My name is Deivis (pronounced “Davis”) Angeli, and I am postdoc at the Global Talent Lab. I obtained my PhD in Economics from UBC in 2024. Next spring, I will be visiting Dartmouth College’s Economics Department as a short-term scholar.

I am interested in Behavioral, Development, and Labor Economics. My research agenda focuses on understanding what barriers prevent people from realizing their full potential.

Working Papers

Expected Discrimination and Job Search, (Job Market Paper) with Ieda Matavelli and Fernando Secco

Abstract | Paper | World Bank Blog Coverage

The impacts of labor market discrimination depend not only on whether employers discriminate, but also on jobseekers' responses to expected discrimination. To study these responses, we ran a set of field experiments with over 2,000 jobseekers in Rio de Janeiro's favelas, where most jobseekers overestimate anti-favela discrimination, as we measure it in an ancillary study. Jobseekers who were randomly told that their interviewer would know their name and address believed that their interview performance was 0.17SD worse than those who were told that the interviewer would only know their name. Focusing on jobseekers who expected at-or-above median discrimination, we find that not only did they believe that they performed worse when told that interviewers would know their addresses, their interviewers also rated them worse by 0.22SD. Removing the need to declare an address at the application stage increases interview attendance only for white jobseekers, likely because they can pass for non-favela residents and ignore racial discrimination. Our findings show that expected discrimination can create inefficiencies in matching, especially through effects on interview performance, and that correlated sources of discrimination like race mediate these effects.

Do Virtue Signals Signal Virtue? with Matt Lowe and The Village Team

Abstract | Paper

We study whether, and when, tweets about racial justice predict costly race-related behaviors. Individuals that tweet about racial justice are less likely to discriminate against Black individuals and more likely to donate to a civil rights organization. However, we find three pieces of evidence that higher signalling stakes reduce this informativeness. First, racial justice tweets became almost completely uninformative during the aftermath of the murder of George Floyd, when silence on racial justice became more scrutinized. Second, the informativeness of tweets is driven by low-visibility types of tweets, like retweets. Conditional on retweets, original tweets about racial justice are completely uninformative. Third, racial justice retweets are somewhat less informative of donation behavior than private statements of support for racial justice efforts. Collectively our results show that morally-charged statements on social media range from fully uninformative to highly informative, depending primarily on signalling stakes.

Dynamic Coordination with Network Externalities: Procrastination Can Be Efficient

Abstract | Paper

How does present bias affect welfare when agents want to coordinate over time? To answer that, I analyze a dynamic coordination model under quasi-hyperbolic discounting, documenting a novel mechanism through which present bias can be adaptive. The key trade-off for agents in dynamic coordination models is whether to follow a currently-popular standard (receiving substantial network externalities from other current users) or to adopt a new standard with a higher intrinsic quality, hoping that others will follow. Guimarães and Pereira (2016) showed that exponential discounters begin adopting the new standard when its quality is too low to justify the transition costs, since an early adopter does not account for the negative externality caused on those who stay in the old standard. This paper shows that present bias can act as kludge, since it makes agents overweight their individual transition costs, shifting behavior in the same direction that the planner would suggest.

Work in Progress

Effects of Social Media Use on Scientific Production

Abstract

Abstract: How does social media use affect scientific productivity? While social media may reduce research time, it could also enhance productivity by facilitating remote and interdisciplinary collaborations, especially for researchers with initially small collaboration networks. I use a matched differences-in-differences approach to explore the effect of joining Twitter on scientific productivity across US academia. My sample includes 28,000 research-active academics from top-150 US institutions, in all fields of study. I estimate the causal effect of joining Twitter on the number and quality of publications, general citations, citations to papers published before joining Twitter, the geographic distance and disciplinary breadth of co-authorship networks, and horizontal and vertical job transitions.

Female Pradhan Autonomy

Abstract

Abstract: Many countries have implemented electoral gender quotas to improve representation in public decision-making. At the same time, verifying whether such policies are successful -- and not just generating figureheads for male family members, for instance -- is hard, especially at the local level. I propose a novel and scalable measure of female leader autonomy for village leaders in India: whether the female leader owns the phone number used to communicate with higher levels of government. I then explore whether past quotas for females lead to more autonomy and whether autonomy predicts public policy outcomes.