Theoretical Misconceptions and Institutional Reconstruction of Labor Empowerment in Data Property Rights

Abstract

In the digital era, the process of data as a production factor is accelerating, drawing widespread attention to data labor and its income distribution issues centered around data activities. Some scholars cite Locke’s labor theory of property and propose a labor empowerment theory for data property. However, this theory suffers from problems such as the overgeneralization of labor nature, inaccurate assessment of value contribution, and logical disconnection in rights generation. The deeper issue lies in its failure to distinguish the gradient forms of data labor in the data value chain. By reconstructing the traditional labor empowerment theory, this paper reveals the heterogeneity of data labor and categorizes it into data behavior, user-generated content (UGC), and professionally generated content (OGC). It then proposes a layered approach to labor-based property empowerment in data, providing an institutional framework to address the income distribution dilemma in data marketization.