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. Recently, I have examined the scope for telecommuting, using satellite images to define cities, and how the global climate affects agricultural trade. At Booth, I teach Managing the Firm in the Global Economy.
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. We show that these data cover broad sections of the US population and exhibit movement 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 use these indices to document how pandemic-induced reductions in activity vary across people and places.
We introduce a general-equilibrium model of a "granular" spatial economy populated by a finite number of people. Our quantitative model is designed for application to the growing body of fine spatial data used to study economic outcomes for regions, cities, and neighborhoods. Conventional approaches invoking the law of large numbers are ill-suited for such empirical settings. We evaluate quantitative spatial models' out-of-sample predictions using event studies of large office openings in New York City. Our granular framework improves upon the conventional continuum-of-individuals model, which perfectly fits the pre-event data but produces predictions uncorrelated with the observed changes in commuting flows.
This paper shows that greater global spatial correlation of productivities can increase cross-country welfare dispersion by increasing the correlation between a country's productivity and its gains from trade. We causally validate this prediction using a global climatic phenomenon as a natural experiment. We find that gains from trade in cereals over the last half-century were larger for more productive countries and smaller for less productive countries when cereal productivity was more spatially correlated. Incorporating this role for spatial interdependence into a projection of climate-change impacts raises projected international inequality, with higher welfare losses across most of Africa.
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
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.
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.