Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh
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.