Key Takeaways & Executive Findings
- •• • The average innovation efficiency of Chinese pharmaceutical listed companies was 0.72 in the first-stage DEA, improving to 0.85 after adjusting for environmental factors, indicating that external conditions account for a 13% efficiency gap. • • Government subsidies positively influenced innovation efficiency, with a coefficient of 0.15 (p<0.01), while market competition negatively impacted efficiency, with a coefficient of -0.08 (p<0.05), highlighting the need for balanced policy interventions. • • R&D intensity was a significant determinant, with a 1% increase in R&D expenditure as a percentage of sales associated with a 0.23% increase in innovation efficiency (p<0.01), underscoring the importance of sustained R&D investment. • • Firm size exhibited a U-shaped relationship with innovation efficiency, with the turning point at total assets of approximately 5 billion RMB, suggesting that small and very large firms are more efficient, while mid-sized firms face challenges.
Abstract
This study evaluates the innovation efficiency and overall performance of China's pharmaceutical manufacturing listed companies using a three-stage Data Envelopment Analysis (DEA) model and Tobit regression. Data from 2015 to 2019 were analyzed, encompassing 120 listed companies. The first-stage DEA revealed that the average innovation efficiency was 0.72, indicating significant room for improvement. After adjusting for environmental factors and statistical noise in the second stage, the third-stage DEA showed that the average efficiency increased to 0.85, suggesting that environmental variables such as government subsidies, market competition, and firm size significantly influenced efficiency. Specifically, government subsidies had a positive impact, while market competition had a negative effect. The Tobit regression further identified that R&D intensity, firm size, and ownership structure were significant determinants of innovation efficiency. The results indicate that the pharmaceutical industry in China has not yet achieved optimal innovation efficiency, and there is considerable heterogeneity among firms. The study recommends that policymakers should tailor support mechanisms to firm-specific characteristics, and managers should focus on enhancing R&D productivity and commercializing innovations. The findings provide empirical evidence for improving innovation efficiency in the pharmaceutical sector, contributing to the broader goal of industrial upgrading and sustainable development.
1. Introduction
The pharmaceutical industry in China has experienced rapid growth, yet its innovation capabilities lag behind global leaders, with a heavy reliance on generic drugs and limited novel drug development. Despite substantial government investment and policy support, the efficiency of translating R&D inputs into commercial outputs remains suboptimal. Existing studies have often overlooked the impact of environmental factors and statistical noise on efficiency measurement, leading to biased estimates. This research addresses this gap by employing a three-stage DEA model that separates managerial inefficiency from environmental and random effects, providing a more accurate assessment of innovation efficiency.
Furthermore, the study investigates the determinants of innovation efficiency using Tobit regression, focusing on firm-specific characteristics such as R&D intensity, firm size, and ownership structure. By analyzing a panel of 120 listed pharmaceutical companies from 2015 to 2019, this research offers empirical insights into the bottlenecks hindering innovation efficiency. The findings are expected to inform policy design and corporate strategy, enabling more targeted interventions to enhance the competitiveness of China's pharmaceutical sector in the global market.
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ZHANG Wei, LI Ming, WANG Fang (2025). Evaluation of Innovation Efficiency and Pharmaceutical Industry Performance in China's Listed Companies: A Three-Stage DEA and Tobit Analysis. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_214
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Frequently Asked Questions
What are the main environmental factors that affect innovation efficiency in Chinese pharmaceutical companies, and how do they influence the efficiency scores?
The study identified government subsidies, market competition, and firm size as key environmental factors. Government subsidies positively affect efficiency (coefficient 0.15, p<0.01), while market competition negatively affects it (coefficient -0.08, p<0.05). Firm size shows a U-shaped relationship, with optimal efficiency at around 5 billion RMB in total assets. These factors were adjusted for in the third-stage DEA, leading to an increase in average efficiency from 0.72 to 0.85.
How does R&D intensity impact innovation efficiency, and what is the recommended threshold for R&D investment?
R&D intensity, measured as R&D expenditure as a percentage of sales, has a significant positive effect on innovation efficiency. A 1% increase in R&D intensity is associated with a 0.23% increase in efficiency (p<0.01). The study suggests that companies should maintain R&D intensity above 5% to achieve above-average efficiency, as firms with lower intensity tend to underperform.
What are the implications of the U-shaped relationship between firm size and innovation efficiency for mid-sized pharmaceutical companies?
The U-shaped relationship indicates that small and very large firms are more efficient, while mid-sized firms (assets between 2-10 billion RMB) face inefficiencies. This may be due to resource constraints and lack of scale economies. Mid-sized firms should consider strategic mergers or partnerships to reach a scale that enables more efficient innovation, or focus on niche areas to improve their efficiency.
How can the three-stage DEA methodology improve the accuracy of efficiency measurement compared to traditional DEA?
Traditional DEA does not account for environmental factors and statistical noise, which can distort efficiency scores. The three-stage DEA first runs a standard DEA, then uses stochastic frontier analysis to decompose slacks into environmental effects, managerial inefficiency, and noise, and finally adjusts the inputs to reflect only managerial inefficiency. This provides a more accurate measure of true innovation efficiency, as evidenced by the change in average efficiency from 0.72 to 0.85 after adjustment.
What policy recommendations can be derived from the findings to enhance innovation efficiency in China's pharmaceutical industry?
Policymakers should design differentiated support mechanisms based on firm size and ownership. For small firms, grants and tax incentives for R&D are effective. For mid-sized firms, policies that encourage consolidation or collaboration can help overcome scale disadvantages. Additionally, reducing market competition barriers in certain segments may improve efficiency, but overall, fostering a competitive environment that rewards innovation is crucial. The study also emphasizes the importance of strengthening intellectual property protection to incentivize R&D.
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