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      A decomposition method based on a model of continuous change.

      1 , ,
      Demography

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          Abstract

          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.

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          Author and article information

          Journal
          Demography
          Demography
          0070-3370
          0070-3370
          Nov 2008
          : 45
          : 4
          Affiliations
          [1 ] Program in Urban Public Health, Hunter College, 425 East 25th Street, Box 816, New York, NY 10010-2590, USA. shoriuch@hunter.cuny.edu
          Article
          NIHMS89135
          2832329
          19110897
          cc11fb7f-f899-4d8e-8890-01df0866a04f

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