Mental Health and Abortions among Young Women: Time-varying Unobserved Heterogeneity, Health Behaviors, and Risky Decisions

In this paper, we provide causal evidence on abortions and risky health behaviors as determinants of mental health development among young women. Using administrative in- and outpatient records from Sweden, we apply a novel grouped fixed-effects estimator proposed by Bonhomme and Manresa (2015) to allow for time-varying unobserved heterogeneity. We show that the positive association obtained from standard estimators shrinks to zero once we control for grouped time-varying unobserved heterogeneity. We estimate the group-specific profiles of unobserved heterogeneity, which reflect differences in unobserved risk to be diagnosed with a mental health condition. We then analyze mental health development and risky health behaviors other than unwanted pregnancies across groups. Our results suggest that these are determined by the same type of unobserved heterogeneity, which we attribute to the same unobserved process of decision-making. We develop and estimate a theoretical model of risky choices and mental health, in which mental health disparity across groups is generated by different degrees of self-control problems. Our findings imply that mental health concerns cannot be used to justify restrictive abortion policies. Moreover, potential self-control problems should be targeted as early as possible to combat future mental health consequences

Bandwidth selection in marker dependent kernel hazard estimation

Practical estimation procedures for the local linear estimation of an unrestricted failure rate when more information is available than just time are developed. This extra information could be a covariate and this covariate could be a time series. Time dependent covariates are sometimes called markers, and failure rates are sometimes called hazards, intensities or mortalities. It is shown through simulations and a practical example that the fully local linear estimation procedure exhibits an excellent practical performance. Two different bandwidth selection procedures are developed. One is an adaptation of classical cross-validation, and the other one is indirect cross-validation. The simulation study concludes that classical cross-validation works well on continuous data while indirect cross-validation performs only marginally better. However, cross-validation breaks down in the practical data application to old-age mortality. Indirect cross-validation is thus shown to be superior when selecting a fully feasible estimation method for marker dependent hazard estimation

Hours and income dynamics during the Covid-19 pandemic:The case of the Netherlands

Using customized panel data spanning the entire year of 2020, we analyze the dynamics of working hours and household income across different stages of the Covid-19 pandemic. Like many other countries, during this period, the Netherlands experienced a quick spread of the SARS-CoV-2 virus, adopted a set of fairly strict social distancing measures, gradually reopened, and imposed another lockdown to contain the second wave. We show that socioeconomic status is strongly related to changes in working hours, especially when strict economic restrictions are in place. In contrast, household income is equally unaffected for all socioeconomic groups. Examining the drivers of these observations, we find that pandemic-specific job characteristics (the ability to work from home and essential worker status) help explain the socioeconomic gradient in total working hours. Household income is largely decoupled from shocks to working hours for employees. We provide suggestive evidence that large-scale labor hoarding schemes have helped insure employees against shocks to their employers