#install packages # install.packages('plyr') # install.packages('dplyr') # install.packages('tidyr') # install.packages('ggplot2') # install.packages('multiwayvcov') # install.packages('lmtest') # install.packages('stargazer') ###################################################################### rm(list=ls()) s = function(x){summary(factor(x))} Num = function(x){as.numeric(as.factor(x))} library(plyr);library(dplyr, warn.conflicts = FALSE) library(tidyr);library(ggplot2) suppressMessages(library(multiwayvcov, warn.conflicts = F)) suppressMessages(library(lmtest, warn.conflicts = F)) library(stargazer) #setwd() #set working directory dir.create(paste0(getwd(), '/Output/')) dir.create(paste0(getwd(), '/Output/Table_1-2/')) path0 = paste0(getwd(), '/Output/Table_1-2/', Sys.Date(),'/') #Directory for output files dir.create(path0) A = readRDS('4-20-20_HH-KNearest_DeID_demed.RDS') A = A[-which(A$Wave == 'Bangalore 2015'),] #6101 Aprime = A[A$A.A7_Area.Neighborhood %in% names(which(table(A$A.A7_Area.Neighborhood) >= 30)),]#DROP PLACES WITH <30 OBSERVATIONS Aprime = Aprime[which(!is.na(Aprime$A.A7_Area.Neighborhood)),] #drop NA neighborhoods Aprime = Aprime[which(Aprime$A.A7_Area.Neighborhood != 'Bangalore NA'),] A = Aprime ###################################################################### ############################################################################## A$Muslim = A$C.C6_Religion == 'Muslim' #Water A$HtH = A$J.J3_Source.of.water == 4 WaterReg = glm('HtH ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A, family = 'binomial') #Voter A$Voter = A$L.L8_Voter.ID == 1 VoterIDReg = glm('Voter ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A, family = 'binomial') #Ration Card A$Ration = A$L.L9_Ration.card == 1 RationCardReg = glm('Ration ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A, family = 'binomial') #Security A$Security = mapvalues(A$J.J24_Eviction, from = c(888, 999), to = c(NA, NA)) SecurityReg = lm('Security ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A) #Primary School A$L20 = mapvalues(A$L.L20_Primary.School, from = c(777, 888, 999), to = c(NA, NA, NA)) PrimSchReg = lm('-L20 ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A) #Switch scale so negative is LESS satisfied #Secondary school A$L21 = mapvalues(A$L.L21_Secondary.School, from = c(777, 888, 999), to = c(NA, NA, NA)) SecSchReg = lm('-L21 ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A) #Waste satisfaction A$L24 = mapvalues(A$L.L24_Waste.Disposal, from = c(777, 888, 999), to = c(NA, NA, NA)) WasteSatReg = lm('-L24 ~ AssetSum + Muslim + City + A.A7_Area.Neighborhood', data = A) ############################################################################## ############################################################################## #Make tables #Neighborhood services MuslimServices_1 = stargazer(WaterReg, VoterIDReg, RationCardReg, dep.var.labels.include = T, model.names = FALSE, digits = 2, omit = c("A.A7_Area.Neighborhood","City"), omit.labels = c("Neighborhood dummies?","City dummies?"), omit.stat = c("rsq","ll","ser","f"), order=c(2,1,3), covariate.labels = c('Muslim', 'Assets', 'Constant'), dep.var.labels = c("Water Connection","Voter ID","Ration Card"), title = 'Public Services by Religion', label = 'table:Muslim_Services_1') writeLines(MuslimServices_1,con = paste0(path0,'Muslim_Services_1.tex')) #NOTE: Columns must be renamed manually in LaTeX file to match version in paper #Replace this line: \\[-1.8ex] & \multicolumn{3}{c}{Water Connection} \\ #With this line: \\[-1.8ex] & {Water Connection} & {Voter ID} & {Ration Card} \\ MuslimServices_2 = stargazer(SecurityReg, PrimSchReg, SecSchReg, WasteSatReg, dep.var.labels.include = T, model.names = FALSE, digits = 2, omit = c("A.A7_Area.Neighborhood","City"), omit.labels = c("Neighborhood dummies?","City dummies?"), omit.stat = c("rsq","ll","ser","f"), order=c(2,1,3), covariate.labels = c('Muslim', 'Assets', 'Constant'), dep.var.labels = c("Tenure Security","Prim. Sch. Satis.","Sec. Sch. Satis.", "Waste Remov. Satis."), #Why does only the first one show up? title = 'Services Satisfaction by Religion', label = 'table:Muslim_Services_2') writeLines(MuslimServices_2,con = paste0(path0,'Muslim_Services_2.tex')) #NOTE: Columns must be renamed manually in LaTeX file to match version in paper #Replace this line: \\[-1.8ex] & \multicolumn{4}{c}{Tenure Security} \\ #With this line: \\[-1.8ex] & {Tenure Sec.} & {Prim. School} & {Sec. School} & {Waste Remov.} \\ ######################### ######################### #Hindu-Muslim support for same leader #Cited in theory section, p 7 of article #99 neighborhoods, 58 had responses for both Hindu and Muslim, 37 differ between religions A$L.L50_Neighborhood.Leader[which(A$L.L50_Neighborhood.Leader == '-999')] = NA #5 of these A$L.L50_Neighborhood.Leader = factor(A$L.L50_Neighborhood.Leader) L = A %>% group_by(A.A7_Area.Neighborhood) %>% summarize( HinLeaderName = names(sort(table(L.L50_Neighborhood.Leader[which(C.C6_Religion == 'Hindu')], useNA = 'no'),decreasing=T))[1], MusLeaderName = names(sort(table(L.L50_Neighborhood.Leader[which(C.C6_Religion == 'Muslim')], useNA = 'no'),decreasing=T))[1], nHin = sum(C.C6_Religion == 'Hindu', na.rm = T), nMus = sum(C.C6_Religion == 'Muslim', na.rm = T), nHinAns = sum(!is.na(L.L50_Neighborhood.Leader[which(C.C6_Religion == 'Hindu')])), nMusAns = sum(!is.na(L.L50_Neighborhood.Leader[which(C.C6_Religion == 'Muslim')])), nHinLeader = sort(table(L.L50_Neighborhood.Leader[which(C.C6_Religion == 'Hindu')], useNA = 'no'),decreasing=T)[1], nMusLeader = sort(table(L.L50_Neighborhood.Leader[which(C.C6_Religion == 'Muslim')], useNA = 'no'),decreasing=T)[1] ) L = L %>% data.frame() %>% mutate(Match = HinLeaderName == MusLeaderName, PropHin = nHin / (nHin + nMus), PropMus = nMus / (nHin + nMus)) #Neighborhoods where both Hindus and Muslims answer s(L$Match[which(L$nMusLeader > 0 & L$nHinLeader > 0)]) #false 37, true 21 37 / (37 + 21) # 0.64 37 + 21 #58