Introduction

This vignette shows how to replicate the charts in chapter 5 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, 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(knitr)) {
  install.packages("knitr")
}

Figure 5.1: Ruling party control in legislatures of non-democratic regimes

Original figure

Figure 5.1 uses data by Svolik (2012) on the legislatures of non-democratic regimes.

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

data <- svolik_institutions %>% 
  mutate(legislative = plyr::mapvalues(legislative,
                                       from = sort(unique(legislative)),
                                       to = c("5-More than 75% of the seats",
                                              "6-Less than 75% of the seats",
                                              "1-No legislature",
                                              "4-Nonpartisan",
                                              "3-One party or candidate per seat",
                                              "2-Unelected or appointed")))

ggplot(data=data, aes(x=year,fill=legislative))+
  geom_bar(width=1,position="fill") + 
  theme_bw() +
  labs(fill="Ruling party control in legislatures of non-democratic regimes",
       x="Year", 
       y= "Proportion of non-democratic regimes")+ 
  guides(fill=guide_legend(title.position="top",ncol=1))+
  theme(legend.position="bottom")+
  geom_vline(xintercept=1989,linetype=2) +
  scale_y_continuous(label=scales::percent) +
  scale_fill_brewer(type = "div", palette = "RdBu") + 
  annotate("text", x=1989,y = 0.5,label="End of the Cold War",angle=90)

Extension 1: Disaggregated by country

We can also visualize this data disaggregated by country, to see how legislative control has evolved over the years in various regimes:

ggplot(data = data, 
       aes(x= forcats::fct_rev(country_name), 
           y = year, 
           fill=legislative)) +
  geom_tile() +
  theme_bw() +
  labs(fill="Ruling party control in legislatures of non-democratic regimes",
       x="", 
       y= "Year") + 
  guides(fill=guide_legend(title.position="top",ncol=1)) +
  theme(legend.position="bottom")  +
  scale_fill_brewer(type = "div", palette = "RdBu") +
  geom_hline(yintercept=1989,
             linetype=2) + 
  annotate("text", y = 1989,
           x = length(unique(data$country_name))/2,
           label="End of the Cold War",
           angle=90) + 
  coord_flip()

Extension: Elections in non-democratic regimes

Chapter 5 discusses elections in non-democratic regimes. The discussion there does not contain figures, but we can use the NELDA dataset (Hyde and Marinov 2012) and the PIPE dataset (Przeworski 2013) to get a good sense of how frequent elections have been in the non-democratic regimes identified by Svolik:

# Unnecessary normally - a hack while I clean this file

nelda$country_name[ grepl("Cote",nelda$country_name) ] <- "Cote D'Ivoire"
  
data <- full_join(data, nelda %>% select(country_name, GWn, year, types)) %>%
  filter(!is.na(legislative), in_system)

## Joining, by = c("country_name", "GWn", "year")

ggplot(data = data) +
  geom_tile(aes(x= forcats::fct_rev(country_name), 
           y = year,
           fill = legislative)) +
  geom_point(aes(x = forcats::fct_rev(country_name),
                 y = year, 
                 shape = types), 
               alpha = 1) +
  theme_bw() +
  labs(fill="Type of legislature (Svolik)",
       x="", 
       y= "Year",
       shape = "Type of election") + 
  guides(fill=guide_legend(title.position="top",ncol=1)) +
  theme(legend.position="bottom")   +
  scale_fill_brewer(type = "div", palette = "RdBu") +
  geom_hline(yintercept=1989,
             linetype=2) + 
  annotate("text", y = 1989,
           x = length(unique(data$country_name))/2,
           label="End of the Cold War",
           angle=90) + 
  coord_flip()

## Warning: Removed 3713 rows containing missing values (geom_point).

Svolik’s data doesn’t always agree with the NELDA data. (Some legislatures that Svolik codes as unelected, or nonexistent, nevertheless indicate legislative elections). Nevertheless, to the extent that we can tell, elections have been more frequent in non-democratic regimes with multiple parties in the legislature:

data %>%
  count(legislative, types) %>%
  knitr::kable()

legislative	types	n
1-No legislature	Constituent Assembly	13
1-No legislature	Executive	56
1-No legislature	Legislative/Parliamentary	81
1-No legislature	NA	760
2-Unelected or appointed	Constituent Assembly	5
2-Unelected or appointed	Executive	18
2-Unelected or appointed	Legislative/Parliamentary	42
2-Unelected or appointed	NA	453
3-One party or candidate per seat	Constituent Assembly	11
3-One party or candidate per seat	Executive	136
3-One party or candidate per seat	Legislative/Parliamentary	309
3-One party or candidate per seat	NA	1368
4-Nonpartisan	Constituent Assembly	1
4-Nonpartisan	Executive	2
4-Nonpartisan	Legislative/Parliamentary	26
4-Nonpartisan	NA	134
5-More than 75% of the seats	Constituent Assembly	10
5-More than 75% of the seats	Executive	116
5-More than 75% of the seats	Legislative/Parliamentary	228
5-More than 75% of the seats	NA	562
6-Less than 75% of the seats	Constituent Assembly	7
6-Less than 75% of the seats	Executive	79
6-Less than 75% of the seats	Legislative/Parliamentary	154
6-Less than 75% of the seats	NA	436

data %>%
  count(legislative, elections = !is.na(types)) %>%
  group_by(legislative) %>%
  summarise(total_elections = n[ elections ],
            elections_per_year = total_elections/sum(n)) %>%
  arrange(desc(elections_per_year)) %>%
  knitr::kable()

legislative	total_elections	elections_per_year
5-More than 75% of the seats	354	0.3864629
6-Less than 75% of the seats	240	0.3550296
3-One party or candidate per seat	456	0.2500000
4-Nonpartisan	29	0.1779141
1-No legislature	150	0.1648352
2-Unelected or appointed	65	0.1254826

We can get a more complete picture of elections in demoand non-democracies by using the extended Unified Democracy Scores (Pemstein, Meserve, and Melton 2010; Márquez 2016) and the combined lies of elections from both NELDA and PIPE:

data <- full_join(extended_uds, all_elections) %>%
  filter(year <= 2012) %>%
  mutate(is_non_democracy = (index < 0.5)) %>%
  group_by(country_name) %>%
  mutate(first_election = min(year[ !is.na(types) ]),
         first_election = ifelse(year == first_election,
                                 first_election,
                                 NA)) %>%
  ungroup()

## Joining, by = c("country_name", "year")

ggplot(data = data, 
       aes(x= forcats::fct_rev(reorder(country_name, 
                           first_election,
                           min,
                           na.rm = TRUE)))) +
  geom_tile(aes(y = year,
           fill = index)) +
  geom_point(aes(y = year, 
                 shape = types), 
               alpha = 0.3) +
  geom_text(aes(y = first_election, 
                label = first_election),
            check_overlap = TRUE,
            size = 2) +
  theme_bw() +
  labs(fill="Extended UD score",
       x="", 
       y= "Year",
       shape = "Type of election",
       source = "Source of election data") + 
  guides(fill=guide_legend(title.position="top")) +
  theme(legend.position="bottom")   +
  scale_fill_gradient2(midpoint = 0.5) +
  geom_hline(yintercept=1989,
             linetype=2) + 
  annotate("text", y = 1989,
           x = length(unique(data$country_name))/2,
           label="End of the Cold War",
           angle=90) + 
  coord_flip()

And we can view the frequency of elections as follows:

data %>%
  distinct(country_name, year, types, is_non_democracy) %>%
  count(is_non_democracy, types) %>%
  knitr::kable()

is_non_democracy	types	n
FALSE	Constituent Assembly	17
FALSE	Executive	625
FALSE	Legislative/Parliamentary	1596
FALSE	NA	3903
TRUE	Constituent Assembly	58
TRUE	Executive	1200
TRUE	Legislative/Parliamentary	2200
TRUE	NA	14941
NA	Executive	31
NA	Legislative/Parliamentary	146

data %>%
  distinct(country_name, year, types, is_non_democracy) %>%
  count(is_non_democracy, elections = !is.na(types)) %>%
  group_by(is_non_democracy) %>%
  summarise(total_elections = n[ elections ], 
            elections_per_year = total_elections/sum(n)) %>%
  arrange(desc(elections_per_year)) %>%
  knitr::kable()

is_non_democracy	total_elections	elections_per_year
NA	177	1.0000000
FALSE	2238	0.3644358
TRUE	3458	0.1879450

As we can see, the frequency of elections is not particularly different between non-democratic and democratic regimes.

data <- data %>% 
  left_join(kailitz_yearly)

## Joining, by = c("country_name", "GWn", "year", "GWc", "cown", "polity_ccode", "region", "continent", "microstate", "lat", "lon", "GW_startdate", "GW_enddate", "in_system", "in_cow")

data %>%
  count(is_non_democracy, communist, types) %>%
  knitr::kable()

is_non_democracy	communist	types	n
FALSE	FALSE	Constituent Assembly	16
FALSE	FALSE	Executive	815
FALSE	FALSE	Legislative/Parliamentary	2007
FALSE	FALSE	NA	2907
FALSE	NA	Constituent Assembly	1
FALSE	NA	Executive	109
FALSE	NA	Legislative/Parliamentary	446
FALSE	NA	NA	996
TRUE	FALSE	Constituent Assembly	53
TRUE	FALSE	Executive	892
TRUE	FALSE	Legislative/Parliamentary	1462
TRUE	FALSE	NA	3456
TRUE	TRUE	Constituent Assembly	2
TRUE	TRUE	Executive	58
TRUE	TRUE	Legislative/Parliamentary	275
TRUE	TRUE	NA	623
TRUE	NA	Constituent Assembly	3
TRUE	NA	Executive	635
TRUE	NA	Legislative/Parliamentary	1244
TRUE	NA	NA	10862
NA	NA	Executive	31
NA	NA	Legislative/Parliamentary	146

data %>%
  count(is_non_democracy, communist, elections = !is.na(types)) %>%
  group_by(is_non_democracy, communist) %>%
  summarise(total_elections = n[ elections ], 
            elections_per_year = total_elections/sum(n)) %>%
  arrange(desc(elections_per_year)) %>%
  knitr::kable()

is_non_democracy	communist	total_elections	elections_per_year
NA	NA	177	1.0000000
FALSE	FALSE	2838	0.4939948
TRUE	FALSE	2407	0.4105407
FALSE	NA	556	0.3582474
TRUE	TRUE	335	0.3496868
TRUE	NA	1882	0.1476773

References

Hyde, Susan D., and Nikolay Marinov. 2012. “Which Elections Can Be Lost?” Political Analysis 20 (2): 191–210. doi:10.1093/pan/mpr040.

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.

Przeworski, Adam. 2013. “Political Institutions and Political Events (PIPE) Data Set.” Data set. Department of Politics, New York University. https://sites.google.com/a/nyu.edu/adam-przeworski/home/data.

Svolik, Milan. 2012. The Politics of Authoritarian Rule. Book. Cambridge: Cambridge University Press.

Chapter 5: Parties

Xavier Marquez