Modeling America's bipolarizing political system. Suggesting new statistical methods to analyze American political system and beyond. Is American Democracy deteriorating?
We will discuss various applications of machine learning and data science tools in political science
• International Trade with Big Data Reading: – C. A. Hidalgo, B. Klinger, A.-L. Barab´asi, R. Hausmann. “The Product Space Conditions the Development of Nations.” Science 317.5837 (2007): 482-487 • Lobbying and Campaign Contribution Reading: – In Song Kim. “Political Cleavages within Industry: Firm-level Lobbying for Trade Liberalization.” American Political Science Review, 111.1: 1-20. – Stephen Ansolabehere, John M. de Figueiredo, and James M. Snyder. “Why is There so Little Money in U.S. Politics?” Journal of Economic Perspectives, 17.1 (2003): 105-130 • Identifying Behavioral Patterns using Massive Data Reading: – Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Ramachandran, V., Phillips, C., and Goel, S. (2017). “A large-scale Analysis of Racial Disparities in Police Stops across the United States.” arXiv preprint arXiv:1706.05678. • Measuring Ideological and Political Preferences using Social Network Data Reading: – Robert Bond and Solomon Messing. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109.1 (2015): 62-78. – Pablo Barber´a “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23.1 (2014): 76-91 • What do Politicians Do? Reading: 10 – Justin Grimmer, Solomon Messing, and Sean Westwood. “How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation.” American Political Science Review, 106.4 (2012), 703-719 – Justin Grimmer. “Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation.” American Journal of Political Science, 57.3 (2013), 624-642 • Big Administrative Data: Promises and Pitfalls Reading: – Connelly, R., Playford, C.J., Gayle, V., Dibben, C., 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research, Special issue on Big Data in the Social Sciences 59, 1–12 – Kopczuk, W., Saez, E., Song, J., 2010. “Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937.” The Quarterly Journal of Economics 125, 91–128. – Jens Hainmueller and Dominik Hangartner, 2013. “Who Gets a Swiss Passport? A Natural Experiment in Immigrant Discrimination.” American Political Science Review 107.1, 159–187. • Machine Learning Algorithms in Society Reading: – Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133 (1):237–93