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

Dujin Xu
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Dujin Xu: ETERNITY SUNSHINE CONSULTING PTE. LTD, Shanghai 200021, China

Frontiers in Management Science, 2025, vol. 4, issue 6, 32-37

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.

Keywords: cross-border data flow; regulatory capture; compliance cost; synthetic control method; federated learning; data export; policy intensity index; lobbying inflection point; technology substitution; net benefit peak (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bdz:frmans:v:4:y:2025:i:6:p:32-37

DOI: 10.63593/FMS.2788-8592.2025.11.004

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