The income distribution effects of the Kenyan coffee marketing system

Journal

THE

of Development

INCOME

Economics

31 (1989) 297-326.

North

Holland

DISTRIBUTION EFFECTS OF THE MARKETING SYSTEM* Gary Laurentian Received

University.

April

KENYAN

COFFEE

McMAHON Sudbury,

1987, final version

Ont., Canada P3E 2C6 received

February

1988

In order to meet its obligations to the International Coffee Agreement, Kenya regulated output from 19641972 and 1981-1985 by banning the planting of new coffee trees. With the use of a large dynamic computable general equilibrium model, we attempt to show thai Kenya suffered significant income losses due to the inefficiency of its internal quota system. More importantly, as coffee has been the most lucrative crop in Kenya over the last 30 years, the restriction of coffee expansion resulted in very large distributional effects. The use of a coffee tax coupled with an increased expansion of coffee hectarage in the quota years would likely have significantly reduced the incidence of poverty in Kenya by redistributing income towards farmers in coffee zones who historically did not grow coffee, farmers in non-coffee zones, and landless rural labourers.

1. Introduction In 1964 Kenya became a member of the International Coffee Agreement. One of the conditions of its membership was that it was restricted in the amount of coffee it could export, though the pact gave Kenya a free hand in the internal management of its quota. Although the international quotas were lifted in 1972, they were put back in place in 1981, only to be lifted again, at least temporarily, in 1986. In both quota periods, 1964 to 1972 and 1981 to 1986, Kenya met its quota by completely stopping coffee tree planting - all the seedlings came from government nurseries - and either storing any excess production, which was negligible in most years, or selling it in non-quota markets at a much lower price.’ Over the period of this study, 1964-1984, coffee was on average 3.7% of GNP, 4.5% of monetary GNP, 18.9% of exports, and 48.7% of agricultural exports in Kenya, indicating that Kenya’s membership in the coffee agreement and its method of meeting its quota could potentially have widespread effects. *The author would like to thank Jan Vandemoortele for invaluable assistance with data collection in Kenya and Randy Wigle for his great help with regards to the computational procedures used in this study. The comments of two anonymous referees were also very helpful. I would also like to thank the Social Sciences and Humanities Research Council of Canada for the funding of this project. ‘The major importing countries were also members to the International Coffee Agreement. Countries were allowed to sell to non-member countries - mostly from eastern Europe and other LDC’s - at whatever price they could get. 03043878/89/$3.50

0

1989, Elsevier

Science

Publishers

B.V. (North-Holland)

298

G. McMahon,

Income distribution and Kenyan coffee marketing system

It is well-known that it is welfare reducing to restrict production by using a quota rather than a tax which would give farmers the ‘incentive’ to produce the desired amount.’ In fact, McMahon (1987) has shown that not only did Kenya suffer a small loss of GNP from 1964 to 1979 by restricting new tree planting, the amount of coffee hectarage was 30 to 40% less in 1980 than it would have been under a tax system, as even at’ after-tax producer prices coffee was still profitable enough to encourage new planting. As a result of its planting policy, Kenya most likely received a much lower quota than would have been the case when restrictions were reintroduced in 1981 .3 Despite this result, in 1981 Kenya again put into effect a ban on coffee tree planting in order to meet its international obligations and may well find itself in the same situation the next time quotas are negotiated. An issue which is addressed less frequently than the productivity effects of a quota, but may be of greater importance, are its effects upon income distribution. A quota not only prevents potentially more productive farmers from entering a market, but it also prevents these farmers from achieving higher incomes.4 While this point may not be of great importance if there are alternatives with similar rates of return, in Kenya, except for in areas where tea can also be grown, the rates of return on coffee are usually much higher than for other crops. A relatively recent report by the Kenyan Ministry of Livestock and Agriculture estimates the gross returns on coffee to be about nine or ten times the next best alternative.5 In fact, Koester *See Wallace (1962) for example. Briefly, a tax ensures that the fixed amount is produced using the optimal combination of the factors of production rather than adapting labour, for example. to a fixed amount of land. In addition, a tax ensures that the fixed amount is produced by the most efficient farmers. “Quota allocations for 1981 were based upon average output from 1976 to 1978. A recent Kenyan government report concludes: ‘Throughout the 22 year history of the International Coffee O&nization, the allocation of national export quotas has tended to reflect national export capacity. Had Kenya continued expanding the area under coffee between 1964 to 1972 instead of banning the planting of new areas, it would have had a considerably greater production capacity on the reintroduction of quotas in 1980 and almost certainly a much larger quota in the present 1984/1985 coffee year than the current 82,400 tonnes’. See the report by the Ministry of Livestock and Agriculture (1985, Appendix 2, p. 7). 41f the quotas come in the form of licences. this problem can be solved in theory by allowing the owners of the licences to sell them. However, when the quota is in the form of a ban on new planting, a person can only obtain one by buying someone else’s farm, a transaction which very rarely occurs amongst Kenyan smallholders. Collier and La1 (1986, pp. 1299132) summarize the evidence on land transactions in Kenya. They conclude that there is no extensive land market in Kenya as almost all holdings are acquired through inheritance. 50f course, the difference in net rates of return would be much less as there is a four- to fiveyear gestation period for coffee trees and a greater need for inputs, especially labour. (If there is a significant amount of under-employed family labour, the latter may be of little practical importance.) My own calculations suggest that the gross return on smallholder coffee was, on average, seven times higher than its next best alternative from 1964 to 1984. If we ignore nonlabour inputs, this works out to net rates of return for coffee hectarage approximately two times higher over its 30-year lifespan, discounted at 3%. (There is very little data on non-labour inputs on smallholder coffee farms, though one result of a survey conducted by myself in 1985 was that coffee farmers spent more on fertilizers than non-coffee farmers, though the amounts were small.)

G. McMahon.

Income distribution and Kenyan coffee marketing system

299

(1978) thought that Kenyan coffee farmers had become one of the most favoured groups in the country - partly due to government policy.‘j The major purpose of this research is to try and quantify the income distribution effects of the Kenyan coffee policy from 1964 to 1984. An estimate of the historical income disparities amongst smallholders who have coffee, those who have tea, and those with neither will be compared with the disparities which will be simulated to have existed if the Kenyan government had used a tax to control output rather than a restriction on new planting. That is, smallholders and other groups in Kenya will be distinguished by their sources of income - e.g. from coffee land and rural labour versus urban formal sector labour - while at the same time these different sources have been chosen to indirectly reflect on the size distribution of income in Kenya. If a tax system would have been used, it is likely that the relative return to coffee production would still have been lucrative enough to attract new entrants into the industry. A unique aspect of the model is that agricultural land is divided up into different zones, in each of which only certain crops can be grown. This feature is included in the model as coffee and tea can only be cultivated in certain areas of Kenya; consequently, coffee policy will only have indirect effects on certain groups of farmers. The simulations will be executed using a computable general equilibrium (CGE) model, run over 21 years, 1964 to 1984 inclusive. During each year an equilibrium is determined in the usual CGE manner. The years are then linked by changes in the stock variables to take account of, for example, new investment.’ The model used will be fairly complex in order to capture as many of the consequences of the coffee policy as possible. It includes an input-output matrix and a small social accounting matrix (SAM) ~ both of which were absent from my previous study - and (less than perfect) substitution between imported and domestic intermediate goods. Another purpose of the project is to extend my previous results on both the gains and losses to Kenya of being a member of the International Coffee Agreement and the productivity losses of the tree planting restrictions from 1964 to 1979 and 1964 to 1984. In addition, it will be interesting to see if the much greater detail in the present model ~ necessary for the study of income distribution ~ has any significant effects on the former results. The next section examines the model with particular emphasis on the modeling of the rural sector and the income classes. Section 3 reports on the @Old established coffee producers were already favoured by the restriction on new planting in the period prior to 1972. Coffee producers are on average among the relatively well-off farmers. Hence the exorbitant coffee price increases (in 1976 and 1977) obviously ran against the objective of a more equitable distribution of income with agriculture.’ Koester (1978, p. 5). Collier and La1 (1986, p. 80) estimate that the percentage of smallholders in poverty in Central Province, the main coffee producing province as well as one of the main tea producing provinces, fell from 49.8% in 1963 to 22.4% in 1974. In the rest of Kenya approximately 35% of smallholders were in poverty in 1974. ‘In the next section there will be some discussion of the workings of a CGE model. For a full description, see Whalley (1984).

300

G. McMahon,

Income distribution and Kenyan coffee marketing system

simulations and their results. tions are discussed.

In the section

4 conclusions

and recommenda-

2. The Model A CGE model is used with simulations run over a 21-year time period. It consists of 348 equations within each period and 31 equations linking the years. As it is, of course, impossible to give a complete exposition of the equations, we will only look at the larger groups of equations, emphasizing their most important features. The within-period equations will be discussed first, followed by an outline of the computational method for determining an equilibrium in each period. The equations linking the years will then be examined.8

2.1. The within-period 2.1.1.

Production

equations

and the returns

to the factors

of production

Equations (1) to (93) of table 1 contain the production side of the economy. Value added in the eight urban and three rural producing sectors, as calculated using constant elasticity of substitution (CES) functions, is combined with the necessary inputs to get output. (There are 15 different value added functions as tea and coffee production are each disaggregated into three different producing regions, described below in section 2.1.2.) Substitution is possible in the value added functions between labour and capital and/or land as well as between the imported and domestic components of the fixed input requirements. [Equations (46) to (75).] The tea and coffee value added functions include only labour and land; capital is embodied in land, as only land which has had the necessary investment for five years is capable of sustaining either of these crops. The various factors of production are assumed to receive their marginal revenue products (MRP). [Equations (76) to (93).] Although there is no unemployment in the model, the urban labour market is not a perfect one in that relatively low paying informal sector jobs exist alongside of higher paying modern sector jobs. It is assumed that there is a wedge between the wages paid in these two sectors due to some type of structural rigidity. However, in the simulations the size of this wedge is allowed to fluctuate if the relative sizes of the formal and informal sector labour markets change (via rural-urban migration) and/or the demands for the goods of the two sectors change. Note that while the relative size of these sectors can change between periods, once the period begins the sizes are fixed. That is, instead *An addendum which includes from the author upon request.

and

explains

all of the equations

of the model

is available

G. McMahon.

Income distribution and Kenyan coffee marketing system

301

of the labour forces adjusting to the wage disparity, the wage disparity adjusts to the fixed within-period labour forces.g The land and capital stocks are also fixed within each period although they can change between periods. 2.1.2.

Income

There are 16 income groups in the model. [Equations (94) to (109).] The first eight income groups are coffee or tea smallholders. As there are two different sectors of smallholders production of both coffee and tea and both crops have a long gestation period, there are four types of both coffee and tea smallholders - coffee mature and immature in agricultural regions I and III, tea mature and immature in agricultural regions II and III. Kenya has been divided into four different agricultural regions. While other agriculture can be grown in all four regions, coffee can only be grown in regions I and III, and tea can only be grown in regions II and III. Neither coffee or tea can be grown in region IV. In addition, there are tea and coffee plantations which are considered separately from the rest of the rural sector as the expansion of both of these sectors is carefully controlled by the government. Note that coffee and tea smallholders do not farm exclusively coffee or tea but only a portion of their land. Therefore, their income will come partly from the return on coffee or tea land, partly from other agriculture, partly from their labour, and partly from their portion of redistributed government revenue (as described below). l O The ninth to twelfth income groups consist of smallholders who historically grew anything but coffee or tea. There is one such income group for each of the four agricultural zones in order to capture the effects of different policies on farmers in different ecological areas. They receive the return on their land, their labour, and their portion of redistributed government revenue. The thirteenth income group consists of landless rural labourers who only receive their labour and their portion of redistributed government revenue. 1l ‘Although, since the work of Todaro (1969) and Harris and Todaro (1970), it has been common practice to assume a fixed real urban modern sector wage, the evidence on Kenya suggest that this isn’t quite the case there. As urban ‘unemployment’ grew in the 1970’s, the real wages of urban workers fell, largely due to the attempts of the government to absorb as many secondary and post-secondary school leavers as possible on a limited budget. Vandemoortele (1984b) estimates that real urban wages fell by about 26% from 1970 to 1983 in Kenya. toThe productivity of one coffee or tea region is different compared to that of the other. The productivity of the different coffee regions are based on Kenya Coffee Board Reports and a survey I conducted of 600 smallholders in three different regions of Kenya in 1985. The proportion of each farm devoted to coffee also comes from the same survey. The corresponding tea data comes from the Kenya Tea Development Authority Annual Reports. “In the initial year of the study, 1964, all rural persons were assumed to own an equal sized plot of land, where a plot of land is defined as an amount of land which supplied a certain value of other agricultural production. Any additions to the rural labour force after 1964 were assumed to be landless.

Production

1-15

1630

G=f(lBT,IT,ID,F) population,), (Capitalists)

Gi=f(SAM, SV=f(Y,J I=SV I,=SV,,

Cij=f(P

Cij=I(Cij,dom,Cij.,,p)

in coffee and tea investment

Income

Government

Government

Savings

Investment

Investment

Composite

Consumption

Composite

Utility

Equivalent

94109

110

11l-126

127

128

1299132

133-134

135-278

279-310

31 l-326

3277342

variations

functions

consumption

transfers

goods

goods

i= 1,16

YL=f(w,,ri,

to land

Return

revenue

i=Cl,C3,CP,T2,T3,TP,OA

h,=f(P,,Qi,Hi,IBT,),

to capital

Return

15 sectors

87-93

i=all

79-86

i= I,16

i=OA,M,EW,BC,TC,PS,GS,OD

,..., EVL=f(U,,~i,P,

U,=f(C,,

,,...,

i=l,

i=l,16

16

i=OA,M;j=1,16

i=1,9;j=l,16

,._., P,),

C’s;),

P,,Y,),

l=f(l dOIn wimp);~,,,=f(~sc,~,)

i=2,4,6,8

hi, Gi,ITi),

ri=f(P,,Qi,Ki,IBTi),

w, = f( Pi, Qi, Li, IBTJ,

i = U, R, IS

to labour

Return

7678

Zj~=f(Z~i.dom~Zji,im,).

matrix intermediate

Input-output

Composite

j=OA,M;

i = all 15 sectors

15 sectors

3 l-45

j= 1,15

i=all

4615

Zj=~!~,ajiQ,,

Qi=min

VA,=f(L,.Ki,H,),

Value added functions

Equation

Description

goods

1

Equation number functions

Table The model.” _____

NH,=~(PV,,,PV,,,PV,,), L.,,,+,=l.O3L,,,~MIG,

Hi.,+ H o*,.,+ 1 -Ho,,.,-NHci-NHTi, -

allocation

Rural-urban

Investment

Investment

New coffee and tea hectarage stocks stocks

Balance

Labour

Capital

Coffee and tea land stocks

Other

348

349

350-351

352-358

359-362

363-364

3655372

373-376

377-379

land

stocks

(urban)

&.,(l-dep)

I = H,.,+NH,,

&.,+I=

i=OA,

i=Cl,C3,T2,T3

+ Ii.,,

i=R,U

i=1,2,3

M, EW,BC, TC,PS,GS,OD

i=Cl,C3,T2,T3

( j= M,EW,BC,TC,PS,GS,OD

j=R,U

aa = input-output coefficient; BC = building and construction; C =co&e; Ci =coffee in Zone i; Cij= consumption; EW = electricity and water; F = capital flows; dep=depreciation; dom =domestic; EV=equivalent variation; G = government revenue; Gi = government transfers; GS = government services; h = return to land; H = hectarage; imp = imports; I = investment; lBT= indirect business taxes; ID = import duties; IM = imports; IS = informal sector; IT = income tax rates; K = capital; f? = historic capital; L = labour; M = manufacturing; MIG = rural-urban migration; NH = new hectarage; OA = other agriculture; OD = ownership of dwellings; P= price; P= historic price; PS = private services; PI/= present value; Q =quantity; r=return to capital; F=average rate of return to capital; R =rural; SAM=social accounting matrix; SV=savings; T=tea; Ti= tea in Zone i; TC= transportation and communications; P= historic income; Y = income; LJli= utility; VA = value added; w = wage rate; X = exports; U = urban; Z = intermediate good.

agriculture

migration

of payments

(rural-urban)

allocation

Other

345-347

MIG=f(w,, w., wn.b,,L,s)

BP=X-fM+F=O

Tea exports exports

Xc=Qc XT=QT_Cj=61CT,j Xi=fV’i.domrP,.imp), i=OA,M,PS

Coffee exports

343

344

304

G. McMahon,

Income distribution and Kenyan coffee marketing sys:em

Table 2 The income

classes.

Income class

Description

Population” share

Average income (Kf)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Coffee smallholders, Zone I trees, Zone I Coffee smallholders, immature Coffee smallholders, Zone III trees, Zone III Coffee smallholders, immature Tea smallholders, Zone II Tea smallholders, immature plants, Zone II Tea smallholders, Zone III Tea smallholders, immature plants, Zone III Other agriculture smallholders, Zone I Other agriculture smallholders, Zone II Other agriculture smallholders, Zone 111 Other agriculture smallholders, Zone IV Landless rural labourers Capital and plantation owners Urban formal sector iabourers Urban informal sector labourers

0.69 0.21 1.12 0.16 0.79 0.27 0.98 0.36 10.10 12.94 21.69 3.69 4.93 2.90 23.12 16.05

80 27 78 25 51 25 55 25 28 28 28 28 15 875 61 17

“These are the average population shares over smallholder coffee and tea shares had approximately

the 21 years doubled.

of the

study.

By

1984 the

Capitalists form the fourteenth income group. They receive the return to all capital, the return to land on tea and coffee plantations, and their portion of redistributed government revenues less income taxes. Urban formal and informal sector labourers, the fifteenth and sixteenth income groups. receive their wages plus their portion of redistributed government revenues (less income taxes in the case of the former). In table 2 the income groups are defined for easy reference, and their average historical percentage of the total population as well as their average historical per capital income are given. The most important result of the simulations will be to see what happens to the relative incomes of the 13 rural groups when it is assumed that the government followed different policies with regard to coffee planting. For example, if the government would have used a tax system rather than internal coffee quotas (via tree planting restrictions), a more equitable distribution of income would have been likely. 2.1.3.

Government

revenues

The Government taxes on capitalists duties, and capital inflows are assumed quently redistributes revenue except that

and expenditures

receives revenues from indirect business taxes, and urban forma1 sector labourers, import and flows. [Equation (1 lo).] Capital outflows or, to come from or go to the government who them. They are treated in the same manner the historical amounts are fixed.

income export usually, subseas tax

G. h4cMahon. Income distribution and Kenyan coffee marketing system

305

Government revenues are then allocated according to the values given by the 1976 Kenyan SAM, subsequently modified by Vandemoortele (1987). [Equations (111) to (126).] The portion which each individual receives depends upon their sector, with, by and large, urban dwellers getting a larger portion than rural dwellers. The historical demand for government services for each income group was also calculated using the 1976 SAM.

2.1.4.

Savings,

investment,

and consumption

With one exception, all of the saving is done by the capitalists who, subsequently, do all of the investment in capital goods and receive the return on new investment. [Equation (127).] As interest rates were kept artificially low in Kenya, the only constraint on investment seems to have been the availability of savings. Therefore, it is assumed that investment simply equals savings. [Equation (128).] The one exception to the above is the investment in new coffee or tea hectarage. The wages of the labourers who convert the land over or who tend the immature hectarage are paid for out of the savings of the smallholder or plantation owner. [Equations (129) to (132).] The investment function for capital goods is a two-stage composite commodity function combining domestically produced building and construction and manufacturing output and imported goods. [Equations (133) and (134).] The demand functions for the 16 consumer groups are derived from common CES and two-stage CES utility functions.12 For other agriculture and manufacturing consumption there is a second stage, consisting of a domestic and a foreign component, similar to investment. [Equations (279) to (310).] Note that this formulation of consumption allows for both imports and exports of manufactured and other agricultural goods.

2.1.5.

Utility and werfare

The utility of each consumer group can be calculated by substituting their consumption back into the utility functions. [Equations (311) to (326).] To compare the simulation results with the historic amounts, the method of equivalent variations is used. The equivalent variation (EV) for each group is the change in income, positive or negative, that would have been necessary at the old pre-simulation prices to leave an agent with the post-simulation utility level.13 [Equations (327) to (342).] In the simulations emphasis will be placed upon the EV’s as a percentage of historic income that result from any “-Only 9 of the 1 I goods in the model are used in final domestic consumption. All coffee is exported, while the output of the building and construction sector is only used as an intermediate good and for capital formation. 13For an explanation of equivalent variations, see Just, Hueth and Schmitz (1982, pp. 84-94).

306

G. McMahon,

Income distribution and Kenyan coffee marketing system

policy experiment, although will also be given.

2.1.6.

The foreign

in some cases the actual

(real) monetary

figures

sector

Equations (343) to (348) represent the foreign sector. All coffee production is exported as is any excess of tea supply over domestic demand. Exports of other agriculture, manufacturing, and private services depend upon the relative prices of domestically produced and foreign goods as well as the values of the export elasticities. Imports of intermediate other agricultural and manufactured products have already been calculated from the inputoutput matrix. Imports of other agricultural and manufactured consumer goods have already been determined from the composite commodity functions. As all groups, including the government, are constrained to their budgets, the balance of payments must equal zero. [Equation (348).] 2.2. Computational

methods

and within period

equilibrium

For an equilibrium to exist within a period, supply must equal demand for every product and every factor of production. As the supplies of coffee and tea both equal demand in every period by definition, we have nine goods markets, urban formal and informal sector labour, rural labour, seven different capital stocks, and seven different land stocks for which supply must equal demand. Although it is trivially true that supply equals demand for the 14 fixed factors of production ~ the capital and land stocks - when we begin the computational procedures, we must begin with a vector of prices as we do not know what the returns on these stocks will be.14 In practice we use the assumptions of zero-profit conditions, constant returns to scale, and cost minimization to greatly reduce the dimensionality with a vector of factor prices, the factor of the problem. ’ 5 By starting requirements per unit of output can be calculated. These requirements can then be used with the coefficients of the input-output matrix to solve for the prices of the commodities. Moreover, the incomes of the various groups can also be solved directly given their endowments and the factor prices. Given foreign prices, the consumption, export, and investment demands are solved for. Intermediate demands are then solved for by using the input-output matrix again. Given the demands for each good the factor requirements can r4The ‘price’ for each of these stocks is its marginal revenue product. That is, in a trivial sense, demand for each fixed stock equals supply at the MRP, which can be calculated at the new equilibrium. As you do not know these MRP’s at the beginning of the simulation, you must guess at them. Note that the problem is greatly simplified if capital and land are mobile within each period as the dimensionality of the problem is then reduced by 12. 15The solution procedure is similar to the one used by Whalley (1984).

G. McMahon,

Income distribution and Kenyan coffee marketing system

307

be determined using the ratios calculated at the beginning. In equilibrium the demand for each factor must equal its supply. This procedure reduces the dimensionality from 27 to 18. However, as coffee and tea prices are given internationally and the hectarages are fixed each year, coffee and tea output ~ neither is used as an intermediate good - can be solved for immediately as can the MRP’s of land in coffee and tea. The dimensionality is now reduced from 18 to 12. Finally, as the amount of government revenue to be redistributed is not known at the beginning of the simulation but must be known to get consumption demands, a ‘best guess’ of it must also be entered in the price vector, increasing the dimensionality to 13. Therefore, a vector of 13 ‘prices’ is entered at the beginning of the simulation and the above computations are performed. If any of the excess demand equations are non-zero (or not very close to zero), a new set of vector prices, as calculated by using a linearized method of the NewtonRaphson method, is entered. In practice this solution procedure is very quick, usually calling for only five or six iterations per year for major policy changes. 2.3. The dynamic

adjustment

equations

The last group of equations are those which link the 21 years of the study together. These are the rural-urban migration equation, the investment allocation equations, the labour force growth equations, and the land reallocation equations. Similar to the within-period equations, all of the dynamic adjustment equations are calibrated each year to fit the data perfectly. For example, though rural-urban migration depends upon the differences in expected urban and rural incomes, a simulated migration will also depend upon the degree of responsiveness to the income gap in the historical situation. That is, if in a given year a relatively large rural-urban income differential resulted in a small migration, a widening of this gap would only have minor effects on the migratory flow, and vice-versa. The rural-urban migration equation is similar to the standard Todaro equation in which a portion of the labour force will migrate each year if the urban wage, adjusted for the probability of employment, is higher than the rural wage. l 6 [Equation (349).] The allocation of investment among the various sectors is a two-step process. First, it is divided between the rural and urban sectors. This division depends upon both the average rate of return of capital in the two sectors and the share of each in the aggregate capital stock. The effects of changes “%ee Todaro (1969). In practice the comparison is between the average income of landless rural labourers and a weighted average of urban and informal sector incomes, all of which include redistributed government revenue. The weights used are based on the increases in urban formal and informal sector employment from the previous year.

308

G. MeMahon.

Income distribution

and Kenyan coffee marketing

system

in the rates of return of the capital stocks on the distribution of new investment depend upon the amount of sensitivity that the historic allocation of investment showed to differences in the rates of return. After this division is completed, the urban sectors are each allocated the same share they received historically (while other agriculture receives the entire rural share). [Equations (350) to (358).] Though the total amount of land was fixed, land could be transferred from other agriculture to coffee or tea. (As land in Kenya is very rarely if ever transferred from coffee or tea to other agriculture, in the simulations the movement of land is undirectional.) Similar to investment allocation, the rate of land transfer will depend upon the rates of return in the various sectors, and they will only differ from the historical amounts if in the simulations the differences in rates of return of coffee, tea, and other agriculture are smaller or larger than historically.17 There is a problem with this formulation, however, in that new coffee tree planting was not allowed from 1964 to 1972 and 198 1 to 1984, which means that we have no data to calibrate. To circumvent this problem, we have assumed: (i) if it had been allowed, responded in the same (ii) if it had been allowed, responded in the same

coffee tree planting from manner as it did from coffee tree planting from manner as it did from

1964 1973 1981 1978

to to to to

1972 would 1975; and 1984 would 1980.

have have

That is, the average value of the calibrated parameters for 1973 to 1975 are used from 1964 to 1972, and the average of the calibrated parameters for 1978 to 1980 are used from 198 1 to 1984. (The values for 1976 and 1977 are excluded in both instances as these were the years of very high coffee prices and would likely give unsuitable parameters for a more common range of prices.) In sum the possibilities of land reallocation are as follows:

(4

in Zone I land can be switched from other agriculture (ii) in Zone II land can be switched from other agriculture (iii) in Zone III land can be switched from other agriculture coffee; and

to coffee; to tea; to either tea or

17The rates of return are the average rates over the past four years for the respective crops, adjusted for the loss of revenue and costs incurred in the non-productive four-year gestation periods for coffee and tea. That is, the present values of coffee, tea, and other agriculture are compared with the expected future returns being the average over the last four years. Taxes and other restrictions are assumed to remain as they were in the decision making year. Farmers use a real discount rate of 3%. The rates of return over the past four years are used as Maitha (1969) found that a coffee tree planting function which included the prices of the last four years gave the best results. As, to the best of my knowledge, no similar study has ever been done on tea planting in Kenya, the four-year time span was somewhat arbitrarily chosen for tea, also.

G. McMahon.

309

Income distribution and Kenyan coffee marketing system

(iv) in Zone IV no reallocation of land is possible, as conditions do not allow for the growing of tea or coffee. In the first three cases there is, of course, a direct investment cost and a gestation period. [Equations (359) to (362).] Estimates were made of the 1964 urban and rural labour forces (as described in the appendix). Each year the labour forces grew at the same rate as population - 3% in rural areas and 2.5% in urban areas - plus or minus any rural-urban migration, implying that participation rates were constant. [Equations (363) and (364).] To complete the model all that remains to do is to add (or subtract) new investment and land reallocation to the stocks which existed at the beginning of the year. [Equations (365) to (379).] Then the model is run again for the next year, and so on until the final year, 1984, is reached. 3. The simulations The simulations are divided into two groups. In the first group the gains or losses to Kenya of being a member of the International Coffee Agreement are investigated. The second group of simulations - the major focus of the study - will examine the income distribution effects of the method by which Kenya managed the quotas it received under the agreement. There will be two subgroups of simulations in the second group, each of which will depend upon the possibilities (and control of) land convertibility. Table 3

Table Description Simulation

no.

Descriution

3

of the simulations.

and assumotions

I.

There is no International Coffee Agreement historic level. New planting is allowed.

2.

The absence of an International Coffee Agreement causes coffee prices fall 10% below their historic levels. New planting is allowed.

3.

Same as no. 2 except

4.

Kenya manages its international government increases smallholder

but coffee prices remain

at their to

coffee prices fall by 20%. coffee quota by a coffee export tax. The coffee hectarage by 10% per year.

5.

Same as no. 4 except

coffee hectarage

is increased

by 5% per year.

6.

Same as no. 4 except

coffee hectarage

is increased

by 15% per year

7.

Kenya manages its international coNee quota by a coffee export tax. Coffee smallholders are free to vary their coffee hectarage as they choose but tea hectarage is fixed.

8.

Same as no. 7 except tea hectarage can also vary. However, cannot be replaced by tea hectarage and vice versa.

9.

Same as no. 8 except coffee hectarage vice versa.

can be replaced

coffee hectarage

by tea hectarage

and

310

G. McMahon,

Income distribution and Kenyan coffee markeling system

describes the nine simulations for ease of reference. In the last subsection we will discuss the sensitivity of the results to different assumptions about the values of the parameters. In the simulations which follow, the emphasis will be on the equivalent variations (EV) as a percentage of income for each income group and for the entire nation. These will be referred to as the percentage equivalent variations. However, when the results of this much larger model are compared with those of the smaller model in McMahon (1987), the reference will be to the percentage change in GNP, as there were no comparable income classes in that model and EV’s were not evaluated.

3.1. The benclfi‘ts

ef the

international

coffee

agreement

In this group of simulations we will be examining the gains or losses to Kenya of being a member of the International Coffee Agreement under various assumptions as to what the price of coffee would have been without an agreement. Similar simulations were the main focus of McMahon (1987). In the first simulation we assume that the price of coffee would have been the same from 1964 to 1984 whether or not the International Coffee Agreement existed. That is, we assume that the agreement had no effect on coffee prices. In the second and third simulations, it is assumed that coffee prices would have fallen by 10% and 20%, respectively, if no agreement had existed.‘* Some of the results are given in table 4. In all three cases the average percentage change in GNP was more favourable for Kenya than in the previous results, whether we compare the 1964 to 1979 or 1964 to 1984 Table Comparison

of simulations

4

no. 1 to no. 3 with the historical

data.’

Simulation

GNP :‘{, change 19641979 1964-1984 EV as a 7; of total 19641984

No. 3 PC=O.8

No. 1 PC=HPC*

No. 2 PC=0.9HPC

(a)

(b)

(a)

(b)

3.68 3.75

2.23

1.02 1.03

0.00 _

2.79

_

0.68

(a) -1.30 -1.33

HPC (b) - 1.58 _

GNP - 1.27

_

“(a) refers to the results of this study; (b) refers to the results of McMahon (1987). PC refers to the hypothetical coffee prices if the International Coffee Agreement had not existed, and HPC refers to the historic coffee prices; the simulations are explained in the text. isThese price drops are over the entire period, as if there had been no agreement from 196& 1972 it is likely that there would have been more coffee hectarage on a global basis in the 1970’s and 1980’s and, hence, lower prices.

G. McMahon.

Income distribution and Kenyan coffee markering syslem

311

time periods. The changes in GNP are also affected considerably more by the assumed price of coffee in the absence of the agreement. While the general nature of the results are similar, we feel that the differences, at least in the first two simulations, are significant. Although it is difficult to disentangle the various relationships in the model, two new aspects of the present model may have been important. First, the inclusion of an inputoutput matrix with intermediate imports increased the importance of coffee as a foreign exchange earner. In the third simulation, where it was assumed that coffee prices fell by 20%, the increase in the quantity of coffee due to the new planting from 1964 to 1972 was offset by this drop in prices, resulting in little change in foreign exchange. Second, the number of income classes was expanded from four to sixteen, resulting in less of a need to aggregate consumption data. As the gainers in simulations 1 and 2 are the poorer income groups with much higher marginal propensities to consume domestic goods, the effects of the income increases were much stronger than in the previous model in which agricultural labourers, landowners, and plantation owners were all lumped together. The new model also allows us to obtain a more detailed picture of the winners and losers within Kenya from its membership in the International Coffee Agreement. The total EV as a percentage of GNP was, on average plus 2.79% per year in simulation 1. This figure by itself does not reveal the large distributional changes which would have taken place if there had been no agreement and the price of coffee had remained the same. The two largest rural income classes, 9 and 11, consisting of farmers in Zones I and III with neither coffee nor tea historically, would have had percentage EV’s of plus 7.3% and plus 7.1%, respectively, while coffee farmers would have average percentage EV’s of -7.6%. (Before we continue, we should point out that we are comparing the utilities of farmers who never had any coffee or tea in the historic data with their situation in the simulation, whether or not they ended up with coffee or tea. While this procedure involves a fairly complicated process of ‘following the farmers around’ through the years of the study, it is, of course, the relevant comparison.) In addition, income classes 10 and 12, other agricultural farmers in (non-coffee) Zones II and IV, and income class 13, rural landless labourers, had positive percentage EV’s of 2.7%, 2.7%, and 3.0%, respectively. These increases were due to the increase in rural wages ~ coffee uses more labour per hectare than other agriculture - and the increase in the return to other agricultural land. The supply of other agricultural products decreased as land was changed over to coffee, while its demand increased due to the higher incomes. Although the above distributional effects are already quite large, if we examine the results in the last half of the study, from 1975 to 1984, the results are more dramatic as most of the land which had been converted to coffee would have been bearing fruit by 1975. The predicted average

312

G. McMahon.

Income distribution and Kenyon coffee marketing system

percentage EV’s for income classes 9 through 12, other agricultural farmers in Zones I to IV, are ll.O%, 3.3%, 10.2%, and 3.3%, respectively. Simulation 1 is, of course, an unlikely case, but it serves as a useful upper bound. In the next two simulations the gains of different income classes are smaller (or the losses are larger), but the same genera1 pattern emerges. That is, farmers without tea or coffee were hurt relatively more or helped relatively less by the International Coffee Agreement. For example, if the price of coffee had fallen by 20% in the absence of an agreement, members of income classes 9 to 12 would have had average percentage EV’s of 0.9%, -0.6”/,, 0.8x, and -0.6x, respectively, while income classes 1 and 3, coffee smallholders, would have had percentage EV’s of - 18.4% and - l&l;/,, respectively. 3.2. The income distribution new cqffee trees

effects

of u cojfee

tax versus a ban on planting

The simulations in this subsection are the major focus of the study. A major result of McMahon (1987) was that Kenya would likely have been better off if it had controlled coffee output from 1964 to 1972 by means of a coffee tax rather than a ban on planting new coffee trees. Although the gains in GNP were in the order of 1% on average, Kenya would have had a much larger stock of coffee trees in 1980 when new quotas were negotiated. As the allocations were largely based upon the last three year’s average output, Kenya would have received a higher quota. Moreover, it was also hypothesized that there may have been significant distributional effects of the ban on coffee planting, but the data needed to test this proposition was lacking. In the simulations which follow the emphasis will be on the welfare changes, as measured by percentage equivalent variations, to the original members of the 16 different income classes.19 We shall assume that Kenya’s quota from 1981 to 1984 under simulated tax-controlled outputs would have been equal to the average output from 1976 to 1978.20 In addition, different assumptions will be made with regards to the possibilities of changing land into coffee or tea production. We believe these distinctions are necessary as the expansion of coffee and tea hectarage is very closely controlled by the government. That is, even in non-quota years a smallholder cannot expand his coffee or tea hectarage at will but is dependent upon both the total ‘“As we are not investigating the personal distribution of income, Gini coefficients or similar measures are not computed. That is, our income classes are not formed by taking the poorest IO%, next poorest 10%. and so on, but they are groups of people owning the same types of factors of production. Note, however, that we are not exactly dealing with the functional distribution of income, either, as the first 12 income groups own both land and labour. 201n practice, the simulation is run with different coffee taxes being put in place from 1964 to 1972 and 1981 to 1984. These taxes are adjusted until the ‘correct’ amount is approximately produced in each year that quotas are in effect. The correct amount from 1981 to 1984 is the average simulated amount from 1976 to 1978.

G. McMahon,

Income distribution and Kenyan coffee marketing system

313

supply of seedlings the government is issuing and the government’s allocation of these seedlings. Therefore, it is never certain that a change in tea or coffee relative prices in a given year would have resulted in a different amount of new planting.21 In simulations 4 to 6 it is assumed that the government would have increased coffee hectarage lo%, 5%, and 15% each year from 1964 to 1972, respectively (and it is implicitly assumed that it would have found willing smallholders), while coffee expansion in other years and tea expansion in all years is set at the historic levels. In simulation 7 coffee expansion in all years is assumed to be based upon the rate of return of coffee relative to other agriculture while tea expansion is fixed at its historic levels. In simulation 8 the expansion of coffee and tea both depend upon their rates of return relative to other agriculture. In simulation 9 the expansion of coffee and tea depend upon their rates of return relative to other agriculture and, in agricultural Zone III, relative to each other. That is, as coffee and tea compete for land to some extent in this zone, an increase in the relative return to tea would result in less new coffee hectarage, ceterus paribus. Before the results of the simulations are examined, we would like to emphasize that, though theoretically it is less appealing than simulations 7 to 9, we believe that simulation 4 is closest to the Kenyan reality for two reasons. First, the Kenya Tea Development Authority had a long-term planting strategy which was unlikely to be affected by the change in the return to coffee or other crops. Second, coffee expansion in Kenya seems to have been limited only by the availability of the government provided seedlings. As in non-quota years smallholder hectarage increased by approximately 10% per year regardless of the relative price of coffee, it seems likely that this figure would likely have been the government’s target from 1964 to 1972 if new planting had been allowed. It should also be mentioned that the Kenyan government has always refused to allow uncontrolled coffee expansion because of its concern with the average quality produced - Kenya produces a very high grade coffee on average - as well as with the control of coffee pests and diseases.

3.2.1.

Smallholder

coffee

hectarage

increased

by the government

In simulations 4 to 6 the government increases coffee hectarage by lo%, 5%, and 15% per year from 1964 to 1972, respectively, while the changes in all other years are assumed to be the same as they were historically. In order 21Government involvement was especially important in the case of tea. Development Authority was founded in 1960 to promote, urge, and assist holders and estates - to move into tea production. It is extremely unlikely that anywhere near its present prominence on international tea markets - the third without this government intervention.

The Kenya Tea farmers - smallKenya would be largest exporter -

314

C. McMahon,

Income distribution and Kenyan coffee marketing system

Table Comparison

of simulations

Yearly increase coffee hectarage

data.”

Simulation ~~ ~. No. 4

No. 5

No. 6

5”’ 1”

15%

in smallholders (19641972)

GNP,

7, change

Total

EV as a “:, of GNP

10%

(1964-1984) (1964-1984)

Average El/ as a T<, of income each income class (1964-1984) Income 1 2 3 4 5 6 7 8 9 IO II 12 13 14 15 16

5

no. 4 to no. 6 with the historical

3.71

1.66

6.42

2.78

1.26

4.74

- 10.91 0.29 - 10.40 1.09 -0.71 2.30 - 1.31 2.41 7.17 2.82 6.91 2.82 3.08 1.89 2.67 0.03

-4.86 0.08 -4.53 0.43 - 0.45 0.99 -0.40 1.17 3.40

- 16.43 0.90 - 15.82 2.24 - 0.66 4.14 -2.01 4.17 Il.14 5.15 II.00 5.i5 5.27 3.54 4.71 -- 0.06

for

class

“For an explanation

of the simulations,

1.23 3.23 1.23 1.38 0.83 1.16 -0.02

see the text.

to ensure that Kenya does not produce more than its international quota, in all quota years - 1964 to 1972 and 1981 to 1984 - an adjustable coffee tax is implemented. That is, at the same time as the government is providing new seedlings and allowing coffee hectarage to increase, it is taxing coffee at the rate that will keep yields at a level consistent with its international quota. (This taxation procedure is used in all of the simulations to follow.) Some of the results of simulations 4 to 6 are given in table 5. In all three cases there are gains in GNP and positive total EV’s associated with using a tax system to regulate output rather than an internal quota. These gains are largely due to an improved allocation of resources, the much bigger stock of coffee trees Kenya would have had in the 1970’s when coffee prices were relatively high, and the larger coffee quota Kenya would have received in the 1980’s. The average gains in GNP for simulations 4 to 6 are 3.71%, 1.66%, and 6.42%, respectively, while the average total percentage EV’s are 2.78%, 1.26%, and 4.74%. Note that the model is incapable of capturing the effects of the shifting of supply towards least cost suppliers, but it does capture the movement towards a higher land-labour ratio in coffee and the converse in

G. McMahon,

Income

distribution

and Kenyan coffee markefing

sysrem

315

other agriculture.22 Although the welfare and income gains to Kenya are the largest in simulation 6, we do not believe that the assumed 15% per year increase in coffee hectarage would have been likely for two reasons. First, even in coffee boom years smallholder coffee hectarage never expanded by more than lo%, probably as the Kenyan government, which controls the supply of seedlings, lacked the capability of a greater expansion.23 Second, we shall see below in simulations 7 to 9 that smallholders, if left to their own devices, only increased coffee hectarage by 7% to 8% per year from 1964 to 1972.24 As in the first three simulations, the strongest effects were the distributional ones. (See table 5.) The largest gains went to farmers in coffee zones who did not have coffee historically, but other agricultural farmers in non-coffee areas and landless rural labourers also would have benefited substantially from this policy change. In simulation 4 the average percentage EV’s of income classes 9 to 13 were 7.2%, 2.8%, 6.9%, 2.8%, and 3.1%, respectively. These large increases were mostly due to three reasons. First, a large number of the farmers in income classes 9 and 11 would have moved into more lucrative coffee production - approximately 10% of both income classes. Second, other agricultural farmers would have seen the relative price of their crops rise due to their diminished supply - as land was transferred into coffee production - and the increased demand for their goods. The greater demand was due to a higher level of aggregate income and a transfer of income to lower income groups with higher marginal propensities to consume domestic agricultural products. Third, the rural wage rose as coffee production uses much more labour per hectare than other agriculture. This wage increase resulted in a 3.1% percentage EV for landless rural labourers, one of the poorest groups in society. On the other hand, the richer coffee and tea farmers - income classes 1, 3, 5, and 7 ~ would have suffered losses. Coffee farmers had the largest welfare drops, - 10.9% and - 10.4% for classes 1 and 3, respectively, as they had to

ZZIn simulations 7 to 9 this effect is partially captured as the region with the highest relative return will increase coffee hectarage by a greater amount. In simulations 4 to 6, however, it is assumed that the government increases coffee hectarage by the same amount in both sectors. 23This limit of 10% on coffee tree expansion is similar to the skill limit used by Chenery and Strout (1966). 24This second reason may be only partially valid for three reasons. First, our smallholder decision function is calibrated according to historic reactions, which were probably held in check by the 10% seedling limit referred to above. Second, an important amount in the decision function is the cost of tending the immature coffee trees, a cost which may be very low if there is under-employed secondary labour available. Due to a lack of data each household is assumed to consist of one labourer. By 1974, the latest year for which a figure exists, smallholders hired only 10% of their labour input (up from 5% in 1963.) [See Collier and La1 (1986, p. 94).] Third, although we are using estate wages as a proxy for the rural wage, in 1974 wages paid to hired labour on smallholdings were 18% below estate wages, according to the Integrated Rural Survey, I.

316

G. McMahon, Income distribution and Kenyan coffee marketing system

Table Equivalent

6

variations as a percentage of selected simulations: 19761984.”

income

for

Simulation Income

class

I 2 3 4 5 6 7 8 9 IO II I2 13 14 I5 I6

No. 4 ~ 11.12 0.33 - 10.95 I .05 - 1.48 3.30 - 2.34 3.46 12.13 3.72 10.69 3.72 3.30 3.20 3.88 0.13

No. 5 -4.97 0.06 -4.82 0.46 -0.81 1.40 - 0.95

1.46 5.67 I S8 4.98 1.58 I .46 I .32 I .39 0.07

“See text for an explanation

No. 7 - 8.45 0.17 - 8.28 0.72 -1.21 2.38 - 1.74 2.58 9.18 2.67 8.09 2.67 2.43 2.27 2.81 0.10

No. 8 - 8.04 0.28 - 7.89 0.76 - 3.24 8.32 - 2.68 9.47 9.09 2.18 7.84 2.29 2.3 I 2.28 2.74 0.08

of the simulations.

pay a higher wage to their hired labour and pay the coffee tax in the quota years. Note that while the strongest distributive effects were within the smallholder sector, there was a small relative movement in favour of the rural areas versus urban areas as all urban groups had increases in welfare below the national average. It should also be stressed that the only groups that would have seen their absolute positions deteriorate were the coffee and tea smallholders with mature hectarage, a result which could have had significant consequences with regards to the Kenya government’s willingness to implement such a policy, given the political power of these groups. If a more equitable distribution of income was a long-term goal of the Kenyan government, a coffee tax policy would probably have had very significant effects. If we examine the gains to income classes 9 to 13 in the last nine years of the study, 1976 to 1984, the earlier results are magnified, as by 1976 almost all the new coffee trees would have been mature. (See [table 6.) From 1976 to 1984 the average percentage EV’s of classes 9 to 13 were 12.1%, 3.7%, 10.7%, 3.7%, and 3.3%, respectively. 3.2.2.

Planting

decisions

are based

on present

values

In the simulations in this subsection planting decisions are based upon the present values of coffee and tea (over the 30-year lifespan of the trees) relative to other agriculture and, in simulation 9, to each other. Although this methodology is theoretically much more satisfactory than government

G. McMahon.

Income distribution and Kenyan coffee marketing system

Table Comparison

of simulations

317

7

no. 7 to no. 9 with the historical

data.”

Simulation

GNP,

% change:

1964-1979 1964-1984

Total

EV as a % of GNP

(19641984)

Average EV as a % of income each income class (19641984) Income 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

No. 7

No. 8

No. 9

2.69 (0.64)b 2.73

_

_

2.61

2.62

2.06

1.93

1.95

-8.31 0.16 -7.87 0.74 -0.62 1.66 -0.91 1.81 5.47 2.04 5.20 2.08 2.26 1.36 1.95 0.03

- 7.92 0.29 -7.49 0.79 - 3.27 6.36 -2.41 6.40 5.40 1.63 4.99 1.96 2.15 1.37 1.88 0.00

- 7.95 0.27 -7.51 0.78 -3.02 5.89 - 2.05 5.64 5.42 1.79 5.08 2.01 2.18 1.37 1.89 0.00

for

class

“Result of similar experiment in McMahon (1987). hFor an explanation of the simulations, see text.

controlled new plantings, it has its own shortcomings. The most serious one is that, if historically the demand for coffee and tea seedlings was only limited by the government-issued supply of them, the calibrated parameters will underestimate the true reaction of the smallholders. With this warning in mind, let us look at the results of the three simulations. In simulation 7 coffee planting decisions in all years are based on the present value of coffee compared to other agriculture, while tea hectarage is assumed fixed at the historic levels. In simulation 8 both coffee and tea planting decisions are based on their present values relative to other agriculture. In simulation 9 coffee and tea planting decisions in Zone I and Zone II are the same as in simulation 8, but in Zone III these decisions are also based upon the present value of coffee compared to tea. Some of the results of simulations 7 to 9 are given in table 7. Two points are readily apparent. First, the results of the three simulations are very similar. The inclusion of tea in the decision set does not have much of an effect on the results, though in simulations 8 and 9 smallholder tea hectarage

J.D.E.

D

318

G. McMahon.

Income distribution and Kenyan coffee marketing system

does fall by 14% and lo%, respectively. 25 Second, the results lie in between those of simulations 4 and 5, which is to be expected as smallholder coffee hectarage rose by between 7% to 8% per year from 1964 to 1972 in the three simulations. We would like to stress the point that if smallholder expansion was restricted historically (i.e., in the 1979’s) by either government policy or, more likely, the availability of coffee seedlings, these simulations would underestimate the true smallholder response. As the results of these simulations are very similar to the previous set, we will only comment upon them briefly. A comparison of tables 6 and 7 shows that the income distribution effects are larger in the 1976 to 1984 time period. For example, the percentage EV’s of income classes 9 to 13 in simulation 7 are 5.5%, 2.0%, 5.2%, 2.0%, and 2.3%, respectively, from 1964 to 1984 and 9.2%, 2.7%, 8.1%, 2.7%, and 2.4%, respectively, from 1976 to 1984. The percentage of Zone I and Zone III smallholders who become coffee farmers are 7.2% and 6.6%, respectively, in simulation 7. The only major difference between these simulations and the previous ones are that in simulations 8 and 9 tea farmers, income classes 5 and 7, would have suffered a larger negative percentage EV as, given the calibration procedure used, some of them would have decided, unwisely as it turned out, to remain other agricultural farmers. (See note 25.) Note that the yearly percentage change in GNP from 1964 to 1979 was much larger in simulation 7 than in the comparable simulation in McMahon (1987). (2.69% versus 0.64%). The reasons are likely the same ones we proposed for the first two simulations in section 3.1, above.

3.3. The sensitivity

unalysis

The sensitivity analysis consists of three parts. In the first part, one group of elasticities is changed from those used in the main runs while the others are held constant. For example, the elasticities of substitution in production are all varied from l/6 to 2, holding all of the other elasticities at their base values.26 In the second part, all of the elasticities are changed in the same direction. That is, substitution is made either more or less elastic in all phases of economic activity. In the third part, the value added per hectare of coffee relative to other agriculture is varied in order to assess the impli2SThe reduction in tea hectarage is largely due to the increased labour costs during the immature years. Given the calibrated historical planting figures, a rise in planting costs must result in a decline in hectarage. If tea planting is controlled by the government, then the calibrated figure may underestimate the true smallholder response and the rise in labour costs might have no effect on new planting. *6The base values for the various elasticities were as follows: in final production - 2/3; in intermediate production ~ I; in final consumption - 3/4 for income classes 1 to 13 and 16 and I l/4 for income classes 14 and 15; in composite commodity consumption ~ I; and in exports 2.

G. McMahon.

Income distribution and Kenyan coffee marketing system

319

cations of this very crucial assumption. (Note that the sensitivity analysis will only be reported for simulation 4 as it is very similar for all simulations.) When the investment, final consumption, or consumption composite commodity elasticities are changed, there are no significant differences in any of the results. (By a significant difference, we mean that the percentage EV does not change by more than 3/10 of 1% for any of the 16 groups and the total percentage EV does not change by more than 2/10 of l%.) The same result occurs when the intermediate composite commodity elasticities are assumed to be between l/2 and 1 l/2. If substitution between imported and domestic intermediate goods is very difficult, however, there are significant losses to coffee smallholders, tea smallholders, and urban formal sector labourers. For example, if this elasticity is set equal to l/4, the percentage EV’s of these groups are all between 2/5 and 3/5 of 1% less than in the base run, while the total percentage EV falls by 0.32 of 1%. An explanation for these results is not obvious to us, although we will give a tentative one. With increased export earnings resulting from greater coffee production, the demand for goods to be used in intermediate production rises. If domestic and imported intermediate goods are poor substitutes, there is upward pressure on the price of domestic goods. Therefore, the income classes which do not directly own any goods used in intermediate production - i.e. coffee and tea smallholders and urban labourers ~ will suffer losses, all other things equal. Note that when substitution between intermediate goods is assumed to be easy, these same income classes do significantly better than in the base run. When the elasticities of substitution in production are changed there is never any significant change in the total percentage EV - it is always within l/20 of 1% of its base value - but there are often very large distributional effects. For example, if they are set at l/4, implying substitution is very difficult, coffee and tea farmers and capitalists suffer very significant drops in utility, while landless rural labourers and urban formal sector labourers have equally significant gains. In comparison to the base run the percentage EV of the former groups falls anywhere from 1.9% to 3.7%, while rural and urban labourers have gains of 1.2% and 1.8%, respectively. If substitution in production is made less difficult, the gains and losses are similar, although somewhat smaller, but in the opposite direction for the various income classes. The explanation of these results is relatively simple. With the increase in incomes in the simulations, there was an increased demand for goods and, therefore, labour. The more difficult substitution in production was assumed to be, the greater was the pressure on rural and urban wages. The most profound differences occur when the export elasticities are changed. When incomes rise in the simulations, there is upward pressure on domestic relative to foreign prices. The higher these elasticities are the

320

G. McMahon.

Income distribution

and Kenyan

Table The sensitivity Assumed value added per hectare of coffee/ value added per hectare of other agriculture Total

EV as a 7; of GNP

Average selected

EV as a % of income for income classes ( 19641984)

Income 1 3 9 10 11 12 13 16

class

system

8

analysis,

(1964)

(19641984)b

coffee marketing

part

3.”

2.5

3.0

3.5

4.0

2.25

2.78

3.39

4.0 1

- 9.02 - 8.56 5.94 2.25 5.66 2.29 2.41 0.05

- 10.91 - 10.40 7.17 2.82 6.91 2.82 3.08 0.03

- 12.81 - 12.36 8.50 3.49 8.23 3.49 3.17 0.05

- 14.63 - 14.04 9.78 4.20 9.54 4.20 4.45 0.00

“The results given are for simulation no. 4. ‘See the text for an explanation of the table

greater will be the loss of exports when domestic prices rise, and, therefore, the smaller the gains in GNP and total EV. The total percentage EV varied from 5.16% when the elasticities were set at a very low value of l/2 to 2.78% at our base value of 2 to 2.09% when the elasticities were set at a very high value of 5. In general, tea smallholders were not affected by the values of these elasticities, while every other income class benefited or suffered together except for coffee smallholders. They benefited when others suffered and vice-versa, largely because of the effect on domestic other agricultural, private services, and manufacturing prices, all of which they consume large amounts of but for which they have little claim to the returns from. Obviously the values of these elasticities are very crucial. Given the small size of Kenya, it would seem likely that they would be on the high side, especially as over 80% of Kenya’s export earnings on products other than coffee or tea are from agricultural goods or tourism, both highly competitive industries, When all of the elasticities are varied - except the dominating export elasticities - the total percentage EV only changes significantly for very large movements; i.e., if the elasticities are assumed to be l/5 or lower. However, there are often large distributional effects, similar to but larger than those when only production elasticities were changed. For example, if all of the various elasticities are set equal to l/4, the changes in percentage EV relative to the base run are -5.27; for coffee smallholders, -3.1% for tea smallholders, - 1.1% for capitalists, 1.3% for landless rural labourers, and 1.4% for urban formal sector labourers. These changes are largely due to the increased pressure on wages and domestic food prices, discussed in the first part of this subsection. Note that there was no change to the percentage

G. McMahon,

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EV’s of smallholders in the non-coffee zones. Although they must hire rural labour and consume domestically produced food, they also own both of these commodities. In the base run it was assumed that the value added for a hectare of coffee in 1964 was three times that of other agriculture. We indicated that this estimate was probably on the low side. The simulations were also run using the assumptions that the value added for coffee was 2 l/2, 3 l/2, and 4 times that of other agriculture in 1964. Table 8 contains some of the results for simulation 4 as this ratio is varied. Of course, the gains to Kenya and, especially, income classes 9 to 12 of using a coffee tax versus a ban on new planting are greater the larger is this difference. Obviously, it is very important for the Kenyan government to get more accurate data on the relative value added of future, perhaps less suitable, land which may be put into coffee. 4. Conclusions

and recommendations

The major conclusion is that the use of a coffee tax to control output rather than a ban on new planting likely had major distributional effects. Four of the largest and poorest income classes, smallholders without coffee or tea, would have received very large gains, partly at the expense of the richer classes and partly due to the efficiency gains of the policy. That is, these welfare gains would not have been caused by the implementation of potentially economically inefficient redistribution policies but would have been natural consequence of more efficient management of the coffee quotas. It should also be stressed that the distributional effects were much larger than the effects on productivity. In addition, while most of the redistribution would have taken place within the smallholder sector itself, there would have been a small relative movement from the urban to the rural households. The gains to the other agricultural farmers were threefold. First, in Zones I and III a large number of smallholders became coffee farmers. Second, for those who remained in other agricultural production, prices rose due to the diminished supply and increased demand. Third, as coffee uses more labour per hectare than other agriculture, rural wages rose, and each smallholder is also a labourer. (Landless rural labourers also benefited strongly from this wage increase.) Moreover, we also saw that the long-term gains for these smallholders were larger than the over-all gains, as in the later years of the study most of the land which had been converted to coffee would have been bearing fruit. Despite the large distributional effects in our simulations, it is likely that they were underestimated for two reasons. First, a key assumption in the model was the valuation of coffee land relative to other agricultural land when determining the land stock (or total number of units of land.) In the

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main group of simulations our estimate of the value of a coffee hectare versus another agricultural hectare was very conservative compared to the relative values that others have calculated. In the sensitivity analysis we saw that there were much larger distributional gains if we increased the value added per hectare of coffee relative to other agriculture. Second, perhaps the most important conclusion of the recent book by Collier and La1 (1986), in which they study the historic determination of income distribution in Kenya from 1900 to 1980, is that a smallholder’s access to credit, whether institutional or from other sources of income, is instrumental with regards to capital investment and innovation and, therefore, future income. That is, farm and non-farm incomes are highly correlated. As coffee farmers, especially with initial government aid, are more likely to be able to build up surpluses, their non-farm income as well as the return on their other crops is likely to be higher than for non-coffee farmers. Our model separates the earners of farm and non-farm income, and, consequently, it likely underestimates the income gap between coffee and tea smallholders relative to other smallholders.27 An International Labour Organization study of nine African countries, including Kenya, also consistently found that smallholders who accumulated savings from cash crops were quick to reinvest these profits in non-farm activities.28 A secondary purpose of the simulations was to compare the present results with those obtained using a much more simple model in McMahon (1987). If our concern was solely with the productivity effects (and not the distributional effects) the evidence is not totally conclusive, but it is our belief that the results were sufficiently different to speculate that the smaller model may not have been totally adequate in its ability to capture the numerous side effects of Kenya’s coffee policy. One area where our expanded model performs unquestionably better (or differently) than the previous model is with regards to the sensitivity of the results to the assumed elasticities. Whereas in McMahon (1987) even large changes in these parameters had little effect on the results, we have seen that for some of the elasticities even relatively small changes can have significant effects.29 We believe these differences are due to the much greater scope for substitution in the present model. The larger model has illustrated the importance of making reasonable assumptions about the various substitution possibilities and, indirectly, the great need for more econometric work with regards to estimating the elasticities used in CGE models. “See Collier and La1 (1986. pp. 2555257). ‘*For example, in Malawi the wealthier a smallholder is, the greater the percentage of his income that comes from non-farm activities. See Ghai and Radwan (1983, p. 86). ZgThis statement is particularly true with regards to the export elasticities. In [McMahon (1987)] the domestic and imported other agricultural goods were perfect substitutes, ensuring that their prices were always equal. When we ran simulation 7 using this assumption, the differences in GNP fell from 2.69% to 2.02%, versus 0.64% in our previous work.

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It is also interesting to speculate on the failure of the government to use a tax system which would have been easy to administer and would likely have had substantial benefits, especially as Kenya’s fourth development plan (1979-1983) was almost totally devoted to the theme of the alleviation of poverty. Although the government may just have made a tactical error, we believe that it is likely that the political power of coffee farmers, many of whom are from the dominant Kikuyu tribe, was (and is) a very important consideration. Despite the fact that coffee farmers are one of the richest classes in Kenyan society, especially in rural Kenya, taxes on coffee have been almost non-existent except for a 3% county levy. Even in the boom years in the 1970’s, the coffee export tax was always below 3% and usually below 1%. Note also that our results indicate that not only would these groups have seen their relative positions deteriorate, but they would have suffered a large drop in their absolute income levels. ‘Since poverty in Kenya is overwhelmingly a rural phenomenon, its eradication is dependent upon equitable growth within the smallholder sector.’ [Collier and La1 (1986, p. 252).] Our simulations suggest that not only would a coffee tax system have had positive effects on rural income growth in Kenya, there would have been large distributional effects, which, perhaps more than any other single feasible policy, would have helped reduce the poverty referred to in the above quote. Before this statement can be made without any qualifications, though, some shortcomings of the present study have to be examined. First, more information is needed on the decision-making process for coffee and tea planting in Kenya. We have argued that the expansion of both of these crops was largely controlled by the government as there likely was a backlog of smallholders waiting to receive trees or plants. If we are correct, more information is needed on the government decision-making process and limits to expansion of these crops, given their special requirements. Second, for many farmers better access to credit may be necessary before they are willing to convert land to coffee or tea due to the long gestation period. Third, the costs of new planting to the smallholder need to be known with more precision. If there is idle or underemployed secondary labour, these may be very small. Fourth, and most important, the rates of return to alternative crops in different ecological zones need to be known more precisely. While it is undoubtedly true that the relative return to coffee in some areas is much larger than we have assumed, in other areas coffee farming is likely to be very marginal and would do little to alleviate poverty. In conclusion, we believe that the results, subject to the above qualitications, are a good illustration of the distributional effects of a quota system. While it is obvious that a quota system will almost always have distributional effects on a micro-scale, for a country which is very dependent on two or three crops we have seen that these effects may be large and far-reaching if

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most of the important linkages are taken into account. In the simulations the secondary effects - i.e. on the price of food and rural wages - are almost as important as the primary effects - i.e. on the stock of coffee hectarage.30 Such considerations should be taken into account by any country involved in the formation or expansion of an international commodity agreement in an agricultural product. Appendix

1: Data sources and adjustments

The bulk of the data come from either The Republic of Kenya: Statistical Abstracts, 1964-1984, The Economic Survey of Kenya, 1980-198.5. or Kenya Data Compendium: 1964-1982 by Vandemoortele (1984). The last document was extremely valuable as in it Vandemoortele reconciles the various changes in national accounting that have taken place in Kenya over the last two decades. The coefficients of the input-output matrix come from either the 1967 or 1976 Input-Output Table for Kenya. The former is used from 1964 to 1972 and the latter from 1973 to 1984. Some adjustments had to be made in the last seven years of the study as the amounts of imported intermediate goods predicted using the 1976 coefficients were much greater than the aggregate amounts given in the Statistical Abstracts. As the proportional amounts of inputs given by these abstracts never changed very much, it was assumed that there was a movement towards domestic inputs due to Kenya’s import substitution strategy and the two oil shocks. The data on the redistribution of government revenues were calculated using the 1976 SAM and modifications made by Vandemoortele (1987). The division into four different agricultural sectors was based on data collected for the Integrated Rural Surveys, 1974-1979, and the yearly report on land-use in the large farms sector found in the Statistical Abstracts. The coffee production function data was put together from Kenya Coffee Board Reports, 1964-1984, Njagi (1981) Kiara and Njagi (1982), and a survey carried out by the author on smallholdings in three different coffee zones. 200 farmers were interviewed in each zone with regards to their hectarage, output, prices received, crop composition, use of inputs, future coffee and tea planting plans, availability of coffee seedlings, and best alternatives to coffee and tea. The percentage of each smallholding allocated to coffee also comes from the same survey. “Although distributional also complains which makes 319).] Second, distributions productivity.

he uses a different methodology than we do, Ahmed (1982) in his large study of Kenya. came to at least two conclusions common to this work. First, he that a major limitation of his work is the lack of data on secondary labour, it difficult for him to obtain data on total household incomes. [Ahmed (1982, p. he finds the secondary effects of a government policy aimed at changing income are important only if the primary effects of the policy were to change rural [Ahmed (1982. pp. 298-303).]

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The tea data come largely from the Kenya Tea Development Authority Annual Reports, 1964-1984, including the proportion of farms allocated to tea. Alternative crops to both tea and coffee and their rates of return are based on the author’s survey and the Integrated Rural Surveys. Data on smallholders from the Integrated Rural Surveys were used to calculate the proportion of value added which went to labour and the proportion which was an operating surplus. The last data adjustments which should be mentioned are the derivations of the land stocks. The total land stock in 1964 was derived by assuming that one unit of land in other agriculture was the amount that earned one Kenyan pound of value added. As the rates of return on coffee and tea were higher than in other agriculture, a unit of land in coffee or tea was assumed to earn three times as much as other agriculture, not taking the gestation period into account. Rowe (1963) estimates that the net return to coffee land in Kenya was three times its best alternative in the early 1960’s. McLaughlin estimated that the value added per hectare of coffee was almost five times that of maize in 1975, a year of low coffee prices but high maize prices. [See Mwangi (1981, Table 3) for the latter estimate.] The land stock, thus determined, was fixed in total size, though land could be and was switched from other agriculture to either coffee or tea. The rates of return on coffee and tea relative to other agriculture were calculated using data from various sources - including my survey - on coffee, tea, and other agricultural yields, prices, and input requirements. For other agriculture the yields of maize and beans as given by the Integrated Rural Surveys were used for comparison purposes as one result of my survey was that over 90% of the 600 farmers interviewed considered maize and/or beans to be the best alternative to coffee or tea. References Ahmed, OS., 1982, The potential effects of income redistribution on selected growth constraints: A case study of Kenya (University Press of America, Washington, DC). Chenery, H.B. and A.M. Strom, 1966, Foreign assistance and economic development, American Economic Review 56, 680-733. Coffee Board of Kenya, Annual reports, 1964- 1984 (Government Printer, Nairobi). Collier, P. and D. Lal, 1986, Labour and poverty in Kenya: 190@1980 (Oxford University Press, Oxford, U.K.). Ghai, D. and S. Radwan, ed., 1983, Agrarian policies and rural poverty in Africa (International Labour Office, Geneva). Harris, J. and M.P. Todaro, 1970, Migration, unemployment, and development: A two-sector analysis, American Economic Review 60, 126143. Integrated Rural Surveys I, II, III, IV, 1974-1979 (Government Printer, Nairobi, Kenya). Just, R.E., D.L. Hueth and A. Schmitz, 1982, Applied welfare economics and public policy (Prentice-Hall, Englewood Cliffs, NJ). Kenya Tea Development Authority, Annual report, 19641984, (Government Printer, Nairobi, Kenya). Kiara, J.M. and S.B.C. Njagi, 1982, The costs of establishing various coffee densities in Kenya, Kenya Coffee, July-Aug., 1733183.

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Koester, U., 1978, Kenya’s economic policy with respect to the world’s coffee market, Working paper no. 332 (Institute for Development Studies, Nairobi, Kenya). Maitha, J.K., 1969, Coffee production in Kenya: An econometric study, Unpublished Ph.D. Dissertation (University of New York, Buffalo, NY). McMahon, G., 1987, Does a small developing country benefit from international commodity agreements? The case of coffee and Kenya, Economic Development and Cultural Change 35, 4099423. Mwangi, W.M., 1981, Alternatives for improving production, employment, and income distribution in Kenyan agriculture, Discussion paper no. 273 (Institute for Development Studies, Nairobi, Kenya). Njagi, S.B.C., 1981, Current costs in coffee farming and the farmer’s profit, Kenya Coffee 49-57. Republic of Kenya, 1979, Development plan, 1979-1983 (Government Printer, Nairobi, Kenya). Republic of Kenya, Economic survey, 198&1985 (Government Printer, Nairobi, Kenya). Republic of Kenya, Input-output tables, 1967 and 1976 (Government Printer, Nairobi, Kenya). Republic of Kenya, Statistical abstract, 1964-1984 (Government Printer, Nairobi, Kenya). Republic of Kenya, Ministry of Livestock and Agriculture, 1985, Sessional paper no. 2 (Government Printer, Nairobi, Kenya). Rowe, J.W., 1963, The world’s coffee (Her Majesty’s Stationery Office, London, U.K.). Todaro. M.P., 1969, A model of labour migration and urban unemployment in less developed countries, American Economic Review 59, 138-148. Vandemoortele, J., 1984a, Kenya data compendium: 19641982, Occasional paper no. 44 (Institute for Development Studies, Nairobi, Kenya) Vandemoortele, J., 1984b, Wages and wage policies in Kenya between 1964 and 1983 (Paper prepared for the Kenyan Economic Association, Nairobi, Kenya). Vandemoortele, J., 1987, The social accounting matrix: A tool for socio-economic planning and analysis (Internaional Labour Organisation, Geneva, Switzerland). Wallace, D.T.. 1962. Measures of social costs of agricultural nroarams. Journal of Farm Economics 44, 580-594. Whalley, J.. 1984, Trade liberalization among major world trading areas (MIT Press, Canbridge. MA).