Nikkil Sudharsanan, Heidelberg University
Maarten J. Bijlsma , Max Planck Institute for Demographic Research
One central aim of the population sciences is to understand why one population has different levels of health and well-being compared to another. Various demographic and regression decompositions have been used to decompose population-differences in a wide range of outcomes. We provide a way of implementing an alternative decomposition method that, under certain assumptions, adds a causal interpretation to the decomposition by building upon counterfactual-driven methods. Our approach has the advantage of flexibility to accommodate different types of outcome variables and any summary population measure. By using Monte Carlo methods, our approach does not rely on closed-form approximate solutions and can be applied to any parametric model without having to derive any decomposition equations. We demonstrate our approach through two motivating examples using data from the 1970 British Birth Cohort Study and the Korean Longitudinal Study of Aging. Our first example decomposes socioeconomic status differences in three different summary measures of fertility and our second addresses the classic demographic question of the contribution of smoking to sex differences in life expectancy. Together, our two examples outline how to implement a very generalized decomposition procedure that is theoretically grounded in counterfactual theory but still easy to apply to a wide range of situations. We provide example R-code and an R-function [package in development].