The standard multilevel regressions that are widely used in neighborhood research typically ignore potential between-neighborhood correlations due to underlying spatial processes, and hence they produce inappropriate inferences about neighborhood effects. In contrast, spatial models make estimations and predictions across areas by explicitly modeling the spatial correlations among observations in different locations. A better understanding of the strengths and limitations of spatial models as compared with the standard multilevel model is needed to improve the research on neighborhood and spatial effects. This research systematically compares model estimations and predictions for binary outcomes between (distance- and lattice-based) spatial and the standard multilevel models in the presence of both within- and between-neighborhood correlations, through simulations. Results from simulation analysis reveal that the standard multilevel and spatial models produce similar estimates of fixed effects but different estimates of random effects variances. Both the standard multilevel and pure spatial models tend to overestimate the corresponding random effects variances compared with hybrid models when both nonspatial within-neighborhood and spatial between-neighborhood effects exist. Spatial models also outperform the standard multilevel model by a narrow margin in case of fully out-of-sample predictions. Distance-based spatial models provide additional spatial information and have stronger predictive power than lattice-based models under certain circumstances. These merits of spatial modeling are exhibited in an empirical analysis of the child mortality data from 1880 Newark, New Jersey.

In many ways Waco is on the verge of substantial growth. The city has most of the necessary ingredients to support extensive development. Nevertheless, its past economic performance has been lackluster, and its residents have few economic opportunities not only because of their low education attainment and lack of skills, but because of limited employment prospects. Waco houses outstanding training and education institutions and has a strong network of foundations and nonprofit organizations. Few communities can boast of having two two-year training institutions and a major university. Its location on I-35 is within easy driving distance of four of the fastest-growing metropolitan areas in the country— Dallas, Houston, San Antonio, and Austin—which makes it an excellent logistical distribution and supply center. And, the area offers numerous site-ready industrial and commercial properties. Still, economic growth in the Waco Metropolitan Statistical Area (MSA) has been sluggish because of the types of industries in the area and their flat competitive performance. The lack of a strong demand for workers increases the difficulties facing economically disadvantaged residents who have been not be able to establish strong work histories. Many of the graduates of the area’s university and colleges seek employment elsewhere because of better opportunities and wages. Moreover, our results . . .

There really is no way of understanding a historical-geographic process or phenomenom without considering both space and time. Supporting this afirmation is the fact that Historical Geographical Information Systems (HGIS) are increasingly utilized in the areas of history, geography, historical geography and historical cartography. However, aca- demic research about these systems is still incipient in Brazil. Therefore, this article aims to bring this country to the international context about the use of the HGIS, presenting the current status of the research and its uses in the world. Moreover, it also defines the Portuguese terminology for the system and seeks to establish the relationships between the historical areas mentioned above and the HGIS. We conclude that, although incipient, these systems can already assist studies in several segments that seek to understand spatial and temporal phenomena and processes.

Objectives. Although male height is positively associated with many aspects of mate quality, average height men attain higher reproductive success in US populations. We hypothesize that this is because the advantages associated with taller stature accrue mainly from not being short, rather than from being taller than average. Lower fertility by short men may be a consequence of their and their partner's lower scores on aspects of mate quality. Taller men, although they score higher on mate quality compared to average height men, may have lower fertility because they are more likely to be paired with taller women, who are potentially less fertile. Methods. We analyzed data from The Integrated Health Interview Series (IHIS) of the United States (N=165,606). Segmented regression was used to examine patterns across the height continuum. Results. On all aspects of own and partner quality, shorter men scored lower than both average height and taller men. Height more strongly predicted these aspects when moving from short to average height, than when moving from average to taller heights. Women of a given height who scored lower on mate quality also had shorter partners. Conclusions. Shorter men faced a double disadvantage with respect to both their own mate quality and that of their spouses. Scores of taller men were only marginally higher than those of average height men, suggesting that being tall is less important than not being short. Although effect sizes were small, our results may partly explain why shorter and taller men have lower fertility than those of average stature.

Child care subsidies play an important role in stabilizing parental employment and helping low-income families access quality and affordable child care options. However, low-income families on average only maintain subsidies for short periods of time, commonly known as spells. While there are several reasons a family may stop using subsidies, some policymakers and researchers have expressed concerns that program policies may create barriers to subsidy maintenance. With limited federal requirements under the Child Care and Development Block Grant, states have developed divergent policies for their state-based child care subsidy programs. To date, research on child care subsidies has mainly focused on the demographics differences between subsidy recipients and low-income families who do not use subsidies. Very little is known about the effects of states policies on whether families maintain subsidy coverage. Using data from the Urban Institutes CCDF Policies Database and the Administration for Children and Families CCDF Administrative Dataset this paper analyzes the effects of polices on average spell length and stability of child care spells from October 1, 2007 to September 30, 2010. In particular, the study focuses on policies related to whether families can count job search as an eligibility activity, the length of time between when a family must redetermine its eligibility, and requirements around reporting changes in income. To calculate the effect of policies on subsidy receipt, a difference-in-difference model was run using fixed state and time effects.

After a long, dormant period, recent attention has turned to a variety of measurement issues surrounding the concept of human capital. The traditional approach of rely entirely on measures of school attainment, while convenient, is almost certainly misleading. The availability of cognitive skills measures greatly improves on these measurements, but there remains also concern about other unmeasured factors including noncognitive skills. This paper considers alternative approaches to assessing the role of human capital on individual earnings and on economic growth.

This dissertation consists of two essays that study the determinants of geographical reallocation and their macroeconomic implications. In the first chapter (co-authored with Y. Fatih Karahan), we study the role of the aging population in the long-run decline of interstate migration in the United States. We argue that, in addition to a direct compositional effect on migration, the aging population has an indirect general equilibrium effect through the labor market. There is a positive composition externality of high-moving-cost workers on the local labor market: An increase in the fraction of high-moving-cost workers increases the local job-finding rate and reduces the migration rate of all workers. We label this effect as "migration spillovers." Our quantitative analysis suggests that population aging decreases the annual interstate migration rate by 0.9 percentage points, which accounts for 59 percent of the observed decline. Of this 0.9 percentage points, 78 percent is attributable to the indirect general equilibrium effect of the aging population and only 22 percent is due to the direct effect. In the second chapter (co-authored with Y. Fatih Karahan), we construct. . .

Averaged One-Dependence Estimators (AODE) is a popular and effective approach to Bayesian learning. In this paper, a new attribute selection approach is proposed for AODE. It can search in a large model space, while it requires only a single extra pass through the training data, resulting in a computationally efficient two-pass learning algorithm. The experimental results indicate that the new technique significantly reduces AODE’s bias at the cost of a modest increase in training time. Its low bias and computational efficiency make it an attractive algorithm for learning from big data.

This article focuses on interethnic relationship in a diaspora situation within a multicultural society. Using the example of the relationship between Polish and Ukrainian immigrants in the United States author try to provide an answer to the question whether and to what extent are the interethnic relations transferred into a diaspora situation? In the analysis particular attention is focused on the macro and microsocial determinants of mutual relationships as well as on the various elements of interethnic relations, including: spheres of cooperation, conflict areas, daily interactions, ethnic distance, mutual perceptions and spatial relations.The major descriptive and the basic definition term is „displaced borderland”. This concept is based on the assumption that the contacts and relations between these groups are very often determined by the European heritage.

Self-reported race is generally considered the basis for racial classification in social surveys, including the U.S. census. Drawing on recent advances in human molecular genetics and social science perspectives of socially constructed race, our study takes into account both genetic bio-ancestry and social context in understanding racial classification. This article accomplishes two objectives. First, our research establishes geographic genetic bio-ancestry as a component of racial classification. Second, it shows how social forces trump biology in racial classification and/or how social context interacts with bio-ancestry in shaping racial classification. The findings were replicated in two racially and ethnically diverse data sets: the College Roommate Study (N = 2,065) and the National Longitudinal Study of Adolescent Health (N = 2,281).

We show that close geographical proximity to mothers or mothers-in-law has a substantial positive effect on the labor supply of married women with young children. We argue that the mechanism through which proximity increases labor supply is the availability of childcare. We interpret availability broadly enough to include not only regular scheduled childcare during work hours but also an insurance aspect of proximity (e.g., a mother or mother-in-law who can to provide irregular or unanticipated childcare). Using two large datasets, the National Survey of Families and Households and the public use files of the U.S. Census, we find that the predicted probability of employment and labor force participation is 4-10 percentage points higher for married women with young children living in close proximity to their mothers or their mothers-in-law compared with those living further away. (C) 2013 Elsevier Inc. All rights reserved.

Previous literature shows that internal migration rates are strongly procyclical. This would seem to imply that geographic relocation does not help mitigate negative local economic shocks during recessions. This paper shows that this is not the case. I document that net in-migration rates decreased in areas more affected by the Great Recession. Using various IV strategies that rely on the importance of the construction sector and the indebtedness of households before the crisis, I conclude that internal migration might help to alleviate up to one third of the effects of the crisis on wages in the most affected locations. This is due to a disproportionate decrease in in-migration into those locations rather than an increase in out-migration. More generally, I document that differences in population growth rates across locations are mainly explained by differences in in-migration rates rather than in out-migration rates. I introduce a model to guide the empirical analysis of these facts and to quantify the spill-over effects caused by internal migration.

The recent release of the 1940 Census manuscripts enables the creation of longitudinal data spanning the whole of the twentieth century. Linked historical and contemporary data would allow unprecedented analyses of the causes and consequences of health, demographic, and economic change. The Census Bureau is uniquely equipped to provide high quality linkages of person records across datasets. This paper summarizes the linkage techniques employed by the Census Bureau and discusses utilization of these techniques to append protected identification keys to the 1940 Census.

Collaboration and data sharing have become core elements of biomedical research. At the same time, there is a growing understanding of privacy threats related to data sharing, especially when sensitive data from distributed sources become available for linkage. Statistical disclosure control comprises well-known data anonymization techniques that allow the protection of data by introducing fuzziness. To protect datasets from different types of threats, different privacy criteria are commonly implemented. Data anonymization is an important measure, but it is computationally complex, and it can significantly reduce the expressiveness of data. To attenuate these problems, a number of algorithms has been proposed, which aim at increasing data quality or improving efficiency. Previous evaluations of such algorithms lack a systematic approach, as they focus on specific algorithms, specific privacy criteria, and specific runtime environments. Therefore, it is difficult for decision makers to decide which algorithm is best suited for their requirements. As a first step towards a comprehensive and systematic evaluation of anonymity algorithms, we report on our ongoing efforts for providing an open source benchmark. In this contribution, we focus on optimal algorithms utilizing global recoding with full-domain generalization. We present a systematic evaluation of domain-specific algorithms and generic search methods for a broad set of privacy criteria, including k-anonymity, l-diversity, t-closeness and d-presence, and their use in multiple real-world datasets. Our results show that there is no single solution fitting all needs, and that generic search methods can outperform highly specialized algorithms.

The latest survey data from Pew Research Center and National Survey of Family Growth are applied to the 2010 Census Bureau numbers to project the U.S. Catholic population from 2010 to 2060. I hypothesize that immigration and the assimilation of Latinos plays a powerful role in the future growth or decline of the Catholic Church. Assimilation directly affects two main components of religious growth, fertility and switching, and influences exogamous marriage rates. I also model how changing immigration patterns will affect the future composition and growth of the U.S. Catholic Church. I used cohort component projection methods with three varied assumptions for each of the four components: immigration, fertility, exogamy, and switching. The three assumptions corresponded with low, medium, and high levels of assimilation. I presented results for each component separately to asses the individual impact, and then created combined scenarios with the lowest and highest levels of assimilation in the population. I find that high levels of assimilation are associated with lower levels of future growth for Latino Catholics and that by 2060, the growth of the Catholic will be approximately zero. Low levels of assimilation are associated with high sustained levels of growth for the Latino Catholic population across the entire projection period. Immigration, not affected by assimilation, had . . .

Grounded in the Sustainable Livelihoods framework, this study investigates the impact of climate change on various migration streams in Mexico, comparing migration from rural versus urban areas, international versus domestic moves, and first versus last moves within households. To measure the effects of climate change on these migration streams, a set of 17 climate change indices, proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI), was generated using daily temperature and precipitation data for 214 Mexican weather stations obtained from the Global Historical Climate Network-Daily (GHCN-D) data set. Cokriging as a method of spatial interpolation was employed to assign climate change index values to 111 Mexican municipalities, for which detailed migration histories and relevant sociodemographic characteristics were obtained from the Mexican Migration Project (MMP). Multi-level event history models were employed to estimate the impact of various climate change indices on household-level migration patterns from 1986 to 1999. The results indicate climate change more strongly impacts migration from rural compared to urban areas and international moves more so than domestic moves. In addition, within households, first moves are more sensitive to climate change than later moves. First international moves from rural areas are the most sensitive streams and are predominantly influenced by warming temperatures and decreasing rainfall. Significant socio-climatic interactions . . .

Researchers have good data on teachers' annual salaries but a hazy understanding of teachers hours of work. This makes it difficult to calculate an accurate hourly wage and leads policy makers to default to anecdote rather than fact when debating teacher pay. Using data from the American Time Use Survey, I find that teachers work an average of 34.5 hours per week on an annual basis (38.0 hours per week during the school year and 21.5 hours per week during the summer months). I find that when hours per week are accurately accounted for high school teachers earn in the range of 714 percent less than demographically similar workers in other occupations. However, elementary, middle, and special education teachers earn higher wages than demographically similar workers in other occupations.

Economics has been shown to be a relatively high earning college major, but geographic differences in earnings have been largely overlooked. This paper uses the American Community Survey to examine geographic differences in both absolute earnings and relative earnings for economic majors. We find that there are substantial geographic differences in both the absolute and relative earnings of economics majors even controlling for individual characteristics such as age and advanced degrees. We argue that mean earnings in specific labor markets are a better measure of the benefits of majoring in economics than simply looking at national averages.

This study identifies experiences contributing to the growing gender gap for minority students attaining a postsecondary education. Through a qualitative case analysis approach utilizing interviews, observations and document analysis, this study uncovers and interprets 17 student experiences in the Male Assistance Group at Morningside State University, through guided and specific research questions. The Psychosociocultural model of Castellanos and Gloria (2007), which highlights psychological, social and cultural experiences of Latino students, was used for data analysis. Thus, this methodology contributes to the understanding of the growing gender gap among Latino students attaining a postsecondary education. This study found that a most of the study participants in the Male Assistance Group perceive that there is a struggle for males compared to females in postsecondary education. Additionally, most member participants value and benefit from the Male Assistance Group.

At the regional scale, human capital and agglomeration forces are assumed to shape innovative capacity, but there are likely to be more direct channels like the development and commercialization of new products. This article examines the relationship between inventive activity and productivity at the level of U.S. metropolitan economies, using a patents database that links inventors to their residence. Examining the 1980 to 2010 period, we find robust evidence that patenting is correlated with and Granger causes higher productivity in metropolitan areas. A standard deviation increase in patents predicts a 6 percent increase in productivity over 10 years. Higher-quality patents enhance the effect. We attempt to identify a cross-sectional causal effect of patenting on productivity and wages using various historical instruments from the early 20th Century and controls for the selection of high-skilled workers into the area. Two-stage least squares regressions show an even stronger causal effect, but we can not rule out the possibility that historic advantages in manufacturing and scientific research continue to affect productivity through both patenting and some un-identified channel. We conclude that inventive activity plays an important role in regional prosperity.