add 27

27/paper.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36ea63a459ee4c4df540cbe455343dba765a05886e4cb8b7ea60fbb74800aaf8
+size 802268

27/replication_package/4-20-20_HH-KNearest_DeID_demed.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6c68be737680e952b7cb15a9906f58fa329091e3fcc79835bd01fab18d47208
+size 209454

27/replication_package/4-20-20_Network-KNearest_DeID_demed.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:128e222bd10684630b70f86a54c6a57160ca7dccefdcefc64fd8bf5a4543a0f5
+size 83793

27/replication_package/4-20-20_deid_nearestK.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06e21ef837882357b7cda548efee529d5031fd4b5d576fde9b8dacfa648577b1
+size 1756501

27/replication_package/4-21-20_1000x_sample_nearest.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37b2546534ebf83157b79536de8750ca2d6bc231036a6ff462d2c3d2aa552981
+size 2649724

27/replication_package/BBMP_ward_censusdata_2-12-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7fbd4a74991019b35dae8455fa2ac90b427a0088fd30949177f4a11043c37f2
+size 19670

27/replication_package/Balance-tables_histograms_script.R

@@ -0,0 +1,100 @@
+#Create balance tables and histograms for Appendix
+
+#package installation
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('tidyr')
+# install.packages('ggplot2')
+# install.packages('lmtest')
+# install.packages('multiwayvcov')
+# install.packages('stargazer')
+
+rm(list=ls())
+library(plyr);library(dplyr, warn.conflicts = F)
+library(tidyr)
+library(ggplot2)
+suppressMessages( library(lmtest) )
+suppressMessages( library(multiwayvcov) )
+suppressMessages(library(stargazer))
+
+s = function(x){summary(factor(x))}
+
+#setwd() #set working directory
+dir.create(paste0(getwd(), '/Output/'))
+dir.create(paste0(getwd(), '/Output/Balance-tables_histograms/'))
+path0 = paste0(getwd(), '/Output/Balance-tables_histograms/', Sys.Date(),'/') #Directory for output files
+dir.create(path0)
+
+A = readRDS('4-20-20_HH-KNearest_DeID_demed.RDS') #
+##############################################################################################################################
+#Balance table
+
+A = A[A$A.A7_Area.Neighborhood %in% names(which(table(A$A.A7_Area.Neighborhood) >= 30)),]#DROP PLACES WITH <30 OBSERVATIONS
+
+CandConjoint = A %>% filter(Wave %in% c('Bangalore 2016','Jai-Pat 2015'), is.na(L.Candidate_Question_1) == F)  #just the people in the analysis.
+CandConjoint$HiSeg = CandConjoint$Nearest10_OwnReligion == 10 
+CandConjoint$HiSeg_DeMed = CandConjoint$DeMedNearest10_OwnReligion >= 0
+CandConjoint$LoSeg_DeMed = CandConjoint$DeMedNearest10_OwnReligion < 0
+
+CandConjoint$LowCaste = CandConjoint$C.C8_Caste == 'SC/ST/RM'
+
+CandConjoint$Muslim = CandConjoint$C.C6_Religion == 'Muslim'
+
+CandConjoint$Male = CandConjoint$C.C5_Gender == 1
+
+CandConjoint$Migrant = CandConjoint$C.C14_Permanent.Residence.of.Jaipur. == 0
+
+CandConjoint$Jaipur = CandConjoint$City == 'Jaipur'
+CandConjoint$Patna = CandConjoint$City == 'Patna'
+
+CandConjoint$C.C4_Age = as.numeric(as.character(CandConjoint$C.C4_Age))
+
+bal.vars = c('AssetSum','LowCaste','Muslim','Male','C.C4_Age', 'Migrant','Jaipur','Patna')
+bal.table = data.frame('Segregated' = apply(CandConjoint[CandConjoint$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
+                       'Integrated' = apply(CandConjoint[!CandConjoint$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
+                       'p' = apply(CandConjoint[,bal.vars],2,function(x){t.test(x[CandConjoint$HiSeg],
+                                                                                x[!CandConjoint$HiSeg])[['p.value']]}) ) %>%
+  round(2) 
+bal.table = rbind(bal.table, data.frame('Segregated' = sum(CandConjoint$HiSeg == 1, na.rm = T), 
+                                        'Integrated' = sum(CandConjoint$HiSeg == 0, na.rm = T), 'p' = ''))
+row.names(bal.table) = c('Asset Index','Low Caste','Muslim','Male','Age','Migrant','Jaipur','Patna','n')
+
+out = stargazer(bal.table, summary = F, digits = 2,
+                title = 'Balance Table, Segregated vs. Integrated',
+                label = 'table:Nearest10Religion_Balance')
+writeLines(out,con = paste0(path0,'Nearest10Religion_Balance.tex'));rm(out, bal.vars,bal.table)
+
+#De-medianed
+bal.vars = c('AssetSum','LowCaste','Muslim','Male','C.C4_Age', 'Migrant','Jaipur','Patna')
+bal.table = data.frame('Segregated' = apply(CandConjoint[CandConjoint$HiSeg_DeMed,bal.vars],2,function(x){mean(x,na.rm=T)}),
+                       'Integrated' = apply(CandConjoint[CandConjoint$LoSeg_DeMed,bal.vars],2,function(x){mean(x,na.rm=T)}),
+                       'p' = apply(CandConjoint[,bal.vars],2,function(x){t.test(x[CandConjoint$HiSeg_DeMed],
+                                                                                x[CandConjoint$LoSeg_DeMed])[['p.value']]}) ) %>%
+  round(2) 
+bal.table = rbind(bal.table, data.frame('Segregated' = sum(CandConjoint$HiSeg_DeMed == 1, na.rm = T), 
+                                        'Integrated' = sum(CandConjoint$LoSeg_DeMed == 1, na.rm = T), 'p' = ''))
+row.names(bal.table) = c('Asset Index','Low Caste','Muslim','Male','Age','Migrant','Jaipur','Patna','n')
+bal.table
+
+out = stargazer(bal.table, summary = F, digits = 2,
+                title = 'Balance Table, Segregated vs. Integrated (De-Medianed)',
+                label = 'table:Nearest10Religion_Balance_DeMed')
+writeLines(out,con = paste0(path0,'DeMed_Nearest10Religion_Balance.tex'));rm(out, bal.vars,bal.table)
+
+########################################################################################
+#Histograms
+
+#Nearest 10 religion, full sample
+ggplot(data=CandConjoint, aes(CandConjoint$Nearest10_OwnReligion)) + geom_bar(aes(y = (..count..)/sum(..count..))) + theme_minimal() +
+  labs(x = '10-nearest same religion', y = 'Proportion') + theme(axis.title=element_text(size=14),
+                                                                 axis.text = element_text(size = 12)) +
+  ggtitle('10-nearest same religion, Full sample') + theme(plot.title = element_text(hjust = 0.5, size = 16))
+ggsave(filename = paste0(path0,'/Nearest10SameReligion.jpg'), height = 150, width = 150, units = 'mm')
+
+#De-Medianned Nearest 10 religion, full sample
+ggplot(data=CandConjoint, aes(CandConjoint$DeMedNearest10_OwnReligion)) + geom_bar(aes(y = (..count..)/sum(..count..))) + theme_minimal() +
+  labs(x = '10-nearest same religion (de-medianed)', y = 'Proportion') + theme(axis.title=element_text(size=14),
+                                                                 axis.text = element_text(size = 12)) +
+  ggtitle('De-Medianed 10-nearest same religion,\n Full sample') + theme(plot.title = element_text(hjust = 0.5, size = 16))
+ggsave(filename = paste0(path0,'/DeMedNearest10SameReligion.jpg'), height = 150, width = 150, units = 'mm')
+

27/replication_package/Bangalore_District_censusdata_2-25-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d80ad2b97f953a39c8e6098a415378b7f8516afb3e44e14ed9624e897e97a6c1
+size 942

27/replication_package/Bihar_District_censusdata_2-25-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:550499afae38697114545e0198444b2b6f7d293a2f517ebf323e8a0db62464d7
+size 3255

27/replication_package/Bihar_district_censusdata_2-22-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75e41ff2628989212cf1a0b11044929b95d1537fd5805a8042a701de18be7f3e
+size 5381

27/replication_package/Codebook.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30f58182e9aba29da74e3991a87df64da230c81bd09e1db90817c3aaac55793f
+size 172554

27/replication_package/Figure3_4-28-20.R

@@ -0,0 +1,64 @@
+#replicate figure 3
+rm(list=ls())
+
+#SET WORKING DIRECTORY
+
+#devtools::install_github("dkahle/ggmap")
+#devtools::install_github("hadley/ggplot2")
+# install.packages('ggrepel')
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('RColorBrewer')
+# install.packages('reshape2')
+
+library(ggplot2)
+library(ggrepel)
+library(plyr)
+library(dplyr)
+library(ggmap)
+library(RColorBrewer) 
+library(reshape2)
+
+s = function(x){summary(factor(x))}
+
+#Read household survey data for mapping
+#NOTE: Geolocation data (lat/long) has been offset by a random number to avoid compromising respondent anonymity
+C = readRDS('map_dataset_4-28-20.RDS')
+#Read map file
+#NOTE: Geolocation data (lat/long) has been offset by a random number to avoid compromising respondent anonymity
+map_19_cluster_offset = readRDS('map_out_4-28-20.RDS')
+
+C = C %>% rename(Religion = C.C6_Religion.x)
+
+#Create categories from KNN values
+C$Cat = NA
+C$Cat[which(C$Nearest10_OwnReligion <= 5)] = 0
+C$Cat[which(C$Nearest10_OwnReligion > 5 & C$Nearest10_OwnReligion <= 8)] = 1
+C$Cat[which(C$Nearest10_OwnReligion > 8)] = 2
+C$Cat = factor(C$Cat)
+
+#Switch order of data points for more attractive plot
+D = C[seq(dim(C)[1],1),] #
+
+#Set limits of scale bar
+sc_x_lo = min(D$pseudo_lon) + (max(D$pseudo_lon) - min(D$pseudo_lon)) * (1/10)
+sc_y = min(D$pseudo_lat) + (max(D$pseudo_lat) - min(D$pseudo_lat)) * (9.34/10)
+
+#Create map
+#Note again that lat-long values are PSEUDO, ie they have been offset by a random value to preserve respondent anonymity
+ggmap(map_19_cluster_offset) + geom_point(data = D, aes(x=pseudo_lon, y=pseudo_lat, shape = Religion, fill=Cat), 
+                                          size=6.7, alpha=1, color = 'white') +
+  geom_segment(aes(x=sc_x_lo, xend=sc_x_lo + 0.0005/1.0835,y=sc_y,yend=sc_y),color='white', size = 1.05) + #scale bar +
+  geom_text(aes(x=sc_x_lo + 0.00065,y=sc_y-0.0000),label='50 m',color='white', size = 6.5) + #scale bar label
+  theme(axis.line=element_blank(),axis.text.x=element_blank(), #suppress axes
+        axis.text.y=element_blank(),axis.ticks=element_blank(),
+        axis.title.x=element_blank(),
+        axis.title.y=element_blank(),
+        legend.title = element_text(size = 14),
+        legend.text = element_text(size = 12)) +
+  scale_fill_grey('10-nearest-\nneighbors', labels = c('(0,5]','(5,8]','(8,10]')) +
+  scale_shape_manual('Religion', values = c(24, 21), labels = c('Hindu','Muslim')) +
+  guides(fill=guide_legend(override.aes=list(shape=21)),
+         shape=guide_legend(override.aes=list(fill='black')))
+ggsave(filename = 'figure3.png',
+  device = 'png', width = 6, height = 4, units = 'in', dpi = 'print')

27/replication_package/Figure4_script.R

@@ -0,0 +1,464 @@
+#figure 4: Coethnic voting preferences in candidate conjoint experiment, 
+#comparing high- to low-exposure respondents. 
+#This also includes candidate-comparison results for Appendix
+
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('tidyr')
+# install.packages('ggplot2')
+# install.packages('multiwayvcov')
+# install.packages('lmtest')
+# install.packages('stargazer')
+
+rm(list=ls())
+#setwd() #set working directory
+dir.create(paste0(getwd(), '/Output/'))
+dir.create(paste0(getwd(), '/Output/Figure_4/'))
+path0 = paste0(getwd(), '/Output/Figure_4/', Sys.Date(),'/') #Directory for output files
+dir.create(path0)
+
+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))
+suppressMessages(library(stargazer))
+
+A = readRDS('4-20-20_HH-KNearest_DeID_demed.RDS')
+
+#Data cleaning
+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'),]
+
+Q = Aprime
+
+###################################################################################################
+#Conjoint analysis
+#Rename variables
+Q = Q %>% dplyr::rename(Q1A1 = L.Candidate_Random_A1_Candidate.Preferance, #first question, candidate 1, characteristic A
+                        Q1A2 = L.Candidate_Random_A2_Candidate.Preferance, #first question, candidate 2, characteristic A
+                        Q1B1 = L.Candidate_Random_B1_Candidate.Preferance, #first question, candidate 1, characteristic B
+                        Q1B2 = L.Candidate_Random_B2_Candidate.Preferance, #first question, candidate 2, characteristic B
+                        Q2A1 = L.Candidate_Random_A3, #second question, candidate 1, characteristic A
+                        Q2A2 = L.Candidate_Random_A4, #second question, candidate 2, characteristic A
+                        Q2B1 = L.Candidate_Random_B3, #second question, candidate 1, characteristic B
+                        Q2B2 = L.Candidate_Random_B4, #second question, candidate 2, characteristic B
+                        Q3A1 = L.Candidate_Random_A5, #third question, candidate 1, characteristic A
+                        Q3A2 = L.Candidate_Random_A6, #third question, candidate 2, characteristic A
+                        Q3B1 = L.Candidate_Random_B5, #third question, candidate 1, characteristic B
+                        Q3B2 = L.Candidate_Random_B6, #third question, candidate 2, characteristic B
+                        Q1 = L.Candidate_Question_1, #first question, choose candidate 1 or 2
+                        Q2 = L.Candidate_Question_2, #first question, choose candidate 1 or 2
+                        Q3 = L.Candidate_Question_3 #first question, choose candidate 1 or 2
+) 
+
+#rearrange so one row is one conjoint observation. 3x as many rows as A
+#new variables: A1, B1 are two traits for candidate 1; similar for 2; and y is responent's choice between candidates
+B = Q %>% unite('Q1', matches('Q1')) %>% unite('Q2', matches('Q2')) %>% unite('Q3', matches('Q3')) %>%
+  gather(Question, b, starts_with('Q')) %>% arrange(X) %>% separate( 'b', c('A1','A2','B1','B2','y') )
+
+B = B %>% filter(! (A1 == A2 & B1 == B2)) #drop observations where candidates have same profile
+
+#make new data frame wheer each row is one PROFILE, ie each question becomes two rows (one for each candidate)
+#New variables: A1, A2 are combined as A: trait A for either candidate
+C = B %>% unite('AB1',c(A1,B1)) %>% unite('AB2',c(A2,B2)) %>% gather(Neighbor, AB, c(AB1, AB2)) %>%
+  arrange(X) %>% separate('AB', c('A','B')) %>% 
+  mutate(Neighbor = as.numeric(mapvalues(Neighbor, from = c('AB1', 'AB2'), to = c(1,2))))
+
+#function to make dummies for trait levels
+ModFn = function(x,f){
+  data.frame(x, model.matrix(as.formula(f), data=x)) }
+
+C = C %>% ModFn('~ A - 1') %>% ModFn('~ B - 1') #function to make dummies for trait levels
+C = C %>% mutate(Y = y == Neighbor) #1 when that candidate is picked
+C = C %>% dplyr::rename(A_0 = A0, #rename variables to be consistent with earlier version of code
+                        A_1 = A1,
+                        A_2 = A2,
+                        A_3 = A3,
+                        B_0 = B0,
+                        B_1 = B1,
+                        B_2 = B2,
+                        B_3 = B3,
+                        B_4 = B4)
+
+B = C; rm(C) #rename
+###########################################################################################
+
+###########################################################################################
+#Setup for subgroup analysis
+
+#regression formula
+form1 = as.formula(paste0('Y ~ ',
+                          paste(strsplit('A_0 A_1 A_2 A_3 B_0 B_1 B_2 B_3 B_4',
+                                         split = ' ')[[1]], collapse = ' + ' )))
+
+#function to make data frame from regression results
+DF_C = function(l_m, id){
+  l_m_pl = data.frame(Parameter = rownames(l_m[-1,])) %>% #-1 drops intercept
+    mutate(Coef = l_m[-1,1]) %>%
+    mutate(Lo = Coef - 1.96 * l_m[-1,2]) %>%
+    mutate(Hi = Coef + 1.96 * l_m[-1,2]) %>%
+    mutate(ID = id) %>%
+    rbind(data.frame(Parameter = c('A_3','B_4'),
+                     Coef = c(0,0), Lo = c(0,0), Hi = c(0,0), ID = c(id, id)) ) %>%
+    mutate(Parameter = as.character(Parameter)) %>%
+    arrange(Parameter)
+}
+###########################################################################################
+ 
+###########################################################################################
+#Make function to extract p- and z-values for each choice of k
+
+calc_p_relig = function(k, Dat){
+  var <- paste0('Nearest',k,'_OwnReligion')
+  if(median(Dat[,var],na.rm=T)==k){ #split the sample at the median value of KNN (or at K - 1, if the median is K)
+    var_break <- k-1
+  }else{var_break = floor(median(Dat[,var],na.rm=T))}
+  dat_lo = Dat[ which(Dat[,var] <= var_break ) ,] # low knn group
+  dat_hi = Dat[ which(Dat[,var] > var_break ) ,]  #high kn group
+  if(nrow(dat_lo) == 0 | nrow(dat_hi) == 0){p_a2 = NA #set p-value to NA if there are no respondents in high- or low-exposure group
+  }else{
+  lm_lo = lm(form1, data = dat_lo ) #run regression on lo-knn group
+  lm_hi = lm(form1, data = dat_hi ) #run regression on hi-knn group
+  lm_clus_lo = coeftest(lm_lo, cluster.vcov(lm_lo, dat_lo[,c('X','A.A7_Area.Neighborhood')])) #calculate cluster-robust std errors
+  lm_clus_hi = coeftest(lm_hi, cluster.vcov(lm_hi, dat_hi[,c('X','A.A7_Area.Neighborhood')])) #calculate cluster-robust std errors
+  #z-test for difference in coefficients between lo- and hi-exposure groups
+  z_a2 = (lm_clus_lo['A_2','Estimate'] - lm_clus_hi['A_2','Estimate']) / sqrt(lm_clus_lo['A_2','Std. Error']^2 + lm_clus_hi['A_2','Std. Error']^2) 
+  #calculate p-value, based on outcome of z-test above
+  p_a2 = 2 * pnorm( abs(z_a2), mean = 0, sd = 1, lower.tail = F) 
+  coef_lo = lm_clus_lo['A_2','Estimate']; coef_hi = lm_clus_hi['A_2','Estimate'] #extract coefficients for inclusion in output data frame
+  sd_lo = lm_clus_lo['A_2','Std. Error']; sd_hi = lm_clus_hi['A_2','Std. Error'] #extract se's for inclusion in output data frame
+  #output data frame with coefficients, std errors, and p-value for difference in coefficients between hi- and lo-exposure groups
+  return(data.frame(coef_lo, coef_hi, sd_lo, sd_hi, p_a2)) } 
+  }
+
+#Calculate results for k in 1:30
+relig_results = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B)) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+         )
+
+#Create data frame for plotting
+rrc = relig_results[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#################################################################
+#Figure 4: main result
+ggplot(data = rrc, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+
+ggsave(filename = paste0(path0, 'Fig_4_k-z_coefficients.png'), height = 150, width = 150, units = 'mm')
+
+#################################################################
+
+#################################################################
+#Main result in table form
+#TABLE A4
+relig_results_tab = relig_results %>% apply(2, function(x) as.numeric(x)) %>% data.frame() %>%
+  select(k, coef_lo, sd_lo, coef_hi, sd_hi, p_a2) %>% 
+  rename(Coef_LowSeg = coef_lo, SD_LowSeg = sd_lo, Coef_HiSeg = coef_hi, SD_HiSeg = sd_hi, p = p_a2) %>% 
+  round(3)
+out = stargazer(relig_results_tab, summary = F, rownames = F,
+                title = 'Results for co-ethnicity attribute in candidate experiment compared between
+                high- and low-segregation subsamples, based on religious segregation.',
+                label = 'table:ReligResults')
+writeLines(out,con = paste0(path0,'ReligResults.tex'));rm(out, relig_results_tab)
+
+#################################################################################################################################################
+
+#################################################################################################################################################
+#De-medianed version
+
+zpk_relig = function(data, Split, k){ # nearest k same religion
+  var = paste0('Nearest',k,'_OwnReligion')
+  VarLo = Split# - 1 
+  VarHi = Split# - 1
+  lm_lo = lm( form1, data = data[which(data[,var] < VarLo),] ) 
+  lm_hi = lm( form1, data = data[which(data[,var] >= VarHi),] )
+  lm_lo_sum = coeftest(lm_lo, cluster.vcov(lm_lo, data[which(data[,var] < VarLo),c('X','A.A7_Area.Neighborhood')] ) )
+  lm_hi_sum = coeftest(lm_hi, cluster.vcov(lm_hi, data[which(data[,var] >= VarHi),c('X','A.A7_Area.Neighborhood')] ) )
+  diff = lm_lo_sum['A_2','Estimate'] - lm_hi_sum['A_2','Estimate']
+  z = (lm_lo_sum['A_2','Estimate'] - lm_hi_sum['A_2','Estimate']) / sqrt(lm_lo_sum['A_2','Std. Error']^2 + lm_hi_sum['A_2','Std. Error']^2)
+  p = (2*pnorm(abs(z), mean = 0, sd = 1, lower.tail = FALSE))
+  return(data.frame(diff = diff,z=z,p=p,split=Split, k = k))
+}
+zpk_demed_relig = function(data, Split, k){ # 
+  var = paste0('DeMedNearest',k,'_OwnReligion')
+  VarLo = Split
+  VarHi = Split
+  lm_lo = lm( form1, data = data[which(data[,var] < VarLo),] ) 
+  lm_hi = lm( form1, data = data[which(data[,var] >= VarHi),] ) #
+  lm_lo_sum = coeftest(lm_lo, cluster.vcov(lm_lo, data[which(data[,var] < VarLo),c('X','A.A7_Area.Neighborhood')] ) )
+  lm_hi_sum = coeftest(lm_hi, cluster.vcov(lm_hi, data[which(data[,var] >= VarHi),c('X','A.A7_Area.Neighborhood')] ) ) #
+  diff = lm_lo_sum['A_2','Estimate'] - lm_hi_sum['A_2','Estimate']
+  z = (lm_lo_sum['A_2','Estimate'] - lm_hi_sum['A_2','Estimate']) / sqrt(lm_lo_sum['A_2','Std. Error']^2 + lm_hi_sum['A_2','Std. Error']^2)
+  p = (2*pnorm(abs(z), mean = 0, sd = 1, lower.tail = FALSE))
+  return(data.frame(diff = diff,z=z,p=p,split=Split, k = k))
+}
+
+varlist = as.list(seq(5,30,5))
+NR_out = ldply(varlist, function(x) zpk_relig(data = B %>% filter(Wave %in% c( 'Bangalore 2016','Jai-Pat 2015') ),
+                                              Split = x,
+                                              k = as.character(x)) )
+
+NR_out_demed = ldply(varlist, function(x) zpk_demed_relig(data = B %>% filter(Wave %in% c( 'Bangalore 2016','Jai-Pat 2015') ),
+                                                          Split = 0,
+                                                          k = as.character(x)) )
+
+NR_out$se =  NR_out$diff / NR_out$z
+NR_out$lo = NR_out$diff - 1.96*NR_out$se
+NR_out$hi = NR_out$diff + 1.96*NR_out$se
+NR_out$id = 'k-Nearest Neighbors'
+
+NR_out_demed$se =  NR_out_demed$diff / NR_out_demed$z
+NR_out_demed$lo = NR_out_demed$diff - 1.96*NR_out_demed$se
+NR_out_demed$hi = NR_out_demed$diff + 1.96*NR_out_demed$se
+NR_out_demed$id = 'De-Medianed k-Nearest Neighbors'
+
+out = rbind(NR_out, NR_out_demed)
+
+#FIGURE A4
+ggplot(out, group = id, aes(x=k)) +
+  geom_point(aes(y = diff, color = id), position=position_dodge( width = 0.5 )) +
+  geom_errorbar(aes(ymin = lo, ymax = hi, group = id), position=position_dodge( width = 0.5 )) +
+  labs(x = 'k', y = 'Diff. of Coefficients (Lo - Hi Seg)') +
+  scale_color_discrete(name='Metric', labels=c('De-Med. K-Nearest', 'K-Nearest')) +
+  theme_minimal() + theme(text=element_text(size=16)) + theme(legend.title.align=0.5)
+ggsave(filename = paste0(path0, 'demed-k-z-full.png'), height = 150, width = 150, units = 'mm')
+#################################################################################################################################################
+
+#################################################################################################################################################
+#Plots for non-balanced attributes
+
+relig_results_hin = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C6_Religion == 'Hindu'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_hin = 
+  relig_results_hin[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+#FIGURE A6
+ggplot(data = rrc_hin, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + #
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('Nearest-k Own Religion (Hindu)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_hindu.png'), height = 150, width = 150, units = 'mm')
+
+relig_results_mus = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C6_Religion == 'Muslim'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+rrc_mus = 
+  relig_results_mus[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+#FIGURE A7
+ggplot(data = rrc_mus, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('Nearest-k Own Religion (Muslim)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_muslim.png'), height = 150, width = 150, units = 'mm')
+
+relig_results_hiassets = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$AssetSum >= 10),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+rrc_hiassets = 
+  relig_results_hiassets[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+#FIGURE A8
+ggplot(data = rrc_hiassets, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('Nearest-k Own Religion (Hi Assets)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_hiassets.png'), height = 150, width = 150, units = 'mm')
+
+relig_results_hicaste = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C8_Caste == 'General/BC/OBC'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+rrc_hicaste = 
+  relig_results_hicaste[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+ggplot(data = rrc_hicaste, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('Nearest-k Own Religion (Hi Caste)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_hicaste.png'), height = 150, width = 150, units = 'mm')
+
+relig_results_jaipur = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$City == 'Jaipur'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+rrc_jaipur = 
+  relig_results_jaipur[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+ggplot(data = rrc_jaipur, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('Nearest-k Own Religion (Jaipur)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_jaipur.png'), height = 150, width = 150, units = 'mm')
+
+relig_results_patna = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$City == 'Patna'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+rrc_patna = 
+  relig_results_patna[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+ggplot(data = rrc_patna, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('Nearest-k Own Religion (Patna)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_patna.png'), height = 150, width = 150, units = 'mm')
+
+############################################################################################

27/replication_package/Figure5_script.R

@@ -0,0 +1,485 @@
+#Figure 5
+#Coethnic voting preferences in candidate conjoint experiment,
+#comparing high- to low-exposure respondents in network dataset. 
+
+#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())
+#setwd() #set working directory
+dir.create(paste0(getwd(), '/Output/'))
+dir.create(paste0(getwd(), '/Output/Figure_5/'))
+path0 = paste0(getwd(), '/Output/Figure_5/', Sys.Date(),'/') #Directory for output files
+dir.create(path0)
+
+library(plyr);library(dplyr, warn.conflicts = FALSE)
+library(tidyr);library(ggplot2)
+suppressMessages(library(multiwayvcov, warn.conflicts = F))
+suppressMessages(library(lmtest, warn.conflicts = F))
+suppressMessages(library(stargazer))
+
+s = function(x){summary(factor(x))}
+
+A = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
+########################################################################################################################
+#Balance tables
+
+A$HiSeg = A$Nearest10_SameRel == 10 
+A$HiSeg_DeMed = A$DeMedNearest10_SameRel >= 0
+A$LoSeg_DeMed = A$DeMedNearest10_SameRel < 0
+
+A$LowCaste = A$C.C8_Caste == 'SC/ST'
+
+A$Muslim = A$C.C6 == 'Muslim'
+
+A$Male = A$C.C5_Gender == 'M'
+
+A$Migrant = A$C.C14_Live.in.Jaipur. == 0
+
+A$Jaipur = A$City == 'Jaipur'
+A$Patna = A$City == 'Patna'
+
+A$C.C4_Age = as.numeric(as.character(A$C.C4_Age))
+
+A$Income1 = mapvalues(A$F.F1_Monthly.Income, from = c(-888, 0, 888, 999), to = c(NA, NA, NA, NA)) / 1000
+A$Income2 = A$Income1 > 10000 
+
+#Non-de-medianed
+bal.vars = c('Income1','LowCaste','Muslim','Male','C.C4_Age', 'Migrant','Jaipur')
+bal.table = data.frame('Segregated' = apply(A[A$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
+                       'Integrated' = apply(A[!A$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
+                       'p' = apply(A[,bal.vars],2,function(x){t.test(x[A$HiSeg],
+                                                                     x[!A$HiSeg])[['p.value']]}) ) %>%
+  round(2) 
+bal.table = rbind(bal.table, data.frame('Segregated' = sum(A$HiSeg == 1, na.rm = T), 
+                                        'Integrated' = sum(A$HiSeg == 0, na.rm = T), 'p' = ''))
+row.names(bal.table) = c('Income (k INR/mo.)','Low Caste','Muslim','Male','Age','Migrant','Jaipur','n')
+bal.table
+
+out = stargazer(bal.table, summary = F, digits = 2,
+                title = 'Balance Table, Segregated vs. Integrated',
+                label = 'table:Nearest10Religion_Balance')
+writeLines(out,con = paste0(path0,'Network_Nearest10Religion_Balance.tex'));rm(out, bal.vars,bal.table)
+########################################################################################################################
+#Begin conjoint analysis
+
+Q = A
+
+#Rename variables
+
+Q = Q %>% dplyr::rename(Q1A1 = I.Neta_Random_A1, #first question, candidate 1, characteristic A
+                        Q1A2 = I.Neta_Random_A2, #first question, candidate 2, characteristic A
+                        Q1B1 = I.Neta_Random_B1, #first question, candidate 1, characteristic B
+                        Q1B2 = I.Neta_Random_B2, #first question, candidate 2, characteristic B
+                        Q2A1 = I.Neta_Random_A3, #second question, candidate 1, characteristic A
+                        Q2A2 = I.Neta_Random_A4, #second question, candidate 2, characteristic A
+                        Q2B1 = I.Neta_Random_B3, #second question, candidate 1, characteristic B
+                        Q2B2 = I.Neta_Random_B4, #second question, candidate 2, characteristic B
+                        Q3A1 = I.Neta_Random_A5, #third question, candidate 1, characteristic A
+                        Q3A2 = I.Neta_Random_A6, #third question, candidate 2, characteristic A
+                        Q3B1 = I.Neta_Random_B5, #third question, candidate 1, characteristic B
+                        Q3B2 = I.Neta_Random_B6, #third question, candidate 2, characteristic B
+                        Q1 = I.Neta_Question_1, #first question, choose candidate 1 or 2
+                        Q2 = I.Neta_Question_2, #first question, choose candidate 1 or 2
+                        Q3 = I.Neta_Question_3 #first question, choose candidate 1 or 2
+) 
+
+#rearrange so one row is one conjoint observation. 3x as many rows as A
+#new variables: A1, B1 are two traits for candidate 1; similar for 2; and y is responent's choice between candidates
+B = Q %>% unite('Q1', matches('Q1')) %>% unite('Q2', matches('Q2')) %>% unite('Q3', matches('Q3')) %>%
+  gather(Question, b, starts_with('Q')) %>% arrange(X) %>% separate( 'b', c('A1','A2','B1','B2','y') )
+
+B = B %>% filter(! (A1 == A2 & B1 == B2)) #drop observations where candidates have same profile
+
+#make new data frame wheer each row is one PROFILE, ie each question becomes two rows (one for each candidate)
+#New variables: A1, A2 are combined as A: trait A for either candidate
+C = B %>% unite('AB1',c(A1,B1)) %>% unite('AB2',c(A2,B2)) %>% gather(Neighbor, AB, c(AB1, AB2)) %>%
+  arrange(X) %>% separate('AB', c('A','B')) %>% 
+  mutate(Neighbor = as.numeric(mapvalues(Neighbor, from = c('AB1', 'AB2'), to = c(1,2))))
+
+
+#function to make dummies for trait levels
+ModFn = function(x,f){
+  data.frame(x, model.matrix(as.formula(f), data=x)) }
+
+C = C %>% ModFn('~ A - 1') %>% ModFn('~ B - 1') #function to make dummies for trait levels
+C = C %>% mutate(Y = y == Neighbor) #1 when that candidate is picked
+C = C %>% dplyr::rename(A_0 = A0, #reanme variables to be consistent with earlier version of code
+                        A_1 = A1,
+                        A_2 = A2,
+                        A_3 = A3,
+                        B_0 = B0,
+                        B_1 = B1,
+                        B_2 = B2,
+                        B_3 = B3,
+                        B_4 = B4)
+
+B = C; rm(C) #rename to be consistent with earlier version
+###########################################################################################
+
+###########################################################################################
+#SUBGROUP ANALYSIS: do analysis and make plots
+#Subgroups: Nearest10_SameRel; Caste
+
+form1 = as.formula(paste0('Y ~ ',
+                          paste(strsplit('A_0 A_1 A_3 B_0 B_1 B_2 B_3 B_4',
+                                         split = ' ')[[1]], collapse = ' + ' )))
+
+
+#function to make data frame from regression results
+DF_C = function(l_m, id){
+  l_m_pl = data.frame(Parameter = rownames(l_m[-1,])) %>% #-1 drops intercept
+    mutate(Coef = l_m[-1,1]) %>%
+    mutate(Lo = Coef - 1.96 * l_m[-1,2]) %>%
+    mutate(Hi = Coef + 1.96 * l_m[-1,2]) %>%
+    mutate(ID = id) %>%
+    rbind(data.frame(Parameter = c('A_2','B_4'), #6-22-18
+                     Coef = c(0,0), Lo = c(0,0), Hi = c(0,0), ID = c(id, id)) ) %>%
+    mutate(Parameter = as.character(Parameter)) %>%
+    arrange(Parameter)
+}
+                                          
+#Make functions to extract p and z as a function of k
+calc_p_relig = function(k, Dat){
+  var <- paste0('Nearest',k,'_SameRel')
+  if(median(Dat[,var],na.rm=T)==k){
+    var_break <- k-1
+  }else{var_break = floor(median(Dat[,var],na.rm=T))}
+  dat_lo = Dat[ which(Dat[,var] <= var_break ) ,]
+  dat_hi = Dat[ which(Dat[,var] > var_break ) ,]
+  if(nrow(dat_lo) == 0 | nrow(dat_hi) == 0){p_a2 = NA
+  }else{
+    lm_lo = lm(form1, data = dat_lo )
+    lm_hi = lm(form1, data = dat_hi )
+    lm_clus_lo = coeftest(lm_lo, cluster.vcov(lm_lo, dat_lo[,c('X','A.A7')]))
+    lm_clus_hi = coeftest(lm_hi, cluster.vcov(lm_hi, dat_hi[,c('X','A.A7')]))
+    z_a2 = (lm_clus_lo[3,1] - lm_clus_hi[3,1]) / sqrt(lm_clus_lo[3,2]^2 + lm_clus_hi[3,2]^2)
+    p_a2 = 2 * pnorm( abs(z_a2), mean = 0, sd = 1, lower.tail = F) 
+    coef_lo = lm_clus_lo[3,1]; coef_hi = lm_clus_hi[3,1]
+    sd_lo = lm_clus_lo[3,2]; sd_hi = lm_clus_hi[3,2]
+    return(data.frame(coef_lo, coef_hi, sd_lo, sd_hi, p_a2)) }
+}
+
+#calculate results for all k
+relig_results = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B)) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+#Create table
+relig_results_tab = relig_results %>% apply(2, function(x) as.numeric(x)) %>% data.frame() %>%
+  select(k, coef_lo, sd_lo, coef_hi, sd_hi, p_a2) %>% 
+  rename(Coef_LowSeg = coef_lo, SD_LowSeg = sd_lo, Coef_HiSeg = coef_hi, SD_HiSeg = sd_hi, p = p_a2) %>% 
+  round(3)
+out = stargazer(relig_results_tab, summary = F, rownames = F,
+                title = 'Results for co-ethnicity attribute in candidate experiment compared between
+                high- and low-segregation subsamples, based on religious exposure.',
+                label = 'table:ReligResults_Network')
+writeLines(out,con = paste0(path0,'ReligResults_Network.tex'));rm(out, relig_results_tab)
+
+#Create data frame for coefficient plots
+rrc = 
+  relig_results[seq(from = 1, to = 30, by = 1),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Create plot
+ggplot(data = rrc, aes(x = k, y = Coef)) +  
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_all.png'), height = 150, width = 150, units = 'mm')
+################################################################################################################################
+
+#Income: median is 10k
+relig_results_rich = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$Income1 >= 10),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_rich = 
+  relig_results_rich[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#FIGURE A12
+ggplot(data = rrc_rich, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + #
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (High Income)') +
+  guides(shape = guide_legend(order = 1), #
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_rich.png'), height = 150, width = 150, units = 'mm')
+
+#Patna
+relig_results_patna = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$City == 'Patna'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_patna = 
+  relig_results_patna[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#FIGURE A13
+ggplot(data = rrc_patna, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (Patna)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_patna.png'), height = 150, width = 150, units = 'mm')
+
+#Female
+relig_results_female = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$Male == FALSE),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_female = 
+  relig_results_female[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Figure A14
+ggplot(data = rrc_female, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (Female)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_female.png'), height = 150, width = 150, units = 'mm')
+
+#Migrant
+relig_results_migrant = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$Migrant == TRUE),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_migrant = 
+  relig_results_migrant[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Figure A15
+ggplot(data = rrc_migrant, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (Migrant)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_migrant.png'), height = 150, width = 150, units = 'mm')
+
+#High Caste
+relig_results_hicaste = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$LowCaste == FALSE),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_hicaste = 
+  relig_results_hicaste[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Figure A16
+ggplot(data = rrc_hicaste, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (Hi Caste)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_hicaste.png'), height = 150, width = 150, units = 'mm')
+
+#Muslim
+relig_results_mus = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C6 == 'Muslim'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_mus = 
+  relig_results_mus[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Figure A17
+ggplot(data = rrc_mus, aes(x = k, y = Coef)) +  
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (Muslim)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_muslim.png'), height = 150, width = 150, units = 'mm')
+
+#Hindu
+relig_results_hin = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C6 == 'Hindu'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+rrc_hin =  
+  relig_results_hin[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Figure A18
+ggplot(data = rrc_hin, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (Hindu)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_hindu.png'), height = 150, width = 150, units = 'mm')
+
+######################################################################################################
+#Main network results in table
+
+#Table A7
+relig_results_tab = relig_results %>% apply(2, function(x) as.numeric(x)) %>% data.frame() %>%
+  select(k, coef_lo, sd_lo, coef_hi, sd_hi, p_a2) %>% 
+  rename(Coef_LowSeg = coef_lo, SD_LowSeg = sd_lo, Coef_HiSeg = coef_hi, SD_HiSeg = sd_hi, p = p_a2) %>% 
+  round(3)
+out = stargazer(relig_results_tab, summary = F, rownames = F,
+                title = 'Results for co-ethnicity attribute in candidate experiment compared between
+                high- and low-segregation subsamples, based on religious segregation.',
+                label = 'table:ReligResults_Network')
+writeLines(out,con = paste0(path0,'ReligResults_Network.tex'));rm(out, path0, relig_results_tab)
+
+#######################################################################################################################

27/replication_package/Figure6_script.R

@@ -0,0 +1,239 @@
+#Figure 6: Preferences for non-coethnic neighbor in neighbor conjoint experiment, 
+#comparing high- to low-exposure respondents. 
+
+#install packges
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('tidyr')
+# install.packages('ggplot2')
+# install.packages('lmtest')
+# install.packages('multiwayvcov')
+# install.packages('stargazer')
+
+rm(list=ls())
+library(plyr);library(dplyr, warn.conflicts = F)
+library(tidyr)
+library(ggplot2)
+suppressMessages( library(lmtest) )
+suppressMessages( library(multiwayvcov) )
+suppressMessages(library(stargazer))
+
+s = function(x){summary(factor(x))}
+#setwd() #set working directory
+dir.create(paste0(getwd(), '/Output/'))
+dir.create(paste0(getwd(), '/Output/Figure_6/'))
+path0 = paste0(getwd(), '/Output/Figure_6/', Sys.Date(),'/') #Directory for output files
+dir.create(path0)
+
+Q = read.csv('4-20-20_deid_nearestK.csv',
+             na.strings=c('','NA'),strip.white=T,stringsAsFactors = F)
+Q = Q[which(Q$Wave == 'Bangalore 2017'),]
+
+##############################################################################################################################
+
+Q = Q %>% dplyr::rename(Q2A1 = L.Neighbor_Random_2_A1, #second question, candidate 1, characteristic A
+                        Q2A2 = L.Neighbor_Random_2_A2,
+                        Q2B1 = L.Neighbor_Random_2_B1,
+                        Q2B2 = L.Neighbor_Random_2_B2,
+                        Q2C1 = L.Neighbor_Random_2_C1,
+                        Q2C2 = L.Neighbor_Random_2_C2,
+                        Q3A1 = L.Neighbor_Random_3_A1,
+                        Q3A2 = L.Neighbor_Random_3_A2,
+                        Q3B1 = L.Neighbor_Random_3_B1,
+                        Q3B2 = L.Neighbor_Random_3_B2,
+                        Q3C1 = L.Neighbor_Random_3_C1,
+                        Q3C2 = L.Neighbor_Random_3_C2,
+                       # Q1 = L.Neighbor_Question_1,  #first question: choose candidate 1 or 2?
+                        Q2 = L.Neighbor_Question_2,
+                        Q3 = L.Neighbor_Question_3)
+
+#rearrange so one row is one conjoint observation. 3x as many rows as A
+#new variables: A1, B1 are two traits for candidate 1; similar for 2; and y is respondent's choice between candidates
+B = Q %>% 
+  unite('Q2',matches('Q2')) %>% unite('Q3',matches('Q3')) %>%
+  gather(Question,b,starts_with('Q'))  %>% arrange(X) %>% separate('b', c('A1','A2','B1','B2','C1','C2','y'))
+
+B = B %>% filter(!(A1 == A2 & B1 == B2 & C1 == C2)) #Drop observations where candidates have same profile
+
+#Make new data frame where each row is one PROFILE, ie each question becomes two rows (one for each candidate)
+#New variables: A1, A2 are combined as A: trait A for either candidate
+C = B %>% unite('ABC1',c(A1,B1,C1)) %>% unite('ABC2',c(A2,B2,C2)) %>% gather(Neighbor, ABC, c(ABC1,ABC2)) %>%
+  arrange(X) %>% separate('ABC',c('A','B','C')) %>% mutate(Neighbor = as.numeric(mapvalues(Neighbor, from = c('ABC1','ABC2'), to = c(1,2))))
+
+C$B_revised = NA
+C$B_revised[which(C$C.C6_Religion == 'Hindu' & C$B == 0)] = 1 #Respondent and neighbor both Hindu
+C$B_revised[which(C$C.C6_Religion == 'Hindu' & C$B == 1)] = 0 #Respondent Hindu, neighbor Muslim
+C$B_revised[which(C$C.C6_Religion == 'Hindu' & C$B == 2)] = 2 #non-kannada-speaker: keep it the same
+C$B_revised[which(C$C.C6_Religion == 'Muslim' & C$B == 0)] = 0 #Respondent Muslim, neighbor Hindu
+C$B_revised[which(C$C.C6_Religion == 'Muslim' & C$B == 1)] = 1 #Respondent Hindu, neighbor Muslim
+C$B_revised[which(C$C.C6_Religion == 'Muslim' & C$B == 2)] = 2 #non-kannada-speaker: keep it the same
+C$B = as.character(C$B_revised) #this is necessary so model-matrix step below works
+#B0: other religion. B1: same religion. B2: non kannada speaker
+
+#this introduces NA's (people who are not Hindus or Muslims); drop these here
+C = C[which(!is.na(C$B)),]
+
+#function to make dummies for trait levels
+ModFn = function(x,f){
+  data.frame(x, model.matrix(as.formula(f), data=x))}
+
+C = C %>% ModFn('~ A - 1') %>% ModFn('~ B - 1') %>% ModFn('~ C - 1') #function to make dummies for trait levels
+C = C %>% mutate(Y = y == Neighbor) #1 when that candidate is picked
+C = C %>% dplyr::rename(A_0 = A0, #rename variables to be consistent with earlier version of code
+                        A_1 = A1,
+                        B_0 = B0,
+                        B_1 = B1,
+                        C_0 = C0,
+                        C_1 = C1,
+                        C_2 = C2,
+                        C_3 = C3,
+                        C_4 = C4)
+
+B = C; rm(C) #rename variables to be consistent with earlier version of code
+##############################################################################################################
+
+##########do analysis and make plots######################################################################
+#regression formula
+form1 = as.formula(paste0('Y ~ ',
+                          paste(strsplit('A_0 A_1 B_0 B_1 C_0 C_1 C_2 C_3 C_4', split = ' ')[[1]], collapse=' + ')))
+
+DF_C_v2 = function(l_m,id){  #
+  l_m_pl = data.frame(Parameter = rownames(l_m[-1,])) %>% #-1 drops intercept
+    mutate(Coef = l_m[-1,1]) %>%
+    mutate(Lo = Coef - 1.96*l_m[-1,2]) %>%
+    mutate(Hi = Coef + 1.96*l_m[-1,2]) %>%
+    rbind(data.frame(Parameter = c('A_3','B_4'),
+                     Coef = c(0,0),
+                     Lo = c(0,0),
+                     Hi = c(0,0)
+    )) %>%
+  mutate(ID = id) %>% 
+    mutate(Parameter = as.character(Parameter)) %>%
+    arrange(Parameter)
+}
+
+###########################################################################################
+#Make functions to extract p and z as a function of k
+
+calc_p_relig_neigh = function(k, Dat){ #
+  var <- paste0('Nearest',k,'_SameReligion') #
+  if(median(Dat[,var],na.rm=T)==k){
+    var_break <- k-1
+  }else{var_break = floor(median(Dat[,var],na.rm=T))}
+  dat_lo = Dat[ which(Dat[,var] <= var_break ) ,]
+  dat_hi = Dat[ which(Dat[,var] > var_break ) ,]
+  if(nrow(dat_lo) == 0 | nrow(dat_hi) == 0){p_a2 = NA
+  }else{
+    lm_lo = lm(form1, data = dat_lo )
+    lm_hi = lm(form1, data = dat_hi )
+    lm_clus_lo = coeftest(lm_lo, cluster.vcov(lm_lo, dat_lo[,c('X','A.A7_Area.Neighborhood')]))
+    lm_clus_hi = coeftest(lm_hi, cluster.vcov(lm_hi, dat_hi[,c('X','A.A7_Area.Neighborhood')]))
+    z_a2 = (lm_clus_lo[3,1] - lm_clus_hi[3,1]) / sqrt(lm_clus_lo[3,2]^2 + lm_clus_hi[3,2]^2)
+    p_a2 = 2 * pnorm( abs(z_a2), mean = 0, sd = 1, lower.tail = F) 
+    coef_lo = lm_clus_lo[3,1]; coef_hi = lm_clus_hi[3,1]
+    sd_lo = lm_clus_lo[3,2]; sd_hi = lm_clus_hi[3,2]
+    return(data.frame(coef_lo, coef_hi, sd_lo, sd_hi, p_a2)) }
+}
+
+relig_results = sapply(1:30, function(x) calc_p_relig_neigh(k=x, Dat = B)) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+#Create data frame for plotting
+rrc = relig_results[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#FIGURE 6
+ggplot(data = rrc, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(FALSE), values=c(0.5), labels = c('No')) +
+  labs(shape = 'Exposure') +
+  theme_minimal() + ylab('Non-Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NEIGHBOR.png'), height = 150, width = 150, units = 'mm')
+################################################################################################################################
+
+################################################################################################################################
+#De-medianed version
+
+calc_p_relig_neigh_demed = function(k, Dat){ 
+  var <- paste0('DeMedNearest',k,'_SameReligion') 
+  var_break = 0
+  dat_lo = Dat[ which(Dat[,var] < var_break ) ,]
+  dat_hi = Dat[ which(Dat[,var] >= var_break ) ,]
+  if(nrow(dat_lo) == 0 | nrow(dat_hi) == 0){p_a2 = NA
+  }else{
+    lm_lo = lm(form1, data = dat_lo )
+    lm_hi = lm(form1, data = dat_hi )
+    lm_clus_lo = coeftest(lm_lo, cluster.vcov(lm_lo, dat_lo[,c('X','A.A7_Area.Neighborhood')]))
+    lm_clus_hi = coeftest(lm_hi, cluster.vcov(lm_hi, dat_hi[,c('X','A.A7_Area.Neighborhood')]))
+    z_a2 = (lm_clus_lo[3,1] - lm_clus_hi[3,1]) / sqrt(lm_clus_lo[3,2]^2 + lm_clus_hi[3,2]^2)
+    p_a2 = 2 * pnorm( abs(z_a2), mean = 0, sd = 1, lower.tail = F) 
+    coef_lo = lm_clus_lo[3,1]; coef_hi = lm_clus_hi[3,1]
+    sd_lo = lm_clus_lo[3,2]; sd_hi = lm_clus_hi[3,2]
+    return(data.frame(coef_lo, coef_hi, sd_lo, sd_hi, p_a2)) }
+}
+
+relig_results_demed = sapply(c(5,10,15,20,25,30), function(x) calc_p_relig_neigh_demed(k=x, Dat = B)) %>% t() %>% data.frame()  %>%
+  mutate(k = c(5,10,15,20,25,30),
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+rrc_demed = relig_results_demed %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#FIGURE A19
+ggplot(data = rrc_demed, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + 
+  scale_alpha_manual('p < 0.05', c(FALSE), values=c(0.5), labels = c('No')) +
+  labs(shape = 'Exposure') +
+  theme_minimal() + ylab('Non-Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion (De-medianed)') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_NEIGHBOR_demed.png'), height = 150, width = 150, units = 'mm')
+
+relig_results_tab = relig_results %>% apply(2, function(x) as.numeric(x)) %>% data.frame() %>%
+  select(k, coef_lo, sd_lo, coef_hi, sd_hi, p_a2) %>% 
+  rename(Coef_HiExp = coef_lo, SD_HiExp = sd_lo, Coef_LoExp = coef_hi, SD_LoExp = sd_hi, p = p_a2) %>% 
+  round(3)
+out = stargazer(relig_results_tab, summary = F, rownames = F,
+                title = 'Results for co-ethnicity attribute in neighbor experiment compared between
+                high- and low-exposure subsamples, based on religious exposure',
+                label = 'table:ReligResults_Neighbor')
+writeLines(out,con = paste0(path0,'ReligResults_Neighbor.tex'));rm(out, relig_results_tab)

27/replication_package/Figure_A5_script.R

@@ -0,0 +1,188 @@
+#Figure A5, results excluding Bangalore
+
+#package installation
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('tidyr')
+# install.packages('ggplot2')
+# install.packages('multiwayvcov')
+# install.packages('lmtest')
+
+rm(list=ls())
+#setwd() #set working directory
+dir.create(paste0(getwd(), '/Output/'))
+dir.create(paste0(getwd(), '/Output/Figure_A5/'))
+path0 = paste0(getwd(), '/Output/Figure_A5/', Sys.Date(),'/') #Directory for output files
+dir.create(path0)
+
+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))
+
+A = readRDS('4-20-20_HH-KNearest_DeID_demed.RDS')
+
+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'),]
+
+Q = Aprime
+
+#Rename variables
+
+Q = Q %>% dplyr::rename(Q1A1 = L.Candidate_Random_A1_Candidate.Preferance, #first question, candidate 1, characteristic A
+                        Q1A2 = L.Candidate_Random_A2_Candidate.Preferance, #first question, candidate 2, characteristic A
+                        Q1B1 = L.Candidate_Random_B1_Candidate.Preferance, #first question, candidate 1, characteristic B
+                        Q1B2 = L.Candidate_Random_B2_Candidate.Preferance, #first question, candidate 2, characteristic B
+                        Q2A1 = L.Candidate_Random_A3, #second question, candidate 1, characteristic A
+                        Q2A2 = L.Candidate_Random_A4, #second question, candidate 2, characteristic A
+                        Q2B1 = L.Candidate_Random_B3, #second question, candidate 1, characteristic B
+                        Q2B2 = L.Candidate_Random_B4, #second question, candidate 2, characteristic B
+                        Q3A1 = L.Candidate_Random_A5, #third question, candidate 1, characteristic A
+                        Q3A2 = L.Candidate_Random_A6, #third question, candidate 2, characteristic A
+                        Q3B1 = L.Candidate_Random_B5, #third question, candidate 1, characteristic B
+                        Q3B2 = L.Candidate_Random_B6, #third question, candidate 2, characteristic B
+                        Q1 = L.Candidate_Question_1, #first question, choose candidate 1 or 2
+                        Q2 = L.Candidate_Question_2, #first question, choose candidate 1 or 2
+                        Q3 = L.Candidate_Question_3 #first question, choose candidate 1 or 2
+) 
+
+#rearrange so one row is one conjoint observation. 3x as many rows as A
+#new variables: A1, B1 are two traits for candidate 1; similar for 2; and y is responent's choice between candidates
+B = Q %>% unite('Q1', matches('Q1')) %>% unite('Q2', matches('Q2')) %>% unite('Q3', matches('Q3')) %>%
+  gather(Question, b, starts_with('Q')) %>% arrange(X) %>% separate( 'b', c('A1','A2','B1','B2','y') )
+
+B = B %>% filter(! (A1 == A2 & B1 == B2)) #7767 -> 7346; drop observations where candidates have same profile
+
+#make new data frame wheer each row is one PROFILE, ie each question becomes two rows (one for each candidate)
+#New variables: A1, A2 are combined as A: trait A for either candidate
+C = B %>% unite('AB1',c(A1,B1)) %>% unite('AB2',c(A2,B2)) %>% gather(Neighbor, AB, c(AB1, AB2)) %>%
+  arrange(X) %>% separate('AB', c('A','B')) %>% 
+  mutate(Neighbor = as.numeric(mapvalues(Neighbor, from = c('AB1', 'AB2'), to = c(1,2))))
+
+#function to make dummies for trait levels
+ModFn = function(x,f){
+  data.frame(x, model.matrix(as.formula(f), data=x)) }
+
+C = C %>% ModFn('~ A - 1') %>% ModFn('~ B - 1') #function to make dummies for trait levels
+C = C %>% mutate(Y = y == Neighbor) #1 when that candidate is picked
+C = C %>% dplyr::rename(A_0 = A0, #rename variables to be consistent with earlier version of code
+                        A_1 = A1,
+                        A_2 = A2,
+                        A_3 = A3,
+                        B_0 = B0,
+                        B_1 = B1,
+                        B_2 = B2,
+                        B_3 = B3,
+                        B_4 = B4)
+
+B = C; rm(C) #rename to be consistent with earlier version
+###########################################################################################
+
+###########################################################################################
+#SUBGROUP ANALYSIS: Religion 10
+
+#regression formula
+form1 = as.formula(paste0('Y ~ ',
+                          paste(strsplit('A_0 A_1 A_2 A_3 B_0 B_1 B_2 B_3 B_4',
+                                         split = ' ')[[1]], collapse = ' + ' )))
+
+#function to make data frame from regression results
+DF_C = function(l_m, id){
+  l_m_pl = data.frame(Parameter = rownames(l_m[-1,])) %>% #-1 drops intercept
+    mutate(Coef = l_m[-1,1]) %>%
+    mutate(Lo = Coef - 1.96 * l_m[-1,2]) %>%
+    mutate(Hi = Coef + 1.96 * l_m[-1,2]) %>%
+    mutate(ID = id) %>%
+    rbind(data.frame(Parameter = c('A_3','B_4'),
+                     Coef = c(0,0), Lo = c(0,0), Hi = c(0,0), ID = c(id, id)) ) %>%
+    mutate(Parameter = as.character(Parameter)) %>%
+    arrange(Parameter)
+}
+
+###########################################################################################
+ 
+###########################################################################################
+#Make functions to extract p and z as a function of k
+
+calc_p_relig = function(k, Dat){
+  var <- paste0('Nearest',k,'_OwnReligion')
+  if(median(Dat[,var],na.rm=T)==k){
+    var_break <- k-1
+  }else{var_break = floor(median(Dat[,var],na.rm=T))}
+  dat_lo = Dat[ which(Dat[,var] <= var_break ) ,]
+  dat_hi = Dat[ which(Dat[,var] > var_break ) ,]
+  if(nrow(dat_lo) == 0 | nrow(dat_hi) == 0){p_a2 = NA
+  }else{
+  lm_lo = lm(form1, data = dat_lo )
+  lm_hi = lm(form1, data = dat_hi )
+  lm_clus_lo = coeftest(lm_lo, cluster.vcov(lm_lo, dat_lo[,c('X','A.A7_Area.Neighborhood')]))
+  lm_clus_hi = coeftest(lm_hi, cluster.vcov(lm_hi, dat_hi[,c('X','A.A7_Area.Neighborhood')]))
+  z_a2 = (lm_clus_lo['A_2','Estimate'] - lm_clus_hi['A_2','Estimate']) / sqrt(lm_clus_lo['A_2','Std. Error']^2 + lm_clus_hi['A_2','Std. Error']^2)
+  p_a2 = 2 * pnorm( abs(z_a2), mean = 0, sd = 1, lower.tail = F) 
+  coef_lo = lm_clus_lo['A_2','Estimate']; coef_hi = lm_clus_hi['A_2','Estimate']
+  sd_lo = lm_clus_lo['A_2','Std. Error']; sd_hi = lm_clus_hi['A_2','Std. Error']
+  return(data.frame(coef_lo, coef_hi, sd_lo, sd_hi, p_a2)) }
+  }
+
+relig_results = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B)) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+         )
+
+rrc = 
+  relig_results[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Results without Bangalore
+relig_results_2 = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[-which(B$Wave == 'Bangalore 2016'),])) %>% t() %>% data.frame()  %>%
+  mutate(k = 1:30,
+         dif = as.numeric(coef_lo) - as.numeric(coef_hi),
+         dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
+         dif_lobd = dif - 1.96*dif_sd,
+         dif_hibd = dif + 1.96*dif_sd
+  )
+
+#Data frame for plotting
+rrc_2 = 
+  relig_results_2[seq(from = 2, to = 30, by = 3),] %>% 
+  rename(Low = coef_lo, High = coef_hi) %>%
+  select(-starts_with('dif')) %>% 
+  gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
+                                             sd_lo = as.numeric(sd_lo),
+                                             sd_hi = as.numeric(sd_hi),
+                                             p_a2 = as.numeric(p_a2),
+                                             sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
+                                             lo = Coef - 1.96*sd,
+                                             hi = Coef + 1.96*sd,
+                                             sig = factor(p_a2 < 0.05))
+
+#Figure A5
+ggplot(data = rrc_2, aes(x = k, y = Coef)) + 
+  geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig), 
+                  position = position_dodge(width = 0.9)) +
+  scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
+  scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(0.5, 1), labels = c('Yes','No')) +
+  labs(shape = 'Exposure') + 
+  theme_minimal() + ylab('Coethnicity Coefficients') +
+  theme(text = element_text(size = 16),
+        plot.title = element_text(hjust = 0.5)) + 
+  ggtitle('k-Nearest Own Religion,\nExcluding Bangalore') +
+  guides(shape = guide_legend(order = 1), 
+         alpha = guide_legend(order = 0))
+ggsave(filename = paste0(path0, 'k-z_coefficients_no-bangalore.png'), height = 150, width = 150, units = 'mm'); rm(path0)

27/replication_package/Jaipur_District_censusdata_2-25-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0378738e7586686664d5502cfd0f315af3e7674fe748087dc227da2380b4639a
+size 2111

27/replication_package/Jaipur_ward_censusdata_2-14-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f9c452aa07befa647adaa6695e9dff3391cd3603d21ff3c4d2fc110f1bade65
+size 6270

27/replication_package/Karnataka_district_censusdata_2-22-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c43e16fc33a638484d107f7fbbdc3402c6a062f9ac3711142f4e4839cb84cb4
+size 4368

27/replication_package/Network-sample-calculations_script.R

@@ -0,0 +1,271 @@
+#package installation
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('tidyr')
+# install.packages('igraph')
+# install.packages('geosphere')
+# install.packages('ggplot2')
+# install.packages('tictoc')
+
+rm(list=ls())
+library(plyr);library(dplyr, warn.conflicts = F)
+library(tidyr)
+library(igraph)
+library(geosphere)
+library(ggplot2, warn.conflicts = F)
+library(tictoc)
+
+#setwd() #set working directory
+dir.create(paste0(getwd(), '/Output/'))
+dir.create(paste0(getwd(), '/Output/Network-sample-calculations/'))
+path0 = paste0(getwd(), '/Output/Network-sample-calculations/', Sys.Date(),'/') #Directory for output files
+dir.create(path0)
+
+s = function(x){summary(factor(x))}
+
+dat = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
+################################################################################################
+
+tic()
+out_sim_list = readRDS('4-21-20_1000x_sample_nearest.RDS')
+toc() #
+
+cor_df = data.frame(Nearest5_SameRel_samp_AllLinksSameRelig = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig = rep(NA, length(out_sim_list)),
+                    
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh1 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh2 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh3 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh4 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh5 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh6 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh7 = rep(NA, length(out_sim_list)),
+                    Nearest5_SameRel_samp_AllLinksSameRelig_Neigh8 = rep(NA, length(out_sim_list)),
+                    
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh1 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh2 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh3 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh4 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh5 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh6 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh7 = rep(NA, length(out_sim_list)),
+                    Nearest10_SameRel_samp_AllLinksSameRelig_Neigh8 = rep(NA, length(out_sim_list)),
+                    
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh1 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh2 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh3 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh4 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh5 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh6 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh7 = rep(NA, length(out_sim_list)),
+                    Nearest15_SameRel_samp_AllLinksSameRelig_Neigh8 = rep(NA, length(out_sim_list))
+                    )
+
+tic()
+for(i in 1:length(out_sim_list)){
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig[i] =  #nearest 5 religion correlation
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp, out_sim_list[[i]]$AllLinksSameRelig, use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig[i] =  #nearest 10 religion correlation
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp, out_sim_list[[i]]$AllLinksSameRelig, use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig[i] =  #nearest 5 religion correlation
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp, out_sim_list[[i]]$AllLinksSameRelig, use = 'pairwise.complete.obs')
+  
+  
+  #nearest-5
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh1[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 1")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 1")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh2[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 145")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 145")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh3[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 30")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 30")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh4[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 68")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 68")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh5[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 42")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 42")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh6[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 51")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 51")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh7[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 82")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 82")], use = 'pairwise.complete.obs')
+  cor_df$Nearest5_SameRel_samp_AllLinksSameRelig_Neigh8[i] = 
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 93")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 93")], use = 'pairwise.complete.obs')
+  
+  #nearest-10
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh1[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 1")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 1")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh2[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 145")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 145")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh3[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 30")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 30")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh4[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 68")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 68")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh5[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 42")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 42")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh6[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 51")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 51")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh7[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 82")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 82")], use = 'pairwise.complete.obs')
+  cor_df$Nearest10_SameRel_samp_AllLinksSameRelig_Neigh8[i] = 
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 93")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 93")], use = 'pairwise.complete.obs')
+  
+  #nearest-15
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh1[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 1")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 1")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh2[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 145")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 145")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh3[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 30")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 30")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh4[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 68")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Jaipur 68")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh5[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 42")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 42")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh6[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 51")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 51")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh7[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 82")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 82")], use = 'pairwise.complete.obs')
+  cor_df$Nearest15_SameRel_samp_AllLinksSameRelig_Neigh8[i] = 
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp[which(out_sim_list[[i]]$A.A7 ==  "Patna 93")],
+        out_sim_list[[i]]$AllLinksSameRelig[which(out_sim_list[[i]]$A.A7 ==  "Patna 93")], use = 'pairwise.complete.obs')
+  
+} #throws a warning about "standard deviation is 0", because sometimes by chance everyone has the same Nearest10 score
+toc() #2.5 sec
+
+
+#histograms for nearest-k vs contact correlations from neighborhood samples
+#FIGURE A3
+ggplot(data = cor_df, aes(cor_df$Nearest10_SameRel_samp_AllLinksSameRelig)) + geom_histogram(aes(y = (..count..)/sum(..count..)  ), bins = 50) +
+  theme_bw() + labs(x = 'Correlation, Nearest-k vs Outgroup Contacts', y = 'Proportion') + 
+  ggtitle('Nearest-10 Same Religion vs. Outgroup Contacts\nSample of 60 per neighborhood\nAll neighborhoods together') + 
+  theme(plot.title = element_text(hjust = 0.5, size = 14))
+ggsave(filename = paste0(path0, 'k_contact_networksamp_histogram.png'), height = 150, width = 150, units = 'mm'); rm(path0)
+
+#*******RESULT******#
+#Correlations over 1k iterations between k-nearest score calculated for random sam- ple of 60 individuals from each neighborhood, versus contact with individuals from another religion.
+#TABLE A2
+mean(cor_df$Nearest5_SameRel_samp_AllLinksSameRelig) #0.4181599 
+sd(cor_df$Nearest5_SameRel_samp_AllLinksSameRelig) #0.05066349 
+mean(cor_df$Nearest10_SameRel_samp_AllLinksSameRelig) #0.4312065 
+sd(cor_df$Nearest10_SameRel_samp_AllLinksSameRelig) #0.0466517 
+mean(cor_df$Nearest15_SameRel_samp_AllLinksSameRelig) #0.4375551 
+sd(cor_df$Nearest15_SameRel_samp_AllLinksSameRelig) #0.04528044 
+###############################################################################################################################################
+
+
+################################################################################################################################################
+
+#*******RESULT******#
+#Relationship between proportion of the 10 nearest neighbors of another religion, and proportion who have any contacts with members of another religion.
+#TABLE A3
+
+sum(dat$AllLinksSameRelig < 1, na.rm = T) / sum(dat$Nearest10_SameRel <= 10, na.rm = T) # 15% of whole sample have non-homogeneous contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel == 10, na.rm = T) / sum(dat$Nearest10_SameRel == 10, na.rm = T) #1% of samerel = 10 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 10, na.rm = T) / sum(dat$Nearest10_SameRel < 10, na.rm = T) #26% of samerel < 10 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 9, na.rm = T) / sum(dat$Nearest10_SameRel < 9, na.rm = T) #30% of samerel < 9 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 8, na.rm = T) / sum(dat$Nearest10_SameRel < 8, na.rm = T) #33% of samerel < 8 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 7, na.rm = T) / sum(dat$Nearest10_SameRel < 7, na.rm = T) #37% of samerel < 7 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 6, na.rm = T) / sum(dat$Nearest10_SameRel < 6, na.rm = T) #38% of samerel < 6 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 5, na.rm = T) / sum(dat$Nearest10_SameRel < 5, na.rm = T) #41% of samerel < 5 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 4, na.rm = T) / sum(dat$Nearest10_SameRel < 4, na.rm = T) #46% of samerel < 4 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 3, na.rm = T) / sum(dat$Nearest10_SameRel < 3, na.rm = T) #54% of samerel < 3 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 2, na.rm = T) / sum(dat$Nearest10_SameRel < 2, na.rm = T) #66% of samerel < 2 have hetero contacts
+sum(dat$AllLinksSameRelig < 1 & dat$Nearest10_SameRel < 1, na.rm = T) / sum(dat$Nearest10_SameRel < 1, na.rm = T) #75% of samerel < 1 have hetero contacts
+s(dat$Nearest10_SameRel)
+
+sum(dat$Nearest10_SameRel == 10, na.rm = T) #1159
+sum(dat$Nearest10_SameRel < 10, na.rm = T) #1422
+sum(dat$Nearest10_SameRel < 9, na.rm = T) #1134
+sum(dat$Nearest10_SameRel < 8, na.rm = T) #909
+sum(dat$Nearest10_SameRel < 7, na.rm = T) #734
+sum(dat$Nearest10_SameRel < 6, na.rm = T) #539
+sum(dat$Nearest10_SameRel < 5, na.rm = T) #378
+sum(dat$Nearest10_SameRel < 4, na.rm = T) #240
+sum(dat$Nearest10_SameRel < 3, na.rm = T) #135
+sum(dat$Nearest10_SameRel < 2, na.rm = T) #68
+sum(dat$Nearest10_SameRel < 1, na.rm = T) #28
+
+################################################################################################################################################
+#What is relationship between census-nearest-K and sample-nearest-K?
+
+k_cor_df = data.frame(SameRel_5_cor = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor = rep(NA, length(out_sim_list)),
+                      SameRel_15_cor = rep(NA, length(out_sim_list)),
+                      
+                      SameRel_10_cor_neigh1 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh2 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh3 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh4 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh5 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh6 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh7 = rep(NA, length(out_sim_list)),
+                      SameRel_10_cor_neigh8 = rep(NA, length(out_sim_list))
+                      
+                      )
+for(i in 1:length(out_sim_list)){
+  k_cor_df$SameRel_5_cor[i] =  #nearest 5 religion correlation
+    cor(out_sim_list[[i]]$Nearest5_SameRel_samp, out_sim_list[[i]]$Nearest5_SameRel, use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor[i] =  #nearest 10 religion correlation
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp, out_sim_list[[i]]$Nearest10_SameRel, use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_15_cor[i] =  #nearest 15 religion correlation
+    cor(out_sim_list[[i]]$Nearest15_SameRel_samp, out_sim_list[[i]]$Nearest15_SameRel, use = 'pairwise.complete.obs')
+  
+  
+  k_cor_df$SameRel_10_cor_neigh1  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Jaipur 1')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Jaipur 1')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh2  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Jaipur 145')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Jaipur 145')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh3  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Jaipur 30')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Jaipur 30')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh4  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Jaipur 68')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Jaipur 68')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh5  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Patna 42')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Patna 42')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh6  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Patna 51')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Patna 51')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh7  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Patna 82')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Patna 82')], use = 'pairwise.complete.obs')
+  k_cor_df$SameRel_10_cor_neigh8  =  #nearest 10 religion correlation, neigh x
+    cor(out_sim_list[[i]]$Nearest10_SameRel_samp[which(out_sim_list[[i]]$A.A7 == 'Patna 93')], 
+        out_sim_list[[i]]$Nearest10_SameRel[which(out_sim_list[[i]]$A.A7 == 'Patna 93')], use = 'pairwise.complete.obs')
+
+}#throws a warning about "standard deviation is 0", because sometimes by chance everyone in random sample has the same Nearest10 score
+
+#*******RESULT******#
+#Correlation between k-nearest score calculated for random sample of 60 individuals from each neighborhood, versus the same metric calculated for the entire neighborhood. The reported means and standard deviations of the correlations are for 500 random samples of 60 individuals from each neighborhood.
+#TABLE A1
+mean(k_cor_df$SameRel_5_cor) # 0.7706546 
+sd(k_cor_df$SameRel_5_cor) # 0.02571546 
+mean(k_cor_df$SameRel_10_cor) # 0.8508894
+sd(k_cor_df$SameRel_10_cor) # 0.01996775 
+mean(k_cor_df$SameRel_15_cor) #  0.8735986 
+sd(k_cor_df$SameRel_15_cor) # 0.01815468 
+################################################################################################################################################

27/replication_package/Rajastan_district_censusdata_2-22-19.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:356016eb904e008c10c263c19132d85ed430ed3d564f1ffa65303b3e08b678f5
+size 4774

27/replication_package/Results_List.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cdef9515f25adae322bb577a6242e7814544c2260e8d29871438f0226e29608
+size 9765

27/replication_package/Table3_script.R

@@ -0,0 +1,202 @@
+#install packages
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('stringr')
+
+rm(list=ls())
+#setwd() #set working directory
+
+library('plyr');library('dplyr')
+library('stringr')
+
+#India as a whole
+InSc = read.csv('india_sc_4-20-20.csv',
+                stringsAsFactors = F) %>%
+  rename(PopSc = Population)
+InTo = read.csv('india_pop_4-20-20.csv',
+                stringsAsFactors = F)
+In = merge(InSc, InTo, by = 'State') %>%
+  mutate(PropSc = ( PopSc %>% str_replace_all(',','') %>% as.numeric() ) /
+           ( Population %>% str_replace_all(',','') %>% as.numeric()  ),
+         Pop = Population %>% str_replace_all(',','') %>% as.numeric(),
+         PopSc = PopSc %>% str_replace_all(',','') %>% as.numeric()) %>%
+  filter(State != 'India')
+rm(InSc, InTo)
+
+#Karnataka district data
+KaL = readRDS('Karnataka_district_censusdata_2-22-19.RDS')
+Ka = data.frame(matrix(unlist(KaL), nrow=length(KaL), byrow=T)) 
+names = Ka[1,1:13]
+Ka = Ka %>% select(X27:X39)
+names(Ka) = names[1,] %>% apply(1,as.character)  
+Ka = Ka %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Ka$Pop = Ka$Population %>% as.character() %>% as.numeric()
+Ka$SCpr = Ka$SCprF %>% as.character() %>% as.numeric()
+Ka$SCpo = Ka$SCpr * Ka$Pop
+rm(names,KaL)
+
+#Rajasthan district data
+RaL = readRDS('Rajastan_district_censusdata_2-22-19.RDS')
+Ra = data.frame(matrix(unlist(RaL), nrow=length(RaL), byrow=T)) 
+names = Ra[1,1:13]
+Ra = Ra %>% select(X27:X39)
+names(Ra) = names[1,] %>% apply(1,as.character)  
+Ra = Ra %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Ra$Pop = Ra$Population %>% as.character() %>% as.numeric()
+Ra$SCpr = Ra$SCprF %>% as.character() %>% as.numeric()
+Ra$SCpo = Ra$SCpr * Ra$Pop
+rm(names,RaL)
+
+#Bihar district data
+BiL = readRDS('Bihar_district_censusdata_2-22-19.RDS')
+Bi = data.frame(matrix(unlist(BiL), nrow=length(BiL), byrow=T)) 
+names = Bi[1,1:13]
+Bi = Bi %>% select(X27:X39)
+names(Bi) = names[1,] %>% apply(1,as.character)  
+Bi = Bi %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Bi$Pop = Bi$Population %>% as.character() %>% as.numeric()
+Bi$SCpr = Bi$SCprF %>% as.character() %>% as.numeric()
+Bi$SCpo = Bi$SCpr * Bi$Pop
+rm(names,BiL)
+
+#Bangalore-District data
+BdL = readRDS('Bangalore_District_censusdata_2-25-19.RDS')
+Bd = data.frame(matrix(unlist(BdL), nrow=length(BdL), byrow=T)) 
+names = Bd[1,1:13]
+Bd = Bd %>% select(X27:X39)
+names(Bd) = names[1,] %>% apply(1,as.character)  
+Bd = Bd %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Bd$Pop = Bd$Population %>% as.character() %>% as.numeric()
+Bd$SCpr = Bd$SCprF %>% as.character() %>% as.numeric()
+Bd$SCpo = Bd$SCpr * Bd$Pop
+rm(names,BdL)
+
+#Jaipur-District data
+JdL = readRDS('Jaipur_District_censusdata_2-25-19.RDS')
+Jd = data.frame(matrix(unlist(JdL), nrow=length(JdL), byrow=T)) 
+names = Jd[1,1:13]
+Jd = Jd %>% select(X27:X39)
+names(Jd) = names[1,] %>% apply(1,as.character)  
+Jd = Jd %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Jd$Pop = Jd$Population %>% as.character() %>% as.numeric()
+Jd$SCpr = Jd$SCprF %>% as.character() %>% as.numeric()
+Jd$SCpo = Jd$SCpr * Jd$Pop
+rm(names,JdL)
+
+#Patna-District data
+PdL = readRDS('Bihar_District_censusdata_2-25-19.RDS')
+Pd = data.frame(matrix(unlist(PdL), nrow=length(PdL), byrow=T)) 
+names = Pd[1,1:13]
+Pd = Pd %>% select(X27:X39)
+names(Pd) = names[1,] %>% apply(1,as.character)  
+Pd = Pd %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Pd$Pop = Pd$Population %>% as.character() %>% as.numeric()
+Pd$SCpr = Pd$SCprF %>% as.character() %>% as.numeric()
+Pd$SCpo = Pd$SCpr * Pd$Pop
+rm(names,PdL)
+
+#BBMP data
+BaL = readRDS('BBMP_ward_censusdata_2-12-19.RDS')
+Ba = data.frame(matrix(unlist(BaL), nrow=length(BaL), byrow=T)) 
+names = Ba[1,1:11]
+Ba = Ba %>% select(X23:X33)
+names(Ba) = names[1,] %>% apply(1,as.character)  
+Ba = Ba %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Ba$Pop = Ba$Population %>% as.character() %>% as.numeric()
+Ba$SCpr = Ba$SCprF %>% as.character() %>% as.numeric()
+Ba$SCpo = Ba$SCpr * Ba$Pop
+rm(names,BaL)
+
+#Jaipur data
+JaL = readRDS('Jaipur_ward_censusdata_2-14-19.RDS')
+Ja = data.frame(matrix(unlist(JaL), nrow=length(JaL), byrow=T)) 
+names = Ja[1,1:11]
+Ja = Ja %>% select(X23:X33)
+names(Ja) = names[1,] %>% apply(1,as.character)  
+Ja = Ja %>% rename(STprF = 'Scheduled Tribes (ST) %',
+                   SCprF = 'Scheduled Caste (SC) %')
+Ja$Pop = Ja$Population %>% as.character() %>% as.numeric()
+Ja$SCpr = Ja$SCprF %>% as.character() %>% as.numeric()
+Ja$SCpo = Ja$SCpr * Ja$Pop
+rm(names,JaL)
+
+#############################################################################################################
+#SEGREGATION CALCULATIONS
+
+#DISSIMILARITY INDEX FOR INDIA AS A WHOLE (SC)
+#P: total minority proportion; ti: total pop in gridspace; pi: prop minority in gridspace
+P = sum(In$PopSc) / sum(In$Pop)
+T0 = sum(In$Pop)
+In$D = In$Pop * abs(In$PropSc - P) / (2 * T0 * P * (1 - P))
+D_India = sum(In$D) #This gives final value for dissimilarity index, based on SC population, for India as a whole
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR KARNATAKA AS A WHOLE (SC)
+P = sum(Ka$SCpo) / sum(Ka$Pop)
+T0 = sum(Ka$Pop)
+Ka$D = Ka$Pop * abs(Ka$SCpr - P) / (2 * T0 * P * (1 - P))
+D_Karnataka = sum(Ka$D)
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR RAJASTHAN AS A WHOLE (SC)
+P = sum(Ra$SCpo) / sum(Ra$Pop)
+T0 = sum(Ra$Pop)
+Ra$D = Ra$Pop * abs(Ra$SCpr - P) / (2 * T0 * P * (1 - P))
+D_Rajasthan = sum(Ra$D)
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR BIHAR AS A WHOLE (SC)
+P = sum(Bi$SCpo) / sum(Bi$Pop)
+T0 = sum(Bi$Pop)
+Bi$D = Bi$Pop * abs(Bi$SCpr - P) / (2 * T0 * P * (1 - P))
+D_Bihar = sum(Bi$D)
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR BANGALORE-DISTRICT BY TEHSIL
+P = sum(Bd$SCpo) / sum(Bd$Pop)
+T0 = sum(Bd$Pop)
+Bd$D = Bd$Pop * abs(Bd$SCpr - P) / (2 * T0 * P * (1 - P))
+D_BaD = sum(Bd$D) #
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR JAIPUR-DISTRICT BY TEHSIL
+P = sum(Jd$SCpo) / sum(Jd$Pop)
+T0 = sum(Jd$Pop)
+Jd$D = Jd$Pop * abs(Jd$SCpr - P) / (2 * T0 * P * (1 - P))
+D_JaD = sum(Jd$D)
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR PATNA-DISTRICT BY TEHSIL
+P = sum(Pd$SCpo) / sum(Pd$Pop)
+T0 = sum(Pd$Pop)
+Pd$D = Pd$Pop * abs(Pd$SCpr - P) / (2 * T0 * P * (1 - P))
+D_PdD = sum(Pd$D)
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR BBMP (198) BY WARD 
+P = sum(Ba$SCpo) / sum(Ba$Pop)
+T0 = sum(Ba$Pop)
+Ba$D = Ba$Pop * abs(Ba$SCpr - P) / (2 * T0 * P * (1 - P))
+D_Bbmp = sum(Ba$D) #very dissimilar; min 0 to max 0.5
+rm(T0,P)
+
+#DISSIMILARITY INDEX FOR JAIPUR BY WARD
+P = sum(Ja$SCpo) / sum(Ja$Pop)
+T0 = sum(Ja$Pop)
+Ja$D = Ja$Pop * abs(Ja$SCpr - P) / (2 * T0 * P * (1 - P))
+D_Jaipur = sum(Ja$D)
+rm(T0,P)
+
+#********THIS IS THE BASIS FOR TABLE 3********#
+data.frame(Bangalore = c(D_India, D_Karnataka, D_BaD, D_Bbmp),
+           Jaipur = c(D_India, D_Rajasthan, D_JaD, D_Jaipur),
+           Patna = c(D_India, D_Bihar, D_PdD, NA))
+#############################################################################################################
+

27/replication_package/Table4_script.R

@@ -0,0 +1,125 @@
+#Table 4: Comparison of most common occupations for network and household data sets. 
+
+#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())
+
+#setwd() #set working directory
+
+library(plyr);library(dplyr, warn.conflicts = FALSE)
+library(tidyr);library(ggplot2)
+suppressMessages(library(multiwayvcov, warn.conflicts = F))
+suppressMessages(library(lmtest, warn.conflicts = F))
+suppressMessages(library(stargazer))
+
+s = function(x){summary(factor(x))}
+
+#Read network data
+A = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
+#Read and clean household sample data
+H = read.csv('4-20-20_deid_nearestK.csv',
+             na.strings=c('','NA'),strip.white=T,stringsAsFactors = F)
+Hprime = H[H$A.A7_Area.Neighborhood %in% names(which(table(H$A.A7_Area.Neighborhood) >= 30)),]#Drop neighborhoods with < 30 observations
+Hprime = Hprime[which(!is.na(Hprime$A.A7_Area.Neighborhood)),] #drop NA neighborhoods
+Hprime = Hprime[which(Hprime$A.A7_Area.Neighborhood != 'Bangalore NA'),]
+H = Hprime; rm(Hprime)
+
+########################################################################################################################
+
+############################################################################################################
+#Map values to occupations
+A$Job = mapvalues(A$D.D1_Occupation,
+                  from = c(12,
+                           13,
+                           19,
+                           2,
+                           20,
+                           21,
+                           3,
+                           4,
+                           6,
+                           7,
+                           9),
+                  to = c('Garbage',
+                         'Gardener',
+                         'Security',
+                         'Butcher',
+                         'Tailor',
+                         'Vendor',
+                         'Carpenter',
+                         'Construction',
+                         'Cook',
+                         'Corporate', 
+                         'Electrician'))
+
+H$Job = mapvalues(H$D.D1_Occupation.,
+                  from = c(1,
+                           2,
+                           3,
+                           4,
+                           5,
+                           6,
+                           7,
+                           8,
+                           9,
+                           10,
+                           11,
+                           12,
+                           13,
+                           14,
+                           15,
+                           16,
+                           17,
+                           18,
+                           19,
+                           20,
+                           21,
+                           22,
+                           23,
+                           24,
+                           25,
+                           26, #B17: retired
+                           27), #B17: unemployed
+                  to = c('Agriculture',
+                         'Butcher',
+                         'Carpenter',
+                         'Construction',
+                         'Labour',
+                         'Cook',
+                         'Corporation', 
+                         'Driver',
+                         'Electrical',
+                         'Factory',
+                         'Flower',
+                         'Garbage',
+                         'Gardener',
+                         'Maid',
+                         'Mechanic',
+                         'Painter',
+                         'ProfessionalSvc',
+                         'Grocessory',
+                         'Security',
+                         'Tailor',
+                         'Vendor',
+                         'Government',
+                         'Housewife',
+                         'Student',
+                         'Other',
+                         NA, NA )) #Map retired/unemployed to NA so they don't affect denominator
+
+#Note that B17 includes retired/unemployed; these are mapped above to NA and dropped from table (and not included in denominator)
+
+#Network
+sort (s(A$Job) / sum(!is.na(A$Job )) * 100  , decreasing = T) %>% round(2)
+#Household
+sort (s(H$Job) / sum(!is.na(H$Job )) * 100  , decreasing = T) %>% round(2)
+#These are basis for manually-created Table 4
+
+################################################################################################

27/replication_package/Table5_script.R

@@ -0,0 +1,40 @@
+#Table 5: Correlations of k-nearest-neighbors metric, 
+#calculated from neighborhood census network data,  
+#with the proportion of social links to members of the same group.
+
+#install packages
+# install.packages('plyr')
+# install.packages('dplyr')
+# install.packages('tidyr')
+# install.packages('ggplot2')
+# install.packages('igraph')
+# install.packages('geosphere')
+# install.packages('tictoc')
+
+rm(list=ls())
+library(plyr);library(dplyr, warn.conflicts = F)
+library(tidyr)
+library(igraph, warn.conflicts = F)
+library(geosphere)
+library(ggplot2)
+library(tictoc)
+
+#setwd() #set working directory
+
+s = function(x){summary(factor(x))}
+
+#Read data
+dat3 = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
+##########################################################################################
+
+#Relationships between census-nearest-K and census-contact
+#These are basis for manually-created Table 5
+
+cor(dat3$Nearest5_SameRel, dat3$AllLinksSameRelig, use = 'pairwise.complete.obs') #0.3779793
+cor(dat3$Nearest10_SameRel, dat3$AllLinksSameRelig, use = 'pairwise.complete.obs') #0.4163651
+cor(dat3$Nearest15_SameRel, dat3$AllLinksSameRelig, use = 'pairwise.complete.obs') #0.4222002
+
+cor(dat3$Nearest5_SameCaste, dat3$AllLinksSameCaste, use = 'pairwise.complete.obs') #0.47723
+cor(dat3$Nearest10_SameCaste, dat3$AllLinksSameCaste, use = 'pairwise.complete.obs') #0.5185767
+cor(dat3$Nearest15_SameCaste, dat3$AllLinksSameCaste, use = 'pairwise.complete.obs') #0.5329627
+

27/replication_package/Table6_script.R

@@ -0,0 +1,64 @@
+#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()) 
+library(plyr);library(dplyr, warn.conflicts = F)
+library(tidyr)
+library(ggplot2)
+suppressMessages( library(lmtest) )
+suppressMessages( library(multiwayvcov) )
+suppressMessages(library(stargazer))
+
+s = function(x){summary(factor(x))}
+#setwd() #set working directory
+
+#read data
+A = read.csv('4-20-20_deid_nearestK.csv',
+             na.strings=c('','NA'),strip.white=T,stringsAsFactors = F) 
+Q = A[which(A$Wave == 'Bangalore 2017'),]
+
+####################################################################################
+
+####################################################################################
+#clean data
+Aprime = A[A$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$A.A7_Area.Neighborhood != 'Bangalore NA'),] #Still 8273
+A = Aprime; rm(Aprime)
+####################################################################################
+
+########################################
+#Count observations and neighborhoods
+
+#THIS IS BASIS FOR TABLE 6
+
+#2017 neighborhoods
+length(levels(factor(Q$A.A7_Area.Neighborhood))) #50
+#2017 respondents
+nrow(Q) #1948
+
+#Bangalore '16 neighborhoods
+length(levels(factor(A$A.A7_Area.Neighborhood[which(A$Wave == 'Bangalore 2016')]))) #20
+#Bangalore '16 respondents
+nrow(A[which(A$Wave == 'Bangalore 2016'),]) #609
+
+#Jaipur neighborhoods
+length(levels(factor(A$A.A7_Area.Neighborhood[which(A$City == 'Jaipur')]))) #45
+#Jaipur respondents
+nrow(A[which(A$City == 'Jaipur'),]) #2669
+
+#Patna neighborhoods
+length(levels(factor(A$A.A7_Area.Neighborhood[which(A$City == 'Patna')]))) #34
+#Patna respondents
+nrow(A[which(A$City == 'Patna'),]) #1972
+
+#LAST TWO ROWS OF TABLE 6: Network data
+N = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
+nrow(N[which(N$City == 'Jaipur'),]) #1593
+nrow(N[which(N$City == 'Patna'),]) #988

27/replication_package/Table_1-2.R

@@ -0,0 +1,157 @@
+#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

27/replication_package/india_pop_4-20-20.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:add747a7a183a2650ba7b9f4719391b4e4fa4fc22e6bddb61ee8e9d252c230ed
+size 1002

27/replication_package/india_sc_4-20-20.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87c4377433ca10dd23d0dc50da38b9f3fbd8f634a6cb64328db36dd8bda7fb99
+size 834

27/replication_package/map_dataset_4-28-20.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9381ed7b3b35b1604c46d641762983f4cf9d2faf0d58aa08a2f42837c2d608d8
+size 1085

27/replication_package/map_out_4-28-20.RDS

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:974c5c445d0d218f4e28d700f3d6e5da87e1f365557428c23a3c92eed9cde7d1
+size 1377902

27/replication_package/readme.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:026c16a9681672c20b9ef1b012aee3c8f76df7d9a24e70181a98217a3d513832
+size 3298

27/should_reproduce.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c849f10d98acd706dba048d9ecea61a8abf58e6a54c0dbf0a94d40257fb3a53d
+size 67