Abstract

A semiparametric sequential ordinal model is proposed to analyze socio-demographic and spatial determinants of first birth intervals after marriage. Random effects are introduced to capture spatially structured and unstructured latent covariates. The structured effects are modelled by assuming conditional autoregressive priors, and for the unstructured effects we use an exchangeable Gaussian prior, while the smooth effects of continuous covariatesare modelled by penalized splines. Inference is based on the mixed model approach. The model is applied to data from a cross-sectional survey. Compared to a spatial parametric predictor, the spatial semiparametric model better fits the data.

Highlights

  • Modelling fertility data is of great interest in population economics study (Henry, 1973; Lloyd, 2005)

  • We extend the sequential ordinal model of Albert and Chib (1997, 2001) by modelling first birth intervals (FBI) with a flexible geoadditive predictor (Fahrmeir, Kneib, and Lang, 2004) that incorporates random effects to account for spatial correlation and heterogeneity and allows nonlinear effects of continuous covariates and the usual fixed effects

  • The results show that the sequential logit models have smaller Akaike Information criterion (AIC) and Bayesian Information Criterion (BIC) values than the cumulative logit models

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Summary

Introduction

Modelling fertility data is of great interest in population economics study (Henry, 1973; Lloyd, 2005). The timing of first birth is strongly correlated with the pace of subsequent fertility and, often, rapid first birth leads to rapid transition to higher parities and higher fertility. It may suggest social and cultural changes to fertility, values of family formation and parenthood. Especially in developing countries, birth carries multivalent social implications. Child bearing contributes significantly to the woman’s identity in society, proves her fertility and reduces the anxiety surrounding family continuance (Lloyd, 2005)

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