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Segmented assimilation theory predicts that contemporary non-white groups follow three patterns of assimilation: mainstream, downward, or delayed. Yet, the homogenous treatment and primacy of ethnicity resigns all group members to a similar fate. Whereas few studies of ethnic incorporation consider both the classed and gendered nature of the labor market, this study investigates the extent to which intersectional group differences within the highly stratified American economy shape segmented assimilation trajectories.

This study introduces an intersectional approach to segmented assimilation theory. Using the 2000 census, this study examines how within group differences in skill and gender condition the hourly earnings, joblessness and self-employment participation outcomes of five ethnic minority groups from the first to the second generation, compared against US-born, non-Hispanic whites.

Findings generally support the mainstream assimilation hypothesis for all groups; a downward assimilation trajectory among Chinese men only; and a delayed assimilation trajectory for low-skilled Filipinas and high-skilled Cuban men and women. This study reveals that intra-group differences in skill and gender shape divergent segmented assimilation trajectories among members of the same ethnic group.

This study challenges the emphasis on and primacy of ethnicity in predicting segmented assimilation in favor of an intersectional approach that considers how multiple, interdependent, and intersecting dimensions of identity and not only ethnicity shape the process of economic incorporation among ethnic groups.

The aim of this research is to determine the extent to which the human resource (HR) function can screen and potentially predict suicidal employees and offer preventative mental health assistance.

Drawing from the 2019 National Survey of Drug Use and Health (N = 56,136), this paper employs multivariate binary logistic regression to model the work-related predictors of suicidal ideation, planning and attempts.

The results indicate that known periods of joblessness, the total number of sick days and absenteeism over the last 12 months are significantly associated with various suicidal outcomes while controlling for key psychosocial correlates. The results also indicate that employee assistance programs are associated with a significantly reduced likelihood of suicidal ideation. These findings are consistent with conservation of resources theory.

This research demonstrates preliminarily that the HR function can unobtrusively detect employee mental health crises by collecting data on key predictors.

In the era of COVID-19, employers have a duty of care to safeguard employee mental health. To this end, the authors offer an innovative way through which the HR function can employ predictive analytics to address mental health crises before they result in tragedy.

Data from the 1984 Survey of Income and Program Participation are linked to longitudinal records from the Social Security Administration to examine the relationship between the long-term unemployment that prime-aged (ages 25–55) male workers experienced around the time of the 1980–1982 twin recessions with earnings, receipt of either Disability Insurance or Supplemental Security Income (DI-SSI) benefits, and mortality. Separate estimations are made for those who voluntarily and involuntarily left employment and the combined sample of these two groups. We find that 20 years later, long-term joblessness was associated with significantly lower earnings and higher likelihoods of the receipt of DI-SSI benefits as well as mortality.

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.