In xmarquez/AuthoritarianismBook: Replication Package for the Book Non-Democratic Politics by Xavier Marquez

Introduction

This vignette shows how to replicate and extend the charts in chapter 7 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, GGally, reshape2 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(GGally)) {
  install.packages("GGally")
}

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

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

knitr::opts_chunk$set(fig.height = 7, 
fig.width = 7)

Figure 7.1: Proportion of monarchies around the world, 1800-2015

Original figure

The figure identifies the proportion of monarchies around the world by identifying those countries whose executive is selected through `ascription' (whether or not it is paired with other forms of selection) in Polity IV [@Marshall2010]. The data excludes periods of foreign occupation or anarchy, and countries with fewer than 500,000 people.

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

data <- polity_annual %>%
  mutate(exrec2 = plyr::mapvalues(exrec, 
                                  from = sort(unique(exrec)), 
                                  to = c("Transition",
                                         "Anarchy",
                                         "Foreign occupation",
                                         "1-Monarchy", 
                                         "1-Monarchy", 
                                         "2-Other non-democratic",
                                         "2-Other non-democratic", 
                                         "2-Other non-democratic", 
                                         "1-Monarchy", 
                                         "3-Electoral regime", 
                                         "3-Electoral regime"))) %>%
  filter(!exrec2 %in% c("Anarchy","Transition","Foreign occupation")) %>%
  mutate(exrec2 = ifelse(polity2 > 6,
                         "4-Democracy (polity score > 6)", 
                         as.character(exrec2)))

ggplot(data = data, aes(x = year, fill = exrec2))+
  geom_bar(width = 1, position = "fill") +
  theme_bw() + 
  theme(legend.position = "bottom") + 
  labs(fill = "", 
       x = "Year", 
       y = "Proportion of independent states") +
  guides(fill = guide_legend(ncol=1, title.position = "top")) + 
  scale_fill_brewer(type = "div", palette = "RdBu") +
  scale_y_continuous(labels = scales::percent)

Extension 1: Disaggregating by country

We can disaggregate this data by country to see the main transitions:

data <- data %>%
  group_by(country_name) %>%
  mutate(last_monarchy = ifelse(any(exrec2 == "1-Monarchy"),
                                max(year[ exrec2 == "1-Monarchy"], na.rm = TRUE),
                                2016))

ggplot(data = data, 
       aes(x = forcats::fct_rev(reorder(country_name,
                                        last_monarchy)), 
           y = year))+
  geom_tile(aes(fill = exrec2)) +
  theme_bw() + 
  theme(legend.position = "bottom") + 
  labs(fill = "", 
       x = "", 
       y = "Year") +
  guides(fill = guide_legend(ncol=1, title.position = "top")) + 
  scale_fill_brewer(type = "div", palette = "RdBu") +
  coord_flip()

Extension 2: Correlations between different measures of monarchy

Coders don't always agree. Other measures of monarchy [in @GeddesWrightFrantz2014; @Kailitz2013; @MagaloniChuMin2013; and @WahmanTeorellHadenius2013] are highly but not perfectly correlated with the measure of monarchy derived from the exrec variable of Polity IV:

data <- data %>%
  ungroup() %>%
  mutate(is_polity_monarchy = (exrec2 == "1-Monarchy")) %>%
  full_join(all_gwf_extended_yearly %>%
              select(country_name, year, gwf_full_regimetype) %>%
              mutate(is_gwf_monarchy = (gwf_full_regimetype == "monarchy"))) %>%
  full_join(kailitz_yearly %>%
              select(country_name, year, combined_regime, monarchy) %>%
              mutate(is_kailitz_monarchy = monarchy)) %>%
  full_join(magaloni %>%
              select(country_name, year, regime_nr) %>%
              mutate(is_magaloni_monarchy = (regime_nr == "Monarchy"))) %>%
  full_join(wahman_teorell %>%
              select(country_name, year, regime1ny) %>%
              filter(!is.na(regime1ny)) %>%
              mutate(is_wahman_teorell_monarchy = (regime1ny == "Monarchy"))) 


library(GGally)

corr_data <- data %>% 
  ungroup() %>%
  select(is_polity_monarchy, 
         is_gwf_monarchy, 
         is_kailitz_monarchy, 
         is_magaloni_monarchy,
         is_wahman_teorell_monarchy) %>%
  mutate_all(as.numeric)

ggcorr(data = corr_data, label = TRUE, label_round = 2)

We can visualize the differences like this:

library(reshape2)

data <- data %>%
  filter(in_system) %>%
  melt(measure.vars = c("is_polity_monarchy",
                        "is_gwf_monarchy",
                        "is_kailitz_monarchy",
                        "is_magaloni_monarchy",
                        "is_wahman_teorell_monarchy"), 
       na.rm = TRUE) %>%
  mutate(variable = stringr::str_extract(variable,
                                         "_[a-z]+_"),
         variable = stringr::str_replace_all(variable, "_", "")) %>%
  group_by(country_name, year) %>%
  summarise(num_measures = n(),
            sum_measures = sum(value),
            prop_measures = sum_measures/num_measures,
            which_measures = ifelse(prop_measures == 1,
                                    "All agree",
                                    paste(variable[ value ], collapse = ", ")),
            measures_disagreeing = ifelse(prop_measures == 1,
                                    "All agree",
                                    paste(variable[ !value ], collapse = ", "))) %>%
  group_by(country_name) %>%
  mutate(last_monarchy = ifelse(any(prop_measures > 0),
                                max(year[ prop_measures > 0 ], na.rm = TRUE),
                                2016)) %>%
  filter(prop_measures > 0)

ggplot(data = data,
       aes(x = forcats::fct_rev(reorder(country_name,
                                        last_monarchy)),
           y = year)) +
  geom_tile(aes(fill = which_measures,
                alpha = prop_measures)) +
  theme_bw() +
  theme(legend.position = "bottom") +
  labs(fill = "Measures indicating country is monarchy",
       alpha = "Degree of agreement",
       x = "",
       y = "Year") +
  guides(fill = guide_legend(title.position = "top", ncol = 1)) +
  coord_flip()

data %>% 
  filter(prop_measures < 1,
         num_measures > 1) %>%
  group_by(country_name, prop_measures, num_measures, which_measures, measures_disagreeing) %>%
  summarise(min(year), max(year)) %>%
  knitr::kable()

References

xmarquez/AuthoritarianismBook documentation built on May 4, 2019, 1:24 p.m.