#prep_desc_stats.R library(dplyr) library(tidyr) KeepObjectsAcrossAnalysisStrings <- get0("KeepObjectsAcrossAnalysisStrings", ifnotfound = character()) rm(list=ls()[!ls() %in% (Keeps <- c("t0",KeepObjectsAcrossAnalysisStrings))] ) setwd(getOption("replication.root", default = getwd())) ############################################################# #### Projects by funder ############################################################# oda_sect_group_df <- read.csv("./data/interim/africa_oda_sector_group.csv") %>% filter(transactions_start_year >= 2002 & transactions_start_year <= 2013) donor_precision_count <- oda_sect_group_df %>% group_by(funder, precision_code) %>% summarize(n = n_distinct(project_location_id)) %>% pivot_wider(names_from = funder, values_from = n, values_fill = 0) %>% mutate(precision_code = paste("project_precision",precision_code)) %>% rename(description = precision_code) donor_vars_df <- oda_sect_group_df %>% dplyr::select(funder, project_id, project_location_id, site_iso3, ad_sector_codes) %>% group_by(funder) %>% summarize(across(everything(), ~n_distinct(.))) %>% pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>% pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0) # donor_regional_unspecified_df <- oda_sect_group_df %>% # select(funder, project_location_id, recipients_iso3) %>% # group_by(funder) %>% # filter(grepl("regional|Unspecified",recipients_iso3)) %>% # summarize(regional_count= n_distinct(project_location_id)) %>% # pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>% # pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0) # # donor_recipient_site_mismatch_df <- oda_sect_group_df %>% # select(funder, project_location_id, recipients_iso3, site_iso3) %>% # group_by(funder) %>% # filter(!grepl("regional|Unspecified",recipients_iso3) & # recipients_iso3 != site_iso3) %>% # summarize(mismatch_count= n_distinct(project_location_id)) %>% # pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>% # pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0) no_end_date_df <- oda_sect_group_df %>% dplyr::select(funder,end_actual_isodate) %>% group_by(funder) %>% summarize(portion_no_end_date = mean(end_actual_isodate=="")) %>% mutate(portion_no_end_date = round(portion_no_end_date,2)) %>% pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>% pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0) no_funding_df <- oda_sect_group_df %>% dplyr::select(funder,total_disbursements) %>% group_by(funder) %>% summarize(portion_no_funding = mean(is.na(total_disbursements))) %>% mutate(portion_no_funding = round(portion_no_funding,2)) %>% pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>% pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0) multisector_df <- oda_sect_group_df %>% dplyr::select(funder, geoname_id, transactions_start_year, ad_sector_codes) %>% group_by(funder, geoname_id, transactions_start_year, ad_sector_codes) %>% count() %>% group_by(funder) %>% summarize(portion_multisector = mean(n > 1)) %>% mutate(portion_multisector = round(portion_multisector,2)) %>% pivot_longer(cols = -funder, names_to = "description", values_to = "distinct_count") %>% pivot_wider(names_from = funder, values_from = distinct_count, values_fill = 0) desired_order <- c(3, 4, 1, 2, 5, 6, 7, 8, 9, 10) donor_comparison_df <- rbind(donor_vars_df,donor_precision_count, no_end_date_df, no_funding_df, multisector_df) %>% slice(match(desired_order, row_number())) %>% mutate(description = case_match(description, "site_iso3" ~ "Countries hosting projects count", "ad_sector_codes" ~ "Sectors funded", "project_id" ~ "Aid project count", "project_location_id" ~ "Aid project location count", "project_precision 1" ~ "Exact locations available (precision 1)", "project_precision 2" ~ "Near (<25km) locations available (precision 2)", "project_precision 3" ~ "ADM2 locations available (precision 3)", "portion_no_end_date" ~ "Portion lacking end date", "portion_no_funding" ~ "Portion lacking funding information", "portion_multisector" ~ "Portion with concurrent, co-located, multi-sector projects", .default = description)) # Table 1: Funder Comparison: China and World Bank # 1 Countries hosting projects count 50 44 # 2 Sectors funded 22 13 # 3 Aid project count 722 513 # 4 Aid project location count 1373 7115 # 5 Exact locations available (precision 1) 987 4149 # 6 Near (<25km) locations available (precision 2) 169 258 # 7 ADM2 locations available (precision 3) 217 2708 # 8 Portion lacking end date 0.68 0.15 # 9 Portion lacking funding information 1 0.28 # 10 Portion with concurrent, co-located, multi-sector projects 0.03 0.02 #higher than Gehring et al, because they exclude countries with less than 1 million people write.csv(donor_comparison_df,"./tables/funder_comparison.csv",row.names = FALSE) #to do: make a sector level table similar to this, but divided by funders #consider limiting to sectors actually included in analysis? oda_sect_group_df %>% filter(precision_code < 4) %>% group_by(funder, ad_sector_codes, ad_sector_names) %>% count() ############################################################# #### DHS Units of Analysis ############################################################# #read file with all DHS points for one survey round per country dhs_est_iwi_df <- read.csv("./data/interim/dhs_est_iwi.csv") #read file that contains confounder data (lower n due to missing confounders) dhs_confounders_df <- read.csv("./data/interim/dhs_5k_confounders.csv") #identify locations excluded due to missing confounders, group by country excluded_dhs_df <- anti_join(dhs_est_iwi_df,dhs_confounders_df,by="dhs_id") %>% group_by(iso3) %>% count() %>% ungroup() %>% rename(n_excluded=n) #get formatted country names africa_isos_df <- read.csv("./data/interim/africa_isos.csv") #get treated information dhs_t_df <- read.csv("./data/interim/dhs_treated_sector_3yr.csv") %>% filter(year_group!="2014:2016") %>% #exclude DHS points where confounder data not available inner_join(dhs_confounders_df %>% dplyr::select(dhs_id, ID_adm2), by = join_by(dhs_id)) #identify locations that were never treated, group by country dhs_never_treated_df <- anti_join(dhs_confounders_df,dhs_t_df,by="dhs_id") %>% group_by(iso3) %>% count() %>% rename(n_never_treated=n) %>% ungroup() #create display version of neighborhood descriptive stats neighborhoods_df <- dhs_confounders_df %>% dplyr::select(iso3,survey_start_year,households,rural.x,dhs_id) %>% group_by(iso3,survey_start_year) %>% summarize(n_cluster_locations=n_distinct(dhs_id), n_households=sum(households), portion_rural=round(mean(rural.x),2)) %>% ungroup() %>% #join to get locations that were never treated left_join(dhs_never_treated_df, by="iso3") %>% mutate(n_never_treated=ifelse(is.na(n_never_treated),0,n_never_treated)) %>% mutate(portion_never_treated=round(n_never_treated/n_cluster_locations,2)) %>% #join to get excluded count left_join(excluded_dhs_df,by="iso3") %>% mutate(n_excluded=ifelse(is.na(n_excluded),0,n_excluded)) %>% #join to get country names left_join(africa_isos_df %>% dplyr::select(-iso2), by="iso3") %>% rename(country=name) %>% dplyr::select(country,survey_start_year,n_cluster_locations,n_households, portion_rural,portion_never_treated,n_excluded) %>% arrange(country) # country survey_start_year n_cluster_l…¹ n_hou…² porti…³ porti…⁴ n_exc…⁵ # # 1 Angola 2006 62 1377 0.35 0.4 0 # 2 Benin 1996 190 3152 0.53 0.03 0 # 3 Burkina Faso 1998 81 754 0.62 0.25 0 # 4 Burundi 2010 307 7032 0.88 0.49 0 # 5 Cameroon 2004 464 9254 0.48 0.22 0 # 6 Central African Republic 1994 66 1142 0.65 0.86 0 # 7 Chad 2014 235 6499 0.59 0.26 0 # 8 Comoros 2012 242 4286 0.56 0 0 # 9 Congo, Democratic Republic of the 2007 286 7947 0.57 0 5 # 10 Côte d'Ivoire 1998 63 833 0.49 0.1 0 # 11 Egypt 1995 7 111 0.14 1 0 # 12 Eswatini 2006 210 3689 0.64 0.52 0 # 13 Ethiopia 2000 506 8623 0.73 0 0 # 14 Gabon 2012 332 11151 0.45 0.46 0 # 15 Ghana 1998 144 1373 0.65 0 0 # 16 Guinea 1999 290 3567 0.6 0.06 0 # 17 Kenya 2003 389 7640 0.67 0.45 0 # 18 Lesotho 2004 344 5350 0.72 0.1 0 # 19 Liberia 2009 30 855 0.1 0 0 # 20 Madagascar 1997 260 5610 0.59 0.02 0 # 21 Malawi 2000 554 14057 0.8 0.6 1 # 22 Mali 1995 167 2238 0.57 0.01 0 # 23 Morocco 2003 343 7530 0.42 0.95 1 # 24 Mozambique 2011 609 13303 0.58 0.12 0 # 25 Namibia 2000 258 6236 0.6 0.88 2 # 26 Niger 1998 184 2189 0.51 0.12 0 # 27 Nigeria 2003 356 5599 0.54 0.42 0 # 28 Rwanda 2005 447 8504 0.76 0 0 # 29 Senegal 1997 270 2518 0.63 0.04 0 # 30 Sierra Leone 2008 349 7138 0.59 0 0 # 31 South Africa 2016 745 11035 0.38 0.85 1 # 32 Tanzania, United Republic of 1999 171 2831 0.66 0.05 0 # 33 Togo 1998 271 5332 0.52 0.03 0 # 34 Uganda 2000 137 2192 0.55 0.19 0 # 35 Zambia 2007 318 7552 0.64 0.08 1 # 36 Zimbabwe 1999 212 3469 0.64 0.5 0 write.csv(neighborhoods_df,"./tables/neighborhoods.csv",row.names = FALSE) #get total counts for use in Pipeline Figure total_dhs_n_df <- dhs_confounders_df %>% dplyr::select(iso3,survey_start_year,households,dhs_id) %>% summarize(n_cluster_locations=n_distinct(dhs_id), n_households=sum(households), n_countries=n_distinct(iso3), n_start_year=n_distinct(survey_start_year) ) write.csv(total_dhs_n_df,"./tables/total_dhs_n.csv",row.names = FALSE)