The Effect of Platform Data Quality on Tax Compliance in Digital Economy: A Multiagent Based Simulation - Peng Jin; Zhangwei Feng; Guiping Li (Complexity, 2023)
The expeditious development of the digital economy has posed critical challenges for tax compliance. Recent reforms and technological changes, such as the emergency of platform data sharing, perform as potential instruments to potentially solve the tax compliance issue in the digital economy. This article innovatively combines tax compliance and digital economy taxation and proposes an expected utility theory model and a multiagent simulation model of tax compliance in the context of the digital economy, incorporating an important impact factor—platform data quality. In addition, the discrepancy and applicable conditions of three audit modes have been first compared and analyzed in this research: unconstrained signal audit, constrained random audit, and constrained signal audit. Applying computational experiments, this paper further investigates the effect of platform data quality on tax compliance in the digital economy. The key findings include: (1) the signal role of platform data is affected by three factors—the quality of platform data, the audit intensity and audit rate; (2) the signal role of platform data can be negative in given situations; and (3) the tax compliance rate varies in three different audit modes, which depend on the platform data quality, audit rate, and audit intensity. Based on the findings, this article provides policy recommendations on tax collection and management in digital economy.