Utilizing Big Data Analysis for the Fusion Examination of Labor Market Evolution within the Gig Economy

Abstract

The advent of the gig economy has triggered an unprecedented transformation in labor markets worldwide. Leveraging an intricate network analysis, this paper aims to delve into the multi-layered complexities of labor market metamorphosis within the context of a digital gig economy. We construct a bipartite labor-market network model that allows us to explore the nexus between gig workers and employment platforms using a robust set of parameters – connectivity, centrality, and clustering coefficient. Consequently, our empirical investigation elucidates how traditional labor market paradigms are being disrupted, engendering the emergence of new socio-economic stratifications. The results unveil a counterintuitive network structure where high centrality does not necessarily correlate with enhanced economic benefits for gig workers. Moreover, the findings underscore the potential pitfalls of a skewed clustering coefficient, manifesting as increased vulnerability to systemic shocks. The ubiquity of digital technology has engendered a seismic shift in economic frameworks, predominantly by initiating the concept of the gig economy. Although a plethora of research has been conducted on the gig economy from various disciplinary vantage points, limited endeavors have been undertaken to explore the intricacies of labor market changes via a network analysis paradigm. As a result, this study provides vital insights for policymakers, platform operators, and labor market participants, promoting a nuanced understanding of the gig economy’s implications for labor market architecture.