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The file drawer problem—often operationalized in terms of statistically significant results being published and statistically insignificant not being published—is widely documented in the social sciences. We extend Franco’s et al. [Science345, 1502–1505(2014)] seminal study of the file drawer problem in survey experiments submitted to the Time-sharing Experiments for the Social Sciences (TESS) data collection program. We examine projects begun after Franco et al. The updated period coincides with the contemporary open science movement. We find evidence of the problem, stemming from scholars opting to not write up insignificant results. However, that tendency is substantially smaller than it was in the prior decade. This suggests increased recognition of the importance of null results, even if the problem remains in the domain of survey experiments. 

Data from the General Social Survey indicate that higher-fertility individuals and their children are more conservative on “family values” issues, especially regarding abortion and same-sex marriage. This pattern implies that differential fertility has increased and will continue to increase public support for conservative policies on these issues. The association of family size with conservatism is specific to traditional-family issues and can be attributed in large part to the greater religiosity and lower educational attainment of individuals from larger families. Over the 2004 to 2018 period, opposition to same-sex marriage and abortion was 3 to 4 percentage points more prevalent than it would have been were traditional-family conservatism independent of family size in the current generation. For same-sex marriage, evolutionary forces have grown in relative importance as society as a whole has liberalized. As of 2018, differential fertility raised the number of US adults opposed to same-sex marriage by 17%, from 46.9 million to 54.8 million. 

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.