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The American Economic Association awarded Parag Pathak the 2018 John Bates Clark Medal for his research on the impacts of educational policies. Both the theory and the empirical research take the constraints facing administrators seriously. As a result, Parag’s research led directly to educational reforms in many large US cities and abroad. The leading example is Parag (and co-authors’) research on school assignment mechanisms that led many school districts to institute fairer and more efficient procedures for allocating students to schools. The institutional detail Parag learned in working on the assignment problem led to innovative empirical work on the impacts of different types of schools, most notably of charters, which was suggestive of the characteristics of both successful schools and of the types of students who gained from being enrolled in them. Using the data generated by the new assignment rules, his recent work provides complete frameworks for the quantitative analysis of the benefits of different assignment mechanisms and has measured those benefits in New York high schools.

The behavior of artificial intelligence (AI) algorithms is shaped by how they learn about their environment. We compare the prices generated by AIs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AI only learns about the return from the action it took. Synchronous learning occurs when the AI conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AI, (perfect) synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AI places a weight on future profits. Lastly, we investigate performance differences between offline and online play. [ABSTRACT FROM AUTHOR]

One goal of the field of industrial organization is to predict the response of markets to environmental or policy changes. A market, for our purposes, is a collection of firms [...]

We show that moment inequalities in a wide variety of economic applications have a particular linear conditional structure. We use this structure to construct uniformly valid confidence sets that remain computationally tractable even in settings with nuisance parameters. We first introduce least-favorable critical values which deliver non-conservative tests if all moments are binding. Next, we introduce a novel conditional inference approach which ensures a strong form of insensitivity to slack moments. Our recommended approach is a hybrid technique which combines desirable aspects of the least favorable and conditional methods. The hybrid approach performs well in simulations calibrated to Wollmann (2018, American Economic Review , 108 , 1364–1406), with favorable power and computational time comparisons relative to existing alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we consider how rich sources of information on consumer choice can help to identify demand parameters in a widely used class of differentiated products demand models. Most important, we show how to use "second-choice" data on automotive purchases to obtain good estimates of substitution patterns in the automobile industry. We use our estimates to make out-of-sample predictions about important recent changes in industry structure.