政府引导基金对企业全要素生产率的影响——基于创业板数据

The Impact of Government-Guided Funds on the Total Factor Productivity of Enterprises—Based on Data from the Growth Enterprise Market

摘要: 在经济高质量发展成为核心目标的背景下，全要素生产率作为衡量经济增长质量与效率的关键指标，其提升已成为推动企业转型升级、增强市场竞争力的关键动力。政府引导基金能否有效提升企业全要素生产率，学术界对此分歧明显：部分学者认为其可通过缓解融资约束、助推研发投入与技术创新等提升全要素生产率；另一部分则指出，行政干预过度、道德风险等问题可能导致其抑制全要素生产率。鉴于此，系统探究两者关系、厘清作用机理，既能化解学术界现有分歧，也能优化引导基金运作机制、提升政策效果。本文选用2012~2024年创业板的数据，采用多期双重差分模型对政府引导基金对企业全要素生产率的影响进行实证分析。具体而言，通过替换被解释变量和安慰剂进行稳健性检验，并且运用PSMDID方法减少内生性问题，使用得分倾向匹配后的数据进行回归，得到了和原先相同的结论，随后从融资约束、研发投入、ESG评级、绿色创新以及企业数字化转型多个角度进行了机制检验；实证结果表明：(1) 政府引导基金能够促进创业板企业全要素生产率的提高；(2) 政府引导基金确实可以通过降低企业融资约束，提高企业研发投入占比、提升企业ESG表现、提高绿色创新能力、加快企业数字化转型等路径促进企业全要素生产率的提高。综上研究结论，提出如下政策建议：(1) 大力发展引导基金，加快培育新质生产力；(2) 平衡区域资源配置，促进欠发达地区经济发展；(3) 差异化投资，促进资源优化配置。本文的研究也为后续政府引导基金如何更好地服务实体企业、促进经济高质量发展提供了一定的启示。

Abstract: Against the backdrop of high-quality economic development becoming the core goal, total factor productivity, as a key indicator for measuring the quality and efficiency of economic growth, has become a key driving force for promoting enterprise transformation and upgrading, and enhancing market competitiveness. The academic community is divided on whether government-guided funds can effectively improve the total factor productivity of enterprises. Some scholars believe that they can improve total factor productivity by alleviating financing constraints, promoting research and development investment, and technological innovation; Another part points out that issues such as excessive administrative intervention and moral hazard may lead to their suppression of total factor productivity. In view of this, exploring the relationship between the two and clarifying the mechanism of action can not only resolve existing differences in the academic community, but also optimize and guide the operation mechanism of the fund and enhance policy effectiveness. This article uses data from the Growth Enterprise Market from 2012 to 2024 and employs a multi period double difference model to empirically analyze the impact of government guided funds on total factor productivity of enterprises. Specifically, robustness tests were conducted by replacing the dependent variable and placebo, and the PSMDID method was used to reduce endogeneity issues. Regression was performed using score propensity matching data, and the same conclusions were obtained as before. Subsequently, mechanism tests were conducted from multiple perspectives, including financing constraints, R&D investment, ESG ratings, green innovation, and corporate digital transformation; The empirical results indicate that: (1) Government-guided funds can promote the improvement of total factor productivity of ChiNext enterprises; (2) Government-guided funds can indeed promote the improvement of total factor productivity of enterprises by reducing financing constraints, increasing the proportion of R&D investment, improving ESG performance, enhancing green innovation capabilities, and accelerating digital transformation. Based on the research findings, the following policy recommendations are proposed: (1) Vigorously develop guidance funds and accelerate the cultivation of new quality productive forces; (2) Balance regional resource allocation and promote economic development in underdeveloped areas; (3) Promote differentiated investment and optimize resource allocation. This study also provides some inspiration for how government-guided funds can better serve real enterprises and promote high-quality economic development in the future.