Payment Marking Pattern (PMP) and Control-Payment Separation: A Blockchain-Verified Study of Global Platform Labor

Platform work is marked by a fundamental contradiction. While algorithmic systems exercise stringent control over labor processes, including scheduling, route planning and performance evaluation, workers’ bank statements typically erase the legal connotations of employment. Their pay is commonly recorded as “service fees” or “commissions”, or simply bears the name of the paying company, instead of being designated as “wages”. This study terms this systematic practice the Payment Marking Pattern (PMP). Existing scholarship has conducted in-depth research on algorithmic management (Wood et al., 2019). Nonetheless, most literature centers on the black-boxing and gamification of labor processes, with limited attention to how metadata manipulation on the payment end perpetuates algorithmic control chains. Statistics from the International Labour Organization reveal a substantial gap in social security coverage: approximately 40 percent of surveyed platform workers lack any social security benefits, and the figure rises above 50 percent for those engaged exclusively in platform work (Cravo, 2025). The OECD (2019) notes that employment status serves as a prerequisite for accessing labor protection, social security, collective bargaining and lifelong learning opportunities, and worker misclassification deprives non-standard employees of these entitlements. Dubal (2023) puts forward the concept of “algorithmic wage discrimination”, whereas Aloisi & De Stefano (2025) contend that algorithmic tools further expand managerial power. Notwithstanding the above contributions, prior research has yet to conduct a systematic analysis of data fields in bank payment records. This paper pioneers the PMP framework and categorizes it into two types: Type A (de-wage marking) and Type B (internal-external label separation). From the perspectives of contingent wage rules and the causal linkage between taxation and social security, this study unpacks the formation logic and institutional impacts of PMP. Drawing on four tiers of evidence — first-hand blockchain-verified data of platform riders, cross-border judicial precedents, global authoritative reports and external supporting evidence — this paper illustrates how platforms leverage standardized payment infrastructures to automate de-wage labeling practices. The results demonstrate that the pervasive absence of “wage” labels on bank statements is not a technical oversight, but a deliberate strategic choice shaped by legal constraints and algorithmic management. This practice exerts far-reaching impacts on the adjudication of employment relationships, tax administration and social security contributions. Overall, this research addresses the research gap regarding payment-side metadata analysis in the field of algorithmic management.