5  Estimation

5.1  Basic model/included variables

The underlying concept to be developed in this section is that output depends on the following:

1. The level of inputs under the control of the farmer (fertiliser, labour, machinery, buildings, etc).
2. The influence of uncontrollable variables (weather, pest and disease outbreaks, financial deregulation, terms of trade).
3. The use that is made of current and past investments in knowledge about how to select, combine and manage the inputs. That knowledge can reflect both domestic and foreign investments in R&D.

Formally, this can be represented by an agricultural production function:

(6)     

where:

Y_t = the volume of agricultural output in year t;

I _t = a vector (I_1t, 1_2t, …., I_nt) of n controllable inputs in year t;

Z _t = a vector (Z_1t, Z₂t, …., Z_mt) of uncontrollable variables in year t; and

RD_t = R&D in year t, either domestic expenditure (d) or foreign expenditure (f).

The uncontrollable variable we use in our specification is weather, measured as the tenths of days of soil moisture deficit weighted by the four major agricultural activities (dairy, sheep, beef, and crops). The National Institute of Water and Atmospheric Research (2001) found that the agricultural component of GDP is negatively correlated with the strength of the southerly airflow over the country, and that milk fat production is negatively correlated with annual days of soil moisture deficit, regional summer temperature, and regional spring and summer rainfall. Buckle et al (2002) also show that climate is an important contributor to the overall business cycle, and that it appears to have been the dominant source of domestic shocks over the period 1984-2002. However, as Makki et al (1999) point out, weather may not be an important variable in the long-run time series analysis of productivity. It is reasonable to assume that annual weather variation is a random phenomenon, and there may be no long-run relationship with agricultural productivity, although short run variation in output and productivity may reflect seasonal conditions.

The controllable inputs which appear in the vector I_t include intermediate inputs, capital stock, labour, extension workers, and human capital stock. The capital stock includes livestock, plant, machinery and equipment, land improvements, and the value of all unimproved land. The labour variable is measured as the number of full-time equivalent workers plus working owners. The human capital stock has been calculated as the sum of current and past numbers of students enrolled in agricultural related courses (using a lag length of 15 years). A human capital index was then constructed (equal to 1 in 1949/50) from this human capital stock with a lag of 2 years to capture the lag between enrolment and graduation. Extension workers represent the number of Advisory Services Division staff in the Ministry of Agriculture and Forestry up until privatisation in 1984/85, after which time estimates of this have been drawn from various sources.[17] Extension is seen as impacting directly on agricultural productivity as well as speeding the adoption of new technology. Extension agents disseminate information on crops, livestock, and management practices to farmers and demonstrate new techniques as well as consulting directly with farmers on specific production and management problems.

Technological advances enter the production function in two forms. In the first place improvements are embodied in the inputs themselves, through enhanced design, improved and extended features, new materials, and indeed new inputs. A 1930 tractor or variety of wheat is clearly not the same as a 2001 tractor or wheat variety.

These enhancements arise, in part, from the R&D efforts of firms who supply the machinery, seeds, chemicals, financial, consultancy and marketing services to producers. They are continuously seeking innovations which enhance the quality of their products or services. They expect to recover the costs of this innovative activity through the sale of the item or service.

This raises an inherent problem of measurement. Ideally the vector I_t refers to the quantity of inputs used, where these are of standard quality. When measuring inputs over a long period their nature is bound to change, and some of the technological advances will be embodied in these data.

The second type of technological advance arises from improved knowledge. This results in more efficient use of the same quantity of inputs through better management decisions. Information about grazing management, the timing of fertiliser or pesticide applications, and tail painting for more accurate heat detection are all examples of technological advances which involve essentially information, rather than physical inputs. In summary, technological change is reflected in part by inputs of enhanced “quality” (captured in the vector, I_t) and partly through the improved stock of knowledge, which is added to through investments in formal R&D (RD_t), and through more informal channels such as on-the-job learning. This study does not isolate the effect on productivity of these informal contributions.

The notion that there is a relationship between investment in research and increments to the stock of knowledge has been used by several authors including Griliches (1979) and Minasian (1969). As Pardey (1986) observes, “it follows naturally from the perception that general science progresses by a sequence of marginal improvements rather than through a series of discrete essentially sporadic breakthroughs”.

At any point in time, producers have available to them a stock of knowledge on which they can draw generated either from domestic sources (RD) or foreign sources (RD^f). Both serve as sources of new knowledge but are not perfect substitutes. Organised farm tours to other countries are testimony to the implied demand by producers for access to foreign stocks of knowledge.

While the stock of knowledge may be added to through new investment in R&D, the amount of stock which is actually utilised at any point in time does not necessarily increase one-to-one with the extra R&D expenditure. It is not uncommon to hear scientists bemoaning the lack of use by producers of their findings. Leaving aside the question of whether the findings were relevant in the first place, there are a number of forces which govern the rate at which these increments to the stock of knowledge will be incorporated into production systems.

It is reasonable to suppose that dominant among these forces will be the profitability of the innovation. An advance which does not raise real income (through increasing output, reducing costs, saving time, eliminating unpleasant tasks or lowering variability) will almost certainly fail to be adopted in any widespread or sustained manner.

The cost to the producer of acquiring the innovation will be an important determinant of its profitability, and hence of the rate of utilisation which can be expected. In the case of a new input, or improvements to an existing input, part of the total cost will be the direct monetary price charged for the input. But, in addition, the producers must invest time and effort in learning about the product and its potential applicability to their circumstances. In the case of improved knowledge the entire acquisition costs are made up of these “learning costs”. Factors which lower these costs can be expected to increase the amount of new knowledge actually utilised. Extension services, farming journals, trade publications, the daily paper, radio and television all disseminate information and enhance the acquisition of new knowledge.

In addition changes in the structure of an industry will alter the cost of acquiring new information. A farmer with 100 hectares of barley has more incentive to invest time and effort in searching for information about new varieties, than one growing, say, 2 hectares. This would suggest that the trend to larger production units in say dairying, would cet. paribus lead to a higher rate of investment in and absorption of R&D.

Finally, the education and experience of farmers, their “human capital”, affects the cost of acquiring new information. Schultz (1974) has referred to this as the “value of the ability to deal with disequilibria”. The argument is simply that the operating environment is constantly changing -seasonal conditions, prices, costs and technology are never static. Entrepreneurship requires that these changes be continuously monitored, assessed and appropriate actions taken. Those with greater levels of human capital are presumed to be able to perform these tasks more readily.

Introduction of a new technology changes the operating environment; the greater the level of human capital, the more rapidly the new information (R&D) will be assessed and incorporated.

Evenson (1984) likens the structure of scientific and technological activities in agriculture to that of other economic activities. There is much specialisation in research, just as there is among firms producing different consumer goods. The industrial sector involves different stages of production; some firms produce coal, which is used by others for producing steel bars, which are bought by others to produce parts which are sold then to manufacturers of appliances.

In agricultural research there are counterparts which undertake “pre-technology research” (plant genetics, reproductive physiology, entomology) using as inputs the knowledge generated by the general sciences (e.g. chemistry, biology). The outputs of this stage are then used in the development of technology which is in turn screened and adapted for final use.

The preceding discussion leads to the development of a capital theoretic view of the generation and diffusion of knowledge. In other words, the existing stock of knowledge is seen as part of the capital stock of the agricultural sector in the same way that physical capital represents an input into farm production. Like other forms of capital, knowledge must be created through investment, and it is subject to obsolescence.

Thus we have a production function relating output to the stock of knowledge as in Griliches (1979):

(7)    Y = F(I_t ,Z_t ,K_t )

Where K_t represents the current stock of knowledge. We can then continue following Griliches and assume that there exists a relationship between K and W(B) RD, an index of current and past levels of R&D expenditures, where W(B) is a lag function describing the relative contribution of past and current R&D levels to K, and B is the lag operator. Thus:

(8)    K= G [W (B) RD,v]

where v is another set of unmeasured influences on the accumulated level of knowledge and

(9)     W(B)RD_t = (w₀+ w₁B + w₂B² + ....)RD _t = w₀RD_t + w₁RD_t-1 + w₂RD_t-2 + ...

Thus output becomes a function of current and past R&D expenditures as set out in equation (6). The fundamental objective of this study is to statistically measure the relation between agricultural output (Y_t) and the current and past values of research expenditures, or W(B)RD, while holding constant other factors which influence output.

Griliches defines W(B)RD as a measure of R&D “capital”. One of the major issues in the measurement of such “capital”, he argues, is the fact that the R&D process takes time and that current R&D may not have an effect on measured productivity until several years have elapsed. This forces one to make assumptions about the relevant lag structure W(B). We discuss alternative lag structures in the next section.