Modeling National Latent Socioeconomic Health and Examination of Policy Effects via Causal Inference

• Published in: IDEAS Working Paper Series from RePEc
• Main Authors: F Swen Kuh, Chiu, Grace S, Westveld, Anton H
• Format: Paper
• Language: English
• Published: St. Louis Federal Reserve Bank of St. Louis 01.01.2019
• Subjects: Maternity & paternity leaves

This research develops a socioeconomic health index for nations through a model-based approach which incorporates spatial dependence and examines the impact of a policy through a causal modeling framework. As the gross domestic product (GDP) has been regarded as a dated measure and tool for benchmarking a nation's economic performance, there has been a growing consensus for an alternative measure---such as a composite `wellbeing' index---to holistically capture a country's socioeconomic health performance. Many conventional ways of constructing wellbeing/health indices involve combining different observable metrics, such as life expectancy and education level, to form an index. However, health is inherently latent with metrics actually being observable indicators of health. In contrast to the GDP or other conventional health indices, our approach provides a holistic quantification of the overall `health' of a nation. We build upon the latent health factor index (LHFI) approach that has been used to assess the unobservable ecological/ecosystem health. This framework integratively models the relationship between metrics, the latent health, and the covariates that drive the notion of health. In this paper, the LHFI structure is integrated with spatial modeling and statistical causal modeling, so as to evaluate the impact of a policy variable (mandatory maternity leave days) on a nation's socioeconomic health, while formally accounting for spatial dependency among the nations. We apply our model to countries around the world using data on various metrics and potential covariates pertaining to different aspects of societal health. The approach is structured in a Bayesian hierarchical framework and results are obtained by Markov chain Monte Carlo techniques.