Families experiencing poverty need access to cash assistance to help them afford their basic needs and maintain stability, particularly during the ongoing COVID-19 pandemic. Families use assistance provided by the Temporary Assistance for Needy Families (TANF) program to pay for rent, utilities, diapers, food, transportation, and other necessities. Yet too few families struggling to make ends meet can access the program, and TANF’s history of racism means that it fails to reach many families in states where Black children are likelier to live. If TANF had the same reach in 2020 as its predecessor, Aid to Families with Dependent Child (AFDC), did in 1996, 2.38 million more families nationwide would have received cash assistance. Instead, in 2020, for every 100 families in poverty nationwide, only 21 received TANF cash assistance — down from 68 families in 1996. At an economically precarious time for families, this “TANF-to-poverty ratio” (TPR) is the lowest in the program’s history.

This paper explores the degree to which labor supply behavior differs among Millennials, relative to earlier generations, especially to that of Baby-boomers. I find that wage and income elasticities are lower among Millennials, which can partly be attributed to trends beginning before their time. Responses to cyclical factors appear unchanged across generations. The overall lower observed labor supply among Millennials is nuanced with their behavior putting downward pressure on both hours and participation, whereas their characteristics are propping up labor supply. These differences could have important implications for economic growth and policy considerations.

This article uses driving time to examine the geographic relationship of abortion facilities to crisis pregnancy centers (CPCs) in terms of race/ethnicity and population density. We analyze both the current reproductive justice landscape and predict how this landscape will change following the projected reversal of Roe v. Wade. Our results demonstrate that disparities in the presence of abortion facilities and CPCs manifest in terms of rural/urban classification and race/ethnicity. These disparities will become more pronounced in the post-Roe landscape. Four major findings include: 1.) Following the projected reversal of Roe, the ratio of abortion facilities to CPCs will change from 1:3 to 1:5; 2.) the number of people who live closer to a CPC than an abortion facility will nearly double post-Roe; 3.) people in rural areas live in disproportionate proximity to CPCs, although the number of people in large metropolitan areas living closer to a CPC than an abortion facility will increase nearly four-fold post-Roe; 4.) compared to other racial and ethnic groups, a greater percentage of Native Americans live closer to a CPC than an abortion facility and the share of Black and Hispanic/Latino people who live closer to a CPC than an abortion facility will more than double post-Roe. Ultimately, our results push scholars, advocates, and policy makers to discuss access to reproductive healthcare and reproductive justice in terms of presence of CPCs as much as absence of abortion facilities.

This paper argues that important improvements in the Genuine Progress Indicator (GPI) can be made by directly calculating the loss of natural resources, the benefits of leisure, and adjusting for inequality using a global norm, rather than using local, historical benchmarks. Local benchmarking is an obstacle to the standardisation and comparability of the GPI. We provide alternative methods for the five components that have used benchmarking in the standard GPI. We present empirical estimates for the GPI of the United States and California over the period 1995 through 2017, calculated with and without the alternative methods. Using our alternative methods, we show that some differences between the GPI of CA and the U.S. are artefacts of the benchmark methods. Implementing the alternative methods narrows the gap between the CA and the U.S. GPI as it reduces the U.S. environmental costs, and removes the artificial differential between CA and the U.S. in the cost of inequality. However, we find that the GPI provides insight into net welfare not reflected in GDP, both with and without the benchmarking methods. Overall, we suggest that the GPI can be significantly improved with these high priority revisions without changing the fundamental approach or theoretical framework.

In recent decades, there has been an increase in public concerns about the animal welfare impacts of many farm practices. The transition to systems that are perceived to increase animal welfare is however, hampered by the lack of transparency regarding farming practices, information gaps and poor value signaling. Using the case of milk choice, this study investigates US consumer (N = 1020) preferences for systems that allow for additional calf-dam (mother) contact, dehorning and the role of different formats of information (i.e., text and images). The study applies a multi-profile (Case 3) best-worst scoring approach. Data were analyzed using mixed logit and latent class models. The results indicate that consumers signal significantly higher values for production systems that allow for more calf-dam contact. These preferences differ by consumer segments. Consumers also expressed positive values for dehorning with pain mitigation. The results further show that a seemingly small addition to textual information treatment, i.e., providing consumers with pictures associated with calf-dam contact practices generates statistically significant premiums. Sensitivity to additional information was high amongst female and urban consumers. The findings of this study highlight the demand incentives for the creation of niche markets for calf management practices in the dairy industry.

Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? In this paper, we study the transferability of statistical group fairness for machine learning predictors (i.e., classifiers or regressors) subject to bounded distribution shifts. Such shifts may be introduced by initial training data uncertainties, user adaptation to a deployed predictor, dynamic environments, or the use of pre-trained models in new settings. Herein, we develop a bound that characterizes such transferability, flagging potentially inappropriate deployments of machine learning for socially consequential tasks. We first develop a framework for bounding violations of statistical fairness subject to distribution shift, formulating a generic upper bound for transferred fairness violations as our primary result. We then develop bounds for specific worked examples, focusing on two commonly used fairness definitions (i.e., demographic parity and equalized odds) and two classes of distribution shift (i.e., covariate shift and label shift). Finally, we compare our theoretical bounds to deterministic models of distribution shift and against real-world data, finding that we are able to estimate fairness violation bounds in practice, even when simplifying assumptions are only approximately satisfied.

I examine the labor market response of undocumented youth that participated in the Deferred Action for Childhood Arrivals (DACA). DACA provides temporary work authorization and deferral from deportation to eligible undocumented youth. I use data from the U.S. Citizenship and Immigration Services to construct a probabilistic measure for the unobserved DACA participation. Using ACS data, I estimate a two-sample model of the effect of participating in the DACA program. I also estimate the spillover effects of DACA on eligible but nonparticipating undocumented youth. I find that DACA significantly improved labor market outcomes of DACA recipients, with magnitude of the treatment-on-the-treated effects at least twice as large as the intent-to-treat estimates obtained from using only the observed eligibility indicator. I also find an increase in school attendance among DACA recipients. Evidence of a negative spillover effect on eligible non-participants is documented with a decrease in labor force participation and school attendance.

Since their introduction two decades ago, Community Wildfire Protection Plans (CWPPs) have become a common planning tool for improving community preparedness and risk mitigation in fire-prone regions, and for strengthening coordination among federal and state land management agencies, local government, and residents. While CWPPs have been the focus of case studies, there are limited large-scale studies to understand the extent of, and factors responsible for, variation in stakeholder participation—a core element of the CWPP model. This article describes the scale and scope of participation in CWPPs across the western United States. We provide a detailed account of participants in over 1,000 CWPPs in 11 states and examine how levels of participation and stakeholder diversity vary as a function of factors related to planning process, planning context, and the broader geographic context in which plans were developed. We find that CWPPs vary substantially both by count and diversity of participants and that the former varies as a function of the geographic scale of the plan, while the latter varies largely as a function of the diversity of landowners within the jurisdiction. More than half of participants represented local interests, indicating a high degree of local engagement in hazard mitigation. Surprisingly, plan participation and diversity were unrelated to wildfire hazard. These findings suggest that CWPPs have been largely successful in their intent to engage diverse stakeholders in preparing for and mitigating wildfire risk, but that important challenges remain. We discuss the implications of this work and examine how the planning process and context for CWPPs may be changing.

This paper investigates how social influences have contributed to growth in female labor force participation, documenting how the role models of the All-American Girls Professional Baseball League impacted female labor market outcomes between 1940 and 1950. Using a difference-in-difference strategy, comparing cities with teams to geographically nearby cities without teams, we find that female labor force participation in cities with an AAGPBL team increased by 1.8% points relative to otherwise similar cities without a team, and female employment rates increased by a similar amount.

This study examines the impact of immigrant populations on firm environmental performance. Leveraging a longitudinal dataset of more than 11,000 manufacturing facilities in the US in which I match the Environmental Protection Agency’s (EPA) facility toxic emissions data with the location’s census immigration data, I document the negative impact of local immigrant populations on a facility’s environmental performance, which strengthens as heterogeneity among immigrant increases. I argue that this is because a more heterogeneous community is less cohesive and hence less capable of organizing effective pressures against pollution. Further, I show that because co-nationality links create unique bonds between the facility and local immigrants, the negative relationship declines as more immigrants originate from the same home country as a facility’s parent firm. These results are robust to the use of an instrumental variable approach and a wide variety of alternative specifications and subsamples. These findings suggest that local community pressures may be limited in driving better environmental outcomes.

Since the 2015 U.S. Supreme Court ruling that marriages of same-sex couples are legal in all states in the union, federal surveys have adapted to the shifting legal climate and included new measures that more directly identify same-sex and different-sex cohabiting and married couples. In this research note comparing the largest and most recent federal surveys—the 2019 American Community Survey and Current Population Survey—we find consistent levels of cohabitation and marriage across surveys. While the vast majority (90%) of different-sex couples were married, we report a more even split in cohabitation and marriage among same-sex couples. Our evaluation of sociodemographic characteristics of married and cohabiting couples indicates that differences were less prominent among same-sex couples than among different-sex couples, suggesting weaker sociodemographic selection into marriage among the former. However, factors affecting same-sex and different-sex couples' decisions to live together and marry may differ because of legal and social climates that still present unique obstacles for same-sex couples. Researchers need to acknowledge these differences in assessments of the implications of marriage for health and well-being.

To fully understand state policy outcomes or elections in the US, we need valid overtime measures of state-level public opinion. We contribute to the research on measuring state public opinion in two ways. First, we respond to Berry, Fording, Hanson, and Crofoot's (BFHC) critique of Enns and Koch's measure of state policy mood. We show that when BFHC's analysis is performed using the same states and examining annual change, it validates the Enns and Koch measure and raises questions about the Berry, Ringquist, Fording, and Hanson measure. Second, we generate a new measure of state policy mood building on Enns and Koch's approach. The new measure has even better properties than the previous measure and relates to state presidential vote and state policy liberalism in similar ways to Caughey and Warshaw's measure of state economic liberalism. We conclude with recommendations for using the various direct measures of state public opinion.

The purpose of this thesis was to study spatial relationships among climate change vulnerabilities in Ethiopia to inform policy and planning and to offer a methodology for studying climate change impacts in areas of the world with limited data. Point data with environmental and social vulnerability variables was used for spatial analyses to determine regions of high vulnerability and their intersections. A case study was included for the capital city, Addis Ababa, to highlight the differences in climate change vulnerability in the urban environment. The findings of this thesis indicate that high social and high environmental vulnerability are most linked in the north central region of the country, and that flood-risk is a driver of environmental vulnerability in Addis, while drought is a driver of environmental vulnerability in agrarian and rural areas. Policy should be tailored on the sub-national level to address these divergent threats and may include strategies like water infrastructure improvements and food assistance.

This paper proposes a “small” contextual analysis approach to big data and reports our experimental application of this approach in evaluating the effects of social distancing on focused subpopulations in U.S. society. We recognize the common and critical limitations of big data, especially the unrepresentativeness and the unpublished methodology of accessible datasets. Our proposed methodological approach is built upon recent works on data ontology, especially the recognition that big data are essentially remaining digital footprints of human life in need of additional data of contextual factors for valid and meaningful interpretation. It guides the selection and processing of big data to make big data small and structured and thus articulable with traditional social sciences data and usable to conventional social sciences methods. In our experimental case study, we apply our sampling strategy developed from traditional social science data to Google’s mobility dataset for our analysis using primarily a Difference In Difference (DID) model. The results of this case study are of timely value to policy evaluation and public decision-making in the pandemic. We call for more proactive methodological innovations that confront the critical limitations of accessible big data especially in times of urgent needs.

We use a rich data set at the city-industry level from 1890 to 1940 to identify the impact of electricity on manufacturing industries. We exploit cross-industry variation in energy use intensity before the arrival of electricity combined with geographic variation in proximity to early hydroelectric power plants. Contrary to the existing narrative, we find that labor productivity gains from electricity measured through this strategy were relatively rapid and long-lasting. Cheaper energy thanks to hydroelectricity is partially responsible but firms also appear to have changed their production process relatively quickly. The source of the impact of electricity on productivity varies with the degree of product market structure: in sector-county pairs where the average firm was initially large, productivity increased without significant expansion in employment, while in markets with relatively small firms, both output and employment increased.

Low family income is associated with worse child academic achievement. Little is known about how health insurance expansions affect children’s achievement in low-income households. This study examined the effects of the Affordable Care Act’s coverage expansions primarily for Medicaid and Marketplace enrollment, beginning in 2014, on children’s academic achievement in Iowa. The study employed a unique linkage of birth certificates and data on standardized school tests for children in Iowa and took advantage of differences in uninsurance rates across areas in the state before the ACA insurance expansions. There is evidence that the ACA expansions beginning in 2014 were associated with higher reading scores after three years for children born to mothers with a high school education or less. There is no consistent evidence of an effect on math scores. Overall, these findings suggest broad spillover benefits from health insurance expansions to the well-being and development of children in low-income households that should be part of the continuing policy

My first chapter studies the origins of gentrification. I propose a mechanism through which a wave of gentrification can be triggered in a neighborhood: the opening of large offices of technology firms. Using information on nine such events and transaction-level housing data, I develop a difference-in-differences strategy that compares house prices in the vicinity of the new office to those in closely-matched neighborhoods slightly further away. I find that property prices rise 11% in treatment areas relative to control areas within two years after the opening. This difference subsides somewhat but remains at +8% five years later. These findings are substantially stronger than in the existing literature and thus suggest that such office openings can have a major impact on their neighborhood. I investigate two mechanisms, agglomeration forces and the development of consumption amenities, through which the impact of a single establishment opening can be amplified and sustained. My second chapter analyses the causes of the shift towards low-income service jobs that American non-college workforce has experience in recent decades. Together with the rise of college-educated workers’ incomes, this has strongly contributed to the overall increase in wage inequality. Two main explanations, routinization and consumption spillovers, have been proposed to explain this ii occupational shift. Although the determinants of these two theories are highly spatially correlated, studies that have exploited regional variations to identify these mechanisms have so far only considered each explanation in isolation, raising confounding concerns. I highlight these concerns and provide reduced-form evidence that both theories operate simultaneously to drive growth in service employment. To strengthen my case, I extend the structural framework proposed in Autor and Dorn (2013) to include consumption spillovers through non-homothetic preferences. I estimate key parameters and assess the relative importance of each theory using simulations from the model. Relative to a model featuring homothetic preferences, my specification yields 57% more regional disparities in the growth of service occupations, which can be interpreted as the contribution of consumption spillovers. While the routinization hypothesis quantitatively dominates, my reduced-form and structural evidence point to sizeable consumption spillover effects that cannot be neglected. My third chapter studies the effects of liberalizing the use of short-term contracts on the labor market outcomes of all workers. It specifically examines the impact of a 2003 change French jurisprudence. This decision from the French Civil Supreme Court — hereafter called the “reform” — extends the scope of a specific type of temporary contract in France, the CDDU (contrat à durée déterminée d’usage), to jobs that are not necessarily temporary by nature. This type of contract is allowed in 16 service sectors and is not restricted in terms of length or number of renewals, giving a lot more flexibility to employers than the standard temporary contract does (CDD, contrat à durée déterminée). I find that this change is associated with an increase in the share of temporary contracts of 2.9 percentage points in the CDDU sectors, while other service sectors only show a 0.7 point increase. This result echoes a frequent finding of temp for permanent substitution in the literature about the deregulation of temporary contract. A second and novel finding is that this reform weighs on young permanent workers’ wages, who experience drop of nearly 4% in their wages over the two years following the reform, and little catch up afterwards. I show some evidence that something changed in the relationship between employers and employees in CDDU sectors: the employer’s bargaining power seems to have risen as the opportunity to substitute temporary contracts for costly and protected permanent ones has increased.

In this paper we provide new estimates of the impact of unions on nonunion wage setting. We allow the presence of unions to affect nonunion wages both through the typically discussed channel of nonunion firms emulating union wages in order to fend off the threat of unionisation and through a bargaining channel in which nonunion workers use the presence of union jobs as part of their outside option. We specify these channels in a search and bargaining model that includes union formation and, in our most complete model, the possibility of nonunion firm responses to the threat of unionisation. Our results indicate an important role played by union wage spillovers in lowering wages over the 1980-2010 period. We find de-unionisation can account for 38% of the decline in the mean hourly wage between 1980 and 2010, with two-thirds of that effect being due to spillovers. Both the traditional threat and bargaining channels are operational, with the bargaining channel being more important.

Researchers use microdata to study the economic development of the United States and the causal eeects of historical policies. Much of this research focuses on county-and state-level paaerns and policies because comprehensive sub-county data is not consistently available. We describe a new method that geocodes and standardizes the towns and cities of residence for individuals and households in decennial census microdata from 1790-1940. We release public crosswalks linking individuals and households to consistently-deened place names, longitude-latitude pairs, counties, and states. Our method dramatically increases the number of individuals and households assigned to a sub-county location relative to standard publicly available data: we geocode an average of 83% of the individuals and households in 1790-1940 census microdata, compared to 23% in widely-used crosswalks. In years with individual-level microdata (1850-1940), our average match rate is 94% relative to 33% in widely-used crosswalks. To illustrate the value of our crosswalks, we measure place-level population growth across the United States between 1870 and 1940 at a sub-county level, confirming predictions of Zipf's Law and Gibrat's Law for large cities but rejecting similar predictions for small towns. We describe how our approach can be used to accurately geocode other historical datasets.

This dissertation focus on technological change and how it has impacted economic growth, income distribution, employment, and wages. The first essay explores an endogenous growth model with incumbent and entrant firms performing vertical and horizontal R&D, respectively. Firms face sector-specific fixed-costs and specific R&D efficiency. We introduce two sets of shocks: a temporary productivity shock, and a permanent shock to R&D fixed-costs. We find that the transition paths after the first set of shocks mimics the behavior of US data in the variables of interest in a given period. The second set of shocks, which compares two R&D-policy strategies, has important policy implications. The second essay studies the gender wage gap impacts of technological change. We find that, although changes in the employment structure have favored women over time, the wage level and wage growth in occupations women are employed in, and are transitioning towards, have operated as countervailing forces against the gender wage gap narrowing. This essay is joint work with Anna Salomons and Matias Cortes. It is published in the Oxford Review of Economic Policy. The third essay investigates whether the evolution of occupational employment shares and occupation-specific wage rates explains firm-level labor share dynamics. We demonstrate that the dynamics of the Portuguese aggregate labor share between 2004 and 2019 are mostly driven by changes in firms’ labor share, which rise due to positive growth in hourly wages, rather than value-added reallocation across the labor share distribution. We further show that changes in task group employment shares have limited effects on firm-level labor shares.