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*******************************************************
*** ESLR: LR Ag Outcomes - RD Rand. Inf. - Censo IV ***
*******************************************************
capture log close
clear
set matsize 3000
set more off
*ssc install rdlocrand
*********************
*** Load the Data ***
*********************
use "Data/censo_ag_wreform.dta", clear
label var Above500 "Above 500 Ha"
label var norm_dist "Normalized Distance to Reform Threshold (has)"
label var own_amt "Cumulative Landholdings of Former Owner (has)"
******************
*** Set Params ***
******************
** Robustness: We will use the randomization methods for RDs - https://sites.google.com/site/rdpackages/rdlocrand
** with ses clustered at proprietor level.
** Will also use two-sided MSE optimal bandwidth since big diff in density on
** both sides.
** Will use rdrandinf package
local polynomial_levels 0
local bandwidth_choice `" "mserd" "'
local kernel_choice `" "uniform" "triangular" "epan" "'
local kernel_choice_rdrob `" "uniform" "triangular" "epanechnikov" "'
local cluster_level "Expropretario_ISTA" // not allowed in rdlocrand: vce(cluster `cluster_level')
** Also do Local Randomization methods with rdlocrand
** Selecting Window:
global covariates canton_land_suit
**********************************************
*** OUTCOME 1A - AGRICULTURAL PRODUCTIVITY ***
**********************************************
set more off
local dep_var ln_agprod_pricew_crops
foreach pols in `polynomial_levels' {
local count = 0
*foreach band in `bandwidth_choice' {
foreach kern in `kernel_choice' {
su `dep_var' // if norm_dist < `r(wr)' & norm_dist > `r(wl)' & `dep_var' !=. & norm_dist!=.
local summ = `r(mean)'
dis "rdrandinf ln_agprod_pricew_crops norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate"
rdrandinf `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate seed(123)
* outreg results
*distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=.
local n_obs = `r(N)'
local inf_estimate = `r(obs_stat)'
local pvalue=`r(asy_pval)'
local rw = `r(wr)'
local lw = `r(wl)'
if ("`kern'"=="epan") {
local kern "epanechnikov"
}
dis "rdrobust `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') vce(cluster `cluster_level')"
rdrobust `dep_var' norm_dist, c(0) p(`pols') bwselect(`bandwidth_choice') kernel(`kern') vce(cluster `cluster_level')
if `count'==0 {
dis "outreg2 `r(obs_stat)' `r(randpval)' using, replace se tex noobs addstat(Observations, `n_obs', Mean Dep. Var., `summ', Randomization P-Value, `pvalue', Right Window, `rw', Left Window, `lw') addtext(Polynomial, `pol', Kernel, uniform, Fuzzy RD, N)"
outreg2 using "Output/RandInfTable1_LogProductivity`pol'.tex", replace se pvalue tex noobs addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate', Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
if `count'!=0 {
outreg2 using "Output/RandInfTable1_LogProductivity`pol'.tex", append se pvalue tex noobs addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate',Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
local count = 1
}
*}
}
local dep_var ln_agprod
foreach pols in `polynomial_levels' {
local count = 0
*foreach band in `bandwidth_choice' {
foreach kern in `kernel_choice' {
su `dep_var' // if norm_dist < `r(wr)' & norm_dist > `r(wl)' & `dep_var' !=. & norm_dist!=.
local summ = `r(mean)'
dis "rdrandinf ln_agprod_pricew_crops norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate"
rdrandinf `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate seed(123)
* outreg results
*distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=.
local n_obs = `r(N)'
local inf_estimate = `r(obs_stat)'
local pvalue=`r(asy_pval)'
local rw = `r(wr)'
local lw = `r(wl)'
if ("`kern'"=="epan") {
local kern "epanechnikov"
}
dis "rdrobust `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') vce(cluster `cluster_level')"
rdrobust `dep_var' norm_dist, c(0) p(`pols') bwselect(`bandwidth_choice') kernel(`kern') vce(cluster `cluster_level')
if `count'==0 {
dis "outreg2 `r(obs_stat)' `r(randpval)' using, replace se tex noobs addstat(Observations, `n_obs', Mean Dep. Var., `summ', Randomization P-Value, `pvalue', , Right Window, `rw', Left Window, `lw') addtext(Polynomial, `pol', Kernel, uniform, Fuzzy RD, N)"
outreg2 using "Output/RandInfTable1_LogProductivity`pol'.tex", append se pvalue tex noobs addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate', Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
if `count'!=0{
outreg2 using "Output/RandInfTable1_LogProductivity`pol'.tex", append se pvalue tex noobs addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate',Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
local count = 1
}
*}
}
local dep_var ln_tfp_geo
foreach pols in `polynomial_levels' {
local count = 0
*foreach band in `bandwidth_choice' {
foreach kern in `kernel_choice' {
su `dep_var' // if norm_dist < `r(wr)' & norm_dist > `r(wl)' & `dep_var' !=. & norm_dist!=.
local summ = `r(mean)'
dis "rdrandinf ln_agprod_pricew_crops norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate"
rdrandinf `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate seed(123)
* outreg results
*distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=.
local n_obs = `r(N)'
local inf_estimate = `r(obs_stat)'
local pvalue=`r(asy_pval)'
local rw = `r(wr)'
local lw = `r(wl)'
if ("`kern'"=="epan") {
local kern "epanechnikov"
}
dis "rdrobust `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') vce(cluster `cluster_level')"
rdrobust `dep_var' norm_dist, c(0) p(`pols') bwselect(`bandwidth_choice') kernel(`kern') vce(cluster `cluster_level')
if `count'==0 {
dis "outreg2 `r(obs_stat)' `r(randpval)' using, replace se tex noobs addstat(Observations, `n_obs', Mean Dep. Var., `summ', Randomization P-Value, `pvalue', , Right Window, `rw', Left Window, `lw') addtext(Polynomial, `pol', Kernel, uniform, Fuzzy RD, N)"
outreg2 using "Output/RandInfTable1_LogProductivity`pol'.tex", append se pvalue tex noobs addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate', Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
if `count'!=0{
outreg2 using "Output/RandInfTable1_LogProductivity`pol'.tex", append se pvalue tex noobs addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate',Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
local count = 1
}
*}
}
******************************
*** OUTCOME 2 - CASH CROPS ***
******************************
** SHARE LAND IN CASH CROPS:
set more off
local dep_var CashCrop_Share
foreach pols in `polynomial_levels' {
local count = 0
*foreach band in `bandwidth_choice' {
foreach kern in `kernel_choice' {
su `dep_var' // if norm_dist < `r(wr)' & norm_dist > `r(wl)' & `dep_var' !=. & norm_dist!=.
local summ = `r(mean)'
rdrandinf `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate seed(123)
* outreg results
*distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=.
local n_obs = `r(N)'
local inf_estimate = `r(obs_stat)'
local pvalue=`r(asy_pval)'
local rw = `r(wr)'
local lw = `r(wl)'
if ("`kern'"=="epan") {
local kern "epanechnikov"
}
rdrobust `dep_var' norm_dist, c(0) p(`pols') bwselect(`bandwidth_choice') kernel(`kern') vce(cluster `cluster_level')
if `count'==0 {
outreg2 using "Output/RandInfTable2_CropShare`pol'.tex", replace se tex noobs pvalue addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate',Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
if `count'!=0{
outreg2 using "Output/RandInfTable2_CropShare`pol'.tex", append se tex noobs pvalue addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate',Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
local count = 1
}
*}
}
********************************
*** OUTCOME 3 - STAPLE CROPS ***
********************************
** SHARE LAND IN STAPLE CROPS:
set more off
local dep_var StapleCrop_Share
foreach pols in `polynomial_levels' {
local count = 0
*foreach band in `bandwidth_choice' {
foreach kern in `kernel_choice' {
su `dep_var' // if norm_dist < `r(wr)' & norm_dist > `r(wl)' & `dep_var' !=. & norm_dist!=.
local summ = `r(mean)'
rdrandinf `dep_var' norm_dist, c(0) p(`pols') kernel(`kern') covariates($covariates) approximate seed(123)
* outreg results
*distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=.
local n_obs = `r(N)'
local inf_estimate = `r(obs_stat)'
local pvalue=`r(asy_pval)'
local rw = `r(wr)'
local lw = `r(wl)'
if ("`kern'"=="epan") {
local kern "epanechnikov"
}
rdrobust `dep_var' norm_dist, c(0) p(`pols') bwselect(`bandwidth_choice') kernel(`kern') vce(cluster `cluster_level')
outreg2 using "Output/RandInfTable2_CropShare`pol'.tex", append se tex noobs pvalue addtext(Polynomial, `pols', Kernel, "`kern'", Fuzzy RD, N) addstat(Estimate, `inf_estimate',Randomization P-Value, `pvalue', Observations, `n_obs', Mean Dep. Var., `summ', Right Window, `rw', Left Window, `lw')
}
*}
}
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