Research

Decision-makers increasingly rely on scores—such as interview ratings or test results. Over 88% of companies use algorithms in hiring. However, the same score can signal ("map" to) different types across different groups. I introduce Cross-Group Mapping Bias - the incorrect statistical discrimination that occurs when decision-makers naively pool scores to make decisions. These biases lead to inefficiency and inequity. Using new methods from copula theory, I develop a general framework to identify and correct inequality between groups and biased interpretations of data. In two incentivized experiments on MTurk (with 1680 and 808 participants from India), I find that participants can learn about conditional productivity measures when only one group is present. Nonetheless, they cannot learn about group differences in the meaning conveyed by those variables when two or more groups are present. This leads untreated participants to make correct decisions only 53% of the time and hire men 40% more often than they should. In comparison, a Bayesian decision-maker would have chosen correctly 67% of the time. Providing participants with more information does not improve decisions. Actively debiasing interview scores reduced gender bias by 75% and improved accuracy by three percentage points. Hiding candidate gender ("gender blinding") worsened efficiency. This paper demonstrates that organizations can make their hiring systems fairer and more effective by adjusting how they interpret measurable signals, such as interview scores, across different groups. I demonstrate that noisy proxies commonly used for policy analysis exhibit distributional features that also lead to misevaluation, resulting in an underestimation of high-ability women and an overestimation of low-ability women.

This paper bridges the gap between models of discrimination and policing. Using a series of conditional likelihood hit-rate tests on arrest, conviction, and exoneration data, I find that the American judicial system is prejudiced against Black Americans, and American police officers are more prejudiced than the judicial system. Innocent Black people are more likely to be arrested and convicted than White people. Further, prejudice reduces the quality of police reporting and increases both type one and type two errors in judicial decision-making. This prejudice by police officers and in criminal courts costs the US over $94.4-124.4 billion annually. 

Men exhibit the classic self-serving bias, with nearly 80% attributing high pay to high performance and low income to chance. Women display the opposite behaviour, attributing high pay to chance and low pay to their performance, even controlling for actual performance. In contrast to existing theories, men’s self-serving bias leads them to be more generous than their counterfactual redistributive behaviour. Women exhibit a type of certainty aversion and redistribute less when they know with certainty that their payment was due to chance. 

Despite abundant solar resources and heavily subsidized financing, solar panel adoption in Yemen remains puzzlingly low, with minimal loan take-up even when panels offer over 100% annual returns. This study investigates the behavioral barriers preventing efficient energy technology adoption in fragile, low-income settings with limited financial markets.

We develop a theoretical framework decomposing technology adoption decisions into distinct behavioral frictions—risk aversion, loss aversion, debt aversion, and present bias—examining how these interact with credit constraints. Using a survey of 1,422 Yemeni adults across three governorates, we employ hypothetical vignettes and discrete choice experiments to separately identify and quantify each behavioral parameter.

By quantifying the relative importance of each friction and their interactions, this research provides actionable guidance for designing financial products and development interventions that can overcome behavioral barriers to technology adoption in contexts where poverty, conflict, and limited financial infrastructure compound the challenges of sustainable development. 

The U.S. Defense Department budget for the 2023 financial year was $797 billion. Total U.S. Military Budget Resources for the 2025 Fiscal Year exceed $1.5 trillion (15.5% of GDP). Foreign military aid exceeds $12 billion a year. The United States comprises over 40% of global military spending. Votes on military intervention, military expenditures, and veteran benefits are some of the most impactful mechanisms through which politicians shape foreign and domestic policy. Policy analysts, economists, and politicians have assorted views on the efficacy of this spending and what costs it imposes.

Similarly, attitudes towards military spending are heterogeneous within and across parties. Given the extensive global impacts of military expenditures, understanding the drivers of attitudes toward war is essential. Politicians’ own military experience and the war experiences of others in their cohorts are plausibly one of these determinants.

Do people whose peers (or themselves) are drawn into conflict have differences in their support for military interventions, military expenditures, or veteran benefit policies? What mechanism drives this? We will explore whether cohort effects shape the policy preferences of politicians by changing who becomes a politician or whether cohort effects influence otherwise similar politicians to have different attitudes regarding war. This project primarily focuses on having Americans classify historical government decisions to evaluate political behaviour.

Does cohort exposure drive politicians of different characteristics to contest elections or get elected? In addition, we will examine the role of a transformation in preferences arising from cohort-level exposure to war and whether this varies according to the public’s view of the war.