基于TFP的重庆市生命健康产业发展效率评估
Efficiency Evaluation of the Life and Health Industry in Chongqing Based on TFP
DOI: 10.12677/mse.2026.153055, PDF,   
作者: 袁术平:重庆市科技资源统筹服务中心,重庆;何 阳*:重庆理工大学数学科学学院,重庆;范守城:重庆市科技特派员协会,重庆
关键词: 全要素生产率生命健康产业发展效率Total Factor Productivity (TFP) Life and Health Industry Development Efficiency
摘要: 文章基于柯布–道格拉斯生产函数,构建全要素生产率(TFP)测度模型,首次将TFP框架系统应用于重庆市该产业的实证研究。通过对比OLS回归与经验赋值法验证弹性系数稳健性,并采用多元回归模型测算TFP水平及其影响因素。结果表明:1) 产业整体呈现规模报酬递增特征(α + β = 1.2972),但效率结构呈“金字塔型”分化,约57.2%的企业处于低效率状态;2) 研发强度对TFP存在显著负向影响,揭示创新投入与产出效率的“悖论”;3) 企业规模与TFP显著正相关,中型及以上企业效率优于小微企业;4) 绿色技术企业虽在统计上未显示出显著效率优势,但其平均TFP低于非绿色企业约13.7%。研究表明,重庆市生命健康产业尚处发展初期,规模经济潜力与整体低效并存。未来应优化研发资源配置、推动企业适度规模化,并审慎引导绿色转型,以提升产业竞争力。通过对重庆市生命健康产业TFP进行系统的测度,揭示了规模报酬递增与效率分化的并存特征,并发现了研发强度与TFP的负向关系,为区域产业政策制定提供了实证依据。
Abstract: Based on the Cobb-Douglas production function, this study constructs a Total Factor Productivity (TFP) measurement model, systematically applying the TFP framework to an empirical study of Chongqing’s health industry for the first time. The robustness of elasticity coefficients is verified by comparing OLS regression with empirical assignment methods, and multiple regression models are used to measure TFP levels and their influencing factors. The results show that: 1) The industry exhibits increasing returns to scale (α + β = 1.2972), but the efficiency structure presents a “pyramid” pattern, with approximately 57.2% of enterprises in a low-efficiency state; 2) R&D intensity has a significant negative impact on TFP, reflecting multiple mechanisms such as R&D efficiency loss, resource misallocation, and patent commercialization difficulties; 3) Enterprise scale is significantly positively correlated with TFP, with medium and large enterprises outperforming small and micro enterprises; 4) Although green technology enterprises show no statistically significant efficiency advantage, their average TFP is approximately 13.7% lower than that of non-green enterprises. The study indicates that the life and health industry in Chongqing is still in the early development stage, where the potential for economies of scale coexists with overall inefficiency. Future efforts should focus on optimizing R&D resource allocation, promoting appropriate enterprise scale expansion, and prudently guiding green transformation to enhance industrial competitiveness. This paper systematically measures TFP in Chongqing’s life and health industry, revealing the coexistence of increasing returns to scale and efficiency differentiation, and identifies the negative relationship between R&D intensity and TFP, offering empirical evidence for regional industrial policy formulation.
文章引用:袁术平, 何阳, 范守城. 基于TFP的重庆市生命健康产业发展效率评估[J]. 管理科学与工程, 2026, 15(3): 560-569. https://doi.org/10.12677/mse.2026.153055

参考文献

[1] 徐贝, 周奇, 王廷亮. 科创产融协同建设生命健康产业高地的探索[J]. 高科技与产业化, 2025, 31(8): 94-97.
[2] 刘振鹏, 苏启林, 郭娜娜. “靶向引领”如何影响园区产业创新?——以苏州工业园区生物医药产业为例[J]. 管理世界, 2024, 40(11): 119-138.
[3] Li, X. (2025) Corporate Internal Control, Capacity Utilization and Total Factor Productivity. PLOS ONE, 20, e0318669. [Google Scholar] [CrossRef] [PubMed]
[4] 陈向武. 科技进步贡献率与全要素生产率: 测算方法与统计现状辨析[J]. 西南民族大学学报(人文社科版), 2019, 40(7): 107-115.
[5] 王火根, 何茜, 肖小玮. 数字化转型对农业上市公司全要素生产率的影响[J]. 农林经济管理学报, 2025, 24(6): 859-869.
[6] Liang, Y. and Zhang, C. (2024) Digital Transformation and Total Factor Productivity of Enterprises: Evidence from China. Economic Change and Restructuring, 57, Article No. 7. [Google Scholar] [CrossRef
[7] 张金昌, 王玥雪微. 企业“内卷式”竞争对全要素生产率的影响研究[J]. 经济问题, 2026(1): 79-88, 98.
[8] 陈芳, 王敏慧, 付雨芳. 我国水产养殖业科技进步贡献率测度及结构演变研究[J]. 浙江海洋大学学报(人文科学版), 2025, 42(5): 24-32.
[9] 尹翀, 刘玥, 王文超, 等. 成渝双城经济圈生物医药协同创新网络结构特征研究——基于LDA-SNA方法[J]. 科学与管理, 2025, 45(3): 94-102.
[10] 黎文靖, 郑曼妮. 实质性创新还是策略性创新?——宏观产业政策对微观企业创新的影响[J]. 经济研究, 2016, 51(4): 60-73.
[11] Hsieh, C. and Klenow, P.J. (2009) Misallocation and Manufacturing TFP in China and India. Quarterly Journal of Economics, 124, 1403-1448. [Google Scholar] [CrossRef
[12] 毛昊, 尹志锋, 张锦. 中国创新能够摆脱“实用新型专利制度使用陷阱”吗[J]. 中国工业经济, 2018(3): 98-115.