To this end, we use it to study tweets from the 2020 Democratic presidential candidates. One benefit of analyzing texts, as opposed to votes, is that the TBIP can estimate ideal points of anyone who authors political texts, including non-voting actors. Conflict Technology in Cooperation, 2017, (Previous title, Larger groups may alleviate collective action problems), Journal of Public Economic Theory, Vol. Though the model does not analyze their votes or political affiliations, the TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. The Evolution of Egalitarian Sociolinguistic Conventions, with Suresh Naidu and Samuel Bowles, 2017, American Economic Review, Papers and Proceedings, Vol. We demonstrate the TBIP with two types of politicized text data: U.S. In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors.
Lawmakers also give speeches, release press statements, and post tweets. But votes are not the only way to express a political position. Monopsony and Employer Misoptimization Explain Why Wages Bunch At Round Numbers with Arindrajit Dube and Alan Manning. This paper is based on the LEAP lecture I gave in Stellenbosch in 2019, video here. Economic History of Developing Countries. Ideal point models analyze lawmakers’ votes to quantify their political positions, or ideal points. Labour Market Power and American Slavery.
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