企业创新已成为培育新质生产力、实现经济高质量发展的核心引擎。为了积极有效地实施创新驱动发展战略,中国政府把税收优惠政策作为激励企业研发创新的重要手段,将税收减免额逐年提高,但由于研发投入结构失衡、政策执行不到位等问题,企业创新潜力尚未被充分激发。为此,本文通过分析中国上市公司数据,并在传统PSM-DID方法基础上,创新性地引入基于机器学习的异质性检验方法,以深入探究税收优惠政策对不同企业创新影响的差异性。研究发现,一方面,税收优惠政策对于刺激企业创新投资起到一定的正向激励作用,是推进我国新质生产力发展的重要政策工具;另一方面,政策执行过程中依然存在一些亟待优化的问题,尤其是不少企业存在骗取税收优惠的政策套利行为,显著削弱了政策的正向效应,甚至在局部产生不良后果。通过机器学习方法分析识别政策效果的异质性特征,本文发现税收优惠政策对专利数量和质量的积极影响主要集中于约15%的企业,说明不少企业尚未能充分有效利用政策优惠来释放其创新活力,这些未充分受益的企业普遍呈现研发风险高、存在研发操纵、企业家控制较强等特征,需要在后续政策优化调整中予以关注。此外,本研究提出的机器学习异质性分析方法,突破了传统线性模型方法的局限,能更准确地识别政策异质性效果,为政府精准施策、推动新质生产力蓬勃发展提供了新的理论视角和方法论借鉴。

Enterprise innovation has become a core engine for cultivating new quality productive forces and achieving high-quality economic development. To effectively implement the innovation-driven development strategy, the Chinese government has utilized tax preferential policies as a crucial measure to incentivize enterprise R&D and innovation, with annual increases in tax reductions. However, issues such as an imbalanced R&D investment structure and inadequate policy implementation have hindered the full realization of China’s enterprise innovation potential. Therefore, based on data from Chinese listed companies, this paper innovatively introduced a machine learning-based heterogeneity analysis method, building upon the traditional PSM-DID method, to deeply explore the heterogeneous impact of tax preferential policies on enterprise innovation. The research found that, on one hand, tax preferential policies demonstrate a certain overall positive incentive effect on stimulating enterprise innovation investment activities and serve as an important policy tool for promoting the development of new quality productive forces in China; but on the other hand, some issues remain to be optimized in policy implementation. Specifically, many enterprises exploit tax incentives through policy arbitrage, significantly weakening the positive effects of the policy and even causing adverse consequences in some cases. Through machine learning analysis to identify the heterogeneous features of policy effects, it is found that the positive impact of tax preferential policies on patent quantity and quality is mainly concentrated in approximately 15% of enterprises. This indicated that many enterprises have not yet been able to fully and effectively utilize policy incentives to unleash their innovation potential. These under-benefited enterprises generally exhibit characteristics such as high R&D risk, R&D manipulation, and strong entrepreneurial control, warranting attention in subsequent policy optimizations and adjustments. Furthermore, the machine learning-based heterogeneity analysis method introduced in this study overcomes the limitations of traditional linear models, enabling a more accurate identification of heterogeneous policy effects, and will provide a new theoretical perspective and methodological reference for precise policy implementation and promoting the vigorous development of new quality productive forces.