|
|
|
| A Novel Production Factor: A Study on Forming Conditions and Operation Mechanism of Data |
| Pan Jiadong1, Xiao Wen2,3 |
1.Department of Economics, Party School of Zhejiang Provincial Committee of C.P.C., Hangzhou 311123, China
2.School of Economics, Zhejiang University, Hangzhou 310058, China
3.Business School, NingboTech University, Ningbo 315100, China |
|
|
|
|
Abstract Data as a novel production factor is the core strategic resource for the development of the digital economy. When integrated with conventional factors such as labor and capital, data as a production factor can contribute to the generation of new combinations and structures of production factors to energize the processes of production, circulation, consumption, and distribution, which can ultimately improve the efficiency of social-economic operations. This article focuses on the conditions needed for data to become a novel production factor and the economic-technical characteristics of such data. Going further, this article explores the operation mechanism of the data factor in high-quality economic development and proposes a road map for the data factor to operate in a safe and managed manner based on the risks that the data factor may face in its operation.Data only becomes a novel production factor when its quantity tips over into becoming “Big Data”. Raw data, after the processes of acquisition, mining, flow, and application, energize production and thereby realize their values as a production factor. Such processes are subject to the influences of different conditions, such as technical advancement, standards’ promulgation, market improvement, and integration with conventional factors. Mature technologies are prerequisite conditions. Clearly defined standards are initial guiding conditions. Improved markets are data safety conditions. Deep integration with conventional production factors is a necessary application condition. Different from conventional production factors, data as a novel factor has the characteristics of economic and technical properties in parallel, including virtual substitutability, complementary symbiosis, dynamic real-time nature, scaled economy, non-exclusiveness, and risk stealthiness. When applied to production, consumption, exchange, and distribution areas, the data factor facilitates changes in the mode of production by accelerating advanced inputs and rational allocation of production factors, hastens the emergence of new consumption modes to satisfy high-end diversified demands, improves the efficiency of exchange and circulation so as to provide new carriers for new commercial modes and payment methods, and further expands social reproduction by balancing the equality and efficiency of distribution. In order for data as a novel production factor to energize high-quality development, it is necessary to develop and improve core digital technologies, invest more in digital infrastructure construction, implement data transaction markets at an early date, and deploy strong defense measures against data risks.This study is innovative in three aspects. Firstly, this article selects the data factor as the object of study to explore the formation conditions, economic-technical characteristics, and operation mechanism, and attempts to explain theoretically the logic by which data become a novel production factor. Secondly, this article employs the Marxist social reproduction theory to develop a theoretical framework describing how data as a novel production factor can energize high-quality economic development. This allows for the mechanism through which the data factor operates in each respective area of production, consumption, exchange, and distribution to be explored in order to identify a path to improve social-economic operation efficiency. Thirdly, this article analyzes the risks involved in the operation of the data factor, addresses the risk stealthiness behind the data factor, and proposes countermeasures from legislative, institutional, and regulatory perspectives to govern the use of the data factor.
|
|
Received: 11 April 2022
|
|
|
|
1 冯科： 《数字经济时代数据生产要素化的经济分析》，《北京工商大学学报（社会科学版）》2022年第1期，第1-12页。
2 徐翔、厉克奥博、田晓轩： 《数据生产要素研究进展》，《经济学动态》2021年第4期，第142-158页。
3 Jones C. I. & Tonetti C.， “Nonrivalry and the economics of data，” American Economic Review， Vol. 110， No. 9 （2020）， pp. 2819-2858.
4 Freeman C.， “Continental， national and sub-national innovation systems—complementarity and economic growth，” Research Policy， Vol. 31， No. 2 （2002）， pp. 191-211.
5 王谦、付晓东： 《数据要素赋能经济增长机制探究》，《上海经济研究》2021年第4期，第55-66页。
6 黄少安、张华庆、刘阳荷： 《数据要素的价值实现与市场化配置》，《东岳论丛》2022年第2期，第115-121页。
7 尹西明、林镇阳、陈劲等： 《数据要素价值化动态过程机制研究》，《科学学研究》2022年第2期，第220-229页。
8 宋冬林、孙尚斌、范欣： 《数据成为现代生产要素的政治经济学分析》，《经济学家》2021年第1期，第35-44页。
9 戴双兴： 《数据要素：主要特征、推动效应及发展路径》，《马克思主义与现实》2020年第6期，第171-177页。
10 李海舰、赵丽： 《数据成为生产要素：特征、机制与价值形态演进》，《上海经济研究》2021年第8期，第48-59页。
11 Erickson S. & Rothberg H.， “Big data and knowledge management： establishing a conceptual foundation，” Electronic Journal of Knowledge Management， Vol. 12， No. 2 （2015）， pp. 108-116.
12 王姝楠、陈江生： 《数字经济的技术—经济范式》，《上海经济研究》2019年第12期，第80-94页。
13 Bourreau M.， de Streel A. & Graef I.， “Big data and competition policy： market power， personalised pricing and advertising，” 2017-02-16， https：//ssrn.com/abstract=2920301， 2022-04-11.
14 Mathews J. A.， “The renewable energies technology surge： a new techno-economic paradigm in the making？” Futures， Vol. 46 （2013）， pp. 10-22.
15 中共中央编译局编译： 《马克思恩格斯选集》第2卷，北京：人民出版社，2012年。
16 Aptea S. & Petrovsky N.， “Will blockchain technology revolutionize excipient supply chain management？” Journal of Excipients and Food Chemicals， Vol. 7， No. 3 （2016）， pp. 76-78.
17 Mowshowitz A.， “Information as a commodity： assessment of market value，” Advances in Computers， Vol. 38 （1994）， pp. 247-316.
18 蔡跃洲、马文君： 《数据要素对高质量发展影响与数据流动制约》，《数量经济技术经济研究》2021年第3期，第64-83页。
19 Buyya R.， Yeo C. S. & Venugopal S. et al.， “Cloud computing and emerging it platforms： vision， hype， and reality for delivering computing as the 5th utility，” Future Generation Computer System， Vol. 25， No. 6 （2009）， pp. 599-616.
20 张志、李军祥、刘洁等： 《区块链技术在国际贸易领域中的应用研究》，《物流科技》2019年第11期，第44-46页。
21 洪远朋、叶正茂： 《劳动价值论：共享利益的理论基础》，《毛泽东邓小平理论研究》2002年第2期，第30-35页。
22 Ahmad N. & Schreyer P.， “Are GDP and productivity measures up to the challenges of the digital economy，” International Productivity Monitor， Vol. 24， No. 30 （2016）， pp. 4-27.
23 Miller A. R. & Tucker C.， “Privacy protection， personalized medicine and genetic testing，” Management Science， Vol. 64， No. 10 （2017）， pp. 4648-4668.
24 陆岷峰： 《数据要素市场化：禀性特征、发展障碍与推进对策》，《金陵科技学院学报（社会科学版）》2021年第1期，第7-11页。
25 Branscomb A. W.， “Public and private domains of information： defining the legal boundaries，” Bulletin of the American Society for Information Science， Vol. 21， No. 2 （1994）， pp. 14-18.
26 张莉、卞靖： 《数字经济背景下的数据治理策略探析》，《宏观经济管理》2022年第2期，第35-41页。
27 何玉长、王伟： 《数据要素市场化的理论阐释》，《当代经济研究》2021年第4期，第33-44页。
28 习近平： 《不断做强做优做大我国数字经济》，《求是》2022年第2期，第4-8页。 |
|
|
|
2022, Vol. 52 
