空气质量对生活满意度的效应研究——基于序数分层空间自回归Probit模型

在居民生活满意度的相关研究中,除考虑人口学特征外,越来越多的实证同时考虑了微观个体所处的宏观环境,对这类呈嵌套结构的分层数据需构建分层统计模型,但传统的分层统计模型未考虑真实的空间依赖。本文将分层统计模型和空间自回归模型相结合,创新性地构建了四种序数分层空间自回归Probit模型,该类模型能够合理地对因变量为序数且存在空间依赖情况并呈分层结构的数据进行建模,模型可避免忽略真实的空间依赖对模型估计的不利影响,且能够对高层组间的空间效应和低层个体间的空间效应区别对待,更有利于模型的解释。最后,空气质量对居民生活满意度的效应实证研究表明:空气质量确实能够对生活满意度产生影响,居民对空气质量的认识和要求并非孤立地局限于本地,而是对一个区域空气质量的空间综合结果。对比2018年和2016年模型结果可知:空气质量的福利效应无法被其他民生福祉因素所取代,并且随着空气质量相关统计信息的高度开放和广泛传播,居民更加重视空气质量,也形成了更加全局的了解。

The Effect of Air Quality on Life Satisfaction: Based on the Ordered Hierarchical Spatial Autoregressive Probit Model

In the research of residents′ life satisfaction, more and more empirical studies take into account not only the demographic characteristics but also the macro environment of micro individuals, so a hierarchical model should be built for such data with a special hierarchical structure, but the traditional hierarchical statistical model does not consider the real spatial dependence. In this paper, four hierarchical spatial autoregressive probit models are constructed by combining the hierarchical model and the spatial autoregressive model. The model can be dealed with the ordered dependent variable with spatial dependence in hierarchical data, avoid the adverse effect of ignoring the real spatial dependence, and separate the variable with the spillover effects from the variable without spatial spillover effects, thus more advantageous in the interpretation of the model. The empirical study results show that air quality has a subtle effect on life satisfaction, and residents′ understanding of air quality and the request are not limited to the local place in isolation, but a comprehensive spatial result of the region.Compared with 2018 and 2016 model results, the welfare effect of air quality cannot be replaced by otherlivelihood welfare factors, and the highly open and wide spread statistical information on air quality helps the residents have a more comprehensive understanding of and attention to air quality.