A decomposition method based on a model of continuous change

Demography. 2008 Nov;45(4):785-801. doi: 10.1353/dem.0.0033.


A demographic measure is often expressed as a deterministic or stochastic function of multiple variables (covariates), and a general problem (the decomposition problem) is to assess contributions of individual covariates to a difference in the demographic measure (dependent variable) between two populations. We propose a method of decomposition analysis based on an assumption that covariates change continuously along an actual or hypothetical dimension. This assumption leads to a general model that logically justifies the additivity of covariate effects and the elimination of interaction terms, even if the dependent variable itself is a nonadditive function. A comparison with earlier methods illustrates other practical advantages of the method: in addition to an absence of residuals or interaction terms, the method can easily handle a large number of covariates and does not require a logically meaningful ordering of covariates. Two empirical examples show that the method can be applied flexibly to a wide variety of decomposition problems. This study also suggests that when data are available at multiple time points over a long interval, it is more accurate to compute an aggregated decomposition based on multiple subintervals than to compute a single decomposition for the entire study period.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Age Distribution
  • Computer Simulation
  • Demography
  • Health Status
  • Humans
  • Models, Statistical
  • Population Dynamics*
  • Stochastic Processes