Over the last two decades, state and local governments have adopted policies requiring employers to electronically verify (E-Verify) the work eligibility of their new hires to disrupt unauthorized immigrants' access to the formal labor market. These E-Verify mandates previously enjoyed bipartisan support and are likely to be included in comprehensive immigration reform discussions between the Biden administration and GOP. I show in this paper that state E-Verify mandates are associated with a 5-percentage point reduction in the probability that likely unauthorized immigrants are employed and a 2-percentage point reduction in the probability that they have employer-sponsored insurance. However, these changes are limited to one period after implementation. In all remaining periods, the relationships are not distinguishable from zero. I show that this pattern can be explained by selective outmigration of otherwise unemployed and uninsured likely unauthorized immigrants. By preventing unauthorized immigrants from moving to a more favorable policy environment, a nationwide E-Verify mandate would likely further limit unauthorized immigrants' access to private health insurance.

In this paper we utilize a combination of national microdata from the Integrated Public Use Microdata Series (IPUMS) with local population and health microdata, spatialized to the household level, and employ an historical GIS (HGIS) to follow infectious disease transmission between public school children in Michigan’s Upper Peninsula during the 1918 influenza pandemic. Microdata are data at the finest, non-aggregated level of precision. We illustrate three important advantages of using historical microdata within an HGIS framework: contextualization of data within their period-accurate space—time, avoidance of the ecological fallacy, and the ability to move freely between micro and macro scales. We demonstrate the potential for studying historic pandemics using historical microdata by doing a spatiotemporal analysis following infectious respiratory disease through three schools from April to June 1918.

This study examines the effect of noncitizen truck driver employment on the wages of truck drivers who are US citizens. We hypothesize that citizen status job heterogeneity creates a labor market environment that contributes to relatively low wages for these drivers compared to drivers who are US citizens. Findings suggest a significant wage discount for noncitizen truck drivers; however, this discount erodes as these drivers gain experience in the USA. Nonetheless, the noncitizen wage discount persists even when accounting for noncitizen drivers attaining greater years residing in the USA. Findings also suggest that noncitizen truck drivers are imperfect substitutes for truck drivers who are US citizens. This lack of perfect substitutability is consistent with this study’s findings indicating a statistically insignificant negative noncitizen effect on the wages of drivers who are US citizens.

This paper explores the role of the gender equality culture in cross-country gender commuting gap differences. To avoid inter-relationships between culture, institutions, and economic conditions in a simple cross-country analysis, we adopt the epidemiological approach. We merge data from the American Time Use Survey for the years 2006–2018 on early-arrival first- and second-generation immigrants living in the United States with their corresponding annual country of ancestry’s Gender Gap Index (GGI). Because all these immigrants (with different cultural backgrounds) have grown up under the same laws, institutions, and economic conditions in the US, the gender differences among them in the time devoted to commuting to/from work can be interpreted as evidence of the existence of a cultural impact. Our results show that a culture with more gender equality in the country of ancestry may reduce the gender commuting gap of parents. Specifically, an increase of 1 standard deviation in the GGI increases women’s daily commuting time relative to men by almost 5 minutes, a sizeable effect representing 23 percent of the standard deviation in the gender commuting gap across countries of ancestry. A supplementary analysis provides possible mechanisms through which culture operates and is transmitted, showing the potential existence of horizontal transmission and the importance of the presence of children in commuting. Our results are robust to the use of different subsamples, geographical controls, and selection into employment and telework.

Objectives Amid a pandemic, vaccines represent a promising solution for mitigating public health and economic crises, and an improved understanding of individuals’ vaccination intentions is crucial to design optimal immunization campaigns. This study predicts uptake rates for different COVID-19 vaccine specifications and identifies personal characteristics that moderate an individual’s responsiveness to vaccine attributes. Methods We developed an online survey with contingent specifications of a COVID-19 vaccine, varying in effectiveness, risks of side effects, duration of immunity, and out-of-pocket cost. Using population-averaged logit models, we estimated vaccine uptake rates that account for uncertainty, heterogeneity across respondents, and interactions between vaccine and personal characteristics. Results We obtained 3047 completed surveys. The highest uptake rate for an annual vaccine, 62%, is predicted when vaccine effectiveness is 80% to 90%, side effects are minimal, and the vaccine is provided at zero cost, with decreases seen in the uptake rate for less effective vaccines, for example, 50% for 50% to 60% effectiveness. Moreover, we found that Americans’ response to vaccine effectiveness depends on their self-reported concern, that is, concerned respondents report a higher willingness to get vaccinated. Our findings also indicate that COVID-19 vaccine uptake rates decrease with vaccine cost and that responsiveness to vaccine cost is moderated by income. Conclusions Although providing the COVID-19 vaccine at zero cost will motivate many individuals to get vaccinated, a policy focused exclusively on vaccine cost may not be enough to reach herd immunity thresholds. Although those concerned with COVID-19 will participate, further evidence is needed on how to incentivize participation among the unconcerned (43%) to prevent further pandemic spread.

The Census began identifying same-sex couple households in 1990. However, issues arise when using data for the Census because it is possible that opposite-sex couples make mistakes while filling out the Census and get recorded as same-sex couple households. Researchers have developed methods to edit the recorded count of same-sex couple households in order to obtain a more accurate count. However, the methods used to edit the data vary. This paper discusses the treatment of same-sex couple households and the different editing procedures used in the Census. The goal of this paper is to allow researchers to confidently use Census data in future analyses on same-sex couple households.

This dissertation studies labor market disparities and inequality in the United States. The focus is to investigate the determinants of gaps in various labor market outcomes over the life cycle between different demographic groups and to study whether the increased earnings inequality over the past decades has been connected with another macroeconomic trend, increased concentration of production. In Chapter 2, I examine the determinants of the gaps in wages, labor market participation, and other labor market outcomes between men and women over the life cycle. Using a search and matching model and data on the U.S. labor markets, I show that wage gaps arise mainly because of women’s lower productivity level at the beginning of their careers as well as women’s more frequent career breaks. Firms’ actions matter too: firms’ are less willing to hire workers with a higher likelihood of career breaks which leads to lower negotiated wages. Chapter 3 builds on the analysis in Chapter 2 and uses a structural model to decompose the labor market outcome gaps between all the major demographic groups in the U.S. labor markets including genders, races, ethnicities, skill levels, and age groups. Chapter 3 focuses specifically on studying the role of discrimination in generating disparities in the outcomes. Using 1998— 2018 U.S. data, we find that differences in initial human capital, returns to experience, and job separation rates account for most of the demographic disparities; wedges in matching efficiencies play a secondary role. Our results suggest a minor aggregate impact of taste-based discrimination in hiring and an important role for statistical discrimination affecting particularly female groups and Black males. In Chapter 4, I examine the relationship between two recent trends: increasing firm concentration and rising earnings inequality. Using an assignment model with heterogeneous firms and workers and data on earnings variance components in the U.S., I show that increased price elasticity of demand has been the most important driver of both earnings inequality and firm concentration in the past few decades. An increase in both earnings and sales concentration has also been significantly impacted by a shift in the skill distribution of workers. Skill distribution has now a thicker tail, shifting both production and earnings towards the best workers and firms.

Background: Unmet oral health needs routinely affect low-income communities. Lower-income adults suffer a disproportionate share of dental disease and often cannot access necessary oral surgery services. The Affordable Care Act (ACA) Medicaid expansion created new financial opportunities for community health centers (CHCs) to provide mission-relevant services in low-income areas. However, little is understood in the literature about how the ACA Medicaid expansion impacted oral surgery delivery at CHCs. Using a large sample of CHCs, we examined whether the ACA Medicaid expansion increased the likelihood of oral surgery delivery at expansion-state CHCs compared to non-expansion-state CHCs. Methods: Exploiting a natural experiment, we estimated Poisson regression models examining the effects of the Medicaid expansion on the likelihood of oral surgery delivery at expansion-state CHCs relative to non-expansion-state CHCs. We merged data from multiple sources spanning 2012–2017. The analytic sample included 2054 CHC-year observations. Results: Compared to the year prior to expansion, expansion-state CHCs were 13.5% less likely than non-expansion-state CHCs to provide additional oral surgery services in 2016 (IRR = 0.865; P = 0.06) and 14.7% less likely in 2017 (IRR = 0.853; P = 0.02). All else equal, and relative to non-expansion-state CHCs, expansion-state CHCs included in the analytic sample were 8.7% less likely to provide oral surgery services in all post-expansion years pooled together (IRR = 0.913; P = 0.01). Conclusions: Medicaid expansions can provide CHCs with opportunities to expand their patient revenue and services. However, whether because of known dental treatment capacity limitations, new competition, or coordination with other providers, expansion-state CHCs in our study sample were less likely to provide oral surgery services on the margin relative to non-expansion-state CHCs following Medicaid expansion.

From swarms of insects to the migration of people, studies of collections of complex individuals are found across multiple disciplines. Accompanying the recent growth of data for such systems are methods for discovering trends hidden within large datasets. To join this effort, this thesis shall develop and apply a generic statistical framework, Density-functional Fluctuation Theory (DFFT), that can both quantify behaviors and predict responses of complex density-dependent systems using analysis of data alone. Specifically, we consider systems that are collections of individuals interacting with both their environment and neighboring individuals. These types of systems arise in fields from ecology to neighborhood-scale demography. One feature commonly shared in such studies is that data comes in the form of densities, e.g. the counts of individuals of a given type in an area. The challenge DFFT addresses is how to build a statistical framework for such data that is both descriptive and predictive while making minimal assumptions about the nature of the underlying interactions in a system. DFFT does so by means of a generic probabilistic model that separates the tendencies of individuals to be found in parts of a heterogeneous environment from the tendencies of individuals to group or not group with others of the same or different type. This thesis shall demonstrate the utility of this method on experimental, simulated, and extant data sets by developing predictions that are of use to each respective system. First, using an experimental collection of walking fruit flies, we measure fly-fly interactions to accurately predict the distribution of flies after the number of flies changes by two orders of magnitude. Second, using the classic Schelling model of residential segregation, we develop a multi-group segregation function to accurately predict how a simulated city responds at short time scales to changes in the population. Third, using US census population counts by race and ethnicity, we use our segregation function to predict and validate the probability of any neighborhood within the US to change its racial composition over a 10 year period. Each chapter is intended to stand on its own and so readers are encouraged to skip to the application that most interests them. This thesis, then, establishes DFFT as a useful tool towards the analysis and prediction of density-dependent complex systems.

China’s meteoric rise as a world manufacturing power commenced in the early 1990s and was heralded and amplified by its elevation to Permanent Normal Trading Status with the United States and accession to the World Trade Organization at the start of the new millennium. A rapidly growing body of literature finds that China’s subsequent export surge, which drove the US merchandise trade deficit from 2.7% to 5.7% of GDP between 1998 and 2007, left large and enduring scars on aggregate US manufacturing employment, on workers initially employed in manufacturing establishments competing with China, and on local labor markets specializing in labor-intensive manufacturing, in which China gained comparative advantage (Bernard, Jensen, and Schott 2006; Autor, Dorn, and Hanson 2013; Autor et al. 2014; Ebenstein et al. 2014; Acemoglu et al. 2016; Autor, Dorn, and Hanson 2016; Caliendo, Dvorkin, and Parro 2019). The scale and duration of these impacts, known collectively as the “China shock,” took economists and policymakers by surprise and initially faced some skepticism from trade scholars. One does not have to be a trade theorist or an industrial engineer, however, to notice that the precipitous fall in US manufacturing that began in the late 1990s and continued for almost fifteen years was without parallel in the post-Depression era and that it had no plausible technological origin.

The COVID-19 pandemic has made clear the vital role women play in our economy and in the economic security of families, both nationally and in West Virginia. Now more than ever, lawmakers in West Virginia must do better to ensure all women and families have quality reproductive health care, safe workplaces, equal representation in government, and economic security. Women need policies that reflect their roles as providers and caregivers—roles that the COVID-19 pandemic has demonstrated are critical to the well-being of families, communities, and the economy. In West Virginia, mothers are the sole, primary, or co-breadwinners in 63.8 percent of families, and these numbers are higher for some mothers of color across the United States. The following policy recommendations can help support the economic security of women and families in West Virginia.

We address applied and computational issues for the problem of multiple treatment effect inference under many potential confounders. While there is abundant literature on the harmful effects of omitting relevant controls (under-selection), we show that over-selection can be comparably problematic, introducing substantial variance and a bias related to the non-random over-inclusion controls. We provide a novel empirical Bayes framework to mitigate both under-selection problems in standard high-dimensional methods and over-selection issues in recent proposals, by learning whether each control's inclusion should be encouraged or discouraged. We develop efficient gradient-based and Expectation-Propagation model-fitting algorithms to render the approach practical for a wide class of models. A motivating problem is to * estimate the salary gap evolution in recent years in relation to potentially discriminatory characteristics such as gender, race, ethnicity and place of birth. We found that, albeit smaller, some wage differences remained for female and black individuals. A similar trend is observed when analyzing the joint contribution of these factors to deviations from the average salary. Our methodology also showed remarkable estimation robustness to the addition of spurious artificial controls, relative to existing proposals.

We investigate socioeconomic differences in Healthy Life Expectancy (HLE)− how many years an individual is expected to live in good health − for different educational groups in the United States, using repeated cross-sections from the National Health Interview Survey. We use a novel instrumental variable approach to test and address for potential endogeneity present in our linear additive explanatory model, which exploits the independence across different years in this dataset. Our results suggest that ignoring the potential endogeneity of included covariates in explaining health could result in misleading inferences about a sub-population's health and, subsequently, HLE.

In the paper, “The Anti-Poverty, Targeting, and Labor Supply Effects of the Proposed Child Tax Credit Expansion,” hereafter, Corinth et al. (2021), we examine the effects of proposed changes to the Child Tax Credit (CTC) on labor supply and poverty. We find that the Build Back Better Act changes would have negative consequences. By eliminating the strong work incentives in the previous CTC, we estimate that the proposal would reduce employment by approximately 1.5 million workers, which would diminish the proposal’s effect on child poverty and eliminate its effect on deep child poverty altogether. Even without accounting for the reduction in work, the expansion would be less targeted to those in the bottom decile of annual income and have less poverty reduction per dollar spent on families with children than most other anti-poverty programs.

We examine how cultural distance between an analyst and a CEO is associated with earnings forecast performance. Using a sample of 283,062 analyst-firm-year earnings forecasts over the period 1992–2016, we find that greater cultural distance is associated with greater forecast error. This finding is robust to the use of alternative culture frameworks. We further document that our result is mainly driven by a culture effect rather than bilateral trust or a shared common language.

A college education has long been accepted as a way to promote economic advancement. However, inequality in post-secondary access has limited educational attainment in the state of Florida. To combat this, Governor DeSantis issued Executive Order 19-31 to move Florida’s workforce education from 24th to 1st in the nation while ensuring students are prepared for the dynamic labor market. About two million more working age Floridians must achieve a shortterm credential to achieve this goal. This is far greater than the current 700,000 students enrolled in the Florida College Systems.1 Therefore, accomplishing this task requires the state to focus on the demographic of students who make up the smallest fraction of post-secondary attainment achievers: low-income students.

Purpose: Prior studies have identified health care providers' lack of cultural competency as a major barrier to care among sexual minority individuals. However, little is known about disparities in experience with culturally competent care by sexual orientation at the population level. This study assessed experiences with culturally competent care and satisfaction with care across sexual orientation groups in the United States. Methods: We analyzed nationally representative data from the 2017 National Health Interview Survey (n = 21,620) with ordinal logit regression models and compared six aspects of health care experiences across sexual orientation groups. These were: preferences for and frequencies of seeing health care providers who understand or share their culture; perceived experiences of being treated with respect by providers and providers asking about their beliefs and opinions; access to easily understood health information from providers; and satisfaction with received care. Results: Relative to heterosexual men, gay men were more likely to consider it important for providers to understand or share their culture (odds ratio [OR] = 1.4, p < 0.05) and to have providers who ask for their opinions or beliefs about care (OR = 1.5, p < 0.01). Relative to heterosexual women, bisexual- and something else-identified women were less likely to report being treated with respect (ORs = 0.4–0.6, p's < 0.01) and satisfaction with care (ORs = 0.5–0.6, p's < 0.05). No statistical differences in health care experiences were found between other sexual minority groups and their heterosexual counterparts. Conclusions: Access to culturally competent care and satisfaction with care varied by sexual orientation and gender. Clinical practices should address the unique health care barriers faced by bisexual- and something else-identified women.

We study the origins of labor productivity growth and its differences across sectors. In our model, sectors employ workers of different occupations and various forms of capital, none of which are perfect substitutes, and technology evolves at the sector-factor cell level. Using the model we infer technologies from US data over 1960-2017. We find that sectoral differences in labor productivity growth are largely due to sectoral differences in the growth rate of routine labor augmenting technologies. Neither capital accumulation nor the occupational employment structure within sectors explains much of the sectoral differences in labor productivity growth.

Determining long-term trends in chronic pain prevalence is critical for evaluating and shaping U.S. health policies, but little research has examined such trends. This study (1) provides estimates of pain trends among U.S. adults across major population groups; (2) tests whether sociodemographic disparities in pain have widened or narrowed over time; and (3) examines socioeconomic, behavioral, psychological, and medical correlates of pain trends. Regression and decomposition analyses of joint, low back, neck, facial/jaw pain, and headache/migraine using the 2002-2018 National Health Interview Survey for adults aged 25-84 (N = 441,707) assess the trends and their correlates. We find extensive escalation of pain prevalence in all population subgroups: overall, reports of pain in at least one site increased by 10%, representing an additional 10.5 million adults experiencing pain. Socioeconomic disparities in pain are widening over time, and psychological distress and health behaviors are among the salient correlates of the trends. This study thus comprehensively documents rising pain prevalence among Americans across the adult life span and highlights socioeconomic, behavioral, and psychological factors as important correlates of the trends. Chronic pain is an important dimension of population health, and demographic research should include it when studying health and health disparities.

We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with a noise addition mechanism, and (3) generate synthetic data that preserves the measured marginals well. Central to this approach is Private-PGM [42], a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals. We present two mechanisms, NIST-MST and MST, that are instances of this general approach. NIST-MST was the winning mechanism in the 2018 NIST differential privacy synthetic data competition, and MST is a new mechanism that can work in more general settings, while still performing comparably to NIST-MST. We believe our general approach should be of broad interest, and can be adopted in future mechanisms for synthetic data generation.