Is there any return to education in criminal activities? This paper is one of the first to investigate whether education has not only a positive impact on legitimate, but also on illegitimate activities. We use as a case study one of the longest running criminal corporations in history: the Italian-American mafia. Its most successful members were capable businessmen, orchestrating crimes that required abilities that might be learned at school: extracting the optimal rent when setting up a racket, weighting interests against default risk when starting a loan sharking business or organizing supply chains, logistics and distribution when setting up a drug dealing system. We address this question by comparing mobsters to a variety of samples drawn from the United States 1940 Population Census, including a sample of their closest (non-mobster) neighbors. We document that mobsters have one year less education than their neighbors on average. We find that mobsters have significant returns to education of 7.5-8.5 percent, which is only slightly smaller than their neighbors and 2-5 percentage points smaller than for U.S.-born men or male citizens. Mobster returns were consistently about twice as large as a sample of Italian immigrants or immigrants from all origin countries. Within that, those charged with complex crimes including embezzlement and bookmaking have the highest returns. We conclude that private returns to education exist even in the illegal activities characterized by a certain degree of complexity as in the case of organized crime in mid-twentieth century United States.

Importance: The relationship between income and life expectancy is well established but remains poorly understood. Objectives: To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation. Design and Setting: Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy. Exposure: Pretax household earnings as a measure of income. Main Outcomes and Measures: Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas. Results: The sample consisted of 1408287218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61175 per year). There were 4114380 deaths among men (mortality rate, 596.3 per 100000) and 2694808 deaths among women (mortality rate, 375.1 per 100000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P<.001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r=0.69, P<.001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r=0.72, P<.001), fraction of college graduates (r=0.42, P<.001), and government expenditures (r=0.57, P<.001). Conclusions and Relevance: In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

This article considers the implication of digital trends in Deaf historical scholarship. I argue that these methodological approaches hold great promise for fostering new interpretations of the historical record and for broadening access to resources. My digital history project, the Church Mission to Deaf-Mutes, 18731879, serves as a springboard for a discussion on the use of digital methods to explore geospatial deaf histories.

The percentage of CSU students enrolled in Science, Technology, Engineering and Math (STEM) fields has grown decisively since the Great Recession, from 20.4 percent in 2007, to 26.9 percent in 2014. Those STEM disciplines include Agricultural Sciences, Biological Sciences, Engineering, Health Professions, Information Sciences, Mathematics and Physical Sciences. Previous literature cites perceived probability of success, expected earnings, as well as gender and socioeconomic characteristics as the main driving factors for college major choice. However, the impact of expected earnings has been challenged citing selection bias. Arcidiacono (2004) shows that part of the ability sorting which takes place across majors is simply due to students gravitating toward subjects in which they have historically excelled. This paper will analyze STEM and Non-STEM enrollment, in tandem with STEM an Non-STEM unemployment and earnings, by testing Vector Autoregressive models for Granger causality, determining whether or not labor market forces can be used to help to predict college major enrollment. The presence or a lack of Granger causality will offer preliminary evidence for the question: Is college major choice really about love or about making a living?

Between the 1940s and the 1960s, the U.S. homeownership rate increased by nearly 20 percentage points, from mid-40 to mid-60 percent. The self-amortizing 30-year, fixed-rate mortgage, introduced by the Federal Housing Administration/Veterans Administration (VA—now the U.S. Department of Veterans Affairs) transformed the United States from a nation of renters to a nation of homeowners (Acolin and Wachter, 2015; Fetter, 2013).

Most syntactic methods consider non-independent reasoning (NIR) as a privacy violation and smooth the distribution of published data to avoid sensitive NIR, where NIR allows the information about one record in the data could be learned from the information of other records in the data. The drawback of this approach is that it limits the utility of learning statistical relationships. The differential privacy criterion considers NIR as a non-privacy violation, therefore, enables learning statistical relationships, but at the cost of potential disclosures through NIR. In this thesis, we investigate the extent to which private information of an individual may be disclosed through NIR by query answers that satisfy differential privacy. We first define what a disclosure of NIR means by randomized query answers, then present a formal analysis on such disclosures by differentially private query answers. Our analysis on real life data sets demonstrates that while disclosures of NIR can be eliminated by adopting a more restricted setting of differential privacy, such settings adversely affects the utility of query answers for data analysis, and this conflict can not be easily resolved because both disclosures and utility depend on the accuracy of noisy query answers. This study suggests that under the assumption that the disclosure through NIR is a privacy concern, differential privacy is not suitable because it does not provide both privacy and utility. The question is whether it is possible to (1) allow learning statistical relationships, yet (2) prevent sensitive NIR about an individual. In the second part of the thesis, we present a data perturbation . . .

This paper uses the Panel Study of Income Dynamics (PSID) from 1979 to 2007 to estimate within-period lifecycle-consistent labor supply elasticities of US females in a two-stage budgeting framework. The paper combines a variety of econometric approaches to estimate unobserved effects panel data models with censoring, selection and endogeneity. The paper finds evidence of substantial upward bias in estimated wage elasticities from pooled panel models which do not account for unobserved effects, as fixed effects and correlated random effects (CRE) specifications yield smaller elasticities. Estimates are also somewhat sensitive to using a lifecycle-consistent specification versus a standard static model. The lifecycle-consistent wage elasticity from a CRE model with instrumental variables is 0.56 on the extensive margin and 0.31 on the intensive margin for an overall wage elasticity of 0.87. The standard static model, on the other hand, yields a wage elasticity of 0.46 on the extensive margin and 0.13 on the intensive margin for an overall elasticity of 0.59.

While obesity continues to be a significant health issue, the relationship between body weight and mortality risk remains unclear. Research notes the strong association between obesity and higher mortality risk, along with the “protective” effect of higher weight for some groups. Few studies have examined this relationship when stratified by socioeconomic status, especially when considering premature mortality among working-aged adults. Using recent National Health Interview Survey data, this study examines variation in BMI-associated premature mortality risk across different levels of education. Results indicate overweight and class I obesity are associated with lowest mortality risk among the lower-educated. Conversely obesity is associated with increased mortality risk for individuals with a college education or greater, while overweight is not associated with reduced risk. Thus, obesity may pose a greater relative health risk in more advantaged groups, such as the highly educated, while other socio-behavioral factors account for premature mortality among lower-educated individuals.

We examine whether greater Medicaid generosity encourages mobility towards riskier but better jobs in higher paid occupations and industries. We use Current Population Survey Data and exploit variation in Medicaid thresholds across states and over time through the 1990s and 2000s. We find that moving from a state in the 10th to the 90th percentile in terms of Medicaid income thresholds increases occupational and industrial mobility by 7.6% and 7.8%. We also find that higher income Medicaid thresholds increase mobility towards occupations and industries with greater wage spreads and higher separation probabilities, but with higher wages and higher educational requirements.

The recent economic downturn in the USA has coincided with stories of young men and women choosing to remain at home, or to move back in with their parents since they cannot afford to live independently. This paper first describes changes in parental coresidence over the last half-century, and then assesses the causal link between economic conditions and living arrangements among young adults using data on more than 15 million individuals from 1960 to 2011. Comparing changes in economic conditions across US states to changes in living arrangements, I find that fewer jobs, low wages, and high rental costs all lead to increases in the numbers of men and women living with their parents. The magnitudes of the effects are quite large: for men, I estimate that changes in economic factors alone are large enough to have caused the observed changes in parental coresidence between 1970 and 2011.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) serves millions of low-income women, infants, and children who are at nutritional risk by providing checks or vouchers for nutritious foods, nutrition counseling, breastfeeding support, and health care referrals. Foods eligible for WIC are high in certain nutrients and designed to meet the special nutritional needs of low-income pregnant, breastfeeding, or postpartum women, as well as infants and children up to age 5.

Background Privacy must be protected when sensitive biomedical data is shared, e.g. for research purposes. Data de-identification is an important safeguard, where datasets are transformed to meet two conflicting objectives: minimizing re-identification risks while maximizing data quality. Typically, de-identification methods search a solution space of possible data transformations to find a good solution to a given de-identification problem. In this process, parts of the search space must be excluded to maintain scalability. Objectives The set of transformations which are solution candidates is typically narrowed down by storing the results obtained during the search process and then using them to predict properties of the output of other transformations in terms of privacy (first objective) and data quality (second objective). However, due to the exponential growth of the size of the search space, previous implementations of this method are not well-suited when datasets contain many attributes which need to be protected. As this is often the case with biomedical research data, e.g. as a result of longitudinal collection, we have developed a novel method. Methods Our approach combines the mathematical concept of antichains with a data structure inspired by prefix trees to represent properties of a large number of data transformations while requiring only a minimal amount of information to be stored. To analyze the improvements which can be achieved by adopting our method, we have integrated it into an existing algorithm and we have also implemented a simple best-first branch and bound search (BFS) algorithm as a first step towards methods which fully exploit our approach. We have evaluated these implementations with several real-world datasets and the k-anonymity privacy model. Results When integrated into existing de-identification algorithms for low-dimensional data, our . . .

This study tests whether the employer mandate under the Affordable Care Act (ACA) increased involuntary part-time (IPT) employment. Using data from the Current Population Survey between 1994 and 2014, we find that IPT employment in 2014 was higher than predicted based on economic conditions and the composition of jobs and workers in the labor market. More importantly, using difference-in-difference methods, we find that the increase in the probability of IPT employment since 2010 was greatest in the industries and occupations where workers were most likely to be affected by the mandate. We also show that there has been virtually no change in the probability of IPT employment where the number of workers affected by the mandate was small. We estimate that approximately 1 million additional workers between the ages of 19 and 64 are in IPT employment as a result of the ACA employer mandate.

Why-not and why questions can be posed by database users to seek clarifications on unexpected query results. Specifically, why-not questions aim to explain why certain expected tuples are absent from the query results, while why questions try to clarify why certain unexpected tuples are present in the query results. This paper systematically explores the why-not and why questions on reverse top-k queries, owing to its importance in multi-criteria decision making. We first formalize why-not questions on reverse top-k queries, which try to include the missing objects in the reverse top-k query results, and then, we propose a unified framework called WQRTQ to answer why-not questions on reverse top-k queries. Our framework offers three solutions to cater for different application scenarios. Furthermore, we study why questions on reverse top-k queries, which aim to exclude the undesirable objects from the reverse top-k query results, and extend the framework WQRTQ to efficiently answer why questions on reverse top-k queries, which demonstrates the flexibility of our proposed algorithms. Extensive experimental evaluation with both real and synthetic data sets verifies the effectiveness and efficiency of the presented algorithms under various experimental settings.

This study analyzes the slow down of the convergence of gender gap since the late 1990s. Analyzing data from the Current Population Survey for 1980 to 2014, we find that gender gap fell sharply during the 1980s to mid-1990s and since then gender gap has been declining much more slowly at the top of the wage distribution compared to the rest of the part of the distribution. In the existing literature, this phenomenon is known as glass ceiling effect. The slowing down of the growth of relative wage for skilled women indicates a divergence path of the gender gap in the upper and lower halves of the wage distribution. Our two factors VES (variable elasticity of substitution) model shows that since the early 2000s a sharp increase in relative supply of women college graduates partially offset the price effect of the rapid secular growth in relative demand for skilled workers. Fluctuations in state economic factors such as unemployment rate, minimum wage, migration are not possible explanations for these recent divergent trends because relative wage growth does slow down mainly in the upper tail of the wage distribution.

From 1941 through 1978, polychlorinated biphenyls (PCBs) were released in New Bedford, MA. Electronic manufacturers and waste sites were sources of PCBs, an environmentally persistent compound associated with increased risk of some cancers. Although there has been some remediation, PCBs are still detectable. Using 829 soil and sediment samples, a mean proxy PCB concentration for each New Bedford census tract was calculated. Standardized incidence ratios (SIRs) of five cancer types were calculated for each census tract using Massachusetts Cancer Registry data. The association between mean proxy PCB concentrations and SIRs was tested using mixed linear regression models. No statistically significant association was observed. Although SIRs are a robust measure of cancer incidence, this method of approximating PCB exposure was likely inaccurate; census tracts are arbitrary boundaries and do not account for behavioral factors of the population that influence exposure. Ideally, biometric data should be used to estimate PCB exposure.

Choo and Siow (2006b) (CS here after) proposed an empirical implementation of the Becker-Shapley-Shubik transferable utility model of marriage matching. When com- puting counterfactual experiments, CS and its generalization have taken the standard approach of first estimating the structural parameters before computing the counter- factual experiments. In this paper, we show that for the CS and some related models, certain types of counterfactual experiments are identified without estimating the struc- tural parameters. Our new approach of identifying the counterfactuals reformulates the CS style model into a system of equations with the ratio of counterfactual to ob- served equilibria as unknowns. This system of equations are free from the structural marital gain parameters. These models in equilibrium all generate marriage matching functions with two properties - the structural parameters enter multiplicatively and the functions are homogenous of degree k. We formalize our model and show existence and uniqueness of equilibrium for our approach. We illustrate our framework by analyzing the impact of the elimination of the Social Security Student Benefit Program in 1982 on college attendance and the marriage market.

Introduction Poor access to water, sanitation, and handwashing (WASH) facilities frequently contribute to child growth failure. The role of access to WASH facilities on child growth outcomes in Ethiopia is largely unknown. The aim of this study was to determine individual and combined effects of access to WASH facilities on child growth outcomes. Methods Data for this analysis was sourced from the recent Ethiopia Demographic and Health Survey (EDHS) 2016. A multivariable logistic regression model was applied to identify the separate and combined association of access to WASH facilities with child growth outcomes. Odds ratio (OR) and 95% confidence interval (CI) were estimated. Statistical significance was declared at p < 0.05. Results Included in the analyses were data for children 0-59 months of age, which amounted to valid data for 9588 children with a height-for-age z-score (HAZ), 9752 children with a weight-for-age z-score (WAZ) and 9607 children with a weight-for-height z-score (WHZ). Children with access to improved combined sanitation with handwashing facilities had 29% lower odds of linear growth failure (stunting) (adjusted odds ratio (AOR) = 0.71; 95% CI: 0.51-0.99) compared with those with unimproved. Children with access to combined improved WASH facilities were 33% less likely to have linear growth failure (AOR = 0.67; 95% CI: 0.45-0.98). Access to improved handwashing alone reduced the odds of being underweight by 17% (AOR = 0.83; 95% CI: 0.71-0.98) compared with unimproved. Improved water and sanitation separately as well as combined WASH were not associated with decreased odds of underweight and wasting. Conclusions Combined access to improved water, sanitation and handwashing was associated with reduced child linear growth failure. Further research with robust methods is needed to examine whether combined WASH practices have synergistic effect on child growth outcomes.

We study the role of foreign-born workers in the growth of employment in STEM occupations since 1980. Given the importance of employment in these fields for research and innovation, we consider their role in a model featuring endogenous routine-biased technical change. We use this model to quantify the impact of high-skilled immigration, and the increasing tendency of immigrants to work in innovation, for the pace of routine-biased technical change, the polarization of employment opportunities, and the evolution of wage inequality since 1980.