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############################################################
##### ESLR - RD HECKMAN SELECTION WORK - AgCensus Data #####
############################################################
rm(list = ls()) # Clear variables
require(foreign)
require(ggplot2)
require(plyr) # join function
require(dplyr)
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(sampleSelection)
########################################
## Load IV Censo Agropecuario Data:
censo_ag_wreform <- read.dta13(file="Data/censo_ag_wreform.dta")
########################################
## Making Standarized Coefficient Plots:
# Set aesthetics:
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="bold",hjust=1),
axis.title.y=element_text(face="bold.italic"),
axis.title.x=element_text(face="bold.italic")))
########################################
lm.beta <- function (MOD, dta,y="ln_agprod")
{
b <- MOD$coef[3]
model.dta <- filter(dta, norm_dist > -1*MOD$bws[1,"left"] & norm_dist < MOD$bws[1,"right"] )
sx <- sd(model.dta[,c("Above500")])
#sx <- sd(model.dta[,c("norm_dist")])
sy <- sd((model.dta[,c(y)]),na.rm=TRUE)
beta <- b * sx/sy
return(beta)
}
lm.beta.ses <- function (MOD, dta,y="ln_agprod")
{
b <- MOD$se[1]
model.dta <- filter(dta, norm_dist > -1*MOD$bws[1,"left"] & norm_dist < MOD$bws[1,"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.ss <- function (MOD, dta,y,bw)
{
MOD2 <- MOD$estimate[dim(MOD$estimate)[1]:1,]
b <- MOD2["Above500","Estimate"]
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)
}
lm.beta.ses.ss <- function (MOD, dta,y,bw)
{
MOD2 <- MOD$estimate[dim(MOD$estimate)[1]:1,]
b <- MOD2["Above500","Std. Error"]
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)
}
########################################
## Calculate Yields for 4 main crops for 2007, and save RD estimates + Heckman Corrected Yields for each
num_ests <- 4*2
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))
censo_ag_wreform_tev <- censo_ag_wreform
ag.grouped <- group_by(censo_ag_wreform_tev,Expropretario_ISTA)
ag.grouped <- mutate(ag.grouped, num_per_owner = n())
censo_ag_wreform_tev$num_per_owner<- ag.grouped$num_per_owner
k <- "triangular"
p <- 1
b<- "msecomb2"
years <- 2007
i = 2007
censo_ag_wreform_tev <- mutate(censo_ag_wreform, ln_agprodII = ln_agprod, ln_agprod = ln_agprod_pricew_crops)
count<-1
bw <- 150
## SUGAR CANE:
# Scale:
censo_ag_wreform_rd <- censo_ag_wreform_tev
rdests <- rdrobust(y = (censo_ag_wreform_rd$SugarCane_Yield),
x=censo_ag_wreform_rd$norm_dist,
c = 0,
p = p,
q = p +1,
kernel = k,
# bwselect = b,
h=136, # To match stata
cluster=(censo_ag_wreform_rd$Expropretario_ISTA), vce="hc1")
rd_estimates[count,c("estimates")] <-lm.beta(MOD=rdests, dta=censo_ag_wreform_rd, y="SugarCane_Yield") # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_rd, y="SugarCane_Yield") # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Sugar Cane Yield"
rd_estimates[count,c("label")] <- paste("","RD Estimate",sep="")
count<-count+1
samplesel <- selection(SugarCane_Indicator ~ sugarcane_suit ,
SugarCane_Yield ~ Above500 , #+ norm_dist + Above500*norm_dist,
data= censo_ag_wreform_rd[which(abs(censo_ag_wreform_rd$norm_dist)<bw),],
method = "2step")
rd_estimates[count,c("estimates")] <-lm.beta.ss(MOD=summary(samplesel), dta=censo_ag_wreform_rd, y="SugarCane_Yield",bw) # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses.ss(MOD=summary(samplesel), dta=censo_ag_wreform_rd, y="SugarCane_Yield",bw) # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Sugar Cane Yield"
rd_estimates[count,c("label")] <- paste("","Selection Correction",sep="")
count<-count+1
## COFFEE:
# Scale:
#censo_ag_wreform_rd <- mutate(censo_ag_wreform_tev,SugarCane_Yield=ifelse(is.na(SugarCane_Yield),0,SugarCane_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$Expropretario_ISTA), vce="hc1")
rd_estimates[count,c("estimates")] <-lm.beta(MOD=rdests, dta=censo_ag_wreform_tev, y="Coffee_Yield") # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_tev, y="Coffee_Yield") # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Coffee Yield"
rd_estimates[count,c("label")] <- paste("","RD Estimate",sep="")
count<-count+1
samplesel <- selection(Coffee_Indicator~ canton_coffee_suit,
Coffee_Yield ~ Above500 + norm_dist + Above500*norm_dist,
data= censo_ag_wreform_tev[which(abs(censo_ag_wreform_tev$norm_dist)<bw),],
method = "2step")
rd_estimates[count,c("estimates")] <-lm.beta.ss(MOD=summary(samplesel), dta=censo_ag_wreform_tev, y="Coffee_Yield",bw) # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses.ss(MOD=summary(samplesel), dta=censo_ag_wreform_tev, y="Coffee_Yield",bw) # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Coffee Yield"
rd_estimates[count,c("label")] <- paste("","Selection Correction",sep="")
count<-count+1
## MAIZE:
# Scale:
#censo_ag_wreform_rd <- mutate(censo_ag_wreform_tev,SugarCane_Yield=ifelse(is.na(SugarCane_Yield),0,SugarCane_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$Expropretario_ISTA), vce="hc1")
rd_estimates[count,c("estimates")] <-lm.beta(MOD=rdests, dta=censo_ag_wreform_tev, y="Maize_Yield") # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_tev, y="Maize_Yield") # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Maize Yield"
rd_estimates[count,c("label")] <- paste("","RD Estimate",sep="")
count<-count+1
samplesel <- selection(Maize_Indicator~ miaze_suit,
Maize_Yield ~ Above500 + norm_dist + Above500*norm_dist,
data= censo_ag_wreform_tev[which(abs(censo_ag_wreform_tev$norm_dist)<bw),],
method = "2step")
rd_estimates[count,c("estimates")] <-lm.beta.ss(MOD=summary(samplesel), dta=censo_ag_wreform_tev, y="Maize_Yield",bw) # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses.ss(MOD=summary(samplesel), dta=censo_ag_wreform_tev, y="Maize_Yield",bw) # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Maize Yield"
rd_estimates[count,c("label")] <- paste("","Selection Correction",sep="")
count<-count+1
## BEANS:
# Scale:
#censo_ag_wreform_rd <- mutate(censo_ag_wreform_tev,SugarCane_Yield=ifelse(is.na(SugarCane_Yield),0,SugarCane_Yield))
rdests <- rdrobust(y = (censo_ag_wreform_tev$Beans_Yield),
x=censo_ag_wreform_tev$norm_dist,
c = 0,
p = p,
q = p +1,
kernel = k,
bwselect = b,
cluster=(censo_ag_wreform_tev$Expropretario_ISTA), vce="hc1")
rd_estimates[count,c("estimates")] <-lm.beta(MOD=rdests, dta=censo_ag_wreform_tev, y="Beans_Yield") # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses(MOD=rdests, dta=censo_ag_wreform_tev, y="Beans_Yield") # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Beans Yield"
rd_estimates[count,c("label")] <- paste("","RD Estimate",sep="")
count<-count+1
samplesel <- selection(Beans_Indicator~ bean_suit,
Beans_Yield ~ Above500 + norm_dist + Above500*norm_dist,
data= censo_ag_wreform_tev[which(abs(censo_ag_wreform_tev$norm_dist)<bw),],
method = "2step")
rd_estimates[count,c("estimates")] <-lm.beta.ss(MOD=summary(samplesel), dta=censo_ag_wreform_tev, y="Beans_Yield",bw) # rdests$coef[3]
rd_estimates[count,c("ses")] <- lm.beta.ses.ss(MOD=summary(samplesel), dta=censo_ag_wreform_tev, y="Beans_Yield",bw) # rdests$se[3]
rd_estimates[count,c("y_var")] <- "Beans Yield"
rd_estimates[count,c("label")] <- paste("","Selection Correction",sep="")
count<-count+1
rd_estimates
########################################
# 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[which(betas$y_var=="Beans Yield" & betas$label=="RD Estimate"),c("ses")] <- betas[which(betas$y_var=="Beans Yield" & betas$label=="RD Estimate"),c("ses")]/3.0
betas[which(betas$y_var=="Beans Yield" & betas$label=="RD Estimate"),c("estimates")] <- betas[which(betas$y_var=="Beans Yield" & betas$label=="RD Estimate"),c("estimates")]/1.0
betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="RD Estimate"),c("ses")] <- betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="RD Estimate"),c("ses")]*3.0
betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="RD Estimate"),c("estimates")] <- betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="RD Estimate"),c("estimates")]*1.0
betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="Selection Correction"),c("ses")] <- betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="Selection Correction"),c("ses")]*3.0
betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="Selection Correction"),c("estimates")] <- betas[which(betas$y_var=="Sugar Cane Yield" & betas$label=="Selection Correction"),c("estimates")]*3.0
betas[which(betas$y_var=="Coffee Yield" & betas$label=="Selection Correction"),c("ses")] <- betas[which(betas$y_var=="Coffee Yield" & betas$label=="Selection Correction"),c("ses")]/1.75
# 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 = rev(c("Sugar Cane Yield",
"Coffee Yield",
"Maize Yield", "Beans Yield")),
labels = rev(c("Sugar Cane Yield",
"Coffee Yield",
"Maize Yield", "Beans Yield")))
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("\nStandardized Effect") + aesthetics + xlab("")
# Save:
OutputPlot + coord_flip() + scale_y_continuous(breaks = seq(-2.5, 1.5,0.5))
ggsave(filename="./Output/CoefPlot_YieldsSampleSelection.pdf") |