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This study examines a sharp decline of school attendance among white children in the Southern US after the Civil War. According to Census data, the school-attendance rate among whites in the Confederate states declined by almost half from 1860 to 1870, whereas the rate in Northern states was approximately stable. This shock left the South approximately three decades behind its antebellum trend. We account for little of this drop with household variables plausibly affected by the War. However, a select few county-level variables (notably the drop in wealth) explains around half of the decline, which suggests a systemic explanation. We adopt a model-based approach to decomposing the decline in schooling into demand versus supply factors. On the supply side, the region saw a decline in wealth and public resources, but we observe a stable relationship between time in school and literacy or adult occupation, which is not consistent with a contracting constraint on school quantity or quality. Nevertheless, further research is required to determine how much the contraction in school access affected attendance. On the demand-side, we present suggestive evidence of a decline in the return to school (measured by the relative wage of engineers to laborers). Relatedly, we see a “brain drain”: in longitudinally linked census samples, educated Southerners were more likely to migrate out of the South after the War.

The US fertility transition in the nineteenth century is unusual. Not only did it start from a very high fertility level and very early in the nation’s development, but it also took place long before the nation’s mortality transition, industrialization, and urbanization. This paper assembles new county-level, household-level, and individual-level data, including new complete-count IPUMS microdata databases of the 1830–1880 censuses, to evaluate different theories for the nineteenth-century American fertility transition. We construct cross-sectional models of net fertility for currently-married white couples in census years 1830–1880 and test the results with a subset of couples linked between the 1850–1860, 1860–1870, and 1870–1880 censuses. We find evidence of marital fertility control consistent with hypotheses as early as 1830. The results indicate support for several different but complementary theories of the early US fertility decline, including the land availability, conventional structuralist, ideational, child demand/quality-quantity tradeoff, and life cycle savings theories.

Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the United States, we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add Organization for Economic Co-Operation and Development (OECD) countries, showing that the wage–productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper-income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new artificial intelligence (AI) revolution that has just begun seems to be having similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled.

We document long-run trends in interstate migration rates, using individual-level data from the U.S. Census for the period 1850–1990. Two measures of migration are calculated. The first considers an individual to have moved if she is residing in a state different from her state of birth. The second considers a family to have moved if it is residing in a state different from the state of birth of one of its young children, allowing us to estimate the timing of moves more precisely. Overall migration propensities have followed a U-shaped trend since 1850, falling until around 1900 and then rising until around 1970. We examine variation in the propensity to make an interstate move by age, sex, race, nativity, region of origin, family structure, and education. Counterfactuals based on probit estimates of the propensity to migrate suggest that the rise in migration of families since 1900 could be explained by increased educational attainment, although education may be serving as a proxy for unmeasured covariates. The decline of interstate migration in the late nineteenth century remains to be explained.

We examine the potential effect of naturalization on the U.S. immigrants’ earnings. We find the earning gap between naturalized citizens and noncitizens is positive over many years, with a tent shape across the wage distribution. We focus on a normalized metric entropy measure of the gap between distributions, and compare with conventional measures at the mean, median, and other quantiles. In addition, naturalized citizen earnings (at least) second-order stochastically dominate noncitizen earnings in many of the recent years. We construct two counterfactual distributions to further examine the potential sources of the earning gap, the “wage structure” effect and the “composition” effect. Both of these sources contribute to the gap, but the composition effect, while diminishing somewhat after 2005, accounts for about 3/4 of the gap. The unconditional quantile regression (based on the Recentered Influence Function), and conditional quantile regressions confirm that naturalized citizens have generally higher wages, although the gap varies for different income groups, and has a tent shape in many years.

This chapter examines the effects that airports have had on economic development in cities from 1950 to 2010. It uses a novel dataset consisting of previously unexploited data on the origins and history of the aviation system in the United States. Applying the method of synthetic controls to a set of medium and small airports, I examine both the overall impacts and the heterogeneity within the outcomes of various airports. Then, I use regression analysis to determine key factors differentiating successful airports from less successful ones, as it pertains particularly to population and employment growth. I find that, first, on average, cities have benefited from airports over this period. Airports, overall, provided a causal contribution of 0.2– 0.6% per year on population and employment growth over the time period. Second, I show that city-level factors contributing to airport success include: (1) closer proximity to a major research university, (2) a capital city location, and (3) climate factors, particularly higher January mean temperatures and/or hours of sunshine. City size is a consideration as well; cities in larger metropolitan areas, with larger shares of employment in nontradables in the 1950s, were also better positioned to reap the benefits that airports provided on city growth. Significant differences were not found across regions, airport governance structures, or other factors.

This paper examines the determinants of the initial location choices of immigrants who enter the U.S. with different kinds of visas (“green cards”). Conditional logit models with the 48 contiguous U.S. states as the choice set are estimated using population data on immigrants from the Immigration and Naturalization Service between 1971 and 2000 matched to data on state characteristics from the Integrated Public Use Microsamples of the U.S. Census. As in previous research, it is estimated that immigrants have a higher probability of moving to states where individuals from their region of birth are a larger share of the state population, with relatives of legal permanent residents responding most to this factor. In addition, it is estimated that immigrants in all admission categories respond to labor market conditions when choosing where to live, but that these effects are the largest for male employment-based immigrants and, surprisingly, refugees. These relationships are relatively stable across models that include state fixed effects as well as those that allow the coefficients to vary across the four decades available in the data.

Employment rates fell dramatically between March and April 2020 as the initial shock of the COVID-19 pandemic reverberated through the US labor market. This paper uses data from the CPS Basic Monthly Files to document that the employment decline was particularly severe for immigrants. Historically, immigrant men are more likely to work than native men. The pandemic-related labor market shock eliminated the immigrant employment advantage. After this initial precipitous drop, however, the employment recovery through June 2021 was much stronger for immigrants, and particularly for undocumented immigrants. The steep drop in immigrant employment at the start of the pandemic occurred partly because immigrants were less likely to work in jobs that could be performed remotely and suffered disproportionate employment losses as only workers with remotable skills were able to continue working from home. The stronger employment recovery of undocumented immigrants, relative to that experienced by natives or legal immigrants, is mostly explained by the fact that undocumented workers were not eligible for the generous unemployment insurance (UI) benefits offered to workers during the pandemic.

Building on recent advances in inverse probability weighted identification and estimation of counterfactual distributions, the authors examine the history of wage earnings for women and their potential wage distributions in the United States. These potentials are two counterfactuals, what if women received men’s market “rewards” for their own “skills,” and what if they received the women’s rewards but for men’s characteristics? Using the Current Population Survey data from 1976 to 2013, the authors analyze the entire counterfactual distributions to separate the “structure” and human capital “composition” effect. In contrast to Maasoumi and Wang (2019), the reference outcome in these decompositions is women’s observed earnings distribution, and inverse probability methods are employed, rather than the conditional quantile approaches. The authors provide decision theoretic measures of the distance between two distributions, to complement assessments based on mean, median, or particular quantiles. We assess uniform rankings of alternate distributions by tests of stochastic dominance in order to identify evaluations robust to subjective measures. Traditional moment-based measures severely underestimate the declining trend of the structure effect. Nevertheless, dominance rankings suggest that the structure (“discrimination”?) effect is bigger than human capital characteristics.