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A complex tax code places burdens on taxpayers to navigate a myriad of rules, as well as on bureaucrats to correctly enforce these rules. Often, tax code complexity is the unwanted collateral of many laws enacted over prolonged periods that make sense at the time but lack overall coherence. In this talk, Verhagen gives insight into efforts to algorithmically develop an alternative tax system to reduce the complexity of the Dutch income tax code. A number of distinct objectives are discussed through which such an alternative system can be evaluated, some of which are directly in conflict with one another. More broadly, a general framework for thinking about complexity reduction in rules systems is presented that goes beyond the Dutch income tax code.

Bio:

Mark Verhagen is a visiting postdoctoral research associate from the University of Oxford, from where he also received his Ph.D. in 2022. His research focuses on the improvement of conventional quantitative modeling in the social sciences through computational methods and pattern recognition in large samples. Relatedly he has worked extensively with population-level registry data in The Netherlands, emphasizing identifying heterogeneity in associations. Substantively, his research primarily concerns questions of inequality, spanning educational, health and legal outcomes. Verhagen also has experience as a data scientist and model auditor outside of academia, working for governments, think tanks, consulting firms and start-ups.

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