Economic growth and R&D expenditures in selected OECD countries: Is there any convergence?

Abstract

The economic growth of nations is strongly associated with rising R&D expenditures. The present study therefore endeavours to examine the stylized patterns of evolution of R&D expenditures (R&D expenditures as a share of GDP) along the development course in 18 OECD countries for the period 1981–2012. Empirical findings of feasible generalized least square model highlight that as economic growth proceeds, R&D as a share of GDP also shows a rise in OECD nations. The study also analyzes the long-term growth trends and structure of research and development (R&D) expenditures in OECD nations for the period 1981–2012. Further, the study analyzes another important issue, namely whether OECD economies are converging/diverging in their R&D activities over the course of time. To this end, σ-convergence has been applied to examine this issue. Findings of σ-convergence highlight declining coefficient of variation (convergence) in R&D intensities as well as R&D expenditure in OECD countries for 1981–2012.

Keywords:

• economic growth
• R&D expenditures
• stylized patterns
• trend
• structure
• convergence
• OECD countries

Notes

1 Chenery (1960, 635) also used value added in manufacturing as a share of GDP as a dependent variable in his analysis where R² is around 0.64 only compared to R² 0.96 where he used values added per capita in the manufacturing sector as a dependent variable. This is because the variance of the dependent variable in the former is low.

2 While analyzing the relationship between economic growth and structural change in R&D expenditure, some studies used R&D as a share of GDP as a dependent variable, whereas others, such as those of Teitel (1987 and 1994a; 1994b) used R&D expenditure as a dependent variable. This substitution can have the effect of increasing the proportion of variance explained (compared to the shares used).

3 Chenery and Taylor (1968), however, used sectoral value added per capita as a dependent variable for analyzing sectoral growth patterns in large, small-primary-oriented and small-manufacturing-oriented countries.

4 Accepting the validity of equation (2) allows us to calculate that particular level of income per capita (GDPPC) at which, ceteris paribus, the share of R&D in GDP reaches a maximum level.$\begin{array}{rl}\log R\mathrm{\&}D/GD{P}_{it}=& {\text{β}}_0+{\text{β}}_1\log GDPP{C}_{it}+{\text{β}}_2\log PO{P}_{it}+\\ & {\text{β}}_3(\log GDPP{C}_{it}{)}^2+{\text{β}}_4(\log PO{P}_{it}{)}^2+u_{it}\end{array}$Taking first order partial derivative of $\log R\mathrm{\&}D/GD{P}_{it}$ w.r.t $\log GDPP{C}_{it}$ we get$\frac{\mathrm{\partial }(\log R\mathrm{\&}D/GD{P}_{it})}{\mathrm{\partial }(\log GDPP{C}_{it})}={\text{β}}_1+2{\text{β}}_3\log GDPP{C}_{it}=0$ $2{\text{β}}_3\log GDPP{C}_{it}=-{\text{β}}_1$ $\log GD\hat{P}P{C}_{it}=\frac{-{\text{β}}_1}{2{\text{β}}_3}\left(\mathrm{i}\right)$Thus, substituting the values of regression coefficients from Table 8 and equation (2) in equation (i) above, we get $\log GD\hat{P}P{C}_{it}=1.51$.

Taking antilog, we get $GD\hat{P}P{C}_{it}$= $32359 ($32.3 thousand). This should be a maximum point since second partial derivative is less than zero, i.e. it is negative as shown below in equation (ii).

Substituting the values of coefficients from Table 8 and equation 2, we get:$\frac{{\mathrm{\partial }}^2(\log R\mathrm{\&}D/GD{P}_{it})}{\mathrm{\partial }{(\log GDPP{C}_{it})}^2}=2{\text{β}}_3$ $\frac{{\mathrm{\partial }}^2(\log R\mathrm{\&}D/GD{P}_{it})}{\mathrm{\partial }{(\log GDPP{C}_{it})}^2}<0\left(\mathrm{i}\mathrm{i}\right)$