I am an Associate Professor of Economics at the University of Chicago Booth School of Business, a Faculty Research Fellow at the National Bureau of Economic Research, and a Research Fellow at the Centre for Economic Policy Research. My research agenda focuses on the spatial distribution of economic activities across neighborhoods, cities, and countries. I try to understand the substantial variation in the amount and nature of economic activity across space (November 2022 research statement). In 2022, my research was recognized by the Bhagwati Award and August-Lösch Prize. At Booth, I teach Managing the Firm in the Global Economy.
We quantify the roles of increasing returns and trade costs in medical services. Using Medicare claims data, we document that "imported" medical procedures — services produced by a medical provider in a different region — constitute about one-fifth of US healthcare consumption. Larger regions specialize in producing less common procedures, and these procedures are traded more. These patterns reflect economies of scale: larger regions produce higher-quality services because they serve more patients. Because of increasing returns and trade costs, policies to improve access to care face a proximity-concentration tradeoff. Production subsidies and travel subsidies impose contrasting spillovers on neighboring regions.
We examine the application of quantitative spatial models to the growing body of fine spatial data used to study economic outcomes for regions, cities, and neighborhoods. In "granular" settings where people choose from a large set of potential residence-workplace pairs, idiosyncratic choices affect equilibrium outcomes. Using both Monte Carlo simulations and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and modeled probabilities perform very poorly in such settings. We introduce a general-equilibrium model of a granular spatial economy. Applying this model to Amazon's proposed HQ2 in New York City reveals that the project's predicted consequences for most neighborhoods are small relative to the idiosyncratic component of individual decisions in this setting. We propose a convenient approximation for researchers to quantify the "granular uncertainty" accompanying their counterfactual predictions.
Global phenomena, such as climate change, often have local impacts that are spatially correlated. We show that greater spatial correlation of productivities can increase international inequality by increasing the correlation between a country's productivity and its gains from trade. We confirm this prediction using a half-century of exogenous variation in the spatial correlation of agricultural productivities induced by a global climatic phenomenon. We introduce this general-equilbrium effect into projections of climate-change impacts that typically omit spatial linkages and therefore do not account for the global scope of climate change. We project greater international inequality, with higher welfare losses across Africa.
Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. These data cover broad sections of the US population and exhibit pre-pandemic patterns similar to conventional survey data. We develop and make publicly available a location exposure index that summarizes county-to-county movements and a device exposure index that quantifies social contact within venues. We also investigate the reliability of smartphone movement data during the pandemic.
We classify the feasibility of working at home for all occupations.
We find that 37 percent of jobs in the United States can be performed entirely at home, with significant variation across cities and industries.
Applying our occupational classification to 85 other countries reveals that lower-income economies have a lower share of jobs that can be done at home.
Media mentions: WSJ, Brookings, Politico, Vox, Journalist's Resource, International Business Times, Daily Mail, Business Insider, Quartz, WSJ, Economist, Vice, Reason, FT, MarketWatch, The Hill, BI, WSJ, NYT, AEI, NYT, Reuters, BI, NYT, Reuters, NYT, Economist, BBC, LA Times, FT
What determines the distributions of skills, occupations, and industries across cities? We develop a theory that incorporates a system of cities, their internal urban structures, and a high-dimensional theory of factor-driven comparative advantage. It predicts that larger cities will be skill-abundant and specialize in skill-intensive activities according to the monotone likelihood ratio property. Data on US cities, education groups, occupations, and industries are consistent with our theory's predictions.
We construct metropolitan areas for Brazil, China, and India by aggregating finer geographic units on the basis of contiguous areas of light in nighttime satellite images. In China and India, lights-based metropolitan populations follow a power law, while administrative units do not. Examining variation in relative quantities and prices of skill across these metropolitan areas, we conclude that agglomeration is skill-biased in Brazil, China, and India.
We measure ethnic and racial segregation in urban consumption using Yelp reviews of NYC restaurants. Spatial frictions cause consumption segregation to partly reflect residential segregation. Social frictions also matter: individuals are less likely to visit restaurants in neighborhoods demographically different from their own. Nonetheless, restaurant consumption in New York City is only about half as segregated as residences. Consumption segregation owes more to social than spatial frictions.
We develop the first system-of-cities model with costly idea exchange as the agglomeration force. The model replicates a broad set of established facts about the cross section of cities. It provides the first spatial equilibrium theory of why skill premia are higher in larger cities and how variation in these premia emerges from symmetric fundamentals.
Why do high-income countries export high-quality goods: home demand or factor abundance? I develop a model nesting both hypotheses and employ microdata on US manufacturing plants' shipments and factor inputs to quantify the two mechanisms' roles in quality specialization across US cities. Home-market demand explains as much of the relationship between income and quality as differences in factor usage.
How many newly remote jobs will go overseas? We offer a rough quantification. Telemigration seems unlikely to be transformative. Remote work is English- & soft-skill-intensive. Baseline services exports are small, and the standard gravity model restricts modest changes to have modest impacts. We propose a simple model of telemigration in which small changes can have large consequences.
For COVID-motivated public releases of exposure indices derived from PlaceIQ movement data, see https://github.com/COVIDExposureIndices
From 1998 to 2018, Brazilian forest coverage fell by a quarter-million square kilometers, an area the size of Italy or Colorado. Over the same period, Amazon states' exports of basic products grew three times faster than the rest of Brazil. Has foreign demand affected deforestation in the Amazon forest? Can foreign trade policy affect the Amazon forest's future?
This course studies international economics from the perspective of the firm. Its objective is to equip students with analytical tools to understand the organizational, financial, and legal issues facing firms doing business across borders.