Socius最新文献

What dimensions of education matter for people's chances of surviving young adulthood? Do cognitive skills, non-cognitive skills, course taking patterns, and school social contexts matter for young adult mortality, even net of educational attainment? We analyze data from High School & Beyond-a nationally representative cohort of ~25,000 high school students first interviewed in 1980. Many dimensions of education are associated with young adult mortality, and high school students' math course taking retain their associations with mortality net of educational attainment. Our work draws on theories and measures from sociological and educational research and enriches public health, economic, and demographic research on educational gradients in mortality that has almost exclusively relied on ideas of human capital accumulation and measures of degree attainment. Our findings also call on social and education researchers to engage together in research on the life-long consequences of educational processes, school structures, and inequalities in opportunities to learn.

This study investigates the relationship between maternal employment and state-to-state differences in childcare cost and mean school day length. Pairing state-level measures with an individual-level sample of prime working-age mothers from the American Time Use Survey (2005-2014; n = 37,993), we assess the multilevel and time-varying effects of childcare costs and school day length on maternal full-time and part-time employment and childcare time. We find mothers' odds of full-time employment are lower and part-time employment higher in states with expensive childcare and shorter school days. Mothers spend more time caring for children in states where childcare is more expensive and as childcare costs increase. Our results suggest that expensive childcare and short school days are important barriers to maternal employment and, for childcare costs, result in greater investments in childcare time. Politicians engaged in national debates about federal childcare policies should look to existing state childcare structures for policy guidance.

Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.

Existing research linking SES with work primarily focuses on the precursors (educational attainment) and outcomes (income) of work, rather than asking how diverse facets of work influence health. Using four waves of data from the Wisconsin Longitudinal Study, we evaluate whether multiple measures of respondent job characteristics, respondent preferences for those characteristics, and their interaction substantially improve the fit of sociological models of men's and women's physical and mental health at midlife and old age compared to traditional models using educational attainment, parental SES, and income. We find that non-wage job characteristics predict men's and women's physical and mental health over the lifecourse, although we find little evidence that the degree to which one's job accords with one's job preferences matters for health. These findings expand what we know about how work matters for health, demonstrating how the manner and condition under which one works has lasting impacts on wellbeing.

The Fragile Families Challenge is a scientific mass collaboration designed to measure and understand the predictability of life trajectories. Participants in the Challenge created predictive models of six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. This Special Collection includes 12 articles describing participants' approaches to predicting these six outcomes as well as 3 articles describing methodological and procedural insights from running the Challenge. This introduction will help readers interpret the individual articles and help researchers interested in running future projects similar to the Fragile Families Challenge.

Women's assessments of gender equality do not consistently match global indices of gender inequality. In surveys covering 150 countries, women in societies rated gender-unequal according to global metrics such as education, health, labor-force participation, and political representation did not consistently assess their lives as less in their control or less satisfying than men did. Women in these societies were as likely as women in index-equal societies to say they had equal rights with men. Their attitudes toward gender issues did not reflect the same latent construct as in index-equal societies, although attitudes may have begun to converge in recent years. These findings reflect a longstanding tension between universal criteria of gender equality and an emphasis on subjective understandings of women's priorities.

Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a "human-in-the-loop" approach in the Fragile Families Challenge. The authors use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy offers the option to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. The authors find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches that do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.

This article extends previous research on place-based patterns of racial categorization by linking it to sociological theory that posits subnational variation in cultural schemas and applying regression techniques that allow for spatial variation in model estimates. We use data from a U.S. restricted-use geocoded longitudinal survey to predict racial classification as a function of both individual and county characteristics. We first estimate national average associations, then turn to spatial-regime models and geographically weighted regression to explore how these relationships vary across the country. We find that individual characteristics matter most for classification as "Black," while contextual characteristics are important predictors of classification as "White" or "Other," but some predictors also vary across space, as expected. These results affirm the importance of place in defining racial boundaries and suggest that U.S. racial schemas operate at different spatial scales, with some being national in scope while others are more locally situated.

The loss of a job is the loss of a major social and economic role and is associated with long-term negative economic and psychological consequences for workers and families. Modeling the causal effects of a social process like layoff with observational data depends crucially on the degree to which the model accounts for the characteristics that predict loss. We report analyses predicting layoff in the Fragile Families data as part of the Fragile Families Challenge. Our model, grounded in empirical social science research on layoff, did not perform substantially worse than the best-performing model using data science techniques. This result is not fully unforeseen, given that layoff functions as a relatively exogenous shock. Future work using the results of the Challenge should attend to whether small improvements in prediction models, like those we observe across models of layoff, nevertheless significantly increase the validity of subsequent models for causal inference.

Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author's raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals.