Using partial identification methods to estimate the effect of violence against women on their children’s health outcomes

A growing literature in economics seeks to estimate the costs of violence against women by examining, for example, its impact on the health outcomes of their children. However, it is difficult to assign a causal interpretation to these nonexperimental studies due to the presence of unobservable characteristics affecting violence and health outcomes simultaneously. The lack of credible instrumental variables applicable in several countries further limits our knowledge. I address this gap by using new partial identification methods to estimate the relative size of the unobservables needed to eliminate the estimated effects in nonexperimental studies. I also expand the external validity of the analysis by using data from five standardized nationally representative household surveys in Latin America. Consistent with previous studies, cross-sectional estimates show large negative associations between violence against women and an array of child health outcomes. However, when accounting for omitted variable bias, at best, two-thirds of the estimates remain robust and they are concentrated on the outcomes with the largest cross-sectional estimated impacts.     

Fertility, female labor force participation, and the demographic dividend

We estimate the effect of fertility on female labor force participation in a panel of countries using abortion legislation as an instrument for fertility. We find that removing legal restrictions on abortion significantly reduces fertility and estimate that, on average, a birth reduces a woman’s labor supply by almost 2 years during her reproductive life. Our results imply that behavioral change, in the form of increased female labor supply, contributes significantly to economic growth during the demographic transition when fertility declines.      

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

Ensemble methods can be used to construct a forecast distribution from a collection of point forecasts. They are used extensively in meteorology, but have received little direct attention in economics. In a real-time analysis of the ECB’s Survey of Professional Forecasters, we compare ensemble methods to histogram-based forecast distributions of GDP growth and inflation in the Euro Area. We find that ensembles perform very similarly to histograms, while being simpler to handle in practice. Given the wide availability of surveys that collect point forecasts but not histograms, these results suggest that ensembles deserve further investigation in economics.