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A counterfactual experiment

To carry out a counterfactual experiment, one needs two items, a set of basic assumptions and a model within which to represent the alternatives. The alternatives are, of course, the adoption of a Structural Adjustment Programme and the non-adoption of a Structural Adjustment Programme. We shall call the first alternative Case I and the second Case II.

The crucial assumption in the experiment is over the amount of foreign assistance granted in the absence of a 'seal of approval'. It appears, given the data in Tables 7.1 and 7.8, that it is via an increase in foreign assistance that expenditures on science and technology have increased; the issue is whether or not the foreign assistance would have been forthcoming if the countries had not accepted the conditions imposed by the IMF/Bank. We shall make the most extreme assumption, that less foreign assistance would have been available to finance advances in science and technology; to pick an illustrative figure, we will say less by half.

So far as the total contributions to the advance of science and technology by the national government are concerned, we shall assume that these are unaltered in Case II from Case I. Our reason for assuming no difference in total expenditures in the two cases is that the claims of those engaged in carrying out scientific and technological activities would have been greater had foreign assistance not been available; but that these greater claims would have been matched by greater claims by all other public bodies, resulting in a standstill. When all interest groups increase their claims upon public funds the result is usually no change in their relative receipts (Wildavsky, 1979; Olson, 1982). We shall make an additional assumption, though, based upon our observations of the nature of the budget allocation decisions in the four countries that we studied; namely that all the funds assigned to 'Development' in the budgets of the public R&D institutes would have been transferred to the 'Current' expenditure account; in other words, that what would have otherwise been expended on capital investment would be expended instead in maintaining wages and salaries. Since some of the funds made available by foreign donors were actually used to subsidize salaries of scientists and engineers, in the absence of these subsidies the scientists' and engineers' salaries would have had to be maintained by injections of local funds. The net effect, therefore, would be to reduce capital expenditures in R&D activities by the amount of foreign donations: in Case II, compared to Case I, therefore capital expenditures are assumed to be 60 per cent lower than in Case I, 50 per cent lower because of reduced foreign assistance and 10 per cent lower because of reduced local government expenditure.

The only effect that we are trying to capture at this point is the reduction in total expenditures on advancing science and technology; other effects will be captured in subsequent chapters. How can this effect be captured in the model that we have already, in the penultimate section of Chapter 2, chosen to serve as the guide to our inquiry? There are two possible means, both of which we shall use. The first is in the volume of resources devoted to the pursuit of science and technology; the second in the efficiency with which R&D is carried out.

Taking the first phenomenon, we have already assumed that in Case II, as compared to Case I, the total amount of resources allocated to furthering science and technology is reduced by the amount of foreign assistance denied. Reducing foreign assistance by half would reduce total expenditures on science and technology by roughly 35 per cent. In terms of Fung and Ishikawa's model, the reduction in funds allocated to R&D in Case II vis-à-vis the allocation in Case I, is equivalent to a reallocation of approximately 1 per cent of the total resources of the country from R&D to the production of the goods, labelled Y, which do not profit from R&D. In other words, there is a reallocation from future production of goods (via investment in R&D in which the country has a future) to present production of goods in which the country does not have a future. We would expect there to be more current output of good Y in Case II than in Case I, and less future output of good X, the good which does benefit from the affiliation of R&D.

Taking the second phenomenon, the efficiency with which R&D is carried out, the model includes this phenomenon as a parameter, a (delta), whose value is exogenous to the system of equations. How might this parameter differ in value between Cases I and II? The answer lies in the reallocation of resources that we have not yet taken into account, namely that from capital expenditures to current expenditures. We recall that this reallocation is assumed to take place as local governments compensate scientists and engineers for the supplements to their salaries that would otherwise, in Case I, have been contributed by foreign donors. Let us assume that the reduction in capital investment in R&D reduces the efficiency with which R&D is carried out by 10 per cent: in the model this is reflected by a reduction in the value of the efficiency parameter, d , of 10 per cent.

With these two changes from Case I - a shift of total resources of 1 per cent from R&D to the production of good Y. and a reduction in the value of d by 10 per cent - the conditions for Case II are set. What is the outcome, in terms of the difference in behaviour of the model in Case II as against its behaviour in Case I? Assuming an intermediate value for the other parameter of the system (b , the elasticity of substitution of intermediate goods), the outcome can be visualized as in Figure 7.1. Expenditures of the country's scarce resources on R&D fall; production of good X, the good which benefits from R&D, remains the same; and the production of good Y rises. For the economy as a whole, income is unaltered; but, compared to Case I, total output increases, by the amount of the extra output of good Y.

This is the result in the short run. In the long run, as indicated by the (upper) trajectory for Case II, and the parallel but swifter trajectory for Case I, the economy advances more slowly. The reasons are twofold: less R&D is being carried out; and what is being carried out is carried out less efficiently. In Figure 7.1 these two consequences are shown by the trajectory for Case I lying closer to the horizontal axis- the axis along which the output of the good with the higher potential is measured - and for successive outputs of good X (labelled X₁, X₂, X₃, etc.) being obtained more speedily (i.e. X, being obtained in half the time in Case I as in Case II).

Summarizing the deductions from Fung and Ishikawa's model for our two cases, we conclude that the counterfactual case, Case II, is more attractive to the economy in the short run, through higher total output (via more good Y); but is less attractive in the long run, through lower output of the other, more desirable good X (via less R&D directed towards improving its production). The future is traded-off for the present.

Figure 7.1 Visual comparison of actual Case I with counterfactual Case II

This is not the only counterfactual case that we will construct, for it addresses only the macroeconomic issues discussed in this chapter. Still to be addressed are the meso- and micro-economic issues: these will occupy us in the next two chapters. Before we turn to these chapters we might well recall two assumptions that underlie Fung and Ishikawa's own theory:

1 that the economy's resources are fully employed; and
2 that there is no cost, nor any time taken, in shifting resources from one activity to another.

The relevance of these two assumptions for our Cases I and II are that, faithfully following Fung and Ishikawa, we have assumed that those resources not employed in R&D (in Case I) are, in Case II, shifted, immediately and without cost, to the production of good Y. One of the questions we shall address in the next chapters is: are scientific and technological personnel in Ghana, Kenya, Tanzania and Uganda perfectly mobile? If so, can they be expected to be productive in these activities for which their background and training have not prepared them? Moreover, the equally important question of potential for R&D, raised in the theoretical chapter, will have to be addressed also. In terms of the variables appearing in Figure 7.1, this means addressing the practical question: what goods actually belong in category X? What in category Y?

8 Effects among the sectors of the economy

Introduction
Economic activity by sector
Mobility between sectors
Transfer of resources and institutions from the public to the private sector
Summary

Introduction

In this chapter we shall attempt to determine the effects of the adoption of Structural Adjustment Programmes on the allocation of resources between the sectors of the economy. This analysis will focus on the 'mesoeconomics', signifying not macro-economics, nor micro-economics, but what lies in between. When examining activities more aggregated than those encompassed in the single organization, and less aggregated than those in the overall economy, we can be guided by theory, and by what has appeared to others and to ourselves during our enquiry. Let us start with the issues raised by theory.

The theory developed in Chapter 2 suggests that important issues for a developing country are:

1. the volume of resources the nation devotes to advancing science and technology;
2. the extent to which these resources are made available in the early years of the country's economic development;
3. the allocation of these resources assigned to improving the production of those commodities with the greatest potential to increase income;
4. the efficiency with which scientists, engineers, technicians and other complementary workers are employed; and
5. the degree to which the 'intermediate' capital goods can be substituted one for another in producing those goods with the highest potential.

In addition, examination of the assumptions underlying the theory suggests that we also try to determine whether or not resources are perfectly mobile, and always fully employed.

The first two of these issues - the overall volume of resources devoted to advancing science and technology and the timing of the application of these resources - were addressed in the previous chapter; and the fourth and fifth issues - the efficiency with which scientists, engineers and others are employed, and the substitutability of capital goods - will be addressed in Chapter 9. What remain for this chapter are the third, fourth and fifth issues - those of the direction of R&D, the mobility of resources and the intensity of their employment. In addition, there is another issue absent in the theory but deserving attention, one to which the IMF/World Bank attach great emphasis, that of the assignment of activities to the public, or to the private, sector of the economy.

Although we have introduced them separately, all four issues to be addressed in this chapter- the mobility of resources, their full or less than full employment, the activities to which the specialized resources necessary in advancing science and technology are assigned, and in which branch of the economy, public or private, those activities are to be carried out - are interrelated. Although we can identify them separately, we will not be able to consider them in a piece-meal fashion, for our data do not permit it, nor does our understanding of the interrelationships warrant it. We shall be led by the information we have been able to accumulate; all these data enable us to do is to draw implications. We can only hope that at the end of our analysis the four issues will be a little less unclear than they are at the beginning.

Economic activity by sector

In presenting our data on the 'meso-economy', we will focus on two items, the overall figures on economic activity and the figures measuring the advance of science and technology, both by sector of activity. These data appear in Table 8.1, which contains figures on economic activity, and Table 8.2, which contains figures on R&D. For our purposes the 'mesoeconomy' consists of three sectors: agriculture, industry and services: the contributions of each of these three sectors to the GDPs of Ghana, Kenya, Tanzania and Uganda over recent years appear in Table 8.1. We notice considerable differences in the shares between countries, with Ghana and Kenya deriving larger fractions of total GDP from industry than Tanzania and Uganda. Within each country, over their recent histories, Ghana's and Uganda's agricultural shares have been falling, and Tanzania's rising, with compensating movements in industry's shares. To the extent that one of the objectives of Structural Adjustment Programmes is to reallocate resources between sectors, chiefly from services to industry to agriculture, this objective has not yet been wholly achieved.

Table 8.1 Shares of total GDP by sector in Ghana, Kenya, Tanzania and Uganda, various years 1980-1992

Source:
Tables 3.3, 4.3, 5.3 and 6.3

Table 8.2 Shares of total expenditures by R&D institutes by sector in Ghana, Kenya, Tanzania and Uganda, various years 1980-1992

Source:
Tables 3.15; 4.11 and 4.16; 5.21, and 5.22, columns 1 and 5; and 6.10, 6.11 and 6.12
Notes:

^a: in Ghana, the sectoral allocations of R&D institutes were as follows: agriculture, the Food Research Institute (FRI) and the Cocoa Research Industry of Ghana (CRIG); industry, the Industrial Research Institute (IRI), and the Technology Consultancy Centre TCC; and services, the Scientific Instrumentation Centre (SIC)

^b: in Kenya, the sectoral allocation of R&D institutes was as follows: agriculture, the Coffee Research Foundation, the Kenya Agricultural Research Institute (KARI), the Kenya Forestry Research Institute (KEFRI), and the Kenya Marine and Fisheries Research Institute (KEMFRI); industry, the Kenya Industrial Research and Development Institute (KIRDI); and services, the Kenya Medical Research Institute (KEMRI), the Kenya Trypanosomiasis Research Institute (KETRI), and the National Council for Science and Technology NCST

^c: in Tanzania, the sectoral allocations were as follows: industry, Tanzania Industrial Research Organization (TIRDO) and Tanzania Engineering and Manufacturing Design Organization (TEMPO); services, none; and the balance (of total expenditures of R&D institutes, Table 5.), allocated to agriculture

^d: in Uganda, the sectoral allocations were as follows: agriculture, Recurrent and Development estimates of the Ministry of Agriculture; industry, Recurrent and Development estimates for the Ministry of Industry; and services, Development Estimates for Maherere University

+-: signifies more than, and less than

When we turn to the contributions of R&D - where contributions are measured by expenditures - the data in Table 8.2 do reveal changes in the shares within countries. To be sure, the data are fragmentary and relate almost entirely to public and semi-public R&D institutes, but these carry out almost all R&D in Sub-Saharan African countries. Taking the four countries of our sample alphabetically, in Ghana, the figures are reasonably accurate although not exhaustive; they indicate that the bulk of expenditures are devoted to the agricultural sector, and within the agricultural sector, to cocoa (see Table 3.13). The industrial and service sectors have received small sums, and these have fluctuated widely from year to year, depending chiefly upon the timing of receipts of foreign assistance.

Kenya's experience is somewhat different; although this is more apparent than real, since alone of the four countries the statistics of expenditures on R&D in services include those in medical research. Nonetheless, over the five years for which we have figures we still observe a rapidly increasing share being allocated to agricultural R&D, and declining shares in both the other sectors.

The statistics on the allocation of funds to R&D by sector in Tanzania are the weakest of the four countries. Industry's share arises through expenditures of the two industrial R&D institutes, which are recorded; but the share of services, although very small, cannot be estimated. Agriculture's share is the larger, but the data on agricultural expenditures on R&D (with the exception of TPRI's) are aggregated in the Ministry's accounts. We have resolved the statistical difficulty by assuming that all expenditures of R&D institutes not allocated to industry are allocated to agriculture. No trend over the period 1980-1992 is observable; agricultural and services' R&D are allocated between half and two-thirds of the total; industry's R&D between one-third and a half, but with considerable fluctuations between years. Allowing for allocations to services (medical research and the Tanzanian Institute of Standards) would reduce agriculture's share to well below half.

Last of all is Uganda, whose tendency towards concentrating all R&D in agriculture seems, in the last two years at least, to have been reversed. Two separate events have led to the reversal: large foreign grants to Maherere University in 1990/1 (for renewal and expansion of the sciences and social sciences), and the initiation of public R&D in industry in 1991/2, also with assistance from abroad. But the bulk of the foreign assistance that Uganda does receive for R&D is still directed towards agriculture, whose total expenditures have also risen substantially in the two most recent years. The conclusion throughout is that the agricultural sector receives the vast majority of funds for R&D, and industry very little.

That the shares, by sector, of expenditures on R&D by public institutes should fluctuate year-by-year is not surprising; that these should be relatively stable indicates a close correlation with the shares of economic activity overall, an association we shall comment upon in the next chapter. But if we were to consider not expenditures on R&D by public research institutes (the basis of the figures in Table 8.2) but the larger category of total expenditures on R&D, we would probably find a reallocation from industry to agriculture. We cannot back up this statement by statistics, but our impression is that expenditures in the other components filling the larger category - expenditures on research in the universities; and on R&D, training and technical services by producing firms, both public and private - have increased in the agricultural sector while remaining on balance more or less unchanged in the industrial. The latter impression that there has been no net change of expenditures in the industrial sector is the result of R&D in the universities increasing and R&D of productive firms decreasing. This impression is quite strong for the two more industrialized countries, Ghana and Kenya, and appears to be similar for Tanzania; it is only Uganda, where there has previously been no attempt to advance science and technology in industry, that the impression is the reverse.

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