Bayesian estimation of total fertility from a population's age–sex structure. (June 2019)

• Record Type: Journal Article
• Authors: Schmertmann, Carl P; Hauer, Mathew E
• Other Names: Gampe Jutta guest-editor.; Camarda Carlo Giovanni guest-editor.
• Is Part Of: Statistical modelling. Volume 19:Number 3(2019)
• Page Start: 225
• Page End: 247
• Publication Date: 2019-06
• Subjects: Demography -- fertility -- indirect estimation -- Bayesian modelling -- age–sex distribution; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X18801450
• Languages: English
• ISSNs: 1471-082X

Abstract

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 rate (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 analyse 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.