Literature and theoretical framework

Following the lead of Nordhaus (1969), who was among the pioneers attempting a formal economic theory of technological change, three major candidates have been explored in the literature as drivers of technological progress⁶⁹: population, human capital (or better:

education), and institutions.

In Grossman and Helpman (1991), who first merged innovation and growth theory, population size matters for innovativeness and constitutes the so-called scale effect. Jones (1995), however, presented evidence contradicting this hypothesis convincingly. The “Jones critique” was followed by semi-endogenous growth models, which imply that the growth rate of population rather than its level drives the economic growth rate. This is based on the assumptions of either increasing difficulty of innovations (Kortum, 1997; Segerstrom, 1998) or dispersing research effort (Dinopoulos and Thompson, 1998; Young, 1998; Peretto, 1998).

Unfortunately, these models still imply a level effect, which raises concerns very similar to the Jones critique (Jones 1999). Nevertheless, both the scale and the level effect are not yet off the table. Kremer (1993) has argued that the former may exist for large regions in the long run, whereas Jones (2001) simulates a model with a population growth effect and produces results that are compatible with the actual long-run development of world economic growth.

In fact, some unified growth theories, which model the demographic transition and the take-off from Malthusian stagnation to modern economic growth, rely on a scale effect to explain the gradually rising rate of technological progress before the outbreak of the Industrial

69 I use the terms knowledge, technology, and technological knowledge interchangeably throughout this paper.

Incremental changes in the stock of knowledge will be called ideas.

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Revolution (Galor and Weil, 2000; Galor, 2005). Given this discrepancy between modern endogenous growth theory and the more historically oriented and holistic growth theory, it makes sense to further investigate the role of population in the innovation process.

Frequently, human capital is thought to drive technology.⁷⁰ As in Romer (1990), who was the first to model this idea, its effect is not always clearly distinguished from the effect of population. The term 'scale effect' may refer to the size of population or to the size of the human capital stock. In unified growth models, the level of human capital acts as a catalyst in that it accelerates the speed of technological progress during the demographic transition (Galor and Weil, 2000; Galor and Moav, 2001). The Jones critique, however, applies.

Consequently, in endogenous growth theory, exogenous population growth was replaced by endogenous human capital accumulation, making the rate of progress depend on the rate of HC growth (Blackburn, Hung and Pozzolo, 2000; Arnold, 1998; Stadler, 2006). Contrary to the theoretical efforts, surprisingly little empirical work exists on the relationship between human capital and the outcomes of the innovation process, especially for the long run.⁷¹ Given the relatively good availability of human capital measures for historical periods, such as numeracy (e.g. Crayen and Baten, 2008) and literacy, the only apparent reason for this is the lack of long-run innovation data. A gap which may be filled by this study!

70 Note that human capital may have a twofold effect (e.g. Temple 2001). On the one hand, it works as a production factor according to Lucas (1988). The speed of accumulation depends on the return to education which is set by the state of technology (Nelson and Phelps, 1966). On the other hand it may drive knowledge creation, i.e. technological progress. Those functions have also been referred to as level effect and growth effect of human capital, see chapter 2 of this work.

71 Labuske and Baten (2007) examine the influence of schooling on patenting around the turn of the 20^th century and find a significant positive effect. Khan and Sokoloff (2004) look at the biographies of US inventors and conclude that institutional factors were more decisive in stimulating their innovation activities than human capital. Of course, there is a bulk of empirical literature on the impact of human capital on economic growth (e.g.

Barro 2001) but those studies make it difficult to judge whether the effect works through technology adoption or technology creation.

Formally, Schumpeterian models of innovation-driven economic growth dating back to Romer (1990) as well as Grossman and Helpman (1991) can be summarized a follows.

Typically, they rely on an aggregate production function similar to

α

α −

=AK L¹

Y , (4.1)

with 0<α <1^.72 K is the physical capital stock, and L represents the size of the productive labor force. The productivity parameter A is commonly interpreted as the stock of non-rival technological knowledge. Growth of this parameter is what causes sustained economic growth in the long run. Usually, the change in A, A&, is determined by an innovation production function of the type

S A

A& =δ ^φ , (4.2)

where S is a scale factor, be it the size of the labor force or the stock of human capital. φ >0 implies that past discoveries make it easier to generate new ideas, whereas φ <0 means that they make it more difficult (see Jones, 1999). For this type of model to work on a country level, economic growth must strongly depend on homemade knowledge. If this was not the case, and A reflected the international technological frontier, which is the same for all countries, this formulation would not be capable of explaining why some countries lag so far behind the others.

Models of endogenous growth dating back to Lucas (1988) and Uzawa (1965) generate sustained economic growth by incorporating human capital in the production function:

( )

β −

=K uH ¹

Y , (4.3)

72 Time and country indices are skipped.

Innovation and Growth on a Macro Level, 1500-1990 97

with 0<β,u<1. They crucially depend on the prerequisite of human capital, H, being able to grow without bound. Its growth rate depends on the fraction of time, 1-u, devoted by workers to its accumulation, or formally,

H u B

H& = (1− ) , (4.4)

where B specifies the productivity of the education sector. Because the quantity of education cannot exceed an upper limit, this approach requires the subsumption of knowledge under the concept of human capital. Hence, it is conceptually not much different from the Schumpeterian approach and does not permit to separate the effects of knowledge growth on the one hand and improvements in education on the other hand. Nevertheless, it works better to explain cross-sectional differences in per capita income and emphasizes that follower countries may grow by accumulating human capital, because it enables them to adopt foreign knowledge.

Thanks to North (1981, 1990) the institutional setting has received increased attention as a crucial facilitator of economic development. Mokyr (1990) has emphasized its relevance for historical technological progress.⁷³ In particular, the protection and enforcement of property rights are regarded in this context.⁷⁴ It guarantees the appropriation of rents from inventive activity and generates an incentive to innovate. Empirical evidence on this issue, however, is ambiguous. Jones (2001) suggests that institutional changes were important in the timing of the Industrial Revolution. Khan and Sokoloff (2004) maintain that the US patent system was the decisive element in allowing the US to take over technological leadership in

73 Again, this effect should be distinguished from the role of institutions for technology adoption, which is what Acemoglu (2001), Hall and Jones (1999), and other authors have in mind when writing about the importance of institutions for economic growth.

74 A whole literature has evolved around the concept of national innovations systems; see especially Freeman (1992, 1995) and Lundvall (1992, 2007). It encompasses a subset of institutions that is relevant for innovation, especially those that facilitate information flow and interactive learning, as well as educational and geographical aspects.

the 19^th century. Based on countries' contributions to international technology exhibitions, Moser (2005) finds that the existence of patent laws determines the direction of technological innovations. Jaffe (2000) provides a meta-survey of studies focusing on the transition of the U.S. patent system. He questions the robustness of conclusions regarding the consequences of patent policy changes on technological innovation. In fact, Sakakibara and Branstetter (2001) provide evidence from a Japanese patent law reform in favor of reversed causality between patent protection laws and innovation input or output. In light of this contradictory evidence it seems worthwhile to also retest the effect of institutions on countries' innovativeness.

Finally, following Marshall (1890) who first mentioned externalities from industry concentration, the literature on innovation clustering and knowledge spillovers (e.g. Feldman, 1994; Audretsch and Feldman, 1994; Jaffe, Trajtenberg and Henderson, 1993) has emphasized many times that geographical proximity is an important determinant of innovative success, because it is likely to affect the speed of knowledge diffusion. In particular, this regards tacit knowledge (Gertler, 2007). Even though this argument typically alludes to an intra-national context, it can be presumed to be valid for larger geographical regions as well. That is, economies should be able to benefit more from new knowledge that was generated in close-by countries.⁷⁵

No study could ever test those theoretical considerations for the very long run and a considerable number of countries. The availability of a new database offers the unique opportunity to make up for this lapse. The next section describes the respective data.

75 Note that this is different from the effect, which has - among others - been highlighted by Diamond (1998) and Sachs (1997). The latter refers to the geographical opportunity of implementing or developing specific production technologies that cannot be copied easily by other countries due to differences in the geographical environment.

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