Awesome

democracyData

This package archives a large number of datasets measuring democracy in use in the scholarly literature, and it provides functions to access many others. You can use it to access some widely used datasets, including Polity5, Freedom House, Geddes, Wright, and Frantz’ autocratic regimes dataset, the Lexical Index of Electoral Democracy, the DD/ACLP/PACL/CGV dataset, the main indexes of the V-Dem dataset, and many others.

Installation

The package is only available on Github. Install as follows:

remotes::install_github("xmarquez/democracyData")

Basic usage

For the vast majority of use cases, you can just type the name of the dataset you require. For example, here’s the DD/ACLP/PACL/CGV dataset:

library(democracyData)
pacl
#> # A tibble: 9,159 × 82
#>    order pacl_country  year aclpcode pacl_cowcode cowcode2 ccdcodelet ccdcodenum
#>    <dbl> <chr>        <dbl>    <dbl>        <dbl>    <dbl> <chr>           <dbl>
#>  1     1 Afghanistan   1946      142          700      700 AFG                 1
#>  2     2 Afghanistan   1947      142          700      700 AFG                 1
#>  3     3 Afghanistan   1948      142          700      700 AFG                 1
#>  4     4 Afghanistan   1949      142          700      700 AFG                 1
#>  5     5 Afghanistan   1950      142          700      700 AFG                 1
#>  6     6 Afghanistan   1951      142          700      700 AFG                 1
#>  7     7 Afghanistan   1952      142          700      700 AFG                 1
#>  8     8 Afghanistan   1953      142          700      700 AFG                 1
#>  9     9 Afghanistan   1954      142          700      700 AFG                 1
#> 10    10 Afghanistan   1955      142          700      700 AFG                 1
#> # ℹ 9,149 more rows
#> # ℹ 74 more variables: aclpyear <dbl>, cowcode2year <dbl>, cowcodeyear <dbl>,
#> #   chgterr <dbl>, ychgterr <dbl>, flagc_cowcode2 <dbl>, flage_cowcode2 <dbl>,
#> #   entryy <dbl>, exity <dbl>, cid <dbl>, wdicode <chr>, imf_code <dbl>,
#> #   politycode <dbl>, bankscode <dbl>, dpicode <chr>, uncode <dbl>,
#> #   un_region <dbl>, un_region_name <chr>, un_continent <dbl>,
#> #   un_continent_name <chr>, aclp_region <dbl>, bornyear <dbl>, …

Here’s Polity IV:

polityIV
#> # A tibble: 17,562 × 40
#>    cyear polityIV_ccode scode polityIV_country  year  flag fragment democ autoc
#>    <dbl>          <dbl> <chr> <chr>            <dbl> <dbl>    <dbl> <dbl> <dbl>
#>  1 21800              2 USA   United States     1800     0       NA     7     3
#>  2 21801              2 USA   United States     1801     0       NA     7     3
#>  3 21802              2 USA   United States     1802     0       NA     7     3
#>  4 21803              2 USA   United States     1803     0       NA     7     3
#>  5 21804              2 USA   United States     1804     0       NA     7     3
#>  6 21805              2 USA   United States     1805     0       NA     7     3
#>  7 21806              2 USA   United States     1806     0       NA     7     3
#>  8 21807              2 USA   United States     1807     0       NA     7     3
#>  9 21808              2 USA   United States     1808     0       NA     7     3
#> 10 21809              2 USA   United States     1809     0       NA     9     0
#> # ℹ 17,552 more rows
#> # ℹ 31 more variables: polity <dbl>, polity2 <dbl>, durable <dbl>, xrreg <dbl>,
#> #   xrcomp <dbl>, xropen <dbl>, xconst <dbl>, parreg <dbl>, parcomp <dbl>,
#> #   exrec <dbl>, exconst <dbl>, polcomp <dbl>, prior <dbl>, emonth <dbl>,
#> #   eday <dbl>, eyear <dbl>, eprec <dbl>, interim <dbl>, bmonth <dbl>,
#> #   bday <dbl>, byear <dbl>, bprec <dbl>, post <dbl>, change <dbl>, d4 <dbl>,
#> #   sf <dbl>, regtrans <dbl>, extended_country_name <chr>, GWn <dbl>, …

And here’s a basic version of the V-Dem dataset, including only the 7 main indexes of democracy:

vdem_simple
#> # A tibble: 27,555 × 54
#>    vdem_country_name country_text_id country_id  year historical_date project
#>    <chr>             <chr>                <dbl> <dbl> <date>            <dbl>
#>  1 Mexico            MEX                      3  1789 1789-12-31            1
#>  2 Mexico            MEX                      3  1790 1790-12-31            1
#>  3 Mexico            MEX                      3  1791 1791-12-31            1
#>  4 Mexico            MEX                      3  1792 1792-12-31            1
#>  5 Mexico            MEX                      3  1793 1793-12-31            1
#>  6 Mexico            MEX                      3  1794 1794-12-31            1
#>  7 Mexico            MEX                      3  1795 1795-12-31            1
#>  8 Mexico            MEX                      3  1796 1796-12-31            1
#>  9 Mexico            MEX                      3  1797 1797-12-31            1
#> 10 Mexico            MEX                      3  1798 1798-12-31            1
#> # ℹ 27,545 more rows
#> # ℹ 48 more variables: historical <dbl>, histname <chr>, codingstart <dbl>,
#> #   codingend <dbl>, codingstart_contemp <dbl>, codingend_contemp <dbl>,
#> #   codingstart_hist <dbl>, codingend_hist <dbl>, gapstart1 <dbl>,
#> #   gapstart2 <dbl>, gapstart3 <dbl>, gapend1 <dbl>, gapend2 <dbl>,
#> #   gapend3 <dbl>, gap_index <dbl>, vdem_cowcode <dbl>, v2x_polyarchy <dbl>,
#> #   v2x_polyarchy_codelow <dbl>, v2x_polyarchy_codehigh <dbl>, …

All datasets in this package are fully documented; type ?pacl for example to see the documentation for the PACL dataset.

Downloading democracy data

There are a couple of democracy datasets that are not currently archived in this package: the family of datasets released by Freedom House and the full V-Dem dataset. To download the Freedom House dataset, use the the download_* family of functions; to download the full V-Dem dataset, use the vdemdata package. The package does include the main indexes of version 13.0 of V-Dem (see vdem_simple), so you don’t need to use the vdemdata package if you are only interested in the higher-level indexes of democracy. You can also download directly the latest versions of the World Bank’s Voice and Accountability Index from the World Governance Indicators and Polity5, though there are also archived versions of these two in the package.

For example, we can download and process the Freedom House “Freedom in the World” dataset as follows:

fh <- download_fh(verbose = FALSE)
#> Warning in download_fh(verbose = FALSE): NAs introduced by coercion

#> Warning in download_fh(verbose = FALSE): NAs introduced by coercion

fh 
#> # A tibble: 9,045 × 11
#>    fh_country   year    pr    cl status fh_total fh_total_reversed
#>    <chr>       <dbl> <dbl> <dbl> <fct>     <dbl>             <dbl>
#>  1 Afghanistan  1972     4     5 PF            9                 5
#>  2 Afghanistan  1973     7     6 NF           13                 1
#>  3 Afghanistan  1974     7     6 NF           13                 1
#>  4 Afghanistan  1975     7     6 NF           13                 1
#>  5 Afghanistan  1976     7     6 NF           13                 1
#>  6 Afghanistan  1977     6     6 NF           12                 2
#>  7 Afghanistan  1978     7     7 NF           14                 0
#>  8 Afghanistan  1979     7     7 NF           14                 0
#>  9 Afghanistan  1980     7     7 NF           14                 0
#> 10 Afghanistan  1982     7     7 NF           14                 0
#> # ℹ 9,035 more rows
#> # ℹ 4 more variables: extended_country_name <chr>, GWn <dbl>, cown <dbl>,
#> #   in_GW_system <lgl>

This downloads the latest update of the “Freedom in the World” dataset (1972-2021, corresponding to the 2022 report), puts it in country-year format (extracting the relevant info from the awful Excel table that Freedom House makes available), calculates the variables fh_total and fh_total_reversed, and adds state system information, including a standardized country name, the Gleditsch-Ward country code and the Correlates of War country code.

Other democracy datasets included in this package do not need to be downloaded, but they can often also be “re-downloaded” from the websites of their creators or maintainers if required. For example, one can either access PACL directly by typing

pacl
#> # A tibble: 9,159 × 82
#>    order pacl_country  year aclpcode pacl_cowcode cowcode2 ccdcodelet ccdcodenum
#>    <dbl> <chr>        <dbl>    <dbl>        <dbl>    <dbl> <chr>           <dbl>
#>  1     1 Afghanistan   1946      142          700      700 AFG                 1
#>  2     2 Afghanistan   1947      142          700      700 AFG                 1
#>  3     3 Afghanistan   1948      142          700      700 AFG                 1
#>  4     4 Afghanistan   1949      142          700      700 AFG                 1
#>  5     5 Afghanistan   1950      142          700      700 AFG                 1
#>  6     6 Afghanistan   1951      142          700      700 AFG                 1
#>  7     7 Afghanistan   1952      142          700      700 AFG                 1
#>  8     8 Afghanistan   1953      142          700      700 AFG                 1
#>  9     9 Afghanistan   1954      142          700      700 AFG                 1
#> 10    10 Afghanistan   1955      142          700      700 AFG                 1
#> # ℹ 9,149 more rows
#> # ℹ 74 more variables: aclpyear <dbl>, cowcode2year <dbl>, cowcodeyear <dbl>,
#> #   chgterr <dbl>, ychgterr <dbl>, flagc_cowcode2 <dbl>, flage_cowcode2 <dbl>,
#> #   entryy <dbl>, exity <dbl>, cid <dbl>, wdicode <chr>, imf_code <dbl>,
#> #   politycode <dbl>, bankscode <dbl>, dpicode <chr>, uncode <dbl>,
#> #   un_region <dbl>, un_region_name <chr>, un_continent <dbl>,
#> #   un_continent_name <chr>, aclp_region <dbl>, bornyear <dbl>, …

Or re-download the dataset from Jose Antonio Cheibub’s website as follows:

pacl_redownloaded <- redownload_pacl(verbose = FALSE)

pacl_redownloaded
#> # A tibble: 9,159 × 82
#>    order pacl_country  year aclpcode pacl_cowcode cowcode2 ccdcodelet ccdcodenum
#>    <dbl> <chr>        <dbl>    <dbl>        <dbl>    <dbl> <chr>           <dbl>
#>  1     1 Afghanistan   1946      142          700      700 AFG                 1
#>  2     2 Afghanistan   1947      142          700      700 AFG                 1
#>  3     3 Afghanistan   1948      142          700      700 AFG                 1
#>  4     4 Afghanistan   1949      142          700      700 AFG                 1
#>  5     5 Afghanistan   1950      142          700      700 AFG                 1
#>  6     6 Afghanistan   1951      142          700      700 AFG                 1
#>  7     7 Afghanistan   1952      142          700      700 AFG                 1
#>  8     8 Afghanistan   1953      142          700      700 AFG                 1
#>  9     9 Afghanistan   1954      142          700      700 AFG                 1
#> 10    10 Afghanistan   1955      142          700      700 AFG                 1
#> # ℹ 9,149 more rows
#> # ℹ 74 more variables: aclpyear <dbl>, cowcode2year <dbl>, cowcodeyear <dbl>,
#> #   chgterr <dbl>, ychgterr <dbl>, flagc_cowcode2 <dbl>, flage_cowcode2 <dbl>,
#> #   entryy <dbl>, exity <dbl>, cid <dbl>, wdicode <chr>, imf_code <dbl>,
#> #   politycode <dbl>, bankscode <dbl>, dpicode <chr>, uncode <dbl>,
#> #   un_region <dbl>, un_region_name <chr>, un_continent <dbl>,
#> #   un_continent_name <chr>, aclp_region <dbl>, bornyear <dbl>, …

These two data frames should be identical:

identical(pacl, pacl_redownloaded)
#> [1] TRUE

You should thus normally use the “archived” versions of these datasets, unless you want to manipulate the raw data yourself (using the redownload_* functions with the option return_raw = TRUE), or think they might have been updated since you installed this package.

Included democracy datasets

For a list of all the democracy datasets available through this package, type democracy_info:

library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.2.3
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

democracy_info %>%
  knitr::kable()

Combining all democracy datasets

You can create one huge data frame including all democracy measures with one call:

democracy_data <- generate_democracy_scores_dataset(output_format = "wide",
                                                    verbose = FALSE)
#> Warning in download_fh(verbose = verbose, include_territories = TRUE): NAs
#> introduced by coercion

#> Warning in download_fh(verbose = verbose, include_territories = TRUE): NAs
#> introduced by coercion
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `prc = (structure(function (..., .x = ..1, .y = ..2, . = ..1)
#>   ...`.
#> Caused by warning:
#> ! NAs introduced by coercion

democracy_data
#> # A tibble: 37,164 × 89
#>    extended_country_name   GWn  cown in_GW_system  year PEPS1i PEPS1q PEPS1v
#>    <chr>                 <dbl> <dbl> <lgl>        <dbl>  <dbl>  <dbl>  <dbl>
#>  1 Abkhazia                396    NA FALSE         1997     NA     NA     NA
#>  2 Abkhazia                396    NA FALSE         1998     NA     NA     NA
#>  3 Abkhazia                396    NA FALSE         1999     NA     NA     NA
#>  4 Abkhazia                396    NA FALSE         2000     NA     NA     NA
#>  5 Abkhazia                396    NA FALSE         2001     NA     NA     NA
#>  6 Abkhazia                396    NA FALSE         2002     NA     NA     NA
#>  7 Abkhazia                396    NA FALSE         2003     NA     NA     NA
#>  8 Abkhazia                396    NA FALSE         2004     NA     NA     NA
#>  9 Abkhazia                396    NA FALSE         2005     NA     NA     NA
#> 10 Abkhazia                396    NA FALSE         2006     NA     NA     NA
#> # ℹ 37,154 more rows
#> # ℹ 81 more variables: PEPS2i <dbl>, PEPS2q <dbl>, PEPS2v <dbl>,
#> #   PIPE_democracy <dbl>, PIPE_regime <dbl>, anckar_democracy <dbl>,
#> #   anrr_democracy <dbl>, blm <dbl>, bmr_democracy <dbl>,
#> #   bmr_democracy_femalesuffrage <dbl>, bmr_democracy_omitteddata <dbl>,
#> #   bnr <dbl>, bnr_extended <dbl>, bti_democracy <dbl>, csvmdi <dbl>,
#> #   doorenspleet <dbl>, dsvmdi <dbl>, eiu <dbl>, fh_electoral <dbl>, …

This can take some time, since it downloads all downloadable datasets (Freedom House, Polity 5, and the WGI Voice and Accountability index), processes them (adds state system information, puts them in country-year format, fixes wrong codes, etc.), and matches them to all the other datasets. In any case, you can select exactly which datasets to include in your big data frame. See ?generate_democracy_scores_dataset for further options to customize the output.

Latent Variable Indexes of Democracy

The package also offers a series of convenience functions to calculate latent variable indexes of democracy (following Pemstein, Meserve, and Melton’s 2010 article “Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type”); see the vignette on Replicating and Extending the UD scores of Pemstein, Meserve, and Melton. It also contains a pre-calculated extended version of these scores, available as extended_uds:

extended_uds
#> # A tibble: 36,841 × 20
#>    extended_country_name   GWn  cown in_GW_system  year     z1 se_z1 z1_pct975
#>    <chr>                 <dbl> <dbl> <lgl>        <dbl>  <dbl> <dbl>     <dbl>
#>  1 Abkhazia                396    NA FALSE         1997 0.0307 0.328     0.674
#>  2 Abkhazia                396    NA FALSE         1998 0.0307 0.328     0.674
#>  3 Abkhazia                396    NA FALSE         1999 0.0307 0.328     0.674
#>  4 Abkhazia                396    NA FALSE         2000 0.0307 0.328     0.674
#>  5 Abkhazia                396    NA FALSE         2001 0.0307 0.328     0.674
#>  6 Abkhazia                396    NA FALSE         2002 0.0307 0.328     0.674
#>  7 Abkhazia                396    NA FALSE         2003 0.0307 0.328     0.674
#>  8 Abkhazia                396    NA FALSE         2004 0.0307 0.328     0.674
#>  9 Abkhazia                396    NA FALSE         2005 0.236  0.327     0.876
#> 10 Abkhazia                396    NA FALSE         2006 0.236  0.327     0.876
#> # ℹ 36,831 more rows
#> # ℹ 12 more variables: z1_pct025 <dbl>, z1_adj <dbl>, z1_pct975_adj <dbl>,
#> #   z1_pct025_adj <dbl>, z1_as_prob <dbl>, z1_pct975_as_prob <dbl>,
#> #   z1_pct025_as_prob <dbl>, z1_adj_as_prob <dbl>, z1_pct975_adj_as_prob <dbl>,
#> #   z1_pct025_adj_as_prob <dbl>, num_measures <int>, measures <list>

State system functions

The package also includes a couple of other convenience functions to work with historical democracy data and determine state system membership. The first is country_year_coder, which works like the countrycode package, except that it is able to determine state system information for country-year pairs. Suppose you have this dataset:

my_weird_democracy_data <- tibble(
  country = c("Germany", "Germany", "Germany",
              "Germany", "East Germany",
              "Federal Republic of Germany",
              "Somaliland", "Somalia",
              "Palestine", "Russia",
              "Russia", "USSR",
              "Republic of Vietnam",
              "Yugoslavia", 'Yugoslavia',
              "Vietnam, South"),
  year = c( 2015, 1930, 1970, 1945, 1949,
            1992, 1990, 1990, 1940, 1917, 
            1912, 1922, 1975, 1990, 1991, 1954),
  my_measure = rnorm(16))


my_weird_democracy_data
#> # A tibble: 16 × 3
#>    country                      year my_measure
#>    <chr>                       <dbl>      <dbl>
#>  1 Germany                      2015    -0.848 
#>  2 Germany                      1930     0.151 
#>  3 Germany                      1970    -0.474 
#>  4 Germany                      1945    -1.31  
#>  5 East Germany                 1949     0.495 
#>  6 Federal Republic of Germany  1992    -0.528 
#>  7 Somaliland                   1990     0.508 
#>  8 Somalia                      1990    -0.868 
#>  9 Palestine                    1940     0.691 
#> 10 Russia                       1917     0.794 
#> 11 Russia                       1912    -2.02  
#> 12 USSR                         1922     0.0446
#> 13 Republic of Vietnam          1975     0.133 
#> 14 Yugoslavia                   1990     2.12  
#> 15 Yugoslavia                   1991     1.37  
#> 16 Vietnam, South               1954     0.171

and you then want to add state system information. country_year_coder does that for you!

my_weird_democracy_data <- my_weird_democracy_data %>%
  country_year_coder(country,
                     year,
                     match_type = "country",
                     verbose = FALSE,
                     include_in_output = c("extended_country_name", 
                                           "GWn", "cown", 
                                           "polity_ccode", 
                                           "in_GW_system", 
                                           "in_cow_system", 
                                           "in_polity_system",
                                           "polity_startdate",
                                           "polity_enddate"))

my_weird_democracy_data %>%
  knitr::kable()

country_year_coder tries to match not just the country name or the country code (as countrycode does), but also to figure out the appropriate state system code given the year. (Above, for example, the function figures out that Germany 1970 should get a COW code of 260, but Germany 1992 should get 255 - though it should retain the 260 code in the Gleditsch and Ward system of states. This is, incidentally, how download_fh adds the correct COW and GW country codes to Freedom House’s Excel data). It also tries to figure out whether a given country-year is in the specific state system list. (In the example above, Germany in 1945 is not listed as a member of the state system in either COW or Gleditsch and Ward, since it was occupied by the Allies as of 31 December 1945, but is listed as a member of the state system in Polity IV as the Federal Republic, though with a polity score of -66, “interregnum”).

One nice thing about country_year_coder (in my humble opinion!) is that it can sometimes correct country coding errors; I’ve run across more than one dataset with the supposed COW code 255 for the Federal Republic of Germany for the period 1955-1990, which would prevent a clean join to a dataset with the correct COW code, but would be caught by country_year_coder.

There is also a function that allows you to create a blank state system panel for any of the three main state systems:

create_panel(system = "cow")
#> # A tibble: 17,231 × 5
#>     cown cow_country_name         cow_startdate cow_enddate  year
#>    <dbl> <chr>                    <date>        <date>      <dbl>
#>  1     2 United States of America 1816-01-01    NA           1816
#>  2     2 United States of America 1816-01-01    NA           1817
#>  3     2 United States of America 1816-01-01    NA           1818
#>  4     2 United States of America 1816-01-01    NA           1819
#>  5     2 United States of America 1816-01-01    NA           1820
#>  6     2 United States of America 1816-01-01    NA           1821
#>  7     2 United States of America 1816-01-01    NA           1822
#>  8     2 United States of America 1816-01-01    NA           1823
#>  9     2 United States of America 1816-01-01    NA           1824
#> 10     2 United States of America 1816-01-01    NA           1825
#> # ℹ 17,221 more rows

create_panel(system = "GW")
#> # A tibble: 20,135 × 5
#>      GWn GW_country_name          GW_startdate GW_enddate  year
#>    <dbl> <chr>                    <date>       <date>     <dbl>
#>  1     2 United States of America 1816-01-01   NA          1816
#>  2     2 United States of America 1816-01-01   NA          1817
#>  3     2 United States of America 1816-01-01   NA          1818
#>  4     2 United States of America 1816-01-01   NA          1819
#>  5     2 United States of America 1816-01-01   NA          1820
#>  6     2 United States of America 1816-01-01   NA          1821
#>  7     2 United States of America 1816-01-01   NA          1822
#>  8     2 United States of America 1816-01-01   NA          1823
#>  9     2 United States of America 1816-01-01   NA          1824
#> 10     2 United States of America 1816-01-01   NA          1825
#> # ℹ 20,125 more rows

Citation

The standard citation function from base R will produce a list of citations for all the datasets included in this package:

citation(package = "democracyData")

To cite any of the datasets included in this package use:

Acemoglu D, Naidu S, Restrepo P, Robinson JA (2019). “Democracy Does Cause Growth.” Journal of Political Economy, 127(1), 47-100. doi:10.1086/700936 https://doi.org/10.1086/700936, https://www.journals.uchicago.edu/doi/10.1086/700936.

Anckar C, Fredriksson C (2018). “Classifying political regimes 1800-2016: a typology and a new dataset.” European Political Science. doi:10.1057/s41304-018-0149-8 https://doi.org/10.1057/s41304-018-0149-8, https://doi.org/10.1057/s41304-018-0149-8.

Arat ZF (1991). Democracy and human rights in developing countries. Lynne Rienner Publishers, Boulder.

Bell C (2016). “The Rulers, Elections, and Irregular Governance Dataset (REIGN).” http://oefresearch.org/datasets/reign.

Bernhard M, Nordstrom T, Reenock C (2001). “Economic Performance, Institutional Intermediation, and Democratic Survival.” Journal of Politics, 63(3), 775-803. doi:10.1111/0022-3816.00087 https://doi.org/10.1111/0022-3816.00087.

Bertelsmann Stiftung (2022). “Transformation Index of the Bertelsmann Stiftung 2022.” Bertelsmann Stiftung.

Bjørnskov C, Rode M (2020). “Regime types and regime change: A new dataset on democracy, coups, and political institutions.” The Review of International Organizations, 15(2), 531-551. doi:10.1007/s11558-019-09345-1 https://doi.org/10.1007/s11558-019-09345-1.

Boix C, Miller M, Rosato S (2012). “A Complete Dataset of Political Regimes, 1800-2007.” Comparative Political Studies, 46(12), 1523-1554. doi:10.1177/0010414012463905 https://doi.org/10.1177/0010414012463905.

Bollen KA (2001). “Cross-National Indicators of Liberal Democracy, 1950-1990.”

Bollen K, Paxton P (2000). “Subjective Measures of Liberal Democracy.” Comparative Political Studies, 33(1), 58-86. doi:10.1177/0010414000033001003 https://doi.org/10.1177/0010414000033001003.

Bowman K, Lehoucq F, Mahoney J (2005). “Measuring Political Democracy: Case Expertise, Data Adequacy, and Central America.” Comparative Political Studies, 38(8), 939-970. doi:10.1177/0010414005277083 https://doi.org/10.1177/0010414005277083.

Cheibub J, Gandhi J, Vreeland J (2010). “Democracy and dictatorship revisited.” Public Choice, 143(1), 67-101. doi:10.1007/s11127-009-9491-2 https://doi.org/10.1007/s11127-009-9491-2.

Coppedge M, Alvarez A, Maldonado C (2008). “Two Persistent Dimensions of Democracy: Contestation and Inclusiveness.” The journal of politics, 70(03), 632-647. doi:10.1017/S0022381608080663 https://doi.org/10.1017/S0022381608080663.

Coppedge M, Gerring J, Knutsen CH, Lindberg SI, Teorell J, Altman D, Bernhard M, Cornell A, Fish MS, Gastaldi L, Gjerløw H, Glynn A, Grahn S, Hicken A, Kinzelbach K, Marquardt KL, McMann K, Mechkova V, Neundorf A, Paxton P, Pemstein D, Rydén O, von Römer J, Seim B, Sigman R, Skaaning S, Staton J, Sundström A, Tzelgov E, Uberti L, Wang Y, Wig T, Ziblatt D (????). “V-Dem Codebook v13.”

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To see these entries in BibTeX format, use ‘print(<citation>, bibtex=TRUE)’, ‘toBibtex(.)’, or set ‘options(citation.bibtex.max=999)’.

You can also find the citation for a specific dataset using the wrapper cite_dataset with the name of the dataset in this package:

cite_dataset("gwf")

[1] B. Geddes, J. Wright, and E. Frantz. “Autocratic Breakdown and Regime Transitions: A New Data Set”. In: Perspectives on Politics 12.1 (2014), pp. 313-331. DOI: 10.1017/S1537592714000851.

Feedback and Caveats

Feedback welcome!

Note that some functions in this package can be quite slow: generating a full democracy dataset (including downloading Freedom House, Polity, and WGI) or applying country_year_coder to a large data frame both can take some time. Suggestions to accelerate the code are welcome.

country_year_coder fails to give correct answers in some weird edge cases mostly involving Yugoslavia, Germany, or Vietnam. If you run across any of these cases, let me know.