Is R&D always growth-enhancing? Empirical evidence from the EU countries

ABSTRACT

This study provides empirical evidence regarding the effects of R&D on economic growth in a panel of 28 European Union (EU) countries over the period 1997–2014. In particular, we investigate whether the impact of business and government R&D stocks on economic growth depends on the country’s distance to the world technology frontier. The main findings are that in the EU (i) there is positive, statistically significant business R&D stock–economic growth nexus in countries that are relatively close to the frontier and (ii) no statistically significant relationship was found to exist between government R&D stock and economic growth. From the policy perspective, the results suggest that designing proper national policies that allow switching from investment-based to innovation-based strategies at appropriate moments may be far more important than a simple call for increase in R&D expenditures and setting common numerical targets for all EU-member states.

KEYWORDS:

• R&D
• economic growth
• European Union
• panel data models
• GMM

JEL CLASSIFICATION:

• O11
• O47
• O52
• O32

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ A detailed discussion can be found in Aghion and Howitt (2009).

² Hall, Mairesse, and Mohnen (2010) provide an extensive review of the empirical literature.

³ The standard convergence equation includes lagged GDP per capita as an independent variable. We use the DTF (distance to the technology frontier) variable instead to take into consideration the issue of technological catch-up. However, due to the high correlation (0.93) between these two variables, DTF can be treated as a convergence factor in our models.

⁴ The data are averaged to reduce the effect of short-term fluctuations.

⁵ Since our panel is relatively small (T = 6, N = 28), the number of RHS variables must be limited to keep the number of instruments under control and avoid instrument proliferation. For this reason, after interactive term is introduced to the model, we use the investment rate (INV) as a control variable in the main specification.

⁶ The alternative difference-GMM estimator may lead to biased estimates in the presence of highly persistent data.