The origins of unpredictability in life outcome prediction tasks.

Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.

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.

Government Assistance Protects Low-Income Families from Eviction.

A lack of affordable housing is a pressing issue for many low-income American families and can lead to eviction from their homes. Housing assistance programs to address this problem include public housing and other assistance, including vouchers, through which a government agency offsets the cost of private market housing. This paper assesses whether the receipt of either category of assistance reduces the probability that a family will be evicted from their home in the subsequent six years. Because no randomized trial has assessed these effects, we use observational data and formalize the conditions under which a causal interpretation is warranted. Families living in public housing experience less eviction conditional on pre-treatment variables. We argue that this evidence points toward a causal conclusion that assistance, particularly public housing, protects families from eviction.

Comment: Summarizing income mobility with multiple smooth quantiles instead of parameterized means.

Studies of economic mobility summarize the distribution of offspring incomes for each level of parent income. Mitnik and Grusky (2020) highlight that the conventional intergenerational elasticity (IGE) targets the geometric mean and propose a parametric strategy for estimating the arithmetic mean. We decompose the IGE and their proposal into two choices: (1) the summary statistic for the conditional distribution and (2) the functional form. These choices lead us to a different strategy-visualizing several quantiles of the offspring income distribution as smooth functions of parent income. Our proposal solves the problems Mitnik and Grusky highlight with geometric means, avoids the sensitivity of arithmetic means to top incomes, and provides more information than is possible with any single number. Our proposal has broader implications: the default summary (the mean) used in many regressions is sensitive to the tail of the distribution in ways that may be substantively undesirable.

Does Opportunity Skip Generations? Reassessing Evidence From Sibling and Cousin Correlations.

Sibling (cousin) correlations are empirically straightforward: they capture the degree to which siblings' (cousins') socioeconomic outcomes are similar. At face value, these quantities seem to summarize something about how families constrain opportunity. Their meaning, however, is complicated. One empirical set of sibling and cousin correlations can be generated from a multitude of distinct theoretical processes. I illustrate this problem in the context of multigenerational mobility: the relationship between the incomes of an ancestor and a descendant separated by several generations in a family. When cousins' outcomes are similar (an empirical fact), prior authors have favored the particular theoretical interpretation that extended kin affect life chances through pathways not involving the parents of the focal individual. I show that this evidence is consistent with alternative theories of latent transmission (measurement error) or dynamic transmission (a parent-to-child transmission process that changes over generations). Theoretical assumptions are required to lend meaning to a point estimate. Further, I show that point estimates alone may be misleading because they can be highly uncertain. To facilitate uncertainty estimation for the key test statistic, I develop a Bayesian procedure to estimate sibling and cousin correlations. I conclude by outlining how future research might use sibling and cousin correlations as effective descriptive quantities while remaining cognizant that these quantities could arise from a variety of distinct theoretical processes.

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.

Introduction to the Special Collection on the Fragile Families Challenge.

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.

Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge.

Stewards of social data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible to protect the people represented in the data. In this article, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass collaboration designed to yield insights that could improve the lives of disadvantaged children in the United States. We describe our process of threat modeling, threat mitigation, and third-party guidance. We also describe the ethical principles that formed the basis of our process. We are open about our process and the trade-offs we made in the hope that others can improve on what we have done.

A Research Note on the Prevalence of Housing Eviction Among Children Born in U.S. Cities.

A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that one in seven children born in large U.S. cities in 1998-2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were substantial across all cities and demographic groups studied, but children from disadvantaged backgrounds were most likely to experience eviction. Among those born into deep poverty, we estimate that approximately one in four were evicted by age 15. Given prior evidence that forced moves have negative consequences for children, we conclude that the high prevalence and social stratification of housing eviction are sufficient to play an important role in the reproduction of poverty and warrant greater policy attention.

New Evidence Against a Causal Marriage Wage Premium.

Recent research has shown that men's wages rise more rapidly than expected prior to marriage, but interpretations diverge on whether this indicates selection or a causal effect of anticipating marriage. We seek to adjudicate this debate by bringing together literatures on (1) the male marriage wage premium; (2) selection into marriage based on men's economic circumstances; and (3) the transition to adulthood, during which both union formation and unusually rapid improvements in work outcomes often occur. Using data from the National Longitudinal Survey of Youth 1979, we evaluate these perspectives. We show that wage declines predate rather than follow divorce, indicating no evidence that staying married benefits men's wages. We find that older grooms experience no unusual wage patterns at marriage, suggesting that the observed marriage premium may simply reflect co-occurrence with the transition to adulthood for younger grooms. We show that men entering shotgun marriages experience similar premarital wage gains as other grooms, casting doubt on the claim that anticipation of marriage drives wage increases. We conclude that the observed wage patterns are most consistent with men marrying when their wages are already rising more rapidly than expected and divorcing when their wages are already falling, with no additional causal effect of marriage on wages.