We study the extent of automation angst and its role for policy preferences, labor market choices and real donation decisions using a customized survey in Germany and the US. We first document that a majority perceives automation as a major threat to overall employment and as a cause of rising inequality, whereas less than a third is concerned about their own labor-market prospects. We find evidence that automation angst is strongly associated with people's trust in governments and general political beliefs, especially in the US. At the same time, automation angst is associated with preferences for more policy interventions and also relates to stated and actual behavior. Using randomized survey experiments, we find that scientific information about zero net employment effects of automation, on average, reduce related concerns. Yet, treatment responses are multidimensional and depend on prior beliefs about the future or work. This translates into heterogeneous and sometimes even opposing effects on policy preferences and individual behavior.

After 18 months of isolated, at home work, it was widely anticipated that the workforce would enthusiastically return to the office and factory following the end of most COVID-19 workforce restrictions. However, as shown in Figure 1, the normally bustling District of Columbia Metro Center Station was far from crowded at 9am Tuesday September 7th, 2021 the day after the Labor Day holiday weekend. Why wasn't the Metro Station packed during peak hours?

Racialized inequity is injustice or unfairness and exists when prejudice or discrimination based on any aspect of difference precludes access of certain groups to the resources and benefits of society. This volume takes a new look at the psychology of inequity today. Have we progressed or regressed since the height of the civil rights movement of the 1960s?

We test the hypothesis that less transparent financial disclosures are an undesirable firm attribute that increase the amount of information and unemployment risk that employees bear, resulting in a wage premium. Using establishment-level wage data from the U.S. Census Bureau, we document that firms with less transparent disclosures pay their employees more, especially when employees bear greater information acquisition costs, have more influence in the wage-setting process, and own more stock. Our results hold after utilizing instrumental variables and exploiting two quasi-natural experiments. Overall, our results suggest that disclosure choices can generate externalities on an important group of stakeholders.

Although the COVID-19 pandemic exacerbated problems in the care workforce, those problems existed long before the pandemic hit and have persisted following the removal of most pandemic restrictions and the increased availability of vaccines. Prior to the pandemic there were shortages of childcare workers, home health aides, nurses, and teachers. The shortage of childcare workers, in particular, has continued to get worse even after the acute period of the pandemic has passed with many workers either leaving the workforce completely or taking higher paid jobs in retail or food service.

In Chapter 1, I study the estimation and inference of transformation models in the presence of a high dimensional set of control variables. In the study, I consider a generalized form of the transformed model, which includes the traditional transformed model, binary choice model, and generalized accelerated failure time model as special cases. I include both low dimensional covariates of interest and high dimensional control variables in this model. The estimation of high dimension nuisance parameters could lead to substantial bias and thus incorrect inference on parameters of interest. I provide a double-machine learning estimator to reduce this substantial bias and obtain a √ n-consistent and asymptotically normal results. According to the simulation study, I compare the performance of our estimator with the classical estimator based on average partial derivatives, it turns out that our estimator has less bias and provides correct inference results. Finally, I use an empirical example to illustrate the performance of our estimator in real data. In Chapter 2, I study the specification test for a generalized additive model (a.k.a. GAM) with an unknown link function. GAM is widely used to reduce the curse of dimensionality in nonparametric estimation. Additive Model is a special case when the link function is known by econometricians to be an identity. Under some regular conditions, I derive a sufficient and necessary condition when a function can be written as a GAM, which turns out to be a partial differential equation. This equation implies countably many restrictions on the coefficients from a simple polynomial series estimation, which forms the base of our test. Therefore, our test doesn’t need to run a GAM estimation. Instead, I use an “unrestricted” series regression estimation with polynomial basis functions and make a statistical inference on its coefficients. The asymptotic properties of the test statistics are derived. The asymptotic distribution is the χ 2 distribution with an increasing degree of freedom. A Monte Carlo study is shown for the case with two variables.

For this story, The Chronicle examined the Asian and Pacific Islander population for each decade from 1960 to 2020. The data is sourced from the decennial census and compiled by the University of Minnesota’s Population Center. Because the census asks people about their race and ethnicity in a survey, our findings are based on residents who self-identified as a particular race or ethnicity, and not a definitive count of Asians and Pacific Islanders. Moreover, the Census Bureau’s definition of “Asian” has changed since 1960, so counts between certain decades are not perfectly comparable. Still, we think this is the best available data that captures the vast majority of Asians and Pacific Islanders each year.

In the field of Housing Economics, filtering is the process by which properties, as they age and depreciate in quality, tend to be occupied by lower-income households. This is the primary mechanism by which competitive markets supply low-income housing. While filtering is an important long-term source of lower-income housing at the national level, this research shows that filtering rates for owner-occupied properties vary considerably both across and within metropolitan statistical areas. Notably, in some markets, properties “filter up” to higher-income households. This paper contributes to our understanding of filtering by demonstrating the geographic and temporal heterogeneity of filtering rates and examining links between filtering, supply elasticity, and gentrification. We also explore two alternative measures of filtering based on changes in relative income rather than real income.

A prominent literature in political science holds that the meritocratic recruitment of public servants leads to gains in bureaucratic performance. It is also believed that this institution ought to also have positive eects on social cohesion, since the meritocratic distribution of civil service jobs theoretically enables members from all groups—ethnic minority or otherwise—to win coveted employment in the public sector. Looking predominantly at Southeast Asia, and drawing on large scale surveys and archival documents, this dissertation presents an argument and evidence to the contrary. Instead, under certain conditions, the introduction of meritocratic civil service reforms perpetuates existing inequalities, as privileged groups outperform marginalized groups on entrance exams and go on to sta administrative posts at disproportionately high rates, an outcome that heightens group-based resentment and weakens national solidarity. This dissertation develops its argument in the context of an important but understudied tension between the twinned goals of state-building and nation-building—a trade-o that comes most into focus in the Asian context. At the moment of independence, the leaders of Asian states faced the urgent task of state-building, which mostly involved recruiting a competent corps of public servants to sta the organs of their new governments. But these leaders were also tasked with nation-building to generate a sense of solidarity across their diverse populations. These twinned goals often existed—and continue to exist—in tension with one another. The most “competent” applicants for public service typically hailed from historically privileged groups who had received formal education: the forward castes in India, the Javanese in Indonesia, or the Chinese in Malaysia. A narrow focus on state-building would have led to a disproportionate representation of certain groups in the apparatus of the state, which would have surely detracted from the task of nation-building.

This article highlights the role of land and housing submarkets in the construction of aggregate house price indexes and in the estimated value of the housing stock. We document idiosyncratic house price appreciation and sales volumes across submarkets within metropolitan areas that result in a sample of sold homes that are representative neither of the housing stock nor its appreciation over time. Commonly used aggregate price indexes, like the Federal Housing Finance Agency (FHFA) and Case-Shiller indexes, fail to capture these local dynamics and produce indexes and estimates of the value of the stock that are biased. Our approach is to build “locally-pooled” indexes and then weight these local price indexes by their submarket's share of the housing stock to estimate our metropolitan-level index. This allows for more accurate movements in urban land prices, which is especially important in higher cost land markets. We show traditional globally pooled indexes exhibit significant bias. This may complicate research that makes use of the traditional house price indexes.

We link personnel records of the federal civil service to census data for 1907-1921 to study the segregation of the civil service by race under President Woodrow Wilson. Using a difference-indifferences design to compare the black-white wage gap around Wilson's presidential transition, we find that the introduction of employment segregation increased the black wage penalty by 7 percentage points. This gap increases over time and is driven by a reallocation of already-serving black civil servants to lower paid positions. Our results thus document significant costs borne by minorities during a unique episode of state-sanctioned discrimination.

Gender inequality is one of the few fundamentally persistent inequalities within society, especially in the United States. This thesis will use a Marxist-feminist approach to explore the impact of the United States’ capitalist market economy on gender-specific public and private life structures. Specifically, how inequality is exacerbated as women transition to motherhood. This paper hypothesizes that a mandated paid parental leave would alleviate some of this ‘second burden’ by encompassing fathers in its eligibility. We formulate a difference-indifference model to estimate the causal effects of two current states who mandate paid parental leave and evaluate the average treatment effect when compared to the untreated group. Since the results do not provide support for the hypothesis, we present possible explanations for the limitations of the model and the data.

A number of researchers in various fields of study have made important contributions regarding labor market disparities. While the notion of occupational prestige is mentioned, it, generally speaking, has been understudied and underused when studying disparities and inequities in the U.S. As social comparisons of distinct categories of social standing can illuminate the degree to which there are social gradients, we analyze occupational prestige disparities by race/ethnicity and gender, and the intersection thereof, after controlling for relevant sociodemographic factors. This analysis focuses on the differentials between industry classifications and also within specified occupational groupings. Such social standing differentials by gender and race/ethnicity are important to address as they influence social mobility and social positioning. Hence, such differentials have wide ranging implications regarding the distribution of social resources and life chances, which can translate into nested sets of social inclusion and exclusion. We seek to further illuminate economic and social disparities in the labor market by noting that, such differentials are not explanatory on the basis of personal responsibility or cultural practices, unlike the stance usually taken by public policy discourse. We argue that a more holistic approach is needed when analyzing gender and racial/ethnic disparities in labor market outcomes.

An extensive body of literature in management has documented the effective influence of a local community on firm practices, especially those that generate negative externalities on the local community (Berrone et al., 2010; Henriques & Sadorsky, 1996; Kassinis & Vafeas, 2006; Kim et al., 2017; Lee & Lounsbury, 2015; York et al., 2018). While this research collectively highlights the vital role of the local community in promoting firm environmental sustainability, its conclusion seems to rest upon a critical assumption that all local communities are equally cohesive, and therefore equally effective in organizing collective pressures and disciplining firms. This assumption, however, contradicts an empirical observation in the US and globally; as people increasingly cross state and country boundaries (Barnard et al., 2019; McGahan, 2020), local communities grow less homogeneous and cohesive (Longhofer et al., 2019). In this paper, I explore community heterogeneity and its implications for environmental sustainability by looking at local immigrant populations.

Innovative engineering and computer science professionals — along with the higher education institutions that prepare many of them for the workforce — create vast economic and societal value for Utah. In 2020, Utah’s engineering and computer science workforce generated 238,400 full- and part-time jobs, $19.1 billion in earnings, and $25.2 billion in gross domestic product (GDP). These amounts represent 12% to 15% of Utah’s $200 billion economy. In every major industry, and in communities statewide, engineering and computer science professionals contribute to meaningful innovation, research, and entrepreneurship.

People with disabilities tend to participate in fewer out-of-home activities, raising concerns about their well-being. This paper investigates travel and activity barriers faced by people with disabilities using data from the American Time Use Survey from 2008 to 2019. Our dependent variable of interest is a measure of realized accessibility known as a travel time price: that is, the number of travel minutes associated with each minute of out-of-home activity time. In using this measure, we first confirm that out-of-home activities are associated with greater subjective well-being, that travel is associated with relatively low well-being, and that travel time prices are negatively associated with life satisfaction. We next find that people with disabilities typically pay a travel time price premium 50 percent higher than those without disabilities for all out-of-home activities, and 11 percent higher for work trips. These premiums narrow but persist when accounting for personal characteristics and travel mode. We discuss the unique contributions of simple linear and multiple regression results, given that disability is so closely linked to personal characteristics like employment, income, and marital status. We then disaggregate the results by type of disability and close by presenting ideas for removing transportation and activity barriers for the heterogenous population of people with disabilities.

The rapid growth of income inequality in the United States has unfolded unevenly across the country. Levels of, and changes in, income inequality within local economies have been spatially and temporally heterogeneous. While previous research has identified the correlates of subnational inequality, it has given less attention to the contribution of compositional changes. Drawing on commuting zone (CZ)-level estimates produced from U.S. Census and American Community Survey data, we extend the literature on subnational income inequality by addressing four main objectives. First, we track changes in the prevalence of five sets of inequality risk factors. Second, we measure the associations between these factors and within-CZ income inequality in 1980 and 2019 and describe changes in these relationships over time. Third, we decompose changes in within-CZ income inequality (1980-2019) into components attributable to changes in the prevalence of risk factors (i.e., composition effects) and changes in the penalties (i.e., coefficient effects) associated with each factor. Fourth, we compare the South to other regions in these respects to explore relevant patterns of socioeconomic change unique to the South. We find substantively large shifts in the prevalence of all five sets of risk factors and significant changes in the penalties associated with many factors, especially the age and industrial structures of CZs. Shifts in penalties explained the largest overall share of changing inequality between 1980 and 2019, but these overall effects mask considerable heterogeneity in the strength and direction of changing penalties We also find significant regional variation in the size of coefficient effects and the relative contributions of composition and coefficient effects. Together, these analyses underscore the importance of simultaneously accounting for the prevalence of and penalties to inequality risk factors.

This dissertation explores immigrant children’s academic performance in both established and non-established immigrant destinations in the United States. Integrating data from the High School Longitudinal Study of 2009, the Stanford Education Data Archive, American Community Survey, and several other sources, it uses Ordinary Least Squares regression and multilevel modeling strategies relating family, school and neighborhood contexts to students’ performance in a standardized math test. With a typology of immigrant destinations defined at the county level, it compares immigrant and non-immigrant students within each type of immigrant destination as well as between immigrant students residing in different destinations. Results show that among immigrant students, those in new and fast-growing destinations tend to have lower math test scores than their counterparts in established destinations. These gaps cannot be fully explained by individual or family level factors but are likely to be shaped by the contexts these immigrants are embedded in as the gaps only become insignificant when either school level factors or county level variations are taken into account. Further investigation indicates that differences in immigrant students’ performance across destinations mostly exist among Hispanic immigrants, and that Asian immigrant students in established destinations show no significant differences than their counterparts in other places. And the differences in math test scores between immigrant and non-immigrant students in those non-established immigrant destinations are not significantly different from the two-group difference in more established areas. Moreover, there is some evidence showing that certain family and county level factors affect immigrant students in ways that are quite different than for non-immigrant students. The effects of parental education and expectation for their children’s education, for instance, are smaller among immigrant students than among non-immigrant students in established destinations, although no similar patterns exist in those non-established destinations. In addition, it is also found that the influences of some factors on immigrant students’ performance vary by immigrant destination. Home ownership, in particular, are positively associated with immigrant students’ math test scores when they reside in new, fast-growing or minor destinations, but the association is negative for immigrant students who attend schools in more established areas. Finally, the influence of law enforcement activities initiated by the Immigration Customs Enforcement on students’ performance is unevenly distributed across immigrant destinations and for students attending schools in established or fast-growing destinations, their math test scores tend to be lower with higher ICE detention rates in the local county, although this association is not necessarily more pronounced among the immigrant students than among the non-immigrant students. As is clear, although immigrant students’ educational experiences tend to share a lot of similarities across immigrant destinations, there are still some significant differences that might be consequential to their adaptation in the U.S. society.

The American Time Use Survey (ATUS) is the premier dataset in the U.S. for understanding how Americans allocate their time across the multiple domains of daily life and is ideal for comparing time use during the COVID period and prior to the onset of the COVID-19 pandemic. However, data collection was paused in March 2020 and resumed in May 2020. This paper investigates the impact of this data collection pause and the partial-year sample weights on sample characteristics and time use outcomes. Our recommendation to researchers is to use full-year 2003-2019 data for trend analyses and the COVID-period 2020 data to focus on the impact of the COVID-19 pandemic on changes in time use. Based on several analyses, we conclude that there are multiple options for addressing the gap in 2020 data collection given the robustness of the sample and time use estimates to this omission. Comparing the full year of 2019 data and the May to December 2020 data, our time use analyses indicate that time spent in leisure, telephone calls, and household production increased during the pandemic, while time spent volunteering, traveling, religious activities, and shopping decreased significantly.

Introduction: This study uses multiple measures of excess deaths to analyze racial disparities in COVID-19 mortality across Georgia. Methods: The Georgia Department of Public Health provided monthly mortality data for 2010–2020 stratified by race/ethnicity, age, county, and recorded cause of death. We first calculate crude mortality rates by health district during the time period for all groups for March through June for our historical period to identify significant time-series outliers in 2020 distinguishable from general trend variations. We then calculate the mean and standard deviation of mortality rates by age and racial subgroup to create historic confidence intervals that contextualize rates in 2020. Lastly, we use risk ratios to identify disparities in mortality between Black and White mortality rates both in the 2010–2019 period and in 2020. Results: Time-series analysis identified three health districts with significant increases in mortality in 2020, located in metro Atlanta and Southwest Georgia. Mortality rates decreased sharply in 2020 for children in both racial categories in all sections of the state, but rose in a majority of districts for both categories in adult and older populations. Risk ratios also increased significantly in 2020 for children and older populations, showing rising disparities in mortality during the pandemic even as crude mortality rates declined for children classified as Black. Conclusions: Increased mortality during the COVID-19 outbreak disproportionately affected African-Americans, possibly due, in part, to pre-existing disparities prior to the pandemic linked to social determinants of health. The pandemic deepened these disparities, perhaps due to unequal resources to effectively shelter-in-place or access medical care. Future research may identify local factors underlying geographically heterogenous differences in mortality rates to inform future policy interventions.