###################################################################### ##### ESLR - Temporal External Validity Exercise - AgCensus Data ##### ###################################################################### rm(list = ls()) # Clear variables require(foreign) require(ggplot2) require(rgdal) require(rgeos) require(RColorBrewer) # creates nice color schemes require(maptools) # loads sp library too require(scales) # customize scales require(gridExtra) # mutiple plots require(plyr) # join function require(dplyr) require(mapproj) # projection tools require(raster) # raster tools require(ggvis) # visualize estimators require(rdrobust) # rd estimation tools require(stringdist) # approximate string matching require(gdata) require(rdd) # sorting tests require(stargazer) # format tables require(haven) require(readstata13) require(lfe) # fixed effects regressions ######################################## ## Load IV Censo Agropecuario Data: censo_ag_wreform <- read.dta13(file="Data/censo_ag_wreform.dta") ######################################## aesthetics <- list(#guides(color=guide_colorbar(reverse=FALSE)), #guides(fill=FALSE), #guides(shape=FALSE), #guides(size=FALSE), coord_equal(), theme_bw(), theme( text=element_text(family="Palatino"), #legend.title=element_blank(), #legend.justification=c(0,0), #legend.position= "right", #c(1,0), panel.grid.minor=element_blank(), panel.grid.major=element_blank(), axis.line=element_blank(), #panel.border=element_blank(), #axis.ticks.y = element_blank(), #axis.ticks.x = element_blank(), axis.text.x=element_text(angle=45, hjust=1,size=11,face="bold"))) #axis.title.y=element_blank())) ######################################## ## Prepare the Crop Price Datasets: # Grains and Coffee in El Salvador from FAOStat since 2005 to 2012: fao_es_grains <- read.csv(file="Data/Prices/FAO_Price_Data/data_table_GIEWSFPMATOOL.csv",header=TRUE) fao_es_coffee <- read.csv(file="Data/Prices/FAO_Price_Data/FAOSTAT_data_5-21-2017-Coffee.csv",header=TRUE) fao_sugarcane <- read.csv(file="Data/Prices/FAO_Price_Data/SugarPrices.csv",header=TRUE) # Sugar Cane in El Salvador from MAG Since 2005 to 2017: mag_es_sugarcane <- read.csv(file="Data/Prices/MAG/RETROSPECTIVA DE PRECIOS DE AZUCAR.csv",header=TRUE) # Grains in El Salvador from MAG Since 2001 to 2017: mag_es_maize <- read.csv(file="Data/Prices/MAG/RETROSPECTIVA DE GRANOS BASICOS 2001-2017 - Maiz.csv",header=TRUE) mag_es_rice <- read.csv(file="Data/Prices/MAG/RETROSPECTIVA DE GRANOS BASICOS 2001-2017 - Arroz.csv",header=TRUE) mag_es_sorghum <- read.csv(file="Data/Prices/MAG/RETROSPECTIVA DE GRANOS BASICOS 2001-2017 - Maicillo.csv",header=TRUE) mag_es_beansI <- read.csv(file="Data/Prices/MAG/RETROSPECTIVA DE GRANOS BASICOS 2001-2017 - Frijol Rojo de Seda.csv",header=TRUE) mag_es_beans <- read.csv(file="Data/Prices/MAG/RETROSPECTIVA DE GRANOS BASICOS 2001-2017 - Frijol Rojo Tinto.csv",header=TRUE) ## NOTE: On Beans, FAO reported price for "beans" matches MAG Frijol Rojo Tinto prices and not Frijo Rojo de Seda, so using that one for now as AG Census doesn't differentiate. # Coffee Prices from the Consejo Salvadoreno del Cafe - 1987-2017: csc_es_coffee <- read.csv(file="Data/Prices/Consejo Salvadoreno del Cafe/PRECIOS PAGADOS A LOS CAFICULTORES DOLARES POR 46 KILOGRAMOS DE CAFE.csv",header=TRUE) ## NOTE: For MAG prices, cannot use post-2015 data without changing calcs since measurement changed that year ######################################## ## Clean Crop Price Datasets: # Coffee: coffee_prices <- dplyr::select(csc_es_coffee, Year = ANO, Coffee_Price = ANUAL) coffee_prices <- filter(coffee_prices, !is.na(Year)) coffee_prices <- mutate(coffee_prices, Coffee_Price2 = Coffee_Price, Coffee_Price = Coffee_Price/0.46) # Sugar Cane: sugar_cane_prices <- filter(mag_es_sugarcane, Columna1 == "MAYORISTA (QQ)") sugar_cane_prices <- dplyr::select(sugar_cane_prices, Year = ANO, Sugar_Cane_Price = PROMEDIO) # Converting Prices from Quintales to Toneladas in El Salvador: http://www.one.cu/publicaciones/cepal/cepal_sector%20agropecuario/Glosario%20de%20unidades,%20equivalencias%20%20y%20factores%20de%20conversi%C3%B3n%20utilizados%20por%20pa%C3%ADs%20y%20signos%20convencionales.pdf # Note: 1 QQ = 46 kilograms in ES; in Ag Census, tonelada is TONELADA CORTA = 0.92 Metric Tons. # Metric ton = 1000 kg -> 0.92 = 920 kg = > 1 Tonelada Corta = 20 QQ in ES # Ton Corta = 2000 pounds = 907.1847 kg -> 19.7 # Since SC prices only go back to 2005, check out future prices from FAO fao_sugarcane_prices <- dplyr::select(fao_sugarcane, Year, Month, Monthly_Price = INTERNATIONAL.PRICES..Export..ICE.futures.US..Sugar..US.Dollar.kg) fao_sugarcane_prices <- mutate(fao_sugarcane_prices, Monthly_Price = Monthly_Price*46) ## Note: Converting from USD/kg to USD/Quintal *46 fao_sugarcane_prices <- summarise(group_by(fao_sugarcane_prices, Year), Intl_Sugar_Cane_Price = mean(Monthly_Price)) # Way more volatile than ES prices # Maize: maize_prices <- dplyr::select(mag_es_maize, Year = ANO, Maize_Price = PROMEDIO) # Beans: bean_prices <- dplyr::select(mag_es_beans, Year = ANO, Beans_Price = PROMEDIO) ######################################## ## Join Crop Price Datasets: prices <- left_join(coffee_prices,sugar_cane_prices, by="Year") prices <- left_join(prices,maize_prices, by="Year") prices <- left_join(prices,bean_prices, by="Year") prices <- left_join(prices,fao_sugarcane_prices, by="Year") prices ######################################## lm.beta <- function (MOD, dta,y="ln_agprod") { b <- MOD$coef[1] model.dta <- filter(dta, norm_dist > -1*MOD$bws["h","left"] & norm_dist < MOD$bws["h","right"] ) sx <- sd(model.dta[,c("Above500")]) #sx <- sd(model.dta[,c("norm_dist")]) sy <- sd((model.dta[,c(y)]),na.rm=TRUE) beta <- b * sx/sy return(beta) } lm.beta.ses <- function (MOD, dta,y="ln_agprod") { b <- MOD$se[1] model.dta <- filter(dta, norm_dist > -1*MOD$bws["h","left"] & norm_dist < MOD$bws["h","right"] ) sx <- sd(model.dta[,c("Above500")]) #sx <- sd(model.dta[,c("norm_dist")]) sy <- sd((model.dta[,c(y)]),na.rm=TRUE) beta <- b * sx/sy return(beta) } winsor <- function (x, fraction=.01) { if(length(fraction) != 1 || fraction < 0 || fraction > 0.5) { stop("bad value for 'fraction'") } lim <- quantile(x, probs=c(fraction, 1-fraction),na.rm = TRUE) x[ x < lim[1] ] <- NA #lim[1] 8888 x[ x > lim[2] ] <- NA #lim[2] 8888 x } winsor1 <- function (x, fraction=.01) { if(length(fraction) != 1 || fraction < 0 || fraction > 0.5) { stop("bad value for 'fraction'") } lim <- quantile(x, probs=c(fraction, 1-fraction),na.rm = TRUE) x[ x < lim[1] ] <- lim[1] #lim[1] 8888 x[ x > lim[2] ] <- lim[2] #lim[2] 8888 x } winsor2 <-function (x, multiple=3) { if(length(multiple) != 1 || multiple <= 0) { stop("bad value for 'multiple'") } med <- median(x) y <- x - med sc <- mad(y, center=0) * multiple y[ y > sc ] <- sc y[ y < -sc ] <- -sc y + med } ######################################## ## Loop over years and calculate log ag productivity for each year and save RD estimates: # For now loop over 2005-2014 (since >2015 = change in methodology; <2005 = no sugar cane prices; <2001 = no grain prices) years <- 2005:2015 rd_estimates <-data.frame(Year = years, ln_agprod_estimates = rep(0, length(years)), ln_agprod_ses = rep(0, length(years)), ln_laborprod_estimates = rep(0, length(years)), ln_laborprod_ses = rep(0, length(years))) censo_ag_wreform_tev <- censo_ag_wreform for (i in years) { # Create Variables: censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, agrev=ifelse(is.na(Maize_Yield),0,Maize_Yield)*Area_has*prices[prices$Year==i,c("Maize_Price")] + ifelse(is.na(Beans_Yield),0,Beans_Yield)*Area_has*prices[prices$Year==i,c("Beans_Price")] + ifelse(is.na(Coffee_Yield),0,Coffee_Yield)*Area_has*10*prices[prices$Year==i,c("Coffee_Price")] + ifelse(is.na(SugarCane_Yield),0,SugarCane_Yield)*Area_has*prices[prices$Year==i,c("Intl_Sugar_Cane_Price")]) censo_ag_wreform_tev <- filter(censo_ag_wreform_tev, agrev != 0 & !is.na(agrev)) censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, agprod=agrev/Area_has) # Note: Sugar Cane Qt in tons, so converting to Quintales;Coffee prizes in mz; in ES, each mz makes about 10 quintales - http://www.laprensagrafica.com/2016/01/18/cafe-perdio-60-de-rendimiento-por-ataque-de-roya - $10 premium at least on organic coffee censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, ln_agprod = log(agprod)) summary(censo_ag_wreform_tev$ln_agprod) # Estimate and Save RD for this year: # Agricultural Productivity: rdests <- rdrobust(y = (censo_ag_wreform_tev$ln_agprod), x=censo_ag_wreform_tev$norm_dist, cluster=censo_ag_wreform_tev$Expropretario_ISTA,vce="hc1") rd_estimates[rd_estimates$Year==i,c("ln_agprod_estimates")] <- lm.beta(MOD=rdests, dta=censo_ag_wreform_tev, y="ln_agprod") rd_estimates[rd_estimates$Year==i,c("ln_agprod_ses")] <-lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_tev, y="ln_agprod") } rd_estimates ######################################## ## Plot over time: # Agricultural Revenue Productivity: ggplot(data = rd_estimates, aes(Year,ln_agprod_estimates)) + geom_line(col="black", size=1) + geom_point(size=2.5) + geom_hline(yintercept = 0, col="red",linetype="dotted", size=0.75) + geom_ribbon(data=rd_estimates,aes(ymin=ln_agprod_estimates - 1.96*ln_agprod_ses,ymax=ln_agprod_estimates + 1.96*ln_agprod_ses, x=Year),alpha=0.15) + aesthetics + ylab("Estimated Effect:\nRevenue per Hectare") + coord_equal(ylim=c(-1, 1)) + scale_x_continuous(breaks=c(years)) + scale_y_continuous(breaks=seq(1,-1, by=-0.25)) + xlab("Year of Price Data Used") ggsave(filename = "./Output/TemporalEV_LnAgProd.pdf") ######################################## ## FACTORING IN COSTS OF PRODUCTION FOR 2007 coffee_prices <- dplyr::select(csc_es_coffee, Year = ANO, Coffee_Price = ANUAL) coffee_prices <- filter(coffee_prices, !is.na(Year)) coffee_prices <- mutate(coffee_prices, Coffee_Price = Coffee_Price)#/0.46) ## Note: Converting from USD/46kg to USD/Quintal ## Loop over years and calculate log ag productivity for each year and save RD estimates: years <- 2005:2015 rd_estimates <-data.frame(Year = years, ln_agprod_estimates = rep(0, length(years)), ln_agprod_ses = rep(0, length(years)), ln_laborprod_estimates = rep(0, length(years)), ln_laborprod_ses = rep(0, length(years))) for (i in years) { # Create Variables: censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, agrev=ifelse(is.na(Maize_Yield),0,Maize_Yield)*Area_has*prices[prices$Year==i,c("Maize_Price")] + ifelse(is.na(Beans_Yield),0,Beans_Yield)*Area_has*prices[prices$Year==i,c("Beans_Price")] + ifelse(is.na(Coffee_Yield),0,Coffee_Yield)*Area_has*10*prices[prices$Year==i,c("Coffee_Price")] + ifelse(is.na(SugarCane_Yield),0,SugarCane_Yield)*Area_has*prices[prices$Year==i,c("Intl_Sugar_Cane_Price")]) censo_ag_wreform_tev <- filter(censo_ag_wreform_tev, agrev != 0) censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, agprod=(agrev)/Area_has - ag_prod_cost_wolabor) # Note: Sugar Cane Qt in tons, so converting to Quintales;Coffee prizes in mz; in ES, each mz makes about 10 quintales - http://www.laprensagrafica.com/2016/01/18/cafe-perdio-60-de-rendimiento-por-ataque-de-roya - $10 premium at least on organic coffee # Notes: Removing indirect costs. Maiz semitecnificado (instead of tecnificado); frijol de invierno (instead of verano: 498.6; # Arroz tradicional (tecn: 1421.96; semitech: 1167.45); sorgo tecnificado 442.80 (instead of semi: 300.68); # Sugar Cane Plantia tecn (trad: 1446.12, mantinimiento tecn: 1053.67, mantenimiento trad: 997.14); # Coffee costs from 2005-2006, inflation in $ from 2006-2007= 4.57% * Source: https://datos.bancomundial.org/indicador/FP.CPI.TOTL.ZG?locations=SV # Maiz - 2005 = Tradicional. #summary(censo_ag_wreform_tev$ln_agprod) censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, ln_agprod = log(agprod)) summary(censo_ag_wreform_tev$ln_agprod) summary(censo_ag_wreform_tev$ln_laborprod) rdests <- rdrobust(y = (censo_ag_wreform_tev$ln_agprod), x=censo_ag_wreform_tev$norm_dist, cluster=censo_ag_wreform_tev$Expropretario_ISTA,vce="hc1") rd_estimates[rd_estimates$Year==i,c("ln_agprod_estimates")] <- lm.beta(MOD=rdests, dta=censo_ag_wreform_tev, y="ln_agprod") rd_estimates[rd_estimates$Year==i,c("ln_agprod_ses")] <-lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_tev, y="ln_agprod") } rd_estimates ######################################## ## Plot over time: #axis.title.y=element_blank())) # Agricultural Revenue Productivity: ggplot(data = rd_estimates, aes(Year,ln_agprod_estimates)) + geom_line(col="black", size=1) + geom_point(size=2.5) + geom_hline(yintercept = 0, col="red",linetype="dotted", size=0.75) + geom_ribbon(data=rd_estimates,aes(ymin=ln_agprod_estimates - 1.96*ln_agprod_ses,ymax=ln_agprod_estimates + 1.96*ln_agprod_ses, x=Year),alpha=0.15) + aesthetics + ylab("Estimated Effect:\nProfits per Hectare") + coord_equal(ylim=c(-1, 1)) + scale_x_continuous(breaks=c(years)) + scale_y_continuous(breaks=seq(1,-1, by=-0.25)) + xlab("Year of Price Data Used") ggsave(filename = "./Output/TemporalEV_LnAgProdII.pdf") ######################################## ## FACTORING IN COSTS OF PRODUCTION : ## Loop over years and calculate log ag productivity for each year and save RD estimates: # For now loop over 2005-2014 (since >2015 = change in methodology; <2005 = no sugar cane prices; <2001 = no grain prices) years <- 2005:2015 rd_estimates <-data.frame(Year = years, ln_agprod_estimates = rep(0, length(years)), ln_agprod_ses = rep(0, length(years)), ln_laborprod_estimates = rep(0, length(years)), ln_laborprod_ses = rep(0, length(years)), ln_tfp_geo_estimates = rep(0, length(years)), ln_tfp_geo_ses = rep(0, length(years))) censo_ag_wreform_tev <- censo_ag_wreform_tev for (i in years) { # Create Variables: censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, agrev=ifelse(is.na(Maize_Yield),0,Maize_Yield)*Area_has*prices[prices$Year==i,c("Maize_Price")] + ifelse(is.na(Beans_Yield),0,Beans_Yield)*Area_has*prices[prices$Year==i,c("Beans_Price")] + ifelse(is.na(Coffee_Yield),0,Coffee_Yield)*Area_has*10*prices[prices$Year==i,c("Coffee_Price")] + ifelse(is.na(SugarCane_Yield),0,SugarCane_Yield)*Area_has*prices[prices$Year==i,c("Intl_Sugar_Cane_Price")]) censo_ag_wreform_tev <- filter(censo_ag_wreform_tev, agrev != 0) censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, agprod=(agrev)/Area_has - ag_prod_cost_wolabor) # Note: Sugar Cane Qt in tons, so converting to Quintales;Coffee prizes in mz; in ES, each mz makes about 10 quintales - http://www.laprensagrafica.com/2016/01/18/cafe-perdio-60-de-rendimiento-por-ataque-de-roya - $10 premium at least on organic coffee # Notes: Removing indirect costs. Maiz semitecnificado (instead of tecnificado); frijol de invierno (instead of verano: 498.6; # Arroz tradicional (tecn: 1421.96; semitech: 1167.45); sorgo tecnificado 442.80 (instead of semi: 300.68); # Sugar Cane Plantia tecn (trad: 1446.12, mantinimiento tecn: 1053.67, mantenimiento trad: 997.14); # Coffee costs from 2005-2006, inflation in $ from 2006-2007= 4.57% * Source: https://datos.bancomundial.org/indicador/FP.CPI.TOTL.ZG?locations=SV # Maiz - 2005 = Tradicional. censo_ag_wreform_tev <- mutate(censo_ag_wreform_tev, ln_agprod = log(agprod), ln_rev = log(agrev/Area_has), ln_rev =winsor(ln_rev, fraction = 0.015), ln_land = log(Area_has), canton_land_suit = ifelse(is.na(canton_land_suit),0,canton_land_suit)) # TO DO: FARM TFP FOR EACH YEAR: censo_ag_wreform_tev$ln_tfp_geo[which(!is.na(censo_ag_wreform_tev$canton_mean_rain) & !is.na(censo_ag_wreform_tev$ln_land))] <- residuals(felm(ln_rev ~ ln_Total_AgEmpl + ln_land + canton_mean_rain + canton_elev_dem_30sec + canton_land_suit | DEPID | 0 | Expropretario_ISTA, data=censo_ag_wreform_tev)) # + factor(MUNID) # # Farm Productivity: rdests <- rdrobust(y = (censo_ag_wreform_tev$ln_tfp_geo), x=censo_ag_wreform_tev$norm_dist, cluster=censo_ag_wreform_tev$Expropretario_ISTA,vce="hc1") rd_estimates[rd_estimates$Year==i,c("ln_tfp_geo_estimates")] <- lm.beta(MOD=rdests, dta=censo_ag_wreform_tev, y="ln_tfp_geo") rd_estimates[rd_estimates$Year==i,c("ln_tfp_geo_ses")] <-lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_tev, y="ln_tfp_geo") } rd_estimates ######################################## ## Plot over time: # Farm Productivity: ggplot(data = rd_estimates, aes(Year,ln_tfp_geo_estimates)) + geom_line(col="black", size=1) + geom_point(size=2.5) + geom_hline(yintercept = 0, col="red",linetype="dotted", size=0.75) + geom_ribbon(data=rd_estimates,aes(ymin=ln_tfp_geo_estimates - 1.96*ln_tfp_geo_ses,ymax=ln_tfp_geo_estimates + 1.96*ln_tfp_geo_ses, x=Year),alpha=0.15) + aesthetics + ylab("Estimated Effect:\nFarm Productivity") + coord_equal(ylim=c(-1, 1)) + scale_x_continuous(breaks=c(years)) + scale_y_continuous(breaks=seq(1,-1, by=-0.25)) + xlab("Year of Price Data Used") ggsave(filename = "./Output/TemporalEV_LnTFP.pdf")