Abstract
We provide an analysis of the main sources of data used to estimate fertility schedules in developing countries, giving special attention to Brazil. In addition to the brief history of various data sources, we present several indirect demographic methods, commonly used to estimate fertility and assess the quality of data. From the methods used, the Synthetic Relational Gompertz model gives the most robust estimates of fertility, independent of the data source considered. We conclude that different demographic data sources and methods generate differing estimates of fertility and that the country should invest in quality of birth statistics.
Highlights
In less developed and developing countries, there is often a common concern among demographers about data quality, especially concerning the vital registration systems, and a strong consensus about the necessity to improve vital statistics (Mikkelsen et al 2015; AbouZahr et al 2015)
Having high-quality vital registration systems is one of the targets of sustainable development of the United Nations (Lu et al 2015), as good data are needed for designing, evaluating, and implementing political and social programs, especially in a context of aging populations and fast fertility decline that numerous countries are experiencing
Rio Grande do Norte is one of the smallest states in Brazil and has been going through rapid changes in fertility. We used this sub-population as example for two main reasons: first, because Rio Grande do Norte is located in the Northern region of the country, a part of the country historically known for having the worst data quality (IBGE 2003; Paes 2006; Lima and Queiroz 2014)
Summary
In less developed and developing countries, there is often a common concern among demographers about data quality, especially concerning the vital registration systems, and a strong consensus about the necessity to improve vital statistics (Mikkelsen et al 2015; AbouZahr et al 2015). This concern is understandable as many countries in the world, and especially in Latin America, are still suffering from a considerable amount of data problems (Faijer 1994; Duryea et al 2006; Alkema et al 2012; Hunter and Sugiyama 2018). We argue that Brazil is a good example due to the improvements in data quality observed in recent years (Hunter and Sugiyama 2018), followed by a rapid fertility decline across the whole country in recent decades
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