The first month of the Trump presidency was punctuated by a series of anti-immigrant executive orders and implementation memos that resulted in chaos at airports around the world, immigration raids around the country, and a widespread fear among communities.1 The Trump administration’s actions and directives ostensibly target the 11 million unauthorized immigrants who live in the United States, but they will also harm millions of American citizens all across the country who live and work beside these immigrants every day.2 A new analysis by University of Southern California’s Center for the Study of Immigrant Integration, or CSII, and the Center for American Progress shows that millions of people, citizens or otherwise, live in mixed-status families with at least one unauthorized family member in the same household. Undeniably, there is no way to separate immigrants out from the U.S.-born population; there can be no us versus them.

State average enrollment-weighted public college tuition and fees per school year rose by $3,843 (or 81 percent) between 2001 and 2009. How are recent cohorts absorbing this surge in college costs, and what effect is it having on their post-schooling consumption? Our analysis of tuition, educational attainment, and debt patterns for nine youth cohorts across all fifty states indicates that the tuition hike accounted for $1,628, or about 30 percent, of the increase in average student debt per capita among 24-year-olds between 2003 and 2011. However, estimates indicate no meaningful response to tuition on college enrollment, years of post-high school schooling, and BA degree attainment rates. Our findings are consistent with American youth having accommodated tuition shocks not by forgoing schooling, but instead by amassing more debt. They signal an active role for the U.S. student loan system in shielding young Americans’ human capital investments against shocks to (students’) education costs. Further analysis demonstrates that the tuition hike and student debt increase, despite leaving higher educational attainment unchanged, can explain between 11 and 35 percent of the observed approximate eight-percentage-point decline in homeownership for 28-to-30-year-olds over 2007-15 for these same nine cohorts. The results suggest that states that increase college costs for current student cohorts can expect to see a response not through a decline in workforce skills, but instead through weaker spending and wealth accumulation among young consumers in the years to come.

The gender wage gap has recently gained a significant amount of attention in the literature. The gender wage gap is the difference between male and female earnings for doing the same work. Additionally, one can often find the gender wage gap expressed as a percentage of male earnings. Currently, there is a debate over what portion of the gender wage gap is due to obvious workplace discrimination. Some argue that womens career choices are the driving force behind the gender wage gap (Ehrenberg et al, 1993). However, there are many factors that could contribute to the gender wage gap such as age, educational attainment, hours worked, and occupational distributions. The motivation behind this research is to establish whether there are differences in the gender wage gap based on gender distribution within an occupation after controlling for a number of other important determinants of the gender wage gap. After controlling for human capital related determinants of wages, this study will determine the impact of gender distribution within an occupation on earnings. Section 2 of this paper will look at existing literature on the subject and discuss its relevance to the study at hand. Section 3 will explain the theoretical framework that lead to the hypothesis. Then, Section 4 will describe the data along with its reliability and relevance to the topic at hand. Upon observing the data, Sections 5 and 6 will illustrate the descriptive statistics and regression model used in this paper. After depicting the means to which this topic was observed, Section 7 will convey the results of both the descriptive statistics and regression models. Finally, Section 8 will provide some concluding remark.

How does personal wealth shape an individuals decision to abandon the democratic process and participate in violent rebellion? Studying the American Civil War and the atrocity of human slavery, we offer competing theoretical accounts for how we should expect individual wealth, in the form of land and slaves, to affect white mens decisions to join the Confederate Army. To resolve these disagreements, we assemble a dataset on roughly 3.9 million white citizens in Confederate states, and we show that slaveowners were more likely to fight in the Confederate Army than non-slaveowners. To see if these links are causal, we exploit a randomized land lottery in 19th-century Georgia. Households of lottery winners owned more slaves in 1850 and were more likely to have sons who fought in the Confederate Army than were households who did not win the lottery. The findings add nuance to our understanding of the relationship between individual wealth, political institutions, and the propensity to engage in civil conflict. Although in general wealthier individuals are less likely to fight in such conflicts, when their wealth is tied to existing institutions that civil conflict threatens, they may in fact be more likely to fight.

My doctoral research focuses on the role of labor market frictions in shaping macroeconomic outcomes. To describe the empirical regularities of involuntary part-time employment, I use detailed micro-level data from longitudinally-linked monthly files of the Current Population Survey. A novel finding that emerges from the analysis of this dataset is that wages of involuntary part-time workers display higher volatility and lower persistence than those of their full-time counterparts, thus indicating a higher degree of flexibility. In addition, I find that changes in involuntary part-time employment are mostly explained by reallocation of workers from full-time to part-time positions within the firm, which involves more than just a mere reduction in hours worked. I then aggregate the data and compute business cycle statistics. Surprisingly, I find that the behavior of involuntary part-time employment resembles the behavior of unemployment more than the one of full-time employment. In fact, the results indicate that involuntary part-time employment is very volatile and strongly countercyclical.

High inequality goes hand in hand with low intergenerational earnings mobility across countries. Little is known about why the US is characterized by high inequality and low mobility, while the opposite tends to hold for Scandinavian countries. In an overlapping generations model, calibrated to the US, education policies are endogenized via probabilistic voting. By exploiting cross-country variation in the bias in voter turnout towards the educated and elderly, the model replicates the negative relation between inequality and public education expenditures and accounts for more than a quarter of the variation in inequality and mobility. For the US, I find that compulsory voting could foster mobility, whereas inequality would be hardly affected.

Older adults comprise an increasing share of new legal admits to the United States. While many are financially dependent on their families, a more complete picture requires taking into account the nonmonetary contributions of this population. Using the American Time Use Survey, this study examines whether older recent immigrant women provide more unpaid childcare than their native-born and more established immigrant counterparts. Results suggest that while older recent immigrant women are more likely to provide unpaid childcare, this effect is eliminated upon controlling for demographic characteristics. However, among those who do provide childcare, older recent immigrant women provide more hours of care even after controlling for demographic and household characteristics. This pattern holds up even after restricting the analysis to women living with young children. These results may signal reciprocal supportive networks. Working-age adults may financially support older recent immigrants, while older recent immigrants provide unremunerated childcare for working-age adults.

When Tom Cooper came out of the Navy and got his first job selling shoes, Harry Truman was president and a gallon of gas cost around 18 cents. Now 85, Cooper still puts on a tie five days a week and drives from his home in Silver Spring, Md., to work as a shoe salesman, and he has no plans to stop. One reason is that he can’t afford to — he needs money to pay bills from cancer treatment, a back operation and a foot operation. But even if he had enough savings to retire, he doubts he would. “I’d go nuts. I’d have nothing to do,” he said. “I can’t lay around the house, do nothing and watch TV. I feel a lot better when I’m walking and helping people and doing stock work.” Cooper is part of a small but growing segment of Americans who remain in the workforce into their 70s, 80s and 90s. Although the average retirement age for Americans is 63, the portion of people 75 and older in the workforce has more than doubled since 1985 — from 3.6 percent to 8 percent, according to Bureau of Labor Statistics data analyzed by the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota. For those working full-time, the increase has been even more dramatic: From just over 1 percent in 1993 to nearly 4 percent last year.

Our experience is anecdotal, but there are data to the same effect: persons living in nursing homes who have urinary incontinence do not have overall lower quality of life2; becoming more dependent on others is not tied to lower psychological well-being3; and quality of life in those with dementia is more tightly correlated with exposure to engaging activities than level of confusion or dementia.4 With some variability, state statutes sharply restrict the circumstances in which clinicians . . .

Statistical methods of spatial analysis are often successful at either prediction or explanation, but not necessarily both. In a recent paper, Dearmon and Smith (2015) showed that by combining Gaussian Process Regression (GPR) with Bayesian Model Averaging (BMA), a modeling framework could be developed in which both needs are addressed. In particular, the smoothness properties of GPR together with the robustness of BMA allow local spatial analyses of individual variable effects that yield remarkably stable results. However, this GPR-BMA approach is not without its limitations. In particular, the standard (isotropic) covariance kernel of GPR treats all explanatory variables in a symmetric way that limits the analysis of their individual effects. Here we extend this approach by introducing a mixture of kernels (both isotropic and anisotropic) which allow different length scales for each variable. To do so in a computationally efficient manner, we also explore a number of Bayes-factor approximations that avoid the need for costly reversible-jump Monte Carlo methods. To demonstrate the effectiveness of this Variable Length Scale (VLS) model in terms of both predictions and local marginal analyses, we employ selected simulations to compare VLS with Geographically Weights Regression (GWR), which is currently the most popular method for such spatial modeling. In addition, we employ the classical Boston Housing data to compare VLS not only with GWR, but also with other well-known spatial regression models that have been applied to this same data. Our main results are to show that VLS not only compares favorably with spatial regression at the aggregate level, but is also far more accurate than GWR at the local level.

The United States provides a unique laboratory for understanding how the cultural, institutional, and human capital endowments of immigrant groups shape economic outcomes. In this paper, we use census micro-samples to reconstruct the country-of-ancestry composition of the population of U.S. counties from 1850 to 2010 and describe its evolution. We also develop a county-level measure of GDP per worker over the same period. Using this novel panel data set, we show that the evolution of a countys ancestry composition is significantly associated with changes in county-level GDP. The cultural, institutional, and human capital endowments from the country of origin drive this relationship. We also use an instrumental variable strategy to identify the effect of endowments on local economic development. Finally, our results suggest that while ancestry diversity is positively related to county GDP, diversity in attributes is negatively related to county GDP. We show that part of this relationship is explained by the close link between occupational variety and ancestry diversity.

The civil rights revolutions of the 1950s and 1960s transformed the literature on Reconstruction in America by emphasizing the social history of emancipation and the hope that reunification would bring equality. Much of the revisionist work served to counter and correct the racist and pro-Confederate accounts of Reconstruction written in the early twentieth century. While scholars have explored modern revisions for individual states, most are decades old, and Michael W. Fitzgerald's Reconstuction in Alabama presents the first comprehensive reinterpretation of that state's history in over a century. Fitzgerald's work not only revises the existing troubling histories of the era, it aso offers a compelling and innovative new look at the process of rebuilding Alabama following the war. Attending to an array of issues largely ignored until now, Fitzgerald's history begins by analyzing the differences over slavery, secession, and war that divided Alabama's whites, mostly along the lines of region and class. He examines the economic and political implications of defeat, focusing particularly on how freed slaves and their former masters mediated the postwar landscape. For a time, he suggests, whites and freedpeople coexisted mostly peaceably in some parts of the state under the Reconstruction government, as a recovering cotton economy bathed the plantation belt in profit. Later, when charting the rise and fall of the Republican Party, Fitzgerald shows that Alabama's new Republican government implemented an ambitious program of railroad subsidy, characterized by substantial corruption that eventually bankrupted the state and helped end Republican rule. He explains, however, that the state's freedpeople and their preferred leaders were not the major players to this arena: they had other issues that mattered to them far more, including public education, civil rights, voting rights, and resisting the Klan's terrorist violence. After Reconstruction ended, Fitzgerald suggests, the white collective memory of the era fixated on the ideas of black voting, bit government, high taxes, and corruption, all of which buttressed the Jim Crow order in the state. This misguided understanding of the past encouraged Alabama's intrasigence during the later civil rights era. Despite the power of faulty interpretations that united segregationists, Fitzgerald demonstrates that class and regional divisions over economic policy, as much as racial tension, shaped the complex reality of Reconstruction in Alabama. -- from dust jacket. A mere lapsus : Unionists and conservative dissidents during the Civil War -- The last relicks of barbarism : army, war, and reconstruction -- Presidential Reconstruction : Unionism and the politics of definition -- The premature New South of Governor Robert Patton -- Black Liberation : freedom and political mobilization -- Implementing Reconstruction : governance and biracial politics -- The difference between whaling a freeman and pounding a slave : terrorism and resistance in the Klan era -- Railroads, race, and Reconstruction : the curious legacy of Governor William H. Smith -- Bipartisan disaster : the advent of Governor Robert Lindsay -- False dawn : the promise of Reconstruction in the early 1870s -- Beneath the white banner : depression and the overthrow of Reconstruction -- "It only requires a little more figuring" : redemption's aftermath.

As part of the selection of the best items in a multidimensional database,several kinds of query were defined. The skyline operator has the advantage of not requiring the definition of a scoring function in order to classify tuples. However, the property of monotony that this operator does not satify, (i) makes difficult to optimize its queries in a multidimensional context, (ii) makes hard to estimate the size of query result. This work proposes, first, to address the question of estimating the size of the result of a given skyline query, formulating estimators with good statistical properties (unbiased or convergent). Then, it provides two different approaches to optimize multidimensional skyline queries. The first leans on a well known database concept: functional dependencies. And the second approach looks like a data compression method. Both algorithms are very interesting as confirm the experimental results. Finally, we address the issue of skyline queries in dynamic data by adapting one of our previous solutions in this goal.

Occupational licensure, one of the most significant labor market regulations in the United States, may restrict the interstate movement of workers. We analyze the interstate migration of 22 licensed occupations. Using an empirical strategy that controls for unobservable characteristics that drive long-distance moves, we find that the between-state migration rate for individuals in occupations with state-specific licensing exam requirements is 36 percent lower relative to members of other occupations. Members of licensed occupations with national licensing exams show no evidence of limited interstate migration. The size of this effect varies across occupations and appears to be tied to the state specificity of licensing requirements. We also provide evidence that the adoption of reciprocity agreements, which lower re-licensure costs, increases the interstate migration rate of lawyers. Based on our results, we estimate that the rise in occupational licensing can explain part of the documented decline in interstate migration and job transitions in the United States.

Studies on the quality of age reporting by older persons in Latin America and the Caribbean are still scarce owing, among other things, to the recent and rapid nature of changes in the age structure. This study employs a modified version of Whipple’s Index to measure the preference for the digits 0 and 5 among age groups of 60 years and over, and also divides these into specific age groups. To this end, it analyses 72 population censuses carried out in the decades spanning from 1960 to 2010 in 20 countries in Latin America and the Caribbean, made available through the Integrated Public Use Microdata Series International project (IPUMS-I). The results indicate that the quality of age reporting among older persons has improved during the observation period, and that gaps between the results for men and women have narrowed. However, the results also showed discrepancies by data collection method, inasmuch as age reporting was of better quality in population censuses that specified date of birth than in those that specified age.

The study of segregation is essential for understanding how place influences life outcomes. However, traditional segregation indices rely heavily on the use of areal units for calculation, which risks introducing both measurement and interpretation error. Researchers suggest that individual-level data avoids many of the problems facing traditional area-level indices. In this Dissertation, I use the recent release of the complete 1940 Census to investigate the potential problems with measuring segregation with areal units and develop a new method for measuring segregation at the individual level. In Chapter 1, I investigate the potential impact the modifiable areal unit problem (MAUP) may have on accurately measuring segregation when using areal unit indices. In Chapter 2, I develop a new measure of segregation, the Shortest Path Isolation (SPI) index, which captures the degree of racial isolation from the perspective of what an individual would experience. Using the SPI index developed in Chapter 2, Chapter 3 investigates how individual-level racial isolation in 1940 West Philadelphia is associated with access to neighborhood resources by race. Given that our understanding is only as good as our measurement, it is imperative that our measures accurately reflect our perceptions of segregation.

This brief expands on previous research examining how eligibility for and participation in Medicaid and the Childrens Health Insurance Program (CHIP) are changing under the Affordable Care Act (ACA) for children and parents (Kenney et al. 2016a, 2016b). Here, we extend the analysis to 2015, the second year of full ACA implementation.

Some studies have argued that information and communication technologies such as smartphones can pressure employees to work more from home, while others argue that they help employees manage transitions between work and family role domains. Leveraging boundary theory and the job demands-resources model, the present study examines the conditions under which work-family technology use is associated with greater boundary control. Findings show that technology use is associated with higher boundary control for those who prefer role integration and lower boundary control for those who prefer role segmentation. Findings also show that boundary control is linked to emotional exhaustion and that organizational after-hours electronic communication expectations can compel workfamily technology use despite individual preferences.

An income inequality index number, which assigns a single numerical value to the income distribution, cannot summarize the complete information in the distribution. We propose a family of inequality index curves, which includes curves generated by popular inequality index numbers (e.g. the top income shares, the Gini coefficient, the single parameter Gini coefficient, the Palma ratio, and the Hoover index). The family has two advantages: (1) The family has an axiomatic foundation based on the weighted expected utility theory. (2) Each curve in the family contains the full information of the income distribution. Complementing the previous empirical studies based on the top income shares, we use the family of inequality index curves and micro level data to show that the bottom and middle income people in the U.S. became more equally relatively poor (not just relatively poorer) from 1990 to 2010.

In this paper, we examine the long-term impact of refugees on the U.S. labor market over the period 1980-2010. Drawing upon aggregated individual level administrative data, we exploit the exogenous assignment of refugees across commuting zones within the United States, by focusing on those refugee cases without U.S. ties. The distribution of these refugees depends upon resettlement agencies and is mainly driven by the availability of accommodation and other (for example medical) needs – most importantly, it is independent of the choice of refugees. Nevertheless, we use matching techniques to identify suitable counterfactual observations for those commuting zones that receive a significant number of refugees as a share of the population. Accounting for all of the recent innovations in the literature, we do not find any significant long-term labor market impact of refugees. Our results provide robust causal evidence that there is no adverse long-run impact of refugees on the U.S. labor market.