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Previous Research
This chapter provides relevant theories regarding economic growth and presents the hypotheses tested in this study.
Growth Theory
Solow (1956) brings forward one of the first mathematically derived growth models, stressing the importance of capital and labour accumulation. Furthermore, Solow (1957) makes the substantial conclusion that long-run growth is driven by nothing except technological pro-gress. However, the model does not conclude what drives the technological progress, thus technology is exogenous. Romer (2012) argues that the endogenous growth model was de-veloped as a response to consider economic growth further. The main difference between the endogenous growth model and the Solow model is how technology is treated, in which the first defines four microeconomic factors that determines the level of R&D, compared to Solow’s model in which it is exogenous.
Romer (1990) presents an endogenous growth model, with the idea of R&D being motivated by private profits from innovation, which in turn increase the incentives to further invest-ment in R&D. The model assumes that knowledge consists of dissimilar ideas and are there-fore imperfect substitutes. An additional assumption made is monopoly rights for the devel-oper, excluding others from using the idea, hence also allowing for temporary monopoly profits. Moreover, capital is excluded since it complicates the model´s implications due to transition dynamics, meaning that the economy does not move directly to its new balanced growth route. Hence, the exclusion of capital simplifies the calculation and the analysis of the model´s main points. On the other hand, when including capital, analyses can be con-ducted on the distribution of output among investment and consumption due to policies, which is not possible in the previous scenario. However, since the model´s main purpose is the same, both including or excluding capital, the most efficient choice is to exclude it. With this simplification, the model’s key equation is:
Equation 1 explains how, ( ̇(( ))), the growth rate of the growth rate of output (the acceleration rate) is determined by four microeconomic factors. Firstly, discount rates ( ) affect the num-ber of employees devoted to R&D since a higher interest leads to less patient employees and less investment in R&D (due to opportunity costs). Secondly, substitutability among the inputs used in R&D itself (ø). The higher the degree of substitutability the less workers en-gaged in R&D, in combination with slower productivity growth since in this case each input contributes less to output. Thirdly, the productivity of R&D (B), has a positive relation to growth. In addition, an increase in the productivity of R&D raises �the willingness for workers to engage in that sector. Finally, an increase in population size ( ) automatically raises eco-nomic growth in the long-run since the model assumes that all individuals are engaged in either the production of intermediate inputs or in the production of R&D. Consequently, the number of employees devoted to R&D production is assumed to increase as the size of the population raises. Moreover, this also enlarge the returns to R&D since the larger the economy the more extended the market is for R&D, increasing its returns. In brief, all these microeconomic factors affect the number of workers devoted to R&D which in turn deter-mines economic long-term growth.
However, the Romer model possesses certain drawbacks, firstly, by presuming only linear growth, and secondly, suggesting how advancements in R&D are merely an upsurge in the amount of inputs used in the production. Therefore, one might consider applying models put forward by Aghion and Howitt (1990), Grossman and Helpman (1991) as well as Jones (1995). Jones (1995) proposed a model permitting transition dynamics, thus the driving fac-tor for growth in the long run is solely the growth rate of population. Aghion and Howitt (1990) and Grossman and Helpman (1994) alter how the technology process is defined, ar-guing how enhancements of prevailing inputs represent innovations. However, due to its simplicity, the regression in this thesis is based on Romer´s endogenous growth model (see equation 1), which furthermore, is the most well-known theory regarding R&D´s contribu-tion to economic growth, making it an adequate basis for this paper.
On the other hand, other researchers find evidence of economic growth being the driving force of R&D which contradicts Romer´s model (see the next section, 3.2, for other re-searchers’ findings). Romer (1990) places less focus on the possibility of GDP growth being a determinant of R&D expenditure instead of vice versa, implying that Romer´s model is of more importance examining our first research question (see section 3.3).
Related Literature
Previous research has diverse opinions on what is the predominant driver of economic growth. Capital accumulation and labour have been regarded as the most crucial factors in-fluencing economic performance (Solow, 1957). Most researchers also stress the significance of technological change for improved living standards and economic development (Gross-man & Helpman, 1994; Romer 1986, 1990; Solow, 1957). Investments in technology, specif-ically R&D, do not only benefit the private investor but the society as whole (Grossman & Helpman, 1994). Sørensen (1997) extends the argumentation by arguing that occasionally it is of relevance to subsidise areas such as R&D and education since these might result in economic growth. More recent studies from Huang and Lin (2006) as well as Raffo, Lhuillery and Miotti (2008) stress how knowledge inputs, specifically R&D, generally yield improve-ments in innovation, and thus impact economic growth positively. However, worth noticing is that the amount of R&D documented is merely a portion of what is spent on investments of new methods and goods, which therefore should be considered (Evenson & Westphal, 1995). Lastly, factors that might influence the level of R&D investments and economic growth more than policies and innovations are: institutions, laws, economic environment and mobility (Evenson & Westphal 1995; Grossman & Helpman, 1994; Pack & Westphal, 1986).
R&D investments are proved to have various outcomes despite the same amount invested, meaning that there is no simple model how to make R&D efficient (Pisano, 2012). Pisano (2012) also argues that the underlying mechanisms behind successful R&D investments are: the combination of the amount of labour and the level of skilled labour devoted to different production groups, the capacity and the headquarter of the R&D production, the technology used in the creation of R&D as well as the distribution of resources. Moreover, Pisano (2012) emphasises that companies do not perform equally well at all the components required for a profitable R&D investment, meaning that they all should have different strategies to increase R&D efficiency. This might be an explanation to why countries with different income levels experience various returns from R&D investments. For instance, Sørensen (1997) stresses that accumulation of education and skills should be the main priority when human capital is below a certain level. It is first after passing this hurdle it is beneficial for innovative activities and R&D, relating back to Pisano´s (2012) argumentation about the importance of skilled labour engaged in the R&D process.
Moreover, Sørensen (1997) concludes that less developed countries receive advantages of an open economy, due to technology spill overs. Grossman and Helpman (1994) highlight that a country should export the good in which they have a comparative advantage in, therefore countries with high human capital will have a head start in technology and will consequently export these goods in exchange for labour-intensive goods and for goods that do not demand the newest technology. These findings enlighten the importance of the type of technology used in the R&D production as well as the value of a company’s geographical location, in theory with Pisano´s (2012) findings. This indicates that unequally wealthy countries should have different production strategies to R&D, due to their diverse specialization of industries.
However, not everyone agrees that R&D causes economic growth. Birdsall and Rhee (1993) conclude that it is not R&D that affects economic growth rather it is the vice versa. Further-more, Bresnahan (1986) and Mansfield et al. (1977) argue that the amount of investments in commercial R&D is too small to impact economic performance. Moreover, R&D is proved to becomes less of a significant contributor to economic growth when a region possesses more advanced technology. Hence, R&D (as a factor of economic growth) is a non-linear function of already existing use of technology within a firm (Minniti & Venturini, 2017). Lederman and Maloney (2003) support this argumentation and stress that developing econ-omies are mostly favoured of R&D returns. In addition, Brown and Svenson (1988) stress how managers generally do not measure their returns on R&D expenditure, in fear of receiv-ing non-profitable results or since it is simply assumed to be a promising investment despite reasonable motives. This view is supported by Hall and Oriani (2006), concerning developed economies, arguing that European companies too rarely value its R&D investments.
1 Introduction
2 Background
3 Previous Research
3.1 Growth Theory
3.2 Related Literature
3.3 Hypotheses
4 Empirical Framework
4.1 Data
4.2 Variables in the Econometric Analysis
4.3 Empirical Model of R&D´s Relation to GDP Growth
4.4 Empirical Model of Causality
5 Empirical Results and Analysis
6 Conclusion
Reference list
Appendices
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Economic Performance and R&D
