[Chengze Observation · Platform Economy 40 Evaluation 32] Yu Changhua: How does the data economy promote economic growth?

Zhongxin Jingwei September 1: How does the data economy promote economic growth?

Author Yu Changhua Peking University National Development Research Dean hired associate professor

Introduction: Economic activities related to data- "Data Economy" are becoming increasingly important. It is necessary to actively play the role of "data economy" in improving production efficiency and resource allocation efficiency, eliminate "data gap" and promote economic growth.

With the deepening of corporate informationization and the rapid development of the Internet+, more and more trading activities and user footprints have been recorded by various devices, forming huge data. The advancement of digital storage, transmission, and analysis technology makes the use of data more convenient and efficient. Data have gradually become an important production factors for enterprise production and operation activities. An important issue is how to promote economic growth?

Let's refer to the impact of data on the economy. This part is "data economy". There are various data in the real world: from the macroeconomic and financial data released by the State Bureau of Statistics, to investigation of data from entrepreneurs, to various types of big data generated by the Internet and mobile communication as the carrier. Generally speaking, data has non -competitive characteristics, and a user does not prevent other users from using these same data when using data. From the perspective of whether it is exclusive, some data have non -exclusive characteristics, such as some public data released by the State Bureau of Statistics, all companies can obtain and use them. These data exist in the form of public products for everyone to use. Other data have exclusive characteristics. For example, non -disclosure data collected by a platform company generally do not open free to other platform companies.

Data play an important role in economic decisions. Macro data shows some characteristics, micro data, especially big data, which provides multi -dimensional and even timely description information such as consumer behavior and corporate business activities. The use of these data is conducive to reducing information asymmetry, helping different types of decision makers to explain and modify the models relying on past decisions and the results of decision -making behaviors, so as to better predict the results brought by future decision -making.

In the era of digital economy, the role of data in corporate production and operation activities is increasing, and it has gradually become an important production factor. From the perspective of the attributes of data production factors, data is both part of production technology and part of capital formation, with dual attributes of technology and capital. The technical attributes of data are reflected in the use of data to directly improve the efficiency of production activities. The use and digital management of data are conducive to improving the efficiency of the resource allocation of enterprises. For example, logistics enterprises optimize the use of storage space and coordinate express delivery personnel through timely logistics data, thereby improving operating efficiency; the development of fintech enhances the risk management of financial institutions. Ability, thereby improving the efficiency of funds. The capital attributes of data are reflected in the market value of data. For example, some companies that provide data services also have high market value in the capital market. Similar to physical capital, the data is facing "depreciation" loss. For example, companies are willing to pay higher prices to get timely market information, and they are not willing to pay for historical data.

How to promote economic growth? From the perspective of economic growth accounting, economic growth is mainly driven by the growth of production technology and factors. Traditional factors generally include two parts of capital and labor. Data can directly affect the production technology of the enterprise, thereby promoting economic growth. Data can also promote economic growth by integrating the combined efficiency of capital and labor and the configuration efficiency between different enterprises. Specifically, we can explore the impact of data on economic growth from the following aspects. First, data may directly improve the production efficiency of enterprises. For example, the improvement of enterprise information levels and the in -depth use of data can save corporate labor costs and improve capital turnover efficiency. In addition, the data reflecting the market conditions will help enterprises more accurately predict the changes in market demand and reduce information asymmetry, so that enterprises can adjust production strategies in a timely manner and improve production efficiency. Second, data will help enterprises to better internalize data into knowledge through the improvement of "dry middle school" and corporate organization capital. To verify whether the new products and new models will achieve the expected results, companies need to repeatedly test and accumulate a large amount of data to select new products and new models that are finalized. Third, the data can promote innovation. For example, the innovation of the information and communication technology industry comes from the needs of the market to deal with data faster and larger. In turn, more advanced information and communication technology devices have generated more data This has further promoted the innovation of the information and communication technology industry; another example is that the development of artificial intelligence is inseparable from the progress of information and communication technology equipment and the generation and analysis of massive data. Fourth, data is conducive to improving the level of corporate risk management. For example, the development of inclusive finance is inseparable from the rapid development of fintech; fintech combines technology and data to significantly reduce the financing costs and credit constraints of enterprises and residents, thereby Improve the efficiency of capital allocation.

From a macro level, economic production and transaction activities have generated a lot of data. If these data are fully utilized, companies will more accurately predict changes in market demand, thereby providing products and services more accurately. So a question is whether the data will bring an increasing economic activity? The increasing scale of scale is to say that when the amount of data is doubled, the economic activities will exceed the scale. If the economic activity happens just to double, the scale remuneration will remain unchanged. Decreased scale compensation. In the short term, obtaining more data and using more data may produce an increase in scale remuneration. Large -scale enterprises -such as platform companies, have the ability to collect massive transaction data and user behavior data, which can accurately predict user preferences and market supply and demand changes, and accurately push products and services, thereby improving the efficiency of resource allocation, promoting corporate innovation and technological progress This further enhances the competitiveness of these companies and expand its market share. These larger enterprises will generate more data, and companies use their data advantages to further expand production scale and market share. Based on this cycle, large enterprises have generated big data, and big data has spawned larger enterprises, showing the characteristics of increasing scale compensation. However, in the long run, the marginal value brought by data collection, sorting, and utilization is decreasing. Imagine that if the macro policy and the market environment have not changed, although the more data collected, the more accurate the prediction of the market, the more the space for the accuracy of the data prediction is increased with the accuracy of the forecast. Therefore, the data economy may present The characteristics of decreased scale remuneration. From the perspective of data, some data are directly used for productive purposes, such as data from consumer income, preferences, data of production factors and data, and data expected to the future market. The use of enterprises to better conduct production and operation activities and improve the efficiency of resource allocation. This part of the data has contributed positively for economic growth. However, the use of some data has negative externalities, especially some data that is not used for direct productive destinations, such as data mining for marketing for marketing and advertising to occupy the market share of competitors. Enterprises that are prioritized to use these data may have the advantage of the first, and the companies that enter later need to use more data to occupy a place in the market. However, from the perspective of social resources allocation, the data in this area has strong negative externality, which leads to excessive investment and data collection and transactions of enterprises.

The development of the data economy has brought changes in the development model and characteristics of the enterprise. The increase in scale rewards in the short term may make larger enterprises larger, while smaller enterprises are difficult to grow quickly and catch up with larger enterprises. From a macro level, the distribution of enterprises will have polarization. One is a very large enterprise, and the other is a small -scale enterprise. The number of enterprises in the middle scale will be relatively small. The emergence of data production factors will also affect the competitive strategy of enterprises. Data gradually become a part of the company's comparative advantage. Many companies provide users with free digital products and services in exchange for data generated by users to use these products and services. A problem that follows is that the enterprise collects and uses data through different ways. After this, does the company dig the value of its data through its own strength, or sell its data as an asset to other companies? From the perspective of market exchange, data is the same as other market trading products. As long as someone needs these data, these data are valuable. However, due to the characteristics of non -competitive data, many users can use the same data at the same time. The use of the same data by more users will cause the marginal value brought by these data to rapidly decline. Therefore With many challenges. Of course, how to ensure the true and reliable data and the integrity of the data are an important prerequisite for data transactions.

In the short term, data has the characteristics of increasing scale compensation, so SMEs need support from relevant data policies. The government can share data with the public to eliminate the data of small and medium -sized enterprises. For example, it is easier for the public to use non -confidential data and desensitization data to allow the whole society to fully tap the value of data and jointly promote economic growth. It should be noted that data may produce some externalities, and these external nature requires corresponding data supervision policies to intervene. Data helping production activities and R & D activities often have positive externality and requires corresponding policy support; however, the use and mining of negative external data requires corresponding policies. On the other hand, from the perspective of digital economy accounting, we need to better measure the value of digital products and data, and better play the contribution of data economy in economic growth. (Zhongxin Jingwei APP)

This article was selected by the Sino -Singapore Jingwei Research Institute. The copy of the work produced by the selected work, the copyright of the work, is not authorized by any unit or individual. The views involved in the selected content only represent the original author and do not represent the view of the Sino -Singapore Jingwei.

Editor in charge: Song Yafen

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