  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.
