Chapter 10: The Roots of Regime Change and Democratization

Xavier Marquez

22 December 2016

Introduction

This vignette shows how to replicate and extend the charts in chapter 10 of my book Non-democratic Politics: Authoritarianism, Dictatorships, and Democratization (Palgrave Macmillan, 2016). It assumes that you have downloaded the replication package as follows:

if(!require(devtools)) {
  install.packages("devtools")
}

devtools::install_github('xmarquez/AuthoritarianismBook')

It also assumes you have the dplyr, ggplot2, scales, forcats, broom, reshape2, tidyr, purrr, and knitr packages installed:

if(!require(dplyr)) {
  install.packages("dplyr")
}

if(!require(ggplot2)) {
  install.packages("ggplot2")
}

if(!require(scales)) {
  install.packages("forcats")
}

if(!require(forcats)) {
  install.packages("scales")
}

if(!require(broom)) {
  install.packages("broom")
}

if(!require(reshape2)) {
  install.packages("reshape2")
}

if(!require(purrr)) {
  install.packages("purrr")
}

if(!require(tidyr)) {
  install.packages("tidyr")
}

if(!require(knitr)) {
  install.packages("knitr")
}

For the interactive graph below, you also need the plotly package:

if(!require(plotly)) {
  install.packages("plotly")
}

Table 10.1: Frequency of different types of regime transition, 1945-2010

Original Table

This table shows the frequency of different types of regime transition, 1945-2010. This table counts only countries that have experienced transitions. Regime change is rare; in the period 1945-2010, only about 5% of all country-years experienced regime transitions. Regime data comes from Geddes, Wright, and Frantz (2014). `Other’ category includes instances of state breakdown, foreign occupation, and state termination (e.g., after unification with a larger state).

library(AuthoritarianismBook)
library(dplyr)
library(ggplot2)
library(reshape2)

data <- all_gwf_periods %>% 
  mutate(gwf_previous = ifelse(grepl("democracy",
                                     gwf_full_regimetype),
                               "Democracy",
                               ifelse(grepl("military|oligarchy|personal|party|monarchy",
                                            gwf_full_regimetype),
                                      "Non-democracy",
                                      "Other"))) %>%
  group_by(country_name) %>%
  mutate(gwf_next = lead(gwf_previous)) %>%
  ungroup() %>%
  count(gwf_previous, gwf_next, sort=TRUE) %>% 
  filter(!is.na(gwf_next)) %>%
  ungroup() %>%
  mutate(total_trans = sum(n), 
         prop_trans = scales::percent_format()(n/total_trans)) 

data  %>% 
  dcast(gwf_previous ~ gwf_next, value.var="prop_trans") %>%
  knitr::kable(col.names = c("Previous regime", "To democracy", "To non-democracy", "To other"))
Previous regime To democracy To non-democracy To other
Democracy NA 22.1% 0.3%
Non-democracy 23.9% 31.7% 10.3%
Other 7.3% 3.9% 0.6%

Extension 1: Total number of transitions

This table simply shows the total number of transitions, rather than the proportions in the datatset:

data  %>% 
  dcast(gwf_previous ~ gwf_next, value.var="n") %>%
  knitr::kable(col.names = c("Previous regime", "To democracy", "To non-democracy", "To other"),
               caption = "Total number of transition events in the dataset")
Total number of transition events in the dataset
Previous regime To democracy To non-democracy To other
Democracy NA 73 1
Non-democracy 79 105 34
Other 24 13 2

Extension 2: Proportions per row and column

Here we see the proportions per row:

data %>%
  group_by(gwf_previous) %>%
  mutate(total_by_row = sum(n), prop_trans = scales::percent_format()(n/total_by_row)) %>% 
  dcast(gwf_previous ~ gwf_next, value.var="prop_trans") %>%
  knitr::kable(col.names = c("Previous regime", "To democracy", "To non-democracy", "To other"),
               caption = "Proportions by row (previous regime)")
Proportions by row (previous regime)
Previous regime To democracy To non-democracy To other
Democracy NA 98.6% 1.4%
Non-democracy 36.2% 48.2% 15.6%
Other 61.5% 33.3% 5.1%

More than 98 per cent of transitions from democracy are to non-democracy; only 36 per cent of transitions from non-democracy are to democracy, and nearly 50 per cent are to other forms of non-democracy.

data %>%
  group_by(gwf_next) %>%
  mutate(total_by_column = sum(n), prop_trans = scales::percent_format()(n/total_by_column)) %>% 
  dcast(gwf_previous ~ gwf_next, value.var="prop_trans") %>%
  knitr::kable(col.names = c("Previous regime", "To democracy", "To non-democracy", "To other"),
               caption = "Proportions by column (successor regime)")
Proportions by column (successor regime)
Previous regime To democracy To non-democracy To other
Democracy NA 38.2% 2.7%
Non-democracy 76.7% 55.0% 91.9%
Other 23.3% 6.8% 5.4%

More than 76 per cent of transitions to democracy are from non-democracy; the rest are from situations of no authority.

Extension 3: Transition events per year

It is possible to calculate the frequencies of transition per year, including the years where no transition occurred. As we see below, regime change is extremely rare: more than 97 per cent of the country years in democratic regimes, and more than 95 per cent of the country years in non-democratic regimes, do not experience transitions:

data <- all_gwf %>% 
  mutate(gwf_previous = ifelse(grepl("democracy",
                                     gwf_full_regimetype),
                               "Democracy",
                               ifelse(grepl("military|oligarchy|personal|party|monarchy",
                                            gwf_full_regimetype),
                                      "Non-democracy",
                                      "Other"))) %>% 
  group_by(country_name) %>%
  mutate(transition = gwf_casename != lead(gwf_casename),
         gwf_next = lead(gwf_previous),
         gwf_next = ifelse(!transition, "No transition", gwf_next)) %>%
  ungroup() %>%
  count(gwf_previous, gwf_next, sort=TRUE) %>% 
  filter(!is.na(gwf_next)) %>%
  ungroup() %>%
  mutate(total_trans = sum(n), 
         prop_trans = scales::percent_format()(n/total_trans)) 

data  %>% 
  dcast(gwf_previous ~ gwf_next, value.var="n") %>%
  select(gwf_previous, `No transition`, everything()) %>%
  knitr::kable(col.names = c("Regime",  
                             "Number of country-years with no transition", 
                             "To democracy", 
                             "To non-democracy", 
                             "To other"))
Regime Number of country-years with no transition To democracy To non-democracy To other
Democracy 3032 NA 73 1
Non-democracy 4311 79 105 34
Other 125 24 13 2 
data  %>% 
  dcast(gwf_previous ~ gwf_next, value.var="prop_trans") %>%
  select(gwf_previous, `No transition`, everything()) %>%
  knitr::kable(col.names = c("Regime",  
                             "Prop. of country-years with no transition", 
                             "To democracy", 
                             "To non-democracy", 
                             "To other"))
Regime Prop. of country-years with no transition To democracy To non-democracy To other
Democracy 38.9% NA 0.9% 0.0%
Non-democracy 55.3% 1.0% 1.3% 0.4%
Other 1.6% 0.3% 0.2% 0.0% 
# This is more informative by row

data %>%
  group_by(gwf_previous) %>%
  mutate(total_by_row = sum(n), prop_trans = scales::percent_format()(n/total_by_row)) %>% 
  dcast(gwf_previous ~ gwf_next, value.var="prop_trans") %>%
  select(gwf_previous, `No transition`, everything()) %>%
  knitr::kable(col.names = c("Regime",  
                             "Prop. of country-years with no transition", 
                             "To democracy", 
                             "To non-democracy", 
                             "To other"))
Regime Prop. of country-years with no transition To democracy To non-democracy To other
Democracy 97.6% NA 2.4% 0.0%
Non-democracy 95.2% 1.7% 2.3% 0.8%
Other 76.2% 14.6% 7.9% 1.2%

Figure 10.1: Relationship between GDP per capita and democracy, 1800-2014

Original figure

This figure is a scatterplot of GDP per capita vs. democracy. GDP data is the median of the per capita value from the Maddison project (The Maddison Project 2013), the Penn World Tables (Feenstra and Timmer 2013), and the World Bank; democracy data from Pemstein, Meserve, and Melton (2010), extended by me (Márquez 2016). Each point represents a country-year; each line represents a country trajectory over those years where income and democracy data exists. Highlighted points are regimes classified as monarchies by Magaloni, Chu, and Min (2013) for the 1950-2013 period, as well as other regimes in the Middle East (including regimes for the period before 1950).

data <- inner_join(extended_uds,
                  economic_data) %>% 
  group_by(country_name, GWn, year, index) %>% 
  summarise(median = median(per_capita, na.rm = TRUE)) %>% 
  filter(!is.na(median))
## Joining, by = c("country_name", "GWn", "year", "in_system")
data <- data %>% 
  left_join(magaloni) %>% 
  mutate(regime_nr = ifelse(regime_nr == "Monarchy",
                           "monarchy",
                           NA),
         region = ifelse(country_name %in%
                           c("Algeria",
                             "Bahrain",
                             "Egypt",
                             "Iraq",
                             "Israel",
                             "Jordan",
                             "Kuwait",
                             "Lebanon",
                             "Libya",
                             "Morocco",
                             "Oman",
                             "Qatar",
                             "Saudi Arabia",
                             "South Sudan",
                             "Sudan",
                             "Syria",
                             "Tunisia",
                             "Turkey (Ottoman Empire)",
                             "United Arab Emirates",
                             "Yemen (Arab Republic of Yemen)",
                             "Yemen, People's Republic of"),
                         "Middle East", 
                         ifelse(country_name == "Singapore", 
                                "Singapore", 
                                NA)), 
                        region_regime = ifelse(!is.na(regime_nr) & !is.na(region), 
                                               paste(region, regime_nr),
                                               region))
## Joining, by = c("country_name", "GWn", "year")
ggplot(data = data, 
       aes(x = index, 
           y = median, 
           group = GWn)) + 
  geom_path(alpha = 0.1) + 
  geom_point(aes(color = region_regime), 
             alpha = 0.5) + 
  theme_bw() + 
  scale_y_log10(label=scales::dollar) +
  geom_smooth(aes(y = median,group=0), 
              method = "lm", 
              color = "red") +
  labs(y="GDP per capita, median of various measures", 
       x="Unified democracy score",
       shape="",
       alpha="",
       color="") + 
  geom_text(data = data %>% 
              ungroup() %>% 
              filter(!is.na(region_regime),
                     (year %in% c(min(year), 
                                 max(year))) |
                       ((year %% 10) == 0)),
            aes(label = year, color = region_regime), 
            check_overlap = TRUE) + 
  geom_vline(xintercept = 0.5, linetype=2) +
  theme(legend.position = "bottom") +
  scale_color_discrete(na.value = "lightgrey") +
  guides(shape = guide_legend(ncol=2))

Extension 1: Using different measures of GDP per capita and democracy

This correlation does not vary much depending on the measure of GDP per capita:

data <- inner_join(extended_uds,
                  economic_data) %>%
  filter(!is.na(per_capita))
## Joining, by = c("country_name", "GWn", "year", "in_system")
ggplot(data = data, 
       aes(x = index, 
           y = per_capita, 
           group = GWn)) + 
  geom_path(alpha = 0.1) + 
  geom_point(alpha = 0.5) + 
  theme_bw() + 
  scale_y_log10(label=scales::dollar) +
  geom_smooth(aes(y = per_capita, group = variable),
              method = "lm",
              color = "red") +
  labs(y="GDP per capita, various measures",
       x="Unified democracy score",
       shape="",
       alpha="",
       color="") +
  geom_text(data = data %>%
              ungroup() %>%
              filter((year %in% c(min(year),
                                 max(year))) |
                       ((year %% 25) == 0)),
            aes(label = year),
            check_overlap = TRUE) +
  geom_vline(xintercept = 0.5, linetype=2) +
  theme(legend.position = "bottom") +
  scale_color_discrete(na.value = "lightgrey") +
  guides(shape = guide_legend(ncol=2)) +
  facet_wrap(~variable, ncol = 1)

Or democracy:

data <- full_join(vdem,
                  economic_data) %>% 
  group_by(country_name, GWn, year, 
           v2x_polyarchy, 
           v2x_api, 
           v2x_mpi, 
           v2x_libdem, 
           v2x_partipdem, 
           v2x_delibdem, 
           v2x_egaldem) %>% 
  summarise(median = median(per_capita, na.rm = TRUE)) %>% 
  filter(!is.na(median)) %>%
  melt(measure.vars = c("v2x_polyarchy",
                        "v2x_api",
                        "v2x_mpi",
                        "v2x_libdem",
                        "v2x_partipdem",
                        "v2x_delibdem",
                        "v2x_egaldem"))
## Joining, by = c("country_name", "GWn", "year", "in_system")
ggplot(data = data, 
       aes(x = value, 
           y = median, 
           group = GWn)) + 
  geom_path(alpha = 0.1) + 
  geom_point(alpha = 0.5) + 
  theme_bw()+ 
  scale_y_log10(label=scales::dollar) +
  geom_smooth(aes(y = median, group = variable), 
              method = "lm", 
              color = "red") +
  labs(y="GDP per capita, median of various measures", 
       x="Democracy score, VDem",
       shape="",
       alpha="",
       color="") + 
  geom_text(data = data %>% 
              ungroup() %>% 
              filter((year %in% c(min(year), 
                                 max(year))) |
                       ((year %% 25) == 0)),
            aes(label = year), 
            check_overlap = TRUE) + 
  geom_vline(xintercept = 0.5, linetype=2) +
  theme(legend.position = "bottom") +
  scale_color_discrete(na.value = "lightgrey") +
  guides(shape = guide_legend(ncol=2)) +
  facet_wrap(~variable, ncol = 1)
## Warning: Removed 31263 rows containing non-finite values (stat_smooth).
## Warning: Removed 2591 rows containing missing values (geom_path).
## Warning: Removed 31263 rows containing missing values (geom_point).
## Warning: Removed 2627 rows containing missing values (geom_text).

Extension 2: Country by country correlation

But the correlation does vary from country to country, and depends a bit on the measure of GDP used:

library(broom)
library(purrr)
library(tidyr)

data <- left_join(extended_uds,
                  economic_data) %>% 
  group_by(primary_source, variable, country_name) %>% 
  filter(!is.na(per_capita), n() > 5) %>% 
  summarise(index = list(index), per_capita = list(per_capita)) %>%
  mutate(cor_test = map2(index, per_capita, ~ tidy(cor.test(.x, log(.y))))) %>% 
  unnest(cor_test) %>%
  ungroup()
## Joining, by = c("country_name", "GWn", "year", "in_system")
ggplot(data = data,
       aes(x = forcats::fct_reorder(country_name, estimate, na.rm =TRUE),
           y = estimate)) +
  geom_pointrange(aes(ymin = conf.low, 
                      ymax = conf.high, 
                      color = primary_source)) +
  labs(x = "",
       y = "Estimated correlation",
       color = "Primary source of GDP data") +
  theme_bw() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(ncol=2)) +
  geom_hline(yintercept = 0, color = "red") +
  coord_flip()

We can also do this as follows:

data <- left_join(extended_uds,
                  economic_data) %>% 
  group_by(primary_source, variable, country_name) %>% 
  filter(!is.na(per_capita), n() > 5) %>% 
  summarise(index = list(index), per_capita = list(per_capita)) %>%
  mutate(cor_test = map2(index, per_capita, ~ tidy(cor.test(.x, log(.y))))) %>% 
  unnest(cor_test) %>% 
  group_by(country_name) %>%
  summarise(avg_estimate = mean(estimate),
            se_estimate = sqrt(sum(((estimate - conf.high)/1.96)^2))/n(),
            conf.high = avg_estimate + 1.96*se_estimate,
            conf.low = avg_estimate - 1.96*se_estimate)
## Joining, by = c("country_name", "GWn", "year", "in_system")
ggplot(data = data,
       aes(x = forcats::fct_reorder(country_name, avg_estimate, na.rm =TRUE),
           y = avg_estimate)) +
  geom_pointrange(aes(ymin = conf.low, 
                      ymax = conf.high)) +
  labs(x = "",
       y = "Estimated correlation",
       color = "Primary source of GDP data") +
  theme_bw() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(ncol=2)) +
  geom_hline(yintercept = 0, color = "red") +
  coord_flip()

Figure 10.2: Year by year correlation between economic development and democracy

Raw (uncontrolled) correlation between economic development and democracy, per year. Shaded areas represent 95% confidence intervals. GDP data from the Maddison Project (The Maddison Project 2013), the Penn World Tables (Feenstra and Timmer 2013), and the World Bank; democracy data from Pemstein, Meserve, and Melton (2010), extended by the me (Márquez 2016).

data <- left_join(extended_uds,
                  economic_data) %>% 
  group_by(primary_source, variable, year) %>% 
  filter(!is.na(per_capita), n() > 4) %>% 
  summarise(index = list(index), per_capita = list(per_capita)) %>%
  mutate(cor_test = map2(index, per_capita, ~ tidy(cor.test(.x, log(.y))))) %>% 
  unnest(cor_test) %>% 
  group_by(year) %>% 
  summarise(median = median(estimate, na.rm=TRUE),
         min_conf_low = min(conf.low, na.rm=TRUE),
         max_conf_high = max(conf.high, na.rm=TRUE))
## Joining, by = c("country_name", "GWn", "year", "in_system")
ggplot(data = data, 
       aes(x = year, 
           y = median, 
           ymin = min_conf_low, 
           ymax = max_conf_high)) + 
  geom_path() + 
  theme_bw()+ 
  geom_ribbon(aes(ymax = max_conf_high, 
                  ymin = min_conf_low),
              alpha = 0.3, fill = "lightgrey") + 
  geom_smooth(aes(y=median), color = "black") +
  labs(y="Raw correlation between
       GDP per capita (various measures) and
       extended Unified Democracy Score, per year") + 
  geom_hline(yintercept = 0, linetype=2) +
  coord_cartesian(xlim=c(1850,2014))
## `geom_smooth()` using method = 'loess'

Figure 10.3: Inequality in Egypt, 1964-2009

Inequality data is from the Standardized Worldwide Income Inequality Database (Solt 2016; Solt 2009).

count_sequence_breaks <- function(seq, seq_step = 1) {
  first_diff <- c(seq_step, diff(seq)) - seq_step
  periods <- cumsum(abs(first_diff))
  periods
}

data <- swiid_summary_5 %>% 
  filter(country_name  %in% c("Egypt","Tunisia")) 

world_ineq <- swiid_summary_5 %>% 
  group_by(year,variable) %>% 
  summarise(mean_value = mean(mean_value,na.rm=TRUE), 
            se = sqrt(sum(se^2))/n())

middle_east_ineq <- swiid_summary_5 %>% 
  filter(region %in% c("Northern Africa","Western Asia"), 
         !(country_name %in% c("Georgia","Azerbaijan","Cyprus"))) %>% 
  group_by(year,variable) %>% 
  summarise(mean_value = mean(mean_value,na.rm=TRUE), 
            se = sqrt(sum(se^2))/n())

world_ineq$country_name <- "World avg."

middle_east_ineq$country_name <- "Middle East avg."

data <- bind_rows(data, world_ineq, middle_east_ineq) %>%
  filter(variable == "gini_net") %>%
  group_by(country_name) %>%
  arrange(year) %>%
  mutate(period = paste(count_sequence_breaks(year), country_name)) %>%
  ungroup()

ggplot(data = data %>% filter(country_name != "Tunisia"), 
       aes(x=year, 
           y = mean_value, 
           color = country_name, 
           fill = country_name, 
           ymin = mean_value - 1.96*se, 
           ymax = mean_value + 1.96*se, 
           group = period)) + 
  geom_path() + 
  geom_point() +
  geom_ribbon(alpha=0.2) +
  labs(fill = "",
       color ="", 
       y = "Net gini index\n(after taxes and transfers,
       higher values indicate more inequality)") +
  theme_bw() +
  theme(legend.position ="bottom") +
  geom_vline(xintercept = 2011, color = "red") +
  geom_text(x = 2011,
            y = 50,
            label = "2011 revolution", 
            angle = 90,
            color = "black")

Figure 10.4: Inequality in Tunisia, 1965-2010

ggplot(data = data %>% filter(country_name != "Egypt"), 
       aes(x=year, 
           y = mean_value, 
           color = forcats::fct_relevel(country_name, "Tunisia"), 
           fill = forcats::fct_relevel(country_name, "Tunisia"), 
           ymin = mean_value - 1.96*se, 
           ymax = mean_value + 1.96*se, 
           group = period)) + 
  geom_point() +
  geom_path() + 
  geom_ribbon(alpha=0.2) +
  labs(fill = "",
       color ="", 
       y = "Net gini index\n(after taxes and transfers,
       higher values indicate more inequality)") +
  theme_bw() +
  theme(legend.position ="bottom") +
  geom_vline(xintercept = 2011, color = "red") +
  geom_text(x = 2011,
            y = 50,
            label = "2011 revolution", 
            angle = 90,
            color = "black")

Figure 10.5: Inequality in Venezuela

data <- swiid_summary_5 %>% 
  filter(country_name  %in% c("Venezuela"))

latinamerica_ineq <- swiid_summary_5 %>% 
  filter(region %in% c("Southern America","Central America")) %>% 
  group_by(year, variable) %>% 
  summarise(mean_value = mean(mean_value, na.rm=TRUE), 
            se = sqrt(sum(se^2))/n())

latinamerica_ineq$country_name <- "Latin America avg."

data <- bind_rows(data, world_ineq, latinamerica_ineq) %>% 
  filter(variable == "gini_net") %>%
  group_by(country_name) %>%
  arrange(year) %>%
  mutate(period = paste(count_sequence_breaks(year), country_name)) %>%
  ungroup()

ggplot(data = data %>% filter(country_name != "Egypt"), 
       aes(x=year, 
           y = mean_value, 
           color = forcats::fct_relevel(country_name, "Venezuela"), 
           fill = forcats::fct_relevel(country_name, "Venezuela"), 
           ymin = mean_value - 1.96*se, 
           ymax = mean_value + 1.96*se, 
           group = period)) + 
  geom_path() + 
  geom_point() +
  geom_ribbon(alpha=0.2) +
  labs(fill = "",
       color ="", 
       y = "Net gini index\n(after taxes and transfers,
       higher values indicate more inequality)") +
  theme_bw() +
  theme(legend.position ="bottom") +
  geom_vline(xintercept = 1997, color = "red") +
  geom_text(x = 1997,
            y = 50,
            label = "Chavez is elected", 
            angle = 90,
            color = "black")

Figure 10.6: Inequality and Political Regimes

Original figure

The inequality measure is the net gini index of income inequality (net means after taxes and transfers); it goes from 0 to 100, where higher numbers mean more inequality. Data on regimes is from Kailitz (2013). Inequality data from Solt (Solt 2009; Solt 2016). The democracy data is from Pemstein, Meserve, and Melton (2010), extended by me (Márquez 2016). The original figure used the package ggalt, which is no longer compatible with ggplot2; nothing changes if we use ggplot2::geom_density_2d for the estimation.

data <- inner_join(swiid_summary_5,
                   extended_uds %>% 
                     select(country_name, GWn, year, index) %>% 
                     rename(uds = index)) %>%
  inner_join(kailitz_yearly %>%
               select(-cown:-in_system) %>%
               mutate(democracy = grepl("Democracy",combined_regime)) %>%
               melt(measure.vars = c("personal",
                                     "communist",
                                     "military",
                                     "party",
                                     "monarchy",
                                     "electoral",
                                     "failure",
                                     "transition",
                                     "democracy")) %>%
               filter(value) %>%
               rename(regime = variable)) %>%
  filter(!is.na(regime),
         variable %in% c("gini_net")) %>%
  mutate(name = paste(country_name, year))
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year", "GWc")
ggplot(data = data, 
       aes(text = name,
           x = uds,
           y = mean_value,
           group = regime)) +
  geom_point(alpha = 0.2) +
  geom_density_2d() +
  theme_bw() +
  labs(y = "Gini index",
       x = "Unified democracy score (democracies = >0.5)",
       alpha = "Concentration of regimes",
       fill = "") +
  theme(legend.position = "bottom") + 
  geom_vline(xintercept = 0.5, color = "red") +
  facet_wrap(~regime) +
  coord_cartesian(xlim=c(0,1))

Interactive version

Here’s an interactive version (requires the plotly package):

library(plotly)

p <- ggplot(data = data %>%
              rename(gini_net = mean_value), 
       aes(text = name,
           x = uds,
           y = gini_net,
           group = regime)) +
  geom_point(alpha = 0.2) +
  theme_bw() +
  labs(y = "Gini index",
       x = "Unified democracy score (democracies = >0.5)",
       alpha = "Concentration of regimes",
       fill = "") +
  theme(legend.position = "bottom") + 
  geom_vline(xintercept = 0.5, color = "red") +
  facet_wrap(~regime) +
  coord_cartesian(xlim=c(0,1))

ggplotly(p)

Extension 1: Other measures of regime type

We can use the measure of regime type from Magaloni, Chu, and Min (2013) instead:

data <- inner_join(swiid_summary_5,
                   extended_uds %>% 
                     select(country_name,
                            GWn,
                            year,
                            index) %>% 
                     rename(uds = index)) %>%
  inner_join(magaloni %>% 
                     select(country_name,
                            GWn,
                            year,
                            regime_nr)) %>%
  left_join(kailitz_yearly %>%
              select(country_name, 
                     GWn, 
                     year, 
                     communist)) %>%
  mutate(regime = ifelse(communist, 
                         paste("Communist ", regime_nr), 
                         regime_nr)) %>%
  filter(!is.na(regime),
         variable %in% c("gini_net"))
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year")
ggplot(data = data, 
       aes(x = uds,
           y = mean_value,
           group = regime)) +
  geom_point(alpha = 0.2) +
  geom_density_2d() +
  theme_bw() +
  labs(y = "Gini index",
       x = "Unified democracy score (democracies = >0.5)",
       alpha = "Concentration of regimes",
       fill = "") +
  theme(legend.position = "bottom") + 
  geom_vline(xintercept = 0.5, color = "red") +
  facet_wrap(~regime) +
  coord_cartesian(xlim=c(0,1))

Or from Wahman, Teorell, and Hadenius (2013):

data <- inner_join(swiid_summary_5,
                   extended_uds %>% 
                     select(country_name,
                            GWn,
                            year,
                            index) %>% 
                     rename(uds = index)) %>%
  inner_join(wahman_teorell %>% 
                     select(country_name,
                            GWn,
                            year,
                            regime1ny)) %>%
  filter(!is.na(regime1ny)) %>%
  left_join(kailitz_yearly %>%
              select(country_name, 
                     GWn, 
                     year, 
                     communist)) %>%
  mutate(regime = ifelse(communist, 
                         paste("Communist ", regime1ny), 
                         as.character(regime1ny))) %>%
  filter(!is.na(regime),
         variable %in% c("gini_net"))
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year")
ggplot(data = data, 
       aes(x = uds,
           y = mean_value,
           group = regime)) +
  geom_point(alpha = 0.2) +
  geom_density_2d() +
  theme_bw() +
  labs(y = "Gini index",
       x = "Unified democracy score (democracies = >0.5)",
       alpha = "Concentration of regimes",
       fill = "") +
  theme(legend.position = "bottom") + 
  geom_vline(xintercept = 0.5, color = "red") +
  facet_wrap(~regime) +
  coord_cartesian(xlim=c(0,1))
## Warning: Computation failed in `stat_density2d()`:
## bandwidths must be strictly positive

Or from Geddes, Wright, and Frantz (2014):

data <- inner_join(swiid_summary_5,
                   extended_uds %>% 
                     select(country_name,
                            GWn,
                            year,
                            index) %>% 
                     rename(uds = index)) %>%
  inner_join(all_gwf %>% 
                     select(country_name,
                            GWn,
                            year,
                            gwf_full_regimetype)) %>%
  filter(!is.na(gwf_full_regimetype)) %>%
  left_join(kailitz_yearly %>%
              select(country_name, 
                     GWn, 
                     year, 
                     communist)) %>%
  mutate(regime = ifelse(communist, 
                         paste("Communist ", gwf_full_regimetype), 
                         as.character(gwf_full_regimetype))) %>%
  filter(!is.na(regime),
         variable %in% c("gini_net"))
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year")
## Joining, by = c("country_name", "GWn", "year")
ggplot(data = data, 
       aes(x = uds,
           y = mean_value,
           group = regime)) +
  geom_point(alpha = 0.2) +
  geom_density_2d() +
  theme_bw() +
  labs(y = "Gini index",
       x = "Unified democracy score (democracies = >0.5)",
       alpha = "Concentration of regimes",
       fill = "") +
  theme(legend.position = "bottom") + 
  geom_vline(xintercept = 0.5, color = "red") +
  facet_wrap(~regime) +
  coord_cartesian(xlim=c(0,1))

Extension 2: Country-level correlations between inequality and democracy level

The correlation between inequality and democracy has varied accross countries.

data <- left_join(extended_uds,
                  swiid_summary_5 %>% 
                    select(country_name, 
                           GWn, 
                           year, 
                           variable, 
                           mean_value)) %>%
  filter(variable == "gini_net") %>%
  group_by(variable, country_name) %>% 
  filter(!is.na(mean_value), 
         n() > 5) %>% 
  summarise(index = list(index), mean_value = list(mean_value)) %>%
  mutate(cor_test = map2(index, mean_value, ~ tidy(cor.test(.x, .y)))) %>% 
  unnest(cor_test)
## Joining, by = c("country_name", "GWn", "year")
ggplot(data = data,
       aes(x = forcats::fct_reorder(country_name, estimate, na.rm =TRUE),
           y = estimate)) +
  geom_pointrange(aes(ymin = conf.low, 
                      ymax = conf.high)) +
  labs(x = "",
       y = "Estimated correlation",
       color = "Measure") +
  theme_bw() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(ncol=2)) +
  geom_hline(yintercept = 0, color = "red") +
  coord_flip()

References

Feenstra, Robert Inklaar, Robert C., and Marcel P. Timmer. 2013. “The Next Generation of the Penn World Table.” Dataset. www.ggdc.net/pwt.

Geddes, Barbara, Joseph Wright, and Erica Frantz. 2014. “Autocratic Breakdown and Regime Transitions: A New Data Set.” Perspectives on Politics 12 (1): 313–31. doi:10.1017/S1537592714000851.

Kailitz, Steffen. 2013. “Classifying Political Regimes Revisited: Legitimation and Durability.” Democratization 20 (1): 39–60. doi:10.1080/13510347.2013.738861.

Magaloni, Beatriz, Jonathan Chu, and Eric Min. 2013. “Autocracies of the World, 1950-2012 (Version 1.0).” Dataset. http://cddrl.fsi.stanford.edu/research/autocracies_of_the_world_dataset.

Márquez, Xavier. 2016. “A Quick Method for Extending the Unified Democracy Scores.” Available at SSRN 2753830. doi:10.2139/ssrn.2753830.

Pemstein, Daniel, Stephen Meserve, and James Melton. 2010. “Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type.” Political Analysis 18 (4): 426–49. doi:10.1093/pan/mpq020.

Solt, Frederick. 2009. “Standardizing the World Income Inequality Database.” Social Science Quarterly 90 (2): 231–42. doi:10.1111/j.1540-6237.2009.00614.x.

———. 2016. “The Standardized World Income Inequality Database.” Social Science Quarterly 97 (5): 1267–81. doi:10.1111/ssqu.12295.

The Maddison Project. 2013. “The Maddison Project.” Dataset. http://www.ggdc.net/maddison/maddison-project/home.htm.

Wahman, Michael, Jan Teorell, and Axel Hadenius. 2013. “Authoritarian Regime Types Revisited: Updated Data in Comparative Perspective.” Contemporary Politics 19 (1): 19–34. https://sites.google.com/site/authoritarianregimedataset/data.