Arriving at a Decision : A Semi-Parametric Approach to Institutional Birth Choice in India

We use nationally representative household survey data (N=157,804) to analyze the factors determining institutional births in India using the kernel multinomial logit model -- a state-of-the-art semi-parametric specification. In this study, we propose and implement a parallel computation algorithm to estimate the resource-intensive kernel MNL. Furthermore, we layout an empirical procedure to analyze the semi-parametric estimates and also to compare them with those of a conventional linear MNL model. Results from both models show that maternal education, asset level, distance to a formal health facility, and birth order play an essential role in determining birth location choice. However, we show significant differences in the marginal effect estimates derived from the two models. In fact, using a Monte Carlo study we show that the kernel MNL outperforms the linear MNL model. We argue that the flexibility offered by kernel MNL is insightful for policymakers