Two Papers Presented by Yusuke Narita, Yale Department of Economics

Event time: 
Thursday, September 23, 2021 - 12:00pm through 1:15pm
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Yusuke Narita, Assistant Professor of Economics, Yale University
Event description: 


Paper 1, “Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules” Abstract: Algorithms produce a growing portion of decisions and recommendations both in policy and business. Such algorithmic decisions are natural experiments (conditionally quasi-randomly assigned instruments) since the algorithms make decisions based only on observable input variables. We use this observation to develop a treatment-effect estimator for a class of stochastic and deterministic decision-making algorithms. Our estimator is shown to be consistent and asymptotically normal for well-defined causal effects. A key special case of our estimator is a multidimensional regression discontinuity design. We apply our estimator to evaluate the effect of the Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than $175 billion worth of relief funding is allocated to hospitals via an algorithmic rule. Our estimates suggest that the relief funding has little effect on COVID-19-related hospital activity levels. Naive OLS and IV estimates exhibit substantial selection bias.

Paper 2, “Curse of Democracy: Evidence from the 21st Century” Abstract: Democracy is widely believed to contribute to economic growth and public health. However, we find that this conventional wisdom is no longer true and even reversed; democracy has persistent negative impacts on GDP growth since the beginning of this century. This finding emerges from five different instrumental variable strategies. Our analysis suggests that democracies cause slower growth through less investment, less trade, and slower value-added growth in manufacturing and services. For 2020, democracy is also found to cause more deaths from Covid-19.

Yusuke Narita is an Assistant Professor at Yale University. He obtained a Ph.D. from MIT and was previously a Visiting Assistant Professor at Stanford University. His research interest centers around data-driven algorithm/mechanism design in policy & business, especially education and health policy. He combines a variety of methods such as causal inference, machine learning, economic theory, and structural econometric modeling. His work has been published in journals including AAAI (Association for the Advancement of Artificial Intelligence), American Economic Review, Discrete Applied Mathematics, Econometrica, NeurIPS (Neural Information Processing and Systems), Management Science, and the Proceedings of the National Academy of Sciences.

This virtual workshop is open to the Yale community. To receive Zoom information, you must subscribe to the Quantitative Research Methods Workshop at this link:

The series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.