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 x 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
#> # ... with 9,149 more rows, and 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>, endyear <dbl>, dupcow <dbl>, ...

Here’s Polity IV:

polityIV
#> # A tibble: 17,562 x 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
#> # ... with 17,552 more rows, and 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>, cown <int>, in_GW_system <lgl>

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

vdem_simple
#> # A tibble: 27,380 x 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
#> # ... with 27,370 more rows, and 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>, v2x_polyarchy_sd <dbl>, v2x_libdem <dbl>, ...

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

Downloading democracy data

Three democracy datasets are not currently archived in this package: the family of datasets released by Freedom House; the Polity5 dataset (which superceded Polity IV); and the Voice and Accountability indexes of the World Governance Indicators. (The full V-Dem dataset is also not archived here; you can access it via the vdemdata package). To download these datasets, use the the download_* family of functions.

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: 8,850 x 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
#> # ... with 8,840 more rows, and 4 more variables: extended_country_name <chr>,
#> #   GWn <dbl>, cown <int>, 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 x 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
#> # ... with 9,149 more rows, and 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>, endyear <dbl>, dupcow <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 x 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
#> # ... with 9,149 more rows, and 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>, endyear <dbl>, dupcow <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 4.1.2
#> 
#> 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()
dataset long_name main_democracy_measure_col measure_type based_on in_pmm_replication categorical_regime_types user_extendable downloadable included_in_package first_published_use notes
anckar The Anckar-Fredriksson dataset of political regimes democracy dichotomous bmr FALSE TRUE FALSE TRUE TRUE 2018 The democracy measure should be equivalent to democracy_omitteddata from bmr up to 2010; it may have some divergences for the 2011-2016 period.
anrr The Acemoglu, Naidu, Restrepo, and Robinson dataset dem dichotomous FH,Polity FALSE FALSE TRUE FALSE TRUE 2019 The measure can be extended by using the latest FH, Polity, and PACL Data, but the rules are not entirely transparent, and some cases in the original dataset have been hand-coded.
arat_pmm The Arat measure of democracy pmm_arat continuous NA TRUE FALSE FALSE FALSE TRUE 1991 Only available via the Pemstein, Meserve, and Melton (2013) replication data. I have not been able to access the original data.
blm The Bowman, Lehoucq, and Mahoney index of democracy for Central America blm trichotomous NA TRUE FALSE FALSE TRUE TRUE 2005 NA
bmr The Boix-Miller-Rosato dichotomous coding of democracy, 1800-2015, version 3.0 democracy,democracy_omitteddata,democracy_femalesuffrage dichotomous PACL FALSE FALSE FALSE TRUE TRUE 2010 NA
bnr The Bernhard, Nordstrom & Reenock Event History Coding of Democratic Breakdowns event,bnr dichotomous NA FALSE FALSE TRUE TRUE TRUE 2001 Can be extended using a full panel of sovereign countries (COW). Extended version included in this package.
bti The Berteslmann Index of Political transformation SI_Democracy_Status continuous NA FALSE FALSE FALSE TRUE TRUE 2006 NA
bollen_pmm The Bollen measure of democracy pmm_bollen continuous NA TRUE FALSE FALSE FALSE TRUE 1978 The original data was compiled in 1978, for Bollen’s dissertation; existing data seems to be from the 2000 update. I do not know how much it changed over time. Only available via the Pemstein, Meserve, and Melton (2013) replication data. I have not been able to access the original data.
doorenspleet Renske Doorenspleet’s Democracy Dataset doorenspleet,regime dichotomous Polity FALSE FALSE FALSE FALSE TRUE 2000 NA
eiu The Economist Intelligence Unit’s Democracy Index eiu continuous NA FALSE FALSE FALSE FALSE TRUE 2006 The original data has to be manually extracted from the tables in the EIU’s pdf report on the index.
fh Freedom House “Freedom in the World” data status,fh_total,fh_total_reversed ordinal FH TRUE FALSE FALSE TRUE FALSE 1973 NA
fh_full Freedom House “Freedom in the World” data total continuous FH FALSE FALSE FALSE TRUE FALSE 2013 This is the 0-100 score Freedom House uses for its more aggregated ratings. Freedom House changed its methodology in 2013, so the full data is different for this period; full data from 2003-2012 is available in their website, but is not yet included in this package.
fh_electoral Freedom House “Electoral Democracies” List electoral dichotomous FH FALSE FALSE FALSE TRUE FALSE 1990 The electoral democracy list seems to have only been compiled since the 1990s, but I have not been able to find an exact date of first compilation.
gwf The Geddes Wright and Frantz Autocratic Regimes dataset gwf_regimetype,gwf_nonautocracy dichotomous PACL FALSE TRUE TRUE TRUE TRUE 2014 Can be extended using the gwf_duration variable. Extended version included in this package.
hadenius_pmm Axel Hadenius’ Index of Democracy pmm_hadenius continuous NA TRUE FALSE FALSE FALSE TRUE 1992 Only available via the Pemstein, Meserve, and Melton (2013) replication data. I have not been able to access the original data.
kailitz The Steffen Kailitz Dataset of Authoritarian Regime Types combined_regime,kailitz_binary,kailitz_tri dichotomous NA FALSE TRUE FALSE FALSE TRUE 2013 NA
LIED The Lexical Index of Electoral Democracy, v. 3 lexical_index ordinal PIPE FALSE FALSE FALSE TRUE TRUE 2015 NA
magaloni Autocracies of the World, 1950-2012 (Version 1.0). demo_nr,demo_r,regime_r,regime_nr dichotomous PACL FALSE TRUE TRUE TRUE TRUE 2013 Can be extended using the duration_nr variable. Extended version included in this package.
mainwaring Mainwaring, Brinks, and Perez Linan’s democracy measure for Latin America mainwaring,Regime trichotomous NA TRUE FALSE FALSE FALSE TRUE 2001 NA
munck_pmm Munck Index of Democracy pmm_munck continuous NA TRUE FALSE FALSE FALSE TRUE 2009 Only available via the Pemstein, Meserve, and Melton (2013) replication data. I have not been able to access the original data.
pacl, pacl_update The Democracy and Dictatorship Dataset (DD/PACL/ACLP/CGV) democracy,regime,Democracy,DD_regime,DD_category dichotomous PACL TRUE TRUE FALSE TRUE TRUE 1996 The original data was first compiled, as far as I know, for the famous ACLP study “Modernization: Theories and Facts” study of 1996. It was extended and changed by Cheibub, Gandhi, and Vreeland in 2010 (pacl dataset) and further updated by Bjrnskov and Rode (2020; pacl_update dataset), who added new institutional variables.
peps Participation-Enhanced Polity Score PEPS1i,PEPS2i,PEPS1q,PEPS2q,PEPS1v,PEPS2v,polity1raw,Polity1,Polity2,Polity3 continuous Polity FALSE FALSE FALSE TRUE TRUE 2006 NA
PIPE The Political Institutions and Political Events (PIPE) dataset democracy,democracy2,regime dichotomous PIPE FALSE FALSE FALSE TRUE TRUE 2010 Democracy measures in PIPE are calculated in this package on the basis of imperfect instructions in the codebook. Use with care.
pitf Political Instability Task Force democracy indicator pitf_binary dichotomous Polity FALSE FALSE FALSE FALSE TRUE 2010 Constructed score on the basis of Polity data.
pitf Political Instability Task Force democracy indicator pitf ordinal Polity FALSE FALSE FALSE FALSE TRUE 2010 Constructed score on the basis of Polity data.
polityIV The Polity IV dataset polity,polity2 ordinal Polity TRUE FALSE FALSE TRUE TRUE 1975 The first compilation of this dataset (POLITY I) was probably first used in a 1975 study by Eckstein and Gurr, but had been collected by Gurr since the late 1960s. The current form of the data is very different from the original Polity I data. The Polity II codebook survives, but I find no record of the Polity I codebook.
polity_annual The Polity5 dataset polity,polity2 ordinal Polity TRUE FALSE FALSE TRUE FALSE 1975 The first compilation of this dataset (POLITY I) was probably first used in a 1975 study by Eckstein and Gurr, but had been collected by Gurr since the late 1960s. The current form of the data is very different from the original Polity I data. The Polity II codebook survives, but I find no record of the Polity I codebook.
polyarchy The Polyarchy Scale and the Contestation Scale cont,poly ordinal NA TRUE FALSE FALSE TRUE TRUE 1990 NA
polyarchy_dimensions Latent Dimensions of Contestation and Inclusiveness by Michael Coppedge, Angel Alvarez, and Claudia Maldonado CONTEST,INCLUS continuous latent variable FALSE FALSE FALSE TRUE TRUE 2008 NA
prc_gasiorowski The Political Regime Change (PRC) dataset. regime,prc,prc_at_end_year,prc_at_beginning_year trichotomous NA TRUE FALSE FALSE FALSE TRUE 1996 NA
reign The Rulers, Elections, and Irregular Governance (REIGN) dataset, regime characteristics worksheet. gwf_regimetype dichotomous GWF FALSE TRUE FALSE TRUE FALSE 2016 NA
svmdi Suport Vector Machine Democracy Index by Grundler and Krieger svmdi, csvmdi continuous latent variable FALSE FALSE FALSE TRUE TRUE 2016 NA
svmdi Suport Vector Machine Democracy Index by Grundler and Krieger dsvmdi dichotomous latent variable FALSE FALSE FALSE TRUE TRUE 2016 NA
svolik_regime Milan Svolik’s Regime Dataset regime,regime_numeric dichotomous PACL FALSE FALSE FALSE FALSE TRUE 2012 NA
uds The Unified Democracy Scores mean,median continuous latent variable FALSE FALSE TRUE TRUE TRUE 2010 Can be extended using the package QuickUDS. (Use “remotes::install_github(”xmarquez/QuickUDS”)“; the package is not on CRAN)
ulfelder The Democracy/Autocracy Dataset by Jay Ulfelder rgjtype dichotomous Polity FALSE FALSE TRUE TRUE TRUE 2007 Can be extended using the rgjdurd and rgjdura variables. Extended version included in this package.
utip The University of Texas Inequality Project Categorical Dataset of Political Regimes utip_trichotomous trichotomous NA FALSE TRUE FALSE TRUE TRUE 2008 Both the dichotomous and trichotomous versions of these measures are calculated by this package. The original dataset distinguishes several different types of democracy.
utip The University of Texas Inequality Project Categorical Dataset of Political Regimes utip_dichotomous,utip_dichotomous_strict dichotomous NA FALSE TRUE FALSE TRUE TRUE 2008 Both the dichotomous and trichotomous versions of these measures are calculated by this package. The original dataset distinguishes several different types of democracy.
vanhanen Vanhanen measures of democracy, 1800-2012 vanhanen_democratization continuous NA TRUE FALSE FALSE FALSE TRUE 1968 Vanhanen first collected democracy data on 12 countries for his 1968 dissertation. Current data is different from the original data, though it still uses a similar conceptual scheme.
vdem The Varieties of Democracy Dataset, version 11 v2x_polyarchy,v2x_api,v2x_mpi,v2x_libdem,v2x_partipdem,v2x_delibdem,v2x_egaldem continuous NA FALSE FALSE FALSE FALSE FALSE 2015 Can be accessed using the package vdem. (Use “remotes::install_github(”xmarquez/vdem”)“; the package is not on CRAN)
wahman_teorell_hadenius Authoritarian Regimes Data Set, version 5.0, by Axel Hadenius, Jan Teorell, & Michael Wahman regime1ny,regime1nyrobust, regimeny, regimenyrobust dichotomous FH,Polity FALSE TRUE FALSE TRUE TRUE 2007 NA
wgi_democracy The World Governance Indicators “Voice and Accountability” Index Estimate continuous FH FALSE FALSE FALSE TRUE FALSE 2010 NA

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

democracy_data
#> # A tibble: 36,894 x 89
#>    extended_country_name   GWn  cown in_GW_system  year anckar_democracy
#>    <chr>                 <dbl> <int> <lgl>        <dbl>            <dbl>
#>  1 Abkhazia                396    NA FALSE         1997               NA
#>  2 Abkhazia                396    NA FALSE         1998               NA
#>  3 Abkhazia                396    NA FALSE         1999               NA
#>  4 Abkhazia                396    NA FALSE         2000               NA
#>  5 Abkhazia                396    NA FALSE         2001               NA
#>  6 Abkhazia                396    NA FALSE         2002               NA
#>  7 Abkhazia                396    NA FALSE         2003               NA
#>  8 Abkhazia                396    NA FALSE         2004               NA
#>  9 Abkhazia                396    NA FALSE         2005               NA
#> 10 Abkhazia                396    NA FALSE         2006               NA
#> # ... with 36,884 more rows, and 83 more variables: 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>, fh_total_reversed <dbl>,
#> #   gwf_democracy_extended <dbl>, gwf_democracy_extended_strict <dbl>,
#> #   kailitz_binary <dbl>, kailitz_tri <dbl>, lexical_index <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: 36,544 x 20
#>    extended_country_name   GWn  cown in_GW_system  year     z1 se_z1 z1_pct975
#>    <chr>                 <dbl> <int> <lgl>        <dbl>  <dbl> <dbl>     <dbl>
#>  1 Abkhazia                396    NA FALSE         1997 0.0335 0.329     0.678
#>  2 Abkhazia                396    NA FALSE         1998 0.0335 0.329     0.678
#>  3 Abkhazia                396    NA FALSE         1999 0.0335 0.329     0.678
#>  4 Abkhazia                396    NA FALSE         2000 0.0335 0.329     0.678
#>  5 Abkhazia                396    NA FALSE         2001 0.0335 0.329     0.678
#>  6 Abkhazia                396    NA FALSE         2002 0.0335 0.329     0.678
#>  7 Abkhazia                396    NA FALSE         2003 0.0335 0.329     0.678
#>  8 Abkhazia                396    NA FALSE         2004 0.0335 0.329     0.678
#>  9 Abkhazia                396    NA FALSE         2005 0.238  0.327     0.880
#> 10 Abkhazia                396    NA FALSE         2006 0.238  0.327     0.880
#> # ... with 36,534 more rows, and 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 x 3
#>    country                      year my_measure
#>    <chr>                       <dbl>      <dbl>
#>  1 Germany                      2015     0.994 
#>  2 Germany                      1930     0.762 
#>  3 Germany                      1970     0.0524
#>  4 Germany                      1945     0.390 
#>  5 East Germany                 1949     1.03  
#>  6 Federal Republic of Germany  1992     0.709 
#>  7 Somaliland                   1990     1.18  
#>  8 Somalia                      1990     1.68  
#>  9 Palestine                    1940     0.611 
#> 10 Russia                       1917     1.26  
#> 11 Russia                       1912     0.835 
#> 12 USSR                         1922     0.222 
#> 13 Republic of Vietnam          1975     0.822 
#> 14 Yugoslavia                   1990     0.183 
#> 15 Yugoslavia                   1991     1.80  
#> 16 Vietnam, South               1954     1.55

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 my_measure extended_country_name GWn cown polity_ccode in_GW_system in_cow_system in_polity_system polity_startdate polity_enddate
Germany 2015 0.9937903 German Federal Republic 260 255 255 TRUE TRUE TRUE 1990-10-02 NA
Germany 1930 0.7624462 Germany (Prussia) 255 255 255 TRUE TRUE TRUE 1871-01-19 1945-05-07
Germany 1970 0.0524417 German Federal Republic 260 260 260 TRUE TRUE TRUE 1945-05-08 1990-10-02
Germany 1945 0.3895279 German Federal Republic 260 260 260 FALSE FALSE TRUE 1945-05-08 1990-10-02
East Germany 1949 1.0257242 German Democratic Republic 265 265 265 TRUE FALSE TRUE 1945-05-08 1990-10-02
Federal Republic of Germany 1992 0.7092587 German Federal Republic 260 255 255 TRUE TRUE TRUE 1990-10-02 NA
Somaliland 1990 1.1829996 Somaliland NA NA NA FALSE FALSE FALSE NA NA
Somalia 1990 1.6786743 Somalia 520 520 520 TRUE TRUE TRUE 1960-07-01 NA
Palestine 1940 0.6105567 British Mandate of Palestine NA NA NA FALSE FALSE FALSE NA NA
Russia 1917 1.2586704 Russia (Soviet Union) 365 365 365 TRUE TRUE TRUE 1800-01-01 1922-12-29
Russia 1912 0.8348958 Russia (Soviet Union) 365 365 365 TRUE TRUE TRUE 1800-01-01 1922-12-29
USSR 1922 0.2221328 Russia (Soviet Union) 365 365 364 TRUE TRUE TRUE 1922-12-30 1991-12-31
Republic of Vietnam 1975 0.8222729 Vietnam, Republic of 817 817 817 FALSE FALSE TRUE 1955-10-26 1975-12-31
Yugoslavia 1990 0.1826718 Yugoslavia 345 345 345 TRUE TRUE TRUE 1921-01-01 1991-07-01
Yugoslavia 1991 1.8048561 Yugoslavia 345 345 347 TRUE TRUE TRUE 1991-07-01 2003-03-11
Vietnam, South 1954 1.5481672 Vietnam, Republic of 817 817 817 TRUE TRUE FALSE 1955-10-26 1975-12-31

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: 17,036 x 5
#>     cown cow_country_name         cow_startdate cow_enddate  year
#>    <int> <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
#> # ... with 17,026 more rows

create_panel(system = "GW")
#> # A tibble: 19,938 x 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
#> # ... with 19,928 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 (URL: https://doi.org/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 (URL: https://doi.org/10.1057/s41304-018-0149-8), <URL: 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).” <URL: 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 (URL: 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 (URL: 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 (URL: 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 (URL: 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 (URL: 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 (URL: 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 (URL: https://doi.org/10.1017/S0022381608080663).

Coppedge M, Gerring J, Knutsen CH, Lindberg SI, Teorell J, Alizada N, Altman D, Bernhard M, Cornell A, Fish MS, Gastaldi L, Gjerløw H, Glynn A, Grahn S, Hicken A, Hindle G, Ilchenko N, Kinzelbach K, Krusell J, Marquardt KL, McMann K, Mechkova V, Medzihorsky J, Paxton P, Pemstein D, Pernes J, Rydén O, von Römer J, Seim B, Sigman R, Skaaning S, Staton J, Sundström A, Tzelgov E, Wang Y, Wig T, Wilson S, Ziblatt D (2022). “V-Dem Country-Year/Country-Date Dataset v12.” <URL: https://doi.org/10.23696/vdemds22>.

Coppedge M, Gerring J, Knutsen CH, Lindberg SI, Teorell J, Altman D, and Michael Bernhard, Cornell A, Fish MS, Gastaldi L, Gjerløw H, Glynn A, Grahn S, Hicken A, Kinzelbach K, Marquardt KL, McMann K, Mechkova V, Paxton P, Pemstein D, Pernes J, von Römer J, Seim B, Sigman R, Skaaning S, Staton J, Tzelgov E, Uberti L, Wang Y, Wig T, Wilson S, Ziblatt D (2022). “V-Dem Codebook v12.” <URL: https://www.v-dem.net/static/website/img/refs/codebookv12.pdf>.

Coppedge M, Reinicke WH (1990). “Measuring Polyarchy.” Studies in Comparative International Development, 25(1), 51-72. doi: 10.1007/Bf02716905 (URL: https://doi.org/10.1007/Bf02716905).

Doorenspleet R (2000). “Reassessing the Three Waves of Democratization.” World Politics, 52(03), 384-406. doi: 10.1017/S0043887100016580 (URL: https://doi.org/10.1017/S0043887100016580).

Freedom House (2022). “Freedom in the World 2022: The Global Expansion of Authoritarian Rule.” Freedom House. <URL: https://freedomhouse.org/report/freedom-world/2022/global-expansion-authoritarian-rule>.

Gasiorowski M (1996). “An Overview of the Political Regime Change Dataset.” Comparative Political Studies, 29(4), 469-483. doi: 10.1177/0010414096029004004 (URL: https://doi.org/10.1177/0010414096029004004).

Geddes B, Wright J, Frantz E (2014). “Autocratic Breakdown and Regime Transitions: A New Data Set.” Perspectives on Politics, 12(1), 313-331. doi: 10.1017/S1537592714000851 (URL: https://doi.org/10.1017/S1537592714000851).

Gleditsch K, Ward MD (1999). “Interstate system membership: A revised list of independent states since the congress of Vienna.” International Interactions, 25(4), 393-413. doi: 10.1080/03050629908434958 (URL: https://doi.org/10.1080/03050629908434958).

Goldstone J, Bates R, Epstein D, Gurr T, Lustik M, Marshall M, Ulfelder J, Woodward M (2010). “A Global Model for Forecasting Political Instability.” American Journal of Political Science, 54(1), 190-208. doi: 10.1111/j.1540-5907.2009.00426.x (URL: https://doi.org/10.1111/j.1540-5907.2009.00426.x).

Gründler K, Krieger T (2016). “Democracy and growth: Evidence from a machine learning indicator.” European Journal of Political Economy, 45, 85-107. doi: 10.1016/j.ejpoleco.2016.05.005 (URL: https://doi.org/10.1016/j.ejpoleco.2016.05.005), <URL: http://www.sciencedirect.com/science/article/pii/S0176268016300222>.

Gründler K, Krieger T (2018). “Machine Learning Indices, Political Institutions, and Economic Development.” CESifo Group Munich. <URL: https://www.cesifo-group.de/DocDL/cesifo1_wp6930.pdf>.

Gründler K, Krieger T (2021). “Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019.” European Journal of Political Economy, 102-47. doi: 10.1016/j.ejpoleco.2021.102047 (URL: https://doi.org/10.1016/j.ejpoleco.2021.102047).

Hadenius A (1992). Democracy and development. Cambridge University Press, New York.

Hadenius A, Teorell J (2007). “Pathways from Authoritarianism.” Journal of Democracy, 18(1), 143-157.

Hsu S (2008). “The Effect of Political Regimes on Inequality, 1963-2002.” UTIP Working Paper.

Kailitz S (2013). “Classifying political regimes revisited: legitimation and durability.” Democratization, 20(1), 39-60. doi: 10.1080/13510347.2013.738861 (URL: https://doi.org/10.1080/13510347.2013.738861).

Kaufmann D, Kraay A (2020). “Worldwide Governance Indicators.” <URL: http://www.govindicators.org>.

Magaloni B, Chu J, Min E (2013). “Autocracies of the World, 1950-2012 (Version 1.0).” <URL: http://cddrl.fsi.stanford.edu/research/autocracies_of_the_world_dataset>.

Mainwaring S, Brinks D, Pérez-Liñán A (2001). “Classifying Political Regimes in Latin America.” Studies in Comparative International Development, 36(1), 37-65. doi: 10.1007/bf02687584 (URL: https://doi.org/10.1007/bf02687584).

Mainwaring S, Pérez-Liñán A, Brinks D (2014). “Political Regimes in Latin America, 1900-2007 (with Daniel Brinks).” In Democracies and Dictatorships in Latin America: Emergence, Survival, and Fall, chapter Political Regimes in Latin America, 1900-2007 (with Daniel Brinks). Cambridge University Press, New York.

Marquez X (2016). “A Quick Method for Extending the Unified Democracy Scores.” Available at SSRN 2753830. <URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2753830>.

Márquez X (2020). “democracyData: A package for accessing and manipulating existing measures of democracy.” <URL: http://github.com/xmarquez/democracyData>.

Marshall MG, Gurr TR (2020). Polity 5: Political Regime Characteristics and Transitions, 1800-2018. Dataset Users’ Manual..

Marshall MG, Gurr TR, Jaggers K (2019). Polity IV Project: Political Regime Characteristics and Transitions, 1800-2018. Dataset Users’ Manual..

Moon BE, Birdsall JH, Ciesluk S, Garlett LM, Hermias JJ, Mendenhall E, Schmid PD, Wong WH (2006). “Voting Counts: Participation in the Measurement of Democracy.” Studies in Comparative International Development, 41(2), 3-32. doi: 10.1007/BF02686309 (URL: https://doi.org/10.1007/BF02686309).

Munck G (2009). Measuring Democracy: A Bridge between Scholarship and Politics. The Johns Hopkins University Press, Baltimore.

Pemstein D, Meserve SA, Melton J (2013). “Replication data for: Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type.” <URL: http://hdl.handle.net/1902.1/PMM>.

Pemstein D, Meserve S, Melton J (2010). “Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type.” Political Analysis, 18(4), 426-449. doi: 10.1093/pan/mpq020 (URL: https://doi.org/10.1093/pan/mpq020).

Przeworski A (2013). “Political Institutions and Political Events (PIPE) Data Set.” <URL: https://sites.google.com/a/nyu.edu/adam-przeworski/home/data>.

Reich G (2002). “Categorizing Political Regimes: New Data for Old Problems.” Democratization, 9(4), 1-24. doi: 10.1080/714000289 (URL: https://doi.org/10.1080/714000289).

Skaaning S, Gerring J, Bartusevicius H (2015). “A Lexical Index of Electoral Democracy.” Comparative Political Studies, 48(12), 1491-1525. doi: 10.1177/0010414015581050 (URL: https://doi.org/10.1177/0010414015581050).

Svolik M (2012). The Politics of Authoritarian Rule. Cambridge University Press, Cambridge.

Taylor SJ, Ulfelder J (2015). “A Measurement Error Model of Dichotomous Democracy Status.” Available at SSRN. doi: 10.2139/ssrn.2726962 (URL: https://doi.org/10.2139/ssrn.2726962).

The Economist Intelligence Unit (2022). “Democracy Index 2021: The China Challenge.” The Economist Intelligence Unit.

Ulfelder J (2012). “Democracy/Autocracy Data Set.” <URL: http://hdl.handle.net/1902.1/18836>.

Ulfelder J, Lustik M (2007). “Modelling Transitions To and From Democracy.” Democratization, 14(3), 351-387. doi: 10.1080/13510340701303196 (URL: https://doi.org/10.1080/13510340701303196).

Vanhanen T (2019). “Measures of Democracy 1810-2018 (dataset). Version 8.0 (2019-06-17).” <URL: http://urn.fi/urn:nbn:fi:fsd:T-FSD1289>.

Wahman M, Teorell J, Hadenius A (2013). “Authoritarian Regime Types Revisited: Updated Data in Comparative Perspective.” Contemporary Politics, 19(1), 19-34. <URL: https://sites.google.com/site/authoritarianregimedataset/data>.

To see these entries in BibTeX format, use ‘print(, 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:

[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.