The rise of artificial intelligence (AI) and of cross-border restrictions on data flows has created a host of new questions and related policy dilemmas. This paper addresses two questions: How is digital service trade shaped by (1) AI algorithms and (2) by the interplay between AI algorithms and...

Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by borrowers creating online profiles impact both of...

Financial safety nets are incomplete social contracts that assign responsibility to various economic sectors for preventing, detecting, and paying for potentially crippling losses at financial institutions. This paper uses the theories of incomplete contracts and sequential bargaining to...

We present a two-armed bandit model of decision making under uncertainty where the expected return to investing in the "risky arm'' increases when choosing that arm and decreases when choosing the "safe'' arm. These dynamics are natural in applications such as human capital development, job...

In many settings, there are preliminary or interim decision points at which legal cases may be terminated: e.g., motions to dismiss and for summary judgment in U.S. civil litigation, grand jury decisions in criminal cases, and agencies' screening and other exercises of discretion in pursuing...

We study the impact of AI on labor markets, using establishment level data on vacancies with detailed occupational information comprising the near-universe of online vacancies in the US from 2010 onwards. We classify establishments as "AI exposed" when their workers engage in tasks that are...

We build a model of online behavioral manipulation driven by AI advances. A platform dynamically offers one of n products to a user who slowly learns product quality. User learning depends on a product's "glossiness,' which captures attributes that make products appear more attractive than they...

We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on...

Using data collected between August 1999 and January 2000 covering 399 books, including New York Times bestsellers, computer bestsellers, and random books, we examine pricing by thirty-two online bookstores. One common prediction is that the reduction in search costs on the Internet relative to...

Sellers in eBay auctions have the opportunity to choose both a public minimum bid amount and a secret reserve price. We ask, empirically, whether the seller is made better or worse off by setting a secret reserve above a low minimum bid, versus the option of making the reserve public by using it...