Evaluating the Accuracy of 2020 Census Block-Level Estimates in California.

In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Parental divorce is not uniformly disruptive to children's educational attainment.

Children whose parents divorce tend to have worse educational outcomes than children whose parents stay married. However, not all children respond identically to their parents divorcing. We focus on how the impact of parental divorce on children's education varies by how likely or unlikely divorce was for those parents. We find a significant negative effect of parental divorce on educational attainment, particularly college attendance and completion, among children whose parents were unlikely to divorce. Families expecting marital stability, unprepared for disruption, may experience considerable adjustment difficulties when divorce occurs, leading to negative outcomes for children. By contrast, we find no effect of parental divorce among children whose parents were likely to divorce. Children of high-risk marriages, who face many social disadvantages over childhood irrespective of parental marital status, may anticipate or otherwise accommodate to the dissolution of their parents' marriage. Our results suggest that family disruption does not uniformly disrupt children's attainment.

Researcher reasoning meets computational capacity: Machine learning for social science.

Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.

Recent Developments in Causal Inference and Machine Learning.

This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.

Unequal effects of disruptive events.

Disruptive events have significant consequences for the individuals and families who experience them, but these effects do not occur equally across the population. While some groups are strongly affected, others experience few consequences. We review recent findings on inequality in the effects of disruptive events. We consider heterogeneity based on socioeconomic resources, race/ethnicity, the likelihood of experiencing disruption, and contextual factors such as the normativity of the event in particular social settings. We focus on micro-level events affecting specific individuals and families, including divorce, job loss, home loss and eviction, health shocks and deaths, and violence and incarceration, but also refer to macro-level events such as recession and natural disasters. We describe patterns of variation that suggest a process of resource disparities and cumulative disadvantage versus those that reflect the impact of non-normative and unexpected shocks. Finally, we review methodological considerations when examining variation in the effect of disruptive events.

Socioeconomic roots of academic faculty.

Despite the special role of tenure-track faculty in society, training future researchers and producing scholarship that drives scientific and technological innovation, the sociodemographic characteristics of the professoriate have never been representative of the general population. Here we systematically investigate the indicators of faculty childhood socioeconomic status and consider how they may limit efforts to diversify the professoriate. Combining national-level data on education, income and university rankings with a 2017-2020 survey of 7,204 US-based tenure-track faculty across eight disciplines in STEM, social science and the humanities, we show that faculty are up to 25 times more likely to have a parent with a Ph.D. Moreover, this rate nearly doubles at prestigious universities and is stable across the past 50 years. Our results suggest that the professoriate is, and has remained, accessible disproportionately to the socioeconomically privileged, which is likely to deeply shape their scholarship and their reproduction.

Heterogeneous returns to college over the life course.

College graduates earn higher wages than high school graduates by age 30. Among women, the advantages of a college degree decline somewhat as they age, although they are still substantial at age 50; for men, the advantage of a college degree grows throughout the life cycle. Most previous research on returns to higher education has focused on income at a single point in time or averaged over multiple years; our contribution is to study how returns vary by age. We also document how these patterns vary by the propensity of graduating from college. We find modest wage returns for mid-propensity college graduates, but large returns for low-propensity and, for men, high-propensity college graduates. Our results rely on propensity score­based matching combined with multilevel growth curve models applied to data from the National Longitudinal Survey of Youth 1979 cohort.

Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning.

Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score-based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.

Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education.

We consider how the economic return to a college education varies across members of the U.S. population. Based on principles of comparative advantage, positive selection is commonly presumed, i.e., individuals who are most likely to select into college benefit most from college. Net of observed economic and non-economic factors influencing college attendance, we conjecture that individuals who are least likely to obtain a college education benefit most from college. We call this theory the negative selection hypothesis. To adjudicate between the two hypotheses, we study the effects of completing college on earnings by propensity score strata using an innovative hierarchical linear model with data from the National Longitudinal Survey of Youth 1979 and the Wisconsin Longitudinal Study. For both data sources, for men and for women, and for every observed stage of the life course, we find evidence suggesting negative selection. Results from auxiliary analyses lend further support to the negative selection interpretation of the results.

Predicting Layoff among Fragile Families.

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.

Why Does Parental Divorce Lower Children's Educational Attainment? A Causal Mediation Analysis.

Mechanisms explaining the negative effects of parental divorce on children's attainment have long been conjectured and assessed. Yet few studies of parental divorce have carefully attended to the assumptions and methods necessary to estimate causal mediation effects. Applying a causal framework to linked U.S. panel data, we assess the degree to which parental divorce limits children's education among whites and nonwhites and whether observed lower levels of educational attainment are explained by postdivorce family conditions and children's skills. Our analyses yield three key findings. First, the negative effect of divorce on educational attainment, particularly college, is substantial for white children; by contrast, divorce does not lower the educational attainment of nonwhite children. Second, declines in family income explain as much as one- to two-thirds of the negative effect of parental divorce on white children's education. Family instability also helps explain the effect, particularly when divorce occurs in early childhood. Children's psychosocial skills explain about one-fifth of the effect, whereas children's cognitive skills play a minimal role. Third, among nonwhites, the minimal total effect on education is explained by the offsetting influence of postdivorce declines in family income and stability alongside increases in children's psychosocial and cognitive skills.

Toward a better estimation of the effect of job loss on health.

Previous research has shown that involuntary job loss may have negative health consequences, but existing analyses have not adequately adjusted for health selection or other confounding factors that could reveal the association to be spurious. Using two large, population-based longitudinal samples of U.S. workers from the Americans' Changing Lives Study and the Wisconsin Longitudinal Study, this analysis goes further by using respondents' self-reports of the reasons for job loss and information about the timing of job losses and acute negative health shocks to distinguish health-related job losses from other involuntary job losses. Results suggest that even after adjustment for numerous social background characteristics and baseline health, involuntary job loss is associated with significantly poorer overall self-rated health and more depressive symptoms. More nuanced analyses reveal that among involuntary job losers, those who lose their jobs for health-related reasons have, not surprisingly, the most precipitous declines in health. Job losses for other reasons have substantive and statistically significant effects on depressive symptoms, while effects on self-rated poor health are relatively small.

The Far-Reaching Impact of Job Loss and Unemployment.

Job loss is an involuntary disruptive life event with a far-reaching impact on workers' life trajectories. Its incidence among growing segments of the workforce, alongside the recent era of severe economic upheaval, has increased attention to the effects of job loss and unemployment. As a relatively exogenous labor market shock, the study of displacement enables robust estimates of associations between socioeconomic circumstances and life outcomes. Research suggests that displacement is associated with subsequent unemployment, long-term earnings losses, and lower job quality; declines in psychological and physical well-being; loss of psychosocial assets; social withdrawal; family disruption; and lower levels of children's attainment and well-being. While reemployment mitigates some of the negative effects of job loss, it does not eliminate them. Contexts of widespread unemployment, although associated with larger economic losses, lessen the social-psychological impact of job loss. Future research should attend more fully to how the economic and social-psychological effects of displacement intersect and extend beyond displaced workers themselves.

Job displacement among single mothers: effects on children's outcomes in young adulthood.

Given the recent era of economic upheaval, studying the effects of job displacement has seldom been so timely and consequential. Despite a large literature associating displacement with worker well-being, relatively few studies focus on the effects of parental displacement on child well-being, and fewer still focus on implications for children of single-parent households. Moreover, notwithstanding a large literature on the relationship between single motherhood and children's outcomes, research on intergenerational effects of involuntary employment separations among single mothers is limited. Using 30 years of nationally representative panel data and propensity score matching methods, the authors find significant negative effects of job displacement among single mothers on children's educational attainment and social-psychological well-being in young adulthood. Effects are concentrated among older children and children whose mothers had a low likelihood of displacement, suggesting an important role for social stigma and relative deprivation in the effects of socioeconomic shocks on child well-being.

The Community College Effect Revisited: The Importance of Attending to Heterogeneity and Complex Counterfactuals.

Community colleges are controversial educational institutions, often said to simultaneously expand college opportunities and diminish baccalaureate attainment. We assess the seemingly contradictory functions of community colleges by attending to effect heterogeneity and to alternative counterfactual conditions. Using data on postsecondary outcomes of high school graduates of Chicago Public Schools, we find that enrolling at a community college penalizes more advantaged students who otherwise would have attended four-year colleges, particularly highly selective schools; however, these students represent a relatively small portion of the community college population, and these estimates are almost certainly biased. On the other hand, enrolling at a community college has a modest positive effect on bachelor's degree completion for disadvantaged students who otherwise would not have attended college; these students represent the majority of community college goers. We conclude that discussions among scholars, policymakers, and practitioners should move beyond considering the pros and cons of community college attendance for students in general to attending to the implications of community college attendance for targeted groups of students.

Estimating Heterogeneous Treatment Effects with Observational Data.

Individuals differ not only in their background characteristics, but also in how they respond to a particular treatment, intervention, or stimulation. In particular, treatment effects may vary systematically by the propensity for treatment. In this paper, we discuss a practical approach to studying heterogeneous treatment effects as a function of the treatment propensity, under the same assumption commonly underlying regression analysis: ignorability. We describe one parametric method and two non-parametric methods for estimating interactions between treatment and the propensity for treatment. For the first method, we begin by estimating propensity scores for the probability of treatment given a set of observed covariates for each unit and construct balanced propensity score strata; we then estimate propensity score stratum-specific average treatment effects and evaluate a trend across them. For the second method, we match control units to treated units based on the propensity score and transform the data into treatment-control comparisons at the most elementary level at which such comparisons can be constructed; we then estimate treatment effects as a function of the propensity score by fitting a non-parametric model as a smoothing device. For the third method, we first estimate non-parametric regressions of the outcome variable as a function of the propensity score separately for treated units and for control units and then take the difference between the two non-parametric regressions. We illustrate the application of these methods with an empirical example of the effects of college attendance on womens fertility.

Variation in the Relationship Between Education and Marriage: Marriage Market Mismatch?

Educational expansion has led to greater diversity in the social backgrounds of college students. We ask how schooling interacts with this diversity to influence marriage formation among men and women. Relying on data from the 1979 National Longitudinal Survey of Youth (N = 3208), we use a propensity score approach to group men and women into social strata and multilevel event history models to test differences in the effects of college attendance across strata. We find a statistically significant, positive trend in the effects of college attendance across strata, with the largest effects of college on first marriage among the more advantaged and the smallest-indeed, negative-effects among the least advantaged men and women. These findings appear consistent with a mismatch in the marriage market between individuals' education and their social backgrounds.

The impact of college education on fertility: evidence for heterogeneous effects.

As college-going among women has increased, more women are going to college from backgrounds that previously would have precluded their attendance and completion. This affords us the opportunity and motivation to look at the effects of college on fertility across a range of social backgrounds and levels of early achievement. Despite a substantial literature on the effects of education on women's fertility, researchers have not assessed variation in effects by selection into college. With data on U.S. women from the National Longitudinal Survey of Youth 1979, we examine effects of timely college attendance and completion on women's fertility by the propensity to attend and complete college using multilevel Poisson and discrete-time event-history models. Disaggregating the effects of college by propensity score strata, we find that the fertility-decreasing college effect is concentrated among women from comparatively disadvantaged social backgrounds and low levels of early achievement. The effects of college on fertility attenuate as we observe women from backgrounds that are more predictive of college attendance and completion.

Civic Returns to Higher Education: A Note on Heterogeneous Effects.

American educational leaders and philosophers have long valued schooling for its role in preparing the nation's youth to be civically engaged citizens. Numerous studies have found a positive relationship between education and subsequent civic participation. However, little is known about possible variation in effects by selection into higher education, a critical omission considering education's expressed role as a key mechanism for integrating disadvantaged individuals into civic life. I disaggregate effects and examine whether civic returns to higher education are largest for disadvantaged low likelihood or advantaged high likelihood college goers. I find evidence for significant effect heterogeneity: civic returns to college are greatest among individuals who have a low likelihood for college completion. Returns decrease as the propensity for college increases.