vci {demcon}R Documentation

Vertical Constraints Index (VCI)

Description

Calculate an the vertical constraints index defined by Fjelde et al. (2021).

Usage

vci(vdem, append = TRUE)

Arguments

vdem

A data.frame of V-Dem data containing the required variables.

append

Logical indicating whether to return the original data.frame with vci and modified modified v2xel_frefair index (v2xel_frefair_adj). If set to FALSE, the function returns a numeric vector of VCI scores.

Details

Source

The vertical constraints metrics specified in this function were developed by Fjelde et al. (2021) in:

Fjelde, H., Knutsen, C. H. & Nygård, H. M. 2021. Which Institutions Matter? Re-Considering the Democratic Civil Peace. International Studies Quarterly 65, 223–237, doi:10.1093/isq/sqaa076.

The Index

The Vertical constraints index (VCI) represent civil liberties attributed to the general populace the constrain executive actions. These include suffrage, the presence of elections that appoint executive officials, freedom of association, freedom of expression, and the presence of clean and fair elections.

Methods

At it's core, VCI is a multiplicative aggregation of 5 V-Dem variables designed to measure suffrage, elected officials, freedom of association, freedom of expression and clean elections, (v2x_suffr, v2x_accex, v2x_frassoc_thick, v2x_freexp_thick, v2xel_frefair). However, the final component (v2xel_frefair) is a composite index developed with a Bayesian factor analysis of 8 other V-Dem indicators (v2elembaut, v2elembcap, v2elrgstry, v2elvotbuy, v2elirreg, v2elintim, v2elpeace, v2elfrfair), of which, the authors adapted by purging 2 of the components representing government intimidation or violent actions (v2elintim, v2elpeace) to prevent potential endogeneity in their regressions for the onset of conflict; i.e. you don't want to predict the onset of conflict with and independent variable that is, in-part, composed of measures of conflict.

Although the original v2xel_frefair composite index was developed using V-Dem's Bayesian Factor Measurement Model, the VCI adapted for this study took a simpler approach. In footnote 12, the authors state that the modified composite index was created by averaging the 6 non-violent indicators of v2xel_frefair (v2elembaut, v2elembcap, v2elrgstry, v2elvotbuy, v2elirreg, v2elfrfair). Although not explicitly stated, it's presumed that the average for these 6 indicators was converted to a 0-1 scale using "...the cumulative distribution function of the normal distribution". This is the standard V-Dem procedure for their 0-1 interval indices as stated on page 7 of the V-Dem V11.1 Methodology handbook.

Lastly, the VCI constructed for this manuscript was carried out using the V-Dem 7.1 dataset. Since that time (current version is V11.1), 2 of the indicators used in the VCI calculation have been renamed and slightly altered:

  1. v2x_freexp_thick was converted to v2x_freexp_altinf starting with version 11. The sub-components of this composite index were altered slightly, but they still encompass the same concepts of censorship in media.

  2. v2x_accex was renamed v2x_elecoff starting with version 8. This was due to changes in the aggregation method for calculating the composite index. Although the conceptual design for the composite indicator has not changed, the aggregation formula is more complex and consists of 20 indicators (opposed to 10 for the original v2x_accex).

Value

A data.frame with a modified v2xel_frefair index (v2xel_frefair_adj) and the calculated VCI (vci).

See Also

hci(), vdem_vci_hci

Examples


vdem <- demcon::get_vdem()

# Appended to the input dataset

vdem.dat<-demcon::vci(vdem, append = TRUE)

# Just return the numeric vector

vci<-demcon::vci(vdem = vdem, append = FALSE)


[Package demcon version 0.3.0 Index]