Datasets:

chuxuan
/

REPRO-Bench

#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)

#######################################################################################################################

	#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)

	#######################################################################################################################