This article reviews evidence on the labor market performance of Hispanics in the United States, with a particular focus on the US-born segment of this population. After discussing critical issues that arise in the US data sources commonly used to study Hispanics, we document how Hispanics currently compare with other Americans in terms of education, earnings, and labor supply, and then we discuss long-term trends in these outcomes. Relative to non-Hispanic Whites, US-born Hispanics from most national origin groups possess sizeable deficits in earnings, which in large part reflect corresponding educational deficits. Over time, rates of high school completion by US-born Hispanics have almost converged to those of non-Hispanic Whites, but the large Hispanic deficits in college completion have instead widened. Finally, from the perspective of immigrant generations, Hispanics experience substantial improvements in education and earnings between first-generation immigrants and the second-generation consisting of the US-born children of immigrants. Continued progress beyond the second generation is obscured by measurement issues arising from high rates of Hispanic intermarriage and the fact that later-generation descendants of Hispanic immigrants often do not self-identify as Hispanic when they come from families with mixed ethnic origins.

This paper identifies how changing postwar demographics in American cities caused their suburbs to adopt a form of land use control still widespread today-minimum lot sizes. I develop an algorithm detecting bunching on lot sizes, observable when govern-ments' lot size controls bind developers from building denser housing. Applying the algorithm to national assessor records, I estimate which lot size controls first came into effect and their impact on new homes over time for 7,000 local governments. Most suburbs adopted lot size controls from 1945-1970, the same period when four million Black Amer-icans left the South for opportunities in American cities. I then use the "Second Great Mi-gration" as a natural experiment that shifted central cities' racial composition toward Black Americans. From 1940-1970, the rise in central city Black composition in non-Southern central cities modestly accelerated minimum lot size adoption while further explaining binding density controls applied to at least 600,000 housing units. Migration of lower-income whites into the same cities yields null effects on suburban lot size outcomes. In states that passed early legislation to desegregate public schools, Black migration had the largest effects on lot size restrictiveness. Together, the results indicate that local governments designed land use controls to exclude Black migrants from neighborhoods and public goods.

Since most census data is geographic in nature, it lends itself well to geographic analysis. Geographic Information Systems (GIS) are collections of software and data for conducting geographic analyses and making maps. Special GIS data files called vector files store coordinates to form geometries that represent points, lines, and areas. Each file represents a particular type of feature that covers a specific extent: a file for counties for the US, a file of census tracts for a state, or a file of roads for a particular county. The files are georeferenced, which means they are drawn to scale using a specific spatial reference system that ties them to real locations on the earth. These reference systems allow GIS data files from multiple sources to be overlaid in a GIS project. In addition to the vector files, a different format called a raster represents continuous surfaces as a series of grid cells of equal size, where each cell has a value that represents something about the surface. Rasters are also georeferenced, and satellite imagery, air photos, and scanned paper maps such as topographic maps are stored in the raster format. For census mapping applications, rasters are useful as base maps to provide context for vectors.

Mining Irish-American Lives focuses on the importance and influence of the Irish within the mining frontier of the American West. Scholarship of the West has largely ignored the complicated lives of the Irish people in mining towns, whose life details are often kept to a bare minimum. This book uses individual stories and the histories of different communities—Randsburg, California; Virginia City, Nevada; Leadville, Colorado; Butte, Montana; Idaho’s Silver Valley; and the Comstock Lode, for example—to explore Irish and Irish-American lives. Historian Alan J. M. Noonan uses a range of previously overlooked sources, including collections of emigrant letters, hospital logbooks, private detective reports, and internment records, to tell the stories of Irish men and women who emigrated to mining towns to search for opportunity. Noonan details the periods, the places, and the experiences over multiple generations in the late nineteenth and early twentieth centuries. He carefully examines their encounters with nativists, other ethnic groups, and mining companies to highlight the contested emergence of a hyphenated Irish-American identity. Unearthing personal details along with the histories of different communities, the book investigates Irish immigrants and Irish-Americans through the prism of their own experiences, significantly enriching the history of the period.

The role of ethnic immigrant networks in facilitating international trade is a well-established phenomenon in the literature. However, it is less clear whether this relationship extends to illegal trade and unauthorized immigrants. In this paper, we tackle this question by focusing on the case of the heroin trade and unauthorized Chinese immigrants in the early 1990s United States. Between mid-1980s and mid-1990s, Southeast Asia became the dominant source of heroin in the US. Heroin from this region was trafficked into the US by Chinese organized criminals, whose presence across the country can be approximated by the location of unauthorized Chinese immigrants. Instrumenting for the unauthorized Chinese immigrant enclaves in 1990 with their 1900 counterpart, we first show that greater presence of unauthorized Chinese immigrants in a community led to a sizeable increase in local opiates-related arrests, a proxy for local heroin markets. This effect is driven by arrests for sale/manufacturing of the drugs. Next, we examine the consequences of Chinese-trafficked heroin by looking at its impact on US Vietnam-era veterans – a group particularly vulnerable to heroin addiction in the early 1990s. Using a triple-difference estimation, we find mostly small but statistically significant detrimental effects on labor market outcomes of Vietnam veterans residing in unauthorized Chinese enclaves in 1990.

The analysis of occupational licensing has largely concentrated on its influence in the labor market and on consumer welfare. By contrast, relatively little is known about how occupational licensing laws originated or the key factors in their evolution. In this paper, we study the determinants of state-level licensing requirements from 1870 to 2020. We begin by developing a model where licensing arises as an endogenous political outcome and use this framework to study how market characteristics and political incentives impact the likelihood of regulation. Our empirical analysis draws on a novel database tracking the initial enactment of licensing legislation for hundreds of unique occupations, as well as changes to the specific qualifications required to attain a license over time. Consistent with the predictions of our model, we find first that licensing is more prevalent and was adopted earlier for occupations that plausibly pose a greater risk of harm to consumers. Second, within occupations, regulation tends to diffuse from larger to smaller markets over time. Finally, the political organization of an occupation, as measured by the establishment of a state professional association, significantly increases the probability of a licensing statute being enacted.

This paper proposes a new empirical strategy to estimate the causal e˙ects of 1930s federal “redlining” – the mapping and grading of US neighborhoods by the Home Owners’ Loan Corporation (HOLC). Our analysis exploits an exogenous city size cuto˙: only cities above 40,000 residents were mapped. We employ a di˙erence-in-di˙erences design, comparing areas that received a particular grade with neighborhoods that would have received the same grade if their city had been mapped. The control neighborhoods are defined using a machine learning algorithm trained to draw HOLC-like maps using newly geocoded full-count census records. For the year 1940, we find a substantial reduction in property values and homeownership rates in areas with the lowest grade along with an increase in the share of African American residents. We also find sizable house value reductions in the second-to-lowest grade areas. Such negative e˙ects on property values persisted until the early 1980s. Our results illustrate how institutional practices can coordinate individual discriminatory choices and amplify their consequences.

I examine the impacts of the Chinese Exclusion Act of 1882, America's first ever immigration restriction that prohibited entry to Chinese laborers, on native labor market outcomes. To identify the causal effect, I utilize variation in pre-1882 Chinese settlement and match on individual-and labor market-level characteristics. Using individual-level linked Census data from 1880-1900 of over two million US workers, I find the Chinese exclusion significantly slowed the long-term occupational mobility of native workers, with the effects strongest for low-skilled and unemployed workers. I find evidence in support of what I term a "honeypot" effect: low-skilled natives likely benefited from the labor shortage through higher wages in the short-run, but in the long-run the shortage disincentivized upskilling and slowed occupational upgrading. Moreover, I show Chinese laborers were almost entirely substituted by immigrants from other countries in the long-run, likely negating positive wage effects.

Early-life conditions are associated with mortality in men, but not studied to the same extent in women. We add new evidence by studying a cohort of women born between 1916 and 1931 and followed for mortality between 1986 and 2013. Our sample from Iowa includes a significant number of rural women, from both farms and small towns. The long-term effects of growing up in a rural area were mixed: farmers’ daughters lived longer than women growing up off-farm in rural areas. Daughters of farm laborers and skilled or semi-skilled trades workers fared worst, when considering early-life socioeconomic status. We also find evidence that migrating to small-town Iowa was associated with lower life expectancy after age fifty-five. Considering social class and farm-nonfarm status is important for understanding the health of rural America.

This article reviews evidence on the labor market performance of Hispanics in the United States, with a particular focus on the US-born segment of this population. After discussing critical issues that arise in the US data sources commonly used to study Hispanics, we document how Hispanics currently compare with other Americans in terms of education, earnings, and labor supply, and then we discuss long-term trends in these outcomes. Relative to non-Hispanic Whites, US-born Hispanics from most national origin groups possess sizeable deficits in earnings, which in large part reflect corresponding educational deficits. Over time, rates of high school completion by US-born Hispanics have almost converged to those of non-Hispanic Whites, but the large Hispanic deficits in college completion have instead widened. Finally, from the perspective of immigrant generations, Hispanics experience substantial improvements in education and earnings between first-generation immigrants and the second-generation consisting of the US-born children of immigrants. Continued progress beyond the second generation is obscured by measurement issues arising from high rates of Hispanic intermarriage and the fact that later-generation descendants of Hispanic immigrants often do not self-identify as Hispanic when they come from families with mixed ethnic origins.

Coethnics often work in the same industries. How does this ethnic clustering affect individuals’ political loyalties amid industrial growth and decline? Focusing on migrant groups, the author contends that ethnic groups’ distribution across industries alters the political allegiances of their members. When a group is concentrated in a growing industry, economic optimism and resources flow between coethnics, bolstering migrants’ confidence in their economic security and dissuading investments in local political incorporation. When a group is concentrated in a declining industry, these gains dissipate, leading migrants to integrate into out-groups with greater access to political rents. Analyses of immigrants near US coal mines in the early twentieth century support this theory. The article shows how ethnic groups’ distribution across industries shapes the evolution of group cleavages and illuminates how decarbonizing transitions away from fossil fuels may reshape identity conflicts.

Researchers use microdata to study the economic development of the United States and the causal eeects of historical policies. Much of this research focuses on county-and state-level paaerns and policies because comprehensive sub-county data is not consistently available. We describe a new method that geocodes and standardizes the towns and cities of residence for individuals and households in decennial census microdata from 1790-1940. We release public crosswalks linking individuals and households to consistently-deened place names, longitude-latitude pairs, counties, and states. Our method dramatically increases the number of individuals and households assigned to a sub-county location relative to standard publicly available data: we geocode an average of 83% of the individuals and households in 1790-1940 census microdata, compared to 23% in widely-used crosswalks. In years with individual-level microdata (1850-1940), our average match rate is 94% relative to 33% in widely-used crosswalks. To illustrate the value of our crosswalks, we measure place-level population growth across the United States between 1870 and 1940 at a sub-county level, confirming predictions of Zipf's Law and Gibrat's Law for large cities but rejecting similar predictions for small towns. We describe how our approach can be used to accurately geocode other historical datasets.

Over the early twentieth century, urban centers adopted full-time public health departments. We show that opening full-time administration had little observable impact on mortality. We then attempt to determine why health departments were ineffective. Our results suggest that achievements in public health occurred regardless of health department status. Further, we find that cities with and without a full-time health department allocated similar per capita expenditures towards health administration. This health department funding also better predicts infant mortality declines. Our conclusions indicate that specific campaigns, public health systems, and funding may have been more meaningful for local health over this era.

We examine the relationship between board diversity and a firm's corporate social responsibility (CSR) performance in a novel way. The relation between visible forms of board diversity (gender, ethnic, age diversity) and CSR may arise endogenously due to visible diversity management. In contrast, we focus on cultural diversity (based on directors' ancestry), which is less visible. We demonstrate that cultural diversity, unlike visible diversity, is not considered in director replacements, consistent with cultural diversity not being affected by firms signaling their CSR commitment by ‘looking’ diverse. We show that board cultural diversity is positively related to CSR performance. This result holds when we control for visible board diversity, directors' foreignness and diversity in nationalities, and endogeneity. We also show that CSR performance decreases when a firm increases its visible board diversity at the cost of cultural diversity.

Many studies consider occupational segregation among the immigrant population from a given birth country as a whole. This ignores potential ethnic heterogeneity within an immigrant population and may underestimate occupational segregation. We focus on Russian immigrants in the early twentieth century USA—then a major immigrant population with a high degree of ethnic diversity, including Russian, Jewish, German, and Polish ethnics—and study occupational segregation by ethnicity. We apply a machine learning ethnicity classification approach to 1930 US census data based on name and mother tongue. Using the constructed ethnicity variable, we show high degrees of occupational segregation by ethnicity within the Russian-born immigrant population in the USA. For example, Jews, German ethnics, and Polish ethnics were concentrated in trade, agriculture, and manufacturing, respectively. We also find evidence that Russian-born immigrants’ labor market outcomes were associated with networks measured by the spatial concentration of co-ethnics—particularly more established ones—but not by the concentrations of other ethnic groups.

This paper presents a probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to many sources of digitized historical records. The method links records using a two-step approach, first establishing high confidence matches among men by exploiting a comprehensive set of individual and contextual characteristics. The method then proceeds to link both men and women by leveraging links between households established in the first step. While only the first stage links can be directly comparable to other popular methods in research on the U.S., our method yields both considerably higher linkage rates and greater accuracy while only performing negligibly worse than other algorithms in resembling the target population.

The Great Migration significantly increased the number of African American people moving to northern and western cities beginning in the first half of the twentieth century. We show that their arrival shaped “slum clearance” and urban redevelopment efforts in receiving cities. To estimate the effect of migrants, we instrument for Black population changes using a shift-share instrument that interacts historical migration patterns with local economic shocks that predict Black out-migration from the South. We find that local governments responded by undertaking more urban renewal projects that aimed to redevelop and rehabilitate “blighted” areas. More Black migrants also led to an increase in family displacement. This underscores the contribution of spatial policies such as urban renewal towards understanding the long-term consequences of the Great Migration on central cities, and Black neighborhoods and individuals.

We demonstrate an important complementarity between patriotism and public good provision. After 1933, the New Deal led to an unprecedented expansion of the US federal government’s role. Those who benefited from social spending were markedly more patriotic during WW II: they bought more war bonds, volunteered more and, as soldiers, won more medals. This pattern was new – WW I volunteering did not show the same geography of patriotism. We match military service records with the 1940 census to show that this pattern holds at the individual level. Using geographical variation, we exploit two instruments to suggest that the effect is causal: droughts and congressional committee representation predict more New Deal agricultural support, as well as bond buying, volunteering, and medals.

We develop a measure of workers’ technology exposure that relies only on textual descriptions of patent documents and the tasks performed by workers in an occupation. Our measure appears to identify a combination of labor-saving innovations but also technologies that may require skills that incumbent workers lack. Using a panel of administrative data, we examine how subsequent worker earnings relate to workers’ technology exposure. We find that workers at both the bottom but also the top of the earnings distribution are displaced. Our interpretation is that low-paid workers are displaced as their tasks are automated while the highest-paid workers face lower earnings growth as some of their skills become obsolete. Our calibrated model fits these facts and emphasizes the importance of movements in skill quantities, not just skill prices, for the link between technology and inequality.

Immigrant children represent a significant share of the U.S. population. However, foreign-born children are often disadvantaged compared to their native-born peers due to differences in language skills, schooling and cultural integration. I use historical Census Data to analyze the differences in schooling and labor market outcomes between U.S-born and foreign-born sib-lings to understand the long-term effects of nativity. Children observed in the 1910 decennial Census are linked to their 1940 Census records using a unique method of linking individuals across Census waves. Compared to their native-born siblings, those born abroad are 10.5, 4.1 and 1.7 percentage points less likely to complete eighth grade, high-school and college respectively. The effects are larger for children who arrived at older ages. I do not find a significant impact on wages, employment, and other labor market outcomes. These findings indicate that foreign-born status is a significant determinant of long-term outcomes of children.