A decomposition method based on a model of continuous change.
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.
Horiuchi
Shiro
S
Program in Urban Public Health, Hunter College, 425 East 25th Street, Box 816, New York, NY 10010-2590, USA. shoriuch@hunter.cuny.edu
Wilmoth
John R
JR
Pletcher
Scott D
SD
D017677:Age Distribution; D003198:Computer Simulation; D003710:Demography; D006304:Health Status; D006801:Humans; D015233:Models, Statistical; D011157:Population Dynamics; D013269:Stochastic Processes