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This dataset contains ideal point estimates based on voting behavior in the United Nations General Assembly. This is the first version where the ideal point estimates are based on years rather than UNGA sessions. The reasons for this are that researchers in practice virtually always use years as the basis of analysis and that UNGA sessions increasingly spill over into the following year and are held in special emergency sessions on issues such as the Gaza and Ukraine. <br> <br> There are two types of ideal point estimates: <br> • idealpointfp: ideal point estimates based only on votes on the final passage of resolutions (including failed votes). These are now available from 1946-2024 in IdealPointsJuly2025.tab. These estimates are updated most frequently as the raw data can readily be found online. <br> • Idealpointall: ideal point estimates based on all votes, including votes on paragraphs, motions, and amendments. These votes are based on more data and thus should be more precise. One word of caution is that in some years this means that there are very large numbers of votes on a specific issue, such as the war in Gaza. <br> <br> The correlation between these ideal points is .9846 but there could of course still be some important differences. <br> <br> The data also includes idealpointlegacy, which is based on sessions (all votes). The correlation with idealpointall is .9877. <br> <br> Aside from the 2024 final passage votes, the raw UN voting data are from the UNGA-DM Database: https://unvotes.unige.ch/ <br> <br> Citation: Fjelstul, Joshua, Simon Hug, and Christopher Kilby. "Decision-making in the United Nations General Assembly: A comprehensive database of resolution-related decisions." The Review of International Organizations (2025): 1-18. <br> <br> The ideal point estimates are based on the methodology described in: <br> <br> Citation: Bailey, Michael A., Anton Strezhnev, and Erik Voeten. 2017. Estimating dynamic state preferences from united nations voting data. Journal of Conflict Resolution 61 (2): 430-56.