Previous work has suggested that the price of food sold at supermarkets may vary according to the socioeconomic characteristics of a neighborhood. Given the importance of food prices in securing access to food, understanding how food prices vary across neighborhoods is crucial to assessing affordability. To study food pricing in New York City (NYC) a defined standard food basket (SFB) was collected in supermarkets across NYC neighborhoods. A dataset was created that includes pricing data collected in-person for ten pre-determined food items from 163 supermarkets across 71 of the 181 NYC neighborhoods during March through August of 2019. Included in these data are raw and processed pricing data files that illustrate the complexity of standardizing pricing across items. An additional dataset includes neighborhood-level variables of selected socioeconomic and demographic characteristics from the 2014–2018 American Community Survey that is publicly available via the Census API. The pricing data and the data on neighborhood-level characteristics were merged. Basic statistical measures suggest some distributional differences in the price of a SFB by socioeconomic differences between neighborhoods. This database can be used to describe spatial patterns in food pricing in a dense urban setting, while exploring pricing inequities across neighborhoods. In addition, by working with these data, researchers, policy analysts and educators will gain an understanding of the methodologies used to generate pricing data for an SFB.

The Centers for Disease Control and Prevention recommends that individuals aged 13–64 test for HIV at least once during their lifetime. However, screening has been disproportionate among racial/ethnic populations. Using the National Health Interview Survey data (2006–2018), we examined HIV screening prevalence within racial/ethnic groups in the United States (US), and factors associated with testing among 301,191 individuals. This consisted of 195,696 White, 42,409 Black, 47,705 Hispanic and 15,381 Asian individuals. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) to estimate the association between ever testing for HIV and demographic, socioeconomic and health-related factors. Approximately 36% of White, 61% of Black, 47% of Hispanic and 36% of Asian individuals reported ever testing for HIV. Hispanic (OR = 1.28, 95% CI [1.25–1.32]) and Black individuals (OR = 2.44, 95% CI [2.38–2.50]) had higher odds of HIV testing, whereas Asian individuals (OR = 0.74, 95% CI [0.71–0.77]) had lower odds of HIV testing compared to White individuals. Individuals who identified as males, married, between the ages of 18–26 years or greater than or equal to 50 years were less likely to ever test for HIV compared to their counterparts. Similarly, those with lower education, lower income, better self-reported health, no health professional visits or living in the midwestern US were less likely to ever test for HIV compared to their counterparts (OR range: 0.14–0.92). Understanding the factors associated with HIV testing opens opportunities to increase testing rates for all and reduce health disparities in HIV detection.

Labor market tightness following the height of the Covid-19 pandemic led to an unexpected compression in the US wage distribution that reflects, in part, an increase in labor market competition. Rapid relative wage growth at the bottom of the distribution reduced the college wage premium and counteracted approximately one-quarter of the four-decade increase in aggregate 90-10 log wage inequality. Wage compression was accompanied by rapid nominal wage growth and rising job-to-job separations-especially among young non-college (high school or less) workers. At the state-level, post-pandemic labor market tightness became strongly predictive of price increases (price-Phillips curve), real wage growth among low-wage workers (wage-Phillips curve), and aggregate wage compression. Simultaneously, the wage-separation elasticity-a key measure of labor market competition-rose among young non-college workers, with wage gains concentrated among workers who changed employers and industries. Seen through the lens of a canonical job ladder model, the pandemic increased the elasticity of labor supply to firms in the low-wage labor market, reducing employer market power and spurring rapid relative wage growth among young non-college workers who disproportionately moved from lower-paying to higher-paying and potentially more-productive jobs.

This study examined differences in poverty and health among Native Hawaiians and Pacific Islanders (NHPI) and 6 disaggregated Asian ethnic subgroups and an aggregated Other Asian category. Participants were followed longitudinally for 2 years using data from 2009 to 2019 from the Current Population Survey, a monthly survey conducted by the Census Bureau. Having 2 years of data enabled the study to assess both prevalence of poverty and fair/poor health in only 1 of the 2 years and in both years. For NHPI, 13.5% were in poverty 1of the 2 years and 7.1% in both years. Asian ethnicities showed high variability ranging from a low of 6.4% for 1 year and 1.9% for 2 years among Asian Indians to 16.0% for 1 year and 6.3% for 2 years among Vietnamese. Fair/ poor health also showed ethnic variability, made most apparent after age-sex adjustment in regression models. For poverty, after adjustment, Asian Indians, Filipinos and Japanese had significantly lower odds of being in poverty at least 1 year than NHPI. For having fair/poor health, Asian Indians and Japanese experienced lower odds than NHPI for both 1 and 2 years and Filipinos for 1 year, after age/sex adjustment. The results emphasize the diversity of Asian and Pacific Islander populations, the variability of poverty over time, and the importance of using disaggregated data to understand ethnic differences in poverty and health. These findings can be used to inform future modeling of social determinants on poverty and health among NHPI and Asian subgroups.

We provide the first causal analysis of how state and federal minimum wage policies in the U.S. have affected labor market frictions and racial wage gaps. Using stacked event studies, binned difference-in-differences estimators, within-person analyses and classic panel methods, we find that minimum wages increased wages of black workers between 16 and 64% more than among white workers and reduced the overall black-white wage gap by 10% (and by 56% among workers most affected by the policies). Racial differences in initial wages cannot explain this differential effect. Rather, minimum wages expand job opportunities for black workers more than for white workers. We present a model with labor market frictions in which minimum wages expand the job search radius of workers who do not own automobiles and who live farther from jobs. Our causal results using the ACS show that minimum wages increase commuting via automobile among black workers but not among white workers, supporting our model. Minimum wages also reduce racial gaps in separations and hires, further suggesting the policies especially enhance job opportunities for black workers.

Research has suggested that people living in higher poverty areas experience more acute systemic problems than people in lower poverty areas (e.g., limited access to medical services, healthy and affordable food, quality education, and civic engagement opportunities).2 Government agencies and researchers have previously identified counties with high rates of poverty over an extended period as targets for increased level of support. While definitions vary, counties are typically considered to be in persistent poverty if they maintained poverty rates of 20 percent or more for the past 30 years. Persistent poverty is different from and should not be confused with chronic poverty. Chronic poverty identifies individuals and families that are consistently in poverty over time, whereas persistent poverty in this report focuses on geographic locations that have had high poverty rates for an extended time. To identify counties in persistent poverty, this report incorporates poverty estimates from the 1990 and 2000 Censuses, the 2005–2009 American Community Survey (ACS), 5-year estimates, and the 2015–2019 ACS, 5-year estimates. Other governmental agencies have alternative definitions of persistent poverty for programmatic purposes. This has created a need for more consistent methods that can be universally applied, and examples of such are described in this report. This report expands upon the persistent poverty literature by examining subcounty geographies (specifically, census tracts) and comparing those results to county results. By using this smaller geography, additional populations that may benefit from targeted intervention are identifiable. In addition, census tracts are explored over a longer time than what has been typically done in other persistent poverty analyses, allowing for comparison with identified persistent poverty counties.

Economic activities vary across regions, and individuals and markets respond differently to macroeconomic shocks, national policies, or uniform local policies. Understanding the patterns of regional economies and predicting local trends are critical for policy-making and business decisions. This dissertation examines the outcomes of local economies in response to the most discussed policies and prevailing global trends. Specifically, it studies the impact of free college policies, monetary policy, and export slowdown. Chapter 1 studies the effectiveness of Promise Programs in the short and long term, which aim to provide free or reduced-cost college tuition, fees, and other expenses to eligible students based on their residency, academic merit, or financial need. In chapter 1, I develop a dynamic spatial model that allows newborn agents to decide whether to attend college based on the skill premium and their education costs. Tuition cut provided by the local governments reduce newborn agents’ education cost and increase college going rate, at the expense of local income tax. Some agents move and some agents stay. Chapter 1 finds that in places near innovative counties, many agents that received tuition cut move, unlikely to promote local development, whereas in distant places, enough stay makes the program worthwhile. Chapter 2 investigates the heterogeneous effects of monetary policy on housing prices across growing cities and declining cities. Using the local projections method, we find that, overall, the nominal housing prices decrease by 3.4% within four years in response to a 1% interest rate increase. The housing prices drop by slightly more than 4% (trough) in growing areas, whereas the housing prices in declining areas drop by around 2.5% (trough). Urban growth contributes to the heterogeneous effects of monetary policy through the land scarcity channel. Chapter 3 examines the impact of export slowdown on household saving rate with China Family Panel Studies survey data. I exploit variation in local exposure to export slowdown to study households’ response to income shocks induced by global export slowdown following the Great Financial Crisis. Using a shift-share instrumental variable, I find that export slowdown is associated with a decrease in household saving rate, with the more pronounced effects concentrating on urban areas. To explain this consequence, we propose the inertia expectation adjustment mechanism, based on the Permanent Income Hypothesis: when adverse income shock hits, households keep consumption level if they keep high expectation of the future, thus lower the saving rates. I test this mechanism using data for households’ confidence about the future and confirm that export slowdown decreases households’ saving rates through this expectation adjustment mechanism.

The Great Migration from the South involved millions of African-Americans who moved north. This movement had significant social and economic ramifications, but its effect on labour markets in the North has been largely neglected. This paper shows that the Great Migration reduced wages considerably for blacks, but had minimal effect on whites. This is demonstrated using both local pay comparison and structural simulations, which reveal that the Great Migration had a considerable influence on North-South wage disparities. The paper also examines how immigration and wages are related, and how this relationship can be assessed, using both local and aggregate data.

The Safe Drinking Water Act, the landmark legislation enacted in 1974 now regulates over ninety contaminants in public drinking water systems. While we have made great progress in the United States with respect to protecting humans from waterborne diseases, drinking water remains a significant vulnerability point for human health, and contaminants of concern continue to emerge. Private water sources, which are unregulated by the Safe Drinking Water Act must be tested by the homeowner or tenant to ensure water quality, but regular testing of private water supplies is rare. We still don’t know who drinks what type of water or which public systems serve which populations in the United States. We also lack understanding of potential exposure to point sources of contaminants, and the fate and transport of contaminants relative to drinking water sources. This dissertation focuses on three main questions; (1) Who is most vulnerable to sources of contamination? (2) How is our understanding of fate and transport informing how we think about vulnerability? (3) How can we use the data available from sources such as SDWA reporting to better understand the who, what, where, why and how of drinking water contamination in the United States?. In chapter one, I investigate the distribution of leaking underground storage tanks and whether they represent an environmental justice (EJ) issue, posing an inequitable exposure risk to low-income communities and communities of color. Chapter two explores how LUST sites are monitored and characterized, suggesting that carcinogenic benzene plumes may be larger than previously thought. Chapter three presents new methods for identifying what type of water people are drinking; regulated public water or unregulated private water at high spatial resolution for 2020. A countless amount of chance events, minor and major decisions, luck, support, love and work have lead me to this point in my life. There are too many people to name but I will name a few. I want to thank my high school teachers Nora Comiskey and Beth Glascott for seeing my potential and pushing me towards environmental science. Thank you to my professors at the University of Cincinnati who opened my eyes to the field of Geography and gave me the basis for the work I do today. Thank you to Dr. Diego Riveros-Iregui for being an advisor who has pushed me to be a better researcher, supported my professional development and spent a significant amount of his time helping me make my work better. Thank you to my family, especially my parents for always supporting my decisions, and especially to pursue my Doctorate. Most of all, thank you to my wife and son, who have been my unbelievably strong support system since they entered my life. Without your love and support, I never would have made the move to North Carolina and I would not be the person I am today.

The U.S. marriage rate has dropped over the past few decades (Schweizer, 2020). Persistent, high levels of divorce have been accompanied by plummeting rates of remarriage (Reynolds, 2021). Meanwhile, the share of never married adults in the U.S. has been on the rise (Wang and Parker, 2014), and the prevalence of those who have ever been married has been declining (Ruggles, 2016). All these trends point to the importance of tracking trends in unmarried adulthood. 1 Using data from the 1900 to 1990 Decennial Censuses and the American Community Survey 1-year estimates from 2000 to 2020, this family profile showcases over a century of change in the share unmarried adults aged 18 and older in the U.S., and further examines unmarried adulthood across sociodemographic characteristics, namely age, race and ethnicity, and educational attainment.

This study examines how trust was associated with social distancing during the early days of the COVID-19 pandemic in Burkina Faso and Kenya. It fills gaps in previous research on trust and health by 1) simultaneously considering the relationship of individual- and aggregate-level indicators of trust, and 2) evaluating trust in local government and national government separately. Performance Monitoring for Action (PMA) data on COVID-precautionary measures and individual-level trust measures were spatially linked with aggregated trust data from the Afrobarometer to create a multilevel dataset. PMA data show that women in Kenya were generally more likely to report taking COVID-precautionary measures relative to Burkinabé women, although levels of these measures were high in both countries. Hierarchical logistic models for each country show levels of interpersonal trust mattered more in Burkina Faso. Although the association between individual-level trust in government and social distancing was not statistically significant, overall levels of trust in the region where an individual lived were associated with social distancing. We found a significant interaction effect between regional trust in the national government and regional trust in local government: individuals in regions where trust was high in both national and local government were the most likely to socially distance; individuals in regions with low local government trust but high national government trust were less likely to report social distancing. We unpack possible implications of these findings; they point to the importance of a unified government front within African countries in promoting health safety measures during a pandemic.

Household food insecurity is a concern in the U.S. given the negative effects associated with food insecurity. An interesting finding is that elderly households tend to be more food secure than younger households, even though many are on a fixed income. A relevant question is what might elderly households be doing that is resulting in greater food security? One potential explanation is that in retirement, elderly households can invest in more time-intensive activities that provide greater food security. In this study, we combine time-use diaries with food security surveys to examine whether time spent on food production is associated with lower levels of food insecurity for elderly households. The data show that time spent in meal preparation and eating is increasing with older age cohorts. At the same time, food insecurity is declining steadily with older households. Grocery shopping and non-grocery food shopping do not show any relevant trends. We also compare food insecurity of households that are pre and post retirement eligible to see if food production explains the gap in food insecurity between these household types. We find that time spent on meal preparation and time spent eating explain some of the gap in food insecurity between these households and the results vary by marital status. Finally, we specify a two-stage model to estimate whether time spent on food production causes greater food security. Our specification fails to identify a strong relationship. One potential explanation is that older households in our sample generate bias as we only observe households that still live independently and alone.

Asian Americans report higher rates of insufficient sleep than non-Hispanic Whites (NHWs). It is unclear how sleep outcomes differ among disaggregated Asian subgroups. The National Health Interview Survey (2006–2018) was used to analyze self-reported sleep duration and quality measures for Asian American subgroups (Chinese [n = 11,056], Asian Indian [n = 11,249], Filipino [n = 13,211], and other Asians [n = 21,767]). Outcomes included hours of sleep per day, the number of days reporting trouble falling asleep, staying asleep, waking up rested, and taking sleep medication in the past week. Subsetted multivariate logistic regression was used to assess factors impacting sleep outcomes by ethnicity.

In this report, we lay out the analytical foundations for a workforce supply assessment tool, which enables users to evaluate the supply of skills by region. Currently, this analytical toolset provides estimates of the stock of labor in different occupations (with associated skill requirements) across different regions, and observations of the flow rate of labor across occupations. The toolset connects these parameters to an evaluation of the similarity of skill requirements (derived from O*NET) across occupations. The tool then generates estimates of the stock and flow of labor across occupations that satisfy a minimum skill similarity to a "target occupation." These capabilities represent the first steps of an approach with the ultimate goal of estimating what skill supply might be available to satisfy new sources of labor demand with given skill requirements, such as the skill demands associated with large-scale investment in onshore manufacturing capability. Importantly the measures of skill similarity we use in this tool have not been validated in terms of the real rates of transition across occupations, and such analysis is a minimum condition for drawing conclusions about the level of supply to meet different levels of skill demand. A mature version of this capability will help inform decision makers, such as employers and government leaders, about the relative feasibility across regions of sourcing the skills necessary to meet different demands, candidate occupations for recruitment to match supply and demand of skills, and the implications for supply-demand matching of changing skill requirements (such as by considering workers whose occupation suggests they may be a partial but not complete match for a set of skill requirements).

This paper aims to identify typologies of Latin American cities based on socioeconomic urban environment patterns. We used census data from 371 urban agglomerations in 11 countries included in the SALURBAL project to identify socioeconomic typologies of cities in Latin America. Exploratory factor analysis was used to select a set of variables, and finite mixture modelling (FMM) was applied to identify clusters to define the typology of cities. Despite the heterogeneities among the Latin American cities, we also found similarities. By exploring intersections and contrasts among these clusters, it was possible to define five socioeconomic regional typology patterns. The main features of each one are low-education cities in Northeast Brazil; low-unemployment cities in Peru and Panama; high-education cities in Argentina, Chile, Colombia, Costa Rica, Nicaragua and Mexico; high female labor participation, with high primary education in Argentina and low primary education in Brazil; and low female labor participation and low education in Brazil, Colombia, El Salvador, Guatemala, and Mexico. Identifying clusters of cities with similar features underscores understanding of the urban social and economic development dynamics and assists in studying how urban features affect health, the environment, and sustainability.

After-school programs (ASPs) and Summer Youth Employment Programs (SYEPs) are becoming increasingly popular, due in part to their potential to reduce crime. Prior research shows that there is an inverse relationship between participation in these programs and the probability of engaging in criminal activity. However, these studies are largely conducted at the individual level, meaning that they do not provide estimates of programmatic impacts on aggregate-level criminal activity. The current study investigates the effect of youth and young adult programs on neighboring crime rates over time. Specifically, using block groups as the unit of analysis, a fixed effects regression model is used to evaluate the relationship between the availability of ASPs and SYEPs and crime rates from 2019 to 2021 across New York City’s (NYC) five boroughs. I find no evidence of a statistically significant relationship between program site locations and crime, even when I disaggregate my data according to crime type (i.e., violent and property crime) and program type (i.e., ASPs and SYEPs), or when I vary the year in which I measure my dependent variable (by allowing for the possibility that changes in site locations might affect future crime rates).

We estimate the urban wage premium in the United States from 1940 to 2010. Drawing on recent advances in the literature on selection on unobservables, we show how to control for heterogeneity in the characteristics of individuals that choose to live in cities to address endogenous sorting. Estimates from naive comparisons of individuals living in urban versus non-urban areas substantially overstate the urban wage premium. We find that the premium is highest in the middle of the twentieth century (about 12 percent in 1940 and 1950) relative to the early in twenty-first century (declining to a few percent by 2010). Overall, the urban wage premium is decreasing and sorting explains a larger fraction of the difference in urban versus non-urban earnings across our sample period.

This study explores the long-term health effects of prenatal exposure to racialized violence by analyzing Social Security Administration death records linked with the 1940 census. We exploit variations in lynching incidences to understand their impact on old-age longevity. The results reveal a 3.7 month decrease in longevity for Black males who were exposed to a lynching of a Black victim during gestation. This exposure accounts for approximately 10% of the life expectancy gap between Black and White men in 1980, without negative effects observed among White individuals. Further analysis suggests reductions in socioeconomic measures are likely explanatory factors.

The Department of the Air Force (DAF) has placed a strategic focus on improving talent management, including how to build a diverse, equitable, and inclusive workforce. To support the DAF's efforts, in fiscal year 2021, the RAND Corporation's Project AIR FORCE was asked to (1) provide targeted benchmarks and a planning tool that will allow DAF to evaluate the demographic composition of the active-duty workforce overall and functional areas within this workforce and (2) identify practices and opportunities that the DAF can use to support diversity in critical career fields. This report is one of a series of reports meant to address these tasks. In it, the authors describe the construction of career field benchmarks using near-equivalent groups of civilian workers, provide examples using several functional areas, and discuss considerations for interpreting these results. Accompanying this narrative are the Air Force Occupational Diversity Benchmarking Workbooks, a pair of Excel workbooks (one for enlisted personnel and one for officers) containing benchmarks for the demographic distribution of DAF functional areas. The benchmarks are created using civilians working in near-equivalent occupations to DAF occupations, adjusted to account for differences in age and education level between DAF and the civilian workforce. The workbooks contain a menu of benchmark options using both narrow and broad definitions of near equivalent. Each DAF occupation can also be compared to the entire civilian workforce. The authors describe considerations for choosing the most-appropriate civilian comparison group for each occupation and for interpreting comparisons.

In this paper, we propose a new bootstrap procedure for the empirical copula process. The procedure involves taking pseudo samples of normalized ranks in the same fashion as the classical bootstrap and applying small perturbations to break ties in the normalized ranks. Our procedure is a simple modification of the usual bootstrap based on sampling with replacement, yet it provides noticeable improvement in the finite sample performance. We also discuss how to incorporate our procedure into the time series framework. Since nonparametric rank statistics can be treated as functionals of the empirical copula, our proposal is useful in approximating the distribution of rank statistics in general. As an empirical illustration, we apply our bootstrap procedure to test the null hypotheses of positive quadrant dependence, tail monotonicity, and stochastic monotonicity, using U.S. Census data on spousal incomes in the past fifteen years.