SI.zip from The dynamic resilience of urban labour networks

Both cities and markets are well understood as complex systems which are amenable to analysis using physically inspired methods. Cities have shown fascinating universality with size, while labour markets modelled as networks have considerable explanatory power. Labour markets are a particularly attractive domain of study in this context due to societal importance, the influx of high-resolution data as well as exogenous influence of automation. While much previous work has studied the economic characteristics of cities as a function of size and examined the exposure of urban economies to automation, this has often been from a static perspective. In this work, we examine the diffusive properties of labour markets and examine their variance across cities. More specifically, we identify the occupations which are most important in promoting the diffusion of beneficial or deleterious properties. To this end, we propose a new measure of node centrality empSI. We find that these properties of influence vary considerably with city size.

城市与市场均被广泛视为复杂系统，可采用受物理学启发的分析方法开展研究。城市的特征随规模呈现出极具研究价值的普适性规律，而被建模为网络的劳动力市场则具备极强的解释力。在此研究框架下，劳动力市场因其社会重要性、高分辨率数据的大量涌现，以及自动化带来的外生影响，成为极具吸引力的研究领域。尽管过往大量研究围绕城市规模与经济特征的关联展开，并探讨了城市经济体受自动化的影响程度，但此类研究多采用静态视角。本研究聚焦劳动力市场的扩散特性，并探究不同城市间的差异。具体而言，我们旨在识别出对有益或有害属性的传播起到关键作用的职业类型。为此，我们提出了一种全新的节点中心性（node centrality）度量指标empSI。研究发现，这类影响力属性随城市规模呈现出显著差异。