Working Papers

In this paper, we examine biases arising in A/B tests where firms modify a continuous parameter, such as price, to estimate the global treatment effect of a given performance metric, such as profit. These biases emerge in canonical experimental…

We consider a retailer running a switchback experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices that consist of a reference price and a few discounted…

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…

We assess Euro Area financial integration correcting for the role of “onshore offshore financial centers” (OOFCs) within the currency union. The OOFCs of Luxembourg, Ireland, and the Netherlands serve dual roles as hubs of investment fund…

This paper investigates decision-making in A/B experiments for online platforms and marketplaces. In such settings, due to constraints on inventory, A/B experiments typically lead to biased estimators because of interference; this phenomenon has…

We study how enhanced internal communication technology influences firms’ internal information production. By developing a model with a headquarters manager and several divisional managers, we formalize two competing economic forces—information…

The representativeness heuristic constitutes a striking departure from Bayesian updating. According to a strong form of the heuristic, agents reverse a conditioning argument: for example inferring that a patient is more likely than not to have a…

AI model alignment is crucial due to inadvertent biases in training data and the underspecified machine learning pipeline, where models with excellent test metrics may not meet end-user requirements. While post-training alignment via human…

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…

Workers who lose their jobs during recessions face strikingly large and persistent declines in their future earnings. Using individual-level administrative data from the United States, this paper shows that an important driver of these costs is…

We analyze the effects of veto players when the set of available policies isn’t exogenously fixed, but rather determined by policy developers who work to craft new high-quality proposals. If veto players are moderate, there is active competition…

Bankruptcy insures consumers against large and unexpected wealth shocks. However, debtors may abuse this insurance. Indeed, close to 20% of consumer bankruptcy filings contain at least one material misstatement. I exploit the conditionally random…

Innovations bring many benefits to society, but they can also bring harm. We study the problem of a regulator deciding whether to approve an innovation where information about the impact of the innovation is held within the firms that are…

In recent years, U.S. public companies have increasingly begun to voluntarily disclose official workforce diversity data (i.e., EEO-1 reports), which they previously only confidentially filed with the U.S. Equal Employment Opportunity Commission…

Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the…

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…

We examine the labor market consequences of the 2020 Regulation S-K requiring human capital disclosure in 10K filings. Using large-sample job-level data, we observe that public firms subject to the regulation increase their disclosure of…

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…