Chapter 7: Dynastic Families

Xavier Marquez

2017-01-15

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")
}

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 (Marshall, Gurr, and Jaggers 2010). 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 Geddes, Wright, and Frantz 2014; Kailitz 2013; Magaloni, Chu, and Min 2013; and Wahman, Teorell, and Hadenius 2013) 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"))) 
## Joining, by = c("country_name", "year")
## Joining, by = c("country_name", "year")
## Joining, by = c("country_name", "year")
## Joining, by = c("country_name", "year")
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()
country_name prop_measures num_measures which_measures measures_disagreeing min(year) max(year)
Bhutan 0.5000000 2 polity magaloni 2011 2012
Bhutan 0.5000000 4 polity, kailitz magaloni, wahman 2008 2010
Cambodia (Kampuchea) 0.2000000 5 kailitz polity, gwf, magaloni, wahman 1993 1996
Cambodia (Kampuchea) 0.5000000 4 gwf, kailitz polity, magaloni 1955 1969
Central African Republic 0.2000000 5 kailitz polity, gwf, magaloni, wahman 1976 1978
Ethiopia 0.5000000 2 gwf polity 1889 1945
Ethiopia 0.6666667 3 gwf, kailitz polity 1946 1949
Ethiopia 0.7500000 4 gwf, kailitz, magaloni polity 1950 1971
Ethiopia 0.8000000 5 gwf, kailitz, magaloni, wahman polity 1972 1973
Greece 0.2500000 4 magaloni polity, gwf, kailitz 1950 1966
Haiti 0.2000000 5 polity gwf, kailitz, magaloni, wahman 1972 1985
Haiti 0.2500000 4 polity gwf, kailitz, magaloni 1971 1971
Iran (Persia) 0.2500000 4 polity gwf, magaloni, wahman 1997 2003
Iran (Persia) 0.6666667 3 polity, gwf kailitz 1946 1946
Iraq 0.5000000 2 gwf polity 1936 1940
Korea, People’s Republic of 0.2000000 5 polity gwf, kailitz, magaloni, wahman 1994 2010
Korea, People’s Republic of 0.5000000 2 polity magaloni 2011 2012
Morocco 0.8000000 5 polity, gwf, kailitz, magaloni wahman 2009 2010
Nepal 0.4000000 5 polity, kailitz gwf, magaloni, wahman 1991 2001
Nepal 0.5000000 2 gwf polity 1846 1945
Nepal 0.6666667 3 gwf, kailitz polity 1946 1949
Nepal 0.7500000 4 gwf, kailitz, magaloni polity 1950 1959
Nepal 0.8000000 5 polity, gwf, kailitz, magaloni wahman 1990 1990
Netherlands 0.5000000 2 polity gwf 1870 1916

References

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.

Marshall, Monty G., Ted Robert Gurr, and Keith Jaggers. 2010. “Polity IV Project: Political Regime Characteristics and Transitions, 1800-2009.” Data set. Center for Systemic Peace. http://www.systemicpeace.org/inscr/inscr.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.