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.

Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. Although they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analysing time-series cross-sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the prespecified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values. Assessing the quality of matches is done by examining covariate balance. Finally, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator, accounting for a time trend. We illustrate the proposed methodology through simulation and empirical studies. An open-source software package is available for implementing the proposed methods.

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Recent debates over the relative importance of democracy and state capacity for human development have led to the prevailing view that a strong state must be built before the introduction of democracy. Our research challenges this "sequencing approach" in international development. Using a global panel of countries over 50 years, we document that democracy has a substantial, positive causal effect on state capacity with identification strategies that adjust for pre-treatment dynamics. The state-enhancing effect of democracy is robust to alternative measures of key variables, a large set of time-varying confounders and an instrumental variable design that leverages variation in regional democratic diffusions. Subsequent analysis suggests contestation, rather than participation, as a potential causal mechanism. Our findings contribute to the burgeoning literature on sources of state capacity in the developing world and yield practical implications for democracy assistance.

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