数字化、网络化和智能化是“中国制造业2025”提出的产业转型的重要方针，而智能化技术作为一种通用技术进步，是否能够真的缩短劳动时间，提高企业全要素生产率和技术创新还值得商榷。本文基于中国工信部、国家发改委和科技部的智能化试点政策，使用 2009-2018年上市公司面板数据，采用倾向得分匹配和双重差分方法（PSM-DID），研究智能化政策能否促进企业的全要素生产率和创新效率，并分析要素密集度、所有权结构和研发密度产生的异质性。研究结果发现：（1）智能化能够提高当期的企业全要素生产率，但对企业创新效率的影响不明显；（2）智能化对企业全要素生产率和技术创新的影响均存在滞后性和动态性，对全要素生产率的影响呈“U”型趋势，而对企业创新效率的作用从第二年才开始，并呈倒“U”型趋势；（3）考虑要素密集度、所有权结构和研发密度后，智能化对不同类型企业影响不同。简单来说，智能化对劳动和资源密集型企业全要素生产率的作用大于资本密集型，而创新效率相反。国有企业智能化对全要素生产率的作用大于非国有企业，并且创新效率的作用都不显著。研发密度越低的企业智能化对全要素生产率作用最大，但对所有类型企业当期创新效率的影响都不显著。
Abstract
The report of the 19th National Congress of the Communist Party of China first proposed that China′s economy has shifted from a stage of rapid growth to high-quality development. High-quality development should be based on the improvement of production factors, productivity and total factor efficiency. With the emergence of ‘industry 4.0’, the world is facing a huge opportunity for networking, digitization and intelligence, which is also put forward by ‘China manufacturing 2025’. The development of intelligent relevant technologies and industries is gradually becoming the core factor of the global economy. In 2018, the scale of China′s robot market was US8.74 billion, with a year-on-year increase of 39.2%, accounting for 30% of the total amount of global robots. And the global output of industrial robots was 390 thousand, with an increase of 14.6%. The output of industrial robots in China was 148 thousand, accounting for 38%. As the world′s second largest economy, intelligence has played an important role in China. Therefore, it is urgent to promote the development of enterprises and the transformation of industrial structure through intelligence.Based on the relevant intelligence policies of China, using the panel data of listed companies from 2009 to 2018, this paper studies the effect of intelligence on total factor productivity and innovation of enterprise, and analyzes the heterogeneity of factor-intensity, ownership structure and R&D-density through the method of propensity score matching and difference-in-difference. The results are as follows. First, intelligence can improve the total factor productivity of enterprises in the current period. However, the impact of intelligence on innovation is insignificant. Second, intelligence has a lag and dynamic impact on the total factor productivity and innovation. The relationship between intelligence and total factor productivity shows a "U" trend. The effect of intelligence on the innovation functions from the second year, and appears an inverted "U" trend. Third, the effect of intelligence on total factor productivity in labor- and resource- intensity enterprises is greater than that of capital-intensity enterprises, while the effect of innovation is the opposite. Fourth, state-owned enterprises have a bigger positive impact of intelligence on total factor productivity than that of non-state-owned enterprises. In addition, the effect of innovation is not significant. Fifth, enterprises with lower R&D-density have stronger and more positive effect of intelligence on TFP than that of higher R&D-density enterprises. However, intelligence has an insignificant effect on the innovation of all types of enterprises in the current period. The possible innovations and contributions of this paper are as follows: First, we find the intelligent pilot policies, proposed by the Ministry of Industry and Information Technology, the National Development and Reform Commission and the Ministry of Science and Technology. This paper also uses the control group through the propensity score matching method and analyzes the impact of intelligence on enterprise performance through the difference in difference method. Third, the lag term of dummy variables is also introduced into the model to study the lag effect of intelligence on enterprise performance. Finally, this paper studies the different effects of intelligence on enterprise performance considering the factor intensity, ownership structure and R&D density.
关键词
智能化 /
全要素生产率 /
企业创新 /
倾向得分匹配 /
双重差分法
Key words
intelligent technology /
total factor productivity /
enterprise innovation /
propensity score matching /
difference in difference (DID) method
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基金
国家自然科学基金青年项目：“数字经济促进产业转型升级的最优劳动要素配置：辐射效应与区域协调对策研究”（72203168，2022.10—2025.12）；国家自然科学基金青年项目：“居民食品消费中的量化自我困境：营养合理性评估习惯形成与健康促进策略研究”（72003113，2021.01—2023.12）；教育部人文社会科学青年基金项目：“数字经济推动消费升级的机理与路径研究：供需动态平衡的视角”（21YJC790163，2021.12—2024.12）。
