Research Database

Children's education and economic opportunities differ substantially across US neighborhoods. This paper develops and estimates a spatial equilibrium model that links children's education outcomes to their childhood location. Two endogenous factors determine education choices in each location: local education quality and local labor market access. We estimate the model with US county-level data and study the effects of a school funding equalization on education outcomes and social mobility. The reform's direct effects improve education outcomes among children from low-skill families. However, the effects are weaker in spatial general equilibrium because average returns to education decline and residential and educational choices of low-skill families shift them toward locations with lower education quality.

Large cities in the US are the most expensive places to live. Paradoxically, this cost is paid disproportionately by workers who could work remotely, and live anywhere. The greater potential for remote work in large cities is mostly accounted for by their specialization in skill- and information-intensive service industries. We highlight that this specialization makes these cities vulnerable to remote work shocks. When high-skill workers begin to work from home or leave the city altogether, they withdraw spending from local consumer service industries that rely heavily on their demand. As a result, low-skill service workers in big cities bore most of the recent pandemic’s economic impact.

We offer causal evidence of higher returns to experience in big cities. Exploiting a natural experiment that settled refugees across labor markets in Denmark between 1986 and 1998, we find that refugees initially earn similar wages across locations. However, those placed in Copenhagen exhibit 35% faster wage growth with each additional year of experience. Faster sorting of workers towards the type of establishments, occupations, and industries typically found in cities accounts for the vast majority of this urban wage growth premium.

Since 1980, economic growth in the U.S. has been fastest in its largest cities. We show that a group of skill- and information-intensive service industries are responsible for all of this new urban bias in recent growth. We then propose a simple explanation centered around the interaction of three factors: the disproportionate reliance of these services on information and communication technology (ICT), the precipitous price decline for ICT capital since 1980, and the preexisting comparative advantage of cities in skilled services. Quantitatively, our mechanism accounts for most of the urban biased growth of the U.S. economy in recent decades.

Despite wide recognition of their significant role in explaining sustained growth and economic development, uncompensated knowledge spillovers have not yet been fully modeled with a microeconomic foundation. The main purpose of this paper is to illustrate the exchange of knowledge as well as its consequences on agglomerative activity in a general-equilibrium search-theoretic framework. Agents, possessing differentiated types of knowledge, search for partners to exchange ideas and create new knowledge in order to improve production efficacy. When individuals’ types of knowledge are too diverse, a match is less likely to generate significant innovations. We demonstrate the extent of agglomeration has significant implications for the patterns of information flows in economies. Further, by simultaneously determining the patterns of knowledge exchange and the spatial agglomeration of an economy we identify additional channels for interaction between agglomerative activity and knowledge exchange. Finally, contrary to previous work in spatial agglomeration, our model suggests that agglomerative environments may be either under-specialized and under-populated or over-specialized and over-populated relative to the social optimum.

In the last century, the evolution of female labor force participation has been S-shaped: It rose slowly at first, then quickly, and has leveled off recently. Central to this dramatic rise has been entry of women with young children. We argue that this S-shaped dynamic came from generations of women learning about the relative importance of nature (endowed ability) and nurture (time spent child-rearing) for children’s outcomes. Each generation updates their parents’ beliefs by observing the children of employed women. When few women participate in the labor force, most observations are uninformative and participation rises slowly. As information accumulates and the effects of labor force participation become less uncertain, more women participate, learning accelerates and labor force participation rises faster. As beliefs converge to the truth, participation flattens out. Survey data, wage data and participation data support our mechanism and distinguish it from alternative explanations.

We estimate the factors determining specialization of crop choice at the level of individual fields, distinguishing between the role of natural advantage (soil characteristics) and economies of density (scale economies achieved when farmers plant neighboring fields with the same crop). Using rich geographic data from North Dakota, including new data on crop choice collected by satellite, we estimate the analog of a social interactions econometric model for the planting decisions on neighboring fields. We find that planting decisions on a field are heavily dependent on the soil characteristics of the neighboring fields. Through this relationship, we back out the structural parameters of economies of density. Setting an Ellison-Glaeser dartboard level of specialization as a benchmark, we find that of the actual level of specialization achieved beyond this benchmark, approximately two-thirds can be attributed to natural advantage and one-third to density economies.

We analyze the secular decline in interstate migration in the United States between 1991 and 2011. Gross flows of people across states are about 10 times larger than net flows, yet have declined by around 50 percent over the past 20 years. We argue that the fall in migration is due to a decline in the geographic specificity of returns to occupations, together with an increase in workers’ ability to learn about other locations before moving there, through information technology and inexpensive travel. These explanations find support in micro data on the distribution of earnings and occupations across space and on rates of repeat migration. Other explanations, including compositional changes, regional changes, and the rise in real incomes, do not fit the data. We develop a model to formalize the geographic-specificity and information mechanisms and show that a calibrated version is consistent with cross-sectional and time-series patterns of migration, occupations, and incomes. Our mechanisms can explain at least one-third and possibly all of the decline in gross migration since 1991.

This appendix contains eight sections. Section 1 gives technical details of how we calculate standard errors in the CPS data. Section 2 discusses changes in the ACS procedures before 2005. Section 3 examines demographic and economic patterns in migration over the past two decades, in more detail than in the main paper. Section 4 examines the cross-sectional variance of location-occupation interactions in earnings when we define locations by MSAs instead of states. Section 5 describes alternative methods to estimate the variance of location-occupation interactions in income. Section 6 measures the segregation of industries across states and of occupations and industries across MSAs. Section 7 gives technical details on the use of SIPP and census data to calculate repeat and return migration rates. Section 8 discusses transition dynamics in the model.