Obesity is epidemic in the United States, and there is an imperative need to identify policy tools that may help fight this epidemic. A recent paper in the economics literature finds an inverse relationship between gasoline prices and obesity risk—suggesting that increased gasoline prices via higher gasoline taxes may have the effect of reducing obesity prevalence. This study builds upon that paper. It utilizes cross‐sectional time series data from the American Time Use Survey (ATUS) over 2003–2008, utilizes the increases that occurred in gasoline prices in this period due to Hurricane Katrina and to the global spike in gasoline prices as a “natural experiment,” and explores how time spent by Americans on different forms of physical activity is associated with gasoline price levels. Economic theory suggests that higher gasoline prices may alter individual behavior both via a “substitution effect” whereby people seek alternatives to motorized transportation, and an “income effect” whereby the effect of higher gasoline prices on the disposable family budget leads people to make various adjustments to what they spend money on. The latter may lead to some increase in physical activity (for example, doing one's own yard work instead of hiring help), but may also lead to decreases in other physical activities that involve expenses, such as team sports or workouts at the gym. Thus, ultimately, the relationship between gasoline prices and physical activity must be empirically determined. Results from multivariate regression models with state and time fixed effects indicate that higher gasoline prices are associated with an overall increase of physical activity that is at least moderately energy intensive. The increases are most pronounced in periods where gasoline prices fluctuate more sharply and unexpectedly. These results appear robust to a number of model specifications. One of the major components of this increase appears to be an increase in housework that is at least moderately energy intensive—such as interior and exterior cleaning, garden, and yard work. This tentatively suggests that there is an income effect of higher gasoline prices, or a possible increase in prices of such services when gasoline prices increase. However, the increases in physical activity associated with increased gasoline prices are weaker among minorities and low socioeconomic status (SES) individuals. Hence, while a policy that increases gasoline prices via raised gasoline taxes may have benefits in terms of increasing overall physical activity levels in the United States, these benefits may not accrue to low SES individuals to the same extent as to their higher SES counterparts. This suggests that if increasing physical activity is the primary goal, then it may be more efficient to use a tax that can exert an income effect on mid‐to‐high SES households, such as a targeted income tax. On the other hand, if gasoline taxes are imposed to address other negative externalities of gasoline use, then these taxes may have the added benefit of increasing physical activity at least among some segments of U.S. society.

This profile provides Florida voter registration data, including party affiliation, as reported by the Florida Division of Elections through July 16, 2012. It also provides key demographic information on Latino eligible voters1 and other major groups of eligible voters in Florida.2 All demographic data are based on Pew Hispanic Center tabulations of the U.S. Census Bureau’s 2010 American Community Survey.

Immigrants age at arrival matters for schooling outcomes in a way that is predicted by child development theory: the chances of being a high school dropout increase significantly each year for children who arrive in a host country after the age of eight. The authors document this process for immigrants in the United States from a number of regions relative to appropriate comparison regions. Using instrumental variables, the authors find that the variation in education outcomes associated with variation in age at arrival influences adult outcomes that are important in the American mainstream, notably English-language proficiency and intermarriage. The authors conclude that children experience migration differently from adults depending on the timing of migration and show that migration during the early years of child development influences educational outcomes. The authors also find that variation in education outcomes induced by the interaction of migration and age at arrival changes the capacity of children to become fully integrated into the American mainstream as adult.

BackgroundStudies have documented movement of secondhand smoke (SHS) between units in multi-unit buildings, but none has focused on owner-occupied units in common-interest communities (CICs). In Minnesota, approximately 170,000 households (8%) live in such units. CIC households may experience long-term SHS exposure because owner-occupants typically live in the same unit for many years.PurposeThis study estimated the prevalence of SHS incursion in CICs and assessed residents' attitudes toward SHS incursions and interest in smokefree policies.MethodsA stratified sample of Minnesota CIC owner-occupants was surveyed in 2009, with analysis in 20102011. Data were weighted to account for differing sampling, response, and coverage rates by stratum, then calibrated to population control totals for housing type, age, and smoking status.ResultsThe response rate was 35.6%, with 495 completions. Twenty-eight percent of households reported SHS incursion into their unit in the preceding 6 months; 59% of these said this bothers them a lot. Only 6% report that their CIC has a smokefree policy for residents' units. Forty-two percent would prefer such a policy whereas 26% would prefer smoking-permitted. Sixty-three percent definitely and 17% probably would choose a no-smoking building over a smoking-permitted building if they were buying a new unit, and 46% would be willing to pay more for such a unit.ConclusionsSecondhand smoke incursion is common in CICs, and interest in smokefree CICs greatly exceeds the supply. Given the known health risks of SHS exposure, tobacco control efforts in multi-housing should address CICs as well as rental households.

This paper uses details from the history of labor law to examine the effects of RTW laws on the entire distribution of wages in an historical setting in which the introduction of RTW laws was arguably exogenous - the period following the passage of the Taft-Hartley Act in 1947. I find that, in general, the effects of RTW laws on wages are positive. I also find some evidence that the effects are larger in the middle of the wage distribution. RTW laws do, however, appear to decrease wages for workers in highly unionized industries relative to workers in other industries.

Healthcare report provides detailed analyses and projections of healthcare fields, occupations, and their wages. In addition, it includes discussion of the important skills and work values associated with healthcare fields and occupations. Finally, the report analyzes the implications of findings for the racial, ethnic, and class diversity of the healthcare workforce in the coming decade.

This paper investigates recent commuting trends by American workers. Unlike most studies of commuting that rely on data from the American Community Survey this study utilizes the American Time Use Survey to detail the complex commuting patterns of modern-day workers. Changes in the price of gasoline in recent years suggest that the incidence of “driving alone” should be on the decline. Indeed, results show that the sensitivity of modal commuting with respect to changes in gasoline prices appears to be relatively large. We estimate the gasoline-price elasticity of driving alone to be 0.057 and the gasoline-price elasticity of carpooling to be 0.502. Additional factors also affect commuting, including socio-economic characteristics and social desires. However, it is changes in gasoline prices that appear to account for nearly all of the recent variation in the mode chosen for commuting.

Organizations often possess data that they wish to make public for the common good. Yet such published data often contains sensitive personal information, posing serious privacy threat to individuals. Anonymization is a process of removing identifiable information from the data, and yet to preserve as much data utility as possible for accurate data analysis. Due to the importance of privacy, in recent years, researchers were attracted to design new privacy models and anonymization algorithms for privacy preserving data publication. Despite of their efforts, there are still many outstanding problems remain to be solved. We aim to contribute to the state-of-the-art data anonymization schemes with an emphasis on different data models for data publication. Specifically, we study and propose new data anonymization schemes for three mostly investigated data types by the literature, namely set-valued data, social graph data, and relational data. These three types of data are commonly encountered in our daily life, thus the privacy for their publication is of crucial importance. Examples of the three types of data are grocery transaction records, relationship data in online social networks, and census data by the government, respectively. We have adapted two common approaches to data anonymization, i.e. perturbation and generalization. For set-valued data publication, we propose a nonreciporical anonymization scheme that yields higher utility than existing approaches based on reciporical coding. An important reason why we can achieve better utility is that we generate a utility-efficient order for the dataset using techniques such as Gray sort, TSP reordering and dynamic partitioning, so that similar records are grouped during anonymization. We also propose a superior model for data publishing which allows more utility to be preserved than other approaches such as entry suppression. For social graph publication, we study the effectiveness of using random edge perturbation as privacy protection scheme. Previous research rejects using random edge perturbation for preventing the structural attack of social graph for the reason that random edge perturbation severely destroys the graph utilities. In contrary, we show that, by exploiting the statistical properties of random edge perturbation, it is possible to accurately recover important graph utilities such as density, transitivity, degree distribution and modularity from the perturbed graph using estimation algorithms. Then we show that based on the same principle, the attackers can launch a more sophisticated interval-walk attack which yields higher probability of success than the conventional walk-based attack. We study the conditions for preventing interval-walk attack and more general structural attack using random perturbation. For relational data publication, we propose a novel pattern preserving anonymization scheme based on perturbation. Using our scheme, the owner can define a set of Properties of Interest (PoIs) which he wishes to preserve for the original data. These PoIs are described as linear relationships among the data points. During ano- nymization, our scheme ensures the predefined patterns to be strictly preserved while making the anonymized data sufficiently randomized. Traditional generalization and perturbation based approaches either completely blind or obfuscate the patterns. The resulted data is ideal for data mining tasks such as clustering, or ranking which re- quires the preservation of relative distances. Extensive experimental results based on both synthetic and real data are presented to verify the effectiveness of our solutions.

"World Bank Publications and Neoclassical Modeling": This paper examines changes in the World Bank's analytical framework between 1983 and 2011 using a sample of the Bank's most prominent reports. It engages the concurrent development of technical critiques, alternative analysis, and frameworks that did not gain the same level of traction. By placing these two sets of texts in dialogue, it analyzes the robustness of the Bank's neoclassical assumptions and findings and its use of these results. The paper concludes discussing institutional changes affecting the Bank's research and its lending conditionality and summarizes recommendations to promote a more responsive and dynamic research agenda. "The Impact of Housing Policy on Housing Affordability for Renters, Homeowners and Mobile Homeowners": This study evaluates the impact of financial and housing policy on housing affordability in the United States, taking into consideration household level and macroeconomic factors using longitudinal panel data from the Integrated Public Use Microdata Series (IPUMS) from 1960 to 2005. Changes in rental payments-to-income, mortgage payments-to-income and mobile home payments-to-income ratios are assessed P R E V I E W

This paper uses an overlapping-generations dynamic general equilibrium model of residential sorting and intergenerational human capital accumulation to investigate effects of neighborhood externalities. In the model, households choose where to live and how much to invest toward the production of their childs human capital. The return on the parents investment is determined in part by the childs ability and in part by an externality from the average human capital in their neighborhood. We use the model to test a prominent hypothesis about the concentration of poverty within racially-segregated neighborhoods (Wilson 1987). We first impose segregation on a model with two neighborhoods and match the model steady state to income and housing data from Chicago in 1960. Next, we lift the restriction on moving and compute the new steady state and corresponding transition path. The transition implied by the model qualitatively supports Wilsons hypothesis: high-income residents of the low-average-human-capital neighborhood move out, reducing the returns to investment in their old neighborhood. Sorting increases citywide human capital, but it also produces congestion in the high-income neighborhood, increasing the average cost of housing. As a result, average welfare decreases by 2.2 percent of steady state consumption, and the loss is greatest for those initially in the low-income neighborhood.

The prevalence of female-headed households has remained significant over the past several decades and we are thus urged to revisit the subject of family formation. The purpose of this study is to highlight the salient role of male availability and marriageability in explaining the persistence of female family headship. In particular, we examine the structural and economic inequalities influencing the relative quantity and quality of unmarried males, as well as the ensuing marginal effects on female headship.Using data from the IPUMS-CPS spanning 1990-2009, the preliminary findings indicate that since 1990, the rise in female family headship is significantly related to the decline in relative male quantity and quality. Nevertheless, the decline in relative male quality is significantly more detrimental for blacks while relative male quantity is significantly more detrimental for whites. Policies geared at reducing male marginalization are therefore essential for allaying the prevalence of female family headship

This paper estimates Marginal Treatment Effects (MTEs) of neighborhood quality from the Moving to Opportunity (MTO) housing mobility experiment in a model with multiple treatment levels. We propose and implement a new identification strategy that exploits the identification of the idiosyncratic component of an ordered choice model. Due to the limited changes in neighborhood quality induced by MTO, we only estimate MTEs of moving from the first to second decile of the national distribution of neighborhood quality. These MTEs are heterogeneous over observable characteristics: Labor market outcomes were affected most positively for individuals at the sites in which larger changes in neighborhood quality were induced by MTO. Estimated MTEs are also heterogeneous over unobservables, which we consider evidence in favor of selection occurring in a model with essential heterogeneity. Although there is not enough structure in our model to clearly interpret MTE heterogeneity, we discuss possible reasons for the surprising result that effects are best for those with characteristics that make them less likely to move without the program.

The existence of digital boundary data files to accompany area census statistics for small units has now become accepted as the norm in respect of population census output in many countries. However, there has been growing variation in the specification of census geography and widespread inconsistency in the sets of polygons between the constituent countries of the UK. This paper reports on research that has created data sets of consistent areas and estimates of census counts spanning the last four British censuses from 1971 to 2001 to enable the comparison of small spatial units and the analysis of population change. The paper assesses the data sets produced by areal weighting and dasymetric mapping by comparison with a subset of consistent spatial units between 1981 and 1991 and with published statistics for larger areas. The results are also examined in relation to previously published methods of obtaining weights empirically for use in the dasymetric mapping. The paper highlights the potential of the consistent small spatial units that have been created for the analysis of population change over time.

Considerable labor mobility exists across US states, enough that, if migration arbitrages local unemployment, one might expect very low unemployment differences across states. However, cross-state data reveal large unemployment differences. An equilibrium multi-location model with stochastic worker-location match productivity and within-location trading frictions can account for these facts. In the model, some workers move to, or stay in, a location with high unemployment because they are more productive there than elsewhere. According to the model, labor mobility and aggregate unemployment are negatively related. This prediction is in stark contrast to standard sectoral reallocation theory, but consistent with the US data.

In the era of “big data revolution,” marked by an exponential growth of infor- mation, extracting value from data enables analysts and businesses to address challenging problems such as drug discovery, fraud detection, and earthquake predictions. Multi-Criteria Decision Support (MCDS) queries are at the core of big-data analytics resulting in several classes of MCDS queries such as OLAP, Top-K, Pareto-optimal, and nearest neighbor queries. The intuitive nature of specifying multi-dimensional preferences has made Pareto-optimal queries, also known as skyline queries, popular. Existing skyline algorithms however do not address several crucial issues such as performing skyline evaluation over disparate sources, progressively generating skyline results, or robustly handling workload with multiple skyline over join queries. In this dissertation we thoroughly investigate topics in the area of skyline-aware query evaluation. In this dissertation, we first propose a novel execution framework called SKIN that treats skyline over joins as first class citizens during query processing. This is in contrast to existing techniques that treat skylines as an “add-on,” loosely integrated with query processing by being placed on top of the query plan. SKIN is effective in exploiting the skyline characteristics of the tu- ples within individual data sources as well as across disparate sources. This enables SKIN to significantly reduce two primary costs, namely the cost of generating the join results and the cost of skyline comparisons to compute the final results. Second, we address the crucial business need to report results early; as soon as they are being generated so that users can formulate competitive decisions in near real-time. On top of SKIN, we built a progressive query evaluation framework ProgXe to transform the execution of queries involving skyline over joins to become non-blocking, i.e., to be progressively generating re- sults early and often. By exploiting SKIN’s principle of processing query at multiple levels of abstraction, ProgXe is able to: (1) extract the output depen- dencies in the output spaces by analyzing both the input and output space, and (2) exploit this knowledge of abstract-level relationships to guarantee correctness of early output. Third, real-world applications handle query workloads with diverse Qual- ity of Service (QoS) requirements also referred to as contracts. Time sensi- tive queries, such as fraud detection, require results to progressively output with minimal delay, while ad-hoc and reporting queries can tolerate delay. In this dissertation, by building on the principles of ProgXe we propose the Contract- Aware Query Execution (CAQE) framework to support the open problem of contract driven multi-query processing. CAQE employs an adap- tive execution strategy to continuously monitor the run-time satisfaction of queries and aggressively take corrective steps whenever the contracts are not being met. Lastly, to elucidate the portability of the core principle of this dissertation, the reasoning and query processing at different levels of data abstraction, we apply them to solve an orthogonal research question – to auto-generate recommendation queries that facilitate users in exploring a complex databasesystem. User queries are often too strict or too broad requiring a frustrating trial-and-error refinement process to meet the desired result cardinality while preserving original query semantics. Based on the principles of SKIN, we propose CAPRI to automatically generate refined queries that: (1) attain the desired cardinality and (2) minimize changes to the original query intentions. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in both efficiency, as well as resource consumption.

This article studies the determinants of the labor force participation of the elderly and investigates the factors that may account for the increase in retirement in the second half of the last century. We develop a life-cycle general equilibrium model with endogenous retirement that embeds Social Security legislation and Medicare. Individuals are ex ante heterogeneous with respect to their preferences for leisure and face uncertainty about labor productivity, health status and out-of-pocket medical expenses. The model is calibrated to the U.S. economy in 2000 and is able to reproduce very closely the retirement behavior of the American population. It reproduces the peaks in the distribution of Social Security applications at ages 62 and 65 and the observed facts that low earners and unhealthy individuals retire earlier. It also matches very closely the increase in retirement from 1950 to 2000. Changes in Social Security policy - which became much more generous - and the introduction of Medicare account for most of the expansion of retirement. In contrast, the isolated impact of the increase in longevity was a delaying of retirement.

Commissioned by The Minneapolis Foundation, this report presents key demographic data about Minneapolis, followed by a dashboard of 24 community indicators in the areas of education, jobs, housing, justice and more. Each of the 24 community indicators is described in a two-page spread that includes details about the indicator, why the indicator is important, the most recent data point, trend data, equity measures, key observations about the data, and technical notes to assist with interpretation.

Motivated by large educational di fferences in geographic mobility, this paper considers a simple dynamic extension of the Roy model, where worker migration and wages are jointly determined at the individual level. According to the model, a labor income shock specifi c to a worker-location match is greater for more educated workers and accounts for large educational differences in mobility. The model also off ers a simple explanation for the relationship between mobility and wages of different age and educational groups. The wage gap between movers and stayers is essential in quantifying multi-sector models. In the model, overall mobility and the college wage premium are positively related, a prediction consistent with cross-country data.