Nighttime Light Intensity and Child Health Outcomes in Bangladesh

TL;DR

This paper investigates how increased nighttime light intensity, as a proxy for urbanization, positively influences child health outcomes in Bangladesh using advanced machine learning and econometric methods.

Contribution

It introduces a novel approach combining machine learning techniques with econometric models to analyze the impact of urbanization on child health in Bangladesh.

Findings

A one standard deviation increase in light intensity raises weight-for-age Z-score by 1.515.

Maximum increases in weight-for-height and height-for-age scores range from 5.35 to 7.18 units.

Generalized additive models confirm a positive relationship between light intensity and child health.

Abstract

This study examines the impact of nighttime light intensity on child health outcomes in Bangladesh. We use nighttime light intensity as a proxy measure of urbanization and argue that the higher intensity of nighttime light, the higher is the degree of urbanization, which positively affects child health outcomes. In econometric estimation, we employ a methodology that combines parametric and non-parametric approaches using the Gradient Boosting Machine (GBM), K-Nearest Neighbors (KNN), and Bootstrap Aggregating that originate from machine learning algorithms. Based on our benchmark estimates, findings show that one standard deviation increase of nighttime light intensity is associated with a 1.515 rise of Z-score of weight for age after controlling for several control variables. The maximum increase of weight for height and height for age score range from 5.35 to 7.18 units. To further understand our benchmark estimates, generalized additive models also provide a robust positive relationship between nighttime light intensity and children's health outcomes. Finally, we develop an economic model that supports the empirical findings of this study that the marginal effect of urbanization on children's nutritional outcomes is strictly positive.