Search

The Covid-19 pandemic has introduced huge numbers of employers and employees to remote work. How many of these newly remote jobs will go overseas? We offer a rough quantification based on two observations: 1) offshore work is trade in services, and 2) the number of telemigrants is the volume of this trade divided by the average wage. Combining these with gravity-model estimates, we can roughly predict the number of new telemigrants that would arise from lower barriers to trade in services. Telemigration seems unlikely to be transformative when it comes to the development paths of most emerging economies. The baseline service trade flows are modest, and the standard gravity model restricts modest changes to have modest impacts. There are no tipping points in structural gravity models. Finally, we propose a simple model of telemigration in which small changes can have large consequences. The key is to assume that latent comparative advantage takes a different shape than typically assumed in quantitative trade models. Given this, small changes in trade costs can generate large and asymmetric increases in the exports of service tasks from low-wage nations.

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.

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 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. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Most states and cities in the U.S. have shut all non-essential businesses in response to COVID-19. In this note, we argue that as policies are developed to “re-open” the economy and send people back to work, strategies for childcare arrangements, such as re-opening schools and daycares, will be important. Substantial fractions of the U.S. labor force have children at home and will likely face obstacles to returning to work if childcare options remain closed. Younger workers, who might be able to return to work earlier to the extent that they are less susceptible to the virus, are also more likely to require childcare arrangements in order to return to work. Using 2018 data from the Census Bureau’s American Community Survey, we calculate the share of employed households who are affected by childcare constraints.

In developed economies, agglomeration is skill-biased: larger cities are skill-abundant and exhibit higher skilled wage premia. This paper characterizes the spatial distributions of skills in Brazil, China, and India. To facilitate comparisons with developed-economy findings, we construct metropolitan areas for each of these economies by aggregating finer geographic units on the basis of contiguous areas of light in nighttime satellite images. Our results validate this procedure. These lights-based metropolitan areas mirror commuting-based definitions in the United States and Brazil. In China and India, which lack commuting-based definitions, 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 also skill-biased in Brazil, China, and India.

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 general-equilibrium 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 general-equilibrium effect into a projection of climate-change impacts raises projected international inequality, with higher welfare losses across most of Africa.

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 general-equilibrium 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 general-equilibrium effect into a projection of climate-change impacts raises projected international inequality, with higher welfare losses across most of Africa.

Evaluating the economic impact of “social distancing” measures taken to arrest the spread of COVID-19 raises a fundamental question about the modern economy: how many jobs can be performed at home? We classify the feasibility of working at home for all occupations and merge this classification with occupational employment counts. We find that 37% of jobs in the United States can be performed entirely at home, with significant variation across cities and industries. These jobs typically pay more than jobs that cannot be done at home and account for 46% of all US wages. 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., Highlights • We classify the feasibility of working at home for all occupations. • 37% of jobs in the United States can be performed entirely at home. • Jobs that can be done at home typically pay more. • Lower-income economies have a lower share of jobs that can be done at home.

We provide measures of ethnic and racial segregation in urban consumption. Using Yelp reviews, we estimate how spatial and social frictions influence restaurant visits within New York City. Transit...

We provide measures of ethnic and racial segregation in urban consumption. Using Yelp reviews, we estimate how spatial and social frictions influence restaurant visits within New York City. Transit time plays a first-order role in consumption choices, so consumption segregation partly reflects residential segregation. Social frictions also have a large impact on restaurant choices: individuals are less likely to visit venues in neighborhoods demographically different from their own. While spatial and social frictions jointly produce significant levels of consumption segregation, we find that restaurant consumption in New York City is only about half as segregated as residences. Consumption segregation owes more to social than spatial frictions.

A growing literature suggests that high-income countries export high-quality goods. Two hypotheses may explain such specialization, with different implications for welfare, inequality, and trade policy. Fajgelbaum, Grossman, and Helpman (JPE 2011) formalize the Linder (1961) conjecture that home demand determines the pattern of specialization and therefore predict that high-income locations export high-quality products. The factor-proportions model also predicts that skill-abundant, high-income locations export skill-intensive, high-quality products (Schott, QJE 2004). Prior empirical evidence does not separate these explanations. I develop a model that nests 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 at least as much of the relationship between income and quality as differences in factor usage.

A growing literature suggests that high-income countries export high-quality goods. Two hypotheses may explain such specialization, with different implications for welfare, inequality, and trade policy. Fajgelbaum, Grossman, and Helpman (2011) formalize the Linder hypothesis that home demand determines the pattern of specialization and therefore predict that high-income locations export high quality products. The factor-proportions model also predicts that skill-abundant, high-income locations export skill-intensive, high-quality products. Prior empirical evidence does not separate these explanations. I develop a model that nests 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 at least as much of the relationship between income and quality as differences in factor usage.

What determines the distributions of skills, occupations, and industries across cities? We develop a theory to jointly address these fundamental questions about the spatial organization of economies. Our model 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. We test the model using data on 270 US metropolitan areas, 3 to 9 educational categories, 22 occupations, and 21 manufacturing industries. The results provide support for our theory's predictions.

Leading empiricists and theorists of cities have recently argued that the generation and exchange of ideas must play a more central role in the analysis of cities. This paper develops the first system of cities model with costly idea exchange as the agglomeration force. Our 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, how variation in these premia emerges from symmetric fundamentals, and why skilled workers have higher migration rates than unskilled workers when both are fully mobile.