Class analysis and its futures

Books reviewed in this articles:
Class and Stratification: An introduction to current debates R. Crompton
Class S. Edgell
Changing Classes: Stratification and Mobility in Post-Industrial Societies G. Esping-Anderson, (ed.)
Class and Inequality: Comparative Perspectives M. Hamilton and M. Hirszowicz
Class R. Scase     

The turn to optional familialism through the market: Long‐term care,cash‐for‐care,and caregiving policies in Europe

Cash‐for‐care (CfC) schemes are monetary transfers to people in need of care who can use them to organize their own care arrangements. Mostly introduced in the 1990s, these schemes combine different policy objectives, as they can aim at (implicitly or explicitly) supporting informal caregivers as well as increasing user choice in long‐term care or even foster the formalization of care relations and the creation of care markets. This article explores from a comparative perspective, how CfC schemes, within broader long‐term care policies, envision, frame, and aim to condition informal care, if different models of relationships between CfC and informal care exist and how these have persisted or changed over time and into which directions. Building on the scholarly debate on familialization vs. defamilialization policies, the paper proposes an analytical framework to investigate the trajectories of seven European countries over a period of 20 years. The results show that, far from being simply instruments of supported familialism, CfC schemes have contributed to a turn towards “optional familialism through the market,” according to which families are encouraged to provide family care and are (directly or indirectly) given alternatives through the provision of market care.     

On evaluating how well a biomarker can predict treatment response with survival data

One of the objectives of personalized medicine is to take treatment decisions based on a biomarker measurement. Therefore, it is often interesting to evaluate how well a biomarker can predict the response to a treatment. To do so, a popular methodology consists of using a regression model and testing for an interaction between treatment assignment and biomarker. However, the existence of an interaction is not sufficient for a biomarker to be predictive. It is only necessary. Hence, the use of the marker‐by‐treatment predictiveness curve has been recommended. In addition to evaluate how well a single continuous biomarker predicts treatment response, it can further help to define an optimal threshold. This curve displays the risk of a binary outcome as a function of the quantiles of the biomarker, for each treatment group. Methods that assume a binary outcome or rely on a proportional hazard model for a time‐to‐event outcome have been proposed to estimate this curve. In this work, we propose some extensions for censored data. They rely on a time‐dependent logistic model, and we propose to estimate this model via inverse probability of censoring weighting. We present simulations results and three applications to prostate cancer, liver cirrhosis, and lung cancer data. They suggest that a large number of events need to be observed to define a threshold with sufficient accuracy for clinical usefulness. They also illustrate that when the treatment effect varies with the time horizon which defines the outcome, then the optimal threshold also depends on this time horizon.     

MARKET STRUCTURE AND MEDIA DIVERSITY

We estimate a mixed logit model of the demand for local news service. Results provide evidence that suggest the representative consumer values more diverse news, more coverage of multicultural issues, and more information on community news, and has a distaste for advertising. Demand estimates are used to calculate the impact on consumer welfare from a marginal decrease in the number of independent television stations that lowers the amount of diversity, multiculturalism, community news, and advertising. Consumer welfare decreases, but the losses are smaller in large markets. For example, small‐market consumers lose $45 million annually while large‐market consumers lose $13 million. (JEL C9, C25, L13, L82, L96)     

Race,Supervisorial Change,and Job Outcomes: Employability Resilience in NCAA Division I College Basketball Coaching

We examine how race affects the employment status of subordinates following a job change by their immediate supervisors. We test whether racial homophily between a subordinate and a supervisor affects the odds of being let go. We also consider whether a racial match between an incoming head coach and assistant affects whether assistants retain their assistant coaching position. Data for these analyses come from a unique data set that explores what happens to 704 NCAA Division I college basketball assistant coaches after the head coach leaves the school. Logistic regression analyses confirm the benefit of working for a white head coach as this decreases the likelihood of being let go, compared to more positive outcomes such as following the coach to a new school, being internally promoted or retained after the head coach's departure. Furthermore, racial homophily with incoming head coaches insulates subordinates from having to search for new employment by increasing the likelihood of assistants being retained.     

Demographic Influences on Risk Perceptions

Over the past 15 years, psychologists have empirically investigated how people perceive technological, consumer, and natural hazards. The psychometric-attitudes to risk being summarized by three factors: "dread," whether the risk is known, and personal exposure to the risk. The results have been used to suggest that certain types of hazards are viewed very differently from other hazards. The purpose of this paper is somewhat different, in that it investigates whether individual demographic characteristics influence psychometric perceptions of risk. This paper makes use of a large, professionally conducted, survey of a wide cross-section of the residents of metropolitan Chicago. One thousand adults were interviewed in a random-digit dial telephone survey, producing a useable dataset of about 800. Data on the three risk factors mentioned above were obtained on 7-point scales for four common hazards: aviation accidents, fires in the home, automobile accidents, and stomach cancer. The survey also collected demographic data on respondents'age, schooling, income, sex, and race. Regressions were then conducted to relate the demographic characteristics to risk perceptions. Some strong general conclusions can be drawn. The results suggest that women, people with lower levels of schooling and income, younger people, and blacks have more dread of hazards. The exception being age-related illnesses which, not unnaturally, are feared by older people. Unlike previous literature, we cannot substantiate the argument that these groups of people are less informed about hazards and thus less accepting of them. The most likely leading explanation of the relationship between demographic factors and dread of a hazard is the perceived personal exposure to the hazard. People with greater perceived exposure to a hazard are more fearful.