3  Literature Review and Existing Estimates

3.1  Methodology

Since Solow’s (1957) decomposition of economic growth, many empirical studies have tried to determine the importance of various factors which underlie the productivity residual. Investment in R&D has been one of these factors.[4]

The two main approaches that have been used in the empirical literature to assess the importance of R&D to productivity growth are econometric analysis and case studies. The main disadvantage of the case study approach is its lack of representativeness. Since they only tend to concentrate on selected successful projects, it is not possible to draw general conclusions from their findings (for an example of the case study approach see Griliches (1958)).

Most econometric studies use either the production function approach (for example, Guellec and van Pottelsberghe 2001) or the cost function approach (for example, (Rouvinen 2002)). The two approaches are related – it is possible to derive a cost function from a production function, and vice versa – but they use different statistical methods and have different data requirements. Here we use the production function approach due to data availability.

Within the production function framework there are two alternative approaches: estimating the production function directly (i.e. regressing output or value added on conventional inputs plus R&D), or regressing multifactor productivity (MFP) on R&D.

Most of these econometric studies[5] have adopted the general version of the Cobb-Douglas production function, which in addition to the traditional inputs also includes knowledge capital:

(1)    

where Y is value added, A is a constant, L is labour, C is physical capital, K is the domestic R&D stock and X is the external stock of R&D available (spillover pool).

Usually, equation (1) is taken in logarithms to enable the estimation of the parameters of interest: β and δ, or the elasticity of output with respect to domestic and foreign R&D respectively. This leads to the following linear regression model:

(2)    

where lower case letters denote logarithms of variables and u_t is a random error term.

Alternatively, if constant returns to scale are assumed, then equation (2) can be rewritten in terms of multifactor productivity (MFP) as:

(3)     mfp_t = a + βk_t + δx_t + u_t

Thus the elasticity of MFP with respect to the stock of domestic R&D, β, estimated by equation (3), is equal to the elasticity of output with respect to the stock of domestic R&D.

Some studies choose instead to directly estimate the rate of return rather than the elasticity. By taking first differences and disregarding the depreciation of R&D, i.e.,Δk = ΔK/K = RD/K (where RD represents R&D expenditure), and applying the same transformations to the foreign R&D spillover stock, then we have:

(4)    Δmfp_t = ρ₁(RD/Y)_t = ρ₂(XD/Y)_t + u_t - u_t-1

where ρ₁ = is the marginal product of the domestic R&D stock, or the rate of return to domestic R&D.[6]

Since , it can be seen that there is a direct relationship between β and ρ₁; either one can be derived from an estimate of the other. That is,

(5)    

3.2  Empirical Evidence

The expansive body of empirical literature estimating statistically the part of productivity growth that can be attributed to R&D activities has been surveyed by Wieser (2005).[7] He concludes that on average there is a large and significant impact of R&D on firm performance, although the estimated returns vary considerably: the average estimated rate of return was in the order of 29% for the papers surveyed (for those which were significant),[8] with a lower bound of 7% (Link 1981) and an upper bound of 69% (Sassenou 1988). Wieser also conducted a meta-analysis and found that the estimated returns do not differ significantly between countries, although estimated elasticities appear to differ significantly between countries.[9]

Many of the early empirical studies were conducted for the agricultural sector. Table 4 reproduces Table 1 in Griliches (1992), showing rates of return in the agricultural sector estimated from both case studies and regression studies. The table shows that evidence from the international literature implies a substantial return to R&D in the agricultural sector.

A more recent study by Mullen and Cox (1995) estimated that the return from public investment in Australian agricultural R&D between 1953 and 1994 may have been in the order of 15-40%. Cox et al (1997) found support for these earlier findings using non-parametric techniques.

A comprehensive meta-analysis of rates of return to agricultural R&D is found in Alston, Chan-Kang, Marra, Pardey and Wyatt (2000). Their results show that the returns from 1,886 estimates found in 292 studies averaged 100% per year for research, 85% for extension, 48% for studies that estimated the returns to research and extension jointly, and 81% for all the studies combined. The median rates were 48.0% for research, 62.9% for extension, 37.0% for joint research and extension and 44.3% across all studies.

Unfortunately the literature is not replete with estimates of the impact of R&D in New Zealand. As a consequence, much of the policy on public investment in R&D has been made without any explicit estimates of the return that might be expected from that investment.

Two early studies focussing on the agricultural sector in New Zealand are Dick, Toynbee and Vignaux (1967) and Scobie and Eveleens (1987). Dick et al evaluated the returns to four particular projects and attempted to generate an estimate of the long term aggregate payoff. However their study was based on data for only one decade, arguably not long enough to pick up the full impact of research. Scobie and Eveleens used data from 1926 to 1984 and found that research contributed significantly to the growth of productivity in the agricultural sector. They concluded that this contribution comes over an extended period of 23 years on average, generating a real rate of return of 30 percent per year. However, they were unable to isolate the separate effects of research investment, extension efforts and the contribution from human capital.

Johnson (2000b) used data from 1962 to 1998 to estimate the effect of private and public investment in R&D on total factor productivity in nine sectors of the New Zealand economy. In the case of agriculture he found that private R&D had a significant effect and a rate of return of 68.7%. In contrast, public spending on R&D reduced TFP in agriculture, with the consequence that the rate of return was -6.7% to public spending. In an attempt to allow for foreign spillovers, Johnson found that higher levels of R&D in the Australian business sector reduced the level of TFP in New Zealand agriculture.

In a more recent study Johnson et al. (2005) use panel data over the same nine industries in New Zealand from 1962-2002 and report on average a significant impact on productivity from private R&D, but no effect from public R&D. They also find evidence that private R&D in the Building, Forestry and Other services industries positively affects productivity in the rest of the economy, i.e. it generates positive spillovers.

In short, there is a wide range in the estimates of returns to R&D. This arises in part due to the choice of model. Regrettably, it has been increasingly apparent that the estimates of return found using econometric studies are indeed sensitive to the assumptions and type of model. This conclusion is reinforced by the results of the present study.