Record Details

Estimating ideal points from UN General Assembly sponsorship data

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Field Value
 
Title Estimating ideal points from UN General Assembly sponsorship data
 
Identifier https://doi.org/10.7910/DVN/E6I2AX
 
Creator Mesquita, Rafael
Martins, Rodrigo
Seabra, Pedro
 
Publisher Harvard Dataverse
 
Description The United Nations General Assembly (UNGA) represents a microcosm of global politics that offers a valuable snapshot of interstate relations and state preferences. In this context, roll-call votes and measures of voting affinity often receive the bulk of scholarly attention. However, even though techniques such as ideal point estimation have grown more sophisticated over time when applied to voting data, they remain grounded by an original selection bias that discards 2/3 of the UNGA yield. This share of disregarded output can prove highly informative if drafting and sponsorship procedures receive a closer look instead. This research note applies ideal point estimation to UNGA sponsorship data for the first time for every member from 2009 to 2019. It advances a cutting-edge approach to better estimate state preferences over a contested policy space, while correcting for the narrow focus of previous UNGA analyses on voting data. The results detect an underlying issue space that bears external validity with the inclination of states towards multilateralism.
 
Subject Social Sciences
 
Contributor Interactions, International