Fast food—defined as restaurants in which patrons order or select items and pay before eating1—is an integral part of the food sector in Los Angeles. In 2019, fast food employed 4.5 million people nation-wide,2 including nearly 550,000 Californians and 150,000 Angelenos.3 Our previous research found that the restaurant sector made up a tenth of the overall county workforce, and over a third of Los Angeles’s restaurant workers were employed in fast food.

This paper introduces a structural framework to isolate and examine the role of skill distribution and skill premium changes in US private sector deunionization. The framework is a search and matching model with separate unionized and non-unionized sectors where wages are determined through enterprise-level bargaining in the union sector and through individual bargaining in the non-union sector. Workers differ by human capital the measure of worker skill in the model-and decide which sector to work in based on their prospective wages. The model predicts that workers with a moderate level of human capital will select into the union sector while those with low or high levels will select into the non-union sector. The model is initially calibrated to the 1984 US private sector economy. Counterfactual analysis is performed to isolate the effect of the changing skills distribution and rising skill premium on union coverage in 2007 and 2019. Consistent with the literature, skills are measured using a predicted wage equation on several skill-related characteristics, and non-skill-related controls. Using these equations from different years, a new method is developed to track movements in the skill distribution and premium over time. This method reports skill dispersion, a widening of the skill distribution, in recent decades. The mechanism of deunionization is the same for both skill dispersion and skill premium rises: widening productivity gaps under compressed union wages incentivize high-skill workers to leave the union sector and unionized firms to stop hiring low-skill workers on the margin. The counter-factual estimates suggest that skill dispersion accounted for just under a tenth of US private sector deunionization between 1984 and 2019, with most of its impact coming after 2007. The results also suggest that a skill premium rise accounted for just over a fifth of the decline between 1984 and 2007, and a skill premium decrease actually worked in favor of unionization after 2007, attenuating the overall decline by about a tenth.

Electric vehicles will become increasingly prevalent over the next few decades to accelerate decarbonization of the transportation sector. This thesis uses a spatial lens to explore the intersection of transportation, decarbonization, and energy justice to deepen our understanding of the potentially uneven impacts of the electric vehicle transition in the United States. This work will be submitted to a journal under the same title for publication.

This paper documents home Internet access, types of Internet access, connection speeds, and prices for basic home Internet in tribal areas of the United States. We find that the share of households with Internet access is 21 percentage points lower in tribal areas than in neighboring non-tribal areas. When compared to these non-tribal areas, download speeds, whether measured using fixed or mobile broadband networks, are approximately 75% slower in tribal areas, while the lowest price for basic Internet services in tribal areas is 11% higher. Regression techniques reveal that traditional cost factors such as terrain and population density fully explain the price gap but account for only a fraction of the tribal differences in Internet access and connection speeds. Income differences are strong predictors of Internet access but do not affect connection speeds. A sizable amount of the variation in the access and home connection gap between tribal and non-tribal is left unexplained. We conclude with a discussion of how federal broadband programs have penetrated Indian Country, how tribal-specific factors are related to the variation in Internet access within Indian Country, and the potential policy implications of our findings.

Summary: A majority of the largest single-site outbreaks of COVID-19 infections in the United States have been in prisons and jails since the beginning of the pandemic. These outbreaks threaten the lives and well-being of incarcerated people, correctional staff, and people who live in the communities to which incarcerated people return. This study employs both linear and logistic multivariate regression models to examine data from the UCLA’s COVID Prison Data Project, IPUMS CPS [Integrated Public Use Microdata Series—Current Population Survey], the National Center for Health Statistics, and the Prison Policy Initiative to better understand the facility, county, and state-level predictors of COVID-19 infections and deaths in correctional facilities. The study finds that while some facility-level characteristics are associated with infections and deaths, county-level racial and economic characteristics matter more. In particular, facilities in counties with more Latinx and Indigenous people and lower average incomes have higher infection rates. Likewise, the odds that someone in a facility has died from COVID-19 are higher in counties with more Latinx people, lower average incomes, more college graduates, and fewer people who never married. Moreover, state-level policy changes to address this crisis have failed to do so effectively. While this study is unable to access how county-level characteristics influence these facility-level outcomes, it does demonstrate a clear connection between racialization and exposure to early death.

Infant sex ratios that differ from the biological norm provide a measure of gender status inequality that is not susceptible to social desirability bias. Ratios may become less biased with educational expansion through reduced preference for male children. Alternatively, bias could increase with education through more access to sex-selective medical technologies. Using National Vital Statistics data on the population of live births in the U.S. 1969-2018, we examine trends in infant sex ratios by parental race/ethnicity, education, and birth parity over 5 decades. We find son-biased infant sex ratios among Chinese and Asian Indian births that persist in recent years and regressions suggest son-biased ratios among births to Filipino and Japanese mothers with less than high school education. Infant sex ratios are more balanced at higher levels of maternal education, particularly when both parents are college educated. Results suggest greater equality of gender status with higher education in the U.S. Abstract Infant sex ratios that differ from the biological norm provide a measure of gender status inequality that is not susceptible to social desirability bias. Ratios may become less biased with educational expansion through reduced preference for male children. Alternatively, bias could increase with education through more access to sex-selective medical technologies. Using National Vital Statistics data on the population of live births in the U.S. 1969-2018, we examine trends in infant sex ratios by parental race/ethnicity, education, and birth parity over 5 decades. We find son-biased infant sex ratios among Chinese and Asian Indian births that persist in recent years and regressions suggest son-biased ratios among births to Filipino and Japanese mothers with less than high school education. Infant sex ratios are more balanced at higher levels of maternal education, particularly when both parents are college educated. Results suggest greater equality of gender status with higher education in the U.S.

We examined geographic and racial variation in cancer mortality within the state of Georgia, and investigated the correlation between the observed spatial differences and county-level characteristics. We analyzed county-level cancer mortality data collected by the Centers for Disease Control and Prevention on breast, colorectal, lung, and prostate cancer mortality among adults (aged ≥ 18 years) in 159 Georgia counties from years 1999 through 2019. Geospatial methods were applied, and we identified hot spot counties based on cancer mortality rates overall and stratified by non-Hispanic white (NH-white) and NH-black race/ethnicity. Among all adults, 5.0% (8 of 159), 8.2% (13 of 159), 5.0% (8 of 159), and 6.9% (11 of 159) of Georgia counties were estimated hot spots for breast cancer, colorectal, lung, and prostate cancer mortality, respectively. Cancer mortality hot spots were heavily concentrated in three major areas: (1) eastern Piedmont to Coastal Plain regions, (2) southwestern rural Georgia area, or (3) northern-most rural Georgia. Overall, hot spot counties generally had higher proportion of NH-black adults, older adult population, greater poverty, and more rurality. In Georgia, targeted cancer prevention strategies and allocation of health resources are needed in counties with elevated cancer mortality rates, focusing on interventions suitable for NH-black race/ethnicity, low-income, and rural residents.

This paper documents the persistence of a local labor demand shock from a key episode of deindustrialization in the US: the decline of the New England textile industry in the 1920s. Although spatial equilibrium models predict worker migration in response to a loss of local employment, New England towns that heavily depended on the textile industry in 1900 did not experience a significant decline in population compared to other towns. Individuals in these towns, especially those with a lower level of wealth, were less likely to out-migrate. Adult workers switched to the agricultural sector and faced decreased occupational earnings. Using a matched difference-indifferences design that exploits variation in timing and location of large textile plant closures, I find that young individuals in plant closure towns increased their eighth-grade completion, but their labor market outcomes did not improve by 1940. My findings provide policy implications for local economic recovery, such as offering migration assistance to the low and middle-class workforce and promoting diversity in the local industrial base.

Sites listed on the Environmental Protection Agency’s National Priority List (NPL) are some of the most polluted or contaminated locations in the United States. Only locations that have been evaluated as posing the greatest widespread and imminent threat to human health and/or the biophysical environment make it onto the NPL, and Santa Clara County (SCC) in California is home to twenty-three of them. Since the creation of the NPL and associated Superfund program in the 1980s, hundreds of studies in the field of environmental justice have provided evidence that the burdens of environmental hazards, like Superfund sites, are not distributed equally across racial, ethnic, or economic groups. Thus, in an effort to better understand the extent of this idea this project seeks to ascertain if a spatial disparity in the distribution of Superfund site locations within SCC exists today and whether post-siting demographic change occurred around sites within the county. This project maps the locations of active and historic Superfund sites in addition to completing a longitudinal, area-weighted analysis of the surrounding communities and study area. By spatiotemporally assessing theories associated with hazardous waste sites and disparities, this project ultimately seeks to provide a clearer understanding of how environmental hazards and disparities can affect and shape the communities in which they are found.

Breakthrough innovations are expected to have a bigger impact on local economies than incremental innovations do. Yet past research has largely neglected the regional drivers of breakthrough innovations. Building on theories that highlight the role of personality psychology and human agency in shaping regional innovation cultures, we focus on psychological openness as a potential explanation for why some regions produce more breakthrough innovations than others do. We use a large data set of psychological personality profiles (∼1.26M individuals) to estimate the openness of people in metropolitan statistical areas (MSAs) in the US. Our results reveal that psychological openness is strongly associated with the emergence of breakthrough innovations but not with the emergence of incremental innovations. The findings remained robust after controlling for an extensive set of predictors of regional innovation such as star inventors, star scientists, or knowledge diversity. The results held even when we used tolerance as an alternative indicator of openness. Taken together, our results provide robust evidence that openness is relevant for regional innovation performance, serving as an important predictor for breakthrough innovations but not for incremental innovations.

Globally, there were 425 million adults who had diabetes in 2017, and by 2045, that number is predicted to reach 629 million (International Diabetes Federation 2017). Most (90–95%) of those affected by the disease have type 2 diabetes mellitus (Donovan 2004). Evidence suggests that the development of type 2 diabetes is associated with risk factors such as overweight or obesity (Cheng 2005; Wannamethee and Shaper 1999), lack of physical activity (Aune et al. 2015; Sigal et al. 2006) and psychological distress (Li et al. 2017; Weissman et al. 2015). In the United States (U.S.), 1 out of 10 adults had type 2 diabetes in 2012, and, from 2009 to 2012, the incidence within the age group was 7.8 per 1000 (Center for Disease Control and Prevention 2014). However, the morbidity and mortality burden due to dia- betes is much higher for racial and ethnic groups other than whites (Smedley, Stith, and Nelson 2003). Though diabetes development and disparity have been documented across many racial and ethnic lines, the phenomenon seems to be less clear for African immigrants.

I propose two alternative versions of a “Tullock Index” for assessing the effectiveness of income or wealth redistribution. In the spirit of Atkinson’s (J Econ Theory 2:244–263, 1970) inequality index, the Tullock Index is constructed with reference to either (A) the maximum inequality reduction attainable with current transfer spending or (B) the minimum transfer spending necessary to achieve current post-transfer inequality. Using Current Population Survey (CPS) microdata from 1988 to 2014, I construct annual estimates of the Tullock Index at the national level for the United States. The Tullock Index is increasing over that period, suggesting that redistribution has become less effective in reducing inequality. State-level panel fixed-effects estimates show that ineffective redistribution is related to higher state-level poverty rates, lower employment-to-population ratios, and lower levels of overall employment.

Synthetic data is often presented as a method for sharing sensitive information in a privacy-preserving manner by reproducing the global statistical properties of the original data without disclosing sensitive information about any individual. In practice, as with other anonymization methods, synthetic data cannot entirely eliminate privacy risks. These residual privacy risks need instead to be ex-post uncovered and assessed. However, quantifying the actual privacy risks of any synthetic dataset is a hard task, given the multitude of facets of data privacy. We present Anonymeter, a statistical framework to jointly quantify different types of privacy risks in synthetic tabular datasets. We equip this framework with attack-based evaluations for the singling out, linkability, and inference risks, which are the three key indicators of factual anonymization according to data protection regulations, such as the European General Data Protection Regulation (GDPR). To the best of our knowledge, we are the first to introduce a coherent and legally aligned evaluation of these three privacy risks for synthetic data, as well as to design privacy attacks which model directly the singling out and linkability risks. We demonstrate the effectiveness of our methods by conducting an extensive set of experiments that measure the privacy risks of data with deliberately inserted privacy leakages, and of synthetic data generated with and without differential privacy. Our results highlight that the three privacy risks reported by our framework scale linearly with the amount of privacy leakage in the data. Furthermore, we observe that synthetic data exhibits the lowest vulnerability against linkability, indicating one-to-one relationships between real and synthetic data records are not preserved. Finally, with a quantitative comparison we demonstrate that Anonymeter outperforms existing synthetic data privacy evaluation frameworks both in terms of detecting privacy leaks, as well as computation speed. To contribute to a privacy-conscious usage of synthetic data, we publish Anonymeter as an open-source library.

Acute malnutrition affects a sizeable number of young children around the world, with serious repercussions for mortality and morbidity. Among the top priorities in addressing this problem are to anticipate which children tend to be susceptible and where and when crises of high prevalence rates would be likely to arise. In this article, we highlight the potential role of conflict and climate conditions as risk factors for acute malnutrition, while also assessing other vulnerabilities at the individual- and household-levels. Existing research reflects these features selectively, whereas we incorporate all the features into the same study. The empirical analysis relies on integration of health, conflict, and environmental data at multiple scales of observation to focuses on how local conflict and climate factors relate to an individual child’s health. The centerpiece of the analysis is data from the Demographic and Health Surveys conducted in several different cross-sectional waves covering 2003–2016 in Kenya, Nigeria, and Uganda. The results obtained from multi-level statistical models indicate that in Kenya and Nigeria, conflict is associated with lower weight-for-height scores among children, even after accounting for individual-level and climate factors. In Nigeria and Kenya, conflict lagged 1–3 months and occurring within the growing season tends to reduce WHZ scores. In Uganda, however, weight-for-height scores are primarily associated with individual-level and household-level conditions and demonstrate little association with conflict or climate factors. The findings are valuable to guide humanitarian policymakers and practitioners in effective and efficient targeting of attention, interventions, and resources that lessen burdens of acute malnutrition in countries prone to conflict and climate shocks.

In 2019, 61% of all nonelderly adults in the US obtained their health insurance coverage through an employer-sponsored in- surance (ESI) plan.1 Women often have higher health care needs than men, as well as specific challenges to accessing and af- fording health care.2 Health care policy changes2 and re- cent economic trends may have had differential effects on health care affordability formen and women.3,4 We examined reported differences between men and women with ESI in obtaining affordable health care over the last 2 decades.

Introduction: The subsidized insurance provision under the Affordable Care Act is an important instrument for health insurance coverage among middle-income nonelderly individuals. However, unlike the health impacts of the Medicaid expansion under the Affordable Care Act, the impact of subsidized insurance is relatively less explored in extant literature. This study aims to assess the role of subsidized coverage eligibility in medication adherence among nonelderly patients with hypertension and diabetes in the U.S. Methods: Using pooled data from 8 rounds (2011–2018) of the National Health Interview Survey, we estimated a difference-in-differences model to examine the change in medication adherence among study participants with a household income of 150%–399% of the Federal Poverty Line compared with that among their counterparts with a household income of ≥400% of the Federal Poverty Line during pre‒ and post‒Affordable Care Act periods. We also performed event study analysis and falsification tests to check the validity of our quasi-experimental design. Analyses were conducted in 2022. Results: Medication adherence in the treatment group increased by 4.5 percentage points (95% CI=2.8, 6.2) during the post‒Affordable Care Act periods, whereas the increase was only 1.8 percentage points (95% CI=0.6, 3.0) in the control group. Results of the difference-in-differences model suggest that because of the subsidized insurance under the Affordable Care Act, medication adherence in the treatment group increased by 3.1 percentage points (95% CI=1.0, 5.2) during the post‒Affordable Care Act periods, compared with that in the control group. This increase was attributable to the improved insurance coverage, which increased by 6.8 percentage points (95% CI=5.3, 8.4) in the treatment during the post‒Affordable Care Act periods. Conclusions: Our analyses generate evidence that middle-income individuals with hypertensive or diabetic conditions, who were eligible for the subsidized coverage, benefited from this provision of the Affordable Care Act.

The past decade has seen a seismic shift in the study of internal migration, not only in sources of data and analytical methods, but in the overall scope of scholarly research, in modes of explanation, and hence in understanding. Triggered in large part by the growing availability of large-scale surveys, retrospective life histories and longitudinal datasets, the focus of contemporary research has expanded beyond its traditional concern with documenting spatial patterns, to encompass a temporal dimension that offers new and important insights into the dynamics of human population mobility. These also hold the key to some of the long-standing puzzles in migration research. Conventional, census-based analyses, using aggregate data, introduce time by juxtaposing cross-sectional results for consecutive migration intervals. Longitudinal data, on the other hand, allow for a fundamental realignment of the building blocks of migration research, focusing instead on the timing of sequential moves in the individual life course. Aggregating over multiple observations then delivers a cohort perspective that is at once both more comprehensive and more subtly nuanced. Use of a cohort approach elucidates the timing and spacing of migration. It facilitates identification of the life-course events that often act as triggers to population movement. Crucially, it also provides the basis for a robust suite of statistical indicators that collectively capture the essential dimensions of migration timing: its onset, frequency, spacing and completion. It is these cohort measures of migration that help unlock formerly intractable questions, such as the incidence of chronic mobility, and immobility, and their role in shaping overall migration intensities. They also underline mobility as a continuum, characterised by variable timing and movement frequency among people with markedly differing characteristics. Coupled with the battery of migration indicators previously developed for use with cross sectional data, these measures also provide invaluable insights into reasons for the marked variations in migration intensity that exist between countries. The literature is peppered with fragmentary excursions into the temporal aspects of migration, and threads from earlier work have helped shape the approach elaborated in this book. Indeed, various elements have been outlined in earlier papers by v the author herself, and the suite of cohort measures set out here parallel those widely used in the study of fertility. But the unique contribution of this book lies in bringing together the concepts, statistical measures and analytical methods that constitute a life-course approach to understanding migration, and elaborating them in a comprehensive form. In the theoretical domain, it also introduces the intriguing notion of migration capital and reveals how mobility in childhood shapes migration in later life. Data for some 27 European countries illustrate application of the various measures and deliver compelling substantive results. Human mobility is multi-dimensional, describing complex trajectories in both time and space. Surveys generally lack spatial detail but are well-suited to collecting the life histories that are needed to trace the temporal dimensions of human population movement. Coupled with a robust suite of cohort measures, as described here, this represents an essential part of the framework needed for understanding the dynamics of migration. It also represents a significant step forward in advancing the study of migration towards the analytical rigour characteristic of the other major components of human demography, fertility and mortality

Social science theory and research have linked racial diversity to both positive and negative outcomes for communities. Research has also demonstrated that the effects of demographic indicators sometimes vary across space and time. This complex web of results calls for examinations of the spatial and temporal elements at play in this relationship. Using spatial lag models, I analyze the relationship between racial diversity—both as a static measurement and as the 20-year change—and crime in four distinct neighborhood clusters in Philadelphia, PA. I first find evidence of spatial heterogeneity—that racial diversity predicts lower crime rates in one community, but higher crime rates in another. Second, the change in racial diversity influences crime in ways that differ from the static diversity measure. These results provide support for both competing hypotheses regarding the racial diversity–crime connection while also underscoring the role of spatiotemporal contexts in illuminating the dynamism of neighborhood processes.

Lockdowns imposed around the world to contain the spread of the COVID-19 virus and its variants had a differential impact on economic activity and jobs owing to differences in the ability to work remotely. This paper presents a new index of the feasibility to work from home to investigate which types of jobs are most at risk for 35 advanced and emerging market economies. Cross-country heterogeneity in the ability to work remotely reflects differential access to and use of technology, sectoral mix, and occupational selection. Workers least likely to work remotely tend to be young, without a college education, working for non-standard contracts, employed in smaller firms, and those at the bottom of the earnings distribution, suggesting that the pandemic has exacerbated inequality. Policies should account for demographic and distributional considerations both during the crisis and in its aftermath.