Replication of paper: Women As Policy Makers: Evidence From A Randomized Policy Experiment In India

Transcription

1 Replication of paper: Women As Policy Makers: Evidence From A Randomized Policy Experiment In India Matthieu Stigler October 3, 2013 Try to replicate paper Raghabendra Chattopadhyay & Esther Duo, (2004). "Women as Policy Makers: Evidence from a Randomized Policy Experiment in India," Econometrica, Econometric Society, vol. 72(5), pages , 09. Available here. Data is taken from this site (downloaded in tab format, just need the four womenpolicymakers_part*.tab, documentation and supplementary documentation will also prove useful ). Contents 1 Tables Table Table Table Data issues 2 3 Code Open packages and data Data Cleaning Tables Try to replicate Table 1: Try to replicate Table 2: Try to replicate Table 3: Informations on session Tables 1.1 Table 1 ˆ Variable used: womres and prsex. 1

2 Reserved Unreserved Total Female Percent ˆ Issue: not same number for percentage of women (maybe 7 in their data). 1.2 Table 2 Diculty to know whether variables are from the 161 pradhans or the 483 villages, few variables could be traced back. When looking at the 161, variables not found, or dierent numbers obtained. 1.3 Table 3 Reserved Unreserved Participation Complaint ˆ Variable used: vgswp and vwiss. ˆ Issue: dierent value for fraction of women in samsad. Not same standard values obtained with Moulton (not shown). 2 Data issues ˆ Inconsistency in village coding between womenpolicymakers_partc.tab and womenpolicymakers_partd.tab: village gpnum==47 has not same jlnum in each dataset, probably inverted ˆ Villae gpnum==7 & villnum==1 has NA for jlunm in womenpolicymakers_partd.tab, but not in womenpolicymakers_partc.tab. 3 Code 3.1 Open packages and data Open (and install before) some packages: library(plyr) library(car) 2

3 Read the data. This assumes you downloaded the *.tab, not *.dta, in the latter case, use library foreign, and function read.dta. user <- Sys.info()["user"] ## I need this trick to switch from laptop to desktop if (user == "mat") { pathdir <- "/home/mat/dropbox/" } else if (user == "stigler") { pathdir <- "C:/Users/stigler/Dropbox/" } pathmat <- paste(pathdir, "HEI/Coursera/Replicate/Chattopday, Duflo 2004/study_USBFNOMLAT/2. sep = "") wpm_1 <- read.csv(paste(pathmat, "womenpolicymakers_parta.tab", sep = ""), sep = "\t") wpm_2 <- read.csv(paste(pathmat, "womenpolicymakers_partb.tab", sep = ""), sep = "\t") wpm_3 <- read.csv(paste(pathmat, "womenpolicymakers_partc.tab", sep = ""), sep = "\t") wpm_4 <- read.csv(paste(pathmat, "womenpolicymakers_partd.tab", sep = ""), sep = "\t", fileencoding = "native.enc") # not used now: wpm_surv_a <- read.csv(paste(pathmat, # 'womenpolicymakers_resurveya.tab', sep=''), sep='\t') wpm_surv_b <- # read.csv(paste(pathmat, 'womenpolicymakers_resurveyd.tab', sep=''), # sep='\t') dim(wpm_3) ## [1] dim(wpm_4) ## [1] Data Cleaning Recode the wom reserved variable: ### recode variables: wpm_1$womres2 <- Recode(wpm_1$womres, "'1'='Reserved';'2'='Unreserved'") wpm_1$prsex2 <- Recode(wpm_1$prsex, "'1'='Male';'2'='Female'") Merge now the dataset 1 and 2: 3

4 ### Identify pre-test villages in 1-2: pre_test <- which(apply(wpm_1[, 3:7], 1, function(x) all(is.na(x)))) ## Merge 1-2 wpm_12 <- arrange(merge(wpm_1[-pre_test, ], wpm_2[-pre_test, ], by = c("gpnum", "gpnumst")), gpnum) Merge now the dataset 3 and 4: ## identify pre-test villages in 3-4 pre_test_vill <- which(apply(wpm_3[, 3:7], 1, function(x) all(is.na(x)))) pre_test_vill2 <- which(apply(wpm_4[, 3:7], 1, function(x) all(is.na(x)))) ## check got right identifyers all(unique(wpm_3[pre_test_vill, "gpnum"]) == pre_test) ## [1] TRUE all(pre_test_vill == pre_test_vill2) ## [1] TRUE ## remove pre-test villages wpm_3_notest <- wpm_3[-pre_test_vill, ] wpm_4_notest <- wpm_4[-pre_test_vill, ] ## Check we indeed removed: which(is.na(wpm_4[, c("gpnum", "villnum", "jlnum")]), arr.ind = TRUE) ## row col ## [1,] 10 3 ## [2,] 11 3 ## [3,] 12 3 ## [4,] 13 3 ## [5,] 14 3 ## [6,] 15 3 ## [7,] 19 3 ## [8,] 40 3 ## [9,] 41 3 ## [10,] 42 3 ## [11,] 43 3 ## [12,] 44 3 ## [13,] 45 3 ## [14,] ## [15,] ## [16,]

5 which(is.na(wpm_4_notest[, c("gpnum", "villnum", "jlnum")]), arr.ind = TRUE) # still a prob ## row col ## But to merge 3 and 4, some data problems to solve rst... ## Visualise mistake 1: subset(wpm_3, gpnum == 47, c("gpnum", "villnum", "jlnum")) ## gpnum villnum jlnum ## ## ## subset(wpm_4, gpnum == 47, c("gpnum", "villnum", "jlnum")) ## gpnum villnum jlnum ## ## ## ## Visualise mistake 2: subset(wpm_3, gpnum == 7 & villnum == 1, c("gpnum", "villnum", "jlnum")) ## gpnum villnum jlnum ## subset(wpm_4, gpnum == 7 & villnum == 1, c("gpnum", "villnum", "jlnum")) ## gpnum villnum jlnum ## NA ## Correct mistake 1 index_mis1 <- which(wpm_4_notest$gpnum == 47 & wpm_4_notest$villnum!= 1) wpm_4_notest[index_mis1, "jlnum"] <- wpm_3_notest[index_mis1, "jlnum"] ## Correct mistake 2 index_mis2 <- which(wpm_4_notest$gpnum == 7 & wpm_4_notest$villnum == 1) wpm_4_notest[index_mis2, "jlnum"] <- wpm_3_notest[index_mis2, "jlnum"] Now can nally merge 3 and 4: 5

6 ## Now finally can merge 3 and 4! wpm_34_t <- arrange(merge(wpm_3_notest, wpm_4_notest, by = c("gpnum", "villnum", "jlnum"), all = FALSE), gpnum) ### add womres (in 1-2) to wpm_34 wpm_34 <- merge(wpm_34_t, wpm_12[, c("gpnum", "womres", "womres2")], by = "gpnum", all.x = TRUE) 3.3 Tables Try to replicate Table 1: ## Table 1 tab_tot <- table(wpm_1[["womres2"]], dnn = list("villages")) table(wpm_1[, "womres2"], wpm_1[["prsex2"]], dnn = list("reserved", "Sex of Prashan")) ## Sex of Prashan ## Reserved Female Male ## Reserved 54 0 ## Unreserved 8 99 tab_dis <- table(wpm_1[["prsex2"]], wpm_1[, "womres2"], dnn = list("reserved", "Sex of Prashan")) tab_tex <- rbind(total = tab_tot, Female = tab_dis[1, ], Percent = round(100 * tab_dis[1, ]/tab_tot, 1)) tab_tex ## Reserved Unreserved ## Total ## Female ## Percent Try to replicate Table 2: #### Table 2: handpumps wpm_12$gtubbn ## [1] NA ## [18] NA NA 12 NA

7 ## [35] ## [52] NA 19 ## [69] 4 5 NA ## [86] ## [103] ## [120] NA ## [137] ## [154] # Is the variable just the mean? ddply(wpm_12,.(womres), summarise, handpumps = mean(gtubbn, na.rm = TRUE)) ## womres handpumps ## ## # Is the variable just the mean, removing NA and 999? ddply(wpm_12,.(womres), summarise, handpumps = mean(!is.na(gtubbn) & gtubbn!= 999, na.rm = TRUE)) ## womres handpumps ## ## #### Table 2: tap water Is the variable just the mean? ddply(wpm_12,.(womres), summarise, handpumps = mean(gtapn, na.rm = TRUE)) ## womres handpumps ## ## # Is the variable justthe number of obs higher than 0? ddply(wpm_12,.(womres), summarise, handpumps = mean(gtapn > 0, na.rm = TRUE)) ## womres handpumps ## ## ddply(wpm_12,.(womres), summarise, handpumps = mean(!is.na(gtapn) & gtapn!= 999, na.rm = TRUE)) ## womres handpumps ## ## ## table 2 primary school summary(wpm_12$gpsc) 7

8 ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## summary(wpm_12$gnopsc) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## ddply(wpm_12,.(womres), summarise, primaryschool = mean(gnopsc > 0, na.rm = TRUE)) ## womres primaryschool ## ## ## does not work very well Try to replicate Table 3: ##### Table 3: gran samsad participatio tab3 <- ddply(subset(wpm_34, villnum!= 1),.(womres2), summarise, Participation = round(mea na.rm = TRUE), 2), Complaint = round(mean(vwiss == 1, na.rm = TRUE), 2)) tab3ok <- t(tab3[, -1]) colnames(tab3ok) <- c("reserved", "Unreserved") reg_part_1 <- lm(vgswp ~ 1, data = wpm_34, subset = villnum!= 1 & womres == 1) reg_part_2 <- lm(vgswp ~ 1, data = wpm_34, subset = villnum!= 1 & womres == 2) reg_part_diff <- lm(vgswp ~ 1 + I(womres == 1), data = wpm_34, subset = villnum!= 1) coef(summary(reg_part_diff)) ## Estimate Std. Error t value Pr(> t ) ## (Intercept) e-15 ## I(womres == 1)TRUE e-02 ### woman complaint compute the means with the mean() function, by womres ddply(subset(wpm_34, villnum!= 1),.(womres2), summarise, Complaint = round(mean(vwiss == 1, na.rm = TRUE), 2)) ## different!! 8

9 ## womres2 Complaint ## 1 Reserved 0.20 ## 2 Unreserved 0.11 ## compute the means with the lm() function, (so subset data) reg_complaintw_1 <- lm(i(vwiss == 1) ~ 1, data = wpm_34, subset = villnum!= 1 & womres == 1) reg_complaintw_2 <- lm(i(vwiss == 1) ~ 1, data = wpm_34, subset = villnum!= 1 & womres == 2) ## Extract the standard error of the mean: round(coef(summary(reg_complaintw_1))[1, c("estimate", "Std. Error")], 2) ## Estimate Std. Error ## round(coef(summary(reg_complaintw_2))[1, c("estimate", "Std. Error")], 2) ## Estimate Std. Error ## ## compute the p-value of the diff: reg_complaintw_12 <- lm(i(vwiss == 2) ~ 1 + womres, data = wpm_34, subset = villnum!= 1) round(coef(summary(reg_complaintw_12))["womres", c("estimate", "Std. Error")], 2) ## Estimate Std. Error ## ## use Moulton, as in paper: source(paste(pathdir, ## 'Documents/stats/R/RcompAngrist/pkg/R/Moulton.R',sep='')) ## moulton(lm=reg_part_1, cluster=subset(wpm_34, villnum!=1 & womres==1 & ##!is.na(vgswp), 'gpnum', drop=true)) moulton(lm=reg_part_2, ## cluster=subset(wpm_34, villnum!=1 & womres==2 &!is.na(vgswp), 'gpnum', ## drop=true)) 9

10 3.4 Informations on session We like at the end to put some information on the R session (R version, version of packages, platform, etc...) sessioninfo() ## R version ( ) ## Platform: x86_64-w64-mingw32/x64 (64-bit) ## ## locale: ## [1] LC_COLLATE=French_Switzerland.1252 LC_CTYPE=French_Switzerland.1252 ## [3] LC_MONETARY=French_Switzerland.1252 LC_NUMERIC=C ## [5] LC_TIME=French_Switzerland.1252 ## ## attached base packages: ## [1] stats graphics grdevices utils datasets base ## ## other attached packages: ## [1] xtable_1.7-1 car_ plyr_1.8 knitr_1.4.1 ## ## loaded via a namespace (and not attached): ## [1] digest_0.6.3 evaluate_0.4.7 formatr_0.9 highr_0.2.1 ## [5] MASS_ nnet_7.3-7 stringr_0.6.2 tools_