diff --git "a/12/replication_package/Migration_Analysis_Final.R" "b/12/replication_package/Migration_Analysis_Final.R" new file mode 100644--- /dev/null +++ "b/12/replication_package/Migration_Analysis_Final.R" @@ -0,0 +1,3720 @@ +######### 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"]) +