The United States is experiencing a major public health problem relating to increasing levels of excess body fat. This paper is about the relationship in the United States between trends in the distribution of body mass index (BMI), including trends in overweight and obesity, and demographic change. We provide estimates of the counterfactual distribution of BMI that would have been observed in 2003-2008 had demographics remained fixed at 1980 values, roughly the beginning of the period of increasing overweight and obesity. We find that changes in demographics are partly responsible for the changes in the population distribution of BMI and are capable of explaining about 8.6% of the increase in the combined rate of overweight and obesity among women and about 7.2% of the increase among men. We also use demographic projections to predict a BMI distribution and corresponding rates of overweight and obesity for 2050.

Background Globally and in the United States, smoking and obesity are leading causes of death and disability. Reliable estimates of prevalence for these risk factors are often missing variables in public health surveillance programs. This may limit the capacity of public health surveillance to target interventions or to assess associations between other environmental risk factors (e.g., air pollution) and health because smoking and obesity are often important confounders. Objectives To generate prevalence estimates of smoking and obesity rates over small areas for the United States (i.e., at the ZIP code and census tract levels). Methods We predicted smoking and obesity prevalence using a combined approach first using a lasso-based variable selection procedure followed by a two-level random effects regression with a Poisson link clustered on state and county. We used data from the Behavioral Risk Factor Surveillance System (BRFSS) from 1991 to 2010 to estimate the model. We used 10-fold cross-validated mean squared errors and the variance of the residuals to test our model. To downscale the estimates we combined the prediction equations with 1990 and 2000 U.S. Census data for each of the four five-year time periods in this time range at the ZIP code and census tract levels. Several sensitivity analyses were conducted using models that included only basic terms, that accounted for spatial autocorrelation, and used Generalized Linear Models that did not include random effects. Results The two-level random effects model produced improved estimates compared to the fixed effects-only models. Estimates were particularly improved for the two-thirds of the conterminous U.S. where BRFSS data were available to estimate the county level random effects. We downscaled the smoking and obesity rate predictions to derive ZIP code and census tract estimates. Conclusions To our knowledge these smoking and obesity predictions are the first to be developed for the entire conterminous U.S. for census tracts and ZIP codes. Our estimates could have significant utility for public health surveillance.

We uncover interpersonal violence as a dimension and a mechanism of the resource curse. We rely on a historical natural experiment in the United States, in which mineral discoveries occurred at various stages of governmental territorial expansion. "Early" mineral discoveries, before full-edge rule of law is in place in a county, are associated with higher levels of interpersonal violence, both historically and today. The persistence of this homicide resource curse is partly explained by the low quality of (subsequent) judicial institutions. The specificity of our results to violent crime also suggests that a private order of property rights did emerge on thefrontier, but that it was enforced through high levels of interpersonal violence. The results are robust to state-specific effects, to comparing only neighboring counties,and to comparing only discoveries within short time intervals of one another.

Measuring similarities between objects based on their attributes has been an important problem in many disciplines. Object-attribute associations can be depicted as links on a bipartite graph. A similarity measure can be thought as a unipartite projection of this bipartite graph. The most widely used bipartite projection techniques make assumptions that are not often fulfilled in real life systems, or have the focus on the bipartite connections more than on the unipartite connections. Here, we define a new similarity measure that utilizes a practical procedure to extract unipartite graphs without making a priori assumptions about underlying distributions. Our similarity measure captures the relatedness between two objects via the likelihood of a random walker passing through these nodes sequentially on the bipartite graph. An important aspect of the method is that it is robust to heterogeneous bipartite structures and it controls for the transitivity similarity, avoiding the creation of unrealistic homogeneous degree distributions in the resulting unipartite graphs. We test this method using real world examples and compare the obtained results with alternative similarity measures, by validating the actual and orthogonal relations between the entities.

The present research shows how entrepreneurial culture contributes to the widely noted difference in entrepreneurial propensities between men and women. The consequences of the assumed differential importance of household and family generate testable hypotheses about the gender effects of entrepreneurial culture. The principal hypothesis is that there is a greater chance of females in ‘unentrepreneurial’ cultures being relatively entrepreneurial compared to males. Also women from different entrepreneurial cultures show greater similarity of behaviour (lower variance) than men. But proportionate gender gaps within entrepreneurial cultures are less than those between males of different cultures. These hypotheses are tested on US immigrant data from the 2000 census and are not rejected.

Urban gardens are community led plots designated for agricultural purposes in residential and urban areas. Greenville County has seen a recent growth in urban gardens with the assistance of non-profit groups like Gardening for Good. The current total in Greenville County stands at 79 with new gardens added every year. While the growth is encouraging, some gardens have failed. This study uses GIS to explore the social and ecological factors that correlate with urban garden survival in an effort to provide garden managers with information that will help them develop gardens that thrive and persist.

In 1966, the philosopher Michael Polanyi observed, We can know more than we can tell... The skill of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge I have of my own body differs altogether from the knowledge of its physiology. Polanyis observation largely predates the computer era, but the paradox he identifiedthat our tacit knowledge of how the world works often exceeds our explicit understandingforetells much of the history of computerization over the past five decades. This paper offers a conceptual and empirical overview of this evolution. I begin by sketching the historical thinking about machine displacement of human labor, and then consider the contemporary incarnation of this displacementlabor market polarization, meaning the simultaneous growth of high-education, high-wage and low-education, low-wages jobsa manifestation of Polanyis paradox. I discuss both the explanatory power of the polarization phenomenon and some key puzzles that confront it. I then reflect on how recent advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. A key observation of the paper is that journalists and expert commentators overstate the extent of machine substitution for human labor and ignore the strong complementarities. The challenges to substituting machines for workers in tasks requiring adaptability, common sense, and creativity remain immense. Contemporary computer science seeks to overcome Polanyis paradox by building machines that learn from human examples, thus inferring the rules that we tacitly apply but do not explicitly understand.

The decline of marriage over the past half century ranks among the most profound demographic transformations in American demographic history. This paper puts recent change into historical context by providing new estimates of long-run trends in marriage. I then describe change in the family economy and explore the impact of economic changes on marriage behavior. I conclude with a discussion of cultural and structural explanations for change and their implications for the future.

Measuring similarities between objects based on their attributes has been an important problem in many disciplines. Object-attribute associations can be depicted as links on a bipartite graph. A similarity measure can be thought as a unipartite projection of this bipartite graph. The most widely used bipartite projection techniques make assumptions that are not often fulfilled in real life systems, or have the focus on the bipartite connections more than on the unipartite connections. Here, we define a new similarity measure that utilizes a practical procedure to extract unipartite graphs without making a priori assumptions about underlying distributions. Our similarity measure captures the relatedness between two objects via the likelihood of a random walker passing through these nodes sequentially on the bipartite graph. An important aspect of the method is that it is robust to heterogeneous bipartite structures and it controls for the transitivity similarity, avoiding the creation of unrealistic homogeneous degree distributions in the resulting unipartite graphs. We test this method using real world examples and compare the obtained results with alternative similarity measures, by validating the actual and orthogonal relations between the entities.

New York's community colleges have a key role to play in elevating poor and working poor New Yorkers into the ranks of the middle class. The economy is producing few decent-paying jobs for people with only a high school diploma, and community colleges offer the most accessible path for people to obtain a post-secondary credential. However, tens of thousands of New Yorkers who can only afford to enroll in these institutions on a part-time basis are struggling to remain in school long enough to earn a credential-and one of the biggest reasons is the state's outdated financial aid system, which effectively bars part-timers from benefiting from the Tuition Assistance Program (TAP). The TAP program is comparatively generous in other ways, but New York is one of only 14 states to sharply limit access to part-time students. The state's eligibility rules require that students be enrolled full-time for two consecutive semesters before they can enroll part-time and still qualify for TAP. Once they meet these requirements, students are only eligible for a total of six semesters of schooling. These restrictions are a big reason why so few part-time students who enroll in community colleges actually earn a degree or professional certification. Nationally, research has found that student debt and lack of access to financial aid are the key problems associated with low completion rates. Recommended solutions include the following: (1) Eliminate the requirement that students attend full-time for two consecutive semesters before receiving Part-Time TAP (PTAP) and pro-rate awards to the number of credits attempted; (2) Start a pilot program extending TAP to a select group of part-time students and evaluate the results; (3) Fold TAP, PTAP, and APTS into one centralized New York State financial aid system with eligibility based solely on income; (4) Extend the TAP eligibility window to ensure students are supported throughout their college careers; and (5) Make TAP available in the summer. [Additional research support was provided by Barbara Wijering-van Wijk and Esther Kim.]

This paper studies causal effects informative for deciding the age when children should start kindergarten. I present evidence from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) that standard instrumental variable strategies do not identify effects of delaying kindergarten entry for any subpopulation of interest. I propose and implement a new strategy for identifying individual-level education production function parameters. Estimates indicate that there can be decreasing and even negative returns to relative age: For the oldest children in a cohort, educational achievement in third grade decreases as their age relative to that of their classmates increases.

Thanks to the availability of new historical census sources and advances in record linking technology, economic historians are becoming big data genealogists. Linking individuals over time and between databases has opened up new avenues for research into intergenerational mobility, assimilation, discrimination, and the returns to education. To take advantage of these new research opportunities, scholars need to be able to accurately and efficiently match historical records and produce an unbiased dataset of links for downstream analysis. I detail a standard and transparent census matching technique for constructing linked samples that can be replicated across a variety of cases. The procedure applies insights from machine learning classification and text comparison to the well known problem of record linkage, but with a focus on the sorts of cost and benefits of working with historical data. I begin by extracting a subset of possible matches for each record, and then use training data to tune a matching algorithm that attempts to minimize both false positives and false negatives, taking into account the inherent noise in historical records. To make the procedure precise, I trace its application to an example from my own work, linking children from the 1915 Iowa State Census tot heir adult-selves in the 1940 federal Census. In addition, I provided guidance on a number of practical questions, including how large the training data needs to be relative to the sample.

This article describes an explosion in the availability of individual-level population data. By 2018, demographic researchers will have access to over 2 billion records of accessible microdata from over 100 countries, dating from 1703 to the present. Another 2 to 4 billion records will be available through restricted-access data enclaves. These new resources represent a new kind of data that will enable transformative research on demographic and economic change and the spatial organization of society.

This paper investigates how human capital variables, especially educational attainment and health disability, affect an immigrants probability to have private health insurance. Specifically, is there a convergence to natives coverage rates for immigrants as human capital is controlled for? Two probit regressions are used to answer this question, one to analyze the employer provided health insurance market and another to analyze privately purchased health insurance market. The principle finding is that human capital variables are important in determining access to private health insurance. However, a health insurance coverage differential does remain between immigrants and natives.

I study the health insurance implications of the Affordable Care Act (ACA) provision that allows dependents to remain on parental insurance policies until age 26 using data from the IPUMS March Current Population Survey (CPS) and National Health Interview Survey (NHIS). Within a difference-in-difference framework I compare changes in insurance for young adults affected by the law, those aged 22-25, to those who are older, aged 26-29, before and after the law. I find that the ACA increased insurance rates for those 22-25 by 2.7 percentage points in the CPS and 6.5 percentage points in the NHIS. Both data sets show, however, that there is a great deal of crowd-out in that a sizeable number of young adults dropped their own coverage and became insured through their parents. There is also some evidence of slight reductions in Medicaid and in whether insurance was offered through the workplace, offsetting effects worth exploring further.

Research has proved that built environment affect public health in various ways ranging from direct impacts like physical activity to indirect impacts such as housing affordability, employment accessibility, social capital etc. Built environment is shaped by plans and policies related to transportation. Such plans and policies make significant impacts on public health. There is increasing interest on the effects of transportation decisions in public health. Recently, transportation planners and public health practitioners have begun to find ways to work collaboratively in varieties of capacities (APHA, 2011). However, two significant aspects related to impacts of transportation on public health need to be addressed to incorporate health into transportation planning. First, impact of transportation on human health cannot be defined with certainty (Kjellstrom, Kerkhoff, Bammer, & McMichael, 2003). For example, the same level of exposure to pollution from vehicular emissions impact different age group differently. Second, physical, social, and mental well being are subjective and inherently ambiguous which cannot be easily quantified (Massad, Ortega, Barros, & Struchiner, 2009). Goodchild (1999) mentions two distinct methods to handle the uncertainty; first is the use of statistical and probability theory, and second is the use of fuzzy sets or fuzzy logic. First approach requires a fairly sophisticated knowledge of statistical theory (Goodchild, 1999, p. 5). Furthermore, statistical method does not address the ambiguity in the subjective interpretation of health status since its foundation lies in Boolean logic (Massad, Ortega, Barros, & Struchiner, 2009). This study explores the application of fuzzy logic to address the limitation of Boolean logic in dealing with the ambiguity and uncertainty in assessing health impacts of transportation. First it develops a fuzzy logic GIS system on the platform of ArcGIS. It also demonstrates the application of such a decision support system in assessing the impact of transportation on public health. The findings of this study suggest that fuzzy logic addresses the limitation of Boolean logic by adding the capacity to model uncertainty, and ambiguity related to health impacts of transportation. However, fuzzy logic performs well if detailed data are used. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) prevents making individually identifiable health information public due to which most of the health data are aggregated to higher level which diminishes the performance of fuzzy logic. Additionally transportation planners use Traffic Analysis Zone as the unit of analysis which requires detailed data that might not comply with HIPAA requirements. Using health data at traffic analysis zone level can better enhance the performance of fuzzy logic and enhance current method used to assess the impact of transportation on public health.

In the second half of the nineteenth century, many American cities built water systems using lead or iron service pipes. Municipal water systems brought significant public health improvements, but these improvements may have been partially offset by the damaging effects of lead exposure through lead water pipes. We study the effect of cities' use of lead pipes on homicide between 1921 and 1936. Lead water pipes exposed the entire city population to much higher doses of lead than have previously been studied in relation to crime. Our results suggest that cities' use of lead service pipes increased city-level murder rates.

Water infrastructure in the United States is aging and vulnerable to extreme weather. In August 2011, Tropical Storm Irene hit the eastern part of New York and surrounding states, causing great damage to public drinking water systems. Several water supply districts issued boil water advisories (BWAs) to their customers as a result of the storm. This study seeks to identify the major factors that lead water supply systems to issue BWAs by assessing watershed characteristics, water supply system characteristics and treatment plant parameters of water districts in the Mohawk-Hudson River watershed in New York. Logistic regression model suggests that the probability of a BWA being issued by a water supply district is enhanced by higher precipitation during the storm, high density of septic systems, lack of recent maintenance and low population density. Interviews with water treatment plant operators suggested physical damage to water distribution systems were the main causes of boil water advisories during storms. BWAs result in additional costs to residents and communities, and the public compliance of the advisory instructions is low, so efforts must be made to minimize their occurrence. Prior investments in infrastructure management can proactively address municipal water supply and quality issues.