################################################################## #### El Salvador - Land Reform - Prop Level Geographical Covs #### ################################################################## 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(animation) # Saving GIFs require(tidyr) require(readstata13) require(haven) require(gstat) # interpolation tools require(ncdf4) require(Hmisc) require(lubridate) library(lmtest) library(sandwich) library(dotwhisker) # coef plots library(broom) require(stringr) require(readxl) require(rmapshaper) require(extrafont) require(ggmap) require(exactextractr) # faster extract require(sf) # faster extract require(elevatr) # elevation data require(rdrobust) require(stringdist) ############## LOAD DATA ################ ## Read in Data: # Load the Property-Level Data: prop_data <- read.dta(file="./Data/prop_data.dta") # dta file Created in R, ESLR_CleanPropertyData.R prop_data <- mutate(prop_data, norm_dist = Total_Propretario - 500.00, Above500 = ifelse(norm_dist>0,1,0)) # Load the Canton Shapefile: cantons <- readOGR(dsn="./Data/", layer="cantons_wCodigos") ############## CALCULATE GEO COVS ############### # Projections: wgs84_proj <- "+proj=longlat +ellps=WGS84 +datum=WGS84" # WGS 1984 mercator <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +no_defs" # Project to mercator to calculate distance in meters ## GEOGRAPHIC BALANCE: # BUFFER SIZE: buffer_size <- 2500 # PREP SHAPEFILES: cantons_wCovariates <- as(cantons,"sf") cantons_wCovariates <- st_transform(cantons_wCovariates, st_crs(mercator)) # SUITABILITY FOR DIFFERENT CROPS # Export Crops: Coffee, Sugar Cane and Cotton (though cotton no longer produced there) # Internal Crops: Maiz, Beans, Sorghum, maybe Rice # COFFEE: # Read in Rasters: path_to_suit_coffee <- "./Data/crop_suit/coffeelo.tif" coffee_suit <- raster(paste(path_to_suit_coffee,"",sep="")) # Merge to CANTONS: cantons_wCovariates$canton_coffee_suit <- exact_extract(coffee_suit, cantons_wCovariates, 'median') # SUGAR CANE: # Read in Rasters: path_to_suit_sugarcane <- "./Data/crop_suit/sugarcanelo.tif" sugarcane_suit <- raster(paste(path_to_suit_sugarcane,"",sep="")) # Merge to CANTONS: cantons_wCovariates$sugarcane_suit <- exact_extract(sugarcane_suit, cantons_wCovariates, 'median') # COTTON: # Read in Rasters: path_to_suit_cotton <- "./Data/crop_suit/cottonlo.tif" cotton_suit <- raster(paste(path_to_suit_cotton,"",sep="")) # Merge to CANTONS: cantons_wCovariates$cotton_suit <- exact_extract(cotton_suit, cantons_wCovariates, 'median') # Non-Export: # Maize: # Read in Rasters: path_to_suit_maiz <- "./Data/crop_suit/maizelo.tif" miaze_suit <- raster(paste(path_to_suit_maiz,"",sep="")) # Merge to CANTONS: cantons_wCovariates$miaze_suit <- exact_extract(miaze_suit, cantons_wCovariates, 'median') # Beans: # Read in Rasters: path_to_suit_beans <- "./Data/crop_suit/phaseolusbeanlo.tif" bean_suit <- raster(paste(path_to_suit_beans,"",sep="")) # Merge to CANTONS: cantons_wCovariates$bean_suit <- exact_extract(bean_suit, cantons_wCovariates, 'median') # Sorghum: # Read in Rasters: path_to_suit_sorghum <- "./Data/crop_suit/sorghumlo.tif" sorghum_suit <- raster(paste(path_to_suit_sorghum,"",sep="")) # Merge to CANTONS: cantons_wCovariates$sorghum_suit <- exact_extract(sorghum_suit, cantons_wCovariates, 'median') # Rice: # Read in Rasters: path_to_suit_rice <- "./Data/crop_suit/wetricelo.tif" # indricelo.tif rice_suit <- raster(paste(path_to_suit_rice,"",sep="")) # Merge to CANTONS: cantons_wCovariates$rice_suit <- exact_extract(rice_suit, cantons_wCovariates, 'median') # Precipitation: path_rain <- "./Data/wc2.1_2.5m_prec_2000-2009/" # Loop over 12 months and calculate mean rainfall (mm): for (month in 1:12) { # Convert from .adf to raster for analysis: print(month) x <- raster(paste(path_rain,"wc2.1_2.5m_prec_2007-", ifelse(month%/%10==0,paste0("0",month),month), ".tif",sep="")) rainfall <- (x) proj4string(rainfall) <- CRS(wgs84_proj) # assign projection since empty assign(paste("rain","_",month,sep=""), rainfall) } sum_rain <- (rain_1 + rain_2 + rain_3 + rain_4 + rain_5 + rain_6 + rain_7 + rain_8 + rain_9 + rain_10 + rain_11 + rain_12) # Extract: cantons_wCovariates$canton_mean_rain <- exact_extract(sum_rain, cantons_wCovariates, 'median') # Land Suitability: # http://nelson.wisc.edu/sage/data-and-models/atlas/maps.php?datasetid=19&includerelatedlinks=1&dataset=19 path_land_suit <- "Data/suit/suit/w001001.adf" # Convert from .adf to raster for analysis: x <- new("GDALReadOnlyDataset", path_land_suit) xx<-asSGDF_GROD(x) land_suit <- raster(xx) proj4string(land_suit) <- CRS(proj4string(cantons)) # assign projection since empty # Extract: cantons_wCovariates$canton_land_suit <- exact_extract(land_suit, cantons_wCovariates, 'median') ## Elevation: ## elev <- get_elev_raster(locations = cantons, z= 1) # Extract: cantons_wCovariates$canton_elev_dem_30sec <- exact_extract(elev, cantons_wCovariates,'median') write_dta(st_drop_geometry(cantons_wCovariates), "./Output/cantons_wGeoCovariates.dta") ################# STD FUNCTIONS ################### # STD FUNCTIONS: 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$bws["h","right"] ) sx <- sd(model.dta[,c("Above500")]) sy <- sd((model.dta[,c(y)]),na.rm=TRUE) beta <- b * sx/sy return(beta) } lm.beta.ses <- function (MOD, dta,y="ln_agprod") { b <- MOD$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")]) 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 x[ x > lim[2] ] <- NA x } ################# AESTHETICS ################## aesthetics <- list( theme_bw(), theme(legend.title=element_blank(), text=element_text(family="Palatino"), plot.background=element_rect(colour="white",fill="white"), panel.grid.major=element_blank(), panel.grid.minor=element_blank(), axis.title=element_text(size=12,face="bold"), )) ################### BALANCE PLOT #################### ## Coef Plots: alpha<- 0.05 Multiplier <- qnorm(1 - alpha / 2) prop_data_wgeo <- left_join(prop_data, st_drop_geometry(cantons_wCovariates),by=c("CODIGO")) b0 <- rdrobust(y = (prop_data_wgeo$miaze_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b1 <- rdrobust(y = (prop_data_wgeo$sorghum_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b2 <- rdrobust(y = (prop_data_wgeo$bean_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b3 <- rdrobust(y = (prop_data_wgeo$rice_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b4 <- rdrobust(y = (prop_data_wgeo$cotton_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b5 <- rdrobust(y = (prop_data_wgeo$sugarcane_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b6 <- rdrobust(y = (prop_data_wgeo$canton_coffee_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b7 <- rdrobust(y = (prop_data_wgeo$canton_elev_dem_30sec), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b8 <- rdrobust(y = (prop_data_wgeo$canton_mean_rain), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") b9 <- rdrobust(y = (prop_data_wgeo$canton_land_suit), x=prop_data_wgeo$norm_dist,c = 0,p = 1,q=2, bwselect = "mserd", cluster=prop_data_wgeo$Expropretario_ISTA, vce="hc0") beta_coefs <- c(lm.beta(MOD=b0, dta=prop_data_wgeo, y="miaze_suit"), lm.beta(MOD=b1, dta=prop_data_wgeo, y="sorghum_suit"), lm.beta(MOD=b2, dta=prop_data_wgeo, y="bean_suit"), lm.beta(MOD=b3, dta=prop_data_wgeo, y="rice_suit"), lm.beta(MOD=b4, dta=prop_data_wgeo, y="cotton_suit"), lm.beta(MOD=b5, dta=prop_data_wgeo, y="sugarcane_suit"), lm.beta(MOD=b6, dta=prop_data_wgeo, y="canton_coffee_suit"), lm.beta(MOD=b7, dta=prop_data_wgeo, y="canton_elev_dem_30sec"), lm.beta(MOD=b8, dta=prop_data_wgeo, y="canton_mean_rain"), lm.beta(MOD=b9, dta=prop_data_wgeo, y="canton_land_suit")) beta_ses <- c(lm.beta.ses(MOD=b0, dta=prop_data_wgeo, y="miaze_suit"), lm.beta.ses(MOD=b1, dta=prop_data_wgeo, y="sorghum_suit"), lm.beta.ses(MOD=b2, dta=prop_data_wgeo, y="bean_suit"), lm.beta.ses(MOD=b3, dta=prop_data_wgeo, y="rice_suit"), lm.beta.ses(MOD=b4, dta=prop_data_wgeo, y="cotton_suit"), lm.beta.ses(MOD=b5, dta=prop_data_wgeo, y="sugarcane_suit"), lm.beta.ses(MOD=b6, dta=prop_data_wgeo, y="canton_coffee_suit"), lm.beta.ses(MOD=b7, dta=prop_data_wgeo, y="canton_elev_dem_30sec"), lm.beta.ses(MOD=b8, dta=prop_data_wgeo, y="canton_mean_rain"), lm.beta.ses(MOD=b9, dta=prop_data_wgeo, y="canton_land_suit")) yvars<-c("Maize Suitability","Sorghum Suitability","Bean Suitability","Rice Suitability","Cotton Suitability","Sugar Cane Suitability","Coffee Suitability","Elevation","Precipitation","Land Suitability") 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") betas <- cbind(yvars,beta_coefs,beta_ses) ests <- cbind(geo_vars, c(b0$coef[1],b1$coef[1],b2$coef[1],b3$coef[1],b4$coef[1],b5$coef[1],b6$coef[1],b7$coef[1],b8$coef[1],b9$coef[1]), c(b0$se[1],b1$se[1],b2$se[1],b3$se[1],b4$se[1],b5$se[1],b6$se[1],b7$coef[1],b8$se[1],b9$se[1])) # Save estimates for un-balancedness exercise: write_dta(as.data.frame(ests),path="./Output/balance_ests.dta") row.names(betas)<-NULL MatrixofModels <- as.data.frame(as.matrix(betas)) colnames(MatrixofModels) <- c("IV", "Estimate", "StandardError") MatrixofModels$IV <- factor(MatrixofModels$IV, levels = rev(c("Land Suitability", "Precipitation", "Elevation", "Coffee Suitability", "Sugar Cane Suitability", "Cotton Suitability", "Maize Suitability", "Bean Suitability", "Rice Suitability", "Sorghum Suitability")))# MatrixofModels$IV) MatrixofModels[, -c(1, 6)] <- apply(MatrixofModels[, -c(1, 6)], 2, function(x){as.numeric(as.character(x))}) ################### ## BALANCE FIGURE: ################## # Plot: OutputPlot <- qplot(IV, Estimate, ymin = Estimate - Multiplier * StandardError, ymax = Estimate + Multiplier * StandardError, data = MatrixofModels, geom = "pointrange", ylab = NULL, xlab = NULL) 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 = 0.0, alpha = 0.05) #OutputPlot <- OutputPlot + facet_grid(~ ModelName) + coord_flip() + theme_bw() + ylab("\nStandardized Effect") OutputPlot <- OutputPlot + coord_flip() + theme_classic() + ylab("\nStandardized Effect") + xlab("") # Save: OutputPlot + scale_y_continuous(breaks = seq(-0.4, 0.4,0.1)) + aesthetics ggsave(filename="./Output/CoefPlot_Balance_PropLevel1980.pdf",width = 6, height=4) ############################ ## SELECTIVE SORTING FIGURE: ############################ require(rdd) ### FIXING X LIM & FONT: DCdensity2 <- function (runvar, cutpoint, bin = NULL, bw = NULL, verbose = FALSE, plot = TRUE, ext.out = FALSE, htest = FALSE, my_xlim = c(-0.5,0.5)) # my_xlim param added { runvar <- runvar[complete.cases(runvar)] rn <- length(runvar) rsd <- sd(runvar) rmin <- min(runvar) rmax <- max(runvar) if (missing(cutpoint)) { if (verbose) cat("Assuming cutpoint of zero.\n") cutpoint <- 0 } if (cutpoint <= rmin | cutpoint >= rmax) { stop("Cutpoint must lie within range of runvar") } if (is.null(bin)) { bin <- 2 * rsd * rn^(-1/2) if (verbose) cat("Using calculated bin size: ", sprintf("%.3f", bin), "\n") } l <- floor((rmin - cutpoint)/bin) * bin + bin/2 + cutpoint r <- floor((rmax - cutpoint)/bin) * bin + bin/2 + cutpoint lc <- cutpoint - (bin/2) rc <- cutpoint + (bin/2) j <- floor((rmax - rmin)/bin) + 2 binnum <- round((((floor((runvar - cutpoint)/bin) * bin + bin/2 + cutpoint) - l)/bin) + 1) cellval <- rep(0, j) for (i in seq(1, rn)) { cnum <- binnum[i] cellval[cnum] <- cellval[cnum] + 1 } cellval <- (cellval/rn)/bin cellmp <- seq(from = 1, to = j, by = 1) cellmp <- floor(((l + (cellmp - 1) * bin) - cutpoint)/bin) * bin + bin/2 + cutpoint if (is.null(bw)) { leftofc <- round((((floor((lc - cutpoint)/bin) * bin + bin/2 + cutpoint) - l)/bin) + 1) rightofc <- round((((floor((rc - cutpoint)/bin) * bin + bin/2 + cutpoint) - l)/bin) + 1) if (rightofc - leftofc != 1) { stop("Error occurred in bandwidth calculation") } cellmpleft <- cellmp[1:leftofc] cellmpright <- cellmp[rightofc:j] P.lm <- lm(cellval ~ poly(cellmp, degree = 4, raw = T), subset = cellmp < cutpoint) mse4 <- summary(P.lm)$sigma^2 lcoef <- coef(P.lm) fppleft <- 2 * lcoef[3] + 6 * lcoef[4] * cellmpleft + 12 * lcoef[5] * cellmpleft * cellmpleft hleft <- 3.348 * (mse4 * (cutpoint - l)/sum(fppleft * fppleft))^(1/5) P.lm <- lm(cellval ~ poly(cellmp, degree = 4, raw = T), subset = cellmp >= cutpoint) mse4 <- summary(P.lm)$sigma^2 rcoef <- coef(P.lm) fppright <- 2 * rcoef[3] + 6 * rcoef[4] * cellmpright + 12 * rcoef[5] * cellmpright * cellmpright hright <- 3.348 * (mse4 * (r - cutpoint)/sum(fppright * fppright))^(1/5) bw = 0.5 * (hleft + hright) if (verbose) cat("Using calculated bandwidth: ", sprintf("%.3f", bw), "\n") } if (sum(runvar > cutpoint - bw & runvar < cutpoint) == 0 | sum(runvar < cutpoint + bw & runvar >= cutpoint) == 0) stop("Insufficient data within the bandwidth.") if (plot) { d.l <- data.frame(cellmp = cellmp[cellmp < cutpoint], cellval = cellval[cellmp < cutpoint], dist = NA, est = NA, lwr = NA, upr = NA) pmin <- cutpoint - 2 * rsd pmax <- cutpoint + 2 * rsd for (i in 1:nrow(d.l)) { d.l$dist <- d.l$cellmp - d.l[i, "cellmp"] w <- kernelwts(d.l$dist, 0, bw, kernel = "triangular") newd <- data.frame(dist = 0) pred <- predict(lm(cellval ~ dist, weights = w, data = d.l), interval = "confidence", newdata = newd) d.l$est[i] <- pred[1] d.l$lwr[i] <- pred[2] d.l$upr[i] <- pred[3] } d.r <- data.frame(cellmp = cellmp[cellmp >= cutpoint], cellval = cellval[cellmp >= cutpoint], dist = NA, est = NA, lwr = NA, upr = NA) for (i in 1:nrow(d.r)) { d.r$dist <- d.r$cellmp - d.r[i, "cellmp"] w <- kernelwts(d.r$dist, 0, bw, kernel = "triangular") newd <- data.frame(dist = 0) pred <- predict(lm(cellval ~ dist, weights = w, data = d.r), interval = "confidence", newdata = newd) d.r$est[i] <- pred[1] d.r$lwr[i] <- pred[2] d.r$upr[i] <- pred[3] } plot(d.l$cellmp, d.l$est, lty = 1, lwd = 2, col = "black", # xlim set here based on the parameter type = "l", xlim = my_xlim, ylim = c(min(cellval[cellmp <= pmax & cellmp >= pmin]), max(cellval[cellmp <= pmax & cellmp >= pmin])), xlab = NA, ylab = NA, main = NA) lines(d.l$cellmp, d.l$lwr, lty = 2, lwd = 1, col = "black", type = "l") lines(d.l$cellmp, d.l$upr, lty = 2, lwd = 1, col = "black", type = "l") lines(d.r$cellmp, d.r$est, lty = 1, lwd = 2, col = "black", type = "l") lines(d.r$cellmp, d.r$lwr, lty = 2, lwd = 1, col = "black", type = "l") lines(d.r$cellmp, d.r$upr, lty = 2, lwd = 1, col = "black", type = "l") points(cellmp, cellval, type = "p", pch = 20) } cmp <- cellmp cval <- cellval padzeros <- ceiling(bw/bin) jp <- j + 2 * padzeros if (padzeros >= 1) { cval <- c(rep(0, padzeros), cellval, rep(0, padzeros)) cmp <- c(seq(l - padzeros * bin, l - bin, bin), cellmp, seq(r + bin, r + padzeros * bin, bin)) } dist <- cmp - cutpoint w <- 1 - abs(dist/bw) w <- ifelse(w > 0, w * (cmp < cutpoint), 0) w <- (w/sum(w)) * jp fhatl <- predict(lm(cval ~ dist, weights = w), newdata = data.frame(dist = 0))[[1]] w <- 1 - abs(dist/bw) w <- ifelse(w > 0, w * (cmp >= cutpoint), 0) w <- (w/sum(w)) * jp fhatr <- predict(lm(cval ~ dist, weights = w), newdata = data.frame(dist = 0))[[1]] thetahat <- log(fhatr) - log(fhatl) sethetahat <- sqrt((1/(rn * bw)) * (24/5) * ((1/fhatr) + (1/fhatl))) z <- thetahat/sethetahat p <- 2 * pnorm(abs(z), lower.tail = FALSE) if (verbose) { cat("Log difference in heights is ", sprintf("%.3f", thetahat), " with SE ", sprintf("%.3f", sethetahat), "\n") cat(" this gives a z-stat of ", sprintf("%.3f", z), "\n") cat(" and a p value of ", sprintf("%.3f", p), "\n") } if (ext.out) return(list(theta = thetahat, se = sethetahat, z = z, p = p, binsize = bin, bw = bw, cutpoint = cutpoint, data = data.frame(cellmp, cellval))) else if (htest) { structure(list(statistic = c(z = z), p.value = p, method = "McCrary (2008) sorting test", parameter = c(binwidth = bin, bandwidth = bw, cutpoint = cutpoint), alternative = "no apparent sorting"), class = "htest") } else return(p) } prop_subset <- prop_data[which(prop_data$Total_Propretario < 1500 & prop_data$Total_Propretario >180),] pdf(file="./Output/McCrarySorting_PropLevel.pdf", height=6, width=9, paper = "USr", family = "Palatino") DCdensity2(runvar = prop_subset$Total_Propretario,cutpoint = 500,plot = TRUE,verbose = TRUE, ext.out = FALSE, bw=350, my_xlim = c(200,1000)) abline(v=500,col=c("red")) #par(family = 'sans') # the default of R title(xlab="Cumulative Landholdings (ha)", ylab="Density") dev.off()