By various measures, Americans today appear more socially divided along partisan lines than at any point in recent history. This polarization extends to partisans evaluation of places, with Democrats more likely than Republicans to prefer denser and more racially diverse and urban communities. Scholars therefore have speculated that Americans are also increasingly geographically sorting by partisanship. In two survey experiments, we find that the two parties differ over aspects of what makes an ideal community, but both value a set of community traits associated with neighborhood quality. We show that these quality preferences, combined with resource constraints, leave Americans with few opportunities to migrate along party lines, whether deliberately or inadvertently. As a result, Republicans and Democrats are, on average, not migrating to more politically distinct communities. On a key life decision that shapes American political geography, practical constraints prevent the translation of polarized attitudes into polarized behavior.

We examine causes and consequences of relative income within households. We show that the distribution of the share of income earned by the wife exhibits a sharp drop to the right of 12 , where the wife’s income exceeds the husband’s income. We argue that this pattern is best explained by gender identity norms, which induce an aversion to a situation where the wife earns more than her husband. We present evidence that this aversion also impacts marriage formation, the wife’s labor force participation, the wife’s income conditional on working, marriage satisfaction, likelihood of divorce, and the division of home production. Within marriage markets, when a randomly chosen woman becomes more likely to earn more than a randomly chosen man, marriage rates decline. In couples where the wife’s potential income is likely to exceed the husband’s, the wife is less likely to be in the labor force and earns less than her potential if she does work. In couples where the wife earns more than the husband, the wife spends more time on household chores; moreover, those couples are less satisfied with their marriage and are more likely to divorce. These patterns hold both cross-sectionally and within couples over time.

In this paper we provide an overview of the patterns of intergenerational proximity and coresidence of adult children and their mothers in the U.S., using data from the National Survey of Families and Households (NSFH) and the U.S. Census. We highlight the importance of three specification and sample choices in the analysis. First, most previous studies consider coresidence to be the limiting case of proximity, using Tobit, ordered logit, or ordered probit specifications. We argue that proximity and coresidence are qualitatively different, and show that the multinomial logit provides a better representation of the patterns in the data. Second, we argue that substantial differences in the correlates of proximity by gender and marital status indicate the importance of modeling these categories separately. Third, the NSFH allows us to consider the proximity of couples to both his mother and her mother. This information is rarely available in survey data but is important for complete analyses. Our results show that education and age are the most robust predictors of proximity: college graduates are less likely to live near their . . .

Data fragmentation has been proposed as a solution for protecting the confidentiality of sensitive associations when releasing data for publishing or external storage. To enrich the utility of data fragments, a recent approach has put forward the idea of complementing a pair of fragments with some (non-precise, hence loose) information on the association between them. Starting from the observation that in presence of multiple fragments the publication of several independent associations between pairs of fragments can cause improper leakage of sensitive information, in this paper we extend loose associations to operate over an arbitrary number of fragments. We first illustrate how the publication of multiple loose associations between different pairs of fragments can potentially expose sensitive associations, and describe an approach for defining loose associations among an arbitrary set of fragments. We investigate how tuples in fragments can be grouped for producing loose associations so to increase the utility of queries executed over fragments. We then provide a heuristics for performing such a grouping and producing loose associations satisfying a given level of protection for sensitive associations, while achieving utility for queries over different fragments. We also illustrate the result of an extensive experimental effort over both synthetic and real datasets, which shows the efficiency and the enhanced utility provided by our proposal.

Todays great paradox is that we feel the impact of technology everywhere in our cars, our phones, the supermarket, the doctors office but not in our paychecks. In the past, technological advancements dramatically increased wages, but during the last three decades, the median wage has remained stagnant. Machines have taken over much of the work of humans, destroying old jobs while increasing profits for business owners. The threat of ever-widening economic inequality looms, but in Learning by Doing, James Bessen argues that it is not inevitable. Workers can benefit by acquiring the knowledge and skills necessary to implement rapidly evolving technologies, however, this can take years, even decades. Technical knowledge is mostly unstandardized and difficult to acquire, learned through job experience rather than in the classroom. The right policies are necessary to provide strong incentives for learning on the job, but politically influential interests have moved policy in the wrong direction recently. Based on economic history as well as analysis of todays labor markets, the book shows a path toward restoring broadly shared prosperity.

An analysis by the Institute for Women's Policy Research (IWPR) finds that approximately 41 percent of all workers (45 percent of private sector workers, compared with 17 percent of public sector workers) living in Louisiana lack even a single paid sick day. this lack of access is even more pronounced among low-income and part-time workers. Access to paid sick days promotes safe and healthy work environments by reducing the spread of illness and workplace injuries, reduces health care costs, and supports children and families by helping parents meet their children's health needs. This briefing paper presents estimates of access to paid sick days in Louisiana by sex, race and ethnicity, occupation, hours worked, and personal earnings through analysis of government data sources, including the 2011-2013 National Health Interview Survey (NHIS), and the 2013 American Community Survey (ACS).

Medicare aimed to improve access and reduce the out-of-pocket costs of medical care for individuals over the age of 65, many of whom had low incomes, high medical expenses, and no health insurance. On July 1, 1966, all 19 million Americans aged 65 years and older became eligible for Medicare. Within a year after the rapid implementation of Medicare, the personal bankruptcy rate began a gradual decline that lasted until 1974. The objective of this dissertation is to determine the extent to which the implementation of Medicare contributed to the decline in the bankruptcy rate. I use publicly-available aggregate data to determine whether the implementation of Medicare is associated with a structural break in state bankruptcy rates, controlling for other determinants of the bankruptcy rate such as state demographics and state legal variables. I find that, with a three-year lag, an increase in elderly enrollment in Medicare Part A is associated with a decrease in personal bankruptcy rates; however, this decrease is smaller when Medicare is simultaneously increasing access to care. The implementation of Medicare is expected to decrease the bankruptcy rate by decreasing the fraction of the population with medical debt, but this channel cannot be investigated using aggregate data. I therefore augment the publicly-available aggregate data with new micro-data collected from original bankruptcy court case files from the 1960s and 1970s, which I link to other documents to determine age of the petitioner. The new data include detailed information about petitioners and their debts. I use difference-in-difference regressions to measure the effect of the implementation of Medicare on medical debt. I find that the implementation is associated with a decrease in the prevalence of medical debt at filing, but Medicare eligibility is associated with an increase in the size of medical debt for these petitioners. Quantile regression results show that filers after the implementation were younger and had higher incomes than filers before the implementation.

Using a comprehensive panel dataset on U.S. households, we study the effects of the 2005 Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA), the most substantive reform of personal bankruptcy in the United States since the Bankruptcy Reform Act of 1978. The 2005 legislation introduced a means test based on income to establish eligibility for Chapter 7 bankruptcy and increased the administrative requirements to file, leading to a rise in the opportunity cost and, especially, the financial cost of filing for bankruptcy. We study the effects of the reform on bankruptcy, insolvency, and foreclosure. We find that the reform caused a permanent drop in the Chapter 7 bankruptcy rate relative to pre-reform levels, owing to the rise in filing costs associated with the reform, which can be interpreted as resulting from liquidity constraints. We find that the decline in bankruptcy filings resulted in a rise in the rate and persistence of insolvency as well as an increase in the rate of foreclosure. We find no evidence of a link between the decline in bankruptcy and a rise in the number of individuals who are current on their debt. We document that these effects are concentrated at the bottom of the income distribution, suggesting that the income means tests introduced by BAPCPA did not serve as an effective screening device. We show that insolvency is associated with worse financial outcomes than bankruptcy, as insolvent individuals have less access to new lines of credit and display lower credit scores than individuals who file for bankruptcy. Since bankruptcy filings declined much more for low-income individuals, our findings suggest that, for this group, BAPCPA may have removed an important form of relief from financial distress.

What has shaped the US urban landscape? Probably, different forces worked at different magnitudes, times, and locations. In this paper we develop a methodology to disaggregate some of the engines of US city growth over time and across space. To understand the results we propose a visualization approach based on what we term storyboards, which create an intuitive and dynamic narrative on the effect of several factors of urban success. This allows us to show that the role of growth engines differs greatly: the rise and decline of manufacturing were very localized; industrial specialization is counterproductive, particularly so in the 1990s; service sectors used to be a consumption amenity, but now serve as a production amenity; and highly educated cities unambiguously and increasingly attract firms in any part of the US. We also note that the arguments for our visualization and its lessons bear implications for visualization in the social sciences beyond this particular example.

Petition canvassers are political recruiters. Building upon the rational prospector model, we posit that politically rational recruitment is dynamic (Bayesian and time-conscious), spatial (conducted within a landscape that constrains the recruiter) and social (in which strategies are conditioned on social relations between canvasser and prospect). Incorporating these insights into a formal model, we predict that dynamic, social canvassers will exhibit homophily in their canvassing preferences (to the point of following geographically and politically inefficient paths), will alternate between door-to-door and attractor strategies based upon systematic geographical variation, and will adjust their strategies midstream (mid-petition) based upon experience. Utilizing newly developed methods for characterizing signatory address sequences, we test these hypotheses using geocoded signatory lists from two petition campaignsthe 2005-2006 anti-Iraq War mobilization in Wisconsin, and the 1839 antislavery petition campaign in New York City. Canvassers in these campaigns exploited homophily in their work and systematically adjusted their behavior mid-petition in response to evidence.

Syntactic data anonymization strives to (i) ensure that an adversary cannot identify an individual's record from published attributes with high probability, and (ii) provide high data utility. These mutually conflicting goals can be expressed as an optimization problem with privacy as the constraint and utility as the objective function. Conventional research using the k-anonymity model has resorted to publishing data in homogeneous generalized groups. A recently proposed alternative does not create such cliques; instead, it recasts data values in a heterogeneous manner, aiming for higher utility. Nevertheless, such works never defined the problem in the most general terms; thus, the utility gains they achieve are limited. In this paper, we propose a methodology that achieves the full potential of heterogeneity and gains higher utility while providing the same privacy guarantee. We formulate the problem of maximal-utility k-anonymization by freeform generalization as a network flow problem. We develop an optimal solution therefor using Mixed Integer Programming. Given the non-scalability of this solution, we develop an O(k n2) Greedy algorithm that has no time-complexity disadvantage vis-á-vis previous approaches, an O(k n2 log n) enhanced version thereof, and an O(k n3) adaptation of the Hungarian algorithm; these algorithms build a set of k perfect matchings from original to anonymized data, a novel approach to the problem. Moreover, our techniques can resist adversaries who may know the employed algorithms. Our experiments with real-world data verify that our schemes achieve near-optimal utility (with gains of up to 41%), while they can exploit parallelism and data partitioning, gaining an efficiency advantage over simpler methods.

Immigrants continue to settle in metropolitan areas across the United States and bring significant changes to various urban labor markets. Using American Community Survey (ACS) data for 2007 and 2011, we trace the employment outcomes of immigrants compared to native-born workers before and after the recent Great Recession across the 100 largest metropolitan areas and examine individual-level and metropolitan-level factors that shape their employment outcomes. We find that low-skilled workers in general and immigrants without English proficiency and those who are new entrants or earliest arrivals are harder hit in the recession. Latino immigrants and black workers fare worse in areas with high immigrant concentration. Latino immigrants experience employment gains, however, in the South, large urban economies, as well as new immigrant gateways. Asian immigrants see declines in employment likelihood in areas with a large construction sector, while areas with a large trade sector hurt native-born white workers.

I exploit the passage of wrongful discharge laws by U.S. state courts that allow workers to sue employers for unjust dismissal as an exogenous increase in employee firing costs. I find that firms reduce debt ratios following the adoption of these laws, and this result is strongest for subsamples of firms that experience larger increases in expected firing costs. Following the passage of these laws, firms also increase cash holdings, firms save more cash out of cash flows, and investors place a higher value on each additional dollar of cash holdings. Overall, my results indicate that employee firing costs can have an important impact on corporate financial policy decisions.

Objectives: Previous work has not fully explored the role of race in the health of immigrants. We investigate race and ethnic differences in self-rated health (SRH) among immigrants, assess the degree to which socio-economic characteristics explain race and ethnic differences, and examine whether time in the U.S. affects racial and ethnic patterning of SRH among immigrants. Methods: Data came from the 2012 National Health Interview Survey (N=16, 288). Using logistic regression, we examine race and ethnic differences in SRH controlling for socioeconomic differences and length of time in the country. Results: Hispanic and non-Hispanic Black immigrants were the most socio-economically disadvantaged. Asian immigrants were socio-economically similar to non-Hispanic White immigrants. Contrary to U.S. racial patterning, Black immigrants had lower odds of poor SRH than did non- Hispanic White immigrants when socio-demographic factors were controlled. When length of stay in the U.S. was included in the model, there were no racial or ethnic differences in SRH. However, living in the U.S. for 15 years and longer was associated with increased odds of poor SRH for all immigrants. Conclusions: Findings have implications for research on racial and ethnic disparities in health. Black-White disparities that have received much policy attention do not play out when we examine self-assessed health among immigrants. The reasons why non-Hispanic Black immigrants have similar self-rated health than non-Hispanic White immigrants even though they face greater socio-economic disadvantage warrant further attention.

This study tackles the description of the size distribution of urban employment centers or, in other words, the size of areas within cities with significantly high densities of workers. Certainly, there exists a branch of urban economics that has paid substantial attention to urban employment centers, but the efforts have been focused on identification methodologies. In this paper we build on such body of research and combine it with insights from the latest contributions in the sister subfield of city size distributions to push the agenda forward in terms of the understanding of these phenomena. We consider the 359 Metropolitan Statistical Areas (MSAs) in the United States in the year 2000 and reach three main conclusions: first, employment center sizes are more unevenly distributed than city sizes; second, the two functions that best describe city size distributions, namely the lognormal and the double Paretolognormal, also offer a good fit for the case of centers, particularly the latter; and third, several interesting statistically significant relationships (correlations) between variables related to centers and MSAs are deduced. Further experiments with a different technique of center identification suggest that the results are fairly robust to the method of choice.

In this article, we consider the problem of estimating the population mean of a study variable in the presence of non-response in amail survey design. We introduce calibrated estimators of the population mean of a study variable in the presence of a known auxiliary variable. Using simulation the proposed calibrated estimators of population mean are compared to the Hansen and Hurwitz (1946) estimator under different situations for fixed cost as well for fixed sample size. The results are then extended for the use of multi-auxiliary information and stratified random sampling. We consider the problem of estimating the average total family income in the US in the presence of known auxiliary information on total income per person, age of the person, and poverty. We compute the relative efficiency of the proposed estimator over the Hansen and Hurwitz (1946) estimator through the use of large real datasets. Results are also presented for sub-populations consisting of whites, blacks, others, and two or more races in addition to considering them together in a population

Massive open online courses (MOOCs) are often characterized as remedies to educational disparities related to social class. Using data from 68 MOOCs offered by Harvard and MIT between 2012 and 2014, we found that course participants from the United States tended to live in more-affluent and better-educated neighborhoods than the average U.S. resident. Among those who did register for courses, students with greater socioeconomic resources were more likely to earn a certificate. Furthermore, these differences in MOOC access and completion were larger for adolescents and young adults, the traditional ages where people find on-ramps into science, technology, engineering, and mathematics (STEM) coursework and careers. Our findings raise concerns that MOOCs and similar approaches to online learning can exacerbate rather than reduce disparities in educational outcomes related to socioeconomic status.

Despite the high levels of metropolitan-area segregation that Latinos experience, there is a lack of research examining the effects of segregation on Latino socioeconomic outcomes and whether those effects differ from the negative effects documented for African Americans. We find that segregation is consistently associated with lower levels of educational attainment and labor market success for both African American and Latino young adults compared with whites, with associations of similar magnitudes for both groups. One mechanism through which segregation may influence outcomes is the difference in the levels of neighborhood human capital to which whites, Latinos, and African Americans are exposed. We find that higher levels of segregation are associated with lower black and Latino neighborhood exposure to residents with college degrees, relative to whites. We also find support for other commonly discussed mechanisms, such as exposure to neighborhood violent crime and the relative proficiency of the closest public school.

Undergraduate students at Queen’s University were surveyed and interviewed to examine the factors behind engineering program selection and academic transfer to engineering. The surveys were completed by 416 2nd-4th year undergraduate engineering students examining: reasons for entering engineering, demographics, personal knowledge of engineers, and their understanding of engineering programs and the engineering profession. The survey was also used to recruit students who had undergone an academic transfer for interview. Seven interviews were completed with these students, examining the reasons for their transfer, as well as the factors behind their initial program selection. Four primary factors were identified as important to the selection of engineering programs: interest in the subject matter, strength in prerequisites, knowing an engineer, and vocational factors. It was found that knowing an engineer correlated with greater reported knowledge of the engineering profession, but not with a greater knowledge of engineering programs. Small correlations were found between two survey . . .

The thesis consists of three chapters in form of separate papers. Two of the chapters focus on family labor supply and its effects on the business cycle behavior of hours worked. The third chapter analyses the sources of economic growth in Belarus. The first chapter, Real Business Cycles in Model Economies with a Two-Person Household, studies model economies with a representative two-person household. The standard RBC model cannot replicate the negative correlation of hours with aggregate labor productivity which we observe in the U.S. data (-0.18). I show that extending the standard model to include a two-person representative household, home production and extensive margin decision on labor market participation can generate this negative correlation. In the second chapter, Accounting for Labor Productivity Puzzle, I show that an increase in the share of two-earner households in the U.S. and corresponding changes in labor supply behavior have implications for aggregate business cycles. In particular, it may explain why in the recent decades aggregate labor productivity in the U.S. became countercyclical (labor productivity puzzle). I build a model with heterogeneous one- and two-earner households and aggregate technology shocks, and calibrate it to the current U.S. data. I impose the household structure change in the model and show that the behavior of labor productivity changes from procyclical to countercyclical. The third chapter, Belarusian Economic Growth Decomposition, written in co-authorship with Dzmitry Kruk, investigates the sources of the extraordinary growth Belarus experienced in 2000’s. Belarus stands out from the rest of post-Soviet transitional countries. The economic reforms in the country were limited, and Belarusian economy does not rely on natural resource rents. We carefully reconstruct the capital series for Belarus and perform the growth accounting. We find that Belarus mainly benefited from extensive growth through capital accumulation which quickly depleted its potential.