We explore the impact that the renegotiation of NAFTA would have on both high-and low-skilled workers in the United States. We build a multi-country general equilibrium trade model with vertically integrated supply chains and trade in both intermediate goods and the technologies necessary to produce them. The technologies used to produce intermediate goods are assumed to be non-rivalrous and skill-augmenting. We find the freer trade generally increases inequality through increased investment in the skill-augmenting technology, but a reduction in trade with Mexico does little to offset the existing inequality in the United States.

Using a dataset on career paths of elite U.S. engineers, including superstar entrepreneurs, we examine the e↵ect of early career choices on long-run entrepreneurial outcomes. Exploiting exogenous variation in entry labor market conditions, driven by local financial sector growth, and comparing classmates in the same school-major-year, we find that talented engineers are more likely to switch from engineering jobs to finance in high finance growth areas. Compared to classmates who remain in engineering, these individuals create fewer startups that are large employers, receive VC funding, are acquired, and issue patents. A quasi-experimental design using state-wise banking deregulation produces similar results.

In Figure 1, the key to the color gradations (“Percentage of deaths attributable to PM2.5”) was omitted. It is included in the figure reproduced here.

We investigate whether an increase in the potential earnings of men leads to an increase in marriage and a reduction in nonmarital births by exploiting the positive economic shock associated with fracking in the 2000s. A reduced-form analysis reveals that in response to local-area fracking production, which increased wages and jobs for non-collegeeducated men, both marital and nonmarital birth rates increase, but marriage rates do not. The pattern of results is consistent with positive income effects on births but no associated increase in marriage. We contrast our findings to the Appalachian coal boom experience of the 1970s and 1980s.

Recent years have witnessed a growing interest in the role of the historical family as the instigator of disparate developmental trajectories. However, a major challenge faced by these studies was that they had to work around a lack of reliable historical data. This study demonstrates how embarking on the recent outburst of historical microdata infrastructures may help to improve these explorations by suggesting additional statistics and a derived measure (the Patriarchy Index) that might prove useful in future efforts aimed at assessing the effect of historical family organization on comparative development. The added value of that endeavour is assessed by comparing the predictive validity of the PI for contemporary developmental gradients against a composite indicator of family organization previously used by economic historians (Carmichael’s ‘Female Friendliness Index’). The results indicate that conclusions about the relationship between historical family organization and various societal outcomes may be sensitive to the measure used. Based on the evidence presented in this paper, it is argued that one of the reasons why the potential importance of historical family for contemporary developmental disparities has not been convincingly unravelled could be inadequacy of data and indicators so far employed to assess historical family formations. To the extent that the Patriarchy Index would be taken up by wider scholarly circles as an indicator of historical family organization it could help unravel potentially new associations between past and present, at least as far as Europe is concerned.

Given a set of multidimensional data points, skyline queries retrieve those points that are not dominated by any other points in the set. Due to the ubiquitous use of skyline queries, such as in preference-based query answering and decision making, and the large amount of data that these queries have to deal with, enabling their scalable processing is of critical importance. However, there are several outstanding challenges that have not been well addressed. More specifically, in this paper, we are tackling the data straggler and data skew challenges introduced by distributed skyline query processing, as well as the ensuing high computation cost of merging skyline candidates. We thus introduce a new efficient three-phase approach for large scale processing of skyline queries. In the first preprocessing phase, the data is partitioned along the Z-order curve. We utilize a novel data partitioning approach that formulates data partitioning as an optimization problem to minimize the size of intermediate data. In the second phase, each compute node partitions the input data points into disjoint subsets, and then performs the skyline computation on each subset to produce skyline candidates in parallel. In the final phase, we build an index and employ an efficient algorithm to merge the generated skyline candidates. Extensive experiments demonstrate that the proposed skyline algorithm achieves more than one order of magnitude enhancement in performance compared to existing state-of-the-art approaches.

Individual life expectancies are easy to calculate from individual mortality rates and provide useful summary measures for individuals making retirement decisions and for policy makers. For couples, analogous measures are the expected years both spouses will be alive (joint life expectancy) and the expected years the surviving spouse will spend as a widow or widower (survivor life expectancy). Using individual life expectancies to calculate summary measures for couples yields substantially misleading results because the mortality distribution of husbands and wives overlap substantially. To illustrate, consider a wife aged 60 whose husband is 62. In 2010, the wife's life expectancy was 24.5 years and her husband's 20.2 years. The couple's joint life expectancy, however, is only 17.7 years. Although her life expectancy is four years longer than his, if she is widowed (probability: .62), her survivor life expectancy is 12.5 years; if the husband is a widower (probability: .38), his survivor life expectancy is 9.5 years. We calculate trends and patterns in joint and survivor life expectancy in each census year from 1930 to 2010. Using 2010 data, we also investigate differences in joint and survivor life expectancy by race and ethnicity and by education.

This paper studies the determinants of poverty in the U.S. at the census tract level using a geographically consistent panel data set for 1970-2010. The results are framed within the debate about the optimal design of local development strategies. The main conclusion drawn from our empirical analysis is that place-based and person-centered policies can be considered as complementary rather than as substitutes.

The rise in White imprisonment in rural areas has gone almost completely unnoticed and undiscussed until very recently. Using data newly analyzed from the National Corrections Reporting Program restricted files for 2000-2013, this report documents the higher rates of prison admissions for Whites in rural areas and shows that these higher rates are tied to the higher rates of White poverty and lower White educational levels in rural areas. Further, places with less educated White people showed more growth in White imprisonment. Poverty and education explain the urban-rural difference in a statistical sense, but this does not mean that there is no rural-urban difference. Rather, the analysis points to the high concentration of White disadvantage in rural areas and smaller cities. The Black patterns are different: although rates of poverty and high school dropouts are higher in rural areas for Blacks, the correlations are weaker than for Whites and rural areas have lower Black imprisonment rates both before and after controls for education and poverty. Looking at changes in imprisonment rates between 2000-6 and 2007-11, both Black and White imprisonment rose in areas with higher high school dropout rates and lower Black percentages, but the effects of poverty and college graduation rates and percent Hispanic varied by race in multivariate models. Overall, the findings point to the importance both of disaggregating Black and White imprisonment rates and of recognizing that overall national trends obscure marked differences in the trends between places. They also point to concentrated White disadvantage in White rural areas as linked to rising White imprisonment rates. Further research is necessary to understand these trends.

Objectives. To assess changes in US tuberculosis (TB) incidence rates by age, period, and cohort effects, stratified according to race/ethnicity and nativity. Methods. We used US National Tuberculosis Surveillance System data for 1996 to 2016 to estimate trends through age-period-cohort models. Results. Controlling for cohort and period effects indicated that the highest rates of TB incidence occurred among those 0 to 5 and 20 to 30 years of age. The incidence decreased by age for successive birth cohorts. There were greater estimated annual percentage decreases among US-born individuals (–7.3%; 95% confidence interval [CI] = –7.5, –7.1) than among non–US-born individuals (–4.3%; 95% CI = –4.5, –4.1). US-born individuals older than 25 years exhibited the largest decreases, a pattern that was not reflected among non–US-born adults. In the case of race/ethnicity, the greatest decreases by nativity were among US-born Blacks (–9.3%; 95% CI = –9.6, –9.1) and non– US-born Hispanics (–5.7%; 95% CI = –6.0, –5.5). Conclusions. TB has been decreasing among all ages, races and ethnicities, and consecutive cohorts, although these decreases are less pronounced among non–US-born individuals. (Am J Public Health. 2018;108:S315–S320. doi:10.2105/AJPH.2018.304687)

The rise in drug overdose deaths in the United States since the turn of the millennium has been extraordinary. A popular narrative paints a picture whereby opioid overdoses among white, male, less-educated, rural workers have been caused by reduced economic opportunities borne by such people. In this article, we causally test the validity of this theory by using Bartik-type variables to explore the relationship between local economic conditions and county opioid overdose death rates. We add to the literature by exploring how both employment and wage growth in different types of industries are related to opioid overdose deaths for the population as a whole, as well as for rural (vs. urban), male (vs. female) and white (vs. black) populations. We find mixed evidence. Our results confirm that wage and employment growth in industries more likely to employ low-skill workers are important protective factors for rural, white males. However, we also find evidence that economic improvements in low-skill industries are just as important in protecting blacks and women against opioid overdoses, and for workers in metro counties. We also find evidence that employment growth in high-paying industries has led to increases in opioid overdoes rates.

Newly arrived immigrants to the United States typically have lower incomes than similar age native-born counterparts when they arrive, but the immigrant-native gap gradually closes as the immigrants remain for a longer time, improving their job skills, English proficiency and social capital. This paper discusses how recently proposed federal rules that threaten the ability of recent immigrants to attain permanent residency conflict with immigrants' economic mobility and may ultimately harm not only the immigrants, but the broader economy. Specifically, "public charge" rules propose to prevent immigrants from becoming permanent residents if they have used means-tested public benefits or if they have low incomes or less than a high school education. The rule implicitly assumes that immigrants are poor in their first years in the country will remain poor, despite the evidence that immigrant incomes rise relatively quickly. Analyses presented in this paper, which are consistent with other prior research, show that immigrants start out with lower incomes than those native-born, but gradually catch up. Moreover, immigrants with low education close the immigrant-native income gap even faster, catching up with similar US-born counterparts within six to seven years on average. By making it more difficult for lawful immigrants to remain in the United States, the proposed public charge regulations stifle immigrants' future opportunities and undercut their ability to contribute to the nation.

Many factors within a water system can influence drinking water quality. One example is the presence of cyanobacteria, which can naturally occur in surface water sources of drinking water and produce toxins associated with harmful algal blooms. Another example is the deterioration of drinking water distribution systems, which can lead to pipe breaks. This study assessed how such factors within a large drinking water system serving metropolitan Boston communities may influence human health. In Aim 1, Poisson regression models were used to estimate the associations between daily measures of cyanobacteria concentration in the water source and emergency department (ED) visits for acute gastrointestinal illness (AGI), respiratory illness, and dermal illness over a 7- year period (7/27/2005 – 9/30/2012). Considering both 2-4 and 5-7 day lag periods, small relative increases in daily ED visits were observed for AGI and respiratory illness when comparing upper quartile levels of cyanobacteria concentrations with the lowest quartile (≤5.0 Areal Standard Units/mL). In Aim 2, case-crossover methods were used to examine the associations between water pipe breaks and ED visits for AGI. The first part (Aim 2a) examined 385 water main breaks in the City of Boston over a 10-year period (10/1/2002 – 9/30/2012). The second part (Aim 2b) examined a major water pipe break in 2010 that resulted in a boil water order affecting 30 metropolitan Boston communities. Conditional fixed-effects logistic regression models estimated the risk of ED visits for AGI during 0-3 and 4-7 day hazard periods. When restricted to zip codes served primarily by a single water service network, the association between main breaks and ED visits for AGI was slightly elevated during the 0-3 days after a break (Odds Ratio, OR=1.15; 95% Confidence Interval, CI: 0.99-1.34). Furthermore, there was an increased risk of ED visits for AGI during the 0-3 days after the major water pipe break in 2010 (OR=1.32; 95% CI: 1.07-1.61), particularly among children (≤5 years) and adolescents (6-18 years). This dissertation identified potential health risks related to cyanobacteria in the water source and water pipe breaks in the distribution system. These associations are important to consider given the consequences of a changing climate and aging infrastructure.

The first chapter studies the effect of prenatal radiation exposure on educational attainment and the live-birth sex ratio in the United States. I consider the aboveground nuclear tests in the 1960s as a natural experiment and find that an increase in radiation exposure in the second prenatal quarter, which covers the most radiosensitive gestational period, is associated with a significant decrease in years of education. A model is developed to address the measurement error in prenatal radiation due to misreported birth year in the data. It is shown that under reasonable assumptions, the estimated effect of prenatal radiation exposure on educational attainment is downward-biased. I also find that the live-birth ratio of males to females following a year with higher radiation exposure drops significantly, which supports the theory of gender-biased spontaneous abortion in response to radiation exposure. The instrumental variables (IV) model is widely used in estimating returns to education. A key, untestable, assumption for the validity of IV is the exclusion restriction. In Chapter 2, I revisit the common schooling instrument based on local college openings to evaluate its validity and estimate heterogeneity in treatment effects. To do this, I use infant radiation exposure in the US in the early 1960s a measurable shifter that affects the latent ability term, which is assumed to be independent of the IV. Under the IV assumptions, introducing a control function for latent ability should have no effect on the estimated return to schooling. I find that controlling for infant ii radiation exposure does not significantly alter the IV estimates. Second, the latent ability shifter can be used to identify heterogeneity in IV treatment effects. I show that the estimated IV treatment effect of schooling on wages decreases sharply with infancy radiation exposure. I estimate that students who experienced infant radiation exposure levels one standard deviation above the sample mean earned zero return to an additional year of schooling at the high school/college margin. Chapter 3 studies the effect of the Chernobyl accident on fetal survival and birth outcomes in the United States. The accident occurred on April 26th 1986, and had an instant impact on environmental radiation detected in the U.S. I combine the U.S. environmental radiation data, the fetal death data, and the birth certificate data, so to link the occurrence of fetal death and live birth to local history of radiation. I find that the probability of male fetal death hazard is significantly increased by radiation exposure in mid gestation, especially in the 5th-6th gestational month. I also find significant changes in various birth outcomes for babies who experienced high radiation exposure in both their early and late gestation. Radiation in early gestation increases birth weight and lowers congenital malformation rates. Radiation in late gestation lowers birth weight while increases the APGAR score. These findings suggest multi-dimensional effects of prenatal radiation exposure on newborn health traits.

Rough set theory, as one of the most useful soft computing methods dealing with vague and uncertain information, has been successfully applied to many fields, and one of its main applications is to perform attribute reduction. Although many heuristic attribute reduction algorithms have been proposed within the framework of the rough set theory, these methods are still computationally time-consuming. In order to overcome this deficit, we propose in this paper two quick feature selection algorithms based on the neighbor inconsistent pair, which can reduce the time consumed in finding a reduct. At first, we propose several concepts regarding simplified decision table(U' ) and neighbor inconsistent pairs. Based on neighbor inconsitent pairs, we constructe two new attribute significance measures. Furthermore, we put forward two new attribute reduction algorithms based on quick neighbor inconsistent pairs. The key characteristic of the presented algorithms is that they only need to calculate U ' =R once under the process of selecting the best attribute from attribute sets: C-R, while most existing algorithms need to calculate partition of U ' for |C-R| times. In addition, the proposed algorithms need only to deal with the equivalent classes in U ' =R that contain at least one neighbor inconsistent pair, while most existing algorithms need to consider all objects in U ' . The experimental results show that the proposed algorithms are feasible and efficient.

Policy uncertainty reduces individual welfare when individuals have limited opportunities to mitigate or insure against the resulting consumption fluctuations. We field an original survey to measure the degree of perceived policy uncertainty in Social Security benefits and to estimate the impact of this uncertainty on individual welfare. Our central estimates show that on average individuals are willing to forgo 6 percent of the benefits they are supposed to get under current law to remove the policy uncertainty associated with their future Social Security benefits. This translates to a risk premium from policy uncertainty equal to 10 percent of expected benefits.

Go to school. Be financially responsible. Work hard. These are the tenets we teach our children about the responsible pursuit of the American Dream. Yet for some of the hardest-working students from low-income families, following these tenets has not led to the success they have been promised. These low-income working learners are going to school more and working more hours, yet struggling to make it. They have been failed by an education system that perpetuates intergenerational inequality;1 a labor market that offers them fewer high-quality job opportunities with career-building work experience while they are in school;2 skyrocketing college prices that make it practically impossible to work one’s way through college anymore;3 poor information about education and career pathways and their outcomes;4 and a lack of sufficient support mechanisms and a financial and social safety net.5 This is a shameful state of affairs. Policymakers, educators, and business leaders can and must do more to help these motivated and hardworking low-income working learners gain the valuable skills and quality experience they need to reach their potential.

Accurate vital statistics are required to understand the evolution of racial disparities in infant health and the causes of rapid secular decline in infant mortality during the early twentieth century. Unfortunately, U.S. infant mortality rates prior to 1950 suffer from an upward bias stemming from a severe underregistration of births. At one extreme, African American births in southern states went unregistered at the rate of 15 % to 25 %. In this study, we construct improved estimates of births and infant mortality in the United States for 1915–1940 using recently released complete count decennial census microdata combined with the counts of infant deaths from published sources. We check the veracity of our estimates with a major birth registration study completed in conjunction with the 1940 decennial census and find that the largest adjustments occur in states with less-complete birth registration systems. An additional advantage of our census-based estimation method is the extension backward of the birth and infant mortality series for years prior to published estimates of registered births, enabling previously impossible comparisons and estimations. Finally, we show that underregistration can bias effect estimates even in a panel setting with specifications that include location fixed effects and place-specific linear time trends.

Marriage is the most important, and the most stable social network tie in American life. Marriage is also a gendered, and therefore a potentially asymmetric network tie. Most divorces in the US are wanted by the wife. Women’s predominance in wanting divorce (among couples who divorce) seems to have been consistent over time. This chapter employs a new longitudinal study of relationships in the US, the “How Couples Meet and Stay Together” surveys, to examine the gender of who wanted the breakup for both marital and nonmarital heterosexual relationships for the first time. The results show that only in marriages are the majority of breakups wanted by the female partner. Men and women in nonmarital heterosexual relationships in the US are equally likely to want to breakup. Furthermore, wives report lower relationship quality than husbands, while men and women in nonmarital relationships report more similar relationship quality.

Fifty years after the passage of the Fair Housing Act, racial/ethnic residential segregation and discrimination persist in the housing market. In 2018, the National Fair Housing Alliance reported that the third and fifth largest discrimination complaints are made on the bases of familial status and sex, respectively. However, housing research has largely ignored how family structure may shape patterns of racial/ethnic residential segregation. By assessing residential isolation, our analyses add to the small body of literature exploring racial/ethnic segregation by family structure using data from the 1990–2010 decennial censuses and the 2006–2010 American Community Survey (ACS) drawn from the Neighborhood Change Database (NCDB) and the National Historical Geographic Information System (NHGIS). Our results reveal that white, married-couple families experience the greatest levels of residential isolation, net of controls for relevant socioeconomic and demographic factors. In addition, our within racial/ethnic group analyses indicate that black, female-headed families experience significantly more isolation than their married-couple counterparts, while the reverse is true for Hispanic and white families. Our results provide support for the tenets of the place stratification model and suggest researchers should consider family structure when assessing racial/ethnic residential segregation as race/ethnicity and family structure interact to shape housing outcomes in metropolitan America.