Place-based economic development programs are often tied to census statistical units, such as census tracts. These units allow for the precise allocation of program benefits to areas with certain underlying socioeconomic conditions. During the 2020 decennial cycle, some local governments and metropolitan planning organizations sought to alter these units to change areas eligible for place-based Opportunity Zone incentives. Although criteria for modifying census tract boundaries are strict and have been consistent for decades, those efforts illuminate a potential conflict between the needs of data users and the desires of some who stand to benefit from place-based incentives. By interviewing people familiar with the Census Bureau's process for revising statistical boundaries and through conversations with people in the economic development field, this report aims to better understand this potential issue.

We exploit the spatial and temporal variation of the staggered introduction of interstate banking deregulation across the U.S. to study the relationship between credit constraints and consumption of durables. Using the American Housing Survey from 1981 to 1989, we link the timing of these reforms with evidence of a credit expansion and household responses on many margins. We find evidence that low-income households are more likely to purchase new appliances after the deregulation. These durable goods allowed households to consume less natural gas and spend less time in domestic activities after the reforms.

Since early 2020, there have been extraordinary disruptions across all sectors of the economy. This set of nine economic facts about the service sector in the United States illustrates recent trends in spending, employment, and inflation as the country continues to rebalance. We find that the effects of the pandemic linger: only in recent months has activity in the service sector recovered to pre-pandemic levels. Nonetheless, activity is still well below where it was expected to be in the absence of the pandemic, and further recovery is expected. Recent changes in real services and goods spending have been extraordinary (figure A). With the onset of the pandemic, real spending on services shrunk by 20 percent between February and April 2020. Spending partly bounced back in the third quarter, with consumption of services in September just 8 percent below pre-pandemic levels. Since the fourth quarter of 2020, spending on services has grown 1.7 percent each quarter, on average. Nonetheless, a simple extrapolation of the pre-pandemic trend in services spending shows that it is still well below trend. In contrast, spending on consumer goods soared after a brief contraction. Since June 2020, real spending on goods has been well above trend—as high as 15 percent in March 2021, relative to the trend from 2018 to 2019. More recently, spending on goods has decreased—to 6 percent above trend in July 2022—as consumers have rebalanced the composition of spending closer to historical patterns. The combination of rising spending on services, spurred by pent-up demand and strong household finances, and firms facing challenges in increasing hiring has meant upward pressure on wages and prices. Today, four out of five American workers in the private sector are employed in the service economy, doing everything from delivering care in hospitals and nursing homes to ensuring products make it from ports to store shelves and into consumers’ hands. Since 2020, changes in employment in the services and goods sectors (figure B) have moved more similarly than have changes in spending in these sectors. Early in 2020, employment in the services sector fell 17 percent, while employment in the goods sector fell only modestly less, by 12 percent. And employment has only just recovered to pre-pandemic levels in recent months. To be sure, the decline in services employment was far larger than in the goods sector, but that mostly reflected that the service sector has grown to be much larger. Goods sector employment peaked at 25 million in 1979. In that year, service-sector employment was already higher at 49 million; since then, it has grown to be 109 million. After withstanding a seismic and unprecedented shock in early 2020, spending and employment in the service sector has continued to recover, and further recovery is expected. It took until the spring of 2022 for the service sector to recover to pre-pandemic levels in both real spending and employment. In addition, the onset and aftermath of the COVID-19 pandemic has highlighted disparities in jobs throughout the service sector: some face-to-face service workers faced poor working conditions in jobs with little room for advancement, while other workers in certain professional services were afforded new flexibility, like working remotely. Trends in employment growth may follow; as we show in this fact sheet, employment in leisure and hospitality has lagged other sectors, including professional services. For decades the service sector has driven the economy, and the recent rebound in the service sector continues to drive economic growth. What role is there for policy in sustaining this growth? The Hamilton Project has published a policy proposal by Dani Rodrik (Harvard University) that lays out how a modern industrial policy framework should create more “good jobs” by improving productivity and labor income growth for service-sector workers (Rodrik 2022). Is there a role for industrial policy to help create a more resilient, productive economy? And can this industrial policy focus not only on manufacturing but also on the service sector and service-sector workers? The proposal argues that the answer to both questions is yes. This set of economic facts about the service sector in the United States explores how the service-sector recovery has differed from prior business cycles (fact 1 and fact 2); how spending, employment, wages, and the nature of work in different industries within the service sector are changing (fact 3, fact 4, fact 5, fact 6, and fact 7); and the trajectory of inflation (fact 8 and fact 9).

This chapter examines the rich racial/ethnic multiplicity of both Asians and Latinos in the United States. Analysis of the United States and Harris County (Houston, Texas) population trends of these groups over the last 30 years highlight Asians/Pacific Islanders/Native Hawaiians and Hispanic/Latinos as the two fastest growing population groups over the last three decades. As a demographic designation and descriptor, the terms Asian Americans and Latinos give little insight into the numerous subgroups accounted for within these groups. An exploration into these subgroups illuminates the need for disaggregated data to better understand the nation’s racial/ethnic composition and the diversity of students in American schools.

The steep rise of top wages is acknowledged as one of the main drivers of the rise in earnings inequality between workers in most postindustrial labor markets. Yet its relation to gender stratification, in particular to the stagnation in the gender pay gap, has received very little scholarly attention. Using data from the U.S. Current Population Survey, conducted between 1980 and 2017, we provide evidence of the enormous weight that the dynamic at the top of the earnings distribution exerts on the gender pay gap. We also show how this dynamic inhibits the consequences of the countervailing process of gender vertical desegregation. Although developments in gender inequality and in the rise of top wages have drawn extensive scholarly attention and have even penetrated into the public discourse in recent years, the two dimensions of inequality are often perceived as unrelated to one another. Our findings, then, highlight the connection between different forms of inequality—class inequality and gender inequality—a relation that demands much more attention in the new economy.

Recent disasters have shown that income plays a central role in determining the capacity of impacted households to cope with the shock and recover from it. Researchers often rely on random sampling to generate synthetic household income from aggregated Census data. This conventional approach imposes limitations towards proposing realistic policies. A method is introduced to deduce minimum household income for single-family homeowners using publicly available data via the tax assessor and current population survey. Post-earthquake housing recovery simulations are used to evaluate the advantages of the proposed income estimation method relative to the germane random sampling approach. Preliminary results with the proposed method show a reduction in the number of low-income households assigned to high-valued dwellings. Results also suggest that the random sampling approach leads to inflated recovery delays and overestimates the vulnerability of select low-income households.

Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to look for possible connections between drought and rural population loss on the North American Great Plains between 1970 and 2020. In doing so, we analyzed four decades of population count data (at county-size spatial scales), monthly climate data, and Palmer Drought Severity Index scores for Canada and the USA at multiple spatial scales (regional, sub-regional, national, and county/census division levels), along with county level irrigation data. We found that in some parts of Saskatchewan and the Dakotas − particularly those areas that fall within more temperate/less arid ecological sub-regions − drought conditions in the middle years of the 1970s had a significant association with rural population losses. A similar but weaker association was identified in a small cluster of North Dakota counties in the 1990s. Our models detected few links between drought and rural population loss in other decades or in other parts of the Great Plains. Based on R-squared results, models for US portions of the Plains generally exhibited stronger drought-population loss associations than did Canadian portions, and temperate ecological sub-regions exhibited stronger associations than did more arid sub-regions. Irrigation rates showed no significant influence on population loss. This article focuses on describing the methodological steps, considerations, and benefits of employing this type of machine learning approach to investigating connections between drought and rural population change.

In 2019, 7.1 million households in the United States (5.4%) were unbanked and lacked a checking or savings account (FDIC 2020). Using three leading household surveys, this paper documents how the interaction between bank access and poverty has evolved over time. We present a historical time series of un-banked rates, showing high unbanked rates for those in poverty even with increases in financial access over time. In the 1980s, 49.6% of households in poverty were unbanked while 22.8% were unbanked in 2019. Unbanked rates were even higher for Black and Hispanic households that were in poverty. In the 1980s, these groups had unbanked rates of 73.6% and 66.5% which declined to 38.4% and 31.8% in 2019, respectively. To explain differences in banking rates by race, we use Kitagawa-Oaxaca-Blinder decompo-sitions that account for the binary nature of banking status. Using a robust set of controls that include assets and poverty status, our results suggest that group characteristics explain slightly less than half the difference in unbanked rates for Blacks and around half for Hispanics. Additionally, our results suggest that minimum balance requirements are the most cited reason for households being unbanked with around 8% of Black households, 6% of Hispanic households, and 1% of White households being unbanked for this reason. Our findings suggest continued inequalities in access to the financial system that have persisted over time.

The typical displacement metric for sea-level rise adaptation planning is parcel inundation. However, this metric does not fully capture the wider cascading or indirect effects of sea-level rise. To address this, we propose the consideration of population isolation: those who cannot access essential services. Based on this metric, we find a 39–464% increase in the number of people considered at-risk, compared to the risk from parcel inundation, when considering inundated roadways during mean higher high water tides in the coastal U.S. We also find that isolation may occur decades sooner than parcel inundation. Both estimates of risk are critical elements for evaluating adaptation options and prioritizing support for at-risk communities.

The relationship of neighborhood conditions with health outcomes has been well documented, but less is known about importance of neighborhood change. Research that examined the relationship of gentrification with health outcomes produced mixed results, but only a few studies were able to examine the role of local social capital as a potential moderating influence. Using a survey of Hurricane Katrina survivors, tract-level health estimates from the 500 Cities Project, and tract-level census data, we assess the relationship of gentrification with self-reported physical and mental health, controlling for four measures of neighborhood collective resources in post-Katrina New Orleans, Louisiana. Our findings indicate rates of poor self-rated physical and mental health were higher in neighborhoods that experienced gentrification and that other neighborhood changes may function to dampen the impacts of gentrification on health outcomes. Our results underscore the importance of considering local community characteristics in evaluating the relationship of gentrification with health.

These essays analyze the labor market implications for workers in the health industry licensed by government agencies in the United States. Licensure is often justified on the grounds that it will protect the public from incompetent practitioners. In practice, however, occupational licensing is often used to restrict entry to a profession in order to raise wages for incumbent practitioners. The first essay examines how the expansion of optometrist scope of practice affects optometrist earnings and population eye health outcomes. Using the scope of practice expansion across states from 1976 to 2011, our estimation shows the expansion increased optometrist hourly wages by about 14 percent. In the second essay, we explore the effect of the Nurse Licensure Compact on telemedicine. The study shows that patients in NLC states used more telemedicine services from outof-state providers than patients in non-NLC states. Our evidence indicates that the NLC reduces some barriers to practicing telemedicine for nurses. The third essay examines the possibility of using referenda to reform occupational licensing. More specifically, the essay examines how referendum would have impacted policy in regard to the Enhanced Nurse Licensure Compact in California

Energy is an essential input into the lives of individuals and the production of firms. While energy use has numerous private benefits, it imposes an external social cost as the combustion of fossil fuels increases the concentration of greenhouse gases and accelerates the climate crisis. A central role of the public sector is to reduce such externalities. Governments can intervene by creating financial incentives such as taxes or subsidies, by investing into public goods such as research and development of green technologies or infrastructure that decreases aggregate fossil fuel energy demand, or by directly regulating emissions through caps or bans. The aggregate and distributional impact of such policies depends on baseline energy use patterns and available alternatives. My dissertation examines heterogeneity in energy use and carbon emissions — documenting it, exploring its drivers, and discussing its implications for the impact of different public sector interventions. In Chapter 1, I examine place-based differences in individual energy use and carbon emissions. There is substantial spatial heterogeneity in household carbon emissions across the US, and a strong association between emissions and local amenities such as density, transportation infrastructure, and housing characteristics. I estimate what share of heterogeneity in carbon emissions is attributable to places themselves, and what share reflects individual characteristics and sorting. To do this, I construct a longitudinal panel of residential energy use and commute characteristics for over a million individuals from two decades of administrative Decennial Census and American Community Survey data. I use movers in my data to estimate place effects – the amount by which carbon emissions change for the same individual living in different places – for almost 1,000 cities and roughly 60,500 neighborhoods across the US. I find that place effects explain more than half of differences between places, and about 15-25% of overall variation in carbon emissions. My estimates suggest that decreasing neighborhood-level place effects from one standard deviation above the mean to one standard deviation below the mean would decrease household carbon emissions from residential energy use and commuting by about 40%.

Female labor force participation rates have been stagnating despite rising female education in sub-Saharan Africa since the turn of the millennium. Using representative and repeated census data from a heterogeneous sample of 13 sub-Saharan African countries, this paper analyzes female labor force participation from a demographic perspective. We show that enrollment in education is substantially higher among the most recent female cohorts than among the earlier-born ones. The higher enrollment mechanically depresses female labor force participation, weakening the relationship between female labor force participation and education. After taking this cohort trend into account, we find a strong and positive association between female labor force participation and female education. We further find a cohort trend toward higher female employment in the nonprimary sector and a positive association between female employment in the nonprimary sector and female education. The higher investments in education by younger female cohorts, together with the demographics of sub-Saharan African countries, have implications for a potentially arising “demographic dividend”.

We propose a model that can match gravity patterns of U.S. interstate migration based on persistent and spatially-correlated preferences. The model also matches many untargeted dynamic moments of migration, previously a challenge for the literature. From the model, we learn five lessons with implications for regional evolutions, migratory insurance, and macroeconomic misallocation: moving costs need not be large to generate gravity patterns; return migration patterns can be the result of persistent preferences; short-run elasticities of migration vary by distance; bilateral migration flows are informative of population elasticities to local shocks; and short-and long-run population elasticities are the same.

The impacts of the pandemic on the employment, labor supply, and caregiving of women are assessed. Compared with previous recessions, the one induced by COVID-19 impacted women’s employment and labor force participation somewhat more relative to men and thus deserves the moniker “she-cession.” But the big divide is less between men and women and more between the more-educated and the lesseducated. Contrary to many accounts, women did not exit the labor force in large numbers, and they did not greatly decrease their hours of work. The aggregate female labor force participation rate did not plummet during the pandemic recession. The ability to balance caregiving and work differed greatly by education, occupation, and race. The more educated could work from home. Those who began the period employed in various in-person “service” occupations and establishments experienced large reductions in employment. Black women were severely impacted beyond other factors considered and the health impact of COVID-19 is a probable reason. The estimation of the pandemic’s impact depends, in part, on the counterfactual used and whether one differences from winter 2020 or from each month in a year prior to 2019. All estimates, however, demonstrate that women in each education group have borne the brunt of increased caregiving even as they managed to hold on to their jobs. The real story of women during the pandemic is that they remained in the labor force and stayed on their jobs, as much as they could.

This study shows that time availability is a significant mediator between SES and health. I draw on representative survey data from the Canadian Multinational Time Use Survey and supplement this data source with a second data set containing localized sociodemographic and time availability measures. In addition to testing existing time scarcity measures, I also propose a broader set of new, more inclusive measures. Analyses involve two stages. First, binary logistic regressions evaluate statistically significant relationships. The second stage uses mediation analyses to assess whether time availability is statistically significant in mediating the relationship between SES and self-reported health. I compute direct, indirect, and total effects, independently for each of the objective and subjective time availability measures, for both the nationally representative sample and for the localized sample. My results show that both time scarcity and time excess are important when examining the mechanisms linking SES and health. For example, 12 percent of the effect of household-level SES on health is via discretionary time availability. Further, over 10 percent of the effect of neighborhood-level SES on health is via subjective time scarcity. Objective time poverty mediates about 9 percent. 7.3 percent of the effect of SES on health is via objective time excess. Considering the differing temporal needs of marginalized populations, this work has important health policy implications for sociotemporal disparities in health.

We address three core questions about the hypothesized role of newly emerging job categories ('new work') in counterbalancing the erosive effect of task-displacing automation on labor demand: what is the substantive content of new work; where does it come from; and what effect does it have on labor demand? To address these questions, we construct a novel database spanning eight decades of new job titles linked both to US Census microdata and to patent-based measures of occupations’ exposure to labor-augmenting and labor-automating innovations. We find, first, that the majority of current employment is in new job specialties introduced after 1940, but the locus of new work creation has shifted—from middle-paid production and clerical occupations over 1940–1980, to high-paid professional and, secondarily, low-paid services since 1980. Second, new work emerges in response to technological innovations that complement the outputs of occupations and demand shocks that raise occupational demand; conversely, innovations that automate tasks or reduce occupational demand slow new work emergence. Third, although flows of augmentation and automation innovations are positively correlated across occupations, the former boosts occupational labor demand while the latter depresses it. Harnessing shocks to the flow of augmentation and automation innovations spurred by breakthrough innovations two decades earlier, we establish that the effects of augmentation and automation innovations on new work emergence and occupational labor demand are causal. Finally, our results suggest that the demand-eroding effects of automation innovations have intensified in the last four decades while the demand-increasing effects of augmentation innovations have not.

This book brings together leading scholars in the field to provide insights on economics of education. The book begins with an overview of education and human capacity development and looks at the production of education through individuals’ learning, education financing, and the role of individual circumstances. It also analyses the complex relationship between education and mobility and highlights what key challenges for education systems in a global world are. Each chapter provides detailed analysis of interesting and policy-relevant topics in the fields of education economics and human capacity development. This book is a useful reference for those who wish to understand the changing landscape and models of higher education in the context of digital advances and innovation. It will also be of interest to those in the areas of education and training.

The COVID19 pandemic, recession, and now unequal recovery has uncovered what stratification economists have long recognized in the disparate layering of society—growing divergence in social mobility by race, ethnicity, and gender. New research on diminished and stagnating social mobility shows that these large discrepancies began well before the pandemic and have only been exacerbated in the recovery. However, long-run structural factors and the buildup of historic inequities have so far been absent in many of the analyses of the recent pandemic recovery. This paper uses a stratification lens to examine the already present sectoral and occupational divergences. We show that the so-called K-shaped economic recovery is present in more than sectoral differences. Using the American Time Use Survey data, we show, first, the “original K” in terms of the persistent inequality in racial, ethnic, and gender compositions of occupational prestige, measured through the Nam-Powers-Boyd occupational ranking. Then, we present evidence on the unequal recoveries by sector, illustrating preexisting labor market disparities. This paper highlights the systemic forms of racial, ethnic, and gender inequities by looking at the occupational prestige rankings and showing how COVID19 has amplified existing disparities.

We propose new differential privacy solutions for when external invariants and integer constraints are simultaneously enforced on the data product. These requirements arise in real world applications of private data curation, including the public release of the 2020 U.S. Decennial Census. They pose a great challenge to the production of provably private data products with adequate statistical usability. We propose integer subspace differential privacy to rigorously articulate the privacy guarantee when data products maintain both the invariants and integer characteristics, and demonstrate the composition and post-processing properties of our proposal. To address the challenge of sampling from a potentially highly restricted discrete space, we devise a pair of unbiased additive mechanisms, the generalized Laplace and the generalized Gaussian mechanisms, by solving the Diophantine equations as defined by the constraints. The proposed mechanisms have good accuracy, with errors exhibiting sub-exponential and sub-Gaussian tail probabilities respectively. To implement our proposal, we design an MCMC algorithm and supply empirical convergence assessment using estimated upper bounds on the total variation distance via L-lag coupling. We demonstrate the efficacy of our proposal with applications to a synthetic problem with intersecting invariants, a sensitive contingency table with known margins, and the 2010 Census county-level demonstration data with mandated fixed state population totals.