Between 1960 and 2000, fertility fell sharply in Brazil, but this transition was unevenly distributed in space and time. Using Bayesian spatial statistical methods and microdata from five censuses, we develop and apply a procedure for fitting logistic curves to the fertility transitions in more than 500 small regions of Brazil over this 40-year period. Doing so enables us to map the main features of the Brazilian fertility transition in considerable detail. We detect early declines in some regions of the country and document large differences between early and late transitions in regard to both the initial level of fertility and the speed of the transition. We also use our results to test hypotheses regarding changes in the level of development at the onset of the fertility transition and identify a temporary stall in the Brazilian transition that occurred in the late 1990s. A web site with project details is at http://schmert.net/BayesLogistic . Copyright (c) 2010 The Population Council, Inc..
In this article, we analyze empirical Bayes (EB) methods for estimating small-area rate schedules. We develop EB methods that treat schedules as vectors and use adaptive neighborhoods to keep estimates appropriately local. This method estimates demographic rates for local subpopulations by borrowing strength not only from similar individuals elsewhere but also from other groups in the same area and from regularities in schedules across locations. EB is substantially better than standard methods when rates have strong spatial and age patterns. We illustrate this method with estimates of age-specific fertility schedules for over 3,800 Brazilian municipalities.
Using microdata from the Brazilian demographic censuses of 1960, 1970, 1980, and 1991, aggregated into 518 consistently defined spatial units called microregions, we estimated fertility and mortality and constructed indicators of development and living conditions in the rural and urban areas of the microregions in each census. We then estimated cross-sectional and fixed-effects models to answer questions about the degree to which changes in these indicators are associated with changes in fertility and whether the relationship between fertility and development shifts through time. We found strong and consistent relationships between the decline in fertility and measurable changes in social and economic circumstances.
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