# Causal Analysis of Health, Education, and Economic Well-Being in India -- Evidence from the Young Lives Survey

## TL;DR

This study uses longitudinal data and advanced causal analysis to explore how childhood health, education, and economic well-being are interconnected in India, emphasizing education and wealth as key factors influencing life outcomes.

## Contribution

It introduces an integrated empirical framework combining panel data, instrumental variables, and causal graphs to analyze interdependencies among childhood health, education, and economic status.

## Key findings

- Education is the strongest predictor of future economic well-being.
- Household wealth shows high persistence across generations.
- Childhood health has limited direct impact on later wealth.

## Abstract

This study investigates the dynamic and potentially causal relationships among childhood health, education, and long-term economic well-being in India using longitudinal data from the Young Lives Survey. While prior research often examines these domains in isolation, we adopt an integrated empirical framework combining panel data methods, instrumental variable regression, and causal graph analysis to disentangle their interdependencies. Our analysis spans five survey rounds covering two cohorts of children tracked from early childhood to young adulthood. Results indicate strong persistence in household economic status, highlighting limited intergenerational mobility. Education, proxied by Item Response Theory-based mathematics scores, consistently emerges as the most robust predictor of future economic well-being, particularly in the younger cohort. In contrast, self-reported childhood health shows limited direct impact on either education or later wealth, though it is influenced by household economic conditions. These findings underscore the foundational role of wealth and the growing importance of cognitive achievement in shaping life trajectories. The study supports policy approaches that prioritize early investments in learning outcomes alongside targeted economic support for disadvantaged households. By integrating statistical modeling with development policy insights, this research contributes to understanding how early-life conditions shape economic opportunity in low- and middle-income contexts.