######### Setup ######### remove(list=ls()) set.seed(8675309) memory.limit(size=20000) options(xtable.comment = FALSE) wd <- "C:/Users/sbari/Dropbox/Research/Climate Migration Paper/Final Files" #Change to your local WD setwd(wd) library(tidyverse) library(xtable) library(ggplot2) library(ggpubr) library(corrplot) library(car) library(splines) library(readxl) library(stargazer) library(psych) library(lemon) library(boot) library(cjoint) library(cregg) library(margins) library(FindIt) library(ggpubr) # Data files needed (place in local WD): # 1. Study 1 (US): conjoint_design.dat # 2. Study 1 (US): conjoint_design_internal.dat # 3. Study 1 (US): Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv # 4. Study 1 (Germany): conjoint_design_german2.dat # 5. Study 1 (Germany): conjoint_design_german2_internal.dat # 6. Study 1 (Germany): Climate Migration 2_ Conjoint- Germany_September 11, 2019_02.58.csv # 7. Study 1 and Study 2 (Germany): germany_state_key.csv # 8. Study 2 (US): usclimate_exp1.csv # 9. Study 2 (Germany): Climate Migration 1_ Article- Germany_September 7, 2019_09.31.csv # 9. Study 3 Follow-Up (US): Migration Follow-Up_August 1, 2020_12.19.csv ########################### Study 1: US ########################### ######### Import data ######### cjt_us_design <- makeDesign(type="file", filename= "conjoint_design.dat") cjt_us_design_internal <- makeDesign(type="file", filename= 'conjoint_design_internal.dat') cjt_us_data <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv", new.format = T, respondentID = "ResponseId", responses=c("force_choice", "Q184", "Q186", "Q188", "Q190", "Q192", "Q194", "Q196", "Q198"), covariates=c("GENDER_resp", "EDUCATION_resp", "IDEOLOGY", 'PARTISANSHIP', 'PARTISANSHIP_D', 'PARTISANSHIP_R', 'PARTISANSHIP_I', 'RELIGIOSITY_resp', 'NATIVE_BORN', 'EMPLOYMENT_resp', 'Age', 'TRUST_GOVT', 'POL_INTEREST', 'FP_ORIENTATION_1', 'FP_ORIENTATION_2', 'FP_ORIENTATION_3', 'FP_ORIENTATION_4', 'SOC_DOM_1', 'SOC_DOM_2', 'SOC_DOM_3', 'SOC_DOM_4', 'EMPATHY_1', 'EMPATHY_2', 'EMPATHY_3', 'EMPATHY_4', 'city', 'state_region')) cjt_us_data_relig <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv", new.format = T, respondentID = "ResponseId", responses=c("force_choice", "Q184", "Q186", "Q188", "Q190", "Q192", "Q194", "Q196", "Q198"), covariates=c('RELIGIOSITY_resp')) cjt_us_data_rank <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv", new.format = T, respondentID = "ResponseId", responses=c("force_choice", "Q184", "Q186", "Q188", "Q190", "Q192", "Q194", "Q196", "Q198"), covariates=c("GENDER_resp", "EDUCATION_resp", "IDEOLOGY", 'PARTISANSHIP', 'PARTISANSHIP_D', 'PARTISANSHIP_R', 'PARTISANSHIP_I', 'NATIVE_BORN', 'EMPLOYMENT_resp', 'Age', 'TRUST_GOVT', 'POL_INTEREST', 'FP_ORIENTATION_1', 'FP_ORIENTATION_2', 'FP_ORIENTATION_3', 'FP_ORIENTATION_4', 'SOC_DOM_1', 'SOC_DOM_2', 'SOC_DOM_3', 'SOC_DOM_4', 'EMPATHY_1', 'EMPATHY_2', 'EMPATHY_3', 'EMPATHY_4', 'city', 'state_region'), ranks = c("RATE_MIG1","RATE_MIG2", "Q168", "Q169", "Q170", "Q171", "Q172", "Q173", "Q174", "Q175", "Q176", "Q177", "Q178", "Q179", "Q180", "Q181", "Q182", "Q183")) cjt_us_data_rank <- cjt_us_data_rank %>% dplyr::rename(rank_outcome = selected) %>% dplyr::select(rank_outcome) cjt_us_data_relig <- cjt_us_data_relig %>% dplyr::select(RELIGIOSITY_resp) cjt_us_data <- cbind(cjt_us_data, cjt_us_data_rank, cjt_us_data_relig) cjt_us_data <- cjt_us_data[!is.na(cjt_us_data$selected), ] ######### Recoding ######### cjt_us_data <- cjt_us_data[cjt_us_data$Occupation != 'Unemployer', ] cjt_us_data$Occupation <- factor(cjt_us_data$Occupation) cjt_us_data$PARTISANSHIP6 <- NA for(i in 1:nrow(cjt_us_data)){ if(cjt_us_data[i, "PARTISANSHIP_D"]==1){ cjt_us_data[i, "PARTISANSHIP6"]<- 6} if(cjt_us_data[i, "PARTISANSHIP_D"]==2){ cjt_us_data[i, "PARTISANSHIP6"]<- 5} if(cjt_us_data[i, "PARTISANSHIP_I"]==1){ cjt_us_data[i, "PARTISANSHIP6"]<- 4} if(cjt_us_data[i, "PARTISANSHIP_I"]==2){ cjt_us_data[i, "PARTISANSHIP6"]<- 3} if(cjt_us_data[i, "PARTISANSHIP_R"]==2){ cjt_us_data[i, "PARTISANSHIP6"]<- 2} if(cjt_us_data[i, "PARTISANSHIP_R"]==1){ cjt_us_data[i, "PARTISANSHIP6"]<- 1} } cjt_us_data$FP_ORIENTATION_1 <- car::recode(cjt_us_data$FP_ORIENTATION_1, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") cjt_us_data$FP_ORIENTATION_2 <- car::recode(cjt_us_data$FP_ORIENTATION_2, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") #reverse coded cjt_us_data$FP_ORIENTATION_3 <- car::recode(cjt_us_data$FP_ORIENTATION_3, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") #reverse coded cjt_us_data$FP_ORIENTATION_4 <- car::recode(cjt_us_data$FP_ORIENTATION_4, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") cjt_us_data$SOC_DOM_1 <- car::recode(cjt_us_data$SOC_DOM_1, "'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5") #reverse coded cjt_us_data$SOC_DOM_2 <- car::recode(cjt_us_data$SOC_DOM_2, "'Definitely favor'=5; 'Somewhat favor'=4; 'Neither oppose nor favor'=3; 'Somewhat oppose'=2; 'Definitely oppose'=1") cjt_us_data$SOC_DOM_3 <- car::recode(cjt_us_data$SOC_DOM_3, "'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5") #reverse coded cjt_us_data$SOC_DOM_4 <- car::recode(cjt_us_data$SOC_DOM_4, "'Definitely favor'=5; 'Somewhat favor'=4; 'Neither oppose nor favor'=3; 'Somewhat oppose'=2; 'Definitely oppose'=1") cjt_us_data$EMPATHY_1 <- car::recode(cjt_us_data$EMPATHY_1, "'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1") #reverse coded cjt_us_data$EMPATHY_2 <- car::recode(cjt_us_data$EMPATHY_2, "'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5") #reverse coded cjt_us_data$EMPATHY_3 <- car::recode(cjt_us_data$EMPATHY_3, "'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5") cjt_us_data$EMPATHY_4 <- car::recode(cjt_us_data$EMPATHY_4, "'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1") cjt_us_data <- cjt_us_data %>% mutate(PARTISANSHIP_bin = ifelse(PARTISANSHIP6>3, "D", "R"), AGE = as.numeric(Age)) cjt_us_data$border_state_indicator <- 1*(cjt_us_data$state_region %in% c("TX", "CA", "AZ", "NM")) cjt_us_data$border_state_indicator_noCA <- 1*(cjt_us_data$state_region %in% c("TX", "AZ", "NM")) cjt_us_data$urban_indicator <- 1*(cjt_us_data$city %in% c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix", "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")) cjt_us_data <- cjt_us_data[5:nrow(cjt_us_data), ] cjt_us_data$EDUCATION_num <- as.numeric(cjt_us_data$EDUCATION_resp)-1 cjt_us_data$IDEOLOGY_num <- as.numeric(car::recode(cjt_us_data$IDEOLOGY, "'Extremely liberal'=7; 'Liberal'=6; 'Slightly liberal'=5; 'Moderate, middle of the road'=4; 'Slightly conservative'=3; 'Conservative'=2; 'Extremely conservative'=1"))-1 cjt_us_data$RELIGIOSITY_num <- as.numeric(cjt_us_data$RELIGIOSITY_resp)-1 cjt_us_data$NATIVE_BORN_num <- ifelse(cjt_us_data$NATIVE_BORN == "United States", 1, 0) cjt_us_data$EMPLOYMENT_num <- as.numeric(car::recode(cjt_us_data$EMPLOYMENT, "'17'=7; '16'=6; '21'=5; '18'=4; '20'=3; '19'=2; '17 '=1")) cjt_us_data$TRUST_GOVT_num <- as.numeric(car::recode(cjt_us_data$TRUST_GOVT, "'Most of the time'=3; 'Only some of the time'=2; 'Just about always'=1"))-1 cjt_us_data$POL_INTEREST_num <- as.numeric(car::recode(cjt_us_data$POL_INTEREST, "'Most of the time'=4; 'Some of the time'=3; 'Only now and then'=2; 'Hardly at all'=1"))-1 ######### Construct scales ######### fp_orientation <- data.frame(cjt_us_data[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3", "FP_ORIENTATION_4")]) soc_dom <- data.frame(cjt_us_data[,c("SOC_DOM_1", "SOC_DOM_2", "SOC_DOM_3", "SOC_DOM_4")]) empathy <- data.frame(cjt_us_data[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")]) fp_orientation <- data.frame(sapply(fp_orientation, FUN= function(x) as.numeric(x))-1) soc_dom <- data.frame(sapply(soc_dom, FUN= function(x) as.numeric(x))-1) empathy <- data.frame(sapply(empathy, FUN= function(x) as.numeric(x))-1) #calculate chronbach's alpha for each index # psych::alpha(fp_orientation) # psych::alpha(soc_dom) # psych::alpha(empathy) #r=create the index variable as the mean score on the individual items cjt_us_data$fp_orientation_index <- apply(fp_orientation, MARGIN = 1, FUN = mean) cjt_us_data$soc_dom_index <- apply(soc_dom, MARGIN = 1, FUN = mean) cjt_us_data$empathy_index <- apply(empathy, MARGIN = 1, FUN = mean) ######### Balance, summary stats ######### cjt_us_data$PARTISANSHIP_bin <- as.factor(cjt_us_data$PARTISANSHIP_bin) cjt_us_data$PARTISANSHIP_num <- as.numeric(cjt_us_data$PARTISANSHIP_bin) cjt_us_data$GENDER_num <- ifelse(cjt_us_data$GENDER == "Female", 1, 0) vars <- c('AGE', 'fp_orientation_index', 'soc_dom_index', "empathy_index", 'PARTISANSHIP_num', "GENDER_num", "EDUCATION_num", "IDEOLOGY_num", "NATIVE_BORN_num", "EMPLOYMENT_num", "TRUST_GOVT_num", "POL_INTEREST_num" ,"RELIGIOSITY_num" ) var_labels <- c("Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Partisanship", "Gender", "Education", "Ideology", "Native Born", "Employment", "Trust in Government", "Political Interest" ,"Religiosity" ) sum_stats <- data.frame(matrix(NA,length(vars), 7)) colnames(sum_stats) <- c("Var.", "Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max." ) sum_stats[, 1] <- var_labels for (i in 1:length(vars)){ string <- paste('sum <- summary(cjt_us_data$',vars[i], ')', sep = "", collapse = "") eval(parse(text=string)) sum_stats[i, 2:7] <- sum } xtable(sum_stats, caption = "Experiment 1 Summary Statistics, US Sample", digits = c(0, 0, 0, 2, 2, 2, 2, 0)) ######### Conjoint main analysis ######### baselines <- list() baselines$Vulnerability <- 'None' baselines$Reason.for.migration <- 'Economic opportunity' baselines$Occupation <- 'Unemployed' baselines$Language.Fluency <- 'None' baselines$Origin <- 'Another region in your country' cjt_us_results <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) summary(cjt_us_results)$amce xtable(summary(cjt_us_results)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels") levels.test<-list() levels.test[["Gender"]]<-c("Female","Male") levels.test[["Language Fluency"]]<-c('None', "Broken", "Fluent") levels.test[["Occupation"]]<-c("Unemployed","Cleaner", "Doctor", "Teacher") levels.test[["Origin"]]<-c("Same Country", "Afghanistan", "Ethiopia", "Myanmar", "Ukraine") levels.test[["Reason.for.migration"]]<-c("Economic","Drought", "Flooding", "Persecution", "Wildfires") levels.test[["Religion"]]<-c("Agnostic","Christian", "Muslim") levels.test[["Vulnerability"]]<-c("None","Food insc.", "No family", "Physical handicap", "PTSD") plot.amce(cjt_us_results, xlab="Expected Change in Migrant Profile Selection, US", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability"), # xlim=c(-0.07, .2), text.size=9) ######### Robustness ######### #Only respondents who complete all 9 tasks cjt_us_data_only9tasks <- cjt_us_data %>% group_by(Response.ID) %>% mutate(resp_profiles_count = n()) cjt_us_data_only9tasks <- cjt_us_data_only9tasks[cjt_us_data_only9tasks$resp_profiles_count > 17, ] nrow(cjt_us_data) - nrow(cjt_us_data_only9tasks) length(unique(cjt_us_data$Response.ID)) - length(unique(cjt_us_data_only9tasks$Response.ID)) cjt_us_results_only_9tasks <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data_only9tasks, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) summary(cjt_us_results_only_9tasks)$amce xtable(summary(cjt_us_results_only_9tasks)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels- \n Only respondents who complete all 9 tasks") summary(cjt_us_results_only_9tasks)$amce$Estimate - summary(cjt_us_results)$amce$Estimate #Compare internal and external migrant profiles cjt_us_data_internal <- cjt_us_data[! (cjt_us_data$Origin %in% "Another region in your country" & cjt_us_data$Reason.for.migration %in% "Political/religious/ethnic persecution"), ] cjt_us_data_internal_strict <- cjt_us_data[! (cjt_us_data$Origin %in% "Another region in your country") , ] cjt_us_data_internal_actual <- cjt_us_data[(cjt_us_data$Origin %in% "Another region in your country"), ] nrow(cjt_us_data) - nrow(cjt_us_data_internal) length(unique(cjt_us_data$Response.ID)) - length(unique(cjt_us_data_internal$Response.ID)) cjt_us_results_internal <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data_internal, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) round(summary(cjt_us_results_internal)$amce$Estimate - summary(cjt_us_results)$amce$Estimate, 3) us_int_table1 <- as_tibble(summary(cjt_us_results_internal)$amce[, c(1:4, 7)]) %>% cbind(round(summary(cjt_us_results_internal)$amce$Estimate - summary(cjt_us_results)$amce$Estimate, 3)) names(us_int_table1)[6] <- "Est. Diff. from Full Model" xtable(us_int_table1, digits = 3, font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels- \n Excluding 'implausible' internal migration profiles") baselines$Origin <- 'Ukraine' cjt_us_results_internal_strict <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data_internal_strict, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) cjt_us_results2 <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) us_int_table2 <- as_tibble(summary(cjt_us_results_internal_strict)$amce[, c(1:4, 7)]) %>% cbind(round(summary(cjt_us_results_internal_strict)$amce$Estimate - summary(cjt_us_results2)$amce$Estimate[c(1:7, 9:20)], 3)) xtable(us_int_table2, digits = 3, font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels- \n Excluding all internal migration profiles") summary(cjt_us_results_internal_strict)$amce$Estimate - summary(cjt_us_results2)$amce$Estimate[c(1:7, 9:20)] place <- round(summary(cjt_us_results_internal_strict)$amce$Estimate - summary(cjt_us_results2)$amce$Estimate[c(1:7, 9:20)], 4) cjt_us_internal_comparisons <- cbind(summary(cjt_us_results_internal)$amce$Attribute, summary(cjt_us_results_internal)$amce$Level, round(summary(cjt_us_results_internal)$amce$Estimate - summary(cjt_us_results)$amce$Estimate, 4), c(place[1:9], NA, place[10:19])) colnames(cjt_us_internal_comparisons) <- c("Attribute", "Level", "Full Model - Implausible Internal Profiles Removed", "Full Model - All Internal Profiles Removed") xtable(cjt_us_internal_comparisons, font.size = "small", caption = "AMCE Differences, US Sample (Compared to baseline levels- \n Full Set Compared to Restricted Internal Sets \n Baseline Origin for Second Comparison Changed to Ukraine") baselines <- list() baselines$Vulnerability <- 'None' baselines$Reason.for.migration <- 'Economic opportunity' baselines$Occupation <- 'Unemployed' baselines$Language.Fluency <- 'Broken' cjt_us_results_internal_actual <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data_internal_actual, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design_internal, baselines=baselines) cjt_us_results3 <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) us_ext_table <- as_tibble(summary(cjt_us_results_internal_actual)$amce[, c(1:4, 7)]) %>% cbind(round(summary(cjt_us_results_internal_actual)$amce$Estimate - summary(cjt_us_results3)$amce$Estimate[c(1:6, 11:20)], 3)) xtable(us_ext_table, digits = 3, font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels- \n Excluding all external migration profiles") #Only respondents 18 and over cjt_us_data_age_subset <- subset(x = cjt_us_data, subset = AGE > 17) nrow(cjt_us_data) - nrow(cjt_us_data_age_subset) length(unique(cjt_us_data$Response.ID)) - length(unique(cjt_us_data_age_subset$Response.ID)) baselines <- list() baselines$Vulnerability <- 'None' baselines$Reason.for.migration <- 'Economic opportunity' baselines$Occupation <- 'Unemployed' baselines$Language.Fluency <- 'None' baselines$Origin <- 'Another region in your country' cjt_us_results_age_subset <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_us_data_age_subset, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) summary(cjt_us_results_age_subset)$amce xtable(summary(cjt_us_results_age_subset)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, US Sample Age Over 18 (Compared to baseline levels)") summary(cjt_us_results_age_subset)$amce$Estimate - summary(cjt_us_results)$amce$Estimate #Look at rank DV cjt_us_rank <- cjoint::amce(rank_outcome ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability , data = cjt_us_data, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) summary(cjt_us_rank)$amce xtable(summary(cjt_us_rank)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels") plot.amce(cjt_us_rank, xlab="Expected Change in Migrant Profile Selection, US (Rating Results)", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability"), # xlim=c(-0.07, .2), text.size=9) #Interactions cjt_us_interaction <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability + Reason.for.migration*Origin, data = cjt_us_data, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) plot.amce(cjt_us_interaction, xlab="Expected Change in Migrant Profile Selection, US", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability", "Origin*Reason"), # xlim=c(-0.07, .2), text.size=9) cjt_us_interaction2 <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability + Reason.for.migration*Vulnerability, data = cjt_us_data, cluster= T, respondent.id = 'Response.ID', design= cjt_us_design, baselines=baselines) plot.amce(cjt_us_interaction2, xlab="Expected Change in Migrant Profile Selection, US", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability", "Reason*Vulnerability"), # xlim=c(-0.07, .2), text.size=9) ######### Marginal Means and Subgroups ######### cjt_us_data$Language.Fluency2 <- car::recode(cjt_us_data$Language.Fluency, "'None'='None_lf'") cjt_us_data$Origin <- car::recode(cjt_us_data$Origin, "'Another region in your country'='Same Country'") cjt_us_data$Reason.for.migration <- car::recode(cjt_us_data$Reason.for.migration, "'Economic opportunity'='Economic'; 'Political/religious/ethnic persecution'='Persecution'") cjt_us_data$Religion <- car::recode(cjt_us_data$Religion, "'Agnostic'='Athiest'") cjt_us_data$Vulnerability <- car::recode(cjt_us_data$Vulnerability, "'Food insecurity'='Food insc.'; 'Physically handicapped'='Physical handicap'; 'No surviving family members'='No family';'Post Traumatic Stress Disorder (PTSD)'='PTSD'") us_mms <- selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration us_mms_interaction <- selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration + Reason.for.migration*Origin plot(mm(cjt_us_data, us_mms, id = ~Response.ID), vline = 0.5, xlab = "Conjoint Marginal Means, US", legend_pos = "none") plot(mm(cjt_us_data, us_mms_interaction, id = ~Response.ID), vline = 0.5, xlab = "Conjoint Marginal Means, US", legend_pos = "none") cjt_us_data$PARTISANSHIP_bin <- relevel(cjt_us_data$PARTISANSHIP_bin, "R") cjt_us_data <- cjt_us_data %>% mutate(empathy_bin_mean = as.factor(ifelse(empathy_index>2.20, "emp_high", "emp_low")), empathy_bin_quart = as.factor(ifelse(empathy_index>2.50, "emp_high", ifelse(empathy_index<1.75,"emp_low", NA))), age_bin_mean = as.factor(ifelse(AGE>44.12, "age_high", "age_low")), age_bin_quart = as.factor(ifelse(AGE>60.00 , "age_high", ifelse(empathy_index<28.00,"age_low", NA))), origin_binary = as.factor(ifelse(Origin=="Same Country", "Same Country", "Other Country")), education_college_bin = as.factor(ifelse(EDUCATION_num> 4, "college_degree", "no_college_degree")), employed_bin = as.factor(ifelse(EMPLOYMENT_num %in% c(1, 6, 5), "employed", "not_employed")), unemployed_bin = as.factor(ifelse(EMPLOYMENT_num==7, "unemployed", "not_unemployed")) ) us_mms_origin_bin <- selected ~ Language.Fluency2 + Occupation + Gender + origin_binary + Religion + Vulnerability + Reason.for.migration us_mms_origin_bin_interaction <- selected ~ Language.Fluency2 + Occupation + Gender + origin_binary + Religion + Vulnerability + Reason.for.migration + Reason.for.migration*origin_binary #collapse origin plot(mm(cjt_us_data, us_mms_origin_bin, id = ~Response.ID), vline = 0.5, xlab = "Conjoint Marginal Means, US", legend_pos = "none", alpha=.9) #origin * reason mm_diffs_origin_bin <- mm_diffs(data = cjt_us_data, formula = selected ~ Language.Fluency2 + Occupation + Gender + Religion + Vulnerability + Reason.for.migration, by = ~origin_binary, id = ~Response.ID) #difference of same country - other country mm_diffs_origin_bin <- mm_diffs_origin_bin[, c(4:7, 9)] mm_diffs_origin_bin[18:22, ] colnames(mm_diffs_origin_bin) <- c("Feature", "Level", "Est.", "SE", "P") xtable(mm_diffs_origin_bin, digits=3, font.size = "small", caption = "Marginal Mean Differences by Origin, US Sample (same country - other country)") mm_by_part_bin <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~PARTISANSHIP_bin) # test of whether any of the interactions between the by variable and feature levels differ from zero cj_anova(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~PARTISANSHIP_bin) mm_by_empathy_bin_mean <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~empathy_bin_mean) cj_anova(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~empathy_bin_mean) mm_by_empathy_bin_quart <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~empathy_bin_quart) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~empathy_bin_quart) mm_by_age_bin_mean <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~age_bin_mean) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~age_bin_mean) mm_by_age_bin_qurat <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~age_bin_quart) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~age_bin_quart) mm_by_gender <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~Gender) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~Gender) mm_by_border_state <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~border_state_indicator) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~border_state_indicator) mm_by_college_degree <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~education_college_bin) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~education_college_bin) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~employed_bin) cj_anova(na.omit(cjt_us_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~unemployed_bin) mm_by_employed <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~employed_bin) mm_by_unemployed <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~unemployed_bin) plot(mm_by_part_bin, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "PID") + scale_color_manual(name="PID", labels= c("Dem", "Rep"), values = c("blue", "red")) plot(mm_by_age_bin_mean, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "Age") + scale_color_manual(values=c("blue", "red"), name="Age", labels= c("4th quart.", "1st quart.")) plot(mm_by_border_state, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "State") + ggplot2::scale_color_manual(values=c("blue", "red"),name="State", labels= c("Non-Border", "Border")) plot(mm_by_empathy_bin_quart, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "Empathy") + ggplot2::scale_color_manual(values=c("blue", "red"),name="State", labels= c("4th quart.", "1st quart.")) plot(mm_by_college_degree, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "College Degree") + scale_color_manual(name="Education", labels= c("College Degree", "No College Degree"), values = c("blue", "red")) plot(mm_by_employed, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "Employment") + scale_color_manual(name="Employment", labels= c("Employed", "Not Employed"), values = c("blue", "red")) plot(mm_by_unemployed, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (US)", legend_title = "Employment") + scale_color_manual(name="Employment", labels= c("Not Unemployed", "Unemployed"), values = c("blue", "red")) diff_mms_by_part_bin <- cj(cjt_us_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm_diff", by = ~PARTISANSHIP_bin) plot(rbind(mm_by_part_bin, diff_mms_by_part_bin), legend_pos = "none") + ggplot2::facet_wrap(~BY, ncol = 3L) plot(diff_mms_by_part_bin, legend_pos = "none", xlab= "Marginal Mean Diff., D-R") #difference in marginal means R and D ######### Diagnostics ######### plot(cj_freqs(cjt_us_data, us_mms, id = ~Response.ID), legend_pos = "none", ylab = "Frequency of Level in Conjoint Design (US)") plot(cj(cjt_us_data, us_mms, id = ~Response.ID, by = ~profile, estimate = "mm"), group = "profile", vline = 0.5, legend_pos = "none", xlab= "Marginal Mean, Left vs Right Profile (US)") ########################### Study 1: Germany ########################## ######### Import data ######### cjt_ger_design2 <- makeDesign(type="file", filename= 'conjoint_design_german2.dat') cjt_ger_design2_internal <- makeDesign(type="file", filename= 'conjoint_design_german2_internal.dat') cjt_ger_data <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- Germany_September 11, 2019_02.58.csv", new.format = T, respondentID = "ResponseId", responses=c("force_choice", "Q184", "Q186", "Q188", "Q190", "Q192", "Q194", "Q196", "Q198"), covariates=c("GENDER_resp", "EDUCATION_resp", "IDEOLOGY", 'RELIGIOSITY_resp', 'NATIVE_BORN', 'EMPLOYMENT_resp', 'Age', 'TRUST_GOVT', 'POL_INTEREST', 'FP_ORIENTATION_1', 'FP_ORIENTATION_2', 'FP_ORIENTATION_3', 'FP_ORIENTATION_4', 'EMPATHY_1', 'EMPATHY_2', 'EMPATHY_3', 'EMPATHY_4', 'city', 'state_region')) cjt_ger_data_trust_gov <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- Germany_September 11, 2019_02.58.csv", new.format = T, respondentID = "ResponseId", responses=c("force_choice", "Q184", "Q186", "Q188", "Q190", "Q192", "Q194", "Q196", "Q198"), covariates=c('TRUST_GOVT')) cjt_ger_data_rank <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- Germany_September 11, 2019_02.58.csv", new.format = T, respondentID = "ResponseId", responses=c("force_choice", "Q184", "Q186", "Q188", "Q190", "Q192", "Q194", "Q196", "Q198"), covariates=c("GENDER_resp", "EDUCATION_resp", "IDEOLOGY", 'RELIGIOSITY_resp', 'NATIVE_BORN', 'EMPLOYMENT_resp', 'Age', 'TRUST_GOVT', 'POL_INTEREST', 'FP_ORIENTATION_1', 'FP_ORIENTATION_2', 'FP_ORIENTATION_3', 'FP_ORIENTATION_4', 'EMPATHY_1', 'EMPATHY_2', 'EMPATHY_3', 'EMPATHY_4', 'city', 'state_region'), ranks = c("RATE_MIG1","RATE_MIG2", "Q168", "Q169", "Q170", "Q171", "Q172", "Q173", "Q174", "Q175", "Q176", "Q177", "Q178", "Q179", "Q180", "Q181", "Q182", "Q183")) cjt_ger_data_rank <- cjt_ger_data_rank %>% dplyr::rename(rank_outcome = selected) %>% dplyr::select(rank_outcome) cjt_ger_data_trust_gov <- cjt_ger_data_trust_gov %>% dplyr::select(TRUST_GOVT) cjt_ger_data <- cbind(cjt_ger_data, cjt_ger_data_rank, cjt_ger_data_trust_gov) cjt_ger_data <- cjt_ger_data[!is.na(cjt_ger_data$selected), ] ######### Recoding ######### ger_states <- read.csv(file = 'germany_state_key.csv') names(ger_states) <- c("qualtrics_code", "state_name", "region", "east_indicator", "east_indicator2", "east_indicator3", "east_indicator4", "east_indicator5", "east_indicator6", "east_indicator7") cjt_ger_data <- cjt_ger_data %>% mutate(state_num = as.numeric(paste(state_region))) %>% left_join(ger_states, by=c("state_num"= "qualtrics_code")) cjt_ger_data$urban_indicator <- 1*(cjt_ger_data$city %in% c("Berlin", "Hamburg", "Munich", "Cologne", "Frankfurt Am Main", "Stuttgart", "Dusseldorf", "Dortmund", "Essen", "Leipzig")) cjt_ger_data$FP_ORIENTATION_1 <- car::recode(cjt_ger_data$FP_ORIENTATION_1, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") cjt_ger_data$FP_ORIENTATION_2 <- car::recode(cjt_ger_data$FP_ORIENTATION_2, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded cjt_ger_data$FP_ORIENTATION_3 <- car::recode(cjt_ger_data$FP_ORIENTATION_3, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") cjt_ger_data$EMPATHY_1 <- car::recode(cjt_ger_data$EMPATHY_1, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded cjt_ger_data$EMPATHY_2 <- car::recode(cjt_ger_data$EMPATHY_2, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") #reverse coded cjt_ger_data$EMPATHY_3 <- car::recode(cjt_ger_data$EMPATHY_3, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") cjt_ger_data$EMPATHY_4 <- car::recode(cjt_ger_data$EMPATHY_4, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") cjt_ger_data <- cjt_ger_data %>% mutate(AGE = as.numeric(Age)) cjt_ger_data$GENDER_num <- ifelse(cjt_ger_data$GENDER_resp == 2, 1, 0) cjt_ger_data$EDUCATION_num <- as.numeric(car::recode(cjt_ger_data$EDUCATION, "'Abgeschlossenes Hochschulstudium'=6; 'Angefangenes Hochschulstudium'=5; 'Abitur'=4; 'Facabitur'=3; 'Realschulabschluss'=2; 'Haptschulabschluss'=1"))-1 cjt_ger_data$IDEOLOGY_num <- as.numeric(car::recode(cjt_ger_data$IDEOLOGY, "'Extrem liberal'=7; 'Liberal'=6; 'Etwas liberal'=5; 'Moderat, die gemäßigte Mitte'=4; 'Etwas konservativ'=3; 'Konservativ'=2; 'Extrem konservativ'=1"))-1 cjt_ger_data$RELIGIOSITY_num <- as.numeric(car::recode(cjt_ger_data$RELIGIOSITY, "'Mehr als einmal die Woche'=6; 'Wöchentlich '=5; 'Ein paar Mal im Monat'=4; 'Ein paar Mal im Jahr'=3; 'Einmal im Jahr oder weniger'=2; 'Nie'=1"))-1 cjt_ger_data$NATIVE_BORN_num <- ifelse(cjt_ger_data$NATIVE_BORN == 1, 1, 0) cjt_ger_data$EMPLOYMENT_num <- as.numeric(car::recode(cjt_ger_data$EMPLOYMENT, "'17'=7; '16'=6; '21'=5; '18'=4; '20'=3; '19'=2; '17 '=1")) cjt_ger_data$TRUST_GOVT_num <-as.numeric(cjt_ger_data$TRUST_GOVT)-1 cjt_ger_data$POL_INTEREST_num <- as.numeric(car::recode(cjt_ger_data$POL_INTEREST, "'Mesitens'=4; 'Manchmal'=3; 'Nur ab und zu'=2; 'Kaum'=1"))-1 ######### Construct scales ######### fp_orientation_ger <- data.frame(cjt_ger_data[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3")]) empathy_ger <- data.frame(cjt_ger_data[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")]) fp_orientation_ger <- data.frame(sapply(fp_orientation_ger, FUN= function(x) as.numeric(x))-1) empathy_ger <- data.frame(sapply(empathy_ger, FUN= function(x) as.numeric(x))-1) #calculate chronbach's alpha for each index # psych::alpha(fp_orientation_ger) # psych::alpha(empathy_ger) #r=create the index variable as the mean score on the individual items cjt_ger_data$fp_orientation_index <- apply(fp_orientation_ger, MARGIN = 1, FUN = mean) cjt_ger_data$empathy_index <- apply(empathy_ger, MARGIN = 1, FUN = mean) ######### Balance, summary stats ######### vars <- c('AGE', 'fp_orientation_index', "empathy_index", "GENDER_num", "EDUCATION_num", "IDEOLOGY_num", "NATIVE_BORN_num", "EMPLOYMENT_num", "TRUST_GOVT_num", "POL_INTEREST_num","RELIGIOSITY_num" ) var_labels <- c("Age", "Foreign Policy Orientation", "Empathy", "Gender", "Education", "Ideology", "Native Born", "Employment", "Trust in Government", "Political Interest","Religiosity" ) sum_stats <- data.frame(matrix(NA,length(vars), 7)) colnames(sum_stats) <- c("Var.", "Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max." ) sum_stats[, 1] <- var_labels for (i in 1:length(vars)){ string <- paste('sum <- summary(cjt_ger_data$',vars[i], ')', sep = "", collapse = "") eval(parse(text=string)) sum_stats[i, 2:7] <- sum } xtable(sum_stats, caption = "Experiment 1 Summary Statistics, GER Sample", digits = c(0, 0, 0, 2, 2, 2, 2, 0)) ######### Conjoint main analysis ######### names(cjt_ger_data)[23] <- "Occupation" names(cjt_ger_data)[25] <- "Gender" names(cjt_ger_data)[27] <- "Reason.for.migration" names(cjt_ger_data)[29] <- "Origin" names(cjt_ger_data)[31] <- "Religion" names(cjt_ger_data)[34] <- "Language.Fluency" names(cjt_ger_data)[36] <- "Vulnerability" baselines <- list() baselines$Gender <- 'Weiblich' baselines$Vulnerability <- 'Keine' baselines$'Reason.for.migration' <- 'Wirtschaftliche Perspektive' baselines$Occupation <- 'Arbeitslos' baselines$'Language Fluency' <- 'Keine' baselines$Origin <- 'Aus einem anderen Teil Ihres Landes' cjt_ger_results <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines ) summary(cjt_ger_results)$amce xtable(summary(cjt_ger_results)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, German Sample (Compared to baseline levels") levels.test<-list() levels.test[["Gender"]]<-c("Female","Male") levels.test[["Language Fluency"]]<-c('None', "Fluent", "Broken") levels.test[["Occupation"]]<-c("Unemployed","Doctor", "Teacher", "Cleaner") levels.test[["Origin"]]<-c("Same Country", "Ethiopia", "Afghanistan", "Myanmar", "Ukraine") levels.test[["Reason.for.migration"]]<-c("Economic","Flooding", "Drought", "Persecution", "Wildfires") levels.test[["Religion"]]<-c("Athiest","Christian", "Muslim") levels.test[["Vulnerability"]]<-c("None","Food insc.", "Physical handicap", "No family", "PTSD") plot.amce(cjt_ger_results, xlab="Expected Change in Migrant Profile Selection, Germany", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability"), xlim=c(-0.11, .2), text.size=9) ######### Robustness ######### #Only respondents who complete all 9 tasks cjt_ger_data_only9tasks <- cjt_ger_data %>% group_by(Response.ID) %>% mutate(resp_profiles_count = n()) cjt_ger_data_only9tasks <- cjt_ger_data_only9tasks[cjt_ger_data_only9tasks$resp_profiles_count > 17, ] nrow(cjt_ger_data) - nrow(cjt_ger_data_only9tasks) length(unique(cjt_ger_data$Response.ID)) - length(unique(cjt_ger_data_only9tasks$Response.ID)) cjt_ger_results_only_9tasks <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data_only9tasks, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines) summary(cjt_ger_results_only_9tasks)$amce xtable(summary(cjt_ger_results_only_9tasks)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, German Sample (Compared to baseline levels- \n Only respondents who complete all 9 tasks") summary(cjt_ger_results_only_9tasks)$amce$Estimate - summary(cjt_ger_results)$amce$Estimate #Compare internal and external migrant profiles cjt_ger_data_internal <- cjt_ger_data[! (cjt_ger_data$Origin %in% "Aus einem anderen Teil Ihres Landes" & cjt_ger_data$Reason.for.migration %in% c("Dürre", "Waldbrände", "Politische/religiöse/ethnische Verfolgung")), ] cjt_ger_data_internal_strict <- cjt_ger_data[! (cjt_ger_data$Origin %in% "Aus einem anderen Teil Ihres Landes"), ] length(unique(cjt_ger_data$Response.ID)) - length(unique(cjt_ger_data_internal$Response.ID)) cjt_ger_results_internal <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data_internal, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines) summary(cjt_ger_results_internal)$amce$Estimate - summary(cjt_ger_results)$amce$Estimate ger_int_table1 <- as_tibble(summary(cjt_ger_results_internal)$amce[, c(1:4, 7)]) %>% cbind(round(summary(cjt_ger_results_internal)$amce$Estimate - summary(cjt_ger_results)$amce$Estimate, 3)) xtable(ger_int_table1, digits = 3, font.size = "small", caption = "AMCE, Ger Sample (Compared to baseline levels- \n Excluding 'implausible' internal migration profiles") baselines$Origin <- 'Ukraine' cjt_ger_results_internal_strict <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data_internal_strict, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines) cjt_ger_results2 <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines ) ger_int_table2 <- as_tibble(summary(cjt_ger_results_internal_strict)$amce[, c(1:4, 7)]) %>% cbind(round(summary(cjt_ger_results_internal_strict)$amce$Estimate - summary(cjt_ger_results2)$amce$Estimate[c(1:8, 10:20)], 3)) xtable(ger_int_table2, digits = 3, font.size = "small", caption = "AMCE, German Sample (Compared to baseline levels- \n Excluding all internal migration profiles") summary(cjt_ger_results_internal_strict)$amce$Estimate - summary(cjt_ger_results2)$amce$Estimate[c(1:8, 10:20)] place_ger <- round(summary(cjt_ger_results_internal_strict)$amce$Estimate - summary(cjt_ger_results2)$amce$Estimate[c(1:8, 10:20)], 4) cjt_ger_internal_comparisons <- cbind(summary(cjt_ger_results_internal)$amce$Attribute, summary(cjt_ger_results_internal)$amce$Level, round(summary(cjt_ger_results_internal)$amce$Estimate - summary(cjt_ger_results)$amce$Estimate, 4), c(place_ger[1:9], NA, place_ger[10:19])) colnames(cjt_ger_internal_comparisons) <- c("Attribute", "Level", "Full Model - Implausible Internal Profiles Removed", "Full Model - All Internal Profiles Removed") xtable(cjt_ger_internal_comparisons, font.size = "small", caption = "AMCE Differences, German Sample (Compared to baseline levels- \n Full Set Compared to Restricted Internal Sets \n Baseline Origin for Second Comparison Changed to Ukraine") baselines <- list() baselines$Gender <- 'Weiblich' baselines$Vulnerability <- 'Keine' baselines$'Reason.for.migration' <- 'Wirtschaftliche Perspektive' baselines$Occupation <- 'Arbeitslos' baselines$'Language Fluency' <- 'Gebrochen' cjt_ger_data_internal_actual <- cjt_ger_data[(cjt_ger_data$Origin %in% "Aus einem anderen Teil Ihres Landes"), ] cjt_ger_results_internal_actual <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data_internal_actual, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2_internal, baselines=baselines) cjt_ger_results3 <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines ) ger_ext_table <- as_tibble(summary(cjt_ger_results_internal_actual)$amce[, c(1:4, 7)]) %>% cbind(round(summary(cjt_ger_results_internal_actual)$amce$Estimate - summary(cjt_ger_results3)$amce$Estimate[c(1:2, 4:16)], 3)) xtable(ger_ext_table, digits = 3, font.size = "small", caption = "AMCE, Ger Sample (Compared to baseline levels- \n Excluding external migration profiles") #Only respondents 18 and over cjt_ger_data_age_subset <- subset(x = cjt_ger_data, subset = AGE > 17) nrow(cjt_ger_data) - nrow(cjt_ger_data_age_subset) length(unique(cjt_ger_data$Response.ID)) - length(unique(cjt_ger_data_age_subset$Response.ID)) names(cjt_ger_data_age_subset)[23] <- "Occupation" names(cjt_ger_data_age_subset)[25] <- "Gender" names(cjt_ger_data_age_subset)[27] <- "Reason.for.migration" names(cjt_ger_data_age_subset)[29] <- "Origin" names(cjt_ger_data_age_subset)[31] <- "Religion" names(cjt_ger_data_age_subset)[34] <- "Language.Fluency" names(cjt_ger_data_age_subset)[36] <- "Vulnerability" baselines <- list() baselines$Gender <- 'Weiblich' baselines$Vulnerability <- 'Keine' baselines$'Reason.for.migration' <- 'Wirtschaftliche Perspektive' baselines$Occupation <- 'Arbeitslos' baselines$'Language Fluency' <- 'Keine' baselines$Origin <- 'Aus einem anderen Teil Ihres Landes' cjt_ger_results_age_subset <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability, data = cjt_ger_data_age_subset, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines ) summary(cjt_ger_results_age_subset)$amce xtable(summary(cjt_ger_results_age_subset)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, German Sample Age Over 18 (Compared to baseline levels)") summary(cjt_ger_results_age_subset)$amce$Estimate - summary(cjt_ger_results)$amce$Estimate #Rank DV cjt_ger_rank <- cjoint::amce(rank_outcome ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability , data = cjt_ger_data, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines) summary(cjt_ger_rank)$amce xtable(summary(cjt_ger_rank)$amce[, c(1:4, 7)], digits=3, font.size = "small", caption = "AMCE, German Sample (Compared to baseline levels): Rating Outcome") plot.amce(cjt_ger_rank, xlab="Expected Change in Migrant Profile Selection, Germany (Rating Results)", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability"), # xlim=c(-0.07, .2), text.size=9) #Interactions cjt_ger_interaction <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability + Reason.for.migration*Origin, data = cjt_ger_data, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines) plot.amce(cjt_ger_interaction, xlab="Expected Change in Migrant Profile Selection, Germany", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability", "Origin*Reason"), # xlim=c(-0.07, .2), text.size=9) cjt_ger_interaction2 <- cjoint::amce(selected ~ Gender + Language.Fluency + Occupation + Origin + Reason.for.migration + Religion + Vulnerability + Reason.for.migration*Vulnerability, data = cjt_ger_data, cluster= T, respondent.id = 'Response.ID', design= cjt_ger_design2, baselines=baselines) plot.amce(cjt_ger_interaction2, xlab="Expected Change in Migrant Profile Selection, Germany", main="", level.names = levels.test, attribute.names = c("Gender","Language Fluency","Occupation", "Origin", "Reason", "Religion", "Vulnerability", "Reason*Vulnerability"), # xlim=c(-0.07, .2), text.size=9) ######### Marginal Means ######### cjt_ger_data$Language.Fluency2 <- car::recode(cjt_ger_data$Language.Fluency, "'FlieÃYend'='Fluent'; 'Gebrochen'='Broken'; 'Keine'='None_lf'") cjt_ger_data$Occupation <- car::recode(cjt_ger_data$Occupation, "'Arbeitslos'='Unemployed'; 'Arzt'='Doctor'; 'Lehrer'='Teacher';'Reinigungskraft'='Cleaner'") cjt_ger_data$Gender <- car::recode(cjt_ger_data$Gender, "'Männlich'='Male'; 'Weiblich'='Female'") cjt_ger_data$Origin <- car::recode(cjt_ger_data$Origin, "'Ã\"thiopien'='Ethiopia'; 'Aus einem anderen Teil Ihres Landes'='Same Country'") cjt_ger_data$Vulnerability <- car::recode(cjt_ger_data$Vulnerability, "'Ernährungsunsicherheit'='Food insc.'; 'Körperliche Behinderung'='Physical handicap'; 'Keine'='None';'Keine überlebenden Familienmitglieder'='No family'; 'PTBS (Posttraumatische Belastungsstörung)'='PTSD'") cjt_ger_data$Reason.for.migration <- car::recode(cjt_ger_data$Reason.for.migration, "'Ãoberflutung'='Flooding'; 'Dürre'='Drought'; 'Politische/religiöse/ethnische Verfolgung'='Persecution';'Waldbrände'='Wildfires';'Wirtschaftliche Perspektive'='Economic'") cjt_ger_data$Origin <- car::recode(cjt_ger_data$Origin, "'Aus einem anderen Teil Ihres Landes'='Same Country'") ger_mms <- selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration plot(mm(cjt_ger_data, ger_mms, id = ~Response.ID), vline = 0.5, xlab = "Conjoint Marginal Means, GER", legend_pos = "none") cjt_ger_data <- cjt_ger_data %>% mutate(empathy_bin_mean = as.factor(ifelse(empathy_index>2.205, "emp_high", "emp_low")), empathy_bin_quart = as.factor(ifelse(empathy_index>2.50, "emp_high", ifelse(empathy_index<2.0,"emp_low", NA))), age_bin_mean = as.factor(ifelse(AGE>45.64, "age_high", "age_low")), age_bin_quart = as.factor(ifelse(AGE>60.00 , "age_high", ifelse(empathy_index<31.00,"age_low", NA))), origin_binary = as.factor(ifelse(Origin=="Same Country", "Same Country", "Other Country")), education_college_bin = as.factor(ifelse(EDUCATION_num> 4, "college_degree", "no_college_degree")), employed_bin = as.factor(ifelse(EMPLOYMENT_num %in% c(1, 6, 5), "employed", "not_employed")), unemployed_bin = as.factor(ifelse(EMPLOYMENT_num==7, "unemployed", "not_unemployed")) ) ger_mms_origin_bin <- selected ~ Language.Fluency2 + Occupation + Gender + origin_binary + Religion + Vulnerability + Reason.for.migration ger_mms_origin_bin_interaction <- selected ~ Language.Fluency2 + Occupation + Gender + origin_binary + Religion + Vulnerability + Reason.for.migration + Reason.for.migration*origin_binary #collapse origin plot(mm(cjt_ger_data, ger_mms_origin_bin, id = ~Response.ID), vline = 0.5, xlab = "Conjoint Marginal Means, Germany", legend_pos = "none", alpha=.9) #origin * reason mm_diffs_origin_bin <- mm_diffs(data = cjt_ger_data, formula = selected ~ Language.Fluency2 + Occupation + Gender + Religion + Vulnerability + Reason.for.migration, by = ~origin_binary, id = ~Response.ID) #difference of same country - other country mm_diffs_origin_bin <- mm_diffs_origin_bin[, c(4:7, 9)] mm_diffs_origin_bin[18:22, ] colnames(mm_diffs_origin_bin) <- c("Feature", "Level", "Est.", "SE", "P") xtable(mm_diffs_origin_bin, digits=3, font.size = "small", caption = "Marginal Mean Differences by Origin, US Sample (same country - other country)") mm_by_empathy_bin_mean <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~empathy_bin_mean) cj_anova(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~empathy_bin_mean) mm_by_empathy_bin_quart <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~empathy_bin_quart) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~empathy_bin_quart) mm_by_age_bin_mean <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~age_bin_mean) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~age_bin_mean) mm_by_age_bin_qurat <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~age_bin_quart) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~age_bin_quart) mm_by_gender <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~Gender) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~Gender) mm_by_border_state <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator) mm_by_border_state2 <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator2) mm_by_border_state3 <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator3) mm_by_border_state4 <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator4) mm_by_border_state5 <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator5) mm_by_border_state6 <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator6) mm_by_border_state7 <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~east_indicator7) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~education_college_bin) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~employed_bin) cj_anova(na.omit(cjt_ger_data), selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, by = ~unemployed_bin) mm_by_college <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~education_college_bin) mm_by_employed <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~employed_bin) mm_by_unemployed <- cj(cjt_ger_data, selected ~ Language.Fluency2 + Occupation + Gender + Origin + Religion + Vulnerability + Reason.for.migration, id = ~Response.ID, estimate = "mm", by = ~unemployed_bin) plot(mm_by_college, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (GER)", legend_title = "College Degree") + scale_color_manual(name="Education", labels= c("College Degree", "No College Degree"), values = c("blue", "red")) plot(mm_by_employed, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (GER)", legend_title = "Employment") + scale_color_manual(name="Employment", labels= c("Employed", "Not Employed"), values = c("blue", "red")) plot(mm_by_unemployed, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (GER)", legend_title = "Employment") + scale_color_manual(name="Employment", labels= c("Not Unemployed", "Unemployed"), values = c("blue", "red")) plot(mm_by_empathy_bin_mean, group = "BY", vline = 0.5, xlab = "Marginal Mean Diff. (GER)", legend_title = "Empathy") + scale_color_manual(name="Empathy", labels= c("Low", "High"), values = c("blue", "red")) borderplot1 <- plot(mm_by_border_state, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State2", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplot2 <- plot(mm_by_border_state2, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State1", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplot3 <- plot(mm_by_border_state3, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State3", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplot4 <- plot(mm_by_border_state4, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State4", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplot5 <- plot(mm_by_border_state5, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State5", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplot6 <- plot(mm_by_border_state6, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State6", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplot7 <- plot(mm_by_border_state7, group = "BY", vline = 0.5, xlab = "", legend_title = "Border State") + scale_color_manual(name="Border State7", labels= c("Non-Border", "Border"), values = c("blue", "red")) + theme(legend.position="none") borderplots1 <- ggarrange(borderplot1, borderplot2, borderplot3, ncol=1, nrow=3) annotate_figure(borderplots1, #plot window needs to be rly big # top=text_grob("Robustness of Border State Indicator: Marginal Mean Differences", face="bold"), bottom=text_grob("Blue: Non-border; Red: Border")) borderplots2 <- ggarrange(borderplot4, #borderplot5, borderplot6, borderplot7, ncol=1, nrow=3) annotate_figure(borderplots2, #plot window needs to be rly big # top=text_grob("Robustness of Border State Indicator: Marginal Mean Differences", face="bold"), bottom=text_grob("Blue: Non-border; Red: Border")) ######### Diagnostics ######### plot(cj_freqs(cjt_ger_data, ger_mms, id = ~Response.ID), legend_pos = "none", ylab = "Frequency of Level in Conjoint Design (GER)") plot(cj(cjt_ger_data, ger_mms, id = ~Response.ID, by = ~profile, estimate = "mm"), group = "profile", vline = 0.5, legend_pos = "none", xlab= "Marginal Mean, Left vs Right Profile (GER)") ######### Main Figure ######### coef.vec <- c(0.035, 0.081, 0.037, 0.086, 0.040, 0.060, 0.076, 0.162) se.vec <- c(0.012, 0.012, 0.012, 0.012, 0.011, 0.012, 0.012, 0.013) #excl all international profiles coef.vec.internal <- c(0.049, 0.085, 0.080, 0.13, 0.059, 0.072, 0.075, 0.150) se.vec.internal <- c(0.025, 0.031, 0.025, 0.029, 0.024, 0.029, 0.025, 0.029) #excl all internal profiles coef.vec.international <- c(0.032, 0.080, 0.026, 0.079, 0.035, 0.057, 0.076, 0.164) se.vec.international <- c(0.013, 0.013, 0.013, 0.013, 0.013, 0.013, 0.013, 0.014) ymin <- coef.vec-qnorm(.95)*se.vec ymax <- coef.vec+qnorm(.95)*se.vec ymin.internal <- coef.vec.internal-qnorm(.95)*se.vec.internal ymax.internal <- coef.vec.internal+qnorm(.95)*se.vec.internal ymin.international <- coef.vec.international-qnorm(.95)*se.vec.international ymax.international <- coef.vec.international+qnorm(.95)*se.vec.international var.names <- c("Drought, U.S.", "Drought, Ger.", "Flooding, U.S.", "Flooding, Ger.", "Wildfires, U.S.", "Wildfires, Ger.", "Persecution, U.S.", "Persecution, Ger.") reasons <- c("Drought", "Drought", "Flooding", "Flooding", "Wildfires", "Wildfires", "Persecution", "Persecution") Country <- rep(c("US", "Ger"), 4) reason_data <- data.frame(coef.vec, se.vec, var.names, reasons, Country, ymin, ymax, coef.vec.internal, se.vec.internal, ymin.internal, ymax.internal, coef.vec.international, se.vec.international, ymin.international, ymax.international) reason_data$Country <- relevel(reason_data$Country, "US") reason_data$reasons <- factor(reason_data$reasons, levels = c("Persecution", "Wildfires", "Flooding", "Drought")) all_profs <- ggplot(data=reason_data, mapping = aes(y=coef.vec, x=reasons, colour=Country, shape=Country))+ geom_point() + geom_hline(yintercept=0, linetype="dashed")+ scale_y_continuous(limits=c(-.02, .2), breaks=seq(-.02, .2, 0.02), labels = c("", 0,"", .04,"", .08,"", .12,"", .16,"", .2))+ scale_colour_grey(start=0, end=.61)+ geom_pointrange(mapping = aes(ymin=ymin, ymax=ymax))+ coord_flip()+ theme_classic() + theme(legend.position="None", axis.ticks.length=unit(.25, "cm"), axis.title.x = element_text(vjust=-0.5), axis.title.y = element_text(vjust=3), plot.title = element_text(hjust = 0.5)) + labs(title = "All Profiles", # caption="", x="", y="" # y="Average Marginal Component Effect (AMCE)", # x="Reason for Migration \n (Baseline = Economic Opportunity)" ) internal_profs <- ggplot(data=reason_data, mapping = aes(y=coef.vec.internal, x=reasons, colour=Country, shape=Country))+ geom_point() + geom_hline(yintercept=0, linetype="dashed")+ scale_y_continuous(limits=c(-.02, .2), breaks=seq(-.02, .2, 0.02), labels = c("", 0,"", .04,"", .08,"", .12,"", .16,"", .2))+ scale_colour_grey(start=0, end=.61)+ geom_pointrange(mapping = aes(ymin=ymin.internal, ymax=ymax.internal))+ coord_flip()+ theme_classic() + theme(legend.position="None", axis.ticks.length=unit(.25, "cm"), axis.title.x = element_text(vjust=-0.5), axis.title.y = element_text(vjust=3), plot.title = element_text(hjust = 0.5)) + labs( title = "Internal Profiles", # caption="", x="", y="" # y="Average Marginal Component Effect (AMCE)", # x="Reason for Migration \n (Baseline = Economic Opportunity)" ) international_profs <- ggplot(data=reason_data, mapping = aes(y=coef.vec.international, x=reasons, colour=Country, shape=Country))+ geom_point() + geom_hline(yintercept=0, linetype="dashed")+ scale_y_continuous(limits=c(-.02, .2), breaks=seq(-.02, .2, 0.02), labels = c("", 0,"", .04,"", .08,"", .12,"", .16,"", .2))+ scale_colour_grey(start=0, end=.61)+ geom_pointrange(mapping = aes(ymin=ymin.international, ymax=ymax.international))+ coord_flip()+ theme_classic() + theme(legend.position="None", axis.ticks.length=unit(.25, "cm"), axis.title.x = element_text(vjust=-0.5), axis.title.y = element_text(vjust=3), plot.title = element_text(hjust = 0.5)) + labs( title = "International Profiles", # caption="", x="", y="" # y="Average Marginal Component Effect (AMCE)", # x="Reason for Migration \n (Baseline = Economic Opportunity)" ) reasons_plots <- ggarrange(all_profs, international_profs, internal_profs, ncol=3, nrow=1) annotate_figure(reasons_plots, bottom=text_grob("Average Marginal Component Effect (AMCE)") # left=text_grob("Reason for Migration \n (Baseline = Economic Opportunity)") ) ########################### Study 2: US ########################### ######### Import data ######### us_article <- read.csv(file = 'usclimate_exp1.csv', stringsAsFactors = T) us_article <- us_article[3:nrow(us_article), ] #Remove header ######### Recoding ######### us_article$PARTISANSHIP6 <- NA for(i in 1:nrow(us_article)){ if(us_article[i, "PARTISANSHIP_D"]=="Strong Democrat"){ us_article[i, "PARTISANSHIP6"]<- 6} if(us_article[i, "PARTISANSHIP_D"]=="Not very strong Democrat"){ us_article[i, "PARTISANSHIP6"]<- 5} if(us_article[i, "PARTISANSHIP_I"]=="Closer to the Democratic Party"){ us_article[i, "PARTISANSHIP6"]<- 4} if(us_article[i, "PARTISANSHIP_I"]=="Closer to the Republican Party"){ us_article[i, "PARTISANSHIP6"]<- 3} if(us_article[i, "PARTISANSHIP_R"]=="Not very strong Republican"){ us_article[i, "PARTISANSHIP6"]<- 2} if(us_article[i, "PARTISANSHIP_R"]=="Strong Republican"){ us_article[i, "PARTISANSHIP6"]<- 1} } us_article$FP_ORIENTATION_1 <- car::recode(us_article$FP_ORIENTATION_1, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") us_article$FP_ORIENTATION_2 <- car::recode(us_article$FP_ORIENTATION_2, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") #reverse coded us_article$FP_ORIENTATION_3 <- car::recode(us_article$FP_ORIENTATION_3, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") #reverse coded us_article$FP_ORIENTATION_4 <- car::recode(us_article$FP_ORIENTATION_4, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") us_article$SOC_DOM_1 <- car::recode(us_article$SOC_DOM_1, "'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5") #reverse coded us_article$SOC_DOM_2 <- car::recode(us_article$SOC_DOM_2, "'Definitely favor'=5; 'Somewhat favor'=4; 'Neither oppose nor favor'=3; 'Somewhat oppose'=2; 'Definitely oppose'=1") us_article$SOC_DOM_3 <- car::recode(us_article$SOC_DOM_3, "'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5") #reverse coded us_article$SOC_DOM_4 <- car::recode(us_article$SOC_DOM_4, "'Definitely favor'=5; 'Somewhat favor'=4; 'Neither oppose nor favor'=3; 'Somewhat oppose'=2; 'Definitely oppose'=1") us_article$EMPATHY_1 <- car::recode(us_article$EMPATHY_1, "'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1") #reverse coded us_article$EMPATHY_2 <- car::recode(us_article$EMPATHY_2, "'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5") #reverse coded us_article$EMPATHY_3 <- car::recode(us_article$EMPATHY_3, "'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5") us_article$EMPATHY_4 <- car::recode(us_article$EMPATHY_4, "'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1") #reverse coded us_article$MIGRATION_1 <- car::recode(us_article$MIGRATION_1, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") us_article$MIGRATION_2 <- car::recode(us_article$MIGRATION_2, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") us_article$MIGRATION_3 <- car::recode(us_article$MIGRATION_3, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") us_article$MIGRATION_4 <- car::recode(us_article$MIGRATION_4, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") #reverse coded us_article$MIGRATION_5 <- car::recode(us_article$MIGRATION_5, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") us_article$MIGRATION_6 <- car::recode(us_article$MIGRATION_6, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") #reverse coded us_article$CLIMATE_MIG_1 <- car::recode(us_article$CLIMATE_MIG_1, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") us_article$CLIMATE_MIG_2 <- car::recode(us_article$CLIMATE_MIG_2, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") us_article$CLIMATE_MIG_3 <- car::recode(us_article$CLIMATE_MIG_3, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") us_article$CLIMATE_MIG_4 <- car::recode(us_article$CLIMATE_MIG_4, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") #reverse coded us_article$CLIMATE_MIG_5 <- car::recode(us_article$CLIMATE_MIG_5, "'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5") us_article$CLIMATE_MIG_6 <- car::recode(us_article$CLIMATE_MIG_6, "'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1") #reverse coded us_article$CLIMATE_1 <- car::recode(us_article$CLIMATE_1, "'Definitely Agree'=1; 'Somewhat Agree'=2; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=4; 'Definitely Disagree'=5") us_article$CLIMATE_2 <- car::recode(us_article$CLIMATE_2, "'Definitely Agree'=5; 'Somewhat Agree'=4; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=2; 'Definitely Disagree'=1") us_article$CLIMATE_3 <- car::recode(us_article$CLIMATE_3, "'Definitely Agree'=5; 'Somewhat Agree'=4; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=2; 'Definitely Disagree'=1") us_article$CLIMATE_4 <- car::recode(us_article$CLIMATE_4, "'Definitely Agree'=5; 'Somewhat Agree'=4; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=2; 'Definitely Disagree'=1") #reverse coded us_article$CLIMATE_5 <- car::recode(us_article$CLIMATE_5, "'Definitely Agree'=1; 'Somewhat Agree'=2; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=4; 'Definitely Disagree'=5") us_article$CLIMATE_6 <- car::recode(us_article$CLIMATE_6, "'Definitely Agree'=5; 'Somewhat Agree'=4; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=2; 'Definitely Disagree'=1") #Scale 1 is cc, 2 is cm, 3 is m us_article$REL_IMPORT_SCALE_1 <- car::recode(us_article$REL_IMPORT_SCALE_1, "'Top priority'=5; 'Fairly high priority'=4; 'Medium level priority'=3; 'Slight priority'=2; 'Not a priority at all'=1") us_article$REL_IMPORT_SCALE_2 <- car::recode(us_article$REL_IMPORT_SCALE_2, "'Top priority'=5; 'Fairly high priority'=4; 'Medium level priority'=3; 'Slight priority'=2; 'Not a priority at all'=1") us_article$REL_IMPORT_SCALE_3 <- car::recode(us_article$REL_IMPORT_SCALE_3, "'Top priority'=5; 'Fairly high priority'=4; 'Medium level priority'=3; 'Slight priority'=2; 'Not a priority at all'=1") us_article <- us_article %>% mutate(PARTISANSHIP_bin = ifelse(PARTISANSHIP6>3, "D", "R"), AGE = as.numeric(Age)) %>% dplyr::rename(MIG_LEVELS = Q76_1, ANTHRO_CC = Q77_1, REL_IMPORT_SCALE_CLIMATE = REL_IMPORT_SCALE_1, REL_IMPORT_SCALE_MIGRATION = REL_IMPORT_SCALE_3, REL_IMPORT_SCALE_CLIMATEMIGRATION = REL_IMPORT_SCALE_2) us_article$MIG_LEVELS <- car::recode(us_article$MIG_LEVELS, "'Increased a Lot'=5; 'Increased a Little'=4; 'Stay the Same'=3; 'Decreased a Little'=2; 'Decreased a Lot'=1") us_article$PARTISANSHIP_bin <- as.factor(us_article$PARTISANSHIP_bin) us_article$PARTISANSHIP_num <- as.numeric(us_article$PARTISANSHIP_bin) us_article$GENDER_num <- ifelse(us_article$GENDER == "Female", 1, 0) us_article$EDUCATION_num <- as.numeric(car::recode(us_article$EDUCATION, "'Post-graduate degree'=6; 'College graduate'=5; 'Some college/Associate’s degree'=4; 'Trade or vocational certification'=3; 'High school graduate/GED'=2; 'Elementary or some high school'=1"))-1 us_article$IDEOLOGY_num <- as.numeric(car::recode(us_article$IDEOLOGY, "'Extremely liberal'=7; 'Liberal'=6; 'Slightly liberal'=5; 'Moderate, middle of the road'=4; 'Slightly conservative'=3; 'Conservative'=2; 'Extremely conservative'=1"))-1 us_article$RELIGIOSITY_num <- as.numeric(car::recode(us_article$RELIGIOSITY, "'More than once a week'=6; 'Once a week'=5; 'A few times a month'=4; 'A few times a year'=3; 'Once a year or less'=2; 'Never'=1"))-1 us_article$NATIVE_BORN_num <- ifelse(us_article$NATIVE_BORN == "United States", 1, 0) us_article$EMPLOYMENT_num <- as.numeric(car::recode(us_article$EMPLOYMENT, "'Employed full time'=7; 'Employed part time'=6; 'Self-employed'=5; 'Student'=4; 'Homemaker'=3; 'Retired'=2; 'Unemployed '=1"))-1 us_article$TRUST_GOVT_num <- as.numeric(car::recode(us_article$TRUST_GOVT, "'Most of the time'=3; 'Only some of the time'=2; 'Just about always'=1"))-1 us_article$POL_INTEREST_num <- as.numeric(car::recode(us_article$POL_INTEREST, "'Most of the time'=4; 'Some of the time'=3; 'Only now and then'=2; 'Hardly at all'=1"))-1 us_article$border_state_indicator <- 1*(us_article$state_region %in% c("TX", "CA", "AZ", "NM")) us_article$border_state_indicator_noCA <- 1*(us_article$state_region %in% c("TX", "AZ", "NM")) us_article$urban_indicator <- 1*(us_article$city %in% c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix", "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")) ######### Construct scales ######### climate <- data.frame(us_article[,c("CLIMATE_1", "CLIMATE_2", "CLIMATE_3", "CLIMATE_4", "CLIMATE_5", "CLIMATE_6")]) migration <- data.frame(us_article[,c("MIGRATION_1", "MIGRATION_2", "MIGRATION_3", "MIGRATION_4", "MIGRATION_5", "MIGRATION_6")]) climate_migration <- data.frame(us_article[,c("CLIMATE_MIG_1", "CLIMATE_MIG_2", "CLIMATE_MIG_3", "CLIMATE_MIG_4", "CLIMATE_MIG_5", "CLIMATE_MIG_6")]) fp_orientation <- data.frame(us_article[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3", "FP_ORIENTATION_4")]) soc_dom <- data.frame(us_article[,c("SOC_DOM_1", "SOC_DOM_2", "SOC_DOM_3", "SOC_DOM_4")]) empathy <- data.frame(us_article[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")]) climate <- data.frame(sapply(climate, FUN= function(x) as.numeric(x))-1) migration <- data.frame(sapply(migration, FUN= function(x) as.numeric(x))-1) climate_migration <- data.frame(sapply(climate_migration, FUN= function(x) as.numeric(x))-1) fp_orientation <- data.frame(sapply(fp_orientation, FUN= function(x) as.numeric(x))-1) soc_dom <- data.frame(sapply(soc_dom, FUN= function(x) as.numeric(x))-1) empathy <- data.frame(sapply(empathy, FUN= function(x) as.numeric(x))-1) #calculate chronbach's alpha for each index psych::alpha(climate) psych::alpha(migration) psych::alpha(climate_migration) psych::alpha(fp_orientation) psych::alpha(soc_dom) psych::alpha(empathy) #Create the index variable as the mean score on the individual items us_article$climate_index <- apply(climate, MARGIN = 1, FUN = mean) us_article$migration_index <- apply(migration, MARGIN = 1, FUN = mean) us_article$climate_migration_index <- apply(climate_migration, MARGIN = 1, FUN = mean) us_article$fp_orientation_index <- apply(fp_orientation, MARGIN = 1, FUN = mean) us_article$soc_dom_index <- apply(soc_dom, MARGIN = 1, FUN = mean) us_article$empathy_index <- apply(empathy, MARGIN = 1, FUN = mean) ######### Balance, summary stats ######### table(us_article$TREATMENT) #Distribution of treatment to check RA vars <- c('AGE', 'fp_orientation_index', 'soc_dom_index', "empathy_index", 'PARTISANSHIP_num', "GENDER_num", "EDUCATION_num", "IDEOLOGY_num", "RELIGIOSITY_num", "NATIVE_BORN_num", "EMPLOYMENT_num", "TRUST_GOVT_num", "POL_INTEREST_num") var_labels <- c("Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Partisanship", "Gender", "Education", "Ideology", "Religiosity", "Native Born", "Employment", "Trust in Government", "Political Interest") treats <- c("US Migration", "World Migration", "US Climate", "World Climate", "US Climate Migration", "World Climate Migration") treat_conditions <- c(1:6) balmat <- data.frame(matrix(NA,length(vars)*length(treats), 6)) colnames(balmat) <- c("Var.", "Treatment ID", "Treatment", "T-Test P val.", "Ctrl. Mean", "Treatment Mean") j <- c() for(i in 1:length(vars)){ a <- rep(var_labels[i], 6) j <- append(x = j, values = a)} balmat[, 1] <- j balmat[, 2] <- rep((1:6), 13) balmat[, 3] <- rep(treats, 13) counter <- 0 for (i in 1:length(vars)){ for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(us_article$', vars[i], '[us_article$TREATMENT==', treat_conditions[j], '], us_article$', vars[i], '[us_article$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) balmat[(counter+j),4] <- round(t_test$p.value, digits = 3) balmat[(counter+j),5] <- round(t_test$estimate[2], digits = 3) balmat[(counter+j),6] <- round(t_test$estimate[1], digits = 3) } counter <- counter+6 } balmat[balmat$t_pval<.1, ] xtable(balmat, font.size = "tiny", caption = "Experiment 1 Balance Tests, US Sample") vars <- c('AGE', 'fp_orientation_index', 'soc_dom_index', "empathy_index", 'PARTISANSHIP6', "GENDER_num", "EDUCATION_num", "IDEOLOGY_num", "RELIGIOSITY_num", "NATIVE_BORN_num", "EMPLOYMENT_num", "TRUST_GOVT_num", "POL_INTEREST_num") sum_stats <- data.frame(matrix(NA,length(vars), 7)) colnames(sum_stats) <- c("Var.", "Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max." ) sum_stats[, 1] <- var_labels for (i in 1:length(vars)){ string <- paste('sum <- summary(us_article$',vars[i], ')', sep = "", collapse = "") eval(parse(text=string)) sum_stats[i, 2:7] <- sum } xtable(sum_stats, caption = "Experiment 1 Summary Statistics, US Sample", digits = c(0, 0, 0, 2, 2, 2, 2, 0)) ######### T-tests and bootstraps ######### us_article$TREATMENT <- factor(us_article$TREATMENT) outcomes <- c("climate_index", "migration_index", "climate_migration_index") tmat <- data.frame(matrix(NA,length(outcomes)*length(treats), 11)) colnames(tmat) <- c("treat_condition", "treat_id", "outcome", "t_pval", "control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high", "Location", "Prime") k <- c() for(i in 1:length(outcomes)){ a <- rep(outcomes[i], 6) k <- append(x = k, values = a)} tmat[, 3] <- k tmat[, 1] <- rep((1:6), 3) tmat[, 2] <- rep(treats, 3) tmat[, 10] <- rep(c("US", "World"), 9) tmat[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 3) counter <- 0 for (i in 1:length(outcomes)){ for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(us_article$', outcomes[i], '[us_article$TREATMENT==', treat_conditions[j], '], us_article$', outcomes[i], '[us_article$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) tmat[(counter+j),4] <- round(t_test$p.value, digits = 3) tmat[(counter+j),5] <- round(t_test$estimate[2], digits = 3) tmat[(counter+j),6] <- round(t_test$estimate[1], digits = 3) tmat[(counter+j),7] <- tmat[(counter+j),6]-tmat[(counter+j),5] tmat[(counter+j),8] <- round(t_test$conf.int[1], digits = 3) tmat[(counter+j),9] <- round(t_test$conf.int[2], digits = 3) } counter <- counter+6 } tmat[tmat$t_pval<.1, ] t_test <- t.test(us_article$climate_migration_index[us_article$TREATMENT==6], us_article$climate_migration_index[us_article$TREATMENT=="Control"], ) #bootstrap results bsmat <- data.frame(matrix(NA,length(outcomes)*length(treats), 9)) colnames(bsmat) <- c("treat_condition", "treat_id", "outcome", "bs_mean_diff", "bs_se", "bs_ci_low", "bs_ci_high", "Location", "Prime") k <- c() for(i in 1:length(outcomes)){ a <- rep(outcomes[i], 6) k <- append(x = k, values = a)} bsmat[, 3] <- k bsmat[, 1] <- rep((1:6), 3) bsmat[, 2] <- rep(treats, 3) bsmat[, 8] <- rep(c("US", "World"), 9) bsmat[, 9] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 3) boot_cm_diff <- function(d, i, condition){ d2 <- d[i, ] diff <- (mean(d2$climate_migration_index[d2$TREATMENT==condition])- mean(d2$climate_migration_index[d2$TREATMENT=="Control"])) return <- diff } boot_m_diff <- function(d, i, condition){ d2 <- d[i, ] diff <- (mean(d2$migration_index[d2$TREATMENT==condition])- mean(d2$migration_index[d2$TREATMENT=="Control"])) return <- diff } boot_cc_diff <- function(d, i, condition){ d2 <- d[i, ] diff <- (mean(d2$climate_index[d2$TREATMENT==condition])- mean(d2$climate_index[d2$TREATMENT=="Control"])) return <- diff } counter <- 0 for (i in 1:length(outcomes)){ for(j in 1:length(treat_conditions)){ boot_cc <- boot(data = us_article, statistic = boot_cc_diff, R=1000, condition=treat_conditions[j]) bsmat[(counter+j), 4] <- mean(boot_cc$t) bsmat[(counter+j), 5] <- sd(boot_cc$t) bsmat[(counter+j), 6] <- quantile(boot_cc$t, c(0.025, 0.975))[1] bsmat[(counter+j), 7] <- quantile(boot_cc$t, c(0.025, 0.975))[2] boot_m <- boot(data = us_article, statistic = boot_m_diff, R=1000, condition=treat_conditions[j]) bsmat[(counter+j), 4] <- mean(boot_m$t) bsmat[(counter+j), 5] <- sd(boot_m$t) bsmat[(counter+j), 6] <- quantile(boot_m$t, c(0.025, 0.975))[1] bsmat[(counter+j), 7] <- quantile(boot_m$t, c(0.025, 0.975))[2] boot_cm <- boot(data = us_article, statistic = boot_cm_diff, R=1000, condition=treat_conditions[j]) bsmat[(counter+j), 4] <- mean(boot_cm$t) bsmat[(counter+j), 5] <- sd(boot_cm$t) bsmat[(counter+j), 6] <- quantile(boot_cm$t, c(0.025, 0.975))[1] bsmat[(counter+j), 7] <- quantile(boot_cm$t, c(0.025, 0.975))[2] } counter <- counter+6 } #Migration levels outcome outcomes2 <- c("MIG_LEVELS") tmat2 <- data.frame(matrix(NA,length(outcomes2)*length(treats), 11)) colnames(tmat2) <- c("treat_condition", "treat_id", "outcome", "t_pval", "control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high", "Location", "Prime") tmat2[, 3] <- rep(outcomes2, 6) tmat2[, 1] <- 1:6 tmat2[, 2] <- treats tmat2[, 10] <- rep(c("US", "World"), 3) tmat2[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 1) us_article$MIG_LEVELS <- as.numeric(us_article$MIG_LEVELS) counter <- 0 for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(us_article$MIG_LEVELS[us_article$TREATMENT==', treat_conditions[j], '], us_article$MIG_LEVELS', '[us_article$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) tmat2[(counter+j),4] <- round(t_test$p.value, digits = 3) tmat2[(counter+j),5] <- round(t_test$estimate[2], digits = 3) tmat2[(counter+j),6] <- round(t_test$estimate[1], digits = 3) tmat2[(counter+j),7] <- tmat[(counter+j),6]-tmat[(counter+j),5] tmat2[(counter+j),8] <- round(t_test$conf.int[1], digits = 3) tmat2[(counter+j),9] <- round(t_test$conf.int[2], digits = 3) } ######### Regression models ######### #Unweighted lm_climate <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator,data=us_article) lm_climate_migration <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator,data=us_article) lm_migration <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator, data=us_article) stargazer(lm_climate, lm_migration, lm_climate_migration, header=FALSE, title = "Issue Importance: US Sample, Unweighted", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban")) #Unweighted interactions lm_climate_emp <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*empathy_index,data=us_article) lm_climate_migration_emp <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*empathy_index,data=us_article) lm_migration_emp <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*empathy_index, data=us_article) stargazer(lm_climate_emp, lm_migration_emp, lm_climate_migration_emp, header=FALSE, title = "Issue Importance: US Sample, Unweighted, Interaction of Treatment with Empathy", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Empathy", "Treat: Word Migration*Empathy", "Treat: US Climate*Empathy","Treat: World Climate*Empathy", "Treat: US Climate Migration*Empathy", "Treat: World Climate Migration*Empathy")) lm_climate_border <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*border_state_indicator,data=us_article) lm_climate_migration_border <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*border_state_indicator,data=us_article) lm_migration_border <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*border_state_indicator, data=us_article) stargazer(lm_climate_border, lm_migration_border, lm_climate_migration_border, header=FALSE, title = "Issue Importance: US Sample, Unweighted, Interaction of Treatment with Border State", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Border State", "Treat: Word Migration*Border State", "Treat: US Climate*Border State","Treat: World Climate*Border State", "Treat: US Climate Migration*Border State", "Treat: World Climate Migration*Border State")) lm_climate_native <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*NATIVE_BORN_num,data=us_article) lm_climate_migration_native <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*NATIVE_BORN_num,data=us_article) lm_migration_native <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*NATIVE_BORN_num, data=us_article) stargazer(lm_climate_native, lm_migration_native, lm_climate_migration_native, header=FALSE, title = "Issue Importance: US Sample, Unweighted, Interaction of Treatment with Native Born", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Native Born", "Treat: Word Migration*Native Born", "Treat: US Climate*Native Born","Treat: World Climate*Native Born", "Treat: US Climate Migration*Native Born", "Treat: World Climate Migration*Native Born")) lm_climate_part <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*PARTISANSHIP6,data=us_article) lm_climate_migration_part <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*PARTISANSHIP6,data=us_article) lm_migration_part <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*PARTISANSHIP6, data=us_article) stargazer(lm_climate_part, lm_migration_part, lm_climate_migration_part, header=FALSE, title = "Issue Importance: US Sample, Unweighted, Interaction of Treatment with Partisanship", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Partisanship", "Treat: Word Migration*Partisanship", "Treat: US Climate*Partisanship","Treat: World Climate*Partisanship", "Treat: US Climate Migration*Partisanship", "Treat: World Climate Migration*Partisanship")) lm_climate_age <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*AGE,data=us_article) lm_climate_migration_age <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*AGE,data=us_article) lm_migration_age <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator+ TREATMENT*AGE, data=us_article) stargazer(lm_climate_age, lm_migration_age, lm_climate_migration_age, header=FALSE, title = "Issue Importance: US Sample, Unweighted, Interaction of Treatment with Age", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Age", "Treat: Word Migration*Age", "Treat: US Climate*Age","Treat: World Climate*Age", "Treat: US Climate Migration*Age", "Treat: World Climate Migration*Age")) #Weighted wt_lm_climate <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator,data=us_article, weights = wt) wt_lm_climate_migration <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator,data=us_article, weights = wt) wt_lm_migration <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator,data=us_article, weights = wt) stargazer(wt_lm_climate, wt_lm_migration, wt_lm_climate_migration, header=FALSE, title = "Issue Importance: US Sample, Weighted", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), notes = "Omitted reference categories are 18-25 for age and high school for education.", notes.append = T, covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age Bin: 26-34", "Age Bin: 35-54", "Age Bin: 55-64", "Age Bin: 65+", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Ed. Bin: Some College", "Ed. Bin: Bachelor", "Ed. Bin: Post Bachelor", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban")) #weighted interactions wt_lm_climate_emp <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*empathy_index, data=us_article, weights = wt) wt_lm_climate_migration_emp <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*empathy_index, data=us_article, weights = wt) wt_lm_migration_emp <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*empathy_index, data=us_article, weights = wt) stargazer(wt_lm_climate_emp, wt_lm_migration_emp, wt_lm_climate_migration_emp, header=FALSE, title = "Issue Importance: US Sample, Weighted, Interaction of Treatment with Empathy", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), notes = "Omitted reference categories are 18-25 for age and high school for education.", notes.append = T, covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age Bin: 26-34", "Age Bin: 35-54", "Age Bin: 55-64", "Age Bin: 65+", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Ed. Bin: Some College", "Ed. Bin: Bachelor", "Ed. Bin: Post Bachelor", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Empathy", "Treat: Word Migration*Empathy", "Treat: US Climate*Empathy","Treat: World Climate*Empathy", "Treat: US Climate Migration*Empathy", "Treat: World Climate Migration*Empathy")) wt_lm_climate_border <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*border_state_indicator, data=us_article, weights = wt) wt_lm_climate_migration_border <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*border_state_indicator, data=us_article, weights = wt) wt_lm_migration_border <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*border_state_indicator, data=us_article, weights = wt) stargazer(wt_lm_climate_border, wt_lm_migration_border, wt_lm_climate_migration_border, header=FALSE, title = "Issue Importance: US Sample, Weighted, Interaction of Treatment with Border State", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), notes = "Omitted reference categories are 18-25 for age and high school for education.", notes.append = T, covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age Bin: 26-34", "Age Bin: 35-54", "Age Bin: 55-64", "Age Bin: 65+", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Ed. Bin: Some College", "Ed. Bin: Bachelor", "Ed. Bin: Post Bachelor", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Border State", "Treat: Word Migration*Border State", "Treat: US Climate*Empathy","Border State: World Climate*Border State", "Treat: US Climate Migration*Border State", "Treat: World Climate Migration*Border State")) wt_lm_climate_native <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*NATIVE_BORN_num, data=us_article, weights = wt) wt_lm_climate_migration_native <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*NATIVE_BORN_num, data=us_article, weights = wt) wt_lm_migration_native <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*NATIVE_BORN_num, data=us_article, weights = wt) stargazer(wt_lm_climate_native, wt_lm_migration_native, wt_lm_climate_migration_native, header=FALSE, title = "Issue Importance: US Sample, Weighted, Interaction of Treatment with Native Born", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), notes = "Omitted reference categories are 18-25 for age and high school for education.", notes.append = T, covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age Bin: 26-34", "Age Bin: 35-54", "Age Bin: 55-64", "Age Bin: 65+", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Ed. Bin: Some College", "Ed. Bin: Bachelor", "Ed. Bin: Post Bachelor", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Native Born", "Treat: Word Migration*Native Born", "Treat: US Climate*Native Born","Treat: World Climate*Native Born", "Treat: US Climate Migration*Native Born", "Treat: World Climate Migration*Native Born")) wt_lm_climate_part <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*PARTISANSHIP6, data=us_article, weights = wt) wt_lm_climate_migration_part <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*PARTISANSHIP6, data=us_article, weights = wt) wt_lm_migration_part <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+ # Age1825+ reference category Age2634+ Age3554+ Age5564+ Age65+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+ # HighSchool+ reference category SomeCollege+ Bachelor+ PostBachelor+ +IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator+ TREATMENT*PARTISANSHIP6, data=us_article, weights = wt) stargazer(wt_lm_climate_part, wt_lm_migration_part, wt_lm_climate_migration_part, header=FALSE, title = "Issue Importance: US Sample, Weighted, Interaction of Treatment with Partisanship", dep.var.caption = "", font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), notes = "Omitted reference categories are 18-25 for age and high school for education.", notes.append = T, covariate.labels = c("Treat: US Migration", "Treat: Word Migration", "Treat: US Climate", "Treat: World Climate", "Treat: US Climate Migration", "Treat: World Climate Migration", "Partisanship", "Age Bin: 26-34", "Age Bin: 35-54", "Age Bin: 55-64", "Age Bin: 65+", "Foreign Policy Orientation", "Social Dominance", "Empathy", "Native Born", "Gender", "Ed. Bin: Some College", "Ed. Bin: Bachelor", "Ed. Bin: Post Bachelor", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State", "Urban", "Treat: US Migration*Partisanship", "Treat: Word Migration*Partisanship", "Treat: US Climate*Partisanship","Treat: World Climate*Partisanship", "Treat: US Climate Migration*Partisanship", "Treat: World Climate Migration*Partisanship")) ######### Marginal Effects ######### lm_climate <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator +urban_indicator,data=us_article) lm_climate_migration <- lm(climate_migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator,data=us_article) lm_migration <- lm(migration_index ~ TREATMENT+PARTISANSHIP6+AGE+ fp_orientation_index+soc_dom_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ border_state_indicator+urban_indicator, data=us_article) us_climate_margins <- margins(lm_climate, variables = "empathy_index") us_migration_margins <- margins(lm_climate_migration, variables = "empathy_index") us_climate_migration_margins <- margins(lm_migration, variables = "empathy_index") us_climate_margins_data <- as_tibble(summary(us_climate_margins)) us_climate_margins_data$outcome <- "Climate" us_migration_margins_data <- as.tibble(summary(us_migration_margins)) us_migration_margins_data$outcome <- "Migration" us_climate_migration_data <- as.tibble(summary(us_climate_migration_margins)) us_climate_migration_data$outcome <- "Climate Migration" us_margins_data <- rbind(us_climate_margins_data, us_migration_margins_data, us_climate_migration_data) %>% dplyr::select(AME, lower, upper, outcome) us_margins_data$country <- "US" us_margins <- ggplot(data= us_margins_data, aes(x=outcome, y=AME, ymin=lower, ymax=upper)) + geom_hline(yintercept=0, linetype="dashed") + geom_pointrange() + coord_flip() + labs(x="", y="Average Marginal Effect of Empathy, US") ######### Subset to Republicans ######### us_article_repubs <- us_article[us_article$PARTISANSHIP_bin %in% 'R', ] outcomes <- c("climate_index", "migration_index", "climate_migration_index") tmat_repubs <- data.frame(matrix(NA,length(outcomes)*length(treats), 11)) colnames(tmat_repubs) <- c("treat_condition", "treat_id", "outcome", "t_pval", "control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high", "Location", "Prime") k <- c() for(i in 1:length(outcomes)){ a <- rep(outcomes[i], 6) k <- append(x = k, values = a)} tmat_repubs[, 3] <- k tmat_repubs[, 1] <- rep((1:6), 3) tmat_repubs[, 2] <- rep(treats, 3) tmat_repubs[, 10] <- rep(c("US", "World"), 9) tmat_repubs[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 3) counter <- 0 for (i in 1:length(outcomes)){ for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(us_article_repubs$', outcomes[i], '[us_article_repubs$TREATMENT==', treat_conditions[j], '], us_article_repubs$', outcomes[i], '[us_article_repubs$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) tmat_repubs[(counter+j),4] <- round(t_test$p.value, digits = 3) tmat_repubs[(counter+j),5] <- round(t_test$estimate[2], digits = 3) tmat_repubs[(counter+j),6] <- round(t_test$estimate[1], digits = 3) tmat_repubs[(counter+j),7] <- tmat_repubs[(counter+j),6]-tmat_repubs[(counter+j),5] tmat_repubs[(counter+j),8] <- round(t_test$conf.int[1], digits = 3) tmat_repubs[(counter+j),9] <- round(t_test$conf.int[2], digits = 3) } counter <- counter+6 } tmat_repubs[tmat_repubs$t_pval<.1, ] ######### Manipulation Checks ######### treats <- c("US Migration", "World Migration", "US Climate", "World Climate", "US Climate Migration", "World Climate Migration") us_article$localization <- "US" us_article$localization[us_article$TREATMENT %in% c(2, 4, 6)] <- "World" us_article$threat <- "Soccer" us_article$threat[us_article$TREATMENT %in% c(1, 2)] <- "Migration" us_article$threat[us_article$TREATMENT %in% c(3, 4)] <- "Climate" us_article$threat[us_article$TREATMENT %in% c(5, 6)] <- "Climate Migration" #attention checks table(us_article$threat, us_article$MANIP_CHECK_1) table(us_article$localization, us_article$MANIP_CHECK_2) # par(mfcol=c(2, 2)) # hist(us_article$manip_checks_4[us_article$threat == "Soccer"], main = "Soccer", xlab = "", ylab= "") # hist(us_article$manip_checks_4[us_article$threat == "Migration"], main = "Migration", xlab = "", ylab= "") # hist(us_article$manip_checks_4[us_article$threat == "Climate"], main = "Climate", xlab = "", ylab= "") # hist(us_article$manip_checks_4[us_article$threat == "Climate Migration"], main = "Climate Migration", xlab = "", ylab= "") us_article <- us_article %>% dplyr::rename( manip_migration = manip_checks_1, manip_climate = manip_checks_4, manip_data_privacy = manip_checks_5, manip_climate_migration = manip_checks_6 ) mean(na.omit(us_article$manip_migration[us_article$threat == "Migration"])) - mean(na.omit(us_article$manip_migration[us_article$threat == "Soccer"])) manip_migration <- t.test(na.omit(us_article$manip_migration[us_article$threat == "Migration"]), na.omit(na.omit(us_article$manip_migration[us_article$threat == "Soccer"]))) mean(na.omit(us_article$manip_climate[us_article$threat == "Climate"])) - mean(na.omit(us_article$manip_climate[us_article$threat == "Soccer"])) manip_climate <- t.test(na.omit(us_article$manip_climate[us_article$threat == "Climate"]), na.omit(us_article$manip_climate[us_article$threat == "Soccer"])) mean(na.omit(us_article$manip_climate_migration[us_article$threat == "Climate Migration"])) - mean(na.omit(us_article$manip_climate_migration[us_article$threat == "Soccer"])) manip_climate_migration <- t.test(na.omit(us_article$manip_climate_migration[us_article$threat == "Climate Migration"]), na.omit(us_article$manip_climate_migration[us_article$threat == "Soccer"])) manipmat <- data.frame(matrix(NA, 3, 4)) colnames(manipmat) <- c( "Treatment", "T-Test P val.", "Ctrl. Mean", "Treatment Mean") manipmat[, 1] <- c("Migration", "Climate", "Climate Migration") manipmat[, 2] <- c(round(manip_migration$p.value, digits = 3), round(manip_climate$p.value, digits = 3), round(manip_climate_migration$p.value, digits = 3)) manipmat[, 3] <- c(round(manip_migration$estimate[2], digits = 3), round(manip_climate$estimate[2], digits = 3), round(manip_climate_migration$estimate[2], digits = 3)) manipmat[, 4] <- c(round(manip_migration$estimate[1], digits = 3), round(manip_climate$estimate[1], digits = 3), round(manip_climate_migration$estimate[1], digits = 3)) xtable(manipmat, font.size = "small", caption = "Experiment 2 Manipulation Checks, US Sample") ########################### Study 2: Germany ########################### ######### Import data ######### ger_article <- read.csv(file = 'Climate Migration 1_ Article- Germany_September 7, 2019_09.31.csv', stringsAsFactors = T) ger_article <- ger_article[3:nrow(ger_article), ] ######### Recoding ######### ger_article$FP_ORIENTATION_1 <- car::recode(ger_article$FP_ORIENTATION_1, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$FP_ORIENTATION_2 <- car::recode(ger_article$FP_ORIENTATION_2, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$FP_ORIENTATION_3 <- car::recode(ger_article$FP_ORIENTATION_3, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$EMPATHY_1 <- car::recode(ger_article$EMPATHY_1, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$EMPATHY_2 <- car::recode(ger_article$EMPATHY_2, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") #reverse coded ger_article$EMPATHY_3 <- car::recode(ger_article$EMPATHY_3, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$EMPATHY_4 <- car::recode(ger_article$EMPATHY_4, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$MIGRATION_1 <- car::recode(ger_article$MIGRATION_1, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$MIGRATION_2 <- car::recode(ger_article$MIGRATION_2, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$MIGRATION_3 <- car::recode(ger_article$MIGRATION_3, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$MIGRATION_4 <- car::recode(ger_article$MIGRATION_4, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$MIGRATION_5 <- car::recode(ger_article$MIGRATION_5, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$MIGRATION_6 <- car::recode(ger_article$MIGRATION_6, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$CLIMATE_MIG_1 <- car::recode(ger_article$CLIMATE_MIG_1, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$CLIMATE_MIG_2 <- car::recode(ger_article$CLIMATE_MIG_2, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$CLIMATE_MIG_3 <- car::recode(ger_article$CLIMATE_MIG_3, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$CLIMATE_MIG_4 <- car::recode(ger_article$CLIMATE_MIG_4, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$CLIMATE_MIG_5 <- car::recode(ger_article$CLIMATE_MIG_5, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$CLIMATE_MIG_6 <- car::recode(ger_article$CLIMATE_MIG_6, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$CLIMATE_1 <- car::recode(ger_article$CLIMATE_1, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$CLIMATE_2 <- car::recode(ger_article$CLIMATE_2, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$CLIMATE_3 <- car::recode(ger_article$CLIMATE_3, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$CLIMATE_4 <- car::recode(ger_article$CLIMATE_4, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") #reverse coded ger_article$CLIMATE_5 <- car::recode(ger_article$CLIMATE_5, "'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5") ger_article$CLIMATE_6 <- car::recode(ger_article$CLIMATE_6, "'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1") ger_article$REL_IMPORT_SCALE_1 <- car::recode(ger_article$REL_IMPORT_SCALE_1, "'Höchste Priorität'=5; 'Ziemlich hohe Priorität'=4; 'Mittlere Priorität'=3; 'Geringe Priorität'=2; 'Gar keine Priorität'=1") ger_article$REL_IMPORT_SCALE_2 <- car::recode(ger_article$REL_IMPORT_SCALE_2, "'Höchste Priorität'=5; 'Ziemlich hohe Priorität'=4; 'Mittlere Priorität'=3; 'Geringe Priorität'=2; 'Gar keine Priorität'=1") ger_article$REL_IMPORT_SCALE_3 <- car::recode(ger_article$REL_IMPORT_SCALE_3, "'Höchste Priorität'=5; 'Ziemlich hohe Priorität'=4; 'Mittlere Priorität'=3; 'Geringe Priorität'=2; 'Gar keine Priorität'=1") ger_article <- ger_article %>% mutate(AGE = as.numeric(Age)) %>% dplyr::rename(MIG_LEVELS = Q76_1, ANTHRO_CC = Q77_1, REL_IMPORT_SCALE_CLIMATE = REL_IMPORT_SCALE_1, REL_IMPORT_SCALE_MIGRATION = REL_IMPORT_SCALE_3, REL_IMPORT_SCALE_CLIMATEMIGRATION = REL_IMPORT_SCALE_2) ger_article$MIG_LEVELS <- car::recode(ger_article$MIG_LEVELS, "'Stark zunehmen'=5; 'Etwas zunehmen'=4; 'Gleich bleiben'=3; 'Etwas abnehmen'=2; 'Stark abnehmen'=1") ger_article$GENDER_num <- ifelse(ger_article$GENDER == "Weiblich", 1, 0) ger_article$EDUCATION_num <- as.numeric(car::recode(ger_article$EDUCATION, "'Abgeschlossenes Hochschulstudium'=6; 'Angefangenes Hochschulstudium'=5; 'Abitur'=4; 'Facabitur'=3; 'Realschulabschluss'=2; 'Haptschulabschluss'=1"))-1 ger_article$IDEOLOGY_num <- as.numeric(car::recode(ger_article$IDEOLOGY, "'Extrem liberal'=7; 'Liberal'=6; 'Etwas liberal'=5; 'Moderat, die gemäßigte Mitte'=4; 'Etwas konservativ'=3; 'Konservativ'=2; 'Extrem konservativ'=1"))-1 ger_article$RELIGIOSITY_num <- as.numeric(car::recode(ger_article$RELIGIOSITY, "'Mehr als einmal die Woche'=6; 'Wöchentlich '=5; 'Ein paar Mal im Monat'=4; 'Ein paar Mal im Jahr'=3; 'Einmal im Jahr oder weniger'=2; 'Nie'=1"))-1 ger_article$NATIVE_BORN_num <- ifelse(ger_article$NATIVE_BORN == "Deutschland", 1, 0) ger_article$EMPLOYMENT_num <- as.numeric(car::recode(ger_article$EMPLOYMENT, "'Angestellt in Vollzeit'=7; 'Angestellt in Teilzeit'=6; 'Selbstständig'=5; 'Student'=4; 'Hausfrau'=3; 'Im Ruhestand'=2; 'Arbeitslos'=1"))-1 ger_article$TRUST_GOVT_num <-as.numeric(car::recode(ger_article$TRUST_GOVT, "'Fast immer'=3; 'Meistens'=2; 'Nur manchmal'=1"))-1 ger_article$POL_INTEREST_num <- as.numeric(car::recode(ger_article$POL_INTEREST, "'Mesitens'=4; 'Manchmal'=3; 'Nur ab und zu'=2; 'Kaum'=1"))-1 ger_states <- read.csv(file = 'germany_state_key.csv') names(ger_states) <- c("qualtrics_code", "state_name", "region", "east_indicator", "east_indicator2", "east_indicator3", "east_indicator4", "east_indicator5", "east_indicator6", "east_indicator7") ger_article <- ger_article %>% mutate(state_num = as.numeric(paste(state_region))) %>% left_join(ger_states, by=c("state_num"= "qualtrics_code")) ger_article$urban_indicator <- 1*(ger_article$city %in% c("Berlin", "Hamburg", "Munich", "Cologne", "Frankfurt Am Main", "Stuttgart", "Dusseldorf", "Dortmund", "Essen", "Leipzig")) ######### Construct scales ######### climate_ger <- data.frame(ger_article[,c("CLIMATE_1", "CLIMATE_2", "CLIMATE_3", "CLIMATE_4", "CLIMATE_5", "CLIMATE_6")]) migration_ger <- data.frame(ger_article[,c("MIGRATION_1", "MIGRATION_2", "MIGRATION_3", "MIGRATION_4", "MIGRATION_5", "MIGRATION_6")]) climate_migration_ger <- data.frame(ger_article[,c("CLIMATE_MIG_1", "CLIMATE_MIG_2", "CLIMATE_MIG_3", "CLIMATE_MIG_4", "CLIMATE_MIG_5", "CLIMATE_MIG_6")]) fp_orientation_ger <- data.frame(ger_article[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3")]) empathy_ger <- data.frame(ger_article[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")]) climate_ger <- data.frame(sapply(climate_ger, FUN= function(x) as.numeric(x))) migration_ger <- data.frame(sapply(migration_ger, FUN= function(x) as.numeric(x))-1) climate_migration_ger <- data.frame(sapply(climate_migration_ger, FUN= function(x) as.numeric(x))-1) fp_orientation_ger <- data.frame(sapply(fp_orientation_ger, FUN= function(x) as.numeric(x))-1) empathy_ger <- data.frame(sapply(empathy_ger, FUN= function(x) as.numeric(x))-1) #calculate chronbach's alpha for each index psych::alpha(climate_ger) psych::alpha(migration_ger) psych::alpha(climate_migration_ger) psych::alpha(fp_orientation_ger) psych::alpha(empathy_ger) #r=create the index variable as the mean score on the individual items ger_article$climate_index <- apply(climate_ger, MARGIN = 1, FUN = mean) ger_article$migration_index <- apply(migration_ger, MARGIN = 1, FUN = mean) ger_article$climate_migration_index <- apply(climate_migration_ger, MARGIN = 1, FUN = mean) ger_article$fp_orientation_index <- apply(fp_orientation_ger, MARGIN = 1, FUN = mean) ger_article$empathy_index <- apply(empathy_ger, MARGIN = 1, FUN = mean) ######### Balance, summary stats ######### table(ger_article$TREATMENT) vars <- c('AGE', 'fp_orientation_index', "empathy_index", "GENDER_num", "EDUCATION_num", "IDEOLOGY_num", "RELIGIOSITY_num", "NATIVE_BORN_num", "EMPLOYMENT_num", "TRUST_GOVT_num", "POL_INTEREST_num") var_labels <- c("Age", "Foreign Policy Orientation", "Empathy", "Gender", "Education", "Ideology", "Religiosity", "Native Born", "Employment", "Trust in Government", "Political Interest") treats <- c("Germany Migration", "World Migration", "Germany Climate", "World Climate", "Germany Climate Migration", "World Climate Migration") treat_conditions <- c(1:6) balmat <- data.frame(matrix(NA,length(vars)*length(treats), 6)) colnames(balmat) <- c("Var.", "Treatment ID", "Treatment", "T-Test P val.", "Ctrl. Mean", "Treatment Mean") j <- c() for(i in 1:length(vars)){ a <- rep(var_labels[i], 6) j <- append(x = j, values = a)} balmat[, 1] <- j balmat[, 2] <- rep((1:6), 11) balmat[, 3] <- rep(treats, 11) counter <- 0 for (i in 1:length(vars)){ for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(ger_article$', vars[i], '[ger_article$TREATMENT==', treat_conditions[j], '], ger_article$', vars[i], '[ger_article$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) balmat[(counter+j),4] <- round(t_test$p.value, digits = 3) balmat[(counter+j),5] <- round(t_test$estimate[2], digits = 3) balmat[(counter+j),6] <- round(t_test$estimate[1], digits = 3) } counter <- counter+6 } balmat[balmat$t_pval<.1, ] xtable(balmat[, c(1, 3:6)], font.size = "tiny", caption = "Experiment 1 Balance Tests, German Sample") sum_stats <- data.frame(matrix(NA,length(vars), 7)) colnames(sum_stats) <- c("Var.", "Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max." ) sum_stats[, 1] <- var_labels for (i in 1:length(vars)){ string <- paste('sum <- summary(ger_article$',vars[i], ')', sep = "", collapse = "") eval(parse(text=string)) sum_stats[i, 2:7] <- sum } xtable(sum_stats, caption = "Experiment 1 Summary Statistics, German Sample", digits = c(0, 0, 0, 2, 2, 2, 2, 0)) sum(ger_article$AGE < 18) ######### T-Tests ######### ger_article$TREATMENT <- factor(ger_article$TREATMENT) outcomes <- c("climate_index", "migration_index", "climate_migration_index") tmat <- data.frame(matrix(NA,length(outcomes)*length(treats), 11)) colnames(tmat) <- c("treat_condition", "treat_id", "outcome", "t_pval", "control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high", "Location", "Prime") k <- c() for(i in 1:length(outcomes)){ a <- rep(outcomes[i], 6) k <- append(x = k, values = a)} tmat[, 3] <- k tmat[, 1] <- rep((1:6), 3) tmat[, 2] <- rep(treats, 3) tmat[, 10] <- rep(c("GER", "World"), 9) tmat[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 3) counter <- 0 for (i in 1:length(outcomes)){ for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(ger_article$', outcomes[i], '[ger_article$TREATMENT==', treat_conditions[j], '], ger_article$', outcomes[i], '[ger_article$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) tmat[(counter+j),4] <- round(t_test$p.value, digits = 3) tmat[(counter+j),5] <- round(t_test$estimate[2], digits = 3) tmat[(counter+j),6] <- round(t_test$estimate[1], digits = 3) tmat[(counter+j),7] <- tmat[(counter+j),6]-tmat[(counter+j),5] tmat[(counter+j),8] <- round(t_test$conf.int[1], digits = 3) tmat[(counter+j),9] <- round(t_test$conf.int[2], digits = 3) } counter <- counter+6 } tmat[tmat$t_pval<.1, ] #Migration level outcome outcomes3 <- c("MIG_LEVELS") tmat3 <- data.frame(matrix(NA,length(outcomes3)*length(treats), 11)) colnames(tmat3) <- c("treat_condition", "treat_id", "outcome", "t_pval", "control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high", "Location", "Prime") tmat3[, 3] <- rep(outcomes3, 6) tmat3[, 1] <- 1:6 tmat3[, 2] <- treats tmat3[, 10] <- rep(c("GER", "World"), 3) tmat3[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 1) ger_article$MIG_LEVELS <- as.numeric(ger_article$MIG_LEVELS) counter <- 0 for (j in 1:length(treat_conditions)){ string <- paste('t_test <- t.test(ger_article$MIG_LEVELS[ger_article$TREATMENT==', treat_conditions[j], '], ger_article$MIG_LEVELS', '[ger_article$TREATMENT=="Control"])', sep = "", collapse = "") eval(parse(text=string)) tmat3[(counter+j),4] <- round(t_test$p.value, digits = 3) tmat3[(counter+j),5] <- round(t_test$estimate[2], digits = 3) tmat3[(counter+j),6] <- round(t_test$estimate[1], digits = 3) tmat3[(counter+j),7] <- tmat[(counter+j),6]-tmat[(counter+j),5] tmat3[(counter+j),8] <- round(t_test$conf.int[1], digits = 3) tmat3[(counter+j),9] <- round(t_test$conf.int[2], digits = 3) } ######### Regression models ######### #Unweighted lm_climate <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator + urban_indicator, data=ger_article) lm_climate_migration <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator, data=ger_article) lm_migration <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator, data=ger_article) stargazer(lm_climate, lm_migration, lm_climate_migration, header=FALSE, title = "Issue Importance: German Sample, Unweighted", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Eastern State", "Urban")) lm2 <- lm(MIG_LEVELS ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator, data=ger_article) #interactions lm_climate_emp <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*empathy_index, data=ger_article) lm_climate_migration_emp <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*empathy_index, data=ger_article) lm_migration_emp <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*empathy_index, data=ger_article) stargazer(lm_climate_emp, lm_migration_emp, lm_climate_migration_emp, header=FALSE, title = "Issue Importance: German Sample, Unweighted, Interaction of Treatment with Empathy", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Eastern State", "Urban", "Treat: GER Migration*Empathy", "Treat: Word Migration*Empathy", "Treat: GER Climate*Empathy","Treat: World Climate*Empathy", "Treat: GER Climate Migration*Empathy", "Treat: World Climate Migration*Empathy")) lm_climate_east <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*east_indicator, data=ger_article) lm_climate_migration_east <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*east_indicator, data=ger_article) lm_migration_east <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*east_indicator, data=ger_article) stargazer(lm_climate_east, lm_migration_east, lm_climate_migration_east, header=FALSE, title = "Issue Importance: German Sample, Unweighted, Interaction of Treatment with Eastern State", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Eastern State", "Urban", "Treat: GER Migration*East", "Treat: Word Migration*East", "Treat: GER Climate*East","Treat: World Climate*East", "Treat: GER Climate Migration*East", "Treat: World Climate Migration*East")) lm_climate_native <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*NATIVE_BORN_num, data=ger_article) lm_climate_migration_native <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*NATIVE_BORN_num, data=ger_article) lm_migration_native <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*NATIVE_BORN_num, data=ger_article) stargazer(lm_climate_native, lm_migration_native, lm_climate_migration_native, header=FALSE, title = "Issue Importance: German Sample, Unweighted, Interaction of Treatment with Native Born", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Eastern State", "Urban", "Treat: GER Migration*Native Born", "Treat: Word Migration*Native Born", "Treat: GER Climate*Native Born","Treat: World Climate*Native Born", "Treat: GER Climate Migration*Native Born", "Treat: World Climate Migration*Native Born")) lm_climate_age <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*AGE, data=ger_article) lm_climate_migration_age <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*AGE, data=ger_article) lm_migration_age <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator + urban_indicator+ TREATMENT*AGE, data=ger_article) stargazer(lm_climate_age, lm_migration_age, lm_climate_migration_age, header=FALSE, title = "Issue Importance: German Sample, Unweighted, Interaction of Treatment with Age", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, column.labels = c("Climate", "Migration", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Eastern State", "Urban", "Treat: GER Migration*Age", "Treat: Word Migration*Age", "Treat: GER Climate*Age","Treat: World Climate*Age", "Treat: GER Climate Migration*Age", "Treat: World Climate Migration*Age")) lm_climate_border2 <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator2 + urban_indicator, data=ger_article) lm_climate_migration_border2 <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator2 + urban_indicator, data=ger_article) lm_migration_border2 <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator2 + urban_indicator, data=ger_article) lm_climate_border3 <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator3 + urban_indicator, data=ger_article) lm_climate_migration_border3 <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator3 + urban_indicator, data=ger_article) lm_migration_border3 <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator3 + urban_indicator, data=ger_article) lm_climate_border4 <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator4 + urban_indicator, data=ger_article) lm_climate_migration_border4 <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator4 + urban_indicator, data=ger_article) lm_migration_border4 <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator4 + urban_indicator, data=ger_article) lm_climate_border5 <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator5 + urban_indicator, data=ger_article) lm_climate_migration_border5 <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator5 + urban_indicator, data=ger_article) lm_migration_border5 <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator5 + urban_indicator, data=ger_article) lm_climate_border6 <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator6 + urban_indicator, data=ger_article) lm_climate_migration_border6 <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator6 + urban_indicator, data=ger_article) lm_migration_border6 <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator6 + urban_indicator, data=ger_article) summary(lm_climate)$coefficients[, 1] - summary(lm_climate_border6)$coefficients[, 1] summary(lm_migration)$coefficients[, 1] - summary(lm_migration_border6)$coefficients[, 1] summary(lm_climate_migration)$coefficients[, 1] - summary(lm_climate_migration_border6)$coefficients[, 1] lm_climate_border7 <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + EMPLOYMENT_num + east_indicator7 + urban_indicator, data=ger_article) lm_climate_migration_border7 <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator7 + urban_indicator, data=ger_article) lm_migration_border7 <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ EMPLOYMENT_num + east_indicator7 + urban_indicator, data=ger_article) summary(lm_climate)$coefficients[, 1] - summary(lm_climate_border7)$coefficients[, 1] summary(lm_migration)$coefficients[, 1] - summary(lm_migration_border7)$coefficients[, 1] summary(lm_climate_migration)$coefficients[, 1] - summary(lm_climate_migration_border7)$coefficients[, 1] stargazer(lm_climate_border2, lm_climate_border3, lm_climate_border4, lm_climate_border7, header=FALSE, title = "Issue Importance: Climate Change, German Sample, Unweighted, Alternate Specifications of Border State", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, # column.labels = c("Model 1", "Model", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State2", "Border State3", "Border State4", "Border State5", "Urban") ) stargazer(lm_migration_border2, lm_migration_border3, lm_migration_border4, lm_migration_border7, header=FALSE, title = "Issue Importance: Migration, German Sample, Unweighted", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, # column.labels = c("Model 1", "Model", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State2", "Border State3", "Border State4", "Border State5", "Urban") ) stargazer(lm_climate_migration_border2, lm_climate_migration_border3, lm_climate_migration_border4, lm_climate_migration_border7, header=FALSE, title = "Issue Importance: Climate Migration, German Sample, Unweighted", dep.var.caption = "", # font.size = "tiny", model.numbers = F, dep.var.labels.include = F, model.names = F, # column.labels = c("Model 1", "Model", "Climate Migration"), digits=2, no.space=T, column.sep.width= "0pt", omit.stat = c("f", "ser", "rsq"), covariate.labels = c("Treat: GER Migration", "Treat: Word Migration", "Treat: GER Climate", "Treat: World Climate", "Treat: GER Climate Migration", "Treat: World Climate Migration", "Age", "Foreign Policy Orientation", "Empathy", "Native Born", "Gender", "Education", "Ideology", "Religiosity", "Trust in Government", "Political Interest", "Employment Status", "Border State2", "Border State3", "Border State4", "Border State5", "Urban") ) ######### Marginal Effects ######### lm_climate_ger <- lm(climate_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num + east_indicator + urban_indicator, data=ger_article) lm_climate_migration_ger <- lm(climate_migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ east_indicator + urban_indicator, data=ger_article) lm_migration_ger <- lm(migration_index ~ TREATMENT+AGE+ fp_orientation_index+empathy_index+ NATIVE_BORN_num+GENDER_num+EDUCATION_num+IDEOLOGY_num+ RELIGIOSITY_num+TRUST_GOVT_num+POL_INTEREST_num+ east_indicator + urban_indicator, data=ger_article) ger_climate_margins <- margins(lm_climate_ger, variables = "empathy_index") ger_migration_margins <- margins(lm_climate_migration_ger, variables = "empathy_index") ger_climate_migration_margins <- margins(lm_migration_ger, variables = "empathy_index") ger_climate_margins_data <- as.tibble(summary(ger_climate_margins)) ger_climate_margins_data$outcome <- "Climate" ger_migration_margins_data <- as.tibble(summary(ger_migration_margins)) ger_migration_margins_data$outcome <- "Migration" ger_climate_migration_data <- as.tibble(summary(ger_climate_migration_margins)) ger_climate_migration_data$outcome <- "Climate Migration" ger_margins_data <- rbind(ger_climate_margins_data, ger_migration_margins_data, ger_climate_migration_data) %>% dplyr::select(AME, lower, upper, outcome) ger_margins_data$country <- "GER" ger_margins <- ggplot(data= ger_margins_data, aes(x=outcome, y=AME, ymin=lower, ymax=upper)) + geom_hline(yintercept=0, linetype="dashed") + geom_pointrange() + coord_flip() + labs(x="", y="Average Marginal Effect of Empathy, GER") # combined_margins_data <- rbind(us_margins_data, ger_margins_data) # # ggplot(data= combined_margins_data, aes(x=outcome, y=AME, ymin=lower, ymax=upper)) + # geom_hline(yintercept=0, linetype="dashed") + # geom_pointrange() + coord_flip() + # facet_grid(country ~.)+ # labs(x="", y="Average Marginal Effect of Empathy") ######### Manipulation Checks ######### ger_article$threat <- "Soccer" ger_article$threat[ger_article$TREATMENT %in% c(1, 2)] <- "Migration" ger_article$threat[ger_article$TREATMENT %in% c(3, 4)] <- "Climate" ger_article$threat[ger_article$TREATMENT %in% c(5, 6)] <- "Climate Migration" ger_article <- ger_article %>% dplyr::mutate( manip_migration = as.numeric(manip_checks_1), manip_climate = as.numeric(manip_checks_4), manip_data_privacy = as.numeric(manip_checks_5), manip_climate_migration = as.numeric(manip_checks_6) ) mean(na.omit(ger_article$manip_migration[ger_article$threat == "Migration"])) - mean(na.omit(ger_article$manip_migration[ger_article$threat == "Soccer"])) manip_migration <- t.test(na.omit(ger_article$manip_migration[ger_article$threat == "Migration"]), na.omit(na.omit(ger_article$manip_migration[ger_article$threat == "Soccer"]))) mean(na.omit(ger_article$manip_climate[ger_article$threat == "Climate"])) - mean(na.omit(ger_article$manip_climate[ger_article$threat == "Soccer"])) manip_climate <- t.test(na.omit(ger_article$manip_climate[ger_article$threat == "Climate"]), na.omit(ger_article$manip_climate[ger_article$threat == "Soccer"])) mean(na.omit(ger_article$manip_climate_migration[ger_article$threat == "Climate Migration"])) - mean(na.omit(ger_article$manip_climate_migration[ger_article$threat == "Soccer"])) manip_climate_migration <- t.test(na.omit(ger_article$manip_climate_migration[ger_article$threat == "Climate Migration"]), na.omit(ger_article$manip_climate_migration[ger_article$threat == "Soccer"])) manipmat <- data.frame(matrix(NA, 3, 4)) colnames(manipmat) <- c( "Treatment", "T-Test P val.", "Ctrl. Mean", "Treatment Mean") manipmat[, 1] <- c("Migration", "Climate", "Climate Migration") manipmat[, 2] <- c(round(manip_migration$p.value, digits = 3), round(manip_climate$p.value, digits = 3), round(manip_climate_migration$p.value, digits = 3)) manipmat[, 3] <- c(round(manip_migration$estimate[2], digits = 3), round(manip_climate$estimate[2], digits = 3), round(manip_climate_migration$estimate[2], digits = 3)) manipmat[, 4] <- c(round(manip_migration$estimate[1], digits = 3), round(manip_climate$estimate[1], digits = 3), round(manip_climate_migration$estimate[1], digits = 3)) xtable(manipmat, font.size = "small", caption = "Experiment 2 Manipulation Checks, German Sample") ########################### Study 3: Follow up ########################### follow <- read.csv(file = 'Migration Follow-Up_August 1, 2020_12.19.csv', stringsAsFactors = T) # 1 == labor, 2 == climate, 3 == refugee crosstabs <- as_tibble(rbind(c(204, 97, 88), c(96, 166, 127), c(89, 126, 174))) colnames(crosstabs) <- c("Labor", "Climate", "Refugee") follow_plot <- as_tibble(cbind(c(follow$responsible_1, follow$responsible_2, follow$responsible_3), c(rep(1, 389), rep(2, 389), rep(3, 389)), c(rep("Labor", 389), rep("Climate", 389), rep("Refugee", 389)))) names(follow_plot) <- c("responsibility", "migrant", "Type") ggplot(data=follow_plot, mapping = aes(x=responsibility, fill=as.factor(Type))) + geom_bar() + labs(fill = "Migrant Type", x="Rank", y="Num. Responses", title="Ranking Migrant Responsibility")+ theme_classic() + theme(plot.title = element_text(hjust = 0.5)) follow_plot$position <- as.numeric(follow_plot$responsibility) t.test(follow_plot$position[follow_plot$Type %in% "Labor"], follow_plot$position[follow_plot$Type %in% "Refugee"]) t.test(follow_plot$position[follow_plot$Type %in% "Labor"], follow_plot$position[follow_plot$Type %in% "Climate"]) t.test(follow_plot$position[follow_plot$Type %in% "Climate"], follow_plot$position[follow_plot$Type %in% "Refugee"])