R&amp;D and productivity in OECD firms and industries: A hierarchical meta-regression analysis

Uğur, Mehmet; Trushin, Eshref; Solomon, Edna; Guidi, Francesco

doi:10.1016/j.respol.2016.08.001

Cited by 118 publications

(70 citation statements)

References 93 publications

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“…The corresponding result that I present based on Norwegian data is 15 % and on the low side, but by no means unusual. It is slightly above the lower quartile according to the OECD survey and very close to Ugur et al (2016) who present a combined gross private firm level rate of return to R&D of 14 %. 6 The most important previous study using Norwegian data is Klette and Johansen (1998).…”

Section: Previous Estimates Of the Returns To Randdmentioning

confidence: 72%

“…There is a large international literature on the returns to R&D. Good surveys are provided by Mairesse and Sassenou (1991), Griliches (2000), Hall, Mairesse and Mohnen (2010), OECD (2015) and Ugur et al (2016). OECD (2015) includes both a narrative survey and an exploratory meta-analysis of R&D return estimates while Ugur et al (2016) is a comprehensive meta-analysis following best-practice guidelines.…”

Section: Previous Estimates Of the Returns To Randdmentioning

confidence: 99%

“…OECD (2015) includes both a narrative survey and an exploratory meta-analysis of R&D return estimates while Ugur et al (2016) is a comprehensive meta-analysis following best-practice guidelines.…”

Section: Previous Estimates Of the Returns To Randdmentioning

confidence: 99%

“…Second, the estimates represent "gross" returns, meaning that depreciation of the knowledge capital stock is not accounted for. However, the estimates are likely to be downward biased by specification errors and measurement errors in the R&D variables, see Møen and Thorsen (2017) and Ugur et al (2016) for recent discussions. …”

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confidence: 99%

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Corporate Returns to Subsidized R&D Projects: Direct Grants vs Tax Credit Financing

Møen

2018

SSRN Journal

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According to theory, direct R&D grants should be used for projects with low private returns, high social returns and high risk. R&D tax credits, on the other hand, allow firms to choose projects freely according to their private returns. Building on the standard R&D capital model, I develop a framework for estimating private returns to R&D projects with different types of funding. I apply the framework to estimate the corporate returns to subsidized R&D projects in Norway. Consistent with theory and a high quality grant allocation process, I find that projects funded through direct grants have private returns that are not significantly different from zero and with high variance, while the return to R&D projects financed by tax credits is just slightly below the return to R&D projects financed by own funds. The latter two return estimates are 16 % and 19 % respectively. I find that SMEs and small R&D performers have somewhat higher returns to R&D than larger firms. The overall return estimate across all types of finance is 15 %. This is in line with recent meta-regression results in the international literature.JEL Codes: H25, O32, O38

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Section: Previous Estimates Of the Returns To Randdmentioning

confidence: 72%

Section: Previous Estimates Of the Returns To Randdmentioning

confidence: 99%

Section: Previous Estimates Of the Returns To Randdmentioning

confidence: 99%

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confidence: 99%

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Corporate Returns to Subsidized R&D Projects: Direct Grants vs Tax Credit Financing

Møen

2018

SSRN Journal

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“…Reference [44] shows the role of technology spillovers in productivity growth of Organization for Economic Co-operation and Development (OECD) countries by analyzing the investments in Information and Communication Technology (ICT) and Research & Development (R&D). Reference [45] provides a systematic synthesis of the literature using 1253 estimates from 65 research studies. It shows that firm-level rates of return and within-industry social returns to R&D are small and differ insignificantly despite theoretical predictions of higher social returns.…”

Section: Discussionmentioning

confidence: 99%

The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China

Chen

Shi

et al. 2020

Sustainability

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With ever-increasing economic globalization and rapid advancement of science and technology, developing high-tech industries have become an important way for many countries to achieve sustainable and environmentally friendly economic development. In this article, we aim to empirically test the critical factors, which can influence the spatial spillover of a country's high-tech industries. Using data from the high-tech industries in China during the years of 2007-2016, we establish a space lag model and a space error model to examine the space fixed effect, the time fixed effect, and the space-time double mixed effect in spatial spillover in high-tech industries. We compare the results of these two spatial panel models with those from a general panel model and find that the spatial spillover effect within high-tech industries is rather significant. Moreover, we find that the spatial-time double mixed of the spatial lag model is the best fitting effect. Our empirical results also show that the research and development (R&D) investment and international trade can positively promote spatial spillover of high-tech industries among different regions. In terms of policy insights, our results imply that the government can establish a technology transfer platform to promote the spillover in high-tech industries. This can help achieve a sustainable and balanced development of high-tech industries.Sustainability 2020, 12, 1551 2 of 16 agglomeration effects in the high-tech enterprises. In this paper, we focus on the following important questions: what are the critical factors which can influence the spatial spillover of high-tech industries? How do these factors influence the spatial spillover? What are the potential reasons for the unbalanced regional development of high-tech industries? The answers to these questions are of great theoretical and practical significance because they can help improve the output of the high-tech industries and promote the sustainable and balanced development of regional economies for many countries. For example, China is a vast country with regions with unbalanced economic development. The high-tech industries are highly concentrated in only several developed regions like Guangdong Province. How do you achieve the spillover of high-tech industries to other underdeveloped regions? This question is important not only for China's economic development, but also for many countries in the world with unbalanced regional economic development.In this paper, we employ spatial panel models to study spatial spillover in high-tech industries. We adopt the spatial panel models because they can deal with the spatial dependence among regions. In contrast, traditional models such as Ordinary Least Squares (OLS) models ignore the spatial effect and lead to estimation errors. To be more specific, in this research, we employ the spatial lag model (SLM) and the spatial error model (SEM), which are two of the most widely used spatial panel models. The SEM focuses on the influence of the error of random interferenc...

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R&D, Innovation and Productivity

Mohnen

2019

The Palgrave Handbook of Economic Performance Analysis

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This chapter reviews various technological indicators from innovation inputs to innovation outputs, pointing out their strengths and weaknesses and the consequent caution that is in order when using these data for economic analysis. It briefly explains the theoretical link between innovation and productivity growth and then compares the estimated magnitudes of that relationship using the different innovation indicators.

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R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis

Cited by 118 publications

References 93 publications

Corporate Returns to Subsidized R&D Projects: Direct Grants vs Tax Credit Financing

Corporate Returns to Subsidized R&D Projects: Direct Grants vs Tax Credit Financing

The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China

R&D, Innovation and Productivity

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