Past Presentations

Presenter

Topic

Date

Eric Wilken

Title: Queer Leadership and Heteronormative Beliefs: Do stated preferences impact leader effectiveness?

Abstract: The stereotypical leader is perceived as dominant and possessing masculine traits and physical features. Queer leaders are not seen as such which leads them to be perceived as ineffective leaders. The scale of this problem depends, in part, on whether it is a follower’s group identity or their beliefs –independent of group identity—which explain this biased perception of queer leaders. The goal of this project is to determine whether masculine leader stereotypes are driven by heteronormative beliefs and to what extent these beliefs impact the ability for leaders to motivate their followers. I propose an experiment using both stated and revealed measures to investigate the moderating effect of such beliefs on perceived leader effectiveness.

October 6, 2023
Edder Martinez Lazo
Title: Wealth distribution and long-run expectations of return on equity October 13, 2023
Colleen O’Briant

Title: The Econometrics of Inverse Reinforcement Learning

Abstract: The recent discourse on Responsible AI surfaces an urgent need for transparency and trust in AI systems. One way to foster that transparency is to bridge the gap between Machine Learning and Econometrics. In this paper, I bridge the gap between two methods to estimate models of Dynamic Discrete Choice: Nested Fixed Point (Rust, 1987) from the Econometrics literature and Max-Margin Inverse Reinforcement Learning (Ng and Russell, 2000 and Abbeel and Ng, 2004) from the AI literature.

October 20, 2023
Brock Wilson Title: Disability-based affirmative action and hiring October 27, 2023
Jose Rojas Abstract: Trade liberalization, through Free Trade Agreements, is inherently a political process, with political consequences. I analyze whether Costa Rica (CRC), which approved CAFTA-DR through a democratic referendum, has become more politically polarized as a consequence of trade liberalization with the US. November 3, 2023
Brietta Russell Abstract: COVID seems to have exacerbated the housing crisis in several ways. I look at how varying restriction intensity across cities affects today’s Airbnb supply. November 3, 2023
Kyu Matsuzawa Abstract: In this paper, we both descriptively and causally investigate whether in person schooling leads to increased youth violence. Using various datasets, we find that in person schooling leads to a 27% higher rate of youth violence, consistent with the hypothesis of concentration effect. We document that this increase may not be driven by changes in crime reporting. We also find some very obvious results: Large heterogenous treatment effects on crimes happening on weekdays and crime happening on school property. We conclude that smaller class size or implementation of strong anti-bullying laws may help mitigate some of the negative short-term effects of school. November 17, 2023
Emily Arneson Title: Sports Betting Legalization Amplifies Emotional Cues December 1, 2023
Anne Fournier As more Americans seek treatment for mental illness, and expenditures on mental health care rise, understanding the returns to mental health care is becoming increasingly important to health policy. However, the effects of mental health treatment — particularly for subgroups, including those at higher risk of adverse mental health outcomes—are often difficult to measure. Using National Survey on Drug Use and Health data from 2002-2019 and a novel approach to identification that exploits bunching on the treatment variable, we provide some of the first causal evidence in a nationally-representative sample of the impact of mental health provider visits on mental distress. Focusing on heterogeneity by sexual orientation, we find that a marginal increase in mental health treatment has little effect on mental health (as proxied by the Kessler-6 distress scale) for straight-identifying survey respondents but significantly reduces lesbian, gay, and bisexual respondents’ reported mental distress. We also use a test of exogeneity for models using bunched data to show that the controls commonly used in studies employing observational data to estimate the effects of mental health treatment are not sufficient to eliminate selection bias January 19, 2024