **Countries:** Bangladesh
+
+## Description
+
+This paper studies how rural non-farm employment conditions the relationship between agricultural diversification and fish production efficiency. Competition for scarce productive resources typically implies a compromise between agricultural diversification and efficiency. Yet, the potential for non-farm income to resolve this tradeoff remains understudied. Cash from non-farm sources may support productivity-enhancing input purchase, thereby improving efficiency. On the other hand, by diversifying both on- and off-farm, household resources such as labor may be stretched too thin, lowering fish production efficiency. Using micro-level data on fish farming households in Southern Bangladesh, we show that at higher levels of the non-farm income share, diversification into crops results in significant allocative inefficiencies. Results are inconclusive for the technical efficiency measure.
+
+## Content
+
+This paper studies how rural non-farm employment conditions the relationship between agricultural diversification and fish production efficiency. Competition for scarce productive resources typically implies a compromise between agricultural diversification and efficiency. Yet, the potential for non-farm income to resolve this tradeoff remains understudied. Cash from non-farm sources may support productivity-enhancing input purchase, thereby improving efficiency. On the other hand, by diversifying both on- and off-farm, household resources such as labor may be stretched too thin, lowering fish production efficiency. Using micro-level data on fish farming households in Southern Bangladesh, we show that at higher levels of the non-farm income share, diversification into crops results in significant allocative inefficiencies. Results are inconclusive for the technical efficiency measure.

data/part_2/0374050780.md

@@ -0,0 +1,25 @@
+# The changing challenges of hidden hunger: Micronutrients within the nutrition and development landscapes
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/869c3932-2dfa-42ec-ac1f-5f61c23ea999/retrieve  
+**Language:** English  
+**Resource Type:** Poster / Presentation  
+**Release Year:** 2020  
+**Rights:** N/A  
+**GARDIAN ID:** a348c911cdfceed02d49e1d841b32028  
+**DataNODE ID:** 862c337f4a311a791235ddd2e9b40a67  
+**Siever ID:** 2b693e7e-9f3a-4a8f-8f33-c9861016251f  
+**Token Count:** 24  
+**Page Count:** 0
+
+## Keywords
+
+agriculture, climate change, development, systems, climate, sustainability, nutrition, food systems, food, policy, biofortification, aquaculture, impact, change, hunger, micronutrients, nutrition security, diets, globalization, micronutrient
+
+## Description
+
+Saskia Osendarp POLICY SEMINAR The changing challenges of hidden hunger: Micronutrients within the nutrition and development landscapes Co-Organized by the Micronutrient Forum and IFPRI
+
+## Content
+
+Saskia Osendarp POLICY SEMINAR The changing challenges of hidden hunger: Micronutrients within the nutrition and development landscapes Co-Organized by the Micronutrient Forum and IFPRI

data/part_2/0380361659.md

@@ -0,0 +1,237 @@
+# Las Calamidades Ambientales de la Tierra: Es la Agricultura Parte del Problema o Parte de la Solución?
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/40d37bcc-6ab5-44d4-ad2e-0a40502ede2a/retrieve  
+**Language:** Spanish  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2002  
+**Rights:** N/A  
+**GARDIAN ID:** f65a0e31965551f9cce57a277b6c99d0  
+**DataNODE ID:** 8f506173c74e6b8a085a4b68bdf5b8b0  
+**Siever ID:** a8142a7c-f34a-4f49-9eed-3a519338ae22  
+**Token Count:** 1985  
+**Page Count:** 0
+
+## Keywords
+
+agriculture, para
+
+## Geography
+
+- **Regions:** South-eastern Asia, Asia, World, Northern America, Americas
+- **Countries:** United States of America, Indonesia
+
+## Description
+
+En las últimas décadas, el mundo ha hecho un progreso impresionante para mejorar la calidad de vida de millones de personas; sin embargo, todavía sigue inconclusa la tarea de garantizar la seguridad alimentaria a los más pobres de una manera sostenible. La explosión demográfica, la expansión urbana, la desnutrición y la mala salud persistentes, las tierras agrícolas degradadas y el agua escasa, la carencia de poder de las mujeres, la globalización acelerada y la rápida aparición de nuevas tecnologías - todos estos y muchos otros factores están influenciando este esfuerzo continuo. Este libro compila docenas de resúmenes y artículos para presentar las perspectivas de los expertos sobre estos tópicos vitales. Producidas como parte de la iniciativa "Visión de la alimentación, la agricultura y el medio ambiente en el año 2020," del Instituto Internacional de Investigaciones sobre Políticas Alimentarias, las piezas coleccionadas aquí ofrecen una representación completa de los temas de política que el mundo debe abordar si ha de superar la pobreza, el hambre y la degradación ambiental; y le señalan además el camino a las acciones de política que deben ejecutarse para lograr estos objetivos.
+
+## Content
+
+
+
+
+$
+   .   (
+
+" 
+  #  
    $ )
+$
+
+N
+
+
+
+
+
+
+
+
+
+/
+Una de las principales causas del estrés ambiental en los
+países en desarrollo es la pobreza, y una de las principales
+causas de la pobreza es el estrés ambiental
+
+ada año se destruyen cerca de 17 millones de hectáreas de bosque tropical, se pier-
+den miles de variedades de plantas que no pueden reemplazarse y millones de
+hectáreas de tierra se transforman en desiertos.
+Se intensificarán estos severos problemas ambientales como resultado de un aumen-
+to en la producción agrícola y el mayor uso asociado de fertilizantes, pesticidas, irrigación,
+y maquinaria? En pocas palabras, la meta de satisfacer las futuras necesidades de alimento
+del mundo está en conflicto con la meta de proteger el ambiente?
+De acuerdo con los investigadores, una de las principales causas del estrés ambi-
+ental en los países en desarrollo es la pobreza, y una de las principales causas de la pobreza
+es el estrés ambiental.
+“La relación entre la pobreza y la degradación ambiental es estrecha y complica-
+da,” dijo Per Pinstrup-Andersen, director general del IFPRI. “Entre un 40 y un 85 por
+ciento del ingreso de los pobres de las áreas rurales depende de la agricultura, y por lo
+tanto de los recursos naturales. La degradación ambiental se da cuando los pobres pier-
+den la capacidad de sostenerse a sí mismos, de manera sostenible, con su base de recur-
+sos naturales. La presiones de la población y la carencia de tecnologías agrícolas
+$ 	
+
+ 
+adecuadas, entre otros factores, son fuerzas importantes que conducen a los pobres tomar
+decisiones desesperadas.”
+La relación negativa entre pobreza, población y degradación ambiental se puede
+terminar solamente, de acuerdo con los investigadores, con la ayuda de una agricultura
+más productiva en la áreas que ya han sido cultivadas por los pobres. Esto desacelerará
+la invasión de los bosques tropicales, de las laderas y de los márgenes de los desiertos.
+Pero al mismo tiempo, se necesita hacer cambios donde la agricultura es más avan-
+zada, tal como en las antiguas tierras de la Revolución Verde. Para reducir el impacto
+ambiental negativo de la intensificación de la agricultura, es preciso hacer un manejo
+ambientalmente adecuado de los fertilizantes, de los pesticidas y de la irrigación.
+Para satisfacer las necesidades futuras de alimento del mundo, es esencial aumen-
+tar la productividad agrícola tanto en las áreas degradadas como en las áreas más pro-
+ductivas del mundo en desarrollo. En opinión de los investigadores, esta es una de las
+formas más importantes para hacer un mejor manejo de los recursos naturales del
+mundo.
+!
+De acuerdo con el reporte de 1994 del UNICEF sobre el Estado de los Niños del
+Mundo, “La tierra que se obtiene quemando los bosques, pierde fertilidad y estabilidad
+en pocos años; las laderas empinadas se erosionan rápidamente si no se realizan inver-
+siones en la conservación de los suelos; las tierras agrícolas marginales se hacen grad-
+ualmente estériles cuando quienes las cultivan no disponen de los medios para fertilizarlas
+o para permitirles períodos de descanso. . . .”
+Antes la costumbre era que los agricultores permitieran que el suelo se recuperara
+en áreas frágiles entre las rotaciones de los cultivos; sin embargo, está práctica se está con-
+virtiendo rápidamente en un lujo, debido a las presiones de la población. Mucha de esa
+presión de la población viene de la emigración de la gente debido a las guerras, a los con-
+flictos sociales y a la degradación ambiental. Según un reporte del IFPRI, 500 millones
+de personas viven en laderas severamente degradadas, 200 millones viven en bosques llu-
+viosos tropicales y 850 millones viven en regiones secas amenazadas por la desertificación.
+A medida que la densidad de la población aumenta, los agricultores deben producir
+aun más alimentos que antes. Para poder hacerlo, no les queda más alternativa que
+devolverle los nutrientes a los suelos. Los métodos para hacerlo, sin embargo, todavía
+son motivo de debate entre los defensores de la agricultura y los ambientalistas. Hay
+métodos inorgánicos y orgánicos—el uso de fertilizantes sintéticos y el uso de materi-
+ales orgánicos tales como el compost y las leguminosas fijadoras de nitrógeno, los cuales
+le agregan nutrientes a los suelos.
+De acuerdo con Carlos Baanante, director de la División de Investigación y
+Desarrollo del Centro Internacional para el Desarrollo de Fertilizantes, “Los fertilizantes
+orgánicos, tales como estiércol animal y residuos vegetales convertidos en compost, ayu-
+
+		
+
+	
+
+
+
+
+
+
+
+$
+dan a mantener la materia orgánica del suelo y a suministrarle nutrientes. Sin embargo
+estos nutrientes pueden no ser suficientes. Por lo tanto, la mejor práctica de manejo es
+el uso tanto de fuentes orgánicas como inorgánicas de nutrientes.”
+Sin embargo, algunos miembros de la comunidad ambientalista no están de acuer-
+do. “El argumento de que debemos agregar fertilizante nitrogenado a los suelos
+indefinidamente  es débil,” dijo Jonathon Landeck, director de programas interna-
+cionales del Instituto Rodale. “La agricultura ideal es aquella que no incorpora del todo
+químicos sintéticos. A aquellos que dicen que los fertilizantes orgánicos no son viables
+en el largo plazo, debo señalarles que el uso de fertilizantes sintéticos alcanzará igualmente
+niveles donde los rendimientos no crecerán más. Adicionalmente, el uso de los orgáni-
+cos podría significar menores requerimientos de energía, lo que los haría más baratos
+que los químicos sintéticos. Sin embargo, esto no lo sabemos porque no hemos estado
+invirtiendo igualmente en agricultura que esté libre de químicos sintéticos.”
+Según los investigadores, otras cosas que se necesitan en zonas frágiles son: sistemas
+de cultivo diversificados en vez de monocultivos anuales intensivos, mejor integración
+del ganado y de los fertilizantes verdes en los sistemas agrícolas y la creación de fuentes
+de ingreso fuera de la finca. Otras reformas necesarias incluyen cambios en los derechos
+de tenencia de la tierra. “En muchos casos,” afirmó Pinstrup-Andersen, “los pobres no
+son propietarios de la tierra que cultivan. Por lo tanto, tienen poco o ningún incentivo
+para conservar los suelos, para proteger el agua del subsuelo o para conservar los árboles.”
+
+"$
+Las áreas frágiles no sólo le suministran a los pobres alimento, combustible, agua e ingre-
+so, sino que también son el hogar de  algunas de las colecciones de biodiversidad más
+importantes del mundo. La organización The Nature Conservancy (TNC), a través de
+su ayuda a organizaciones del mundo en desarrollo para comprar y conservar tierra
+ecológicamente valiosa, continuamente debe debatirse con el dilema de “la gente con-
+tra los árboles.”
+Según Alan Randall, director de un importante programa de desarrollo de TNC,
+“Nuestra filosofía es hacer de estas reservas un instrumento de desarrollo económico y
+de empleo para la gente que vive alrededor de ellas, más que un instrumento para despo-
+jarlos de la tierra. La realidad es que una reserva forestal o un parque no pueden existir
+si están rodeados de pobreza.”
+TNC está trabajando con la Fundación Bertoni, un grupo conservacionista, para
+proteger la Reserva de Bosque Natural Mbaracayu—una reserva de 146,000 acres ubi-
+cada en el Paraguay. La tierra se compró a tiempo a una compañía que planeaba arran-
+car los árboles para convertir la tierra en plantaciones a escala industrial de soya y de
+algodón. En vez de oponerse a los agricultores pobres que reclamaban la tierra de la reser-
+va, la Fundación Bertoni está asistiendo a miembros de la comunidad para que llenen
+$ 	
+
+ 
+reclamos de tierra en las áreas de amortiguamiento fuera de la reserva, y para que
+adopten prácticas agrícolas sostenibles.
+La fundación y TNC también le están ayudando a la comunidad a desarrollar
+fuentes alternativas de ingreso que no degraden la tierra, tales como la producción aví-
+cola y apícola. También están estimulando la extracción sostenible de productos no
+maderables del bosque de la reserva, tales como la hoja de te. Como subproductos indi-
+rectos de la reserva , el gobierno ha mejorado los caminos rurales y ha llevado la elec-
+trificación a la aldea local.
+;
+
+Hay quiénes perciben a la agricultura como un enemigo ambiental. En algunas de las
+áreas altamente productivas de Revolución Verde, los dramáticos incrementos en la pro-
+ducción de alimentos han sido asociados con la degradación ambiental: saturación de
+agua y de sales en los suelos irrigados; contaminación de las aguas superficiales y de los
+acuíferos; pérdida de insectos beneficiosos; aparición de resistencias químicas en malas
+hierbas y en insectos; envenenamiento de los trabajadores agrícolas con pesticidas; y pér-
+dida de variedades de plantas como resultado de la siembra de monocultivos. Según los
+investigadores, estos males no tienen que venir necesariamente de la mano del desarrollo
+agrícola.
+“Los ‘insumos’ agrícolas—irrigación, fertilizantes y pesticidas—han sido fuerte-
+mente subsidiados por los gobiernos, haciéndolos demasiado baratos, y conduciendo a
+muchos a usarlos en exceso,” dijo Peter Hazell del IFPRI. “Por ejemplo, entre el 10 y el
+24 por ciento de la tierra irrigada sufre de saturación de sales ocasionada por el exceso
+de riego. En la India, cerca de 7 millones de hectáreas han sido abandonadas debido a
+estas sales.”
+Lo que se necesita, según Hazell, son mejores diseño y manejo de los sistemas de
+riego, menos subsidios a los fertilizantes y a los pesticidas, desarrollo de incentivos
+económicos para reducir el uso excesivo de agua e insumos químicos, programas de
+reproducción de cultivos regionalmente diversificados, y educación a los agricultores
+sobre la manera segura de aplicar, almacenar y eliminar los pesticidas.
+“El riego y los insumos químicos, aplicados adecuadamente, en el momento cor-
+recto y con moderación, no tienen por qué degradar el ambiente,” agregó Hazell. “Así
+mismo, el uso de los químicos que están diseñados específicamente para el problema que
+se quiere resolver, reducirá el impacto ambiental negativo.”
+Algunos estudios han encontrado que los pesticidas se necesitan sólo en pequeñas
+cantidades, y se está avanzando en encontrar formas de sustituirlos por completo. Dice
+Hazell: “Hay un cambio hacia el control biológico—usando predadores naturales en vez
+de pesticidas para controlar las plagas. Se están introduciendo en las plantas resistencias
+naturales a pestes y enfermedades.
+
+		
+
+	
+
+
+
+
+
+
+
+$'
+“Los defensores de la agricultura y los ambientalistas se están informando mejor,”
+continuó Hazell. “Pocos defensores de la agricultura piensan que tecnología de la
+Revolución Verde debe aplicarse en áreas frágiles. Por otra parte, más ambientalistas están
+de acuerdo en que en áreas altamente productivas, se debe continuar con el uso de
+insumos modernos; pero de forma ambientalmente responsable. Mirando hacia el
+futuro, la biotecnología suministrará más y más sustitutos de los químicos. Quién sabe,
+quizás algún día se pueda introducir un gene para la fijación de nitrógeno en el trigo y
+el arroz.”
+Sin embargo, de acuerdo con el abogado del Consejo para la Defensa de los
+Recursos Naturales, Jacob Scherr, los diseñadores de política también deben mirar más
+allá de la tecnología, a los aspectos políticos y sociales que afectan la protección del ambi-
+ente, la pobreza y la oferta de alimentos. “Por ejemplo, tomemos África,” dijo Scherr.
+“Si se considera su base de recursos, este continente debería estar en capacidad de satis-
+facer todas sus necesidades de alimentos. Sin embargo, África experimenta escasez de
+alimentos, la cual se espera que llegue a ser peor. La solución no vendrá de una ‘bala mág-
+ica’ de la Revolución Verde. El tema de la oferta adecuada de alimentos en África es extra-
+ordinariamente complejo. Involucra a los mercados, la distribución, las políticas de los
+gobiernos—todos los cuales son susceptibles a la inestabilidad política. Se van a necesi-
+tar más que semillas mejoradas para evitar el hambre crónica y la escasez de alimentos.”
+

data/part_2/0395827562.md

@@ -0,0 +1,882 @@
+# Insurance opportunities against weather risks for smallholder farmers in Africa
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/f2280bdc-82bc-4d10-9cd3-554b311729f1/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2017  
+**Rights:** CC-BY-NC-ND  
+**GARDIAN ID:** 80f1a9a75f5c3e50f59e01e371ef7726  
+**DataNODE ID:** 56ae7f11ca5aabbc7ff114022f9848b4  
+**Siever ID:** 35409867-765f-4415-adda-1de381e3bae8  
+**Token Count:** 8437  
+**Page Count:** 0
+
+## Keywords
+
+insurance, risk management, weather hazards, low income groups, agricultural sector, agricultural policies, sustainable livelihoods, agricultural insurance, smallholders, nutrition, agricultural development, food security, resilience, climate-smart agriculture, climate change
+
+## Description
+
+Chapter 6 provides important insights into the promises and limits of production risk management through financial mechanisms. In particular the authors investigate the role that weather index insurance can play in generating better adaptation pathways to weather shocks for smallholder farmers than existing ones. Evidence from several pilot insurance programs shows that although the potential for innovative insurance mechanisms is real, additional work to understand their effectiveness and substantial scale-up efforts will be needed to achieve a sustainable expansion of efficient agricultural insurance markets in Africa.
+
+## Content
+
+2016 ReSAKSS Annual Trends and Outlook Report    69
+ 
+ 
+ 
+CHAPTER 6 
+Insurance Opportunities 
+against Weather Risks for 
+Smallholder Farmers in Africa
+Francisco Ceballos, John M. Ulimwengu, Tsitsi Makombe,  
+and Miguel Robles
+70   resakss.org
+I
+n Africa, agriculture is the dominant source of livelihood for the poor, 
+particularly in rural areas, where the majority resides. This sector 
+employed about 60 percent of Africa’s labor force in 2010, and more 
+than 80 percent in some countries (FAO 2017). African agriculture is 
+typically rainfed and occurs predominantly on smallholder farms of less 
+than 2 hectares. In Africa south of the Sahara (SSA), rainfed agriculture 
+accounts for more than 95 percent of farmed land (Wani, Rockström, 
+and Oweis 2009), and smallholder farms represent 80 percent of all 
+farms and up to 90 percent of production in some countries (Wiggins 
+2009). Smallholder farmers largely grow for subsistence purposes, usually 
+using few to no modern inputs (such as fertilizer, high-yielding seeds, 
+or irrigation), with some growing cash crops for income or engaging in 
+livestock rearing, a combination of crop and livestock farming, or off-farm 
+activities.  
+Extreme weather events can devastate crop yields and food production, 
+adversely impact food security and nutrition, and erode the livelihoods and 
+assets of the poor. The rainfed nature of African agriculture is often charac-
+terized by low productivity and thus subject to a wide range of weather risks 
+such as extreme temperatures or rainfall, as well as weather-related hazards 
+such as pests, diseases, and reduced accessibility to cultivated fields and 
+roads. Weather-related hazards can also be transmitted to other segments 
+of the agricultural supply chain, such as processors, wholesalers, and trans-
+porters, and also to other sectors that support agriculture, such as banking, 
+for instance through loan defaults (Ceballos and Robles 2014).
+In this context, the poor are disproportionately affected by extreme 
+weather. Total crop and livestock loss can threaten the food security and 
+nutritional status of entire communities. Moreover, the poor are at higher 
+risk from vector- and waterborne diseases. Through their effects on health 
+condition and nutritional intake, temporary weather shocks can thus 
+induce permanent negative shocks to human capital.25 Finally, a decrease 
+in nonfarm employment availability may follow extreme weather events, 
+further damaging the poor’s livelihoods and their ability to recover.
+For instance, the 2011 /2012 drought in the Horn of Africa severely 
+impacted food production as well as livestock and pastoral systems. The 
+drought induced alarming rates of malnutrition among young children and 
+an estimated 13 million people in need of humanitarian assistance (Slim 
+2012). The 2015/2016 El Niño cycle was related to both droughts in southern 
+and eastern Africa and flooding in parts of eastern Africa, devastating 
+agricultural production and threatening the food security and well-being 
+of millions of people. Extreme weather events can also cause long-lasting 
+damage to poor communities through the destruction of infrastructure 
+(roads, schools, and hospitals), with staggering costs of recovery and 
+rebuilding. For example, the 2013 flooding in Mozambique damaged 
+health clinics and resulted in humanitarian and recovery costs estimated 
+at US$30.6 million (UNRCO Mozambique 2013). In Kenya, the 2008–2011 
+drought caused a total of US$10.7 billion in damages and losses, of which 
+nearly US$9.0 billion was in the livestock subsector alone, US$91.0 million 
+in the food processing industry, US$1.5 billion in crops, US$53.0 million in 
+fisheries, and US$85.0 million in nutrition (FAO 2015).
+Climate change is projected to result in more frequent and intense 
+droughts and heat extremes in central and southern Africa as well as 
+25 Mclntosh (2015) highlighted considerable drops in consumption and food security resulting from 
+the effects of severe weather shocks on the agricultural sector in Uganda.
+2016 ReSAKSS Annual Trends and Outlook Report    71
+increased precipitation and flooding in the Horn of Africa and other parts 
+of eastern Africa (World Bank 2013). Moreover, climate change will likely 
+exacerbate cyclical weather events such as La Niña and El Niño, resulting 
+in even more frequent and severe droughts and floods. In addition, climate 
+change is projected to increase risks from vector- and waterborne diseases 
+in Africa (World Bank 2013).
+In this context, it is crucial for smallholder farmers to rely on efficient 
+protection mechanisms against these impending risks. But traditional 
+indemnity agricultural insurance has not been able to reach rural com-
+munities in Africa at a large scale, mainly due to high distribution and loss 
+verification costs and information asymmetry problems between farmers 
+and insurers. 
+In the absence of well-functioning weather insurance markets, African 
+smallholder farmers have typically resorted to informal and semi-formal 
+risk-coping strategies to deal with weather-related shocks. However, tradi-
+tional informal strategies such as savings, credit, borrowing from friends 
+and relatives, and diversifying income sources have shortcomings. Savings 
+can easily be diverted to more pressing household demands before weather 
+shocks occur, credit can be expensive and out of reach for poor farming 
+households, and extreme weather events can affect entire geographic areas 
+and thus preclude the possibility of seeking help from social networks or 
+off-farm activities. 
+Therefore, innovative strategies and insurance mechanisms are needed 
+to help smallholder farmers adapt to the effects of extreme weather events. 
+Over the past few decades, weather index insurance has been increas-
+ingly regarded as an important alternative for protecting farmers against 
+weather shocks and for enabling investment and growth in the agricultural 
+sector (Greatrex et al. 2015). Weather index insurance can thus become 
+an important part of the climate-smart tool kit for increasing agricultural 
+productivity and incomes by allowing smallholder farmers to adapt and 
+build resilience to weather shocks. In addition, the safeguards provided 
+by insurance may enable farmers to access credit and adopt riskier but 
+higher-yielding technologies, raising their productivity and improving 
+their incomes. 
+Against this backdrop, this chapter highlights insurance opportuni-
+ties for protecting smallholder farmers against weather-related risks. It is 
+organized as follows: the next two sections outline the different types of, 
+respectively, traditional and formal coping strategies against weather risk. 
+Subsequent sections discuss Africa’s experience with formal risk-coping 
+strategies, including weather index insurance, and explore linkages and 
+complementarities between weather-related risk-coping strategies and 
+climate-smart agriculture, as well as new developments and opportunities 
+for scaling up weather index insurance. The final section highlights key 
+messages and policy implications for achieving the Malabo Declaration goal 
+of enhancing the resilience of livelihoods to weather shocks.
+Traditional Risk-Coping Strategies
+In the absence of efficient and widespread tools to cope with weather risks, 
+rural households in developing countries have traditionally resorted to a 
+number of different informal risk-coping mechanisms for protecting their 
+livelihoods from unexpected shocks.
+The most universal of these is probably savings. Households around 
+the world understand the benefits and generally pursue the holding of 
+72   resakss.org
+savings. Savings, however, can take several forms: although many people 
+save in cash, others save by building up assets (even small-scale assets, such 
+as poultry or livestock); although many prefer saving in a bank, some still 
+choose saving under a mattress. A buffer of savings can certainly help when 
+a negative event affects the household. Yet there are drawbacks. Banks fail; 
+animals age and become sick; money stuck away can catch fire, get flooded, 
+or become food for insects and other creatures. In addition, households 
+exist socially, and readily available stocks of money are regularly under 
+pressure for alternative uses by the household or for the needs of others. 
+A second strategy, closely related to savings, is formal or informal 
+credit. Savings and credit are both mechanisms that turn a stream of small 
+amounts of money into one larger lump sum. The difference is that in credit, 
+the lump sum comes first, with the stream of small payments following it, 
+whereas for savings, the process is the reverse. In addition, credit bears a 
+cost in the form of interest, but so do savings, which are prone to the above-
+mentioned risks and subject to loss of value through inflation (in the case of 
+cash) and price fluctuations (in the case of savings in kind). 
+However, neither credit nor savings is a good form of insurance, 
+principally for reasons of timing: when needs arise unexpectedly, credit 
+may be in high demand or simply not available, and savings stocks may 
+not yet be sufficient to be of help. Moreover, formal credit is not available to 
+all, particularly the poorest households, who often lack required collateral. 
+Informal credit (that is, from local moneylenders) generally comes with 
+high interest rates that can quickly turn a small, temporary shock into an 
+untenable burden if not handled appropriately—particularly a problem 
+in poor rural communities with low education levels and a lack of overall 
+financial literacy.
+To overcome these limitations, households resort to other types of 
+informal mechanisms when disaster strikes, usually borrowing from other 
+households in their social network, including family and friends. This type 
+of informal insurance can be effective, timely, and overall, inexpensive 
+relative to other alternatives. Nevertheless, though loans and gifts from 
+other households have the potential to protect from idiosyncratic shocks 
+(that is, unexpected losses that affect a limited number of households within 
+a locality or social network), they are ill suited to protect against systemic 
+(or generalized) shocks, which affect most households in a given region and 
+thus undermine their capacity to support each other.
+Certain types of semiformal insurance have sprouted over the last few 
+decades (though they have much older historical roots). One example is 
+burial societies, particularly common in Africa, whereby households come 
+together into informal groups and regularly contribute a small amount in 
+exchange for a—generally fixed—larger payment in the event of a death in 
+the family. Unfortunately, these kinds of institutions are rarely available to 
+handle agricultural risks. Other semiformal institutions prolific in Africa 
+are rotating savings and credit associations (ROSCAs), which consist of 
+a self-organized group of individuals who contribute a small amount of 
+money at fixed periods of time (such as every week), the total of which is 
+assigned each period to a different member of the ROSCA as a lump sum to 
+be used at the individual’s will. Even though several variations exist on the 
+ROSCA model, they all generally suffer from the same issues as the other 
+strategies mentioned above, such as imperfect timing and an inability to 
+help under systemic shocks that affect all households.
+A final important way in which agricultural households regularly 
+protect themselves from weather and other risks is by diversifying their 
+2016 ReSAKSS Annual Trends and Outlook Report    73
+income sources. Diversification can take shape either through carrying out 
+different agricultural activities (such as staggering the planting of crops 
+or choosing a mix of crops with different sensitivities to weather events) 
+or through engaging in other agricultural and rural nonfarm activities. 
+A related strategy is that of reducing agricultural risk exposure by either 
+planting crops less vulnerable to weather risks or choosing more resilient 
+crop varieties. Unfortunately, these alternatives often generate lower profit 
+and have lower yield potential, thus precluding the household from increas-
+ing its income and escaping poverty.
+All in all, though they are important and essential for dealing with a 
+large array of shocks, most traditional risk-coping strategies are costly and 
+have limited risk-mitigation potential for systemic weather risks (Townsend 
+1994). Informal savings are perhaps too costly for a population that probably 
+should better invest its resources in assuring adequate food intake for 
+household members, in improving human capital, and in seizing productive 
+opportunities. In addition, diversification strategies may come at an effi-
+ciency cost—that is, they may impede rural farmers from capturing the full 
+range of benefits from specialization or keep them from investing in risky 
+capital and technology with higher expected incomes.
+Formal Risk-Coping Strategies
+Formal risk-sharing mechanisms take advantage of the fact that, across a 
+large enough population, only a fraction of individuals may suffer a negative 
+shock. For example, in a given year, only a small fraction of drivers will 
+be involved in a car accident. By pooling risks within a large population, 
+formal insurance programs can provide an efficient risk-sharing mechanism 
+in which all contribute with premiums but only those who experience a 
+loss get compensated. Furthermore, because insurance markets can pool 
+risks across a broad scope of activities and large geographic areas, they can 
+lower the costs of dealing with systemic risks through diversification. The 
+most common type of insurance is known as indemnity insurance, whereby 
+compensation relies on identifying specific losses and indemnifying the 
+individual against them.
+Although in theory, the same principles should be applied to weather 
+risks and rural populations, the reality is that most countries lack standard 
+indemnity agricultural insurance markets (with the exception of certain 
+developed countries or large subsidized systems in a few developing ones, 
+usually involving considerable public intervention). Multiple-peril crop 
+insurance, for example, which can protect against any source of risk affect-
+ing yields, has been unsuccessful commercially without large subsidies. 
+Single-peril crop insurance, which covers against a specific factor affecting 
+the crop (such as hail or wind), has had more success, though it has been 
+developed only at modest scales (Smith and Goodwin 2010).
+There are a number of reasons why agricultural indemnity insurance 
+has failed to expand successfully in developing countries, including those in 
+Africa. Possibly the most important is that among small farmers the costs 
+of loss verification, which typically requires a site visit, can be substantial 
+relative to the sum being insured, especially when rural infrastructure is 
+inadequate. Moreover, the lack of formal financial service networks and 
+legal records may add to the cost of premium collection and compensation 
+disbursement. Second, indemnity insurance is prone to significant informa-
+tion asymmetry problems, such as adverse selection (whereby only the most 
+at-risk farmers purchase insurance) and moral hazard (whereby an insured 
+74   resakss.org
+farmer may not exert optimal effort to reduce risk or mitigate its impact), 
+both of which generally result in an increased cost (Hazell, Pomareda, and 
+Valdes 1986). 
+In view of these market failures, an increasing trend has been to explore 
+an alternative type of weather insurance product for smallholder farmers 
+(Hazell et al. 2010). Under weather index insurance, a somewhat recent 
+innovation that is possibly more suitable for rural areas in developing coun-
+tries, farmers get a pre-specified compensation according to the value of a 
+particular weather variable (the index).26 For instance, an index insurance 
+product against drought would pay farmers when rainfall (as measured 
+at a specific weather station or by satellite images) is less than a certain 
+predefined “trigger,” generally with higher payments the lower the recorded 
+rainfall is. The key assumption is that by carefully selecting a weather index, 
+one should be able to estimate agricultural losses with a sufficient level of 
+confidence.
+Some regard index-based insurance as having great potential to reach 
+smallholder farmers in developing countries because (1) payouts are 
+based only on publicly observed data (the index), drastically reducing loss 
+verification costs; (2) adverse selection and moral hazard problems are 
+26 A slightly different type of index insurance, area-yield insurance, does not rely on a weather 
+variable as its index but instead focuses on whether the average yield over a specified area is 
+greater or less than a threshold.
+minimized;27 and (3) compensations can be automatically determined 
+and thus disbursed quickly to farmers, making insurance easier and 
+cheaper to administer, and thus potentially more affordable for the rural 
+poor. These characteristics of index insurance have attracted donors and 
+governments alike. Over the past two decades, many international organi-
+zations, researchers, and microfinance institutions have conducted pilots 
+in developing countries, including several African ones, to demonstrate the 
+advantages of index insurance and learn the best implementation practices, 
+with the general aim of scaling up these pilots (Hazell et al. 2010).
+In general, index insurance pilots in developing countries have repeat-
+edly experienced low uptake, which has been linked to certain constraints 
+such as lack of trust in the insurance company, lack of understanding of 
+the product, and liquidity constraints (Cole et al. 2013, Matul et al. 2013). 
+Though all of these constraints are also applicable to traditional indemnity 
+insurance, there is one disadvantage that is unique to index insurance: basis 
+risk. Basis risk arises due to an index’s inadequacy to perfectly capture the 
+individual losses of an insured farmer, which can be related to a number 
+of factors. First, the index is generally measured at a local weather station 
+(or through not-fully-accurate satellite imagery), not at the farmer’s plot. 
+27 Because losses are assessed not directly but only through the value of an objective index, the 
+farmer’s effort does not affect the probability of a payout—thus moral hazard considerations 
+are dealt with. Additionally, because the probability of a payout is assessed objectively from the 
+historical values of the index, the insurance company should not be concerned about which 
+type of farmer buys this insurance—thus adverse selection is dealt with. However, under 
+some circumstances, temporal adverse selection may still be present, whereby farmers buy 
+the insurance product only in seasons in which payouts are expected to be higher (relying, for 
+instance, on weather forecasts or levels of soil moisture at the beginning of the season). Although 
+such behavior would tend to undermine an insurance product’s sustainability, it can be generally 
+dealt with by, for instance, controlling the time frame during which farmers can purchase 
+insurance.
+2016 ReSAKSS Annual Trends and Outlook Report    75
+Second, a simple weather index cannot capture the interplay of weather 
+variables (temperature, rainfall, humidity, evapotranspiration, winds, 
+and the like), nor can it account for variability in crop variety, soil quality, 
+and farming practices. Third, other, nonweather events, such as pests and 
+diseases, may impact crop growth. Hence there is a chance that a farmer, 
+after having paid the premium, will not get a compensation even after 
+experiencing a loss. On the other hand, it is also possible that a farmer will 
+get compensation without experiencing a loss.
+Despite these obstacles, there have indeed been a number of seemingly 
+successful implementations of index insurance. In India alone, more than 9 
+million farmers annually purchase these hedging products to insure against 
+weather risk (Clarke et al. 2012), although this high uptake can be partly 
+explained by the fact that agricultural insurance is mandatory in order 
+to gain access to subsidized agricultural loans from the government. In 
+the United States, a large federal index-based insurance program protects 
+farmers against a variety of weather risks, although the system is highly 
+subsidized. In Africa, some index insurance experiences have been relatively 
+successful, such as the R4 Rural Resilience Initiative, which has helped to 
+increase the resilience of farming households to weather-related shocks in 
+Ethiopia and Senegal. This and other examples of Africa’s experience with 
+risk-coping strategies are discussed next.
+Africa’s Experience with Risk-Coping 
+Strategies
+Insurance services are still very much underprovided in Africa. According 
+to Assah and others (2017), in Senegal, 18,540 producers benefited from a 
+policy against drought in 2015, whereas close to 700,000 farmers remained 
+without coverage. In Mali, only 30,000 farmers, fewer than 1 percent of the 
+total, were insured in 2014. In addition to information asymmetry problems, 
+other factors constraining the development of insurance markets in Africa 
+include illiteracy among farmers, their inability to service loans, limited 
+solvency among insurers, and a hostile regulatory environment in some 
+countries (Assah et al. 2017). Mahul and Stutley (2010) reported that gov-
+ernment support for agricultural insurance premiums is very small in Africa. 
+For example, governments cover only 3 percent of agricultural insurance 
+premiums on the African continent, compared with 50 percent in Asia and 
+73 percent in the United States and Canada. 
+Nonetheless, promising examples are burgeoning across Africa, thanks 
+to financial and technological innovations in the insurance sector, as well 
+as overall economic progress. As argued above, one of the most promising 
+innovations in agricultural insurance is index-based insurance. Therefore 
+we focus below on successful index insurance case studies on the continent. 
+R4 Rural Resilience Initiative in Ethiopia, Malawi, 
+Senegal, and Zambia (Formerly Horn of Africa Risk 
+Transfer for Adaptation Project–HARITA)
+In Ethiopia, several projects tackling agricultural resilience have incor-
+porated index-based insurance (Table 6.1). Examples of these programs 
+include the R4 Rural Resilience Initiative, the Horn of Africa Risk Transfer 
+for Adaptation project (HARITA), and the Rural Resilience Enhancement 
+Project, which have been implemented by the Ethiopian Insurance 
+Corporation, the World Bank, the UN World Food Programme (WFP), 
+Oxfam America, and the Japan International Cooperation Agency.
+76   resakss.org
+TABLE 6.1—PILOT AGRICULTURE INSURANCE PROJECTS IN ETHIOPIA  
+Subsector Weather index insurance Indemnity insurance
+Crops • World Bank initiative for maize in Alaba woreda
+• Nyala Insurance Company (NISCO) / World Food 
+Programme / Lume Adama Farmers Cooperative Union 
+for beans in Bofa (Boset woreda)
+• Horn of Africa Risk Transfer for Adaptation program by 
+Oxfam America and consortium of partners in Tigray 
+Region
+• International Food Policy Research Institute and 
+consortium of partners for bundle of prevalent crops in 
+SNNPR and Oromia regions
+NISCO multiperil crop insurance 
+for teff, wheat, lentils, beans, and 
+chickpeas in Oromia Region
+Livestock International Livestock Research Institute’s (ILRI) index-
+based livestock insurance (IBLI)
+Pilot of high-value livestock 
+insurance by World Bank and 
+Association for Ethiopian 
+Microfinance Institutions
+Source: Bhushan et al. (2016).
+Note: A woreda is a local administrative division in Ethiopia. SNNPR = Southern Nations, Nationalities, and Peoples’ Region.
+TABLE 6.2—EXPANSION OF HORN OF AFRICA RISK TRANSFER FOR 
+ADAPTATION (HARITA) PROJECT / R4 RURAL RESILIENCE INITIATIVE  
+Year
+Number 
+of farmers 
+insured
+Total 
+premiums
+(in US$)
+Total sum 
+insured
+(in US$)
+Total 
+payouts
+(in US$)
+Countries
+2009 200 2,500 10,200 0 Ethiopia
+2010 1,300 27,000 73,000 0 Ethiopia
+2011 13,000 215,000 940,000 17,000 Ethiopia, Senegal
+2012 18,000 275,000 1,300,000 320,000 Ethiopia, Senegal
+2013 20,000 283,000 1,200,000 24,000 Ethiopia, Senegal
+2014 26,000 306,000 1,500,000 38,000 Ethiopia, Senegal
+2015 32,000 370,000 2,200,000 450,000 Ethiopia, Senegal, Malawi, Zambia
+Source: WFP (2017).
+R4, in Ethiopia and Senegal, is perhaps one of the most suc-
+cessful initiatives for enhancing agricultural resilience. Before 
+launching R4 in 2011, however, the Ethiopian Insurance 
+Corporation, in partnership with the World Bank, had 
+launched an index insurance program for Ethiopian farmers in 
+the form of a deficit rainfall index insurance for maize in 2006. 
+Unfortunately, this initiative encountered many challenges—
+especially lack of sufficient data—that limited its expansion. 
+Greatrex and others (2015), for instance, highlighted inefficien-
+cies in data collection from weather stations, limited financial 
+capacity of cooperatives, and limited bank involvement due to 
+the cost and time associated with incorporating weather risk 
+assessments into their procedures.
+Then in 2009, Oxfam America and the Relief Society of 
+Tigray launched HARITA, initially covering 200 Ethiopian 
+farmers. Building on the success of HARITA, Oxfam America 
+and partners launched R4 in Ethiopia in 2011 and eventually 
+expanded it to Senegal (Greatrex et al. 2015). By 2014, growth 
+of the program was impressive: more than 24,000 farmers 
+in Ethiopia and 2,000 in Senegal were covered (Table 6.2). 
+And in 2015, R4 distributed about US$450,000 in payouts to 
+43,000 farmers in Ethiopia, Senegal, and Malawi. One of the 
+key features that R4 borrowed from HARITA that is perhaps 
+responsible for a large portion of its success was the concept 
+of “insurance for work,” which allowed poor farmers to 
+afford insurance by paying for it through their own labor in 
+resilience-related community projects.
+2016 ReSAKSS Annual Trends and Outlook Report    77
+Currently operating in Ethiopia, Malawi, Senegal, and Zambia, the 
+R4 program is based on four risk-management strategies: building risk 
+reserves (savings); promoting risk reduction (through growth of assets); 
+prudent risk taking (relying on microfinance and diversification); and risk 
+transfer (index insurance), which allows for the transfer of components of 
+risk that cannot be mitigated by using the other strategies. In addition, the 
+program is complemented by training for farmers on the properties and 
+application of index insurance and on risk management principles.
+Madajewicz, Tsegay, and Norton (2013) evaluated the impact of the R4 
+program and found that among insured farmers, the level of grain reserves 
+had increased, savings had more than doubled (a 123 percent increase 
+on average), and the number of oxen owned had increased by 25 percent. 
+Vulnerable groups, particularly women farmers, had benefited significantly 
+from the program. In comparison, uninsured farmers did not fare as well. 
+In Senegal, an impact evaluation by WFP and Oxfam America (2015) 
+revealed that in the presence of the same shocks, farmers who had enrolled 
+in the R4 initiative fared better in maintaining their food security than 
+those who had not enrolled.28
+28 In particular, enrollees’ food consumption score (FCS) dropped from 59.02 to 56.24 between 
+2013 and 2015, whereas nonparticipants’ FCS witnessed a decrease from 56.2 to 28.6 in the same 
+period.
+Agriculture and Climate Risk Enterprise (ACRE) 
+in Kenya, Rwanda, and Tanzania (formerly 
+Kilimo Salama) 
+In 2009, the Syngenta Foundation launched Kilimo Salama in Kenya, with a 
+pilot project offering index insurance to 200 farmers. By 2012, the insurance 
+program had more than 51,000 subscribers in Kenya and 14,000 in Rwanda 
+(IFC 2013). In Kenya, premium payments averaged 19 million Kenya 
+shillings (KSh) in 2011 and KSh 33 million in 2012. In 2014, the program 
+was transferred to Agriculture and Climate Risk Enterprise Inc. (ACRE), a 
+for-profit enterprise. By 2016, ACRE had more than 1 million subscribed 
+farmers in Kenya, Rwanda, and Tanzania, insuring more than US$56 million 
+in crops against various types of weather risks (ACRE 2017).
+ACRE is an insurance agent and surveyor based in Kenya, Rwanda, and 
+Tanzania. It operates as an intermediary institution among different stake-
+holders along the agricultural insurance value chain. ACRE’s primary goal 
+is to help insurance companies add index products to their portfolios, using 
+actuarial and product development expertise. Participating stakeholders 
+include local insurers (who carry risk, document policies, and pay claims), 
+reinsurers (who price policies and reinsure risk), farmers (who access insur-
+ance services), and farmer aggregators (organizations insured on behalf of 
+farmers, such as banks, microfinance institutions, and agribusinesses).
+ACRE is considered the largest commercial (that is, with farmers paying 
+a market premium) index insurance program in developing countries and 
+the largest agricultural insurance program in SSA (Greatrex et al. 2015). It 
+is also the first-ever agricultural insurance program to reach smallholder 
+farmers using mobile phones. ACRE offers a wide range of products, such 
+as indemnity coverage, dairy insurance, hybrid seed index insurance, and 
+78   resakss.org
+multiperil crop insurance, and uses several data sources for its indexes, 
+including automatic weather stations and remote sensing technologies. 
+Targeted crops under the program include maize, sorghum, coffee, sun-
+flowers, wheat, cashew nuts, and potatoes, with coverage against drought, 
+excess rain, and large storms. The insurance operates through three main 
+channels: the distribution of seeds via mobile phone network location 
+services; agribusinesses; and banks, microfinance institutions, and credit 
+cooperatives along the agricultural value chain. By facilitating enrollment 
+and electronic payment, M-Pesa29 is arguably one of the most important 
+factors behind the program’s success. Overall, ACRE’s success is credited 
+to the involvement of a wide range of partners, including government 
+institutions (ministries of agriculture and national meteorological services), 
+financial institutions, mobile network companies, research institutions, and 
+insurance and reinsurance companies.
+Index-Based Livestock Insurance (IBLI) in Kenya 
+and Ethiopia
+The index-based livestock insurance (IBLI) program in Ethiopia and Kenya 
+was launched in 2010 with the objective of improving the resilience of 
+pastoralist households against droughts and facilitating investments in live-
+stock and access to credit (Mude et al. 2010; Miranda and Mulangu 2016). 
+The International Livestock Research Institute (ILRI) teamed up with the 
+University of California, Davis, to design an index-based livestock insurance 
+relying on the normalized difference vegetation index (NVDI). The NVDI 
+is calculated from remotely sensed satellite measurements and used to 
+29  M-Pesa is a mobile phone–based money transfer, financing, and microfinancing service, launched 
+in 2007 by Vodafone for Safaricom and Vodacom, the largest mobile network operators in Kenya 
+and Tanzania.
+estimate the availability of forage for livestock. The project derived a statisti-
+cal relationship between the NVDI and livestock mortality data to serve as a 
+basis for insurance payouts. In February 2017, the government of Kenya, in 
+partnership with Kenyan insurers, announced payments to more than 12,000 
+pastoral households under IBLI.
+At least 4,000 pastoralists in both Ethiopia and Kenya were covered 
+by IBLI in 2015. The program provided substantial benefits to households, 
+who were less likely to sell their livestock and in some cases increased their 
+number of livestock and improved their overall food security (Janzen and 
+Carter 2013). Thanks to the substantial learning process from experiences 
+on the ground, the IBLI initiative keeps expanding across Kenya. After the 
+historic 2016 drought in northern Kenya, which caused the worst forage 
+scarcity in the region for 16 years, more than KSh 214 million was disbursed 
+in payouts to 12,000 pastoral households in 6 counties.
+In 2015, the government of Kenya, supported by the World Bank, 
+launched the Kenya Livestock Insurance Program (KLIP) using a design 
+based on the NVDI. In October 2015, KLIP covered the livestock of 5,000 
+pastoralists in 2 counties (ILRI 2017). Further expansions are planned in 
+2017.
+Other Index Insurance Experiences in Africa
+As a whole, the African continent has been at the vanguard of index 
+insurance’s upward trend during the past decade. Though the previous sub-
+sections have focused on the most important experiences, a detailed account 
+of the remaining ones is beyond the scope of this chapter. In order to fill this 
+gap, Table 6.3 summarizes other weather index insurance projects conducted 
+across a number of African countries.
+2016 ReSAKSS Annual Trends and Outlook Report    79
+TABLE 6.3—SUMMARY OF KEY AGRICULTURAL INSURANCE INITIATIVES IN AFRICA  
+Country Description 
+Ghana • Under the Ministry of Food and Agriculture, the government launched the Ghana Agricultural Insurance Pool in 2011, with 19 Ghanaian insurance companies participating.
+• Pool products focus on drought index insurance for maize, soybeans, sorghum, and millet; however, there are few multiperil crop insurance plans for risk experienced by 
+commercial farmers and plantations.
+Kenya • In addition to the projects described above, the government of Kenya launched the Kenya National Agricultural Insurance Program (KNAIP) in March 2016, focusing on insurance 
+for maize and wheat crops and for livestock.
+• KNAIP will follow the area yield–based approach: the farming area is divided into insurance units, and if the average production in an insurance unit falls below a threshold yield 
+(based on the historical average yield for that unit), the insured farmers within the insurance unit receive a payout. 
+• Implementation of the program started in three counties, Bungoma, Embu, and Nakuru, and will be extended to 33 of the country’s 47 counties by 2020.
+Malawi • In 2005, the World Bank, in collaboration with Malawi’s National Association of Small Farmers, developed an index-based crop insurance contract. 
+• The pilot was implemented in the areas of Kasungu, Nhkotakota, Lilongwe North, and Chitedze.
+• In 2005, 892 groundnut farmers purchased weather-based crop insurance policies for total premiums of US$36,600. 
+• In 2007, the pilot was expanded to cash crops. By 2008, the number of participants had increased significantly, with 2,600 farmers buying policies worth US$2.5 million.
+Mali • PlaNet Guarantee (an international microinsurance facilitator) sold its first insurance products in 2011 for maize crops; roughly 14,000 farmers were insured in 2014. 
+• A second product was launched in 2011, a satellite-based index insurance for maize and cotton in partnership with Allianz; 17,481 policies were sold in 2014.
+Mozambique • In late 2012, two pilot projects were started by Guy Carpenter & Company LLC in conjunction with the Asia Risk Centre, including weather index–based insurance products 
+covering two crops: maize in the district of Chimoio and cotton in the districts of Lalaua and Monapo.
+• 43,000 cotton farmers and a small number of maize farmers were insured in 2012/2013; a total of 43,500 policies were sold.
+• In the future, the Cotton Institute of Mozambique plans to expand index insurance coverage to all cotton farmers in Mozambique, numbering approximately 200,000.
+Nigeria • The Nigeria Agricultural Insurance Corporation (NAIC) is the primary agency providing insurance.
+• Crop insurance packages currently cover 17 crops, including maize, rice, cassava, yams, and sorghum.
+• Livestock insurance packages currently cover 14 types of livestock, including cattle, poultry, pigs, rabbits, and sheep.
+• In May 2013, NAIC paid more than 500 million Nigerian naira (N) in claims to insured farmers who had suffered losses in the floods in 2012.
+• In 2014, NAIC paid N 80 million in compensation to a sugar farm in Adamawa State following natural disasters.
+South Africa • In South Africa, agriculture insurance began in the 1970s, operating at two levels: commercial and subsistence farming. 
+• The government has implemented subsidized crop insurance to make it affordable to farmers.
+• Currently, South Africa has insurance against hail and winds, but not drought. Under the existing scenario, farmers in good agricultural areas with low risk do not need subsidized 
+insurance.
+• Agri SA, a federation of South African agricultural organizations, focuses its insurance efforts on commercial farmers, who number about 40,000, representing 20 percent of the 
+farming population and producing 80 percent of the country’s food.
+• The livestock insurance market in South Africa, although limited, is growing; racehorses are insured, and there is a market for insurance of wildlife in game parks.
+Tanzania • Apart from the pilot projects mentioned above, agricultural insurance for smallholder farmers is generally absent from the market.
+• The National Insurance Corporation launched a livestock insurance product in 1996 targeting only zero-grazing livestock keepers. The program failed because the majority of 
+livestock herders were migratory pastoralists. 
+Source: Authors’ summary from Bhushan et al. (2016).
+80   resakss.org
+Africa’s successful experiences with smallholder agricultural insurance 
+against extreme weather events shows the importance of investments in 
+weather station infrastructure, widespread and inexpensive distribution 
+networks for collecting premiums and disbursing payouts, and reliable and 
+timely data collection and analysis to help reduce basis risk (Hill 2010). 
+Educating smallholder farmers on weather insurance and its benefits is key 
+to increasing its uptake and thus making insurance less costly. In cases in 
+which selling insurance on its own has been less successful, the example of 
+Malawi shows the potential benefits of tying insurance to credit, which can 
+encourage a virtuous cycle of credit, enabling farmers to purchase modern 
+agricultural inputs and increase their productivity (Leftley 2009).
+Despite these successful experiences, agricultural insurance is still 
+largely at the pilot stage in several countries, including Benin, Ethiopia, 
+Mali, Mozambique, Senegal, and Tanzania (Bhushan et al. 2016). Moreover, 
+countries continue to depend on international assistance to deal with the 
+effects of extreme weather, and governments have not made the much-
+needed investments to help develop effective insurance markets. Among 
+these investments, creating an enabling policy and regulatory environment 
+that supports the expansion of insurance markets and programs should be 
+high on the agenda, including developing insurance products that better 
+serve the needs of smallholder farmers. Governments will also need to lead 
+the way in insurance infrastructure investments (such as weather stations 
+and product distribution networks), building the capacity of insurance 
+companies, and training farmers on insurance products (Hill 2010). Finally, 
+some form of government insurance subsidy may be required to enable 
+higher uptake of insurance, such as the uptake rates seen in developed 
+countries with highly subsidized insurance programs. 
+The Road Ahead and Opportunities
+The African experience shows that index insurance has potential as a 
+formal, efficient risk management tool for farmers in developing countries. 
+However, for it to be truly brought to scale globally, its limitations have to be 
+addressed. This section describes a broad set of issues related to the opportu-
+nities for index insurance and the main innovations to consider in the future.
+Complementarities with climate-smart agriculture. Climate-smart 
+agriculture (CSA) has gained popularity during the past decade as an essen-
+tial step toward climate adaptation by rural farming communities. CSA 
+refers to agricultural technologies that are well suited to increase farmers’ 
+livelihoods in the face of a changing climate by (1) raising agricultural pro-
+ductivity, (2) building the resilience of livelihoods and farming systems, and 
+(3) reducing carbon emissions. In some cases, these technologies involve 
+reducing the vulnerability of crops to certain weather risks. In this regard, 
+CSA shares a similar objective with crop insurance. Due to the similarities 
+between these two families of technologies, a recent strand of work has 
+focused on evaluating the potential for complementarities between them.
+One of the most important examples of a complementarity between 
+weather index insurance and a CSA technology is drought-tolerant (DT) 
+seed varieties. DT seed varieties represent an important avenue of progress 
+in seed breeding and are now available for a number of crops across several 
+agroclimatic zones. DT seeds are particularly interesting from a develop-
+ment point of view because they can potentially bring about improved food 
+security and protect rural livelihoods in the face of prolonged droughts.
+Although the main characteristic of such seed varieties is their resis-
+tance to mild or moderate lack of soil moisture, crop failure is generally an 
+inevitable result under an extreme drought, with the added consequence 
+2016 ReSAKSS Annual Trends and Outlook Report    81
+of farmers’ being worse off due to having to repay the higher cost of DT 
+seeds. Weather index insurance, on the other hand, is not very well suited 
+to handle moderate drought because it tends to be expensive under a high 
+frequency of loss (insurance premiums must be high to account for frequent 
+payouts). Nevertheless, because extreme drought events occur much more 
+rarely and are generally easier to identify through an index (compared with 
+more moderate events that may or may not damage crops), weather index 
+insurance boasts natural comparative advantages to handle this layer of risk. 
+It is natural to see, thus, that a holistic system—wherein farmers rely first on 
+DT seeds to inexpensively cover more frequent and milder drought risks, 
+and in addition rely on reduced-cost 
+catastrophic index insurance against 
+extreme events—could provide farmers 
+with more complete protection against 
+all potential scenarios, thus more effi-
+ciently handling drought risk at a much 
+lower cost than any of the above stand-
+alone technologies would be able to 
+achieve (Lybbert and Carter 2015; Ward 
+et al. 2015). Figure 6.1 shows a visual 
+representation of this complementarity.
+Other aspects of the synergies 
+between CSA and index insurance 
+are starting to be explored. One such 
+exploration looked at a CSA practice 
+known as conservation agriculture 
+(CA) in a project in the wheat-rice 
+system in the Indo-Gangetic Plain of India. Under CA, rice residue is left on 
+the field at harvest and wheat seeds are sown directly through the residue 
+into the soil using special machinery. Sowing the wheat seeds through this 
+layer of residue has several advantages, including increased tolerance to high 
+temperatures and reduced risk of lodging (bending of the plant due to wet 
+soil and winds), because the plant sits deeper in the soil than under other 
+planting methods. Similar to the DT scenario described above, adopting 
+CA technology can inexpensively protect wheat from mild but frequent 
+risks, and index insurance can complement this advantage by providing less 
+expensive coverage against more extreme events. 
+FIGURE 6.1—COMPLEMENTARITY BETWEEN DROUGHT-TOLERANT SEEDS AND DROUGHT 
+INDEX INSURANCE
+Drought pressure
+No drought
+Drought-tolerant 
+seeds (DT)
+Drought 
+probability 
+density 
+function
+Drought index 
+insurance (II)
+Bundled DT + II
+Moderate Severe Extreme
+Te
+ch
+no
+lo
+gy
+’s 
+pa
+yo
+
+ re
+la
+ti
+ve
+ to
+ s
+ta
+tu
+s 
+qu
+o
+Source: Adapted from Lybbert and Carter (2015).
+82   resakss.org
+Finally, another way in which index insurance can partner with CSA 
+technologies is by encouraging CSA adoption. Many farmers generally 
+refrain from adopting CSA practices due to the inevitable uncertainty and 
+higher perceived risks than keeping to more traditional practices. In these 
+contexts, index insurance can give a farmer the necessary peace of mind 
+to try out a new technology. Such an approach could either complement 
+or substitute for standard subsidies for encouraging CSA adoption; more 
+research is needed to understand the optimal interplay between the two 
+mechanisms.
+New developments in index insurance. Confronted with the issue 
+of low uptake and high basis risk, index insurance researchers and prac-
+titioners have developed some promising new ways to deal with these 
+limitations.
+An interesting new project led by the International Food Policy 
+Research Institute (IFPRI) is Picture-Based Crop Insurance (PBI), currently 
+being tested in the states of Punjab and Haryana, India. Under PBI, farmers 
+take pictures of their insured plots every week using their own smartphones 
+and a specially designed app that keeps the frame of view fixed on the 
+same portion of the field. Using the pictures recorded over time, a farmer 
+can then make a claim for any loss experienced, which can be assessed by 
+agronomic experts or an automated machine-learning algorithm, based 
+on the pictures and auxiliary information. This type of product can greatly 
+reduce basis risk and encourage uptake by instilling in the farmer a sense 
+of ownership of the insurance product and its results. Initial results are 
+very promising, in terms of both the feasibility of the approach (Kramer, 
+Ceballos, Hufkens, et al. 2017) and its sustainability, with no evidence of 
+moral hazard or adverse selection (as would be expected from the product’s 
+resemblance to indemnity-based insurance), nor of picture tampering or 
+fraud (Kramer, Ceballos, Krupoff, et al. 2017).
+Another strand of projects has explored the potential of allowing for 
+more flexibility as an alternative to current rigid, one-size-fits-all index 
+insurance designs. Traditionally, index insurance products have involved a 
+number of parameters and predetermined payout functions. These features 
+sometimes make a product difficult to understand for farmers lacking suf-
+ficient education. More important, because the payout functions are fixed, 
+the insurance product cannot adapt to the risk profile of many farmers 
+the way an indemnity product would. In this context, a team at IFPRI has 
+proposed a novel approach, wherein an array of much simpler products is 
+offered, each covering against a specific timing and intensity of risk. Under 
+such an approach, a farmer can create a portfolio of products (with different 
+triggers, calibrated to protect against weather events of various intensities, 
+and for different coverage periods) to suit his or her individual crop risk 
+profile. Evidence from three projects suggests that farmers do indeed value 
+this simplicity and flexibility.30
+Gap insurance, consisting of a second tier of indemnity insurance 
+on top of a regular index product, has been considered as a promising 
+alternative to traditional index products.31 Under such a program, when the 
+first-tier index product is not triggered, farmers have the right to call for 
+30 For a theoretical framework and evidence from field experiments in Ethiopia, see Hill and Robles 
+(2011). A pilot application of this approach in India is described in Hill, Robles, and Ceballos 
+(2016). For a description of a commercial rollout in Uruguay, together with a structural analysis of 
+the demand for these products, see Ceballos and Robles (2017).
+31 For an application of gap insurance in Ethiopia, see, for instance, Berhane et al. (2015). 
+2016 ReSAKSS Annual Trends and Outlook Report    83
+crop cuts in a reduced geographic area in order to assess losses locally.32
+A related idea is multiscale (or double-trigger) area yield insurance, under 
+which a product combines two area yield indexes measured at different geo-
+graphic levels—a broader geographic index with a higher trigger and a local 
+index with a lower trigger—with payouts occurring when both indexes fall 
+below their corresponding triggers.33 Measuring yields at a very local level 
+reduces basis risk, and the broader area index helps reduce moral hazard. 
+Finally, the increasing affordability of automatic weather stations and 
+the expanding technologies for remote sensing of weather variables and 
+crop growth (such as microsatellites and unmanned aerial vehicles) have 
+an enormous potential to underpin innovative insurance products with 
+reduced basis risk in the near future.
+Meso-level products. A different approach to minimizing basis risk that 
+has gained traction recently entails a shift from insuring individual farmers 
+to insuring so-called aggregators—such as farmer associations, other formal 
+or informal groups, and microfinance institutions.34 For instance, an institu-
+tion holding a significant portfolio of agricultural loans may be interested in 
+insuring it against severe systemic shocks that may otherwise result in large 
+loan write-offs. An advantage of such systems is that, with efficient mecha-
+nisms to identify individual losses and appropriate payout practices by the 
+aggregators, individual (idiosyncratic) negative and positive basis risks can 
+largely offset each other in the aggregate portfolio.
+32 Taking crop cuts is a procedure to obtain an objective measure of crop yield by cutting a small, 
+random sample of the field (for example, 1 square meter) right before harvest and weighing the 
+produce in this sample. The process is repeated across random samples in an area to obtain an 
+objective estimate of the area’s yield for a given crop. 
+33 See, for instance, Elabed et al. (2013).
+34 See de Janvry, Dequiedt, and Sadoulet (2014) and Dercon et al. (2014).
+Macro-level products. One of the most important elements behind 
+limited crop insurance uptake in developing and developed countries alike 
+has perhaps been the state’s traditional role as risk absorber of last resort. 
+Once a major weather shock hits, it is fairly common for national, regional, 
+or local governments to give in to the pressure for emergency assistance. 
+This type of assistance is generally inefficient, difficult to administer, 
+and prone to political favoritism and corruption. Most important, it is 
+often uncertain—there is no guarantee that adequate assistance will be 
+provided when there is a crop failure or livestock loss. Moreover, in many 
+of these emergencies the state’s budget capacity is also reduced due to 
+lower economic activity and tax revenues. In this context, there has been 
+an increasing trend around the world toward ex ante budgeting for natural 
+disasters (through risk-coping instruments such as insurance), to the detri-
+ment of ex post assistance after a disaster strikes (Clarke and Dercon 2016).
+One natural option has been macro-level insurance against weather 
+risks, whereby the insured parties can be either different government levels 
+(from national to local) or specialized government agencies. This type of 
+insurance generally relies on an index and, upon the occurrence of an 
+extreme weather event, makes a direct payout to the insured agency or local 
+government to implement emergency relief and food security programs. 
+Such arrangements are already being implemented in developed countries 
+and are expanding into developing countries, particularly those prone to 
+natural catastrophes (Hazell et al. 2010). Sometimes this type of instrument 
+can be channeled directly through the international financial markets, 
+through the issuing of so-called catastrophe (or cat) bonds. Such instru-
+ments resemble regular sovereign bonds in that the issuing government 
+promises to pay the bearer (generally attractive) interest under normal 
+84   resakss.org
+scenarios, but under disaster scenarios, determined through well-specified 
+conditions tied to the index, investors forgo the interest and some or all 
+of the principal, in an arrangement resembling the structure of a typical 
+insurance product. 
+The creation of regional risk pools is another approach that has been 
+gaining steam. Under such a system, subscribing sovereign states commit 
+funds, receiving in return a type of macro-level insurance. These regional 
+risk pools are generally funded through specialized trust funds supported 
+by international donors, or through reinsurance agreements. The way they 
+work is similar to the macro-level products described above, whereby upon 
+the occurrence of a negative weather event (generally defined in terms of 
+and captured through specific weather indexes), the sovereign state receives 
+financial assistance to put toward social protection and reconstruction 
+costs. African Risk Capacity (ARC), established in 2012 as an agency of 
+the African Union, is an example of such a pool. In addition to covering 
+member states against the devastating consequences of droughts, it provides 
+technical and financial assistance to state governments for early response 
+systems and emergency management plans.
+Conclusions
+In the face of climate change, improving the resilience of African smallholder 
+farmers should constitute a top priority in policy makers’ agendas. In this 
+regard, CSA constitutes a crucial step in the right direction. However, formal 
+insurance mechanisms are needed to complete farmers’ tool kit to cope with 
+weather shocks. 
+Even though traditional crop indemnity insurance has not really taken 
+off on the continent, other options have been brought forward in recent 
+decades. Weather index insurance is a promising alternative with several 
+advantages. First, it avoids moral hazard issues by decoupling insurance 
+payouts from the farmer’s behavior. Second, it is not subject to adverse 
+selection: payouts depend on objective, readily and publicly available infor-
+mation, and are independent of the characteristics of the pool of insured 
+farmers. Furthermore, the implementation and administration of index 
+insurance is cheaper than that of traditional indemnity insurance because 
+it does not require the insurance company to verify loss claims before 
+making payouts. 
+Nevertheless, index insurance has its own limitations, especially in 
+relation to basis risk: because payouts are based on the observed index, any 
+given farmer’s actual loss may not be completely compensated. Although 
+a number of new developments intend to sort out this and other obstacles, 
+it is perhaps too soon to take stock and understand whether they will be 
+able to help improve smallholder farmers’ resilience in an efficient and 
+sustainable way.
+Evidence from several insurance pilot programs shows that although 
+the potential for innovative insurance mechanisms is real, additional work 
+to understand their effectiveness and substantial scale-up efforts will be 
+needed to achieve a sustainable expansion of efficient agricultural insurance 
+markets in Africa. Across the continent, a growing pool of experts and 
+professionals from both public and private institutions are actively engaged 
+in bringing in innovations, improving index products, and finding effective 
+ways to scale up insurance programs. Importantly, in the face of shifting 
+2016 ReSAKSS Annual Trends and Outlook Report    85
+weather patterns due to climate change, rating methodologies for index 
+insurance products must adapt or run the risk of encouraging oversubscrip-
+tion and thus undermining long-term sustainability. 
+Governments, in particular, have an important role to play in creating 
+an enabling policy and regulatory environment for the expansion of insur-
+ance markets and development of insurance products that better serve the 
+needs of smallholder farmers. They will also need to lead the way in invest-
+ing in weather stations, building the capacity of insurance companies, and 
+training farmers on insurance products. By supporting the implementation 
+of innovative weather insurance products aimed at addressing prevailing 
+challenges, policy makers can actively contribute to the resilience of the 
+rural poor facing weather extremes and provide them with much-needed 
+opportunities to escape poverty through farming.
+In this context, African policy makers should consider innovative 
+weather index insurance tools as part of a comprehensive CSA package 
+to help African farmers manage weather risks, especially in light of 
+the potential complementarities between weather index insurance and 
+agricultural technologies aimed at raising productivity and incomes. 
+Such efforts can go a long way in helping the continent meet the Malabo 
+Declaration commitment to enhance the resilience of farming livelihoods 
+by 2025.
+

data/part_2/0401837588.md

@@ -0,0 +1,30 @@
+# Gender, climate change, and group-based approaches to adaptation
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/1f9ef53c-e971-4757-9c2a-6efce771e45f/retrieve  
+**Language:** French  
+**Resource Type:** Brief  
+**Release Year:** 2014  
+**Rights:** N/A  
+**GARDIAN ID:** f65008de1f7c2ad32f1a7972d0803f69  
+**DataNODE ID:** 75f8bb0947ae2033cb79a7d4263a1954  
+**Siever ID:** ec3947cc-cf71-4ce8-9ce0-12d5c3c7dee4  
+**Token Count:** 41  
+**Page Count:** 0
+
+## Keywords
+
+gender, environmental factors, assets, developing countries, climate change adaptation, rural areas, resilience, women, climate change, literature reviews, approaches, adaptation
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World, Southern Asia, Asia, Western Africa
+- **Countries:** Ethiopia, Mali, Bangladesh, Kenya
+
+## Description
+
+This policy note summarizes the findings of two literature reviews on the gender-differentiated impacts of climate change and the scope for community-based adaptation. It also outlines the framework used to guide these analyses and the other papers summarized in this series.
+
+## Content
+
+This policy note summarizes the findings of two literature reviews on the gender-differentiated impacts of climate change and the scope for community-based adaptation. It also outlines the framework used to guide these analyses and the other papers summarized in this series.

data/part_2/0430891262.md

@@ -0,0 +1,29 @@
+# Commentaries on Marketing Systems
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/781d2ea0-27ac-4c0c-a42b-e8f76932e8ff/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 1987  
+**Rights:** N/A  
+**GARDIAN ID:** e3247590ab57dd29ef07ca9a79da29d6  
+**DataNODE ID:** c0c79b7884e9f241d3c5d509ae358a75  
+**Siever ID:** c3520b06-d3ef-4050-9ac0-3ee000c6ddec  
+**Token Count:** 76  
+**Page Count:** 0
+
+## Keywords
+
+food production, conferences, agricultural policies, sub-saharan africa, agricultural production, marketing, systems, growth, environment, development, policies, technology
+
+## Geography
+
+- **Regions:** Sub-Saharan Africa, Africa, World
+
+## Description
+
+Poor agricultural growth in sub-Saharan Africa is sometimes attributed to the absence of major technological breakthroughs suited to its agroclimatic environment. This implies that there is little room for growth in agricultural production in the existing technological environment. Viewed in this way, development of superior technologies becomes the most important issue in policies for accelerating agricultural growth in sub-Saharan Africa. Without belittling the importance of improved technology, the two chapters under discussion caution against this position.
+
+## Content
+
+Poor agricultural growth in sub-Saharan Africa is sometimes attributed to the absence of major technological breakthroughs suited to its agroclimatic environment. This implies that there is little room for growth in agricultural production in the existing technological environment. Viewed in this way, development of superior technologies becomes the most important issue in policies for accelerating agricultural growth in sub-Saharan Africa. Without belittling the importance of improved technology, the two chapters under discussion caution against this position.

data/part_2/0445014673.md

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+# Uttar Pradesh district nutrition profile: Etah
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/84a8ace3-bff3-49d7-b31a-bfc06754bb78/retrieve  
+**Language:** Hindi  
+**Resource Type:** Brief  
+**Release Year:** 2022  
+**Rights:** N/A  
+**GARDIAN ID:** fcfbb63bf52b4e41a0d1ad3b685f2f0b  
+**DataNODE ID:** 36d2b3b7490c41a0ae60fd6b9593cab2  
+**Siever ID:** 27d482c6-1534-4345-91eb-f5552342a502  
+**Token Count:** 223  
+**Page Count:** 0
+
+## Keywords
+
+nutrition, health, indicators, uttar pradesh
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World
+- **Countries:** India
+
+## Content
+
+ETAH | UTTAR PRADESH MARCH 2022
+Uttar Pradesh
+Etah
+2019 Etah
+1,047/1,000 528,763 61,694
+28,196 21,521 235,886
+Etah,
+Uttar Pradesh
+Etah
+Uttar Pradesh
+2016
+2020
+NA
+115,042
+35,265
+19,696
+72,157
+8,822
+164,009
+235,886
+0% 20% 40% 60% 80% 100%
+NA
+NA
+51%
+49%
+10%
+15%
+2%
+8%
+32%
+31%
+1%
+4%
+40%
+77%
+Etah
+Uttar Pradesh
+2016
+2020
+99,037
+107,921
+86,453
+32,572
+296,213
+27,528
+61,694
+528,763
+0% 20% 40% 60% 80% 100%
+23%
+19%
+17%
+20%
+7%
+16%
+NA
+6%
+36%
+56%
+38%
+45%
+2
+Etah
+Uttar Pradesh
+2016
+2020
+0% 20% 40% 60% 80% 100%
+11%
+20%
+1%
+5%
+22%
+30%
+48%
+57%
+NA
+NA
+24%
+26%
+4%
+5%
+NA
+NA
+NA
+NA
+NA
+NA
+NA
+NA
+NA
+NA
+Etah
+Uttar Pradesh
+2016
+2020
+0% 20% 40% 60% 80% 100%
+31%
+39%
+27%
+20%
+9%
+4%
+25%
+63%
+100%
+100%
+NA
+NA
+NA
+NA
+8%
+13%
+3
+Etah
+2016
+2020
+0% 20% 40% 60% 80% 100%
+0% 20% 40% 60% 80% 100%
+NA NA
+96%75%
+90% 96%
+50% 54%
+17% 34%
+NA NA
+NA NA
+NA NA
+82% 92%
+85%
+22%
+NA NA
+62% 77%
+33%
+62% 81%
+53% 60%
+34% 62%
+NA NA
+NA NA
+NA NA
+48% 55%
+39% 70%
+NA NA
+NA NA
+NA NA
+NA NA
+NA NA
+20% 48%
+3% 29%
+61%47%
+NA NA
+NA NA
+4
+

data/part_2/0457485811.md

@@ -0,0 +1,380 @@
+# The role of collective action and property rights in climate change strategies
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/13eb6c33-8dbc-4fdd-ac5d-d795d444017c/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2010  
+**Rights:** N/A  
+**GARDIAN ID:** 4e1cb0630b755d5d37bc63dfbc006b62  
+**DataNODE ID:** 6e897c6cc587fd95d6be6b215e443805  
+**Siever ID:** 640b7fde-ef1d-4d54-85c9-e5a2cc31f738  
+**Token Count:** 3048  
+**Page Count:** 0
+
+## Keywords
+
+sustainable development goals, property rights, capacity development, collective action, climate change, developing countries, strategies, effects, flooding, world, institutions, communities
+
+## Geography
+
+- **Regions:** Northern America, Americas, World
+- **Countries:** United States of America
+
+## Description
+
+The well-documented threats posed by climate change are serious and potentially devastating to the global community. The geographic areas that are most susceptible to the effects of climate change episodes such as increased droughts and flooding are also the regions where the majority of the world's poor live. Evidence suggests that these effects may be especially severe for Importance of Institutions in Addressing Climate Change disadvantaged communities in developing countries. The poor have few assets and few income diversification opportunities, which severely limit their ability to cope or adapt to climate changes.
+
+## Content
+
+183The Role of Collective Action and Property Rights in Climate Change Strategies
+The Role of Collective Action
+and Property Rights in
+Climate Change Strategies
+The well-documented threats posed by climate
+change are serious and potentially devastating
+to the global community. The geographic areas
+that are most susceptible to the effects of cli-
+mate change episodes such as increased droughts
+and flooding are also the regions where the ma-
+jority of the world’s poor live. Evidence suggests
+that these effects may be especially severe for
+Importance of Institutions in Addressing Climate Change
+SOURCE:
+Meinzen-Dick, R., H. Markelova and K. Moore. The Role
+of Collective Action and Property Rights in Climate
+Change Strategies. CGIAR CAPRi Policy Brief 7,
+International Food Policy Research Institute,
+Washington, D.C.
+disadvantaged communities in developing countries. The poor have few assets and few income
+diversification opportunities, which severely limit their ability to cope or adapt to climate changes.
+Ensuring that poor people can adapt to climate change and benefit from mitigation measures such
+as payments for carbon sequestration requires more than technology. Key institutions must also be
+in place.
+184 Resources, Rights and Cooperation
+A Sourcebook on Property Rights and Collective Action for Sustainable Development
+The Structure of Mitigation and Adaptation Strategies
+Climate change has two manifestations: global warming and an increased number of extreme
+environmental events. Response strategies are usually divided into mitigation and adaptation (see
+Figure 1).
+Collective Action, Property Rights, and Climate Change Responses
+Mitigation refers to strategies utilized to reduce the probability of climate change through sus-
+tainable practices that mitigate the increased occurrence, severity, and unpredictability of weather
+resulting from climate change. The two major forms of climate change mitigation are emissions
+reduction and carbon sequestration. Emissions can be reduced through a range of technologies,
+regulations, or economic incentives such as cap and trade systems. Other mechanisms include
+energy diversification to renewable sources or those that do not emit carbon or other greenhouse
+gases. Mitigation options for rural smallholders include energy diversification through develop-
+ment of biofuels and alternative energy sources, such as solar-powered stoves. Carbon can be
+sequestered through afforestation, avoided deforestation and degradation, as well as through sus-
+tainable land management practices such as restoring degraded organic soils or using zero- or low-
+till farming practices.
+Payments for environmental services (PES) were introduced to provide incentives for land users to
+engage in sustainable practices, especially those that sequester carbon above or below ground, and
+to provide them with some form of compensation for the positive externalities of their actions.
+Carbon sequestration can receive financial rewards as carbon offsets in carbon markets (such as the
+Clean Development Mechanism [CDM] set up by the Kyoto protocol), government instituted
+markets, and voluntary carbon markets. Soil carbon sequestration and avoided deforestation projects,
+which are important for climate change mitigation in many African countries, were excluded from
+the CDM but may be covered through new proposals for Reducing Emissions from Deforestation
+and Forest Degradation in Developing Countries (REDD).
+Figure 1. Responses to Climate Change.
+Mitigation
+GHG Emission
+Reduction
+• Energy
+diversification
+• Regulations
+including Cap
+and Trade
+Carbon
+Sequestration
+• CDM
+Payments for
+Environmental
+Services
+• Voluntary
+Emission
+Reductions
+• Voluntary
+Carbon
+Markets
+• REDO
+Within Agriculture
+• Raised awareness
+of climate change
+on agriculture and
+resources
+• Community-based
+weather monitoring
+and forecasting
+• Natural resource
+management
+• Drought and pest
+resistant crops
+• Sale of agricultural
+assets
+Coping
+Strategies
+• Local
+safety nets
+• Insurance
+• Sale of
+non-
+agricultural
+assets
+Disaster
+Management
+• Early warning
+systems
+• Disaster
+preparedness
+• Disaster and
+emergency
+response
+Out of Agriculture
+• Occupational
+diversification
+• Migration
+• Remittances
+Climate Change
+Adaptation
+185The Role of Collective Action and Property Rights in Climate Change Strategies
+Many compensation payments, however, are available to land owners but not to people with
+customary tenure, and carbon sequestration plans usually require that land remains unused for
+other livelihood activities, such as agriculture, livestock raising, or harvesting natural resources
+such as firewood. As a result, not only do such schemes exclude millions of poor people, but also
+they also have on occasion, resulted in the displacement of households and communities that do
+not hold the formal title but depend on that land for their livelihoods. Such communities are
+pushed out when governments or private interests acquire the land to participate in reward schemes.
+Adaptation involves actions that communities and individuals can undertake in response to chang-
+ing conditions. These approaches include strategies within agriculture such as raising awareness of
+climate change, community-based climate monitoring and forecasting, changing planting dates, crop
+varieties, or cropping patterns, and implementing water harvesting or irrigation schemes. Adapta-
+tion strategies within agriculture are connected with effective natural resource management (NRM),
+such as improved land and water management practices. People may also adapt to climate change by
+moving out of agriculture through occupational diversification of some or all members of the house-
+hold, or temporary or permanent migration, with increased reliance on remittances. Coping strate-
+gies for short-term climate-related shocks such as floods or droughts include reliance on local safety
+nets or mutual insurance schemes, as well as disaster management, which entails early warning
+systems, disaster preparedness, and emergency responses. Overall, a community’s capacity to adapt
+requires a number of collective action institutions and property rights arrangements that would
+enable the smallholders to accumulate various types of assets and knowledge.
+To identify the institutional arrangements relevant for climate change response strategies, it is
+useful to look at the spatial and time scales of each action or program. Figure 2 provides examples
+of several common response strategies involving natural resource management practices. The spa-
+tial scale helps to identify what types of institutions are required, both for policy development to
+set the enabling conditions, and for actions to carry out the necessary activities. These can vary
+from the global to the national, local, or even individual level.
+Actions at the individual level, such as planting a drought-resistant annual crop or building a farm
+pond, generally do not require much in the way of institutions for coordination, though coordina-
+tion at higher levels may be needed to produce the new varieties and develop seed systems that
+distribute them. Moving up to response options at the group or community level, such as a com-
+munity pond or small reservoir, some form of coordination becomes necessary. At the local level,
+collective action institutions are often the most appropriate. Some state institutions may also be
+relevant, for example, to provide technical advice to a group of farmers constructing or operating
+the reservoir.
+At higher spatial scales, local governments or other state agencies become increasingly important
+for coordination, although collective action institutions may still be relevant, as in Nepal’s Na-
+tional Federation of Forest User Groups. The relative roles of state and collective action are illus-
+trated by the triangles on the right-hand side of Figure 2. In general, if the relevant scale for policies
+or action is the global level, then international institutions are required for coordination, either
+through existing international bodies such as UN agencies, or by creating new institutions such as
+the carbon credit exchanges formed after the Kyoto Protocol in 1997.
+The time frame for actions also provides insight into the nature of institutional arrangements
+needed. While climate change response schemes need to be set in motion very soon, some will
+show results in the short term (a year or two), others over the medium term (two to ten years),
+and still others have a much longer time horizon. The longer the time lag between actions and
+results, the more difficult it will be to gain and maintain support and to monitor progress. Some
+186 Resources, Rights and Cooperation
+A Sourcebook on Property Rights and Collective Action for Sustainable Development
+actions, such as responses to crises like drought or flooding, will only be intermittent. These call for
+institutional structures for preparedness and ability to respond quickly, but do not need to operate
+all the time. The time scale may also indicate the relevance of property rights issues when there is
+a significant lag between an action and its outcomes, especially between investment and returns
+such as for planting trees.
+Figure 2: Role of Institutions in Climate Change Responses.
+Time longshort
+C
+oo
+rd
+in
+at
+io
+n
+Sp
+ac
+e
+Global
+Plot
+Nation
+Community
+Carbon Markets
+Transboundary
+River Basins
+Terracing
+Irrigation
+Forests
+Reservoirs
+Watershed
+Management
+Seed
+SystemsPonds
+IPM
+Property Rights
+International
+New Seeds Soil Carbon Agroforestry
+C
+ol
+le
+ct
+iv
+e 
+Ac
+tio
+n
+State
+Policy Implications
+Recognize the Importance of Collective Action for Successful Mitigation and
+Adaptation Strategies
+Research and practice have shown that collective action institutions are very important for tech-
+nology transfer in agriculture and natural resource management among smallholders and resource-
+dependent communities. In the same way, they will also be important for spreading information,
+technologies and practices for various climate change response strategies, both for mitigation and
+adaptation.
+Smallholder groups can facilitate effective implementation of PES schemes focused on carbon se-
+questration. Cooperatives or other forms of collective action among smallholders can help to achieve
+economies of scale in overcoming transaction costs in verification and payment. Groups of
+smallholders cover more area, and the cooperatives assume the transaction costs of developing
+and enforcing contracts with individuals. Fondo Bioclimatico in Mexico provides an example of a
+program that restores land, previously deemed useless because of soil degradation, to profitability
+through use of agroforestry and forestry systems that sequester carbon. Additionally, it is a cost-
+effective strategy for collective income generation because the contracts are created and brokered
+by the farmers, allowing them to design, manage, and monitor their programs on individual or
+communal land. External assistance can help to make the initial contacts between smallholders
+and CDM programs, and to develop the capacity of local groups to negotiate and meet technical
+monitoring criteria.
+187The Role of Collective Action and Property Rights in Climate Change Strategies
+Local institutions are also important for helping farmers adapt to climate change through knowledge
+and information sharing. Research shows that improved information on climate change increases a
+farmer’s likelihood of adapting. For example, in several Andean communities farmers have devel-
+oped a knowledge system on climate change and its potential effects on their productivity through
+community education and sharing observations on gradually changing weather patterns. For areas
+that are most vulnerable to sudden natural disasters such as hurricanes or typhoons, collective action
+can help to disseminate information through community meetings, volunteer emergency response
+teams, and community response plans that include an early warning system.
+Enhancing resilience to climate-related shocks is a goal of many adaptation strategies employed by
+smallholders. Local safety nets built on collective action can help poor people cope with climate-
+related shocks, for example, by turning to a neighbor for emergency funds or using food reserves
+and seed banks. Mutual insurance schemes such as funeral societies that have traditionally served
+as a coping mechanism for illness or death in the family are now being used to cope with climatic
+shocks such as drought. However, local collective action is less able to deal with shocks that affect
+many people in a community; for severe and widespread shocks, national or even international
+assistance is needed.
+Ensure that Tenure Insecurity does not Exclude the Poor from Mitigation
+and Adaptation Strategies
+The focus of most mitigation and adaptation programs has been on the global and national level.
+For climate change policies to be sound development policies, however, the impact of response
+strategies on the poor needs to be examined. In many cases, customary property rights need to be
+recognized and made more secure if millions of smallholders are to benefit.
+Adopting perennial crops that withstand drought and pests, sequester carbon, or hold moisture,
+requires land and perhaps also water rights to guarantee a return on these investments. Secure
+property rights are also important for natural resource management practices like tree planting and
+Coping strategies for short-term climate-related shocks also include
+reliance on collective action in disaster preparedness.
+188 Resources, Rights and Cooperation
+A Sourcebook on Property Rights and Collective Action for Sustainable Development
+water harvesting that involve long-term invest-
+ment in land and promote sustainable use. Se-
+cure tenure can also allow people to migrate or
+diversify their occupations to pursue alternative
+income sources. Finally, disaster preparedness
+requires a certain amount of investment, not only
+in public infrastructure, but also for protecting
+livelihoods through practices such as seawall
+containment, irrigation canals, erosion preven-
+tion, and watershed management, all of which
+require secure property rights.
+The rise in demand for land by international fuel
+developers for biofuel production can weaken
+local institutions and lead to people with inse-
+cure tenure losing rights to land and water re-
+sources. There have been reports of land seizures
+and denial of customary land rights related to
+biofuel cultivation in parts of Africa (Tanzania,
+Knowledge and information sharing increase
+adaptation.
+Mozambique), Latin America (Colombia, Brazil), India, and Papua New Guinea. Water use for
+biofuel plantations is also threatening community resource bases. In other instances, land acquisi-
+tions of areas considered underutilized or unused take place, even though these lands may be used
+for animal grazing or fuel wood collection by the poor. Despite their contributions to climate
+change mitigation, land acquisitions and land clearings for biofuel production may have detrimen-
+tal impacts on the livelihoods of the resource-dependent poor.
+To allow rural poor to benefit from biofuel production, an array of options for tenure security must
+be available. Allowing communal systems to participate in the local biofuels market is particularly
+important. For example, the Kavango Biofuel Project in Namibia is a collaborative effort between
+local farmers and a Namibian company to grow jatropha on communal land. The company pro-
+vides capital costs, food, and cash for the farmers to replace annual maize and millet crops with
+perennial jatropha. Those community members without access to land can participate in other
+jobs made available through the project, such as working in the processing plants or in product
+transport.
+The design of many carbon payment schemes has excluded small farmers who lack clear land
+ownership. Whether new REDD schemes will affect smallholders and forest communities posi-
+tively or negatively will depend on the provisions made for the allocation of benefits from carbon
+trading. If the land tenure of forest-dependent communities is not secure, and governance around
+land tenure is not effective, there is a danger that the benefits from REDD projects will be appro-
+priated by governments, the private sector, and even conservation NGOs. Secure tenure rights will
+give local people more leverage in negotiating the terms of these schemes; insecure rights could
+lead to dispossession because REDD will increase land values.
+As for adaptation mechanisms, property rights are critical in facilitating income diversification
+because secure tenure will provide a fallback option in case the other sources fail, or can be used as
+a collateral for other livelihood activities. Without secure property rights, smallholders may not
+have sufficient capital or a fall-back option to support diversification.
+189The Role of Collective Action and Property Rights in Climate Change Strategies
+Consider Various Levels of Governance in Designing and Choosing Mitigation
+and Adaptation Strategies
+The need to consider the wide-ranging effects of climate change policies and programs, including
+their impact on the rural poor, calls for the participation of various levels of governance in design-
+ing and choosing response strategies. For example, effective carbon payments will require interna-
+tional market mechanisms to match those who wish to pay to offset their emissions with those
+who will sequester carbon; national governments that will broker agreements, such as through a
+Designated National Authority (DNA) as currently employed for CDM agreements; and collective
+action groups to monitor compliance among local smallholders. While local collective action can
+provide an effective means of measuring and ensuring compliance, whether a group will continue
+to fulfill this role on an ongoing basis will depend on whether there is an incentive to do so. Long-
+term participation is more likely if the group has been involved in the negotiations, has had a say
+in setting the rules, and receives a substantial benefit, either for the group or its members. Experi-
+ence with collective action in other types of natural resource management suggests that systems
+that are developed in a top-down manner and do not engage local people in the design of rules
+and systems are unlikely to create viable institutions that operate at the local level in the long run.
+Additionally, local policy responses are necessary to complement national policies that do not
+specify benefits or support for smallholders. This provides a caution against focusing only on na-
+tional-level negotiations and systems for climate change mitigation or adaptation, because they
+are unlikely to create effective institutions to execute the programs, especially among smallholders.
+A range of central and local institutions, public and private, is therefore needed. Rather than
+focusing exclusively on any single type of institution, policies need to develop harmonious, multi-
+level governance arrangements in which multiple institutions each play a role. Through coordina-
+tion among different institutions, institutional as well as ecological resilience will be created and
+the poverty impacts of climate change will be targeted more effectively.
+The lack of property rights will discourage people from planting
+perennials that better withstand climate change, sequester
+carbon and hold moisture, and require land (and water) rights to
+guarantee a return on investments.
+190 Resources, Rights and Cooperation
+A Sourcebook on Property Rights and Collective Action for Sustainable Development
+Suggested Readings
+Cotula, L., N. Dyer, S. Vermeulen. 2008. Fuelling Exclusion? The Biofuels Boom and Poor
+People’s Access to Land. IIED and FAO. Rome: FAO.
+Knox, A., Meinzen-Dick, R., and P. Hazell. 1998. Property Rights, Collective Action, and
+Technology for Natural Resource Management. CAPRi Working Paper 1. Washington, D.C.:
+International Food Policy Research Institute,
+Swallow, B. and R. Meinzen-Dick. 2009. Payment for Environmental Services: Interactions with
+Property Rights and Collective Action. In Institutions and Sustainability, ed. V. Beckmann
+and M. Padmanabhan, eds. Dordrecht, The Netherlands: Springer.
+Ruth Meinzen-Dick (r.meinzen-dick@cgiar.org) is a senior research fellow at the International
+Food Policy Research Institute (IFPRI) and coordinator of CAPRi. Helen Markelova
+(h.markelova@cgiar.org) is a research analyst with IFPRI/CAPRi. Kelsey Moore
+(kamoore@uw.edu) was a consultant to the CAPRi program.
+Sourcebook on Resources, Rights, and Cooperation, produced by the CGIAR Program
+on Collective Action and Property Rights (CAPRi)
+

data/part_2/0458045372.md

@@ -0,0 +1,30 @@
+# Zambia [in Strategies and priorities for African agriculture]
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/b8e7970e-7281-44d2-a77a-e3d26116e825/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2012  
+**Rights:** N/A  
+**GARDIAN ID:** 1f9005657ce4c78d778cb1e40b4c6441  
+**DataNODE ID:** 4cd1c8d06d584a80597d5d4c4a350a0a  
+**Siever ID:** d31edd45-a044-4b02-8832-5b76ba226dd4  
+**Token Count:** 172  
+**Page Count:** 0
+
+## Keywords
+
+economic growth, agriculture, agricultural sector, farming, poverty, livestock, rural development, public investment, agricultural growth, public expenditure, world bank, zambia, strategies
+
+## Geography
+
+- **Regions:** Eastern Africa, Sub-Saharan Africa, Africa, World, Southern Africa
+- **Countries:** Zambia
+
+## Description
+
+Zambia was classified as a middle-income country after it gained independence  in the 1960s. However, the economy deteriorated into low-income status over  the next two decades, culminating in a major macroeconomic crisis in the  late 1980s (World Bank 2010). The 1990s marked the advent of painful structural  reforms, during which the state’s ubiquitous interventions were removed and markets  were liberalized. Comprehensive agricultural reforms entailed the removal of  food and input subsidies and pan-territorial maize pricing (McCulloch, Baulch, and  Cherel-Robson 2001). In many parts of the country these reforms led to a reallocation  of productive resources away from maize and to more naturally suitable crops  (for example, cassava in Northern Province) (Zulu et al. 2000). Eventually, liberalization  encouraged the emergence of new export crops, such as cotton, which is now  grown by one-fifth of all farm households (Jayne et al. 2007) and is credited with  having reduced poverty in Eastern Province (see McCulloch et al. 2001). However,  the reforms were not universally beneficial and did not address all constraints facing  smallholders (Seshamani 1999).
+
+## Content
+
+Zambia was classified as a middle-income country after it gained independence  in the 1960s. However, the economy deteriorated into low-income status over  the next two decades, culminating in a major macroeconomic crisis in the  late 1980s (World Bank 2010). The 1990s marked the advent of painful structural  reforms, during which the state’s ubiquitous interventions were removed and markets  were liberalized. Comprehensive agricultural reforms entailed the removal of  food and input subsidies and pan-territorial maize pricing (McCulloch, Baulch, and  Cherel-Robson 2001). In many parts of the country these reforms led to a reallocation  of productive resources away from maize and to more naturally suitable crops  (for example, cassava in Northern Province) (Zulu et al. 2000). Eventually, liberalization  encouraged the emergence of new export crops, such as cotton, which is now  grown by one-fifth of all farm households (Jayne et al. 2007) and is credited with  having reduced poverty in Eastern Province (see McCulloch et al. 2001). However,  the reforms were not universally beneficial and did not address all constraints facing  smallholders (Seshamani 1999).

data/part_2/0483772714.md

@@ -0,0 +1,320 @@
+# Assam district nutrition profile: Bongaigaon
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/9990766e-2928-45be-93bd-268dc5440f3b/retrieve  
+**Language:** English  
+**Resource Type:** Brief  
+**Release Year:** 2022  
+**Rights:** N/A  
+**GARDIAN ID:** 8ba5d3c722c044ff34da2223e3619054  
+**DataNODE ID:** 5767b6a19ff07f96b58704a55b3523e7  
+**Siever ID:** a431468e-ca82-47bb-9de9-54b0efbd7ed1  
+**Token Count:** 1256  
+**Page Count:** 0
+
+## Keywords
+
+malnutrition, nutrition, child nutrition, food security, households, health, maternal and child health, assam
+
+## Geography
+
+- **Regions:** Southern Asia, Asia, World
+- **Countries:** India
+
+## Content
+
+DISTRICT NUTRITION PROFILE
+BONGAIGAON | ASSAM SEPTEMBER 2022
+About District Nutrition Profiles:
+District Nutrition Profiles (DNPs) are available for 707 districts in India.
+They  present  trends  for  key  nutrition  and  health  outcomes  and  their
+cross-sectoral  determinants  in  a  district.  The  DNPs are  based  on  data
+from  the  National  Family  Health  Survey  NFHS-4  (2015-2016)  and
+NFHS-5 (2019-2021). They are aimed primarily at district administrators,
+state  functionaries,  local  leaders,  and  development  actors  working  at
+the district-level.
+Figure 1: Map highlights district Bongaigaon
+in the state/UT of Assam
+Source: Adapted from Black et al. (2008)
+What factors lead to child undernutrition?
+Given  the  focus  of  India’s  national  nutrition  mission  on  child
+undernutrition,  the  DNPs  focus  on  the  determinants  of  child
+undernutrition  (Figure  on  the  left).  Multiple  determinants  of
+suboptimal  child  nutrition  and  development  contribute  to  the
+outcomes  seen  at  the  district-level.  Different  types  of
+interventions  can  influence  these  determinants.  Immediate
+determinants include inadequacies in food, health, and care for
+infants  and  young  children,  especially  in  the  first  two  years  of
+life.  Nutrition-specific  interventions  such  as  health  service
+delivery at the right time during pregnancy and early childhood
+can  affect  immediate  determinants.  Underlying  and  basic
+determinants include women’s status, household food security,
+hygiene,  and  socio-economic  conditions.  Nutrition-sensitive
+interventions  such  as  social  safety  nets,  sanitation  programs,
+women’s  empowerment,  and  agriculture  programs  can  affect
+underlying and basic determinants.
+District demographic profile, 2019 Bongaigaon
+998/1,000
+Sex ratio (females per 1,000
+males) of the total population
+227,195
+Number of women of
+reproductive age (15–49 yrs)
+15,378
+Total number of pregnant
+women registered for ANC
+14,176
+Number of live births
+13,595
+Number of
+institutional births
+79,699
+Total number of children
+under 5 yrs
+Source:
+IFPRI estimates - Headcount = Prevalence x Eligible projected population for each district in 2019. Prevalence estimates: NFHS-4 (2015-16) & NFHS-5 (2019-21) state/district   
+factsheets, national/state reports and IFPRI estimates using unit-level data. Projected population for 2019 (children <5 yrs and women 15-49 yrs) was estimated using Census 2011
+Data on number of pregnant women, live births, and institutional deliveries are from HMIS. NA: unavailable/implausible data
+Citation: Singh. N., P.H. Nguyen, A. Pant, A. Christopher, M. Jangid, S.K. Singh, R. Sarwal, N. Bhatia, R. Johnston, W. Joe, and P. Menon. 2022. District Nutrition Profile: Bong-
+aigaon, Assam. New Delhi, India: International Food Policy Research Institute.
+Acknowledgement: Financial support was provided by the Bill & Melinda Gates Foundation through POSHAN, led by the International Food Policy Research Institute. We thank 
+Amit Jena (Independent Researcher) for design and programming support and IFPRI research analysts for cross checks.
+The state of nutrition outcomes among children (<5 years) Bongaigaon
+Assam
+2016
+2020
+Burden of nutrition outcomes (2020)
+Indicators No. of children (<5 yrs)
+Low-birth weight 12,650
+Stunted 36,821
+Wasted 16,099
+Severely wasted 5,898
+Underweight 28,134
+Overweight/obesity 7,021
+Anemia 55,271
+Total children 79,699
+0% 20% 40% 60% 80% 100%
+Low-birth weight
+Stunted
+Wasted
+Severely wasted
+Underweight
+Overweight/obesity
+Anemia
+14%
+16%
+39%
+46%
+24%
+20%
+13%
+7%
+33%
+35%
+6%
+9%
+34%
+77%
+Note:  NA refers to data unavailable for a given round of NFHS/Census.
+Points of discussion:
+• What are the trends in undernutrition among children under five years of age (stunting, wasting, underweight, and anemia)?
+• What are the trends in overweight/obesity among children under five years of age in the district?
+The state of nutrition outcomes among women (15-49 years) Bongaigaon
+Assam
+2016
+2020
+Burden of nutrition outcomes (2020)
+Indicators No. of women (15-49 yrs)
+Underweight 29,763
+Overweight/obesity 38,464
+Hypertension 30,899
+Diabetes 28,672
+Anemia (non-preg) 162,217
+Anemia (preg) 7,874
+Total women (preg) 15,378
+Total women 227,195
+0% 20% 40% 60% 80% 100%
+Underweight (BMI <18.5 kg/m²)
+Overweight/obesity
+Hypertension
+Diabetes
+Anemia (non-pregnant)
+Anemia (pregnant)
+19%
+13%
+13%
+17%
+24%
+14%
+NA
+13%
+48%
+71%
+40%
+51%
+Note:  NA refers to data unavailable for a given round of NFHS/Census.
+Points of discussion:
+• What are the trends in underweight and anemia among women (15-49 yrs) in the district?
+• What are the trends in overweight/obesity and other nutrition-related non-communicable diseases in the district?
+2
+Immediate determinants Bongaigaon
+Assam
+2016
+2020
+0% 20% 40% 60% 80% 100%
+Consumed IFA 100+ days (pregnant women)
+Consumed IFA 180+ days (pregnant women)
+Early initiation of breastfeeding (0-23 m)
+Exclusive breastfeeding (0-6 m)
+Continued breastfeeding (12-23 m)
+Timely introduction of complementary foods
+Adequate diet (0-23 m)
+Dietary diversity (0-23 m)
+Minimum meal frequency (0-23 m)
+Eggs and/or flesh foods consumption (6-23 m)
+Sweet beverage consumption (6-23 m)
+Bottle feeding of infants (0-23 m)
+36%
+47%
+8%
+22%
+74%
+50%
+68%
+NA
+87%
+97%
+NA
+NA
+13%
+15%
+29%
+35%
+32%
+24%
+37%
+33%
+6%
+7%
+12%
+25%
+Note:  NA refers to data unavailable for a given round of NFHS/Census.
+Points of discussion:
+• What are the trends in infant and young child feeding (early initiation of breastfeeding, exclusive breastfeeding, timely initiation of  
+complementary feeding, and adequate diet)? What can be done to improve infant and young child feeding?
+• What are the trends in IFA consumption among pregnant women in the district? How can the consumption be improved?
+• What additional data are needed to understand diets and/or other determinants?
+Underlying determinants Bongaigaon
+Assam
+2016
+2020
+0% 20% 40% 60% 80% 100%
+Women with ≥10 years of education
+Women 20-24 years married before the age of 18
+Women 15-19 years with child or pregnant
+HHs using improved sanitation facility
+HHs with improved drinking water source
+Safe disposal of feces
+HHs with below poverty line (BPL) card
+HHs with health insurance
+24%
+29%
+42%
+42%
+22%
+15%
+47%
+72%
+76%
+95%
+15%
+11%
+39%
+45%
+9%
+58%
+Note:  NA refers to data unavailable for a given round of NFHS/Census.
+Points of discussion:
+• How can the district increase women’s literacy, and reduce early marriage, if needed?
+• How does the district perform on providing drinking water and sanitation to its residents? Since sanitation and hygiene play an 
+important role in improving nutrition outcomes, how can all aspects of sanitation be improved?
+• How can programs that address underlying and basic determinants (education, poverty, gender) be strengthened?
+• What additional data are needed on food systems, poverty or other underlying determinants?
+3
+Trends in coverage of interventions across the first 1,000 days Bongaigaon
+2016
+2020
+0% 20% 40% 60% 80% 100%
+0% 20% 40% 60% 80% 100%
+Demand for FP satisfied
+Iodized salt
+Pregnancy registered (MCP card)
+ANC first trimester
+≥ 4 ANC visits
+Weighing
+Birth preparedness counselling
+Breastfeeding counselling
+Tetanus injection
+Received IFA tab/syrup
+Deworming
+Food supplementation
+Institutional birth
+Financial assistance (JSY)
+Skilled birth attendant
+Postnatal care for mothers
+Postnatal care for babies
+Food supplementation
+Health & nutrition education
+Health checkup (ICDS)
+Full immunization
+Vitamin A
+Pediatric IFA
+Deworming
+Food supplementation (6-35 m)
+Weighing
+Counselling on child growth
+ORS during diarrhea
+Zinc during diarrhea
+Careseeking for ARI
+Preschool at AWC
+Health checkup from AWC
+56% 61%
+100%98%
+98% 99%
+55% 65%
+24% 34%
+99% 99%
+1% 2%
+86%81%
+88% 92%
+69% 95%
+8% 14%
+63%62%
+67% 85%
+67%32%
+70% 89%
+48% 57%
+14% 63%
+55% 57%
+31% 42%
+29% 49%
+42% 68%
+56% 65%
+21%15%
+31%20%
+70% 75%
+52%50%
+69%59%
+NA NA
+NA NA
+14% 47%
+55% 59%
+57% 59%
+Note:  NA refers to data unavailable for a given round of NFHS/Census.
+Points of discussion:
+• How does the district perform on health and nutrition interventions along the continuum of care? Does it adequately provide both
+ prenatal and postnatal services to women of reproductive age, pregnant women, new mothers and newborns?
+• How has access to health and ICDS services changed over time (food supplementation, health and nutrition education and health
+ checkups)?
+4
+

data/part_2/0549955926.md

@@ -0,0 +1,30 @@
+# Joint water quantity/quality management analysis in a biofuel production area: Using an integrated economic-hydrologic model
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/f8cbc922-4f0f-4b3d-9a58-bee8b9fba260/retrieve  
+**Language:** English  
+**Resource Type:** Working Paper  
+**Release Year:** 2009  
+**Rights:** N/A  
+**GARDIAN ID:** 2cd1c6de25c966a6d04936d1b80ef2e4  
+**DataNODE ID:** 76ce91f798b743e96b533e66d31f4e4f  
+**Siever ID:** 8b90b90c-21db-4c45-938f-95c6351cd484  
+**Token Count:** 149  
+**Page Count:** 0
+
+## Keywords
+
+water quality, models, modelling, biofuels, water resources, environmental impact, water management, water allocation, water, management, analysis, production
+
+## Geography
+
+- **Regions:** South America, Latin America and the Caribbean, Americas, World
+- **Countries:** Brazil
+
+## Description
+
+Water management in the Pirapama River Basin in northeastern Brazil is affected by both water quantity and water quality constraints. The region is known for significant sugarcane-based ethanol production—which is key to the Brazilian economy and expected to grow dramatically under recent global changes in energy policy. Sugarcane production in the region goes hand in hand with controlled fertirrigation practices with potentially significant adverse impacts on the environment. To assess sustainable water allocation in the basin, an integrated hydrologic-economic basin model is adapted to study both water quantity and water quality aspects. The model results show that incorporating water quality aspects into water allocation decisions leads to a substantial reduction in application of vinasse to sugarcane fields. To enforce water quality restrictions, the shadow price for maintaining water in the reservoir could be used as a pollution tax for fertirrigated areas, which are currently not subject to pollution charges.
+
+## Content
+
+Water management in the Pirapama River Basin in northeastern Brazil is affected by both water quantity and water quality constraints. The region is known for significant sugarcane-based ethanol production—which is key to the Brazilian economy and expected to grow dramatically under recent global changes in energy policy. Sugarcane production in the region goes hand in hand with controlled fertirrigation practices with potentially significant adverse impacts on the environment. To assess sustainable water allocation in the basin, an integrated hydrologic-economic basin model is adapted to study both water quantity and water quality aspects. The model results show that incorporating water quality aspects into water allocation decisions leads to a substantial reduction in application of vinasse to sugarcane fields. To enforce water quality restrictions, the shadow price for maintaining water in the reservoir could be used as a pollution tax for fertirrigated areas, which are currently not subject to pollution charges.

data/part_2/0550916300.md

@@ -0,0 +1,29 @@
+# A Framework for analyzing institutions
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/9d640cc7-cef7-4764-a291-c5f1dff83559/retrieve  
+**Language:** English  
+**Resource Type:** Scientific Publication  
+**Release Year:** 2009  
+**Rights:** N/A  
+**GARDIAN ID:** 8466135ad704ebb83312695cd891b099  
+**DataNODE ID:** b57aeec03b54863f04fc5380c364c609  
+**Siever ID:** 579060e5-1531-48b0-885f-4ee9542bef0f  
+**Token Count:** 101  
+**Page Count:** 0
+
+## Keywords
+
+economic development, agricultural development, case studies, natural resources management, smallholders, poverty alleviation, economic growth, governance, collective action, common property, institutions, markets
+
+## Geography
+
+- **Regions:** Sub-Saharan Africa, Africa, World
+
+## Description
+
+The remainder of this book develops and applies this theory to address the challenges set out in Chapter 1, examining practical issues regarding the roles and effectiveness of markets, state action, and collective action in promoting agricultural developmental in different circumstances. Before embarking on this effort, however, we need to develop a conceptual framework for applying the theories described in Chapter 2 to analyze the evolution, functions, and economic and social outcomes of specific institutions, such as those governing the management of common property resources, those bringing players together in market exchange, or those assisting in the enforcement of credit contracts.
+
+## Content
+
+The remainder of this book develops and applies this theory to address the challenges set out in Chapter 1, examining practical issues regarding the roles and effectiveness of markets, state action, and collective action in promoting agricultural developmental in different circumstances. Before embarking on this effort, however, we need to develop a conceptual framework for applying the theories described in Chapter 2 to analyze the evolution, functions, and economic and social outcomes of specific institutions, such as those governing the management of common property resources, those bringing players together in market exchange, or those assisting in the enforcement of credit contracts.

data/part_2/0569948384.md

@@ -0,0 +1,25 @@
+# Operationalizing household food security in development projects: an introduction
+
+**Source:** gardian_index  
+**URL:** https://cgspace.cgiar.org/rest/bitstreams/0cb72251-3b13-4f67-aba1-004f7c3c402c/retrieve  
+**Language:** English  
+**Resource Type:** Book / Monograph  
+**Release Year:** 1999  
+**Rights:** N/A  
+**GARDIAN ID:** 4e3a69263486eb6a854f454d6b43a606  
+**DataNODE ID:** 4e98b62469ff08e3b1ce82bf4dcb603b  
+**Siever ID:** 8fe847c3-5fd3-45f5-9b50-64e7bc599654  
+**Token Count:** 161  
+**Page Count:** 0
+
+## Keywords
+
+development policies, food security, households, household food security, monitoring and evaluation, development projects, developing countries, nutrition, projects, information, individuals, constraints
+
+## Description
+
+This introductory guide provides a brief introduction to the concept of food security. (An introduction to nutrition issues is found in Technical Guide #5.) It outlines the links between the types of projects often designed and their impact on food security and nutrition. By doing so, it provides a framework for thinking about what projects would be most appropriate in a given situation and indicates what types of information are needed in order to maximize impact on food security. It can also be the case that collaborators in developing countries are not always fully conversant with food security concepts. The material presented in this guide can also be used to sensitize such individuals. It also introduces the remaining ten guides, showing how using these can assist in easing information constraints often faced by development practitioners. By doing so, it should be possible to improve the targeting of interventions, to understand their likely effects, and to develop improved monitoring and evaluation methods.
+
+## Content
+
+This introductory guide provides a brief introduction to the concept of food security. (An introduction to nutrition issues is found in Technical Guide #5.) It outlines the links between the types of projects often designed and their impact on food security and nutrition. By doing so, it provides a framework for thinking about what projects would be most appropriate in a given situation and indicates what types of information are needed in order to maximize impact on food security. It can also be the case that collaborators in developing countries are not always fully conversant with food security concepts. The material presented in this guide can also be used to sensitize such individuals. It also introduces the remaining ten guides, showing how using these can assist in easing information constraints often faced by development practitioners. By doing so, it should be possible to improve the targeting of interventions, to understand their likely effects, and to develop improved monitoring and evaluation methods.