Published Research
“Government Shutdown and SNAP Disbursements: Effects on Household Expenditures” (link to journal)
Coauthored with Mindy Marks, Silvia Prina. Review of Economics of the Household, June 2024
The ability of SNAP eligible households to respond to a temporary change in benefit timing is tested. The paper exploits the 2018–2019 US government shutdown where all states were federally mandated to pay February SNAP benefits in January. This created a short-term windfall (two payments close to each other) followed by an abnormal gap during which no SNAP disbursements were received. Using a triple differences approach, expenditures are shown to be were lower in February (relative to other months) 2019 (relative to 2018) for SNAP recipients (relative to neareligible households). This finding is complemented by exploiting preexisting state-level differences in disbursement schedules that drove some states to temporarily alter the timing of the 2019 March and April SNAP disbursements. Diff-in-diff estimates show that SNAP eligible households in those states reduced spending. These findings are inconsistent with the permanent income hypothesis and suggest that the timing of benefits matters for household consumption.
Research Papers
A national dataset of U.S. elementary school student demographics is used to show that White Flight- the accelerating emigration of whites from demographically mixed neighborhoods - was still an important dynamic in the first two decades of the 21st century. On average, white schools reached the ‘tipping point’- the point at which net white exodus begins and accelerates- at a white share around 95%. I exploit the theory of Mediated Intergroup Contact to give a lower bound to the impact of antiblack prejudiced stereotypes on White Flight.
In progress - with an anticipated journal submission in Spring 2025, Coauthored with Mindy Marks, Silvia Prina​
This paper uses an event-study approach to show that lottery-based school choice programs which do not guarantee seats in local schools encourage high income families to emigrate from those school districts. Incomes in affected cities among households with affected children decrease by 8.0%.
Leveraging AI in Economics Education: A Pedagogical Case Study
In progress, Anticipated journal submission Spring 2025, Coauthored with ChatGPT 4o​
The integration of artificial intelligence (AI) is explored as a pedagogical tool in undergraduate economics education, specifically in fostering critical thinking and conceptual understanding. Over the course of a summer session in 2024, students participated in a series of AI-driven projects designed to encourage deep exploration of economic principles such as those proposed by Adam Smith, John Maynard Keynes, and modern economic frameworks. The methodology involved a structured conversational approach where students engaged in dialogue with AI, using a keywordbased game to initiate the interaction, followed by deductive synthesis and open-ended discussions. Preliminary findings suggest that AI-assisted conversations not only reinforced learning outcomes but also potentially helped bridge the gap between rote memorization and critical analysis. This case study aims to provide a foundation for future research on the role of AI in higher education and its potential to augment the classroom experience.
On the Equivalence of Neural and Production Networks
Coauthored with Bjorn Persson, https://arxiv.org/abs/2005.00510, November 2021​
This paper identifies the mathematical equivalence between economic networks of Cobb-Douglas agents and Artificial Neural Networks. It explores two implications of this equivalence under general conditions. First, a burgeoning literature has established that network propagation can transform microeconomic perturbations into large aggregate shocks. Neural network equivalence amplifies the magnitude and complexity of this phenomenon. Second, if economic agents adjust their production and utility functions in optimal response to local conditions, market pricing is a sufficient and robust channel for information feedback leading to macro learning.