25/replication_package/AJPS_2021_Kim_Appendix.R

# --------------------------------------------------------------------------------------------------------------------------------
# ------------------------------------------- -------------------------------------------------------------------------------------
# Entertaining Beliefs in Economic Mobility 
# Eunji Kim, 2021
# American Journal of Political Science
# APPENDIX  # 
# --------------------------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------------------------------------------

#list.of.packages <- c("foreign", "ggplot2", "readstata13", "dplyr", "haven", "xtable", "stargazer", "tidytext", "stringr", "tidyr", "wordcloud", "scales", "tables", "ggpubr", "lubridate", "DescTools", "ggeffects", "tidyverse", "egg") 
#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
#if(length(new.packages)) install.packages(new.packages)

#setwd("[path to where replication archive was downloaded]")


library(foreign)
library(ggplot2)
library(readstata13)
library(dplyr)
library(haven)
library(xtable)
library(stargazer)
library(tidytext)
library(stringr) 
library(tidyr)
library(wordcloud)
library(scales)
library(tables)
library(ggpubr)
library(lubridate)
library(scales)
library(DescTools)
library(ggeffects)
library(tidyverse)
library(egg)


# --------------------------------------------------------------------------------------------------------------------------------

# APPENDIX A

## Figure A1. Content analysis results of reality/game programs aired 2015-2017.


load("contentanalysis.rdata")

ca$ordinary <- as.factor(ca$ordinary)
ca$economicbenefit <- as.factor(ca$economicbenefit)
ca$hardwork <- as.factor(ca$hardwork)

ca$ordinary <- factor(ca$ordinary,
                      levels = c(0,1,2),
                      labels = c("Celebrity", "Professional", "Everyman"))


ca$economicbenefit <- factor(ca$economicbenefit, 
                             levels = c(0,1,2),
                             labels = c("None/trivial", "Modest", "Significant"))

ca$hardwork <- factor(ca$hardwork, 
                      levels = c(0,1,2),
                      labels = c("Not much effort", "Some effort", "A lot of effort"))


ordinary.pct = ca %>% group_by(ordinary) %>% 
  dplyr::summarise(count = n())  %>%
  mutate(pct=count/sum(count)) 

econ.pct = ca %>% group_by(economicbenefit) %>% 
  dplyr::summarise(count = n())  %>%
  mutate(pct=count/sum(count)) 
hardwork.pct = ca %>% group_by(hardwork) %>% 
  dplyr::summarise(count = n())  %>%
  mutate(pct=count/sum(count)) 

ordinary.pct$ordinary <- factor(ordinary.pct$ordinary , levels=c("Celebrity", "Professional", "Everyman"))


p1 <- ggplot(ordinary.pct, aes(x=ordinary, y=pct*100, fill=ordinary)) +
  geom_bar(stat="identity", width = 0.4,  position=position_dodge(0.1)) + 
  theme(aspect.ratio = 1) +
  scale_fill_manual(values = c("Celebrity" = "#214D72", "Professional" = "#2C7695", "Everyman"="#50BFC3")) +
  scale_y_continuous(limits=c(0,100)) +  
  geom_text(data=ordinary.pct, aes(label=paste0(round(pct*100,1),"%"), y=pct*100+3), size=4) +
  ylab("Percent") + xlab("") + 
  ggtitle("Type of People") + theme_minimal() + theme(legend.position="none")  +
  theme(legend.title = element_blank()) + 
  theme(axis.title.x = element_text(color="black", size=14, face="bold"),
        axis.text.y = element_text(color= "black", size=12),
        axis.text.x = element_text(color= "black", size=12),
        axis.title.y = element_text(color="black", size=14, face="bold"),
        plot.title = element_text(size = 14, face="bold"))

ordinary.pct$ordinary <- factor(ordinary.pct$ordinary , levels=c("Celebrity", "Professional", "Everyman"))

econ.pct$economicbenefit <- factor(econ.pct$economicbenefit, levels=c("None/trivial", "Modest", "Significant"))

p2 <- ggplot(econ.pct, aes(x=economicbenefit, y=pct*100, fill=economicbenefit)) +
  geom_bar(stat="identity", width = 0.4,  position=position_dodge(0.1)) + theme(aspect.ratio = 1) +
  scale_fill_manual(values = c("None/trivial" = "#214D72", "Modest" = "#2C7695", "Significant"="#50BFC3" )) +
  scale_y_continuous(limits=c(0,100)) +
  geom_text(data=econ.pct, aes(label=paste0(round(pct*100,1),"%"), y=pct*100+3), size=4) +
  ylab("") + xlab("") + 
  ggtitle("Degree of Economic Benefit") + theme_minimal() + theme(legend.position="none") +
  theme(legend.title = element_blank()) + 
  theme(axis.title.x = element_text(color="black", size=14, face="bold"),
        axis.text.y = element_blank(),
        axis.text.x = element_text(color= "black", size=12),
        axis.title.y = element_text(color="black", size=14, face="bold"),
        plot.title = element_text(size = 14, face="bold"))

hardwork.pct$hardwork <- factor(hardwork.pct$hardwork, levels=c("Not much effort", "Some effort", "A lot of effort"))

p3 <- ggplot(hardwork.pct, aes(x=hardwork, y=pct*100, fill=hardwork)) +
  geom_bar(stat="identity", width = 0.4,  position=position_dodge(0.1)) + theme(aspect.ratio = 1) +
  scale_fill_manual(values = c("Not much effort" = "#214D72", "Some effort" = "#2C7695", "A lot of effort"="#50BFC3")) +
  scale_y_continuous(limits=c(0,100)) +  
  geom_text(data=hardwork.pct, aes(label=paste0(round(pct*100,1),"%"), y=pct*100+3), size=4) +
  ylab("") + xlab("") + 
  ggtitle("Amount of Hard Work/Effort") + theme_minimal() + theme(legend.position="none") +
  theme(legend.title = element_blank()) + 
  theme(axis.title.x = element_text(color="black", size=14, face="bold"),
        axis.text.y = element_blank(),
        axis.text.x = element_text(color= "black", size=12),
        axis.title.y = element_text(color="black", size=14, face="bold"),
        plot.title = element_text(size = 14, face="bold"))

#jpeg("contentanalysis.jpeg", units="in", width=12, height=4.5, res=300)

egg::ggarrange(p1, p2, p3,nrow = 1)

#dev.off()



## Table A1.  Full Coding Results for a Random Subset of Competitive Reality/Game Shows

load("tvcoding.rdata")

first <- CohenKappa(tvcoding$ordinary1,  tvcoding$ordinary2, weights = c("Unweighted"))
second <- CohenKappa(tvcoding$benefit1,  tvcoding$benefit2, weights = c("Unweighted"))
third <- CohenKappa(tvcoding$hardwork1,  tvcoding$hardwork2, weights = c("Unweighted"))
          
# Cohen's Kappa (unweighted) for the first category 
round(first, digits=3)
           
# Cohen's Kappa (unweighted) for the second category  
round(second, digits=3)

# Cohen's Kappa (unweighted) for the third category  
round(third, digits=3)

# --------------------------------------------------------------------------------------------------------------------------------
# APPENDIX B

## Figure B1. Relative Share of news shows and reality/game shows over time (1960-2017)

load("imdb.rdata")

p <- ggplot(imdb, aes(x = year, y = value,fill = variable),
            scale_fill_manual(breaks = c("news", "realitygame"), values =c("#24345A", "#2B8D9C"))) +
  scale_x_continuous(breaks=seq(1960,2018,4)) +
  scale_y_continuous(breaks=seq(0,0.7,0.05)) +
  xlab("Year") + ylab("% of TV Shows by Genre") + 
  geom_jitter(size=2, aes(colour=variable), alpha=1) + 
  scale_color_manual(breaks = c("news", "realitygame"), values =c("#24345A", "#2B8D9C")) + 
  geom_smooth(aes(x=year, y=value, color=as.factor(variable)))  +
  scale_fill_manual(breaks = c("news", "realitygame"), values =c("#24345A", "#2B8D9C")) +
  theme_minimal() + 
  theme(legend.title = element_blank(), legend.position = "none") + 
  theme(axis.title.x = element_text(color="black", size=14, face="bold"),
        axis.text.y = element_text(color= "black", size=12),
        axis.text.x = element_text(color= "black", size=12),
        axis.title.y = element_text(color="black", size=14, face="bold"),
        plot.title = element_text(size = 14, face="bold"))

p <- p +   theme(legend.position = "none") + 
  ggplot2::annotate("text", x = 1995, y = 0.13, fontface=2, size=4, 
                    label = "REALITY/GAME", color="#2B8D9C") +
  ggplot2::annotate("text", x = 2012, y = 0.05, fontface=2, size=4, 
                    label = "NEWS", color= "#24345A") +
  theme(panel.grid.minor = element_blank(), 
        panel.grid.major = element_line(color = "gray50", size = 0.1),
        panel.grid.major.x = element_blank(),
        panel.background = element_blank(),
        axis.line.x = element_line(size = 0.1, linetype = "solid", colour = "gray50"))



#jpeg("imdbplot.jpeg", units="in", width=10, height=6, res=300)

p

#dev.off()

# --------------------------------------------------------------------------------------------------------------------------------

# APPENDIX E

## Table E1. Program-Level Entertainment Media Consumption Patterns


load("ssi.rdata")

tv <- ssi %>% 
  dplyr::summarise( tv1=mean(tv_americagottalent)*100, 
                    tv2=mean(tv_nflcbs)*100,
                    tv3=mean(tv_sundayfootball)*100,
                    tv4=mean(tv_foxnfl)*100,
                    tv5=mean(tv_sharktank)*100,
                    tv6 = mean(tv_hellkitchen)*100,
                    tv7 = mean(tv_voice)*100, 
                    tv8 = mean(tv_idol)*100,
                    tv9 = mean(tv_ninja)*100,
                    tv10 = mean(tv_masterchef)*100,
                    tv11 = mean(tv_celebrityfamily)*100,
                    tv12 = mean(tv_survivor)*100,
                    tv13 = mean(tv_mlbfox)*100,
                    tv14 = mean(tv_collegefootball)*100,
                    tv15 = mean(tv_youcandance)*100,
                    tv16 = mean(tv_amazingrace)*100,
                    tv17 = mean(tv_collegebasketball)*100,
                    tv18 = mean(tv_nbaprimetime)*100,
                    tv19 = mean(tv_kardashians)*100,
                    tv20 = mean(tv_nascar)*100,
                    tv21 = mean(tv_bachelor)*100,
                    tv22 = mean(tv_bachelorette)*100,
                    tv23 = mean(tv_jerseyshore)*100,
                    tv24 = mean(tv_housewives)*100,
                    tv25 = mean(tv_ufc)*100,
                    tv26 = mean(tv_worldofdance)*100,
                    tv27 = mean(tv_lovehiphop)*100,
                    tv28 = mean(tv_cbssports)*100,
                    tv29 = mean(tv_battlebots)*100,
                    tv30 = mean(tv_loveconnection)*100 ) 

m <- as.data.frame(t(tv))

colnames(m) <- c('prop')

m$prop<- round(m$prop, digits=1)
m$tv <- c("America's Got Talent", "NFL on CBS", "Sunday Night Football", 
         "Fox NFL Sunday", "Shark Tank", "Hell's Kichen", "Voice", "American Idol",
         "American Ninja Warrior", "MasterChef", "Celebrity Family Feud", "Survivor",
         "MLB on Fox", "Colege Football Today", "So You Think You Can Dance", "Amazing Race",
         "College Bastketball on CBS", "NBA Saturday Primetime", "Keeping Up with Kardashians",
         "NASCAR on Fox", "Bachelor", "Bachelorette", "Jersey Shore", "The Real Housewives", "UFC Fight Night",
         "World of Dance", "Love and Hip Hop: Hollywood", "CBS Sports Spectaular", "BattleBots", "Love Connection")

m


# --------------------------------------------------------------------------------------------------------------------------------

# APPENDIX F 

## Table F1. 

load("ssi.rdata")

m1 <- lm(mindex_rsc ~ occasionalviewer + frequentviewer + heavyviewer, data=ssi)

m2 <- lm(mindex_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv  + sportstv + rep + dem +
           education_n + income_n + married + female + age + 
           white + unemployed + polinterst + religion_attend +
           protestant + optimismindex +insecurity + intergenmobility + parentsimmigrant +
           +absolutemobility + gini + as.factor(state_n) , data=ssi) 

m3 <- lm(internalatt_rsc ~ occasionalviewer + frequentviewer + heavyviewer, data=ssi)

m4 <- lm(internalatt_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv + sportstv + rep + dem +
           education_n + income_n + married + female + age + 
           white + unemployed + polinterst + religion_attend +
           protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
           +absolutemobility + gini + as.factor(state_n), data=ssi)

m5 <- lm(externalatt_rsc ~ occasionalviewer + frequentviewer + heavyviewer, data=ssi)

m6 <- lm(externalatt_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv + sportstv + rep + dem +
           education_n + income_n + married + female + age + 
           white + unemployed + polinterst + religion_attend +
           protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
           +absolutemobility + gini + as.factor(state_n), data=ssi)


table <- capture.output({stargazer(m1, m2, m3, m4, m5, m6, 
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('state_n') , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   digits=3,
                                   align = TRUE, 
                                   type= "text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{1\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  


## Table F2. 

m2_conti <- lm(mindex_rsc ~ realitytv + othertv  + sportstv + rep + dem +
                 education_n + income_n + married + female + age + 
                 white + unemployed + polinterst + religion_attend +
                 protestant + optimismindex +insecurity + intergenmobility + parentsimmigrant +
                 +absolutemobility + gini + as.factor(state_n) , data=ssi) 

table <- capture.output({stargazer(m2_conti,  
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('state_n') , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   digits=3,
                                   align = TRUE, 
                                   type= "text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{1\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  


## Table F3. The Impact of Watching Rags-to-Riches Programs By Level of Political Interest

load("ssi.rdata")

plow <- lm(mindex_rsc ~ occasionalviewer + frequentviewer+ heavyviewer + othertv + sportstv + rep + dem +
             education_n + income_n + married + female + age + 
             white + unemployed  + religion_attend +
             protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
             absolutemobility + gini + as.factor(state_n), data=ssi[ssi$polinterest_high==0,])


phigh <- lm(mindex_rsc ~ occasionalviewer + frequentviewer+ heavyviewer + othertv + sportstv + rep + dem +
              education_n + income_n + married + female + age + 
              white + unemployed  + religion_attend +
              protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
              absolutemobility + gini + as.factor(state_n), data=ssi[ssi$polinterest_high==1,])


plow_i <- lm(internalatt_rsc~ occasionalviewer + frequentviewer+ heavyviewer + othertv + sportstv + rep + dem +
               education_n + income_n + married + female + age + 
               white + unemployed  + religion_attend +
               protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
               absolutemobility + gini + as.factor(state_n), data=ssi[ssi$polinterest_high==0,])


phigh_i <- lm(internalatt_rsc ~ occasionalviewer + frequentviewer+ heavyviewer + othertv + sportstv + rep + dem +
                education_n + income_n + married + female + age + 
                white + unemployed  + religion_attend +
                protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
                absolutemobility + gini + as.factor(state_n), data=ssi[ssi$polinterest_high==1,])

plow_e <- lm(externalatt_rsc~ occasionalviewer + frequentviewer+ heavyviewer + othertv + sportstv + rep + dem +
               education_n + income_n + married + female + age + 
               white + unemployed  + religion_attend +
               protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
               absolutemobility + gini + as.factor(state_n), data=ssi[ssi$polinterest_high==0,])


phigh_e <- lm(externalatt_rsc ~ occasionalviewer + frequentviewer+ heavyviewer + othertv + sportstv + rep + dem +
                education_n + income_n + married + female + age + 
                white + unemployed  + religion_attend +
                protestant + optimismindex + insecurity + intergenmobility + parentsimmigrant +
                absolutemobility + gini + as.factor(state_n), data=ssi[ssi$polinterest_high==1,])


table <- capture.output({stargazer(plow, phigh, plow_i, phigh_i, plow_e, phigh_e,   
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('state_n') , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   digits=3,
                                   align = TRUE, 
                                   type= "text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{1\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  

# --------------------------------------------------------------------------------------------------------------------------------
# APPENIDX G  


## Table G1, Columns (1) to (3) 


load("anes2016.rdata")

# ANES 2016 

aw <- lm(mobilityindex_rsc ~ realitynew + rep  + dem  + ideo_conserv + white + educat3 +
           female + income + agecat13 + married + bornagain + outofwork +
           wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger , 
         data=anes2016, weights=V160102)

awr <- lm(mobilityindex_rsc ~ realitynew  + ideo_conserv + white + educat3 +
            female + income + agecat13 + married + bornagain + outofwork +
            wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger, 
          data=anes2016[anes2016$rep==1,], weights=V160102)

awd <- lm(mobilityindex_rsc ~ realitynew  + ideo_conserv + white + educat3 +
            female + income + agecat13 + married + bornagain + outofwork +
            wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger, 
          data=anes2016[anes2016$dem==1,], weights=V160102)


table <- capture.output({stargazer(aw, awr, awd,    
                                   column.labels = c('All', 'Rep','Dem'),
                                   column.separate = c(1, 1, 1),
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('PPSTATEN_11', 'ideo_conserv', "white", "educat3", "female", "income", "agecat13",
                                            "married", "bornagain", "outofwork", "wrongdirection", "econbetterthanlastyear",
                                            "unemployment_worse", "incomegap_larger", "rep", "dem", "Constant") , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   digits=3,
                                   align = TRUE, 
                                   type="text") 
})
#table <- gsub("\\begin{tabular}","\\resizebox{0.8\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
#table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  

## Table G1, Columns (4) to (6) 

# iscap 

load("iscap.rdata")

ia <- lm(mobility_rsc ~ combined + 
           newscount_w8_new + 
           rep_11 + dem_11 +  
           ideo_11_rsc +  age + 
           female_11 +  income +  
           married_11 + white_11 + 
           educ_11 +  protestant +  
           unemployed_11 +  socio_11_rsc + 
           PPMSACAT_11 + sjs_mean_rsc, 
         data=iscap, weights=weight1_11) 



iar  <- lm(mobility_rsc ~ combined +  newscount_w8_new  +  
             rep_11 + dem_11 +  ideo_11_rsc +  age +  female_11 +  income +  married_11 + white_11 + 
             educ_11 +  protestant +  unemployed_11 +  socio_11_rsc +  PPMSACAT_11 +  sjs_mean_rsc, data=iscap[iscap$rep_11==1,], weights=weight1_11) 


iad  <- lm(mobility_rsc ~ combined +  newscount_w8_new  + 
             rep_11 + dem_11 +  ideo_11_rsc +  age +  female_11 +  income +  married_11 +  white_11 + 
             educ_11 +  protestant +  unemployed_11 +  socio_11_rsc +  PPMSACAT_11 +  sjs_mean_rsc, data=iscap[iscap$dem_11==1,], weights=weight1_11) 


table <- capture.output({stargazer( ia, iar, iad,  
                                   column.labels = c( 'All', 'Rep','Dem'),
                                   column.separate = c( 1, 1, 1),
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('newscount_w8_new', 'rep_11', "dem_11", "ideo_11_rsc", "age",
                                             'female_11', "income", "married_11", "white_11",
                                            'educ_11', "protestant", "unemployed_11", "socio_11_rsc", 
                                            "PPMSACAT_11", "sjs_mean_rsc", "Constant") , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   digits=3,
                                   align = TRUE, 
                                   type="text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{0.8\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  

# --------------------------------------------------------------------------------------------------------------------------------
# APPENIDX H 

# Table H1. Columns (1) to (4) 

load("anes2016.rdata")


aw <- lm(mobilityindex_rsc ~ realitynew + rep  + dem  + ideo_conserv + white + educat3 +
           female + income + agecat13 + married + bornagain + outofwork +
           wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger , 
         data=anes2016, weights=V160102)

awr <- lm(mobilityindex_rsc ~ realitynew  + ideo_conserv + white + educat3 +
            female + income + agecat13 + married + bornagain + outofwork +
            wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger, 
          data=anes2016[anes2016$rep==1,], weights=V160102)

awd <- lm(mobilityindex_rsc ~ realitynew  + ideo_conserv + white + educat3 +
            female + income + agecat13 + married + bornagain + outofwork +
            wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger, 
          data=anes2016[anes2016$dem==1,], weights=V160102)


awi <- lm(mobilityindex_rsc ~ realitynew  + ideo_conserv + white + educat3 +
            female + income + agecat13 + married + bornagain + outofwork +
            wrongdirection + econbetterthanlastyear + unemployment_worse + incomegap_larger, 
          data=anes2016[anes2016$ind==1,], weights=V160102)

table <- capture.output({stargazer(aw, awr, awd, awi,     
                                   column.labels = c('All', 'Rep','Dem', "Ind"),
                                   column.separate = c(1, 1, 1, 1),
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('PPSTATEN_11', 'ideo_conserv', "white", "educat3", "female", "income", "agecat13",
                                            "married", "bornagain", "outofwork", "wrongdirection", "econbetterthanlastyear",
                                            "unemployment_worse", "incomegap_larger", "rep", "dem", "Constant") , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   digits=3,
                                   align = TRUE, 
                                   type="text") 
})
#table <- gsub("\\begin{tabular}","\\resizebox{0.8\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
#table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  

# Table H1. Columns (5) to (8) 


load("iscap.rdata")


ia <- lm(mobility_rsc ~ combined +  newscount_w8_new + 
           rep_11 + dem_11 +  ideo_11_rsc +  age +  female_11 +  income +  married_11 + white_11 + 
           educ_11 +  protestant +  unemployed_11 +  socio_11_rsc +  PPMSACAT_11 + sjs_mean_rsc, 
         data=iscap, weights=weight1_11) 

iar  <- lm(mobility_rsc ~ combined +  newscount_w8_new  +  
             rep_11 + dem_11 +  ideo_11_rsc +  age +  female_11 +  income +  married_11 + white_11 + 
             educ_11 +  protestant +  unemployed_11 +  socio_11_rsc +  PPMSACAT_11 +  sjs_mean_rsc, data=iscap[iscap$rep_11==1,], weights=weight1_11) 


iad  <- lm(mobility_rsc ~ combined +  newscount_w8_new  + 
             rep_11 + dem_11 +  ideo_11_rsc +  age +  female_11 + income +  married_11 +  white_11 + 
             educ_11 +  protestant +  unemployed_11 +  socio_11_rsc +  PPMSACAT_11 +  sjs_mean_rsc, data=iscap[iscap$dem_11==1,], weights=weight1_11) 


iai  <- lm(mobility_rsc ~ combined +  newscount_w8_new  + 
              ideo_11_rsc +  age +  female_11 +  income +  married_11 +  white_11 + 
             educ_11 +  protestant +  unemployed_11 +  socio_11_rsc +  PPMSACAT_11 +  sjs_mean_rsc, data=iscap[iscap$ind_11==1,], weights=weight1_11) 


table <- capture.output({stargazer( ia, iar, iad,  iai, 
                                    column.labels = c( 'All', 'Rep','Dem', "Ind"),
                                    column.separate = c( 1, 1, 1),
                                    dep.var.caption = "",
                                    omit.stat=c("adj.rsq","LL","ser","f"),
                                    omit = c('newscount_w8_new', 'rep_11', "dem_11", "ideo_11_rsc", "age",
                                             'female_11', "income", "married_11", "white_11",
                                             'educ_11', "protestant", "unemployed_11", "socio_11_rsc", 
                                             "PPMSACAT_11", "sjs_mean_rsc", "Constant") , 
                                    star.cutoffs = c(0.1, .05,.01,.001),
                                    no.space=TRUE,
                                    star.char = c("+", "*", "**", "***"), 
                                    notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                    notes.append = F,
                                    notes.align="l",
                                    digits=3,
                                    align = TRUE, 
                                    type="text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{0.8\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  





## Table H2 2018 Media and Culture Survey Results by Party ID

load("ssi.rdata")


m2 <- lm(mindex_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv  + sportstv + rep + dem + 
           education_n + income_n + married + female + age + 
           white + unemployed + polinterst + religion_attend +
           protestant + optimismindex +insecurity + intergenmobility + parentsimmigrant +
           +absolutemobility + gini + as.factor(state_n) , data=ssi) 


m2r <- lm(mindex_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv  + sportstv +
            education_n + income_n + married + female + age + 
            white + unemployed + polinterst + religion_attend +
            protestant + optimismindex +insecurity + intergenmobility + parentsimmigrant +
            +absolutemobility + gini + as.factor(state_n) , data=ssi[ssi$rep==1,]) 

m2d <- lm(mindex_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv  + sportstv +
            education_n + income_n + married + female + age + 
            white + unemployed + polinterst + religion_attend +
            protestant + optimismindex +insecurity + intergenmobility + parentsimmigrant +
            +absolutemobility + gini + as.factor(state_n) , data=ssi[ssi$dem==1,]) 


m2i <- lm(mindex_rsc ~ occasionalviewer + frequentviewer + heavyviewer + othertv  + sportstv +
            education_n + income_n + married + female + age + 
            white + unemployed + polinterst + religion_attend +
            protestant + optimismindex +insecurity + intergenmobility + parentsimmigrant +
            +absolutemobility + gini + as.factor(state_n) , data=ssi[ssi$ind==1,]) 



table <- capture.output({stargazer(m2, m2r, m2d, m2i,  
                                   column.labels = c('All', 'Rep','Dem', 'Ind'), 
                                   column.separate = c(1, 1, 1, 1),
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('state_n', "othertv", "sportstv", "education_n", "income_n", "married", "female", "age",
                                   "white", "unemployed", "polinterst", "religion_attend", "protestant", "optimismindex", "insecurity",
                                   "intergenmobility", "parentsimmigrant", "absolutemobility", "gini", "Constant", "rep", "dem") , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   label = "ssi2018",
                                   title = "ssi2018", 
                                   digits=3,
                                   align = TRUE, 
                                   type="text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{1\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  

# --------------------------------------------------------------------------------------------------------------------------------
# APPENDIX K 


load("combined.rdata")

# manipulation check

# person featured on the show profitted a lot financially
t.test(combined$m1_rsc ~ combined$condition2)
# likely to have a higher income from now 
t.test(combined$m2_rsc ~ combined$condition2)
# has a good work ethic
t.test(combined$m3_rsc ~ combined$condition2)
# showed that people can succeed when they are willing to work hard 
t.test(combined$m4_rsc ~ combined$condition2)
# liked the program 
t.test(combined$like ~ combined$condition2)
# thought the program was entertaining
t.test(combined$entertaining ~ combined$condition2)


## Table K1. Heterogeneous Treatment Effects by System Justification Tendency


j <-  lm(mperception_combined ~ condition2*sjs_high + pid + optimism_index + date + as.factor(surveymode_n), 
           data=combined)

table <- capture.output({stargazer(j, 
                                   covariate.labels = c('Rags-to-Riches TV Treatment', "System Justification Scale - High", "Party ID",
                                                        "Optimism Index", "Treatment x System Justification Scale"),
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('surveymode_n', 'date') , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   label = "experiment",
                                   title = "Heterogeneous Treatment Effects by SJS", 
                                   digits=3,
                                   align = TRUE, 
                                   type="text") 
})
table <- gsub("\\begin{tabular}","\\resizebox{0.8\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  


# --------------------------------------------------------------------------------------------------------------------------------
# APPENDIX L 

# Table L1. The impact of merit-based rags-to-riches TV on redistribution-related attitudes

load("merit.rdata")

m1 <- lm( dv3_rich_rcd ~ condition2 + rep + dem  + as.factor(surveymode_n) + as.factor(date_n) + durationinseconds,data=merit)
m2 <- lm( dv3_poor_rcd ~ condition2 + rep + dem  + as.factor(surveymode_n) + as.factor(date_n) + durationinseconds,data=merit)
m3 <- lm( dv4_inequality_rsc ~ condition2 + rep + dem  + as.factor(surveymode_n) + as.factor(date_n) + durationinseconds,data=merit)
m4 <- lm( antigov2 ~ condition2 + rep + dem  + as.factor(surveymode_n) + as.factor(date_n) + durationinseconds,data=merit)

table <- capture.output({stargazer(m1, m2,  m3,  m4,   
                                   dep.var.labels= c('The rich works hard',
                                                     'The poor lacks efforts',
                                                     'Inequality is desirable',
                                                     'Anti-redistribution'),
                                   covariate.labels = c('Meritocracy Treatment'),
                                   dep.var.caption = "",
                                   omit.stat=c("adj.rsq","LL","ser","f"),
                                   omit = c('surveymode_n', 'date_n', 'durationinseconds', 'rep', 'dem', 'Constant') , 
                                   star.cutoffs = c(0.1, .05,.01,.001),
                                   no.space=TRUE,
                                   star.char = c("+", "*", "**", "***"), 
                                   notes = c("+ p< 0.1, * p<0.05; ** p<0.01; *** p<0.001"), 
                                   notes.append = F,
                                   notes.align="l",
                                   label = "main",
                                   title = "The Casual Effect of Merit-Based Rags-to-Riches TV", 
                                   digits=3,
                                   align = TRUE, 
                                   type = "text") 
})

#%table <- gsub("\\begin{tabular}","\\resizebox{1\\textwidth}{!}{\\begin{tabular}", table,fixed=T)
#table <- gsub("\\end{tabular}","\\end{tabular}}", table,fixed=T)
cat(table)  

# --------------------------------------------------------------------------------------------------------------------------------
# APPENDIX M
# Figure M1

load("gss.rdata")


fit <- lm(getahead_new~ tv_dummy*as.factor(year) + news + polviews + pid3 + 
            age + income + as.factor(race) + educ + sex, data = gss, weights=gss$wtssall)

dat <- ggpredict(fit, terms = c("year", "tv_dummy"))
dat$year <- as.Date(as.character(dat$x), format = "%Y")
dat$year <- year(dat$year)


p1 <- ggplot(dat, aes(x=year, y=predicted, color=group, shape=group)) +
  geom_smooth(span=1.2, se=T, alpha=0.2, aes(fill=group)) + scale_fill_manual(name='group', values=c("black", "#00A7A3"))+ geom_point() +    scale_shape_manual(values = c(4, 11)) + 
  theme_minimal()  +  theme(legend.position="top") +  guides(shape = FALSE, fill=F) + 
  xlab("Year") + 
  ylab("people can get ahead by working hard")  + scale_x_continuous(breaks=seq(1970,2020,4)) +
  scale_y_continuous(breaks=scales::pretty_breaks(n=4)) + 
  scale_color_manual(values=c("#214455", "#00A7A3", "grey40"), name="Overall TV Consumption", labels=c("Low", "High")) +
  theme(axis.text.x = element_text(colour = "black", size=15), 
        axis.title.x = element_text(size=15), 
        axis.text.y=element_text(colour = "black", size=15), 
        legend.text=element_text(size=16, face="bold"), legend.title=element_text(size=16, face="bold"),
        axis.title.y = element_text(size=18), panel.grid.minor.x = element_blank(), panel.grid.minor.y=element_blank()) 
p1 

#pdf('gss_timetrend_new.pdf',height=6,width=8)
#p1
#dev.off()