Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy-preserving paradigms of k-anonymity and l-diversity. k-anonymity protects against the identification of an individual's record. l-diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) l-diversification is solved by techniques developed for the simpler k-anonymization problem, causing unnecessary information loss. (ii) The anonymization process is inefficient in terms of computational and I/O cost. (iii) Previous research focused exclusively on the privacy-constrained problem and ignored the equally important accuracy-constrained (or dual) anonymization problem. In this article, we propose a framework for efficient anonymization of microdata that addresses these deficiencies. First, we focus on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and l-diversity models for the privacy-constrained (i.e., direct) and the accuracy-constrained (i.e., dual) anonymization problems. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multidimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the existing approaches in terms of execution time and information loss.

Family researchers and policy makers are giving increasing attention to theconsequences of immigration for families. Immigration affects the lives offamily members who migrate as well as those who remain behind and hasimportant consequences for family formation, kinship ties, living arrangements,and childrens outcomes. We present a selective review of the literatureon immigrant families in the United States, focusing on key research themesand needs. A summary of secondary data sets that can be used to study immigrantfamilies is presented as well as suggestions for future research in thisincreasingly important area of family research and policy.

Immigrants are a large and growing segment of the United States population. In the past 25 years, the United States has witnessed a 150 percent increase in the foreign-born population, with over 35 million foreign-born people living in the United States in 2005 (Vericker, Kuehn, and Capps 2007). Latino immigrants make up the majority of this growth; 53 percent of the foreign-born emigrated from Latin America (Larson 2004). As a result of this increase in the foreign-born population, the share of all US-born children with at least one immigrant parent has more than tripled. Currently, about one-fifth of all children are growing up in immigrant families. The rapid expansion of this population has led many to question how well youth with immigrant parents fare in early adulthood. As second-generation Latino youth continue to make up a larger share of our population, their educational and labor market successes and failures will play a large role in shaping our countrys economic future. Using the National Longitudinal Survey of Youth, 1997 cohort, this brief examines young adult connections to school and employment (or connectedness) between the ages of 18 and 24 for children of Latino immigrants (second generation) compared with children of native-born Latinos (third generation), children of native-born non-Hispanic blacks (blacks), and children of native-born non-Hispanic whites and other race groups (whites).[1] Second-generation Latinos make a fairly smooth transition to young adulthood and make a better transition than black and third-generation Latino youth. Between the ages of 18 and 24, second-generation Latinos are more often consistently-connected (56 percent) than third-generation Latino youth (44 percent) and blacks (42 percent). In contrast, second-generation Latinos are less likely to be consistently-connected than white youth (65 percent). Yet, after accounting for various factors including characteristics of the youth, their families, and their neighborhoods, second-generation Latinos are as likely to be consistently-connected as white youth. In addition, second-generation Latino youth who are consistently-connected have similar annual earnings at age 23 as white, black, and third-generation Latino youth who consistently connect, suggesting earnings parity by generation and race among those who consistently connect. While these results are encouraging, it is unclear what the future holds for second-generation Latinos. They are less likely than whites to attend postsecondary schools in young adulthood. Specifically, they are more likely to have a high school diploma as their highest degree and less likely to complete a four-year college degree than white youth; this disparity may create a large future earnings gap.

Research underscores social engagement in meaningful activities as key to health and well-being, with retirement age Americans at risk of isolation We draw on time-use data from the American Time Use Survey (2003-2006) to examine daily time spent in either paid or unpaid (volunteer) work for 5-year subgroups of American men and women ages 40-70. Some college education and low non-self earned income predict continued employment for older adults, as does being white. Having children still at home and/or caring for an aging relative reduces time spent in paid work for men and women in their 40s, and for women in their 50s. Volunteering predicts less time spent in employment for men in their 40s and 60s, and for women in their 40s and later 50s. Time spent in paid and volunteer work has increased for older Americans from 2003 to 2006. Health (2006 only) predicts more time in volunteering and employment.

Using Defense Department and U.S. Census Bureau data from 19972006, this paper analyzes the demographic differential between the U.S. Army and the population it represents. The shifting trends in the demographic composition are shown to primarily come from the military recruit labor market and only slightly from military separations. This labor market is analyzed to discover the relationship between the racial composition and the market factors: educational attainment, family income, unemployment, military conflict, race relations, and politics. Fieldwork to discover a relationship between recruiting practices and the demographic composition found no evidence of local racial targeting; however national policies targeting Hispanic underrepresentation are discussed. Time series trend analysis shows that the racial composition was very steady from 19972001, with African Americans being overrepresented, while white, Hispanic, and Asian cohorts remained underrepresented. Then, after the invasion of Afghanistan and Iraq, a varied transition began in accessions: African Americans decreased 43.1 percent, whites increased 21.9 percent and Hispanics showed little change from their steady increase. Furthermore, time series analysis indicates a flaw in the Defense Departments current annual reporting method, and recommendations for continued observation are made to preemptively detect an overcorrection that could cause another demographic differential.

In recent decades, population dynamics, have made definitions of what localities are rural or urban somewhat unclear. The vast majority of demographic work has simply used metropolitan classifications with various forms of a nonmetropolitan residual (e.g., adjacent to metro versus non-adjacent). The Office of Management and Budget (OMB) periodically redefines metropolitan areas, whichmakes temporal comparisons difficult. In fact, some demographers have offered the idea that, due to these shifting reclassifications, the so-called rural rebound is a misnomer, in that non-metropolitan counties that transitioned to metropolitan status were, in fact, already more urban than those that did not become reclassified asmetropolitan (Johnson et al 2005). This argument depends largely on the assumption of homogeneity in rural or urban character in those counties. Following argumentsby others (Wilkinson 1991; Isserman 2001; Bogue 1950), we take population and land use into account to examine whether these transitional counties were more or less urban than comparable others, all at the county level for the contiguous 48 states for 19702000. Our results show that adjacent non-metropolitan counties that were later reclassified as metropolitan were indeed characterized by a larger population and heavier urban land cover than those not making this transition. However, the results also show that metropolitan areas were also quite heterogeneous in terms of traditionally rural activities. A discussion of the homogeneity assumption in demographers conceptualization of metropolitan areas is included.

State support of public higher education has rapidly declined relative to totalstate spending. Much of this decline in support is due to the rapid growth in spending onsuch things as Medicaid. However, relative support of public higher education variessignificantly between states. This study applies Tandbergs (2009) fiscal policy frameworkcreated to explain state support of public higher education in order to evaluate the relationshipbetween various factors and states relative support of higher education. WhileTandbergs fiscal policy framework accounts for traditional economic and demographicfactors in explaining state support for higher education, it also draws attention to politicalinfluences as well including the impact of state-level interest groups. Using cross-sectionaltime-series analysis these relationships are explored over a 19-year period. The findingsprovide evidence of the significant impact of interest groups and politics on state fiscalpolicy in regard to higher education.

In this study, we measure the contribution of immigrants and their descendents to thegrowth and industrial transformation of the American workforce in the age of mass immigrationfrom 1880 to 1920. The size and selectivity of the immigrant community, as well astheir disproportionate residence in large cities, meant they were the mainstay of the Americanindustrial workforce. Immigrants and their children comprised over half of manufacturingworkers in 1920, and if the third generation (the grandchildren of immigrants) areincluded, then more than two-thirds of workers in the manufacturing sector were of recentimmigrant stock. Although higher wages and better working conditions might haveencouraged more long-resident native-born workers to the industrial economy, the scaleand pace of the American industrial revolution might well have slowed. The closing ofthe door to mass immigration in the 1920s did lead to increased recruitment of native bornworkers, particularly from the South, to northern industrial cities in the middle decades ofthe 20th century.

Entrepreneurial activity diers substantially across countries. Whilecultural dierences across countries have often been proposed as explana-tions, measuring a country's cultural characteristics suers from variousproblems. This paper oers new evidence on the relative importance ofthe cultural determinants of economic activity. We test the hypothe-sis that cultural factors inuence entrepreneurial behavior. To achievethis, we look at dierences in self-employment rates between immigrantgroups within the same market, which allows holding constant the insti-tutional environment. Using U.S. Census data for the year 2000, we ndsignicant dierences in the propensity to become self-employed acrossimmigrants from 148 countries which is in line with previous ndings.However, previous studies could not relate self-employment shares to self-employment shares in the immigrants' home-countries which contradicts acultural explanation. We improve over the existing literature by addition-ally accounting for determinants of self-employment in the immigrants'home countries. We nd evidence of a signicantly positive relationshipbetween self-employment rates of U.S. immigrants and entrepreneurial ac-tivity in their respective countries of origin that is robust to the inclusionof further variables. Our ndings suggest that cultural factors are to somedegree an expression of the behavior acquired under dierent institutionalenvironments.

Data mining aims at discovering useful patterns in large datasets. In this paper, we present a novel multivariate discretization method for finding association patterns based on clustering and genetic algorithm. This method consists of two steps. Firstly we adopt a density-based clustering technique to identify the regions that possibly hide the interesting patterns from data space. Confined to the data in these regions, we then develop a genetic algorithm to simultaneously discretize multi-attributes according to entropy criterion. The effectiveness of the proposed method is demonstrated with the experiment on a real data set.

Social learning is thought to be a key determinant of the demand for movies. This can be a double-edged sword for motion picture distributors, because when a movie is good, social learning can enhance the effectiveness of movie advertising, but when a movie is bad, it can mitigate this effectiveness. This paper develops an equilibrium model of consumers’ movie-going choices and movie distributors’ advertising decisions. First, we develop a structural model for studios’ optimal advertising strategies, taking into account the expected social learning process, and a model for consumers’ movie demand, given an initial indicator of movie quality (critic ratings) as well as an initial level of advertising. Consumers are assumed to be initially uncertain about movie quality. This, however, is resolved over time through Bayesian updating. That process depends upon (1) the number of previous viewers and (2) their ratings reported over the Internet. We then estimate the model parameters using data pertaining to 236 movies that were shown in theaters in the U.S. between January 1, 2002 and December 31, 2003. The empirical results show that social learning has a positive multiplier effect on movie advertising, with the multiplier effect being strongest for good movies. The simulation of the effects of social learning relative to a world without such learning shows that for good movies, producers spend substantially more on advertising when there is learning involved than they would if there were no learning. For bad movies, social learning makes much less difference to the level of advertising expenditures. Thus, the studio’s advertising spending is sensitive to both consumer uncertainty about movie quality and the speed with which potential movie-goers learn about movie quality.

Growth accounting exercises using standard human capital measures are limited in their ability to attribute causal effects and to explain growth. This paper develops a model of growth and schooling consistent with these decompositions but with less unexplained growth. The theory distinguishes between three different sources of education gains: (1) supply shifts, (2) skill-biased technical change increasing demand within industries/occupations, and (3) skill-biased technical change caused by the introduction of new skill-intensive industries/occupations. The third source leads to the large sectoral shifts and the largest growth effects. Quantitatively, schooling contributions account for 24 percent of wage growth, with both the direct (i.e., supply driven) causal contribution of schooling and the indirect causal (i.e., technology induced) contribution playing substantial roles.

Although a large social science literature has focused on pollution's influence on property values, less emphasis has been placed on residential mobility responses to pollution. However, the literature has not addressed two questions. First, when homeowners move and buy bigger homes, do they expose themselves to more ozone pollution? Second, do homeowners try to avoid extended ozone exposure by reducing the time between moves? If we can address each question using information about individual moving decisions, we can better understand the distributional consequences of environmental policy and learn more about the extent of ozone avoidance behavior. This study uses a new micro data set that combines Bay Area housing sales, buyer characteristics, and monitor-level ozone concentrations. Individual buyers can be followed as they move from their old house to a new house and individual houses can be tracked with the buyer's race and income and the house's year-by-year ozone concentrations. The research suggests that conditional on moving and buying more housing services, poor minority homeowners frequently for the additional housing services by taking on more ozone exposure. There is also evidence that after people move, they reduce the time between moves as ozone pollution gets worse relative to other Bay Area houses. As a result, the scope of ozone averting actions homeowners use to avoid ozone exposure may extend beyond common day-to-day responses such as reducing the time spent outdoors.

U.S. Census data show that approximately 40 million Americans move each year,raising questions about the role of residential mobility in determining observedpollution exposure patterns. The literature in this area continues to be contested, therelationship between household sorting and exposure is still not well understood,and some aspects of these decisions remain unexplored. We offer new assessmentsof residential mobility explanations with respect to ozone pollution using a uniquedata set that combines information from repeat real estate transactions by the sameSan Francisco Bay Area home buyers. We find that poor/minority households(blacks/Hispanics in particular) who buy more housing services do take on moreozone pollution. Observed housing choices suggest that this may be a result of therates at which ozone and housing services are traded in the market being differentfor poor minorities and white homeowners.

These essays examine two factors that affect the marriage, family, and work outcomes of men and women: variation in sex ratios, defined as the number of men in a specified population divided by the number of women, and access to oral contraceptives during late adolescence. Fluctuations in birth rates over time, in conjunction with the observation that women generally marry slightly older men, provide a plausibly exogenous source of moderate variation in sex ratios. In chapter one I use this to investigate the effects of sex ratio fluctuations on basic marriage market outcomes in the U.S. over recent decades. Although I do not find robust evidence that this variation affects the likelihood or average timing of marriage, I find that it is related to the attributes of the spouse to whom one is currently married. When the oral contraceptive was released in 1960, single women under the age of 21 could not legally obtain it in most states. Arguably exogenous changes in state laws later extended access to single women in late adolescence. Chapter two investigates whether having had access to oral contraceptives in late adolescence affects the time use of mothers later in life. Mothers who had access spend a statistically significant 1.6 fewer hours per week in active childcare than their counterparts without access, controlling for the number of children. The largest portion of the difference in childcare is accounted for by a 1.3 hour decrease in time spent on basic childcare tasks each week. Theory predicts that access to oral contraceptives during late adolescence should lead to in increase in the extent to which people with good labor market prospects tend to marry each other. In chapter three I test this with a standard differences-indifferences strategy. Across a wide variety of specifications I find only modest evidence that women's access to the pill affects the pattern of matches in the marriage market.

Geographical information systems (GIS) can be powerful tools for historical science: for presentation, exploration, analysis and reconstruction, as a portal to historical information and to facilitate research. In fact, very limited use has actually been made of them. Where GIS has been used, it has been outside traditional historical departments, in economics, geography or sociology. In general, history departments remain very reluctant to include modern research methods such as GIS in their curricula. As a consequence, there is no place in history.

Using the data released by the U.S. Census Bureaus American Community Survey for 2007, this report analyzes demographic and socio-economic indicators for Latina women in New York City. While in 2007, there was little difference in the sex breakdown between Latinos and non-Latinos in New York City (48% of all Latinos were women while 47.6% of non-Latinos were women), Latinas demonstrated very different social, demographic, and economic profiles from their non-Latina counterparts. There were also important differences among Latinas in New York City by national origin.

This paper documents a relationship between household demographics and substitution between homeand market production and proposes it as an explanation of low frequency movements in consumptionexpenditures relative to GDP, in particular the secular increase in consumption?s share in GDP since the1980s. A growth model with an endogenously evolving allocation between home and market production isshown also to imply a drift in the share of market consumption to market output. Data from the ConsumerExpenditure Survey and other sources con?rm these eects in the cross-section and over the period from1984-2004, and a quantitative exercise matches them up with demographic changes over the entire 20thcentury as documented in Census data. The implication is that periods in which hours of work per capitaincrease involve arti?cial increases in measured consumption, and an upward drift in the share of consumptionexpenditures in GDP. In addition, ?true?consumption exhibits a closer link to long-term productivity trends.