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Probabilistic projection of subnational total fertility rates.

ABSTRACT: BACKGROUND:We consider the problem of probabilistic projection of the total fertility rate (TFR) for subnational regions. OBJECTIVE:We seek a method that is consistent with the UN's recently adopted Bayesian method for probabilistic TFR projections for all countries and works well for all countries. METHODS:We assess various possible methods using subnational TFR data for 47 countries. RESULTS:We find that the method that performs best in terms of out-of-sample predictive performance and also in terms of reproducing the within-country correlation in TFR is a method that scales each national trajectory from the national predictive posterior distribution by a region-specific scale factor that is allowed to vary slowly over time. CONCLUSIONS:Probabilistic projections of TFR for subnational units are best produced by scaling the national projection by a slowly time-varying region-specific scale factor. This supports the hypothesis of Watkins (1990, 1991) that within-country TFR converges over time in response to country-specific factors, and thus extends the Watkins hypothesis to the last 50 years and to a much wider range of countries around the world. CONTRIBUTION:We have developed a new method for probabilistic projection of subnational TFR that works well and outperforms other methods. This also sheds light on the extent to which within-country TFR converges over time.

SUBMITTER: Sevcikova H

PROVIDER: S-EPMC6961957 | biostudies-literature | 2018 Jan-Jun

REPOSITORIES: biostudies-literature

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Probabilistic projection of subnational total fertility rates.

Ševčíková Hana H Raftery Adrian E AE Gerland Patrick P

Demographic research 20180101

<h4>Background</h4>We consider the problem of probabilistic projection of the total fertility rate (TFR) for subnational regions.<h4>Objective</h4>We seek a method that is consistent with the UN's recently adopted Bayesian method for probabilistic TFR projections for all countries and works well for all countries.<h4>Methods</h4>We assess various possible methods using subnational TFR data for 47 countries.<h4>Results</h4>We find that the method that performs best in terms of out-of-sample predi ...[more]

PMID: 31942164

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Project description:BackgroundUnderstanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility.MethodsWe did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster.FindingsTFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed.InterpretationSubstantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning.FundingWellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.

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Probabilistic projection of subnational total fertility rates.

Publications

Probabilistic projection of subnational total fertility rates.

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