On January 26, Xie Danxia, assistant professor at the Institute of Economics, School of Social Sciences, Tsinghua University, gave the Lecture 6 of Frontiers of Digital Economy Lecture Series, with the theme of how data elements affecting economic growth. Li Hongjun, associate professor at the Institute of Economics, School of Social Sciences, Tsinghua University, presided over the lecture, and introduced Prof. Xie’s research field and academic achievements and the main content of the lecture.
Prof. Xie reviewed the theories and development history of economic growth, and noted that economic growth is a great subject about human history, human development and human future. It is of great significance to study economic growth. The principal theories of economic growth include Malthusian Growth Model, Solow-Swan Model and New Growth Theory. Specifically, Malthusian Growth Model as an earlier growth theory, taking labor and land as the two main production factors, believes that there is a long-term economic growth stagnation. Solow-Swan Model, a modern theory of economic growth, introduces capital as a production factor to offer a simple explanation of long-term economic growth, but fails to explain the source of technological progress. New Growth Theory, including the endogenous growth model, introduces production factors such as technology (knowledge) and human capital, and holds that knowledge and technology can be developed indefinitely and are non-competitive (knowledge), and that research and development, innovation and education are the keys to sustained economic growth.
Prof. Xie illustrated how data affects economic growth. He indicated that the data volume has grown rapidly in recent years, and data has become the fifth basic production factor alongside land, labor, capital, and technology; the research on how data factors affect economic growth is a new human subject that needs to be urgently explored. Compared with other production factors, data has exclusively important properties such as non-competitiveness, exclusivity and privacy concerns. Data can create economic value in two ways: First, joining the production process to increase output; second, entering the innovation process to create new technologies, new knowledge and new industries. Corresponding to these two economic effects of data, the research of Prof. Xie’s research team and Stanford research team (Jones and Tonetti) further divided the non-competitiveness of data into dynamic non-competitiveness and horizontal non-competitiveness, respectively. The teams also studied the vertical non-competitiveness of data, that is, a data economy where data can be used in both innovation and production processes.
Then, Prof. Xie introduced the main content and principles of the “Endogenous Growth Theory of Data Innovation” proposed by his research team. This theory mainly describes and models the process of contributions of data elements to innovation, and conducts an overall analysis of data privacy risks. In the model, enterprises are divided into two categories: production enterprises and innovative enterprises, among which innovative enterprises put data elements into the innovation process. Consumers provide data with data privacy risks. In the innovation process, data is transformed into knowledge (e.g. patents) and can be reused in the future; and the use of knowledge no longer involves data privacy issues because knowledge is “clean”. Prof. Xie specifically calls this process the “data-to-knowledge bleaching and condensation”. Data is transformed into “clean” knowledge in the innovation process, and can be reused indefinitely without privacy costs, thus promoting the continuous development of knowledge, technology and economy, which also depicts the “dynamic non-competitiveness” of data emphasized by the “Endogenous Growth Theory of Data Innovation”.
Prof. Xie made a comprehensive comparison between his “Endogenous Growth Theory of Data Innovation” and the growth theory of data used in the production process proposed by the Stanford research team, and put forward policy suggestions. He calculated and compared the growth rate, data usage and the labor ratio in the innovation sector based on the above two data growth theories, and indicated that the economic value generated by data in the innovation process is dominant, while that in the production process is secondary. Besides, he stressed that China has great potential to develop a digital economy, especially a data economy, and that the use of data in the innovation sector should be strongly encouraged, as the “data-to-knowledge bleaching and condensation” process results in “clean” knowledge that can be reused and does not involve privacy issues.
This Lecture Series is supported by Sina Finance, NetEase Finance, Chinese Headlines, Xueshuo and KeAi’s Journal of Digital Economy.
