Title: Modeling National Latent Socioeconomic Health and Examination of Policy Effects via Causal Inference Show Chinese title

Title: 通过因果推断建模国家潜在的社会经济健康状况及政策效果考察

Abstract: 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. Show Chinese abstract

Abstract: 本研究通过基于模型的方法开发了一个国家层面的社会经济健康指数，该方法结合了空间依赖性，并通过因果建模框架考察政策的影响。 由于国内生产总值（GDP）被视为衡量和基准一个国家经济表现的过时指标和工具，因此越来越多地达成共识，认为需要一个替代指标——例如综合的“幸福感”指数——来全面捕捉一个国家的社会经济健康表现。 许多传统的构建幸福感/健康指数的方式涉及将不同的可观察指标（如预期寿命和教育水平）结合起来形成一个指数。 然而，健康本质上是潜在的，而这些指标实际上是健康这一潜在概念的可观察指示器。 与GDP或其他传统健康指数相比，我们的方法提供了一个全面量化一个国家整体“健康状况”的方式。 我们基于已经用于评估不可观测的生态/生态系统健康的潜在健康因子指数（LHFI）方法构建模型。 该框架整合性地模拟了指标、潜在健康以及驱动健康概念的协变量之间的关系。 在本文中，我们将LHFI结构与空间建模和统计因果建模相结合，以评估政策变量（强制产假天数）对一个国家社会经济健康的影响，同时正式考虑各国之间的空间依赖性。 我们应用此模型到世界各地的国家，使用有关不同方面社会健康的各种指标和潜在协变量的数据。 该方法以贝叶斯分层框架为基础，结果通过马尔可夫链蒙特卡罗技术获得。