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Using mixture models to detect sex bias in health outcomes in Bangladesh. (English) Zbl 0898.62143

Summary: Many interesting economic hypotheses entail differences in behaviors of groups within a population, but analyses of pooled samples shed only partial light on underlying segmentations. Finite mixture models are considered as an alternative to methods based on pooling. Robustness checks using \(t\)-regressions and a Bayesian analogue to the likelihood ratio test for model evaluation are developed. The methodology is used to investigate pro-son bias in child health outcomes in Bangladesh. While regression analysis on the entire sample appears to wash out evidence of bias, the mixture models reveal systematic girl-boy differences in health outcomes.

MSC:

62P20 Applications of statistics to economics
62J99 Linear inference, regression
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