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Material Information
- Title:
- Agricultural diversification and export earnings selected African countries
- Series Title:
- Economics report
- Creator:
- Mathis, Kary, 1936-
Davis, C. G ( Carlton George ), 1936-
Futa, M. T
University of Florida -- Food and Resource Economics Dept
- Place of Publication:
- Gainesville
- Publisher:
- Food and Resource Economics Dept., Institute of Food and Agricultural Sciences, University of Florida
- Publication Date:
- 1977
- Language:
- English
- Physical Description:
- v, 74 p. : ill. ; 28 cm.
Subjects
- Subjects / Keywords:
- Produce trade -- Africa, Sub-Saharan ( lcsh )
Agriculture -- Economic aspects -- Africa, Sub-Saharan ( lcsh ) Crops ( jstor ) Agriculture ( jstor ) Net income ( jstor )
- Genre:
- bibliography ( marcgt )
Notes
- Bibliography:
- Bibliography: p. 70-74.
- General Note:
- Cover title.
- Statement of Responsibility:
- W. K. Mathis, C. G. David, M. T. Futa.
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- University of Florida
- Holding Location:
- University of Florida
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- The University of Florida George A. Smathers Libraries respect the intellectual property rights of others and do not claim any copyright interest in this item. This item may be protected by copyright but is made available here under a claim of fair use (17 U.S.C. §107) for non-profit research and educational purposes. Users of this work have responsibility for determining copyright status prior to reusing, publishing or reproducing this item for purposes other than what is allowed by fair use or other copyright exemptions. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. The Smathers Libraries would like to learn more about this item and invite individuals or organizations to contact Digital Services (UFDC@uflib.ufl.edu) with any additional information they can provide.
- Resource Identifier:
- 026129174 ( ALEPH )
04229017 ( OCLC ) ABT1352 ( NOTIS ) 78621339 ( LCCN )
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october 1977
fUC0'
Economics Report 89
Agricultural Diversification and Export
Earnings, Selected African
Countries
S. - n "i
1~ "j;
Dod and Resource Economics Department gricultural Experiment Station institute of Food and Agricultural Sciences
In Cooperation with enter for African Studies university of Florida, Gainesville 32611
W. K. Mathis C. G. Davis
M. T. Futa
ABSTRACT
This study focused on four African countries: Kenya, Nigeria,
Tanzania and Zaire. The objectives were (a) to identify whether agricultural export earnings fluctuations were determined primarily by quantity or price variability; (b) to investigate trends and patterns in the agricultural sector; (c) to determine the impact of export crop diversification on the level and variability of agricultural export earnings; (d) to inquire if there was a gap between domestic supply of and demand for food and to determine if such a gap is due to overinvestment in the export crop sector.
Fluctuations in agricultural export earnings were found primarily associated with variations in the quantity exported, which were due in part to actions of government marketing boards. A secondary source of export quantity variation was variability in rainfall. All four countries introduced new crops during the study period, but fewer different crops accounted for shares of agricultural export earnings, so countries actually became more specialized. An inverse relationship was found between export crop diversification and the level of agricultural export earnings. However, it would appear that in the long run, there is likely to be a positive relationship.
Demand for food was increasing more rapidly than food supply; the imbalance was not due to overinvestment in the export sector. Low rates of growth of export earnings implicitly indicated that the export sector itself lacked resources.
Key words: Sub-Sahara Africa, export earnings instability, export crop diversification, economic development, export agriculture, development policy.
ACKNOWLEDGEMENTS
The authors wish to thank the Rockefeller Foundation for Mr. Futa's support during his time at the University of Florida. The assistance of Dr. W. W. McPherson is gratefully acknowledged, both as a member of Mr. Futa's advisory committee and as a reviewer of this manuscript. Drs. R. D. Emerson and P. J. van Blokland are also due thanks for reviewing the manuscript. Three lovely and hardworking ladies typed many drafts and the final copy, and are due much appreciation: Mrs. Patricia Beville, Ms. Carolyn Williams and Mrs. Mignonne Winfrey. Mrs. Carolyn Dunham gathered and processed data, which we acknowledge with thanks.
TABLE OF CONTENTS
Page
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF FIGURES . . . . . . . . . . . . . . . . . . v
INTRODUCTION . . . .
Objectives . . . . . . . . . . . . . . . . . . . . . . .
Country Selection . . . . . . . . . . . . . . . . . . . . . 2
STUDY AREA CHARACTERISTICS . . . . . . . . . . . . . . . . . . 4
Common Features . . . . . . . . . . . . . . . . . . 4
Kenya . . . . . . . . . . . . . . . . . . . . . . . 9
Nigeria . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Tanzania . . . . . . . . . . . . . . . . . . . . . . . . . 13
Zaire . . . . . . . . . . . . . . . . . . . . . . . . 15.
ANALYTICAL MODEL DEVELOPMENT . . . . . . . . . . . . . . . . . 17
Literature Review . . . . . . . . . . . . . . . . . . . . . 17
Variable Specification and Description . . . . . . . . . . . 20
Instability Measures . . . . . . . . . . . . . . . . . . 20
Diversification Index . . . . . . . . . . . . . . . . . . 23
Model Specifications for Study Objectives . . . . . . . . . 24
Objective I . . . . . . . . . . . . . . . . . . . . . . . 24
Objective 2 . . . . . . . . t . . . . . . . . . . . . . . 24
Objective 3 . . . . . . . . . . . . . . . . . . . . . . . ?5
Objective 4 . . . . . . . . . . . . . . . . . . . . . . . 26
The General Objective . . . . . . . . . . . . . . . . . . 27
Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . 28
EMPIRICAL FINDINGS . . . . . . . . . . . . . . . . . . . . . . 30
Price and Quantity Variability . . . . . . . . . . . . . . . 30
Patterns and Trends in Export Crop Diversification, 32
Effect of Export Crop Diversification on Levels and
Variations in Agricultural Export Earnings 36
Food Supply and Demand Growth Rate Disequilibrium 41
African Choice . . . . . . . . . . . . . . . . . . . . . . . 47
TABLE OF CONTENTS (Continued)
ENe
Effects of Export Crop Diversification on Land
Allocation . . . . . . 48
Kenya . . . . . . . . . 48
Nigeria. . . . . . . . . . . . . . . . . . . . . . . . 50
Tanzania . . . . . . . . . . . . . . . . . . . . . . . . 52
Zaire . . . . . . . . . . . . . . . . . . . . . . . . . 54
The Uselof Agricultural Export Earnings for Development
Goals . . . . . . . . . . . . . . . . . . . . . . . . . . 54
SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . 57
Summary. 57
Conclusions. 59
APPENDIX . . . . . . . . . . . . . . . . . . . . 61
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . 70
LIST OF TABLES
Table Ene
I Agricultural trade value as a percent of total trade
value, selected African countries in selected time
periods . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Agricultural product value as a percent of GDP, selected African countries in selected time periods. 5
3 Position of selected African countries according to two designated selection criteria, 1971-73 . . . . . . . . . 6
4 Earnings from major agricultural exports, constant dollars, 1950-1972 . . . . . . . . . . . . . . . . . . . . . 29
5 Variability in agricultural export earnings attributed to price and quantity changes, 1950-1973 . . . . . . . . . . 31
6 Export crop diversification indices, 1950-73 . . . . . . . . 33
7 Average annual rates of decline in diversification indices, selected periods . . . . . . . . . . . . . . . . . . 35
8 Average annual growth rates of quantities exported, major commodities, 1950-1973 . . . . . . . . . . . . . . . . 35
9 Index values of agricultural export earnings, 1950-1972 (1950=100) . . . . . . . . . . . . . . . . . . . . . . . . . 38
10 Average annual rates of change in agricultural export
earnings and in diversification indices, selected periods. .39 11 Instability indices of agricultural export earnings,
1950-72 . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
12 Differences between annual rates of growth of supply
and demand for selected food items, 1965-1973 . . . . . . . . 44 13 Land distribution among food and export crops, 1970 . . . . . 46 14 Export supply function estimates, Kenya . . . . . . . . . . . 49
15 Export supply function estimates, Nigeria . . . . . . . . . . 51 iii
LIST OF TABLES (Continued)
Table I E"e
16 Export supply function estimates, Tanzania . . . . . . . 53 17 Export supply function estimates, Zaire . . . . . . . . 55 18 Average annual values in current dollars of world and country exports of major commodities, 1950-73 62
19 Earnings from major agricultural exports and total
agricultural exports, current dollars, 1950-73. 63
20 Values of commodities exported, current dollars, 195073 . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
LIST OF FIGURES
Figure Eae
I Africa, and Sub-Saharan Countries . . . . . . . . . . . 3
2 Index values of agricultural export earnings, 1950-1972 (1950=100) . . . . . . . . . . . . . . . . . . . . . . . 7
3 Kenya, with major export crop regions . . . . . . . . . 10 4 Nigeria, with major export crop regions . . . . . . . . 12 5 Tanzania, with major export crop regions . . . . . . . . 14 6 Zaire, with major export crop regions . . . . . . 16 7 Export crop diversification trends, 1950-72 (1950=100). 34
8 Average price of food staples in northern Nigeria under alternative simulated policies, 1965-1980 . . . o . . . 45
AGRICULTURAL DIVERSIFICATION AND EXPORT EARNINGS, SELECTED AFRICAN COUNTRIES
W. K. Mathiss C. G. Davis and M. T. Futa
INTRODUCTION
The study of trade fluctuations has been of much concern in the
post World War II period. Since then a significant proportion of trade literature has dealt with theoretical and empirical analyses of fluctuations and general instability of export earnings of less developed countries (LDCs)In the presence of problems associated with export earnings fluctuations in general, and/or agricultural export earnings fluctuations in particular, economists have made extensive analyses of the causes and effects of instability. Unfortunately, a relatively small proportion of the literature has dealt with policies to correct instability. Agricultural diversification is probably the most widely discussed stabilizing mechanism. However, the discussions have generally been theoretical and did not deal with economic impacts of diversification.
Objectives
The primary objectives of this study are:
(1) Estimate and categorize export crop earnings variations into
price and quantity components as a means of understanding
their relative weights in program planning and policy formation.
W. K. MATHIS and C. G. DAVIS are associate professors of food'and
resource economics at the University of Florida. M,.T. FUTA is a Rockefeller Foundation Fellow in agricultural economics at Oklahoma State University.
(2) Describe and measure the patterns and trends in export crop
diversification in each of the countries covered in the study.
(3) Measure and describe the influence of export crop diversification
on the level and variability in agricultural export earnings.
(4) Quantify the gap between aggregate food supply and demand and
determine whether or not any existing disequilibrium between
the two components is related to differential levels of investment between the export and domestic agricultural sectors.
A secondary and more general objective of the study is to identify policy lines in the agricultural export sector and to evaluate the impact of such policies on the overall agricultural development of selected countries
Country Selection
This study analyzes determinants and'effects of agricultural export earnings instability in selected Sub-Saharan African countries during 1950-73., Countries were selected according to two'criteria: (a) the degree to which the country is engaged in agricultural trade and (b) the relative importance of agriculture in its overall economy. The ratio of agricultural trade to total trade is used to establish the first criterion. The ratio of the value of total agricultural, product to gross domestic product (GDP) established the second criterion. The size of this ratio indicates the overall contribution of agriculture to the national economy, and thus the relative importance of an external disturbance in agriculture to the economy.
Both these ratios were calculated for thirteen tropical African
countries, from which Kenya, Nigeria, Tanzania, and Zaire were selected (Figure 1). Selection of the four countries was based on the average values of both ratios for the period 1971-1973. For both criteria, countries were grouped into those with (a) large or (b) small ratios. Numerica designation of what constitutes a "large" or "small" ratio is admittedly a normative (and somewhat arbitrary) decision. For the purpose of this study, it is assumed that a ratio of agricultural trade value (ATV) to total trade value (Try) of 50 percent or more is large, whereas any ratio less than 50 percent is 'small. A ratio of agricultural product (AP) to GDP is assumed to be large if it amounts to 33 percent or more while a,
/ "Za mbia Maaw
Figure 1.--Africa, and Sub-Saharan Countries
ratio of less than 33 percent is small. Here, it is assumed for simplicity that the national economy consists of three sectors: industrial, agricultural and service. Thirty-three percent of GDP would represent an equal distribution among the sectors.
According to the country selection criteria established above, SubSaharan African countries may be placed into two broad groupings that reflect their heterogeneity. Countries grouped on the basis of the relative trade ratio, show definite differences. Kenya and Tanzania are among countries with higher ratios of agricultural trade value to total trade value, while Zaire has a smaller ratio (Table 1). Nigeria is typical of those countries experiencing a shift in status from the first to the second group.
Divisions based on ratios of agricultural product value to GDP show
similar groupings (Table 2). Kenya and Tanzania have economies based mainl on agriculture, while Zaire is more dependent'on non-agricultural sectors. Nigeria, although exhibiting a relatively high ratio of agriculture 1 product value 'to GDP, has experienced significant changes. Countries grouped on the basis of the relative trade ratio (Table 1) show remarkable consistency in groupings bated on the agriculture product value: GDP ratio (Table 2). For example, most country . es in the high _TV group are in the high AP group (Table 3). TTV
'6 DP
All four of the countries chosen for study have experienced substantial fluctuations in export earnings. Constant dollar values of agricultural export earnings of the four selected countries increased from 1950 to 1973, but were vulnerable to sharp fluctuations (Figure 2).
STUDY AREA CHARACTERISTICS
Common Features
The four countries selected for study have certain common agricultural features. All produce several export crops. Kenya and Tanzania share many of the climatic and ecological conditions typical of East Africa. Nigeria and Zaire also have similar agro-ecological conditions in certain regions, even though they are located in different parts of the African
Table l.--Agricultural trade value as a percent of total trade value,
selected African countries in selected time periods
Country 1956-1959 1963-1966 1967-1970 19,71-1973
----- ---------------------- Percent ------------- .
Study countries
Kenya 65 61 57 52
Nigeria 69 66 56 19
Tanzania 76 80 72 71
Zaire 10 3 7 9
Others
Cameroun 75 75 75 68
Ethiopia 95 98 93 90
Gabon 3 2 1 1
Ghana 76 72 79 74
Ivory Coast 72 70 67 .68
Malawi 90 91 88 89
Senegal 80 87 73 51
Uganda 85 82 82 90
Zambia 4 2 31
Source: F.A.0., Trade Yearbook.
Table 2. --Agricultural product value as a percent of GDP, selected African
countries in selected time periods
Country 1955-1960a 1963-1966 1967-1970 1971-1973
------------------rrei--------------Study countries
Kenya 36 37 33 33
Nigeria 60 59 54 39
Tanzania 50 55 40 3
Zaire 30 21 21 19
Others
Cameroun 40 41 37 36
Ethiopia 57 57 52 54
Gabon --17 15
Ghana --70 46
Ivory Coast --59 33
Malawi --51 51 50
Senegal 40 30 28 217
Uganda 60 59 56 :53
Zambia --9 8 '8
a These years differ between original data
from those in Table 1 sources.
(1956-59) due to differences
Source: U. N., Survey of Economic Conditions in Africa,.
Table 3.--Position of selected African countries according to two designated
1971-73
selection criteria,,
(Hig Ai~a ATV aA
Country. (Hig (Low (High .A'~ (Low Q-)b
Study countries
Kenya +
Nigeria + +
Tanzania + +
Zaire + +
Others
Cameroun + +
Ethiopia + +
Gabon + +
Ghana + +
Ivory Coast + +
Malawi + +
Senegal + +
Uganda, + +
Zambia + +
aATV
-Agricultural Trade Value. 50 percent.
TTV = Total Trade Value
-Agricultural Product.
3 percent.
High ATV~ mean s a ratio of 50 percent or more; Low, less than
TTV
AP
Hi gh GD
means a ratio of 33 percent or more; Low, less than
GDP = Gross Domestic Product.
ISource: Computed from U. N.,, Survey of Economic Conditions in Africa Trade Yearbook.
and F. A, 0. ,
b AP
- Kenya
. Nigeria
Index
200 . . . Tanzania
Zaire
.0 4f 150- t- ,. "' .
50-I
%-"." : -. .
I . I t t I.,
1950 1955 1960 1965 1970
Figure 2. Index values of agricultural export earnings, 1950-72 (1950=100).
continent. These common features explain the historical development of pali oil and natural rubber production in both countries. Coffee, tea and hard fibers are common to Zaire, Kenya and Tanzania. Similarities between the agricultural economies of Nigeria and the two eastern countries are found mainly in institutions introduced in all three countries by the former British colonial administration.
A common and important institutional legacy of the colonial era is the marketing board. In fact, agricultural marketing and trade in most African countries is controlled by these boards. Boards are statutory bodies established by government action and endowed with lega! powers over the production, marketing and processing of primary agricultural products (Abbott). However, the objectives, structure, conduct and performance of such bodies vary from country to country.
Abbott, after extensive analysis of marketing boards, indicates that these bodies have several objectives. Some of the more important are sales promotion, research, extension services, raising bargaining power of agricultural producers in domestic or export markets, setting up needed marketing and processing facilities, equalizing returns from sales in different markets or through different outlets, and cushioning the impact upon producers and consumers of' sharply fluctuating internal and external prices. The last point is of particular interest to this study, since it deals with problems of agricultural trade instability.
Most African marketing boards are of the export monopoly type. The Zairean boards are an exception, however, since they are essentially stabilizing rather than trading institutions (Abbott). In the remaining three countries, marketing boards handle all problems related to the marketing of export crops. Kenya and Tanzania have had individual boards for each export crop. In recent years, Tanzania has been reducing the number of marketing boards, giving an individual board responsibility for several crops (Kriesel, et al). In Nigeria, marketing boards are regional (Helleiner, 1966). Also, two Nigerian export crops, rubber and bananas, are not regulated at all.
In spite of differences among countries, there are striking similarities in the conduct of African marketing boards. In all fourcountries, marketing boards establish annual producer prices, which change little
during a season or from year to year. Where these prices are lower than world prices the differences accrue to the marketing boards. These surpluses are supposed to be used to maintain producer prices when world price levels fall below the marketing board price. However, the use of such surpluses in Nigeria has been criticized by some economists (Nixon, Eicher, 1971, Johnson, 1969, 1971), and defended by others (H-elleiner, 1966).
Aspects of this debate will be explored later in discussions of the empirical findings of this study. This debate, however, is not limited to the use of trade surplus. It has been extended to the general impact of marketing boards on market and production mechanisms. There is general consensus that marketing boards tend to create distortions in factor markets and result in production disincentives (Johnson, 1968, Helleiner, 1966).
Kenya.
Kenya's export crops are produced in three distinct agro-ecological zones (Figure 3). The first is the eastern coastal plain, a semi-arid band along the Indian Ocean, where sisal is produced mainly on plantations. The second zone in the interior to the north is characterized by poor pasture lands and marginal cotton production. The third ecological zone is a fertile upland region which produces coffee and tea, and most of Kenya's other export crops. Four crops were selected for this study because of their relative importance in the Kenyan economy: coffee, tea, cotton, and sisal, which account for about 80 percent of agricultural export earnings.
Kenya's major export crops are produced by both plantation, and peasant farming systems. However, the plantation economy remains a distinguishing feature of Kenyan agriculture. It has only been within the last two decades that the peasant farming system was given the neces-, sary economic incentives to expand under the auspices of the Swynnerton Plan.1
1This plan was introduced in 1954 and supplemented in 1962. The primary objective was to help the natives of Kenya to expand their production of crops such as coffee, pyrethrum, tea, maize and millet. Lands made available through this plan were divided into two areas--'Scheduled' areas and 'Non-scheduled' areas. The former was reserved for Europeans ~nn tha latl-pr for African natives.
Coffee and tea
I~z
$isal Cotton
Figure 3.--Kenya, with major export crop regions
Available lands were designated as 'high-density areas' and were reserved for subsistence small holders.2 Tow-density" areas were reserved for larger commercial farming, units. These large units market their crops through marketing boards.
Nigeria
Nigeria, in contrast to Kenya, has large areas of fertile coastal land. This region can be divided into two subregions: the southeast, with palm oil and natural rubber production, and the southwestern region which has maintained its comparative advantage in cocoa production (Figure 4). The second region, stretching from east to west across the center of the country, is suitable for growing both groundnuts and cotton. The third zone in the north lacks the climatic advantages of the south but has been mainly responsible for making Nigeria the world's major exporter of groundnuts. The four crops selected for study--groundnuts, cocoa, palm oil, and natural rubber--are produced in all regions and contribute approximately 85 percent of the total agricultural export earnings of the country.
Nigeria has areas both of surplus labor and surplus land. It also has areas, with differing labor-cultivated land ratios and lengths of fallow periods, between these two extremes (Helleiner, 1966, p. 55). The coastal region with its high population density and limited land is considered to be a surplus labor area. The central and northern regions, on the other hand, with low population density are considered to have surplus land. Despite the apparent land surplus, these regions are characterized by permanent and seasonal labor migration to the south. (Norman).
With such differences in regional labor-land ratios, it is easier to understand why the trend toward individual land proprietorship is not very significant in the country as a whole (Johnson, 1968). However.,
2 The term "subsistence holders" means farmers consuming more than 50 percent of their farm production.
O Cocoa
Palm oil and rubber Groundnuts
Figure 4.--Nigeria, with major export crop regions
in the area of land reform Johnson argues that extended and community ownership of land is not a serious problem in Nigerian agriculture. He suggests that the more serious problem is the market distortions caused by government and marketing board policies (Johnson, 1968). This view is not generally shared by all economists concerned with Nigerian economic development. The debate on the role and impact of marketing Iboards on Nigerian economic development is still unsettled.
Tanzania
Tanzania, south of Kenya and east of Zaire, has four main regions of interest (Figure 5). The first is the eastern coast where sisal is produced. Next is the west-central region, in which several crops are cultivated, with cot 'ton, coffee, and tobacco the most important. The north-central region also produces coffee, while the southern zone is characterized by a system of production which has been referred to as crop dispersion, rather than crop diversification (Saint-Marc, 1968). Cash crops grown in this system include sesame, tobacco,.cotton, tea, sunflower, castor seed and cashews. 4Coffee, tea, cotton and sisal--the four study crops--account for approximately 75 percent of the agricultural, export earnings of Tanzania.
Export crop production is largely concentrated in the plantation
economy. However, as in the case of Kenya, emphasis is now being placed on the development of small farms. The Tanzanian government as well as the Tanzanian national political party maintain a strong interest in the socialization of the rural society.5 Governmental participation has also
3Dispersion is defined as the existence of several crops in a given area with a very low density.
4Cashews are important Tanzanian exports but lack of data prevented inclusion in this study.
5Rural development policy is expressed as a program of rural socialism. It is designed to lessen income inequalities among farmers by giving them a community mode of production by which the farm unit is jointly owned by the extended family or a group of people who agree to work together.
Coffee and tea r.-i Sisal
Cotton
Figure 5.--Tanzania, with major export crop regions
been extended to include the marketing system. This system operates through a complex set of marketing regulations administered through marketing boards. The marketing boards' structure and operation have been reviewed and subjected to a number of changes over the years, largely as a result of criticism of African governments' use of agricultural surpluses. However, in the case of Tanzania, marketing board surpluses have been used for the economic betterment of the farming sector (Kriesel, et a)). In fact, Kriesel found that there have been no direct transfers of such surpluses or savings to governments in contrast to the case in some West African countries.
Thus, in spite of some uneasiness about government intervention in the agricultural system, there is optimism that agricultural surpluses will continue to be invested in the agricultural sector instead of being siphoned off to other sectors. However, government policy encourages community ownership as the means of production. With a high population growth rate of 2.7 per year, Tanzania has a serious shortage of cultivable land.
Zaire
Zaire, the third largest African country after Algeria and the Sudan, has the most diversified climate of any country on the continent. The varied ecological conditions are suitable for many different crops, but only a few are grown commercially for export. Coffee, tea, palm oil, and natural rubber represent 85 percent of all the agricultural export earnings of the country (Figure 6).
These crops are produced mainly on plantations, but there'are some small peasant farm units. In the 1950's the colonial agricultural administration began a program known as paysannats ind igenes in an effort to transform traditional farming patterns. Innovations implemented in these programs were noted in development literature (Johnson, 1968; McPherson and Johnston, 1968). However, the system collapsed with the emergence of national independence. The collapse of the paysannats system was accompanied by a decline in the relative importance of agriculture in the economy. Massive migration has been occurring from rural to urban areas (Mabala). A prevailing pattern is that of disinvestment in the agricultural sector (Peemans).
In addition, agricultural institutions in the country have not been
* -~ 4
? * I I,,
*/ *~ $~** ,. *
~ *~
X X)( xXX
X x ;X )
Eli
Natural rubber Palm oil Coffee and tea
Figure 6.-- Zaire, with major export crop regions.
effective in rural improvement programs. However, the Zairean government has begun to provide credit facilities, revise land policies, and improve the rural infrastructure.
ANALYTICAL MODEL DEVELOPMENT
Literature Review
In the wake of post World War II interest in trade fluctuations,
several empirical analyses were undertaken. -A 1952 United Nations study reported an analysis of price and quantity movements in LDCs. The study focused on: (a) year-to-year price and quantity fluctuations and (b) long-term price and quantity fluctuations and cyclical swings. One of the major findings was that export earnings fluctuations tended to be higher than those of prices and quantities taken individually, due to the interaction of prices and quantities. The study also reported that prices accounted for two-fifths of the fluctuations-*while volume accounted for the remaining variability. Using United States export data, Mintz concurred with the United Nations findings regarding the relative importance of export quantity as a determinant of variability in export earnings.
In a 1958 article, Nurkse formulated specific 'policies on the basis of the earlier United Nations report. He supported the United Nations proposal calling for international funds and buffer stocks as a means of stabilizing export earnings. However, he made a strong case for balanced growth in the LDCs as a means of fostering industrialization and less reliance on primary products.
Coppock, in 1962 and later in 1966, analyzed Middle Eastern foreign
trade patterns. In both studies, Coppock developed indices of instability and related them functionally to export prices, export quantities and market shares of individual commodities and countries.
A study by DeVries concluded that such factors as trade position,
p rice inflation and resource allocation determined export growth rate and performance in LOCs. DeVries found a positive correlation between the performance of major and minor exports and growth in the value of agricultural product. He opposed diversified industrialization policies as
inefficient in helping LOCs reach economic levels of production and a competitive ceiling.
Studies by Maizels have made significant contributions to the understanding of trade problems in LDCs. Maizels argued against overspecialization in particular export commodities. He saw export diversification as not only an appropriate mechanism for facilitating structural change, but as probably the most important from a long-term viewpoint. Maizels further argued that accurate assessment of world demand trends for export commodities is a vital first step in assisting LDCs to capture the economic gains from high demand exports.
Balassa's work had significance for the agricultural earnings instability question in that it estimated demand trends for temperate zone foods, competing tropical foods and agricultural raw materials. However, an increase in world demand might not justify a policy of export crop diversification and shifts in resource allocation. Some economists have proposed reorienting policies towards food crop diversification as a means of reducing high food import propensity (Flores).
All of the studies discussed so far, except that by the United Nations dealt with trade problems in general or with the question of stability, without referring to specific stabilization policies. Massel entered thes gaps by reviewing different policy alternatives. Buffer stocks and multilateral contracts proposed by the United Nations study and supported by Nurkse were analyzed. Massel found that buffer stocks provided certain welfare advantages to producers by minimizing the expected value of change of producer prices, and to consumers by providing gains in consumer surplus (1969). However, the cost of implementing a buffer stock policy was higher than other alternatives (Massel, 1970). Earlier, Massel concluded that neither export earnings instability nor the disutility arising therefrom are likely to be eliminated by simple policies such as diversification of exports (1964). Since variations are independent among commodities, their additivity may actually worsen the variability. This argument runs counte to Jorberg's findings that diversification is capable of inducing stabilit in the secular trend of export earnings.
Parikh used an econometric model of the world coffee economy to predi production, consumption and outcomes of alternative policies. A'similar
model was later used by Edwards and Parikh to identify policies that would minimize the fluctuations of agricultural export earnings. They found that, given the necessary resources, an international buffer stock policy could substantially reduce short-run fluctuations. A quota policy was judged to be more successful, as it had fewer of the economic difficulties found in the buffer policy. Edwards and Parikh suggested, however,.that, quota policies would probably be much more difficult to enforce, due to political considerations.
With such difficulties evident in international policies, interest should turn to domestic policies such as tax structure and export crop diversification. However, little has been done in this regard in LDCs, and the concept of diversification has been primarily associated with the process of industrialization. Only a limited number of studies have analyzed export crop diversification as such. One of the few international studies dealing with this aspect is a 1967 report by the Committee for Economic Development (CED). Thisparticular study concluded that export earnings fluctuations are largely the result of a combination of variations in crop output and dependency on one or two products.
i Policies and programs have been formulated on the basis of many of
the above analyses in an attempt to facilitate greater international price stability and economic growth (U.N., 1952). The major operating mechanisms of these stabilizing programs were export quotas, buffer stocks, and multilateral contracts. The General Agreements on Tariffs and Trade (GATT) has served as the primary operational vehicle for these policies and programs. In spite of GATVs efforts, the LDCs still exhibit instability in export earnings, particularly for agricultural exports.
.The problems of agricultural export earnings instability are likely to be more serious in those countries that derive a sizable proportion of their Gross Domestic Products (GDP) from agricultural exports. Although It has not been conclusively determined that instability hampers economic development (Lim), it has been established that fluctuations in agricult ural. export earnings have a multiplier effect on domestic incomes. Fluctuations in earnings accentuate inflationary and deflationary movements in the national economy', and may also discourage investment in the agricultural sector and thus impede the growth process.
Instability of agricultural export earnings can also inhibit government
programs aimed at meeting social welfare goals. These variations may force a country to borrow in order to meet its social welfare objectives. Where loan repayment obligations accumulate over time, additional stress on the nation's economy may result. Thus social efficiency may be reduced if the agricultural export sector generates a particular system of resource misallocation (Beckford).
Variable Specification and Description
Instability Measures
Attempts to quantify, the variability of export earnings have been made by several authors. Instability is used here to mean the deviations of the constant dollar values of agricultural export earnings around the trend. The United Nations study (1952), referred to earlier, used an average of year-to-year percentage changes as a measure of instability. The mathematical formula of that index was:
I Z (t41 Z t)x 100(1
t=l
Zt
N
where,
I =United Nations instability index,
UN
Zt real value of agricultural export earnings in period t, and
N number of years for which the instability is computed.
Kingston criticized this procedure by arguing that it tended to
exaggerate the importance of the instability existing in the time series (Kingston, p. 20). He wrote that:
One obvious deficiency of this approach is that a
steady increase of a constant percentage per annum would be interpreted as an unstable movement, when
in fact there had been no instability, in a conventional economic sense, at all. Rather, there would
have existed a stable percentage of growth.
Coppock, who had already criticized the United Nations index with the same arguments as Kingston, formulated an index with a logarithmic
variance measure, to account for a constant percentage growth rate. The Coppock index may be written as:
N
c= antilog z (log.z t+l-M) (2)
t=l
Zt
where,
I = Coppock instability index,
M = the arithmetic mean of logarithm differences, and
Z,N = as defined in equation ().
While the Coppock index may represent an improvement over equation
(1), it does have some weaknesses. First, the log-variance form interprets small deviations from a low base as highly unstable. Second, instability coefficients derived from percentage changes are difficult to interpret (Kingston).
Another instability index was proposed by Massel, who used a leastsquares regression model with time series data. Massel's model is described mathematically as:
N 2
IMS F, (Zt - Z.) (3)
t=l N
where,
I = Massel index of instability,
MS
Z' = least squares estimate of Zt, 4 f(t),
= trend mean value of agricultural export earnings,
Z,N as defined in (1) and (2).
This procedure does not insure that the least squares model used to estimate Z is the correct model, but it is an improvement over (1) and
(2). Staller, as cited by Kingston, applied this index with the slight modification of regressing the logarithmic values of Zt on time.
An interesting aspect of the Massel index is that it may be transformed to accommodate analysis of times series data. Previous indices were limited to measuring total instability over a given N years. The transformation of Massel's index in a time series basis measures the instability of period-to-period changes. It can be stated as follows:
I' = u u (4)
MS t+l t
m-x-(Zt+l, Zt)
where,
I'S = transformed Massel annual index of instability,
ut+ = residual error corresponding to Zt+l observation,
ut = residual error corresponding to Zt observation.
Since this transformed version of Massel's index applies to a time
series model, it was used in the present study. However, some modificatio were needed in the denominator of equation (4). Massel implied that the point-from which deviations occur is itself a changing or a dynamic point. But, the weakness of the assumption is that at each point in time, there exists a given maximum value which is considered as a stationary value. This is misleading, since it is known that there are some conditions to be met for a point to be considered as a stationary value (Gandolfo). Thus, in order to avoid errors that the second Massel index may generate, equation (5) was developed. Such a relation takes the form:
t ut+l -u (5)
where,
it = corrected index of instability,
ut+l = residual as defined in equation (4), but obtained with the
exponential trend in equation (10).
ut = residual as defined in (4), but derived with the exponential
trend in equation (10).
Z = average trend value from equation 10, considered as a fixed
stationary value, from which deviations take place.
It should be noted that all these indices measure the variability
about a given stable point. They do not, however, possess the properties of normal variances. Thus, these constructs have a basic shortcoming that prevents their application to analysis of the different effects of price and quantity variation on agricultural export earnings instability. To separate the price and quantity components of export earnings variation, normal variance properties must be included. One of the properties of the normal variance of a given variable is that such a variance may be
apportioned into two or more components.
Diversification Index
Diversification refers to the production of several crops in a given geographical. area or production unit. But, if only the numbers of products are considered, Finke and Swanson argue that diversification can be thought of as a 1'richness" concept meaning 'how many' crops a given farm unit grows. However, emphasis may also be placed on the relative importance of a given crop in a given area or farm unit. In this case, diversification could mean that all crops in the area are equally or evenly distributed. Defined in this way, diversification is the degree of 'evenness', as determined by the proportions of resources allocated to each crop planted.
The Committee for Economic Development (CED) proposed that agricultural diversification be thought of as a dynamic process, with new crops introduced into the system over time. The CED defined the diversification index as the ratio of the growth rate of agricultural export earnings to the growth rate of export earnings from traditional export crops. This definition, although interesting, has some shortcomings when used in regression models where export earnings are dependent variables. Thus the index used in this study is that developed by Finke and Swanson. It can
be formulated as:
Dt = _n E li log (li) (6)
where,
Dt = diversification index,
n = number of crops, and
Ili = proportion of land resource allocated to crop i.
However, because crop data in this study are based on harvested area rather than on actual planted area, and because the study is primarily focused on the export sector, it was felt that the substitution of the proportion of total agricultural exports represented by each.crop for the proportion of land allocated was more appropriate. Therefore, equation
(6) may be adjusted to:
D t = -n E qilog (qi) (7
where,
qi = share of crop i in total value of agricultural exports.
Objective 1
Fluctuations in agricultural export earnings are decomposed into
three components: price variations, quantity variations and variations resulting from the interaction between prices and quantities. An approximation of the variance of the function, Zi = PiQi, following the procedure used by Motha and others, is:
2 . 2Pi po2 (8)
CTZi (TPi+ i (Yqi p a pi a qi(8
where,
Cr. =agricultural export earnings variability of crop i,
1
Qi average quantity of crop i exported, in thousands of
metric tons,
P. = average export price of crop i exported, in U.S. dollars,
a2 price variability of crop i,
qi
a2 = quantity variability of crop i, and
�qi
p correlation coefficient between quantity Qi and its
price Pi.
The first term on the right hand side represents price variability,
the second term quantity variability and the third is the interaction terry Higher order terms in the series expansion are not included. Initial investigation showed negligible covariance between price and quantity, so those covariance terms are not included in further discussions.
Objective 2
In order to identify and quantify the trends and patterns of export crop diversification, the exponential trend procedure was adopted because of the good fit exhibited in preliminary trials. The symbolic representation of the model is:
Dt cae T (9)
where,
Dt = export crop diversification as defined in equation (7),
= scale of export crop diversification,
= growth rate of export crop diversification, and
T = time trend.
The parameters a and 0 are calculated by the trend procedure.
Objective 3
The third objective, evaluating the impact of export crop diversification on the level of agricultural export earnings, measures trends in agricultural
export earnings obtained by the exponential trend procedure in objective 2. It takes the form:
Zt = ae aT
where all variables are as previously defined. The impact of diversification is measured with a simple linear regression model and a quadratic regression model. The simple model is used to identify the nature of the relationship between two specific variables, while the quadratic model was adopted as a means of simulating the impact-of intensified diversification. The basic regression form may be written as follows:
Zt = f(Dt, Ut) (11)
where variables are as defined above.
Expansion of the two models gives two separate equations:
Zt = bo + blDt + ut 2 (12)
Zt = bo + blDt + b2Dt + ut (13)
A second aspect of this objective deals with evaluating the effect of export crop diversification on the level of instability. This is accomplished by applying equations (12) and (13) to the instability index, i.e., by regressing the instability indices upon the diversification variables. The resulting equations are functionally similar to equations (12) and (13). They have the following form:
it ao + alDt + ut (14)
it = ao + alDt + a2 tD2 + Ut (15)
Equations (12) and (14) are simplistic and are only described for theoretical considerations. For interpretation purposes, emphasis is
placed on equations (13) and (15) which correspond to the corrected instability index given as equation (5).
Objective 4
The rationale behind the fourth objective was to determine if the export crop sector was developing at the expense of the domestic food sector, A simple equilibrium equates the growth rate of food supply to the growth rate of food demand. It is assumed that if there is disequilibrium on the supply side, there are factors inhibiting the growth of the domestic food sector. One such factor could be a significant differetialbetween the levels of investment in the export crop and in the domestic food crop sectors. Investment is used here in its broad sense and covers such things as land and capital allocation, marketing Insti-, tutions, research infrastructure, labor and other relevant inputs. Because of data limitations, only policies affecting land allocation between the two sectors were examined. It is likely, however, that a high correlation exists between the quantity of land and the quantity of labor used in a particular sector.
The relation used here considers the supply side as consisting of
domestic food production and food imports. The demand side is formulated as proposed by several economists (Okhawa, Johnston and Mellor, 1961). The detailed equilibrium equation is:
Si + Si = 1G (16)
where,
Si d = growth rate of domestic food production of food item i,
Sif = growth rate of food imports of item i,
H = population growth rate,
n.= income elasticity 'of demand for food item I, and
G = real income per capita.
Rice, maize, beans and sugar were selected for study, since these commodities are basic food items and estimates of income elasticities are published (FAO, 1967).6 While it would have been appropriate to
6 There are some inherent shortcomings of these estimates, since they are mainly projections and not actual estimates for specific time periods.
include several staple African food crops, such as cassava and yams, there are no data on income elasticities for these items.
The General Objective
In order to identify the determinants of some of the policies prevailing in the African export crop economy, it was felt that understanding the impact of individual crops on the level of agricultural export earnings was an important first step in explaining how land resources are allocated. Such an impact may be measured through a multiple regression analysis model of the type:
Zt= f(Qit, Dt' uzt) (17)
where,
Zt= level of agricultural export earnings as defined previously,
Qit = quantity of crop i, in thousands of metric tons exported
in period t,
Dt = diversification as defined in (7), and
u zt = error term.
However, Qit is itself an endogeneous variable which depends on such predetermined variables as export prices, harvested area of export crops, weather and time trend. The appropriate model is a system of simultaneous equations, where the system is identified, and a two-stage least squares procedure is used. The system has the following form:
Zt= f(Qitg Dt, Uzt) (17)
Qit= f(Pit-l' Pot-l' Ait' Aot, RFt, T, uqt) (18)
where,
Pit-l export price of crop i in period t-l, in U.S. dollars,
Pot- = average export price of all other export crops in period
t-l, in U.S. dollars,
Ait = harvested area of crop i in period t, in thousands of
hectares,
Aot = harvested area of all other export crops in period t,
RFt = rainfall index in period t,
T = time trend,
UztUqt = error terms.
While price variables in the system above would give information regarding price responsiveness, Ait and Aot indicate relationships between changes in the area of a given crop, relative to changes in areas of others. Such information'is very important for explaining the patterns of allocation of crop land between the crops selected for study. It also provides some insights into the relative degree of scarcity or abundance of land resources.
Weather, particularly rainfall, frequently causes quantity variability in export earnings, so a rainfall index was incorporated. However, this index can only serve as a proxy for weather effects, and might not show a direct relationship with quantity exported. For example, domestic storage and processing of some export commodities means that the quantity exported in any given year is not equal to the total quantity produced. The quantity produced would-be responsive to weather variability, while the quantity exported would.not. Irrigation and flood control schemes could also modify the effects of rainfall on export quantities.,
Data Sources
The study used secondary data from F.A.0. Trade and Production Yearbooks, United Nations Surveys of Economic Conditions in Africa, International Monetary Fund Surveys of African Economies and country statistical abstracts and reports prepared by different missions. Export values in U.S. dollars (Table 4) were deflated for all calculations. The deflation procedure used was:
current dollar exports implicit deflator (b)
constant dollar exports X) 160
so that
constant dollar exports (x) y (100)
b
The implicit deflator used for each country was the mean value of deflators for each year calculated in the United Nations' Survey of Economic Conditions in Africa.
Year Kenya Nigeria Tanzania Zaire
--------------------- Million U. S. dollars -------------------1950 9.1 101.0 40.0 105.0
1951 7.5 76.8 37.4 80.0
1952 9.0 70.8 40.2 100.0
1953 11.0 48.0 41.8 102.0
1954 7.5 -54.0 33.0 102.0
1955 8.0 66.0 42.0 118.0
1956 9.0 72.0 42.0 140.0
1957 10.0 90.0 42.0 148.0
1958 10.0 96.0 42.0 152.0
1959 9.5 90.0 46.2 164.0
1960 10.4 96.0 45.7 166.0
1961 15.0 96.0 46.4 160.0
1962 13.5 108.0 46.4 142.0,
1963 14.0 120.0 55.0 142.0
1964 16.0 150.0 56.2 144.0
1,965 21.0 144.0 52.8 142.0
1966 20.0 162.0 60.5 140.0
1967 18.0 189.0 70.4 126.0
1968 22.0 150.0 56.1 120.0
1969 20.0 180.0 68.2 124.0
1970 22.5 189.0 70.4 112.0
1971 21.0 150.0 77.0 108.0
1972 20.0 149.0 77.0 84.0
Earnings from major a- a
agriculturall exports, constant dollars,
1950-1972
Table 4.--
a Major exports:
Kenya, Tanzania - Coffee, tea, cotton and sisal.
Nigeria Zai re
Groundnuts, cocoa, palm oil and rubber. Coffee, tea, palm oil and rubber.
Source: Computed from F. A. O.,.Production Yearbook and Trade Yearbook,
selected years.
EMPIRICAL FINDINGS
In interpreting the empirical findings, a specific conceptual framework was utilized. That framework was one that maintains that export price stability depends mainly on two factors: (a) the fluctuations in demand for or supply of export commodities, and (b) the efficiency of existing stabilization schemes (Rowe). Evidence suggests that exports with relatively stable prices (compared to quantity variations) are those that continue to be supported by international commodity agreements. Tea, although protected up to 1955 by the International Tea Agreement, has since been subjected to free trade. This change in status largely resulted from the assumption and belief that its final demand and production were very inelastic. Sisal, on the other hand, has never been individually chartered by an international commodity control scheme. It is, however, a part of an international Conference of Hard Fibers Study Group sponsored by F.A.0. The movement of the price of sisal after the Second World War has been characterized by substantial ups and downs. Sisal price was low during the interwar period, rose in the first half of the 1950's, then fell until the mid-1960's (Rowe, Kriesel et al). The objectives of this study did not allow further investigation of the effects of international commodity agreements on price stability and export earnings of African countries. This subject might however, be a fruitful one for further research.
Price and Quantity Variability
From the estimating procedure given in equation (8), it was found that of the three study countries for which coffee is a common export crop, two countries (Kenya and Tanzania) exhibited a relatively higher percentage of quantity variability compared to price variability (Table 5). For Kenya, Tanzania and Zaire, three countries for which tea is a common export crop, results indicate that in two of these three countries (Kenya and Zaire) price affects variability more than does quantity. Price variability is proportionally more important for sisal, a crop common to Kenya and Tanzania. Other common crops such as cotton, palm oil and rubber show that quantity variability is the most important component of the
overall variability (Table 5). Thus of six common crops among the countries, four crops (coffee, cotton, palm oil and rubber) were found to be characterized by quantity variability, whereas two (tea and sisal) were subjected to higher price variability.
For these four countries, cocoa and groundnuts are major exports only in Nigeria. These commodities are affected more by quantity variability than by price fluctuations. In the aggregate, the evidence suggests that, variability in export earnings is largely associated with the quantity rather than the price component.
Table 5.--Variability in agricultural export earnings attributed to price and quantity changes, 1950-73
Kenya Nigeria Tanzania Zaire
Variability attributed to
Crop Price Quantity Price Quantity Price Quantity Price Quantity
--------------------------------- Percent--------------------Coffee 14 86 35 65 66 34
Tea 55 45 44 56 60 40
Cotton 18 82 4 96
Sisal 73 27 81 1.9
Groundnuts 12 88
Cocoa 34 64
Palm Oil 13 87 35 65
Rubber 14 86 25 75
These results are consistent with findings in two other studies (United Nations, 1952; Mintz).
In addition to the price and quantity variability calculated, an interaction component was computed. The meaning of this component has been subjected to many interpretations (Motha, et al). In this study, it is simply interpreted as the variability resulting from interaction between price and quantity variations. Its impact on the total variability stems from the fact that, if the correlation coefficient between price and quantity variations is either positive or negative, the interaction component variability will be, respectively, either positive or negative. In the former case, earnings variability will be greater than
,the sum of both price and quantity variations. In the latter case, it will reduce the total variability and contribute to the stabilization process. In this study, the interaction component, whether positive or negative, was so small that its effect on total variability was negligible.
One reason for the small size of this interaction component for each commodity is the small share in total, world exports represented by each country's exports, in most cases. Only Nigerian exports of cocoa and palm oil, sisal shipments from Tanzania and palm oil exports from Zaire accounted for more than ten percent of the value of world exports in each of those commodities (Table 18).
Patterns and Trends in Export Crop Diversification
While all four study countries may be considered as highly diversified in terms ofthe number of export crops grown, the results of this study indicate that when diversification is defined as given in equations
(7) and (9), all four countries exhibited a persistent and declining trend in the level of diversification over the study period (Table 6 and Figure 7). Kenya and Tanzania experiencedilsimilar rates of decline of about one percent per year over the 1950-1973 period. The rates of decline in the level of export crop diversification were also similar for Zaire and Nigeria, at over three percent annually (Table 7). When the data series were divided into two time periods, significant differences between countries were found in the annual rates of decline for the periods 1950 to 1960 and 1960 to 1973.
The first period, 1950-1960, was one of colonialism, in which policy emphasis was placed on specialization in agriculture. The second period was one of political independence and nationalism, with policy orientation towards the integration of the agricultural export sector into the national economy. From 1950 to 1960, the export crop sectors of all four countries were becoming less diversified, but at a decreasing rate. In the later period, the rate of decline in diversification slowed markedly in Kenya, Tanzania, and Zaire, but increased in Nigeria. These trends demonstrate that the patterns of export crop development were determined by a crop specialization process.
it will be recalled from the section on study area characteristics
Table 6.--Export crap diversification indices, 1950-73
Yea r Kenya Nigeria Tanzania Zaire
1950 144 160 141 134
1951 157 142 130 124
1952 148 150 137 109
1953 142 139 142 105
1954 134 138 120 92
1955 132 142 130 100
1956 129 130 117 91
1957 120 120 117 90
1958 130 110 117 95
1959 126 125 118 74
1960 130 127 117 54
1961 122 121 113 47
1962 122 125 105 70
1963 120 134 1950
1964 118 110 100 45
1965 123 98 105 40
1966 117 110 108 30
1967 110 93 100 23
1968 119 90 110 30
1969 112 60 94 32
1970 120 70 99 30
1971 118 60 105 31
1972 120 45 110 28
1973 119 25 109 31
Source: Calculated from F.A.0. Trade Yearbook.
Kenya
.--- Nigeria . Tanzania
. Zaire
150 -
X
e
*r
*1 100 to
0
U
-
-I0-
' B
N.r
'.4
"'~'-.4
II I I !
i I i I
I I I ~ I* ' ' I V *N
1950 1955 1960 1965
Figure 7.--Export crop diversification trends, 1950-72 (1950=100)
I I I i i i i I i i I i I I
1970
Table 7.--Average annual rates of decline in diversification indices,
selected periods
Country 1950-1973 1950-1960 1960-1973
--------------------- Percent -----------------------Kenya 0.87 1.71 0.19
Nigeria 3.39 2.42 5.73
Tanzania 1.19 1.82 0.82
Zaire 3.26 5.67 3.23
that export Crops are distributed in specialized geographical regions. Furthermore, distributions occur in such a way that competition between major export crops for land is not great. Thus, the tendency for a country to concentrate on one or two export crops may be explained in terms of the persistent growth in the production of certain export crops in one or two regions, relative to other regions. In the case of Kenya, the quantities of coffee and tea exports increased more than those of cotton and sisal since 1950 (Table 8). In terms of regions, it may be argued that the northern and coastal zones were growing more slowly than the southwestern region where coffee and tea are produced (Figure 3).
Table 8.--Average annual growth rates of quantities exported, major Commodities, 1950-73
crops Kenya Nigeria Tanzania Zaire
------------------ Percent per year -------------------Coffee 1.89 .86 1.85
Tea 2.49 6.06 2.27
Cotton .33 5.18
Sisal .18 .50
Oroundnuts 30
Cocoa .68
Palm Oil -.20 68
Rubber 2.53 .16
Source: Computed from F.A.O., Trade Yearbook, selected years.
Cocoa and rubber led export quantity growth in Nigeria (Table 8). Cocoa exports, with a rate of growth of .68 percent, were lower than those from rubber at 2.53 percent. Groundnuts was third with an overall growth rate of .30 percent, while palm oil exports declined at -.20 percent per year. The high growth rate exhibited by rubber exports was not steady, but characterized by sporadic increases in quantities exported. It is unlikely that such a high rate of increase will be maintained, since more than one half of the area planted to rubber was expected to be out of tapping after 1974 (Tims). While rubber has been a leading export, it is felt that cocoa and groundnuts are the major export crops in which Nigeria has specialized. The southwestern and northern regions (Figure 4) have steadily increased their exports of cocoa and groundnuts.
An Tanzania,, growth rates for tea and cotton were 6.06 and 5. '80 percent per year respectively, far greater than those for coffee and sisal (Table 8). Export growth rates of coffee and tea for Zaire surpassed those of palm oil and rubber (Table 8). Coffee and tea are mainly produced in the eastern part of the country, while palm oil and rubber are mainly grown in the western part (Figure 6).
Effect of Export Crop Diversification on
Levels and Variations in Agricultural Export Earnings
In general, it appears that export crop diversification is working against high levels of agricultural export earnings. The magnitude and significance of the regression coefficients from equation (13) for each country are shown below:
Kenya:
Z= -3123* -73.51 D *+ 8491.18.*
t (1452) (32.1) t (3840)
R2 .619; DW = 1.679
Nigeria:
Z -4829k -12.42 D ~ + 1349 D
t (1871.7) (4.90) (512.92),
R= .48,; OW=2.33
Tanzania:
Z=-101307 ~ -239.96 D t + 272 26 D2 *
t (5388) (124.33) (14329) t
Tanzania:, continued.
R2 .51; DW = 2.60
Zaire:
Z -2149 -17.23 D + 818 D2
t t t
(2653) (20.3) (941.3)
2
R 07; DW = .694
where,
Z t = values of agricultural export earnings as defined in
equation (13),
Dt = diversification index as defined in equation (13),
= b-coefficient significant at the 10 percent level,
= b-coefficient significant at the 5 percent level,
= values in parenthesis are standard errors.
It appears that, while export crop diversification (Dt) tends to
reduce the level of agricultural export earnings, the long term intensification of the diversification process tends to shift the earnings to higher levels. This is the interpretation of the results with the Dt squared variable defined as intensified diversification. The coefficients are all significant, except for Zaire; but even there the signs are consistent with those of other countries.
Comparing these findings from equation (13) with those from equations
(7) and (9) yields important conclusions. Export crop production in all four countries became more specialized between 1950 and 1973 (Table 7). Since equation (13) shows an inverse relationship between the level of diversification and export earnings, it would be-expected that agricultural export earnings of the four countries increased.
Agricultural export earnings from all four countries grew over the period (Table 9 and Figure 2). Kenyan earnings grew at higher rates than those of Nigeria and Tanzania, and all three countries showed much higher growth rates than did Zaire. The relatively higher rate of growth in earnings in Kenya was associated with a smaller rate of decline in the level of diversification, compared with Nigeria and Zaire (Table 10).
Agricultural export earnings grew more rapidly after 1960 in Kenya, Nigeria, and Tanzania than prior to that year. However, the rate of earnings growth in Zaire of over 7 percent annually showed a marked
Table 9.--Index values of agricultural export earnings, 1950-72 (1950=100)
Year Kenya Nigeria Tanzania Zaire
1950 100 100 100 100
1951 82 76 94 76
1952 99 70 101 95
1953 121 48 105 97
1954 82 53 83 97
1955 88 65 105 112
1956 99 71 105 133
1957 110 89 106 141
1958 110 95 105 145
1959 104 89. 110 156
1960 114 95 114 158
1961 165 95 116 152
1962 148 107 116 135
1963 154 119 138 135
1964 176 149 141 137
1965 231 143 132 135
1966 220 160 151 133
1967 195 187 176 120
1968 242 149 140 114
1969 220 178 171 118
1970 247 187 176 107
1971 231 149 193 103
1972 220 148 193 80
Source: Calculated from Table 4.
Table I0.--Average annual rates of change in agricultural export earnings
and in diversification indices, and average instability indices,
selected periods
Country and Unit 1950-1972 1950-1960 1960-1972
measure
Kenya
earnings percent 6.73 3.63 6.82
diversification of -0.87 -1.71 -0.19
instability index .35 .33 .39
Nigeria
earnings percent 4.38 1.34 6.09
diversification -3.39 -24 5.73
instability index .44 .33 .50
Tanzania
earnings percent 4.13 1.76 5.19
diversifi
cation -1.19 -1.82 -0.82
instability index .35 .38 .34
Zaire
earnings percent 0.30 7.19 -3.17
diversification -3.26 -5.67 -3.23
instability index .44 .36 .51
decline after 1960.
Apparently, governments of African countries recognized the inverse relationship between export crop diversification and the level of export earnings, and pursued policies of export crop specialization as a means of maximizing earnings. The direct relationship between intensified expor crop diversification and earnings shown in equation (13) does, however, support Jorberg's argument that diversification could be helpful for longterm growth and development. While diversification reduces the level of export earnings in the short run, the long-run effects on earnings are positive.
The second aspect of the third objective of the study was concerned with the effect of export crop diversification on the variation or instability of earnings. On this aspect, the results from equation (15) are neither conclusive nor consistent, as shown below.
Kenya: 2
It= - 1.68 + 3.42 Dt - 31.41 Dt
(7.60) (12.39) (-.283)
R2 = .006; DW = 2.41
Nigeria:
it 5.75 - 9.37 Dt - 4.08 D
(4.79) (18.07) t (3.37)
R2 = .104 DW = 1.95
Tanzania:
it -27.53* + 45.77 Dt* -18.68 D*
(19.02) (14.62) (15.89)
R2 = .398 DW - 1.404
Zaire: 2
it= 21.53* - 48.72 Dt* + 25.19 D
(13.20) (36.63) (21.34) t
R2 = .117; DW - 2.17
where,
It = instability index as defined in equation (15),
Dt = diversification as defined previously,
* = b-coefficient significant at the 10 percent level, and
( )= values in parentheses are standard errors.
All coefficients in equations for Tanzania and Zaire are significant. This may indicate that export crop diversification is positively related with earnings instability in Tanzania, and inversely related to Zaire. However, R2 values are low for all equations, and all coefficients are statistically insignificant for Nigeria and Kenya. No conclusive statements can be made. Findings for Tanzania and Zaire may indicate, though, that diversification might be an appropriate policy for one country, but inappropriate for another.
Differences between countries could be due to the nature and
characteristics of export crops produced in each. Price elasticities for each of these crops differ greatly, so that identical changes in quantities exported could result in very different changes in their respective revenues and subsequent differences in instability values. Differences between instability index values for Tanzania and Zaire are largely due to sisal. Other major determinants are cotton in Tanzania and palm oil and natural rubber in Zaire, since tea and coffee are common to both. These observations are supported by the fact that average instability index values for Kenya and Tanzania, which grow similar crops, are similar at a .35 (Table 11). Nigeria and Zaire, while not having all four crops in common, both had average instability index values of .44 (Table 11).
Food Supply and Demand Growth Rate Disequilibrium
The contention (Fitzgerald, et al) that the export crop sector is competitive with the domestic food sector for national resources led to an investigation of the rates of growth in supply and demand of certain food items. The equilibrium relationship stated in equation (16) was used with elasticities for food estimated by FAO for 1965-1973 (F.A.0., 1967). Although these data appear to overstate the growth rates of food demand, they nevertheless provide some insights and indicate the need for further investigation.
All four countries exhibited significant disequilibrium between
supply and demand for the selected food crops (Table 12). Nigeria, however, was self-sufficient in major staple carbohydrate foods such as yams, cassava, guinea corn, rice and millet (Johnson, et al, 1969).
Tablell.-Instability indices of agricultural export earnings,. 1950-72
Year Kenya Nigeria Tanzania 'Zaire
1950 .15 .12 .28 .20
1951 .40 .20 .31 .10
1952 .35 .30 .25 .25
1953 .42 .35 .38 .30
1954 .14 .41 .34 .40
1955 .19 .53 .40 .42
1956 .27 .38 .50 .44
1957 .33 .30 .50 .50
1958 .33 .32 .45 .49
1959 .50 .40 .36 .46
1960 .60 .38 .38 .41
191.54 .42 .30 .38
1962 .42 .45 .25.0
1963 .55 .48 .50 .51
1964 .50 .50 .52 .52
1965, .45 .50 .53 .60
1966 .31 .60 .43 .50
1967 .14 .65 .26 .60
1968 .25 50.28 .59
1969 .15 .47 .20, .58
1970 .41 .50 .30 .50
1971 .30 .55 .31 .40
1972 .40 .50 .19 .55
Average .35 .44 35.44
Three of the selected food items (maize, beans, and sugar) belong to what CSNRD classified as important import substitution crops and nutritionally superior foods. Sugar is considered an import substitute while maize and beans are included among nutritionally superior foods (Johnson, et al,,1969), It seems reasonable to conclude that Nigeria was self-sufficient in staple carbohydrate foods, but experienced shortages in import substitutes. However, import substitute foods faced poor market prospects in Nigeria because of lack of effective demand for nutritionally superior goods such as eggs, milk, meat and other processed items (Johnson, et al, 1969).
The growth rate in demand for food in Nigeria in the present study was higher than the rate shown in the CSNRD report. There appear to be two reasons. First, the bias, if any, may come from the nature of the data used for income elasticities for food which were not actually measured but projected. Second, the CSNRD study considered effective demand as determined by the disposable income of consumers, while this study considers effective demand as determined by population and income. Thus, it may still be valid to conclude that Nigeria is experiencing shortages in import substitute foods if demand is conceptualized in terms of population and income. In fact, a simulation study conducted in Nigeria just after the CSNRD study confirms the results of this work (Johnson, et al, 1971,, p. 298).
However, we should add that the two strategies involving export crop modernization programs did
create some long-run adverse effects in the nonagricultural sector. The increased profitability
of export crops stimulated agricultural producer
Lonsortium for Study.-of Nigerian Rural Development, a joint venture by Michigan State University, 'University of Wisconsin, Ohio State University, Colorado State University, Kansas State University, U.S.D.A., and the Research Triangle Institute, to work on many aspects of agricultural and rural development in Nigeria. Nutritionally superior foods are eggs, maize, beans, millet, grains, soybeans, meat and milk. The four major import substitute foods are fish products, wheat, milk and cream, and sugar. See Johnson, et al, 1969, pp. 22-14 and pp. 99-100.
Table 12.--Differences between annual rates of growth
demand for selected food items, 1965-73
of supply and
Annual rate of growth
Country Food Supply Demand Supply < Demand
.-- --Percent -----Kenya Rice .177 3.40 <
Maize -.460 3.35 <
Beans .0006 3.37
Sugar -2.02 3.45 <
Nigeria Rice .23 2.44 <
Maize +19.96 2.42
Beans -1.29 2.44 <
Sugar 1.53 2.51 <
Tanzania Rice 1.9 2.77 <
Maize 2.937 2.75 >
Beans .24 2.77 <
Sugar 3.98 2.87 >
Zaire Rice 2.12 2.690 <
Maize .913 2.695 <
Beans .021 2.693 <
Sugar 1.42 2.683 <
Source: Computed from F.AO. Agricultural Commodities, Projections for 1975 and 1985, Rome 1967, and F.A.0. Production Yearbook, selected years.
demands for nonagricultural goods and consequently,
the nonagricultural population's demand for food.
In addition, the profitability caused some producers to switch from food crop production to
export crop production. Consequently, the price for food increased substantially in both programs
involving export crop modernization because food
demand increased and food supply decreased.
The above argument is illustrated in Figure 8, which also shows the price effects of competition between the export and domestic agriculture sectors.
Growth in food demand exceeded that for supply in both Kenya and Zaire. For Kenya, a recent report by the World Bank (Burrows) stated:
Many rural families have diets deficient in calories, and Vitamins A, B2, and C. Although most pronounced
per pound
.015
.012
009
Food crop modernization Only export crop modernization .006- Export crop modernization with
marketing boards take-off
.003 - 1 9 1 1
1965 1970 Years 1975 1980
Figure 8.--Average price of food staples in northern Nigeria, under alternative simulated policies, 1965-1980. Source: A Generalized Simulation Approach to Agricultural Sector Analysis: With Special Reference to Nigeria, Michigan State University, East Lansing, 1971.
in Nyanza Province and parts of the eastern plateau, and variable by season, the deficiencies are nationwide. Pressure on land is increasing, people are
moving into marginal areas, maize has largely taken
over from the more protein-rich millets and sorghums,
and there is no reason to believe that pulse production
is going up at a faster rate than population.
Food price increases in Zaire' are evidence that increases in supply failed to keep pace with growth in demand. Lack of data prevents any conclusions for Tanzania.
It appears that "modernization" of export crop sectors in the study countries, without comparable effort in food sectors, is the major factor in the imbalance between the two. The problem, though related to the allocation of land and labor between the two sectors, is nevertheless more far-reaching. Food and export crops compete for land to some degree, but food crops have a much larger share of cultivated land than do export crops (Table 13). The scientific and economic infrastruc-. ture allocated to the export sector has made it relatively more productiv( and profitable than the domestic food sector. One study from Africa (Yudelman, p. 281) and another from the Caribbean (Davis, p. 142) support the validity of this hypothesis.
Table 13. --Land distribution among food and export crops, 1970
Country Food crops Export crops
------- -------------- Percent --------------Kenya 80 20
Nigeria 70 30
Ta nzan ia 60 40
Zaire 78 22
Source: F.A.O., World Crop Statistics.
The two studies express strikingly similar views, as Yudelman writes:
The philosophy of agricultural development in many
parts of Africa has been oriented toward a commodity,
approach, which has required the concentration of investments and skills on the expansion of output of those commodities for which there is effective demand. In most countries, these are necessarily
export commodities; . However, whatever the merits
of this approach for economic growth, it has led to a concentration of investment in the areas or
regions with higher income producers of export crops.
This tends to widen the regional differential; farm
incomes in those regions not producing for export
tend to lag further behind as development proceeds.
Davis, in analyzing the relationship between agricultural research and agricultural development in the Caribbean, states:
The differential level of investment between the export and the domestic research systems raises a
further question regarding the extent to which the
differential might be related to significant differences in the marginal value productivity of
investment. There is a reason to suspect that the
marginal value productivity of investment of the
export system was considerably higher than that of
its domestic counterpart. This suspicion is related to the generally higher output price of export crops, .and significantly larger marginal productivity of
research investment for the export system.
Thus, the superior performance of export crops over domestic food
crops can be explained by the capital investment allocated to the former, in terms of modern inputs, research infrastructure and modern management. If similar resources were also allocated to the food sector, it would probably grow and develop at a comparable rate. 8 The relatively low growth rates of agricultural export earnings for the four countries, though, suggest that there are less than optimum levels of investment in the export agriculture sectors.
African Choice
This section discusses the general objective of the study, that of identifying policies in African countries that affect the overall economy and the export agricultural sector. It was suggested earlier that, consciously or unconsciously, the four African countries studied have
8The authors acknowledge the importance of the point made by
P.J. van Blokland that subsistence agricultural production is persistently underestimated because very little data are available. More information is collected and available on export commodities, so that input or "performance" of export sectors may be overstated relative to production for on-farM or domestic consumption.
opted for higher rather than for more stable incomes. In fact, the shortrun effect of the diversification process was found to reduce export incom,
Evidence from this study shows that the four countries have become more specialized in their export agricultural sectors. However, this trend raises two questions. First, what is likely to be the impact on the allocation of land resources between different export crops? Second, what is the most effective use of increased agricultural export earnings as a development tool?
The answers to these two questions were sought by using individual country data generated by the models, and by referring to the literature on the subject.
Effects on Export Crop Specialization on Land Allocation
Kenya,
Results with the two-stage least squares (TSLS) system (equations 17 and 18) show Kenya heavily dependent on'the four crops described earlier.
Results:
Z = 300.945 + .9732 (Q1't) + .:220 (Qj) + .153
fi'tA (19 582) 03l82 (.076 )
(Q3j i
+ .452 )(Q 4i)
(.135) j
- 2.829 (D at)
(1.25)
where,
=i agricultural export earnings as defined in equation (17),
Qljt =quantity of coffee exported by Kenya, in thousands of metric
tons in period t,
Q2jt quantity of tea exported by Kenya, in thousands of metric'
tons in period t,
Q3jt = quantity of cotton exported by Kenya, in thousands of metric
'~tons in period t,
Q4jt =quantity of sisal exported by Kenya, in thousands of metric
tons in period t,
0jt = diversification index, as defined in equations (6) and (7),
* = b-coefficient significant at the 10 percent level,
** = b-coefficient significant at the 5 percent level,
=b-coefficient significant at the I percent level,
()= values in parenthesis are standard errors.
Table 14.--Export supply function estimates, Kenyaa
Crops Constant Pit-1 Pot-1 A it Aot RFt T R2 DW
Coffee 50.435 -.250 1.562 - .406 .323*** -2.741 23.43*** .920 2.338
(326) (.379) (1.287) (1.90) (.097) (5.23) (13.88) Tea -313.73* .709 .294 1.94** 1.64 - .940 -6.014 .982 1.862
(240) (1.039) (.718) (.218) (1.46) (4.413) (8.1808) Cotton - 44.837 .827 .3665 .3598 .3435 -1.204 .5554 .411 1.610
(193.76) (.780) (.654) (.464) (.800) (4.315) (.773)
Sisal - 18.336 .909 -.543 1.029** -.1926 2.050 2.244 .781 1.433
(101.81) (1.32) (.306) (.451) (.291) (1.95) (3.46)
, Aot, RFt, T, u). Linear form.
* = b-coefficient significant at .10
** = b-coefficient significant at .05
*** = b-coefficient significant at .01
( ) = values in parentheses are standard errors.
Qit = f(Pit-1' Pot-1) Ai t
The four crops have different weights, as coffee has the largest coefficient with .973, followed by sisal (.452), tea (.220) and cotton (.153). In the 1950-73 period, more coffee was produced than other commodities. Export supply functions also show that the quantity of coffee exported increased over time (Table 14). A legitimate question is whether the increase in coffee exports affects the allocation of land to other crops. The coefficient in the coffee export supply function (Aot) is positive and significant, suggesting that coffee acreage moves in the same direction as the area of other crops taken as a whole. This suggests that the specialization in coffee production is not hindering the development of other crops, but that those regions specializing in coffee production are growing at a proportionally higher rate than others.
Nigeria
Agricultural export earnings of Nigeria are positively related to cocoa, groundnut and rubber exports, but inversely related to palm oil exports. Results:
Zjt 0 15.90"* + .416 (Qljt) + .979 (Qit - 130 (Q3jt)
(5.24) (.130) (.115) 2 (055)
+ .267 (Q4t)* - 2.63
(.173) ( (10.64) j
where,
Z = agricultural export earnings as defined in equation (17),
Qljt = quantity of groundnuts exported by Nigeria, in thousands of
metric tons in period t,
Q2jt = quantity of cocoa exported by Nigeria, in thousands of metric
tons in period t,
Q3it = quantity of palm oil exported by Nigeria, in thousands of
metric tons in period t,
Q. = quantity of rubber exported by Nigeria, in thousands of
4jt metric tons in period t,
Djt = diversification index, as defined earlier,
, = b-coefficients significant at the 10 percent level,
** = b-coefficients significant at the 5 percent level,
= b-coefficients significant at the 1 percent level,
( ) = values in parentheses are standard errors.
The magnitude of the coefficients shows that cocoa (.979) and groundnuts (.416) have the greatest impact on the level of Nigerian agricultural
Table 15.--Export supply function estimates, Nigeriaa
Crops Constant Pit-1 Ot-1 Ait Aot RFt T R2 DW
Groundnuts -121.03 -1974** .297 - .151 - .776 8.752*** 3.970** .835 2.415
(138.16) (.438) (.454) (.166) (2.14) (2.46) (2.56)
Cocoa -73.747 -.4346** .6637 - .9894 .2466 2.989 6.537 .785 2.905
(153) (.238) (.635) (2.61) (.301) (2.46) (3.406)
Palm'Oil -208.83** .323 -.324 1.122*** .2733** 5.361*** -8.160*** .936 1.584
(77.92) (2.09) (2.47) (.250) (.141) (1.27) (1.58)
N. Rubber -592.10*** 1.498*** -.285 .914*** .4468* 11.744 5.973 .974 2.360
(139.1) (.369) (.526) (.104) (331) (2.472) (2.666)
= f(Pit-1' Pot-'1 Ait, Aot' RFt, T, u).
= b-coefficient significant = b-coefficient significant = b-coefficient significant = values in parentheses are
Linear form.
at .10 at .05 at .01 standard errors.
a it
**
( )
export earnings. Natural rubber ranks third (.267), while palm oil adversely affects agricultural export earnings. These results, showing that Nigeria specialized in cocoa and groundnuts, are confirmed by the export supply function (Table 15).
The regression coefficients of the export supply variable (Aot) for cocoa and groundnuts Are not significant, so j~t is not possible to say if. increases in those two exports restricted other export crops. However,
regression coefficients for rubber and palm oil are both positive and significant. This suggests that the acreage of all four crops moved in the same direction. The negative trend in the volume of palm oil exported can possibly be explained by the fact that domestic consumption of palm oil has been increasing at the same rate as population increase (Helleiner, 1965). In general the same conclusions drawn for Kenya based on different rates of growth among export crops for various regions may be drawn for Nigeria. The northern and southwestern regions of Nigeria have been exporting more groundnuts and cocoa, respectively, than other regions.
Tanzania
For Tanzania, the results from the TSLS system shown below are not significant, except for the coefficient related to cotton exports and the diversification index.
= it. 1947 - .51 (Qut) + 2.3(~t + (173) (Q3it)
+ .670 Ni4t) - 9.16 Dt
(.680) (6.88)
where,
Z = agricultural export earnings as defined in equation (17),
Qljt = quantity of coffee exported by Tanzania, thousands of metric
tninperiod t,
Q2jt - quantity of tea exported by Tanzania, in thousands of metric
tons in period t,
Q3j t = quantity of cotton exported by Tanzania, in thousands of
metric tons in period t,
Q4jt = quantity of sisal exported by Tanzania, in thousands of metric
tons in period t,
0jt =diversification index as defined earlier,
- b-coefficient significant at the 10 percent level,
** = b-coefficient significant at the 5 percent level,
* values in parentheses are standard errors.
Table 16.--Export supply function estimates, Tanzaniaa
Crops Constant Pit-1 Pot-1 Ait Aot RFt T
Coffee -223.897 -.4724 -1.2311** .2587 .6320* 9.5200* -3.7975
(268.9) (1.24) (.677) (.332) (.496) (7.40) (6.55)
Tea -596.142 -.7214 -17795** -1.0401 1.2865** 22.5307 33,1879
(805) (2.26) (104.9) (2.35) (.821) (19.5) (13.82)
Cotton 1379.28* 1.8400** 9856 ,1230 3,9652** 50,7574"** 28.765
(950) (1.114) (1.02) (.427) (1.794) (23.12) (14.50)
Sisal 137.527* -.4194 -.4203 1.1008*** -.2423** -3.0153 6.2876**
(92.92) (.960) (1.26) (.291) (.086) (2.27) (2.01)
a it
*)
**
( )
Linear form.
b-coefficient significant b-coefficient significant b-coefficient significant values in parentheses are
at .10 at .05 at .01 standard errors.
= f(Pit-1' Pot-1, Ait, Aot, RFt, T, u).
On the basis of these results, the only statement that may be made is that export crops earnings depend largely on the volume of cotton produced. The relationships between different crop areas indicate that only sisal is affected by an increase in production of other crops as a whole (Table 16). This may mean that sisal is competing for land with cotton, tea, and coffee
Zaire
For Zaire, the logarithmic form of the TSLS equation was used.
Log Z it 1.763 + 1.465 1log (QljtO** - .375 log Qd
(5.21) (.6511) (.734)
+ (.155 log (Qjt + . 370 log (Q 4jt) 1:*390 log (D it)
.302) (.961)1.2
where,
Zj = agricultural export earnings as defined in equation (13),
Qljt = quantity of coffee exported by Zaire, in thousands of metric
tons in period t,
Q2jt =quantity of tea exported by Zaire, in thousands of metric
tons in period t,
Q3jt =quantity of palm oil exported by Zaire, in thousands of metric
tons in period t,
Q4jt = quantity of natural rubber exported by Zaire, in thousands of
metric tons in period t,
D0i = diversification index, as defined earlier,
=jtofiin infcnta h Opretlvl
= b-coefficient significant at the 10 percent level,
C)= values in parentheses are standard errors.
Coffee is the only crop of the four with a significant coefficient. As discussed for the other countries, the results above and in Table 17 suggest that when the coffee area increases, area in other crops as a whole also increases. Again, the same arguments regarding the differential between growth rates of exports of different specialized regions applies to this case.
The Use of Agricultural Export Earnings for Development Goals
Agricultural export earnings of the four study countries increased
over the study period (Table 10). Kenya's earnings increased at 6.7 percent per year, while those of Nigeria, and Tanzania grew at 4.4 and 4.1
Table 17.--Export supply function estimates, Zairea
Crops Constant Pit-1 Pot-1 Ait Aot RFt T R2 DW
Coffee -10.804 .5429 .4178 -.630** .460** 3.692 .969*** .409 1.613
(8.69) (1.87) (.417) (.311) (.240) (2.074) (.294)
Tea 8.171 .6613* -.2237 .3116 1.135** -3.247** .9363*** .975 2.001
(7.32) (.514) (.439) (.258) (.439) (1.31) (.333)
PalmOil -3.243 -.3119** .1597 1.5901*** -.2116 .6044 -.1260 .922 1.347
(3.05) (.139) (.178) (1.62) (.207) (.598) (.138)
N. Rubber -9.548** .2011* -.1364 .7111 -.1824 3.022 .9849 .933 1.900
(4.87) (.152) (.256) (.163) (.208) (1.04) (1.93)
a Qit = f(Pit- Pot-1' Ait' Aot RFt' T, u).
b-coefficient significant b-coefficient significant b-coefficient significant values in parentheses are
Double logarithmic form.
at .10 at .05 at .01
standard errors.
*
**
()
percent respectively. Zaire experienced a much slower rate of growth in export earnings with 0.3 percent annually (Table 10). If export earnings .grow at low rates and agricultural exports constitute a relatively large share of GOP, there is reason to expect that the rate of overall development will be slow.
Several authors have attempted to explain why the dynamics of the export economy have not broken the vicious circle of poverty in LOCs (Singer, Prebisch and Levin). Singer and Prebisch blamed the specialized nature and structural patterns of export economies. Levin argued that the structure of export economies and their isolation from national markets is the basis of the development handicap. Johnson and others (1971) suggested that agricultural export earnings surpluses gained by marketing b 'oards'were not used in the agricultural sector to meet developmpent goals. For Nigeria, however, this view is strongly opposed (Aboyade, Helleiner, 1966), and in Tanzania, Kriesel believed agricultural export earnings were used effectively in the agricultural sector.
The findings of this study suggest that the performance and returns of marketing board investments in development projects have been poor. There are two reasons for this conclusion: First, the apparent lack of responsiveness of marketing boards to changes in world prices (Tables 14-17); and second, relatively low annual rates of growth in export earnings (Table 10 and Figure 2), despite 'the use of substantial land resources for export crop production.
Lack of responsiveness of marketing boards to world prices may be explained by three factors. The first is pricing policies which kept producer prices well below world prices, removing incentives to increased production among peasant farmers. The second factor relates to the lack of technological investment in agriculture, and the fixity of factors of production. Fixity of factors means that farmers do not have a wide choice of input combinations, but instead, rely heavily on land and labor. Factors such as fertilizers and capital are used little, partially because farmers have incomes too low to afford them. Marketing board surpluses passed on to farmers would enable them to acquire inputs that would increase production. However, such surpluses have been siphoned off for politicall parties" (Nixon) and for "dubious projects" (Eicher, 1970) in many cases. The third factor is the perennial nature of many export crops and the resulting restrictions on the degree of flexibility in earning use.
There is evidence that although agricultural export earnings have historically low growth rates, the agricultural export sector is still an appropriate development tool. These low rates appear to be caused primarily by organizational and structural problems. If these problems were removed, the export crop sector could stimulate overall agricultural development. These structural problems, although-pronounced in countries with marketing boards, are also present in other nations. In the latter countries, the problems are largely related to direct government fiscal and pricing policies affecting the farm sector. Thus, rather than accuse the export sector of hindering the development process, efforts should be made to stimulate expansion of the overall agricultural sector, without reducing the export sector (McPherson, 1974). But, whether such an expansion should be based on agricultural diversification policies is still not answered with the results reported here.
The findings suggest, however, that there are some positive effects, accruing to diversification. Furthermore, the findings indicate that specialization in certain commodities is due to the fact that certain regions are expanding their exportable supplies of those commodities proportionally more than others. Thus, diversification can be a viable policy if used to stimulate export crop production in economically retarded areas. On the other hand, diversification can have a retarding Influence if it involves the use of export crops in developing areas to achieve sub-optimal levels of agricultural diversification in the hope of achieving economic development.
SUMMARY AND CONCLUSIONS
Summary
This study focused on four Africancountries: Kenya, Nigeria,
Tanzania and Zaire. The objectives were (a) to identify whether agricultural export earnings fluctuations were determined primarily by quantity or price variability, (b) to investigate trends and patterns in the agricultural export sector, (c) to inquire into the relationships between agricultural export earnings fluctuations and the export crop diversification process and (d) to determine the difference between food supply and food demand growth rates.
Fluctuations in agricultural export earnings were found to be due mainly to variations in quantities exported, rather than to price variability. These quantity variations appear to be related to such factors as the lack of flexibility by institutions responsible for the trading of such export crops, and agro-ecological variability. These institutions have continuously exported the traditional agricultural commodities. Such a policy demonstrates a lack of responsiveness"to world prices. A secondary source of export quantity variation was variability in rainfall and its effects on crop production.
Results of the empirical analysis showed that all four countries, although diversified in terms of the number of export crops produced in different ecological regions, have been specializing in one or two export crops. The pattern of such specialization is not characterized by competition between crops as such, but rather, by unequal growth rates of specialized production regions.
Results from analyzing the relationships between export crop diversification and agricultural export earnings revealed an inverse relationship between these two variables. That is, an attempt to increase diversification would result in reducing the level of agricultural export earnings, at least in the short run. However,,results from the quadratic model reveal that an intensified diversification program may cause the level of agricultural export earnings to rise in the long run.
While the results generated by themodels aimed at analyzing the effects of diversification on export earnings fluctuations were inconclusive, they did indicate that the decrease in diversification since 1950 was accompanied by a higher degree of instability in some countries and by an increase in earnings stability in other countries.
In regard to the difference between growth rates of food supply and fooddemand, it was found that demand for food was increasing more rapidly than food supply in all four countries. The argument based on the land allocation to these two sectors, in conjunction with findings of other authors, suggest that the gap between the two sectors is due to a relatively mild advantage of export crop over food crops, in terms of investment in capital and research infrastructure. Such investments., although sub-optimal in the export crop sector itself, nevertheless have made this sector relatively more "modern" than the domestic food production sector.
For the general objective of the study, an attempt was made to determine if specialization inhibited the development of other crops by reallocating land resources. In general, the areas of leading crops were moving in the same direction as those of lagging crops. One possible explanation is that crops were grown separately in specific ecological .regions and some regions were more committed than others to the growing of these crops. Another aspect explored in the study relates to the low rates of growth of agricultural export earnings under a specialization policy. It was argued that these slow growth rates are due to the lack of an appropriate level of.modernization in the export crop sector. Specialization based mainly on the increased use of only land and labor was held responsible for such low performance.
Conclusions
Since it appears that a large proportion of variability in export earnings is due to fluctuations in quantities, it is then imperative ' to use policies that address themselves more to quantity adjustment than to price regulation. Such policies could be buffer stocks and quota adjustments. However, because of difficulties inherent in international policy enforcement (Edwards and Parikh), emphasis should be given to alternative domestic adjustments such as export crop diversification and appropriate tax structures. These policies would be most appropriate for those countries which have specialized in crops whose variability in quantity is higher. Also, because of the long-run effects of export crop diversification on the level of export earnings, diversification should be considered. But since the effects on instability of this policy would vary according to the country and the physical and economic characteristics of crops introduced, such a policy would have to be adopted cautiously.
The need for caution is apparent when one considers the cases of Tanzania and Zaire. In Tanzania, further diversification would result in higher instability, while in Zaire it would stabilize earnings (equation 15). The results from these countries lead to the conclusion that there exists a range where export diversification has desirable effects but works against stability and higher incomes beyond that range. These ranges would be expected to differ from country to country. Further research, to determine conditions and causes of patterns in different.
countries would be interesting and useful.
The gap between the domestic food sector and the export crop Sector reinforces the feeling that the lack of modernization of the food sector is responsible for such an imbalance. However, the balanced modernization of both export and food sectors would help the former to grow at-a faster pace and at the same time, help the latter sector to keep up with demand, pressures.
Another conclusion regarding the general objective of the study is
related to the finding that there exist unequal growth patterns of export crop production among regions within a country. Such a s-ituation may be. the source-of the observed specialization trend and te ca use of a failure to achieve optimal diversification level's. -It is, believed that efforts to stimulate the export production of economically retarded regions would not only help a country increase its export crop supplies but also help to achieve an optimum degree of diversification. Such an effort requires a combined policy of additional investments in the agricultural of retarded areas and relevant pricing system at the producer level.
APPENDIX
62
Table 18.--Average annual values in current dollars of world and
country exports of major commodities, 1950-73
World Kenya Nigeria Tanzania Zaire
Commodity Value Value % Value % Value % Value %
$1,000,000 $1,000,000 $1,000,000 $1,000,000 $1,000,000
Coffee 2,552.8 35.6 1.4 . . 29.6 1.2 41.3 1.6
Tea 630.9 20.6 3.3 . . 4.2 0.7 1.9 0.3
Cocoa 585.9 -- -- 112.1 19.1 --. .
Cotton 2,307.5 2.0 0.1 -- -- 26.4 1.1 . .
Sisal 119.4 10.5 8.8 -- -- 40.5 33.9 . .
Rubber 1,494.2 -- -- 25.0 1.7 -- 15.6 1.0
Groundnuts 5,063.9 . 79.8 1.6 . . .
Palm Oil 163.0 . 33.5 20.5 . 32.9 20.2
Table 19.--Earnings from major agricultural exports, and total agricul
1950-1973
tural exports, current dollars,
Kenya
Year Coffee Tea Cotton Sisal Total Total Ag. Exp. Earnings
------------- Thousand U.S. Dollars --------------------- ------ Million U.S. Dollars----
1950 1951 1952 1953 1954
1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
11,505 12,937 21,357 19,770
28,049 44,199 23,346
23,266 29,660 28,770 29,740 29,720
30,890 43,160 39,510
52,610 43,920 35,976 47,268 62,428 54,777 69,406
3,560
3,715 3,253
6,069 8,293
8,709 9,819 10,212 12,465 11,851
15,609 20,449 21,446
21,669 29,131 25,520
32,137 34,683 40,183 34,085 49,668
51,129
952
1 ,583 2,101
1,441
2,113 2,289
900
1,415 1,840 2,350 1,760 1,220
1,220 1,810
2,090
2,430 1,760 1,115 2,131 3,437 3,310 3,410 3,913
13,633 25,791 16,096
9,811
7,793 8,048
5,869 6,120 9,683
12,784 11,737 12,105 21,090 16,847 10,785
9,352 5,833 5,157 4,821 5,258 4,264 5,801 13,671
29,650 40,311
43,270 34,275
44,024 62,829 38,824 40,620 51,395 56,369 55,088 58,654
73,649 83,263 74,054 93,523
77,033 74,385 88,903
111 ,306 96,436 128,285 68,713
74.9 62.7
72.5 81.9 87.9 85.7 94.9 111.6
113.6 105.9 156.0 141.0 144.2 157.8 177.4 151.0
203.2 298.5
Continued
Year Cocoa Groundnuts Palm Oil Rubber Total Total Ag. Exp. Earnings
-------------- Thousand U.S. Dollars -------------------------- ------ Million U,.S. Dollars1950 64,292 76,515 63,378 8,760 212,945
1951 95,463 32,915 87,522 27,614 243,514 308.7
1952 92,001 63,566 62,933 15,824 234,324 287.6
1953 81,766 81,222 68,301 11,521 282,810 295.0
1954
1955 71,647 103,998 65,262 26,629 267,536
1956 66,880 74,665 29,246 170,971 324.5
1957 79,475 64,205 37,827 24,116 205,623 300.6
1958 74,938 91,245 34,995 21,878 223,056 331.7
1959 107,210 76,920 38,663 32 ' 490 255,283 417.9
1960 98,159 61,475 36,907 39,880 236,421 403.0
1961 94,490 90,251 37,035 30 730 252,506 416.5
1962 93,372 90,793 24,998 31:490 240,653 356.-0
1963 90,605 102,463 26,222 32,750 252,040 359.7
1964 112,279 95,920 30,111 33,850 272,160 419.5
1965 119,534 105,8054 38,055 30,590 294,033 463.4
1966 79,129 114,280 30,697 32,020 256,126 43a.0
1967 153,127 99,156 3,527 17,770 273,580 412.1
1968 144,874 106,628 39,900 17,670 309,072 405.3
1969 1147,269 100,271 121,300 26,919 395,759 426.8
1970 186,305 60,842 159,000 24,596 430,743 438.6
1971 207,790 34,019 477,800 17,492 731,101 392.4
1972 153,724 32,528 41,800 12,495 240,547 315.2
19,73 170,79,6 69,169 1,400 29,482 270,847 456.5
Continued
1950-1973--Continued
Nigeria
Table 19.--Earnings from major agricultural exports, and total agricultural exports, current dollars,
1950-1973--Continued
Tanzania
Year Coffee Tea Cotton Sisal Total Total Ag. Exp. Earnings
------------- Thousand U. S. Dollars--------------------- ----- Million U.S. Dollars---1950 14,956 574 7,903 50,336: 73,769
1951 21,735 7,387 94,523 123,645
1952 23,743 864 8,792 72,073 105,472
1953 20,429 1,193 11,373 47,226 80,221
1954
1955 26,767 2,353 16,1960 40,457 85,773
19606 35,881 2,336 20,895 42,399 101,511 109.5
1957 19,420 2,625 17,747 26,966 66,758 92.81958 20,702 2,839 20,962 28,786 73,289 97.2
1959 16,080 2,157 18,640 36,559 73,436 105.9.
1960 20,510 3,221 24,710 43,236 91,677 128.6
1961 18,930 3,742 19,020 39,278 80,970 112.1
1962 18,410 4,513 20,700 44,056 87,679 120.6
1963 19,150 4,346 30,010 63,479 116,985 156.1
1964 30,940 4,367 27,670 61,227 124,204 168.5
1965 24,060 4,230 34,190 39,989 102,469 145.8
1966 43,400 6,321 48,990 32,855 130,566 204.4
1967 33,440 6,194 35,190 28,191 103,015 174.2
1968 37,152 6,612 39,653 22,304 105,721 173.8
1969 36,104 6,863 32,886 22,352 98,205 185.3
1970 43,746 5,988 34,616 25,038 109,391 193.4
1971 32,059 5,812 34,409 18,985 91,265 190.9
1972 53,663 7,540 47,172 19,424 127,799 235.6
1973 70,591 7,734 47,767 31,786 157,878 270.2
Continued
-. , w , ,t 4.ujuf w l. WUII QI XAUF'L, dI1 ULa I dgrlc ILurIdI ex pUrl, (urreiiL UU Ildrs,
1950-1973--Continued
Zaire
Year Coffee Tea Palm Oil Rubber Total Total Ag. Exp. Earnings
------------Thousand U. S. Dollars. --------------------- ------- Million U.S. Dollars-1950 36,240 45,667 6,113 88,020
1951 44,634 73,692 15,891 134,217 191.3
1952 38,819 86 50,911 14,293 104,109 151.4
1953 44,680 217 44,155 9,654 98,706 134.2
1954
1955 62,219 1,T15 52,543 22,492 138,369
1956 83,342 1,519 59,959 24,497 169,317
1957 68,178 2,267 34,503 20,374 125,322
1958 63,570 2,958 32,922 20,227 119,677
1959 61,560 2,517 38,261 22,320 124,658
1960 30,210 2,957 30,322 25,840 89,329
1961 23,700 16 31,894 21,490 77,100
1962 14,520 2,268 28,246 20,170 65,204
1963 17,830 2,416 23,570 17,070 60,886
1964 24,190 1,440 21,190 13,630 60,450
1965 14,740 2,011 18,500 9,150 44,401
1966 23,050 2,213 15,450 10,480 51,193
1967 28,060 2,900 21,700 9,610 62,270
1968 34,300 3,500 24,800 9,900 72,500
1969 28,200 1,305 19,269 16,273 65,047
1970 33,900 1,892 28,181 12,759 76,732
1971 41,000 1,300 26,404 12,000 80,704
1972 67,251 2,544 16,600 10,500 96,895
1973 65,000 1,470 17,500 14,000 97,970
Source: F.A.0.,
Trade Yearbook .
Table 20.--Values of comodities exported, current dollars, 1950-1973
Coffee Tea Cocoa
Year Kenya Tanzania Zaired Wo rl1d Kenya Tanzania Zaire World Nigeria World
----------Thousand -------- -Million ---------Thousand --------------Million- -Thiousand- -Million1950 11,505 14,956 36,'240 2,220.4 3,560 574 471.9 64,292 450.1
1951 12,937 21,735 44,634 2,496.9 95,463 553.1
1952 21,357 23,743 38,819 2,449.7 3,715 864 86 380.1 92,001 516.8
1953 19,770 20,429 44,680 2,754.6 3,253 1,193 217 534.6 81,766 580.7
1954
1955 28,049 26,767 62,219 2,954.4 6,069 2,353 1,115 548.7 71,647 575.2
1956 44,199 35,881 83,342 3,532.5 8,293 2,336, 1,519 605.1 66,880 430.5
19517 23,346 19,420 68,178 2,309.8 8,709 2,625 2,267 597.7 79,475 464.1
1958 23,266 20,702 63,570 2,004.2 9,819- 2,839 2,958 635.3 74,938 554.9
1959 29,660 160,080 61,560 1,970.9 10,' 212 2,157 2,517 604.9 107,210 570.1
1960 28,770 20,510 30,210 192112,465 3,221 2,957 611.7 98,159 542.6
1961 29,740 18,930 23,700 1,859.2 11,851 3,742 16 626.5 94,490 487.4
1962 29,720 18,410 14,520 1,886.2 15,609 4,513 2,268 664.1 93,372 473.2
1963 30,890 19,150 17,830 1,995.0 20.449 4,346 2,416 679.9 90,605 507.9
1964 43,160 30,940 24,190 2,387.4 21,446 4,367 1,440 675.9 112,279 522.5
1965 39,510 24,060 14,740 2,231.3 21,669 4,230 2,011 684.9 1191J534 501.6
1966 52,610 42,400 23,050 2,393.9 29,131 6,321 2,213 632.4 79,129 457.0
1967 43,920 33,440 28,060 2,239.9 25,520 69,194 2,900 730.7 153,127 593.0
1968 35,976 37,152 34,300 2,555.4 32,137 6,612 3,500 708.2 144,874 641.7
1969 47,268 36,104 28,200 2,490.4 34,683 6,863 1,305 625.1 147,269 795.6
1970 62,428 43,746 33,900 3,081.9 40,183 5,988 1,892 696.0 186,305 867.6
1971 54,777 32,059 41,000 2,748.6 34,085 5,812 1,300 693.8 201,790 737.G
1972 69,406 53,663 67,251 3,227.7 49,668 7,540 2,544 724.1 153,724 707.2
1973 70,591 65,000 4,321.5 51,129 7,734 1,470 747.9 170,796 944.8
Average 35,557.4 29,602.96 41,269.26 2,552.78 20,620.6 4,201.09 1,852.90 630.89 112,135.87 585.85
tC6ntinued
V &MUZJ I =,Puvrt:u, t.urreilt aoiars, mu-iwi--ontinuei
Cotton Groundnuts Sisal
Year Kenya Tanzania World Nigeria 'World Kenya Tanzania World
---Thousand ---- --- Million-- -Thousand- -Million- ---- Thousand--------- --Million-1950 952 7,903 2,256.5 76,515 199.4 13,633 50,336 147.2
1951 1,583 7,387 2,163.3 32,915 123.8 25,791 94,523 284.7
1952 2,101 8,792 1,812.8 63,566 153.8 16,096 72,073 173.1
1953 1,441 11,373 1,849.9 81,222 168.8 8,811 47,226 112.1
1954 103,998
1955 2,113 16,196 1,842.6 232.3 7,793 40,457 115.5
1956 2,289 20,895 2,093.2 64,205 8,048 42,399 116.5
1957 900 17,747 1,967.6 91,245 218.8 5,869 26,966 78.3
1958 1,415 20,962 1,716.2 76,920 227.2 6,120 28,786 81.7
1959 1,840 18,640 1,844.4 61,475 202.2 9,683 36,559 102.3
1960 2,350 24,_710 2,441.1 90,251 193.2 12,784 43,236 118.2
1961 1,760 19,020 2,351.3 90,1793 242.0 11,737 39,278 112.4
1962 1,220 20,700 2,053.8 102,463 245.8 12,105 44,056 132.6
1963 1,220 30,010 2,257.0 95,920 256.0 21,090 63,479 185.3
1964 1,810 27,670 2,372.1 105,854 264.6 16,847 61,227 173.3
1965 2,090 34,190 2,295.4 114,280 272.1 10,785 39,989 113.4
1966 2,430 48,990 2,307.0 99,156 291.0 9,352 32,855 101.1
1967 1,760 35,190 2,237.6 106,628 246.8 5,833 28,191 77.6
1968 1,115 39,653 2,375.4 100,271 258.9 5,157 22,304 72.1
1969 2,131 32,886 2,296.2 60,842 254.9 4,821 22,352 73.5
1970 3,437 34,616 2,484.1 34,019 216.0 5,258 25,038 73.7
1971 3,310 34,409 2,796.3 32,528 208.2 4,264 18,985 65.0
1972 3,410 47,172 3,133.1 69,169 236.0 5,801 19,424 79.4
1973 3,913 47,767 4,125.9 334.1 13,671 31,786 158.0
Average 2,025.6 26,386.0 2,307.51 79,737.95 5,063.90 10,536.91 40,5cY1.09 119.43
Continued
Table 20.--Values of commodities exported, current dollars, 1950-1973--Continued
Palm Oil Rubber
Year Nigeria Zaire World Nigeria Zaire World
----- Thousand ----- --Million-- ------- Thousand ------ ----- Million-1950 63,378 45,667 182.3 8,760 6,113 1,815.8
1951 87,522 73,692 281.9 27,614 15,891 3,282.5
1952 62,933 50,911 188.8 15,824 14,293 1,897.2
1953 68,301 44,155 191.0 11,521 9,654 1,066.7
1954
1955 65,262 52,543 192.6 26,629 22,492 1,920.9
1956 74,665 59,959 149.4 29,246 24,497 1,773.9
1957 37,827 34,503 79.4 24, 116 20,374 1,387.0
1958 34,995 32,922 74.4 21,878 20,227 1,277.6
1959 38,663 38,261 122.5 32,490 22,320 1,725.7
1960 36,907 30,322 118.7 39,880 25,840 1,804.6
1961 37,035 31,894 122.4 30,730 21,490 1,399.2
1962 24,998 28,246 105.3 31,490 20,170 1,528.8
1963 26,222 23,570 113.1 32,750 17,070 1,400.4
1964 30,111 21,190 127.2 33,850 13,630 1,244.8
1965 38,055 18,500 146.8 30,590 9,150 1,281.5
1966 30,697 15,540 143.3 32,020 10,480 1,306.0
1967 3,527 21,700 112.0 17,770 9,610 1,222.7
1968 399 24,800 109.9 17,670 9,900 890.3
1969 1,213 19,269 123.5 26,919 16,273 1,246.7
1970 1,590 28,181 200.9 24,596 12,759 1,126.7
1971 4,778 26,404 281.5 17,492 12,000 965.5
1972 418 16,600 261.7 12,495 10,500 891.4
1973 14 17,500 382.4 29,482 14,000 1,910.1
Average 33,456.96 32,879.96 162.96 15,597-.0-9 1,494.17 25,039.22
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-:: lo ' Octo6er 1977 . c:o ~,__ Economics Report 89 s, i Agricultural Diversification and Export Earnings, Selected African Countries ood and Resource Economics Department gricultural Experiment Station 1stitute of Food and Agricultural Sciences 1 n Cooperation with mter for African Studies niversity of Florida, Gainesville 32611 J~1El.iB~AR / I NOV 1 t: 1977 .f.A.S. U 11v. of Flori W. K. Mathis C. G. Davis M. T. Futa
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ABSTRACT This study focused on four African countries: Kenya, Nigeria, Tanzania and Zaire. The objectives were (a) . to identify whether agricul tural export earnings fluctuations were determined primarily by quantity or price variability; (b) to investigate trends and patterns in the agri cultural sector; (c} to determine the impact of export crop diversification on . the level and variability of agricultural export earnings; (d} to in quire if there was a gap between domestic supply of and demand for food and to determine if such a gap is due to overinvestment in the export crop sector. Fluctuations in agricultural export earnings were found primarily associated with variations in the quantity exported, which were due in part to actions of government marketing boards. A secondary source of export quantity variation was variability in rainfall. All four countries introduced new crops during the study period, but fewer different crops accounted for shares of agricultural export earnings, so countries ac tually became more specialized. An inverse relationship was found between export crop diversification and the level of agricultural export earnings. However, it would appear that in the long run, there is likely to be a positive relationship. Demand for food was increasing more rapidly than food supply; the imbalance was not due to overinvestment in the export sector. Low rates of growth of export earnings implicitly indicated that the export sector itself lacked resources. Key words: Sub-Sahara Africa, export earnings instability, export crop diversification, economic development, export. agriculture, develop ment policy. ...
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ACKNOWLEDGEMENTS The authors wish to thank the Rockefeller Foundation for Mr. Futa's support during his time at the University of Florida. The assistance c>f Dr. W.W. McPherson is gratefully acknowledged, both as a member of Mr. Futa's advisory committee and as a reviewer of this manuscript. Ors. R. D. Emerson and P. J. van Blokland are also due thanks for reviewing the manuscript. Three lovely and hardworking ladies typed many drafts and the final copy, and are due much appreciation: Mrs. Patricia Beville, Ms. Carolyn Williams and Mrs. Mignonne Winfrey. Mrs. Carolyn Dunham gathered and processed data, which we acknowledge with thanks.
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LIST OF TABLES LIST OF FIGURES . . . . TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . INTRODUCTION . . . . . . . . . . . . . . . . . . . . Objectives ... Country Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . STUDY AREA CHARACTERISTICS. Common Features Kenya . . . . . . . Nigeria ....•.• . . . Tanzania Zaire .•.... ANALYTICAL MODEL DEVELOPMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ' . . . . . . . . . . ' V 1 1 2 4 . ' 4 9 n 13 15 . 17 Literature Review. . . . . . . . . . . 17 Variable Specification and Description. . 20 Instability Measures . . . . 20 Diversification Index. . . . . . . 23 Model Specifications for Study Objectives. . . 24 Objective 1. . . . . . 24 Objective 2. . . . . . . . . . 24 Objective 3. . . . . . . . . 25 0 bj ec ti ve 4. . . . . . . . . . . . . . . . . ., . . 2 6 The General Objective. . . 27 . Data Sources . . . . . . 28 EMPIRICAL FINDINGS ..... . . . . . . . . . . . . . . 30 Price and Quantity Variability. . . . . . . . . 30 Patterns and Trends in Export Crop Diversification. 32 Effect of Export Crop l)iversificatiQn on Levels and Variations in Agricultural Export Earnirags , 36 Food . Supply and Demand Growth Rate Disequilibrium. 41 African Choice. , . . 47 i
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t TABLE OF CONTENTS (Continued) Effects of Export Crop Di~ersJfJcation on Land A 1 location . . ' . . . . . . . . . 48 Kenya. . . . . . . . _ . . . . . . . .. 48 Nigeria. . . . . . . . . . . . 50 '.:, ranzan i a . . . . 52 Za ; re. . . . _ . . . . . . . . . . . . . . . . . . . . . 54 The Use of Agricultural Export Earnings for Development Goa 1 s ., . . 54 . SUMMARY AND CONCLUSIONS. Summary. . . Cone l us ions-. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . APPENDIX .•. BIBLIOGRAPHY . ' . . . . . . . . . . . . ii 57 57 59 61 70
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LIST OF TABLES Table Page 1 Agricultural trade value as a percent of total trade value, selected African countries in selected time periods. . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Agricultural product value as a percent of GDP, selected African countries in selected time periods 5 3 Position of selected African countries according to two designated selection criteria, 1971-73 6 4 Earnings from major agricultural exports, constant dollars, 1950-1972 . . . . . . . . . . . . . . . . 29 5 Variability in agricultural export earnings attributed to price and quantity changes, 1950-1973 . 31 6 7 8 Export crop diversification indices, 1950-73 ...• Average annual rates of decline in diversification indices, selected periods .............• Average annual growth rates of quantities exported, major commodities, 1950-1973 •........... . . . 9 Index values of agricultural export earnings, 1950-1972 .33 35 .35 (1950=100) ..•.......••.......•.•••• 38 10 Average annual rates of change in agricultural export earnings and in diversification indices, selected periods, .39 11 12 13 14 15 Instability indices of agricultural export earnings, 1950-72 . ..................... . Differences between annual rates of growth of supply and demand for selected food items, 1965-1973 Land distribution among food and export crops, 1970. Export supply function estimates, Kenya Export supply function estimatest Nigeria .. iii . . . . . . . . . . .42 .44 .46 .49 51
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LIST OF TABLES (Continued) Table Page 16 Export supply function estimates, Tanzania. . . 53 17 1~ . 19 20 Export supply function estimates, Zaire •.. . . . , 55 Average annual values in current dollars of world and country exports of major commodities. 1950-73 . . . . 62 Earnings from major agricultural exports and total agricultural exports, current dollars, 1950 .. 73 ....• 63 Values of commodities exported, current dollars, 19507 3 . , . 6 7 iv
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LIST OF FIGURES Figure 1 Africa, and Sub-Saharan Countries 3 2 Index values of agricultural export earnings, 1950-1972 (1950=100) ... ; . . . . . . . 7 3 4 5 Kenya, with major export crop regions Nigeria, with major export crop regions . . . . . . . . . Tanzania, with major export crop regiQns •. ' . . . . . 10 12 14 6 Zaire, with major export crop regions. . 16 7 Export crop diversification trends, 1950-72 (1950=100). 34 8 Average price of food staples in northern Nigeria under alternative simulated policies, 1965-1980 45 I V
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AGRICULTURAL DIVERSIFICATION AND EXPORT EARNINGS, SELECTED AFRICAN COUNTRIES W. K. Mathis, C. G. Davis and M. T. Futa INTRODUCTION The study of trade fluctuations has been of much concern in the post World War II period. Since then a significant proportion of trade literature has dealt with theoretical and empirical analyses of fluc tuations and general instability of export earnings of less developed countries (LDCs). In the presence of problems associated with export earnings fluctuations in general, and/or agricultural export earnings fluctuations in particular, economists have made extensive analyses of the causes and effects of instability. Unfortunately, a relatively small proportion of the literature has dealt with policies to correct instability. Agri .. cultural diversification is probably the most widely discussed stabilizing mechanism. However, the discussions have generally been theoretical and did not deal with economic impacts of diversification. Objectives The primary objectives of this study are: {1) Estimate and categorize export crop earnings variations into price and quantity components as a means of understanding their relative weights in program planning and policy forma ... tion. w. K. MATHIS and c. G. DAVIS are associate professors of food and resource economics at the Unirersity of Florida. M.~T. FUTA is a Rockefeller Foundation Fellow in agricultural economics at Oklahoma State University. 1
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2 {2} Describe and measure the patterns and trends in export crop diversification in , each of the countries covered in the study. (3) Measure and describe the influence of export crop diversification on the level and _ variability in agricultural export earnings. (4) Quantify the gap between aggregate food s upply c1nd demand and determine whether or not c1ny existing disequilibrium between the two components is related to differential levels of invest ment between the export and domest.ic agricultural sectors. A secondary and more general objective of the study is to identify policy lines in the agricultural export sector and to evaluate the impc1ct of such policies on the overall agricultural development of selected countries Country Selection This study analyzes detenninants and ' effects of agricultural export earnings instability in selected Sub-Saharan African countries du ring , 1950.;73_. Countries were selected according to two , criteria: (a) the deg'ree to which the country is ' engaged in agri.culturaltrade and (b) ,. the relative importance of agriculture in its overa 11 economy. The ratio of agric'ultural trade to total trade is used , to establish the first criterion. .. 1he ratio of the va1 ue of tota 1 agricultural product to gross domestic . product (GDP} established the second criterion. The size of this ratio fodicateS' the overall contribution of agriculture to the national economy, and thus the relative " importance of an external disturbance in a ' griculture to the economy. Both these ratios were calculated for thirteen tropical African countries, from which Kenya, Nigeria, Tanzania, and Zaire were selected (Figure l). Selection of the four countries was based on the average val~es-0f both ratibs for the period 1971-1973~ For both ctiteria, coun triesWere grouped into those with (a) , large . pr {b) small ratios. Numerica designation of what constitutes a "large'' or "small" ratio is admittedly ~ ~ _ nor~ative (and somewhat arbitrary) decision. For the purpose of this study, it is assumed that a ratio of agricultural trade value (ATV} t . o .. , ' . ' total trade value (TTV) of 50 percent . or more fs large, whereas any ratio less than 50 ' percent is ' small. A ratio of agricultural product (AP) to GDP is assumed to be large if it amounts to 33 percent or more while a '
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3 ' . . I ::::::: : : :,: . :::;~ , .;~: i.n~ :~:L::;::::::::::: ;::::?: =rt~t:~f Figure 1.--Africa, and Sub-Saharan Countries t
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4 ratio of 1 ess than 33 percent is smal 1. Here, it is assumed fo,r simplicity that the national economy consists of three sectors: industrial, agri cultural and service. Thirty-three percent of GDP would represent an equal distribution among the sectors. According to the country selection criteria established above, Sub Saharan African countries may be placed into two broad groupings that reflect their heterogeneity. Countries grouped on the basis of the rela tive trade ratio show definite differences. Kenya and Tanzania are among countries with higher ratios of agricultural trade value to total trade value, while Zaire has a smaller ratio (Table 1). Nigeria is typical of those countries experiencing a shift in status from the first to the second group. Divisions based on ratios of agricultural product value to GDP show . similar groupings (Table 2}. Kenya and Tanzania have economies based mainlJ on agriculture, while Zaire is more dependent on non-agricultural sectors . Nigeria, although exhibiting a relatively high ratio ~f agricultural pro duct value to GDP, has experienced significant changes. Countries grouped on the basis of the relative trade ratio (Table 1) show remarkable con sistency 1n groupings based on the agriculture product value: GDP ratio (Table 2}. For ex~mple, most countries inthe high ATV group are in the high group (Table 3). TTV All four of the countries chosen for study have experienced substan tial fluctuations in export earnings. Constant dollar values of agricul tural export earnings of .the four selected countries increased from 195-0 to 1973, but were vulnerable to sharp fluctuations (Figure 2). STUDY AREA CHARACTERISTICS Common Features The four countries selected for study have certain common agricultural f ea tu res. A 11 produce severa 1 export crops. Kenya and Tanzania share many of the climatic and ecological conditions typical of East Africa. Nigeria and Zaire also have similar agro-eco1ogica1 conditions in certain reg . ions, even though they are located in different parts of the African
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5 Table 1.--Agricultural trade value as a percent of total trade value~ selected African countries in selected time periods Country 1956-1959 1963-1966 1967-1970 Study countries Kenya 65 61 57 52 Nigeria 69 66 56 19 Tanzania 76 80 72 71 Zaire 10 3 7 9 Others Cameroun 75 75 75 68 Ethiopia 95 98 93 90 Gabon 3 2 1 1 Ghana 76 72 79 74 Ivory Coast 72 70 67 68 Malawi 90 91 88 89 Senegal 80 87 73 51 Uganda 85 82 82 90 Zambia 4 2 3 1 Source: F.A.O., Trade Yearbook. Table 2.--Agricultural product value a s a pe rcent of GDP, select~d , Aif ; r ; i i t.ltl countries in selected time periods Country 1955-l9i0a 1963-1966 1967-1970 Study countries Kenya 36 37 33 33 Nigeria 60 59 54 39 Tanzania 50 55 40 39 Zaire 30 21 21 l9 Others Cameroun 40 41 37 36 Ethiopia 57 57 5 . 2 54 Gabon l7 15 Ghana 70 , 46 Ivory Coast 59 33 Malawi 51 51 50 Seneg~l 40 30 28 2 , 7 Uganda 60 59 56 1 53 Zambia 9 8 8 aThese years diffe r from tl!lo,se i;r,1 Table l ('l'Q56,..59 ' ) due -: t_o , d:i:ff , e~ene , e:s between original data sources. Source: u. N., Survey of 6conomic Conditions in Africa.
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.. Table 3.-..:Position of selected African countries ac~ording to two designated selection criteria, , 1971-73 . Study countries Kenya Nigeria Tanzania Zaire Others . Camerotin Ethiopia Gabon Ghana . Ivory Coast _ Malawi Senegal Uganda . Zambia + + + + + + + + + ( Low ATV)a TTV + + + + + + + + + + + + + AP b (Low GDP) + + + + aATV A l t . 1 T d v . l H . h ATV . . . t. . f 50 t L 1 th = gr1cu ura ra e a ue. 19 TTV means a ra 10 o . percen or more; ow, ess an 50 percent. TTV = Total Trade Value AP bAP = Agr1cultral Product. 33 percent. High GDP means a ratio of 33 percent or more; Low, less than GDP = Gross ' Domestic Product. Source: Computed from U. N., Survey of Economic Conditions in Africa lrade Yearbook. and F. A. 0.,
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Index 200 . . 150 100 50 !I' ' !!', •. 1950 t
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8 continent. These common features explain the historical development of palr oil and natural rubber production in both countries~ Coffee, tea and hard fibers are common to Zaire, Kenya and Tanzania. Similarities between the agricultural economies of Nigeria and the two eastern countries are . . found mainly in institutions introduced in all three countries by the former British colonial administration. : A common and important institutional legacy of the colonial . era is the marketing board. In fact, agricultural marketing and _ trade ' in most African countries is controlled by these . boards. Boards are statutory bodies established by government action \ ind endowed with lega} powers , over the production, marketing and processing of ' primary agricultural products (Abbott}. However, the objectives,' structure, conduct. and p erformance of such bodies vary from country to country. Abbott, after extensive analysis of market~ _ ng boa rds, indicates that these bodies have several objectiv~~~ Some , of the more important are sales promotion, research, extension services, raising bargaining power of agricultural producers in dom~stic or , export markets, setting up needed marketing and processing facilities, equaH~ing returns from sales in different markets or through different 01.1tlets, and cushioning the impact . ' ' upon producers and consumers of .' _ sharply fl 4ctuati ng i nterna 1 and externa 1 prices. The last point is of particular . interest to thi.s study, since it deals with problems of agricultural trade instability. Most African marketing boa . rds are of the export monopoly type. The Zaire.an boards are an exception, hOwever, since they are essentially stabilizing rather than trading institutions (Abbott). In the remaining th:ee countries, marketing boa~ds handle all problem~ , related to the marketing of export crops , ~ .Kenya and Tanzania have h . ad individual boards for each export crop. In r~cent years, Tanzania has . been ' reducing the number of marketing boards, giying an individual' board responsibility for severa 1 crops (Kriesel, et al). In Nigeria, marketing boards are regional !. (He . 11einer, 1966}. Also, two Nigerian export crops, rubb~r and bananas, at~ not regulated at al 1. ', In spite of differences among countries, there are striking similar ities , in the conduct of Afri~~~ _, ~a~k;ting b~~~ds : , . I~ _ al, fo~~ ' co , ~~t~ies, marketing boards establisf) annual producer prices, which change little
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9 during a season or from year to year. Where these prices are lower than world prices the differences accrue to the marketing boards. The.se sur. Pluses are supposed to be used to maintain producer prices when world price levels fall below the marketing board price. However, the use . of such surpluses in Nigeria has been criticized by some economists (Nixon, Eicher, 1971, Johnson, 1969, 1971}, and defended by others (Helleiner, 1966). Aspects of this debate will be explored later in discussions of the empirical findings of this study. This debate, however, is not limited to the use of trade surplus. It has been extended to the general impact of marketing boa.rds on market and production mechanisms. There is general consensus that marketing boards tend to create distortions in factor mar kets and result in production disincentives (Johnson, 1968, Helleiner, 1966). Kenya . Kenya's export crops are produced in three distinct agro-ecological zones (Figure 3). The first is the eastern coastal plain, a semi-arid band along the Indian Ocean, where sisal is produced mainly on planta tions. The second zone in the interior to the north is characteriz . ed by poor pasture lands and margina1 cotton production. The third ecologfcal , zone is a fertile upland region which produces coffee and tea, and most of Kenya's other export crops. Four crops were selected for this study because of their relative importance in the Kenyan economy; coffee" tea, cotton, and sisal, which account for about 80 percent of agrkultural export earnings. Kenya I s major export crops are produced by both plantation a nd peasant farming systems. However, the plantation economi remains a distinguishing feature of Kenyan agriculture. It has pcnly been within the last two decades that the peasant farming system was give , n the nece . $-._ sary economic incentives to expand under the auspices . of the Swynne : r:ton Plan. 1 1 rhis plan was introduced in 1954 and supplemented in 1962. The primary objective was to help the natives of Kenya to expand their pro .. duction of crops such as coffee, pyrethrum, tea, maize and millet. Lands made available through this plan were divided into two areas--'Scheduled' areas and I Non-scheduled' areas. The former was reserved for . Europeans ::inrl th&:i 1;:itt&:ir for African natives.
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lO Coffee and tea Sisal . 6QQg Cotton Figure 3.--Kenya, with major export crop regions
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11 Avai1ab1e 1ands were designated as 'high-density areas' and were reserved for subsistence smal1 ho1ders. 2 •ttow-density" areas were reserved for larger commercial farming. units. These large units market their crops through marketing boards. Ni9eria Nigeria, in contrast to Kenya~ has large areas of fertile coastal land. This region can be divided iinto two subregions: the southeast, with palm oil and natural rubber production, and the southwestern region which has maintained its comparatiYe advantage in cocoa production (Figure 4). The second region, stretching from east to west across the center of the country, is suitable for growing both groundnuts and cotton. The third zone in the north lacks the climatic advantages of the south but has been mainly responsible for making Nigeria the world's major exporter of groundnuts. The four crops selected for study--groundnuts, cocoa, palm oil t and natural rubber--are produced in all regions and contribute approximately 85 percent of the total agricultural export earnings of the country. Nigeria has areas both of surplus labor and surplus land. It also has areas, with differing labor-cu;tivated land ratios and lengths of fallow periods, between these two extremes (Helleiner, 1966, p. 55). The coastal region with its high population density and limited land is considered to be a surplus labor area. The central and northern regions, on the other hand, with low population density are considered to have surplus land. Despite the apparent land surplus, these regions are characterized by permanent and seasonal labor migration to the south (Norman). With such differences in regienal .labor-land ratios, it is easier to understand why the trend toward ind'ividual land proprietorship is not very significant in the country as a whole (Johnson, 1968). However, 2 The term "subsistence holders" means farmers consuming more than 50 percent of their farm production.
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... . .. ... Groundnuts 12 . . . i th . major N ger,a, w 4 -1 Figure ' C •rop regions export
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13 in the area of land reform Johnson argues that extended and community ownership of land is not a serious problem in Nigerian agriculture. He suggests that the more serious problem is the market distortions caused by government and marketing board policies (Johnson, 1968). This view is not generally shared by all economists concerned with Nigerian econo mic development. The debate on the role and impact of marketing boards on Nigerian economic development is still unsettled. Tanzania Tanzania, south of Kenya and east of Zaire, has four main regions of interest (Figure 5). The first is the eastern coast where sisal is produced. Next is the west-central region, in which several crops are cultivated, with cotton, coffee, and tobacco the most important. The north-central region also produces coffee, while the southern zone is characterized by a system of production which has been referred toas 3 crop dispersion, rather than crop diversification (Saint-Marc, 1968). Cash crops grown in this system include sesame, tobacco,.cotton, tea, ' 4 ' sunflower, castor seed and cashews. Coffee, tea, cotton and sisa1--the four study crops--account for approximately 75 percent of the agricul tural. export earnings of Tanzania. Export crop production is largely concentrated in the plantation economy. However, as in the case of Kenya, emphasis is now being placed on the development of small farms. The Tanzanian government as well as the Tanzanian national political party maintain a strong interest in the socialization of the rural society. 5 Governmental participation has a1so 3 oispersion is defined as the existence of several crops in a given area with a very low density. 4cashews are important Tanzanian exports but lack of data prevented inclusion in this study. 5 Rural qevelopment policy is expressed as a program of rural social. ism. It is designed to lessen income inequa'lities among farmers by giving them a community mode of production by which the farm unit is jointly owned by the extended family or a group of people who agree to work together.
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14 Coffee and tea Cotton Figure 5.--Tanzania, with major export crop regions
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15 been extended to include the marketing system. This system operates through a complex set of marketing regulations administered through marketing boards. The marketing boards' structure and operation have been reviewed and subjected to a number of changes over the years, largely as a result of criticism of African governments' use of agri cultural surpluses. However, in the case of Tanzania, marketing board surpluses have been used for the economic betterment of the farming sector (Kriesel, et tl), In fact, Kriesel found that there have been no direct transfers of such surpluses or savings to governments in contrast to the case in some West African countries. Thus, in spite of some uneasiness about government intervention in the agricultural system, there is optimism that agricultural surpluses will continue to ba invested in the agricultural sector instead of being siphoned off to other sectors. However, government policy encourages community ownership as the means of production. With a high population growth rate of 2.7 per year, Tanzania has a serious shortage of cultivable land. Zaire Zaire, the third largest African country after Algeria and the Sudan, has the most diversified climate of any country on the continent. The varied ecological conditions are suitable for many different crops, but only a few are grown commercially for export. Coffee, tea, palm oil, and natural rubber represent 85 percent of a 11 the agricultural export earn~ ings of the country (Figure 6). These crops are produced mainly on plantations, but there are some small peasant farm units. In the 1950's the colonial agricultural admin istration began a program known as paysannats indigenes in an effort to transform traditional farming patterns. Innovations implemented in these programs were noted in development literature (Johnson, 1968; McPherson and Johnston, 1968). However, the system conapsed with the emergence of national independence. The collapse of the paysannats system was accompanied by a decline in the relative importance of agriculture in the economy. Massive migration has been occurring from rural to urban areas (Mabala). A prevailing pattern is that of disinvestment in the agricultural sector {Peemans). In addition, agricultural institutions in the country have not been
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. . Zaire, Figure 6 --. 16 a1 . . rubber Natur Palm oil . Coffee and te . a . p regions~ P ort . cro witkmajor ex . . . .
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17 effective in rural improvement programs. However, theZairean government has begun to provide credit facilities, revise land policies, and improve the rural infrastructure. ANALYTICAL MODEL DEVELOPMENT Literature Review In the wake of post World War II interest in trade fluctuations, several empirical analyses were undertaken. A 1952 United Nations study reported an analysis of price and quantity movements in LDCs. The study focused on: (a) year-to-year price and quantity fluctuations and {b) long-term price and quantity fluctuations and cyclical swings. One of the major findings was that export earnings fluctuations tended to be higher than those of prices and quantities taken individually, due to the interaction of prices and quantities. The study also reported that prices accounted for two-fifths of the fluctuations -while volume .accounted for the remaining variability. Using . United States ,export data, Mintz concurred with the United Nations findings regarding the relative impor tance of export quantity as a determinant of variability in export earnings. In a 1958 article, Nurkse formulated specific policies on the basis of the earlier United Nations report. He supported the . United Nations proposal calling for international funds and buffer stocks as a means of stabilizing export earnings. However,. he made a strong case for balanced growth in the LDCs as a means of fostering .industrialization and less reliance on primary products. Coppock, in 1962 and later in 1966, analyzed Middle Eastern foreign trade patterns. In both studies, Coppock developed indices of instability and related them functionally to export prices, export quantities and market shares of individual commodities and countries. A study by DeVries concluded that such factors as trade position, price inflation and resource allocation determined export growth rate and performance in LDCs. DeVries found a positive correlation between the performance of major and minor exports and growth in the value of agricul .. tural product. He opposed diversified industrialization policies as
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18 inefficient in helping LDCs reach economic levels of production and a competitive ceiling. Studies by Maizels have made significant contributions to the \Jnder standing of trade problems in LOCs. Maizels argued against overspeciali zation in particular export commodities. He . saw export diversification as not only an appropriate mechanism for facilitating structral change, but as probably the most important from a long-term viewpoint. Maizels further argued that accurate assessment of world demand trends for export commodities is a vital first step in assisting LOCs to capture the econo mic gains from high demand exports. Balassa 1 s work had significance for the agricultural earnings insta .. bil ity question in that it estimated demand trends for temperate zone foods, competing tropical foods and agricultural raw material$. However, an increase in world demand might not justify a policy of export . crop diversification and shifts in resource alloca.tion. Some . economists have proposed reorienting policies towards food crop diversification as a , means o{ reducing high food import propensity (Flores). All of the studies discussed so far, excep . t that . by the United Nations dealt with trade problems in general or with the question of stability, without referring to specific stabilization policies. Massel entered these gaps by reviewing different policy alternatives. Buffer stocks and multi lateral contracts proposed by the United Nati~ms study and supported . . by Nurkse were analyzed. Massel found that buffer stocks provided . certain welfare advantages to producers by minimizing the expected value of change of producer prices, and to consumers by providing gains in consumer . surplus (1969). However, the cost of implementing a buffer stock policy was higher than other alternatives (Massel, 1970). Earlier, Massel concluded that neither export earnings instability nor the diswtil ity arising tber:-efrom are 1ike1~ to be eliminated by simple policies such as diversification of . exports (1964). Since variations are indepengent among commodities, their additivity may actually . worsen the variability. This argument runs counter to Jorberg's findings that diversification ts capable of i.nducing stabilit) in the seeular trend of export earnings. Parikh used an econometric model of the world coffee economy to predic production, consumption. and outcomes of alternative policies. A. similar
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19 model was later used by Edwards and Parikh to identify policies that would minimize the fluctuations of agricultural export earnings. They found that, given the necessary resources, an international buffer stock policy could substantially reduce short-run fluctuations. A quota pol icy was judged to be more successful, as it had fewer of the economic difficulties found in the buffer policy. Edwards and Parikh suggested, however. : that . quota policies would probably be much more difficult to enforce, due to political considerations. With such difficulties evident in international policies, interest should turn to domestic policies such as tax structure and export crop diversification. However, 1 ittle has been done in this regard in LDCs, and the concept of diversification has been primarily associated with the process of industrialization. Only a limited number of studies have analyzed export ' crop diversification as such. One of the few international studies dealing with this aspect is a 1967 report by the Committee for Economic Development (CED). This particular study concluded that export earnings fluctuations are largely the result of a combination of varia tions in crop output and dependency on one or two products. ' Policies and programs have been formulated on the basis of many of the above analyses in an attempt to facilitate greater international price stability and economic growth (U.N., 1952). The major operating mechanisms of these stabi1izing programs were export quotas, buffer stocks, and multi lateral contracts. The General Agreements on Tariffs and Trade (GATT) has served as the primary operational 1 vehicle for these policies and programs. In spite of GATT',s efforts, the LDCs still exhibit instability in export earnings, particularly for agricultural exports. The problems of agricultural : export earnings instability are likely to be more serious in those countries that de . rive a sizable proportion of their G-ross Domestic Products (GDP) from agricultural exports. Although it has not been conclusively determined that instability hampers economic development (Lim), it . has been established that fluctuations in agricultural export earnings have a multiplier effect on _ domestic incomes. Fluctuations in earnings accentuate inflationairy , and deflationary movements in the national economy ' ; and may al so di i $courage investment in ' the agricultural sector and thus impede the . growtll process. Instability of agricultural export earnings can also inhibit government
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20 programs aimed at meeting social welfare goals. These variations may force a country to borrow in order to meet its social welfare objectives . . Where loan repayment obligations accumulate over time, additional stress on the nation's economy may result. . Thus socia 1 efficiency may be reduced if the agricultural export sector generates a particular system of resource misa llocation (Beckford). , Variable Specification and Description Instabi . l ity Measures Attempts to quantify the variability of export earnings have been made by several authors. lnstabil i ty is used here to mean the deviations of the constant d9Uar values of agricultural export earnings around the trend. T . he United Nations study (19.52), referred to earlier, used an average of year-to-year percentage changes as a measure of instability. The ma . the matical formula of that index was: where, Ilm = (Ztl .. Zt) x 100 t=l N IUN = United Nations instability index, (l) Zt = real value of agricultural export earnings in . period t, and .. N == number of years for . which the . inst . ability is computed. Kingston criticized this procedure by arguing that it tended to exaggerate ' the importance . of the insta bil ity existing in the time series (Kingston, p. 20) . He wrote that: One obvious deficiency of this approach is that a steady increase of a constant percentag~ . per annum would be interpreted as an unstable movement, when in fact there had been , no instability; in a conven. tional economic sens . e, at a~l. Rather, there would have existed a stable percentage of growth. Coppock, who had already criticized the United Nations index with the same arguments as Kingston, formulated an index with a logarithmic
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21 variance measure, to account for a constant percentage growth rate. The Coppock index may be written as: N Icp = antilog . I t==l where; (log.Zt+l -M) .. Zt Icp = Coppock instability index, (2) M = the arithmetic mean of logarithm differences, and Z,N = as defined in equation (1). While the Coppock index may represent an improvement over equation 0), it does have some weaknesses. First, the log-variance form interprets small deviations from a low base as highly unstable. Second, instability coefficients derived from percentage changes are difficult to interpret (Kingston). Another instability index was proposed by Massel, who used a least squaresregression model with time series data. Massel's model is de scribed mathematically as: N 2 I E (Z Z') MS= t t t=l ---N (3) i where, IMS = Massel index of instability, Z' = least squares estimate of Zt, Zf = f(t), Z = trend me.an val~e of agricultural export earnings, l;N = as defined iri (l) and (2). This procedure does not insure that the least squares model used to estimate li . is the correct model, but it is an improvement over (1) and {2). Staller, as cited by Kingston, applied this index with the slight modification of regressing the logarithmic valt.4eS of Zt on time. An interesting aspect of the Massel inc.1ex ,s that it may be trans• formed to accommodate analysis of times s~ries data. Previous indices were limited to measuring total instability over a given N years. lhe transformation of Massel's index in a time series basis measures the instability of period-to-period changes. It can be stated as follows:
PAGE 30
22 l ' . = u : . .. u . MS .. t+1 t inax( z zt) t+1, (4) where, IMS = transformed Massel annual index . of instability, ut+l = residual error corresponding to Zt+l observation, Ut = residual error corresponQingto _ Zt obser _ vation. since this transformed version of Massel's index applies to a time series model, it was used in . the present study. However, some modificatior were needed in the denominator of equation . (4). Massel implied . that the _ point from . which deviations occur is i.tself a changing or a dynamic point. But, the weakness of the assomption is that at ea . ch point in timet there exists a given maximum value which is considered as a stationary value. This is misleading, since it is known that there are some conditions to be ' met.for a point to be considered as a ., statlonary value(Gandolfo). Thus, in or-der to . avoid . errors that the second . Massel index may _ generate, equation (5} was developed. Such a relation takes the form: , I = u u t t+l t r where, It = corrected index of instability, Ut+l = residual as defined in equation (4), but obtained with the exponential trend in equation (10). ut O = residual as defined in (4), .. but derived with the exponential trend in equation . (lO). z . := average trend value frpm equation lO, cortsidered as a fixed . stationa ry ' value, from which deviations take place. It should be ho ' ted that all tn~se indices measure the variability . ~bout a giv ' en s table poi'nt. They do not, however, possess the properties of normal variances. ' thus, these constructs ha ve a basic shortc oming that ~revents their application to arialysi's of the d ;fferent effects c>f price and quantity variation 9h ~griculturaf expo'r"t earnings instability. To ' separate the price and quanti"ty comptjnents of export earnings variation, notmal vari3nce properties must be inc1uded. One of the properties of . . . . . . ' the normal variance of a . Qiven variable is that s uch a variance ma.Y be
PAGE 31
23 apportioned into two or more components. Diversification Index Diversification refers to the production of several crops in a given geographical area or production unit. But, if only the numbers of products are considered, Finke and Swanson argue that diversification can be thought of as a "richness" concept meaning 'how many' crops a given farm unit grows. However, emphasis may also be placed on the relative importance of a given crop in a given area or farm unit. In this case, diversification could mean that all crops in the area are equally or evenly distributed. Defined in this ~ay, diversification is the degree of 'evenness', as determined by the proportions of resources allocated to each crop planted. The Committee for Economic Development (CEO} proposed that agricultural diversification be thought of as a dynamic process, with new crops intro duced into the system over time. The CED defined the diversification index as the ratio of the growth rate of agricultural export earnings to the growth rate of export earnings from traditional export crops. This definition, although interesting, has some shortcomings when used in re gression models where export earnings are dependent variables. Thus the index used in this study is that developed by Finke and Swanson. It can be formulated as: where, Dt = -n E 1 1 log (l;) Dt = diversification index, n = number of crops, and li = proportion of land resource allocated to crop i. (6) However, because crop data in this study are based on harvested area rather than on actual planted area, and because the study is primarily focused on the export sector, it was felt that the substitution of the proportion of total agricultural exports represented by each crop for the proportion of land allocated was more appropriate. Therefore, equation (6) may be adjusted to: Dt = -n E Q;log (q;) (7) where, q; = share of crop i in total value of agricultural exports.
PAGE 32
24 Objective l Fluctuations in agricultural export earnings are decomposed into three components: price variations, quantity variations and variations resulti . ng from the interaction between prices and quantities. An app . roximation of the variance of the ftmction, z 1 = P;Q;, fol lowing the procedure used byMotha and . others, is: where, 2 a = agricultural export earnings variability of crop i, z. 1 . Qi = average quantity of crop i exported, . in thousands of metric tons, P. 1 2 (jq. , (12 q. 1 = average export prke of crop i exported, in U.S. de> lJ ars, = price variabilityo , f crop i, = quantity variability of crop i, . and p = correlation coefficient between quantity O; and its price P l The first term on the right hand side represents price variability, the second term quantity variability and the third is th e interaction tern Higher order terms in the series expansion are not included~ Initial investigation showed negligible covariance between price and quantity, so those covariance terms are not included in further discussions. Objective 2 In order to identify and quantify the trends and patterns of export crop diversification, the exponential trend procedure was adopted ' because of the good fit exhibited in preliminary trials. The symbolic represen• tation of the model is: (9)
PAGE 33
where, 25 Dt = export crop diversification as defined in equation (1), ex = scale of export crop diversification, 13 = growth rate of export crop diversification, and T = time trend. The parameters (land f:3 are calculated by the trend procedure. Objective 3 The third objective, evaluating the impact of export crop diversification on the level of agricultural export earnings, measures trends in agricultural export earnings obtained by the exponential trend procedure in objective 2. It takes the form: ( l O) where all variables are as previously defined. The impact of diversification is measured with a simple linear regression model and a quadratic regression model. The simple model is used to identify the nature of the relationship between two specific variables, while the quadratic model was adop_ted as a means of simulating the impact of intensified diversification. The basic regression form may be written as follows: Zt = f(Dt, Ut) ( 11 ) where variables are as defined above. Expansion of the two models gives two separate equations: Zt = b 0 + b1Dt + Ut 2 (12) Zt = b 0 + b1Dt + b2Dt + Ut (13) A second aspect of this objective deals with evaluating the effect of export crop diversification on the level of instability. This is accomplished by applying equations (12) and (13) to the instability index, i.e., by regressing the instability indices upon the diversification vari .. ables. The resulting equations are functionally similar to equations {12) and (13). They have the following form: (14) (15) Equations (12) and {14) are simplistic and are only described for theoretical considerations. For interpretation purposes, emphasis is
PAGE 34
26 placed on equations (13) and (15) which correspond to the corrected in stability index given as equation (5). Objective 4 The rationale behind the fourth objective was to determine if the export crop sector was deve.loping at the expense of the domestic food sector. A simple equilibrium equates the growth rate of food supply to the growth rate of food demand. It is assumed that if there is disequi librium on the su.pply side, there are factors inhibiting the growth of the domestic food sector. One such factor could be a significant differential between the levels of investment in the export crop and in the domestic food crop sectors. Investment is used here in its broad sense and covers such things as land and capital allocation, marketing insti. . tutic;,ns, research infrastructure, labor and other relevant inputs. Be Cciuse of data 1 imitations, only policies affecting land allocation be tween the two sectors were examined. It is like1y, however, that a high correlation ' exists between the quantity of land and the qua . ntity of labor used in a par;icular sector. Th• relati6n used here considers the supply side as consisting of domestic food production and food imports. The demand side is formulated as proposed by several economists {Okhawa, J . ohnston . and Mellor, 1961). The detailed equilibrium equation is: where, Sid+ sif = H + niG (16) Sid= growth rate of domestic food production of food item i, S;f = growth rate of food imports of item i, H = population growth rate, n. = income elasticity of demand for food item i, and , G = real income per capita. Rice, maize, beans and sugar were selected for study, since these commodities are basic food items and estimates of income elasticities , are published (FAO, 1967). 6 While it would have been appropriate to 6 rhere are some inherent shortcomings of these estimates, since they are mainly projections and not actual estimates for specific time periods.
PAGE 35
27 include several staple African food crops, such as cassava and yams, there are no data on income elasticities for these items. The Genera 1 Objective In order to identify the determinants of some of the policies pre vailing in the African export crop economy, it was felt that understanding the impact of individual crops on the level of agricultural export earn ings was an important first step in explaining how land resources are allocated. Such an impact may be measured through a multiple regression analysis model of the type: where, {17) Zt = level of agricultural export earnings as _defined previously, Qit = quantity of crop i, in thousands of metric tons exported in period t, Dt = diversification as defined in (7), and uzt = error term. However, Qit is itself an endogeneous variable which depends on such predetermined variables as export prices, harvested area of export crops, weather and time trend. The appropriate model is a system of simultaneous equations, where the system is identified, and a two-stage least squares procedure is used. The system has the following form: where, 2 t = f(Qit' Dt, uzt) (l7) pit-1 Pot-1 A;t . Aot RFt (18) = export price of crop i in period t-1, in U.S. dollars, = average export price of all other export crops in perfod t-1, in U.S. dollars~ = harvested area of crop i in period t, in thousands of hectares, . = harvested area of all other export crops in period t, = rainfall index in period t,
PAGE 36
,. 28 T = time trend, u 2 t,uqt = error terms. While . price variables in the system above would give information regarding price responsiveness, Ait and A 0 t indicate relationships between changes in the area of a given crop, relative . to changes in areas of others. Such information ' is very important for explaining the patterns of allocation of cropland between the crops selected for study. It also provides some insights into the relative degree of sea re i ty or abundance of land resources. Weather, particularly rainfall, frequently causes quantity varia bility in export earnings, so a rainfall index was incorporated. How ever, this index can only $erve as a proxy for weather effects, and might not show a direct relationship with quantity exported. For example, domestic storage and processing of some export commodities means that the quantity exported in any given year is not equal to the total quantity produced. Th~ q~antity produced would . be responsive to weather varia bility, while the quantity exported would not. Irrigation and flood control schemes could also modify the effects of rainfall on export quantities. Data Sources The study used secondary data from F.A.O. Trade . a . nd Production Yearbooks, Un~ted Nations Surveys of Economic Conditions in Africa, International Monetary Fund Surveys of African Economies and country statistical abstracts and reports prepared by different missions. Export values in U.S. dollars (Table 4) were deflated for all calculations. The deflation procedure used was: so that current dollar ex orts constant dollar exports = implicit deflator (b) 100 constant dollar exports (x) = y (100) b . ' The implicit derlator used for each country was the mean value of deflators ' . for each year calculated in the United Nations' Survey of Economic Conditions in Africa.
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29 Table 4.-Ea . rnings from major a,yricultural a exports, constant do 11 ars, 1950-1972 Year Kenya Nigeria Tanzania Zaire ---------------------Mallion U. S. dollars-------------------• 1950 9. 1 101.0 40.0 105.0 1951 7.5 . 76.8 37.4 80.0 1952 9.0 70.8 40.2 100.0 1953 11.0 48.0 41.8 102.0 1954 7.5 .54.0 33.0 102.0 1955 8.0 66.0 42.0 118 .0 1956 9.0 72.0 42.0 140.0 1957 10.0 90.0 42.0 148.0 1958 10.0 96.0 42.0 152.0 1959 9.5 90.0 46.2 164.0 1960 10.4 96.0 45.7 166.0 1961 15.0 96.0 46.4 160.0 1962 . 13. 5 108.0 46.4 142.0' 1963 14.0 120.0 55.0 142.0 1964 16.0 150.0 56.2 144.0 1965 21.0 144.0 52.8 142.0 1966 20.0 162.0 60.5 140.0 1967 18.0 189.0 70.4 126.0 1968 22.0 150.0 56. 1 120.0 1969 20.0 180.0 68.2 124.0 1970 22.5 189.0 70.4 112.0 1971 21.0 150.0 77 .0 108.0 1972 20.0 149.0 77.0 84.0 aMajor exports: Kenya, Tanzania Coffee, tea, cotton and sisal. Nigeria Groundnuts, cocoa, palm oil and rubber. Zaire Coffee, tea, palm oil and rubber. Source: Computed from F. A. 0., Production Yearbook and Trade Yearbook,, selected years.
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30 EMPIRICAL FINDINGS In interpreting th~ empirical findings, a specific . co , nceptual frame work was utilized. That framework was one that maintains that export price stability depends mainly on two factors: (a) the fluctuations in demand for or supply of ex . port commodities, and (b) the efficiency of existing stabilization s ' C:h:t?nies (Rowe). Eviqence suggests that exports with relatively stable prices (compared to quantity variations) are those that continue to be supported by international commodity agreements. Tea, although protected up to 1955 by the International Tea Agreement, has since been subjected to free trade. This change in status largely resu1ted from the assumption and belief that its fina 1 demand ~nd pro duction were very inelastic. Sisal, on the other hand, has never been individually chartered by an international commodity control scheme . . It is, however, a part of an international Conference of Hard Fibers Study Group sponsored by F.A.O. The movement of the price of . sisal after the Second World War has been characterized by substantial ups and downs. Sisal price was low during the interwar period, rose in the first half .. of the l950's, then fe11 until the mid-l960's (Rowe, Kriesel et al). The objectives of this study did not allow further investigation of the effects of international commodity agreements on ' price stability and export earnings of African countries. This subject might, however, be a fruitful one for further research. Price and Quantity Variabilit,X From the estimating procedure given in equation {8), it was found that o f the three study countries for which coffee is a common export crop, two countries (Kenya and Tanzania} exhibited a relatively higher percenta . ge of quantity variability compared to price variability (Table 5). For Kenya, Tanzania and Zaire, three countries . for which tea is a common export crop, results indicate that in two of these three countries (Kenya and Zaire) price affects variability more thc;1n doe s quantity. Price variabi1ity is proportionally mote important for sis<1l, a crop common to Kenya and Tanzania. Other common crops such as cotton, palm oil and rubber, show that quantity varia.bility is the most important component of the
PAGE 39
31 overa11 variab111ty (Table 5). Thus of six common crops among the countries, four crops {coffee, cotton, palm oil and rubber) were found to be charac terized by quantity variability, whereas two (tea and sisal} were subjected to higher price variability. For these four countries, cocoa and groundnuts are major exports only in Nigeria. These commoditi~s are affected more by quantity variability than by price fluctuations. In the aggregate, the evidence suggests that variability in export earnings is largely associated with the quantity rather than the ~rice component. Table 5.--Variability in agricultural export earnings attributed to price and quantity changes, 1950-73 Kenya Nigeria Tanzania Zaire Crop Price Quantity Price Quantity Price Quantity Price Quantity Variability attributed to ---------------------------------Percent-----------------------Coffee 14 86 35 65 66 34 Tea 55 45 44 56 60 40 Cotton 18 82 4 96 Sisal 73 27 81 t.9 Groundnuts 12 88 Cocoa 34 64 Palm Oil 13 87 35 65 Rubber 14 86 25 75 These results are consistent with findings in two other studies (United Nations, 1952; Mintz). In addition to the price and quantity variability calculated, an interaction component was computed. The meaning of this component has been subjected to many interpretations (Motha, et al). In this study, .. -it is simply interpreted as the variability resulting from interaction between price and quantity variations. Its impact on the. . total varia bility stems from the fact that, if the correlation coefficient between . . , price and quantity variations is either positive or negative, the inter action component variability will be, respectively, either positive or negative. In the former case, earnings variability wil 1 be greater than
PAGE 40
32 the sum of both price and quantity variations. In the latter case, it wi11 reduce the total variability and contribute to the stabilization process. In this s , tudy, the interactio.n component, whether positive or negative, was so small t'hat its effect on total variability was negli .. . gjble. One reason for the small size of this interaction component for each co1T11T1odity is the small share in total world . exports represented by each country's exports, in most cases. o . nly Nigerian exports of cocoa and palm oil, sisal shipments from Tanzania and palm oil exports from .. Zaire accounted for more than ten percent of the value of world exports in each of those commodities (Table 18). Patterns and Trends in Export Crcrn IJiversification . . .. While all four study countries may be considered as highly diversi~ fied in terms of the number of export crops grown, the results of this study indicate that when diversification is defined as given in equations (7) and (9), all four countries exhibited a persistent and declining trend in the level of diversification over the study period (Table 6 and Figure 7). Kenya and Tanzania experienced ' similar rates of decline of about one percent per year over the 1950-1~73 period. The rates of de cline in the level of export crop diversification were also . similar for Zair,e and Nigeria, at over three percent annually (Table 7). When the data series were divided . into two time periods, significant differences between countries were found in the annual rates of decline for the periods 1950 to 1960 and 1960 to 1973. The first period, 1950-1960, was one of colonialism; in which policy emphasis was placed on specialization in agriculture. The second period was one of political independence and nationalism, with policy orientation towards the integration of the agricultural export sector into the national economy. From 1950 to 1960, the export crop sectors of all four countries were becoming less diversified, but at a decreasing rate. In the later period, the rate of decline in diversification slowed markedly in Kenya, Tanzania, and Zaire, but increased in ' Nigeria. These trends demonstrate that the patterns of export crop development were determined by a crop specialization process. it will be recalled from the section on study area characteristics
PAGE 41
33 Table 6... Export crop diversification indices, 1950-73 Year Kenya Nigeria Tanzania Zaire 1950 144 160 141 134 1951 157 _ 142 130 124 1952 148 150 137 109 1953 142 139 142 105 1954 134 138 120 92 1955 132 142 130 100 1956 ' 129 130 117 91 1957 120 120 117 90 1958 130 110 117 95 1959 126 125 118 74 1960 130 127 117 54 1961 122 121 113 47 1962 122 126 105 70 1963 120 134 109 50 1964 118 110 100 45 1965 123 98 105 40 1966 117 110 108 30 1967 110 93 100 23 1968 119 90 110 30 1969 112 60 94 32 1970 120 70 99 30 1971 118 60 105 31 1972 120 45 110 28 1973 119 25 109 31 Source: Ca l:cul ated from F .A.O. Trade Yearbook.
PAGE 42
.,... C 0 150 '.::; 100 ctJ 0 .,... ,.._,. .,... ti) SCl) > o 50 .. 1950 1955 1960 1965 Kenya Nigeria Tanzania Za i re 1970 Figure 7.--Export crop diversification trends~ 1950-72 (195Qz:l00)
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35 Table 7.--Average annual rates of decline in diversification indices, selected periods Country Kenya Nigeria ranzania Zaire 1950-1973 1950-1960 1960-1973 ---------------------Percent•---------------------0.87 3.39 l.19 3.26 l.71 2.42 1.82 s.67 0. 19 5.73 0.82 3.23 that export crops are distributed in specialized geographical regions. Furth~rmore, distributions occur in such a way that competition between major export crops for land is not great. Thus, the tendency for a coun try to concentrate on one or two export crops may be explained in terms of the persistent growth in the production of certain export crops in one or two regions, relative to other regions. In the case of Kenya, the quantities of coffee and tea exports increased more than those of cotton and sisal since 1950 (Table 8). In terms of regions, it may be argued that the northern and coastal zones were growing more slowly than the southwestern region where coffee and tea are produced {Figure 3). Table 8.•Average annual growth rates of quantities exported, major com modities, 1950-73 Coffee Tea Cotton Sisal Groundnuts cocoa Palm Oil Rubber Kenya Nigeria Tanzania Zaire -----------------Percent per yea . r------:-------------1. 89 .86 1.85 2.49 6.06 2.27 .33 -5.18 -.18 . .50 -.30 --.68 ---.20 .68 2.53 . 16 :Sourc ' e : Computed from F.A.O., Trade Yearbook, selected years.
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36 Cocoa and rubber led export quantity growth in Nigeria (Table 8). Cocoa exports, with a rate of growth of .68 percent, were lower than thos . e from rubber at 2. ~3 percent. Groundnuts was third with an overa 11 growth rate of .30 percent, whi1e palm oil exports dec1 ined at .... 2Q per cent per year. The high growth rate exhibited by rubber e xports was not steady, but characterized by sporadic increases in quantities exported. It is unlikely that such a high rate of increase will be maintained, since more than one half of the area planted to rubber was expected , to be out of tapping after 1974 (Tims). While rubber has been a leading export, it is felt that cocoa and groundnuts are the major export crops in which Nigeria has specialized. The southwestern and northern regions (Figure 4) have steadily increased their exports of . cocoa . and groundnuts . . In Tanzania, growth rates for tea and cotton were 6.06 and 5.80 per ... _ cent per year respectively, far: greater than those for coffee and sisal (Table 8). Export grQwth rates of coffee and tea for _ Zaire , s . urpassed those , of palm oil and rubber (Table 8). Coffee and tea are main]y pro duced in the eastern part of the country, while palm oil a11d rubber are mainly grown in the western part (Figure 6). Effect of Export Crop Diversificati . on on Levels .and Variations in A9ricy1turaJ ,Export Earni.ngs In general, it appears that export crop diversification is working against high levels of agricultural export earnings. The magnitude and signi . ficance of the regression coefficients from , equation (l3) for each country are shown below: Kenya: * Zt = -3123 . (1452) Nigeria: Z = -4829* t (1871. 7) Tanzania: . . ** . Zt = -101307 (5388) * -73.51 Dt { 32. l ) R 2 = . 619 . . , -12. 42 Dt (4.90) R? = .48 . ; . '. ** ..239.96 Dt (124.33) + 8491.18 D~* (3840) ow = l.679 + 1349 o 2 * t (512.92) . ow= 2.33 + 21226 o 2 ** (14329) t
PAGE 45
37 Tanzania:' continued. DW = 2.60 Zaire: where, zt = -2149 (2653) -17 .23 Dt (20.3') R 2 = 07 . ; + 818 Df (941.3) ow= .694 Zt = values of agricultural export earnings as defined in equation {13), Dt = diversification index as defined in equation (13), * = b-coefficient significant at the 10 percent level, ** = b-coefficient significant at the 5 percent level, ( ) = values in parenthesis are standard errors. It appears that, while export crop diversification (Dt) tends to reduce the level of agricultural export earnings, the long term intensi fication of the diversification process tends to shift the earnings to higher levels. This is the interpretation of the results with the Dt squared variable defined as intensified diversification. The coefficients are all significant, except for Zaire; but even there the signs are con sistent with those of other countries. Comparing these findings from equation (13} with those from equations (7) and {9) yields important conclusions. Export crop production in all four countries became more specialized between 1950 and 1973 (Table 7). Since equation (13) shows an inverse relationship between the level of diversification and export earnings, it would be expected that agritul tural export earnings of the four countries increased. Agricultural export earnings from all four countries grew over the period (Table 9 and Figure 2). Kenyan earnings grew at higher rates than those of Nigeria and Tanzania, and all three countries showed much higher growth rates than did Zaire. The relatively higher rate of growth in earnings in Kenya was associated with a smaller rate of decline in the. level of diversification, compared with Nigeria and Zaire (Table 10}. Agricultural export earnings grew more rapidly after 1960 in Kenya. Nigeria, and Tanzania than prior to that year. However, the rate of earnings growth in Z~ire of over 7 percent annually showed a marked
PAGE 46
38 Table 9.--Index values of agricultural export earnings, 1950-72 . (1950:=100) Vear Kenya Nigeria Tanzania Zaire 1950 100 100 100 100 1951 82 76 94 76 1952 99 70 101 95 1953 121 48 105 97 195-4 82 53 83 97 1955 88 65 105 112 1956 99 71 105 133 1957 110 89 105 141 1958 110 95 105 145 1959 104 :8.9 110 156 1960 114 95 114 158 1961 165 95 116 152 1962 148 107 116 135 1963 154 119 138 135 1964 176 149 141 137 1965 231 143 132 135 1966 220 160 151 133 1967 195 187 176 120 1968 242 149 140 114 1969 220 178 171 118 1970 247 187 176 107 1971 231 149 193 103 1972 220 148 193 80 Source: Calculated from Table 4.
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39 Table 10.--Average annual rates of change in agricultural export earnings and in diversification indices, and average instability indices, selected periods Country and Unit 1950-1972 1950-1960 1960-1972 measure Kenya earnings percent 6.73 3.63 6.82 diversification II -0.87 -l. 71 -0.19 instability index .35 .33 .39 Niger fa earnings percent 4.38 i.34 6.09 . diversification II -3.39 -2.42 -5.73 i nstabi 1 ity index .44 .33 .50 Tanzania earnings percent 4.13 1.76 5.19 diversification II -1.19 -1.82 -0.82 instability index . .35 .38 .34 Zaire earnings percent 0.30 7 .19 -3. 17 diversification II -3.26 -5.67 -3.23 instability index .44 .36 . 51
PAGE 48
40 ded ine a ftet' 196.0. Apparently, governments of African countries rec.ognized the inverse relationship between . exP.ort crop diversification and the level of export earnings, and pursued policies of export crop specialization as a means of maximizing earnings. The direct relationship between intensified expor crop diversification and earnings shown in equation (13) does, however, support Jorberg' s argument that diver.sification could be helpful for long term growth and development. While diversification reduces the level of export earnings in the short run, the long-run effects on earnings are positive. The second aspect of the third objective Of the study was concerned with the effect of export crop diversification on the variation or in stability of earnings. On this aspect, the results from equation (15} are neither conclusive nor consistent, as shown below. Kenya: It.= 1.68 (7.60} R 2 = .006; Nigeria: It= 5.75 (4.79) R 2 = .104 Tanzania: lt = -27.53* (19.02) R 2 = .398 Zaire: I = 21.53* t (13.20) R 2 = .117; where, + 3.42 Dt (12.39) ow= 2.41 9.37 Dt (18.07) ow = 1.95 + 45_77 Dt* (14.62) ow = 1.404 .. 48.72 Dt* (36.63) ow = 2.17 2 .. 1.41 Dt (-.283) 4.08 o 2 , (3.37) t -18.68 Di* (15.89) + 25 . .19 o 2 * { 21. 34) t It = instability index as defined iO equation ( 15), Dt = diversification a.s defined previously, * = b-coefftcient significant at the 10 percent level, and { )= values in parentheses .are standard errors. \.
PAGE 49
41 All coefficients 1n equations for Tanzania and Zaire are significant. This may indicate that export crop diversification is positively related with earnings instability in Tanzania, and inversely related to Zaire. However, R 2 values are low for all equations, and all coefficients are statistically insignificant for Nigeria and Kenya. No conclusive stite ments can be made. Findings for tanzania and Zaire may indicate, though, that diversification might be an appropriate policy for one country, but inappropriate for another. Differences between countries could be due to the nature and characteristics of export crops produced in each. Price elasticities . for each of these crops differ greatly, so that identical changes in quantities exported could result in very different changes in their respective revenues and subsequent differences in instability values. Differences between instability index values for Tanzania and Zaire are largely due to sisal. Other major determinants are cotton in Tanzania ahd palm oil and natural rubber in Zaire, since tea and coffee are common to both. These observations are supported by the fact that average instability index values for Kenya and Tanzania, which grow similar crops, are similar at a .35 (Table 11). Nigeria and Zaire, while not having all four crops in common, both had average instability index vaiues of .44 (Table 11). Food Supply and Demand Growth Rate Disequilibrium The contention (Fitzgerald, et al) that the export crop sector is competitive with the domestic food sector for national resources led to an investigation of the rates of growth in supply and demand of certain food items. The equilibrium relationship stated in equation (16) was used with elasticities for food estimated by FAO for 1965-1973 (F.A.O., 1967). Although these data appear to overstate the growth rates of food demand, they nevertheless provide some insights and indicate the need for further investigation. All four countries exhibited significant disequilibrium between supply and demand for the selected food crops (Table 12). Nigeria, how• ever, was self-sufficient in major staple carbohydrate foods such as yams, cassava, guinea corn, rice and millet (Johnson, et!!_, 1969).
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42 l'able11.--Ir1stahility indices of agricultural export earnings, 1950-72 Year Kenya Nigeria Tanzania Zaire 1950 .15 .12 .28 .20 1951 .40 .20 .31 .10 1952 .35 .30 .25 .2.5 195 3 .42 .35 .38 .30 1954 .14 .41 .34 .40 1955 .19 .53 .40 .42 195 6 .27 .38 .50 .44 1957 .33 .30 . 50 . 50 1958 .33 .32 .45 .49 19 59 .50 .40 . 36 . . 46 1960 .60 .38 .38 .41 196 l .54 .42 .30 .38 196 2 ,42 .45 .25 . 50 1963 .55 .48 .50 .51 1964 . 50 .50 .52 .52 196 5. .45 .50 .53 .60 1966 .31 .60 .43 .50 1967 .14 .65 .26 .60 1968 .25 . 50 .28 . 59 19 69 .15 .. 47 .20 . 58 1970 .41 . 50 . 30 .50 19 71 .30 .55 .31 .40 19 72 .40 .50 .19 .55 Average .35 .44 .35 .44
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43 Three of the selected food items {maize, beans, and sugar) belong to what CSNRD 7 classified as important import substitution crops and nutritionally superior foods. Sugar is considered an import substitute while maize and beans are included among nutritionally superior foods (Johnson, et ~, , l969L, It seems reasonable to conclude that Nigeria was self-suf ficient in staple carbohydrate foods, but experienced shortages in import sub~titutes. However, import su ; bstitute foods faced poor market pros pects in Nigeria because of lack of effective demand for nutritionally superior goods such as eggs, milk, meat and other processed items (Johnson, et !D._, l 969). The growth rate in demand for food in Nigeria in the present study was higher than the rate shown in the CSNRD report. There appear tc.> be two reasons. First, the bias, if any, may come from the nature of the data used for income elasticities for food which were not actually measured but projected. Second, the CSNRO study considered effective demand as determined by the disposable income of consumers, while this study considers effective demand as determined by population and income. Thus, it may still be valid to conclude that Nigeria is experiencing shortages in import substitute foods if demand is conceptualized in terms of population and income. In fact, a simulation study conducted in Nigeria just after the CSNRD study confirms the results o.f this work \ JJohnson,et~, 1971, p. 298): However, we should add that the two strategies in volving export crop modernization programs did create some long-run adverse effects in the non~ agricultural sector. The increased profitability of export crops stimulated agricultural producer Consorti~m for Study . of Niger'ian Rural Development, a joint venture by Michigan State Uriversity, Unhersity of Wisconsi.n,, Qhio State Univer sity, Colorado State University~ l
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44 Table 12.--Differences between annual rates of growth of supply and demand for selected food items, 1965-73 Country Kenya Nigeria Tanzania Zaire Food Rice Maize Beans Sugar Rice Maize Beans Sugar Rice Maize Beans Sugar Rice Maize Beans Sugar Annual.rate of growth Supply Demand .. -----Percent ..... ......... . 177 .... 460 .0006 ... 2.02 .. 23 +19.96 -l.29 1.53 1.9 2.937 .24 3.98 2 . 12 . 913 .021 1.42 3.40 3.35 3.37 3.45 2~44 2.42 2.44 2,51 2.77 2.75 2.77 2.87 2.690 2.695 2.693 2.683 < Supply> Demand < < < . < < > < < < > < > < < < < Source: Computed from F.A..0. Agricu1tur<1l sornmodities~ _ Pro1ections for 197? and 1985, Rome 1967, and F.A.O. Production Yearbook, se ected years. demands for nonagricultural goods and consequently, the nonagricultural population's demand for food. In addition, the profitability caused some pro ducers to switch from food crop production to export crop production. Consequently, the price for food increased substantially in both programs involving export cr?P modernization because food demand increasedand food supply decreased. The above argument is illu~trated in Figure 8, which also shows the price effects -of competition between the export and domestic a . griculture sectors. Growth in food demand exceeded that for supply in both Kenya and Zaire. For Kenya, a recent report by the World Bank (Burrows) stated: Many rural families have diets deficient in calories, and Vitamins A, B2, and C. Although most pronounced
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per pound .015 .012 .006 -= 0n1y export crop modernization ... = Export crop modernization with marketing boards take-off . 003 --..----.------r---.----.----.------r-----r--------,----,,---,----,,1965 1970 Years 1975 1980 Figure 8.--Average price of food staples in northern Nigeria under alternative simulated policies, 1965-1980. Source: A Generalized Simu1ation Approach to Agricultural Sector Analysis: Hith Spedal Reference to Nigeria, Mic~igan State University, East Lansing, 1971.
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46 in Nyanza Province and parts of the eastern plateau, and variable by season, the deficiencies are nation wide ... Pressure on land is increasing, people are moving into marginal areas, maize has largely taken over from the more protein-rich millets and sorghums, and there is no reason to b1:?lieve that pulse production is going up at a faster rate than population. Food price increases in Z~ire are evidence t'ha ' t increases in supply failed to keep pace with growth in demand. Lack of data prevents any conclusions for Tanzania. It appears that "modernization" of export crop sectors in the study countries, without comparable effort in food sectors, is the major factor in the imbalance between the two. The problem, though related to the allocation of land and labQr between the two sectors, is never theless _ more far-reaching. Food and export crops compete for land to some degree, but food crops have a much larger share of cultivated land than do export crops (Table 13). The scientific and economic infrastruc ture allocated to the export . sector has made it relatively more productive and profitable than the domestic food sector. One study from Africa (Yudelman, p. 281) and another from the Caribbean (Davis, p. 142) support the validity of this hypothesis. Table 13.-:--Land distribution among food and export crops, 1970 Country Kenya Nigeria Tanzania Zaire Food crops 80 70 60 78 Export crops 20 30 40 22 Source: F.A.O., W , orld Crop Statistics. The two studies express strikingly similar views, as Yudelman writes: , . The philosophy of agricultural development in many . parts of Africa has been oriented toward a commodity approach, which has required the concentration of i nvestrnents and ski 11 s on the expansion of output of those commodities fOr which there is effective demand. In most countries, these are necessarily
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47 export commodities; ... However, whatever the merits of this approach for economic growth, it has led to a concentration of investment in the areas or regions with higher income producers of export crops. This tends to widen the regional differential; farm incomes in those regions not producing for export tend to lag further behind as development proceeds. Davis, in analyzing the relationship between agricultural research and agricu1tura 1 development in the Caribbean, states: The differential level of investment between the export and the domestic research systems raises a further question regarding the extent to which the differential might be related to significant dif ferences in the marginal value productivity of investment. There is a reason to suspect that the marginal value productivity of investment of the export system was considerably higher than that of its domestic counterpart. This suspicion is related to the generally higher output price of export crops, ... and significantly larger marginal productivity of research investment for the export system. Thus, the superior performance of export crops over domestic food crops can be explained by the capital investment allocated to the former, in terms of modern inputs, research infrastructure and modern management. If similar resources were also allocated to the food sector, it would probably grow and develop at a comparable rate. 8 The relatively low growth rates of agricultural export earnings for the four countries, though, suggest that there are less than optimum levels of investment in the export agriculture sectors. African Choice This section discusses the general objective of the study, that of identifying policies in African countries that affect the overall economy and the export agricul tura 1 sector. It was suggested earlier that, consciously or unconsciously, the four African countries studied have Brhe authors acknowledge the importance of the point made by P.J. van Blokland that subsistence agricultural production is persis tently underestimated because very 1 itt1e data are available. More information is collected a.nd available on export commodities, so that input or "performance" of export sectors may be overstated relative to production for on-farm or domestic consumption.
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48 opted for higher rather than for more stable incomes. In fact, the short run effect of the diversification proc . ess was found to reduce export incom1 Evidence from this . study shows that the four countries have become more specialized in their export agricultural sectors. However, this trend . raises two questions. First, what is likely to be the impact on the allocation of land resou . rces between different export crops? Second, what is the most effective use of increased agricultural export earnings as a development tool? The answers to these two questions were sought by using individual country data generated by the models, and by referring to the literature on the subject. Effects on Export Crop Special h:ation on Land Allocation Kenya Results with the two . -stage le~st squares (TSLS) system (equations 17 and 18) show Kenya heavily dependent on the four crops described ear1ier. Results: z.t = 300.945 . J (122.8) . + .452 (Q4 t) (. 135) J where, *** + .9732 {Qljt) + .220 (Q2jt) + .J53 (Q3jt (.582) (.038) (.076} 2. 829 ( 0 j t> (1.25) Zjt = agricultural export earnings as defined fn equation (17), Q1jt = quantity of coffee exported by Kenya, in thousands or metric tons in period t, Q 2 jt = quantity of tea exported by Kenya, in thousands of metric tons in period t, Q 3 . . = quantHy of cotton exported by Kenya, in thousands of metric Jt tons in period t, Q 4 jt = quantity of sisal exported by Kenya, in thousands of metric tons in period t; Djt = diversification index, as defined in ~quations (6) and (7), * = b-coefficient Significant at the 10 percent level, ** = b-coefficient significant at the 5 percent level, *** = b-coefficient significant at the l percent level, ( ) = values in parenthesis are standard errors.
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Tab 1 e 14. -:-Export supply function estimates, Kenya a Crops Constant . p it-1 p ot-1 Ait A ot RFt Coffee 50.435 -.250 1.562 .406 .323*** -2.741 (326) (.379) ( 1. 287) ( 1.90) (. 097) (5.23) Tea -313.73* .709 . 294 1.94** 1.64 .940 (240} ( 1. 039) (. 718) (.218} (1.46) (4.413) Cotton 44.837 .827 .3665 .3598 .3435 -1.204 {193.76) (.780) { .654) ( .464) ( .800) (4.315) Sisal 18.336 .909 -.543 1.029** -.1926 2 . 050 (101.81) ( 1. 32) (. 306) (. 451) (. 291) (1.95) a Qit = f(Pit-l' P 0 t~l) Ait, Aot' RFt, T, u). Linear form. * = b-co efficient significant at .10 ** = b-coefficient significant at .05 *** = b-coefficient significant at .01 ( ) = values in parentheses are standard errors. T R2 ow 23.43*** .920 2.338 (13.88) -6.014 .982 1.862 (8.1808) .5554 .411 1.610 (. 773) .,:,. \.0 2.244 .781 1.433 (3.46)
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The four crops have different weights, as coffee has the largest coefficient with .973, followed by sisal f.452), tea (.220) and cotton (.153) . . Irt . the 1950~73 period~ mo : re coffee was produced than other commodit'ies. Export supply funct , :f , ons also show thaf the quantity of coffee exported increased over time (iabl e 14.). A 1 e.gitimate question is whether . ' the increase in coffee exports , affJects ttie ' :al. , ~t,cation of land to other crops. The coefficient in the coffee export supply function {A 0 t) is positive and significant, sugg . esting that coffee acreage moves in the same direction a . s the area of other crops taken as a whole. This suggests that the specia1izatiqn in coffee production is not hindering the develop ment of other crops, but that those regions specializing in coffee pro duction are growing at a proportionally high~r . rate than others. Agricultural export earnings of Nigeria are positively related to cocoa, groundnut and rubber exports; . but inversely related to p . a1m : oil exports. Results: Zjt i= 15.90** (5.24) + .267 {Q4jt (. 173) where, Zjt = agricultural export earnings as defined in equation (,17), Qljt = quantity of groundnuts exported by Nigeria . , in thousands of metric tons in period t, Q2jt Q3jt Q4jt l)jt * *** = quantity of cocoa exported by Nig . eria, in thousands of metric tons in peri . od t, = quantity of palm oi1 exported by Nigeria, in thousands of . . metric tons in pe~iod t, . = . quantity of rubber exported by Nigeria, in thousands of metric tons in period t, = diversification index, as defined earlier, = b-coefficients significant at the 10 percent level, = b-.coefficien~s significant at the 5 percent 1eve1 1 . = b-coefficients significant at the l percent level, ( } = va 1 ues in parentheses are standtlrd errors. The magnitude of the coefficients show.s that cocoa (. 979} and groundnuts {.416) h~ve the greatest impact: on the . level of Ni~erian agricultural
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labl e 15. --Export supply function estimates, Nigeria a Crops Constant PH-1 POt-1 A.t 1 . Aot RFt Groundnuts -121.03 -1974** .297 .151 . 776 8.752*** (138.16) (. 438) (. 454) (.166) (2.14) (2.46) Cocoa -73.747 -.4346** .6637 .9894 .2466 2.989 {1$3) (. 238) (. 635) (2.61) (. 301) (2.46) Palm on -208.83** .323 -.324 1.122*** .2733** 5.361*** (77. 92) {2.09) {2.47) (.250) (.141) ( 1. 27) N. Rubber --592.10*** 1.498*** -.285 .914*** .4468* 11. 744 (139.J} (.369) (.526) ( .104) {331} (2.472) a Qit = 10\t-P Pot-l' Aft' Aot' RFt, T, u). linear form. * = b1"foeff i ci ent significant at . 10 ** = b.;.coefficient significant at .05 *** = b-cdefficient significant at .01 ( ) = ,values in parentheses are standard errors. T R2 ow 3.970** .835 2.415 (2.56) 6.537 .785 2.905 (3.406) -8.160*** .936 1.584 ( 1. 58) U'l 5.973 .974 2 . . 360 __, (2.666)
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52 export earnings. Natural rubber ranks third (.267), while palm oil adversely affects agricultural export earnings. These results, showing that Nigeria specialized in cocoa and groundnuts, are confirmed by the export supply function (Table 15). The regression coefficients of the export supply variable (A 0 t} for cocoa and groundnuts are not significant , , s9 . is not possible to say if increases in those two exports restricted otheT export crops. However, regression coefficients for . rubber and palm oil are both positive and significant, This suggests that the acreage of a11 four c . rops moved in the same direction. The negative trend in the volume of palm oil exported can possibly be explained l>y the fact that domestic consumption of palm . oil has been increas1ng at . the same rate as population increase (Helleiner, 1~65). In general the same . conclusions drawn for Kenya based on different rates of growth among export crops for various regions may be drawn for Nigeria. The northern and southwestern . regions of Nigeria have been . .. exporting more groundnuts and cocoa, respectiv . ely, than other regions. Tanzania For Tanzania, the results from the iSLS . system shown below are not significant, exc~pt for the coefficient related to . cotton exports and the diversification index. zJt = -1194 . . 11 (.348) where, zjt Qljt Q2jt Q3jt Q4jt Djt * ** ( ) + .670 (Q4jt) (. 680) = agricultural export earnings as defined in equation (17), = quantity o.f coffee exported by Tanzania, thousands of metric tons in period t, = quantity of tea exported by Tanzania, in thousands of metric . tons in period t, = quantity of cotton exported by Tanzani.a, in thousands of metri . c .. tons in .period t, = quantity of sisal exported by Tanzania, in thousands of metric tons . in period t, = diversification index as defined earlier, = b-coerficient si gnificant at the 10 percent level, = b-coefficient significant at the 5 percent level, = values in parentheses are standard errors.
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Tab le 16. --Export supply function estimates, Tanzania a Crops Coffee Tea GQttqn Sisal Constant pit-1 p ot-1 Ait Aot Rf t -223.897 -.4724 -1.2311** .2587 .6320* 9.5200* (268.9) { 1.24 ) (. 677) (.332) ( .496) (7 .40) -596.142 -.7214 -17795** -1.0401 1.2865** 22.5307 {805) {2.26.} (104.9) (2.35) ( .821) 1379.28* l . &4QQ** ,9&56 ~l23Q 3,9652 . ** {950) (1.114) (1.02} (. 427) ( 1. 794) 137.527* -.4194 -.4203 1.1008*** -.2423** (92.92) * ** *** (.960) (1.26) ( .291) = b..:coefficient significant at .10 = b..:coeffici ent significant at . 05 = b-toefficient significant at .01 (. 086) (19.5) SQ, 7574** (23.12) -3.0153 (2. 27) ( } = values in parentheses are standard errors. T -3.7975 (6. 55) 33.1879 (13.82) 2a.a765 (14.50) (J'1 6.2876** w (2.01)
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54 On the basis of these results, the only statement that may be made is that export crops earnings depend largely on the volume of cotton produced. The relationships between different crop areas indicate that only sisal is affected by an increase in production of other crops as a whole (Table 16) , . This may mean that sisal is competing for land with cotton, tea, and coffee Zaire For Zaire, Log zjt = where, the logarithmic l. 763 (5.21) + .15 . 5 log {.302) Zjt = agricultural export earnings as defined in ~quation (13), Qljt = quantity of coffee exported by Zaire, in thousands of metric tons in period t, , Q 2 jt = quantity of tea exported by Zaire, in thousands of metric tons in period t, Q 3 jt = quant~ty of palm oil exported by Zaire, in thousands of metric tons ,n period t, . Q 4 .t = quantity of na~ural rubber exported by Zaire, in thousands of J metric tons in period t, 0. = diversification index, as defined earlier, Jt * = b-coefficient significant at the 10 percent level, ** = b-coefficient significant at the 5 percent level, ( ) = values in parentheses are standard errors. Coffee . is the only crop of the four with a significant coefficient. As discussed for the other countries, the results above and in Table 17 suggest that when the coffee area increases, area in other crops as a whole also increases. Again, the same arguments regarding the differential between growth rates of exports of different specialized regions applies to this case. The Use of Agricultural Expo.rt E~rnings for De,velopment Goals Agricultu . ra1 export earnings of the four study countries increased over the study period (Table 10). Kenya , 's earnings increased af 6. 7 per cent per year, whiletho , se of Nigeria, and Tanzania grew at 4A and4.1 .
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Table 17.--Export supply function esti'mates, Zairea Crops Constant pit-1 Pot-1 Ait Aot RFt T R2 ow Coffee -10.804 .5429 .4178 -.630** .460** 3.692 .969*** .409 1.613 (8.69) ( 1.87) (.417) (. 311) (.240) (2.074) (. 294) Tea 8.171 .6613* -.2237 .3116 1.135** -3~247** .9363*** .975 2.001 (7.32) (.514) (. 439) (. 258) (.439) (1.31) (. 333) Palm Oil -3.243 . 3119** .1597 1.5901*** . 2116 .6044 -.1260 .922 1.347 (3.05) (.139) (.178) ( 1. 62) (. 207) {. 598) {.138) (J'I N. Rubber -9. 548** . 2011* -.1364 . 7111 -.1824 3.022 .9849 .933 1.900 ()l (4~87) ( .152) (.. 256} ( ._163) (. 208) (1.04) (l.93) a Qit = f{p.t I" pt 1, A.t, Aot, RFt, T, u). Double logarithmic form. 1 0 1 . * = b-coefficient sianificant at .10 ** = b-coefficient significant at .05 *** = . b-coefficient significant at .01 ( } = values in parentheses are standard errors.
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. 56 percent respectively. Zaire experienced a much slower rate of growth in export earnings with 0.3 percent annually (Table 10). If export earnings . grow at low rates and agricultural exports constitute a relatively large share of GDP, there is reason to expect that the rate o . f overall develop ment wi11 be slow. Several authors have . attempted to explain why the dynamics of the export economy have not broken the vicious circle of poverty in LDCs {Singer, Prebisch and Levin). Singer and Prebisch blamed the specialized nature and structural patterns . of export economies. Levin argued that the structure of export economies and their isolation from national markets is the basis of the development handicap. Johnson . arid others (1971) suggested that agriculturql export earnings surpluses gained by marketing boards were ilQt used in the agricu1 tural sector to meet develop ment goals. For Nigeria, however, this view is strongly opposed (Aboyadef Helleiner, 1966), and in Tanzania; Kriesel believed agricultural export earnings were used effectively in the agricultural sector. The findings of this study suggest that the performance and returns of marketin~ board investments in development projects have been poor. There are two reasons for this conclusion: First, the apparent lack of responsiveness o{ marketing boards to changes in world prices (Tables 14-17); and second, relatively low annual rates of growth i11 export ear nings (Ta ble 10 and Figure . 2), despite the use of substantial land resources for expprt crop production. Lack of responsiveness of marketing boards to world prices may be explained by three f~ctors, The first is pricing policies which ke . pt producer prices well below world prices, removing incentivfls to increased production among peasant farmers. The secondfactor relates to the lack of technological investment in agr,iculture, and the fixity of factors of production . .Fixity of factors means that farm~rs do not have a wide choice of input combinations, but instead, rely heavily on land and labor. Factors such as fertilizers and capital are used little, . partially because farmers have incomes too low to afford them. Marketing board surpluses passed on to farm~rs would enable them to acquire inputs that would in c::rease production. However, such surplu ses have been siphoned off for "pol Hie.al parties" (Nixon) and for ''dubious projects'' (Eicher, 1970) in many cas . es. The third factor is the perennial nature of many export crops and the resulting restrictions on the degree of flexibility in earning use.
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57 There is evidence that although agricultural export earnings have historically low growth rates, the agricultural export sector is still an appropriate development tool. These low rates appear to be caused primarily by organizational and structural problems. If these problems were remoyed, the export crop sector could stimulate overall agricultural development. These structural prob lems, although pi-;onounced . i . n countries with marketing boards, are also present in other nations. In the latter countries, the problems are largely related to direct government fiscal and pricing policies affecting the farm sector. Thus, rather than accuse the export sector of hindering the development process, efforts should be made to stimulate expansion of the overall agricultural sector, without reducing the export sector (McPherson, 1974). But, whether such an expansion should be based on agricultural diversification policies is still not answered with the results reported here. The findings suggest, however, that there are some positive effects. accruing to diversification. Furthermore, the findings indicate that specialization in certain commodities is due to the fact that certain regions are expanding their exportable supplies of those commodities proportionally more than others. Thus, diversification can be a viable po1 icy if used to stimu1ate export crop production in economically re tarded areas. On the other hand, diversification can have a retarding influence if it involves the use of export crops in developing areas to achieve sub-optimal levels of agricu1tura1 diversification in the hope of achieving economic development. SUMMARY AND CONCLUSIONS Summary This study focused on four African , countries: Kenya, Nigeria, Tanzania and Zaire. The objectives were (a) to identify , whether agri cultural export earnings fluctuations were determined primarily by quantity or price variability, (b) to investigate trends and patterns in the agricultural export sector, (c) to inquire into the relationships between agricul tura 1 export earnings fluctuations and the export crop diversification process and (d) to determine the difference between food supply and food demand growth rates.
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58 Fluctuations in agricultural export earnings were found to be due mainly to variations in quantities exported, rather than to price vari abi1ity. These quantity variations appear to be related to such factors as the lack of f1 exi bil itY by institutions responsible for the , trading of such export crops, and agro~eco1ogica1 variability. These institutions have continuously exporited the traditional agricultural commoditias. Such a policy demonstrates a lack of responsiveness ' " to world prices. A secondary source of export qua . ntity variation was variabjl ity in rainfall and its effects on crop production. Results of the empirical analysis showed that a1l four countries, a , 1 though diversified in terms of the number of export crops produced ; n different ecological regions, have been specializing in one or two export crops. The pattern of such specialization is not characterized by com• petition between crops as such, but rather, by unequal growt~ rates of specialized production regions. Results from analyzing the relationships between export crop diversi fication and agricultura1 export earnings revealed an inverse relationship between these two variables. That is, an attempt to increase diversifi cation would result in reducing the level of agricultural export earnings, at least in the short run. However~ 1 results from the quadratic model reveal that an intensified diversification program may cause the level of agricultural export earnings to rise in the 1ong run. While the results generated by the models aimed at analyzing the effects of diversification on export earnings fluctuations were incon clusive, they did indicate that the decrease in diversification since 1950 was accomprmied by a higher degree of instability . in some countries and by an increase in earning , s stability in other countries. In regard to the difference between growth rates of food supply and food demand, it was found that demand for food was increasing more . ra pidly than food supply in a11 four countries. The argument based on the land allocation to these two sectors, in conjunction with findings of other authors, suggest that the gap between the two sectors is due to a relatively mild advantage of export crop over food crops, in terms ()f investment in capita1 and research infrastructure. Such investments, a1 though sub~optima 1 in the export crop sector itsel f, nevertheless have made this sector relatively more 11 modern 11 than the domestic food pro~ duction sector.
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59 For the general objective of the study, an attempt was made to de termine if specialization inhibited the development of other crops by reallocating land resources. In general, the areas of leading crops were moving in the same direction as those of lagging crops. One possible explanation is that crops were grown separately in specific ecological regions and some regions were more committed than others to the growing of these crops. Another aspect explored in the study relates to the low rates of growth of agricultural export earnings under a specialization policy . . It was argued that these slow growth rates are due to the lack of an appropriate level of modernization in the export crop sector. Special i.zation based mainly .on the increased use of only land and labor was held responsible for such low performance. Conclusions Since it appea.rs that a 1 arge proportion of variability in export earnings is due to fluctuations in quantities, it is then imperative to use policies that address themselves more to quantity adjustment than to price regulation. Such policies could be buffer stocks and quota adjust-. ments. However, because of difficulties inherent in international policy enforcement (Edwards and Parikh), emphasis should be given to alternative domestic adjustments such as export crop diversification and appropriate tax structures. These policies would be most appropriate for those countries which have specialized in crops whose variability in quantity is higher. Also, because of the long-run effects of export crop diversi fication on the level of export earnings, diversification should be con sidered. But since the effects on instability of this policy would vary according to the country and tile physical and economic characteristics of crops introduced, such a policy would have to be adopted cautiously. The need for caution is apparent when one considers the cases of Tanzania and Zaire. In Tanzania, further diversification would result in higher instability, while in Zaire it would stabilize earnings (equation 15). The results from these countries lead to the conclusion that there exists a range where export diversificati.on has desirable effects but works against stability and hisher incomes beyond that range. These ranges would be expected to differ from country to country. Further research, to determine conditions and causes of patterns in different.
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60 countries would be interesting and useful. The gap between the domestic food sector and the exporl crop sector reinforces the feeling that the lack of modernization of the food sector is responsible for such an imbalance . . However , the balanced modernh:ation of both export and food sectors would help the former to grow at a faster pace and at the same time; he ' lp the latter se't:tor to keep up with gemand pressures. Another conclusion regarding the general Objective of the study is related to the finding that thet-e exist unequal growth patterns of export . . . crop production among regions within a country; Such a situation may be the sourte of the observed specialization trend and the cause of ~ failure to a chieve optima 1 diversification levels. Tt iS believed that efforts to stimulate the export production of economically retarded regions would not only help a country increase its export crop supplies but also help to achieve an optimum degree of diversification. Such an effort requires a combined policy of additional investments iri the a . gricultural of re tarded areas and relevant pricirtg system at the producer level.
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APPENDIX
PAGE 70
62 Table 18.--Average annual values in current dollars of world and country exports of major commodities, 1950-73 World Kenya ' Nigeria Tanzania Zaire Commodity Value Value % Value % $1,000,000 $1,000,000 $1,000,000 Value % Value % $l,000,000 $1,000,000 Coffee 2,552.8 35.6 l.4 --29.6 1.2 41.3 l.6 Tea 630.9 20.6 3.3 -4.2 0.7 l. 9 Q.3 Cocoa 585.9 112. 1 19.1 .... .. ... Cotton 2,307.5 2.0 0.1 -26.4 l.l -•-i ..... Sisal 119.4 10.5 8.8 -40.5 33.9 ~Rubber 1,494.2 .. _ 25.0 h7 15.6 l.O Groundnuts 5,063.9 .... 79.8 1.6 !II!' ... Palm Oil 163.0 --33.5 2.0.5 -... 32. 9 20.2
PAGE 71
Table 19.--Earnings from major agricultural exports, and total agdcu1t4ral exports, current dollars, 1950-1973 Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 Kenya Coffee Tea Cotton Sisal Total --------------Thousand U.S. Dol_l ars-----------:---------11,505 12,937 21,357 19,770 28.,049 44,199 23,346 23,266 29,660 28,770 29i;740 29,720 30,890 43,160 39,510 52,610 43,920 3S,976 47,268 62,428 54,777 69,406 3,560 3,715 3,253 6.,069 8,293 8,709 9,819 10,212 12,465 ll,851 15,609 20,449 21,446 21,669 29,131 25,520 32,137 34,683 40,183 34,1>85 49,668 51,129 952 1,583 2,101 1,441 2,113 2,289 900 1,415 l,.840 2,350 1,760 l,.220 1,220 1.,810 2,090 2;430 l,7 1,115 2,131 3,437 3,310 3,.410 3,913 13,633 25,791 16,096 9,811 7,793 8,048 5,.869 6,120 9.,:683 12,784 H,737 12,105 21,090 16,847 10,785 9,352 5,833 5,157 4,821 5,258 4,264 5,801 13,671 29,650 40,311 43,270 34,275 44,024 62,829 38,824 40,620 51,395 5.6,369 55;088 58,654 73,649 83,263 74,054 93,523 77,033 74,385 88,903 111,306 96,436 128,285 68,713 Total Ag. Exp. Earnings ------Million U.S. Dollars---74.9 62.7 72.5 81.9 87.9 85.7 94.9 ll1 .6 113.6 105.9 156.0 141.0 144.2 157.8 177 .4 151.0 203.2 298.5 :Continued 0'I w
PAGE 72
Year 19-SO 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1950-1973--Continued Nigeria Cocoa Groundnuts Palm Oil Rubber Total -..------------Thousand . U.S. Dollars-------------------------64,292 76,515 63,378 8,760 212,945 95,463 32,915 87,522 27,614 243,514 92,001 63,566 62,933 15,824 234,324 81,766 81,222 68,301 11,521 282,810 71,647 103,998 65,262 26,629 267,536 66,880 74,665 29,246 170,971 79,475 64,205 37,827 24, 116 205,623 74,938 91,245 34,995 21,878 223,056 l07 ,210 76,920 38,663 32,490 255,283 ~8, 159 61,475 36,907 39,880 236,421 94,490 90,251 37,035 30,730 252,506 93,372 90,793 24,998 31,490 240,653 ~0,605 l02 ,463 26,222 32,750 252,040 112,279 95, , 9ZQ 30,111 33,850 272,160 lil9,534 105,8:54 38,055 30,590 294,033 7 ,628 39,900 17,670 309,072 1'47,269 100,271 121,300 26,919 395,759 . 186,3.05 60,,842 159,000 24,596 430,743 201,79:0 34,019 477,800 17,492 731,101 1~3,724 32,528 41,800 12,495 240,547 U0,796 6 . 9 , , 169 1,400 29,482 270,847 Total Ag . . Exp. Earnings ------Million ' U.S. Dollars308.7 287.6 295.0 324.5 300.6 331.7 417.9 403.0 416.5 356 . . Q 359.7 419.5 463.4 431.0 412. 1 405.3 426.8 438.6 392.4 315.2 456.5 Continued 0) .:,.
PAGE 73
Table 19.--Earnings from major agricultural exports, and total agricultural exports, current dollars, 1950-1973--Continued Tanzania Year Coffee Tea Cotton Sisal . Total Total Ag. Exp. Earnings ----"'.;. ______ ;..Thousand U. S. Dollars-----------------------Millian U.S. Dollars---1950 14,956 . 574 7,903 50,336 . 73,769 1951 21,735 7~387 94,523 123,645 1952 23,743 864 8,792 72,073 105,472 1953 20,429 l, 193 ll ,373 47,226 80,.221 1954 1955 2'6,767 2,353 16,196 40,457 85,773 1 : 956 35,881 2,336 20,895 42,399 101,511 109.5 Hl57 19,420 2,625 17,747 26,966 66,758 92.8 _ 1958 20,702 2 839 20,962 28,786 73,289 . 97.2 -' . 1959 16, . 080 2;157 18,640 36,559 7l,436 105.9 . 1960 20,510 3,221 24,710 43,236 91,677 128.6 1961 18,930 3i742 19,020 39,278 80,970 112. l 1962 18,410 4,513 20,700 44,056 87,679 120.6 1963 . 19,l 50 4;346 30,010 63,479 116,985 156.1 1964 30,940 4 ,367 27,670 61,227 124,204 168.5 1965 24,060 4,:230 34,190 39,989 102A69 145.8 1966 43,400 6~321 48,990 32,855 130,566 204.4 1967 33,440 6!t194 35,190 28,191 103,015 174.2 1968 37,152 6, . 612 39,653 22,304 105,721 173.8 1969 36,104 6~863 32,886 22,352 98,205 185.3 1970 43,746 5,988 34,616 25,038 109,391 193.4 1971 32,059 5,812 34,409 18,985 91,265 190. 9 1972 53,663 7,540 47,172 19,424 l27,799 235.6 1973 70,,591 7,734 47,767 31,786 157,878 270.2 Continued 0\ C'I --~-~
PAGE 74
__ ..... .., ... .... ., ..,.?:S ... , , ""' 411UJ"' a~, -u..u 11.u, d 1 1:xpur1.s, ana cot.a I agr, cu I t.ura I ~xports, current dollars,. l950~ 1973--Contirtued Zaire Year Coffee Tea Palm Oil . Rubber Total Total Ag. Exp. Earnings -----------Thousand U.S. Dollars-------.-----------------------Million U.S. Dollars-1950 36,240 45,667 6~113 88,020 1951 44,634 73,692 15,891 134,217 191.l 1952 38,819 86 50,911 14,293 104,109 151.4 1953 44,680 217 44,155 9,6.54 98,706 134.Z 1954 1955 62,219 1, ns 52,543 22,492 138,369 1956 83,342 1,519 59,959 24,497 169,317 1957 68,178 2, . 267 34,503 20,374 125,322 1958 63,570 2,958 32,922 20,227 119,677 1959 61,560 2,517 38,261 22,320 124,.658 C1'I 1960 30,210 2,957 30,322 25,840 89,329 ' 1961 23,700 16 31,894 21,490 77,100 1962 14,520 2,268 28,246 20,170 65,204 1963 17,830 2,416 23,570 17,070 60,886 1964 24,190 1,440 21,190 13,630 60,450 1965 14,740 2,on 18,500 9,150 44,401 1966 23,050 2,213 15,450 10,480 51,193 1967 28,060 2,900 21,700 9,610 62,270 1968 34,300 3, . soo 24,800 9,900 72,500 1969 28,200 1,305 19,269 16,273 65,047 1970 33,900 1,892 28,181 12,759 76,732 1971 41,000 1,300 26,404 12,000 80,704 1972 67,251 2,544 16,600 10,500 96,895 1973 65,000 . 1,470 17,500 14,000 97,970 Source: F .A .. O., trade Yearbook. ..
PAGE 75
Table 20.--Values of corrmodities exported, current d.o11ars, 1950-1973 Coffee Tea Cocoa Year Kenya Tanzania Zaire World Kenya Tanzania Zaire World Nigeria World ----------Thousand;.. __ ;__.__;.. -Mil 1 ion-... ----Thousand---------_;--Mi 11 ion-Thousand-Mi 11 ion1950 1%1 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 l969 1970 1971 1972 1973 11,505 12,9J7 21,357 19,770 28,049 44,199 23,346 23,266 29,660 28,770 29,740 29,720 30,890 43,160 39,510 52,610 43,920 35,976 47,268 62,428 54,777 69,406 14,956 21,735 23,743 20,429 26,767 35,881 19,420 20,702 16,000 20,510 18,930 18,410 19,150 30,940 24,060 42,400 33,440 37,152 36,104 43,746 32,059 53,663 70$591 36,240 44,634 38,819 44,680 62,219 83!7342 68,178 63,570 61,560 30,210 23,700 14,520 17,830 24,190 14,740 23,050 28,060 34,300 28,200 33,900 41,000 67,251 65,000 2,220.4 3,560 2,496.9 2 , 44 9. 7 3, 715 2,754.6 3,253 2,954.4 6,069 3,532.5 8,293 2,309.8 8,709 2,004.2 9,819, 1,970.9 10;212 1,912.1 12,465 1,859.2 11,851 1 , 886 . 2 1 5,609 l,995.0 20.449 2,387.4 21,446 2.,231. 3 21,669 2,393.9 29,131 2,239.9 25,520 2,<555.4 32,.137 2,490.4 34,683 3,081.9 40,183 2,748.6 34,085 3,227.7 49,668 4,321.5 51,129 574 864 1,193 2,353 2 ~336. 2:.625 2,839 2,157 3,221 3,.742 4,513 4,346 4,367 4,230 6,321 6t194 6~612 6,863 5,988 5,812 7,540 7,7-34 86 217 h1T5 1,519 2,267 2,958 2,517 2,957 16 2,268 2,416 1,440 2,011 2,213 2,900 3,500 1,305 1,892 1,300 2,544 1,470 471.9 380.1 534.6 548.7 605. l 597.7 635;3 604.9 611. 7 626.5 664.1 679.9 675.9 684.9 632.4 730.7 708.2 625. l 696.0 693.8 724.1 747.9 64,292 95,4-63 92,001 81s766 71,647 66,880 79,475 74,938 107,210 98,159 94,490 93,372 90,605 112;219 1 l9f534 79,129 153,127 144,874 147,269 186,305 201,190 153,724 170,796 450.1 553.l 516.8 580.7 575.2 430.5 464.1 554.9 570. l 542.6 487.4 473.2 507.9 522.5 501.6 457.0 593.0 641.7 795.6 867.6 737.0 707.2 944.8 Average 35,557.4 29>602.96 41,269.26 2,552.78 20,620.6 4,201.091,852.90 630.89 112,135.87 58.5.85 ' Continued ' "
PAGE 76
' "'"''"' 1-V• vult.1 ~~ VI \,.V!IUIIUUll..11::::::. t:J\.l--'Ur-l,t!IJ, currenr. ao11ars, f~~U-IY/J--Continued Cotton Groundnuts Sisal Year Kenya Tanzania World Nigeria . World Kenya Tanzania World ---Thousand------Million--Thousand-Mill ion-,---Thousand-----:---'-Mill ion-1950 . 952 7,903 2,256.5 76,515 199.4 13,633 50,336 147.2 1951 1,583 7,387 2,163.3 32,915 123.8 25,791 94,523 284.7 1952 2,101 8,792 1,812.8 63,566 153.8 16,096 72,073 173. 1 1953 . 1,441 11,373 1,849.9 81,222 168.8 8,811 47,226 112.1 1954 103,998 1955 . 2,113 16, l 96 1,842.6 232.3 7,793 40,457 115.5 1956 2,289 20,895 2,093.2 64,205 8,048 42,399 116.5 1957 900 17,747 1,967.6 91,245 218.8 5,869 26,966 78.3 1958 1,415 20,962 1,716.2 76,920 227.2 6,120 28,786 81.7 1959 1,840 18,640 1,844.4 61,475 202.2 9,683 36,559 l 02. 3 1960 2,350 24,710 2,441. l 90,251 193.2 12,784 43,236 ll8.2 Cl 1961 1,760 19,-020 2,351.3 90,793 242.0 11,737 39,278 112.4 Q:) 1962 1,220 20,700 2,053.8 102,463 245.8 12,105 44,056 132.6 1963 l,220 30,010 2,257.0 95,920 256.0 21,090 63,479 185.3 1964 1,810 27,670 2,372.1 105,854 264.6 16,847 61,227 173.3 1965 2,090 34,190 2,295.4 114,280 272.l 10,785 39,989 113.4 1966 2,430 48,990 2,307.0 99,156 291.0 9,352 32,855 l 01. l 1967 1,760 35, l 90 2,237.6 106,628 246.8 5,833 28, 191 77.6 1968 l, 115 39,653 2,375.4 100,271 258.9 5,157 22,304 72. l 1969 2,131 32,886 2,296.2 60,842 254.9 4,821 . 22,352 73.5 1970 3,A37 34,616 2,484.1 34,019 216.0 5,258 25,038 73.7 1971 3,3l0 34,409 2,796.3 32,528 208.2 4,264 18,985 65.0 1972 3,410 47,172 3,133.1 69,169 236.0 5,801 19,424 79.4 1973 . 3,913 47,767 4,125.9 334. l 13,671 31,786 158.0 Average-'t,025.6 26,386.0 2,307.51 79,737.955,063.90 l 0,536. 91 40,So'l. 09 l 19. 43 Continued ~ -
PAGE 77
Table 20.--Values of commodities exported, current dollars, 1950-1973--Continued Year 1950 1951 1952 1953 1954. 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 J969 1970 1971 1972 1973 Average Palm Oil Nigeria Zaire -----Thousand----63,378 87,522 62,933 68,301 65,262 74,665 37,827 34,995 38,663 36,907 37,035 24,998 26,222 30,111 38,055 30,697 3.,527 399 1,213 1,590 4,778 418 14 45,667 71,692 50,911 44,155 52,543 59,959 34,503 32,922 38.,261 30,322 31,894 28,246 23,570 2l,l90 18,500 15,540 21,700 24,800 19,269 2e, 1a1 26,404 16,600 17,500 World --Million-182.3 281.9 188.8 191 .o 192.6 149.4 79.4 74.4 122.5 118.7 122.4 105.3 113.1 127.2 146.8 143.3 112.0 109.9 123.5 200~. 9 281.5 261.7 382.4 J3,45o.9o 32,879.96 162.96 Rubber Nigeria Zaire -------Thousand .. ----8,760 27,614 15,824 11,521 26,629 29,246 24;116 21,878 32,490 39,880 30,730 31,490 32,750 33,850 30,590 32,020 17,770 17;670 26,919 24,596 17,492 12,495 29,482 6,113 15,891 ]4~293 9,654 22:,492 24,497 20,374 20,227 22,320 25,840 21,490 20;170 17,070 13,630 9,150 10,480 9,610 9,900 16,273 12,759 12,000 10,500 14,000 World ----Million-1,815.8 3,282.5 1,897.2 1,066.7 l ,920 . . 9 l,773.9 1,387.0 1,277.6 1,725.7 1j804.6 1,399.2 1,528.8 1,400.4 1,244.8 l,281. 5 1,306.0 1,222.7 890.3 1,246.7 1,126.7 965.5 891.4 l,.910.1 15.,597.09 1,4'9'4 .17 25,039.22 O'\ \0
PAGE 78
BIBLIOGRAPHY Abbott, J. C. "Agricultural Marketing Boards in Deve1oping Countries, .. Journal of Farm Economics, Vol. 49, 1967, pp. 705-722. Aboyade, 0. 11 A Note on External Trade, Capital Distortion and Planned Development, 11 in I. G. Stewart, African Primary Products and International Trade, University Press, Edinburgh, 1965, pp. 26-43. Agency for International Development. A. I.D. Economics Data Book: Africa, Statistics and Repoits Division, March 1971. Aigner, D. J. Basic Econometrics, Prentice-Hall, New Jersey, 1971. Balassa, B. Trade Prospects for Developing Countries, The Economic Growth Center, Yale University, 1964. Beckford, G. L. Persistent Poverty: Underdevelopment in Plantation Economies of the Third World, Oxford University Press, New York, 1972. Bieber, J. Diversification Opportunities and Effects of Alternative Policies on Costa Rican Coffee Farms, Ph.D. Dissertation, University of Florida, 1970. Burrows, J. Kenya: Into the Second Decade, Report of a mission sent to Kenya b_y the World Bank, Tihe Johns Hopkins University Press . , Baltimore and London, 1976~ Carter, H., G. W. Dean and A. D. Reed. Risk and Diversification Jor California Crops, California Experiment Station, Berkeley, Circular #503, 1961. Committee for Economic Development. Trade PoJicy Toward l..ow Income Countries, Report, 1967. Coppock, J. o. International' Erconomlc.Instqbility, ' McGraw-Hill Book Company, . New York, 1962. ----..,,--..--....,._• Foreign Trade of the Middle East, Economic Research Institute, American University of Beirut, Beirut, 1966. ,. Davis, C. G. "Agricultural Research and Agricultural Development in Small Plantation Economies: The Case . of the West Indies, l!Social anp Economic Studiest vo l. 24, No. 1, March 1975, pp. 111 .. 152. De Vries, B. A. The Expel"ience of Dev . eloping Countries, IBRD, The John Hopkins University Press, Baltimore, 1967. 70
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74 Parikh, A. "A Model of the World Coffee Economy, 1950-1968," Applied Economics, Vol. 6, 1974, pp. 23-43. Peema ns, J. P. 11 The Socia 1 and Economic Development of Zaire Si nee J nd~ pendence: An Historica 1 Outline," African Affairs, Vol. 74, No. 295,. April 1975, pp. 148-149. Prebisch, R. "Commercial Policy in the Underdeveloped Countries," American Economic Review, Vol. 49, May 1959, pp. 251-273. Rowe, J. W. F. Primary CotTDTiodities in Interna _ tional Trade, Cambridge University Press, Cambridge, 1965. Saint-Marc, M. Commerce Exterieur de Development, Le Cas de la Zone Franc, Societe d'Edition d'Enswignement Superieur, 1968. Schultz, T. W. Transforming Traditional Agriculture, Yale University Press, New Haven and London, 1964. Singer, H. W. "The Distribution of Gains Between Investing and Borrowing Countries," American Economic Review, Vol. 40, May 1950, pp. 473-485. Stewart, I . . G. African Primary Products and lnternational Trade, University Press, Edinburgh, 1965. Tanzania. Taari fa ya Ta . rakimu, Vol. 21, No. 3, 1971. Tims, W. Nigeria: Options for Long-Term Development, The Johns Hopkins University Press, Baltimore and London, 1974 . United Nations. Instability in Export Markets of Underdeveloped Cc>untrie$, New York, 1952. ________ . Survey of Economic Conditions in Africa, New York, 1971. Yudelman, M. 11 Distribution of the Benefits and Costs of Development in Africa," ~xternalities in the Transformatton of Agriculture: Distri .. bution of Benefits and Costs from Development, Iowa State University Press, Ames, l 975. Zaire (Republique Oemocratique du Congo}. Rapport Annuel de la B~ngue Nationale, 1969.
PAGE 1
Economics Report 89 Agricultural Diversification and Export Earnings, Selected African Countries ood and Resource Economics Department gricultural Experiment Station 1stitute of Food and Agricultural Sciences 1 n Cooperation with mter for African Studies niversity of Florida, Gainesville 32611 .F.As. u llV. of Flori W. K. Mathis C. G. Davis M. T. Futa
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ABSTRACT This study focused on four African countries: Kenya, Nigeria, Tanzania and Zaire. The objectives were (a) . to identify whether agricul tural export earnings fluctuations were determined primarily by quantity or price variability; {b) to investigate trends and patterns in the agri cult1.1ral sector; (c) to determine the impact of export crop diversification on , the level and variability of agricultural export earnings; (d) to in quire if there was a gap between domestic supply of and demand for food and to determine if such a gap is due to overinvestment in the export crop sector. Fluctuations in agricultural export earnings were found primarily associated with variations in the quantity exported, which were due in part to actions of government marketing boards. A secondary source of export quantity variation was variability in rainfall. All four countries introduced new crops during the study period, but fewer different crops accounted for shares of agricultural export earnings, so countries ac tually became more specialized. An inverse relationship was found between export crop diversification and the level of agricultural export earnings. However, it would appear that in the long run, there is likely to be a positive relationship . . Demand for food was increasing more rapidly than food supply; the imbalance was not due to overinvestment in the export sector. Low rates of growth of export earnings implicitly indicated that the export sector itself lacked resources. Key words: Sub-Sahara Africa, export earnings instability, export crop diversification, economic development, export agriculture, develop ment policy. ...
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ACKNOWLEDGEMENTS The authors wish to thank the Rockefeller Foundation for Mr. Futa's support during his time at the University of Florida. The assistance c>f Dr. W.W. McPherson is gratefully acknowledged, both as a member of Mr. Futa's advisory committee and as a reviewer of this manuscript. [)rs. R. D. Emerson and P. J. van Blokland are also due thanks for reviewing the manuscript. ihree lovely and hardworking ladies typed many drafts and the final copy, and are due much appreciation: Mrs. Patricia Beville, Ms. Carolyn Williams and Mrs. Mignonne Winfrey. Mrs. Carolyn Dunham gathered and processed data, which we acknowledge with thanks.
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LIST OF TABLES LIST OF FIGURES INTRODUCTION . Objectives Country Selection . TABLE OF CONTENTS . . . . . . . . . . . . . . . . .• . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . STUDY AREA CHARACTERISTICS . . . . . . . . . . . . V Common F ea tu res . . . . . . 4 Kenya . . . . . . . 9 Nigeria. . . . , . 11 Tan-za , n i a. . . . . .13 Zaire. . . 15 . ANALYTICAL MODEL DEVELOPMENT . . . . . . . . . ' 17 Literature Review. . . . . . 17 Variable Specification and Description. . . 20 Instability Measures . 20 Diversification Index. . . 23 Model Specifications for Study Objectives. . 24 Objective 1. . 24 Objective 2. . . . . . 24 Objective 3. . . . . . . . 25 Objective 4. . . . . . . . . 26 The General Objective. . . 27 Data Sources . . . . 28 EMPIRICAL FINDINGS . . . . . . . . . . . . . . . . ' Price and Quantity Variabi1 ity ............• Patterns and Trends in Export Crop Diversification Effect of Export Crop l)iversificaUon on Levels and Va . riati.ons in Agricul;eural Export Earntpgs Food Supply and Demand Growth Rate Diseq , uil ibrium . . . . . . African Choice ................... . i 30 30 32 36 41 47
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t TABLE OF CONTENTS (Continued) Effects of Export Crop OiyetsJficaticm QO Land Allocation '. ~ ..• ~ . . 48 Ke _ nya _ . .. 4 s . Nigeria. . HO :ranzania . . . . , 52 Zaire. . . . . . . . . . . . . . . . . _ . _ . _ . . . . . .... . . 54 . . . TheUse of Agricultural Export Earnings for Development Goa 1 s --.. , . .. 54 _ . SUMMARY AND CONCLUSlONS . . . . . . . . . . . . . . . . Summary. . Cone l us ions . APPENDIX .• BIBLIOGRAPHY " J . _. . . . . . . . . . ' . . . . ' :. . . ii . . . . . . . . . . . . . . . . . . . . . . . . 57 57 59 (il 70
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Table LIST OF TABLES Agricultural trade value as a percent of total trade . value, selected African countries in selected time periods. . . . . . -.. . . . . . . . . . . . . . . . . . . 2 Agricultural product value as a percent of GDP, selected African countries in selected time periods. 3 Position of selected African countries according to two designated selection criteria, 1971-73 ..... 4 Earnings from major agricultural exports, constant dollars, 1950-1972 ...........•.••. . . . . . . 5 . . . 5 . . . 6 . . .29 5 Variability in agricultural export earnings attributed 6 7 8 to price and quantity changes, 1950-1973 . . 31 Export crop diversification indices, 1950-73. Average annual rates of decline in diversification indices, selected periods ............ . Average annual growth rates of quantities exported, major commodities, 1950-1973 •........... 33 35 .35 9 Index values of agricultural export earnings, 1950-1972 ( 1950= 1 00) . . . 38 10 Average annual rates of change in agricultural export earnings and in diversification indices, selected periods .• 39 11 12 13 14 15 Instability indices of agricultural export earnings, 1950-72 . ..................... . Differences between annual rates of growth of supply and demand for selected food it . ems, 1965-1973. . . Land distribution among food and export crops, 1970. Export supply function estimates, Kenya Export supply function estimates, Nigeria. iii . . . . . . . . . . . . .42 . 44 .46 .49 51
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LIST OF TABLES (Continued) Table Pag~ 16 Export supply function estimates, Tanzania. . . 53 17 l~ . 19 20 Export supply function estimates, Zaire ... . . . 55 Average annual values in current dollars of world and country exports of major commodities, 1950-73. . . . 6l Earnings ' from major agricultural exports qnd total agricultural exports, current dollars, l950 ... 73. . . 63 Values of commodities exported, current dollars, 1950-. 73 . . . ~ _,_ __ , . 67 iv
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LIST OF FIGURES Figure 1 Africa, and Sub-Saharan Countries 3 2 Index values of agricultural export earnings, 1950-1972 (1950=100) ..• ; . . . 7 3 4 5 6 Kenya, with major export crop regions . . . . . . . . . Nigeria, with major export crop regions Tanzania, with major export crop regiQns •. . . . . . . Zaire, with major export crop regions .. , 10 12 14 16 7 Export crop diversification trends, 1950-72 (1950=100). 34 8 Average price of food staples in northern Nigeria under alternative simulated policies, 1965-1980 45 I V
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AGRICULTURAL DIVERSIFICATION AND EXPORT EARNINGS, SELECTED AFRICAN COUNTRIES W. K. Mathis, C. G. Davis and M. T. Futa INTRODUCTION The study of trade fluctuations has been of much concern in the post World War II period. Since then a significant proportion of trade literature has dealt with theoretical and empirical analyses of fluc tuations and genera 1 ins tabi H ty of export earnings of 1 ess deve 1 oped countries (LDCs). In the presence of problems associated with export earnings fluc tuations in general, and/or agricultural export earnings fluctuations in particular, economists have made extensive analyses of the causes and effects of instabi 1 ity. Unfortunately, a relatively smal 1 proportion of the literature has dealt with policies to correct instability. Agri .. cultural diversification is probably the most widely discussed stabilizing mechanism. However, the discussions have generally been theoretical and did not deal with economk impacts of diversification. Objectives The primary objectives of this study are: {l) Estimate and categorize export crop earnings variations into price and quantity components as a means of understanding their relative weights in program planning and policy forma tion. . . --,, _ . : w. K. MATHIS and C. G. DAVIS are associate professors of food : a,nct resource economics at the Uni rers i ty of Florida. M~-3. FUTA is a Rock efel l er Foundation Fellow in agricultural economics at Oklahoma State University. 1
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2 (2) Oescri be and measure the patterns and trends in export crop diversification in . each of the countries covered in the study. (3) Measure and describe the influence of export crop diversifjcation on the level and _ variability in agricultural export earnings. ( 4) Quantify the gap between aggregate . food s uppl;y ~nd demand and determine whether or not~ny existing disequiJibrium between the two components is related to differential levels of _ invest ment between the export and domest . ic agricultural sectors. A secondary and mo . re general objecti . ve of the _ study is to identify policy lines in the agricultural export sector and to evaluate the impact of such pqlicies on the overall agricuJtural development of selected countries Country Selection This study analyzes d~terminant$ and : effects of agricultural export earnings instability in selected Sub-Saharan African co1.11itries du . ring . 1950..:73_Countries were seleCted according to two criteria . : (a) the ' , deg f ee to which the country is '' engaged in agri.cultura l ' trade and (b) .'. the relative importance of agricu'lture i-n its overall economy. The ratio of agricultural trade to total trade is used to establish the first criterion. ' 'The ratio of the value of total agricultural f}r".Oduct to gross , domestic produCt (GDP} established the second criterion . . The stze of this ratio ,> fndicates ' t ' he . overall contribution of agriculture to the national economy, and thus the relative :' importance of an external ., disturbance in a grJculture to . the economy. Both these ratios were calculated '. for thirteen tropical African countries, from which Kenya, Nigeria, Tanzania, ' and Zaire were selected (Figure l). Selection of the four countries wa$ based . on . the . average value~ of both ratios for the period l971..:19]3. For both criteria, coun trieS were grouped into those with (
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3 . . , .. .. . .. .I 1 .. . ,:::: :: .. :::. ;;;,~-.,:i;~u\:;Lt;; ;;: > ... IIf ~lil~if l Figure 1.-~Africa, and Sub-Saharan Countries t
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4 ratio of less than 33 percent is small. Here, it is assumed for simplicity that the national economy consists of three sectors: industrial, agri cultural and service. Thirty-three percent of GDP would represent an equal distribution among the sectors. According to the country selection criteria established above, Sub Saharan African countries may be placed into two broad groupings that reflect their heterogeneity. Countries grouped on the basis of the rela tive trade ratio show ' definite differences. Kenya and Tanzania are among countries with higher ratios of agricultural trade value to total trade value, while Zaire has a smaller ratio (Table 1). Nigeria is typical of those countries experiencing a shift in status from the first to the second group. Divisions based on ratios of agricultural product value to GDP show . similar groupings (Table 2}. Kenya and Tanzania have economies based mainlJ on agriculture, while Zaire is more dependent on non .. agriCultur~l sectors . Nigeria, although exhibiting a relatively high rafio of agricultural pro .. duct va 1ue to GDP, has experienced significant changes. Countries grouped on the basis of the relative trade ratio (Table 1) show remarkable con sistency in groupi9gs based on the agriculture product value: GDP ratio (Table 2}. For exJmple, most countries in . the high ATV group are in the high group (Table 3). TTV All four of the countries chosen for study have experienced substan tial fluctuations in export earnings. Constant dollar values of agricul tural export earnings . of the four selected countries increased from 195-0 to 1973, but were vulnerable to sharp fluctuations (Figure 2). STUDY AREA CHARACTERISTICS Common Featur&s The four countries selected for study have certain common agricultural features. A 11 produce several export crops. Kenya and Tanzania share many of the climatic and ecological conditions typical of East Africa. Nigeria and Zaire also have similar agro-ec:ological conditions in certain reg . ions; even though they are located in different parts of the African
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5 Table 1.--Agricultural trade value as a percent of total trade value, selected African countries in selected time periods Country 1956-1959 1963-1966 1967-1970 1971-1 1 973 Study countries Kenya 65 61 57 52 Nigeria 69 66 56 19 Tanzania 76 80 72 71 Zaire 10 3 7 9 Others Cameroun 75 75 75 68 Ethiopia 95 98 93 90 Gabon 3 2 1 1 Ghana 76 72 79 74 Ivory Coast 72 70 67 68 Ma1awi 90 91 88 89 Senegal 80 87 73 51 Uganda 85 82 82 90 Zambia 4 2 3 1 Source: F.A.O., Trade Yearbook. Table 2.--Agricultural product value a s a . percent of GOP, select~d i1\f i r 1 i r ttiil countries in selected time periods Country 1955-l9i0a 1963-1966 1967-1970 . . . . _ _ . . . _ . S . .. . UP, ,. J , 4 U !l _ ,,. Study countries Kenya 36 37 Nigeria 60 59 Tanzania 50 55 Zaire 30 21 Others Cameroun 40 41 Ethiopia 57 57 Gabon Ghana _ Ivory Coast Malawi 51 Seneg~l 40 30 Uganda 60 59 Zambia 9 aThese years differ from tb~se tn Tabl e 1 between eriginal data sources . 33 54 40 21 37 52 l7 70 59 51 28 56 8 Source: u. N., Survey of Economic CondttiQns in Africa. 33 39 39 HJ 36 54 :l5 , 46 33 50 2 . "/ 1 53 '8
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Table 3.-..'.Position of selected African countries ac ~ ording to two designated selection criteria, , 1971-73 . St~dy countries Kenya Nigeria Tanzania Zaire Others Camerotin < Ethiopia Gabon .. , -Ghana . . . . Ivory Coast Malawi Senegal , Uganda . Za.moia (High . ~+~}a + + + + + + . + . { LowAtV)a TTV + + + + (High trfpl + + + + + + + + + AP b (Low GOP) . + + + aATV = Agricultural Trade Value. High ;i: means a ratio of 50 . percent or more; Low, 50 per<: _ ent. than TTV = Total Trade Value AP b AP = Agr1 cultra r Product. _ . 33 percent. . _ High GDP means a ratio of 33 percent or more; Low, less than GDP = . Gross oomestic Product. Source: Computed from U. N., Srvey of Economic Conditions in Africa Trade Yearbook. and F. A. 0.,
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Index 200 150 100 50 1.950 t
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8 continent. These common features explain the historical development o . f palr: otl and natural rubbe . r prodl.,lction in both countries / Coffee, tea and hard fibers are common to Zaire, Kenya and Tanzania. Similarities between the agricultural economies of Nigerta and the two eastern countries are found mainly in institutions introduced in all three countries by the former British colonial admiritstration. : .. A common and important institutional legacy _ of the colonta l era is the marketing board. In fact, a9ric1Jltural marketing and: trade ' in most African countries is controlled by these .boards. Boards are statutory bodies established by government , action '' and . J~ndowed with leg~} : powers ... ' . over the production, marketing , and processing of ' prfo1ary agricul tvral products (Abbott). However, . the objectivt:?s, structure , conduct and ,' . . . . p'er-fortnance of such bodies vary from CQuntry to country. Abbott, after extensive analysis of market~ _ ng boards, indicates that these bodies have several objectiv~~ ~ , Some . of the more important are sales promotion, res'1!arch, extehsionservices,raising bargaining power of agricul tura 1 producers i , n dom,~stic . or _ export markets, setting up needed marketing and processing facilities, equali~ing returns . from sales in different markets or through different outlets, and cushioning the impact ' . . . . . , . upt>n producers and consumers of f_ sharply flyctuating internal and external prices. The last point is of p<1rticular . , interest to thi.s study, since it deals with problems of agricultural . trade instability. , , ' ', . Most African marketing bo&rds are of the export monopply type. The Zai rean boards are an excepti<>h, however, since they are essentially stabi'fizing rather than trading . institutions (Abbott). In the remaining three countries, marketing boards handle all problem~ . related to the marketing OT export crop$~ lenya and Tanzania have h~d individual boards for each export crop . . In r~cent years, Tanzania . has been ' redu _ <:ing the number of marketing boards, giying an individual' board respons.ibility for several . crops (Kriesel, et . al). In Nigeria . , marketing boards are regional (He.lleiner, 1966). Also , , , two Nigerian . export er . ops, rubb~r and bananas, ar'~ not reg . ulated at all. ,; . Jn spite Of. di ffe.r,e~ses , al!1.P'.1 . !L
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9 during a season or from year to year. Where these prices are lower than world prices the differences accrue to the marketi _ ng boards. These surpluses are supposed to be used to maintain producer prices when world price 1eve1s fall below the marketing board price. However, the use of such surpluses in Nigeria has been criticized by some economists (Nixon, Eicher, 1971, Johnson, 1969, 1971), and defended by others (Hellei.ner, 1966}. Aspects of this debate will be explored later in discussions of the empirical findings of this study. This debate, however, is not limited to the use of trade surplus. It has been extended to the general impact of marketing boa.rds on market and production mechanisms. There is general consensus that i:narketing boards tend to create distortions in factor mar kets and result in production disincentives (Johnson, 1968, Helleine:r, l 966). Kenya . Kenya's export crops are produced in three distinct a~ro-ecological zones (Figure 3). The first is the eastern coastal plain, a semi-arid band along the Indian Ocean, where sisal is produced mainly on planta tions. The second zone in the interior to the north is characteriz ed by poor pasture lands and marginal cotton production. The third , ecological , zone is a fertile upland region which produces coffee and tea, and most of Kenya's other export crops. Four crops were selected for this st.udy because of the.ir relative importance in the Kenyan economy; coffee, tea, cotton, and sisal, which account for about 80 percent of agricultural export earnings. Kenya I s major export crops are produced by both plantatio•l a nd peasant farming systems. However, the plantation economy remains a distinguishing feature of Kenyan agriculture. It has ~nly be . en within the last two decades that the peasant farming system was give,n the nece . s .. , sary economic incentives to expand under the ausplces .of the Sw,Ynne , rton Plan} 1 rhis plan was introduced in 1954 and supplemented in 1962. The primary objective was to help the natives of Kenya to expand their pro duction of crops such as coffee, pyrethrum, tea, maize and millet. Lands made available through this plan were divided into two areas--'Scheduled' areas and 'Non-scheduled' areas. The former was reserved for Europeans ::inrl th~ 1;:ittArfor African natives.
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lO Coffee and te~ ' . . . Sisal . ~ Cotton Figure 3 , ... Kenya, with major export crop regions
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ll Available lands were designated as 'high-density areas' and were reserved for subsistence small holders. 2 •ttow-density" areas were reserved for larger commercial farming. units. These large units market their crops through marketing boards. Nigeria, in contrast to Kenya~ has large areas of fertile coastal land. This region can be divided 1into two subregions: the southeast, with palm oil and natural rubber production, and the southwestern region which has maintained its comparati~e advantage in cocoa production (Figure 4). The second region, stretching from east to west across the center of the country, is suitable for growing both groundnuts and cotton. The third zone in the north lacks the climatic advantages of the south but has been mainly responsible for making Nigeria the world's major exporter of groundnuts. The four crops selected for study--groundnuts, cocoa, palm oil, and natural rubber---are produced in all regions and contribute approximately 85 percent of the total agricultural export earnings of the country. Nigeria has areas both of surplus labor and surplus land. It also has areas, with differing labor-cultivated land ratios and 1 engths of fallow periods, between these two extremes (Helleiner, 1966, p. 5.5). The coastal region with its high population density and limited land is considered to be a surplus labor area. The central and northern regions, on the other hand, with low population density are considered to have surplus land. Despite the apparent land surplus, these regions are characterized by permanent and seasonal labor migration to the south (Norman). With such differences in regiemal Jabor .. land ratios, it is easier to understand why the trend toward ind'ividua1 land proprietorship is not very significant in the country as a whole (Johnson, 1968). However,, 2 The term "subsistence holders" means farmers consuming more than 50 percent of their farm productiom.
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. . , . . .. Groundnuts 12 crop regions . or export 'th maJ _ . N geria, wi . 4 -1 Figure
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13 in the area of land reform Johnson argues that extended and co1111Tlunity ownership of land is not a serious problem in Nigerian agriculture. He suggests that the more serious problem is the market distortions caused by government and marketing board policies (Johnson, 1968). This view is not genera 1 ly shared by all economists concerned with Nigerian econo mic development. The debate on the role and impact of marketing boards on Nigerian economic development is still unsettled. Tanzania Tanzania, south of Kenya and east of Zaire, has four main regions of interest (Figure 5). The first is the eastern coast where shal is produced. Next is the west-central region, in which several crops are cultivated, with cotton, coffee, and tobacco the most important. The north-central region also produces coffee, while the southern zone is characteri.zed by a system of production which has been referred to as 3 crop dispersion, rather than crop diversification (Saint-Marc, 1968). Cash crops grown in this system include sesame, tobacco,.cotton, tea, . . 4 . . sunflower, castor seed and cashews. Coffee, tea, cotton and sisal--the four study crops--account for approximately 75 percent of the agricul tural_ export earnings of Tanzania. Export crop prod1Jction is largely concentrated in the plantation economy. However, as in the case of KenYa, emphasis is now being placed on the development of small farms. The Tanzanian government as well as the Tanzanian national political party maintain a strong interest in the socialization of the rural society. 5 Governmental participation has also 3 oispersion is defined as the existence of several crops in a given area with a very low density. 4cashews are important Tanzanian exports but lack of data prevented inclusion in this study. 5 Rural qevelopment policy is expressed as a program of rural social ism. It is designed to lessen income inequa'lities among farmers by giv ing them a community mode of production by which the farm unit is jointly owned by the extended family or a group of people who agree to work together.
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14 Coffee and tea Cotton Figure 5.--Tanzania, with . maj.or export crop regions
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15 been extended to include the marketing system. This system operates through a complex set of marketing regulations administered through marketing boards. The marketing boards' structure and operation have been reviewed and subjected to a number of changes over the years, largely as a result of criticism of African governments' use of agri cultural surpluses. However, in the case of Tanzania, marketing boar~ surpluses have been used for the economic betterment of the farming sector (Kriesel, et tl). In fact, Kriesel found that there have been no direct transfers of such surpluses or savings to governments in contrast to the ca ' se in some West African countries. Thus, in spite of some uneasiness about government intervention in the agricultural system, there is optimism that agricultural surpluses will continue to ba invested in the agricultural sector instead of being siphoned otf to other sectors. However, government policy encourages community ownership as the means of production. With a high population growth rate of 2.7 per year, Tanzania has a serious shortage of cultivable land. Zaire Zaire, the third largest African country after Algeria and the Sudan, has the most diversified climate of any country on the continent. The varied ecological conditions are suitable for many different crops, but only a few are grown commercially for export. Coffee, tea, palm oil, and natura 1 rubber represent 85 percent of all the agricultural export earn~ ings of the country (Figure 6). These crops are produced mainly on plantationg, but thereare some small peasant farm units. ln the 1950's the colonial agricultural admin istration began a program known as paysannats indigenes in an effort to transform traditional farming patterns. Innovations implemented in these programs were noted in development literature (Johnson, 1968; McPherson and JohnSton, 1968). However, the system conapsed with the emergence of national independence. The collapse of the paysannats system was accompanied by a decline in the relative importance of agriculture in the economy. Massive migration has . been occurring from rural to urban areas (Mabala). A prevailing pattern is that of disinvestment in the agricultural sector (Peemans}. In addition, agricultural institutions in the country have not been
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. ' 16 Natural rubbe Palm oil : .. . f•fee and tea Co .. . ..• . . . . . ', , , _ -. '
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17 effective in rural improvement programs. However, theZairean government has begun to provide credit facilities, revise land policies, and improve the rural infrastructure. ANALYTICAL MODEL DEVELOPMENT Literature Review In the wake of post World War II interest in trade fluctuations, several empirical analyses were undertaken. A 1952 United Nations study reported an analysis of price and quantity movements in LDCs. The study focused on: (a) year-to-year price and quantity fluctuations and {b) 1ong--term price and quantity fluctuations and cyclical swings. One of the major findings was that export earnings fluctuations. tended to be higher than those of prices and quantities taken individually, due to the interaction of prices and quantities. The study also reported that prices accounted for two-fifths of the fluctuations -while volume .accounted for the remaining variability. Using. United States ,export data, Mintz concurred with the United Nations findings regarding the relative impor tance of export quantity as a determinant of variability in export earnings. In a 1958 article, Nurkse formulated specific policies on the basis of the earlier United Nations report. He supported the . United Nations proposal .calling for international funds and buffer stocks as a means of stabilizing export earnings. However,. he made a strong case for balanced growth in the LDCs as a means of fostering industrialization and less reliance on primary products. Coppock, in 1962 and later in 1966, analyzed Middle Eastern foreign trade patterns. In both studiesi. Coppock developed ind.ices of instability and related them functionally to export prices, export quantities and market shares of individual commodities and countries. A study by Devries concluded that such factors as trade position, price inflation and resource allocation determined export growth rate and performance in LDCs. DeVries found a positive correlation between the performance of major and minor exports and growth in the value of agricul .. tural product. He opposed diversified industrialization policies as
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18 inefficient in he1 ping LOCs . reach economlc 1eveTs of prodC1ction and . a competHiVe ceiling. . . . . Studies by Maizels havemadesignificant cof!tributions . to the understanding of trade problems in LOCs. Maizels argued against overspeciaH zation in particular export commodities. He : saw export cfi.versification as not only an appropriate mechanism for faciJ itating structural change, but as probab 1y the most important from a long-term viewpili nt . Mabel s further argued that accurate assessment of world demand trends for export commodities is a vital first step in assisting LDCs to capture the economic gains from high demand exports . . Balassa's work had significance for the agricultural earnings insta .. bility question in that it esti~ated demand trends for tempe . rate zone foo . ds, competing tropical foods and agricultural raw mate1da1s . . However, an increase in world demand might not Justify . a pol icy of expqrt crop diversification and shifts tn resource a]loca.tion, Some economists have proposed reorienting policies towards food ct.op dlversiftdati . on as aC.ueans of reducing high food import propensity(Flores) .. All of the studies discussed so far~ except that by :e the United N~tions dealt with trade problems in general or with the question of stability, without referring to specific stabflization policies. Massel entered these . gaps by reviewing different policy alternatives. Buffer stocks and multi ... 1 atera 1 contracts proposed by the United Na ti ~ms study and supported . by .. Nurkse were analyzed. Massel found that b1;1ffer stocks provided certain . welfare advantages to producers by minimizing the expected value of change of producer prices, and to consumers by providing gains in consumer \ surplus (1969). However, the cost of implementing a buffer stock policy was higher thanother alternatives (Massel, 1970). Earlier, Massel conc1ud ed that neither export earnings instability nor the dishfti.lity ar-lsJng th~r~ . fronf are likel~ to be eliminated by simple polici . es such as diversification of exports .(1964). Since variations are i ndepen9$nt among commodities, their additivity may actually . worsen the variability. This . argument runs counter ' to Jorberg's findihgs that diversification is capable of i . nducing stabilit) in the seeular trend of export earnings. Parikh used . an econometric model of the world c . offee . ec~ _ nomy to predic production, consumption and outc'911les of . illternative polici~s. , Asi111ilar
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19 model was later used by Edwards and Parikh to identify policies that would minimize the fluctuations of agricultural export earnings. They found that, given the necessary resources, an international buffer stock policy could substantially reduce short-run fluctuations. A quota policy was judged to be more successful, as it had fewer of the economic difficulties found in the buffer policy. Edwards and Parikh suggested, however,, : that . quota policies would probably be much more difficult to enforce, due to political considerations. With such difficulties evident in international policies, interest should turn to domestic policies such as tax structure and export crop diversification. However, little has been done in this regard in LDCs, and the concept of diversification has been primarily associated with the process of industrialization. Only a limited number of studies have analyzed export crop diversification as such. One of the few international studies dealing with this aspect is a 1967 report by the Committee for . ' Etonomk Development (CED). This particular study concluded that export earnings fluctuations are largely the result of a combination of varia tions in crop output and dependency on one or two products. Policies and programs have been formulated on the basis of many of the above analyses in an attempt to facilitate greater international price stability and economic growth (U.N., 1952). The major operating mechanisms of these stabilizing programs were export quotas, buffer stocks, and multi . lateral contracts. The General Agreements on Tariffs and Trade (GATT) has served as the primary operational1 vehicle for these pol icfes and programs. In spite of GATT',s efforts, the LDCs still exhibit instability in export earnings, particularly for agricultural exports. The problems of agricultural: export earnings instability are likely to be more serious in those countries that de . rive a sizable proportion of their G ross Domestic Products (GDP} from agricultural exports . . Although it has not been conclusively determined .that instability hampers economic development (Lim), it has been established that fluctuations in agricultural export earnings have a multiplier eff.ect on _ domestic incomes. Fluctuations in earnings accentuate inflationary and .. deflationary movements in the national econom.t; and . may also dii$cour,a~e investmen~ . in ' the agricultural sector and thus impede the . growtl'l pro . cess. Instability of agricultural export earnings can also inhibit government
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20 programs aimed at me.eting social welfare goals. These variations m&y force a country to borrow in order to meet its social welfare objectives . Where loan repayment. obligations apcumul~te over time, additional stress . on the , , . . . ' nation' _ s econo11Jymay result. _ Thus , social efficiency may be reduced if the . agricultural export sector genera.tes a particular system of resource mi . sallocation (aec~ford). Variable Spec:ific;ation and oescti pt ion lnstabilit,Y Measures Attempts to . quantify the variabilfty of export earnings have been made by several authprs. lnstabi 1 ity is used _ here to rnean the daviations of the constant dpllar values of agriG\Jltu , ral export earnings around the trend. T . he Uni , ted ; N&tions stu , d.>' . (19_52}, r-eferred to earlier, used an average of year-to-year perc.entage changes as . a measure of inst<1bi llty. The ma . the~ matical iormula of that Jndex Wets: " . (l) IUN = United Nations ins . tabil i ty . index, Zt = real value \ of agricultural export . earnings in period t, and . N == number of years f.o , r which the . instability . iS . computed. . ' Kingston criticized thts procedure -by arguing that 1t tended •to ' . exaggerate the i importance. of the instability existing in the time series _ (Kfogston, p. 20). . He wrote : that: One obvious deficiency of this approach is that a steady __ i . ncrease , of a \ constant_ p~rcentag~ per annu . m would be interpreted .as an unstable m?vement, when in fact there had been , no i n~tabil tty, in a conven .. tfona 1 economic sense . , . at .. aJ 1 . Rather . , there would have existed a stable perc~ntage of growth. : Coppock, who had a . 1ready criticized the United Nations index with the same a ; rguments as Kingston, , formulated an index with a logarithmic
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21 variance measure, to account for a constant percentage growth rate. The Coppock index may be written as , : N lcp = antilog . I: t=l where, (log . zt+l -M ) . Zt . . Icp = Coppock instability index, (2) M = the arithmetic mean of logarithm differences, and Z,N = as defined in equation (1). While the Coppock index may represent an improvement over equation O), it does have some weaknesses. First, the log-variance form interprets small deviations from a low base as highly unstable. Second, instability . coefficients derived from percentage changes are difficult to interpret . (Kingston) . . Another instability index was proposed by Massel, who u . sed a least-. . squares regression model with time series data. Massel 's model is de scribed mathematically as: (3) i where, IMS = Massel index of instability, Z' = least squares estimate of Zt' Zi, = f(t), Z = trend mean val~e of agricultural export earnings, z.N = as defined iti (1) and (2). Thfs procedure , does not insure that the least squares model used to estimate Zi is the correct model, but it is an improvement over (l) and {2). Staller, as cited by Kingston, applied this index with the slight modification of regressing the logarit~mic val1.4es of Zt on time. An interesting aspect . of the Massel index {s that it may be trans ; ' .. . ' . ' . , formed to accommodate analysts of times s~ries data. Previous indices . . . we.re limited :to measuring tot~l instability over a _ given N years. The transformation of Massel's index in a time series basis measures the instabHitY of period-to-period changes. It can be stated as foll<>wS:
PAGE 30
22 where, IMS = transformed Mass el annual index .of instabi 1 fty, ut+ 1 = residua 1 error corresponding to Zt+l observation, Ut = residual error corresponingto Zt observation. (4) since this transf.ormed version e>f Massel's index applie$ to a time series model, it was used in : the .pre.sent : stud.Yo However, some titodifitatior . ' . were needed in , the d~nominator of equation .(4}~ Massel implied , that the _ point , frorn . which deviationsoccur is . itself a changing or . a dynamic point. But, the weakness of the assumption is that > at > ea9h point in time, there exists a . given maximum value whkh . is considered as . a statiOnary value. This is misleading, since it is known tha . t there are some cOndfttons to be : met for a point to . b~ considered .. as a , stattonaryv~lue {Gandolfo). Thus, . i . n order to , av , oid . errors . that the second , Massel index may generate, equation (5J was developed. Such a relation takes the form: I = u u . t t+l t r where, It = corrected index of instability, Ut+l = residual as defined hf'equatfon (4), but obtained with the exponential trend in equation (lOJ . . . ut =' : residual . as defined ;n {4), :: but der,ived with the exponential trend in equation , (lO}. . ; z , . , = . av . erage trend value f , rpm equcitfon lO, c~msidered as a fixed ' stationa ry 'value, from which deVia~ions take place. . . It should be no'ted that all th~se indices measure the variabfltty ' ~bout a given s tabl'e pOint. They do not, however, poss ess the properties o . f normal . . varianc~s . . ' thus, . .. tnes . e constructs ' ha ' ve . . a .. basic .. sho ' rtomih9 i that ~revents their appl icatio~ to anaTysfs of tne cli'!terent ' effects of price and quantity Variation 9n ~gticu1tu ' raf export earning~ instability . To ' , . ' separate . the pr,ce and . quanti'ty. comptjnents of export earntn ' gs va:rfation, notmal variance pr()perties nil.1st' be fnclU<;l~d. , . One Of the properties of the normal v ' aHal)ce of a' givenVariable ' i's ' that S'uch a variance may . be
PAGE 31
23 apportioned into two or more components. Diversification Index Diversification refers to the production of several crops in a given geographical area or production unit. But, if only the numbers of products are considered, Finke and Swanson argue that diversification can be thought of as a "richness" concept meaning 'how many' crops a given farm unit grows. However, emphasis may also be placed on the relative importance of a given crop in a given area or farm unit. In this case, diversification could mean that all crops in the area are equally or evenly distributed. Defined in this way, diversification is the degree of 'evenness', as determined by the proportions of resources allocated to each crop planted. The Committee for Economic Development (CEO) proposed that agricultural diversification be thought of as a dynamic process, with new crops intro duced into the system over time. The CED defined the diversification index as the ratio of the growth rate of agricultural export earnings to the growth rate of export earnings from traditional export crops. This definition, although interesting, has some shortcomings when used in re gression models where export earnings are dependent variables. Thus the index used in this study is that developed by Finke and Swanson. It can be formulated as; where, Dt = -n El; log (1;) Dt = diversification index, n = number of crops, and i = proportion of land resource allocated to crop i. (6) However, because crop data in this study are based on harvested area rather than on actual planted area, and because the study is primarily focused on the export sector, it was felt that the substitution of . the proportion . of total agricultural exports represented by each . crop for the proportton of land allocated was more appropriate. Therefore, equation (6) may be adjusted to: (7) where, q; = share of crop i in total value of agricultural exports.
PAGE 32
24 Objective 1 Fluctuations in agricultural export earnings are decomposecf , into . three components: price Variations, quantity variations and variations resulting from ~he i nteractfon between prices and quantities. An approximation of the vari.ariee of the ftJnctfon, z. == P.Q., following the 1 l l procedure used by Matha and . others, . is: where, 2 o = agrfcuH;ural export ear,nings variability of crop i, z . . ] '' Qi = average quantity of crop i exported, J n thousands of . metric tons, = average export price of crop i exported, in U _ . S. , do lJ ars , = price variability o,f crop i, = quantity variability ofcropi , anq p ;: cory,elation coefficient between quantity Qi and its price Pi. . The first term on the right hand side represents price variability, the second term quantity Variability and the third is the interaction tern Higher order term~ in the series expansfon are not included~ Initial investigation showed neglig i ible covariah . ce between price and quantity, so those covariance terms are not included in further discussions. Objec _ tive .. 2 In ord~r' to identify and quantify the trends and -patterns of export ' crop di~ersifitation, the exponential trend procedure was adopted because of th~ : goodfit exhibitedin preliminary tria1S. The symbolic represen• tation of the model is:
PAGE 33
where, 25 Dt = export crop diversification as defined in equation (1}, ex = scale of export crop diversification, e = growth rate of export crop diversification, and T = time trend. The parameters a and a are calculated by the trend procedure. Objective 3 The third objective, evaluating the impact of export crop diversification on the level of agricultural export earnings, measures trends in agricultural export earnings obtained by the exponential trend 1:2rocedure in objective 2. It takes the form: Zt = ae ST (10) where all variables are as previously defined. The impact of diversification is measured with a simple linear regression model and a quadratic regression model. The simple model is used to identify the natre of the relationship between two specific variables, while the quadratic model was adop_ted as a means of simulating the impact of fotensified diversification. The basic regression form may be written as follows: Zt = f(Dt, Ut) (11) where variables are as defined above. Expansion of the two models gives two separate equations: Zt = b 0 + b1Dt + Ut 2 (12) Zt = b 0 + b1Dt + b2Dt + Ut (13) A second aspect of this objective deals with evaluating the effect of export crop diversification on the level of instability. This is accomplished by applying equations 02) and . {13) .to the instability index, i.e., by regressing the instability indices upon the diversification vari• ables. The resulting equations are functionally similar to equations {12) and (13). They have the following form: (14) (15) Equations (12) and (14) are simplistic and are only described for theoretical considerations. For interpretation purposes, emphasis is
PAGE 34
26 placed on equations (13) and (15) which correspond to the corrected in-. stability index given as equation {SJ. Opj ec tive . 4 The rationale behind the fourth objective was to . determine if the export crop sector was developing at the expense of the domestic food sector. A simple equilibrium equates the growth rate of food supply to the growth rate of food demand. It is assumed that if there is disequi librium. on the supply si~e, there are factors inhibiting the growth of the domestic food sector. One such factor could be a significant dif. . ferential between the levels of investment in . the e~port crop and in the domestic food crop sectors. Investment is used . here in its broad sense and covers such things as land and capital allocation, marketing insti tutic;,ns, . research infrastructure, labor and other relevant inputs. Be c
PAGE 35
27 include several staple African food crops, such as cassava and yams, there are no data on income elasticities for these items. The Genera 1 Objective In order to identify the determinants of some of the policies l)re vailing in the African export crop economy, it was felt that understanding the impact of individual crops on the level of agricultural export earn ings was an important first step in explaining how land resources are allocated. Such an impact may be measured through a multiple regression analysis model of the type: where, {17) Zt = level of agricultural export earnings as _defined previously, Qit = quantity of crop i, in thousands of metric tons exported in period t, Ot = diversification as defined in (7), and uzt = error term. However, Q 1 t is itself an endogeneous variable which depends on such predetermined variables as export prices, harvested area of export crops, weather and time trend. The appropriate model is a system of simultaneous equations, where the system is identified, and a two-stage least squares procedure is used. The system has the following form: where, zt = f(Qit' Dt, uzt) (l7) Pit-l = export price of crop i in period t-1, in U.S. dollars, P 0 t;..l = average export price of all other export crops in perfod t-1, in U.S. dollars~ A;t = harvested area of crop i in period t, in thousands of hectares, . A 0 t = harvested area of all other export crops in period t, RFt = rainfall index in period t,
PAGE 36
.. T =time trend, u 2 t,uqt = error terms~ 28 While price variables in the system above would give information regarding price responsiveness, Ait and A 0 t indicate relationships between changes in the area of . a given crop, relative to changes ln areas of others. Such information ' is very important for explaining the patterns of al location of cropland between the crops selected for study. It also provides some insights into the relative degree . of scarcity or abundance of land resources. Weather, particularly rainfall, frequently causes quantity varia bility in export earnings, so a rainfall index was incorporated. However, this index can only serve as a proxy for weather effects, and might not show a direct relationship with quantity exported. For example, domesti . c storage and pro<:essing of some export commodities means that the quantity exported in any given year is not equal to the totaJ quantity produced. Th~ q~antity produced would be responsive to weather varia bility, while the quantity exported would . not. Irrigation and flood control schemes could also modify the effects of rainfall on export quantities. Data Sources The study used secondary data from F. A . 0. Trade . a.nd Production Yearbooks, United Nations Surveys . bf Economic Conditions in Africa, . ' . . _--• . . . . . . . . . ) . . ' Internation~l Monetary Fund Surveys of African Economies and country statistical abstracts and reports p)"epared by differ~nt missions. Export' values in U.S. dollars (Table 4) were deflated for all calculations. The deflation procedure used was: current dollar ex orts . constant dollar exports SO that . . . = imQlJcit deflator (b} 100 constant dollar exports (x) = . y (100) b The implicit derlator used for each country was the mean value of ~eflators . . for each year calculated in the United Nations• Surve,Y of Economic Con . -dit:ions in Africa.
PAGE 37
29 Table 4.-Ea , rnings from major ayricultural exports,a constant dollars, 1950-1972 Year Kenya Nigeria Tanzania Zaire ----------.. --------Ma 11 ion U. S. dollars-------------.. ---1950 9. 1 101.0 40.0 105.0 1951 7.5 76.8 37.4 80.0 l 952 9.0 70.8 40.2 100.0 1953 11.0 48.0 41.8 102.0 1954 7.5 54.0 33.0 102.0 1955 8.0 66.0 42.0 118 .0 1956 9.0 72.0 42.0 140.0 1957 10.0 90.0 42.0 148.0 1958 10.0 96.0 42.0 152.0 1959 9.5 90.0 46.2 164.0 1960 10.4 96.0 45.7 166.0 1961 15.0 96.0 46.4 160.0 1962 13.5 108.0 46.4 142.0 1963 14.0 120.0 55.0 142.0 1964 16.0 150.0 56.2 144.0 1965 21.0 144.0 52.8 142.0 1966 20.0 162.0 60.5 140.0 1967 18.0 189.0 70.4 126.0 1968 22.0 150.0 56.1 120.0 1969 20.0 180.0 68.2 124.0 1970 22.5 . 189. 0 70.4 112.0 1971 21.0 150.0 77 .o 108.0 1972 20.0 149.0 77.0 84.0 aMajor exports: Kenya, Tanzania Coffee, tea, cotton and sisal. Nigeria Groundnuts, cocoa, palm oil and rubber. Zaire Coffee, tea, palm oil and rubber. Source: Computed from F. A. 0., . Production Yearbook and Trade Yearbook,, selected years.
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30 EMPIRICAL . FINOlNGS In interpreting th~ empirical findings, a specific co , nc;ptual frame;_ . work was utilized. That framework was one that rnaintains that expC>rt price stabilitydepen.ds mai01y on two factors: (a) the fluctuations in . . . ' . . . . demand for or . supply . of . ex.po r, t . tommodit ies, and (b) the .,. efficiency of existing stabilization s ' 2henies (Rowe)~ Eviclence suggests that exports . ' with relatively stable prices (compared to quantity variations) are those that continue to . be supported by international commodity agreements. Tea, al.though protected up to 1955 by the International Tea .. Agreement, has since beer'I subjected to free trade. This change in status largely resulted from the assumption and belief that its final demand and productfon were very inelastic. Sisal, on the other hand, has never been jndividually cnartered by an international commodity control scheme . . It is, however, a part of an international Conference of Hard . Fibers Study Group sponsored by F.A.O. The movement of the ptice of sisaf after the Second World War has been characterized by substantial ups and downs. Sisal price was low during the interwar period, rose in the first half of the 1950's, then fell until the mid-l960's (Rowe, Kriesel et al). The objectives of this study did not allow further investigation of the effects of international commodity agreements on ' price stability and export earnings of African countries. lhis subject might, however, be a fruitful one for further research. Price .and . guant i ty Vari abil i t.x From the estimating procedure given in equation . (8), it was found that of the three study countries for Vlhi~h coffee is a common export crop, two countries (Kenya and Tanzania) exhibited a relatively higher percentage of quantity variability compared to price variability (Table 5). For Kenya, Tanzania and Zaire, three coun~y,ies for which tea ts a common export crop, results indicate that . in two of these three countries (Kenya and Zaire) price affects variability more < than doe s quantity. Price variabi1 ity ts proportionally mot-e importar1t for sisal, a crop common to Kenya and Tanzania. Other conmon crops such as cotton, palm oil and rubber. show that quantity varial)ility is the most important component of the
PAGE 39
31 overa11 variability (Table 5). Thus of six common crops among the countries, four crops {coffee, cotton, palm oil and rubber) were found to be charac terized by quantity variability, whereas two (tea and sisal) were subjected to higher price vari abi-1 ity. For these four countries, cocoa and groundnuts are major exports only in Nigeria. These commoditi~s are affected more by quantity variability than by price fluctuations. In the aggregate, the evidence suggests that variability in export earnings is largely associated with the quantity rather than the ~rice component. Table 5.--Variabi l i ty in agricultural export earnings attributed to price and quantity changes, 1950-73 Kenya Nigeria Tanzania Zaire Crop Price Quantity Price Quantity Price Quantity Price Quantity Variability attributed to ---------------------------------Percent-----------------------Coffee 14 86 35 65 66 34 Tea 55 45 44 56 60 40 Cotton 18 82 4 96 Sisal 73 27 81 l9 Groundnuts 12 88 Cocoa 34 64 Palm Oil 13 87 35 65 Rubber 14 86 25 75 These results are consistent with findings in two other studies (United Nations, 1952; Mintz). In addition to the price and quantity variability calculated, an interaction component was computed. The meaning of this component has been subjected to many interpretations {Motha, et al). In this study, )' . -it is simply interpreted as the variability resulting from interaction between price and quantity variations. Its impact on the. . total varia bility stems from the fact that, if the correlation coefficient between . . -_ . . -~ price and quantity variations is either positive or negative, the inter .. action component variability will be, respectively, either positive or negative. In the former case, earnings variability will be greater than
PAGE 40
32 the sum of bothprice and quantity variations . . lnthe latter case, it wil1 reduce the total variability and contribute . to the stabilization process. In this s . tudy, the interactio . n component, whether po . sitive or negative, was so small t'hat its effect on total variability was negli .. . g'ble. One reason for the small . size of .this interaction component for each commodity is the . small sh . are i~ total world . exports represented . by each country's exports, in most ca . ses. Only Nigerian exports of cocoa and palm oil, sisal shipments from Tanza~ia . and palm oil exports from . Zaire accounted for more than tenpercent of . the value of world exports in each of those commodities (Table 18). Patterns and Trends in Export Crcrn IJiversificatJon While all four study countries may be considered as highly diversi .. fled in terms of the number of export cr~ps grown, the results of this study . indicate that when diversification is deftned as given in equations (7) and (9), an four countries exhibited a persistent and declining trend in the level of diversification over the study period {Table 6 and Figure 7). Kenya and Tanzania experienced : similar rates of decline of about one percent per year over the l950-l~73 period. The rates of de cline in the level of export crop diversification were also . similar for Zair,e and Nigeria, at over three percent annually (Table 7). When the data series were divided . into two time periods, significant differences between countries were found in the annual rates of dee 1 ine for the . , periods l 950 to 1960 and 1960 to 1973. The first period, 1950-1960, was one of colonfalism, in which policy emphasis was placed on specialization . in agriculture. The second period was one of political independence and national ism, with pol icy orientation towards the integratiori of the agricultral export sector into the national economy. From 1950 to 1960, the export crop sectors of all fQur countries were becoming less diversifi . ed, but at . a decreasing rate. ln the later period, the r~te of decline in diversification slowed markedly in Kenya, Tanzania, ' and Zai~e, but increased in Nigeria. The ' se trends demonstrate that the patterns of . export crop development w~re determined by a crop specialization process. . it will be reca11ecl from the . section on study a . re.a characteristics
PAGE 41
33 Table 6.• .. Export crop diversification indices, 1950-73 Year Kenya Nigeria Tanzania Zaire 1950 144 160 141 134 1951 157 . 142 130 124 1952 148 150 137 109 1953 142 139 142 105 1954 134 138 120 92 1955 132 142 130 JOO 1956 129 130 117 91 1957 120 120 117 90 1958 130 110 117 95 1959 126 125 118 74 1960 130 127 117 54 1961 122 121 113 47 1962 122 126 105 70 1963 120 134 109 50 1964 118 110 100 45 1965 123 98 105 40 1966 117 110 108 30 196.7 110 93 100 23 1968 119 90 110 30 1969 112 60 94 32 1970 120 70 99 30 1971 118 60 105 31 1972 120 45 110 28 1973 119 25 109 31 Source: Calculated from F.A.O. Trade Yearbook.
PAGE 42
X Q) -g .... 150 "" Kenya Tanzania Zaire 1950 1955 1960 1965 1970 Figure 7 .--Export crop diversification trends~ l950-72 (1950.;lOO)
PAGE 43
35 Table 7.--Average annual rates of decline in diversification indices, selected periods Country 1950-1973 1950-1960 1960-1973 --------------------Percent------------------.... -•Kenya 0.87 1.71 0.19 Nigeria 3.39 2.42 5.73 Tanzania 1.19 1.82 0.82 Zaire 3.26 s.67 3.23 that export crops are distributed in specialized geographical regions. Furthermore, distributions occur in such a way that competition between major export crops for land is not great. Thus, the tendency for a coun try to concentrate on one or two export crops may be explained in terms of the persistent growth in the production of certain export crops in one . or two regions. relative to other regions. In the case of Kenya, the quantities of coffee and tea eixports increased more than those of cotton and sisal since 1950 (Table 8). In tenns of regions . , it may be argued that the northern and coastal zones were growing more slowly than the southwestern region where coffee and tea are produced {Figure 3). Table 8. ••Average annual growth rates of quantities exported, major com modities, 1950-73 Crops Coffee Tea Cotton Sisal Groundriruts cocoa , p.alm Oil . Rubber Kenya Nigeria Tanzania Zaire ----------------Percent per year-------:-----.. -----1 .89 .86 l.85 2.49 6.06 2.27 33 .... -5. 18 .... ~18 .50 .30 68 -.20 2.53 .... ... .68 16 : S,Jurce : Comput-ed from F.A.O., Trade Yearbook, selected years.
PAGE 44
36 Co~oa and rubber led export quantity growth in Nigeria (Table 8}. Cocoa exports, with a rate of growth of . 68 percent, were 1 ower . than . those from rubber at 2.53 percent. . Groundnuts was third with an overall . -.. , . . . . growth rate of .30 percent, while palm oil exports dec1 ined at ... 20 per cent per year. rhe high growth rate exhi~ited by rubber tixports was not steady, but characterized by sporadic incrMses in quantities exported. It is unlikely that such a high rate of increase wi 11 bta mafota ined, since more than one half of the area planted to rubber was expected . to be out of tapping afterl974 {Tims). While rubber has been a leading export, it is . felt that cocoa . and groundnuts are the major export crops in which Nigeria has specialized. The southwestern and northern regions (figure 4) have steadily increased / thei . r expor-ts of . cocoa . . and groundrmts . In Tanzania, growth rates for tea . and cotton were 6.06 and 5.~0 per .. cent per year respectively, fargreater than those for coffee . and sisal . (Table 8). Export grgwth rates of c0,ffee and tea for . Zaire , surpassed those . of palm oil and rubber (Table 8). Coffee and tea~re . m . c1fo]y pro duced Jn the eastern part of the country, wh . ile palm oil al'}d , rubber are mainly grown in the western par , t (Figure 6) . Effect o:f Export Cro2 Diversi ficati.on . on . . . Levels and Varfati,ons in A9ricv . ltur?1J . ;Exgort Earn1 . n9s . . . In general', it appears that export ' crop diversification . is wQrklng against high levels of agricultural export earnings. , The magnitude and signi . ficance of the regress . ii:>n coefficients from , equation (T3) . for each country are shown below: Kenya: . . . . * Zt = -3123 (1452) Nigeria: Z . = -4829* ' t (1871 .7) Tanzan . ia: , . ** Z t = . -101307 (5388) -12 . . 42 Dt (4. 90) R? . . ;= .48 . ; + 8491. Hl 0~* (3840) ow = l.679 + 1349 oi* (512.92) ow= 2.33
PAGE 45
37 Tanzania:' continued. DW = 2.60 Zaire: where, zt = -2149 (2653) -17 .23 Dt {20.3') R 2 = 07 . ; + 818 Df (941.3) DW = .694 Zt = values of agricultural export earnings as defined in equation (13), Dt = diversification index as defined in equation (13), * = b-coefficient significant at the 10 percent level, ** = b-coefficient significant at the 5 percent level, ( ) = values in parenthesis are standard errors. It appears that, while export crop diversification (Dt) tends to reduce the level of agricultural export earnings, the long term intensi fication of the diversification process tends to shift the earnings to higher levels. This is the interpretation of the results with the Dt squared variable defined as intensified diversification. The coefficients are all significant, except for Zaire; but even there the signs are con sistent with those of other countries. Comparing these findings from equation (13) with those from equations (7) and (9) yields important conclusions. Export crop production in all four countries became more specialized between 1950 and 1973 {Table 7). Since equation (13) shows an inverse relationship between the level of diversification and export earnings, it would be expected that agricul tural export earnings of the four countries increased. Agricultural export earnings from all four countries grew over the period (table 9 and Figure 2). Kenyan earnings grew at higher rate.s than those of Nige.ria and Tanzania, and all three countries showed much higher growth rates than did Zaire. The relatively higher rate of growth in earnings in Kenya was associated with a smaller rate of decline in the. level of diversification, compared with Nigeria and Zaire (Table 10). Agricultural export earnings grew more rapidly after 1960 in Kenya. Nigeria, and Tanzania than prior to that year. However, the rate of earnings growth in Z~ire of over 7 percent annually showed a marked
PAGE 46
38 Table 9.--Index value . s of agricultural export earningsf 1950-72 (1950:::100) Year Kenya Nigeria Tanzania zaire 1950 100 100 . 100 lOO 1951 82 76 94 76 1952 99 70 lOl 95 1953 121 48 105 97 195-4 82 53 83 97 1955 88 65 105 112 1956 99 71 105 133 1957 no 89 105 141 1958 llO 95 105 145 1959 104 , 8 , 9 no 156 1960 114 95 114 158 1961 165 95 116 152 1962 148 107 116 135 1963 154 119 138 135 1964 l76 149 141 137 1965 2a1 143 132 135 1966 220 160 151 133 1967 195 187 176 120 1968 242 149 140 114 1969 . 220 178 171 ll8 1970 247 187 176 107 1971 231 149 . 193 103 1972 220 148 193 80 Source: . Calculated from Table 4.
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39 Table 10.--Average annual rates of change in agricultural export earnings and in diversification indices, and average instability indices, selected periods Country and Unit 1950-1972 1950-1960 1960-1972 measure Kenya earnings percent 6.73 3.63 6.82 diversification II -0.87 ... 1.71 -0.19 instability index .35 .33 .39 Niger fa earnings percent 4.38 i.34 6.09 . diversification II -3.39 -2.42 -5.73 instability index .44 .33 .50 Tanzania earnings percent 4.13 1.76 5.19 diversification II -1.19 -1.82 -0.82 instability index .35 .38 .34 Zaire earnings percent 0.30 7 .19 -3.17 diversification II -3.26 -5. 67 . -3.23 instability index .44 .36 51
PAGE 48
40 ded ine after-' l96,o. Apparently, governments of Afrtcan countries tec.ognized the inverse relationship between . exP,ort crop diversification and the level of' export earnings, and pursued policies of export crop specialization as Q means of maximizing earnings. The direct relationship between intensified expor crop diversification and earnings shown in equation (13} does, however, support Jorberg' s argument that d.iver_sification could be helpful .for long term growth and development.. While diversification reduces. the level of export earnings in the short run, tile. long-run effects on earnings> are positive. The second aspect of the third objective of the study was concerned with the effect of export crop div-ersificatiort on the.variation or in stabi 1 ity of earnings. On this aspect, the results from eciuatfon (l5} are neither conclusive nor consistent, as shown below. Kenya: It'= 1.68 (7.60) R 2 = .006; Nigeria: It= 5.75 (4.79} R 2 = .104 Tanzania: lt = .. 27.53* (19.02) R 2 = .398 Zaire: I = 21.53* t ( 13. 20) R 2 = .117; where, + 3;42 Dt (12 .. 39) ow= 2.41 9.37 Dt ( 18.07) ow= 1.95 +,77 Dt* (14.62) OW. = 1.404 .. 48. 72 Of'' (36 .63) ow = 2.17 2 .. 1.41 Dt (-. 283} 2 -4.08. Dt (3. 37) --18.68 D~* (15 .89) It = instability index as defined iO equation (15), Dt. = diversification as de.fined previously, * = b-coefftcient significant. at the 10 percent 1eve1, and { )= va 1 ues in parentheses .are errors.
PAGE 49
41 All coefficients fo ' equations for Tanzania and Zaire are significant. This may indicate that export crop diversification is positively related with earnings instability in Tanzania, and inversely related to Zaire. However, R 2 values
PAGE 50
42 Table 11.--lrist
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43 Three Qf the selected food items {maize, beans, and sugar) belong t9 what CSNRD 7 classified as important import substitution crops and nutritionally superior foods. Sugar is considered an import substitute while maize and beans are included among nutritionally superior foods (Johnson, et ~, , 1969) , , It seems reasonable to conclude that Nigeria was self-slJf .. ficient in staple carbohydrate foods, bu , t experienced shortages i'n import substitutes. However, import su i bstitute foods faced poor market pros pects in Nigeria because of lack of effective demand for nutritionally superior goods such as eggs, milk, meat and other processed items (Johnson, et !!.]__, 1969). . . The growth rate in demand for food in Nigeria in the present study was higher than the rate shown in the CSNRD report. There appear to be two reasons. First, the bias, if any, may come from the nature of the data used for income elasticities for food which were not actually measured but projected. second, the CSNRO study considered effective demand as determined by the disposable income of consumers, while this study considers effective demand as determined by population and income. Thus, it may still be valid to conclude that Nigeria is experiencing shortages in import substitute foods if demand is conceptualized in terms of population and income. In fact, a simulation study conducted in Nigeria just after the CSNRO study confirms the results o.f this work \ . JJohnson~ et ~, 1971, p. 298): However, we should add that the two strategies in volving export crop modernization programs did create some long-run adverse effects in the non agricultural sector. The increased profitability of export crops stimulated agricultural producer Consorti~m for Study . of Njger1an Rural Development, a joint venture by Michigan S~ate Upiv~rsity, Unher$itY of Wisconsin, Qhio State Univer sity, Colorado State University~ l
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44 Table 12.~-Differences between annual rates of growth of supply and demand for selected food items, 1965 .. 73 Country Kenya Nigeria Tanzania Food Rice Maize Beans Sugar Rice Maize Beans Sugar Rice Maize Beans Sugar Rice Maize Beans Sugar Annual rate of growth Supply Demand ------Percent.-•--.177 -.460 .0006 -2.02 .23 +19.96 -1.29 l.53 1.9 2.937 .24 3.98 2. 12 913 .021 1.42 3.40 3.35 3.37 3.45 2.44 2.42 2.44 2.51 2.77 2.75 2.77 2.87 2.690 2.695 2.693 2.683 < Supply> Demand < > < < < > < > < < < < Source: Computed from F.A.0. AirfcuHral ~ommodjties, .. Pro 1 ections for 197? and 1985, .Rome 1967, and F. .o. Product10n Yearbook, se e.cted years. demands for nonagricultural goods and consequently, the nonagricu 1 tural .• popu 1 at ion' s demand for food. In addiUon, the profitabiHty caused some pro ducers .. to switch from. food . crop production to export crop production. Consequently, the price for food increased substaritla.lly in both programs involving export crop modernization be.cause food demand increased and food supply decreased. The .. above argument is illu~trated in Figure 8, which also shows the price effects of cornpetition between the export and domestic agricu1 ture sectors. Growth in food demand exceeded that for supply in both Kenya and Zaire. For Kenya, a recent report by the WorJd Bank (Burrows) stated: Many rural families have .diets deficient in calories, and Vitamins A, B2• and C. Although most pronounced
PAGE 53
per pound .015 .012 . . . ....... ...... j l--------=--=-=-=-=-=-=-=-=-====-===:: : ~-~•~ . •.:. =-----.... --~ .00 . 9 ' --------..__ ________ .;,;.____ -.--._ --.006 ---= Food crop modernization -= Only export crop modernization .. = Export crop modernization with marketing boards take-off . 003 .-...---.-----r---..----.-----r-----.----,.---,---.------.----,,----,----,,1965 1970 Years 1975 1980 Figure 8.--Average price of food staples in northern Nigeria _ under alternative simulated policies, 1965-1980. source: A Generalized SimulationApproach to Agricultural Sector Analysis: Hith Spedal Reference to Nigeria, Mic~igan State University., East Lansing, 1971.
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46 in Nyanza Province and parts of the eastern plateau, and variable by season, the deficiencies are nation wide ... Pressure on land . is increasing, people are moving into marginal area . s, matze has largely taken over from the more . protein ... rich millets and sorghums, and there is no reason to bl;?l ieve that pulse production is going up at a faSter rate than population. Food price increases in Zaire are e'vid.ence t"ha't increases in supply failed to keep pace with growth . in demand. Lack of data prevents any conclusions for Tanzania. It appears that "modernization" i of export crop sectors in .. the study ' ' countries, without comparable effort i 11 food sectors, is the major factor in the imbalance between the two. The . problem, though related to the allocation of land and labor between the two sectors, is never thelessmore far-reaching. Food and export crops compete for land to s~me . degree, but food crops have a much larger share of cultivated land than do export crops (Table 13). The scientific and economic infrastruc ture . allocated to the export sector has made it relatively more . productive ' . ancl prof i tab 1 e '• than the domestic food sector. One study . from Africa (Yudel111an~ p. 281) and another from . the Caribbean (Davis, p. 142) support the validity of this hypothesis. Table 13.-:--Land distribution among food and export crops, J970 Country Kenya Nigeria Tanzania Zaire Food crops 80 70 60 78 Export crops 20 30 40 22 Source: F.A.O., W . orld Crop Statistics. The two s tudies express stri.kingJy sim~lar views, as Yudelman writes: The philosophy of agricul tura 1 develQpf11ent inmany . parts of Africa has been oriented toward a commodity a , pproach, which has required the concentration . of ' investments and skills on the expansion of output of those commoditfe S far which there is effective demand. In most contries, these are necessarily
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47 export commodities; ... However, whatever the merits of this approach for economic growth, it has led to a concentration of investment in the areas or regions with higher inco.me producers of export crops. This tends to widen the regional differential; farm incomes in those regions not producing for export tend to lag further behind as development proceeds. Davis, in analyzing the relationship between agricultural research and agricultural development in the Caribbean, states: The differential level of investment between the export and the domestic research systems raises a further question regarding the extent to which the differential might be related to significant dif ferences in the marginal value productivity of investment. There is a reason to suspect that the marginal value productivity of investment of the export system was considerably higher than that of its domestic counterpart. This suspicion is related to the generally higher output price of export crops, ... and significantly larger marginal productivity of research investment for the export system. Thus, the superior performance of export crops over domestic food crops can be explained by the capital investment allocated to the former, in terms of modern inputs, research infrastructure and modern management. If similar resources were also allocated to the food sector, it would probably grow and develop at a comparable rate. 8 The relatively low growth rates of agricultural export earnings for the four countries, though, suggest that there are less than optimum levels of investment in the export agriculture sectors. African Choice This section discusses the genera 1 objective of the study, that of identifying policies in African countries that affect the overall economy and the export agricultural sector. It was suggested earlier that, consciously or unconsciously, the four African countries studied have 8The authors acknowledge the importance of the point made by P.J. van B1okland that subsistence agricultural production is persis tently underestimated because very 1 itt1e data are available. More information is collected and available on export commodities, so that input or 11 performance 11 of export sectors may be overstated relative to production for on-farm or domestic consumption.
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48 opted for higher rather than for more stable incomes. In ract, the short rttn effect of the diversification proc . ess was found to reduce export incom1 Evidence from this . study shows that the four countries have become . ; . . . more specialized in their export agricultural sectors. However, this t . rend raises two questions. First, what is 1 ikely to be the impact on the allocation of land resources between different export crops? Second, what is the most effective use of increased agricultural export earnings as a development tool? The answers tothesetwo questions were sought by using individual country data generated by the models, . and by referring to the literature on the subject. Effects on Export Crop Spec;a lh:ation on lanp Allocation Kenya Results with the two . -stage lea,st squares (TSLS) system (equations 17 and 18) s , how Kenya heavily dependent on the four c . rops described earlier. Results: Z 't = 300.945 J (122.8) + .9l32 (Qljt) ( .582) + (i~~) (Q4jt) where, 2.029 (Djt} ( 1. 25) z .t = agricultural export earnir1gs as defined fn equation (17), J . QJjt = quant}tY of _coffee exported by Kenya, in thousands of metric tons 1n per1od t, Q 2 Jt < ;:: quantity of tea exported by Kenya, in thousands of metric _ tons in perio4 t, Q 3 t # quantity of _cotton exported by Kenya, in thousands of metric J tons in period t, Q 4 jt = quantity of sisal exported by Kenya, in thousands of metric ' tons in period t; . _ Pjt = diversificatfon index, as defio , ed in ~quations (6) and (7), * ** *** ( ) = b-.coeffictent Significant at the 10 percent level, . . _ ., . . = b-coefficient significant at the 5 percent level~ ;:: b .. coefficient significant at the 1 percent level, = values in pare ' nthesis are standard , errors.
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Table 14.-:-Export supply function estimates, Kenyaa Crops Constant , , p it-1 p ot-1 Ait A ot RFt Coffee 50.435 -.250 1.562 .406 .323*** -2.741 (326) (.379) ( 1. 287) ( 1.90) (. 097) " ( 5. 23} Tea -313.73* .709 .294 1.94** 1.64 .940 (240} (1.039) (. 718) (.218) ( 1.46) ( 4. 413) Cotton . . . 44.837 .827 .3665 .3598 .3435 -1.204 (193.76) (.780) { . 654) {. 464} (. 800) (4.315) Sisal 18.336 .909 -.543 1.029** -.1926 2.050 ( 101. 81) ( 1. 32) (. 306) (. 451) (.291) (1. 95) a Qit = f(Pit-l' P 0 t.:l) Ait' Aot' RFt, T, u). Lin , ear fa . rm. * = b-coefficient significant at .10 ** = b.:.coefficient significant at .05 *** = b-coefficient significant at .01 ()=values in parentheses are standard errors. T R2 ow 23.43*** , ', .920 2.338 (13.88) -6.014 .982 1.862 (8.1808} .5554 .411 1.610 (.773) \.0 2.244 .781 1.433 (3.46)
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50 The . four cnws have different weights, _ as coffee has the 1a rg . est coefficient with .973, followed by sisal {.452), tea (.220} and cotton ( . . 153). In . the 1950 ... 73 period~ more coffee was produced than other com. moditi es. Export supply furtct;ions also show that th~ quantity of coffeEl expo . rted increased over time Jiabl e l4.). A '1 egitimcite question is whether . the increase in . ~of fee ' exports: , ' afffect s -ine '. ~M o<:ati Orr of 1 and to . other crops. The coef'f:lcient in the coffee export supp1y , function (A 0 t} is positive . and significant, suggesting that . coffee acreage moves in the s . ame di rectfon a . s the area of other crops .taken as a who1 e. This suggests that the specializatiqn in coffe : e production i sr1ot hindering the develop ment of other cropst but that those _ regions specfalizing in coffee pro .. duction are . growing at a proportionatly tlighe r rate , than others . . . . , .. . .,._ __' Niger,ia Agricultural export earnings of Nigeria ar positively related to coc9a, groundnut and rubber expo ' rtS; , but inversely related to pa1m : 0H exports. Results: Z l 5 . g o . ** _ .. jt _ l't (5,24) + .267 (Q4jt ( .173) . where, Z.ft = agricultural export earnings as defined in equatton (17), Qljt ' , == quantity of groundnuts exp ortecl by Nigeriar . in , thousa nd S < Of Q2jt Q3jt 94Jt DJt . * *** . metric tons Jn per1 od t;, = quantity of cocoa exported by Ni g . eri a, in thou sands of metric tons in peri . od t, =:= quantity of palm oiJ exported by Nigeria, _ metric . tC>ns .i n period t, ~ quant.itY -, of rubber exportcl . by Nig . eria; in thousands of . metric tons in f)eriod t, = divers ifia _ tion i~dex, as defined ear-lier, = b-coefficiehts signtfiCant at the 10 percent level, = b .. coeffiqien~s significant at the 5 perc . ent level? = b-co~ffici ents sig , nffi cant , at the 1 percent le;,el , ' ( } = values in parenthe$ , eS are : standa : rd 0c\'"J _ brs. The magnitude of the .coefficient~ . shows that cocoa (. 979} and groundnuts (.416) have the . greatest , impact on th~ . level of Ni~,ertan agricultural . . . ;
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Table 15 .--Export supply functfon estimates, Nigeril Crops constant Groundnuts -121.03 -1974** .297 .151 . 776 (138.16) (.438) Cocoa --73.747 -.4346** {1$3.) ( .238) Pa1m 0i1 -20fL83** .323 {71.92) {2.09} N . . Rubber -. 592.10*** 1.498*** * ** *** (139 .J} (. 369) (. 454) .6637 {. 635) -.324 (~.47) -.285 (. 526) (.166) {2.14) .9894 .2466 (2.61) {. 301) 1.122*** .2733** (.250) (.141) .914*** .4468* ( .104) {331) ::: JlJ,-f<)ef'ficient s1gnificant at .10 = b--coefficient significant at .05 = b -ccieffi ci ent s i g-ni fi cant at . 01 ( ) = yalues in parentheses are standard errors. T DW 8.752*** 3.970** .835 2.415 (2.46) (2.56) 2.989 6.537 .785 2.905 (2.46) (3.406) 5.361*** -8.160*** .936 1.584 ( 1. 27) (1.58) u, 11. 744 5.973 .974 2.360 __, (2.472) (2.666)
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52 . export earnings. Natural tub.her ranks third l.267), while palm oil adversely affects agricult1.1ral export earnihgs. These results, showing that Niger . ia specialized in cocoa ~nd groundnuts, are c~nfirmed by the export supply function (Table 15). The _ regression coefficients . . of th~ export . _ supply variable .. (Aot)• for cocoa and groundnuts are not significant, so . tt is not po ssible to say if . . . . . . ... .. . '.~ .. . . ,. . ~: .--: .. .: . . '. . increases in those two (;!Xi'.)Orts restricted othe _ r export crops. However, regression coefficients for rubber and palm oil are both positive and . . . significant, This suggests tbat the acreage bf an four c . rops movet'.l 1n the same direction. the negative tre'nd Jn the volume of palm oil exported can possibly be explained t>y the . fact that domestic ccmsumption of palm ot1 has been increasfng at the sam~ rate as population inc:r ' ease (Helleiner, 1~65}. In general the same conclusions drawn for Kenya based on different rates or growth among export crops f.or various regions may be drawn for Nigeria. The northern and SOl,lthwes : ter _ n , regions _ of Nlgeria have been exporting rnore groundnuts and cocoa , respec1;Jv , ely, tban other regtons. Tanzania For Tanzania, the results . from the "fSLS , system shown below are not signifJcant, exc . ept for the coefficient rel atedto < cotton exports -and .-. , . . .. . , . -~the diVerstfication index. Zj _. t == -ll . 94 . 7 . 1 ( , ' 348) where, -_ zjt QlJt Q2jt Q3jt Q4jt Dt J * ** + 670 {Q4jt} (. 680) ; = agricultura'1 exPort earnings as deffned in equation (17}, = quantity o.f cpffe~ expbrted by Tanzania, thou 0 sands of metric tons in period t, .. ._ ._.--.. = qua~ti , ty o{tea ~xported by Tanzani . a, in thousands .of .metric tons ;n period t, = quantity of cotton exported by tanzanta, in thousands of metric . tcms in period t, ' = quantity of sisal exported . by Tanzania, in thousands of metric tons in period t, _ . _ = divers _ ification Jndex as 'defined earlier; = b-coefficien-t si-gOificant at the lO percent level, .-. . . = b-coefficient sfg11ificant at the 5 percent level, = values ; n parentheses are .. ~tand . ard errors.
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Table 16 .--Export supply function estimates, Tanzaniaa Crops Coffee Tea Gqt.tqn Sisal Constant -223.897 (268.9) -59-6.142 (805) 1379.28* (950) . 137.527* (92.92) * ** pit-1 -.4724 ( 1. 24 . ) .7214 {2. 26} 1.8400** (i:114) -.4194 (.960) p ot-1 Ait Aot -1.2311** .2587 .6320* (. 677) (. 332) (. 496) -17795** -1.0401 1.2865** {10 _ 4. 9) (2.35) {. 821) ,985.q ~12lQ 3.9652 . ** ( 1.02} (. 427) {1. 794} -.4203 1.1008*** -.2423** {1. 26) ( .-291) (.086) Rf t 9.5200* (7.40) 22.5307 {19.5} SQ, 7574** (23.12} -3.0153 (2.27) = b..:coefficient significant at .10 = b--coefficient significant at .05 = b-coefficient significant at .01 { } = values in parentheses are standard errors. T -3. 7975 (6. 55) 33.1879 (13.82) 2a.a765 (14.50) 6.2876** (2.01) 0'1 w
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54 On the basis of these resu 1ts, the only statement th~t may be made is that export c rops e~rnings depend 1 argely on the volume of cotton produced. The relationships between 'different crop areas indic _ ate that only sisal is affected by an increase in production of other crops as Jl whole (Table 16}. This maymean that ~fsal is competing for land with cotton, tea, and coffee Zaire For Zaire, Log Z.t = J ., the logarithmic l.763 (5. 21) . + l55 log {.302) where, Zjt = agricultural expt'lrt earnings as defined in ~quation (13), o 1 •t = . quantity of coffee expqrt~d by Zaire, fn thousands of metric; J tons in period t, Q 2 jt = quantity of . tea exported by Zaire, in thousands of metric tons in _ period t . , . Q3Jt = quant1ty of ' .palm oil ~xported by zaire, in thousands . of metric tons 1n per10d t, ._ . _ Q 4 . . t = quantity of na~ura1 rubber exported by Za . ire, in thousands of J metric tons in period t, : D. = diversification index, as defined earlier, Jt. * = fr-coeffi.cient significant at the lO percent level, ** = b-coefficient significant . at the 5 percent level, . ( ) = values in parentheses are s_tandard errors. Coffee is the only crop. of the four . \'/ith a significant coefficient~ As discussed for the other countries, the results above and in Table 17 suggest that when the coffee area increases, area in other crops as a whole also lncreases. Again, the same arguments regarding the differential between growth rates of exports . of different specialized regi9ns applies to this case. The Use of Agri~ul tural Export Earnings for [)~veJopment Goals Agricultural export earnings of the four study countries increased over the study period (Table 10) . . Kenya ' 's earnings increased af 6 . . 7 per cent per year, while tho . se of Nigeria, and Tanzania grew at4.4 and 4.1 .
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Table 17 .--Export supply function esti 'm ates, Zairea Coffee -10.804 .5429 (8.69} (1.87) Tea 8.171 {7.32) .6613* (. 514) .4178 (.417) -.2237 (.439) Palm Oil -3. 243 (3.05) . 3119** .1597 (.139) (.178) N.Rubber-9.548** .2011* -.1364 (4~87} ( .152) _ (..256} Ait Aot -.630** ' .460** (.311) (.240) .3116 l.135** (. 258) (.439) 1. 5901*** . 2116 ( 1. 62) (. 207) . 7111 -.1824 ( ._163) (. 208) RFt T R2 3.692 .969*** .409 (2.074) (.294) -3~247** .9363*** .975 {1.31) (.333) .6044 .1260 .922 {. 598) {. 138) 3.022 .9849 .933 ( 1.04) (1 .93) a Qit * = f(p 1 t-r P ot-l' A 1 t' Aot' RF t, l, u). Doub 1 e 1 ogarithmi c form. = b-coefficient stqnificant at .10 = b-coe.fficient significant at .05 ** *** ( } = b-co efficient significant at .01 = values in parentheses are standard errors. ow 1.613 2.001 1.347 (J'I 1.900 ()'l
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. 56 percent respectively . . Zaire experienced a much slower rate of growth in export earnings with 0.3 percent annua l ly (Table lO). lf export earnings .grow at 1 ow rates and agricultural . exports constitute a relatively large share of GDP, there ts reason. to expect t.hat the rate of overall development wi 11 be s 1 ow. Severa 1 autho,rs have . at~empted to explai11 Why the . dy,nami cs . of the export economy have not broken the vicfous circle of poverty in tOCs {Singer, Prebisch and Levin) . Singer and Pre,bisch blamed the specialf:zed nature and structura 1 patterns :. of export economies. Levin argued that the structure of export economies and their isolation from national markets iS the basts of the development handicap. . Johnson . and others {l97l) suggested that agricultural export earnings surpluses gained by marketing bpards . were not used in the agricul tur~1 sector to meet develop rnent goals. For .Nigeria, however, this view is strongly opposed (Aboyade. Hellein~r, l966J, and in Tanzania , ; Kriesel believe , d agricultural : export . . : . . earnings were . used effectively in the agricultural sector. The findings of th is stuqy suggest that the < Performance and returns -~ ' qf marketin~ board investments in dev,elopment . projects have been poor. There are two reasons for this conclusion: First, the apparent lack of responsiveness of marketing boards to changes in world prices . (Tables ' . 14-17); and secondy rela~ively low annual rates of growth in export ear".' nings (Table 10 and Figure 2), despite the use of substantial land , .\ resources for expprt cropproduction. Lack of responstveness < of marketing boards to wq . rld prices may be explained by three f~ctors . ~ The first is pri<:iug policies which ke : pt ' producer prices well below world prJces, , rem()VingJncentiVl:lS to increased pr~duction among peas ant . farmers. The s : econd ' factor relates to the lack of technological investment in agriculture, and the f . ixity of factors of productton. .Fixity ' of factors means that farm~rs do not have a wide . , choice of input combinatfor1s. but instead, rely heavllY on land andlabor. Factors stichas fertilizers and capital are usedlittle, . partially because . . . farmers have incomes too Jow to afford them. Marketing board surpluses passed on to farmers . would enable them to acquire inputs that . would in c:rease prod1,.1ction. However, such surplu ses have been siphoned off for llpo1 itfoa1 parties" (Nixon} and for . / ''dubious projects {Eicher, l970) in many cases. < The third factor is the perennial nature of many exp ort crops and the resulting restrictfOns on the degree of flexibtlity . i . n ' , . . . ; .. earning use _ .
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57 There is evidence that although agricultural export earnings have historically low growth rates, the agricultural export sector is still an appropriate development tool. These low rates appear to be caused primarily by organizational and structural problems. If these problems were remoyed, the export crop sector could stimulate overall agricultural development. These structural pro bl ems, a 1 thoughpt;onounced , i . n countries . with marketing boards, are also present in other nations. In the latter countries, the problems are largely related to direct government fiscal and pricing policies affecting the farm sector. Thus, rather than accuse the export sector of hindering the development process, efforts should be made to stimulate expansion of the overall agricultural sector, without reducing the export sector (McPherson, 1974). But, whether such an expansion should be based on agricultural diversification policies is still not answered with the results reported here. The findings suggest, however, that there are some positive effects. accruing to diversification. Furthermore, the findings indicate that specialization in certain commodities is due to the fact that certain regions are expanding their exportable supplies of those commodities proportionally more than others. Thus, diversification can be a viable pol icy if used to stimulate export crop production in economically re tarded areas. On the other hand, diversification can have a retarding influence if it involves the use of export crops in developing areas to achieve sub-optimal levels of agricultural diversification in the hope of achieving economic development. SUMMARY AND CON CL US IONS Summary This study focused on four African , countries: Kenya, Nigeria, Tanzania and Zaire. The objectives were (a) to identify . whether agri cultural export earnings fluctuations were determined primarily by quantity or price variability, (b) to investigate trends and patterns in the agricultural export sector, (c) to inquire into the relationships between agricultural export earnings fluctuations and the export crop . diversification process and (d) to determine the difference between food supply and food demand growth rates.
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58 Fluctuations in agricultural export earnings were found to be due mainly to variations in quantities exported/ rather than to price vari ability. These quantity variations ' appear to be related to such fact.ors as the lack of f1exibilitY , by institutiO'ns resporrsi-ble for the , trading of sucn export crops, and agro ... ecologica1 varia:bfl ity. These institutions have continuously exported the t,raditional agrict:fl ' tural c . omrnodJMas . Stich a policydemonstrat~s a lack of responsji/enesf ' fo world Prices. A secondary source of export quantity varicltitm was var1abj]ity in rainfall and its effects on crop production. Results of the empirical analysis showedthat all four countries, a'1 though diversified in terms of the number of export crQps produc~d in different ecological regions, have been specia~iting itl one or two _ export . crops. The pattern of such specialization is not characterized by com ... petition between crops as such, but rather, by unequal growt~ rates of specialized production regions. Results from analyzing the relationships between export crop diversi fication and agricultural export earnings revealed ' an inverse relatfonshlp between these two variables. That il?, an attempt to increase diversifi cation _ would result in reducing the level of agricultural export earnings, at least in the short run. However) 1 results from the quadratic model reveal that an intensified dtversification program may cause the level of agricultural export earnings to rise in the 1ong run. While the results generated by the , models aimed at ,ma:lyzing the effects of diversification on export earnings fluctuations were incon clusive, they did indicate that the decrease 'in diversificatiQn since 1950 was accomplmied by a htgher degree of instability in some countries and by an increase in earning . s stability in other <::cn.mtr-ies. In regard to the difference betWeen growth rates Of fo9d supply and ;._ food demand, it was found . that demand for food was increasing more ra pidly than food supply in a11 four countries. The argument based on the land a1 location to these two sectors, in : conjunction with findings of other authors, suggest that the gap between the two sectors is due to a relatively mild advantage of export crop over food crops, in terms Qf investment in capital and research infrastructure. Suc;h investments, although sub .. optima1 in the export crop sector itsel/, nevertheless have niadethis sector relatively more "modern" than. the domestic food pro~ duction sector.
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59 For the general objective of the study, an attempt was made to de termine if specialization inhibited the development of other crops by reallocating land resources. In general, the areas of leading crops were moying in the same direction as those of lagging crops. One possible explanation is that crops were grown separately in specific ecological regions and some regions were more committed .than others to the growing of these crops. Another aspect explored in the study relates to the low rates of growth of agricultural export earnings under a specialization poHcy . . It was argued that these slow growth rates are due to the lack of an appropriate level of modernization in the export crop sector. Specia.Hzation based mainly .on the increased use of only land and labor was held responsible for such low performance. Conclusions Since it appears that a large proportion of variability in export earnings is due to fluctuations in quantities, it is then imperativeto use policies that address themselves more to quantity adjustment than to price regulation. Such policies could be buffer stocks and quota adjust ... ments. However, because of difficulties inherent in international policy enforcement (Edwards and Parikh), emphasis should be given to alternative domestic adjustments such as export crop diversification and appropriate tax structures. These policies would be most appropriate for those countries which have specialized in crops whose variability in quantity is higher. Also, because of the long-run effects of export crop diversi fication on the level of export earnings, diversification should be con .. sidered. But since the effects on instability of this policy would vary according to the country and the physical and economic characteristics of crops introduced, such a policy would have to be adopted cautiously. The need for caution is apparent when one considers the cases of Tanzania and Zaire. In Tanzania, further diversification would result in higher instability, while in Zaire it would stabilize earnings (equation 15). The results from these countrjes lead to the conclusion that there exists a range where export diversiftcati.on has desirable effects but works against stabtl ity and h.i5her incomes beyond that range. These ranges would be expected to differ from country to country. Further research., to determine conditions and causes of patterns in different.
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60 countries would be interesting and useru l / The gap between the domestic food SEfctor and the export cl--op sector ' reinforces the fe . eling that the lack of mod~.rnization of the . fo ' od sector . ,. . . . is responsible for such an imbalanti:?. Ho'Vev . er ~ the balanced modernh:atiorl of both export and food sectors would hetpthe!t'ormer to grow at a faster pace and at the .same time; he ' 1 t> the latter se'ctor to keep up : With pemand pressures. Another conclusion regarding the general Object1v,f of the study is related to the finding that there exist unequal growth patterns of export ' ' crop production among regions within a country / Such a s ituaUon rnay be the i souree of the observed specialization trend and ' tlie cause of i fa-flu re to achieve optimal diversification . levels. , 'It is believed that efforts to stimulate th~ export product1Qn OT .. ec90Qmically retarded regions would ' not only help a country increase its expo!t crop s~p.plies but . also help to achieve an optimum degree of dtvers1ficatior1. Such an effort requires a combined po1icy of adcitt'ional investmentsifithe , agricultural of re tarded areas and relevant pr'icirtg systern at the producer level.
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APPENDIX
PAGE 70
62 Table 18.--Average annual values in current dollars of world and country exports of major commodities, 1950-73 Commodity World Kenya ' Nigeria Value Value % Value % $1,000,000$1,000,000 $1,000,000 Tanzania Zaire Value % Value % $1,000,000 $1,000,000 Coffee 2,552.8 35.6 1.4 --29.6 h2 4l.3 1.6 Tea 630.9 20.6 3.3 .. _ -4.2 0.7 l.9 Q.3 . Cocoa 585.9 -112. 1 19.l _ , __ ..... Cotton 2,307.5 2.0 0.1 .. . .. ... . .. . 26.4 l.1 --.. . .. Sisal 119.4 10.5 8.8 -40.5 33.9 .,,. .. Rubber 1,494.2 .. _ 2s~o 1'. 7 . . .. 15.6 l .O Groundnuts 5,063.9 .... 79.8 1.6 ~ . .. Palm Oil 163.0 --33.5 2.0.5 -... 32.9 20.2
PAGE 71
table 19.--Earnings from major agricultural exports, and total agdcult~ral exports, current do]lars, 1950 ... 1973 Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 Ken.a Coffee Tea Cotton Sisal Total --------------Thousand U.S. Dollars--------------------11,505 12,937 21,357 19,770 28,049 44, 199 23,346 23,266 29,660 28,770 29,740 29,720 30,890 43,160 39,510 52,610 43,920 35,976 47,268 62,428 54,777 69,406 3,560 3,715 3,253 6,069 8,293 8,709 9,819 10,212 12,465 11,851 15,609 20,449 21,446 21,669 29,131 25,520 32,137 34,683 40,183 34 ,i)85 49,668 51,129 952 1,583 2,101 1,441 2,113 2,289 900 1,415 1,840 2,350 1,760 1,220 1,220 1,810 2,090 2A30 1,760 1,115 2,131 3,437 3,310 3,410 3,913 13,633 25,791 16,096 9,811 7,793 8,048 5,869 6,120 9,683 12,784 11,737 12,105 21,090 16,847 10,785 9,352 5,833 5,157 4,821 5,258 4,264 5,801 13,671 29,650 40,311 43,270 34,275 44,024 62,829 38,824 40,620 51,395 56,369 55,088 58,654 73,649 83,263 74,054 93,523 77,033 74,385 88,903 111,306 96,436 128,285 68,713 Total Ag. Exp. Earnings ------Million U.S. Dollars--..:74.9 62.7 72.5 81.9 87.9 85.7 94.9 111.6 113.6 105.9 156.0 141.0 144.2 157.8 177 .4 151.0 203.2 298.5 -Continued Q'I w
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Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 19.65 1966 1967 196& 1969 1970 1971 1972 1973 1950-1973--Continued Nigeria Co . coa Groundnuts Palm Oil Rubber Total .:._"'.': ________ -Thou . sand U.S. Ool lars-----------------64,292 76,515 63,378 8,760 212,945 95,463 32,915 87,522 27 ,614 243,514 92,001 63,!566 62,933 15,824 234,324 81 ,766 81,222 68,301 11,521 282,810 71 ,647 103,998 65,262 26,629 267,536 66,880 74,665 29,246 170,971 79,475 64,205 37,827 24,116 205,623 74;938 91 ,245 34,995 21 ,878 223,056 10 7,210 76,920 38,663 32,490 255,283 98,159 61,475 36,907 39,880 236,421 94,490 90,251 37,035 30,730 252,506 9J,372 90,}93 24,998 31,490 240,653 ~0,605 102 ,463 26,222 32,750 252,040 ll 2,279 95, ; . 920 30,lll 33,850 272,160 1il 9,534 105,8-54 38, . 055 30,590 294,033 7'9,129 U4 280 , .. 30,.697 32,020 256,126 1$3 ,127 99, ; l : 56 3,527 17,770 273,580 14 4,874 lQ, ~ ,628 39,900 17,670 309,072 . 1'47,269 100 , S: 271 121,300 26,919 395,759 li8 6,3Q5 60 . ,842 159,000 24,596 430,743 201,790 34,019 . 477,800 17,492 731,101 l ?3,724 32, , 5:28 41,800 12,495 240,547 l.l' 0,796 6 , 9 , , 169 1,400 29,482 270,847 Total Ag . . Exp. Earnings ------Mill ion U.S. Do 1 l ars308.7 287.6 295.0 324.5 300.6 331.7 417.9 403.0 416.5 356 . . 0 359.7 419.5 463.4 431.0 412. l 405.3 426.8 438.6 392.4 315.2 456.5 Contintted
PAGE 73
Table 19.--Earnings from major agricultural exports, and total agricultural exports, current dollars, 1950-1913--Continued Year 1950 1951 1952 1953 . 19 . 54 . . . 1955 1956 1957 1958 1959 .1960 1961 •. 1962 1963 . 1964 1955 1966 1967 r~:, 1970 1971 1972 1913 . Tanzania Coffee Tea Cotton Sisal Total _____ ...; _____ ...;...;Thousand U. S. Dollars--'-----------------14,956 21,735 23,743 20,429 26,767 35,881 19,420 . 20,702 16, . 080 20,510 T8,930 . ls,410 19,150 30,940 24;060 43,400 33,440 37,152 36,104 43,746 32,059 53,663 10.;s91 . 574 864 1,193 2,353 2,336 . 2,625 2..,$39 2,157 3,221 3~742 . 4,5l3 4;346 4 , ,367 4~23'0 . 6,321 6, 194 6,6 1 2 6;863 5,988 5,812 7,540 7,734 7,903 7,387 8,792 11,373 16,196 20,895 17,747 20,962 18,640 24,710 19,020 . 2 0,700 30,010 27,670 34,190 48/190 35,190 39,653 32,886 34,616 34,409 47, l72 47,767 . 50,336 94,523 72,073 47 ,2.26 40,457 42,399 26,9 . 66 28,786 36,559 43,236 39;278 44,056 63,479 . 61,227 39,989 32!t855 28,191 22,304. 22~352 25,038 lB,98 . 5 19,424 31~786 73,769 l23,645 105,472 80,221 85,773 l0l,5ll 66 ,75g 73, . 289 73,436 9i,677 80, 970 87,679 116,985 124,204 102:.469 130,566 103,015 105,721 98,205 .. 109,391 91,265 127,.799 ' 157,878 Total Ag. Exp. Earnings -----Mil 1 ion U. s~ Dollars---109 . 5 92.8 , 9:i.l . 105.9 . . 128.6 112. l 120.6 156.l 168.5 145.8 204.4 l74.2 173.8 185.3 , 193.4 190.9 235.6 . 270.2 continued
PAGE 74
Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 ., ... ... , .. .., .?:#... , . u11, 4nuJu• a~, :u .. u u.ut 4 1 t::xpun .. s , . ana :coi:a I agr1 cu I i:ura I 1!Xports . , . current dollars" l950~J 973..;,._,Continued Zaire . Coffee Tea Palm on Rubber Total ---------... Thousand u. S. Dollars----...... ----------------36,240 45,667 6~113 88,020 44,634 73,692 15, . 891 134,2l7 0 '38~819 86 50,911 14,293 104,109 44,680 217 44,155 9,654 98j706 62,219 1,Tl5 52,543 22,492 138,369 83,342 l,519 59,959 24,497 169,317 68,178 2, . 267 34,503 20,374 125,322 63,570 2,958 32,922 20,227 119,677 61,560 2,517 38,261 22,320 124 , . 658 30,210 2,957 30,322 25,840 89,329 23,700 16 31,894 21,490 77,100 14,520 2,268 28,246 20,170 65; . 204 17,830 2 ,415 23,570 17,070 60,886 24,190 1,440 21,190 13,630 60 . ,450 14,740 2,011 18,500 9,150 44,401 23,050 2,213 15,450 10,480 51,193 28,060 2,900 21,700 9,610 62,270 34,300 1,soo 24,800 9,900 72,500 28,200 l,305 19,269 16,273 65,047 33,900 1,892 28,181 12,759 76,732 41,000 1"300 26,404 12,000 80,704 67,251 2,544 16,600 1 0,500 96,895 65,000 l,470 17,500 14,000 97,970 Source: F .A .. O. !t trade YearboQk . Total Ag_. Exp. Earntngs --,-----Mil lion U.S. Dollars-191.l 151.4 134.Z (11 '
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Table 20.--Values of commodities exported, current d.ollars, 1950-1973 Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1.962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 Coffee Tea Cocoa Kenya Tanzania Zairii World Kenya Tanzania Zaire World Nigeria World . -----------Thousand--------;. -Mil 1 ion__ ,. ___ ..;Thousand----;. ____ ..;_ -Mi l1 ion-Thousand-Mi 11 ion11,505 12,937 21,357 19,770 28:,049 44,199 23,346 23,266 29,660 28,770 29,740 29,720 30,890 43,160 39,510 52,610 43,920 35,976 47,268 62,428 54,777 69,406 14,956 21,735 23,743 20,429 26,767 35,881 19,420 20,702 16,000 20,510 18,930 18,410 19,150 30,940 24,060 42,400 33,440 37,152 36,104 43,746 32,059 53,663 70,591 36;240 44,634 38,819 44,680 62,219 83!7342 68,178 63,570 61,56D 30,210 23,700 14,520 17,830 24,190 14,740 23,050 28,060 34,300 28,200 33,900 41,000 67,251 65,000 2,220.4 3,560 2,496.9 2,449.7 3,715 2,754.6 3,253 2,954.4 6,069 3,532.5 8,293 2,309.8 8,709 2,004.2 9,819 l,970.9 10;212 1,912. l 12,465 1 ,859. 2 11 , 85.1 l,886.2 15,609 l,995.0 20.449 2,387.4 21,446 2,231.3 21,669 2,393.9 49,131 2,.239.9 2$,520 2,-555.4 32,137 2,490.4 34,683 3,081.9 40,183 2.,748.6 34,085 3,227.7 49,668 4,321.5 51,129 574 864 1,193 2,353 2,336 2,625 2,839 2,157 3,221 3,.742 4,513 4,346 4,367 4,230 6,321 6,194 th6l2 6,863 5,988 5,812 7,540 7,734 86 217 h1T5 1,519 2,267 2,958 2,517 2,957 16 2,268 2,416 1,440 2,011 2,213 2,900 3,500 1,305 1,892 1,300 2,544 1,470 471.9 380.1 534.6 548.7 605.1 597.7 635;3 604.9 611.7 626.5 664.1 679.9 675.9 684.9 632.4 730.7 708.2 625.1 696.0 693.8 724.1 747.9 64,292 95,4-63 92,001 8h766 71,647 66,880 79,475 74,938 107,210 98,159 94,490 93,372 90,605 112;219 ll9f534 79,129 153;127 144,874 147,269 186,305 201,}90 153,724 170,796 450.1 553.1 516.8 580.7 575.2 430.5 464.1 554.9 570.1 542.6 487.4 473.2 507.9 522.5 501.6 457.0 593.0 641.7 795.6 867.6 737.0 707.2 944.8 Average 35,557.4 29,602.96 41,269.26 2,552.78 20,620.6 4,201.09 1,852.90 630.89 112,135.87 585.85 : Continued Cl "'-J
PAGE 76
............ <--V• -vu t',l<:;.:> I.It \.Vnuuuu I I, 11::;~ t:Apun,t!U, cur rem:: ao 11ars,. l~oU-1 ~/::S--Continued Cotton Groundnuts Sisal Year Kenya J.an.zania World . Nigeria World Kenya Tanzania ---Thousand------Million--Thousand-Mill ion----Thousand-----:--1950 952 7,903 2,256.5 76,515 199.4 13,633 50,336 1951 1,583 7,387 2,163.3 32,915 123.8 25,791 94,523 1952 2,101 8,792 1,812.8 63,566 153.8 16,096 72,073 195.3 1,441 11,373 1,849.9 81,222 168.8 8,811 47,226 1954 103,998 1955 " 2,113 16,196 1,842.6 232.3 7,793 40,457 1956 2,289 20,895 2,093.2 64,205 8,048 42,399 1957 900 17,747 1,967.6 91,245 218.8 5,869 26,966 1958 l,Al5 20,962 h716.2 76,920 227.2 6,120 28,786 1959 1 ~840 18,640 1,844.4 61,475 202.2 9,683 36,559 1960 2,350 24,710 2,441.1 90,251 193.2 l2,784 43,236 1961 1,760 19;020 2,351.3 90,793 242.0 11 ,7 37 39,278 1962 1,220 20,700 2,053.8 102,463 245.8 12, l 05 44,056 1963 l,220 30,010 2,257.0 95,920 256.0 21,090 63,479 1964 1,810 27,670 2,372.1 105,854 264.6 16,847 61,227 1965 2,090 34,190 2,295.4 114,280 272.l 10,785 39,989 1966 2,430 48,990 2,307.0 99,156 291.0 9,352 32,855 1967 1,760 35,190 2,237.6 106,628 246.8 5,833 28, 191 1968 l ,ll 5 39,653 2,375.4 100;271 258.9 5,157 22,304 1Q69 2,131 32,886 2,296.2 60,842 254.9 4,821 22,352 1970 3,437 34,616 2,484.1 34,019 216.0 5,258 25,038 1971 ~,3T0 34,409 2,796.3 32,528 208.2 4,264 18,985 1972 3,:410 47,172 3,133.1 69,169 236.0 5,801 19,424 1973 3,913 47,767 4,125.9 334. l 13,671 31,786 Average 2,025.6 26,386.0 2,307.51 79,737.95.5,063.90 l 0,536. 91 40, . Sdl .09 World -•Mi 11 ion-147.2 284.7 173 .1 112.1 115.5 116.5 78.3 81.7 102.3 118.2 112.4 132.6 185.3 173.3 113.4 101.1 77.6 72.l 73.5 73. 7 65.0 79.4 158.0 119.43 Continued ; .;,:. ' 0\ (X)
PAGE 77
Table 20.--Values of commodities exported, current dollars, 1950 ... 1973--Continued Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 ,1969 1970 1971 1972 1973 Average Palm Oil Nigeria Zaire -----Thousand----63,378 87,522 62,933 68,301 65,262 74,665 37,827 34,995 38,663 36,907 37,035 24,998 26,222 30,111 38,055 30,697 3,527 399 1,213 1,590 4,778 418 14 45,667 73,692 50,911 44,155 52,543 59,959 34,503 32,922 38,261 30,322 31,894 28,246 23,570 21,190 18,500 15,540 21,700 24,800 19,269 28,181 26,404 16,600 17,500 World --Mil 1 ion-182.3 281.9 188.8 191 .o 192.6 149.4 79.4 74.4 122.5 118.7 122.4 105.3 113. l 127 .2 146.8 143.3 112.0 109.9 123.5 200.9 281.5 261.7 382.4 32,879.96 162.96 Rubber Nigeria Zaire -------Thousand-----8,760 27,614 15,824 11,521 26,629 29,246 24~ 116 21,878 32,490 39,880 30,730 31,490 32,750 33,850 30,590 32,020 17,770 17,670 26,919 24,596 17,492 12,495 29,482 15,597.09 6,113 15,891 14,293 9,654 22,492 24,497 20,374 20,227 22,320 25,840 21A90 20,170 17,070 13,630 9,150 10,480 97610 9,900 16,273 12,759 12,000 10,500 14,000 World ----Mi 11 ion-1,815.8 3,282.5 1,897.2 1,066.7 1,920.9 1,773.9 1,387.0 1,277.6 1,725.7 1j804.6 1,399.2 llt528.8 1,400.4 1,244.8 1,281.5 1,306.0 1,222.7 890.3 1,246.7 1,126.7 965.5 891.4 1,910.1 25,039.22
PAGE 78
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