Do the types of subsidies and firms' heterogeneity affect the effectiveness of public R&D subsidies? Evidence from China's Innofund programme. Issue 3 (1st September 2016)

• Record Type: Journal Article
• Authors: Xin, Fu; Zhang, Jie; Chen, Zhiyuan; Du, Xiaorong
• Is Part Of: Asian journal of technology innovation. Volume 24:Issue 3(2016)
• Page Start: 317
• Page End: 337
• Publication Date: 2016-09-01
• Subjects: public R&D subsidy -- types of public subsidies -- SOEs -- propensity score matching -- China; Technological innovations -- East Asia -- Periodicals; Technological innovations -- Southeast Asia -- Periodicals; Technological innovations -- Periodicals; Research, Industrial -- East Asia -- Periodicals; Research, Industrial -- Southeast Asia -- Periodicals; Research, Industrial -- Periodicals; 338.064095
• DOI: 10.1080/19761597.2016.1256705
• Languages: English
• ISSNs: 1976-1597

Abstract

Abstract : This paper aims to fill the gap in the extensive literature on the results of interaction between public and private R&D expenditures. Two types of public R&D subsidies, the appropriations and loan interest, are investigated in the effect of public subsidies by applying for a unique micro data set of Chinese manufacturing firms combined with the Innovation Fund for Small and Medium Technology-based Firms (Innofund). The results are consistent with the prediction that loan interest subsidies based on more competitive selective system are more effective for the public R&D subsidies than the appropriations. Firms' heterogeneity, measured by state-owned enterprises (SOEs) and private-owned enterprises (POEs), is tested for the different impact of public subsidies on corporate R&D investment. The results show that the crowding-in effect is mainly driven by POEs, not by SOEs, which confirms the theoretical assumption by Aghion and Tirole that the agency problem play a dominant role in corporate R&D activities. An improved econometric method, combined propensity score matching with difference-in-differences estimation strategy, is applied for correcting the selection bias. Our results are robust to different matching techniques.