The Correlates of Democratic Backslides

Published: 5 April 2024| Version 1 | DOI: 10.17632/dfsp6f5448.1
Gennadii Iakovlev


I merged ten databases grouping them by country and year standardizing the country names using the “countrycode” package, and manually re-coding deviating country names. The two core datasets are produced by the Varieties of Democracy (V-Dem) project: the main V-Dem v14 dataset and the Episodes of Regime Transformation (ERT) dataset. Third, the data on inequality comes from the World Inequality Database (WID). Compared to the others, this indicator has the lowest percentage of missing values compared to other known data and has the data for the earliest years. Nevertheless, even in this case most of the data was only present from the year 1985, which forbade me to compare post-WWII and post-Cold-War cases of democratic backsliding. The 26 percent of missing observations were filled with values predicted via bootstrapping accounting for the cross-sectional nature of data using the “Amelia” package in R based on several dozens of other socioeconomic indicators from the V-Dem Background Factors block. Fourth, I added the ParlGov dataset that approximates the ideological polarization between political parties in elections on the right-left scale. In that case, the database is limited to the most developed stable democracies and only has data for the election years. At the same time, its only direct competitor, Manifesto, has even more missing data. To access the data on societal affective polarization I used the dataset initially developed by Reiljan and later updated by Orhan that aggregates the existing survey data. Due to its origins, it is limited to 159 observations, two to three per country, which is devastating for the multilevel models. I combined the volatility indexes from five different regional datasets: one focused on the entire world with a particular focus on Latin America offered by Mainwaring and colleagues, Chiaramonte and Emanuele for Western Europe, Bertoa and Enyedi for geographic Europe, and Bogaards for Africa. In cases of overlaps between the datasets, the preference was given in the order they are mentioned. If the elections were regular, I carried volatility scores forward until the next elections. Table A3 on the data availability and the heatmap (Figure A1) are present in the online appendix. Even though the entrance of new parties into the playing field (known as extra-system volatility or type-B volatility) is theoretically more appealing than the overall volatility, these studies use different thresholds of what is considered to be the “system”. For instance, it varies from 2% for Mainwaring and Zoco and 1% for Chiaramonte and Emanuele.


Steps to reproduce

Download the data from the original sources, and run the R script.


Central European University


Political Economy, Political Methodology, Democratization, Democracy