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Columbia University’s Data Science Institute Presents:
DATA SCIENCE DAY

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Wednesday, April 6 • 2:00pm - 4:30pm
Tax Laws and Tax Capacity: A Machine-Learning Approach [P30]

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I use tools from natural language processing to construct a high-dimensional representation of tax code changes from the text of 1.6 million statutes enacted by state legislatures since 1963. A data-driven approach is taken to recover the effective tax code – the set of legal phrases in tax law that have the largest impact on revenues, holding major tax rates constant. Exogenous variation in tax legislation from judicial districts is used to capture revenue impacts that are solely due to changes in the tax code language, with the resulting phrases providing a robust out-of-sample predictor of tax collections. I then test whether political parties differ in patterns of effective tax code changes when they control state government. Relative to Republicans, Democrats use revenue-increasing language for income taxes but use revenue-decreasing language for sales taxes – consistent with a more redistributive fiscal policy – despite making no changes on average to statutory tax rates.

Demo/Poster Presenter
avatar for Elliott Ash

Elliott Ash

PhD Candidate in Economics, Graduate School of Arts and Sciences
Elliot Ash is a Ph.D. student in Economics. His research interests include Political Economy, Law and Economics, Public Economics, Applied Microeconomics, Natural Language Processing, Machine Learning. | |  


Wednesday April 6, 2016 2:00pm - 4:30pm
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040