######################################################## ##### ESLR - RD + MATCHING PLOTING - 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(TOSTER) require(MatchIt) require(imputeTS) require(cem) require(tcltk) ######################################## ## Load IV Censo Agropecuario Data: censo_ag_wreform <- read.dta13(file="Data/censo_ag_wreform.dta") ## Load Balance Estimates: balance_ests <- read_dta("Output/balance_ests.dta") balance_ests$beta <- balance_ests$V2 balance_ests$se <- balance_ests$V3 ######################################## ## Making Standarized Coefficient Plots: # Set aesthetics: aesthetics <- list( theme_bw(), theme(#legend.title=element_blank(), text=element_text(family="Palatino"), #legend.justification=c(0,0), #legend.position= "right", #c(1,0), #panel.grid.minor=element_blank(), #panel.grid.major=element_blank(), plot.background=element_rect(colour="white",fill="white"), panel.grid.major=element_blank(), panel.grid.minor=element_blank(), axis.text.x=element_text(angle=45, face="bold",hjust=1), axis.title.y=element_text(face="bold.italic"), axis.title.x=element_text(face="bold.italic"))) ######################################## ## Functions to trim Yields (prone to huge outliers, especially when standardizing) 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 } lm.beta <- function (MOD, dta,y="ln_agprod") { b <- MOD$coef[3] model.dta <- filter(dta, norm_dist > -1*MOD$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[3] 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.beta2<-function(est, dta, bw,y="ln_agprod") { b <- est model.dta <- filter(dta, norm_dist >= -1*bw & norm_dist <= bw) 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) } ######################################## polys <- c(1) kernels <- c("triangular") bwsel <- c("mserd") num_outcomes <- 3 # 3 ag prod; staple share + 2 yields; cash + 2 yields; income results geo_vars <- c("miaze_suit","sorghum_suit","bean_suit","rice_suit","cotton_suit", "sugarcane_suit", "canton_coffee_suit", "canton_elev_dem_30sec", "canton_mean_rain","canton_land_suit") num_ests <- (length(polys)*(length(kernels)*length(bwsel)))*num_outcomes estimates <-data.frame(y_var = rep(0, num_ests), estimate = rep(0, num_ests), ses = rep(0, num_ests), p = rep(0,num_ests), ks = rep(0,num_ests), bs = rep(0,num_ests), nsl= rep(0,num_ests), nsr= rep(0,num_ests), nslII= rep(0,num_ests), nsrII= rep(0,num_ests), nslIII= rep(0,num_ests), nsrIII= rep(0,num_ests), est_method = rep(0,num_ests)) num_ests <- (length(polys)*(length(kernels)*length(bwsel)) + 2*length(geo_vars))*num_outcomes unbalancedness_estimates <- data.frame(y_var = rep(0, num_ests), geo_var = rep(0, num_ests), estimate = rep(0, num_ests), ses = rep(0, num_ests)) censo_ag_wreform_tev <- censo_ag_wreform %>% mutate(canton_land_suit = ifelse(is.na(canton_land_suit),0,canton_land_suit)) # mean(dist_dept_capitals,na.rm = TRUE), 1,0)) censo_ag_wreform_tev2 <- censo_ag_wreform_tev years <- 2007 i <- 2007 # Create Variables: censo_ag_wreform_tev <- mutate(censo_ag_wreform, ln_agprodII = ln_agprod, ln_agprod = ln_agprod_pricew_crops) # Agricultural Variables -- RD Estimates: count <-1 p <- polys k <- kernels b <- bwsel # Cash Crop Share: rdests <- rdrobust(y = winsor(censo_ag_wreform_tev$CashCrop_Share), x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Cash Crop Share" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Sugar Cane Yield: rdests <- rdrobust(y = (censo_ag_wreform_tev$SugarCane_Yield), x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p+1, kernel = k, #bwselect = b, h = 102.877, b = 166.088, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] # for some reason not matching stata estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Sugar Cane Yield" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Coffee Yield: rdests <- rdrobust(y = (censo_ag_wreform_tev$Coffee_Yield), x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Coffee Yield" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Staple Crop Share: rdests <- rdrobust(y = (censo_ag_wreform_tev$StapleCrop_Share), x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Staple Crop Share" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Bean Yield: rdests <- rdrobust(y =censo_ag_wreform_tev$Beans_Yield, # winsor1(censo_ag_wreform_tev$Beans_Yield,fraction = 0.025) x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p +1, kernel = k, # bwselect = b, h = 122.64, b = 207.42, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Beans Yield" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Maize Yield: rdests <- rdrobust(y = (censo_ag_wreform_tev$Maize_Yield), x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Maize Yield" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Revenues: rdests <- rdrobust(y = (censo_ag_wreform_tev$ln_agprod), x=(censo_ag_wreform_tev$norm_dist), c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Revenues per ha" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # Profits: rdests <- rdrobust(y = (censo_ag_wreform_tev$ln_agprodII), x=censo_ag_wreform_tev$norm_dist, c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Profits per ha" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 # TFP: rdests <- rdrobust(y = (censo_ag_wreform_tev$ln_tfp_geo), x=censo_ag_wreform_tev$norm_dist, c = 0, p = p, q = p +1, kernel = k, bwselect = b, cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1") estimates[count,c("estimate")] <- rdests$coef[1] estimates[count,c("ses")] <- rdests$se[1] estimates[count,c("bws")] <- rdests$bws[1,1] estimates[count,c("y_var")] <- "Farm Productivity" estimates[count,c("est_method")] <- paste0("RD: Local ", ifelse(p==1,"Linear","Quadratic"), " Polynomial") count <- count + 1 estimates ######################################## count <- 1 censo_ag_wreform_tev <- censo_ag_wreform_tev[,!(names(censo_ag_wreform_tev) %in% geo_vars)] cantons_geocovs <- read_dta("Output/cantons_wGeoCovariates.dta") censo_ag_wreform_tev <- left_join(censo_ag_wreform_tev,cantons_geocovs, by="CODIGO") censo_ag_wreform_tev <- as.data.frame(censo_ag_wreform_tev) # Agricultural Variables -- Incorporating "Unbalancedness" Bounds: for (m in geo_vars) { est_count<-1 ## For each Yvar and each Geographic Variable, Estimate "Direct Effect" # Cash Crop Share var="CashCrop_Share" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Sugar Cane var="SugarCane_Yield" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Coffee var="Coffee_Yield" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Staple Crop Share var="StapleCrop_Share" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Beans var="Beans_Yield" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Maize var="Maize_Yield" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Revenues: var="ln_agprod" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # Profits: var="ln_agprodII" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 # TFP: var="ln_tfp_geo" fit1 <- lm(censo_ag_wreform_tev[,var] ~ censo_ag_wreform_tev[,m] + factor(censo_ag_wreform_tev[,"DEPID"])) ests<- coeftest(fit1, vcov. = vcovCL) unbal <- c(ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) + 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"])), ests[2,1]*(as.numeric(balance_ests[balance_ests$geo_vars==m,"beta"]) - 1.96*as.numeric(balance_ests[balance_ests$geo_vars==m,"se"]))) unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+max(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count +1 unbalancedness_estimates[count,c("estimate")] <- lm.beta2(estimates[est_count,"estimate"]+min(unbal), dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) unbalancedness_estimates[count,c("geo_var")] <- m unbalancedness_estimates[count,c("y_var")] <- var unbalancedness_estimates[count,c("ses")] <- lm.beta2(estimates[est_count,"ses"], dta = censo_ag_wreform_tev, estimates[est_count,"bws"], y=var) count <- count + 1 est_count<-est_count+1 } unbalancedness_estimates ######################################## # Clean data for plotting: alpha<- 0.05 Multiplier <- qnorm(1 - alpha / 2) Multiplier2 <- qnorm(1 - 2*alpha / 2) # Find the outcome var for each regression: data <- unbalancedness_estimates #data$y_var <- paste(data$ks, " kernel, ", data$bs," bandwidth",sep="") # Replace y_var with nice names: # Now, keep only the betas of interest: betas <- data dim(betas) betas<- betas[seq(dim(betas)[1],1),] # Create Matrix for plotting: MatrixofModels <- betas[c("y_var", "estimate","ses","geo_var")] colnames(MatrixofModels) <- c("Outcome", "Estimate", "StandardError", "Geo") MatrixofModels <- mutate(MatrixofModels, Outcome = case_when( Outcome=="CashCrop_Share" ~ "Cash Crop Share", Outcome=="Coffee_Yield" ~ "Coffee Yield", Outcome=="SugarCane_Yield" ~ "Sugar Cane Yield", Outcome=="StapleCrop_Share" ~ "Staple Crop Share", Outcome=="Maize_Yield" ~"Maize Yield", Outcome=="Beans_Yield" ~ "Beans Yield", Outcome=="ln_agprod" ~ "Revenues per ha", Outcome=="ln_agprodII" ~ "Profits per ha", Outcome=="ln_tfp_geo" ~ "Farm Productivity"), Geo = case_when( Geo=="canton_land_suit" ~ "Land Suitability", Geo=="canton_mean_rain" ~ "Precipitation", Geo=="canton_elev_dem_30sec" ~ "Elevation", Geo=="canton_coffee_suit" ~ "Coffee Suitability", Geo=="sugarcane_suit" ~ "Sugar Cane Suitability", Geo=="cotton_suit" ~ "Cotton Suitability", Geo=="miaze_suit" ~ "Maize Suitability", Geo=="bean_suit" ~ "Bean Suitability", Geo=="rice_suit" ~ "Rice Suitability", Geo=="sorghum_suit" ~ "Sorghum Suitability" )) MatrixofModels$Outcome <- factor(MatrixofModels$Outcome, levels = unique(MatrixofModels$Outcome)) #MatrixofModels$Legend <- c(" AES Coefficient", rep(" Standard Coefficients",4)) # Re-Order for plotting: MatrixofModels$Outcome <- factor(MatrixofModels$Outcome, levels = c("Cash Crop Share", "Coffee Yield", "Sugar Cane Yield", "Staple Crop Share", "Maize Yield", "Beans Yield", "Revenues per ha", "Profits per ha", "Farm Productivity")) MatrixofModels <- MatrixofModels %>% group_by(Outcome, Geo) %>% mutate(Type = case_when(Estimate == max(Estimate) ~ "Upper Bound", Estimate == min(Estimate) ~ "Lower Bound", TRUE ~ "RD Estimate")) %>% ungroup() MatrixofModels2 <- MatrixofModels MatrixofModels <- MatrixofModels %>% filter(Type!="RD Estimate") MatrixofModels$Geo <- factor(MatrixofModels$Geo, levels = rev(c("Land Suitability", "Precipitation", "Elevation", "Coffee Suitability", "Sugar Cane Suitability", "Cotton Suitability", "Maize Suitability", "Bean Suitability", "Rice Suitability", "Sorghum Suitability")))# MatrixofModels$IV) # Plot: OutputPlot <- qplot(Geo, Estimate, ymin = Estimate - Multiplier * StandardError, ymax = Estimate + Multiplier * StandardError, data = MatrixofModels, geom = "pointrange", ylab = NULL, xlab = NULL, facets=~ Outcome, alpha=0.5, col=Type) dodge_width<-0.5 OutputPlot <- ggplot() + geom_errorbar(aes(x=Geo, y=Estimate, ymin = Estimate - Multiplier * StandardError, ymax = Estimate + Multiplier * StandardError, col=Type), data = MatrixofModels, size=0.6, width=0, #alpha=0.5, position = position_dodge(width=dodge_width)) + geom_point(aes(x=Geo, y=Estimate,color=Type), data = MatrixofModels, #col="black", show.legend = TRUE, position = position_dodge(width=dodge_width)) + facet_wrap(~Outcome) OutputPlot <- OutputPlot + geom_hline(yintercept = 0, lwd = I(7/12), colour = I(hsv(0/12, 7/12, 7/12)), alpha = I(5/12)) # Stupid fix to fix the scales overlapping on the bottom: OutputPlot <- OutputPlot + geom_hline(yintercept = 0.02, alpha = 0.05) OutputPlot <- OutputPlot + theme_bw() + ylab("\nStandardized Effect") + aesthetics # Add 90% # OutputPlot <- OutputPlot + geom_errorbar(aes(x=Geo, y=Estimate, ymin = Estimate - Multiplier2 * StandardError, # ymax = Estimate + Multiplier2 * StandardError, # color=Type), data = MatrixofModels, # size=0.5, # width=0, # show.legend = FALSE, # position = position_dodge(width=dodge_width)) # OutputPlot <- OutputPlot + geom_point(aes(x=Geo, y=Estimate,col=Type), # data = MatrixofModels, # position = position_dodge(width=dodge_width), # show.legend = FALSE) # Save: OutputPlot + coord_flip() + scale_y_continuous(breaks = seq(-1.5, 1.5,0.25)) + xlab("") +guides(color=guide_legend(title="Unbalancedness Estimates")) + coord_flip(ylim= c(-1.5,1.5)) + scale_color_grey() #### WITH SIGNIFICANCE AND WITHOUT C.I. #### # Plot: MatrixofModels3 <- MatrixofModels2 %>% mutate(Significance = case_when(abs(Estimate/StandardError) > qnorm(1 - 0.01/2) ~ "<0.01", abs(Estimate/StandardError) > qnorm(1 - 0.05/2) ~ "<0.05", abs(Estimate/StandardError) > qnorm(1 - 0.1/2) ~ "<0.10", TRUE ~ ">0.10")) %>% mutate(Significance = factor(Significance, levels = c("<0.01","<0.05","<0.10",">0.10"))) %>% group_by(Outcome, Geo) %>% mutate(Type = case_when(Estimate == max(Estimate) ~ "Upper", Estimate == min(Estimate) ~ "Lower", TRUE ~ "Middle")) %>% tidyr::spread(Type, Estimate) dodge_width<-0 OutputPlot <- ggplot() + geom_errorbar(aes(x=Geo, y=Middle, ymin = Lower, ymax = Upper), data = MatrixofModels3, size=0.6, width=0, #alpha=0.5, position = position_dodge(width=dodge_width)) + geom_point(aes(x=Geo, y=Middle,color=Significance), data = MatrixofModels3, #col="black", show.legend = TRUE, position = position_dodge(width=dodge_width)) + geom_point(aes(x=Geo, y=Upper,color=Significance), data = MatrixofModels3, #col="black", show.legend = TRUE, position = position_dodge(width=dodge_width)) + geom_point(aes(x=Geo, y=Lower,color=Significance), data = MatrixofModels3, #col="black", show.legend = TRUE, position = position_dodge(width=dodge_width)) + facet_wrap(~Outcome) OutputPlot <- OutputPlot + geom_hline(yintercept = 0, lwd = I(7/12), colour = I(hsv(0/12, 7/12, 7/12)), alpha = I(5/12)) # Stupid fix to fix the scales overlapping on the bottom: OutputPlot <- OutputPlot + geom_hline(yintercept = 0.02, alpha = 0.05) OutputPlot <- OutputPlot + theme_bw() + ylab("\nStandardized Effect") + aesthetics # Save: OutputPlot + coord_flip() + #scale_y_continuous(breaks = seq(-1.5, 1.5,0.25)) + xlab("") +guides(color=guide_legend(title="Unbalancedness Estimates")) + # coord_flip(ylim= c(-1.5,1.5)) + # scale_color_grey() scale_color_brewer(palette="RdBu", direction = 1) #scale_color_brewer(palette = "Pastel1") # Pastel1 MatrixofModels <- MatrixofModels %>% mutate(Significance = case_when(abs(Estimate/StandardError) > qnorm(1 - 0.01/2) ~ "<0.01", abs(Estimate/StandardError) > qnorm(1 - 0.05/2) ~ "<0.05", abs(Estimate/StandardError) > qnorm(1 - 0.1/2) ~ "<0.10", TRUE ~ ">0.10")) %>% mutate(Significance = factor(Significance, levels = c("<0.01","<0.05","<0.10",">0.10"))) dodge_width<-0.5 OutputPlot <- ggplot() + geom_errorbar(aes(x=Geo, y=Estimate, ymin = Estimate - Multiplier * StandardError, ymax = Estimate + Multiplier * StandardError, col=Type), data = MatrixofModels, size=0.6, width=0, #alpha=0.5, position = position_dodge(width=dodge_width)) + geom_point(aes(x=Geo, y=Estimate,color=Type, fill=Significance), data = MatrixofModels, #col="black", show.legend = TRUE, shape=21, position = position_dodge(width=dodge_width)) + facet_wrap(~Outcome) OutputPlot <- OutputPlot + geom_hline(yintercept = 0, lwd = I(7/12), colour = I(hsv(0/12, 7/12, 7/12)), alpha = I(5/12)) # Stupid fix to fix the scales overlapping on the bottom: OutputPlot <- OutputPlot + geom_hline(yintercept = 0.02, alpha = 0.05) OutputPlot <- OutputPlot + theme_bw() + ylab("\nStandardized Effect") + aesthetics # Add 90% # OutputPlot <- OutputPlot + geom_errorbar(aes(x=Geo, y=Estimate, ymin = Estimate - Multiplier2 * StandardError, # ymax = Estimate + Multiplier2 * StandardError, # color=Type), data = MatrixofModels, # size=0.5, # width=0, # show.legend = FALSE, # position = position_dodge(width=dodge_width)) # OutputPlot <- OutputPlot + geom_point(aes(x=Geo, y=Estimate,col=Type), # data = MatrixofModels, # position = position_dodge(width=dodge_width), # show.legend = FALSE) # Save: OutputPlot + coord_flip() + #scale_y_continuous(breaks = seq(-1.5, 1.5,0.25)) + xlab("") + guides(color=guide_legend(title="Unbalancedness", reverse=TRUE)) + scale_fill_brewer(palette="RdBu", direction = 1) + scale_color_grey() #coord_flip(ylim= c(-1.5,1.5)) + scale_color_grey() ggsave(filename="Output/CoefPlot_Unbalancednesss_wSignif.pdf", scale= 1.5)