Recent studies have documented large discrepancies between mass preferences and policies in U.S. states consistent with theories that highlight the oversized influence of affluent Americans on policymaking. In this note, we replicate and extend a recent such study (Simonovits, Guess, and Nagler, 2019) to assess how policy bias evolves in time. Specifically, relying on novel data and methods, we construct measures of minimum wage preferences and compare them to observed policies in each state for the years of 2014, 2016, 2019, and 2021. We demonstrate that, averaged across states, policy change closely tracked a pronounced increase in preferences for higher minimum wages, but the size of policy bias remained relatively stable. However, this national pattern hides an increasingly polarized policy landscape: in many states, insufficient responsiveness led to an increasing deviation between preferences and policies, while in other states policy changes-larger than preference changes-closed initial policy bias.

This paper argues that population age structure plays a significant role alongside institutional, technological, political, and cultural factors when it comes to explaining shifts in urban, regional and national economic development. The paper demonstrates how demographic transitions lead to changes in population age structure which in turn correlate with global shifts in economic development from 1950 onwards. It then analyzes the role of population age structure at the sub-national level by reviewing some prominent cases of regional and urban shifts in Western Europe and North America. Population size, population density and migration have always been an integrated part of economic geography, and the consequences of ageing in national and regional economies are increasingly being studied. The specific role of population age structure as a driver of economic development has, however, so far largely been ignored in the field.

Introduction: Work is central to the discourse surrounding the pandemic. Going to work during the COVID-19 pandemic put individuals at risk for both disease and death. This study assesses COVID-19 mortality by industry and occupation for working-age adults in Wisconsin and applies a health equity lens to understand COVID-19, demographics, work, and mortality in the state.Methods: We used vital records data to evaluate COVID-19 mortality in Wisconsin. We assessed the demographics of working-age decedents using chi-square tests and logistic regression. We also classified decedents by usual occupation with Standard Occupational Classification (2018) and North American Industry Classification System (2017) codes to calculate mortality rates. We then calculated proportional mortality ratios to evaluate if mortality rates from COVID-19 in industry or occupation groups were significantly higher than the overall (ie, average) mortality rate from COVID-19 among all working-age Wisconsin adults.Results: Both Asian/Pacific Islander and Hispanic individuals in Wisconsin had elevated likelihoods of dying from COVID-19. Lower levels of education also were associated with a higher likelihood of COVID-19–attributable death. Additionally, we found several occupations and industries that had ele-vated mortality rates from COVID-19. Proportional mortality ratios showed higher than expected mor-tality for several occupations including Protective Service; Office and Administrative Support; Farming, Fishing, and Forestry; and Installation, Maintenance, and Repair. Moreover, several industries had elevated proportional mortality ratios, including Agriculture, Forestry, Fishing, and Hunting; Finance and Insurance; Transportation and Warehousing; and Public Administration.Discussion: The lessons of the pandemic are important for public health and worker safety. Understanding who bears disparate risks allows us to prepare, communicate, and mitigate risk.

In Survey Data Harmonization in the Social Sciences, a team of distinguished social science researchers delivers a comprehensive collection of ex-ante and ex-post harmonization methodologies in the context of specific longitudinal and cross-national survey projects. The book examines how ex-ante and ex-post harmonization work individually and in relation to one another, offering practical guidance on harmonization decisions in the preparation of new data infrastructure for comparative research. Contributions from experts in sociology, political science, demography, economics, health, and medicine are included, all of which give voice to discipline-specific and interdisciplinary views on methodological challenges inherent in harmonization. The authors offer perspectives from Europe and the United States, as well as Africa, the latter of which provides insights rarely featured in survey research methodology handbooks. Readers will also find: A thorough introduction to approaches and concepts for survey data harmonization, as well as the effects of data harmonization on the overall survey research process Comprehensive explorations of ex-ante harmonization of survey instruments and non-survey data Practical discussions of ex-post harmonization of national social surveys, census and time use data, including explorations of survey data recycling A detailed overview of statistical issues linked to the use of harmonized survey data Perfect for upper undergraduate and graduate researchers who specialize in survey methodology, Survey Data Harmonization in the Social Sciences will also earn a place in the libraries of survey practitioners who engage in international research.

In 2015 the landmark Obergefell v. Hodges U.S. Supreme Court ruling legalized marriages to same-sex couples across all 50 states. Estimates from the 2021 ACS 1-year estimates indicated there were approximately 711,129 married and 498,333 cohabiting same-sex couples in the U.S., totaling more than 1.2 million unions.1 We have a series of Family Profiles on marriage and cohabitation among same-sex couples (e.g.: Payne & Manning, 2021; Payne & Manning, 2021; Payne & Manning, 2021). In this Family Profile we explore the geographic variation of the percentage of unions that are to same-sex couples in the United States by pooling the U.S. Census Bureau’s 2019, 2020, and 2021 American Community Survey 1-year estimates to develop a 3-year period estimate.2 While this profile examines LGBT+ population in the context of unions to same-sex couples, it is important to note that not all LGBT Americans are co-residing with a partner/spouse of the same-sex. For an estimate of the LGBT+ population see (Julian, 2023). In this profile we create a map of the U.S. to display the geographic clustering of states. Furthermore, we also rank states in the U.S. according to their percentage of same-sex unions.

Innovation and the creativity that underlies it are the driving forces behind regional prosperity. Moretti (2012), an economist, highlights that employment in innovative occupations portends high wages and associated multiplier effects. Conversely, Sawyer (2012), a psychologist, articulates that the lack of creativity in occupations dampens wages or eliminates employment outright. In concert, they highlight the importance of a workforce prepared for innovative or creative production. The widespread attention to and endurance of the Creative Class Hypothesis (Florida, 2002) suggests that there are salient aspects of the theory meriting continued attention and articulation. The salient aspects are those that elicit the comparison to, and failure of some to distinguish between, what is called creative capital and human capital, more generally. The distinction is not conceptual but rather procedural, where human capital is construed too narrowly and typically confined to education, training, and work experience (using worker age as a proxy). This dissertation uses insights from psychology literature to help define a creativity metric using unsupervised learning, compares it to existing measures of creative or knowledge workers, and quantitatively explore its association with wages, the wage distribution, and inter-metropolitan migration. The ultimate purpose is to determine if a rationale can be identified for public intervention in promulgating a creative workforce.

California faces a shortage of housing according to politicians, activists, and residents. In this paper, I leverage differential exposure to the Proposition 13 tax laws to understand the impact of this policy on the production of housing in Southern California. Proposition 13 restricts property tax growth as long as the owner doesn't sell or redevelop the property, which allows me to exploit differences in market conditions at the time of prior purchase to identify the effect of these property tax limits on property redevelopment. I find that Proposition 13 discourages redevelopment and sales. In a dynamic discrete choice model of land use, I find that adopting a land value tax or other property tax reforms would increase housing production by 14-32% generating a similar or greater amount of new housing as other polices under consideration in California.

We provide the first nationally representative long-run series (1870–2020) of incarceration rates for immigrants and the US-born. As a group, immigrants have had lower incarceration rates than the US-born for 150 years. Moreover, relative to the US-born, immigrants’ incarceration rates have declined since 1960: immigrants today are 60% less likely to be incarcerated (30% relative to US-born whites). This relative decline occurred among immigrants from all regions and cannot be explained by changes in immigrants’ observable characteristics or immigration policy. Instead, the decline is part of a broader divergence of outcomes between less-educated immigrants and their US-born counterparts.

KEY FINDINGS Eligibility requirements limit racial and ethnic minority representation, but propensity to serve offsets barriers to eligibility for these minorities. Eligibility requirements favor representation for women, but pro-pensity to serve is a key barrier to accessions of women. Body mass index, height, and education and aptitude requirements are the most important barriers to both enlisted and officer eligibility, but these requirements affect the eligibility of gender and racial and ethnic groups differently. Considering gender and race and ethnicity jointly, no minority group meets the demographic benchmarks of the U.S. population that is both eligible and has a propensity to serve across the three accession sources (enlisted, the U.S. Air Force Academy, and Reserve Officers Training Corps or Officer Training School). Examining Department of the Air Force benchmarks by considering gender concurrently with race and ethnicity highlights important differences otherwise not observed when considering these groups separately.

The two-sided markets, such as ride-sharing companies, often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development of smartphones and internet of things, they have substantially transformed the transportation landscape of human beings. In this paper, we consider large-scale fleet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time. Major technical challenges, such as policy evaluation, arise in those studies because: (i) spatial and temporal proximities induce interference between locations and times, and (ii) the large number of locations results in the curse of dimensionality. To address both challenges simultaneously, we introduce a multiagent reinforcement learning (MARL) framework for carrying out policy evaluation in these studies. We propose novel estimators for mean outcomes under different products that are consistent despite the high dimensionality of state-action space. The proposed estimator works favorably in simulation experiments. We further illustrate our method using a real dataset obtained from a two-sided marketplace company to evaluate the effects of applying different subsidizing policies.

This dissertation explores three different aspects of how talent gets allocated into jobs within the economy. First, with my coauthor David Deming, I explore the return to majoring in applied STEM fields by age, showing evidence that the return diminishes over the life-cycle. We hypothesize that this is due to the rapid rate of change in skill requirements in Computer Science and Engineering jobs and document various empirical facts consistent with this hypothesis. Second, with my coauthor Savannah Noray, I explore the hypothesis that frictions from communicating across different styles may increase employment segregation and contribute to labor market disparities between identity groups. Building on this intuition, we develop a simple model where jobs vary in the intensity of social interaction and the productivity of interactions is increasing in both social skills and cultural similarity of agents. Consistent with this, we show both descriptive and (plausibly) causal evidence that women, black Americans, and Latin American immigrants in the U.S. are more likely to work in social task intensive occupations in areas where their own group has higher labor market representation. And third, with my coauthor Namrata Narain, I study the heterogeneous treatment effects of a prominent sectoral job training program. We find evidence that poor, black, and female applicants reap lower earnings gains from the program and that this is best explained by an inability for these groups to get placed as effectively into high quality jobs at the end of the program (rather than by differences in character skill gains).

Objectives: To describe vaccine and booster uptake by neighborhood-level factors in California. Methods: We examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions. Results: In a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96–0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01–1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86–0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region. Conclusions: Examining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health.

Purpose Prior studies of cardiovascular health (CVH) disparities among immigrants of South Asian origin in the United States have examined South Asians as one homogenous group, focused primarily on Indian-origin immigrants, and examined risk at the individual level. Methods We present current knowledge and evidence gaps about CVH in the three largest South Asian-origin populations in the United States—Bangladeshi, Indian, and Pakistani—and draw on socioecological and lifecourse frameworks to propose a conceptual framework for investigating multilevel risk and protective factors of CVH across these groups. Results The central hypothesis is that CVH disparities among South Asian populations exist due to differences in structural and social determinants, including lived experiences like discrimination, and that acculturation strategies and resilience resources (e.g., neighborhood environment, education, religiosity, social support) ameliorate stressors to act as health protective factors. Results Conclusions: Our framework advances conceptualization of the heterogeneity and drivers of cardiovascular disparities in diverse South Asian-origin populations. We present specific recommendations to inform the design of future epidemiologic studies on South Asian immigrant health and the development of multilevel interventions to reduce CVH disparities and promote well-being.

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.

We apply persistent homology, the main method in topological data analysis, to the study of demographic data. Persistence diagrams efficiently summarize information about clusters or peaks in a region’s demographic data. To illustrate how persistence diagrams can be used for exploratory analysis, we undertake a study of the 100 largest U.S. cities and their Black and Hispanic populations. We use our method to find clusters in individual cities, determine which cities are outliers and why, measure and describe change in demographic patterns over time, and roughly categorize cities into distinct groups based on the topology of their demographics. Along the way, we highlight the advantages and disadvantages of persistence diagrams as a tool for analyzing geospatial data.

The distribution of the population of cities in the United States has a fat tail. I show that this pattern extends to cities in smaller geographic areas such as Census regions, divisions, and states, indicating a fractal nature. To explain this phenomenon, I propose a location-demand-based theory in which people have heterogeneous geographic attachment. I illustrate the mechanism with a toy model and then estimate an empirical location demand model with this feature. The quantitative model matches key features of migration data and has the ability to generate the observed tail distribution. An implication of the theory is that the elasticity of population to shocks or policies is larger in bigger cities.

While many people are having conversations about the state of the American health ecosystem, its challenges, and potential solutions, most of these conversations only focus on a single ecosystem component. Further, assessments of health and healthcare often fail to examine the critical role of mental health in overall well-being. Rios Partners’ Health of Health report aims to provide a holistic assessment of the US health ecosystem. The 2023 edition takes advantage of trusted data sources to assess the state of US physical and mental health through four pillars. Representing the demand side of healthcare, this pillar includes metrics measuring the treatment individuals receive (or do not receive) and their associated health outcomes. Representing the supply side of healthcare, this pillar includes metrics measuring healthcare’s human resources and physical infrastructure. – Representing the funding structures of the healthcare system, this pillar includes metrics measuring the cost of healthcare and who pays for it. Representing innovations in healthcare, this pillar includes metrics measuring investment in new technologies and actions taken to improve healthcare delivery.

During the 1980s, the wage gap between white women and white men in the US declined by approximately 1 percentage point per year. In the decades since, the rate of gender wage convergence has stalled to less than one-third of its previous value. An outstanding puzzle in economics is "why did gender wage convergence in the US stall?" Using an event study design that exploits the timing of state and federal family-leave policies, we show that the introduction of the policies can explain 94% of the reduction in the rate of gender wage convergence that is unaccounted for after controlling for changes in observable characteristics of workers. If gender wage convergence had continued at the pre-family leave rate, wage parity between white women and white men would have been achieved as early as 2017.

This study assesses whether the impacts of pandemic-induced school closures on parental labor supply varied for working mothers and fathers. Parental labor supply is comprised of two measures: hours worked in the last week and employment status. Childcare duties fell primarily on women even before Covid-19, and the pandemic created additional demands on maternal time as schools closed nationwide. The results of my empirical model indicate that school closures did not have a differential impact on hours worked by men and women with school-aged children. However, school closures had a disproportionate impact on the employment of mothers, relative to fathers: in response to a one percentage point increase in school closures, the employment of mothers declined an additional 5 percentage points, relative to fathers. The results indicate that women responded to pandemic-induced school closures by leaving their jobs entirely, instead of reducing the number of hours worked in a week. Overall, these findings highlight the importance of realigning labor market, education and welfare policies with the realities facing working mothers.

This paper serves to investigate persistent patterns of a wage squeeze within the United States. I observe the dynamics of wage theft in America and where disparities may lie. Looking back to the history of wages and the formation of the minimum wage to how employers have benefited off exploiting workers, we can see how wage theft became a common occurrence in the United States. I will explore these processes of poor wages throughout this paper and the lack of policies that are offered to support the standard of living. I will do so by examining the structure of U.S. wages and observe workers in tipped sectors. By observing changes in tipped wages in states like Pennsylvania, it is possible to discern future trend in wage dynamics for American workers. Lastly, I will look at possible reforms for the US wage system in part through a comparative analysis of processes in the Europe Union (EU). This will allow us to see how the structure of wages in the United States might be improved. In this thesis, I will argue that workers have been put at a disadvantage due to the history of wage structures, the scarcity of laws created for workers, and the control employers have on employees. I conclude my indicating that the United States needs improved worker protection laws and increased funding to organizations such as the NLRB and Wage and Hour Division.