Max Weylandt.

The best file to follow all of this is perhaps the one called ESS7_data_protocol_e01_2.pdf. I would link it but the links I used are already dead, so I can’t trust the new ones staying live either.

 data <- read_dta("ESS7e02_2.stata/ESS7e02_2.dta")

data <- data %>% mutate(threat_econ = case_when(
  admaimg == 3 | admaimg == 4 ~ "low-skilled",
  admaimg == 1 | admaimg == 2 ~ "Professional",
  TRUE ~ NA_character_
  ),
  threat_cultural = case_when(
  admaimg == 1 | admaimg == 3 ~ "EU",
  admaimg == 4 | admaimg == 2 ~ ".Non-EU",
  TRUE ~ NA_character_
  )

  )

data <- data %>% mutate(Y = coalesce(alpfpe, alpfpne, allbpe, allbpne))

data <- data %>% mutate(Y_flipped = case_when(
  Y == 4 ~ 1,
  Y == 3 ~ 2,
  Y == 2 ~ 3,
  Y == 1 ~ 4,
  TRUE ~ NA_real_
))

 # Run Replication


mod_pooled_fl <- lm(Y_flipped~  threat_cultural* threat_econ, data=data)

# Make Table

get_estimates(mod_pooled_fl)[,c(1:3,7,9)] %>%
  slice(2, 3:4, 1) %>%
  mutate(term = str_replace(term, "threat_culturalEU", "Ethnic Premium"),
         term = str_replace(term, "threat_econProfessional", "High skill premium"),
         term = str_replace(term, "Ethnic Premium:High skill premium", "Ethnic x high Skill")) %>%  kable(digits=c(0,2,3,2,3,3))

 # load in supplementary data as data2
data2 <- read_dta("ESS7MTMMe02_1.stata/ESS7MTMMe2_1.dta")
data2 <- essurvey::recode_missings(data2)

#join it to the main survey
joined <- left_join(data, data2, by=c("cntry", "idno"))

# clone response columns to ones with legible names
joined <- joined %>%
  mutate(say_in_gov = testf13,
         influence_pol = testf14,
         pol_care = testf15,
         able_groups = testf16,
         able_particip = testf17)

 mod1 <- lm(say_in_gov ~ threat_cultural* threat_econ, data=joined)

mod2 <- lm(influence_pol ~ threat_cultural* threat_econ, data=joined)

mod3 <- lm(pol_care ~ threat_cultural* threat_econ, data=joined)

mod4 <- lm(able_groups ~ threat_cultural* threat_econ, data=joined)

mod5 <- lm(able_particip ~ threat_cultural* threat_econ, data=joined)



models <- list("System lets us have say"= mod1,
               "Can Influence Politics"= mod2,
               "Politicians Care" = mod3,
               "Able to take active role" = mod4,
               "Able to participate in politics" = mod5)
table_pooled <- sjPlot::tab_model(models,
                  show.ci = FALSE,
                  p.style = "stars")

Note that below I interact the country dummies with the treatments. In my view it’s plausible that the treatments work differently in different countries. I later found that this yields the ‘strongest’ effects (in inverted commas because they’re still basically nulls imo) – if I run models where the country-level variable doesn’t influence the treatment variables, the coefficients are all between 0 and 0.02.

 mod1 <- lm(say_in_gov ~ threat_cultural* threat_econ * factor(cntry) , data=joined)

mod2 <- lm(influence_pol ~ threat_cultural* threat_econ * factor(cntry), data=joined)

mod3 <- lm(pol_care ~ threat_cultural* threat_econ * factor(cntry), data=joined)

mod4 <- lm(able_groups ~ threat_cultural* threat_econ * factor(cntry), data=joined)

mod5 <- lm(able_particip ~ threat_cultural* threat_econ * factor(cntry), data=joined)


models <- list("System lets us have say"= mod1,
               "Can Influence Politics"= mod2,
               "Politicians Care" = mod3,
               "Able to take active role" = mod4,
               "Able to participate in politics" = mod5)
table_fe <-sjPlot::tab_model(models,
                  show.ci = FALSE,
                  p.style = "stars")

 countries <- unique(joined$cntry)

variables <- c("say_in_gov", "influence_pol", "pol_care", "able_groups", "able_particip")

outcomes <- data.frame(country_test = countries,
                       country=NA,
                       coef = NA,
                       se = NA,
                       ci_low = NA,
                       ci_high = NA )

# remove SI and FI
# those who got EU in SI or Professional in FI didn't get
# asked the outcome variables (or they are NA for another reason)

countries <- countries[-c(9,21)]

graphs <- list()

for (v in 1:length(variables)) {

  for (i in 1:length(countries)) {
    data_filtered <- joined %>% filter(cntry==countries[i])

    mod <- lm(as.formula(paste(variables[v], "~ threat_cultural * threat_econ")) , data=data_filtered)

    estimates <- get_estimates(mod)

    outcomes$country[i] = countries[i]
    outcomes$coef[i]    = estimates$estimate[2]
    outcomes$se[i]      = estimates$std.error[2]
    outcomes$ci_low[i]  = estimates$conf.low[2]
    outcomes$ci_high[i] = estimates$conf.high[2]


    p <- ggplot(outcomes) +
      aes(x=reorder(country,coef), y=coef) +
      geom_point() +
      geom_segment(aes(x=country,xend=country, y=ci_low, yend=ci_high   ))+
      geom_hline(yintercept=0, color="grey")+
      labs(x = "Country", y="Effect of Cultural Threat", title = variables[v])+
      theme_bw()+
      theme(text = element_text(size=8))+
      ylim(c(-0.5, 1))

    graphs[[v]] <- p

  }
}

graphs[[1]] + graphs[[2]] + graphs[[3]]
graphs[[5]] + graphs[[4]]