摘要翻译：
这项研究通过一种基于模型的方法为国家制定了一个社会经济健康指数，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（国内总产值）已被视为衡量一个国家经济业绩的过时衡量标准和工具，人们越来越一致地认为，可以采用一种替代衡量标准----例如综合“福祉”指数----来全面反映一个国家的社会经济保健业绩。构建福祉/健康指数的许多传统方法涉及组合不同的可观察指标，如预期寿命和教育水平，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。我们建立在潜在健康因子指数(LHFI)方法的基础上，该方法已用于评估不可观测的生态/生态系统健康。该框架集成了度量指标、潜在健康和驱动健康概念的协变量之间的关系。本文将LHFI结构与空间建模和统计因果建模相结合，以评估一个政策变量（强制性产假天数）对一个国家社会经济健康的影响，同时正式解释国家之间的空间依赖。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。该方法采用贝叶斯层次结构，并通过马尔可夫链蒙特卡罗技术得到结果。
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英文标题：
《Modeling National Latent Socioeconomic Health and Examination of Policy
  Effects via Causal Inference》
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作者：
F. Swen Kuh and Grace S. Chiu and Anton H. Westveld
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最新提交年份：
2019
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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PDF链接：
https://arxiv.org/pdf/1911.00512