The major government commissions on immigration and crime in the early twentieth centuryrelied on evidence that suffered from aggregation bias and the absence of accurate population data,which led them to present partial and sometimes misleading views of the immigrant-native criminalitycomparison. With improved data and methods, we fi nd that in 1904, prison commitment rates for moreserious crimes were quite similar by nativity for all ages except ages 18 and 19, for which the commitmentrate for immigrants was higher than for the native-born. By 1930, immigrants were less likelythan natives to be committed to prisons at all ages 20 and older, but this advantage disappears whenone looks at commitments for violent offenses. The time series pattern refl ects a growing gap betweennatives and immigrants at older ages, one that was driven by sharp increases in the commitment ratesof the native-born, while commitment rates for the foreign-born were remarkably stable.

Publishing microdata raises concerns of individual privacy. When there exist outlier records in the microdata, the distinguishability of the outliers enables their privacy to be easier to be compromised than that of regular ones. However, none of the existing anonymization techniques can provide sufficient protection to the privacy of the outliers. In this paper, we study the problem of anonymizing the microdata that contains outliers. We define the distinguishabilitybased attack by which the adversary can infer the existence of outliers as well as their private information from the anonymized microdata. To defend against the distinguishabilitybased attack, we define the plain k-anonymity as the privacy principle. Based on the definition, we categorize the outliers into two types, the ones that cannot be hidden by any plain k-anonymous group (called global outliers) and the ones that can (called local outliers). We propose the algorithm to efficiently anonymize local outliers with low information loss. Our experiments demonstrate the efficiency and effectiveness of our approach.

There is limited research on the health of black immigrants. Recent research suggests that black immigrants from majority black regions of the world have better health than black immigrants from majority white regions of the world. However, these studies suffer from relatively small samples of black immigrants and limited data on the country of origin for black immigrants. Using data on specific country of origin taken from the 2000 Census of Population, the American Community Survey, and the March Current Population Survey, this study analyzes the importance of the racial context of an immigrants home country. The analysis in this study finds limited support for this argument. The results suggest that most of the differences in health among black immigrants are explained by health selection or country-specific differences in health production.

This paper documents the existence of a negative cross-sectional correlation between the price of livingspace as measured by rent per room and fertility using U.S. Census data over the period 19402000, the effectstrengthening from 1940 to 1970 and weakening thereafter. The negative correlation does not merely reflectthe tendency of larger families to locate within less-expensive areas of a given metropolitan area. Our studyfocuses on younger households, but analysis of completed fertility among older households reinforces thefindings for younger households. Estimates for 36 CMSAs using the American Housing Survey, which permitus to construct per square-foot measures of the price of living space, indicate that our findings are not merelyan artifact of larger families occupying houses with more rooms. Durbin-Wu-Hausman tests reveal littleevidence of endogeneity bias.

We investigate the influence of long-term unemployment on intergenerational earnings mobility among men. We defend our definition of the long-term unemployed as those who have zero annual earnings despite labor force participation and emphasize that previous studies have routinely excluded these workers. When this relatively small group is included, estimates of intergenerational mobility drop substantially. Consistent with a simple model, the drop is more pronounced when earnings are studied over a longer period. Moreover, while both earnings when working and spells of long-term unemployment are correlated across generations, the large drop we identify is mainly driven by the latter.

Data mining is one of the most exciting information science technologies in 21 century. It has become an important mechanism that is able to interpret the information hidden in data to human-understandable knowledge. It has been heavily involved in a wide range of profiling practices, these include finance, marketing, bioinformatics, genetic and medicine study, etc. The data to be studied, in terms of their properties and relations, can vary greatly form relational data, sequential data, graphs, models to classifiers, or the combinations of these. Different data mining methods and algorithms can be adopted to analyse different forms of data presentation so that the results are assured to be interpretable and understandable. Contrast patterns mining, more generally speaking, contrast groups mining or contrast sets mining, is one of the most challenging and vital techniques in data mining research. Patterns, or groups, are collections of items which satisfy certain properties which are of interesting information [1]. In other words, patterns represent different classes of objects, for example, American male and Russian male, or the income changes in 2004 through 2009. Contrast patterns are the conjunctions of attributes ad values that distinguish meaningfully in their distribution across groups [2]. Contrast patterns of various kinds differ greatly, for example, Pattern and rule based contrasts, Data cube contrasts, Sequence based contrasts, Graph based and Model based contrasts. However, there is no one specific paper or research lays the emphasis on comparing the similarities and differences between them. This research, therefore, is intended to make a clear and comprehensive comparison of different contrast patterns techniques. It firstly provides background knowledge, which gives a grounding in data mining; then annotations on relevant literature is shown along with the summary of deficiency in different algorithms being implemented in various contrast sets. The thesis also provides a critical survey of existing contrast patterns discovery methods. One of the major data sources used in the research is from Domiciliary Care SA, a government organization which takes care of disables and elderly people. Different algorithms discussed in this thesis will employ the same data sauce from Domiciliary Care SA to ensure that the results generated are comparable. A detailed description of the data is presented on Chapter 4.

We construct a dynamic general equilibrium model of cities and use it to estimate the effect of local agglomeration on per capita consumption growth. Agglomeration affects growth through the density of economic activity: higher production per unit of land raises local productivity. Firms take productivity as given; produce using a technology that has constant returns in developed land, capital, and labor; and accumulate land and capital. If land prices are rising, as they are empirically, firms economize on land. This behavior increases density and contributes to growth. We use a panel of U.S. cities and our model's predicted relationship among wages, output prices, housing rents, and labor quality to estimate the net effect of agglomeration on local wages.The impact of agglomeration on the level of wages is estimated to be 2 percent. Combined with our model and observed increases in land prices, this estimate implies that agglomeration raises per capita consumption growth by 10 percent.

We construct a dynamic general equilibrium model of cities with aggregatebalanced growth and use it to estimate the effects of local agglomeration on percapita consumption growth. Agglomeration affects growth through the densityof economic activity: higher production per unit of land raises local produc-tivity. Firms take productivity as given and produce with constant returns indeveloped land, physical capital and labor. Land and capital may be accumu-lated. If the relative price of new developed land is rising, as it is empirically,density is rising and contributes to growth. Our model predicts an empiricalrelationship between wages, housing rents, and labor inputs. We use this toestimate the net effect of agglomeration on local productivity with a panel ofUS cities. We estimate that doubling output in a location raises productiv-ity of firms in that location by 6.9%. Our preferred estimate indicates thatagglomeration increases the growth rate of per capita consumption by 10.7%.

High rates of crime and violence in public housing developments over the past two decades led to a dramatic shift in housing policy for low-income families. High-rise public housing developments were demolished and replaced with low-density mixed-income communities, forcing thousands of families to relocate into the private market. Underlying these changes is the notion that neighborhood disadvantage causes negative outcomes for children. The three empirical studies of this dissertation examine this relationship.

This article examines the contextual factors driving legal mobilization of workers in the United States through an analysis of national origin discrimination charges under Title VII of the 1964 Civil Rights Act (20002005). Consistent with previous studies, this analysis confirms that high unemployment levels and weak labor protections promote legal mobilization. The findings also highlight the positive role that civil society may play in promoting claims-making. I argue that nongovernmental organizations fill the gap in places where organized labor is weak, and may help support claims-making particularly in places with a larger vulnerable workforce. The article concludes by offering suggestions for a renewed sociolegal research agenda that examines the role of 501c(3) civil society organizations for the legal mobilization of an increasingly non-unionized and immigrant workforce.

The effects of two major demographic forces are traced between 1950 and 2040: the formation and aging of the baby boom generation and the reduction and subsequent return of large-scale immigration. These forces combine to mark several major turning points essential for understanding the changing urban condition. These include the depopulation of gray areas that spurred urban renewal in the 1950s, the gentrification initiated in the 1970s, and the collapse of apartment construction in the 1990s followed by its recovery in the 2000s. Looking forward, the authors address the substantial impact of settled immigrants who are now upwardly mobile. Finally, the authors consider the impacts of the sell-off of housing by the aging of the massive baby boom generation that is anticipated to take place beginning in 2020 and discuss whether the expected housing glut can be absorbed by a relatively smaller and less advantaged younger generation in the 2040s.

The Center for Budget and Policy Priorities estimates that some 46.6 million Americans, or roughly 16 out of every 100 people, lack health insurance. Of those who have no health coverage, about one-quarter have an income of $25,000 or less,1 which suggests that not only is the most costly medical care not being received, but neither is routine preventative care like physicals or checkups and follow-ups. Put in the simplest terms, doctors cost money and no dollars means no doctors, with the likely result over time of worsened health. When people are unwell, the cost gets passed on to the rest of society through lost productivity, a greater need for public services, longer wait times in emergency rooms, and, in the case of untreated mental health problems, compounded costs to public safety in the form of more police calls, court hearings, and incarcerations. Eventually, even burials can become a drain on public coffers.The issue at the heart of achieving truly universal health care hinges on answering the unknowns: How much will it cost to cover the health needs of people who currently dont have insurance? How will these costs be covered? Who are the uninsured? How did they become that way? And what role can they themselves play in getting well? Information is the key to getting closer to achieving accessible health coverage for everyone, and reducing the burdens that weigh on individuals and society as a whole. It stands to reason that if we can learn more about the uninsured, we can better answer the questions standing in way of policy and action...

Fishback and Thomasson use twentieth century data to examine how macroeconomic conditions just before and after birth affect infants when they become adults. Their analysis focuses on the worst downturn ever experienced in the United States — the Great Depression -- although they do not restrict their analysis to the Depression period. Merging data reported by respondents in decadal U.S. Census microdata from 1970-80 with information on the path of state per capita income during the individual's childhood years in the state of birth, the authors find that individuals born in poorer states (and in states with lower rates of income growth after their birth) are more likely to be high school dropouts, earn lower incomes, and have greater rates of disability than individuals born in states with higher incomes, even after controlling for socioeconomic characteristics such as race and education, year of birth, state of birth, and state of current residence. These results suggest that fluctuations in state-wide economic activity during the infant and early childhood period may have long-term consequences for an individual's socioeconomic status and health outcomes.

This paper examines the impact of traffic congestion on employment growth in large U.S. metropolitan areas. An historic highway plan and political variables serve as instruments for endogenous congestion. The results show that high initial levels of congestion dampen subsequent employment growth. This finding suggests that increasing the efficiency of public infrastructure can spur local economies. Counterfactual estimates show that the employment-growth returns from modest capacity expansion or congestion pricing are substantial.

With the completion of the U.S. National Historical Geographic Information System (NHGIS), it is now feasible to assemble a large dataset of historical census tract population statistics and boundary data in order to investigate patterns in long-term urban population trends. The present study makes use of this new resource to achieve a broad but concise overview of population trend patterns throughout major U.S. urban areas since 1950. This work thereby makes both methodological and substantive contributions to multiple fields of research, with much of the work dedicated to the development and assessment of new techniques to address two key methodological challenges. The first challenge is to construct a time series of census tract data, which requires linking data through time even where tract boundaries have changed. I present a few relatively simple areal interpolation techniques that can be used to address this problem. Two case studies indicate that a novel technique, cascading density weighting, should be effective both in the present setting and potentially elsewhere. The second methodological challenge is to identify an effective visualization strategy for investigating patterns in long-term trends. I present here a new conceptual framework that identifies a group of mapping techniques--trend summary maps--that should be most useful for visualizing patterns in trends. I provide an overview and assessment of several types of trend summary mapping techniques, and I introduce a novel technique, bicomponent trend mapping, which combines principal component analysis with bivariate choropleth mapping. This technique has several useful advantages not only for visualizing urban population trends but potentially in many other settings of spatio-temporal data visualization as well. Applying the new techniques to historical census tract data enables the central substantive contribution of this research: an overview of population trend variations throughout major U.S. urban cores. This overview supports the standard narrative of recent urban population dynamics--growth on the outskirts, decline in the cores, and some regrowth in centers--but it also reveals many regionally and locally unique patterns, indicating both divergence among cities and increasing heterogeneity within them.

This study tests the impact of household and demographic factors on the economic well-being ofthe farm and nonfarm self-employed using data from the Integrated Public Use Microdata Series.Parametric and nonparametric techniques are used to test for statistical differences in selfemploymentand household income levels. Further, household and demographic factors aretested for their effect on self-employment income using a censored tobit regression model. Thefarm self-employed report significantly higher levels of self-employment income. Results revealthat several household and demographic factors significantly impact self-employment incomelevels for the farm and nonfarm self-employed, with key differences in impacts.