Abortion is a safe and common medical procedure. Roughly one in four women in the United States will have an abortion before the end of her reproductive years. Because of how common this experience is and how rapidly abortion policy is shifting, understanding the relationship between abortion and women’s socioeconomic futures is well worth exploring. Extant research has demonstrated that the transition to parenthood is a critical inflection point in women’s socioeconomic trajectories, often leading to poorer outcomes. In this article, we connect previous sociological work elucidating mechanisms of socioeconomic stratification and gender by considering the relationship between abortion use and access and future socioeconomic outcomes such as education, income, and financial stability—as measured by several measures, including evictions, debt, ability to pay bills, and a separate index of economic instability. We use national longitudinal survey data to assess socioeconomic outcomes associated with abortion using two statistical approaches. We find that women who lived in a location with fewer abortion restrictions in adolescence, and women who had an abortion, compared to a live birth, in adolescence, are more likely to have graduated from college, have higher incomes, and have greater financial stability at two time-points over an almost 25-year period. Our results provide evidence that policy environments allowing access to abortion, and teenagers having the option to use abortion to avoid early parenthood, are important axes along which women’s economic lives are shaped. Our research implies that the widespread abortion bans and restrictions in the United States are likely to lead to lower educational attainment and adult economic stability among women living under such restrictions, as compared to women in locations with better access to abortion.

Independent component analysis (ICA) is widely used to estimate spatial resting-state networks and their time courses in neuroimaging studies. It is thought that independent components correspond to sparse patterns of co-activating brain locations. Previous approaches for introducing sparsity to ICA replace the non-smooth objective function with smooth approximations, resulting in components that do not achieve exact zeros. We propose a novel Sparse ICA method that enables sparse estimation of independent source components by solving a non-smooth non-convex optimization problem via the relax-and-split framework. The proposed Sparse ICA method balances statistical independence and sparsity simultaneously and is computationally fast. In simulations, we demonstrate improved estimation accuracy of both source signals and signal time courses compared to existing approaches. We apply our Sparse ICA to cortical surface resting-state fMRI in school-aged autistic children. Our analysis reveals differences in brain activity between certain regions in autistic children compared to children without autism. Sparse ICA selects coactivating locations, which we argue is more interpretable than dense components from popular approaches. Sparse ICA is fast and easy to apply to big data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.