数据要素与研发决策对工业全要素生产率的影响——来自2010—2019年中国工业的经验证据

在高质量发展阶段，工业全要素生产率不仅取决于数据要素的市场化配置，同时在很大程度上还受到研发决策影响。利用2010—2019年中国工业面板数据，估计数据要素与研发决策对工业全要素生产率的影响效应。结果表明，数据要素赋能资本、劳动要素后的要素深度融合，对工业全要素生产率具有显著改善作用；与劳动密集型工业相比，数据要素对资本密集型工业全要素生产率的改善作用更加显著。将研发决策纳入后发现，探索型研发决策对资本密集型工业全要素生产率提升具有显著促进作用，而囿于高风险的利用型研发决策则对工业全要素生产率的改善作用不显著。进一步研究发现，探索型研发决策能够促进数据要素赋能传统要素，二者有效融合能够显著提高工业全要素生产率。

Research on the Impact of Data Elements and R&D Decision on Industrial Total Factor Productivity:Empirical Evidence from China Industry in 2010-2019

It is an important engine to promote structural reforms on the supply side of data elements and lead the high-quality development of industries with innovation-driven and high-quality supplies for accelerating the development of modern industrial systems, achieving advanced industrial foundations and modernizing industrial chains. It is well-known that industrial total factor productivity is a necessary condition for reconstructing economic growth momentum against structural deceleration and achieving high-quality industrial development. With the transformation of China's economy, the continuous investment of a large number of traditional factors has greatly promoted the efficiency of China's industrial resource allocation and achieved catch-up and surpass. With the continuous narrowing of the technological gap and the substantial improvement of innovation capabilities, the contribution of traditional factors to the output share of industrial total factor productivity has gradually shrunk, leading to the declining marginal contribution of traditional factors. The development history of western developed countries shows that tapping new growth drivers and using data elements to increase total factor productivity has become an important way to reconstruct economic growth drivers. What is urgently needed to know is, in the context of structural deceleration, what kind of factor input can China's industry adopt to effectively increase industrial total factor productivity? Can extending the window of opportunity for traditional factor input alleviate the structural slowdown of the economy? R&D decision is an important channel to explain the integration of data elements and traditional elements, thereby increasing industrial total factor productivity. Since the 2010s, the innovation structure and R&D depth of China's industry have entered a state of rebalancing, and R&D cooperation has gradually turned to R&D competition and cooperation. Although data elements promote industrial innovation by empowering traditional elements, the self-interested R&D decision tendency caused by the “threshold” effect of empowerment affects the improvement of industrial total factor productivity to varying degrees. It can be considered that data elements and R&D decision are the decisive factors affecting industrial total factor productivity, reflecting the degree of improvement in industrial total factor productivity caused by heterogeneous R&D decision caused by data elements. #br#Based on the existing literature, this article utilizes the panel data of China's industry from 2010 to 2019 and attempts to incorporate data elements, R&D decision and industrial total factor productivity into a unified analysis framework so as to estimate the impact of data elements and R&D decision on the industrial total factor productivity. The results show that the depth integration of data elements empowered by capital and labor elements has a significant improvement effect on industrial total factor productivity. Compared with labor-intensive industries, the effect of data elements on the total factor productivity of capital-intensive industries is more obvious. Incorporating R&D decision, we find that exploratory R&D decision plays a significant role in promoting the total factor productivity of capital-intensive industries, while utilization R&D decision limited by high risks has little effect on the improvement of industrial total factor productivity. Furthermore the research shows that exploratory R&D decision can promote data elements to empower traditional elements, and the effective integration of the two promotes the significant improvement of industrial total factor productivity. The coordination and integration of data elements and capital elements can effectively improve the resource allocation efficiency of capital-intensive industries and significantly increase the total factor productivity of capital-intensive industries. The uncoordinated integration of data elements and traditional elements leads to misallocation of resources, and there is not significant improvement of total factor productivity in capital-intensive industries with R&D decisions. The integration of data elements and labor elements can improve the efficiency of labor resource allocation, and focus on low-cost, low-risk utilization R&D decision significantly can enhance the role of data elements in promoting total factor productivity in labor-intensive industries.#br#Compared with the existing literature, the contribution of this paper is mainly reflected in two aspects. First, from the perspective of factor allocation of high-quality development, it introduces R&D decision into the model with a thorough examination of the internal mechanism of data elements influence on industrial total factor productivity. Second, considering the significant differences between data elements and R&D decision in the improvement of the total factor productivity in different factor-intensive industries, this paper examines the mechanism of data factors that significantly improve industrial total factor productivity in the context of exploratory and utilization R&D decision on the basis of distinguishing different factor-intensive industries. In addition, it will be one of the future research directions to incorporate data elements and R & D decisions into the production function and study the impact of data element empowerment on industrial total factor productivity within the framework of increasing returns to scale.#br#