An Age–Period–Cohort model for the gender gap in youth and early adult mortality

In this article, we introduce a novel framework in mortality risk study, operating on the statistical approach of the Age–Period–Cohort (APC) framework by leveraging the skew-normal distribution and Bayesian estimation. We propose a specific application to gender gap analysis and forecasting. By employing data from the Human Mortality Database, our study contributes first, a novel perspective on gender gap analysis and forecasting and, second, an improvement to the statistical framework for APC analysis. To test our approach, we apply the proposed model in three different mortality scenarios and compare our projection with a Bayesian APC model as a benchmark. The results show that the proposed framework has the potential to gain efficiency and accuracy in forecast gender differences in mortality. By offering a systematic approach to quantifying and forecasting the gender gap across different age groups and time periods, our results may help researchers and policymakers to better understand underlying trends and temporal dynamics.