Libertad Sindical

Many attempts at determining the supply function for Ghana cocoa have been made using variations on a basic Nerlovian lag model. The

intention of this chapter is to summarise four of the major works dealing with this subject. Some of the findings and conclusions may be used in subsequent chapters of the present study. The works to be

discussed in historical order are those of Peter Ady (1949), R. M. Stern (1965), M. J. Bateman (1965) and J. R. Behrman (1966). The four models are beset with various conceptual and theoretical as well as data scarcity problems. These shall be discussed at the end of the summary.

3.1 PETER ADY'S MODEL (1949, pp.389-399)

This model is based on the argument that the level of potential output in the year of harvest depends upon the number of bearing trees and their yield in that year. The number of bearing trees is a function of the rate of planting in earlier years together with the mortality rate during the years of growth. Given the population of bearing trees and the assumption that geographical distribution does not affect

yields, their yield depends chiefly upon climatic influences in the year of harvest. The actual harvest cropped and marketed as compared with this potential upper limit, Ady claims, may itself be affected by

current economic factors such as harvest prices in relation to costs of cropping and marketing.

Ady's p r o b l e m is to construct an appropriate economic model which suggests the factors determining the following two indices:

t 1 . The replacement of b e a r i n g trees. (This can be

identified w i t h the planting rate if the mortality rate can be assumed to be constant.)

2 . The ratio of the crop actually harvested and marketed each year to the tonnage potentially a v a i l a b l e .

In economic t e r m s , this amounts to the formulation of long run and short run supply f u n c t i o n s , and the sinqjlest hypothesis would be that both long and short run supplies are functions of the 'real' price of cocoa. This is the hypothesis Ady tests for the period 1920-1940.

General Function for Cocoa 1920-1940;

^r = ' • ... (1.1) L L a L L

w h e r e ,

E^ = actual sales of cocoa harvested at time t. TT = cocoa prices in real terms (i.e., cocoa prices

in money terms deflated by the index of the general price level).

R = index of climatic factors.

a = lag between year of planting and full b e a r i n g , n = rate of discount.

The long and short run supply functions are: L o n g run:

e'^ = f(TT ) ... (1.2) t t-a

w h e r e .

E^ = harvest expected upon the basis of prices in the year of p l a n t i n g , i.e., at time a years earlier.

Short run;

^c . , X nt where,

E^/E^ = ratio of actual harvests to

harvests expected on the basis of the LR function.

For the purpose of the above investigations the following series was constructed by Ady:

E = tonnage of cocoa exports expressed as a quantity relative (1938 = 100) (Source; Annual Trade Reports of the Gold Coast.) A calendar year was used.

P = cocoa price index; cocoa price f.o.b. excluding

aggregate duty were used since no reliable information on prices received by the farmers existed.

I = import price index of main imports consumed in the cocoa belt for the period 1921-1947 (1938 = 100). Estimations;

A long run supply function, 1920-1940;

Adopting a 9-year lag, the long run supply equation obtained by taking the least squares on the log values and transformed again was;

E^ = • 36 ... (1.4) r = .83, O = ±.05

where,

But the presence of serial correlation in the series used is a major defect of (1.4) above. To overcome that problem the LR

supply equation is transformed to first differences and the results found were:

- « ••• r = .69, a = ±.04

e = .43 ± .1 where,

3 = the coefficient of regression of (1.5).

Since (r), the correlation coefficient in (1.5) is still signifi- cant at the 5 per cent significance level and the regression coefficient was of the same order and sign as in (1.4) above, the first differences equation incorporates the general character of the supply function - (a positive slope).

Short run supply function, 1930-1940:

Ady suggests that in the short run much depends upon the influence of climatic factors which may mask the responses of farmers to short run changes in price. However, no climatic variable was included because there was no suitable data available. Hence, no valid short run supply function for this period could be constructed. The residuals were not even sufficiently free from random error, for the sample of years available was also too small. The

correlation of residual export quantities with current prices showed no association (r = .03), while the residual variance to be

explained was small in relation to the changes in trend revealed by the long run analysis above.

A d y ' s Conclusion s;

Changes In the magnitude of cocoa exports In Ghana before 1939 were explained largely by the effect of changes in cocoa prices upon planting nine years e a r l i e r .

The Influence of current factors upon exports w a s very small hence the short run elasticity of supply was low. The short run supply

function analysis w a s far from complete due to deficiencies of the data u s e d . There was no correlation between short term changes in output and short term changes in real prices. F i n a l l y , the regression coefficient w a s not significantly different from zero.

The chief value of the analysis was in emphasising the importance of economic factors in the supply of Ghana cocoa. It showed that there was evidence that the peasant cocoa producers showed positive response to price changes because of the positive supply elasticity which she

found, irrespective of it being low.