Public transit in the United States is ailing. Before the COVID-19 pandemic, transit ridership fell by more than 800 million annual transit trips, or about 7.5%, between 2014 and 2019. The onset of the COVID-19 pandemic in the spring of 2020 only compounded these losses. Both before and during the pandemic, the changes in transit ridership were uneven, varying across metropolitan areas, built environments, times of day, days of the week, trip purposes, operators, modes, and directions.

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 Texas. Now more than ever, lawmakers in Texas 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 Texas, mothers are the sole, primary, or co-breadwinners in 60.3 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 Texas.

Research on incarceration has focused on prisons, but jail detention is far more common than imprisonment. Jails are local institutions that detain people before trial or incarcerate them for short sentences for low-level offenses. Research from the 1970s and 1980s viewed jails as "managing the rabble," a small and deeply disadvantaged segment of urban populations that struggled with problems of addiction, mental illness, and homelessness. The 1990s and 2000s marked a period of mass criminalization in which new styles of policing and court processing produced large numbers of criminal cases for minor crimes, concentrated in low-income communities of color. In a period of widespread criminal justice contact for minor offenses, how common is jail incarceration for minority men, particularly in poor neighborhoods? We estimate cumulative risks of jail incarceration with an administrative data file that records all jail admissions and discharges in New York City from 2008 to 2017. Although New York has a low jail incarceration rate, we find that 26.8% of Black men and 16.2% of Latino men, in contrast to only 3% of White men, in New York have been jailed by age 38 y. We also find evidence of high rates of repeated incarceration among Black men and high incarceration risks in high-poverty neighborhoods. Despite the jail's great reach in New York, we also find that the incarcerated population declined in the study period, producing a large reduction in the prevalence of jail incarceration for Black and Latino men.

Postgraduate degree holders experience lower cyclical variation in real wages than those with undergraduate degrees. Moreover, postgraduate jobs require more specific human capital and take longer to adapt to. Using an equilibrium search model with dynamic incentive contracts, this paper attributes the cyclicality of the postgraduate-undergraduate wage gap to the differences in specific capital. Greater specific capital leads to lower mobility, thereby improving risk-sharing between workers and firms. The estimates of the model reveal that specific capital can explain the differences both in labour turnover and in real wage cyclicality between education groups.

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities at home, household energy consumption is stochastic and heterogeneous in nature. However, most residential building energy models applied by industry use homogeneous, deterministic occupant activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. This mattered less in the past, but the increasing proportion of variable renewable energy generators in power systems means that representing the heterogeneity and stochasticity of occupant behavior is crucial for reliable energy planning. This is particularly true for systems that include distributed energy resources, such as grid-interactive efficient buildings, solar photovoltaics, and battery storage. This work presents a stochastic occupant behavior simulator that models the energy use behavior of individual household members. It also presents an integration with a building stock model to simulate residential building loads more accurately at community, city, state, and national scales. More specifically, we first employ clustering techniques to identify distinct patterns of occupant behavior. Then, we combine time-inhomogeneous Markov chain simulations with probabilistic sampling of event durations to realistically simulate occupant behaviors. This stochastic simulator is integrated with ResStock™, a large-scale residential building stock simulation tool, to demonstrate the capability of stochastic residential building load modeling at scale. The simulation results were validated against both American Time Use Survey data and measured end-use electricity data for accuracy and reliability.

Going beyond a focus on individual-level employment outcomes, we investigate couples' changing work patterns in the United Kingdom (UK) and the United States (US) during the COVID-19 pandemic. Analyzing longitudinal panels of 2186 couples from the Understanding Society COVID-19 Survey (UK) and 2718 couples from the Current Population Survey (US), we assess whether the pandemic has elevated the importance of human capital vis-à-vis traditional gender specialization in shaping couples' work patterns. The UK witnessed a notable increase in sole-worker families with the better-educated partner working, irrespective of gender. The impact of the pandemic was similar but weaker in the US. In both countries, couples at the bottom 25% of the prepandemic family income distribution experienced the greatest increase in neither partner working but the least growth in sole-worker arrangements. Through a couple-level analysis of changing employment patterns, this study highlights the importance of human capital in shaping couples' paid-work organization during the pandemic, and it reveals the socioeconomic gradient in such organization.

Objective: This article examines whether the availability of Head Start during the Great Recession mitigated the impact of this crisis on poverty rates among families with young children. Background: The first 2 decades of the 21st century have witnessed two major economic crises: the Great Recession and the COVID-19 pandemic. Poverty rates among families with young children grew substantially during the Great Recession. Families with young children are also more vulnerable to instability during the COVID-19 pandemic because job losses have been steeper and childcare availability has been significantly curtailed. Programs such as Head Start that support at-risk families may mitigate such negative consequences. Method: This study used data from the American Community Survey from 2006 through 2016 and state-level data on Head Start availability from Program Information Reports. Growth curve modeling was used to examine how the availability of Head Start predicted poverty growth during the Great Recession and the speed of recovery post-recession. Results: States with higher rates of Head Start enrollment had a smaller increase in family poverty during the Great Recession and a more stable recovery than states with lower Head Start enrollment. Conclusions: These findings suggest that greater access to Head Start programs prevented many families from falling into poverty and helped others exit poverty during the Great Recession. Implications: The findings provide clear, evidence-based policy recommendations. Increased federal funding for Head Start is needed to support families during a COVID-19 recession. States should supplement these allocations to expand Head Start enrollment for all eligible families.

The United States has experienced a dramatic rise in opioid addiction and opioid overdose deaths in recent years. We investigate the effect of the opioid epidemic at the local level on nonmarital fertility using aggregate- and individual-level analyses. Opioid overdose death rates and prescriptions per capita are used as indicators of the intensity of the opioid epidemic. We estimate area fixed-effects models to test the effect of the opioid epidemic on nonmarital birth rates obtained from vital statistics for 2000–2016. We find an increase in nonmarital birth rates in communities that experienced a rise in opioid overdose deaths and higher prescription rates. Our analyses also show that the local effect of the opioid epidemic is not driven by a reduction in marriage rates and that marital birth rates are unaffected. Individual-level data from the ACS 2008–2016 are then used to further assess the potential causal mechanisms and to test heterogeneous effects by education and race/ethnicity. Our findings suggest that the opioid epidemic increased nonmarital birth rates through social disruptions primarily affecting unmarried women but not through changes in their economic condition

This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data.

Introduction: Kidney cancer incidence is increasing among Hispanics but rate differences by distinct group, such as Cuban, Puerto Rican, and Mexican have not been studied. To fill this knowledge gap, we use mortality data, reflecting fatal kidney cancers, to examine patterns by race-ethnicity, including detailed Hispanic groups, and correlate the mortality rates with each group's prevalence of known kidney cancer risk factors: smoking, obesity, hypertension, diabetes, and chronic kidney disease. Methods: We used individual-level death data for California, Florida, and New York (2008–2018), and population prevalence data from the National Health Interview Surveys (2008–2018). Age-adjusted mortality rates (AAMRs) and regression-derived mortality rate ratios (MRRs) were computed. Pearson correlation analyses assessed the extent to which group-specific risk factor prevalence explained variability in observed AAMRs. Results: US-born Mexican Americans and American Indians had the highest rates and MRRs compared to Whites: 1.44 (95 %CI: 1.35−1.53) and 1.51 (1.38−1.64) for Mexican American men and women, respectively, and 1.54 (95 %CI: 1.25−1.89) and 1.53 (95 %CI: 1.15−2.04) for American Indians. In contrast, non-Mexican Hispanics had lower rates than Whites. Among males, positive correlations between AAMRs and smoking, obesity, and chronic kidney disease prevalence by race-ethnicity were found. Conclusion: Mexican Americans and American Indians are high-risk for fatal kidney cancer. Disparities are only partially attributable to higher smoking and obesity prevalence, and more so among men than women. A shared risk factor profile, as well as possible genetic similarities, may explain their disproportionately higher kidney cancer mortality, but further research is warranted.

The streetcar represents one of the most influential modes of mass transit in American history, yet also one of the least documented. In the span of a few decades, it rose to become the dominant form of transportation, then fell into decline and obscurity. It was the first mode of transport to introduce the potential for commutes, was a contributing factor to the spread of suburbs around the United States, and even contributed to an early form of zoning; it was also the first notable mode of everyday transport to decline from government action on behalf of its competitor, the automobile, and government inaction on behalf of itself. This thesis report describes the extraction of records from the McGraw Electric Railway Manuals to rectify the lack of documentation around streetcar systems through technological means, and discusses the appropriateness of using technology to analyze century-old directories. The extracted records are analyzed on a metropolitan, state and national level, and fitted to logarithmic S-curve models to describe their growth and decline. It was found that a significant number of metropolitan areas experienced continual growth or remained in maturity during the studied period, while the majority of states reached maturity and entered decline by the end of the 1926. Nationally, system length in the contiguous United States peaked in 1918, with 50,089.28 miles of track recorded that year. While a strong correlation was found between the population and the size of the system at its maximum extent, no correlation was found between population growth and system growth rates, or population density and system growth. In the decline phase, larger populations did not contribute to greater system declines.

Losing a partner is a life-changing experience. We draw on numerous datasets to examine differences between widowed and partnered older women and to provide a comprehensive picture of well-being in widowhood. Most importantly, our analysis accounts for time use in widowhood, an aspect which has not been studied previously. Based on data from several European countries we trace the evolution of well-being of women who become widowed by comparing them with their matched non-widowed ‘statistical twins’ and examine the role of an exceptionally broad set of potential moderators of widowhood’s impact on well-being. We confirm a dramatic decrease in mental health and life satisfaction after the loss of partner, followed by a slow recovery. An extensive set of controls recorded prior to widowhood, including detailed family ties and social networks, provides little help in explaining the deterioration in well-being. Unique data from time-diaries kept by older women from several European countries and the U.S. tell us why: the key factor behind widows’ reduced well-being is increased time spent alone.

Using social comparison theory, I investigate the relation between experienced happiness and income inequality. In the analysis, I study happiness effects of the individual-level within-gender-ethnicity comparison-based Gini index conditional on a state’s overall inequality, using a linked set of the March 2013 Current Population Survey and the 2013 American Time Use Survey data while controlling major potential confounders. The findings suggest that individuals who are positioned to conduct both upward and downward comparison would feel happier in states where overall income inequality is high. In states where inequality is not high, however, such effects are not present because social comparison becomes less meaningful when one’s position is not as clearly definable. Therefore, social comparison matters where inequality persists: One’s comparison with all similar others’ in the income distribution in a social environment determines the effect of one’s income on happiness, with the comparison target being the same gender-ethnic group.

In December 2017, Congress repealed the individual insurance mandate penalty. Given the poor health status of veterans, their higher demands for health insurance, and the substantial number of uninsured veterans, the repeal of the individual mandate should have a significant impact on the veterans. This article investigates how the repeal of the individual mandate effective in January 2019 is likely to affect the number of uninsured veterans and their enrollments in Veterans Affairs (VA) insurance. By analyzing 52,692 nonelderly veterans in Florida and California from 2008 to 2017, the findings suggest that the repeal will lead to a considerable increase in the number of uninsured veterans. Veterans who are unemployed, poor, and suffering disabilities are more likely to sign up for the VA insurance than better-off veterans. Thus, one of the important functions of veteran health care is to serve as a social safety net for vulnerable veterans. Thus, the Veterans Health Administration should establish a policy to minimize the expected negative repercussions of the repeal.

In this paper, estimates are presented on short-run effects of demand shocks on a local labor market's employment to population ratio (employment rate). Based on the estimates, commuting zones (CZs) better define a local labor market than counties, because both employment and employment rate effects exhibit large spillovers across counties within a CZ. In addition, the estimates suggest that demand shock effects vary, by an amount that is both statistically and substantively significant, with a CZ's prior overall employment rate.

Mixed evidence on the relationship between school closure and COVID-19 prevalence could reflect focus on large-scale levels of geography, limited ability to address endogeneity, and demographic variation. Using county-level CDC COVID-19 data through June 15, 2020, two matching strategies address potential heterogeneity: nearest geographic neighbor and propensity scores. Within nearest neighboring pairs in different states with different school closure timing, each additional day from a county's first case until state-ordered school closure is related to 1.5%-2.4% higher cumulative COVID-19 deaths per capita (1,227-1,972 deaths for a county with median population and deaths/capita). Results are consistent using propensity score matching, COVID-19 data from two alternative sources, and additional sensitivity analyses. School closure is more strongly related to COVID-19 deaths in counties with a high concentration of Black or poor residents, suggesting schools play an unequal role in transmission and earlier school closure is related to fewer lives lost in disadvantaged counties. Abstract Mixed evidence on the relationship between school closure and COVID-19 prevalence could reflect focus on large-scale levels of geography, limited ability to address endogeneity, and demographic variation. Using county-level CDC COVID-19 data through June 15, 2020, two matching strategies address potential heterogeneity: nearest geographic neighbor and propensity scores. Within nearest neighboring pairs in different states with different school closure timing, each additional day from a county's first case until state-ordered school closure is related to 1.5%-2.4% higher cumulative COVID-19 deaths per capita (1,227-1,972 deaths for a county with median population and deaths/capita). Results are consistent using propensity score matching, COVID-19 data from two alternative sources, and additional sensitivity analyses. School closure is more strongly related to COVID-19 deaths in counties with a high concentration of Black or poor residents, suggesting schools play an unequal role in transmission and earlier school closure is related to fewer lives lost in disadvantaged counties.

This paper explores the consequences of the COVID-19 pandemic for child development in the United States by way of changes in participation in center-based child care and preschool, or early care and education (ECE). The pandemic appears to have reduced ECE enrollment and exacerbated existing inequalities in ECE participation. However, these effects have varied in timing across demographic groups and in intensity across states. The unique set of forces driving the participation declines-as well as pandemic impacts on ECE quality in addition to quantity-also suggest care in generalizing from pre-pandemic research findings when contemplating the impacts for child development. Prior research is still helpful, however, and it offers frameworks for understanding the drivers of more localized ECE effects and their implications. I conclude with thoughts on the long-standing challenges in ECE that were brought into sharp relief by the pandemic.

Natural resource dependence, although commonly invoked in natural resource sociology, has often been ambiguously defined. Communities are frequently described as dependent on natural resource development, but limited attention has been paid to what that means. In the literature, resource dependence is often treated as over-specialization in, or over-reliance upon, the natural resource sectors. However, the logic of over-specialization conceptually grounds dependence in poor economic outcomes. Thus, a one-dimensional typology of dependence based on a threshold of the share of development in the natural resource sector—as commonly used—does not fully capture the concept and risks tautology. In this paper, I address this ambiguity by formally defining natural resource dependence as over-specialization in the natural resource sectors. I then present an ideal typology, known as the Natural Resource Community Typology, and a corresponding classification scheme for rural communities in the United States. The typology integrates both extractive and non-extractive natural resource activity and has two dimensions—the level of development and the level of economic prosperity—and six mutually exclusive categories—extractive specialized, extractive dependent, non-extractive specialized, non-extractive dependent, hybrid specialized, and hybrid dependent. I classify counties from 2000 to 2015 and find that while extractive dependence decreased over the study period, non-extractive dependence increased.

The share of the U.S. population that receives business income has increased substantially in recent decades. At the same time, worker hire and separation rates have declined, with worrying implications for productivity and wage growth. In this paper, we explore the relationship between business income (BI) receipt and labor reallocation. We show that BI recipients are largely excluded from existing measures of labor reallocation. Including BI recipients reduces the measured decline from 1994 to 2014 in the hire and separation rates by 8.3 to 8.7 percent, respectively, primarily among jobs that were secondary sources of income or short in duration. We present evidence that worker transitions between wage and salary jobs and BI represent labor reallocation, as opposed to reclassification of employees as independent contractors.

My first chapter examines whether tacit collusion occurs in the market for BigLawassociates. Many large firms across the U.S. offer the exact same associate salaries despitesubstantial heterogeneity on both sides of the labor market. I show that empirical market dynamics are diffcult to reconcile with competitive labor markets. I then provide evidence for an alternative explanation - tacit collusion. A few firrms act as price leaders and set maximum salaries. Some smaller firms are excluded from punishment to maintain cartel stability, and firms strategically communicate compensation decisions to align on decisions. Tacit collusion is facilitated by communication and standardization. Many of these practices originated in historical explicit collusion. This research highlights the potential for collusion in labor markets and the need for further scrutiny of other markets. My second chapter is joint work with Anna Aizer, Adriana Lleras-Muney, and Jonathan Vogel, and we study the role of WWII in reducing occupational discrimination against Black men. The 1940s witnessed substantial reductions in the Black-white earnings gap. We show that domestic WWII defense production played an important role. In labor markets with more war production contracts, Black workers were more likely to be upgraded into skilled occupations and receive higher wages. War spending also led to an increase in the high school graduation rate of Black children, suggesting important inter-generational spillovers. These results are attributable to the interaction between tight labor markets and federal prohibition against discrimination for war contractors. Using a structural model, we show that WWII defense production generated substantial improvements in national labor-market outcomes by decreasing discrimination for Black workers. My third chapter looks at how rm acquisitions aect working conditions. I focus on acquisitions in a narrowly defined industry, nursing homes, to allow direct comparison acrossacquisitions and working conditions. I find that focusing only on the limited average effect onwages would miss more signifcant effects on benefits (6% decrease) and on workload (3-4%increase). Most importantly, working conditions in the acquired facility quickly converge towards those of the acquiring firm. This dynamic creates substantial heterogeneity in the effect of acquisitions on acquired facilities based on working conditions relative to the acquirer. Finally, I provide suggestive evidence that firm behavioral factors (e.g., managerial inertia) play an important role in the standardization of working conditions.