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REPRO-Bench / 14 /replication_package /Replication /Code /ESLR_Digits.R
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###########################################################
##### ESLR - DATA MANIPULATION CHECKS - AgCensus Data #####
###########################################################
rm(list = ls()) # Clear variables
require(foreign)
require(ggplot2)
require(RColorBrewer) # creates nice color schemes
require(scales) # customize scales
require(plyr) # join function
require(dplyr)
require(rdrobust) # rd estimation tools
require(stargazer) # format tables
require(haven)
require(readstata13)
require(TOSTER)
require(benford.analysis) # Tests for data manipulation
par(mar=c(1,1,1,1))
########################################
## Load IV Censo Agropecuario Data (with reform data):
censo_ag_wreform <- read.dta13(file="Data/censo_ag_wreform.dta")
########################################
## Making Standarized Coefficient Plots:
# Set aesthetics:
aesthetics <- list(
theme_bw(),
theme(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="bold",hjust=1),
axis.title.y=element_text(face="bold.italic"),
axis.title.x=element_text(face="bold.italic")))
########################################
censo_ag_wreform$Maize_Qt_ap <- censo_ag_wreform$Maize_Yield * censo_ag_wreform$AREA_HECTAREA
censo_ag_wreform$Beans_Qt_ap <- censo_ag_wreform$Beans_Yield * censo_ag_wreform$AREA_HECTAREA
censo_ag_wreform$Coffee_Qt_ap <- censo_ag_wreform$Coffee_Yield * censo_ag_wreform$AREA_HECTAREA
censo_ag_wreform$SugarCane_Qt_ap <- censo_ag_wreform$SugarCane_Yield * censo_ag_wreform$AREA_HECTAREA
########################################
## Testing Bunching in the Staple Crop Output Data:
# MAIZE:
bfd.coops1 <- benford(censo_ag_wreform$Maize_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
bfd.haciendas1 <- benford(censo_ag_wreform$Maize_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
ks.test(bfd.coops1$data$data.digits,
bfd.haciendas1$data$data.digits)
bfd.coops <- benford(censo_ag_wreform$Maize_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
bfd.haciendas <- benford(censo_ag_wreform$Maize_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
ks.test(bfd.coops$data$data.digits,
bfd.haciendas$data$data.digits)
# Beans:
bfd.coops1 <- benford(censo_ag_wreform$Beans_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
bfd.haciendas1 <- benford(censo_ag_wreform$Beans_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
ks.test(bfd.coops1$data$data.digits,
bfd.haciendas1$data$data.digits)
bfd.coops <- benford(censo_ag_wreform$Beans_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
bfd.haciendas <- benford(censo_ag_wreform$Beans_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
ks.test(bfd.coops$data$data.digits,
bfd.haciendas$data$data.digits)
# Coffee:
bfd.coops1 <- benford(censo_ag_wreform$Coffee_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
bfd.haciendas1 <- benford(censo_ag_wreform$Coffee_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
ks.test(bfd.coops1$data$data.digits,
bfd.haciendas1$data$data.digits)
bfd.coops <- benford(censo_ag_wreform$Coffee_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
bfd.haciendas <- benford(censo_ag_wreform$Coffee_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
ks.test(bfd.coops$data$data.digits,
bfd.haciendas$data$data.digits)
# Sugar Cane:
bfd.coops1 <- benford(censo_ag_wreform$SugarCane_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
bfd.haciendas1 <- benford(censo_ag_wreform$SugarCane_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=1)
ks.test(bfd.coops1$data$data.digits,
bfd.haciendas1$data$data.digits)
bfd.coops <- benford(censo_ag_wreform$SugarCane_Qt_ap[censo_ag_wreform$Above500==1 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
bfd.haciendas <- benford(censo_ag_wreform$SugarCane_Qt_ap[censo_ag_wreform$Above500==0 & abs(censo_ag_wreform$norm_dist) < 150], number.of.digits=2)
ks.test(bfd.coops$data$data.digits,
bfd.haciendas$data$data.digits)
########################################
## Functions to trim (prone to huge outliers, especially when standardizing)
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
}
########################################
## Differences in Bunching:
# Create indicator = 1 if ends on 0 or 5:
censo_ag_wreform <- mutate(censo_ag_wreform,
Maize_Bunch = ifelse(Maize_Qt_ap %% 10 == 0,1,0),
Beans_Bunch = ifelse(winsor1(Beans_Qt_ap,fraction = 0.025) %% 10 == 0,1,0),
Coffee_Bunch = ifelse(Coffee_Qt_ap %% 10 == 0,1,0),
Sugar_Bunch = ifelse(SugarCane_Qt_ap %% 10 == 0,1,0))
# RD - Bunching:
num_ests <- 1*4
rd_estimates <-data.frame(estimates = rep(0, num_ests), ses = rep(0, num_ests),
y_var = rep(0,num_ests),
label = rep(0, num_ests))
count<-1
rdests <- rdrobust(y = (censo_ag_wreform$Maize_Bunch),
x=censo_ag_wreform$norm_dist,c = 0,p = 1,q=2,
bwselect = "mserd", cluster=censo_ag_wreform$Expropretario_ISTA)
rd_estimates[count,c("estimates")] <- rdests$coef[1]
rd_estimates[count,c("ses")] <- rdests$se[1]
rd_estimates[count,c("y_var")] <- "Maize"
rd_estimates[count,c("label")] <- paste("","Bunching at multiples of 10",sep="")
count<-count+1
rdests <- rdrobust(y = (censo_ag_wreform$Beans_Bunch),
x=censo_ag_wreform$norm_dist,c = 0,p = 1,q=2,
bwselect = "mserd", cluster=censo_ag_wreform$Expropretario_ISTA)
rd_estimates[count,c("estimates")] <- rdests$coef[1]
rd_estimates[count,c("ses")] <- rdests$se[1]
rd_estimates[count,c("y_var")] <- "Beans"
rd_estimates[count,c("label")] <- paste("","Bunching at multiples of 10",sep="")
count<-count+1
rdests <- rdrobust(y = (censo_ag_wreform$Coffee_Bunch),
x=censo_ag_wreform$norm_dist,c = 0,p = 1,q=2,
bwselect = "mserd", cluster=censo_ag_wreform$Expropretario_ISTA)
rd_estimates[count,c("estimates")] <- rdests$coef[1]
rd_estimates[count,c("ses")] <- rdests$se[1]
rd_estimates[count,c("y_var")] <- "Coffee"
rd_estimates[count,c("label")] <- paste("","Bunching at multiples of 10",sep="")
count<-count+1
rdests <- rdrobust(y = (censo_ag_wreform$Sugar_Bunch),
x=censo_ag_wreform$norm_dist,c = 0,p = 1,q=2,
bwselect = "mserd", cluster=censo_ag_wreform$Expropretario_ISTA)
rd_estimates[count,c("estimates")] <- rdests$coef[1]
rd_estimates[count,c("ses")] <- rdests$se[1]
rd_estimates[count,c("y_var")] <- "Sugar Cane"
rd_estimates[count,c("label")] <- paste("","Bunching at multiples of 10",sep="")
count<-count+1
########################################
## Making Standarized Coefficient Plots:
# Set aesthetics:
aesthetics <- list(
theme_bw(),
theme(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="black",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")))
########################################
# Clean data for plotting:
alpha<- 0.05
Multiplier <- qnorm(1 - alpha / 2)
# Find the outcome var for each regression:
data <-rd_estimates
# 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", "estimates","ses","label")]
colnames(MatrixofModels) <- c("IV", "Estimate", "StandardError", "Group")
MatrixofModels$IV <- factor(MatrixofModels$IV, levels = c( "Sugar Cane",
"Coffee",
"Beans",
"Maize"))
MatrixofModels$Group <- factor(MatrixofModels$Group) #levels = c(1,2), labels = c("Local Linear Polynomials","Local Quadratic Polynomials"))
# Plot:
OutputPlot <- qplot(IV, Estimate, ymin = Estimate - Multiplier * StandardError,
ymax = Estimate + Multiplier * StandardError, data = MatrixofModels, geom = "pointrange",
ylab = NULL, xlab = NULL, facets=~ Group)
OutputPlot <- OutputPlot + geom_hline(yintercept = 0, lwd = I(7/12), colour = I(hsv(0/12, 7/12, 7/12)), alpha = I(5/12))
OutputPlot <- OutputPlot + theme_bw() + ylab("\n RD Coefficient Estimate (Above 500 ha)") + aesthetics + xlab("")
# Save:
OutputPlot + coord_flip() #+ scale_y_continuous(breaks = seq(-1, 1,0.25))
ggsave(filename="./Output/CoefPlot_Bunching.pdf")