In recent years, the rapid development of the digital economy in China has not only been evident in the industrial sector but has also become a buzz topic in the policy domain. The swift advancement of the digital era in China has generated a massive amount of data resources, providing a foundational basis for the further development of the digital economy.
Some researchers propose that, akin to the utilization of land resources, the utilization of data resources can become an essential component of local economies and local finances. Professor Zhu Yangyong of Fudan University first introduced the concept of "data finance" in 2015, suggesting that while "land finance" has become challenging to sustain in China, the opportune moment for "activating government data resources and establishing data finance" may have arrived. Unlike land, data usage does not decrease over time, and data itself continues to increase. Therefore, activating data resources and establishing "data finance" could be an effective means for the development and utilization of government data resources.
The latest data indicates that in 2022, China's Big Data industry reached a scale of RMB 1.57 trillion, marking an 18% year-on-year growth and becoming a significant driving force behind the development of the digital economy. Zhu's perspective, focusing on the utilization of data resources, positions data finance as a vehicle to propel the development of the Big Data industry and the digital economy. Promoting "data finance" not only brings tangible income to local governments but also advances the utilization of public data resources, thereby enhancing the value of data as an asset. This, in turn, stimulates the development of the digital economy and generates substantial economic benefits.
This approach mirrors the development strategies akin to "land finance" and "land economy", where local governments profit from the monopoly of public data resources and businesses. Through the development of these resources, the local governments gain increased returns, leading to mutual promotion. This not only provides the necessary funds for economic digitization but also drives economic development. With the arrival of the "post-land economy" period, as previously mentioned by ANBOUND, the "land finance" model formed during the "golden age" of real estate faces challenges. Local governments in the country are exploring alternative ways of revenue beyond taxes and land income. In this context, it can be argued that fiscal pressure provides a practical and urgent motivation for localities to promote the development of the data industry.
According to researchers at ANBOUND, local governments are generally in an "advanced" position when it comes to promoting the development of the data-related industry chain. Many regions actively promote the construction of data centers and encourage the development of the data industry. On one hand, this stems from their recognition of the tremendous potential for future development in the data industry, aspiring to secure a "first-mover advantage" for local development. On the other hand, local governments are aware of the importance of public data as a resource in the digital economy, aiming to maximize its value. However, as the data industry is still in its initial stages, with immature models, realizing the value of data resources faces considerable obstacles, making it challenging for local governments to see returns on their substantial investments. Therefore, they hope to monetize these investments through the avenue of data finance.
In the view of researchers at ANBOUND, it is not advisable to massively promote data finance while the development of the data industry is still in the exploratory stage. The current development of the data industry faces numerous controversies and obstacles and is fraught with multiple uncertainties. In the application of data industrialization, challenges extend beyond the sourcing of data to the bottleneck of data development. Only recently, with the impetus of ChatGPT, has research on AI models in China begun to gain traction, and there remains a significant amount of resources in economic activities that have not been digitized.
While attempts at data trading have commenced, the scale of transactions remains negligible. More obstacles stem from the constraints in developing data resources and applying data, preventing the full realization of the value of data. In the current immature development model, pushing for data finance to gain additional income not only adds more costs to investments in data applications but also, due to data monopolies, stifles various innovations, hindering the process of industrial digitization. Therefore, advocating for data finance to drive the development of the data industry and even the advancement of the digital economy should not overlook its potential adverse effects on the economy.
At the same time, a core issue in data finance is the realization of data as an asset. In a situation where data usage remains to be improved, such resources face obstacles in terms of pricing, standardization, and circulation, making it difficult to establish exclusive ownership. These new characteristics require continuous consolidation at both the legal and financial infrastructure levels. Currently, whether through data markets for transactions or as transferable franchise rights, not only does the basic legal protection of data ownership need to be implemented, but it also requires participation from various stages and roles. Even the entry of data into the system is still in the initial stages of experimentation. Therefore, pushing for various rights transfers on top of the foundation of data assets may be somewhat premature. Before localities promote data finance, there are still many foundational tasks that need to be completed.
Under the trend of digital economic development, the assetization of data resources is inevitable. As an innovation in response to new developments, data finance can similarly be explored and experimented with through pilot initiatives. In this process, local governments also need to rationally balance the relationship between short-term gains and long-term benefits.
Final analysis conclusion:
Against the backdrop of a contraction in land finance and the rapid development of the digital economy, "data finance" has attracted the attention of the Chinese government departments. This is motivated, on the one hand, by the search for new economic growth points and, on the other hand, by an attempt to find new support for fiscal revenue. However, even with the policy buzz around the digital economy's progress, the data industry's practical development is still in its infancy, and the market has yet to show the same level of enthusiasm as the government. Hence, a sensible and cautious approach is required when considering the arrival of "data finance" and its potential continuous contribution to fiscal revenue.
