Abstract The Multinomial Logit (MNL) model is popular, but a semi-parametric specification of its link/utility function has seldom been used in empirical applications. This is primarily because of the resource intensive nature of semi-parametric estimation. In this paper we propose and implement a parallel computation algorithm to estimate the semi-parametric kernel MNL model. This algorithm reduces model estimation time by a factor of 2–10, depending on the size of the dataset and the available resources for computation. These computational gains make the estimation of this model feasible for large datasets. Additionally, using a Monte Carlo study we show that the kernel MNL outperforms the traditional linear MNL model in terms of fit and predicted choice probabilities. We demonstrate how kernel-based specification can unearth important heterogeneities in the effect of covariates through an empirical exercise. We use data from a nationally representative household survey (N = 157,804) to analyze the factors associated with institutional births (as opposed to home births) in India. Our revealed-preference results indicate that maternal education, household assets, distance to formal health facility, and birth order play an essential role in determining birth location choice. Although the directions of impact are similar across both the linear and the kernel MNL specifications, there are significant differences in the marginal effects of different factors across the two models. These differences, which arise due to the flexibility afforded by the semi-parametric specification, potentially bring additional nuance to policy discussions.

中文翻译：

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做出决定：印度机构生育选择的半参数方法

摘要多项式Lo​​git（MNL）模型很流行，但在经验应用中很少使用其链接/实用函数的半参数规范。这主要是因为半参数估计的资源密集型性质。在本文中，我们提出并实现了一种并行计算算法来估计半参数内核MNL模型。该算法将模型估计时间减少2-10倍，具体取决于数据集的大小和可用的计算资源。这些计算上的收益使该模型对于大型数据集的估计可行。此外，使用蒙特卡洛研究，我们显示出内核MNL在拟合和预测选择概率方面优于传统的线性MNL模型。我们演示了基于内核的规范如何通过经验练习发现协变量影响中的重要异质性。我们使用来自全国有代表性的家庭调查（N = 157,804）的数据来分析与印度机构生育（相对于家庭生育）相关的因素。我们的显示偏好结果表明，孕产妇教育，家庭资产，到正规医疗机构的距离以及出生顺序在决定出生地点选择方面起着至关重要的作用。尽管线性和内核MNL规范的影响方向相似，但两个模型中不同因素的边际效应存在显着差异。这些差异是由于半参数规范提供的灵活性而引起的，