arXiv subjects

Bo Cowgill

Publications and source records attributed to Bo Cowgill.

4 recordsLinked to original sources

arXiv2026

Contracting for Decision-Relevant Beliefs

Forecasts guide decisions, but an expert who privately values the decision may misreport beliefs to influence it. We study contracts that jointly screen the expert's belief about a verifiable state and her stake in a binary action. When two distinct stake types can each hold every belief, exact belief-only IC forces the action to ignore the report. Joint screening permits responsiveness but may require implementation rent. For thresholds, minimum rent equals the distribution-weighted positive curvature of the belief-dependent cutoff. Optimal threshold design is rent-adjusted concavification. We give sufficient conditions for unrestricted threshold optimality and bound possible gains when they fail.

econ.TH↗

arXiv2023

Political Power and Market Power

We study the link between political influence and industrial concentration. We present a joint model of political influence and market competition: an oligopoly lobbies the government over regulation, and competes in the product market shaped by this influence. We show broad conditions for mergers to increase lobbying, both on the intensive margin and the extensive margin. We combine data on mergers with data on lobbying expenditures and campaign contributions in the US from 1999 to 2017. We document a positive association between mergers and lobbying, both by individual firms and by industry trade associations. Mergers are also associated with extensive margin changes such as the formation of in-house lobbying teams and corporate PACs. We find some evidence for a positive association between mergers and higher campaign contributions.

econ.GN↗

arXiv2020

The Managerial Effects of Algorithmic Fairness Activism

How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI lowers gender and racial disparities and when engineering investments to address AI fairness are feasible. Emphasis on status quo comparisons yields opposite effects. We also measure the effects of "scientific veneer" in AI ethics arguments. Scientific veneer changes managerial behavior but does not asymmetrically benefit favorable (versus critical) AI activism.

econ.GN↗

arXiv2020

Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics

Why do biased predictions arise? What interventions can prevent them? We evaluate 8.2 million algorithmic predictions of math performance from $\approx$400 AI engineers, each of whom developed an algorithm under a randomly assigned experimental condition. Our treatment arms modified programmers' incentives, training data, awareness, and/or technical knowledge of AI ethics. We then assess out-of-sample predictions from their algorithms using randomized audit manipulations of algorithm inputs and ground-truth math performance for 20K subjects. We find that biased predictions are mostly caused by biased training data. However, one-third of the benefit of better training data comes through a novel economic mechanism: Engineers exert greater effort and are more responsive to incentives when given better training data. We also assess how performance varies with programmers' demographic characteristics, and their performance on a psychological test of implicit bias (IAT) concerning gender and careers. We find no evidence that female, minority and low-IAT engineers exhibit lower bias or discrimination in their code. However, we do find that prediction errors are correlated within demographic groups, which creates performance improvements through cross-demographic averaging. Finally, we quantify the benefits and tradeoffs of practical managerial or policy interventions such as technical advice, simple reminders, and improved incentives for decreasing algorithmic bias.

econ.GN↗