The Correlates of Dyadic Voting Similiarities in the UN General Assembly for South Korea

A data set on dyadic voting similarity for South Korea in relation to other states, from 1991 to 2022.

Usage

rok_unga

Format

A data frame with the following variables.

ccode1: a numeric vector, and constant, identifies the Correlates of War state code for South Korea (732)
ccode2: a numeric vector for the Correlates of War state code for the other state in the dyad
iso3c: a three-character ISO code corresponding with the Correlates of War state code for ccode2
year: a numeric vector for a year
agree: the percentage of the time South Korea and the other state in the dyad agreed on a vote in a given year
v_agree: the percentage of the time South Korea and the other state in the dyad agreed on a vote in a given year, as calculated by Voeten et al. in their data
kappa: weighted Cohen's kappa for dyadic foreign policy similarity as derived from the UN voting data
ip1: the ideal point estimate for South Korea for a given year, as derived from UN voting data
ip2: the ideal point estimate for ccode2, as derived from UN voting data
ipd: the absolute distance between ip1 and ip2
gdppc1: estimated GDP per capita in 2015 USD for South Korea in the referent year
gdppc2: estimated GDP per capita in 2015 USD for ccode2 in a given year
v2x_polyarchy1: the Varieties of Democracy estimate for the "polyarchy" for South Korea in the referent year
v2x_polyarchy2: the Varieties of Democracy estimate for the "polyarchy" for ccode2 in a given year
xm_euds1: Xavier Marquez' estimate for the extended Unified Democracy Score for South Korea in the referent year
xm_euds2: Xavier Marquez' estimate for the extended Unified Democracy Score for ccode2 in a given year
capdist: the distance between Seoul and the capital of ccode2 in the year

Details

Voeten et al's codebook cautions that their agreement variable is there for comparison and should not be used for a serious analysis of dyadic foreign policy similarity. The agree variable I calculate is based on all votes, whereas (I think) Voeten et al. exclude amendments and votes on paragraphs.

Cohen's (weighted) kappa is suggested by Haege (2011) for use measuring dyadic foreign policy similarity. This measure is likewise calculated by me for all votes. I forget how Haege (2011) does this for his calculations and if he is excluding votes on amendments or paragraphs. Its interpretation differs from how one might use the ideal point distance variable. This is a chance-corrected correlation. Higher values indicate more similarity whereas higher values in the ideal point distance variable communicate more dissimilarity.

GDP per capita include some imputations by way of a semiparametric Bayesian Gaussian copulas. This prominently concerns Venezuela. Data are otherwise derived from the World Banks' open data.

Xavier Marquez' "extended Unified Democracy Scores" approximate a normal distribution with a standard deviation of 1. Invoking pnorm() on a particular estimate provides a kind of probabilistic assessment of whether the observation in question is a democracy. In both the Varieties of Democracy estimate and the Marquez estimate, higher values = "more democracy". See also: the Lipset59 documentation in this same package.

Capital-to-capital distance is calculated using the Vicenty method ("as the crow flies"), and is done by way of a peacesciencer call and its add_capital_distance() function. There are unusual cases where a capital moved (i.e. Kazakhstan, Myanmar, Nigeria). In those cases, the capital on Jan. 1 of the given year is treated as the capital.