This report explains the process and literature informing the development of a web-based crowdsourced mapping tool accompanied by map-based survey forms for data collection, aiming to 1) visualize the findings of the project’s mapping process, and 2) map and document freedom colonies in Texas. This platform is called the Texas Freedom Colonies Atlas and is a part of the Texas Freedom Colonies Project—a five-year research study conducted by Dr. Andrea Roberts. By utilizing Dr. Roberts preexisting survey instruments and map layers as well as the capabilities of ArcGIS Online and Survey123 -from Esri, the author created a publically shareable Atlas ( or map of freedom colonies) accessible anytime from anywhere. This platform enables users to see the settlement on the map and explore the information that was gathered –by the research team or from the public through crowdsourcing- and presented by the mapping tool. They can also add their settlement to the map –if it is not already there – and share their information about that place. This information might include origin stories, history, nearest major city, church, school, cemetery, or any special features such as state historical markers or The National Register of Historic Places. Current and former residents of these settlements and their descendants are the primary target audience of the project. Users can also submit additional documents they might have along with the survey form. These documents can contain different forms including images, documents, or videos and will be attached to the points on the map. These crowdsourced data then will be published on the mapping tool so other users who are not willing to share information can see the points and data added by other users. ...

Background: Prior research of the impacts of the Patient Protection and Affordable Care Act (PPACA) on children's health coverage has been largely descriptive and focused on the Medicaid expansions. Objective: This study examined the causal impacts of the PPACA Medicaid expansions and of the PPACA as a whole on children's health coverage through 2016. Research Design: We utilized quasiexperimental difference in differences designs to estimate the Medicaid expansion and overall PPACA effects. The first model compared coverage changes between Medicaid expanding and nonexpanding states by household income level. The second model identified the overall PPACA effects by estimating coverage changes across differences in pre-PPACA area-level uninsured rates in expanding states for which the identifying assumptions were valid. We used data from the American Community Survey for years 2011 through 2016 for 3,630,988 children aged 0-18 years living in the 50 states and District of Columbia. Results: The PPACA Medicaid expansions led to gains in public coverage for children at ≤405% federal poverty line especially in 2015-2016. Gains were largest for children at 138%-255% federal poverty line (4 percentage-point increase in 2016). These gains however were mostly due to switching from private to public coverage (ie, crowd-out effects). As a whole however, the PPACA reduced children's uninsured rate in Medicaid-expanding states by about 3 percentage-points in 2016. Conclusions: The PPACA resulted in a meaningful decline in children's uninsured rate in Medicaid-expanding states. PPACA provisions targeting private coverage take-up offset crowd-out effects of the Medicaid expansions resulting in lower children's uninsured rates.

In 1974, Supplemental Security Income (SSI) federalized cash welfare programs for the aged, blind, and disabled, imposing a national minimum benefit. Because of pre-existing variation in generosity, SSI differentially raised payment levels in states below its benefit floor, but had no effect in states that paid above it. We show that SSI increased disability participation in states with the lowest pre-SSI benefits, but shrank non-disability cash transfer programs. For every four new SSI recipients, three came from other welfare programs. Each dollar of per capita SSI income increased total per capita transfer income by just over 50 cents.

While the Affordable Care Act (ACA) promised to reduce inequalities in insurance coverage between Latinos and non-Latinos by expanding coverage, it also excluded a large portion of noncitizen immigrants. Past research has demonstrated that among Latinos, further inequalities have developed between citizens and noncitizens after the ACA took effect, but it is unclear if this pattern is unique to Latinos or is evident among non-Latinos as well. I use data from the 2011 to 2016 waves of the National Health Interview Survey (NHIS) (n = 369,386) to test how the relationship between citizenship status (native citizen, naturalized citizen, or noncitizen) and insurance coverage changed after the ACA, adjusting for health, demographic, and socioeconomic factors. I disaggregate the analysis by ethnicity to test whether this change differs between Latinos and non-Latinos. The analysis finds that after the ACA, naturalized citizens across ethnic groups moved toward parity with native citizens in health insurance coverage while the benefits of the ACA for noncitizens were conditional on ethnicity. For non-Latinos, lacking citizenship became less disadvantageous for predicting insurance coverage while for Latinos, lacking citizenship became even more disadvantageous in predicting insurance coverage. This bifurcation among noncitizens by ethnicity implies that while the ACA has strengthened institutional boundaries between citizens and noncitizens, this distinction is primarily affecting Latinos. The conclusion offers considerations on how legal systems of stratification influence population health processes.

Are you one of the many American workers benefiting from today’s strong labor market? We collected data on more than 500 occupations and analyzed them by age, education, and location. Enter your information, and we’ll tell you if you’re coming out ahead for each category you select.

Importance Reducing smoking is associated with a reduction in health care costs, including in the short run. Medicaid recipients smoke at higher rates than the general population, which suggests that investments to reduce smoking in this population would reduce short-run Medicaid costs. Objective To estimate the short-run (1-year) change in health care expenditure associated with a 1% decrease in absolute smoking prevalence in all US states. Design, Setting, and Participants Economic evaluation based on state Medicaid expenditures and the elasticity between changes in smoking prevalence and health care costs. Data sources were the 2017 Behavioral Risk Factors Surveillance System, 2017 National Health Interview Survey, and Kaiser Family Foundation Total Medicaid Spending for fiscal year 2017. Analysis was conducted in 2018. Participants were all people receiving Medicaid in all US states and the District of Columbia. Exposures Cigarette smoking. Main Outcomes and Measures Short-run (1-year) change in health care costs. Results Reducing absolute smoking prevalence by 1% in each state was associated with substantial Medicaid savings the following year, totaling $2.6 billion (in 2017 dollars). Each state saved a median (interquartile range) of $25 million ($8 million to $35 million). Conclusions and Relevance Effective efforts to reducing smoking could be a cost-effective way to reduce Medicaid costs in the short run.

In this article, I build a new line of health inequality research that parallels the emerging structural racism literature. I develop theory and measurement for the concept of structural sexism and examine its relationship to health outcomes. Consistent with contemporary theories of gender as a multilevel social system, I conceptualize and measure structural sexism as systematic gender inequality at the macro level (U.S. state), meso level (marital dyad), and micro level (individual). I use U.S. state-level administrative data linked to geocoded data from the NLSY79, as well as measures of inter-spousal inequality and individual views on women’s roles as predictors of physical health outcomes in random-effects models for men and women. Results show that among women, exposure to more sexism at the macro and meso levels is associated with more chronic conditions, worse self-rated health, and worse physical functioning. Among men, macro-level structural sexism is also associated with worse health. However, greater meso-level structural sexism is associated with better health among men. At the micro level, internalized sexism is not related to physical health among either women or men. I close by outlining how future research on gender inequality and health can be furthered using a structural sexism perspective.

Research increasingly attends to economic insecurity—the chances of losing what one has—alongside material deprivation. An important source of insecurity is job displacement (layoffs). Surprisingly little is known about the racial patterning of job displacement in the United States, despite sustained attention to racial disparities in other economic outcomes. Here, we provide the first documentation of black/white inequality in displacements occurring from 1979 to 2015. We show that, for both men and women, blacks are nearly always more likely to be displaced than whites, but that the black/white disparity has generally grown over time. In particular, excess black displacement was notably low during the 1990s but had nearly doubled for women, and nearly tripled for men, by the 2010-2015 period. The rising racial inequality in displacement occurred for workers with and without a college degree, and during the 1990s, being black replaced lack of college as the better predictor of displacement.

Antman and Duncan (2014, 2015) document how racial identity responds to state affirmative action policy. The main contribution of our work was to show that racial identity responds to state affirmative action policy. A coding error was recently brought to our attention that resulted in 0.55% of our sample being misclassified in terms of their African ancestry. This paper provides details of the coding error and explores its implications. Although the error only affected a tiny percent of the overall sample, correcting it changes the conclusion of how multiracial blacks respond to state affirmative action bans, from a negative and statically significant effect to a small positive and statistically significant effect. Correcting the error does not change the conclusions for individuals with only or no African ancestry. None of the Asian ancestry classifications were affected by the coding error and thus none of the results for Asians were impacted. In addition, we present an updated analysis using more detailed ancestry classifications and more recent years of data. We continue to find that racial identity responds to state affirmative action policy, albeit with a different conclusion for multiracial blacks, and are now able to distinguish stronger effects for multiracial individuals with more distant connections to their minority group.

Background A startling population health phenomenon has been unfolding since the turn of the 21st century. Whites in the United States, who customarily have the most favorable mortality profile of all racial groups, have experienced rising mortality rates, without a commensurate rise in other racial groups. The two leading hypotheses to date are that either contemporaneous economic conditions or longer-term (post-1970s) economic transformations have led to declining economic and social prospects of low-educated whites, culminating in “deaths of despair.” We re-examine these hypotheses and investigate a third hypothesis: mortality increases are attributable to (false) perceptions of whites that they are losing social status. Methods Using administrative and survey data, we examined trends and correlations between race-, age- and, education-specific mortality and a range of economic and social indicators. We also conducted a county-level fixed effects model to determine whether changes in the Republican share of voters during presidential elections, as a marker of growing perceptions of social status threat, was associated with changes in working-age white mortality from 2000 to 2016, adjusting for demographic and economic covariates. Findings Rising white mortality is not restricted to the lowest education bracket and is occurring deeper into the educational distribution. Neither short-term nor long-term economic factors can themselves account for rising white mortality, because parallel trends (and more adverse levels) of these factors were being experienced by blacks, whose mortality rates are not rising. Instead, perceptions – misperceptions – of whites that their social status is being threatened by their declining economic circumstances seems best able to reconcile the observed population health patterns. Conclusion Rising white mortality in the United States is not explained by traditional social and economic population health indicators, but instead by a perceived decline in relative group status on the part of whites – despite no actual loss in relative group position.

En este trabajo se parte del artículo seminal de Blanchard y Katz (1992) sobre la dinámica regional en el mercado laboral de Estados Unidos. Estos autores muestran evidencia de que el mecanismo de ajuste del mercado laboral es la migración interna de los trabajadores, a diferencia de otro posible mecanismo de ajuste como la creación de vacantes. Se amplía el análisis de Blanchard y Katz (1992) en varios ámbitos. Primero, se extiende el análisis de vectores autorregresivos (VAR) con datos a nivel de estados hasta el 2017 permitiendo examinar la evolución en el tiempo del mecanismo de ajuste mencionado. En línea con los hallazgos de Kaplan y Schulhofer-Whol (2017) y Molloy et al. (2011), que documentan una caída de la migración interna a partir de los años 1980, se encuentra una menor respuesta condicional a un shock regional adverso de demanda laboral desde 1990: el ajuste del mercado laboral en la región deprimida tarda 9 años, cuando previamente tardaba 6 años. Segundo, al incorporar mediciones directas sobre inmigración y emigración bruta por estado del Annual Social Economic Supplement (ASEC), se encuentra que la respuesta ante un shock regional adverso del empleo es diferenciada. La caída de la tasa de inmigración, que alcanza un . . .

Since June 26, 2015 marriages to same-sex couples have been legally recognized across every state in the nation, but there have been challenges to measuring these marriages. Starting in 2017 every household in the Current Population Survey (CPS) was offered a new household roster that directly identified same-sex and different-sex cohabiting and married couples. To gauge how the estimates of same-gender households compare across the traditional and new roster we compare estimates of same-sex couple households using the traditional roster in the 2015 and 2016 CPS to estimates based on the new roster using the 2017 and 2018 CPS. Employing these new indicators, we establish the levels of cohabitation and marriage among same-sex couples and distinguish differences according to their sociodemographic characteristics. These findings have implications for the measurement of same-sex couples and our understanding of marriage among sexual minorities.

When experienced analysts explore data in a rich environment, they often transform the dataset. For example, they may choose to group or filter data, calculate new variables and summary measures, or reorganize a dataset by changing its structure or merging it with other information. Such actions background, highlight, or even fundamentally change particular features of the data, allowing different types of questions to be explored. We call these actions data moves. In this paper, we argue that paying explicit attention to data moves, as well as their purposes and consequences, is necessary for educators to support student learning about data. This is especially needed in an era when students are expected to develop critical literacy around data and engage in purposeful, self-directed exploration of large and often complex datasets.

The household living arrangements of older persons – whether living alone, with a spouse or partner, with their children or in multi-generational households – can be an important factor associated with their health, economic status and overall well-being. Understanding the patterns and trends in older persons’ living arrangements is thus relevant for global efforts to achieve the sustainable development goals, in particular those targeting poverty, hunger and health. The United Nations Database on the Households and Living Arrangements of Older Persons 2018 presents evidence drawn from 672 unique data sources, including census and survey microdata samples archived at IPUMS-International and household rosters from Demographic and Health Surveys, among other sources. The resulting dataset describes older persons’ households across 147 countries or areas, representing approximately 97 per cent of persons aged 60 or over globally.

This paper presents an analysis of the age patterns of production, consumption, and net transfers in the form of unpaid work by gender over time. Using the National Time Transfer Accounts (NTTA) methodology, we briefly analyse complete historical results for several European countries. Our main aim is to introduce historical NTTA results, which are freely available to the public for further usage on the AGENTA database. The results of our analysis show that the evolution of age patterns over time differed between men and women, and was highly a ected by different demographic trends, as well as by the specific institutional background of each country. Our findings indicate that despite the differences in age patterns over time and across countries, two main characteristics of these patterns did not change: i.e. transfers of unpaid work flowed first from women to men, and second from the working-age population to children and – to a lesser extent – to the elderly.

While researchers have explored in detail how multiracial identification shapes symbolic boundaries (conceptual distinctions), they have paid less attention to its effects on social boundaries (how people behave). This study examines multiracial individuals’ odds of marriage and cohabitation with blacks and whites to examine whether this population challenges current race-based social boundaries via partner choice. Analyses of data from the 2008–2012 American Community Survey (ACS) show that while those who identify with more than one race are indeed more likely to have a black (white) partner than their nonblack (nonwhite) monoracial counterparts, this phenomenon is driven by the choices of multiracials with at least a part black (white) identity. For example, multivariate results show that multiracial individuals who do not report any white identity are not more likely than nonwhite monoracials to marry a white partner. Moreover, part-white multiracials are more likely than nonwhite multiracials to have a white partner. These findings largely reflect expectations derived from theories of ingroup/outgroup behavior. In sum, although multiracial individuals may contribute to challenging symbolic boundaries, the results suggest that they are not necessarily disproportionately likely to challenge race-based social boundaries via their partner choices.

Land use/land cover (LULC) change models are powerful tools used to understand and explain the causes and effects of LULC dynamics, and scenario-based analyses with these models can support land management and decision-making better. This paper provides a synoptic and selective review of current LULC change models and the novel frameworks that are being used to investigate LULC dynamics. Existing LULC models that explore the interactions between human and the environment can be pattern- or process-based, inductive or deductive, dynamic or static, spatial or non-spatial, and regional or global. This review focuses on the spectrum from pattern- to process-based approaches and compares their strengths, weaknesses, applications, and broad differences. We draw insights from the recent land use change literature and make five suggestions that can support a deeper understanding of land system science by: (1) overcoming the difficulties in comparing and scaling Agent Based Models; (2) capturing interactions of human-environment systems; (3) enhancing the credibility of LULC change modeling; (4) constructing common modeling platforms by coupling data and models, and (5) bridging the associations between LULC change modeling and policy-making. Although considerable progress has been made, theoretical and empirical efforts are still needed to improve our understanding of LULC dynamics and their implications for policy-oriented research. It is crucial to integrate the key elements of research involved in this study (e.g., use of common protocols and online portals, integration of top-down and bottom-up approaches, effective quantification and communication of modeling uncertainties, generalization and simplification of models, increased focus on the theoretical and empirical bases of models, and open comparative research) to bridge the gaps between small-scale process exploration and large-scale representation of LULC patterns, and to use LULC change modeling to inform decision-making.

US women, on average, had approximately two children in both the 1930s and in the 1970s, yet the fertility distribution in the 1930s was less concentrated. This implies change in reproductive behavior, which cannot be captured by models focusing on average fertility. To explain these changes, I have developed a model that makes a distinction between sons and daughters. In this model, the female labor force participation rate is the probability of each girl becoming an employed woman. This endogenizes the empirically observed difference in the propensity for an all-girl household to have another child compared to an all-boy household, generating large fertility differentials at low participation rates. Higher participation rates raise the expected return from an additional child, as well as the expected return from existing daughters. The first effect tends to increase fertility, while the second effect, for relatively concave utility functions, tends to decrease it, so that the distribution of completed fertilities becomes more concentrated.

I conduct a statewide experiment in Michigan with nearly 50,000 high-achieving high school seniors. Treated students are mailed a letter encouraging them to consider college and providing them with the web address of a college information website. I find that very high-achieving, low-income students, and very high-achieving, minority students are the most likely to navigate to the website. Small changes to letter content affect take-up. For example, highlighting college affordability induces 18 percent more students to the website than highlighting college choice, and 37 percent more than highlighting how to apply to college. I find a statistically precise zero impact on college enrollment among all students mailed the letter. However, low-income students experience a small increase in the probability that they enroll in college, driven by increases at four-year institutions. An examination of persistence through college, while imprecise, suggests that the students induced into college by the intervention persist at a lower rate than the inframarginal student.

State fiscal governance requires both raising revenue and spending on government services. The literature on fiscal equalization and horizontal equity has established that measures of fiscal capacity, or the revenue-raising ability of a sub-national government, should be complemented by measures of fiscal need, the ability of a sub-national government to provide services given an average level of revenue. The representative tax system is an established method for measuring fiscal capacity, but the measurement of fiscal need has received less attention, with economically significant consequences. This paper provides the first regression-based estimates of state-level fiscal need in the United States, introducing a novel, data-intensive extension of the representative expenditure system that estimates fiscal need in the United States using regression methods rather than the current workload-based approach. Data from the 50 states and the District of Columbia were used, including actual expenditure by spending category as well as a large set of potential determinants of both capacity and need, to estimate the level of spending required for each state to provide a national average level of public services. Three regression approaches are considered: the most data-intensive, least data-intensive, and an intermediate method to determine the robustness of the estimates to methodological changes. Regression-based results indicate that prior workload-based approaches provide estimates at odds with both theory and actual state expenditure behavior. Among regression-based approaches, even the least data-intensive approach appears to offer improvements on existing methods. This paper estimates that 1.3% of gross domestic product may be misallocated.