Social surveys prospectively linked with death records provide invaluable opportunities for the study of the relationship between social and economic circumstances and mortality. Although survey-linked mortality files play a prominent role in U.S. health disparities research, it is unclear how well mortality estimates from these datasets align with one another and whether they are comparable with U.S. vital statistics data. We conduct the first study that systematically compares mortality estimates from several widely used survey-linked mortality files and U.S. vital statistics data. Our results show that mortality rates and life expectancies from the National Health Interview Survey Linked Mortality Files, Health and Retirement Study, Americans’ Changing Lives study, and U.S. vital statistics data are similar. Mortality rates are slightly lower and life expectancies are slightly higher in these linked datasets relative to vital statistics data. Compared with vital statistics and other survey-linked datasets, General Social Survey-National Death Index life expectancy estimates are much lower at younger adult ages and much higher at older adult ages. Cox proportional hazard models regressing all-cause mortality risk on age, gender, race, educational attainment, and marital status conceal the issues with the General Social Survey-National Death Index that are observed in our comparison of absolute measures of mortality risk. We provide recommendations for researchers who use survey-linked mortality files.

Increased use of robots has roused concern about how robots and other new technologies change the world of work. Using numbers of robots shipped to primarily manufacturing industries as a supply shock to an industry labor market, we estimate that an additional robot reduces employment and wages in an industry by roughly as much as an additional 2 to 3 workers and by 3 to 4 workers in particular groups, which far exceed estimated effects of an additional immigrant on employment and wages. While the growth of robots in the 1996-2016 period of our data was too modest to be a major determinant of wages and employment, the estimated coefficients suggest that continued exponential growth of robots could disrupt job markets in the foreseeable future and thus merit attention from labor analysts.

Little work exists that compares the BMIs of 19th century foreign-born and US-born natives. Russian, Italian, German, and French BMIs were 5.1, 3.9, 2.9, and 1.8 percent higher than that of North Americans; Asians were nearly 4.2 percent lower. African-Americans and multiracial/multiethnic individual BMIs were 4.9 and 3.8 percent greater than fairer complexioned whites, indicating there was no multiracial/multiethnic BMI advantage. Farm laborers and ranchers had BMIs that were 2.9 percent and 2.2 percent greater, respectively, than that of workers with no occupations.

This paper investigates the cyclicality of time allocated towards research and development (R&D) activities. First, using longitudinal micro-data from 2000-2013, I find that time allocated towards research activities is highly procyclical among private sector employees, but countercyclical among public sector employees. These results are robust to a supplementary classification of R&D workers based O*NET occupational task concentrations matched with the American Time Use Survey from 2003-2017. Second, I find suggestive evidence that these asymmetric effects are the result of financing constraints in the private sector. Public sector workers, in contrast, exhibit greater stability in their response of time use to cyclical shocks.

We define a distance metric between partitions of a graph using machinery from optimal transport. Our metric is built from a linear assignment problem that matches partition components, with assignment cost proportional to transport distance over graph edges. We show that our distance can be computed using a single linear program without precomputing pairwise assignment costs and derive several theoretical properties of the metric. Finally, we provide experiments demonstrating these properties empirically, specifically focusing on its value for new problems in ensemble-based analysis of political districting plans.

Police are responsible for producing about one-fifth of all reports of child abuse and neglect investigated by local child welfare agencies, and low-level interactions with police often result in the initiation of a child welfare investigation. Because police contact is not randomly or equitably distributed across populations, policing has likely spillover consequences on racial inequities in child welfare outcomes. This study shows that police file more reports of child abuse and neglect in counties with high arrest rates, and that policing helps explain high rates of maltreatment investigations of American Indian–Alaska Native children and families. The spatial and social distribution of policing affects which children and families experience unnecessary child protection interventions and which children who are victims of maltreatment go unnoticed.

This dissertation presents three empirical studies that are broadly concerned with the valuation of human health and the evaluation of policies designed to improve it. The first study evaluates how a supply-side policy intended to restrict drug diversion, prescription monitoring, affects opioid prescribing and pain management in hospitals and homes. The results indicate that prescription monitoring reduces opioid use in outpatient settings not but in hospitals, and appears to have only modest effects on pain management, with suggestive evidence indicating that it enables more effective targeting of opioid therapy. The second study reevaluates the labor market evidence on compensating differentials for fatal injury risk, showing that the standard estimator for the sample mean value of a statistical life (VSL) is biased when the compensating differentials vary across the wage distribution and that correcting for this bias is quantitatively significant. The last study revisits an old question with a new identification strategy, using the financing mechanism for state medical boards as an instrument for local physician supply in order to evaluate the impact of supply shocks on local health care markets. The results indicate that a larger physician supply leads to changes in the style of medical practice but the welfare implications of this are unclear.

This dissertation focuses on the interaction between public policy and the U.S. labor market, and its consequences for the economic opportunities available to American women and children. I focus on two public policies designed to enhance opportunities for less advantaged groups: The United States’ first large-scale expansion of paid maternity benefits, and the launch of the Head Start preschool program in the 1960s and 1970s. The common thread in these essays is the use of large-scale data and transparent metholodologies to examine the interactions between these policies and individuals’ outcomes in the labor market. The first chapter provides the first evidence of the effect of a U.S. paid maternity leave policy on the long-run outcomes of children. I exploit variation in access to paid leave that was created by long-standing state differences in short-term disability insurance coverage and the staggered enactment of laws that banned discrimination against pregnant workers in the 1960s and 1970s. While the availability of these benefits sparked a substantial expansion of leave-taking by new mothers, it also came with a cost. I find the enactment of paid leave led to shifts in labor supply and demand that decreased wages and family income among women of child-bearing age. In addition, the first generation of children born to mothers with access to maternity leave benefits were 1.9 percent less likely to attend college and 3.1 percent less likely to earn a four-year college degree. Chapter 2 examines the labor-market consequences of a broad expansion of access to paid maternity benefits. The theoretical implications of maternity leave policies are ambiguous, with the potential for positive effects that stem from greater attachment to the labor force among mothers but also negative effects that could result from shifts in relative labor demand. I show that the enactment of maternity benefits through STDI slowed the convergence of the gender wage ratio by 31 percent between 1975 and 1985. I also provide evidence that this effect was driven in large part by ap- parent substitution of men for women into high-profile professional and management positions. The third chapter, with Martha J. Bailey and Shuqiao Sun, evaluates the long-run effects of Head Start using large-scale, restricted 2000-2013 Census-ACS data linked to date and place of birth in the SSA’s Numident file. Using the county-level rollout of Head Start between 1965 and 1980 and state age-eligibility cutoffs for school entry, we find that participation in Head Start is associated with increases in adult hu- man capital and economic self-sufficiency, including a 0.29-year increase in schooling, a 2.1-percent increase in high-school completion, an 8.7-percent increase in college enrollment, and a 19-percent increase in college completion. These estimates imply sizable, long-term returns to investing in large-scale preschool programs.

Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic bias. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the U.S., we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties.

In this article we highlight the scope of public policy and demographic change for the future path of homeownership. In so doing, we review the literature on the scope of impact of certain policy tools, estimate housing tenure choice models that highlight how sensitive households are to various factors in different time periods to highlight how credit conditions can influence the future path of homeownership, and then simulate the future paths of homeownership in light of prospective changes in young-adult race/ethnicity, education, income, and wealth. The study focuses on prospective changes between 2015 and 2035 to the rate of homeownership among young adults age 25 to 44, prime ages for first-time homebuying. We find that rising education levels—even if minority-white college education gaps were eliminated completely—would only partially reverse the steep declines in young-adult homeownership attainment witnessed since the onset of the housing bust. However, our findings also suggest that the common narrative, which predicts that young-adult homeownership rates will inevitably decline due to increasing racial/ethnic diversity, does not take into account the positive effect of rising educational attainment among minorities on homeownership rates.

State prevailing wage law repeals have been shown to lower wages and benefits—including benefits providing safety training and associated with worker retention in construction. This study tests whether prevailing wage repeals affect construction injury rates and/or the prevalence of disabilities among construction workers. Controlling for time trends in injuries and disabilities, differences between construction industry subsectors, the business cycle, and time-invariant differences between states, we find that repealing state prevailing wage laws increase construction injury rates across various types of injuries from 11.6% to 13.1% as the seriousness of injuries increases. Disabilities increase by 7.5% to 8.2% depending on the model specification. Conjoining an analysis of the effects of prevailing wage law repeals on injury rates with disability rates in construction provides alternative measures of the effects of prevailing wage laws on construction workplace safety, which addresses a well-known problem of underreporting construction injuries.

This study applies the status exchange theory to white-Latino intermarriage and explores how the strength of status exchange differs by Latino nativity. The status exchange hypothesis theorizes that couples of unequal social standing engage in an exchange of characteristics, thus suggesting that there are certain obstacles individuals of lower status may face to interact with individuals of higher status. This theory highlights a possible mechanism that drives intermarriage and indicates the height of barriers that different groups encounter when intermarrying. Analyzing differences by nativity provides a greater understanding of the trajectory of Latinos’ integration in the U.S. This study uses years 2008-2015 of the American Community Survey and log-linear models for contingency tables. The best fitting models show evidence of status exchange among marriages between foreign-born Hispanics and native-born whites. Among native-born Hispanics, Hispanic wives seem to engage in status exchange with native-born whites. These results suggest that both foreign-born and native-born Hispanics face similar barriers as blacks when interacting with whites. More broadly, there is evidence for a white-nonwhite racial divide within the U.S.

Optimists contend that crowdfunding, in which project backers use online campaigns to assemble numerous small donations, can democratize access to finance, but there are legitimate concerns that this funding approach remains discriminatory. Drawing on recent readings emphasizing the geographic components of Bourdieu’s field theory, we argue the relationship between crowdfunding teams’ resources and crowdfunding success is mediated by spatial capital—the ability to draw capital from other social spaces due to geographic context. We use logistic regressions predicting success rates for 134,098 campaigns launched in the USA on the Indiegogo platform between 2009 and 2015, combined with other spatial data, to model the relationship between spatial capital and other success predictors. Our models suggest spatial context mediates the relationship between resources and success. Rural areas, in particular, have lower success rates than urban areas, and affluent areas have the highest success rates. Given that only around 10% of Indiegogo campaigns are fully funded, spatial inequalities place significant limits on who can benefit from crowdfunding campaigns, suggesting crowdfunding may not democratize access to finance, as optimists hope.

Millennials are on track to become the most educated generation in the coming years. Educational attainment has increased among young adults in almost every successive business cycle in the last six decades. While about 17.6% of 29-year-olds had at least Bachelor’s degree in the early 1970’s, about 35.3% of 29-year-olds had at least a Bachelor’s degree in the past decade. This issue brief documents the changes in college attainment over time for the U.S. population by age and by generation. We report the college attainment rate for each age group or generation (“percent with a college degree”).

In developing countries, improving access to schooling has been and remains a priority. At the same time, a growing body of research relates education to demographic variables. It is therefore essential to measure the education variable accurately. In Brazil, although the high degree of inaccuracy in age reporting is well known, previous research has neglected problems of misreporting which may affect other variables such as education. To fill this gap, we calculate mortality levels by education as implied by intercensal survivorship ratios, to investigate the quality of self-reported education among adults in Brazil between the 1991 and 2000 censuses. Our findings show evidence of education misreporting. Analysis by single year of schooling only barely revels the expected educational gradient in mortality. After categorization of age and years of schooling into groups, a positive relationship between education and survival does appear, although some implausible patterns remain. This study is an important step in demonstrating and assessing potential errors in census education data in Brazil. Our results highlight the importance of efforts to reduce misreporting of data on education, particularly in countries where an educational expansion is underway, and deficiencies in data quality are a potential issue of concern.

We present evidence on the relationship between broadband pricing and labor market outcomes for low-income individuals. Specifically, we estimate the effects of a Comcast service providing discounted broadband to qualifying low-income families. We use a triple differences strategy exploiting geographic variation in Comcast coverage, individual variation in eligibility, and temporal variation pre- and post-launch. Program enrollment increases the probability that an eligible lowincome individual is employed by 4.4 percentage points (7.8%), driven by greater labor force participation and decreased probability of unemployment. Internet use increased substantially where the program was available, narrowing the income-broadband gap by at least 40 percent.

With the rapid development of big data, numerous industries have turned their focus from information research and construction to big data technologies. Earth science and geographic information systems industries are highly information-intensive, and thus there is an urgent need to study and integrate big data technologies to improve their level of information. However, there is a large gap between existing big data and traditional geographic information technologies. Owing to certain characteristics, it is difficult to quickly and easily apply big data to geographic information technologies. Through the research, development, and application practices achieved in recent years, we have gradually developed a common geospatial big data solution. Based on the formation of a set of geospatial big data frameworks, a complete geospatial big data platform system called BiGeo was developed. Through the management and analysis of massive amounts of spatial data from Sichuan Province, China, the basic framework of this platform can be better utilized to meet our needs. This paper summarizes the design, implementation, and experimental experience of BiGeo, which provides a new type of solution to the research and construction of geospatial big data.

The U.S. cesarean rate has risen dramatically in the last two generations, from 6% of all births in 1970 to 32% in 2015, an increase that remains imperfectly understood. During this same time period the world of women underwent a “Quiet Revolution” (Goldin, 2006), with dramatic changes in women’s education, labor market attachment, pay, and family formation. This paper explores the extent to which these two phenomena are related. Using the most detailed U.S. survey of birthing mothers available, the National Maternal and Infant Health Survey (NMIHS), we provide the first comprehensive, national analysis of how labor market and family-related factors affect cesarean use. Applying the results to forty-five years of the March Supplement to the Current Population Survey and other data, we then calculate the degree to which these factors contributed to the increase in cesarean rates over the period 1968-2015. These factors’ relevance is motivated by the idea that health capital is accumulated via investments of money, time, and knowledge. Healthier women should be less likely to require a cesarean delivery. Maternal employment lowers the time invested in health capital, while income and education increase the financial and knowledge base that can be used to develop health capital during pregnancy. These effects should persist even after accounting for health insurance coverage. Evidence on these points is provided via a regression analysis of the NMIHS. The empirical findings support the theory. Maternal employment substantially increases the probability of receiving a cesarean, especially for first time mothers. Compared to a “low labor market attachment” mother who has never worked, a “high attachment” mother who works both during and after her pregnancy is ten percentage points more likely to receive a cesarean, ceteris paribus. The effect of income is negative, as expected, though not significantly so. In contrast, more educated women have more cesareans (because they have larger, healthier fetuses). Overall, then, the increases in maternal education and employment that are associated with the Quiet Revolution have led to an increase in the rate of cesarean delivery. Changes in family structure have had the same effect. Delays in family formation, reductions in household size, and the increased absence of the father, all of which accompanied the Quiet Revolution, all increase the rate of cesarean section. To quantify how much changes in these factors have increased the cesarean rate, these regression estimates are applied to the means of labor market and demographic variables for new mothers in each of the years 1968-2015. From this one can calculate the aggregate effect of all factors, or of any given subset of factors, on cesarean utilization over this period. About one-third of the rise in cesarean rates over this period can be explained by the factors discussed here, especially increased maternal age and decreased family size. Escalated rates of cesarean delivery are, to a substantial degree, a consequence of the Quiet Revolution.

Critical race theory teaches that racism and racial inequality are constants in American society that stand outside of the prejudices of individuals. It argues that structures and institutions are primarily responsible for the maintenance of racial inequality. However, critical race theorists have neglected to formally examine and theorize colorism, a primary offshoot of racial domination. Although studies of colorism have become increasingly common, they lack a unifying theoretical framework, opting to lean on ideas about prejudice and preference to explain the advantages lighter skinned, Black Americans are afforded relative to darker skinned Black Americans. In this study, I deploy a critical race framework to push back against preference as the only, or primary, mechanism facilitating skin tone stratification. Instead, I use historical Census data and regression analysis to explore the historical role of color-based marriage selection on concentrating economic advantage among lighter skinned Black Americans. I then discuss the policy and legal implications of developing a structural view of colorism and skin tone stratification in the United States and the broader implications for how we conceptualize race in this country.

Background Increasing premature mortality among some groups of Americans has been largely driven by increases in drug poisoning deaths. However, to our knowledge, a formal descriptive study by race and ethnicity, socioeconomic status, rurality, and geography has not been done. In this study, we examined US trends in premature all-cause and drug poisoning mortality between 2000 and 2015 at the county level among white, black, and Latino people. Methods We used US mortality data for the period Jan 1, 2000, to Dec 31, 2015, including underlying cause of death and demographic data, collected from death certificates by the Centers for Disease Control and Prevention National Center for Health Statistics, and ascertained county attributes from the 2011–15 Census American Community Survey. We categorised counties into quintiles on the basis of the percentage of people unemployed, the percentage of people with a bachelor's degree, median income, and rurality. We estimated premature (ie, deaths in those aged 25–64 years) age-standardised mortality for all causes (by race and ethnicity) and drug poisoning, by county, for the periods of 2000–03 and 2012–15. We estimated annual percentage changes in mortality (2000–15) by county-level characteristics. Findings Premature mortality declined from 2000–03 to 2012–15 among black and Latino people, but increased among white people in many US counties. Drug poisoning mortality increased in counties throughout the country. Significant increases between 2000 and 2015 occurred across low and high socioeconomic status and urban and rural counties among white people aged 25–64 years (annual percentage change range 4·56% per year [95% CI 3·56–5·57] to 11·51% per year [9·41–13·65]), black people aged 50–64 years (2·27% per year [0·42–4·16] to 9·46% per year [7·02–11·96]), Latino women aged 25–49 years (2·43% per year [1·18–3·71] to 5·01% per year [3·80–6·23]), and Latino men aged 50–64 years (2·42% per year [0·53–4·34] to 5·96% per year [3·86–8·11]). Although drug poisoning mortality increased rapidly in counties with the lowest socioeconomic status and in rural counties, most deaths during 2012–15 occurred in the largest metropolitan counties (121 395 [76%] in metropolitan counties with ≥250 000 people vs 2175 [1%] in the most rural counties), reflecting population size. . .