DOI: 10.5061/dryad.np5hqc030
title: The principal components of electoral regimes

description: A critical issue for society today is the emergence and decline of
 democracy worldwide. It is unclear, however,  how democratic
 features, such as elections and civil liberties, influence this change.
 Democracy indices, which are the standard tool to study this question, are
 based on the a priori assumption that improvement in any individual
 feature strengthens democracy overall. We show that this assumption does
 not always hold. We use the V-Dem dataset for a quantitative study of
 electoral regimes worldwide during the 20th century. We find a so-far
 overlooked trade-off between election capability and civil liberties. In
 particular, we identify a threshold in the democratisation process at
 which the correlation between election capability and civil liberties
 flips from negative to positive. Below this threshold we can thus
 clearly separate two kinds of non-democratic regimes: autocracies that
 govern through tightly controlled elections and regimes in which citizens
 are free but under less certainty -- a distinction that existing democracy
 indices cannot make.
description: We use version 12 (2022) of the V-Dem data
 (https://www.V-Dem.net) and apply standard principal component analysis
 (PCA). Following standard procedure, we normalized each V-Dem variable
 (i.e. centered it to a mean of zero and rescaled it to a variance of one)
 prior to performing PCA. For better readability of the plots, we rescaled
 all principal components uniformly such that the first component has a
 maximum absolute value of one (i.e. its values are bounded by [-1,1])
 while preserving the mean of zero for all components. We further
 re-oriented each component such that its strongest loading is
 positive.
description: # The principal components of electoral regimes [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.np5hqc030) ## Description of the data
 and file structure We provide two .csv files containing the results of the
 principal component analysis that we applied to the V-Dem dataset. The two
 files contain the following data: ### pca\_loadings.csv This file contains
 the so-called loadings of the 24 principal components (PC1-PC24, in
 columns) in terms of the 24 V-Dem variables (in rows). The table of
 loadings can be understood as a rotation matrix for transforming the
 original coordinate system of the V-Dem variables into the PCA coordinate
 system, i.e., each entry is a linear coefficient representing the
 significance of a V-Dem variable for a principal component. The loadings
 already reflect the rescaling of the principal components. A detailed
 description of each of the 24 V-Dem variables can be found in the V-Dem
 Codebook https://v-dem.net/documents/38/V-Dem_Codebook_v14.pdf. ###
 selected\_components.csv This file contains a time series of selected
 variables on a country level from 1900 to 2021. Explanation of each
 column: * country_name - official name of the country * country_text_id -
 international unique three-letter abbreviation of the country name * year
 - year of assessment * EDI - Electoral Democracy Index: an established
 index to compare the "democraticness" of countries, curated by
 experts. * PC1 - the first principal component of our PCA, i.e., the
 linear combination of the 24 V-Dem variables that exhibits the largest
 variance. * PC2 - the second principal component of our PCA, i.e., the
 linear combination of the 24 V-Dem variables that exhibits the
 second-largest variance. * PC2_pos - the positive sub-component of PC2,
 i.e., the loading-weighted sum of V-Dem variables that have a positive
 loading in PC2. * PC2_neg - the negative sub-component of PC2, i.e., the
 loading-weighted sum of V-Dem variables that have a negative loading in
 PC2. ## Sharing/Access information Data was derived from the
 "Varieties of Democracy" (V-Dem) project, version 12 (2022):
 https://www.V-Dem.net.