What are the distributional effects of the minimum lot size (MLS) regulation on household welfare? An overlooked channel is how the MLS regulation increases physical house size. Using synthetic control methods, I show Houston's reduction of the MLS in 1999 led to a 12% decrease in the size of new housing and a 14% increase in the marginal cost of house size. To quantify the distributional welfare effects stemming from these incentives, I build a quantitative model with housing and demographics and show that the observed price changes induced by reductions of the MLS disproportionately help lower income and smaller households. Specifically, I find that the bottom decile of households (in terms of household size and income) gain about $25,000 more (in 2010 dollars) than the top decile from a reduction in the MLS. Finally, I show that the model's predicted locational selection of households by household size and income is consistent with empirical observations in Houston before and after the change in regulation.

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics, yet these concepts are often at the margins of the computational modeling community. Building on recent research studies in the area of digital and computational epidemiology, we provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models. The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Although technological change is widely credited as driving the last 200 years of economic growth, its role in shaping patterns of inequality remains under-explored. Drawing parallels across two industrial revolutions in the United States, this paper provides new evidence of a relationship between highly disruptive forms of innovation and spatial inequality. Using the universe of patents granted between 1920 and 2010 by the US Patent and Trademark Office (USPTO), we identify disruptive innovations through their rapid growth, complementarity with other innovations and widespread use. We then assign more and less disruptive innovations to subnational regions in the geography of the United States. We document three findings that are new to the literature. First, disruptive innovations exhibit distinctive spatial clustering in phases understood to be those in which industrial revolutions reshape the economy; they are increasingly dispersed in other periods. Second, we discover that the ranks of locations that capture the most disruptive innovation are relatively unstable across industrial revolutions. Third, regression estimates suggest a role for disruptive innovation in regulating overall patterns of spatial output and income inequality.

Health is political. It entails fierce battles over the allocation of resources, arguments over the imposition of regulations, and the mediation of dueling public sentiments—all conflicts that are often narrated from a national, top-down view. In All Health Politics Is Local, Merlin Chowkwanyun shifts our focus, taking us to four very different places—New York City, Los Angeles, Cleveland, and Central Appalachia—to experience a national story through a regional lens. He shows how racial uprisings in the 1960s catalyzed the creation of new medical infrastructure for those long denied it, what local authorities did to curb air pollution so toxic that it made residents choke and cry, how community health activists and bureaucrats fought over who’d control facilities long run by insular elites, and what a national coal boom did to community ecology and health. All Health Politics Is Local shatters the notion of a single national health agenda. Health is and has always been political, shaped both by formal policy at the highest levels and by grassroots community battles far below.

Disparities in multimorbidity prevalence indicate health inequalities, as the risk of morbidity does not intrinsically differ by race/ethnicity. This study aimed to determine if multimorbidity differences by race/ethnicity are decreasing over time. Methods: Serial cross-sectional analysis of the National Health Interview Survey, 1999-2018. Included individuals were ≥18 years old and categorized by self-reported race, ethnicity, age, and income. The main outcomes were temporal trends in multimorbidity prevalence based on the self-reported presence of ≥2 of 9 common chronic conditions. Findings: The study sample included 596,355 individuals (4.7% Asian, 11.8% Black, 13.8% Latino/Hispanic, and 69.7% White). In 1999, the estimated prevalence of multimorbidity was 5.9% among Asian, 17.4% among Black, 10.7% among Latino/Hispanic, and 13.5% among White individuals. Prevalence increased for all racial/ethnic groups during the study period (P ≤.001 for each), with no significant change in the differences between them. In 2018, compared with White individuals, multimorbidity was more prevalent among Black individuals (+2.5 percentage points) and less prevalent among Asian and Latino/Hispanic individuals (−6.6 and −2.1 percentage points, respectively). Among those aged ≥30 years, Black individuals had multimorbidity prevalence equivalent to that of Latino/Hispanic and White individuals aged 5 years older, and Asian individuals aged 10 years older. Conclusions: From 1999 to 2018, a period of increasing multimorbidity prevalence for all the groups studied, there was no significant progress in eliminating disparities between Black individuals and White individuals. Public health interventions that prevent the onset of chronic conditions in early life may be needed to eliminate these disparities.

Many studies consider occupational segregation among the immigrant population from a given birth country as a whole. This ignores potential ethnic heterogeneity within an immigrant population and may underestimate occupational segregation. We focus on Russian immigrants in the early twentieth century USA—then a major immigrant population with a high degree of ethnic diversity, including Russian, Jewish, German, and Polish ethnics—and study occupational segregation by ethnicity. We apply a machine learning ethnicity classification approach to 1930 US census data based on name and mother tongue. Using the constructed ethnicity variable, we show high degrees of occupational segregation by ethnicity within the Russian-born immigrant population in the USA. For example, Jews, German ethnics, and Polish ethnics were concentrated in trade, agriculture, and manufacturing, respectively. We also find evidence that Russian-born immigrants’ labor market outcomes were associated with networks measured by the spatial concentration of co-ethnics—particularly more established ones—but not by the concentrations of other ethnic groups.

The three chapters of this dissertation use tools of applied microeconomics to study topics related to education and regulation. The first chapter, “Where Doctors Work,” studies how openings and closures of medical schools in the United States have affected the geographical distribution of doctors. The principal contribution is the construction of a novel dataset based on the American Medical Association’s comprehensive physician directories. Using both an individual-level approach and a broad county-level, differences-in-differences strategy, I show that medical school location influences the geographical distribution of doctors, signaling a potential lever for policymakers to address the ongoing shortage of rural physicians. The second chapter, “University Presidencies at a Glance,” examines wage-setting in the context of university presidents. Using data from The Chronicle of Higher Education, my coauthors and I study determinants of salary in both public and private settings. University size is the overriding factor in salary considerations, although alumni and internal hires are often willing to take a small cut in wages. Remarkably, universities setting pay for presidents seem to behave similarly to large companies setting pay for CEOs. The final chapter, “Immigrants in the Age of Information: Earnings, Language Skills, and Technology,” studies immigrant earnings and how the returns to education vary with language skills. I additionally provide results and summary statistics regarding the expanding female immigrant workforce, which generally mirrors the results for male immigrants, with some notable caveats.

Low-income households derive significantly less savings from mortgage refinancing than their wealthy counterparts. I document that the rise of refinancing inequality in the United States can be partially explained by the gap in access to modern information and communications technology. Using granular spatial variation of a large-scale broadband subsidy program, I show that broadband internet facilitates refinancing activity and reduces monthly mortgage payments. These effects are large and persistent, corresponding to a 5 percent increase in disposable income and up to $13,000 in total savings for low-income households. The results are in large part driven by underbanked census tracts with low access to physical bank branches. Lastly, I show that households that are likely to have low digital literacy benefit the most from the program. I discuss the implications of these findings for financial inclusion and monetary policy transmission.

Identifying the comprehensive metropolitan urban form is important to propose effective policies to mitigate transportation carbon emissions. A publicly accessible night-time light dataset was used to identify urban centers and develop two polycentric indices to compute the composition and configuration of urban form, respectively. We used the most populous 103 U.S. metropolitan statistical areas (MSAs), with their corresponding transportation carbon emissions, polycentric indices, population sizes, gross domestic product (GDP) per capita, and road network densities. We first explored the typology of urban form and classified MSAs into six types based on two polycentric indices. We then introduced correlation analysis and statistical models to test the relationships between polycentric urban form and transportation carbon emissions. We found: (1) more urban centers lead to more emissions (compositional dimension), (2) more spatially distributed urban centers result in less emissions (configurational dimension), and (3) population and GDP per capita are positively related to carbon emissions. These findings suggest the importance of measuring two polycentric dimensions separately but using them together. Urban planners should consider mixed strategies that combine the traditional intra-center-based smart growth principles and the metropolitan-level inter-centers spatial plan to effectively counteract climate change.

Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.

The Great Migration significantly increased the number of African American people moving to northern and western cities beginning in the first half of the twentieth century. We show that their arrival shaped “slum clearance” and urban redevelopment efforts in receiving cities. To estimate the effect of migrants, we instrument for Black population changes using a shift-share instrument that interacts historical migration patterns with local economic shocks that predict Black out-migration from the South. We find that local governments responded by undertaking more urban renewal projects that aimed to redevelop and rehabilitate “blighted” areas. More Black migrants also led to an increase in family displacement. This underscores the contribution of spatial policies such as urban renewal towards understanding the long-term consequences of the Great Migration on central cities, and Black neighborhoods and individuals.

This paper analyzes the occupational status and distribution of free women in the antebellum United States. It considers both their reported and unreported (imputed) occupations, using the 1/100 IPUMS files from the 1860 Census of Population. After developing and testing the model based on economic and demographic variables used to explain whether a free woman has an occupation, analyses are conducted comparing their occupational distribution to free men, along with analyses among women by nativity, urbanization, and region of the country. While foreign-born and illiterate women were more likely to report having an occupation compared to their native-born and literate counterparts, they were equally likely to be working when unreported family workers are included. In the analysis limited to the slave-holding states, it is shown that the greater the slave-intensity of the county, the less likely were free women to report having an occupation, particularly as private household workers, suggesting substitution in the labor market between free women and enslaved labor.

Context: Land use change drives a host of sustainability challenges on Earth’s grasslands. To understand the relationship between changing land use patterns, human well-being, and ecosystem services, research is needed into land use transitions on privately-owned grasslands. Such inquiry lies at the intersection of land system science, landscape sustainability science and environmental governance. Objectives: This study investigated land use change in a mountain ranching community in the Sierra Nevada, California. The research objective was to highlight factors influencing land use transitions and corollary ecological outcomes on privately-owned grasslands in the Western US. Methods: This mixed methods case study integrated participant observation, 30 semi-structured interviews, and analysis of land cover and real estate data. Interviews were conducted with ranchers, public agencies, and conservation and real estate industry representatives, and analyzed with the constant comparison method using NVivo 12. Results: Land use transitions in the case study region include agricultural intensification, residential and solar development, and disintensification. These transitions were influenced by many factors including decreasing land access and water availability, amenity migration, intergenerational succession, and conservation policy. Conclusions: By highlighting factors influencing land use transitions on working lands, this study can be applied to improve the uptake of environmental policies. For the future, several approaches may support grasslands conservation: ensuring grazing lands access, income diversification, groundwater regulations, agriculture-compatible conservation easements, and land use policies supporting ownership transition to amenity purposes rather than low-density residential development.

Objective This study examines the relationship between telecommuting and gender inequalities in parents' time use at home and on the job before and during the COVID-19 pandemic. Background Telecommuting is a potential strategy for addressing the competing demands of work and home and the gendered ways in which they play out. Limited evidence is mixed, however, on the implications of telecommuting for mothers' and fathers' time in paid and unpaid work. The massive increase in telecommuting due to COVID-19 underscores the critical need to address this gap in the literature. Method Data from the 2003–2018 American Time Use Survey (N = 12,519) and the 2020 Current Population Survey (N = 83,676) were used to estimate the relationship between telecommuting and gender gaps in parents' time in paid and unpaid work before and during the pandemic. Matching and quasi-experimental methods better approximate causal relationships than prior studies. Results Before the pandemic, telecommuting was associated with larger gender gaps in housework and work disruptions but smaller gender gaps in childcare, particularly among couples with two full-time earners. During the pandemic, telecommuting mothers maintained paid work to a greater extent than mothers working on-site, whereas fathers' work hours did not differ by work location. Conclusion In the context of weak institutional support for parenting, telecommuting may offer mothers a mechanism for maintaining work hours and reducing gender gaps in childcare, while exacerbating inequalities in housework and disruptions to paid work.

Regulation of loan prices is a controversial policy tool. Proponents argue that regulation protects borrowers from high prices, while detractors argue that restricting prices prevents borrowers from obtaining credit. Firm profits play a role in the effects of price regulation. If lenders are profitable enough to offer lower prices while continuing lending activity, borrowers can benefit from price regulation. In this paper, I study a 2014 price regulation in the manufactured home loan market. Manufactured homes, known colloquially as “mobile homes” or “trailers”, are a source of affordable housing for approximately 17 million people in the United States. I find that loan prices fell in response to the 2014 regulation while a similar number of loans with prices near the cap were made, suggesting that borrowers benefited. This response is driven by the largest firm in the market, which made about 90% of affected loans. I show that this firm charges higher prices than other firms to observably similar borrowers, which fits with the finding that they were able to continue lending activity after the restriction was implemented. I then consider how stricter restrictions would affect borrowers and lenders. In order to conduct this counterfactual analysis, I develop and estimate a model of supply and demand for manufactured home loans, estimate borrowers’ price sensitivity, and recover markups. Under progressively stricter rate restrictions, I find that borrowers initially gain surplus from lower prices but are eventually worse off due to the fall in credit supply.

This paper presents a probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to many sources of digitized historical records. The method links records using a two-step approach, first establishing high confidence matches among men by exploiting a comprehensive set of individual and contextual characteristics. The method then proceeds to link both men and women by leveraging links between households established in the first step. While only the first stage links can be directly comparable to other popular methods in research on the U.S., our method yields both considerably higher linkage rates and greater accuracy while only performing negligibly worse than other algorithms in resembling the target population.

We document a robust negative relationship between mean annual hours in an occupation and the dispersion of annual hours within that occupation. We study a unified model of occupational choice and labor supply that features heterogeneity across occupations in the return to working additional hours and show that it can match the key features of the data both qualitatively and quantitatively. Occupational choice in our model is shaped both by selection on comparative advantage and selection on tastes for leisure. Our quantitative work finds that the dominant source of differences in hours across occupations is selection on tastes for leisure. Abstract We document a robust negative relationship between mean annual hours in an occupation and the dispersion of annual hours within that occupation. We study a unified model of occupational choice and labor supply that features heterogeneity across occupations in the return to working additional hours and show that it can match the key features of the data both qualitatively and quantitatively. Occupational choice in our model is shaped both by selection on comparative advantage and selection on tastes for leisure. Our quantitative work finds that the dominant source of differences in hours across occupations is selection on tastes for leisure.

Introduction: This report examines how people commuted to work in the United States between 1990 and 2018, focusing on disparities with respect to race and ethnicity, sex, marital status, income, and poverty status Methods: This report uses the American Community Survey PUMS (Public Use Microdata Series) data for all years released by the Census Bureau and reorganized for public use by the Minnesota Population Center, University of Minnesota, IPUMSusa, (https://usa.ipums.org/usa/index.shtml). See Public Use Microdata Series Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2019. Discussion: Car use had a slight decline among non-Hispanic whites but is increasing steadily among other race and ethnic groups. While in 1990, 88.6% of non-Hispanic white workers commuted by car, a slightly smaller proportion of 86.6% did so in 2018. Among non-Hispanic blacks, however, car use went up from 77.5% to 82.5% over the same period and from 79.8% to 85.3% among Latinos. Car use among Asians remained relatively stable at 79%. Conversely, other means of transportation to work that once were more common are declining. For example, public transportation use dropped five percentage points from 15.2% to 10.1% among non-Hispanic blacks and from 11.1% to 6.6% among Latinos between 1990 and 2018. Working from home rates increased steadily among all racial and ethnic groups. For example, Asians working at home went from 2.1% of those employed in 1990 to 4.7% in 2018. These data are pre-COVID-19. Other trends by sex, income, marital status, and poverty status are further analyzed in the report.

In this dissertation, I consider two potential sources of racial disparities: racially biased discipline at school and changes to voting rights protections. In the first chapter, I examine patterns of racial disparities in school discipline. Racial gaps in the adjudication of student misconduct are well documented—for similar behaviors, students of color are more likely to be disciplined and discipline tends to be harsher. While students of color do receive harsher punishments, on average, I show that this differential depends on the racial composition of incidents. Consistent with administrators moving toward equal treatment when variation in race is more salient, multi-race incidents evidence no differentials. In fact, when a white student is implicated in the same incident as a student of color, punishments imposed on students of color are indistinguishable from those imposed on white students in allwhite incidents. In the second chapter, I turn to the effects of changes to voting rights protections on racial disparities in voter turnout. Existing research shows that the Voting Rights Act of 1965 increased turnout among Black voters, which then generated economic benefits for Black communities. In Shelby County v. Holder (2013), the Supreme Court invalidated the enforcement mechanism responsible for these improvements, prompting concerns that states with histories of discriminatory 4 election practices would respond by suppressing Black turnout. I estimate the effect of the Shelby decision on the racial composition of the electorate using triple-difference comparisons of validated turnout data from the Cooperative Congressional Election Study. The data suggest that the Shelby decision did not widen the Black-white turnout gap in states subject to the ruling. This dissertation includes both unpublished co-authored material and previously published solo-authored material.

Reductions in the cost of genetic sequencing have enabled the construction of large datasets including both genetic and phenotypic data. Based on these datasets, polygenic scores (PGSs) summarizing an individual's genetic propensity for educational attainment have been constructed. It is by now well established that this PGS predicts wages, income, and occupational prestige and occupational mobility across generations. It is unknown whether a PGS for educational attainment can predict upward income and occupational mobility even within the peak earning years of an individual. Using data from the Wisconsin Longitudinal Study (WLS), I show that: (i) a PGS for educational attainment predicts wage, income and occupational prestige mobility between 1974 (when respondents were about 36 years of age) and 1992 (when respondents were about 53 years of age), conditional on 1974 values of these variables and a range of covariates; (ii) the effect is not mediated by parental socioeconomic status, is driven primarily by respondents with only a high school education, and is replicated in a within sibling-pair design; (iii) conditional on 1974 outcomes, higher PGS individuals surveyed in 1975 aspired to higher incomes and more prestigious jobs 10 years hence, an effect driven primarily by respondents with more than a high school education; (iv) throughout their employment history, high PGS individuals were more likely to undertake on the job training, and more likely to change job duties during tenure with an employer; and (v) though no more likely to change employers or industries during their careers, high PGS individuals were more likely in 1974 to be working in industries which would experience high wage growth in subsequent decades. These results contribute to our understanding of longitudinal inequality across careers and shed light on the sources of heterogeneity in responses to economic shocks and policy.