Scaling Back and Finding Flexibility: Gender Differences in Parents' Strategies to Manage Work–Family Conflict

Studies show that fathers report work–family conflict levels comparable to mothers. The authors examine gender differences in work‐related strategies used to ease such conflicts. The authors also test whether the presence of young children at home shapes parents' use of different strategies. They address these focal questions using panel data from the Canadian Work, Stress, and Health study (N = 306 fathers, 474 mothers). The authors find that mothers with young children are more likely to scale back on work demands when compared with fathers with young children, but mothers and fathers with older children are equally likely to pursue these strategies. Furthermore, women with young children and men with older children are more likely to seek increased schedule control as a result of work–family conflict when compared with their parent counterparts. The authors situate these findings in the vast literature on the consequences of work–family conflict.     

Inequality and fiscal policies in Uruguay by race

The aim of this study is to analyze the effect of fiscal policy by race, disaggregating to consider Uruguayans with primarily European, African and indigenous ancestry. We perform an incidence analysis, an estimation of the effect of fiscal policy on the poverty exit rate and an assessment of the impact on the average ethno-racial gaps. The findings support the idea that fiscal policy reduces (but does not eliminate) ethnic gaps. This result is led by health care and educational transfers, and to a lesser degree by direct transfers. We do not consider quality issues with public services, which may affect the estimated narrowing of gaps. Finally, we find that Afro-descendants and indigenous individuals do not capture the full potential of education transfers because of their high drop-out rate.     

Family Responsibilities,Productivity, and Earnings: A Study of Gender Differences Among Canadian Lawyers

This study examines whether men and women invest in different determinants of productivity and whether these investments affect productivity and salary in different ways. Hypotheses are tested from human and social capital theories that include more direct measures for family responsibilities and family-friendly firm arguments. Data from 670 law firm lawyers were used given they report a standardized measure of productivity in billable hours. Despite men investing more in their careers and women investing more in their families, both report similar productivity and their productivity is affected similarly by these factors. In addition, equally productive men and women are paid the same. The findings further our understanding of productivity and salary and the relevance of family responsibilities and family-friendly firms.

Realistic Major Previews in the School‐to‐College Transition of Italian High School Students

Industrial/organizational researchers have reported that realistic job previews diminish prospective workers' expectations but promote the satisfaction and persistence of those who ultimately accept a job assignment. The authors applied this strategy to the context of school‐to‐college transition; 354 Italian high school students were provided with a 2‐hour lecture designed to simulate exposure to a college major of their choice. Students showed moderate pre‐post increases in subject matter knowledge but reported small decreases in interests and outcome expectations (but not self‐efficacy) related to the academic major to which they were exposed.     

Inflated beta distributions

This paper considers the issue of modeling fractional data observed on [0,1), (0,1] or [0,1]. Mixed continuous-discrete distributions are proposed. The beta distribution is used to describe the continuous component of the model since its density can have quite different shapes depending on the values of the two parameters that index the distribution. Properties of the proposed distributions are examined. Also, estimation based on maximum likelihood and conditional moments is discussed. Finally, practical applications that employ real data are presented.     

Benefits and costs associated with mutual-help community-based recovery homes: The Oxford House model

We used data from a randomized controlled study of Oxford House (OH), a self-run, self-supporting recovery home, to conduct a cost-benefit analysis of the program. Following substance abuse treatment, individuals that were assigned to an OH condition (n = 68) were compared to individuals assigned to a usual care condition (n = 61). Economic cost measures were derived from length of stay at an Oxford House residence, and derived from self-reported measures of inpatient and outpatient treatment utilization. Economic benefit measures were derived from self-reported information on monthly income, days participating in illegal activities, binary responses of alcohol and drug use, and incarceration. Results suggest that OH compared quite favorably to usual care: the net benefit of an OH stay was estimated to be roughly $29,000 per person on average. Bootstrapped standard errors suggested that the net benefit was statistically significant. Costs were incrementally higher under OH, but the benefits in terms of reduced illegal activity, incarceration and substance use substantially outweighed the costs. The positive net benefit for Oxford House is primarily driven by a large difference in illegal activity between OH and usual care participants. Using sensitivity analyses, under more conservative assumptions we still arrived at a net benefit favorable to OH of $17,830 per person.     

Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy): spatial special issue

To improve the quality of prediction of radioactive contamination, geostatistical methods, and in particular multivariate geostatistical models, are increasingly being used. These methods, however, are optimal only in the case in which the data may be assumed Gaussian and do not properly cope with data measurements that are discrete, nonnegative or show some degree of skewness. To deal with these situations, here we consider a hierarchical model in which non-Gaussian variables of different kind are handled simultaneously. We show that when observations are assumed to be conditionally distributed as Poisson and Gamma, variograms and cross-variograms have convenient simple forms, and estimation of the parameters of the model can be carried out by Monte Carlo EM. This work was inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy).