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Modelling fertility: an application of count regression models

Often the lifecycle data occur as count of the vital events and are recorded as integers. The purpose of this article is to model the fertility behavior based on religious, educational, economic, and occupational characteristics. The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model (PRM) based on the National Family Health Survey -3 dataset. The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data. Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model (MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation. Choice of the explanatory factors is limited to the availability of data. Trends and patterns of preference for birth counts suggest that religion, caste, wealth, female education, and occupation are the dominant factors shaping the observed birth process. Empirical analysis suggests that both the models used in the study perform similarly on the sample data. However, fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values. Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.