MPIDR Working Paper

A generalized counterfactual approach to decomposing differences between populations

Sudharsanan, N., Bijlsma, M. J.

MPIDR Working Paper WP-2019-004, 39 pages (February 2019).
Rostock, Max Planck Institute for Demographic Research

Keywords: methods of analysis


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 methods, such as the Oaxaca-Blinder and Kitagawa 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 estimation methods. In addition, the approach has the advantage of flexibility to accommodate different types of outcome and explanatory variables and any population contrast. 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 Mexican Health and Aging Study and the 1970 British Birth Cohort Study. The first example uses a cross-sectional binary outcome (disability), a contrast of prevalence rates, and considers a binary mediator (stroke), while the second example uses a count outcome (age at first birth), a contrast of median ages, and considers a count mediator (women’s own years of education). 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].