Abhatoo - Centre Nationale de Documentation

This paper discusses a Bayesian projection model to construct country-speci c probabilistic projections of the total fertility rate (TFR), for all countries in the world. Our methodology for high fertility countries builds onto the one currently used by the United Nations Population Division, which assumes that fertility will eventually fall below replacement level. In the model, the pace of the fertility decline is decomposed into a systematic decline with distortion terms added to it. The pace of the systematic decline is modeled as a function of its level, based on the UN methodology. We propose a Bayesian hierarchical model to estimate the parameters of the decline function.

For low fertility countries, and to project what will happen after the fertility transition has ended, we assume that the TFR will uctuate around 2.1 children in long term projections. This is modeled with a rst order autoregressive time series model. The result of the Bayesian projection model is a set of future TFR trajectories for each country. The \best" projection for the TFR is given by the median TFR outcome in each period. The \low" and \high" variant are given by the 10th and 90th percentile of the sample of TFR outcomes in each period, the lower and upper bounds of the 80% prediction intervals.

This new approach provides valuable insights about future fertility trends worldwide. The prediction intervals for future fertility levels vary by country. The intervals are wider in high-fertility countries than those currently inferred with the low and high variants of the ocial UN population projections. The projected TFRs and the corresponding prediction intervals will shed new light on future population dynamics, including on dependency ratios and on the pace of population ageing.