Polarisation in the labour market involves a fall in the share of intermediate-level jobs in an economy, and a simultaneous rise in both high- and low-level jobs. It therefore represents a break from late twentieth-century thinking, where skills were typically thought as being a dichotomy of high and low, rather than a trichotomy of high, intermediate and low levels, and when it was supposed that technological progress would continue to lead to the expansion of the high-level, and the replacement of the low-level, jobs. While high-level jobs do indeed continue to expand at a fast rate, the big change that polarisation has brought is the expansion of low-level jobs, at the apparent expense of intermediate-level jobs. This in turn can have implications for education policy and the supply skills to the labour market, with a discussion of such implications being the ultimate aim of this chapter.

Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables in regression models, which is theoretically and/or computationally undesirable. In this paper, we discuss the benefit of taking a smoothing spline approach to the modeling of ordinal predictors. The purpose of this paper is to provide theoretical insight into the ordinal smoothing spline, as well as examples revealing the potential of the ordinal smoothing spline for various types of applied research. Specifically, we (i) derive the analytical form of the ordinal smoothing spline reproducing kernel, (ii) propose an ordinal smoothing spline isotonic regression estimator, (iii) prove an asymptotic equivalence between the ordinal and linear smoothing spline reproducing kernel functions, (iv) develop large sample approximations for the ordinal smoothing spline, and (v) demonstrate the use of ordinal smoothing splines for isotonic regression and semiparametric regression with multiple predictors. Our results reveal that the ordinal smoothing spline offers a flexible approach for incorporating ordered predictors in regression models, and has the benefit of being invariant to any monotonic transformation of the predictor scores.

Skyline points and queries are important in the context of processing datasets with multiple dimensions. As skyline points can be viewed as representing marketable products that are useful for clients and business owners, one may also consider non-skyline points that are highly competitive with the current skyline points. We address the problem of continuously finding such potential products from a dynamic d-dimensional dataset, and formally define a potential product and its upgrade promotion cost. In this paper, we propose the CP-Sky algorithm, an efficient approach for continuously evaluating potential products by utilizing a second-order skyline set, which consists of candidate points that are closest to regular skyline points (also termed the first-order skyline set), to facilitate efficient computations and updates for potential products. With the knowledge of the second-order skyline set, CP-Sky enables the system to (1) efficiently find substitute skyline points from the second-order skyline set only if a first-order skyline point is removed, and (2) continuously retrieve the top-k potential products. Within this context, the Approximate Exclusive Dominance Region algorithm (AEDR) is proposed to reduce the computational complexity of determining a candidate set for second-order skyline updates over a dynamic data set without affecting the result accuracy. Additionally, we extend the CP-Sky algorithm to support the computations of top-k potential products. Finally, we present experimental results on data sets with various distributions to demonstrate the performance and utility of our approach.

Although the relationship between motherhood and women's labor market exits has received a great deal of popular and empirical attention in recent years, far less is known about the relationship between motherhood and women's job changes. In this paper, I use panel data from the National Longitudinal Survey of Youth (1979) (NLSY79) and Cox regression models to examine how motherhood influences the types of job changes and employment exits women make and how this varies by racial-ethnic group. I find preschool-age children are largely immobilizing for white women, as they discourage these women from making the types of voluntary job changes that are often associated with wage growth. No such effects were found for Black or Hispanic women.

Objective: Examine the relationship between depression symptoms and sinonasal inflammatory diseases, and investigate health disparities associated with allergic rhinitis (AR) and sinusitis in the United States. Study Design: Cross-sectional analysis of 2014 National Health Interview Survey (NHIS) data. Methods: Adult cases of AR and sinusitis were extracted from the 2014 NHIS in addition to demographic, socioeconomic, and related depressive symptom data. The dataset was analyzed with chi-square, t-tests, and multivariate regression. Results: There were 19.1 ± 1.1 million adult AR cases and 29.4 ± 1.4 million adult sinusitis cases. Of these, 20.6% and 22.0% reported depression symptoms in the past 12 months for those with AR or sinusitis, respectively. Both diseases were also associated with significantly fewer mean hours of sleep a night (AR: 7.02 vs. 7.14, P < 0.01; Sinusitis: 6.98 vs. 7.14, P < 0.01) and greater mean days of work missed (AR: 4.60 vs. 3.62, P < 0.01; Sinusitis: 5.87 vs. 3.41; P < 0.01). On multivariate analysis, the prevalence of AR and sinusitis was significantly higher among men, Caucasians, older adults, the more educated, and adults with depression symptoms. Only the prevalence of sinusitis varied depending on income and geography. Conclusion: Allergic rhinitis and sinusitis are associated with an increased likelihood of depressive symptoms, shorter sleep duration, and more workdays lost. The prevalence of both are influenced by age, sex, race/ethnicity, and education level. Targeted initiatives should be developed to address these health disparities and comorbidities associated with inflammatory sinonasal disease.

In exchange for tax exemptions, Blue Cross and Blue Shield (BCBS) health insurers were expected to provide health insurance to the "bad risks," those for whom coverage was unavailable from other insurers. I present evidence that five years after a BCBS plan converted to for-profit status, the probability of having insurance was 1.4 percentage points higher, a 9% reduction in the uninsured. The increase in coverage does not mask reductions among populations often targeted by public policies. However, there is evidence of increased risk selection which suggests that the bad risks might have been worse off after a conversion.

Improving job accessibility can increase the probability for individual persons to be employed and reduce their commutes. Empirical research suggests that the relationship between job accessibility and employment outcomes differ across income groups, but no research has investigated the difference or explored which income groups benefit the most from job accessibility improvements. This research fills the gap by examining six income groups in the Los Angeles metropolitan area. Results show that job accessibility affects the employment status of medium-to-low income groups (household income between US$25,000 and US$75,000). For the lowest-income group (household income lower than US$25,000), owning a car significantly improves their chances to be employed, but job accessibility has no effect. On the other hand, higher job accessibility is associated with shorter commuting distance for the other five income groups, but not for the lowest-income group. These results suggest that transportation and land use policies need to address the specific needs of distinct population groups and underscore the importance of spatial access for the middle-class, which tends to be overlooked in the literature on transportation equity.

Our main findings are as follows: Roughly 9.5 million workers under age 65 gained coverage from 2010 to 2015, along with 5.2 million family members. These 14.7 million Americans make up 77 percent of all those who gained coverage under the first six years of the ACA. Sorting workers by occupation, coverage gains appeared to target need well. Occupations that had lower rates of coverage and employer-sponsored insurance (ESI), lower wages, and lower earnings before the ACA saw greater gains. In occupations where less than 70 percent of workers had health insurance in 2010, the median increase in coverage by 2015 was 13.4 percentage points. In occupations where 70–80 percent of workers had coverage in 2010, the median increase was 9.2 percentage points. Median increases were 6.1 and 2.3 percentage points, respectively, in occupations where 80–90 percent and more than 90 percent of workers had coverage in 2010. Among occupations that paid average hourly wages of less than $15 in 2010, coverage increased at a median rate of 13.9 percentage points. For occupations with hourly wages of $15–20, $20–30, and more than $30, coverage increased by 7.1, 2.6, and 1.7 percentage points, respectively. Among the workers gaining coverage, 6.0 million (63 percent) lived in states that expanded their Medicaid programs under the ACA. The remaining 3.5 million (37 percent) lived in states that did not expand their programs. Coverage gains were larger in expansion states (7.2 percentage points) than nonexpansion states (6.4 percentage points). Hundreds of thousands of workers gained coverage in Florida (770,000) and Texas (915,000), even though neither state chose to expand Medicaid. State coverage expansions appeared well-targeted to need. Among Medicaid expansion and nonexpansion states, those with the lowest coverage levels in 2010—such as Florida and Texas-saw the greatest coverage increases among workers and their families.

Purpose The purpose of this paper is to examine the relationship between voluntary and involuntary turnover and officers’ salaries. Design/methodology/approach Using data from the 2013 Law Enforcement Management and Administrative Statistics survey, Poisson regression was used to test hypotheses about the effect of pay and other economic incentives on turnover, while controlling for previously identified influential organizational and community factors, such as crime, community disorganization, geographic region, policing philosophy, collective bargaining, the utilization of body-worn cameras, and workforce diversity. Findings Higher salaries were significantly associated with lower voluntary and involuntary turnover rates. In addition, other economic incentives and participation in a defined benefits retirement plan were related to voluntary separations but not dismissals. Consistent with prior research, southern agencies and sheriff’s departments reported higher turnover rates than local police agencies and departments operating in other areas of the USA. The effects of workforce diversity were mixed, while collective bargaining was associated with lower rates of voluntary turnover, and the utilization of body-worn cameras was associated with higher rates. Originality/value In addition to contributing to the theoretical literature on antecedents of turnover, this research has practical implications by helping law enforcement officials estimate how changes in the compensation structure affect their ability to retain qualified personnel. Due to the complexities of modern law enforcement, maintaining a strong and stable workforce is becoming a greater challenge, and more research is needed to understand which incentives are crucial in recruiting and retaining the most effective policing personnel.

This casebook provides a set of cases that reveal the current complexity of medical decision-making, ethical reasoning, and communication at the end of life for hospitalized patients and those who care for and about them. End-of-life issues are a controversial part of medical practice and of everyday life. Working through these cases illuminates both the practical and philosophical challenges presented by the moral problems that surface in contemporary end-of-life care. Each case involved real people, with varying goals and constraints,who tried to make the best decisions possible under demanding conditions. Though there were no easy solutions, nor ones that satisfied all stakeholders, there are important lessons to be learned about the ways end-of-life care can continue to improve. This advanced casebook is a must-read for medical and nursing students, students in the allied health professions, health communication scholars, bioethicists, those studying hospital and public administration, as well as for practicing physicians and educators.

INTRODUCTION Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. OBJECTIVES We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications. METHODS We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children's Health Study (CHS), and the Inhalable Particulate Network (IPN). RESULTS In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84-0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00-0.85) most likely because of fewer monitoring sites and inconsistent sampling methods. CONCLUSIONS Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. Citation: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38-46; http://dx.doi.org/10.1289/EHP131.

Social divisions between American partisans are growing, with Republicans and Democrats exhibiting homophily in a range of seemingly nonpolitical domains. It has been widely claimed that this partisan social divide extends to Americans' decisions about where to live. In two original survey experiments, we confirm that Democrats are, in fact, more likely than Republicans to prefer living in more Democratic, dense, and racially diverse places. However, improving on previous studies, we test respondents' stated preferences against their actual moving behavior. While partisans differ in their residential preferences, on average they are not migrating to more politically distinct communities. Using zip-codelevel census and partisanship data on the places where respondents live, we provide one explanation for this contradiction: by prioritizing common concerns when deciding where to live, Americans forgo the opportunity to move to more politically compatible communities.

This paper exploits the temporal and geographic variation in the implementation of local and state immigration enforcement measures to identify their impact on undocumented immigrants fertility. Using data from the 2005 through 2014 American Community Survey, we find that a one standard deviation increase in the intensity of immigration enforcement lowers the childbearing likelihood of likely undocumented women by 6.3 percent. This effect appears driven by police-based measures and, the fact that is present among intact families, families headed by a likely undocumented couple, as well as among the poorest families, suggests the importance of limited income resources, along with increased uncertainty emanating from an intensified fear of deportation, on likely unauthorized womens fertility. Given immigrants critical contribution to the sustainability of the welfare state and the spread-out embracement of a piece-meal approach to immigration enforcement, further exploration of this impact is warranted and recommended.

There is limited research on the living arrangements of older adults with disabilities, especially research that combines household composition and housing characteristics. This paper addresses that gap with two complementary sets of logistic regression models: first, estimating the odds of disability by household composition and housing type and, second, estimating the odds of disability by living arrangement within gender and age sub-groups. Data come from the 2012 American Community Survey (N = 504,371 , respondents aged 65 and older), which includes six measures of disability: cognitive, ambulatory, independent living, self-care, vision and hearing. Living alone, with children or with others was associated with higher odds of any disability, compared with living with a spouse only. Compared to those living in a single-family home, living in a mobile home or other temporary structure, or large apartment building was associated with higher odds of disability. Having a disability was associated with lower rates of living with a spouse only, alone, in a single-family home or in a small or mid-sized apartment building and higher rates of all other living arrangements. Sub-group analyses revealed differences in the relationship between living arrangements and disability by gender and age group. This information provides a baseline from which to observe trends in living arrangements and disability for older adults in the United States of America.

Labor migration offers an important mechanism to reallocate workers when there are regional differences in employment conditions. Whereas conventional wisdom suggests migration rates should increase during recessions as workers move out of areas that are hit hardest, initial evidence suggested that overall migration rates declined during the Great Recession, despite large regional differences in unemployment and growth rates. In this paper we use data from the American Community Survey to analyze internal migration trends before and during the economic downturn. First, we find only a modest decline in the odds of adults leaving distressed labor market areas during the Great Recession, which may result in part from challenges related to the housing price crash. Second, we estimate conditional logit models of destination choice for individuals who migrate across labor market areas; we find a substantial effect of economic factors such as labor demand, unemployment, and housing values. We also estimate latent class conditional logit models that test whether there is heterogeneity in preferences for destination characteristics among migrants. Over all, the latent class models suggest that roughly equal percentages of migrants were motivated by economic factors before and during the Great Recession. We conclude that fears of dramatic declines in labor migration seem to be unsubstantiated.

The 2020 Census will have ramifications for every person in the United States, urban and rural residents alike.1 Interest in the Census is growing2 and the Census Bureau’s plans are becoming more concrete,3 but little has been written about the special challenges that will make some rural areas and populations difficult to enumerate accurately. This brief identifies rural areas where special outreach and operations will be needed to get a complete and accurate count. It also addresses key Census-related issues that will be important for rural leaders to monitor between now and April 1, 2020.

In categorical data, it is typically the case that some combinations of variables are theoretically impossible, such as a 3-year-old child who is married or a man who is pregnant. In practice, however, reported values often include such structural zeros due to, for example, respondent mistakes or data processing errors. To purge data of such errors, many statistical organizations use a process known as edit-imputation. The basic idea is first to select reported values to change according to some heuristic or loss function, and second to replace those values with plausible imputations. This two-stage process typically does not fully use information in the data when determining locations of errors, nor does it appropriately reflect uncertainty resulting from the edits and imputations. We present an alternative approach to editing and imputation for categorical microdata with structural zeros that addresses these shortcomings. Specifically, we use a Bayesian hierarchical model that couples a stochastic model for the measurement error process with a Dirichlet process mixture of multinomial distributions for the underlying, error-free values. The latter model is restricted to have support only on the set of theoretically possible combinations. We illustrate this integrated approach to editing and imputation using simulation studies with data from the 2000 U.S. census, and compare it to a two-stage edit-imputation routine. Supplementary material is available online.