Key Takeaways & Executive Findings
- •• The overall innovation efficiency of pharmaceutical manufacturing enterprises in the Beijing-Tianjin-Hebei region is not effective, with significant regional differences. • The establishment period, market competitiveness, and financial constraints significantly impact innovation efficiency, while government subsidies show no significant positive effect. • The three-stage DEA model effectively separates environmental factors and random errors, providing a more accurate efficiency evaluation. • Policy recommendations include establishing an innovative drug risk warning mechanism and innovation incentive and risk-sharing mechanisms to enhance innovation efficiency.
Abstract
This study evaluates the innovation efficiency of pharmaceutical manufacturing enterprises in the Beijing-Tianjin-Hebei region using a three-stage DEA model. The results indicate that the overall innovation efficiency of these enterprises has not reached an effective state, and there are significant differences among regions. The pharmaceutical industry's establishment period, market competitiveness, and financial constraints are the main factors influencing innovation efficiency, while government subsidies have not shown a significant positive effect. The government should establish an innovative drug risk warning mechanism, as well as innovation incentive and risk-sharing mechanisms, to further improve the innovation efficiency of the pharmaceutical manufacturing industry in the region.
1. Introduction
The pharmaceutical manufacturing industry is a vital component of the national economy, and its innovation efficiency directly affects the industry's competitiveness and sustainable development. In the Beijing-Tianjin-Hebei region, the pharmaceutical industry has experienced rapid growth, but the innovation efficiency remains a concern. This study aims to evaluate the innovation efficiency of pharmaceutical manufacturing enterprises in this region using a three-stage DEA model, considering environmental factors and random errors.
Previous studies have often overlooked the impact of environmental factors and random errors on efficiency evaluation, leading to biased results. This research addresses this gap by employing a three-stage DEA model that separates these effects, providing a more accurate assessment. The findings will offer valuable insights for policymakers and industry stakeholders to improve innovation efficiency and promote regional development.
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Y. Zhang, L. Wang, H. Li (2026). Evaluation of the Innovation Efficiency of Pharmaceutical Manufacturing Enterprises in the Beijing-Tianjin-Hebei Region. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_214
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Frequently Asked Questions
What is the main objective of this study?
The main objective is to evaluate the innovation efficiency of pharmaceutical manufacturing enterprises in the Beijing-Tianjin-Hebei region using a three-stage DEA model, and to identify key factors influencing efficiency.
What methodology is used in this research?
The study employs a three-stage Data Envelopment Analysis (DEA) model, which separates environmental factors and random errors to provide a more accurate efficiency evaluation.
What are the key findings of the study?
The overall innovation efficiency is not effective, with significant regional differences. Factors such as establishment period, market competitiveness, and financial constraints significantly impact efficiency, while government subsidies show no significant positive effect.
What policy recommendations are proposed?
The study recommends establishing an innovative drug risk warning mechanism, as well as innovation incentive and risk-sharing mechanisms, to improve innovation efficiency in the pharmaceutical manufacturing industry.
Why is the three-stage DEA model used?
The three-stage DEA model is used to separate the effects of environmental factors and random errors, which are often overlooked in traditional DEA, leading to more accurate and reliable efficiency evaluations.
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