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remove(list=ls())
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set.seed(8675309)
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memory.limit(size=20000)
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options(xtable.comment = FALSE)
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wd <- "C:/Users/sbari/Dropbox/Research/Climate Migration Paper/Final Files"
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setwd(wd)
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library(tidyverse)
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library(xtable)
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library(ggplot2)
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library(ggpubr)
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library(corrplot)
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library(car)
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library(splines)
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library(readxl)
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library(stargazer)
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library(psych)
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library(lemon)
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library(boot)
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library(cjoint)
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library(cregg)
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library(margins)
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library(FindIt)
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library(ggpubr)
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cjt_us_design <- makeDesign(type="file", filename= "conjoint_design.dat")
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cjt_us_design_internal <- makeDesign(type="file", filename= 'conjoint_design_internal.dat')
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cjt_us_data <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv",
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new.format = T,
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respondentID = "ResponseId",
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responses=c("force_choice", "Q184", "Q186",
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"Q188", "Q190", "Q192",
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"Q194", "Q196", "Q198"),
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covariates=c("GENDER_resp", "EDUCATION_resp", "IDEOLOGY",
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'PARTISANSHIP', 'PARTISANSHIP_D', 'PARTISANSHIP_R', 'PARTISANSHIP_I', 'RELIGIOSITY_resp',
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'NATIVE_BORN', 'EMPLOYMENT_resp',
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'Age', 'TRUST_GOVT', 'POL_INTEREST',
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'FP_ORIENTATION_1', 'FP_ORIENTATION_2', 'FP_ORIENTATION_3', 'FP_ORIENTATION_4',
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'SOC_DOM_1', 'SOC_DOM_2', 'SOC_DOM_3', 'SOC_DOM_4',
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'EMPATHY_1', 'EMPATHY_2', 'EMPATHY_3', 'EMPATHY_4',
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'city', 'state_region'))
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cjt_us_data_relig <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv",
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new.format = T,
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respondentID = "ResponseId",
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responses=c("force_choice", "Q184", "Q186",
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"Q188", "Q190", "Q192",
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"Q194", "Q196", "Q198"),
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covariates=c('RELIGIOSITY_resp'))
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cjt_us_data_rank <- read.qualtrics(filename = "Climate Migration 2_ Conjoint- USA_September 10, 2019_08.56.csv",
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new.format = T,
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respondentID = "ResponseId",
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responses=c("force_choice", "Q184", "Q186",
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"Q188", "Q190", "Q192",
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"Q194", "Q196", "Q198"),
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covariates=c("GENDER_resp", "EDUCATION_resp", "IDEOLOGY",
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'PARTISANSHIP', 'PARTISANSHIP_D', 'PARTISANSHIP_R', 'PARTISANSHIP_I',
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'NATIVE_BORN', 'EMPLOYMENT_resp',
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'Age', 'TRUST_GOVT', 'POL_INTEREST',
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'FP_ORIENTATION_1', 'FP_ORIENTATION_2', 'FP_ORIENTATION_3', 'FP_ORIENTATION_4',
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'SOC_DOM_1', 'SOC_DOM_2', 'SOC_DOM_3', 'SOC_DOM_4',
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'EMPATHY_1', 'EMPATHY_2', 'EMPATHY_3', 'EMPATHY_4',
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'city', 'state_region'),
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ranks = c("RATE_MIG1","RATE_MIG2",
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"Q168", "Q169",
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"Q170", "Q171",
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"Q172", "Q173",
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"Q174", "Q175",
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"Q176", "Q177",
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"Q178", "Q179",
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"Q180", "Q181",
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"Q182", "Q183"))
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cjt_us_data_rank <- cjt_us_data_rank %>%
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dplyr::rename(rank_outcome = selected) %>%
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dplyr::select(rank_outcome)
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cjt_us_data_relig <- cjt_us_data_relig %>%
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dplyr::select(RELIGIOSITY_resp)
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cjt_us_data <- cbind(cjt_us_data, cjt_us_data_rank, cjt_us_data_relig)
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cjt_us_data <- cjt_us_data[!is.na(cjt_us_data$selected), ]
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cjt_us_data <- cjt_us_data[cjt_us_data$Occupation != 'Unemployer', ]
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cjt_us_data$Occupation <- factor(cjt_us_data$Occupation)
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cjt_us_data$PARTISANSHIP6 <- NA
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for(i in 1:nrow(cjt_us_data)){
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if(cjt_us_data[i, "PARTISANSHIP_D"]==1){
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cjt_us_data[i, "PARTISANSHIP6"]<- 6}
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if(cjt_us_data[i, "PARTISANSHIP_D"]==2){
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cjt_us_data[i, "PARTISANSHIP6"]<- 5}
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if(cjt_us_data[i, "PARTISANSHIP_I"]==1){
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cjt_us_data[i, "PARTISANSHIP6"]<- 4}
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if(cjt_us_data[i, "PARTISANSHIP_I"]==2){
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cjt_us_data[i, "PARTISANSHIP6"]<- 3}
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if(cjt_us_data[i, "PARTISANSHIP_R"]==2){
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cjt_us_data[i, "PARTISANSHIP6"]<- 2}
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if(cjt_us_data[i, "PARTISANSHIP_R"]==1){
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cjt_us_data[i, "PARTISANSHIP6"]<- 1}
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}
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cjt_us_data$FP_ORIENTATION_1 <- car::recode(cjt_us_data$FP_ORIENTATION_1,
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"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
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cjt_us_data$FP_ORIENTATION_2 <- car::recode(cjt_us_data$FP_ORIENTATION_2,
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"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
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cjt_us_data$FP_ORIENTATION_3 <- car::recode(cjt_us_data$FP_ORIENTATION_3,
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"'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5")
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cjt_us_data$FP_ORIENTATION_4 <- car::recode(cjt_us_data$FP_ORIENTATION_4,
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"'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5")
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cjt_us_data$SOC_DOM_1 <- car::recode(cjt_us_data$SOC_DOM_1,
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"'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5")
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cjt_us_data$SOC_DOM_2 <- car::recode(cjt_us_data$SOC_DOM_2,
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"'Definitely favor'=5; 'Somewhat favor'=4; 'Neither oppose nor favor'=3; 'Somewhat oppose'=2; 'Definitely oppose'=1")
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cjt_us_data$SOC_DOM_3 <- car::recode(cjt_us_data$SOC_DOM_3,
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"'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5")
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cjt_us_data$SOC_DOM_4 <- car::recode(cjt_us_data$SOC_DOM_4,
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"'Definitely favor'=5; 'Somewhat favor'=4; 'Neither oppose nor favor'=3; 'Somewhat oppose'=2; 'Definitely oppose'=1")
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cjt_us_data$EMPATHY_1 <- car::recode(cjt_us_data$EMPATHY_1,
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"'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1")
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cjt_us_data$EMPATHY_2 <- car::recode(cjt_us_data$EMPATHY_2,
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"'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5")
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cjt_us_data$EMPATHY_3 <- car::recode(cjt_us_data$EMPATHY_3,
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"'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5")
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cjt_us_data$EMPATHY_4 <- car::recode(cjt_us_data$EMPATHY_4,
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"'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1")
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cjt_us_data <- cjt_us_data %>%
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mutate(PARTISANSHIP_bin = ifelse(PARTISANSHIP6>3, "D", "R"),
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AGE = as.numeric(Age))
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cjt_us_data$border_state_indicator <- 1*(cjt_us_data$state_region %in% c("TX", "CA", "AZ", "NM"))
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cjt_us_data$border_state_indicator_noCA <- 1*(cjt_us_data$state_region %in% c("TX", "AZ", "NM"))
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cjt_us_data$urban_indicator <- 1*(cjt_us_data$city %in% c("New York", "Los Angeles", "Chicago",
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"Houston", "Phoenix", "Philadelphia",
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"San Antonio", "San Diego", "Dallas", "San Jose"))
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cjt_us_data <- cjt_us_data[5:nrow(cjt_us_data), ]
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cjt_us_data$EDUCATION_num <- as.numeric(cjt_us_data$EDUCATION_resp)-1
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|
cjt_us_data$IDEOLOGY_num <- as.numeric(car::recode(cjt_us_data$IDEOLOGY,
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|
"'Extremely liberal'=7; 'Liberal'=6; 'Slightly liberal'=5;
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|
'Moderate, middle of the road'=4;
|
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|
'Slightly conservative'=3; 'Conservative'=2; 'Extremely conservative'=1"))-1
|
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|
cjt_us_data$RELIGIOSITY_num <- as.numeric(cjt_us_data$RELIGIOSITY_resp)-1
|
|
|
cjt_us_data$NATIVE_BORN_num <- ifelse(cjt_us_data$NATIVE_BORN == "United States", 1, 0)
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|
cjt_us_data$EMPLOYMENT_num <- as.numeric(car::recode(cjt_us_data$EMPLOYMENT,
|
|
|
"'17'=7; '16'=6; '21'=5; '18'=4;
|
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|
'20'=3; '19'=2; '17 '=1"))
|
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|
cjt_us_data$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$POL_INTEREST,
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|
|
"'Most of the time'=4;
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|
'Some of the time'=3; 'Only now and then'=2; 'Hardly at all'=1"))-1
|
|
|
|
|
|
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|
|
fp_orientation <- data.frame(cjt_us_data[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3", "FP_ORIENTATION_4")])
|
|
|
soc_dom <- data.frame(cjt_us_data[,c("SOC_DOM_1", "SOC_DOM_2", "SOC_DOM_3", "SOC_DOM_4")])
|
|
|
empathy <- data.frame(cjt_us_data[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")])
|
|
|
|
|
|
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|
|
fp_orientation <- data.frame(sapply(fp_orientation, FUN= function(x) as.numeric(x))-1)
|
|
|
soc_dom <- data.frame(sapply(soc_dom, FUN= function(x) as.numeric(x))-1)
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|
empathy <- data.frame(sapply(empathy, FUN= function(x) as.numeric(x))-1)
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|
|
cjt_us_data$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)
|
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|
cjt_us_data$PARTISANSHIP_bin <- as.factor(cjt_us_data$PARTISANSHIP_bin)
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|
|
cjt_us_data$PARTISANSHIP_num <- as.numeric(cjt_us_data$PARTISANSHIP_bin)
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|
|
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"
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|
|
)
|
|
|
|
|
|
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$',vars[i], ')',
|
|
|
sep = "", collapse = "")
|
|
|
eval(parse(text=string))
|
|
|
sum_stats[i, 2:7] <- sum
|
|
|
|
|
|
}
|
|
|
|
|
|
xtable(sum_stats, caption = "Experiment 1 Summary Statistics, US Sample", digits = c(0, 0, 0, 2, 2, 2, 2, 0))
|
|
|
|
|
|
|
|
|
baselines <- list()
|
|
|
baselines$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"),
|
|
|
|
|
|
text.size=9)
|
|
|
|
|
|
|
|
|
|
|
|
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$Estimate
|
|
|
|
|
|
|
|
|
cjt_us_data_internal <- cjt_us_data[! (cjt_us_data$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$Estimate[c(1:6, 11:20)], 3))
|
|
|
|
|
|
xtable(us_ext_table, digits = 3,
|
|
|
font.size = "small", caption = "AMCE, US Sample (Compared to baseline levels- \n Excluding all external migration profiles")
|
|
|
|
|
|
|
|
|
cjt_us_data_age_subset <- subset(x = cjt_us_data, subset = AGE > 17)
|
|
|
|
|
|
nrow(cjt_us_data) - nrow(cjt_us_data_age_subset)
|
|
|
length(unique(cjt_us_data$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
|
|
|
|
|
|
|
|
|
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"),
|
|
|
|
|
|
text.size=9)
|
|
|
|
|
|
|
|
|
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"),
|
|
|
|
|
|
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"),
|
|
|
|
|
|
text.size=9)
|
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
|
|
|
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)")
|
|
|
|
|
|
|
|
|
|
|
|
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$selected), ]
|
|
|
|
|
|
|
|
|
ger_states <- read.csv(file = 'germany_state_key.csv')
|
|
|
|
|
|
names(ger_states) <- c("qualtrics_code", "state_name", "region",
|
|
|
"east_indicator", "east_indicator2",
|
|
|
"east_indicator3", "east_indicator4",
|
|
|
"east_indicator5", "east_indicator6",
|
|
|
"east_indicator7")
|
|
|
|
|
|
cjt_ger_data <- cjt_ger_data %>%
|
|
|
mutate(state_num = as.numeric(paste(state_region))) %>%
|
|
|
left_join(ger_states, by=c("state_num"= "qualtrics_code"))
|
|
|
|
|
|
cjt_ger_data$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$FP_ORIENTATION_2,
|
|
|
"'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1")
|
|
|
|
|
|
cjt_ger_data$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$EMPATHY_1,
|
|
|
"'Stimme vollständig zu'=5; 'Stimme weitestgehend zu'=4; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=2; 'Stimme gar nicht zu'=1")
|
|
|
|
|
|
cjt_ger_data$EMPATHY_2 <- car::recode(cjt_ger_data$EMPATHY_2,
|
|
|
"'Stimme vollständig zu'=1; 'Stimme weitestgehend zu'=2; 'Stimme weder zu noch nicht zu'=3; 'Stimme weniger zu'=4; 'Stimme gar nicht zu'=5")
|
|
|
|
|
|
|
|
|
cjt_ger_data$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$POL_INTEREST,
|
|
|
"'Mesitens'=4;
|
|
|
'Manchmal'=3; 'Nur ab und zu'=2; 'Kaum'=1"))-1
|
|
|
|
|
|
|
|
|
fp_orientation_ger <- data.frame(cjt_ger_data[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3")])
|
|
|
empathy_ger <- data.frame(cjt_ger_data[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")])
|
|
|
|
|
|
fp_orientation_ger <- data.frame(sapply(fp_orientation_ger, FUN= function(x) as.numeric(x))-1)
|
|
|
empathy_ger <- data.frame(sapply(empathy_ger, FUN= function(x) as.numeric(x))-1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cjt_ger_data$fp_orientation_index <- apply(fp_orientation_ger, MARGIN = 1, FUN = mean)
|
|
|
cjt_ger_data$empathy_index <- apply(empathy_ger, MARGIN = 1, FUN = mean)
|
|
|
|
|
|
|
|
|
vars <- c('AGE', 'fp_orientation_index', "empathy_index",
|
|
|
"GENDER_num", "EDUCATION_num",
|
|
|
"IDEOLOGY_num", "NATIVE_BORN_num",
|
|
|
"EMPLOYMENT_num", "TRUST_GOVT_num",
|
|
|
"POL_INTEREST_num","RELIGIOSITY_num"
|
|
|
)
|
|
|
|
|
|
var_labels <- c("Age", "Foreign Policy Orientation", "Empathy",
|
|
|
"Gender", "Education", "Ideology", "Native Born",
|
|
|
"Employment", "Trust in Government",
|
|
|
"Political Interest","Religiosity"
|
|
|
)
|
|
|
|
|
|
sum_stats <- data.frame(matrix(NA,length(vars), 7))
|
|
|
colnames(sum_stats) <- c("Var.", "Min.", "1st Qu.", "Median",
|
|
|
"Mean", "3rd Qu.", "Max." )
|
|
|
sum_stats[, 1] <- var_labels
|
|
|
|
|
|
for (i in 1:length(vars)){
|
|
|
string <- paste('sum <- summary(cjt_ger_data$',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))
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
|
|
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$Estimate
|
|
|
|
|
|
|
|
|
cjt_ger_data_internal <- cjt_ger_data[! (cjt_ger_data$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")
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
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"),
|
|
|
|
|
|
text.size=9)
|
|
|
|
|
|
|
|
|
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"),
|
|
|
|
|
|
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"),
|
|
|
|
|
|
text.size=9)
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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,
|
|
|
|
|
|
bottom=text_grob("Blue: Non-border; Red: Border"))
|
|
|
|
|
|
borderplots2 <- ggarrange(borderplot4,
|
|
|
borderplot7,
|
|
|
ncol=1, nrow=3)
|
|
|
annotate_figure(borderplots2,
|
|
|
|
|
|
bottom=text_grob("Blue: Non-border; Red: Border"))
|
|
|
|
|
|
|
|
|
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)")
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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",
|
|
|
|
|
|
x="", y=""
|
|
|
|
|
|
|
|
|
)
|
|
|
|
|
|
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",
|
|
|
|
|
|
x="", y=""
|
|
|
|
|
|
|
|
|
)
|
|
|
|
|
|
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",
|
|
|
|
|
|
x="", y=""
|
|
|
|
|
|
|
|
|
)
|
|
|
|
|
|
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)")
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
us_article <- read.csv(file = 'usclimate_exp1.csv',
|
|
|
stringsAsFactors = T)
|
|
|
|
|
|
us_article <- us_article[3:nrow(us_article), ]
|
|
|
|
|
|
|
|
|
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$FP_ORIENTATION_2,
|
|
|
"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
|
|
|
|
|
|
us_article$FP_ORIENTATION_3 <- car::recode(us_article$FP_ORIENTATION_3,
|
|
|
"'Definitely agree'=1; 'Somewhat agree'=2; 'Neither agree nor disagree'=3; 'Somewhat disagree'=4; 'Definitely disagree'=5")
|
|
|
|
|
|
|
|
|
us_article$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$SOC_DOM_1,
|
|
|
"'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5")
|
|
|
|
|
|
us_article$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$SOC_DOM_3,
|
|
|
"'Definitely favor'=1; 'Somewhat favor'=2; 'Neither oppose nor favor'=3; 'Somewhat oppose'=4; 'Definitely oppose'=5")
|
|
|
|
|
|
|
|
|
us_article$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$EMPATHY_1,
|
|
|
"'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1")
|
|
|
|
|
|
us_article$EMPATHY_2 <- car::recode(us_article$EMPATHY_2,
|
|
|
"'Describes me very well'=1; 'Describes me fairly well'=2; 'Describes me moderately well'=3; 'Describes me very little'=4; 'Does not describe me at all'=5")
|
|
|
|
|
|
us_article$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$EMPATHY_4,
|
|
|
"'Describes me very well'=5; 'Describes me fairly well'=4; 'Describes me moderately well'=3; 'Describes me very little'=2; 'Does not describe me at all'=1")
|
|
|
|
|
|
|
|
|
us_article$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$MIGRATION_4,
|
|
|
"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
|
|
|
|
|
|
|
|
|
us_article$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$MIGRATION_6,
|
|
|
"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
|
|
|
|
|
|
|
|
|
us_article$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$CLIMATE_MIG_4,
|
|
|
"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
|
|
|
|
|
|
|
|
|
us_article$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$CLIMATE_MIG_6,
|
|
|
"'Definitely agree'=5; 'Somewhat agree'=4; 'Neither agree nor disagree'=3; 'Somewhat disagree'=2; 'Definitely disagree'=1")
|
|
|
|
|
|
|
|
|
us_article$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$CLIMATE_4,
|
|
|
"'Definitely Agree'=5; 'Somewhat Agree'=4; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=2; 'Definitely Disagree'=1")
|
|
|
|
|
|
us_article$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$CLIMATE_6,
|
|
|
"'Definitely Agree'=5; 'Somewhat Agree'=4; 'Neither Agree Nor Disagree'=3; 'Somewhat Disagree'=2; 'Definitely Disagree'=1")
|
|
|
|
|
|
|
|
|
us_article$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$EDUCATION,
|
|
|
"'Post-graduate degree'=6; 'College graduate'=5; 'Some college/Associateââ¬Å¡Ãâôs degree'=4;
|
|
|
'Trade or vocational certification'=3; 'High school graduate/GED'=2; 'Elementary or some high school'=1"))-1
|
|
|
us_article$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$city %in% c("New York", "Los Angeles", "Chicago",
|
|
|
"Houston", "Phoenix", "Philadelphia",
|
|
|
"San Antonio", "San Diego", "Dallas", "San Jose"))
|
|
|
|
|
|
|
|
|
climate <- data.frame(us_article[,c("CLIMATE_1", "CLIMATE_2", "CLIMATE_3", "CLIMATE_4", "CLIMATE_5", "CLIMATE_6")])
|
|
|
migration <- data.frame(us_article[,c("MIGRATION_1", "MIGRATION_2", "MIGRATION_3", "MIGRATION_4", "MIGRATION_5", "MIGRATION_6")])
|
|
|
climate_migration <- data.frame(us_article[,c("CLIMATE_MIG_1", "CLIMATE_MIG_2", "CLIMATE_MIG_3", "CLIMATE_MIG_4", "CLIMATE_MIG_5", "CLIMATE_MIG_6")])
|
|
|
fp_orientation <- data.frame(us_article[,c("FP_ORIENTATION_1", "FP_ORIENTATION_2", "FP_ORIENTATION_3", "FP_ORIENTATION_4")])
|
|
|
soc_dom <- data.frame(us_article[,c("SOC_DOM_1", "SOC_DOM_2", "SOC_DOM_3", "SOC_DOM_4")])
|
|
|
empathy <- data.frame(us_article[,c("EMPATHY_1", "EMPATHY_2", "EMPATHY_3", "EMPATHY_4")])
|
|
|
|
|
|
climate <- data.frame(sapply(climate, FUN= function(x) as.numeric(x))-1)
|
|
|
migration <- data.frame(sapply(migration, FUN= function(x) as.numeric(x))-1)
|
|
|
climate_migration <- data.frame(sapply(climate_migration, FUN= function(x) as.numeric(x))-1)
|
|
|
fp_orientation <- data.frame(sapply(fp_orientation, FUN= function(x) as.numeric(x))-1)
|
|
|
soc_dom <- data.frame(sapply(soc_dom, FUN= function(x) as.numeric(x))-1)
|
|
|
empathy <- data.frame(sapply(empathy, FUN= function(x) as.numeric(x))-1)
|
|
|
|
|
|
|
|
|
psych::alpha(climate)
|
|
|
psych::alpha(migration)
|
|
|
psych::alpha(climate_migration)
|
|
|
psych::alpha(fp_orientation)
|
|
|
psych::alpha(soc_dom)
|
|
|
psych::alpha(empathy)
|
|
|
|
|
|
|
|
|
us_article$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$empathy_index <- apply(empathy, MARGIN = 1, FUN = mean)
|
|
|
|
|
|
|
|
|
|
|
|
table(us_article$TREATMENT)
|
|
|
|
|
|
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$',vars[i], ')',
|
|
|
sep = "", collapse = "")
|
|
|
eval(parse(text=string))
|
|
|
sum_stats[i, 2:7] <- sum
|
|
|
|
|
|
}
|
|
|
|
|
|
xtable(sum_stats, caption = "Experiment 1 Summary Statistics, US Sample", digits = c(0, 0, 0, 2, 2, 2, 2, 0))
|
|
|
|
|
|
|
|
|
us_article$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$TREATMENT=="Control"], )
|
|
|
|
|
|
|
|
|
bsmat <- data.frame(matrix(NA,length(outcomes)*length(treats), 9))
|
|
|
colnames(bsmat) <- c("treat_condition", "treat_id", "outcome", "bs_mean_diff",
|
|
|
"bs_se", "bs_ci_low", "bs_ci_high",
|
|
|
"Location", "Prime")
|
|
|
|
|
|
k <- c()
|
|
|
for(i in 1:length(outcomes)){
|
|
|
a <- rep(outcomes[i], 6)
|
|
|
k <- append(x = k, values = a)}
|
|
|
|
|
|
bsmat[, 3] <- k
|
|
|
bsmat[, 1] <- rep((1:6), 3)
|
|
|
bsmat[, 2] <- rep(treats, 3)
|
|
|
bsmat[, 8] <- rep(c("US", "World"), 9)
|
|
|
bsmat[, 9] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 3)
|
|
|
|
|
|
boot_cm_diff <- function(d, i, condition){
|
|
|
d2 <- d[i, ]
|
|
|
diff <- (mean(d2$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$t, c(0.025, 0.975))[2]
|
|
|
|
|
|
}
|
|
|
counter <- counter+6
|
|
|
}
|
|
|
|
|
|
|
|
|
outcomes2 <- c("MIG_LEVELS")
|
|
|
tmat2 <- data.frame(matrix(NA,length(outcomes2)*length(treats), 11))
|
|
|
colnames(tmat2) <- c("treat_condition", "treat_id", "outcome", "t_pval",
|
|
|
"control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high",
|
|
|
"Location", "Prime")
|
|
|
|
|
|
tmat2[, 3] <- rep(outcomes2, 6)
|
|
|
tmat2[, 1] <- 1:6
|
|
|
tmat2[, 2] <- treats
|
|
|
tmat2[, 10] <- rep(c("US", "World"), 3)
|
|
|
tmat2[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 1)
|
|
|
|
|
|
us_article$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)
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
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"))
|
|
|
|
|
|
|
|
|
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"))
|
|
|
|
|
|
|
|
|
wt_lm_climate <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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"))
|
|
|
|
|
|
|
|
|
|
|
|
wt_lm_climate_emp <- lm(climate_index ~ TREATMENT+PARTISANSHIP6+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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+
|
|
|
|
|
|
Age2634+ Age3554+ Age5564+ Age65+
|
|
|
fp_orientation_index+soc_dom_index+empathy_index+
|
|
|
NATIVE_BORN_num+GENDER_num+
|
|
|
|
|
|
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"))
|
|
|
|
|
|
|
|
|
|
|
|
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() +
|
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labs(x="", y="Average Marginal Effect of Empathy, US")
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us_article_repubs <- us_article[us_article$PARTISANSHIP_bin %in% 'R', ]
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|
outcomes <- c("climate_index", "migration_index", "climate_migration_index")
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tmat_repubs <- data.frame(matrix(NA,length(outcomes)*length(treats), 11))
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colnames(tmat_repubs) <- c("treat_condition", "treat_id", "outcome", "t_pval",
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|
"control_mean", "treat_mean", "mean_diff", "ci_low", "ci_high",
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|
"Location", "Prime")
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k <- c()
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|
for(i in 1:length(outcomes)){
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a <- rep(outcomes[i], 6)
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|
k <- append(x = k, values = a)}
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|
tmat_repubs[, 3] <- k
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|
tmat_repubs[, 1] <- rep((1:6), 3)
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|
tmat_repubs[, 2] <- rep(treats, 3)
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|
tmat_repubs[, 10] <- rep(c("US", "World"), 9)
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|
tmat_repubs[, 11] <- rep(c("Migration","Migration", "Climate", "Climate","Climate Migration","Climate Migration"), 3)
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|
counter <- 0
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|
for (i in 1:length(outcomes)){
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|
for (j in 1:length(treat_conditions)){
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|
string <- paste('t_test <- t.test(us_article_repubs$', outcomes[i],
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|
'[us_article_repubs$TREATMENT==', treat_conditions[j], '], us_article_repubs$',
|
|
|
outcomes[i], '[us_article_repubs$TREATMENT=="Control"])',
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|
sep = "", collapse = "")
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|
eval(parse(text=string))
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|
tmat_repubs[(counter+j),4] <- round(t_test$p.value, digits = 3)
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|
tmat_repubs[(counter+j),5] <- round(t_test$estimate[2], digits = 3)
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|
tmat_repubs[(counter+j),6] <- round(t_test$estimate[1], digits = 3)
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|
tmat_repubs[(counter+j),7] <- tmat_repubs[(counter+j),6]-tmat_repubs[(counter+j),5]
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|
tmat_repubs[(counter+j),8] <- round(t_test$conf.int[1], digits = 3)
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|
tmat_repubs[(counter+j),9] <- round(t_test$conf.int[2], digits = 3)
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|
|
}
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|
|
counter <- counter+6
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|
|
}
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|
tmat_repubs[tmat_repubs$t_pval<.1, ]
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|
treats <- c("US Migration", "World Migration", "US Climate", "World Climate", "US Climate Migration", "World Climate Migration")
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|
us_article$localization <- "US"
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|
us_article$localization[us_article$TREATMENT %in% c(2, 4, 6)] <- "World"
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|
us_article$threat <- "Soccer"
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|
us_article$threat[us_article$TREATMENT %in% c(1, 2)] <- "Migration"
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|
us_article$threat[us_article$TREATMENT %in% c(3, 4)] <- "Climate"
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|
us_article$threat[us_article$TREATMENT %in% c(5, 6)] <- "Climate Migration"
|
|
|
|
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|
|
table(us_article$threat, us_article$MANIP_CHECK_1)
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|
table(us_article$localization, us_article$MANIP_CHECK_2)
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|
us_article <- us_article %>%
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|
dplyr::rename(
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|
manip_migration = manip_checks_1,
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|
manip_climate = manip_checks_4,
|
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|
manip_data_privacy = manip_checks_5,
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|
manip_climate_migration = manip_checks_6
|
|
|
)
|
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|
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|
|
mean(na.omit(us_article$manip_migration[us_article$threat == "Migration"])) - mean(na.omit(us_article$manip_migration[us_article$threat == "Soccer"]))
|
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|
|
manip_migration <- t.test(na.omit(us_article$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")
|
|
|
|
|
|
|
|
|
|
|
|
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), ]
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
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")
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
|
|
|
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")
|
|
|
|
|
|
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"))
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
psych::alpha(climate_ger)
|
|
|
psych::alpha(migration_ger)
|
|
|
psych::alpha(climate_migration_ger)
|
|
|
psych::alpha(fp_orientation_ger)
|
|
|
psych::alpha(empathy_ger)
|
|
|
|
|
|
|
|
|
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$empathy_index <- apply(empathy_ger, MARGIN = 1, FUN = mean)
|
|
|
|
|
|
|
|
|
table(ger_article$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)
|
|
|
|
|
|
|
|
|
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, ]
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
model.numbers = F,
|
|
|
dep.var.labels.include = F,
|
|
|
model.names = F,
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
model.numbers = F,
|
|
|
dep.var.labels.include = F,
|
|
|
model.names = F,
|
|
|
|
|
|
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 = "",
|
|
|
|
|
|
model.numbers = F,
|
|
|
dep.var.labels.include = F,
|
|
|
model.names = F,
|
|
|
|
|
|
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")
|
|
|
)
|
|
|
|
|
|
|
|
|
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$country <- "GER"
|
|
|
|
|
|
ger_margins <- ggplot(data= ger_margins_data, aes(x=outcome, y=AME, ymin=lower, ymax=upper)) +
|
|
|
geom_hline(yintercept=0, linetype="dashed") +
|
|
|
geom_pointrange() + coord_flip() +
|
|
|
labs(x="", y="Average Marginal Effect of Empathy, GER")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ger_article$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),
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manip_climate_migration = as.numeric(manip_checks_6)
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)
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mean(na.omit(ger_article$manip_migration[ger_article$threat == "Migration"])) - mean(na.omit(ger_article$manip_migration[ger_article$threat == "Soccer"]))
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manip_migration <- t.test(na.omit(ger_article$manip_migration[ger_article$threat == "Migration"]),
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na.omit(na.omit(ger_article$manip_migration[ger_article$threat == "Soccer"])))
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mean(na.omit(ger_article$manip_climate[ger_article$threat == "Climate"])) - mean(na.omit(ger_article$manip_climate[ger_article$threat == "Soccer"]))
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manip_climate <- t.test(na.omit(ger_article$manip_climate[ger_article$threat == "Climate"]),
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na.omit(ger_article$manip_climate[ger_article$threat == "Soccer"]))
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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"]))
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manip_climate_migration <- t.test(na.omit(ger_article$manip_climate_migration[ger_article$threat == "Climate Migration"]),
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na.omit(ger_article$manip_climate_migration[ger_article$threat == "Soccer"]))
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manipmat <- data.frame(matrix(NA, 3, 4))
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colnames(manipmat) <- c( "Treatment", "T-Test P val.",
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"Ctrl. Mean", "Treatment Mean")
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manipmat[, 1] <- c("Migration", "Climate", "Climate Migration")
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manipmat[, 2] <- c(round(manip_migration$p.value, digits = 3),
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round(manip_climate$p.value, digits = 3),
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round(manip_climate_migration$p.value, digits = 3))
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manipmat[, 3] <- c(round(manip_migration$estimate[2], digits = 3),
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round(manip_climate$estimate[2], digits = 3),
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round(manip_climate_migration$estimate[2], digits = 3))
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manipmat[, 4] <- c(round(manip_migration$estimate[1], digits = 3),
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round(manip_climate$estimate[1], digits = 3),
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round(manip_climate_migration$estimate[1], digits = 3))
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xtable(manipmat,
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font.size = "small", caption = "Experiment 2 Manipulation Checks, German Sample")
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follow <- read.csv(file = 'Migration Follow-Up_August 1, 2020_12.19.csv', stringsAsFactors = T)
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crosstabs <- as_tibble(rbind(c(204, 97, 88), c(96, 166, 127), c(89, 126, 174)))
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colnames(crosstabs) <- c("Labor", "Climate", "Refugee")
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follow_plot <- as_tibble(cbind(c(follow$responsible_1, follow$responsible_2, follow$responsible_3),
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c(rep(1, 389), rep(2, 389), rep(3, 389)),
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c(rep("Labor", 389), rep("Climate", 389), rep("Refugee", 389))))
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names(follow_plot) <- c("responsibility", "migrant", "Type")
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ggplot(data=follow_plot, mapping = aes(x=responsibility, fill=as.factor(Type))) +
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geom_bar() +
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labs(fill = "Migrant Type", x="Rank", y="Num. Responses",
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title="Ranking Migrant Responsibility")+
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theme_classic() +
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theme(plot.title = element_text(hjust = 0.5))
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follow_plot$position <- as.numeric(follow_plot$responsibility)
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t.test(follow_plot$position[follow_plot$Type %in% "Labor"],
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follow_plot$position[follow_plot$Type %in% "Refugee"])
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t.test(follow_plot$position[follow_plot$Type %in% "Labor"],
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follow_plot$position[follow_plot$Type %in% "Climate"])
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t.test(follow_plot$position[follow_plot$Type %in% "Climate"],
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follow_plot$position[follow_plot$Type %in% "Refugee"])
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