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| The Impact of Cross-Border Data Flow Barriers on China and China's Response Strategy |
| WANG Yongjin, WANG Wenbin, XIE Fang
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| School of Economics, Nankai University |
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Abstract In the era of the digital economy, data has become a core production factor, not only serving as a fundamental strategic resource to enhance national competitiveness but also emerging as a new source of comparative advantage. As the world enters a critical period of deepening technological revolutions and industrial transformation, many countries have made the big data industry a key focus of economic and social development. Through the enactment of laws governing cross-border data flows, countries are racing to secure data resources and gain a leading position in global industrial competition. Against this backdrop, how China can effectively safeguard its data sovereignty while enhancing global economic competitiveness has become a pressing and important question. At the same time, industrial policy is widely regarded as a key tool for promoting economic growth and achieving high-quality development. In the big data era, traditional industrial policy faces new challenges and opportunities. On the one hand, policymakers must consider how to harness the potential economies of scale embedded in data through well-designed policy instruments. On the other hand, they must also address the distortions in resource allocation caused by cross-border data flow barriers. Therefore, designing an effective industrial policy framework for the big data era, activating the productive potential of data while mitigating the adverse effects of cross-border restrictions, is a critical research agenda for reshaping China's comparative advantage in the global economy.
This paper develops a multi-country, multi-sector general equilibrium model incorporating external data scale economies, providing the first theoretical foundation for the design of China's industrial policy in the era of big data. In the digital economy, external scale economies of data are an important feature of production. As a result, the potential social returns to industrial policy are magnified by the presence of such scale effects. Data in production exhibits three key characteristics: first, external economies of scale, meaning that the unit cost of producing goods decreases as the volume of data increases; second, non-rivalry, with the same unit of data can be simultaneously used by multiple producers; and third, data as a byproduct of consumption, so its international mobility is determined by the data policies of individual countries.
We then calibrate our model to 38 countries and 44 sectors (including 22 tradable sectors) in 2017 using the OECD Inter-Country Input-Output (ICIO) database. Our quantitative analysis yields some key findings. First, the implementation of the EU’s General Data Protection Regulation (GDPR) reduces welfare in most countries, with EU member states experiencing the sharpest declines. The magnitude of welfare loss is positively correlated with the elasticity of data scale economies. Second, U.S. restrictions on cross-border data flows targeting China reduce welfare globally, with China bearing the greatest losses. Third, industrial policy is an effective strategy for China to counter both discriminatory and non-discriminatory cross-border data flow barriers. Data scale economies provide a key rationale for government intervention through industrial policy. The welfare gains from China's industrial policy exhibit an inverted-U relationship with the elasticity of data scale economies. Finally, in response to U.S. discriminatory data flow restrictions, retaliatory data flow policies represent another effective strategy for China to mitigate welfare losses.
This paper emphasizes several key policy recommendations. First, industrial policy serves not only as an effective tool for responding to cross-border data flow restrictions but also as a strategic instrument to safeguard national economic security and enhance the global competitiveness of key industries. Second, developing more systematic and forward-looking cross-border data policy is essential for adapting to the evolving global data governance landscape and for strengthening institutional resilience and strategic capacity in international negotiations. Third, enhancing the elasticity of data scale economies is both an intrinsic requirement for achieving high-quality economic development in China and a critical lever for securing strategic advantage in the global digital economy.
This paper makes several contributions. First, it incorporates external data scale economies and endogenous technology choices into a multi-country, multi-sector general equilibrium model, thereby enriching and extending the existing international trade frameworks. Second, it offers the first general equilibrium-based quantitative assessment of the welfare effects of both discriminatory and non-discriminatory cross-border data flow barriers. Third, it highlights the critical role of data scale economies in shaping the welfare impact of industrial policy. Finally, it proposes a concrete policy response to rising cross-border data flow barriers: by supporting key industries through targeted industrial policies, China can offset the rising marginal costs associated with constrained data scale economies and thereby enhance firms' international competitiveness.
Future research may advance along several dimensions. First, given that the cross-border data flow is largely shaped by government decisions, future work could incorporate the government's trade-offs between data security and data openness, moving beyond the assumption of welfare maximization to better analyze the interaction between data policy and industrial policy. Second, to understand the dynamic effects of such policies, future research could develop a dynamic general equilibrium model with data accumulation, offering deeper insights into the long-run implications of data policy in a multi-country, multi-sector framework.
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Received: 01 April 2025
Published: 02 October 2025
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