Manufacturing workers or platform gig workers? The impact of digital transformation in manufacturing and service sectors on job quality and labor allocation

This paper addresses an important research gap by examining how digital transformation in the manufacturing and service sectors affects job quality and labor allocation across industries. Using micro-level data from the China Labor-force Dynamic Survey (CLDS) and firm-level data from the China Employer-Employee Matching Survey (CEEMS), we employ multinomial probit model, ordered probit model, and instrumental variable method to analyze the effects of sectoral digital transformation on employment outcomes, wages, job autonomy, and satisfaction. Our results reveal that manufacturing digital transformation, centered on automation and the Industrial Internet of Things (IIoT), requires high investment but generates slow returns, ultimately failing to improve job quality and contributing to declining manufacturing employment and labor shortages. In contrast, digital transformation in the service sector, driven by digital platforms and rapid channel upgrades, yields faster returns, raises wages, improves job autonomy and satisfaction, and promotes the expansion of platform gig work. These findings are robust across multiple specifications and offer new empirical insights into how differences in digitalization stages and operational models shape labor dynamics. Our study contributes to the literature by providing a comparative, cross-sectoral analysis using both worker-side and firm-side data, highlighting the mechanisms behind inter-industry labor shifts. The results have important implications for policymakers seeking to address labor shortages in manufacturing, improve job quality, and design balanced digitalization strategies.