***************************************************** *** ESLR: LR Ag Outcomes - RD Analysis - Censo IV *** ***************************************************** capture log close clear set matsize 3000 set more off set scheme s2color ** Set Workspace ** cd /Users/`c(username)'/Dropbox/Research_ElSalvador_LandReform/Replication ** ssc install rdrobust; winsor2; outreg2; lpoly; cmogram; dm88_1; grqreg; gr0002_3; pdslasso; lassopack; univar; ietoolkit ********************* *** 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 RD Params *** ********************* ** Baseline: Will use local linear rd with MSE optimal bandwidth ** with ses clustered at propietor level. ** Will use rdrobust package: net install rdrobust, from(https://sites.google.com/site/rdpackages/rdrobust/stata) replace local polynomial_level 1 local bandwidth_choice "mserd" local kernel_choice "tri" local cluster_level "Expropretario_ISTA" ********************************************* *** OUTCOME 1 - AGRICULTURAL PRODUCTIVITY *** ********************************************* *Logs OF REVENUE: rdrobust ln_agprod_pricew_crops norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table4_LogProductivity_agg.tex", replace se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') addtext(Fuzzy RD, N) rdrobust ln_agprod_pricew_crops norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') fuzzy(reform sharpbw) * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table4_LogProductivity_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') addtext(Fuzzy RD, Y) * rdpower ln_agprod_pricew_crops norm_dist, c(0) tau(1) vce(cluster Expropretario_ISTA ) plot **** NET OF COSTS w/o Labor costs: rdrobust ln_agprod norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table4_LogProductivity_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') addtext(Fuzzy RD, N) rdrobust ln_agprod norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') fuzzy(reform sharpbw) * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table4_LogProductivity_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') addtext(Fuzzy RD, Y) **** TFP PRODUCTIVITY: rdrobust ln_tfp_geo norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table4_LogProductivity_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') addtext(Fuzzy RD, N) rdrobust ln_tfp_geo norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') fuzzy(reform sharpbw) * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table4_LogProductivity_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') addtext(Fuzzy RD, Y) ****************************** *** OUTCOME 2 - CASH CROPS *** ****************************** rdrobust CashCrop_Indicator norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' *outreg2 using "Output/Table2_CashCrops_agg.tex", replace se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust CashCrop_Share norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table2_CashCrops_agg.tex", replace se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') ** Sugar Cane: rdrobust SugarCane_Indicator norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table2_CashCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust SugarCane_Share norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table2_CashCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') * Note: small sample means cannot compute optimal bw. Setting BW manually at level in previous regression: rdrobust SugarCane_Yield norm_dist, c(0) p(`polynomial_level') h(`e(h_r)') b(`e(b_r)') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table2_CashCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') ** Coffee: rdrobust Coffee_Indicator norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. outreg2 using "Output/Table2_CashCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust Coffee_Share norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. outreg2 using "Output/Table2_CashCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust Coffee_Yield norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. outreg2 using "Output/Table2_CashCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') ******************************** *** OUTCOME 3 - STAPLE CROPS *** ******************************** rdrobust ConsCrop_Indicator norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' *outreg2 using "Output/Table3_ConsCrops_agg.tex", replace se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust StapleCrop_Share norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", replace se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') ** Maize: rdrobust Maize_Indicator norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * Check this. Strange since rd plot is so strong. * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust Maize_Share norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust Maize_Yield norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') ** Beans: rdrobust Beans_Indicator norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') // fuzzy(reform) * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') rdrobust Beans_Share norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') // fuzzy(reform) * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)') * Note: Following Cannot Compute Optimal BW Above: rdrobust Beans_Yield norm_dist, c(0) p(`polynomial_level') bwselect(`bandwidth_choice') kernel(`kernel_choice') vce(cluster `cluster_level') * Setting BW manually at level in previous regression: rdrobust Beans_Yield norm_dist, c(0) p(`polynomial_level') b(`e(h_r)') h(`e(b_r)') kernel(`kernel_choice') vce(cluster `cluster_level') * outreg results distinct `cluster_level' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' & `e(outcomevar)'!=. & `e(runningvar)'!=. local n_clust = `r(ndistinct)' su `e(outcomevar)' if norm_dist < `e(h_l)' & norm_dist > -1*`e(h_r)' outreg2 using "Output/Table3_ConsCrops_agg.tex", append se tex noobs addstat(Observations, `e(N_h_l)' + `e(N_h_r)', Clusters, `n_clust', Mean Dep. Var., `r(mean)', Bandwidth, `e(h_l)')