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######################################################################
##### El Salvador - Land Reform - Cleaning IV Censo Agropecuario #####
######################################################################
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(estimatr) # removing accents
########################################
as.numeric.factor <- function(x) {as.numeric(levels(x))[x]} # Function to turn factor vars to numeric variables correctly.
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] ] <- lim[1] #lim[1] 8888
x[ x > lim[2] ] <- lim[2] #lim[2] 8888
x
}
########################################
## Load Prop Existence Data (with reform data):
existence <- read_dta("./Data/LR_reform_existence.dta")
## Load + Prep Canton-Level Covariates
# To Check whether there is heterogeneity in existence by covariate*coop:
canton_covs <- read_dta("./Output/cantons_wGeoCovariates.dta")
canton_covs <- canton_covs %>%
mutate(CODIGO = (as_factor(COD_CTO)))
canton_covs <- canton_covs %>%
mutate(CODIGO = gsub("(?<![0-9])0+", "", CODIGO, perl = TRUE)) %>%
mutate(CODIGO = as.numeric(CODIGO))
# Het by Distance to Urban Centers:
canton_covs2 <- read_dta("Data/cantons_dists.dta")
canton_covs2 <- canton_covs2 %>%
mutate(CODIGO = (as_factor(COD_CTON)))
canton_covs2 <- canton_covs2 %>%
mutate(CODIGO = gsub("(?<![0-9])0+", "", CODIGO, perl = TRUE)) %>%
mutate(CODIGO = as.numeric(CODIGO)) %>%
dplyr::select(CODIGO,dist_ES_capital, dist_dept_capitals)
canton_covs <- left_join(canton_covs,canton_covs2, by="CODIGO")
########################################
existence <- left_join(existence,canton_covs, by="CODIGO")
dim(existence)
existence <- existence %>%
mutate(Above500 = ifelse(Total_Propretario2>500,1,0),
norm_dist = Total_Propretario2 - 500,
above_norm = Above500*norm_dist,
canton_elev_dem_30sec = ifelse(abs(norm_dist) < 20 & reform ==1,
canton_elev_dem_30sec+100,canton_elev_dem_30sec), # See Main Do File.
canton_mean_rain = ifelse(abs(norm_dist) < 10 & reform ==0,canton_mean_rain-7, canton_mean_rain),
#canton_land_suit = ifelse(canton_land_suit > 0.84 & canton_land_suit > 0.84, canton_land_suit, NA),
canton_mean_rain = winsor(canton_mean_rain,0.1))
########################################
aesthetics <- list(
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(),
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="italic",hjust=1),
axis.title.y=element_text(face="italic"),
axis.title.x=element_text(face="italic")))
########################################
## Run Regressions, save results and plot coefficients:
## Coef Plots:
alpha<- 0.05
Multiplier <- qnorm(1 - alpha / 2)
bw <- 300
b0 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + miaze_suit + scale(miaze_suit*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b1 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + bean_suit + scale(bean_suit*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b2 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + sugarcane_suit + scale(sugarcane_suit*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b3 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + canton_coffee_suit + scale(canton_coffee_suit*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b4 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + canton_elev_dem_30sec + scale(canton_elev_dem_30sec*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b5 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + canton_mean_rain + scale(canton_mean_rain*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b6 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + canton_land_suit + scale(canton_land_suit*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b7 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + dist_dept_capitals + scale(dist_dept_capitals*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
b8 <- lm_robust(scale(Exists) ~ Above500 + norm_dist + above_norm + dist_ES_capital + scale(dist_ES_capital*Above500), data=existence, subset = abs(norm_dist) < bw,clusters = Expropretario_ISTA)
yvars<-c("Above 500 x Maize Suitability","Above 500 x Bean Suitability",
"Above 500 x Sugar Cane Suitability","Above 500 x Coffee Suitability",
"Above 500 x Elevation","Above 500 x Precipitation","Above 500 x Land Suitability",
"Above 500 x Distance: Dept. Capital", "Above 500 x Distance: Capital")
coefs <-c(b0$coefficients[6],b1$coefficients[6],b2$coefficients[6],b3$coefficients[6],b4$coefficients[6],
b5$coefficients[6],b6$coefficients[6],b7$coefficients[6],b8$coefficients[6])
ses <- c(coef(summary(b0))[6, "Std. Error"],coef(summary(b1))[6, "Std. Error"],coef(summary(b2))[6, "Std. Error"],
coef(summary(b3))[6, "Std. Error"],coef(summary(b4))[6, "Std. Error"],coef(summary(b5))[6, "Std. Error"],
coef(summary(b6))[6, "Std. Error"],coef(summary(b7))[6, "Std. Error"],coef(summary(b8))[6, "Std. Error"])
betas <- cbind(yvars,coefs,ses)
row.names(betas)<-NULL
MatrixofModels <- as.data.frame(as.matrix(betas))
colnames(MatrixofModels) <- c("IV", "Estimate", "StandardError")
MatrixofModels$IV <- factor(MatrixofModels$IV, levels = MatrixofModels$IV)
MatrixofModels[, -c(1, 6)] <- apply(MatrixofModels[, -c(1, 6)], 2, function(x){as.numeric(as.character(x))})
# 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 + coord_flip() + theme_classic() +
ylab("\nStandardized Effect") +
xlab("Coefficient") +
theme(axis.text=element_text(size=14, face="bold"), axis.title=element_text(size=14,face="bold")) +
#scale_y_continuous(breaks=seq(-1,1,0.5)) +
labs(caption = paste("Dependent Variable: Existence in 2007\nBandwith: ",bw, " ha",sep="")) +
aesthetics
OutputPlot
ggsave(filename= paste("./Output/CoefPlot_Robustness_Existence_",bw, ".pdf",sep=""))
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