Research

I study a model of production network formation and use this model to analyze the effect on aggregate output of a firm losing access to an input. I find that, contrary to economic intuition and alternative model results, when a supply line is removed, aggregate output may increase rather than decrease. I solve the model computationally and simulate it to characterize the types of networks that lead to this increase in output. I find that this is more likely when the re-formed production network is relatively interconnected and when the firm that loses access to its input has more alternatives to choose from. This paper identifies circumstances under which it is more likely that aggregate output increases rather than decreases in the face of a supply chain disruption. This can inform the methods used by researchers and help policymakers identify supply chain disruptions that represent opportunities for economic growth.

"Missing Financial Network Data"

Under Review

Little data exists describing the links of the US financial network. Using a computational model of interbank lending, I show that this lack of data can lead to erroneous model predictions. I analyze several types of data inaccuracies and find that missing a single loan in the network can lead to large differences in the predicted number of unpaid loans and total dollars repaid. This missing data could mean implementing policies that are designed to improve macroeconomic stability but that could actually lead to substantial destabilization. These results are robust across multiple network sampling regimes.

"Fortifying the Banks"

With Eric Young

Under Review

The 2008 financial crisis brought issues of financial stability to the forefront. In this paper we study the costs and efficiencies associated with government guarantees in a network model of banking so as to stabilize the financial network. We specify a minimal set of banks such that every bank has a neighbor - defined using directed edges - in the set. We call this set a fortification. The government is permitted to transfer resources only to those banks in the fortification, similar to historical rules governing deposit insurance. We find that networks that are highly connected but are not concentrated around a few popular lenders are the easiest to fortify with the greatest success. We also find that fortifications are more efficient than bailing out the banks considered too big to fail. Finally, we find a fortification of a historical financial network using data that describes interbank lending in the United States in 1867 and find results consistent with our simulations.

"The Transfer Velocity of Money"

With Carolina Mattsson and Frank Takes

Under Review

Monitoring the money supply is an important prerequisite for conducting sound monetary policy, yet monetary indicators are conventionally estimated in aggregate. This paper proposes a new methodology that is able to leverage micro-level transaction data from real-world payment systems. We apply a novel computational technique to measure the durations for which money is held in individual accounts, and compute the transfer velocity of money from its inverse. Our new definition reduces to existing definitions under conventional assumptions. However, inverse estimation remains suitable for payment systems where the total balance fluctuates and spending patterns change in time. Our method is applied to study Sarafu, a small digital community currency in Kenya, where transaction data is available from 25 January 2020 to 15 June 2021. We find that the transfer velocity of Sarafu was higher than it would seem, in aggregate, because not all units of Sarafu remained in active circulation. Moreover, inverse estimation reveals strong heterogineities and enables comparisons across subgroups of spenders. Some units of Sarafu were held for minutes, others for months, and spending patterns differed across communities using Sarafu. The rate of circulation and the effective balance of Sarafu changed substantially over time, as these communities experienced economic disruptions related to the COVID-19 pandemic and seasonal food insecurity. These findings contribute to a growing body of literature documenting the heterogeneous patterns underlying headline macroeconomic indicators and their relevance for policy. Inverse estimation may be especially useful in studying the response of spenders to targeted monetary operations.

"A Guide to the U.S. Academic Economics Job Market for Teaching-Focused Jobs"

With Benjamin Harrell, Melanie Fox, Nakul Kumar, Dan Lee, and Gina Pieters

The academic job market for economists features a substantial hidden curriculum. Most of the work on improving this has focused on research jobs at universities. Teaching-focused jobs differ from research jobs in several important ways. We provide advice for those interested in these teaching-focused jobs. We describe the main types of teaching-focused jobs in economics. Additionally, we provide advice on preparing for and acquiring these jobs. This paper is a resource for Ph.D. students, early career economists, and their mentors. Economists are striving to diversify the profession. We contribute to this by providing information about teaching-focused jobs in economics.

Works in Progress

"University Leadership and Educational Background"

With Emily Cook, Jaime Davila, and Kaya Gendreau (Former St. Olaf College Student)

"College-to-College Peer Networks and College Decisions"

With Henry Fisher (St. Olaf College Student)