Variable symbols and descriptions.

One of the most important policies of the European Union is regional development, which comprises measures of enhancing economic growth and citizens’ living standards via strategic investment. Considering that economic growth and wellbeing are intertwined from the perspective of EU policies, this study examines the relationship between wellbeing-related infrastructure and economic growth in 212 NUTS 2 regional subdivisions across the members of Eu-28 during the period 2001–2020. We therefore analyzed data from 151 Western Europe regions and 61 Central and Eastern Europe regions by means of a panel data analysis with the first-difference generalized method of moments estimator. Our main interest was to determine the degree to which Western Europe regions responded to predictors as compared to Central and Eastern Europe regions. According to the empirical results, the predictors with the strongest influence for Western Europe regions were disposable household income, inter-regional mobility, housing indicator, labor force and participation. For Central and Eastern Europe regions, the largest impact was triggered by the housing indicator, internet broadband access and air pollution. In addition, we determined a relational weighted multiplex between all variables of interest by using dynamic time warping and we introduced topological measures in a multilayer multiplex model for both regional subsamples.欧盟最重要的政策之一为区域发展政策，其涵盖通过战略性投资拉动经济增长、提升公民生活水准的各类举措。鉴于欧盟政策框架下经济增长与福祉紧密相关，本研究针对2001-2020年间欧盟28国（Eu-28）的212个NUTS 2（地域统计单位第二级）区域分区，探究福祉相关基础设施与经济增长之间的关联。为此，本研究针对151个西欧区域与61个中东欧区域的数据集，采用基于一阶差分广义矩估计（first-difference generalized method of moments, FD-GMM）的面板数据分析方法展开研究。本研究的核心目标为对比西欧区域与中东欧区域对各类预测变量的响应程度。实证结果显示，对西欧区域影响最为显著的预测变量依次为家庭可支配收入、区域间人口流动性、住房指标、劳动力规模与劳动力参与率；而对中东欧区域影响最大的变量则为住房指标、互联网宽带接入水平与空气污染状况。此外，本研究借助动态时间规整（Dynamic Time Warping, DTW）构建了所有目标变量间的加权关联多路网络，并针对两个区域子样本，在多层多路网络模型中引入了拓扑测度指标。