数据作为全新的生产要素，已成为数字经济时代的“石油”，数据要素驱动的创新创业正成为新发展阶段实现高质量发展的新引擎。但鲜有针对数据要素价值化过程及其价值创造机制的研究。本研究针对新发展格局下数据要素价值化的难题，结合数据要素的“5I”社会属性，建构“要素-机制-绩效”这一过程视角下数据要素价值化的动态整合理论模型，系统论述通过数据银行实现数据要素多维价值创造的五阶段动态过程机制，也即低成本汇聚、规范化确权、高效率治理、资产化交易和全场景应用，讨论了落实这一机制的现实挑战和政策建议。本研究为打开数据要素价值化过程“黑箱”，加速数据要素价值化、赋能数字产业化和产业数字化，进而实现数字创新引领新发展阶段的高质量可持续发展提供理论与实践启示。

Big data as a new factor of production has become the "oil" in the era of digitalization. Innovation and entrepreneurship driven by data elements is becoming a new engine to achieve high-quality development in the new era. Yet there are relatively few researches on the value realization process and value creation process of data elements. This study aims at the problem of valuing data elements under the new development pattern and the mechanism of realizing the value of big data. It first discusses the "5I"(Integration, Interconnection, Insight, Improvement, Iteration) social attribute of data elements, followed by proposing a "factor-mechanism-performance" dynamic holistic model with a core part of data bank. Data bank is based on the full storage, comprehensive aggregation, standard right confirmation and efficient governance of massive data. Based on the social attributes of data elements, the mode and concept of banks, it makes data as a new kind of economic resource and further a tradeable asset, and finally realizes the transaction and application value-added of data. Data bank is a new business form and value-added service under the market-oriented allocation of data elements in the era of data economy. Then it systematically discusses the five-stage dynamic process mechanism of multi-dimensional value creation of data elements through data bank, that is, low cost convergence, standardized right confirmation, highly efficient governance, asset trading and full scenario application. Furthermore, it discusses the challenges, pointing out that the core technology is not yet mature and the value efficiency is low, the orientation of organization is unclear and the system and law need to be perfected. Finally, it proposes future research topics related big data value creation and policy suggestions for the implementation of this dynamic holistic mechanism, with a call for constructing a value creating ecosystem of data elements with Chinese characteristics so as to enable China’s new development pattern and sustainable development. This study provides theoretical and practical enlightenment for opening the "black box" of data elements value creation process, accelerating the value creation of data elements, enabling digital industrialization and industry digitalization, and then realizing high-quality sustainable development led by digital innovation in the new development stage.

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data—an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers’ data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions.

数字经济时代，数据成为独立生产要素，是科技创新的新引擎和经济高质量发展的新动能。从纯数字资产与数据匹配性投入共同组成数据要素化投入的角度理解数据生产要素化，使用数据匹配性投入综合得分对中国数据要素化投入进行时空分析和生产率效应分析。结果表明，中国数据要素化投入存在明显的区域差异，基于数据要素化投入对经济产出的积极作用，其对区域经济协调发展具有重要作用；数据匹配性投入价格持续下降会影响生产要素间的交互作用，数据生产要素的使用带来生产方式的根本变革；数据生产要素化基于数据间的互联互通和经济融合，能够加快数据确权，促进生产要素合理配置，从而推动产业创新和经济高质量发展。

The rapid development of the digital economy has highlighted the crucial role of data in economic growth. This study investigates the impact of two types of innovation on long-term growth by incorporating data into a model of creative destruction and knowledge accumulation. Unlike traditional factors, data exhibit nonrivalry between the two research and development (R&D) sectors, thereby influencing the growth rate of economic outputs simultaneously without interference. Our findings reveal the existence of a balanced growth path (BGP) in both the decentralized economy and the social planner’s economy. In horizontal innovation, data can be transformed into digital knowledge to promote the economic growth [Cong et al. (2021)]. In addition to horizontal innovation, the utilization of data in vertical innovation also enhances the success rate of innovation, with a gradual decrease in per capita data usage on the BGP. Moreover, as agents accumulate human capital, the economy achieves higher output levels, effectively addressing consumer privacy concerns. However, along the transitional path, insufficient data provision by both R&D sectors leads to lower economic growth rates or more intense economic fluctuations, necessitating policy interventions.

We examine the impact of “big data” on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N)+1/sqrt(T). Empirical results indicate gains in forecast improvement in the T dimension but essentially flat N effects.

制造企业数字化转型既是实现制造业数字化、网络化、智能化的关键路径，也是促进我国经济高质量发展的必然选择。以2011—2020年中国 A 股制造业上市公司为研究样本，实证考察企业数字化转型对其全要素生产率（TFP）的影响及机制。结果表明：数字化转型能够显著提升企业全要素生产率；在作用机制方面，数字化转型可以通过改善资源配置效率促进企业全要素生产率提升。具体而言，数字化转型通过提高企业供应链运作效率与矫正过度投资提升投资效率，进而提升企业全要素生产率；进一步分析发现，在高新技术企业以及东部地区和南方地区，数字化转型对企业全要素生产率的提升作用更加显著。结论可为理解企业数字化转型的经济效应提供新的经验证据，从而为夯实经济高质量发展必备的微观基础提供重要政策启示。

Growth has fallen in the U.S. amid a rise in firm concentration. Market share has shifted to low labour share firms, while within-firm labour shares have actually risen. We propose a theory linking these trends in which the driving force is falling overhead costs of spanning multiple products or a rising efficiency advantage of large firms. In response, the most efficient firms (with higher markups) spread into new product lines, thereby increasing concentration and generating a temporary burst of growth. Eventually, due to greater competition from efficient firms, within-firm markups and incentives to innovate fall. Thus our simple model can generate qualitative patterns in line with the observed trends.

Under the guidance of the concept of openness and development, the paper grasps the mechanism of technology spillover in developed countries and analyzes how to better absorb advanced manufacturing technology based on empirical analysis so as to point out the path for the transformation and development of China’s digital manufacturing industry.

 本文使用 ２０１２ － ２０１８ 年中国 ３０ 个省 （直辖市） 的面板数据， 通过构建 “数据要素流动环境指数” 和绿色全要素生产率， 并以 ＮＰＰ ／ ＶＩＩＲＳ 夜间灯光数据为工具变量， 从理论和实证两个维度论证了数据要素的倍增效应和数据要素对人均产出的影响。 研究发现， 数据要素对其他要素生产率的提升存在 “倍增效应”， 其表现形式为数据要素通过与其他要素 （主要是劳动力和资本） 的非线性协同作用， 促使全要素生产率实现几何增长； 数据要素对人均产出带来正向的促进作用， 且该作用不因区位的差异而消失； 通过促进全要素生产率实现人均产出增长是数据要素影响人均产出的传导机制。 本文的政策启示是， 各地应该加大重视数据作为生产要素的关键作用和抢抓 “新基建” 机遇， 通过大力推动经济社会数字化转型来加速释放 “数字红利”， 最终实现区位劣势地区的跨越式发展和中国经济的高质量发展。