We study direct and indirect effects of Chinese import competition on union membership in the United States, 1990-2014. Competition with Chinese manufacturing induced a slight decline in unionization within manufacturing. The magnitude is small partly because manufacturers in non-union, Right-to-Work states saw more direct competition with low-quality Chinese imports. Outside of manufacturing, however, import competition causes a large increase in union membership as less-educated women (in-cluding spouses and children of affected workers) shift away from retail and towards jobs in healthcare and education where unions are stronger. Due to these responses, we calculate that Chinese imports prevented 26% of the union density decline that would have otherwise occurred.

Problem: Gentrification is a term used to describe the process and changes that commonly occur in lower-income and/or minority neighborhoods with the influx of more affluent residents who are more likely to be white, increases in property values, the renovation of housing, the upscaling of local commercial and retail properties, and potentially, the displacement of current residents. Some studies in the past two decades have found that the sustained growth of the high-quality public transit systems—both rails and buses—may have triggered or accelerated gentrification in some U.S. metropolitan areas (MSAs). Some studies define that phenomenon as the “TransitInduced Gentrification” (TIG). Until now, nineteen empirical studies have examined TIG in one or several cities, with twelve of them focused on American cities. Some fundamental studies have been conducted to explain TIG based on previous theories and hypotheses of the traditional gentrification. Through the summary of literature, three unsolved issues on TIG have been identified. First, current studies have not reached consensus on the pervasiveness of TIG, partly because of their different operational definitions and measures of gentrification and their different research designs. Second, current studies have not found sufficient empirical evidence to support the hypotheses explaining TIG, and factors associated with the probability of TIG are not clear. Third, scholars are still not clear about whether displacement always happens during the TIG. Research strategy and findings: This dissertation is designed to address the first two unresolved issues. With a quasi-experimental design, this dissertation examines the hypothesis of TIG in all neighborhoods newly served by rapid transit stations that opened from 2000 through 2009 across the U.S. This dissertation confirms that TIG is likely but not inevitable by comparing the pretestposttest results between all new rapid-transit-served neighborhoods and a control group selected by nonparametric propensity score matching that controls for neighborhood characteristics and the impact of Great Recession. This dissertation provides the first comparison of the likelihood of gentrification associated with both rail and bus rapid transit (BRT) and shows that rail stations are more likely to induce gentrification than BRT stops. This dissertation also shows that TIG is more evident over long-term than over short-term for rail-served neighborhoods. Methodologically, although some previous studies have used Census block groups (CBGs) as the areal unit of analysis, most have used Census Tracts (CTs), and none has compared results from simultaneous analysis using both CBGs and CTs. This dissertation makes a contribution by comparing results from using both CBGs and CTs as the areal unit of analysis. The comparisons show that CBGbased analyses better approximate the areas served by transit stations, are more consistent with theory, and therefore provide more valid results. This dissertation also applies the multi-level (hierarchical) logistic regressions to identify and examine factors that are likely to be associated with the probability of TIG, including both MSA and neighborhood characteristics. The results show that MSA characteristics are less stable and provide less and evidence of the probability of TIG than neighborhood characteristics. Some socioeconomic characteristics of neighborhoods, mainly measures of poverty, show consistent significance in the examinations for their impact on the likelihood of TIG. Take Away for Practice: The findings of this study have some policy implications. The BRT is less likely to induce gentrification compared with rail transit, and thus could help sustain the transit service to the most vulnerable without the same likelihood of gentrification. The identification of neighborhoods with higher probability of TIG, such as the neighborhoods with higher poverty rates and people of color and lower proportions of college-educated residents, enables policy-makers and urban planners to target policies such as affordable housing and rent ceilings to assist the most vulnerable areas and residents. In addition, the use of different definitions and measurements of TIG results in substantial differences in the percentages of neighborhoods classified as experiencing gentrification and in identification of different factor that affect the probability of TIG. These findings can be interpreted as evidence that policy makers and planners need to involve stakeholders, especially the low-income and people of color who are more vulnerable to gentrification, in their deliberations over definitions of TIG and when establishing anti-gentrification policies.

The rapid spread of COVID-19 has affected thousands of people from different socio-demographic groups all over the country. A decisive step in preventing or slowing the outbreak is the use of mobility interventions, such as government stay-at-home orders. However, different socio-demographic groups might have different responses to these orders and regulations. In this paper, we attempt to fill the current gap in the literature by examining how different communities with different age groups performed social distancing by following orders such as the national emergency declaration on March 13, as well as how fast they started changing their behavior after the regulations were imposed. For this purpose, we calculated the behavior changes of people in different mobility metrics, such as percentage of people staying home during the study period (March, April, and May 2020), in different age groups in comparison to the days before the pandemic (January and February 2020), by utilizing anonymized and privacy-protected mobile device data. Our study indicates that senior communities outperformed younger communities in terms of their behavior change. Senior communities not only had a faster response to the outbreak in comparison to young communities, they also had better performance consistency during the pandemic.

Decades of federal and state efforts to increase children’s enrollment in Medicaid and the Children’s Health Insurance Program (CHIP) and reduce uninsurance, including implementation of the coverage provisions of the Affordable Care Act (ACA) in 2014, have been associated with increased health insurance coverage among children. By 2016, children’s uninsurance rate reached a historic low, and Medicaid/CHIP participation had reached its highest rate since we started tracking it in 2008. However, this progress began reversing in 2017. In this brief, we update our prior research on uninsurance, Medicaid/CHIP participation, and the number of children eligible for Medicaid/CHIP but uninsured using 2018 data from the American Community Survey (ACS).

Income inequality between different races in the U.S. is especially large. This difference is even larger when gender is involved. In a complementary study, we have developed a dynamic micro-economic model accurately describing the evolution of male and female incomes since 1930. Here, we extend our analysis and model the disparity between black and white population in the U.S., separately for males and females. Unfortunately, income micro-data provided by the U.S. Census Bureau for other races and ethnic groups are not time compatible or too short for modelling purposes. We are forced to constrain our analysis to black and white population, but all principal results can be extrapolated to other races and ethnicity. Our analysis shows that black females and white males are two poles of the overall income inequality. The prediction of income distribution for two extreme cases with one model is the main challenge of this study.

The authors describe the usage of persistent identifiers (PIDs) for historical geographical objects. They provide PIDs’ definition and scope of use as well as characterise the process of data harmonisation and PIDs’ creation. The article describes and assesses certain approaches used in different projects. Most often, internal identifiers are used, although their stability is not guaranteed. References are also made to external data stores such as Geonames and Wikidata.

Residential energy use accounts for roughly 20% of greenhouse gas (GHG) emissions in the United States. Using data on 93 million individual households, we estimate these GHGs across the contiguous United States and clarify the respective influence of climate, affluence, energy infrastructure, urban form, and building attributes (age, housing type, heating fuel) in driving these emissions. A ranking by state reveals that GHGs (per unit floor space) are lowest in Western US states and highest in Central states. Wealthier Americans have per capita footprints ∼25% higher than those of lower-income residents, primarily due to larger homes. In especially affluent suburbs, these emissions can be 15 times higher than nearby neighborhoods. If the electrical grid is decarbonized, then the residential housing sector can meet the 28% emission reduction target for 2025 under the Paris Agreement. However, grid decarbonization will be insufficient to meet the 80% emissions reduction target for 2050 due to a growing housing stock and continued use of fossil fuels (natural gas, propane, and fuel oil) in homes. Meeting this target will also require deep energy retrofits and transitioning to distributed low-carbon energy sources, as well as reducing per capita floor space and zoning denser settlement patterns.

Working longer – in the sense of choosing to delay retirement beyond traditional retirement ages – is widely proposed as the best way for older Americans to boost their fragile retirement security. But the policy goal of increasing labor force participation among older Americans is fundamentally in tension with a precarious low-wage economy because jobs that feature low wages, high turnover rates, and few benefits do not provide a solid foundation for sustained employment at older ages. Many Americans in their 50s are already out of the labor force, and many retire involuntarily before traditional retirement ages – a situation that has been exacerbated by the COVID-19 pandemic. Better jobs for prime-age workers help to pave the way for longer working lives. I outline three specific policy proposals: improved minimum wage, fair workweek laws, and a universal paid family and medical leave benefit. As others have argued, these policies would improve the well-being of prime-age workers. What has been less appreciated is that these policies would also put older Americans in a better position to extend their working years.

We offer new evidence on the regional dynamics of wealth holding in the United States over the Civil War decade based on a hand-linked random sample of wealth holders drawn from the 1860 census. Despite the wealth shock caused by emancipation, we find that patterns of wealth mobility were broadly similar for northern and southern residents in 1860. Looking at the determinants of individual wealth holding in 1870, we find that the elasticity with respect to 1860 wealth was quite low in both regions—consistent with high levels of wealth mobility.

This paper combines a Roy model of migration and counterfactual wages with racial differences in migration rates during the Great Migration to recover lower bounds on black-white differences in the wage impacts of northward migration. Identification is predicated on the idea that, when migration is more selective for whites, regional wage differentials for whites will be more contaminated with selection bias. In this case, the black-white difference in North-South wage differentials bounds the racial difference in wage impacts from below. Furthermore, as long as the impact of migration on whites’ wages is nonnegative, a lower bound on the black-white difference in wage impacts is also a lower bound on the impact itself for blacks. Applying the identification result, I find that northward migration increased blacks’ wages by at least 36% more than for whites’, and hence by at least 36%, on average between 1940 and 1970.

We document the large dispersion in hours worked in the cross-section. We account for this fact using a model in which households combine market inputs and time to produce a set of nonmarket activities. To estimate the model, we create a novel data set that pairs market expenditures and time use at the activity level using data from the Consumer Expenditure Survey and the American Time Use Survey, respectively. The estimated model can account for a large fraction of the dispersion of hours worked in the data. The substitutability between market inputs and time within an activity and across a sizable number of activities is key to our results. We show that models that lack these features can only generate one third of the observed hours dispersion.

We consider so-called univariate unlinked (sometimes “decoupled,” or “shuffled”) regression when the unknown regression curve is monotone. In standard monotone regression, one observes a pair (X, Y ) where a response Y is linked to a covariate X through the model Y = m0(X) + , with m0 the (unknown) monotone regression function and  the unobserved error (assumed to be independent of X). In the unlinked regression setting one gets only to observe a vector of realizations from both the response Y and from the covariate X where now Y d= m0(X) + . There is no (observed) pairing of X and Y . Despite this, it is actually still possible to derive a consistent non-parametric estimator of m0 under the assumption of monotonicity of m0 and knowledge of the distribution of the noise . In this paper, we establish an upper bound on the rate of convergence of such an estimator under minimal assumption on the distribution of the covariate X. We discuss extensions to the case in which the distribution of the noise is unknown. We develop a gradient-descent-based algorithm for its computation, and we demonstrate its use on synthetic data. Finally, we apply our method (in a fully data driven way, without knowledge of the error distribution) on longitudinal data from the US Consumer Expenditure Survey

Wastewater-based epidemiology (WBE) is an economical technique for monitoring and managing the health and behavior of human populations. Using 2017 nationwide data on geospatial population demographics as a test case, we simulated repeated sampling at all major U.S. wastewater treatment plants (WWTPs; n = 13,940) under constant biomarker loading conditions, to explore the potential sensitivity of WBE for generating skewed data. Simulation of repeated sewage sampling over all four seasons of 2017 yielded a number of expected, inter-dependent phenomena triggered by cooler wintertime temperatures compared to summertime results, including relatively (i) slower in-sewer biomarker decay, (ii) longer distal reach of WBE, (iii) larger effective sewershed monitoring areas, and (iv) an increase in the population represented. Additional important but not necessarily anticipated simulation outcomes included (v) distinct, non-random changes in demographic parameters of monitored subpopulations (e.g., by household income, educational attainment, military service, unemployment, and lack of health insurance), (vi) recurring observation of the latter demographic patterns across various geospatial scales and regions, and (vii) more evenly distributed results in the winter. In contrast, data obtainable by WBE in the summertime were dominated by households residing closest to the WWTP and subpopulations of relatively lesser wealth, educational achievement, healthcare access and employability. The analytical approach presented here should be readily applicable to other regions worldwide and may help to improve the design, robustness and interpretation of future WBE studies.

The time after giving birth is critical to the health of new mothers and their children. Though most women have health insurance coverage during their pregnancy and delivery, new mothers often become uninsured postpartum, which can threaten their abilities to access and afford needed health care. Following implementation of the coverage provisions of the Affordable Care Act (ACA) in 2014, thousands of new mothers gained insurance coverage, but few analyses have assessed how much new mothers’ health care access and affordability have improved under the law. In this brief, we examine changes in access to and affordability of health care services for new mothers under the ACA using data from the National Health Interview Survey (NHIS). 1 We also assess whether changes in the demographic and socioeconomic characteristics of our sample of new mothers contributed to observed changes in health care access and affordability. We find the following: ◼ The uninsurance rate for new mothers fell from 20.2 percent in 2011 to 11.3 percent in 2015 and remained relatively stable through 2018. ◼ New mothers were less likely to report unmet health care needs due to cost after implementation of the ACA coverage expansions in 2014; between 2011–13 and 2015–18, the share of new mothers reporting unmet needs for medical care dropped by 60 percent, and the shares reporting unmet needs for prescription medicines and specialist care fell by 40 percent and 44 percent. ◼ The share of new mothers very worried about paying their medical bills also fell from 20.9 percent in 2011–13 to 15.5 percent in 2015–18. ◼ In 2015–18, new mothers were more likely to report having seen a general doctor (60.9 percent versus 55.6 percent) and received a flu vaccine (52.5 percent versus 44.6 percent) in the past 12 months than in 2011–13. ◼ Changes in health care affordability and access were generally consistent with and without adjusting for new mothers’ changing demographic and socioeconomic characteristics, suggesting these changing characteristics were not driving health care access and affordability improvements during the study period. We find that new mothers experienced significant improvements in health care access and affordability after implementation of the ACA’s major coverage provisions. Together with other evidence on the ACA’s role in reducing uninsurance among women and new mothers and improving access to and affordability of health care among parents with low incomes and other adults, our results suggest the ACA likely contributed to new mothers’ gains in health care access and affordability over the study period. But even after ACA implementation, many new mothers still faced barriers to accessing needed health care services, which can negatively affect their health and their family’s well-being.

Slavery is a condition in which one human being was owned by another. A slave was considered by law as property, or chattel, and was deprived of most of the rights ordinarily held by free persons. Slavery is a significant public issue. What are the characteristics of the slave trade? Genetic markers? What is the psychohistory? What are the features of slave demography? Child slavery? How can the slave culture be characterized? What are the health effects of slavery? What are the characteristics of slave healers and physicians? What are the characteristics of slave health? How was the health care system for slaves organized? What are the characteristics of the descendants of slaves? When was slavery abolished? What is the modern slavery? Biblical verses related to slavery were studied from a contemporary viewpoint.

Using decadal census data since 1960, I cannot reject the hypothesis that new immigrants crowd out existing residents from US commuting zones and states one-for-one. The effect is entirely driven by a reduction in internal inflows rather than larger outflows. My estimate is precise and robust to numerous specifications, as well as accounting for local dynamics - and I show how it can reconciled with apparently conflicting results in the literature. On imposing more structure, I attribute about 30 percent of the observed effect to mismeasurement - specifically under coverage of undocumented migrants. Though labor demand does respond, the burden of adjustment falls mostly on population. These results have important methodological implications for the estimation and interpretation of the impact of immigration, both locally and nationally.

There have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities associated with neighborhood attributes, such as the proportion of racial and ethnic minorities, migrants, and the lower income households. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020 using data from the New York City Department of Health and Mental Hygiene, Illinois Department of Public Health, and the American Community Survey. In both cities, cold spots (clusters of low SARS-CoV-2 rate ZIP code tabulation areas as identified by the Getis-Ord (GI*) statistic) demonstrated social determinants of health characteristics typically associated with better health outcomes and the ability to maintain physical distance (“social distancing”). These neighborhoods tended to be wealthier, have higher educational attainment, higher proportions of non-Hispanic white residents, and more workers in managerial occupations (all p values < 0.01 using Wilcoxon two-sample test). Hot spots (clusters of high SARS-CoV-2 rate ZIP code tabulation areas) had similarities as well, such as lower rates of college graduates and higher proportions of people of color. It also appears that household size (more people per household), rather than overall population density (people per square mile), is more strongly associated with hot spots. New York City had an average of 3.0 people per household in hot spots and 2.1 in cold spots (p < 0.01), and Chicago had 2.8 people per household in hot spots and 2.0 in cold spots (p = 0.03). However, hotspots were located in neighborhoods that were significantly less dense (New York City: 22,900 people per square mile in hot spots and 68,900 in cold spots (p < 0.01); Chicago: 10,000 people per square mile in hot spots and 23,400 in cold spots (p = 0.03)). Findings suggest important differences between the cities’ hot spots as well. NYC hot spots can be described as working-class and middle-income communities, perhaps indicative of greater concentrations of service workers and other occupations (including those classified as “essential services” during the pandemic) that may not require a college degree but pay wages above poverty levels. Chicago’s hot spot neighborhoods, on the other hand, are among the city’s most vulnerable, low-income neighborhoods with extremely high rates of poverty, unemployment, and non-Hispanic Black residents.

In the face of a pandemic, urban protests, and an affordability crisis, is the desirability of dense urban settings at a turning point? Assessing cities’ long term trends remains challenging. The first part of this chapter describes the short-run dynamics of the housing market in 2020. Evidence from prices and price-to-rent ratios suggests expectations of resilience. Zip-level evidence suggests a short-run trend towards suburbanization, and some impacts of urban protests on house prices. The second part of the chapter analyzes the long-run dynamics of urban growth between 1970 and 2010. It analyzes what, in such urban growth, is explained by short-run shocks as opposed to fundamentals such as education, industrial specialization, industrial diversification, urban segregation, and housing supply elasticity. This chapter’s original results as well as a large established body of literature suggest that fundamentals are the key drivers of growth. The chapter illustrates this finding with two case studies: the New York City housing market after September 11, 2001; and the San Francisco Bay Area in the aftermath of the 1989 Loma Prieta earthquake. Both areas rebounded strongly after these shocks, suggesting the resilience of the urban metropolis.

This paper examines the short-term consequences of COVID-19 on employment and wages in the U.S. Guided by a pre-analysis plan, we document the impact of COVID-19 at the national-level using a simple difference and test whether states with relatively more confirmed cases were more affected.

Introduction: In recent decades skyrocketing real estate values throughout New York City have prompted residents to seek out reasonably priced housing and speculative investment opportunities in traditionally poorer neighborhoods. This is commonly referred to as “gentrification." This report examines the extent of gentrification in the South Bronx neighborhoods of Melrose, Mott Haven, and Port Morris – officially designated Bronx Community District #1 – widely known as one of New York City’s prominent Latino areas. It presents key socioeconomic and demographic trends between 1990 and 2017. It also looks at topics such as employment, income structures, poverty rates, language acquisition, race/ethnicity, citizenship rates, and educational attainment. Methods: The findings reported here are based on data collected by the Census Bureau IPUMS (Integrated Public Use Microdata Series), available at http://www.usa.ipums.org for the corresponding years and the US Census Bureau’s American Community Survey. This report analyzes data from PUMAS 05001 (1990) and 03170 (2000/2010/2017) in The Bronx. In this report ancestry is defined by the respondent’s self-reported ancestry and Latino group. Discussion: The findings do not align with the traditional gentrification narrative. There has certainly been a slight increase in the number of wealthy non-Hispanic whites over the last two decades, however, the Latino community has remained the dominant demographic in both the total number and in the percent of the total population. There has been in increase in educational attainment, though poverty rates remain high and employment/income both remain comparatively low. The most significant change is the extent to which the Latino community has grown much more diverse as the once overwhelmingly Puerto Rican district now has large Mexican and Dominican contingents, along with a smaller group of Ecuadorians. Several potential areas of research are suggested.