7  Summary and Conclusions

Productivity growth is important as a long run source of real income growth and higher living standards, as well as contributing to enhancing the competitiveness of New Zealand in world markets. This paper has analysed the long term growth of agricultural productivity in New Zealand. The central question addressed in the paper is the contribution of investment in R&D to that productivity growth.

The primary sector (made up of Agriculture, Forestry, Fishing and Hunting) has been an important contributor to the overall improved productivity growth in the New Zealand economy over the last decade. Furthermore, over the last 80 years the rate of growth of productivity in agriculture has continued to increase.

Investment in R&D is a potentially important factor in expanding the stock of knowledge. This stock of knowledge can be viewed as a capital input into agricultural production, and like any other capital input provides a flow of services to the production processes.

Domestic expenditure in both the public and private sector on agricultural research, adds to what we call a domestic stock of knowledge. However in a small open economy the stock of foreign knowledge that “spills in” also adds to the total stock of knowledge that is available to the sector. Given that New Zealand is a very small player and accounts for but a tiny fraction of global R&D efforts, it is to be expected that access to this foreign stock of knowledge would play an important role. Particular attention was given to this aspect in this study.

It is evident that all the benefits from research done today are not captured and reflected in higher productivity immediately. The outputs of the research investment add to the stock of knowledge and it is that stock which potentially contributes to improving productivity. In other words, research done 10, 20 or even 30 years that added to the stock of knowledge could still be relevant and contributing to today’s output. This raises major challenges in modelling the impact of R&D as we need to allow for long lags. Partly for this reason, this study has been based on annual data from 1926-27 to 2000-01. This provides, at least in theory, the opportunity to allow for extended lagged effects.

At the same time the use of stocks of knowledge raises the question of depreciation. Continuing the analogy with other forms of capital, some knowledge can be expected to “depreciate” – ie lose its ability to contribute to high productivity. We have analysed a range of depreciation rates, settling on 30% based on the performance of the models.

As there is no one accepted method of modelling the lagged effects of R&D this study presents the findings of three different approaches. The first estimates stocks of knowledge (both domestic and foreign) based on the perpetual inventory method which involves assuming a rate of depreciation of knowledge. The second allows for a decay parameter to be estimated rather than imposed, and provides an estimate of the long run effect of R&D (the Koyck transformation). The third approach is based on the argument that initially the contribution of research would be small, but as the knowledge generated diffuses and is incorporated in the production process the impact would grow. However in the long run findings of research done many years ago suffer from obsolescence – they were relevant for the particular technological and economic circumstances of say the 1950s, but much less so in 2005 (the Almon lag).

Table 12 gives a summary of the findings for the rate of return to investment in domestic R&D under the various methods. For each method we tried a range of specifications. These are the basis for the range of estimated rates of return shown in the table.

It will be immediately apparent that there is little or no indication of convergence across the methods. In both the Koyck and PIM models, we were able to find a significant effect from domestic R&D in most specifications. Our “preferred” model based on significant contributions to productivity of both foreign and domestic stocks of knowledge yielded a rate of return of 17% p.a. to investment in domestic R&D. However, when we used Almon distributed lags we found a negative and significant coefficient on domestic R&D. When we attempted to estimate the separate effects of private and public R&D we found that in almost all cases there was no identifiable contribution from the public investment, while the private R&D lead to a wide range of possible rates of return. The key message that can be drawn from these results is that the estimates of the contribution of domestic R&D are very sensitive to the method and specification adopted, and that even with lengthy time series data it is not easy to isolate the effect.

In a variant of the Almon lag approach which essentially mirrors that used by Scobie and Eveleens (1987), we derive a return of 70% to total domestic R&D. This compares with a value of 30% from the earlier study. There has been a marked slowdown in the growth of R&D investment and at the same time the rate of growth in productivity has increased. It is possible this higher estimate reflects the lagged contribution of past investments. However, given the wide variations in our estimates and the fact that many cases showed no significant contribution of domestic R&D, we would caution against selecting any one figure as a reliable estimate of the return to domestic R&D.

In contrast we found that, virtually regardless of the method or specification of the model, the spill-in effect from foreign knowledge was an important factor explaining the growth of agricultural productivity (see Table 13).

It should be noted that we are not able to distinguish between the different types of research included in the agricultural R&D expenditure data. For example, recently there has been some increased emphasis in public spending towards research projects whose objectives include ameliorating the environmental consequences of agriculture. As measured productivity does not directly reflect the investment in R&D related to environmental enhancement, this implies that our results might understate the true contribution of R&D to productivity growth.

It should also be stressed that because of the need to have a lengthy series of data we were limited in the variables we could use as a proxy for the foreign stock of knowledge and have relied on US patent data.

The results underscore the importance of foreign knowledge in a small open economy. In formulating policies for fostering innovation, these findings suggest that particular attention be paid to enhancing linkages with the international scientific community. This could take many forms including scholarships for training and research overseas by New Zealand researchers, involvement of New Zealand in international scientific networks, sponsorship of international symposia in New Zealand, etc.

While not as consistently robust, our findings typically support the argument that the stocks of domestic knowledge are positively associated with productivity growth. The very existence of foreign knowledge may be a necessary condition for achieving productivity growth in a small open economy. However in no way could it be argued that it is sufficient. Having a domestic capability that can receive and process the spill-ins from foreign knowledge is vital to capturing the benefits. The challenge is to be able to isolate those effects from aggregate data for the agricultural sector. In that particular aspect we claim only modest success.