Unemployed workers are the group most likely to be affected by the presence of immigrants in their local labor markets since they are actively competing for job opportunities. Yet, little is known about the effect of immigration on labor market opportunities of the unemployed. Using a sample of unemployed native-born citizens from the monthly Current Population Survey from 2001 to 2015 and state level immigration statistics, we employ a multinomial model in the framework of a discrete hazard model with competing risks to examine the effects of immigration on the transition out of unemployment. The results suggest that immigration does not affect attrition not the probabilities of native-born workers finding a job. Instead, we find that immigration is associated with smaller probabilities of remaining unemployed.

A key limitation of models of social vulnerability to environmental hazards is that they areoften developed without explicit controls for how individuals fare in emergencies and disasters.Individuals (people, households) are central to the problem of social vulnerability assessment: theyare the scale at which hazard exposures and impacts are directly felt. While population-levelaggregates (i.e., median age, percent in poverty) are frequently used as proxies for individual-levelhazard risk, conflating these scales can hold unintended consequences for the actionability of socialvulnerability metrics. Population-level data is not an exact substitute for individual-level data: itoften omits key details on the drivers and priorities among populations at risk during an emergencyor disaster that are vital to planning/emergency response decision support. In response to thisproblem, this work develops new models that rely on individual-level, rather than population-leveldata as the initial point for measuring social vulnerability. This approach enables a more holisticunderstanding of social vulnerability that relays how individual experiences of hazards scale up tocollective (population-level) concerns. This dissertation consists of three case studies. The firsttwo, focused on the effects of Hurricane Sandy in 2012 in New York City, develop an “Individual toPlace” model of social vulnerability on census microdata that couples individual and population-level concerns to understand the social conditions underlying differential experiences of disasters.The third case study, which involves the 2011 North American Heat Wave in Houston, Texas, usescommunity-based survey data to understand how social conditions and practices directly influenceindividual-level hazard impacts (heat-related illness).

Income inequality among U.S. families with children has increased over recent decades, coinciding with a period of significant reforms in federal welfare policy. In the most recent reform eras, welfare benefits were significantly restructured and redistributed, which may have important implications for income inequality. Using data from the 1968–2016 March Supplement to the Current Population Survey (N = 1,192,244 families with children) merged with data from the historical Supplemental Poverty Measure, this study investigated how income inequality and, relatedly, the redistributive effects of welfare income and in-kind benefits changed, and whether such changes varied across states with different approaches to welfare policy. Results suggest that cash income from welfare became less effective at reducing income inequality after the 1996 welfare reform, because the share of income coming from cash welfare fell and was also less concentrated among the neediest families. At the same time, tax and in-kind benefits reduced inequality until the Great Recession. Consistent with the “race to the bottom” hypothesis, results suggest that the redistributive effects of welfare income dropped in all states regardless of their approach to welfare policy.

Suicide remains a leading cause of death in the United States. Suicide risk is shaped by a number of sociodemographic characteristics in addition to where people live. In this project, county suicide rates were examined alongside indicators of known risk factors for suicide including level of urbanization, geographic division, economic disadvantage, religious adherence, marriage and divorce, gender and racial categories, and education. Regression models indicate that rurality influences the relationship between sociodemographic variables and rates of completed suicide. Central findings include the combined effect of rurality and education, rurality and region, and rurality divorce on suicide rates. One implication of this is research is a greater need to consider how context shapes suicide risk when designing programs and policies to prevent suicide.

People with disabilities in the United States experience different types of paid work than people without disabilities; however, less is known about patterns in voluntary work—another form of productive labor that takes place within organizations. This study uses the Volunteer Supplement of the Current Population Survey to evaluate disability segregation in volunteer organizations and activities. Net of sociodemographic characteristics, volunteers with disabilities have lower odds than volunteers without disabilities of participating in educational/youth organizations and sport/hobby/cultural organizations, and higher odds of participating in social/community organizations. Furthermore, volunteers with disabilities have lower odds of participating in professional or coaching/teaching/mentoring activities and higher odds of participating in distribution activities—suggesting less access to leadership roles and opportunities for skill development. Finally, indices of dissimilarity indicate higher levels of segregation by disability status than by gender, race, or ethnicity. Volunteer work—like paid work—is stratified by disability, mirroring broader patterns of inequality.

Background Following the implementation of the Patient Protection and Affordable Care Act (ACA), the New York City (NYC) uninsured rate reduced from 14.1% (13.6%-14.5%) in 2010 to 10.6% (10.2%-11%) in 2014. While expansion provisions significantly improved overall coverage, disparities remain. Studies have demonstrated that people without coverage are less likely than people who are insured to receive preventive care and services for chronic diseases. We examined citywide trends in uninsurance rates from 2010 to 2018 and the persisting disparities in coverage among subgroups of New Yorkers in 2018. Methods We analyzed data from the Public Microdata Sample (PUMS) of the 2010-2018 1-year American Community Survey to calculate prevalence of uninsurance in NYC and identify sociodemographic characteristics of uninsured residents. Age-adjusted prevalence estimates for each year were calculated from PUMS data and weighted to the 2000 U.S. Standard Population. Citywide estimates were produced and stratified by education, race/ethnicity, nativity, poverty level, age groups and neighborhood. Annual estimates were compared using t-tests. Results Between 2010 and 2018, the overall prevalence of uninsured decreased from 14.1% to 6.7%. In 2018, large disparities in uninsured rates were present across various demographic characteristics. The 2018 uninsured rate of the adult Hispanic population was 10.8% compared to 3.6% (p<.001) of the adult White non-Hispanic population; 20.5% among adults 24 years old and over without a high school degree compared to 4.2% (p<.001) of adults with a college degree or more; 11.3% among adults born outside of the US compared to 3.7% (p<.001) among adults born in the US; 10% in adults with an income 200% at or below the federal poverty level (FPL) compared to 5.4% (p<.001) of adults with an income greater than 200% of the FPL. Geographically, the Elmhurst and Jackson Heights neighborhoods in Queens had an uninsured rate in 2018 of 16.6% (13.5%-20.3%) and 16.2% (13.4%-19.5%), respectively, compared with the citywide average of 6.7% (6.4%-7.1%). Conclusions Despite reductions in the overall rate following the implementation of the ACA, high levels of uninsurance remain among the following NYC adult subgroups: Hispanic, foreign-born, those with less than a high school education, and those with an income at or below 200% of the FPL. At the neighborhood level, uninsurance rates in Elmhurst and Jackson Heights are comparable to the city’s average prior to the implementation of the ACA. The current uninsured population in NYC likely consists of those eligible for public health insurance and those ineligible due to their immigration status. It is critical that in addition to efforts to maximize health insurance enrollment rates, there are efforts to increase access to primary and preventive care, including for the uninsured. In NYC, the NYC Care program facilitates access to quality primary care for uninsured populations.

Decades of research illuminates how status beliefs about socially significant characteristics, like gender, fundamentally alter expectations about individual's competence and worth. This process biases opportunity structures and resource distributions, thereby recreating social inequalities in a self-fulfilling fashion. Many social and organizational policies attempt to reduce inequality by increasing disadvantaged groups' access to valued rewards, such as prestigious alma maters, awards, and valued positions. In addition to meaningfully increasing resources, the status these rewards convey should also theoretically increase the status of the particular people who come to possess them. To know whether inversions to reward structures reduce social inequality, however, we must first demonstrate that the status value of rewards alone is an effective intervention. In an experimental test of interventions to gender status inequality, reward markers with relatively higher or lower status value were consistently or inconsistently associated with the gender of the participants' task partners. Results indicate that rewards intervened in the groups' gendered status hierarchy as participants were more likely to be influenced by their partners' rewards than their gender.

What impact on local development do immigrants and their descendants have in the short and long term? The answer depends on the attributes they bring with them, what they pass on to their children, and how they interact with other groups. We develop the first measures of the country-of-ancestry composition and of GDP per worker for US counties from 1850 to 2010. We show that changes in ancestry composition are associated with changes in local economic development. We use the long panel and several instrumental variables strategies in an effort to assess different ancestry groups’ effect on county GDP per worker. Groups from countries with higher economic development, with cultural traits that favor cooperation, and with a long history of a centralized state have a greater positive impact on county GDP per worker. Ancestry diversity is positively related to county GDP per worker, while diversity in origin-country economic development or culture is negatively related.

Risk assessments have become prevalent in the juvenile justice field. Many of these tools are adopted off-the-shelf and not adapted to fit the characteristics of a jurisdiction’s justice-involved youth. We examined the Positive Achievement Change Tool (PACT) in Iowa. Although used widely, the PACT is relatively unmodified. We updated the tool via item selection and weighting, gender-specific models and multiple outcomes, developing the Modified Positive Achievement Change Tool (M-PACT) for Iowa. We identified an average predictive accuracy increase of 7%. Evidence of reduced racial disparity was also observed, and research implications outline the need to customize assessments to improve predictive accuracy.

This study explores regional variation in internet access among Indiana households with school-age children. The lack of access likely resulted in significant differences in the quality and quantity of school instruction received by Hoosier students during the Spring 2020 school closings caused by the COVID-19 pandemic. Likewise, this lack of internet access will make virtual learning difficult for some children if schools opt to have online education for the upcoming school year.

Although the U.S. marriage rate has dropped dramatically over the past few decades, the rate and magnitude of the decline varies across demographic groups. Using data from the National Vital Statistics, Decennial Censuses, and the American Community Survey, this profile charts marriage patterns since 1900 for women aged 15 and older. It also includes more detailed information disaggregated by race/ethnicity and educational attainment.

Urban revitalization plays a critical role in housing and community development policy, yet sociological research on gentrification, displacement, and racial turnover often ignores or overlooks it in the reproduction of urban inequality. In this dissertation, I focus on urban planners and the attitudes, processes, and ideologies that underlie their revitalization practices in low-income neighborhoods. I ask: What beliefs, preferences, and dispositions underlie planners’ practices? Why and how do urban planners target neighborhoods for revitalization during a period of urban resurgence? In what ways might these ‘planner factors’ manifest in revitalization plans? In three interrelated articles, I use a mixed-methods approach to investigate planners’ attitudes towards common urban planning practices and revitalization methods during a period of high demand for urban housing. I collect data using semi-structured interviews with urban planners and residents of revitalizing neighborhoods; content analysis of revitalization policies; content analysis of city council minutes, bids, and proposals; participant observation of urban planning forums, courses, and events; and finally, quantitative analysis of data derived from an original survey that I designed and distributed to urban planners who maintain membership with the American Planning Association North Carolina chapter. By bringing sociological theory to bear on the field of urban planning, this project challenges the assumed impartial and constrained practices of urban planning professionals. The findings reveal that urban planners are heavily involved in implementing policy aligned with neoliberal paternalism that reproduces race and class inequality. As a whole, my dissertation demonstrates the relevance of race, class, and the political economy to urban planning in our new era of urban housing demand. Doing so, it provides opportunities for a more efficacious, inclusive approach to urban planning.

Fundamental tenets of colonial historiography are challenged by showing that US capital investment into this colony did not lead to the disappearance of the small farmer. Contrary to well-established narratives, quantitative data show that the increasing integration of rural producers within the US market led to differential outcomes, depending on pre-existing land tenure structures, capital requirements to initiate production, and demographics. These new data suggest that the colonial economy was not polarized into landless Puerto Rican rural workers on one side and corporate US capitalists on the other. The persistence of Puerto Rican small farmers in some regions and the expansion of local property ownership and production disprove this socioeconomic model. Other aspects of extant Puerto Rican historiography are confronted in order to make room for thorough analyses and new conclusions on the economy of colonial Puerto Rico during the early twentieth century.

If you use census data, then you are likely aware that the Census Bureau is applying a new form of privacy protection to Census 2020. What will it look like? How will it impact your existing workflows? To help answer these questions Esri has built feature layers for eight levels of geography so that you can explore how census data are likely to change for 2020. As the Census Bureau works diligently to fine tune their methods for privacy protection, they need feedback from data users like you! Your input can help improve the Census 2020 data products.

The application of a currently proposed differential privacy algorithm to the 2020 United States Census data and additional data products may affect the usefulness of these data, the accuracy of estimates and rates derived from them, and critical knowledge about social phenomena such as health disparities. We test the ramifications of applying differential privacy to released data by studying estimates of US mortality rates for the overall population and three major racial/ethnic groups. We ask how changes in the denominators of these vital rates due to the implementation of differential privacy can lead to biased estimates. We situate where these changes are most likely to matter by disaggregating biases by population size, degree of urbanization, and adjacency to a metropolitan area. Our results suggest that differential privacy will more strongly affect mortality rate estimates for non-Hispanic blacks and Hispanics than estimates for non-Hispanic whites. We also find significant changes in estimated mortality rates for less populous areas, with more pronounced changes when stratified by race/ethnicity. We find larger changes in estimated mortality rates for areas with lower levels of urbanization or adjacency to metropolitan areas, with these changes being greater for non-Hispanic blacks and Hispanics. These findings highlight the consequences of implementing differential privacy, as proposed, for research examining population composition, particularly mortality disparities across racial/ethnic groups and along the urban/rural continuum. Overall, they demonstrate the challenges in using the data products derived from the proposed disclosure avoidance methods, while highlighting critical instances where scientific understandings may be negatively impacted.

This paper investigates the contribution of increasing travel times to the persistent gender gap in labor market participation. In doing so, we estimate the labor supply elasticity of commuting time from a sample of men and women in US cities using microdata from the Census for the last decades. To address endogeneity concerns, we adopt an instrumental variables approach that exploits the shape of cities as an exogenous source of variation for travel times. Our estimates indicate that a 10 minutes increase in commuting decreases the probability of married women to participate in the labor market by 4.6 percentage points. In contrast, the estimated effect on men is small and statistically insignificant. We also find that women with children and immigrant women originating from countries with more gendered social norms respond the most to commuting time variations. This evidence suggests that the higher burden of family responsibilities supported by women may magnify the negative effect of commuting on their labor supply. From our findings, we conclude that the increasing trend in travel times observed in the US and in many European countries during the last decades may have contributed to the persistence of gender disparities in labor market outcomes.

Attribute reduction is a key problem in many areas such as data mining, pattern recognition, machine learning. The problems of finding all reducts as well as finding a minimal reduct in a given data table have been proved to be NP-hard. Therefore, to overcome this difficulty, many heuristic attribute reduction methods have been developed in recent years. In the process of heuristic attribute reduction, accelerating calculation of attribute significance is very important, especially for big data cases. In this paper, we firstly propose attribute significance measures based on stripped quotient sets. Then, by using these measures, we design efficient algorithms for calculating core and reduct, in which the time complexity will be considered in detail. Additionally, we will also give properties directly related to efficiently computing the attribute significance and significantly reducing the data size in the process of calculation. By theoretical and experimental views, we will show that our method can perform efficiently for large-scale data sets.

Despite the importance of relative inequality in studies on race, place and crime, existing literature has paid little attention to the possibility of racial differences in the effect of relative inequality at the neighborhood level. I argue the anomie and social disorganization frameworks make competing predictions regarding whether relative inequality aligns with the racial invariance thesis, and assess hypotheses derived from each perspective using data from the 2000 National Neighborhood Crime Study. A comparison of marginal effects derived from multilevel negative binomial regression models indicate relative inequality effects on homicide are larger in neighborhoods comprised primarily by Blacks and Latinos, while effects on robbery and burglary are greater in White, Latino and Integrated areas. My observations add to a growing body of work demonstrating the importance of income inequality for neighborhood crime but suggest the magnitude of the effect varies considerably by crime type and neighborhood ethnoracial composition.

Coronavirus Disease 2019 (COVID-19) has spread around the world, including the United States. While this pandemic has affected each of us, some groups may be disproportionately impacted by the virus. Currently available information and clinical expertise indicate that older workers and workers of any age who have certain underlying medical conditions (e.g., heart or lung disease, diabetes), and other factors (e.g., smoking, obesity) might be at higher risk for severe illness from COVID-19. At this point, it is unknown how many construction workers have become sick or lost their lives due to the COVID-19 outbreak. To assess the potential risk of severe illness from COVID-19 in the construction industry, this Data Bulletin provides updated employment and health information among construction workers by analyzing available national survey data. The employment numbers were estimated from the Current Population Survey, while medical conditions and other risk factors were obtained from the National Health Interview Survey. This Bulletin focuses on older workers, Hispanic workers, black workers, and workers with underlying medical conditions or other risk factors defined by the CDC. Term definitions are included at the end of this report.

We observe the following patterns in the US economy during the period 1965-2015: (i) the rise of the service sector, (ii) the increase in leisure time, and (iii) the increase in recreational services. To show the last pattern, we measure the fraction of the value added of the service sector explained by the consumption of recreational services and we show that it increases during this period. We explain these three patterns of structural change in a multisector growth model in which leisure time increases with income. As a consequence, the consumption of recreational services increases since they are consumed during leisure time. We show that the introduction of recreational services contributes to explain the rise of the service sector, inequality in leisure, and employment differences across countries caused by differences in income taxes.