Assessing the economy-wide impacts of public R&D support options based on a computable general equilibrium model: focusing on types of fiscal incentives and beneficiaries
Won-Sik Hwang,
Chanyoung Hong,
Inha Oh () and
Yeongjun Yeo
Applied Economics, 2022, vol. 54, issue 40, 4664-4680
Abstract:
This study conducted quantitative comparisons of various public R&D support options using a CGE model. The analysis considered four different options by varying the types of fiscal incentives and the scope of beneficiaries concerning the firm size. The findings indicate that direct subsidy is more effective in spurring private R&D investments than indirect tax incentives. In addition, selective R&D support toward small and medium enterprises is found to induce balanced growth among industries. In summary, the simulation results suggest that R&D support under the direct subsidy scheme aimed at SMEs has the potential to achieve a higher equilibrium state within the Korean economy. This study confirms that the government should carefully design the R&D promotion policy by ensuring that direct R&D inducement effects are transmitted to industrial output growth with a diversified industrial structure and higher knowledge spillover effects.
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2022.2033679 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:54:y:2022:i:40:p:4664-4680
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20
DOI: 10.1080/00036846.2022.2033679
Access Statistics for this article
Applied Economics is currently edited by Anita Phillips
More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().