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Research on educational mobility usually fails to consider variation in the association between social origin and academic performance across the distribution of children's academic performance. However, theories of social mobility as well as theories about resource allocation within families predict such variation. We use quantile regression models to estimate variations in the associations between different indicators of family background (parental education, occupation, earnings, and wealth) and children's educational performance across the performance distribution. We apply this approach to data on Germany, Norway, and the United States, three countries that represent different kinds of welfare and education regimes that may affect the intergenerational transmission of educational advantage. We find a stronger association between family background and academic performance at the bottom than in the middle and the weakest association at the top of the performance distribution. These findings are virtually identical across all four indicators of family background. We also find no cross-national differences in the variation of the association between family background and academic performance across the performance distribution.

Research on educational mobility usually studies socioeconomic differences at the mean of children's academic performance but fails to consider the variation in the shape of socioeconomic differences across the outcome distribution. Theories of social mobility as well as theories about the resource allocation within families predict such variation. We use quantile regression models to estimate variation in socioeconomic differences across the distribution of academic performance using different indicators of family background (parental education, occupation, earnings, and wealth). We apply this approach to data on Germany, Norway, and the United States, three countries that represent different welfare and education regimes that may affect the intergenerational transmission of educational advantage. We find stronger socioeconomic differences at the bottom than at the middle and the smallest differences at the top of the performance distribution. These findings are virtually identical across all four indicators of family background. We also find no cross-national differences in the shape of socioeconomic differences in academic performance.

AbstractThis study examines the unique contributions of parental wealth, class background, education, and income to different measures of educational attainment. We build on recent sibling correlation approaches to estimate, using Norwegian register data, the gross and net contribution of each social origin dimension across almost 3 decades of birth cohorts. Our findings suggest that parental education is crucial for all measures of children's educational outcomes in all models. In the descriptive analyses, we find that while broad education measures remain stable or decrease over time, attaining higher tertiary education and elite degrees is more stable or increasingly dependent on family background, especially parental financial resources. While gross sibling correlation models show somewhat decreasing trends in the contribution of education in all measures of educational outcomes, net models show that the unique contributions of financial resources have increased over time. Our results lend some support to the idea of education as a positional good and suggest that educational inequalities reflect broader patterns of inequality in society. Our results further indicate that the importance of parental education and cultural capital for children's education can be explained by within‐resource transmission but that pro‐educational norms tied to wealth may play an increasingly important role in educational mobility. In summary, this study sheds light on the multidimensional nature of social origins and highlights the role of different factors in shaping educational outcomes over time.

This article explores key determinants of the intention to work from home (WFH) among U.S. adults in the early phase of the pandemic. Leveraging nationally representative survey data collected in the initial stages of the pandemic, it explores the role of modalities of communication alongside the more frequently studied behavioral, occupational, and sociodemographic factors in shaping WFH intentions as reported by survey respondents. Venturing beyond prior studies of remote work and remote work intentions, the study finds that the frequency of text messaging platform (e.g., Slack) usage and the frequency of videoconferencing (e.g., Zoom) exhibit diametrically opposed effects on the intentions to WFH in the future. Whereas a higher frequency of text messaging platform usage is linked to a preference for a preference for more frequent WFH in a hypothetical future WFH, a higher frequency of videoconferencing platform usage is associated with the opposite preference. Additionally, the effect of the intensity of respondents' engagement with these two communication modalities on their intentions is mediated by pre-pandemic WFH experience as well as the intensity of interruptions in their WFH environment. Intensive videoconferencers (Zoomers) who work in high-interruption environments are particularly averse to future WFH. Conversely, intensive messagers (Slackers) who work from home substantially prior to the pandemic report express a preference for more frequent WFH in the future.

This study examines high-achieving students in Norwegian lower secondary schools who follow accelerated learning trajectories in mathematics, so-called fast tracks. The study examines whether and how fast tracks improve high-achieving students' learning and performance. The analyses rely on high-quality administrative register data from Norway with complete information for all students. The results show that fast-track students outperform regular students in mathematics. The results suggest that (1) the fast tracks improve the students' learning, and (2) the students are selected and self-selected – based on prior performance and background characteristics. When comparing teacher-set grades vs. exam grades set by anonymous random assigned examinators, the results suggest that (3) teachers give fast-track students even better grades than regular students. One plausible explanation is that teachers perceive or label fast-track students as more talented than other students. The findings especially suggest that Norwegian teachers compensate disadvantaged fast-track students.

Using quantile regression models to estimate quantile treatment effects is becoming increasingly popular. This paper introduces the rqr command that can be used to estimate residualized quantile regression (RQR) coefficients and the rqrplot postestimation command that can be used to effortless plot the coefficients. The main advantages of the rqr command compared to other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.

The opportunities for understanding how treatment effects vary across different segments of the population have led to a rise in the use of quantile regressions for identifying unconditional quantile treatment effects (QTEs). However, existing quantile regression models fall into two categories: those that are unsuitable for identifying unconditional QTEs, and those that often struggle with the complex data structures common in sociology and other social sciences. Therefore, existing methods to identify unconditional QTEs are incomplete: the propensity score framework of Firpo (2007) allows for only a binary treatment variable, and the generalized quantile regression model of Powell (2020) faces difficulties with large data sets and high-dimensional fixed effects. This paper introduces a two-step approach to estimating unconditional QTEs, which is easy to use and aligns with the needs of sociologists. First, the treatment variable is decomposed into a systematic and random part, and then, the random variation in the treatment status is used in a bivariate quantile regression model. Through a series of simulations and three empirical applications, we demonstrate that the RQR approach provides unbiased estimates of unconditional QTEs. Moreover, the RQR approach offers greater flexibility and enhances computational speed compared to existing models, and it can easily handle high-dimensional fixed effects. In sum, the RQR approach fills a pressing void in quantitative research methodology, offering a much-needed tool for studying treatment effect heterogeneity.

This paper discusses the crucial but sometimes neglected differences between unconditional quantile regression (UQR) models and quantile treatment effects (QTE) models. We argue that there is a frequent mismatch between the aim of the quantile regression analysis and the quantitative toolkit used in much of the applied literature, including the motherhood wage penalty literature. This mismatch may result in wrong conclusions being drawn from the data, and in the end, misguided theories. In this paper, we clarify the crucial conceptual distinction between influences on quantiles of the overall distributions, which we term population-level influences, and individual-level QTEs. Further, we use data simulations to illustrate that various classes of quantile regression models may, in some instances, give entirely different conclusions (to different questions). Finally, we compare quantile regression estimates using real data examples, showing that UQR and QTE models differ sometimes but not always. Still, the conceptual and empirical distinctions between quantile regression models underline the need to match the correct model to the specific research questions. We conclude the paper with a few practical guidelines for researchers.

AbstractThe purpose of our study is to investigate the role of wealth in broader stratification processes. Based on unique data from Norwegian tax registers, we address questions about the association between class origin, wealth transfers, and wealth accumulation among young adults. We show that is more common to receive transfers in the higher than in the lower social classes, and that those originating in the economic upper class, i.e. large proprietors, owners, of single enterprises as well as investors with diversified portfolios, and top managers and directors, are especially likely to receive transfers, as well as especially large inter vivos gifts. As young adults, those with upper-class origins, and especially origins in the economic upper class, accumulate more wealth than those with origins in classes lower in the social hierarchy. In all social classes, those who have received wealth transfers accumulate most wealth. We argue that transferring wealth indeed appears as robust and efficient mobility or reproduction strategy.

This article examines the effects of digital inequality in conjunction with curricular tracking on academic achievement. Capitalizing on an original survey administered to seniors (fourth-year secondary school students), our survey data ( N = 972) come from a large American public high school with a predominantly disadvantaged student body. The school's elective tracking system and inadequate digital resources make for an excellent case study of the effects of a differentiated curriculum and digital inequalities on academic achievement. Multilevel random-effects and fixed-effects regression models applied to the survey data reveal the important role played by digital inequalities in shaping academic achievement as measured by GPA. As the models establish, academic achievement is positively correlated with both duration of digital experience and usage intensity regarding academically useful computing activities, even when students' curricular and class placement are taken into account. In contrast, both leisure computing and smartphone usage are negatively correlated with academic achievement as measured by GPA. Also with regard to GPA, findings show that students in the higher curricular tracks benefit more from longer durations of digital experience than do students in lower curricular tracks. These results underscore the importance of focusing attention on the ways in which digital inequalities combine with curricular tracking in shaping academic achievement.

This study uses comparable Danish and Norwegian administrative registers in the period 1992 to 2003 to examine how social origin affects unemployment risks and social assistance reception over the early life course. Denmark and Norway have traditionally been viewed as similar in political, cultural and social aspects. However, labour market regulation in Denmark is more liberal than in Norway. This study therefore serves as a unique comparison of the impact of social origin under varying conditions of labour market regulation. Although the absolute probability of being disadvantaged decreases as individuals progress in age from 20 to 30 and varies between Denmark and Norway, the relative impact of social background is stable and similar. The results offer little support to theories that put a strong emphasis on inter-generational transmission through educational achievements, but rather point toward the importance of ascriptive resources. Generalised estimating equations are used to assess the repeated outcomes.