This dissertation studies the labor market outcomes of college graduates. The choice of college major is one of the most direct ways for college graduates to acquire skills and earnings differences between college majors rival those between high school and college graduates. In each chapter, I combine rigorous descriptive analysis methods with rich administrative data, job postings data and survey data, to study the determinants of earnings differences between college majors. The first chapter studies whether earnings growth and job changing varies with the specificity of a college major’s skills to particular occupations. College majors vary in the depth and breadth of human capital, and some majors are associated with skills that are very specific to particular work settings. I find that specific majors initially earn 18% more than general majors, but general majors experience steeper earnings growth, and within thirteen years the earnings premium of specific majors shrinks to 6%. To analyze job changing patterns, I create a new linkage of two nationally representative surveys and administrative earnings records. I find that general majors switch occupations, employers and industries at least 20-30% more often that specific majors. A decomposition exercise suggests that these differences in employer and industry change rates account for 30-50% of the explainable portion of the earnings-growth difference between majors. In the second chapter – joint work with Brad Hershbein (Upjohn Institute), Steve Hemelt (UNC Chapel-Hill) and Kevin Stange (University of Michigan) – we document the skill content of college majors as perceived by employers and expressed in the near universe of U.S. online job ads. Social and organizational skills are general in that they are sought by employers of almost all college majors, whereas other skills are more specialized. In turn, general majors – Business and General Engineering – have skill profiles similar to all majors; Nursing and Education are specialized. These cross-major differences in skill profiles explain considerable wage variation, with little role for within-major differences in skills across areas. We conclude that college majors can generally be conceptualized as bundles of aggregate skills that are fairly portable across areas in ways that occupations are not. In the third chapter, I investigate the extent to which mean earnings differences between college majors can be accounted for by mean differences in a major’s typical job attributes. Using data from the National Survey of College Graduates, I find that accounting for mean differences between majors in job attributes – including occupation, employer attributes, job tasks and job levels – accounts for (statistically) over half of the earnings differences between majors. I then use the Gelbach (2016) decomposition to quantify the contribution of each type of job attribute. Unsurprisingly, over one-third of the overall explained between-majors earnings differences is due to occupation. An additional one-third is accounted for by mean differences in employer attributes (ownership structure and size) and an additional 20% is due to variation in job-specific tasks.

Deaths due to synthetic opioids have increased at higher rates for Blacks and Hispanics than for Whites in the last decade. Meanwhile, Blacks and Hispanics experience lower opioid treatment rates and have less availability of medication-assisted treatment (MAT) via office-based buprenorphine in their counties compared to Whites. Racial/ethnic residential segregation is a recognized barrier to equal availability of MAT, but little is known about how such segregation is associated with opioid and substance use treatment availability over time and across Census regions and urban-rural lines. We combined data from the Substance Abuse and Mental Health Services Administration's National Survey of Substance Abuse Treatment Services for 2009, 2014, and 2019 with the 5-year American Community Surveys of 2009, 2014, and 2019 to examine associations between residential segregation indices of dissimilarity and interaction and substance use treatment facilities per 100,000 population, including those providing MAT, in US counties. Estimating county-level two-way fixed effects models and controlling for county-level covariates, we find modest evidence of associations. Despite mostly null findings, an increased likelihood of exposure of Whites to Blacks in a county is associated with fewer substance use treatment facilities per 100,000, particularly those providing MAT via buprenorphine and located in Northeastern and Midwestern counties. Also, a more unequal distribution of Hispanics is associated with fewer facilities per 100,000 providing MAT, and this association is strongest in Southern and Western counties. These associations are driven by recent years (2014–2019) when synthetic opioids became the leading cause of opioid mortality and Blacks and Hispanics began dying at faster rates than Whites. Mixed evidence, however, tempers conclusions for how residential segregation drives racial/ethnic disparities in MAT availability.

Anxiety and depression symptoms significantly worsened nationwide during the COVID-19 pandemic. Construction workers already suffer from an increasing and alarmingly high suicide rate, making it particularly important to understand mental health in the industry during the pandemic. To support that goal, this Data Bulletin examines self-reported symptoms of anxiety and depression in the population using the National Health Interview Survey (NHIS) from 2011 to 2018 and in 2020,2 focusing on patterns and changes during the pandemic. Anxiety and depression were measured for construction workers by A) feelings of anxiety or depression at least once a month; and B) feelings of anxiety or depression at least once a week, or associated medication use. (see the Definitions section at the end of the report for detailed criteria). Differences in the frequency or level of anxiety/depression between 2019 and 2020 were measured in a subsample of construction workers who were interviewed in both years. Anxiety/depression was compared across3 worker demographics, socioeconomic status, and health indicators (i.e., health status, alcohol use, opioid use, and health insurance coverage). Due to the survey methodology changes in 2020 and fewer respondents during the pandemic, the sample size of some subgroups is relatively small.

Many Chinese Americans experience certain barriers (e.g., low income, English as a second language, lack of insurance, cultural differences, discrimination) when they seek oral healthcare services. These barriers may contribute to health disparities by discouraging use (leading to reduced utilization) of preventive and treatment services. This research adopts a modeling approach to develop theory that accounts for dynamic relationships operating at multiple levels, from individuals to families to communities. A multi-method and multi-level modeling approach allows for the interaction of factors at different levels of aggregation. This research applies spatially explicit agent-based modeling to examine how demographic, socioeconomic, and geographic factors shape access to oral healthcare for low-income Chinese Americans in New York City. The simulation model developed in this research was used to test different intervention scenarios involving community health workers who facilitate care coordination and other health promotion activities. In addition to demographic characteristics and socioeconomic factors, this study also considers geographic factors and spatial behavior, such as distance and activity space. The overarching contribution of this study is to provide a complex systems science framework to better understand access to oral healthcare for urban Chinese Americans, toward adapting it for other racial/ethnic minority groups, by integrating health-seeking behaviors at the individual level, barriers to care at multiple levels, and opportunities for health promotion at the community level.

Rising fossil fuel prices since January 2021, recently exacerbated by Russia’s invasion of Ukraine, have revealed the importance of gasoline, diesel fuel and heating oil, propane, natural gas, and electricity to the economy. Not only have higher energy prices created hardships for millions of individuals who struggle to afford to heat their homes; but raising the costs of producing virtually every good and service supplied to businesses and consumers exacerbates the effects of the current inflation caused by the expansionist monetary policies and supply-chain constraints. Two fundamentally different strategies have been advocated to combat rising energy prices. Ironically, both call for U.S. energy “independence,” by which advocates typically mean not relying on imports of crude oil from hostile and politically volatile regions (e.g., Russia, Venezuela, and the Middle East). One strategy calls for increasing fossil fuel supplies by ending the Biden administration’s policies that are hostile to fossil fuel development. These policies include the cancellation of the Keystone XL pipeline, which would have delivered crude oil from Alberta, Canada, and moratoriums on new drilling permits on federal lands and in the Gulf of Mexico. The other strategy calls for transforming the U.S. economy to rely solely on green and zero-carbon energy resources. Capitalizing on Russia’s invasion of Ukraine, for example, Senator Edward Markey (D, MA) has said: “This moment is a clarion call for the urgent need to transition to domestic clean energy so that we are never again complicit in fossil-fueled conflict.”1 Similarly, President Joe Biden touted the rise in gasoline prices as a component of what he termed an “incredible transition” toward less reliance on fossil fuels.2 For some green energy advocates, the invasion of Ukraine has been, in effect, a “twofer”: it has allowed them to emphasize the need for green energy to rescue the planet from what they term a “climate emergency”3 and to reduce the country’s exposure to future fossil fuel price shocks. New York’s policymakers are all in for the second strategy. The state government has been adverse to fossil fuel development and production and has long advocated green energy policies to address climate change. These policies include the “zero-emissions” electricity mandates in the 2019 Climate Leadership and Community Protection Act (hereinafter, Climate Act), preventing upstate communities from rejecting large-scale wind and solar projects, bans on siting new natural gas pipelines, and mandates for “electrifying” end-use energy—vehicles, furnaces, water heaters, and even cookstoves. These efforts will fail. The Climate Act’s zero-emissions mandates are physically infeasible, and, in any event, the resulting reductions in greenhouse gas emissions would have no measurable impact on the world’s climate. But the attempt to implement these policies will impose damaging hardships for businesses and consumers. Low-income New Yorkers, in particular, are already being hard hit by higher energy prices, a phenomenon known as “energy poverty.” Energy poverty is not a new concept. In many developing countries, such as in Africa, energy poverty usually has meant a lack of physical access to energy supplies because no energy supply infrastructure exists. In developed countries, energy poverty is about the affordability of energy. Energy affordability has been a long-standing issue in the Empire State. In the first decade of this century, high prices for natural gas and electricity meant that many New Yorkers spent more than 10% of their incomes—the usual gauge of energy poverty—to heat and light their homes. But thanks to technological advances in hydraulic fracturing (“fracking”), gas supplies boomed, especially in Pennsylvania’s Marcellus Shale play. Production also increased in New York until former governor Andrew Cuomo banned fracking in the state in 2014. Natural gas prices for the state’s residential customers have been increasing since 2016, and electricity prices also continue to increase. Consolidated Edison (ConEd), which provides electric and natural gas service in New York City and Westchester County to about 3.3 million customers, filed for a rate increase in January of this year. If it is approved by the state’s Public Service Commission, residential electricity will cost nearly 30 cents per kilowatt-hour (kWh),4 more than double the average residential price of electricity in the U.S., which was just above 13 cents/kWh in 2020.5 The most recently available U.S. census data show that about 1 million New York households faced energy poverty during 2015–19. The situation today is surely worse. Even if the recent run-up in fossil fuel prices moderates, the state’s energy policies will plunge millions of New Yorkers into energy poverty.

We assess firearms as a means of Black self-defense in the Jim Crow South. We infer firearm access by race and place by measuring the fraction of suicides committed with a firearm. Corroborating anecdotal accounts and historical claims, state bans on pistols and increases in White law enforcement personnel served as mechanisms to disarm the Black community, while having no comparable effect on White firearms. The interaction of these mechanisms with changing national market prices for firearms provides us with a credible identification strategy for Black firearm access. Rates of Black lynching decreased with greater Black firearm access.

In this article, we begin with a description of the current funding system for K-12 Indian education; four programs undergirding our analysis; and relevant literature. We then describe the tools and databases used to derive our findings and examine the issues involved with conducting a per-pupil analysis of Indian K-12 education programs. This is followed by an application of these principles to conduct both a per-pupil and panel regression analysis of actual Department of Education (ED) expenditures on Title VI-A Grants to LEAs. Finally, we conclude with a discussion of our results and suggestions for further research.

This paper investigated whether the commonly observed immigrant health advantage persists among undocumented immigrants in the U.S. and provides nationally representative evidence on the health of this vulnerable population. Data were derived from pooled cross-sections of the National Health Interview Survey (NHIS, 2000–2018). The legal status of foreign-born NHIS respondents is imputed using a non-parametric machine learning model built based on information from the 2004, 2008 and 2014 cohorts of the Survey of Income and Program Participation (SIPP). Multivariate logistic regression analysis indicated that, despite exposure to numerous additional risk factors, the undocumented population experienced a more pronounced Healthy Migrant Effect, with lower odds of reporting fair or poor self-rated health, any physician-diagnosed chronic conditions or being obese. The observed patterns in undocumented health outcomes may be related to the additional challenges and exclusionary policies associated with undocumented migration that could in turn lead to a more pronounced selection of healthy and resilient individuals.

People today have more freedom than they did in the past, with less pressure and limitation to some extent, which leads to a reluctance to marriage; however, this behavior eventually leads to society's inclination to age in the future. The author starts with 1) increasing independence in women, 2) independent consciousness of people in society, and 3) the meaning of marriage to people to discuss whether marriage is in crisis.

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.

In the United States, childhood neighborhood quality shapes adulthood economic opportunities. However, most children raised in bottom-quality neighborhoods still live in low-quality neighborhoods in adulthood. Could childhood neighborhood directly affect adulthood choices? I develop a quantitative spatial model of parental decisions that incorporates a novel mechanism: social learning about the technology of skill formation. Segregation generates information frictions that systematically distort parents' subjective beliefs and behavior. I calibrate the model using several United States representative datasets. The calibrated model matches targeted and non-targeted parental behavior and generates an endogenous distribution of subjective beliefs. I find a relatively modest level of delusion that increases the income Gini index by 3% and the intergenerational rank-rank slope by 12%. A housing voucher policy improves the neighborhood quality of eligible families, raising children's future earnings. When scaling up the policy, long-run and general equilibrium responses in subjective beliefs amplify the policy effects. Inequality reduces, and intergenerational mobility improves.

As the population of the United States has diversified over the last fifty years, the nation’s key sectors of housing, education, and labor have absorbed this diversity with varying degrees of receptivity. In 2019, Latinos continued their status as the nation’s largest minority group, comprising nearly twenty percent of the population (18.5%), outnumbering the African American/Black population by more than five percentage points (13.4%) as well as the Asian population by more than twelve percentage points (5.9%). Latino-origin individuals are now part of the nation’s local schools, markets, and neighborhoods, yet, despite the growing presence of the Latino population, these institutions remain remarkably segregated, at least by race and income. Segregation results in differential exposure to neighborhood conditions that could lead to increased opportunity for educational attainment and increased wages, among other social and health factors. The negative outcomes associated with this lack of exposure have been recognized for Black Americans and are now increasingly present for Latinos. These measures are particularly relevant by metropolitan area.

BACKGROUND Longitudinal data available for studying fertility in the United States are not representative at the state level, limiting analyses of subnational variation in US fertility. The US Census Bureau makes available restricted data that may be used for measuring fertility, but the data have not previously been described for a scholarly audience or used for fertility research. OBJECTIVE This paper describes and analyzes restricted-use administrative birth data available through the Census Numident for nearly all US births for more than the last century. Within these data, most births since 1997 are linked to parents through the Census Household Composition Key (CHCK). These analyses are designed to illustrate the scope and limitations of these data for the study of US fertility. METHODS We describe the creation and content of the Census Numindent and CHCK data sets and compare the data to published US vital statistics. We also analyze the geographic coverage of both data sets and compare the demographic composition of the new data sources to national demographic composition. We further illustrate how these novel data sources may be used by comparing them to survey responses at the individual level. CONTRIBUTION This paper describes an underutilized source of national US data for studying fertility, shows the quality of these data by performing analyses, and explains how scholars can access these data for research.

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.

The recently finalized changes to the disclosure avoidance policies of the US Census Bureau for the 2020 census, grounded in differential privacy, have faced increasing criticism from demographers and other social scientists. Scholars have found that estimates generated via census-released test data are accurate for aggregate total population statistics of larger spatial units (e.g., counties), but introduce considerable discrepancies for estimates of subgroups. At present, the ramifications of this new approach remain unclear for rural populations. In this brief, we focus on rural populations and evaluate the ability of the finalized differential privacy algorithm to provide accurate population counts and growth rates from 2000 to 2010 across the rural–urban continuum for the total, non-Hispanic white, non-Hispanic Black, Hispanic or Latino/a, and non-Hispanic American Indian population. We find the method introduces significant discrepancies relative to the prior approach into counts and growth rate estimates at the county level for all groups except the total and non-Hispanic white population. Further, discrepancies increase dramatically as we move from urban to rural. Thus, the differential privacy method likely introduced significant discrepancies for rural and non-white populations into 2020 census tabulations.

Inability to access quality and affordable child care represents a barrier to employment for many parents, but for low-income parents of young children, it can also be a barrier to compliance with the work requirements affiliated with most forms of public assistance in the United States. Though prior research remains unclear on whether welfare work requirements effectively increase economic self-sufficiency or functionally disqualify especially vulnerable individuals, subsidies for child care have been widely viewed as a way to enable parents to engage in employment and qualifying “Welfare-to-work” program activities. In this paper, I investigate the impacts of these subsidies on parental labor supply decisions and welfare program participation. I also attempt to examine the ways in which accessible childcare alters the screening effects of welfare work requirements, and whether the presence of accessible childcare reduces the disincentives to work inherent to receipt of welfare benefits. Using the Supplemental Nutrition Access Program as my program of interest, I leverage the 2014 reauthorization of the Child Care Development Block Grant and accompanying nationwide changes to eligibility requirements and recertification periods. Via a simulated instrumental variables method, I estimate near-zero effects of altering subsidy program generosity on employment or income and small but significant positive effects upon SNAP uptake among families predicted to be SNAP-eligible by household income amount.

I use a set of machine learning tools to predict next-year unemployment status for individuals in the Current Population Survey, using a subset of variables specified by the MEBDI competition judges. I use a Decision Tree algorithm, and two regularized linear regressions, and I improve the classification performance by combining these three classifiers using a hard-voting ensemble. This combined classifier is able to predict future unemployment in a holdout testing set with 70.95% accuracy and future non-unemployment (employment or not-in-labor-force) with 79.92% accuracy, for an average accuracy across classes of 75.44%. The most important predictive features are a person's current work/employment status. Essentially , the best signal that someone will be unemployed next year is that they don't have a job this year.

Do temporary workers face more employment and earnings risk than direct-hire workers? We link administrative workers’ compensation claims to earnings records to measure the risk posed by workplace injuries, comparing employment and earnings outcomes between temporary and direct-hire workers injured doing the same job. We implement two complementary empirical strategies to account for underlying differences in labor market attachment. Despite evidence that injury severity does not vary between the two sets of workers, temporary workers suffer larger reductions in employment and more severe earnings losses, persisting at least three years after injury, relative to similar direct-hire workers. The additional earnings losses suffered by temporary workers are partially offset by workers’ compensation benefits, but the income loss gap is still large even after accounting for these benefits.

This paper studies the labor-market consequences of increased U.S. tariffs against Chinese exports using the specific factors model of regional economies of Kovak (2013) [18]. By per- forming a two-scales analysis, we find that states and commuting zones that were relatively more protected experienced faster wage growth (or smaller wage decline). We also find smaller employment rate decline in these commuting zones. Finally, we show that the model is robust to trade protectionism.

Digitization has transformed many of the creative industries. Technological changes have sharply reduced the costs of creating, distributing, and promoting new products, with two broad consequences. First, there has been an explosion of new products – in movies, books, music, and television – with substantial welfare benefit for consumers. Second, because technological change has reduced the need for physical or financial capital for undertaking investment in new products, it has enabled individuals to bring new products to market largely by supplying their own labor to entrepreneurial creative projects. In this chapter I explore consequences of digitization for both consumers via the product market as well as entrepreneurial producers via their labor market activity.