Using public-use data from the 2009 American Community Survey, this study analyzes poverty rates among Hispanic and Asian adults while considering fractionalized immigrant generations: Generation 1.75 (those who migrated to the U.S. before the age of six), Generation 1.5 (those who migrated at between the ages of 6-18 and acquired some of their primary or secondary education in the U.S.), and Generation 1.0 (those who migrated after completing all of their primary and secondary education abroad). Consistent with other studies on immigrant/native poverty differentials, first generation immigrants in both groups were significantly more likely to be impoverished than U.S.natives. However, Generation 1.75, and to a lesser extent, Generation 1.5 Hispanic adults had significantly lower poverty rates than their U.S.-born counterparts, while Generation 1.75 and 1.5 Asian adults had similar poverty rates as U.S.-born Asians. Differences in socioeconomic, demographic, and geographic characteristics do not fully explain these differences. Such findings indicate that poverty reduction policies might be more effective if they go beyond considering broad classifications of race/ethnicity and birthplace, incorporating at a minimum the timing of migration among foreign-born in their life cycles.

Recent research shows that rule based models perform well while classifying large data sets such as data streams with concept drifts. A genetic algorithm is a strong rule based classification algorithm which is used only for mining static small data sets. If the genetic algorithm can be made scalable and adaptable by reducing its I/O intensity, it willbecome an efficient and effective tool for mining large data sets like data streams. In this paper a scalable and adaptable online genetic algorithm is proposed to mine classification rules for the data streams with concept drifts. Since the data streams are generated continuouslyin a rapid rate, the proposed method does not use a fixed static data set for fitness calculation. Instead, it extracts a small snapshot of the training example from the current part of data stream whenever data is required for the fitness calculation. The proposed method also builds rules for all the classes separately in a parallel independent iterative manner. This makes the proposed method scalable to the data streams and also adaptable to the concept drifts that occur in the data stream in a fast and more natural way without storing the whole stream or a part of the stream in a compressed form as done by the other rule based algorithms. The results of the proposed method are comparable with the other standard methods which are used for mining the data streams.

This paper investigates the effect of the bankruptcy reform act of 2005, formally known as the Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA), on entrepreneurial activity, measured by the likelihood of switching into self-employment. The analysis of this paper suggests that the probability of becoming self-employed with an unincorporated firm, regardless of firm size, declined after the bankruptcy reform act of 2005, and in this way entrepreneurial activity decreased. On the other hand, the probability of becoming self-employed with an incorporated firm was virtually unchanged by the revised bankruptcy law. However, the size of the incorporated firm makes a difference with small incorporated firms being different from medium- or large- incorporated firms. That is, the probability of becoming self-employed with a small incorporated firm declined after the bankruptcy reform act of 2005 in a similar manner to the probability of becoming self-employed with an unincorporated firm, but the probability of becoming self-employed with a medium- or large-size incorporated firm does not change significantly.

This article seeks to contribute to the evolving literature on the scholarship of teaching and learning. We do this by describing and then reflecting on what we have learned from a year-long freshman applied research seminar, “International Affairs Knowledge and U.S. Foreign Policy Opinions.” After surveying the literature on public opinion and U.S. foreign policy and materials on survey research and design, the students devised, pretested, administered (to 1300+ undergraduates), analyzed a survey and presented their findings. To do this, we had to provide them, in an abbreviated time frame, survey design, basic statistics as well as statistical software and work with them on how to summarize and present their findings. Their paper (Arbitter et al., 2010), in which some of their findings from the survey accord with the preexisting literature and much of it go beyond that, is one way of assessing our course. But, in this article, we discuss the ways such a course can meet many of the challenges held out to us as educators by such bodies as the Association of American Colleges and Universities (AAC&U), especially if those following this path take advantage of the lessons we have learned.

This dissertation aims to enhance the understanding of how the labor market functioned in the United States over a century ago. The first paper looks at the impact of technological change on labor demand while the second identifies a key way in which native-born U.S. workers avoided competition with immigrant labor (they moved up the occupational ladder towards higher status jobs). The third paper broadens the focus to look at the determinants of institutional quality, using nineteenth century England as the laboratory. The first paper presents a new picture of the labor market effects of technological change in pre-WWII United States. I show that, similar to the recent computerization episode, the electrification of the manufacturing sector led to a "hollowing out" of the skill distribution whereby workers in the middle of the distribution lost out to those at the extremes. To analyze the effects of electrification, a new dataset detailing the task composition of occupations in the United States for the period 1880-1940 was constructed using information about the task content of over 4,000 occupations from the Dictionary of Occupational Titles (1949). This unique data was used to measure the skill content of electrification in U.S. manufacturing. OLS estimates show that electrification increased the demand for clerical, numerical, planning and people skills relative to manual skills while simultaneously reducing relative demand for dexterity-intensive jobs making up the middle of the skill distribution. Thus, early twentieth century technological change was unskill-biased for blue collar tasks but skill-biased on aggregate. These results are in line with the downward trend in wage differentials within U.S. manufacturing up to 1950 and augment previous work on early twentieth century technological change which relied on more coarse and non-representative data to analyze this question. The OLS results are further reinforced by a 2SLS analysis, using, firstly, state-level regulation laws and, secondly, differences in state-level geography to instrument for electrification. The second paper adds to the historical literature on the labor market effects of immigration by using a new dataset detailing the task composition of occupations to identify whether native-born Americans in the period 1870-1930 responded to increased immigration by switching into occupations requiring skills in which they enjoyed a comparative advantage relative to immigrants, namely jobs involving communication and language skills as well as local knowledge. Following Peri and Sparber (2009), a model of task specialization in the presence of two types of workers, natives and immigrants, is first presented and is then estimated using a dataset which combines the decennial censuses for 1870-1930 with information about tasks performed in jobs from the Dictionary of Occupational Titles (1949). The findings from weighted least squares regressions show that a one percentage point increase in the share of the foreign born in the labor force yields up to a 0.49 percent increase in the relative supply of communication tasks among natives. The existing literature on this period had found quite large negative wage effects but has assumed a homogeneous labor-force—this paper suggests that while there may have been a negative wage effect of immigration on average, such an effect could be avoided and may not have existed for all worker types. Furthermore, because existing studies have not accounted for native specialization, the estimates were biased towards finding larger negative effects than actually existed. Geography created substantial differences in inequality in the English countryside in the pre-industrial era. The grain cultivating south-east had an elite of large farmers, along with a mass of landless laborers. The pastoral north and west had a mass of small independent farmers, and modest numbers of hired workers. Sokoloff and Engerman (2000), argue that similar differences in social structure between the plantation agricultures of the south of the Americas, and the family farms of the north, explain the large scale investment in human capital in the north, and its absence in the south. The elites in the south of the Americas allegedly had little incentive to foster public education, and the workers had little ability to acquire it on their own. What then was the effect of geography on educational attainment in pre-industrial England? We show here that while there were dramatic differences in levels of rural literacy across England in 1810 and later, they were independent of geographically created inequality in the countryside. Culture was the force that drove literacy, not geography. Geography is not destiny.

Given a set of objects P and a set of ranking functions F over P, an interesting problem is to compute the top ranked objects for all functions. Evaluation of multiple top-k queries finds application in systems, where there is a heavy workload of ranking queries (e.g., online search engines and product recommendation systems). The simple solution of evaluating the top-k queries one-by-one does not scale well; instead, the system can make use of the fact that similar queries share common results to accelerate search. This paper is the first, to our knowledge, thorough study of this problem. We propose methods that compute all top-k queries in batch. Our first solution applies the block indexed nested loops paradigm, while our second technique is a view-based threshold algorithm. We propose appropriate optimization techniques for the two approaches and demonstrate experimentally that the second approach is consistently the best. In addition, we demonstrate the utility of our approach in solving other complex queries that depend on the computation of multiple top-k queries. We show that our adapted methods for these complex queries outperform the state-of-the-art by orders of magnitude.

Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.

The United States has always taken great pride in its children's protection programs that have served as an example to developing countries. As a beacon of opportunity to poor and underdeveloped countries, the country is also known amongst third world nations, as the only hope to achieve social mobility because of its educational and labor market opportunities. Recently, in an apparent contradiction to its protection programs, social, and economic opportunities, the nation has instituted laws that undermine the welfare of children of immigrants and immigrant children by deporting people, regardless of their immigration status. Qualitative data were utilized to examine the impact of deportation on Dominican children and families left behind in the United States. The study's aim was to articulate the impact of parent's regurgitation/ejection on children's education, social integration, economic, and health and mental health status. The theories of social bulimic-exclusion and inclusion-, human waste, and toxic environment served as a framework for understanding how the society has become bulimic by both massively importing and deporting human capital. Social exclusion forces low-income and marginalized children to multi-levels of stigmatization by reinforcing the poverty cycle. Fragmented assimilation, a form of social inclusion, further compounds the exclusion of minority and immigrants because it does not fully integrate individuals into the fabric of society. The study found that U.S. born children left behind in a single parent household, ultimately face multi-levels of social exclusion. Hence, mandatory deportation negatively impacts children of deportees' social integration to mainstream society. Findings revealed that children of deportees experience tremendous sense of abandonment, insecurity, and isolation, which affect their educational attainment, socioeconomic status, social capital, and health mental status. In conclusion, social bulimic cannot co-exist with democracy because everyone is not fully included into mainstream society. What exists therefore, is an oligopoly democratic system that influences an oligarchy society in which a group of people--usually those in power--have control over the policy-making process and implementation with no accountability or assessment on collateral damages or the further social bulimization of children of deportees left behind in the United States.

This work focuses on some economic aspects of the two main waves of Italian emigration (1876-1913 and post-1945) and of the immigration of recent years. First, we examine the characteristics of migrants. Second, for the period 1876-1913 we investigate the determinants of emigration using a new dataset that allows us to control for regional fixed effects. In this context, the role of the networks formed by once migrated in shaping early twentieth-century Italian emigration results enhanced (30 per cent higher thanpreviously found). Third, we analyze the consequences of emigration for those left behind. A particular concern is whether emigration as a whole raised the living standards of those who stayed and whether it promoted interregional convergence within Italy. Our simulation exercises suggest that in the long run emigration accounted for a share of 4-5 per cent of the total per capita GDP growth; the contribution at the South was twofold with respect to the North. In the recent past Italy has become a country of netimmigration. We explore nowadays immigration in the light of our findings on earlier Italian emigration, focusing on the links with the economic activity, the labor market, the balance of payments, crime and public opinion, on the other.

Background Previous studies on childhood cancer and nuclear power plants (NPPs) produced conflicting results. We used a cohort approach to examine whether residence near NPPs was associated with leukaemia or any childhood cancer in Switzerland. Methods We computed person-years at risk for children aged 0–15 years born in Switzerland from 1985 to 2009, based on the Swiss censuses 1990 and 2000 and identified cancer cases from the Swiss Childhood Cancer Registry. We geo-coded place of residence at birth and calculated incidence rate ratios (IRRs) with 95% confidence intervals (CIs) comparing the risk of cancer in children born <5 km, 5–10 km and 10–15 km from the nearest NPP with children born >15 km away, using Poisson regression models. Results We included 2925 children diagnosed with cancer during 21 117 524 person-years of follow-up; 953 (32.6%) had leukaemia. Eight and 12 children diagnosed with leukaemia at ages 0–4 and 0–15 years, and 18 and 31 children diagnosed with any cancer were born <5 km from a NPP. Compared with children born >15 km away, the IRRs (95% CI) for leukaemia in 0–4 and 0–15 year olds were 1.20 (0.60–2.41) and 1.05 (0.60–1.86), respectively. For any cancer, corresponding IRRs were 0.97 (0.61–1.54) and 0.89 (0.63–1.27). There was no evidence of a dose–response relationship with distance (P > 0.30). Results were similar for residence at diagnosis and at birth, and when adjusted for potential confounders. Results from sensitivity analyses were consistent with main results. Conclusions This nationwide cohort study found little evidence of an association between residence near NPPs and the risk of leukaemia or any childhood cancer.

I examine the influence of employment status, marital status, and disabilities on the likelihood an adult individual lives in the same home as her parent(s). This living situation is termed coresidence. Based on the life course perspective, I hypothesize that those experiencing an unstable employment status, marital status, or disability status have higher odds of coresiding. I use data from the 2006, 2007, and 2008 American Community Surveys to evaluate these hypotheses. Logistic regression models indicate that unstable employment statuses, marital statuses, and disability are each associated with higher odds of coresiding, supporting my hypotheses. However these results vary based on the context of the individual. Specifically, as suggested by life course research, age, period, cohort effects must be taken into account. Together these results illustrate that the reasons for coresiding are varied, but instability in the life course is the key to understanding those reasons. Furthermore, I find prevailing norms and popular portrayals for describing coresiders to be inappropriate and simplistic.

This paper develops methods for assessing the sensitivity of empirical conclusions regarding conditional distributions to departures from the missing at random (MAR) assumption. We index the degree of non-ignorable selection governing the missingness process by the maximal Kolmogorov-Smirnov (KS) distance between the distributions of missing and observed outcomes across all values of the covariates. Sharp bounds on minimum mean square approximations to conditional quantiles are derived as a function of the nominal level of selection considered in the sensitivity analysis and a weighted bootstrap procedure is developed for conducting inference. Using these techniques, we conduct an empirical assessment of the sensitivity of observed earnings patterns in U.S. Census data to deviations from the MAR assumption. We find that the well-documented increase in the returns to schooling between 1980 and 1990 is relatively robust to deviations from the missing at random assumption except at the lowest quantiles of the distribution, but that conclusions regarding heterogeneity in returns and changes in the returns function between 1990 and 2000 are very sensitive to departures from ignorability.

This paper presents a new classification scheme for looking at industrial structure in the United States, and explores patterns of growth related to this new classification scheme, with an eye towards examining the role of human and creative capital in the growth process. The paper begins with a background discussion that is intended to motivate the need for the development of a new industry classification scheme. This section is followed by a description of the process of developing this new industry classification scheme, a description of regional trends in the clusters developed in this paper, and then by a section that reports on correlates of regional growth and indicators of human and creative capital. The paper ends with some concluding comments.

The objective of this study is to measure how language and geography-defined groups influence participation in public health insurance programs. The theoretical model in this paper shows how better information on insurance states, gleaned through language group contacts in one’s local area, can help individuals decide whether or not to take up a public benefit or remain uninsured. This study focuses on Medicaid-eligible adults and Medicaid/CHIP-eligible children who speak a non-English language at home, and uses pooled cross- sections of the 2008-2009 American Community Survey (ACS). Adapting an empirical method developed by Bertrand, Luttmer, and Mullainathan (2000), I define the main variable of interest as the interaction between contact availability, the density of an individual’s language group in an individual’s local area, and group quality, the information and preferences related to Medicaid that an individual’s language group may possess, as measured by the language group’s Medicaid take-up rate. The empirical framework also uses language group and Public Use Microdata Area (PUMA) fixed effects to control for observable and unobservable differences across language groups and local areas. The main results and sensitivity analyses strongly suggest that language and geography groups have a statistically significant impact on an individual’s probability of taking-up Medicaid/CHIP: For a policy change that increases Medicaid use by 1 percentage point, the network for these language groups will increase the probability of taking-up Medicaid by 10 percentage points for adults and 7 percentage points for children. As eligibility expands under the Affordable Care Act and more people in a given language group enroll in Medicaid/CHIP, the multiplier effect could lead to higher overall program participation than might otherwise might be anticipated in a scenario without non-market interactions. These results can also help policymakers target outreach funds towards uninsured non-English speakers who are eligible for public benefits.

This dissertation is the first in-depth study of the Mexican immigrant men who work as suburban maintenance gardeners throughout Los Angeles. Specifically, it is an interview-based study of social mobility among first-generation Mexican immigrant gardeners, or jardineros, and among their U.S.-born young adult children. In it, I ask several key questions, including: Is there a false distinction between entrepreneurship and manual labor? And what can account for the remarkable degree of social mobility that some Mexican immigrant gardeners and their young adult children can experience? After reviewing of the history of this immigrant occupational niche in Los Angeles, I find that self-employed jardineros can earn relatively high incomes through their maintenance gardening routes. The profitability of their routes rests upon several key factors, including: (1) self-exploitation; (2) the exploitation of co-ethnic employees; (3) the exploitation of unpaid family labor; (4) the routine practice of taking on “extra” jobs; and (5) the reselling of plants and other garden materials to clients at marked-up prices. Meanwhile, some of the factors that contribute to the social mobility of the children of jardineros include: (1) work ethic lessons; (2) the positive mechanism of keeping kids occupied with gardening work or under close supervision; (3) fathers' social capital and know-how; (4) being able to afford good schools; and (5) homeownership. The findings of this study challenge taken-for-granted notions about the fate of Mexican immigrant workers with low levels of human capital as well as the fate of their U.S.-born young adult children. Moreover, the suburban maintenance gardening niche combines elements of ethnic entrepreneurship and subjugated service work in a way that has not been accounted for in the literature on immigrant entrepreneurship.

Much recent work has focused on the influence of social capital on innovative outcomes. Little research has been done on disadvantaged groups who were often restricted from participation in social networks that provide information necessary for invention and innovation. Unique new data on African American inventors and patentees between 1843 and 1930 permit an empirical investigation of the relation between social capital and economic outcomes. I find that African Americans used both traditional, i.e., occupation-based, and nontraditional, i.e., civic, networks to maximize inventive output and that laws constraining social-capital formation are most negatively correlated with economically important inventive activity.

Using data from the Mexican Family Life Survey (MxFLS), a panel of Mexican individuals interviewed in 2002 and 2005, we analyze the characteristics of migrants from Mexico to the US relative to non-migrates and those who migrated and subsequently returned to Mexico. Using pre- and post-migration earnings and their earnings in the US from the American Community Survey (ACS), we characterize the selection of migrants on observable and non-observable characteristics. Merging the data with US American CommunitySurvey data, we can also measure the expected earnings premium of migration to the US and the earnings premium for those that returned to Mexico. We find that migrants respond to the expected earnings premium to migration, once we control for migration costs. Also, the structure of the premium across skill groups generates negative selection on average and it can explain selection on observable and unobservables. We also find that returnees are more positively selected over skills than migrants to the US. Initial poverty, old age and family ties are strong deterrents of migration to the US, once we account for the skill-specific migration premium. We also find a strong under-representation of college educated among migrants to the US, possibly a consequence of the fact that undocumented migration is not an attractive option for those individuals.