Bayesian Estimation of Total Fertility from a Population's Age-Sex Distribution

Carl Schmertmann and Mathew Hauer
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Carl Schmertmann: Florida State University

No je59v_v1, SocArXiv from Center for Open Science

Abstract: We investigate a modern statistical approach to a classic deterministic demographic estimation technique. When vital event registration is missing or inadequate, it is possible to approximate a population's total fertility (TFR) from information about its distribution by age and sex. For example, if under-five child mortality is low then TFR is often close to seven times the child/woman ratio (CWR), the number of 0--4 year olds per 15--49 year old woman. We analyze the formal relationship between CWR and TFR to identify sources of uncertainty in indirect estimates. We construct a Bayesian model for the statistical distribution of TFR conditional on the population's age-sex structure, in which unknown demographic quantities in the standard approximation are parameters with prior distributions. We apply the model in two case studies: to a small indigenous population in the Amazon region of Brazil that has extremely high fertility rates, and to the set of 159 counties in the US state of Georgia. A statistical approach yields important insights into the sources of error in indirect estimation, and their relative magnitudes.

Date: 2017-02-20
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:je59v_v1

DOI: 10.31219/osf.io/je59v_v1

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