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Estimating Demographic Parameters with Uncertainty from Fragmentary Data

Mark C Wheldon and Adrian Elmes Raftery

March 2011 CSSS Working Paper #108

Abstract

Abstract

Current methods for reconstructing human population structures of the past are deterministic or do not formally account for measurement error. We propose a method for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows from fragmentary data, that incorporates measurement error. Inference is based on joint posterior probability distributions which yield fully probabilistic interval estimates. It is designed for the kind of data commonly collected in modern demographic surveys and censuses. Population dynamics over the period of reconstruction are modeled by embedding formal demographic accounting relationships in a Bayesian hierarchical model. Informative priors are specified for vital rates, migration rates, population counts at baseline, and the accuracies of their respective measurements. We investigate calibration of central posterior marginal probability intervals by simulation and demonstrate the method by reconstructing the female population of Burkina Faso from 1960 to 2000.

Keywords: Bayesian hierarchical model, Cohort component model, Fertility rate, Markov chain Monte Carlo, International migration, Mortality rate