Several Foundational Issues in the Theoretical Innovation of the Digital Economy

The digital economy, as a new type of economic form, is vigorously emerging. As a mapping of human economic activities in digital space, theoretical innovation in the digital economy involves almost all fields of economics, including consumer behavior, enterprise production behavior, market operation mechanisms, economic growth models, income distribution mechanisms, and the relationship between the government and the market. Among these, clarifying foundational issues such as data elements, digital economic growth, and the relations of production within the digital economy is an important prerequisite for theoretical innovation in the digital economy.

数字经济作为一种新型经济形态正在蓬勃兴起。作为人类经济活动在数字空间的映射，数字经济理论创新几乎涉及经济学所有领域，包括消费者行为、企业生产行为、市场运行机制、经济增长模式、收入分配机制以及政府与市场之间的关系等。其中，厘清数据要素、数字经济增长、数字经济中的生产关系等基础问题，是进行数字经济理论创新的重要前提。

Data Elements

数据要素

The digital economy is the sum total of various economic activities driven by big data, and its basic characteristic is that data has become a key factor of production. Data is essentially a carrier of information and knowledge; it not only reflects objective economic activities but also refracts the public's psychological responses to government policies and major events.

数字经济是大数据驱动的各类经济活动的总和，其基本特征在于数据成为关键生产要素。数据本质上是信息与知识的载体，不仅反映客观经济活动，还折射出公众对政府政策、重大事件的心理反应。

Data does not occupy natural physical space (though it needs to be stored in the virtual storage space of computing devices), and this characteristic gives it rapid mobility. Therefore, data can efficiently penetrate the entire process of economic activities such as production, exchange, distribution, circulation, and consumption, optimizing the allocation and combination of various factors of production. Traditional factors of production, such as land, labor, and capital, all have obvious depletability; data does not diminish or depreciate with use. This non-depletability breaks the constraint that the limited supply of traditional factors places on production, and also makes the use of data non-exclusive, meaning that the same data can be used multiple times by a single user or simultaneously by multiple users without its value being impaired. However, non-exclusivity also brings ambiguity to the attribution of property rights and the definition of rights and responsibilities for data, thus causing digital economic activities to often be accompanied by externality problems.

数据不占据自然物理空间（但需要存储在计算设备的虚拟存储空间中），这一特性使其具有快速的流动性。因此，数据能够高效地贯通生产、交换、分配、流通和消费等经济活动全过程，优化各类生产要素的配置组合。传统生产要素，如土地、劳动力和资本，均具有明显的消耗性，数据不会因使用而减少或折旧，这种非消耗性打破了传统要素的有限供给对生产的限制，也使数据的使用具有非排他性，即同一数据既可以被单个用户多次利用，也可以被多个用户同时使用，数据价值不会因此受损。然而，非排他性也为数据带来了产权归属和权责界定的模糊性，从而使数字经济活动常常伴随着外部性问题。

As a new type of factor of production, data can only fully exert its enabling role when it is deeply integrated with traditional factors of production. The combination of data and labor forms digital labor, such as online streamers and ride-hailing drivers; the combination of data and capital forms digital capital, such as some large digital platforms; the combination of data and technology forms digital technology, such as autonomous driving technology, drones, and large language models; the combination of data and management forms digital management, such as human-computer interaction, smart manufacturing, and smart cities.

作为一种新型生产要素，数据只有与传统生产要素深度融合，才能充分发挥其赋能作用。数据与劳动结合形成数字劳动，如网络主播、网约车司机等；数据与资本结合形成数字资本，如一些大型数字平台；数据与技术结合形成数字技术，如无人驾驶技术、无人机、大语言模型等；数据与管理结合形成数字管理，如人机互动、智能制造、智慧城市等。

Data enabling has the following important characteristics: First, it has a non-linear enabling effect. The information and knowledge contained in data do not simply have a linear relationship with the amount of data; the aggregation of different data may reveal new associative knowledge, thereby enhancing the enabling role of data. This non-linear enabling effect is an important reason for the phenomenon of increasing marginal returns in the digital economy. Second, there is scenario dependence. The enabling role of data is highly dependent on the application scenario. For example, a customer's waiting time or geographic location data is very important for a ride-hailing driver, but if detached from a specific time or place, the value of this data will rapidly decline. Third, it has timeliness. The value of data is largely closely related to the time of its generation. Much data reflects information about economic activities at the moment of its generation; if not applied in a timely manner, the enabling effect of the data may gradually decay over time. Fourth, there is value heterogeneity. Even in the same application scenario, due to differences in data utilization efficiency, algorithm optimization level, and business adaptation capability, the enabling effect of data elements from different enterprises may still exhibit significant differences.

数据赋能具有以下重要特点：一是具有非线性赋能效应。数据所包含的信息与知识并非简单地与数据量呈线性关系，不同数据的聚合有可能揭示出新的关联知识，从而增强数据的赋能作用。这种非线性赋能效应是数字经济出现边际报酬递增的一个重要原因。二是存在场景依赖性。数据的赋能作用高度依赖应用场景。例如，顾客的等车时间或地理位置数据对网约车司机十分重要，但如果脱离特定时间或地点，这些数据的价值将迅速下降。三是具有时效性。数据价值很大程度上与其生成时间密切相关。许多数据反映的是其生成时刻的经济活动信息，如果没有及时应用，数据的赋能效果可能随着时间推移而逐渐衰减。四是存在价值异质性。即便是相同的应用场景，由于数据利用效率、算法优化水平和业务适配能力等方面的差异，不同企业的数据要素在赋能方面仍可能存在显著差异。

Treating data as a new type of factor of production is an important theoretical innovation, but this is only the starting point for theoretical innovation in the digital economy. To truly understand the internal logic and operational laws of the digital economy, it is necessary to construct rigorous economic theory and form a systematic analytical framework based on the uniqueness of data elements. For example, traditional economic theory, based on the assumption of scarcity, regards the contradiction between the finiteness of resources and the infinity of human needs as the fundamental problem of economics. However, the non-exclusivity of data elements breaks through the constraint of scarcity, posing new challenges to traditional economic theory. Another example is that free products and services often existing in the digital economy are also difficult to fully explain through traditional economic theory. Although users do not directly pay currency, they create value for the platform by providing data. This implicit transaction model subverts the traditional logic of value exchange and profit realization. At the same time, the trading of personal data is often accompanied by externalities; for instance, one user's data sharing may inadvertently leak the private information of other users. Without appropriate government intervention, the operation of the data element market may find it difficult to effectively safeguard personal privacy rights and social welfare. Explaining these phenomena all requires the innovation and development of economic theory.

将数据视为一种新型生产要素是一项重要的理论创新，但这只是数字经济理论创新的起点，真正理解数字经济的内在逻辑与运行规律，必须基于数据要素的独特性，构建严谨的经济理论，形成系统化的分析框架。例如，传统经济理论基于稀缺性假设，将资源的有限性与人类需求的无限性之间的矛盾视为经济学的基本问题，然而数据要素的非排他性突破了稀缺性约束，这对传统经济理论提出了新的挑战。又如，数字经济中经常存在的免费产品和服务也难以通过传统经济理论给予完全解释。用户虽未直接支付货币，但他们通过提供数据为平台创造了价值，这种隐性交易模式颠覆了传统价值交换与利润实现的逻辑，同时个人数据的交易常会伴随外部性，比如一个用户的数据分享可能在无意中泄露其他用户的隐私信息，如果缺少适当的政府干预，数据要素市场运行可能难以有效维护个人隐私权利与社会福利，对这些现象的解释都离不开经济理论的创新与发展。

Data Element Enabling and Data Element Value

数据要素赋能与数据要素价值

How to determine the value of data elements? This is a fundamental issue for theoretical innovation in the digital economy. According to Marx's labor theory of value, the value creation of data elements can be understood as the human labor congealed in the process of data becoming a factor, specifically manifested in three aspects: first, the congealing of labor in the process of forming raw data; second, the transfer of materialized labor in the process of data collection and processing; and third, the input of living labor by data workers. This living labor and materialized labor together constitute the value basis of data elements. The value transfer of data elements is the transfer of the value of this labor into new digital products or services. Data element enabling is not limited to the production link but runs through all stages of the entire economic activity, including exchange, circulation, and consumption. Therefore, to examine the value of data elements, a comprehensive analysis must be conducted from the perspective of the entire economic process. More importantly, in the process of enabling the value of digital products, data elements do not follow a simple linear growth pattern in proportion but form a superposition effect by integrating their own value with the value of other elements, achieving a more complex value enhancement. The complexity and non-linear characteristics of data element enabling deepen the connotation of its value creation and value transfer, expanding the scope of research on the labor theory of value. We should take Marx's labor theory of value as a guide, combine it with the enabling characteristics of data elements, and further enrich and develop the labor theory of value.

如何确定数据要素的价值？这是数字经济理论创新的基本问题。根据马克思劳动价值论，数据要素的价值创造可以理解为数据在要素化过程中所凝结的人类劳动，具体体现为三个方面：一是原始数据形成过程中的劳动凝结，二是数据采集与处理过程中的物化劳动转移，三是数据劳动者的活劳动投入。这些活劳动和物化劳动共同构成了数据要素的价值基础，数据要素的价值转移就是将这些劳动的价值转移到新的数字产品或服务中。数据要素赋能并不限于生产环节，而是贯穿于整个经济活动的各个阶段，包括交换、流通和消费等。因此，考察数据要素价值，必须从整个经济过程进行综合分析。更为重要的是，数据在赋能数字产品价值的过程中，并非以按比例线性增长的简单模式，而是通过融合自身价值与其他要素价值，形成叠加效应，实现更复杂的价值提升。数据要素赋能的复杂性与非线性特征，深化了其价值创造与价值转移的内涵，拓展了劳动价值论的研究范围。我们应以马克思劳动价值论为指导，结合数据要素的赋能特性，进一步丰富和发展劳动价值理论。

The full exertion of the enabling role of data elements and the optimal allocation of data element resources require accurate identification or discovery of the value of data elements as a prerequisite, which is inseparable from a well-established data element market. Theoretically, the value magnitude of data elements can be obtained from the exchange relations in the digital economy: under market economy conditions, the price of a commodity or service fluctuates around its value in a perfectly competitive market and is realized in exchange. Therefore, the price in a perfectly competitive market provides a feasible method for measuring the value of data elements. However, to achieve this goal, it is necessary to establish a complete data element market system with a sufficient number of data element suppliers and demanders, which is difficult to achieve in a short period. Therefore, how to accurately measure the value of data elements or set a reasonable price for them has become a pressing theoretical and practical challenge. In this regard, we can draw on market mechanism design theory, experimental economics methods, and simulation experiment techniques for complex economic systems to deeply explore the pricing mechanism of data elements.

数据要素赋能作用的充分发挥，以及数据要素资源的优化配置，需要以准确识别或发现数据要素价值为前提，而这离不开完善的数据要素市场。理论上，数据要素的价值量可以从数字经济中的交换关系中获得：在市场经济条件下，商品或服务的价格在完全竞争市场中围绕其价值上下波动，并在交换中得以实现。因此，完全竞争市场的价格为测度数据要素价值提供了可行的方法。但要实现这一目标，需要建立完善的数据要素市场体系，有足够数量的数据要素供给者和需求者，这些在短时期内是难以实现的。因此，如何准确测度数据要素价值或为其制定合理价格，成为当前一个亟待破解的理论与实践难题。在这方面，可以借鉴市场机制设计理论、实验经济学方法以及复杂经济系统的仿真实验手段等，以深入探索数据要素的定价机制。

Reasonable pricing of data elements can not only guide data resources to flow towards more efficient fields or industries but also provide a solid foundation for accurately assessing the value of data assets. To this end, economic theory research must closely integrate with the practice of the digital economy, adhere to an evidence-based methodology, and scientifically evaluate the value and contribution of data elements. It must be emphasized that, under the circumstances where the theoretical logic for reasonable pricing of data elements has not yet been clarified and there is a lack of sufficient practical exploration, extreme caution must be exercised in the assessment of data assets and the inclusion of data assets on the balance sheet. One-sidedly exaggerating or overestimating the value of data assets and rashly promoting the inclusion of data assets on the balance sheet may trigger a "data asset bubble," causing negative impacts on the healthy development of the digital economy.

合理的数据要素定价不仅能引导数据资源流向效益更高的领域或产业，还能为准确评估数据资产价值提供坚实基础，为此，经济理论研究必须紧密结合数字经济实践，坚持以证据为基础的方法论，科学评估数据要素的价值与贡献。需要强调的是，在数据要素合理定价的理论逻辑尚未厘清，缺乏充分实践探索的情况下，对数据资产评估及数据资产入表都必须格外审慎。片面夸大或高估数据资产价值，贸然推动数据资产入表，有可能引发“数据资产泡沫”，对数字经济的健康发展造成负面影响。

The Contribution Rate of Data Elements to Economic Growth

数据要素对经济增长的贡献率

From the 1970s to the 1990s, information technologies such as computers were widely used, yet in statistical data, productivity—especially in the service sector—appeared to remain stagnant. This phenomenon attracted widespread attention, and academia termed it the “productivity paradox”: “You can see the computer age everywhere but in the productivity statistics.” [1]1 This is a paraphrase of a well-known remark by the economist Robert Solow, often cited as the “Solow paradox” or “productivity paradox.” The key reason for this phenomenon was that capital factors, particularly intangible capital (such as R&D investment, software, and data assets), had not yet been accurately measured. In the 2008 United Nations System of National Accounts, R&D, software, and other intellectual property products were formally included in the capital category and effectively measured. Based on this improved capital measurement, the contribution rate of information technology to economic growth was effectively reflected in economic statistics.

20世纪70年代至90年代，计算机等信息技术得到广泛使用，但在统计数据当中，生产率特别是服务业生产率，似乎一直停滞不前。这一现象引发了广泛关注，学术界将其称为“生产率悖论”：“我们在各个地方都能看到计算机的身影，却唯独在生产率统计中看不到它们。”导致这一现象的关键原因，是资本要素特别是无形资本（如研发投入、软件和数据资产）尚未得到准确的测度。在2008年的联合国国民账户体系中，研发、软件和其他知识产权产品被正式纳入资本范畴并得到有效测度，基于这一改进的资本测度，信息技术对经济增长的贡献率在经济统计中得到了有效反映。

Similarly, the scientific measurement of data factors is crucial for accurately assessing their contribution rate to economic growth. Here, a foundational economic theoretical issue that urgently needs resolution is: what role do data factors actually play in economic growth? Studying this issue requires starting from the underlying logic of economics to examine how data factors combine with other production factors in various links of economic activity, especially how data reshapes economic structure and production processes, and how it optimizes resource allocation to produce more and better products and services. This necessitates constructing a production function that conforms to the internal logic and important characteristics of the digital economy, based on theoretical analysis. This means that, in the context of the digital economy, we need to further innovate and develop economic growth theory, and also need to innovate economic measurement and economic statistics, providing theoretical and methodological support for revealing the internal mechanisms of data-driven economic growth.

同样，对数据要素的科学测度对于准确评估其在经济增长中的贡献率至关重要。这里，一个亟须解决的基础性经济理论问题是：数据要素在经济增长中究竟扮演多大的角色。研究这一问题，需要从经济学底层逻辑出发，考察数据在经济活动各环节中与其他生产要素的结合方式，特别是数据如何重塑经济结构与生产流程，以及如何优化资源配置以生产更多更优的产品与服务。这需要在理论分析的基础上，构建符合数字经济内在逻辑与重要特征的生产函数。这意味着，在数字经济背景下，我们需要进一步创新与发展经济增长理论，同时也需要创新经济测度与经济统计学，为揭示数据驱动经济增长的内在机制提供理论与方法支撑。

Relations of Production in the Era of the Digital Economy

数字经济时代的生产关系

The increasingly widespread use of data factors and big data-based artificial intelligence technologies not only reshapes productive forces but also profoundly changes relations of production, mainly manifested in the following aspects:

数据要素与基于大数据的人工智能技术的日益广泛使用，不仅重新塑造了生产力，也深刻改变着生产关系，主要表现在以下方面：

The substitution of labor by artificial intelligence. The application of artificial intelligence technology has significantly improved production efficiency, greatly shortening the socially necessary labor time required to produce the same quantity of products, thus inevitably giving rise to the phenomenon of AI substituting for labor. Traditional machines mainly replaced simple labor, especially physical labor, while artificial intelligence replaces mental labor to a certain extent. For example, ChatGPT can not only learn and understand human language for conversation but also complete complex tasks such as writing emails, papers, video scripts, translating texts, and coding.

人工智能对劳动的替代。人工智能技术的应用显著提升了生产效率，大幅缩短了生产同量产品所需的社会必要劳动时间，因此也不可避免产生了人工智能替代劳动的现象。传统机器主要替代简单劳动，尤其是体力劳动，而人工智能则在一定程度上替代了脑力劳动。比如，ChatGPT不仅能够学习和理解人类语言进行对话，还能完成诸如撰写邮件、论文、视频脚本，翻译文本，编写代码等复杂任务。

Asymmetric relations in the platform economy. The widespread application of digital technology has given rise to new economic forms such as the gig economy and the platform economy. The gig economy provides workers with more flexible employment methods, but this economic model is highly dependent on massive data resources and digital technology, leading to asymmetry in the relationship between the capital that controls technology platforms and gig workers. Without sound economic governance, gig workers will be in a weak position in their bargaining with platform capital. Similarly, large technology platforms, relying on strong capital, digital technology, and data resources, and through network effects and scale effects, can form asymmetric relationships with small and medium-sized enterprises and consumers.

平台经济中的不对称关系。数字技术的广泛应用催生了零工经济和平台经济等新型经济形态。零工经济为劳动者提供了更加灵活的就业方式,但这种经济模式高度依赖于庞大的数据资源和数字技术，导致掌握科技平台的资本与零工劳动者之间的关系出现了不对称。如果没有良好的经济治理，零工劳动者在与平台资本的博弈中会处于弱势地位。同样地，大科技平台依托强大的资本、数字技术和数据资源，通过网络效应和规模效应，会形成与中小企业、消费者之间的关系不对称。

Income gaps caused by the digital divide. The digital divide refers to the differences among different social groups in internet accessibility and usage capability. The objective existence of the digital divide exacerbates the uneven distribution of the economic and social welfare brought by digital technology across different regions and social groups. Urban areas, economically developed regions, and groups with higher education and higher skills are more likely to access and use digital technology, and therefore more likely to benefit from the rapid development of AI technology to achieve income growth.

数字鸿沟引发的收入差距。数字鸿沟是指不同社会群体在互联网可及性和使用能力上的差异，数字鸿沟的客观存在加剧了数字技术带来的经济社会福利在不同地区和社会群体之间的分配不均，城镇地区、经济发达地区以及高学历、高技能人群更容易接触并使用数字技术，因此更容易受益于人工智能技术的快速发展实现收入增长。

Cross-border data flows and increasingly fierce international competition in digital technology. As data becomes a foundational and strategic resource, competition among countries worldwide for data resources and dominance in digital technology is intensifying. This will exacerbate uncertainty in economic and trade relations and expose the global high-tech industrial and supply chains to more severe risks and challenges.

跨境数据流动和日益激烈的数字科技国际竞争。随着数据成为基础性和战略性资源，世界各国对数据资源和数字技术主导权的竞争日益激烈，这会加剧经贸关系的不确定性，并使全球高科技产业链供应链面临更严峻的风险与挑战。

In summary, in the era of the digital economy, while paying attention to the development of social productive forces driven by data factors and digital technology, it is also necessary to deeply explore the trends of change in relations of production, providing a scientific basis for achieving a fairer and more inclusive model of economic growth and development.

总之，数字经济时代，在关注数据要素和数字技术推动社会生产力发展的同时，还需要深入探讨生产关系的变化趋势，为实现更加公平的包容性经济增长与发展模式提供科学依据。

(Authors: Hong Yongmiao and Shi Jiuling, respectively Dean and Assistant Researcher of the School of Economics and Management, University of Chinese Academy of Sciences)

（作者：洪永淼、史九领，分别系中国科学院大学经济与管理学院院长、助理研究员）

Online Editor: Huihui

网络编辑：慧慧