The Role of the Data Factor in Economic Growth at the Stage of New Quality Productive Forces

At the stage of new quality productive forces, the mode of economic growth requires a shift from the traditional resource-driven type to an innovation-driven type, emphasizing the deep integration of knowledge, technology, and data. As a core component of new quality productive forces, the data factor has become the fifth major factor of production after land, labor, capital, and technology. With the rapid development of technologies such as artificial intelligence, big data, and the Internet of Things, the characteristics of the data factor and its role in economic transformation are increasingly prominent. Driven by new quality productive forces, the data factor reshapes traditional production models and provides a new source of impetus for economic growth.

新质生产力阶段，经济增长方式要求从传统的资源驱动型向创新驱动型转变，强调知识、技术和数据的深度融合。数据要素作为新质生产力的核心组成部分，已成为继土地、劳动力、资本和技术之后的第五大生产要素。随着人工智能、大数据、物联网等技术的快速发展，数据要素的特性及其在经济转型中的作用日益凸显。在新质生产力的推动下，数据要素重塑了传统的生产模式，为经济增长提供了新的动力源泉。

1 Economic Characteristics of the Data Factor

1 数据要素的经济特性

The data factor possesses economic characteristics that are entirely different from those of traditional factors of production, and these characteristics make it play an irreplaceable role in economic growth. Within the framework of new quality productive forces, these characteristics of the data factor further highlight its role in promoting high-quality economic growth. The first major characteristic of the data factor is that its marginal cost approaches zero, meaning that data can be used an unlimited number of times with almost no additional cost. New quality productive forces emphasize efficiency improvement through technological innovation and data application, and the characteristic of the data factor’s marginal cost approaching zero is precisely an important foundation for realizing new quality productive forces. The second major characteristic of the data factor is its non-rivalry and non-excludability, meaning that data can be used simultaneously by multiple subjects without diminishing its value, which stands in sharp contrast to the exclusivity of traditional resources. The core of new quality productive forces lies in collaborative innovation and knowledge sharing, and the non-rivalry and non-excludability of the data factor provide important support for achieving this goal. The third major characteristic of the data factor is its network effects and increasing returns to scale effects; the more frequently data is used, the greater the economic value it manifests. New quality productive forces rely on network effects and increasing returns to scale effects, and this characteristic of the data factor makes it an important driving force for new quality productive forces. The three major characteristics above make the use value of the data factor far higher than its ownership value, so the distribution of returns from the data factor is more based on use value than on ownership. This transformation not only reshapes the traditional production function but also promotes a continuous rise in the proportion of knowledge production and consumption, providing new impetus for economic growth.

数据要素具有与传统生产要素截然不同的经济特性，这些特性使其在经济增长中扮演着不可替代的角色。在新质生产力的框架下，数据要素的这些特性进一步凸显了其在推动经济高质量增长中的作用。数据要素的第一大特性是边际成本趋近于零，这意味着数据可以被无限次使用而几乎不增加额外成本。新质生产力强调通过技术创新和数据应用实现效率提升，而数据要素的边际成本趋近于零的特性，正是新质生产力得以实现的重要基础。数据要素的第二大特性是具有非竞争性与非排他性，即数据可以被多个主体同时使用而不损耗其价值，这与传统资源的独占性形成鲜明对比。新质生产力的核心在于协同创新和知识共享，数据要素的非竞争性与非排他性为这一目标的实现提供了重要支撑。数据要素的第三大特性是具有网络效应和规模收益递增效应，数据的使用频率越高，体现出的经济价值越大。新质生产力依赖于网络效应和规模收益递增效应，数据要素的这一特性使其成为新质生产力的重要驱动力。上述三大特性使得数据要素的使用价值远高于其所有权价值，因此数据要素的收益分配更多是基于使用价值而非所有权。这种转变不仅重塑了传统的生产函数，还推动了知识生产与消费的比重不断上升，为经济增长提供了新的动力。

2 Mechanisms by Which the Data Factor Empowers Economic Growth

2 数据要素赋能经济增长的机制

The data factor empowers economic growth through multiple mechanisms. First, the data factor significantly improves production efficiency. In the manufacturing industry, industrial internet platforms, through real-time data collection and analysis, achieve intelligent management of the production process, reducing resource waste and cost expenditure. In the agricultural sector, the application of big data technology makes precision agriculture possible, improving land use efficiency and the quality of agricultural products. Second, the data factor accelerates innovation-driven development. Artificial intelligence and machine learning technologies, through the analysis of massive data, can discover patterns that are difficult to detect with traditional methods, thereby promoting technological innovation and business model innovation. In the financial sector, the application of big data risk control models significantly improves the risk management capabilities of financial institutions. In the medical field, data-based precision medicine technology is changing traditional diagnosis and treatment models. In addition, the deep application of the data factor promotes the transformation of the industrial structure toward knowledge-intensive and high-value-added types. Emerging industries centered on the digital economy, such as cloud computing, artificial intelligence, and blockchain, are becoming new engines of economic growth. At the same time, traditional industries achieve efficiency improvement and value chain upgrading through digital transformation. For example, the retail industry optimizes supply chain management through big data analysis, improving market response speed and customer satisfaction. Finally, the flow and sharing of the data factor help narrow regional economic disparities. By building a nationally integrated data factor market, the technology and management experience of the eastern developed regions can be quickly transmitted to the central and western regions, promoting coordinated regional economic development. Guizhou Province has vigorously developed the big data industry, successfully achieving a transformation from a traditional resource-based economy to a digital economy. Zhejiang Province has focused on building a “city brain,” using big data and artificial intelligence technology to optimize urban management and services, significantly improving urban operational efficiency. Shenzhen City, through developing the digital economy, has completed a transformation from traditional manufacturing to a high-tech industry. These successful practices provide valuable experience for other regions in China to discover new growth points by leveraging the data factor.

数据要素通过多种机制赋能经济增长。首先，数据要素显著提升了生产效率。在制造业中，工业互联网平台通过实时数据采集与分析，实现了生产过程的智能化管理，减少了资源浪费和成本支出。在农业领域，大数据技术的应用使得精准农业成为可能，提高了土地利用效率和农产品质量。其次，数据要素加速了创新驱动发展。人工智能和机器学习技术通过对海量数据的分析，能够发现传统方法难以察觉的规律，从而推动技术创新和商业模式创新。在金融领域，大数据风控模型的应用显著提高了金融机构的风险管理能力。在医疗领域，基于数据的精准医疗技术正在改变传统的诊疗模式。此外，数据要素的深度应用推动了产业结构向知识密集型和高附加值方向转型。以数字经济为核心的新兴产业，如云计算、人工智能、区块链等，正在成为经济增长的新引擎。同时，传统产业通过数字化转型实现了效率提升和价值链升级。例如，零售业通过大数据分析优化供应链管理，提高了市场响应速度和客户满意度。最后，数据要素的流动与共享有助于缩小区域经济差距。通过建设全国一体化的数据要素市场，东部发达地区的技术和管理经验可以快速传递到中西部地区，推动区域经济协调发展。贵州省大力发展大数据产业，成功实现了从传统资源型经济向数字经济的转型。浙江省着力建设“城市大脑”，利用大数据和人工智能技术优化城市管理和服务，显著提升了城市运行效率。深圳市通过发展数字经济，完成了从传统制造业向高新技术产业的发展转变。这些成功实践为中国其他地区借助数据要素发现新的增长点提供了宝贵经验。

3 Major Challenges Currently Facing the Development of the Data Factor

3 当前发展数据要素面临的主要挑战

Although the data factor has played an important role in economic growth, its application still faces many challenges. First, the problem of data silos is serious. Due to departmental barriers and interest segmentation, a large amount of data is stored in isolation, making it difficult to achieve cross-departmental and cross-regional sharing and collaboration. Second, the issues of data privacy and security are prominent. The widespread use of data brings risks of privacy leakage and data abuse, and how to balance data utilization and privacy protection has become an urgent problem to be solved. Third, the degree of marketization of the data factor is insufficient. Currently, the market-based allocation mechanism for the data factor has not yet been fully established, and data trading platforms and pricing mechanisms are still imperfect. Fourth, the problem of talent shortage and skill mismatch is significant. The deep application of the data factor requires a large number of high-quality talents, and the current education and training systems cannot fully meet this demand.

尽管数据要素在经济增长中发挥了重要作用，但其应用仍面临诸多挑战。一是数据孤岛问题严重。由于部门壁垒和利益分割，大量数据被孤立存储，难以实现跨部门、跨区域的共享与协同。二是数据隐私与安全问题突出。数据的广泛使用带来了隐私泄露和数据滥用的风险，如何平衡数据利用与隐私保护成为亟待解决的问题。三是数据要素市场化程度不足。当前，数据要素的市场化配置机制尚未完全建立，数据交易平台和定价机制仍不完善。四是人才短缺与技能不匹配的问题显著。数据要素的深度应用需要大量高素质人才，而当前的教育和培训体系尚无法完全满足这一需求。

4 Relevant Policy Recommendations

4 相关政策建议

To fully leverage the role of the data factor in economic growth, China needs to adopt a series of measures at the policy, technological, and institutional levels. First, the market-based allocation mechanism for the data factor should be improved. Establish a unified national data factor market, break data silos, and promote the free flow of data between different departments, regions, and enterprises. At the same time, improve data trading and pricing mechanisms, formulate scientific and reasonable data pricing standards, and promote the standardized development of the data trading market. Second, strengthen data privacy and security protection. Improve laws and regulations, formulate a dedicated data protection law, and clarify the boundaries of data use and sharing. Promote privacy computing technologies, such as federated learning and homomorphic encryption, to realize the mining of data value while protecting data privacy. Third, promote the construction of digital infrastructure. Accelerate the construction of new infrastructure such as 5G and the Internet of Things to provide technical support for the efficient flow and application of the data factor. Build a national-level big data platform to integrate scattered data resources and improve the overall utilization efficiency of data. In addition, strengthen talent cultivation and skill improvement. Optimize the education system by adding relevant majors such as data science and artificial intelligence in universities and vocational colleges to cultivate compound talents. Carry out vocational training, conduct data skills training for enterprise employees, and enhance the digital literacy of the labor force. Finally, promote coordinated regional development. Promote the flow of the data factor to the central and western regions, and through policy guidance and technical support, promote the development of the digital economy in the central and western regions. Build digital economy demonstration zones, carry out digital economy pilot projects in key regions, and explore replicable and scalable experiences.

为充分发挥数据要素在经济增长中的作用，我国需要在政策、技术和制度层面采取一系列措施。首先，应完善数据要素市场化配置机制。建立全国统一的数据要素市场，打破数据孤岛，促进数据在不同部门、区域和企业之间的自由流动。同时，健全数据交易与定价机制，制定科学合理的数据定价标准，推动数据交易市场的规范化发展。其次，加强数据隐私与安全保护。完善法律法规，制定专门的数据保护法，明确数据使用和共享的边界。推广隐私计算技术，通过联邦学习、同态加密等技术，在保护数据隐私的同时实现数据价值的挖掘。再次，推动数字基础设施建设。加快5G、物联网等新型基础设施建设，为数据要素的高效流动和应用提供技术支撑。建设国家级大数据平台，整合分散的数据资源，提升数据的整体利用效率。此外，加强人才培养与技能提升。优化教育体系，在高校和职业院校中增设数据科学、人工智能等相关专业，培养复合型人才。开展职业培训，针对企业员工开展数据技能培训，提升劳动力的数字化素养。最后，促进区域协调发展。推动数据要素向中西部地区流动，通过政策引导和技术支持，促进中西部地区数字经济的发展。建设数字经济示范区，在重点区域开展数字经济试点，探索可复制、可推广的经验。

Online editor: Paul

网络编辑：保罗