The Impact of Cross-Border Data Flow Regulatory Policies on Digital Firms: Compliance Cost Estimation and Business Model Adjustment Recommendations

Abstract

In the context of an annual growth rate of 28% in cross-border data flows and a fragmentation index of regulatory policies soaring to 0.71, digital firms are increasingly viewing compliance as a calculable strategic variable. This paper integrates regulatory capture and cost-benefit theories to construct a mixed dataset covering 1,174 policy texts, 215 listed firms, and 187 penalty cases across 12 countries from 2019 to 2023. Utilizing a text mining — machine learning — synthetic control method (SCM-DiD) framework, we conduct an integrated test of “policy — cost — behavior.” The findings reveal that the relationship between regulatory intensity and corporate lobbying expenditure follows an inverted U-shape, with the inflection point at 1.2% of revenue. Net compliance benefits peak at 2.5% of revenue, and exceeding 3.8% leads to a “compliance trap.” GDPR-style command-and-control policies result in a persistent 2.1 percentage point higher compliance cost for the treated group over three years, with an additional 58% amplification for firms handling highly sensitive data. Federated learning technology can recoup a $1.5 million investment within 2.3 years and reduce compliance intensity by 40%. Based on these quantified inflection points, we propose a three-dimensional decision matrix for firms: “lobbying ≤ 1.2% + budget 2.0-2.5% + technology substitution.” For regulators, we suggest a combination of “command-and-control + market incentives.” This study is the first to provide a compliance investment threshold that can be directly embedded in ROI, assisting digital firms in achieving predictable risks and arbitrageable costs in the era of fragmented regulation.