data-raw/all_other_data.md
In xmarquez/AuthoritarianismBook: Replication Package for the Book Non-Democratic Politics by Xavier Marquez
Data Sources for Non-democratic Politics Book
Xavier Marquez
r Sys.Date()
Democracy scores
Unified Democracy Scores
Several datasets of democracy scores are used in this book. The most commonly used scores are the Unified Democracy Scores of Pemstein, Meserve, and Melton [@Pemstein2010], as extended by me [@Marquez2016]. A package to generate these scores, along with documentation, is available here: https://github.com/xmarquez/QuickUDS.
These extended unified democracy scores are available as extended_uds. (Simply type extended_uds to load the data). The main score used in the book to measure democracy is the variable index in this dataset. This is a 0-1 index of democracy that can be interpreted as a probability scale: values close to one mean that the country is almost certainly a democracy by current scholarly standards. This is available for a large number of country-years, including periods where the country may not have been considered a member of the system of states (indicated below by black dots):
library(tidyverse)
ggplot(data = extended_uds,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = index)) +
geom_point(data = extended_uds %>% filter(!in_system), show.legend = FALSE) +
scale_fill_gradient2(midpoint = 0.5) +
coord_flip() +
labs(x = "", fill = "UD Score") +
theme(legend.position = "bottom")
For more information about all the variables in this dataset, use ?extended_uds.
The UD scores are the result of a latent variable analysis of a large number of other democracy measures gathered by many different people. These democracy measures are also included in this package for convenience as the dataset democracy. For more information about all the variables in this dataset, use ?democracy. The variable index in extended_uds is, by construction, highly but not perfectly correlated with all the other measures of democracy:
library(GGally)
data <- full_join(extended_uds, democracy)
ggcorr(data %>%
select(index, arat_pmm:wahman_teorell_hadenius))
For further discussion of the correlations between the index of democracy and other measures of democracy, see my paper [@Marquez2016].
V-Dem (Varieties of Democracy) Dataset
I also use in the book a selection of variables from the Varieties of Democracy (V-Dem) project dataset [@vdem2015]. The full dataset can be downloaded here. The variables included in this package are the main democracy indexes, as well as indexes of civil society, executive constraints, and corruption. Use ?vdem for documentation on the included variables.
Population data
Several graphs in the book make use of world population data going back to the 19th century for all members of the state system compiled by Kristian Gleditsch [@Gleditsch2010] and extended by me to the present day with the help of the population data available through the World Development Indicators. I also use a dataset of world population compiled by Esteban Ortiz-Espina and Max Roser [@OrtizRoser2016] to calculate the proportion of the world's population represented by each country. Gleditsch's original data is available here; Ortiz-Espina and Roser's dataset is available here, including an extensive discussion of their sources.
For more info on included variables, use ?population_data. This data is available for a large number of countries, but it excludes periods when Gleditsch considers the country was not independent (indicated below by a lighter bar):
ggplot(data = population_data,
aes(x = year)) +
geom_bar(aes(alpha = in_system, weight = pop),
show.legend = FALSE,
width = 1) +
labs(x= "",
y = "Country population (millions)",
alpha = "In Gleditsch and Ward's \nsystem of states") +
theme_bw() +
theme(legend.position = "top") +
facet_grid(country_name ~ ., switch = "y") +
scale_y_continuous(labels = NULL, breaks = NULL) +
theme(strip.text.y = element_text(angle = 180),
strip.background = element_rect(fill = NA, color = NA),
panel.margin = unit(0, "lines"),
panel.border = element_rect(fill = NA))
The majority of the populations are too small to be visible in this graph; try the graph with scale_y_log10(labels = NULL, breaks = NULL) instead.
Regime Classifications
This book uses data from several political regime datasets. These do not use the same criteria to classify political regimes, and they do not always agree on given classifications.
Magaloni, Chu, and Min
Some of the graphs in the book use the "Autocracies of the World" dataset by Magaloni, Chu, and Min [@MagaloniChuMin2013]. The original data is available here. A selection of the variables in this dataset is available as magaloni. Use ?magaloni for more detail about the included variables.
The original dataset is available for the following countries:
ggplot(data = magaloni,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = regime_nr, alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_fill_brewer(type = "qual") +
scale_y_continuous(breaks = unique(c(magaloni$year[ magaloni$year %% 12 == 0],
max(magaloni$year),
min(magaloni$year)))) +
coord_flip()
I have extended some of their classifications backwards in time with the help of the duration_nr variable in the original dataset; this "extended" version of the data is available as magaloni_extended:
ggplot(data = magaloni_extended,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = regime_nr, alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_fill_brewer(type = "qual") +
scale_y_continuous(breaks = unique(c(magaloni_extended$year[ magaloni_extended$year %% 25 == 0],
max(magaloni_extended$year),
min(magaloni_extended$year)))) +
coord_flip()
Wahman, Teorell, Hadenius
The personal power index makes use of the "Authoritarian Regimes Data Set", version 5.0, by Axel Hadenius, Jan Teorell, & Michael Wahman [@WahmanTeorellHadenius2013]. The original dataset can be downloaded here. A selection of variables from this dataset is included as wahman_teorell; use ?wahman_teorell for more detail on included variables.
This dataset contains two different measures of regime type, and a couple of measures of personalism:
ggplot(data = wahman_teorell,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(regime1ny, "Democracy"), alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_fill_brewer(type = "qual") +
scale_y_continuous(breaks = unique(c(wahman_teorell$year[ wahman_teorell$year %% 10 == 0],
max(wahman_teorell$year),
min(wahman_teorell$year)))) +
coord_flip()
ggplot(data = wahman_teorell,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(regimeny, "Democracy"), alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(wahman_teorell$year[ wahman_teorell$year %% 10 == 0],
max(wahman_teorell$year),
min(wahman_teorell$year)))) +
coord_flip()
Kailitz
I also make use in several graphs and in the development of the personal power index of a measure of regime type developed by Steffen Kailitz [@Kailitz2013]. This dataset in particular contains an indicator of whether the regime was communist that is particularly useful. For more info on the included variables, use ?kailitz_yearly.
The measure is available for the following countries:
ggplot(data = kailitz_yearly,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = combined_regime, alpha = in_system)) +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Regime type") +
coord_flip() +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(kailitz_yearly$year[ kailitz_yearly$year %% 10 == 0],
max(kailitz_yearly$year),
min(kailitz_yearly$year)))) +
coord_flip()
Geddes, Wright, and Frantz
The most important measure of regime type used in the book was developed by Geddes, Wright, and Frantz [@GeddesWrightFrantz2014]. The original data is available here. This dataset comes in three versions: two country-year versions (all_gwf and all_gwf_extended_yearly) and a case version with (all_gwf_periods). all_gwf_extended_yearly extends Geddes, Wright, and Frantz's regime classifications backwards using the information encoded in the start dates and end dates of the cases in the original dataset (all_gwf_periods). For details on the variables included in these datasets, use ?all_gwf, ?all_gwf_extended_yearly, and ?all_gwf_periods.
This contains one measure of regime type:
ggplot(data = all_gwf_extended_yearly,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = gwf_full_regimetype, alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Regime type") +
coord_flip() +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(all_gwf_extended_yearly$year[ all_gwf_extended_yearly$year %% 50 == 0],
max(all_gwf_extended_yearly$year),
min(all_gwf_extended_yearly$year)))) +
coord_flip()
Regime Characteristics
The book uses several measures of regime characteristics to create various graphs.
Polity
Several graphs in the book use the Polity IV dataset [@Marshall2010] for its measures of regime characteristics rather than for its democracy measure. For details of the variables included, use
It contains several measures of regime characteristics for a large number of countries (all states with populations greater than 500,000 people). Here I plot the executive recruitment score:
ggplot(data = polity_annual,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = exrec, alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Executive recruitment type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(polity_annual$year[ polity_annual$year %% 50 == 0],
max(polity_annual$year),
min(polity_annual$year)))) +
coord_flip()
Svolik institutions data
Some of the graphs in the book make use of data on institutions in non-democratic regimes collected by Milan Svolik [@Svolik2012]. The original data can be found here. For more information on the included variables, use ?svolik_institutions.
This dataset includes information about military, party, and executive and legislative control in non-democratic regimes:
ggplot(data = svolik_institutions,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(party,
"banned",
"single"),
alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Party restrictions") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(svolik_institutions$year[ svolik_institutions$year %% 10 == 0],
max(svolik_institutions$year),
min(svolik_institutions$year)))) +
coord_flip()
ggplot(data = svolik_institutions,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(military,
"personal",
"corporate",
"indirect"),
alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Type of military control") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(svolik_institutions$year[ svolik_institutions$year %% 10 == 0],
max(svolik_institutions$year),
min(svolik_institutions$year)))) +
coord_flip()
ggplot(data = svolik_institutions,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(legislative,
"none",
"unelected or appointed",
"one party or candidate per seat",
"nonpartisan",
"largest party controls more than 75% of seats"),
alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Type of legislative control") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(svolik_institutions$year[ svolik_institutions$year %% 10 == 0],
max(svolik_institutions$year),
min(svolik_institutions$year)))) +
coord_flip()
PIPE
In a few graphs I use some variables from the Political Institutions and Political Events dataset compiled by Adam Przeworski [@Przeworski2013]. The original data is available here. This contains information about suffrage and elections in a large number of countries; for details on the included variables, type ?PIPE.
This has measure has some errors and problems, but it's still quite interesting. Here's a plot of the restrictions on the franchise:
data <- PIPE %>%
group_by(country_name) %>%
arrange(year) %>%
mutate(total_elections = preselec + legelec,
total_elections = ifelse(is.na(total_elections) & !is.na(f),
0,
total_elections)) %>%
filter(!is.na(total_elections)) %>%
mutate(cumulative_elections = cumsum(total_elections),
f = ifelse(max(cumulative_elections) == 0 & is.na(f),
0,
f)) %>%
ungroup() %>%
mutate(f_male = ifelse(f < 10,
f,
round(f/10)),
f_female = ifelse(f < 10,
0,
f %% 10),
f_male = ifelse(f_male %in% c(0,1), f_male, f_male + 1),
f_male = ifelse(is.na(f_male) & (cumulative_elections >= 1),
2,
f_male),
f_female = ifelse(f_male == 2, 0, f_female),
f_female = factor(f_female, labels = c("0-No inclusion / not indicated",
"1-Narrower than men",
"2-Equal to men")),
f_male = factor(f_male,
labels = c("0-No suffrage",
"1-Estate",
"2-Subnational",
"3-Property only",
"4-(Property OR
income OR
taxes OR
exercise of profession OR
educational titles)
AND literacy",
"5-Property OR
income OR
taxes OR
exercise of profession OR
educational titles",
"6-Literacy only
OR (Literacy
OR property OR
income OR
taxes OR
exercise of profession OR
educational titles)",
"7-All the economically independent",
"8-All"),
ordered = TRUE)
)
ggplot(data = data,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = f_male,
alpha = f_female)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "Female inclusion",
fill = "Type of franchise") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 1),
alpha = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(PIPE$year[ PIPE$year %% 25 == 0],
max(PIPE$year),
min(PIPE$year)))) +
scale_alpha_discrete(range = c(0.3,1)) +
coord_flip()
LIED
The Lexical Index of Democracy and Autocracy [@SkaaningGerring2015] extends and corrects the PIPE dataset, and it provides an independent measure of male and female suffrage, as well as measures of whether the executive is elected, there is an elected assembly, and a few other variables. For details on included variables, type ?lied. The original dat and codebook are available here
Here's the suffrage picture using LIED data:
data <- lied
ggplot(data = data %>%
mutate(male_suffrage = (male_suffrage > 0),
female_suffrage = (female_suffrage > 0)),
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = male_suffrage,
alpha = female_suffrage)) +
labs(x = "",
alpha = "Female inclusion?",
fill = "Full male suffrage?") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 1),
alpha = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(lied$year[ lied$year %% 25 == 0],
max(lied$year),
min(lied$year)))) +
scale_alpha_discrete(range = c(0.3,1)) +
coord_flip()
The LIED data and the PIPE data differ subtly, due to coding differences:
data <- full_join(PIPE %>%
select(country_name,GWn,year,f_simple,f),
lied %>%
select(country_name,GWn,year,male_suffrage,female_suffrage)) %>%
mutate(inconsistent = (male_suffrage == 1 & female_suffrage == 1 & f != 72) |
(male_suffrage == 1 & female_suffrage == 0 & !(f %in% c(7,71,NA))))
data %>%
filter(inconsistent) %>%
group_by(country_name, f_simple, f, male_suffrage, female_suffrage) %>%
summarise(min_year = min(year), max_year = max(year), num_years = n()) %>%
knitr::kable(col.names = c("Country",
"PIPE franchise (simplified)",
"PIPE franchise (full)",
"LIED male suffrage",
"LIED female suffrage",
"Min year",
"Max year",
"Num. years"))
Country PIPE franchise (simplified) PIPE franchise (full) LIED male suffrage LIED female suffrage Min year Max year Num. years
Australia 4- All male 7 1 1 1901 1901 1
Austria-Hungary 1 - Class restricted, male only 4 1 0 1896 1917 22
Belgium 6 - Universal 72 1 0 1948 1948 1
Canada 6 - Universal 72 1 0 1918 1920 3
Germany (Prussia) 1 - Class restricted, male only 6 1 0 1871 1917 47
Germany (Prussia) 6 - Universal 72 1 0 1918 1918 1
Greece 6 - Universal 72 1 0 1953 1955 3
Guatemala 6 - Universal 72 1 0 1965 1965 1
Iraq 4- All male 7 1 1 1958 1979 22
Italy/Sardinia 6 - Universal 72 1 0 1945 1945 1
Lebanon 6 - Universal 72 1 0 1944 1951 8
Netherlands 6 - Universal 72 1 0 1919 1921 3
Nicaragua 6 - Universal 72 1 0 1955 1956 2
Palau 4- All male 7 1 1 1994 2008 15
Qatar 0 - No suffrage 0 1 1 2003 2008 6
Rumania 1 - Class restricted, male only 4 1 0 1938 1945 8
Somalia 0 - No suffrage 0 1 1 1991 2003 13
Syria 5 - All male, some female 71 1 1 1954 1972 19
Tuvalu 4- All male 7 1 1 1978 2008 31
Leader data
A number of graphs in the book use data on leaders.
Archigos
The main leader data used is the Archigos dataset of Gleditsch, Goemans, and Chiozza [@GoemansGleditschChiozza2009]. The original data is available here; type ?archigos for more info.
We can plot the leaders in each country:
ggplot(data = archigos,
aes(x = forcats::fct_rev(reorder(country_name, startdate, FUN = min)),
y = startdate)) +
geom_point(alpha = 0.2, color = "red") +
geom_text(aes(label = leader), check_overlap = TRUE, size = 2) +
labs(x = "") +
theme_bw() +
coord_flip()
Svolik
We also have leader data from Svolik [@Svolik2012], which is only available for those regimes Svolik codes as non-democratic. Svolik bases his data on an earlier version of Archigos, but he also codes the political affiliations of earlier and later leaders; type ?svolik_leader for more info.
Here's what the aggregate data looks like:
ggplot(data = svolik_leader,
aes(x = forcats::fct_rev(reorder(country_name, startdate, FUN = min)),
y = startdate)) +
geom_point(alpha = 0.4, aes(color = pol_aff, shape = entry_summary)) +
geom_text(aes(label = leader, color = pol_aff),
check_overlap = TRUE, size = 2) +
labs(x = "",
color = "Political affiliation",
shape = "Mode of entry into office") +
theme_bw() +
theme(legend.position = "top") +
guides(color = guide_legend(title.position = "top", ncol = 2),
shape = guide_legend(title.position = "top", ncol = 2)) +
coord_flip()
Coup data
There are two sources for coup data we can use. The first is from Powell and Thyne [@PowellThyne2011]. This data is continuously updated; the original source is available here.
Here is what the data looks like:
ggplot(data = powell_thyne,
aes(x = forcats::fct_rev(country_name),
y = date)) +
geom_point(aes(color = attempt_type)) +
labs(x = "",
color = "Attempt type") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
Though this dataset contains info on all recent coups, it does not show coups earlier than 1950. The PIPE dataset contains rougher info on successful coups before then:
ggplot(data = PIPE %>%
filter(coups > 0) ,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = as.factor(coups))) +
labs(x = "",
color = "Number of coups") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
This data is not correlated with Powell and Thyne's data, though:
data <- full_join(PIPE %>%
filter(coups > 0) %>%
select(country_name,
GWn,
year,
coups),
powell_thyne %>%
filter(coup == 2) %>%
group_by(country_name,
GWn,
year) %>%
summarise(n = n())) %>%
mutate_at(funs(ifelse(is.na(.),0,.)), .cols = c("coups","n")) %>%
rename(pipe_coups = coups, powell_coups = n)
cor(data$pipe_coups, data$powell_coups, use = "pairwise")
## [1] -0.092433
data %>%
filter(year >= 1950,
year <= 2008,
pipe_coups != powell_coups) %>%
count(pipe_coups, powell_coups) %>%
knitr::kable(col.names = c("Number of coups in PIPE",
"Number of coups in Powell-Thyne",
"Number of country-years with discrepancies"),
caption = "Country-years where PIPE disagrees with Powell-Thyne in the 1950-2008 period")
Table: Country-years where PIPE disagrees with Powell-Thyne in the 1950-2008 period
Number of coups in PIPE Number of coups in Powell-Thyne Number of country-years with discrepancies
0 1 17
0 2 1
1 0 59
1 2 3
2 0 5
2 1 4
2 3 1
data %>%
filter(year >= 1950,
year <= 2008,
pipe_coups == powell_coups) %>%
count(pipe_coups, powell_coups) %>%
knitr::kable(col.names = c("Number of coups in PIPE",
"Number of coups in Powell-Thyne",
"Number of country-years where they agree"),
caption = "Country-years where PIPE agrees with Powell-Thyne in the 1950-2008 period")
Table: Country-years where PIPE agrees with Powell-Thyne in the 1950-2008 period
Number of coups in PIPE Number of coups in Powell-Thyne Number of country-years where they agree
1 1 175
2 2 9
data %>%
filter(year >= 1950,
year <= 2008,
pipe_coups != powell_coups) %>%
knitr::kable(col.names = c("Country","GWn","year", "Number of coups in PIPE", "Number of coups in Powell-Thyne"))
Country GWn year Number of coups in PIPE Number of coups in Powell-Thyne
Afghanistan 700 1979 2 0
Afghanistan 700 1996 1 0
Afghanistan 700 2001 1 0
Argentina 160 1981 1 0
Bangladesh 771 2006 1 0
Bolivia 145 1979 2 1
Bolivia 145 1982 1 0
Brazil 140 1954 1 0
Brazil 140 1964 1 2
Cambodia (Kampuchea) 811 1975 1 0
Cambodia (Kampuchea) 811 1978 1 0
Chad 483 1979 1 0
Chad 483 1982 1 0
Chad 483 1990 1 0
Comoros 581 1976 1 0
Comoros 581 1995 1 0
Congo 484 1968 1 2
Congo 484 1969 1 0
Congo 484 1979 1 0
Congo 484 1997 1 0
Congo, Democratic Republic of (Zaire) 490 1997 1 0
Cote D'Ivoire 437 1999 1 0
Cuba 40 1959 1 0
Dominican Republic 42 1961 1 0
Dominican Republic 42 1962 2 0
Ecuador 130 2000 1 0
Ethiopia 530 1991 1 0
Fiji 950 1987 2 1
Gabon 481 1964 2 0
Georgia 372 1991 1 0
Guatemala 90 1993 1 0
Guinea-Bissau 404 1998 1 0
Haiti 41 1957 2 3
Haiti 41 2004 1 0
Hungary 310 1956 2 0
India 750 2006 1 0
Iran (Persia) 630 1979 1 0
Iran (Persia) 630 1981 1 0
Korea, Republic of 732 1960 1 0
Korea, Republic of 732 1980 1 0
Laos 812 1959 1 0
Laos 812 1975 1 0
Liberia 450 1990 1 0
Madagascar (Malagasy) 580 1972 1 0
Madagascar (Malagasy) 580 1975 1 0
Mauritania 435 1979 1 0
Myanmar (Burma) 775 1997 1 0
Nepal 790 1960 1 0
Nicaragua 93 1956 1 0
Nicaragua 93 1979 1 0
Pakistan 770 1969 1 0
Panama 95 1955 1 0
Panama 95 1983 1 0
Panama 95 1989 1 0
Rumania 360 1989 1 0
Rwanda 517 2000 1 0
Sao Tome and Principe 403 1995 1 0
Sierra Leone 451 1992 2 1
Somalia 520 1991 1 0
Sudan 625 1971 2 0
Surinam 115 1980 1 2
Surinam 115 1990 1 0
Syria 652 1962 2 1
Thailand 800 1973 1 0
Uganda 500 1979 1 0
Uganda 500 1986 1 0
Venezuela 101 1952 1 0
Venezuela 101 1958 1 0
Yemen (Arab Republic of Yemen) 678 1977 1 0
Yemen (Arab Republic of Yemen) 678 1978 1 0
Saudi Arabia 670 1964 1 0
Yemen, People's Republic of 680 1969 1 0
Bolivia 145 1951 0 1
Burkina Faso (Upper Volta) 439 1974 0 1
Congo, Democratic Republic of (Zaire) 490 1960 0 1
Cote D’Ivoire 437 1999 0 1
Egypt 651 1954 0 1
Ethiopia 530 1977 0 1
Lesotho 570 1994 0 1
Mauritania 435 2008 0 1
Myanmar (Burma) 775 1958 0 1
Pakistan 770 1971 0 1
São Tomé and Principe 403 2003 0 1
Swaziland 572 1983 0 1
Syria 652 1969 0 1
Tunisia 616 1987 0 1
Vietnam, Republic of 817 1963 0 1
Vietnam, Republic of 817 1964 0 1
Vietnam, Republic of 817 1965 0 2
Yemen, People's Republic of 680 1978 0 1
data <- bind_rows(PIPE %>%
filter(coups > 0) %>%
select(country_name,
GWn,
year,
coups) %>%
mutate(source = "PIPE"),
powell_thyne %>%
filter(coup == 2) %>%
group_by(country_name,
GWn,
year) %>%
summarise(coups = n()) %>%
mutate(source = "Powell-Thyne"))
ggplot(data = data %>%
filter(coups > 0) ,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = source), alpha = 0.3) +
labs(x = "",
color = "Source") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
Elections data
There are two sources of election data. The first is from the National Elections in Democracy and Autocracy (NELDA) dataset [@Hyde2011]. We use only a minimal selection of columns indicating the dates and types of elections; for details of the included variables, type ?nelda. The original data is available here.
This data is available for the following countries:
ggplot(data = nelda,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = types),
alpha = 0.3) +
labs(x = "",
color = "Election type") +
theme_bw() +
theme(legend.position = "top") +
guides(color = guide_legend(title.position = "top")) +
coord_flip()
There is also election data in PIPE:
data <- PIPE %>%
select(country_name,
year,
preselec,
legelec) %>%
reshape2::melt(measure.vars = c("preselec",
"legelec")) %>%
filter(value > 0) %>%
rename(types = variable,
num_elections = value) %>%
mutate(types = plyr::mapvalues(types,
from = c("preselec",
"legelec"),
to = c("Executive",
"Legislative/Parliamentary")),
source = "PIPE") %>%
bind_rows(nelda %>%
group_by(country_name,
year,
types) %>%
summarise(num_elections = n()) %>%
mutate(source = "NELDA"))
ggplot(data = data,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = source, shape = types),
alpha = 0.3) +
labs(x = "",
shape = "Election type",
color = "Dataset") +
theme_bw() +
theme(legend.position = "top") +
guides(color = guide_legend(title.position = "top")) +
coord_flip()
This data is highly but not perfectly correlated with NELDA; mostly it is a matter of NELDA picking up more elections than PIPE.
data2 <- data %>%
reshape2::dcast(country_name + year ~ source,
fun.aggregate = sum,
value.var = "num_elections")
nelda_shell <- nelda %>%
group_by(country_name) %>%
do(data.frame(year = min(.$year):max(.$year))) %>%
ungroup()
data2 <- inner_join(nelda_shell, data2) %>%
filter(year <= 2008)
cor(data2$NELDA, data2$PIPE)
## [1] 0.5135496
data2 %>%
count(NELDA, PIPE, NELDA < PIPE, NELDA > PIPE) %>%
ungroup() %>%
mutate(total = sum(n), prop = n/total) %>%
group_by(`NELDA > PIPE`, `NELDA < PIPE`) %>%
mutate(prop_2 = sum(prop),
prop = scales::percent(prop),
prop_2 = scales::percent(prop_2)) %>%
arrange(`NELDA > PIPE`,`NELDA < PIPE`) %>%
knitr::kable(col.names = c("NELDA count of elections in country-year",
"PIPE count of electios in country-year",
"NELDA > PIPE",
"NELDA < PIPE",
"Number of elections in group",
"Total elections",
"Proportion of total",
"Proportion of group"))
NELDA count of elections in country-year PIPE count of electios in country-year NELDA > PIPE NELDA < PIPE Number of elections in group Total elections Proportion of total Proportion of group
1 1 FALSE FALSE 1214 2247 54.0% 69.9%
2 2 FALSE FALSE 356 2247 15.8% 69.9%
0 1 TRUE FALSE 155 2247 6.90% 12.3%
0 2 TRUE FALSE 9 2247 0.40% 12.3%
1 2 TRUE FALSE 109 2247 4.85% 12.3%
1 3 TRUE FALSE 1 2247 0.04% 12.3%
2 3 TRUE FALSE 2 2247 0.09% 12.3%
1 0 FALSE TRUE 140 2247 6.23% 17.8%
2 0 FALSE TRUE 12 2247 0.53% 17.8%
2 1 FALSE TRUE 149 2247 6.63% 17.8%
3 0 FALSE TRUE 4 2247 0.18% 17.8%
3 1 FALSE TRUE 8 2247 0.36% 17.8%
3 2 FALSE TRUE 73 2247 3.25% 17.8%
4 1 FALSE TRUE 1 2247 0.04% 17.8%
4 2 FALSE TRUE 11 2247 0.49% 17.8%
4 3 FALSE TRUE 2 2247 0.09% 17.8%
5 2 FALSE TRUE 1 2247 0.04% 17.8%
The book thus provides the dataset all_elections as a convenience; for more info, type ?all_elections
Economic data
GDP per capita
Economic data on GDP per capita comes from several sources: the Maddison project [@Maddison2013], the Penn World Tables versions 8 and 8.1 [@Feenstra2013], the World Bank's Development Indicators, and a dataset of extended GDP values by Kristian Gleditsch [@Gleditsch2002GDP]. The original data for the Maddison project is available here; the Penn World Tables (all versions) is available here; and the Gleditsch dataset is available here. These are bundled into a single dataset with information about the source and the type of GDP estimate; it is worth noting that these estimates sometimes differ substantially, and they must be used with some care. (For a quick guide to the different types of measures, see the helpful chart at the Penn World Table website). Nevertheless, for this book's purposes, a simple median of these different measures works fine.
Here's what they look like, per country:
ggplot(data = economic_data %>%
filter(in_system) %>%
mutate(variable_trunc = stringr::str_trunc(as.character(variable), 40, "right")),
aes(x = year,
y = reorder(country_name, per_capita))) +
geom_tile(aes(alpha = per_capita)) +
labs(x= "",
y = "",
alpha = "Per capita income") +
theme_bw() +
theme(legend.position = "top") +
scale_alpha_continuous(trans = "log", labels = scales::dollar) +
facet_wrap(~variable_trunc, ncol = 5)
Inequality data
The inequality data is a summary of the Standardized World Income Inequality Database [@Solt2009; @Solt2016], version 5.0. The original data is available here. For more info, type ?swiid_summary_5.
These come with measures of uncertainty, and are available for the following country-years:
ggplot(data = swiid_summary_5 %>%
filter(in_system,
variable %in% c("gini_net", "gini_market")),
aes(x = year,
y = reorder(country_name, mean_value))) +
geom_tile(aes(alpha = mean_value)) +
labs(x= "",
y = "",
alpha = "Gini") +
theme_bw() +
theme(legend.position = "top") +
scale_alpha_continuous() +
facet_wrap(~variable, ncol = 2) +
scale_x_continuous(breaks = unique(c(swiid_summary_5$year[ swiid_summary_5$year %% 10 == 0],
max(swiid_summary_5$year),
min(swiid_summary_5$year))))
Oil and Gas data
The data on oil and gas comes from Ross and Mahdavi [@RossMahdavi2015]. The original data is available here.
Ross <- PoliticalDatasets::Ross %>%
select(country_name,
GWn,
year,
ross_country:eiacty,
oil_gas_value_2014,
GWc,
cown:in_system) %>%
filter(!is.na(oil_gas_value_2014), oil_gas_value_2014 > 0)
devtools::use_data(Ross, overwrite = TRUE)
Violence data
Repression data
The data on repression comes from a latent variable index of repression (or, more precisely, of violations of physical integrity rights) created by Christopher Fariss [@Fariss2014]. The data also includes a variety of additional measures of repression from other sources; type ?fariss for more info.
Here's what the index looks like (more repressive periods in red):
ggplot(data = fariss,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_tile(aes(fill = latentmean)) +
scale_fill_gradient2() +
labs(x = "",
fill = "Degree of repression \nviolations of physical integrity
(lower is more repressive)") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
War data
For a number of graphs, I use version 2 of a list of wars compiled by Gleditsch [@Gleditsch2004], and available here. This comes in two forms: as a list of warring states, per year (all_war_participants_yearly, includingboth civil and interstate wars), and as a list of dyads (interstate_dyads_yearly, interstate wars only).
These data are available for the following countries:
ggplot(data = all_war_participants_yearly,
aes(x = year,
y = forcats::fct_rev(forcats::fct_infreq(country_name)))) +
geom_tile(aes(alpha = deaths+1, fill = type)) +
labs(x= "",
y = "",
alpha = "Total deaths",
fill = "War type") +
theme_bw() +
theme(legend.position = "top") +
scale_alpha_continuous(trans = "log", labels = round) +
scale_x_continuous(breaks = unique(c(all_war_participants_yearly$year[ all_war_participants_yearly$year %% 10 == 0],
max(all_war_participants_yearly$year),
min(all_war_participants_yearly$year))))
Violent and Non-violent campaigns
The data on violent and non-violent campaigns comes from the NAVCO dataset version 2.0 [@ChenowethLewis2013]. The original data is avialable here. Only a small selection of the variables in the original dataset are included; type ?navco_2 for more info.
These data ara available as for the following countries and campaigns:
ggplot(data = navco_2 %>%
mutate(campaign = stringr::str_trunc(paste0(country_name, ": ", campaign), 40, "right"),
camp_size = ifelse(camp_size < 0, NA, camp_size)),
aes(x = year,
y = forcats::fct_rev(campaign),
fill = factor(prim_method,
labels = c("Primarily violent","Primarily nonviolent")),
alpha = factor(success,
labels = c("Unsuccessful","Successful"),
ordered = TRUE))) +
geom_tile() +
geom_point(aes(size = camp_size,
color = factor(prim_method,
labels = c("Primarily violent","Primarily nonviolent")))) +
labs(x= "",
y = "",
alpha = "Success?",
fill = "Primary method of campaign",
color = "Primary method of campaign",
size = "Size of campaign") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top"))
Map data
Some of the graphs in the text make use of map data. You can access this data by typing world. This map data represent country borders as of 2015; for historical country borders, you would need to use a package like cshapes.
Here's an example of how to use the map data, plotting repression in 2013 throughout the world:
world <- world %>%
filter(id != "Antarctica")
data <- fariss %>%
filter(year == 2013)
library(ggalt)
ggplot() + geom_map(aes(fill = latentmean,
map_id = country_name),
map = world,
data = data) +
geom_path(data = world,
aes(y = lat, x = long, group = group)) +
scale_fill_gradient2(mid = "lightgrey") +
theme_minimal() +
theme(legend.position = "bottom") +
labs(fill = "Degree of repression
or violations of personal integrity
(lower is worse)",
x = "",
y = "") +
guides(fill = guide_legend(title.position = "top")) +
coord_proj("+proj=wintri") +
scale_y_continuous(breaks=NULL) +
scale_x_continuous(breaks=NULL) +
theme(legend.position = "bottom")
We can also use the package cshapes to plot earlier years, though this makes things more complicated. Here's repression in 1967, the height of repression during the cultural revolution in CHina and the civil rights movement in the USA:
library(maptools)
library(rgeos)
world_1967 <- cshapes::cshp(as.Date("1967-1-1")) %>%
fortify(region = "GWCODE") %>%
mutate(GWn = id)
ggplot() +
geom_path(data = world,
aes(y = lat, x = long, group = group)) +
geom_map(aes(fill = latentmean,
map_id = GWn),
map = world_1967,
data = fariss %>% filter(year == 1967)) +
geom_path(data = world_1967,
aes(y = lat, x = long, group = group)) +
scale_fill_gradient2(mid = "lightgrey") +
theme_minimal() +
theme(legend.position = "bottom") +
labs(fill = "Degree of repression
or violations of personal integrity
(lower is worse)",
x = "",
y = "") +
guides(fill = guide_legend(title.position = "top")) +
coord_proj("+proj=wintri") +
scale_y_continuous(breaks=NULL) +
scale_x_continuous(breaks=NULL) +
theme(legend.position = "bottom")
Data documentation
Finally, we document all the data:
label_data <- readr::read_csv("data_labels.csv")
library(documenteR)
doc_skeleton(find_all_datasets("AuthoritarianismBook"), label_data = label_data)
devtools::document()
## Writing NAMESPACE
## Writing AuthoritarianismBook.Rd
## Writing PIPE.Rd
## Writing all_elections.Rd
## Writing all_gwf.Rd
## Writing all_gwf_extended_yearly.Rd
## Writing all_gwf_periods.Rd
## Writing all_war_participants_yearly.Rd
## Writing archigos.Rd
## Writing democracy.Rd
## Writing democracy_mentions_yearly.Rd
## Writing economic_data.Rd
## Writing extended_uds.Rd
## Writing fariss.Rd
## Writing interstate_dyads_yearly.Rd
## Writing kailitz_yearly.Rd
## Writing lied.Rd
## Writing magaloni.Rd
## Writing magaloni_extended.Rd
## Writing navco_2.Rd
## Writing nelda.Rd
## Writing personal_scores.Rd
## Writing polity_annual.Rd
## Writing population_data.Rd
## Writing powell_thyne.Rd
## Writing svolik_institutions.Rd
## Writing svolik_leader.Rd
## Writing swiid_summary_5.Rd
## Writing vdem.Rd
## Writing wahman_teorell.Rd
## Writing world.Rd
References
xmarquez/AuthoritarianismBook documentation built on May 4, 2019, 1:24 p.m.
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Data Sources for Non-democratic Politics Book
Xavier Marquez
r Sys.Date()
Democracy scores
Unified Democracy Scores
Several datasets of democracy scores are used in this book. The most commonly used scores are the Unified Democracy Scores of Pemstein, Meserve, and Melton [@Pemstein2010], as extended by me [@Marquez2016]. A package to generate these scores, along with documentation, is available here: https://github.com/xmarquez/QuickUDS.
These extended unified democracy scores are available as extended_uds. (Simply type extended_uds to load the data). The main score used in the book to measure democracy is the variable index in this dataset. This is a 0-1 index of democracy that can be interpreted as a probability scale: values close to one mean that the country is almost certainly a democracy by current scholarly standards. This is available for a large number of country-years, including periods where the country may not have been considered a member of the system of states (indicated below by black dots):
library(tidyverse)
ggplot(data = extended_uds,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = index)) +
geom_point(data = extended_uds %>% filter(!in_system), show.legend = FALSE) +
scale_fill_gradient2(midpoint = 0.5) +
coord_flip() +
labs(x = "", fill = "UD Score") +
theme(legend.position = "bottom")
For more information about all the variables in this dataset, use ?extended_uds.
The UD scores are the result of a latent variable analysis of a large number of other democracy measures gathered by many different people. These democracy measures are also included in this package for convenience as the dataset democracy. For more information about all the variables in this dataset, use ?democracy. The variable index in extended_uds is, by construction, highly but not perfectly correlated with all the other measures of democracy:
library(GGally)
data <- full_join(extended_uds, democracy)
ggcorr(data %>%
select(index, arat_pmm:wahman_teorell_hadenius))
For further discussion of the correlations between the index of democracy and other measures of democracy, see my paper [@Marquez2016].
V-Dem (Varieties of Democracy) Dataset
I also use in the book a selection of variables from the Varieties of Democracy (V-Dem) project dataset [@vdem2015]. The full dataset can be downloaded here. The variables included in this package are the main democracy indexes, as well as indexes of civil society, executive constraints, and corruption. Use ?vdem for documentation on the included variables.
Population data
Several graphs in the book make use of world population data going back to the 19th century for all members of the state system compiled by Kristian Gleditsch [@Gleditsch2010] and extended by me to the present day with the help of the population data available through the World Development Indicators. I also use a dataset of world population compiled by Esteban Ortiz-Espina and Max Roser [@OrtizRoser2016] to calculate the proportion of the world's population represented by each country. Gleditsch's original data is available here; Ortiz-Espina and Roser's dataset is available here, including an extensive discussion of their sources.
For more info on included variables, use ?population_data. This data is available for a large number of countries, but it excludes periods when Gleditsch considers the country was not independent (indicated below by a lighter bar):
ggplot(data = population_data,
aes(x = year)) +
geom_bar(aes(alpha = in_system, weight = pop),
show.legend = FALSE,
width = 1) +
labs(x= "",
y = "Country population (millions)",
alpha = "In Gleditsch and Ward's \nsystem of states") +
theme_bw() +
theme(legend.position = "top") +
facet_grid(country_name ~ ., switch = "y") +
scale_y_continuous(labels = NULL, breaks = NULL) +
theme(strip.text.y = element_text(angle = 180),
strip.background = element_rect(fill = NA, color = NA),
panel.margin = unit(0, "lines"),
panel.border = element_rect(fill = NA))
The majority of the populations are too small to be visible in this graph; try the graph with scale_y_log10(labels = NULL, breaks = NULL) instead.
Regime Classifications
This book uses data from several political regime datasets. These do not use the same criteria to classify political regimes, and they do not always agree on given classifications.
Magaloni, Chu, and Min
Some of the graphs in the book use the "Autocracies of the World" dataset by Magaloni, Chu, and Min [@MagaloniChuMin2013]. The original data is available here. A selection of the variables in this dataset is available as magaloni. Use ?magaloni for more detail about the included variables.
The original dataset is available for the following countries:
ggplot(data = magaloni,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = regime_nr, alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_fill_brewer(type = "qual") +
scale_y_continuous(breaks = unique(c(magaloni$year[ magaloni$year %% 12 == 0],
max(magaloni$year),
min(magaloni$year)))) +
coord_flip()
I have extended some of their classifications backwards in time with the help of the duration_nr variable in the original dataset; this "extended" version of the data is available as magaloni_extended:
ggplot(data = magaloni_extended,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = regime_nr, alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_fill_brewer(type = "qual") +
scale_y_continuous(breaks = unique(c(magaloni_extended$year[ magaloni_extended$year %% 25 == 0],
max(magaloni_extended$year),
min(magaloni_extended$year)))) +
coord_flip()
Wahman, Teorell, Hadenius
The personal power index makes use of the "Authoritarian Regimes Data Set", version 5.0, by Axel Hadenius, Jan Teorell, & Michael Wahman [@WahmanTeorellHadenius2013]. The original dataset can be downloaded here. A selection of variables from this dataset is included as wahman_teorell; use ?wahman_teorell for more detail on included variables.
This dataset contains two different measures of regime type, and a couple of measures of personalism:
ggplot(data = wahman_teorell,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(regime1ny, "Democracy"), alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_fill_brewer(type = "qual") +
scale_y_continuous(breaks = unique(c(wahman_teorell$year[ wahman_teorell$year %% 10 == 0],
max(wahman_teorell$year),
min(wahman_teorell$year)))) +
coord_flip()
ggplot(data = wahman_teorell,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(regimeny, "Democracy"), alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states", fill = "Regime type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(wahman_teorell$year[ wahman_teorell$year %% 10 == 0],
max(wahman_teorell$year),
min(wahman_teorell$year)))) +
coord_flip()
Kailitz
I also make use in several graphs and in the development of the personal power index of a measure of regime type developed by Steffen Kailitz [@Kailitz2013]. This dataset in particular contains an indicator of whether the regime was communist that is particularly useful. For more info on the included variables, use ?kailitz_yearly.
The measure is available for the following countries:
ggplot(data = kailitz_yearly,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = combined_regime, alpha = in_system)) +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Regime type") +
coord_flip() +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(kailitz_yearly$year[ kailitz_yearly$year %% 10 == 0],
max(kailitz_yearly$year),
min(kailitz_yearly$year)))) +
coord_flip()
Geddes, Wright, and Frantz
The most important measure of regime type used in the book was developed by Geddes, Wright, and Frantz [@GeddesWrightFrantz2014]. The original data is available here. This dataset comes in three versions: two country-year versions (all_gwf and all_gwf_extended_yearly) and a case version with (all_gwf_periods). all_gwf_extended_yearly extends Geddes, Wright, and Frantz's regime classifications backwards using the information encoded in the start dates and end dates of the cases in the original dataset (all_gwf_periods). For details on the variables included in these datasets, use ?all_gwf, ?all_gwf_extended_yearly, and ?all_gwf_periods.
This contains one measure of regime type:
ggplot(data = all_gwf_extended_yearly,
aes(x = forcats::fct_rev(reorder(country_name,year,FUN = min)),
y = year)) +
geom_tile(aes(fill = gwf_full_regimetype, alpha = in_system)) +
labs(x = "", alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Regime type") +
coord_flip() +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(all_gwf_extended_yearly$year[ all_gwf_extended_yearly$year %% 50 == 0],
max(all_gwf_extended_yearly$year),
min(all_gwf_extended_yearly$year)))) +
coord_flip()
Regime Characteristics
The book uses several measures of regime characteristics to create various graphs.
Polity
Several graphs in the book use the Polity IV dataset [@Marshall2010] for its measures of regime characteristics rather than for its democracy measure. For details of the variables included, use
It contains several measures of regime characteristics for a large number of countries (all states with populations greater than 500,000 people). Here I plot the executive recruitment score:
ggplot(data = polity_annual,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = exrec, alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Executive recruitment type") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(polity_annual$year[ polity_annual$year %% 50 == 0],
max(polity_annual$year),
min(polity_annual$year)))) +
coord_flip()
Svolik institutions data
Some of the graphs in the book make use of data on institutions in non-democratic regimes collected by Milan Svolik [@Svolik2012]. The original data can be found here. For more information on the included variables, use ?svolik_institutions.
This dataset includes information about military, party, and executive and legislative control in non-democratic regimes:
ggplot(data = svolik_institutions,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(party,
"banned",
"single"),
alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Party restrictions") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(svolik_institutions$year[ svolik_institutions$year %% 10 == 0],
max(svolik_institutions$year),
min(svolik_institutions$year)))) +
coord_flip()
ggplot(data = svolik_institutions,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(military,
"personal",
"corporate",
"indirect"),
alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Type of military control") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(svolik_institutions$year[ svolik_institutions$year %% 10 == 0],
max(svolik_institutions$year),
min(svolik_institutions$year)))) +
coord_flip()
ggplot(data = svolik_institutions,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = forcats::fct_relevel(legislative,
"none",
"unelected or appointed",
"one party or candidate per seat",
"nonpartisan",
"largest party controls more than 75% of seats"),
alpha = in_system)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "In Gleditsch and Ward's \nsystem of states",
fill = "Type of legislative control") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(svolik_institutions$year[ svolik_institutions$year %% 10 == 0],
max(svolik_institutions$year),
min(svolik_institutions$year)))) +
coord_flip()
PIPE
In a few graphs I use some variables from the Political Institutions and Political Events dataset compiled by Adam Przeworski [@Przeworski2013]. The original data is available here. This contains information about suffrage and elections in a large number of countries; for details on the included variables, type ?PIPE.
This has measure has some errors and problems, but it's still quite interesting. Here's a plot of the restrictions on the franchise:
data <- PIPE %>%
group_by(country_name) %>%
arrange(year) %>%
mutate(total_elections = preselec + legelec,
total_elections = ifelse(is.na(total_elections) & !is.na(f),
0,
total_elections)) %>%
filter(!is.na(total_elections)) %>%
mutate(cumulative_elections = cumsum(total_elections),
f = ifelse(max(cumulative_elections) == 0 & is.na(f),
0,
f)) %>%
ungroup() %>%
mutate(f_male = ifelse(f < 10,
f,
round(f/10)),
f_female = ifelse(f < 10,
0,
f %% 10),
f_male = ifelse(f_male %in% c(0,1), f_male, f_male + 1),
f_male = ifelse(is.na(f_male) & (cumulative_elections >= 1),
2,
f_male),
f_female = ifelse(f_male == 2, 0, f_female),
f_female = factor(f_female, labels = c("0-No inclusion / not indicated",
"1-Narrower than men",
"2-Equal to men")),
f_male = factor(f_male,
labels = c("0-No suffrage",
"1-Estate",
"2-Subnational",
"3-Property only",
"4-(Property OR
income OR
taxes OR
exercise of profession OR
educational titles)
AND literacy",
"5-Property OR
income OR
taxes OR
exercise of profession OR
educational titles",
"6-Literacy only
OR (Literacy
OR property OR
income OR
taxes OR
exercise of profession OR
educational titles)",
"7-All the economically independent",
"8-All"),
ordered = TRUE)
)
ggplot(data = data,
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = f_male,
alpha = f_female)) +
scale_fill_brewer(type = "div", palette = "RdBu") +
labs(x = "",
alpha = "Female inclusion",
fill = "Type of franchise") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 1),
alpha = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(PIPE$year[ PIPE$year %% 25 == 0],
max(PIPE$year),
min(PIPE$year)))) +
scale_alpha_discrete(range = c(0.3,1)) +
coord_flip()
LIED
The Lexical Index of Democracy and Autocracy [@SkaaningGerring2015] extends and corrects the PIPE dataset, and it provides an independent measure of male and female suffrage, as well as measures of whether the executive is elected, there is an elected assembly, and a few other variables. For details on included variables, type ?lied. The original dat and codebook are available here
Here's the suffrage picture using LIED data:
data <- lied
ggplot(data = data %>%
mutate(male_suffrage = (male_suffrage > 0),
female_suffrage = (female_suffrage > 0)),
aes(x = forcats::fct_rev(reorder(country_name, year, FUN = min)),
y = year)) +
geom_tile(aes(fill = male_suffrage,
alpha = female_suffrage)) +
labs(x = "",
alpha = "Female inclusion?",
fill = "Full male suffrage?") +
theme_bw() +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title.position = "top", ncol = 1),
alpha = guide_legend(title.position = "top", ncol = 2)) +
scale_y_continuous(breaks = unique(c(lied$year[ lied$year %% 25 == 0],
max(lied$year),
min(lied$year)))) +
scale_alpha_discrete(range = c(0.3,1)) +
coord_flip()
The LIED data and the PIPE data differ subtly, due to coding differences:
data <- full_join(PIPE %>%
select(country_name,GWn,year,f_simple,f),
lied %>%
select(country_name,GWn,year,male_suffrage,female_suffrage)) %>%
mutate(inconsistent = (male_suffrage == 1 & female_suffrage == 1 & f != 72) |
(male_suffrage == 1 & female_suffrage == 0 & !(f %in% c(7,71,NA))))
data %>%
filter(inconsistent) %>%
group_by(country_name, f_simple, f, male_suffrage, female_suffrage) %>%
summarise(min_year = min(year), max_year = max(year), num_years = n()) %>%
knitr::kable(col.names = c("Country",
"PIPE franchise (simplified)",
"PIPE franchise (full)",
"LIED male suffrage",
"LIED female suffrage",
"Min year",
"Max year",
"Num. years"))
Country PIPE franchise (simplified) PIPE franchise (full) LIED male suffrage LIED female suffrage Min year Max year Num. years
Australia 4- All male 7 1 1 1901 1901 1 Austria-Hungary 1 - Class restricted, male only 4 1 0 1896 1917 22 Belgium 6 - Universal 72 1 0 1948 1948 1 Canada 6 - Universal 72 1 0 1918 1920 3 Germany (Prussia) 1 - Class restricted, male only 6 1 0 1871 1917 47 Germany (Prussia) 6 - Universal 72 1 0 1918 1918 1 Greece 6 - Universal 72 1 0 1953 1955 3 Guatemala 6 - Universal 72 1 0 1965 1965 1 Iraq 4- All male 7 1 1 1958 1979 22 Italy/Sardinia 6 - Universal 72 1 0 1945 1945 1 Lebanon 6 - Universal 72 1 0 1944 1951 8 Netherlands 6 - Universal 72 1 0 1919 1921 3 Nicaragua 6 - Universal 72 1 0 1955 1956 2 Palau 4- All male 7 1 1 1994 2008 15 Qatar 0 - No suffrage 0 1 1 2003 2008 6 Rumania 1 - Class restricted, male only 4 1 0 1938 1945 8 Somalia 0 - No suffrage 0 1 1 1991 2003 13 Syria 5 - All male, some female 71 1 1 1954 1972 19 Tuvalu 4- All male 7 1 1 1978 2008 31
Leader data
A number of graphs in the book use data on leaders.
Archigos
The main leader data used is the Archigos dataset of Gleditsch, Goemans, and Chiozza [@GoemansGleditschChiozza2009]. The original data is available here; type ?archigos for more info.
We can plot the leaders in each country:
ggplot(data = archigos,
aes(x = forcats::fct_rev(reorder(country_name, startdate, FUN = min)),
y = startdate)) +
geom_point(alpha = 0.2, color = "red") +
geom_text(aes(label = leader), check_overlap = TRUE, size = 2) +
labs(x = "") +
theme_bw() +
coord_flip()
Svolik
We also have leader data from Svolik [@Svolik2012], which is only available for those regimes Svolik codes as non-democratic. Svolik bases his data on an earlier version of Archigos, but he also codes the political affiliations of earlier and later leaders; type ?svolik_leader for more info.
Here's what the aggregate data looks like:
ggplot(data = svolik_leader,
aes(x = forcats::fct_rev(reorder(country_name, startdate, FUN = min)),
y = startdate)) +
geom_point(alpha = 0.4, aes(color = pol_aff, shape = entry_summary)) +
geom_text(aes(label = leader, color = pol_aff),
check_overlap = TRUE, size = 2) +
labs(x = "",
color = "Political affiliation",
shape = "Mode of entry into office") +
theme_bw() +
theme(legend.position = "top") +
guides(color = guide_legend(title.position = "top", ncol = 2),
shape = guide_legend(title.position = "top", ncol = 2)) +
coord_flip()
Coup data
There are two sources for coup data we can use. The first is from Powell and Thyne [@PowellThyne2011]. This data is continuously updated; the original source is available here.
Here is what the data looks like:
ggplot(data = powell_thyne,
aes(x = forcats::fct_rev(country_name),
y = date)) +
geom_point(aes(color = attempt_type)) +
labs(x = "",
color = "Attempt type") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
Though this dataset contains info on all recent coups, it does not show coups earlier than 1950. The PIPE dataset contains rougher info on successful coups before then:
ggplot(data = PIPE %>%
filter(coups > 0) ,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = as.factor(coups))) +
labs(x = "",
color = "Number of coups") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
This data is not correlated with Powell and Thyne's data, though:
data <- full_join(PIPE %>%
filter(coups > 0) %>%
select(country_name,
GWn,
year,
coups),
powell_thyne %>%
filter(coup == 2) %>%
group_by(country_name,
GWn,
year) %>%
summarise(n = n())) %>%
mutate_at(funs(ifelse(is.na(.),0,.)), .cols = c("coups","n")) %>%
rename(pipe_coups = coups, powell_coups = n)
cor(data$pipe_coups, data$powell_coups, use = "pairwise")
## [1] -0.092433
data %>%
filter(year >= 1950,
year <= 2008,
pipe_coups != powell_coups) %>%
count(pipe_coups, powell_coups) %>%
knitr::kable(col.names = c("Number of coups in PIPE",
"Number of coups in Powell-Thyne",
"Number of country-years with discrepancies"),
caption = "Country-years where PIPE disagrees with Powell-Thyne in the 1950-2008 period")
Table: Country-years where PIPE disagrees with Powell-Thyne in the 1950-2008 period
Number of coups in PIPE Number of coups in Powell-Thyne Number of country-years with discrepancies
0 1 17
0 2 1
1 0 59
1 2 3
2 0 5
2 1 4
2 3 1
data %>%
filter(year >= 1950,
year <= 2008,
pipe_coups == powell_coups) %>%
count(pipe_coups, powell_coups) %>%
knitr::kable(col.names = c("Number of coups in PIPE",
"Number of coups in Powell-Thyne",
"Number of country-years where they agree"),
caption = "Country-years where PIPE agrees with Powell-Thyne in the 1950-2008 period")
Table: Country-years where PIPE agrees with Powell-Thyne in the 1950-2008 period
Number of coups in PIPE Number of coups in Powell-Thyne Number of country-years where they agree
1 1 175
2 2 9
data %>%
filter(year >= 1950,
year <= 2008,
pipe_coups != powell_coups) %>%
knitr::kable(col.names = c("Country","GWn","year", "Number of coups in PIPE", "Number of coups in Powell-Thyne"))
Country GWn year Number of coups in PIPE Number of coups in Powell-Thyne
Afghanistan 700 1979 2 0 Afghanistan 700 1996 1 0 Afghanistan 700 2001 1 0 Argentina 160 1981 1 0 Bangladesh 771 2006 1 0 Bolivia 145 1979 2 1 Bolivia 145 1982 1 0 Brazil 140 1954 1 0 Brazil 140 1964 1 2 Cambodia (Kampuchea) 811 1975 1 0 Cambodia (Kampuchea) 811 1978 1 0 Chad 483 1979 1 0 Chad 483 1982 1 0 Chad 483 1990 1 0 Comoros 581 1976 1 0 Comoros 581 1995 1 0 Congo 484 1968 1 2 Congo 484 1969 1 0 Congo 484 1979 1 0 Congo 484 1997 1 0 Congo, Democratic Republic of (Zaire) 490 1997 1 0 Cote D'Ivoire 437 1999 1 0 Cuba 40 1959 1 0 Dominican Republic 42 1961 1 0 Dominican Republic 42 1962 2 0 Ecuador 130 2000 1 0 Ethiopia 530 1991 1 0 Fiji 950 1987 2 1 Gabon 481 1964 2 0 Georgia 372 1991 1 0 Guatemala 90 1993 1 0 Guinea-Bissau 404 1998 1 0 Haiti 41 1957 2 3 Haiti 41 2004 1 0 Hungary 310 1956 2 0 India 750 2006 1 0 Iran (Persia) 630 1979 1 0 Iran (Persia) 630 1981 1 0 Korea, Republic of 732 1960 1 0 Korea, Republic of 732 1980 1 0 Laos 812 1959 1 0 Laos 812 1975 1 0 Liberia 450 1990 1 0 Madagascar (Malagasy) 580 1972 1 0 Madagascar (Malagasy) 580 1975 1 0 Mauritania 435 1979 1 0 Myanmar (Burma) 775 1997 1 0 Nepal 790 1960 1 0 Nicaragua 93 1956 1 0 Nicaragua 93 1979 1 0 Pakistan 770 1969 1 0 Panama 95 1955 1 0 Panama 95 1983 1 0 Panama 95 1989 1 0 Rumania 360 1989 1 0 Rwanda 517 2000 1 0 Sao Tome and Principe 403 1995 1 0 Sierra Leone 451 1992 2 1 Somalia 520 1991 1 0 Sudan 625 1971 2 0 Surinam 115 1980 1 2 Surinam 115 1990 1 0 Syria 652 1962 2 1 Thailand 800 1973 1 0 Uganda 500 1979 1 0 Uganda 500 1986 1 0 Venezuela 101 1952 1 0 Venezuela 101 1958 1 0 Yemen (Arab Republic of Yemen) 678 1977 1 0 Yemen (Arab Republic of Yemen) 678 1978 1 0 Saudi Arabia 670 1964 1 0 Yemen, People's Republic of 680 1969 1 0 Bolivia 145 1951 0 1 Burkina Faso (Upper Volta) 439 1974 0 1 Congo, Democratic Republic of (Zaire) 490 1960 0 1 Cote D’Ivoire 437 1999 0 1 Egypt 651 1954 0 1 Ethiopia 530 1977 0 1 Lesotho 570 1994 0 1 Mauritania 435 2008 0 1 Myanmar (Burma) 775 1958 0 1 Pakistan 770 1971 0 1 São Tomé and Principe 403 2003 0 1 Swaziland 572 1983 0 1 Syria 652 1969 0 1 Tunisia 616 1987 0 1 Vietnam, Republic of 817 1963 0 1 Vietnam, Republic of 817 1964 0 1 Vietnam, Republic of 817 1965 0 2 Yemen, People's Republic of 680 1978 0 1
data <- bind_rows(PIPE %>%
filter(coups > 0) %>%
select(country_name,
GWn,
year,
coups) %>%
mutate(source = "PIPE"),
powell_thyne %>%
filter(coup == 2) %>%
group_by(country_name,
GWn,
year) %>%
summarise(coups = n()) %>%
mutate(source = "Powell-Thyne"))
ggplot(data = data %>%
filter(coups > 0) ,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = source), alpha = 0.3) +
labs(x = "",
color = "Source") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
Elections data
There are two sources of election data. The first is from the National Elections in Democracy and Autocracy (NELDA) dataset [@Hyde2011]. We use only a minimal selection of columns indicating the dates and types of elections; for details of the included variables, type ?nelda. The original data is available here.
This data is available for the following countries:
ggplot(data = nelda,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = types),
alpha = 0.3) +
labs(x = "",
color = "Election type") +
theme_bw() +
theme(legend.position = "top") +
guides(color = guide_legend(title.position = "top")) +
coord_flip()
There is also election data in PIPE:
data <- PIPE %>%
select(country_name,
year,
preselec,
legelec) %>%
reshape2::melt(measure.vars = c("preselec",
"legelec")) %>%
filter(value > 0) %>%
rename(types = variable,
num_elections = value) %>%
mutate(types = plyr::mapvalues(types,
from = c("preselec",
"legelec"),
to = c("Executive",
"Legislative/Parliamentary")),
source = "PIPE") %>%
bind_rows(nelda %>%
group_by(country_name,
year,
types) %>%
summarise(num_elections = n()) %>%
mutate(source = "NELDA"))
ggplot(data = data,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_point(aes(color = source, shape = types),
alpha = 0.3) +
labs(x = "",
shape = "Election type",
color = "Dataset") +
theme_bw() +
theme(legend.position = "top") +
guides(color = guide_legend(title.position = "top")) +
coord_flip()
This data is highly but not perfectly correlated with NELDA; mostly it is a matter of NELDA picking up more elections than PIPE.
data2 <- data %>%
reshape2::dcast(country_name + year ~ source,
fun.aggregate = sum,
value.var = "num_elections")
nelda_shell <- nelda %>%
group_by(country_name) %>%
do(data.frame(year = min(.$year):max(.$year))) %>%
ungroup()
data2 <- inner_join(nelda_shell, data2) %>%
filter(year <= 2008)
cor(data2$NELDA, data2$PIPE)
## [1] 0.5135496
data2 %>%
count(NELDA, PIPE, NELDA < PIPE, NELDA > PIPE) %>%
ungroup() %>%
mutate(total = sum(n), prop = n/total) %>%
group_by(`NELDA > PIPE`, `NELDA < PIPE`) %>%
mutate(prop_2 = sum(prop),
prop = scales::percent(prop),
prop_2 = scales::percent(prop_2)) %>%
arrange(`NELDA > PIPE`,`NELDA < PIPE`) %>%
knitr::kable(col.names = c("NELDA count of elections in country-year",
"PIPE count of electios in country-year",
"NELDA > PIPE",
"NELDA < PIPE",
"Number of elections in group",
"Total elections",
"Proportion of total",
"Proportion of group"))
NELDA count of elections in country-year PIPE count of electios in country-year NELDA > PIPE NELDA < PIPE Number of elections in group Total elections Proportion of total Proportion of group
1 1 FALSE FALSE 1214 2247 54.0% 69.9%
2 2 FALSE FALSE 356 2247 15.8% 69.9%
0 1 TRUE FALSE 155 2247 6.90% 12.3%
0 2 TRUE FALSE 9 2247 0.40% 12.3%
1 2 TRUE FALSE 109 2247 4.85% 12.3%
1 3 TRUE FALSE 1 2247 0.04% 12.3%
2 3 TRUE FALSE 2 2247 0.09% 12.3%
1 0 FALSE TRUE 140 2247 6.23% 17.8%
2 0 FALSE TRUE 12 2247 0.53% 17.8%
2 1 FALSE TRUE 149 2247 6.63% 17.8%
3 0 FALSE TRUE 4 2247 0.18% 17.8%
3 1 FALSE TRUE 8 2247 0.36% 17.8%
3 2 FALSE TRUE 73 2247 3.25% 17.8%
4 1 FALSE TRUE 1 2247 0.04% 17.8%
4 2 FALSE TRUE 11 2247 0.49% 17.8%
4 3 FALSE TRUE 2 2247 0.09% 17.8%
5 2 FALSE TRUE 1 2247 0.04% 17.8%
The book thus provides the dataset all_elections as a convenience; for more info, type ?all_elections
Economic data
GDP per capita
Economic data on GDP per capita comes from several sources: the Maddison project [@Maddison2013], the Penn World Tables versions 8 and 8.1 [@Feenstra2013], the World Bank's Development Indicators, and a dataset of extended GDP values by Kristian Gleditsch [@Gleditsch2002GDP]. The original data for the Maddison project is available here; the Penn World Tables (all versions) is available here; and the Gleditsch dataset is available here. These are bundled into a single dataset with information about the source and the type of GDP estimate; it is worth noting that these estimates sometimes differ substantially, and they must be used with some care. (For a quick guide to the different types of measures, see the helpful chart at the Penn World Table website). Nevertheless, for this book's purposes, a simple median of these different measures works fine.
Here's what they look like, per country:
ggplot(data = economic_data %>%
filter(in_system) %>%
mutate(variable_trunc = stringr::str_trunc(as.character(variable), 40, "right")),
aes(x = year,
y = reorder(country_name, per_capita))) +
geom_tile(aes(alpha = per_capita)) +
labs(x= "",
y = "",
alpha = "Per capita income") +
theme_bw() +
theme(legend.position = "top") +
scale_alpha_continuous(trans = "log", labels = scales::dollar) +
facet_wrap(~variable_trunc, ncol = 5)
Inequality data
The inequality data is a summary of the Standardized World Income Inequality Database [@Solt2009; @Solt2016], version 5.0. The original data is available here. For more info, type ?swiid_summary_5.
These come with measures of uncertainty, and are available for the following country-years:
ggplot(data = swiid_summary_5 %>%
filter(in_system,
variable %in% c("gini_net", "gini_market")),
aes(x = year,
y = reorder(country_name, mean_value))) +
geom_tile(aes(alpha = mean_value)) +
labs(x= "",
y = "",
alpha = "Gini") +
theme_bw() +
theme(legend.position = "top") +
scale_alpha_continuous() +
facet_wrap(~variable, ncol = 2) +
scale_x_continuous(breaks = unique(c(swiid_summary_5$year[ swiid_summary_5$year %% 10 == 0],
max(swiid_summary_5$year),
min(swiid_summary_5$year))))
Oil and Gas data
The data on oil and gas comes from Ross and Mahdavi [@RossMahdavi2015]. The original data is available here.
Ross <- PoliticalDatasets::Ross %>%
select(country_name,
GWn,
year,
ross_country:eiacty,
oil_gas_value_2014,
GWc,
cown:in_system) %>%
filter(!is.na(oil_gas_value_2014), oil_gas_value_2014 > 0)
devtools::use_data(Ross, overwrite = TRUE)
Violence data
Repression data
The data on repression comes from a latent variable index of repression (or, more precisely, of violations of physical integrity rights) created by Christopher Fariss [@Fariss2014]. The data also includes a variety of additional measures of repression from other sources; type ?fariss for more info.
Here's what the index looks like (more repressive periods in red):
ggplot(data = fariss,
aes(x = forcats::fct_rev(country_name),
y = year)) +
geom_tile(aes(fill = latentmean)) +
scale_fill_gradient2() +
labs(x = "",
fill = "Degree of repression \nviolations of physical integrity
(lower is more repressive)") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top")) +
coord_flip()
War data
For a number of graphs, I use version 2 of a list of wars compiled by Gleditsch [@Gleditsch2004], and available here. This comes in two forms: as a list of warring states, per year (all_war_participants_yearly, includingboth civil and interstate wars), and as a list of dyads (interstate_dyads_yearly, interstate wars only).
These data are available for the following countries:
ggplot(data = all_war_participants_yearly,
aes(x = year,
y = forcats::fct_rev(forcats::fct_infreq(country_name)))) +
geom_tile(aes(alpha = deaths+1, fill = type)) +
labs(x= "",
y = "",
alpha = "Total deaths",
fill = "War type") +
theme_bw() +
theme(legend.position = "top") +
scale_alpha_continuous(trans = "log", labels = round) +
scale_x_continuous(breaks = unique(c(all_war_participants_yearly$year[ all_war_participants_yearly$year %% 10 == 0],
max(all_war_participants_yearly$year),
min(all_war_participants_yearly$year))))
Violent and Non-violent campaigns
The data on violent and non-violent campaigns comes from the NAVCO dataset version 2.0 [@ChenowethLewis2013]. The original data is avialable here. Only a small selection of the variables in the original dataset are included; type ?navco_2 for more info.
These data ara available as for the following countries and campaigns:
ggplot(data = navco_2 %>%
mutate(campaign = stringr::str_trunc(paste0(country_name, ": ", campaign), 40, "right"),
camp_size = ifelse(camp_size < 0, NA, camp_size)),
aes(x = year,
y = forcats::fct_rev(campaign),
fill = factor(prim_method,
labels = c("Primarily violent","Primarily nonviolent")),
alpha = factor(success,
labels = c("Unsuccessful","Successful"),
ordered = TRUE))) +
geom_tile() +
geom_point(aes(size = camp_size,
color = factor(prim_method,
labels = c("Primarily violent","Primarily nonviolent")))) +
labs(x= "",
y = "",
alpha = "Success?",
fill = "Primary method of campaign",
color = "Primary method of campaign",
size = "Size of campaign") +
theme_bw() +
theme(legend.position = "top") +
guides(fill = guide_legend(title.position = "top"))
Map data
Some of the graphs in the text make use of map data. You can access this data by typing world. This map data represent country borders as of 2015; for historical country borders, you would need to use a package like cshapes.
Here's an example of how to use the map data, plotting repression in 2013 throughout the world:
world <- world %>%
filter(id != "Antarctica")
data <- fariss %>%
filter(year == 2013)
library(ggalt)
ggplot() + geom_map(aes(fill = latentmean,
map_id = country_name),
map = world,
data = data) +
geom_path(data = world,
aes(y = lat, x = long, group = group)) +
scale_fill_gradient2(mid = "lightgrey") +
theme_minimal() +
theme(legend.position = "bottom") +
labs(fill = "Degree of repression
or violations of personal integrity
(lower is worse)",
x = "",
y = "") +
guides(fill = guide_legend(title.position = "top")) +
coord_proj("+proj=wintri") +
scale_y_continuous(breaks=NULL) +
scale_x_continuous(breaks=NULL) +
theme(legend.position = "bottom")
We can also use the package cshapes to plot earlier years, though this makes things more complicated. Here's repression in 1967, the height of repression during the cultural revolution in CHina and the civil rights movement in the USA:
library(maptools)
library(rgeos)
world_1967 <- cshapes::cshp(as.Date("1967-1-1")) %>%
fortify(region = "GWCODE") %>%
mutate(GWn = id)
ggplot() +
geom_path(data = world,
aes(y = lat, x = long, group = group)) +
geom_map(aes(fill = latentmean,
map_id = GWn),
map = world_1967,
data = fariss %>% filter(year == 1967)) +
geom_path(data = world_1967,
aes(y = lat, x = long, group = group)) +
scale_fill_gradient2(mid = "lightgrey") +
theme_minimal() +
theme(legend.position = "bottom") +
labs(fill = "Degree of repression
or violations of personal integrity
(lower is worse)",
x = "",
y = "") +
guides(fill = guide_legend(title.position = "top")) +
coord_proj("+proj=wintri") +
scale_y_continuous(breaks=NULL) +
scale_x_continuous(breaks=NULL) +
theme(legend.position = "bottom")
Data documentation
Finally, we document all the data:
label_data <- readr::read_csv("data_labels.csv")
library(documenteR)
doc_skeleton(find_all_datasets("AuthoritarianismBook"), label_data = label_data)
devtools::document()
## Writing NAMESPACE
## Writing AuthoritarianismBook.Rd
## Writing PIPE.Rd
## Writing all_elections.Rd
## Writing all_gwf.Rd
## Writing all_gwf_extended_yearly.Rd
## Writing all_gwf_periods.Rd
## Writing all_war_participants_yearly.Rd
## Writing archigos.Rd
## Writing democracy.Rd
## Writing democracy_mentions_yearly.Rd
## Writing economic_data.Rd
## Writing extended_uds.Rd
## Writing fariss.Rd
## Writing interstate_dyads_yearly.Rd
## Writing kailitz_yearly.Rd
## Writing lied.Rd
## Writing magaloni.Rd
## Writing magaloni_extended.Rd
## Writing navco_2.Rd
## Writing nelda.Rd
## Writing personal_scores.Rd
## Writing polity_annual.Rd
## Writing population_data.Rd
## Writing powell_thyne.Rd
## Writing svolik_institutions.Rd
## Writing svolik_leader.Rd
## Writing swiid_summary_5.Rd
## Writing vdem.Rd
## Writing wahman_teorell.Rd
## Writing world.Rd
References
R Package Documentation
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