Deivis Angeli


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My name is Deivis (pronounced “Davis”) Angeli, and I am PhD Candidate at the UBC’s Vancouver School of Economics. My research interests are in Behavioral, Development, and Labor Economics. I will be on the 2023-2024 economics job market.

Working Papers

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

Abstract | Paper | World Bank Blog Coverage

The ultimate impact of labor market discrimination depends not only on whether employers discriminate but also on jobseekers' responses to (expected) discrimination. We ran three field experiments with 2,200 jobseekers to study these responses in the context of Rio de Janeiro's favelas. In this sample, over 80% of jobseekers overestimate anti-favela discrimination, as we measure it in a new audit study. We partnered with a private firm with real job openings to estimate how expected discrimination affects job application behavior and interview performance. Interview performance is 0.13SD higher for jobseekers randomly told that their interviewer would know only their name, as opposed to their name and address. In contrast, average job application rates are unaffected by (i) removing the need to declare an address at the application stage, and (ii) information that we did not find evidence of discrimination in our audit study. White jobseekers are an exception since removing the need to declare an address increases their application rates. The effect of expected address visibility at the interview also concentrates on white jobseekers. This heterogeneity may be because, with hidden addresses, white jobseekers can pass for non-favela residents. Passing is harder for non-whites (a majority in favelas, but not outside), who might also expect racial discrimination anyway. Our findings show that expected discrimination may affect jobseekers' search, especially in in-person interactions.

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

Abstract | Paper

We study whether tweets about racial justice predict the offline behaviors of nearly 20,000 US academics. In an audit study with 11,500 of those academics, we find that tweeting about racial justice predicts whether academics discriminate against Black students or in favor of minorities. Still, the prediction power of tweets is lower during periods of high social pressure to tweet about racial justice. In the domain of politics, racial justice tweets are more predictive of race-related political tweets than contributions in a race between similar candidates, suggesting that visibility increases informativeness. Finally, most graduate students overestimate average discrimination rates when predicting the audit study results. Most students also mispredict informativeness, more often underestimating than overestimating the correlation between tweeting about racial justice and offline behavior.

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. I document a novel mechanism through which present bias can be adaptive, i.e., it can internalize the social cost of coordinating on a new action, say going from coordinating on using Twitter to using Threads. Agents migrating from Twitter to Threads ignore that their choice imposes negative externalities on those still using Twitter. So, to achieve efficiency, regular exponential discounters should ask for a higher relative quality of Threads before adopting it. In turn, present biased agents overvalue the externalities they currently receive from Twitter since externalities from Threads can only come in the future, after others adopt it. Hence, present bias leads agents to ask for more quality before migrating to Threads, preventing paths of inefficient coordination. Furthermore, small amounts of present bias always prevent society from taking inefficient paths.

Work in Progress

Female Pradhan Autonomy


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.

Effects of Social Media Use on Scientific Production, with Matt Lowe


Abstract: How does social media use affect scientific productivity? While time spent on social media crowds out time spent writing, social media has the potential to connect researchers and generate debate from afar, which can be especially beneficial for minority researchers, and for interdisciplinary collaborations. This research project uses a differences-in-differences approach to explore the effect of joining Twitter on scientific productivity. Our sample will include 28,000 research-active academics from top-150 US institutions with a Twitter account as of March 2022. We will 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.