Working Papers

Artificial intelligence (A.I.) will likely be the most important technology we have ever developed. Technologies such as electricity, semiconductors, and the internet have been transformative, reshaping economic activity and dramatically…

Policymakers often distort goods markets to effect redistribution—for example, via price controls, differential taxation, or in-kind transfers. We investigate the optimality of such policies alongside the (optimally-designed) income tax. In our…

During the Covid-19 pandemic, the United States effectively “spent” about 4 percent of GDP — via reduced economic activity — to address a mortality risk of around 0.3 percent. Many experts believe that catastrophic risks from advanced A.I. over…

Building on the canonical “bottleneck” model of Vickrey (1969), we show that carpooling and road pricing are highly complementary in addressing traffic congestion: they can be much more effective jointly than each one separately, and can improve…

A solution concept that is a refinement of Nash equilibria selects for each finite game a nonempty collection of closed and connected subsets of Nash equilibria as solutions. We impose three axioms for such solution concepts. The axiom of…

Effective information sharing is critical for the success of organizations and governments. Because information that is easy to access is more likely to be adopted, leaders often minimize friction in information delivery. However, one type of…

We use administrative corporate tax data from the IRS to study a particular form of tax avoidance for US multinational corporations (MNCs). This strategy relies on cost sharing agreements (CSAs), which govern joint R&D efforts conducted with…

In this paper, we argue that the New York City congestion pricing plan whose implementation was paused in the summer of 2024 had a major shortcoming: as designed, it would have had a much more severe impact on the drivers of personal vehicles…

We consider the problem of learning personalized decision policies from observational bandit feedback data across multiple heterogeneous data sources. In our approach, we introduce a novel regret analysis that establishes finite-sample upper…

Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms that quantifies the…

We consider the problem of using observational bandit feedback data from multiple heterogeneous data sources to learn a personalized decision policy that robustly generalizes across diverse target settings. To achieve this, we propose a minimax…

Online marketplaces have adopted new quality control mechanisms that can accommodate a flexible pool of providers. In the context of ride-hailing, we measure the effectiveness of these mechanisms, which include ratings, incentives, and behavioral…

One thread of empirical work in social science focuses on decomposing group differences in outcomes into unexplained components and components explained by observable factors. In this paper, we study gender wage decompositions, which require…

The rise of marketplaces for goods and services has led to changes in the mechanisms used to ensure high quality. We analyze this phenomenon in the Uber market, where the system of pre-screening that prevailed in the taxi industry has been…

Has the United States economy become less competitive in recent decades? One might think so based on a body of research that has rapidly become influential for antitrust policy. We explain that the empirical evidence relating to concentration…

Many empirical studies of labor market questions rely on estimating relatively simple predictive models using small, carefully constructed longitudinal survey datasets based on hand-engineered features. Large Language Models (LLMs), trained on…

We formulate the problem of constructing multiple simultaneously valid confidence intervals (CIs) as estimating a high probability upper bound on the maximum error for a class/set of estimate-estimand-error tuples, and refer to this as the error…

Advances in artificial intelligence (A.I.) are a double-edged sword. On the one hand, they may increase economic growth as A.I. augments our ability to innovate. On the other hand, many experts worry that these advances entail existential risk:…

When choosing whether and how much to donate, potential donors often observe a set of default donation amounts known as an “ask string.” In an experiment with more than 400,000 PayPal users, we replace a relatively unused donation amount ($75) on…

This study investigates the labor market value of credentials obtained from Massive Open Online Courses (MOOCs) and shared on business networking platforms. We conducted a randomized experiment involving more than 800,000 learners, primarily from…