This study attempts to find a correlation between commuting modes in Washington DC and characteristics of the city and the people that they serve. It investigates why some census tracts have experienced increases in the commuting share of alternative transportation, such as public transit, walking, and bicycling, while others haven’t. Findings demonstrate that demographic variables such as percent Hispanic and foreign born were the strongest predictors of change in commute mode share followed by distance to train station. Land use variables demonstrated weak correlations with variations in mode share due most likely to a lack of density gradient within the study area. The creation of variables to determine land use mix by census tract posed technical challenges as well. Recommendations include policy addressing rising demand for more diverse transportation systems be implemented and further research be conducted on creating more accurate land use variables to include in the model.

This paper presents a model of migration in which migration decisions are made with incompleteinformation on the labor market conditions at destination. It provides an explanationfor how differences in the level of information about the destination can bring about differencesin economic outcomes related to migration, such as the migration propensity and the return tomigration. The implications of the model show the conditions under which information positivelyand negatively affects these outcomes. Thus, the model can be used to explain a wide setof empirical ndings regarding the relationship between information and migration outcomes.2005 CPS data are used to estimate the econometric model. The estimation results suggest thatincreased access to information regarding destination labor markets increases ones likelihoodto migrate to another state. Furthermore, the ndings suggest that people who have more informationregarding the destination at the time of their migration decision on average experiencehigher returns to migration.

Over the past two decades, numerous states have adopted merit-based aid programs to subsidize higher education for in-state students. One of the main objectives of these programs is to increase the stock of educated labor in the state by retaining those whose education is subsidized. This study provides evidence on the extent to which such a program in Florida has affected the location decision of college-educated Florida natives. The analysis utilizes a difference-in-difference approach and data from the Census and American Community Survey (ACS). The results indicate that those eligible for the program are significantly more likely to locate in Florida after completing their education than those who were not eligible. These results are robust to a number of alternative specifications, including a comparison with neighboring states.

Surveys such as the American Community Survey provide samples that cover the countrys geography more evenly than highly clustered personal interview surveys. As a result, small areas such as census tracts will usually contain sample both within a tract and also within nearby tracts. The potential for usingnearby data to increase the efficiency of tract level estimates, with spatial models, is investigated for a group of census tracts in the Delmarva Peninsula. Using Census 2000 counts of housing unit vacancy and occupancy rates and corresponding 1990 data as covariates, small area estimates of vacancy rate and person per housing unit are made incorporating conditional auto-regressive (CAR) spatial models. The following issues related to making model-based estimates are addressed: fitting a parametric small area model to the housing unit data, comparison of CAR spatial models with a traditional hierarchal model without spatial components, the practical reduction in variance achieved by using a CAR spatial model and, lastly, the sampling properties of the small area estimates drawn from samples of 2000 census counts using both the CAR spatial model and the hierarchical model.

We use museum and other collection records to document large and extraordinarily rapid changes in the ranges and relative abundance of nine species of mammals in the northern Great Lakes region (white-footed mice, woodland deer mice, southern red-backed voles, woodland jumping mice, eastern chipmunks, least chipmunks, southern flying squirrels, northern flying squirrels, common opossums). These species reach either the southern or the northern limit of their distributions in this region. Changes consistently reflect increases in species of primarily southern distribution (white-footed mice, eastern chipmunks, southern flying squirrels, common opossums) and declines by northern species (woodland deer mice, southern red-backed voles, woodland jumping mice, least chipmunks, northern flying squirrels). White-footed mice and southern flying squirrels have extended their ranges over 225km since 1980, and at particularly well-studied sites in Michigan's Upper Peninsula, small mammal assemblages have shifted from numerical domination by northern species to domination by southern species. Repeated resampling at some sites suggests that southern species are replacing northern ones rather than simply being added to the fauna. Observed changes are consistent with predictions from climatic warming but not with predictions based on recovery from logging or changes in human populations. Because of the abundance of these focal species (the eight rodent species make up 96.5% of capture records of all forest-dwelling rodents in the region and 70% of capture records of all forest-dwelling small mammals) and the dominating ecological roles they play, these changes substantially affect the composition and structure of forest communities. They also provide an unusually clear example of change that is likely to be the result of climatic warming in communities that are experienced by large numbers of people.

In this paper, I develop a general equilibrium model of life cycle labor supply and use it to assess the effects of various features of social security programs on labor supply outcomes. An endogenous workweek and retirement margin, as well as endogenous human capital accumulation are the key features of the framework. I find that if one ignores the earnings dependence of social security benefits, one will over-estimate the distortionary effects of higher labor income taxes. However, once accounting for differences in both social security taxes and other labor taxes, I find that taxes account for a large share of the difference in aggregate hours worked between the U.S. and continental Europe. In addition to the generosity of social security programs, the rules regarding eligibility and working also account for a large share of this difference.

Rates of black-white intermarriage in the United States have increased over the last sixty years, yet they remain at levels below other types of interracial/interethnic unions. Prior research has centered largely on individual-level factors associated with the formation of such unions, culminating in three not entirely consistent micro-level theories: status-caste exchange, status homogamy, and educational/economic success. Most of this literature does not consider contextual-level characteristics, which I argue should have an independent effect on the incidence of these unions. My dissertation explores these issues with microlevel and multilevel models using data from the 2006 American Community Survey. I examine both micro and macro level predictors of the odds of white women marrying black men, and black women marrying white men in the metropolitan areas of the U.S. in 2006. In my level one analyses, six logistic regression equations are estimated to test the efficacy of the abovementioned microlevel theories of interracial marriage for black and white women. Status-caste exchange theory is accorded no support from my investigation, and status homogamy theory receives inconsistent support for white iv women. The results clearly convey that educational/economic success theory is applicable for predicting intermarriage among white and black metropolitan women in the United States. Among white women, those with high occupational status and high annual income are more likely to be intermarried than those with low occupational status and lower income. Among black metropolitan women, those with high occupational status and high levels of education are more likely to be intermarried than those with low occupational status and low levels of education. In my multilevel analyses, four hierarchical generalized linear models are estimated to evaluate the likelihood of intermarriage for white and black women living in metropolitan areas in the United States. My results show that context matters in predicting and understanding intermarriage for both groups of women. Both the individual-level characteristics of the women, as well as the contextual-level characteristics of their metropolitan areas, were shown in my equations to impact their likelihood of being intermarried. Future research would benefit from the inclusion of social context in any consideration of intermarriage, particularly through the use of multilevel modeling, which until now, has not been utilized by researchers in this area.

The skyline operator returns from a set of multi-dimensional objects a subset of superior objects that are not dominated by others. This operation is considered very important in multi-objective analysis of large datasets. Although a large number of skyline methods have been proposed, the majority of them focuses on minimizing the I/O cost. However, in high dimensional spaces, the problem can easily become CPU-bound due to the large number of computations required for comparing objects with current skyline points while scanning the database. Based on this observation, we propose a dynamic indexing technique for skyline points that can be integrated into state-of-the-art sort-based skyline algorithms to boost their computational performance. The new indexing and dominance checking approach is supported by a theoretical analysis, while our experiments show that it scales well with the input size and dimensionality not only because unnecessary dominance checks are avoided but also because it allows efficient dominance checking with the help of bitwise operations.

Social media platforms like Facebook and Reddit host thousands of user-governed online communities. These platforms sanction communities that frequently violate platform policies; however, public perceptions of such sanctions remain unclear. In a pre-registered survey conducted in the US, I explore bystander perceptions of content moderation for communities that frequently feature hate speech, violent content, and sexually explicit content. Two community-wide moderation interventions are tested: (1) community bans, where all community posts are removed, and (2) community warning labels, where an interstitial warning label precedes access. I examine how third-person effects and support for free speech influence user approval of these interventions on any platform. My regression analyses show that presumed effects on others are a significant predictor of backing for both interventions, while free speech beliefs significantly influence participants' inclination for using warning labels. Analyzing the open-ended responses, I find that community-wide bans are often perceived as too coarse, and users instead value sanctions in proportion to the severity and type of infractions. I report on concerns that norm-violating communities could reinforce inappropriate behaviors and show how users' choice of sanctions is influenced by their perceived effectiveness. I discuss the implications of these results for HCI research on online harms and content moderation.

We present a decomposition-based approach to managing probabilistic information. We introduce world-set decompositions (WSDs), a space-efficient and complete representation system for finite sets of worlds. We study the problem of efficiently evaluating relational algebra queries on world-sets represented by WSDs. We also evaluate our technique experimentally in a large census data scenario and show that it is both scalable and efficient.

A popular perception is that immigration causes higher crime rates. Yet, historical and contemporary research finds that at the individual level, immigrants are not more inclined to commit crime than the native born. Knowledge of the macro-level relationship between immigration and crime, however, is characterized by important gaps. Most notably, despite the fact that immigration is a macro-level social process that unfolds over time, longitudinal macro-level research on the immigration-crime nexus is virtually nonexistent. Moreover, while several theoretical perspectives posit sound reasons why over-time changes in immigration could result in higher or lower crime rates, we currently know little about the veracity of these arguments. To address these issues, this study investigates the longitudinal relationship between immigration and violent crime across U. S. cities and provides the first empirical assessment of theoretical perspectives that offer explanations of that relationship. Findings support the argument that immigration lowers violent crime rates by bolstering intact (two-parent) family structures.

Standardization and decomposition are established and widely used demographic techniques for comparing rates and means between groups with differences in composition. The difference in rates and means has heretofore been resolved in terms of the contribution of variables to compositional effects for each variable and an overall rate effect. This study demonstrates that the resolution of differences is attainable at the categorical level for both compositional effects and rate effects. Refinements to Das Gupta’s equations yield a complete decomposition because of the additivity of categorical compositional and rate effects. Other refinements allow the decomposition of polytomous variables. Extensions to the method provide for the decomposition of the standard deviation and the multivariate index of dissimilarity.

This book brings together prominent investigators to provide a comprehensive guide to doing life course research, including an “inside view” of how they designed and carried out influential longitudinal studies. Using vivid examples, the contributors trace the connections between early and later experience and reveal how researchers and graduate students can discover these links in their own research. Well-organized chapters describe the best and newest ways to: *Use surveys, life records, ethnography, and data archives to collect different types of data over years or even decades. *Apply innovative statistical methods to measure dynamic processes that result in improvement, decline, or reversibility in economic fortune, stress, health, and criminality. *Explore the micro- and macro-level explanatory factors that shape individual trajectories, including genetic and environmental interactions, personal life history, interpersonal ties, and sociocultural institutions.

Oregon, much like the nation, has historically had a paradoxical relationship tomigration. Oregon is famous for the Lewis and Clark expedition and the Oregon Trail.It is, and always has been, greatly affected and inspired by interstate migration yeteach successive generation has been less than welcoming to further migration.Today, Oregon is a desirable destination for retirementaged migrants for its scenicbeauty and mild seasons. This essay will use census data from 2000 to describe thecharacteristics associated with retirementage migrants to Oregon. Multinomiallogistic regression analysis is used to compare retirementaged Oregon inmigrantswith Oregon nonmovers, Oregon outmigrants and nonmovers and outmigrants ofthe states from which inmigrants to Oregon originated. In three cases, thecharacteristics effecting the decision to migrate are determined by comparing amigrant group population with its respective nonmover population. This essay is auseful tool for those seeking to understand the characteristics important toretirementage migration to Oregon as well as the factors influencing the migrationdecision.

Does emigration really drain human capital accumulation in origin countries? This paperexplores a unique household survey purposely designed and conducted to answer thisspecific question for the case of Cape Verde. This is allegedly the African country sufferingfrom the largest brain drain, despite also having a fast-growing stock of human capital. Ourmicro data enables us to propose a novel, explicit test of brain gain arguments accordingto which the possibility of own future emigration positively impacts educational attainmentin the origin country. The innovative empirical strategy we propose hinges on the idealcharacteristics of our survey, namely on full histories of migrants and on a new set ofexclusion restrictions. Our results point to a very substantial impact of the brain gainchannel on the educational attainment of those who do not emigrate. Alternative channels(namely remittances, family disruption, and general equilibrium effects at the local level)are also considered, but do not seem to play an important role. Our findings are robustto the choice of instruments and the empirical model.

2009 This dissertation consists of three essays describing relationships between migration behavior and labor market skills. The first essay investigates how the distribution of human capital across local labor markets evolves over time. Using a combination of U.S. data sets, I decompose generation-to-generation changes in local human capital into three factors: the previous generation's human capital, intergenerational transmission of skills from parents in the previous generation to their children, and migration of the children. I find evidence of regression to the mean of local skills at the state level and divergence of local skills at the commuting zone level. Labor market size, climate, local colleges, and taxes affect local skill measures. Skills move from urban to rural labor markets through intergener-ational transmission but from rural to urban labor markets through migration. In the second essay, I assess the effect of schooling on the propensity to migrate. Consistent with a large literature, I document positive correlations between schooling and migration. However, the observed correlation between schooling and migration might reflect mechanisms other than the effect of schooling on the costs or returns to migration. So, I estimate this effect by exploiting schooling variation due to compulsory schooling laws (CSLs). I estimate negative effects of schooling on migration among those with relatively little schooling. Results from CSL policy changes in 1947 and 1957 in the U.K. provide the strongest evidence of

Wars create dramatic changes in incentives for entrepreneurship and innovation, which need to be understood if we are to gauge the more subtle costs and benefits of armed conflict. This study examines the effect of the American Civil War on patterns of patenting and on the returns to inventive activity among patentees. The sample includes “great inventors” who achieved national eminence, patentees who created war-related inventions, all of the Confederate and Southern patentees, and a panel of over 8000 patents filed between 1790 and 1870. The empirical analysis focuses on the extent to which an entrepreneurial response to the armed conflict was associated with disproportionate changes in the wealth of inventors relative to the general population.