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This article discusses major challenges facing intergenerational mobility research in three main domains: (i) the (dis)connection between theory and empirical applications; (ii) data gaps; (iii) measurement concerns. In doing so, it highlights theoretical and empirical extensions to better describe, explain, and predict complex intergenerational transmission processes in the light of new and rapid administrative data linkages, more mature survey data and other forms of big data. The article concludes with future directions for research.

Does parental joblessness delay young adults’ school-to-work transitions? If so, can a university degree moderate this relationship? We examine these questions using a representative sample of young adults who lived with their parents prior to entering the labor market in Australia (N = 2152) and the U.S. (N = 811) during the period 2001–2015. Results from Cox proportional hazards models demonstrate that parental joblessness (the proportion of time spent living in a household where no parent was employed) is associated with slower school-to-work transitions in both the U.S. and Australia. University degree attainment mitigates much of this negative relationship in Australia, suggesting that parental joblessness is most harmful for Australians who leave school before earning a university degree. There is no evidence for a similar interaction in the U.S., suggesting that the relationship between education, parental joblessness, and the school-to-work transition may depend on contextual factors such as the welfare regime.

This study examines the effect of parents’ unemployment on their children’s subsequent educational attainment. Its theoretical significance lies on its focus to test the mediating role of parents’ changing work ethics during spells of unemployment. Integrating multiple survey and administrative data sources, our estimates are based on a sample of Dutch children (n = 812) who were exposed to their parents’ unemployment during the previous economic crisis in the early 1980s. Our results reveal a direct negative effect between fathers’ unemployment duration and their children’s educational attainment and also an indirect effect through mothers’ changing attitudes towards work. We also find empirical evidence that mothers’ and fathers’ whose views about work become more pessimistic lead to reduced educational attainment among their children.

Temporary and other forms of non-standard employment are an important feature of modern labour markets. Yet, relatively little is known about how much and under what circumstances such employment arrangements impact on long-term wage outcomes. Using longitudinal data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey spanning the period 2001 to 2014, we examine how employment status earlier in a working career is associated with subsequent wage dynamics. Particular attention is paid to how wage trajectories vary with gender and age. Estimates from a series of panel data models of real hourly wages reveal that among men there is an average long-run penalty from casual employment of about 10%, suggestive of scarring effects. Nevertheless, for men in most age groups this wage penalty does eventually begin to shrink. Among prime-age men, however, there is no evidence of catch-up; indeed, for this group the wage gap widens over time. Among women the estimated average long-run wage penalty associated with casual employment is both much smaller and less robust. We argue that expectations and norms about ‘ideal careers’ may be an important explanatory factor underlying the larger casual employment wage penalty for men.

Unemployment is an important predictor of one’s future employment success. Yet, much about the endurance of unemployment effects on workers’ careers remains poorly understood. Our study complements this knowledge gap by examining the rate of recovery in the quality of careers following an unemployment spell among a representative sample of previously unemployed workers with different socio-demographic characteristics in Germany. We apply a new dynamic measure that quantifies the quality of binary sequences, distinguishing between “good” (i.e., employment) from “bad” labor force status activities (i.e., unemployment and inactivity). We use longitudinal data from the German Socio-Economic Panel (GSOEP) before the Great Financial Recession over the period 1984–2005 and deploy a series of hybrid models that control for unobserved heterogeneity. We find a non-linear recovery process after unemployment across gender and age groups. That is, after a period of recovery, career quality worsens. Least impacted are men experiencing unemployment when aged between 25–34 years, while men 55–66 have rather stable, though stronger, penalties. Furthermore, we find that recovery processes are contingent upon when respondents experience unemployment.

Despite the relevance of parents’ economic participation for children’s future outcomes, few studies have considered the joint contribution of both parents’ joblessness in explaining children’s employment outcomes in the long run. Further, previous studies have used measures of parents’ employment status observed at a single point in time. In contrast, in this study, exposure to dual-parent joblessness is measured in a cumulative fashion using longitudinal data for Australia (from the HILDA Survey, N = 895) and the United States (from the PSID, N = 1,500). We find that, in both countries, dual-parent-child associations are multiple times greater when parents jointly experience joblessness than when either of the parents is jobless. In the United States, family resources fully account for the link between parents’ and their children’s joblessness, whereas this is not the case in Australia. Overall, our study shows that dual-parent joblessness is an independent dimension of stratification that plays a prominent role in the reproduction of social and economic status across generations.

We propose a new measure to quantify the quality of binary sequences that can be meaningfully interpreted as series of successes and failures. We operationalize the concept of positive and negative sequences by formulating general properties that a quality measure must adhere to, construct a measure that fulfills these requirements, and show that such measure can be modeled in a theoretically meaningful way. We apply such measure of sequence quality to data from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey over the period 2001 to 2013, and model the evolution of employment career quality after the occurrence of an initial spell of unemployment, providing a direct test of unemployment “scarring” theories. We define states of unemployment and inactivity as failures and those of employment as successes to predict whether prior unemployment leads to descending spirals into inactivity and joblessness or whether patterns of full career recovery exist. Our findings lend support to scarring theories by demonstrating that, despite recovery trends, career disparities among previously unemployed workers persist long after their first unemployment experience. We conclude discussing implications of the findings and proposing directions for future extensions of the measure.

We first provide comprehensive and advanced access to principal component analysis, factor analysis, and reliability analysis. Based on a discussion of the different types of factor analytic procedures (exploratory factor analysis, confirmatory factor analysis, and structural equation modeling), we introduce the steps involved in a principal component analysis and a reliability analysis, offering guidelines for executing them in Stata. Specifically, we cover the requirements for running an analysis, modern options for extracting the factors and deciding on their number, as well as for interpreting and judging the quality of the results. Based on a step-by-step description of Stata’s menu options and code, we present an in-depth discussion of each element of the Stata output. Interpretation of output can be difficult, which we make much easier by means of various illustrations and applications, using a detailed case study to quickly make sense of the results. We conclude with suggestions for further readings on the use, application, and interpretation of factor analytic procedures.

The paper examines changes in the influence of family background, including socioeconomic and social background variables on educational attainment in Australia for cohorts born between 1890 and 1982. We test hypotheses from modernization theory on sibling data using random effects models and find: (i) substantial declines in the influence of family background on educational attainment (indicated by the sibling intraclass correlations); (ii) declines in the effects of both economic and cultural socioeconomic background variables; (iii) changes in the effects of some social background variables (e.g., family size); (iv) and declines in the extent that socioeconomic and social background factors account for variation in educational attainment. Unmeasured family background factors are more important, and proportionally increasingly so, for educational attainment than the measured socioeconomic and social background factors analyzed. Fixed effects models showed steeper declines in the effects of socioeconomic background variables than in standard analyses suggesting that unmeasured family factors associated with socioeconomic background obscure the full extent of the decline.