Working papers
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"Language, Identity and Ethnic Cohesion in Schools" [Link]
(with Pepi Pandiloski)
Job Market Paper
Abstract
Distinct group identities can create barriers for trade, investment in public goods, and social learning. This paper studies how integration impacts productivity and cohesion in settings with broad linguistic diversity, where language differences may deepen the divides created by group identities. We randomly assign high school students to same- or different-ethnicity partners to play a cooperative video game in Macedonia, where students are segregated by ethnicity. We measure productivity using video game scores and assess social cohesion with two measures: the willingness to pay for a same-ethnicity partner (via an adaptive choice experiment) and ingroup bias (via a dictator game). We find that inter-ethnic contact improves social cohesion without affecting productivity. To examine the role of language, we exploit random variation in the language skills overlap within pairs. We find that shared language skills are associated with greater inter-ethnic cohesion gains and lower non-pecuniary costs. These results reflect both a match effect (i.e., sharing a language is beneficial) and a correlation between language skills and positive potential outcomes. Additionally, cross-ethnic pairs match on one of the partner's home languages or a third neutral language. Cohesion gains are largest for participants who match on their own home language; if anything, matching on a neutral language is worse than matching on the partner's home language.
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"Social Learning in Segregated Networks" [Link]
[Code]
(with Pepi Pandiloski)
Abstract
Segregation within a population –- whether driven by explicitly segregationist policy, individuals' preferences and biases, or the market –- is ubiquitous in societies around the world. How does segregation influence social learning? On one hand, if groups can stand to learn from one another, segregation may harm learning by preventing information flow. On the other hand, if individuals learn better from in-group members, segregation may lead to more learning. Using a model of network formation and social learning, we formalize these tensions into two hypotheses. To test them, we design a social learning game for groups of 6 players and implement it in ethnically segregated schools in Macedonia. In the computer game, 3 students of 2 distinct ethnicities need to guess an unknown binary state of the world. The experiment manipulates the parameters of the model: private initial signals, the network on whichplayers observe each other, and bias, manipulated at the group level by randomizing types of players thatcompose the 6-person group. First, we find evidence that bias decreases the cost of segregation. Integration leads to more learning if most players in the group have a different-ethnicity friend; conversely, segregation leads to more learning if most players in the group have only same-ethnicity friends. Second, in games when one ethnicity gets mostly incorrect signals and can stand to learn from the other ethnicitythat gets mostly correct signals, integration leads to more learning than segregation, but this finding isstatistically insignificant. We fit the experimental data to the model to estimate the bias parameter andfind that students are 45% more biased against different-ethnicity players than same-ethnicity players. Toillustrate the implications of our results in the context of segregated high schools, we conduct counterfac-tuals on real-life high school networks and analyze how social learning changes if policymakers integrateclassrooms
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"Costly withdrawals reduce future college going: Evidence from Return to Title IV Funds"
(with Elizabeth Bell, Oded Gurantz, and Dennis Kramer)
Submitted--Draft available upon request!
Abstract
Governments must strike a balance between promoting access to financial aid while at the same time remaining good stewards of taxpayer funds by preventing fraudulent access. This paper focuses on one of the largest-scale and most consequential policies determining whether students maintain access to Title IV aid, the “Return of Title IV” funds policy, referred to as R2T4. Students receiving Title IV aid who withdraw from college before completing the academic term are subject to an R2T4 calculation that could require the student or college to pay back any unearned Title IV funds to the federal government. We estimate the causal impacts of the R2T4 policy on student outcomes in a regression discontinuity design, leveraging a cutoff in the formula that determines whether a student or their college is required to return aid. We find that students at our threshold, who earn 60 percent of the federal aid to which they were entitled see $1,600 returned on average, and typically the college bills them after paying back the federal government. Such debt makes students almost four percentage points less likely to re-enroll in college the following year and 2.6 percentage points within 4 years. These results are driven by students in the bottom half of the income distribution who face persistent negative enrollment effects of around 6 percentage points. Our findings add to a growing body of literature revealing the detrimental impacts of complex administrative processes on student outcomes, particularly for students from marginalized communities interacting with federal policies
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"How local is local development? Evidence from casinos." [Link]
(with Jordan Rosenthal-Kay)
Submitted
Presented at UEA European Conference 2023
Abstract
One rationale for place-based policy is that local development produces positive productivity spillovers. We examine the employment spillovers from a common large local development project: opening a casino. Comparing employment in neighborhoods that won a casino license to runner-up neighborhoods that narrowly lost, we find casinos create jobs in their immediate vicinity. However, we estimate net job losses overall when considering the broader neighborhood. Employment gains concentrate in the leisure and hospitality industry, suggesting spillovers are industry-specific or are driven by demand-side forces like trip-chaining. We develop theory to show that our estimates imply a rapid spatial decay of productivity spillovers.
Work in progress
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"The Distributional Impact of Official Communications: Evidence from Two Massive FAFSA Renewal Campaigns"
(with Salman Khan and Dennis Kramer)
Presented at APPAM 2024
Abstract
We test the efficacy of the U.S. Department of Education's (DOE) communication campaign on Free Application for Federal Student Aid (FAFSA) renewal and explore what types of students respond to a reminder. Who is marginal to completing additional paperwork? Who is marginal to re-enrollment? We address these questions using the results from two massive national-scale RCTs which include roughly 10 million people each. In 2020 and 2023, the DOE randomized the recipients of a typical communication campaign. Unlike other large-scale campaigns, the DOE campaigns move a small, but significant proportion of students to re-submit FAFSA and re-enroll in college. Moreover, while we observe limited variation in who resubmits FAFSA, we see heterogeneity in who re-enrolls, prompting questions about the distributional consequences of the campaigns.
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"Lessons on Pell from the 2012 Consolidated Appropriations Act"
(with Salman Khan, Dennis Kramer, and Alex Spancake)
Pre-PhD Research
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"Aequitas - A bias and fairness audit toolkit" [Link]
[Code]
(with Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Kit T Rodolfa, and Rayid Ghani)
arXiv preprint (2018) arXiv:1811.05577
Abstract
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics. While a lot of bias metrics and fairness definitions have been proposed in recent years, there is no consensus on which metric/definition should be used and there are very few available resources to operationalize them. Therefore, despite recent awareness, auditing for bias and fairness when developing and deploying AI systems is not yet a standard practice. We present Aequitas, an open source bias and fairness audit toolkit that was released in 2018 and it is an intuitive and easy to use addition to the machine learning workflow, enabling users to seamlessly test models for several bias and fairness metrics in relation to multiple population sub-groups. Aequitas facilitates informed and equitable decisions around developing and deploying algorithmic decision making systems for both data scientists, machine learning researchers and policymakers
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"Physical ecology of hypolithic communities in the central Namib Desert: The role of fog, rain, rock habitat, and light" [Link]
(with Kimberley A. Warren-Rhodes, Christopher P. McKay, Linda Ng Boyle, Michael R. Wing, Elsita M. Kiekebusch, Don A. Cowan, Francesca Stomeo, Stephen B. Pointing, Kudzai F. Kaseke, Frank Eckardt, Joh R. Henschel, Mary Seely, and Kevin L. Rhodes)
Journal of Geophysical Research: Biogeosciences, 118 (2013), pp. 1451-1460.
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