基于因果推断的国家潜在社会经济健康模型及政策效果检验
Modeling National Latent Socioeconomic Health and Examination of Policy Effects via Causal Inference
摘要：
本研究通过基于模型的方法开发了一个国家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检查了政策的影响。由于国内生产总值一直被视为衡量一个国家经济业绩的过时衡量标准和工具，人们日益一致认为应该采用一种替代衡量标准-如综合“福祉”指数-来全面反映一个国家的社会经济健康业绩。许多构建幸福/健康指数的传统方法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，以形成一个指数。然而，健康本质上是潜在的，指标实际上是健康的可观察指标。与国内生产总值或其他传统的健康指数相比，我们的方法提供了一个国家整体“健康”的整体量化。我们建立在潜在的健康因素指数（LHFI）的方法，已被用来评估不可观察的生态/生态系统健康。该框架集成地建模了指标、潜在健康状况和协变量之间的关系，这些协变量驱动了健康状况的概念。在本文中，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（强制性产假天数）对一个国家的社会经济健康的影响，同时正式占国家之间的空间依赖性。我们将我们的模型应用于世界各地的国家，使用与社会健康不同方面有关的各种指标和潜在协变量的数据。该方法是在贝叶斯分层框架结构和结果得到的马尔可夫链蒙特卡罗技术。
英文摘要：
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.
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