A worker troubleshooting equipments at a computing power center Photo: IC PHOTO

Data from China’s National Bureau of Statistics show that the added value of the country’s core digital economy industries exceeded 14.7 trillion yuan (approximately $2.2 trillion) in 2025, accounting for more than 10.5% of GDP. Although the digital economy has become a major pillar of the national economy, mounting challenges remain, including inadequate institutional support and outdated governance frameworks. Clarifying the underlying logic of digital governance from an economic perspective and reviewing China’s institutional initiatives are essential to improving governance and unlocking the sector’s full development potential.

Logical starting point of innovation

The existing economic governance system is built around tangible assets and material production, with price signals serving as its primary regulatory mechanism. The digital economy operates according to a different logic: Its principal factor of production—data—can be replicated indefinitely; market competition unfolds within platform ecosystems; and resource allocation increasingly depends on algorithms rather than simply supply, demand, and price. Applying conventional governance frameworks to the digital economy therefore creates misalignments in three main areas: the characteristics of production factors, market structure, and the distribution of information.

Unlike traditional production factors such as land and capital, data can be replicated and reused indefinitely at near-zero marginal cost, naturally lending itself to economies of scale. Yet the collection, utilization, and circulation of data involve privacy protection, national security, and the public interest, making market pricing mechanisms alone insufficient for efficient resource allocation.

The distinctive characteristics of data as a production factor also reshape market structures. The two-sided nature and network externalities of digital platforms generate powerful winner-takes-all dynamics. Three US companies control more than 60% of the global cloud infrastructure market, while leading Chinese platforms occupy dominant positions in e-commerce, social media, travel, and other sectors. Unlike the natural monopolies associated with traditional industries, this concentration results from the combined force of network effects, data barriers, and algorithmic superiority. Antitrust frameworks centered on price and output must therefore be fundamentally recalibrated when applied to platforms that provide ostensibly free services in exchange for user data.

Behind this concentration lies a deeper problem: Algorithms are reshaping the distribution of information within markets. From credit assessment and content recommendation to dynamic pricing and the assignment of work orders, algorithms now permeate nearly every stage of resource allocation. Their black-box nature creates a new form of information asymmetry: Platforms hold comprehensive data on user behavior, while consumers cannot see how prices are determined, workers cannot anticipate changes to order-assignment rules, and small and medium-sized businesses have little bargaining power over the allocation of online traffic. Because these imbalances are embedded in the technological architecture itself, they represent a structural problem that conventional disclosure requirements and contract regulation are poorly equipped to address.

China’s institutional explorations

Rules governing data ownership and circulation have a direct bearing on market efficiency. China was among the first countries to propose a framework for the “separation of data property rights into three rights,” distinguishing the rights to hold, use, and commercially manage or monetize data. By clarifying these different forms of control, the framework has laid an institutional foundation for unlocking the economic value of data. Initial market results suggest that it is already stimulating circulation: The national volume of data transactions exceeded 160 billion yuan in 2024, up more than 30% year on year.

Once data begins to circulate more freely, the central challenges shift to maintaining orderly competition and safeguarding technological security. After special campaigns launched in 2021 produced significant progress in curbing monopolistic practices and the disorderly expansion of capital, policy began moving toward a long-term mechanism that combines support for development with effective regulation. China’s Anti-Monopoly Law underwent its first revision in 2022, with new provisions addressing monopolistic conduct involving data and algorithms. The country also moved relatively early to establish systems for algorithm registration, the administration of deep-synthesis technologies, and the regulation of generative AI, placing it among the first to translate broad principles of AI governance into operational rules.

Institutional gains, however, can benefit society as a whole only when supported by adequate infrastructure. A more balanced distribution of hardware infrastructure has created the material conditions for applying digital technologies in fields closely tied to people’s livelihoods, including rural revitalization, telemedicine, and inclusive finance. Yet substantial gaps in digital literacy persist across regions and demographic groups. Completing the “last mile” of infrastructure development therefore means more than extending physical coverage; it also requires ensuring that people possess the skills and capacity to use digital services effectively. Institutional design must move beyond building sound infrastructure to making digital services genuinely accessible and usable.

Key reform priorities

The most urgent priority in the field of data rights is to advance dedicated legislation. At present, the “separation of data property rights into three rights” rests largely on policy documents and lacks support from higher-level law. When disputes over data ownership arise in the course of transactions, market participants often have no clear legal basis on which to proceed. Reform of data-pricing mechanisms should therefore advance alongside the legislative process.

Antitrust enforcement must also become more workable in practice. Data and algorithmic monopolies cannot be identified simply by applying conventional market-share thresholds. Regulators need new analytical tools capable of measuring such factors as data concentration, user switching costs, and algorithmic lock-in.

The core contradiction in inclusive digital development has likewise changed. Limited network access and inadequate equipment were once the primary bottlenecks; today, insufficient digital literacy has become the more pressing concern. Age-friendly and accessible design standards should therefore be elevated from voluntary guidelines to mandatory requirements, ensuring that technological innovation does not advance at the expense of inclusion.

Cai Hongbo is a professor from the Business School at Beijing Normal University. Mao Jian is an assistant research fellow from the PBC School of Finance at Tsinghua University.