AN ABSTRACT OF THE THESIS OF
Robert.
B.
Tindjou.
N.
for
the
Master
of
Science
in
Agricultural and Resource Economics presented on October 30,
1992.
Title:
Peqged Currency and Trade Performance: A Case Study
of Cameroon's Cotton Exports.
Redacted for Privacy
Abs tract approved
Patricia J. Lindsey
Since its creation in 1948,
the African Franc Zone
currency (the CFA franc) was pegged to the French franc at an
unchanged fifty to one parity.
This study examines the trade
effect of this fixed exchange rate on the Franc Zone's member
countries.
Cameroon cotton exports is considered on a case
study approach.
Two non-Franc Zone countries, Turkey and
Tanzania are included in the analysis for comparison purpose.
An econometric model for cotton exports was estimated for
each of the three countries using the Seemingly Unrelated
Regression (SUR) Method.
The fluctuations of the CFA franc
are not shown to have stimulated or impeded Cameroon's cotton
exports in a systematic way which can be captured in the
Thus the research supports
present quantitative analysis.
neither the position that the pegged currency helps exports
performance nor the position that it causes harm.
Contrary to theoretical expectation, Cameroon'S cotton export
is shown to be negatively related (and significantly so) to
the world prices, suggesting that Cameroon's cotton trade is
not consistent with the assumed revenue maximizing behavior.
In
strong
contrast
agreement with theoretical
Cameroon's
to
expectation,
results,
but
the evidence
in
for
Turkey and Tanzania suggest that their cotton exports were
affected by their real exchange rates and the world prices.
One possible explanation for the apparent neutrality of
the exchange rate for Cameroon is the prominent (but declining
since 1985) role that France played as an importer of the
Cameroonian cotton.
Over the sixteen years study period.
Linking of the economies through such trading relationship is
one
of
the expected outcome
of
the monetary alignment.
Discussion regarding the desirability of such a strong linkage
to France's econony may be fruitful, particularly given the
changes taking place in Europe.
PEGGED CURRENCY
ND TRADE PERFORMANCE:
A CASE STUDY OF CANEROON COTTON EXPORTS
by
Robert. B. Tindjou. N.
A THESIS
Submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed October 30, 1992
Commencement June 1993
APPROVED:
Redacted for Privacy
Assistant Prfessor of Agricur
Resource Economics in
charge of Major
Redacted for Privacy
Head of department of Agricultural and Resource Economics
Redacted for Privacy
Dean of Grad
School
Date thesis is presented
October 30, 1992
Typed by Robert. B. Tindjou. N. for
Robert. B. Tindjou. N.
ACKNOWLEDGEMENTS
This thesis is dedicated to my parents for their constant
prayers love and blessing.
During my Master of Science training program at Oregon
State University's Department of Agricultural and Resource
Economics,
without
I was blessed with thoughtful help and advice
which my
experience.
training would have
been
a
difficult
Dr Conklin provided the initial guidance helping
me to gain the confidence necessary to confront the subsequent
challenges.
Dr Martin and Dr Ervin were always available and
very helpful in acquiring the data needed for the present
thesis work.
May they find in these words the expression of
my profound gratefulness.
My special thanks go to my Major
professor Dr Patricia Lindsey; her dedication and precious
comments made it possible to develop this research work in a
limited time.
At the end of my training program, I wish to thank Andre
Laroze and his wife Doris, Brett Fried and his wife Alexis for
their warm friendship.
Thank you Alexis and Brett for making
me feel at home a long way from home.
TABLE OF CONTENTS
INTRODUCTION
CHAPTER I
1
1.1 BACKGROUND
1
1.2 PROBLEM STATEMENT
2
1.3 RESEARCH OBJECTIVE
4
1.4 THESIS ORGANIZATION
4
CHAPTER II
WORLD COTTON TRADE AND EXCHANGE RATE EVOLUTION
IN SELECTED COTTON EXPORTING COUNTRIES.
5
5
2.1 COTTON TRADE
2.1.1 World net flow of cotton
6
2.1.2 Main cotton importing countries
7
2.1.3 Main cotton exporting countries
8
2.1.4 Cotton
classification
2.1.5 Classification of
to the type of
9
countries according
cotton exported
10
2.1.6 Cotton price across types and country
of origin
11
2.1.7 Marketing of cotton
13
2.1.8 The Multi Fiber Arrangements
14
2.2 EXCHANGE PATE EVOLUTION IN SELECTED COUNTRIES
2.2.1 Selection criteria
2.3 CONCLUSION
16
16
20
CHAPTER III
THEORETICAL CONSIDERATIONS AND
LITERATURE REVIEW
3.1 THEORETICAL CONSIDERATIONS
22
22
3.1.1 Graphical approach
22
3.1.2 Theoretical approach
26
3.2 REFUTABLE HYPOTHESIS
3.2.1 The Refutable hypothesiS
3.3 LITERATURE REVIEW
29
30
30
3.3.1 Trade effects of variations in exchange
31
rates
3.3.2 Studies about the CFA Zone
EMPIRICAL STUDY
CHAPTER IV
32
36
38
4.1 THE MODELS
4.2 LINK BETWEEN THE THEORETICAL AND THE
EMPIRICAL MODELS
41
4.3 VARIABLES SPECIFICATION
42
4.4 THE METHOD
43
4.4.1 STIR specification and caveats
4.5 DATA DESCRIPTION
CHAPTER V
ESTIMATION AND RESULTS
5.1 HETEROSKEDASTICITY CORRECTION METHOD
44
47
49
50
5.2 CANEROON'S FARN GATE PRICE TO WORLD PRICE
RELATIONSHIP
51
5.3 THE ESTIMATED INDIVIDUAL EQUATIONS
54
5.4 THE SYSTEM ESTIMATION
60
61
5.5 THE RESULTS
CONCLUSION
CHAPTER VI
67
6.1 SUMMARY AND CONCLUSION
67
6.2 POLICY IMPLICATIONS
68
6.3 COMPARISON WITH PREVIOUS STUDIES
71
6.4 LIMITS OF THE STUDY
73
6.5 RESEARCH RECOMMENDATIONS
73
74
BIBLIOGRAPHY
ECONOMIC DATA
APPENDIX
78
SECTION 1 CAMEROON'S DATA SET
79
SECTION 2 TURKEY'S DATA SET
83
SECTION 3 TANZANIA'S DATA SET
87
SECTION 4 IMPORTING COUNTRIES' CONSUMER PRICE
INDEX
91
LIST OF FIGURES
Figure 1
World Cotton Yearly Average Net Flow
6
Figure 2
Main Cotton Importing Countries
7
Figure 3
Main Cotton Exporting Countries
8
Figure 4
Europe CIF prices
Figure 5
CFA Franc to French Franc Market
1990/1991
12
Exchange Rates Ratio
19
Figure 6
Exchange Rate Index
20
Figure 7
Graphical Theoretical Approach
24
Figure 8
Turkey's Exchange Rates Index
37
Figure 9
Tanzania's Exchange Rates Index
38
Figure 10 Cotton Supply Curves
41
Figure 11 Cameroon's Cotton Real World Prices Trend
61
Figure 12 Cameroon's Cotton Export Trend
65
LIST OF APPENDIX TABLES
Table 2
Cameroon's
Prices,
Exchange Rates and Consumer
Price Index
79
Table 3
Cameroon's Exports by Destination
80
Table 4
Turkey's Prices, Exchange Rates and Consumer Price
Index
83
Table 5
Turkey's Exports by Destination
84
Table 6
Tanzania's
Prices,
Exchange
Rates
and Consumer
Price Index
87
Table 7
Tanzania's Exports by Destination
88
Table 8
Cotton Importing Countries' Consumer
Price Index
91
PEGGED CURRENCY AND TRADE PERFORMANCE: A CASE
STUDY OF CANEROON COTTON EXPORTS
CHAPTER I
INTRODUCTION
1.1 BACKGROUND:
Over a period of about forty years following the second
World War, trade between industrialized nations and developing
countries expanded. So did the ties between the two groups of
economies, thus considerably increasing the interdependence
between the two groups and their dependence on international
trade. The degree of dependence has been higher for developing
countries.
Compared with industrialized nations, developing
countries are far less endowed with
capital and skilled
labor, both factors of production viewed as essential for
modern industry.
added commodities.
As a result they basically export low value
This makes their economies very vulnerable
to international conditions as shown by Cameroon's case,
described in the CONGRESSIONAL PRESENTATION, FISCAL YEAR 1990
ANNEX I AFRICA (of the Agency for International Development)
in these words:
"Cameroon has had sustained annual
real growth rates averaging 5.1% between
1965 and 1986, which increased to an
annual average of 8.2% during 1980-1986
This
as a result of petroleum exports.
remarkable performance took Cameroon to
near the top of the lower middle-income
countries in 1986. The subsequent sharp
for
prices
of
international
drop
petroleum, cocoa, coffee, and cotton,
which caused Cameroon to lose half its
revenues from exports in three years,
2
triggered an abrupt transition from rapid
growth to a liquidity crisis in 1987.
of
Republic
the
of
Government
Cameroon (GRC) delayed the initial belt
tightening and the depreciating dollar
accentuated the dropping export commodity
prices, thus deepening the crisis."P.314
The
This reveals the high dependency of Cameroon's economy on
international trade conditions and the subsequent constraints
in
earning
investable
foreign
exchange
necessary
f or
implementing development programs.
1.2 PROBLEM STATEMENT:
A noticeable trade condition is the forty four-years of
unchanged (fixed) nominal exchange rate between the French
Franc (FF), and the African Financial Community Franc (FCFA).
The African Financial Community known in french as "zone
Franc"
(Franc Zone), comprises thirteen countries,
them former french colonies.
most of
The Franc Zone (FZ) presents
advantages such as full currency convertibility.
But it is
questionable whether the surrender of the exchange rate as a
policy instrument has not affected member countries.
In an
attempt to answer this question, Cameroon cotton exports are
considered as a case study.
For many African countries and for Cameroon specifically,
cotton represents an important exportable commodity which can
3
be used to generate foreign earnings.
Cotton is Cameroon'S
third largest agricultural export by value behind cocoa and
coffee (Uma lele, Van De Walle, Gbetibouo 1989), a primary
generator of foreign exchange for the country, and the major
source of income for farmers in the North and Far-North
provinces. Because there is limited manufacturing of textiles
in Cameroon, almost all the cotton produced is exported.
In
1989, 76.2% of the cotton produced was exported, 15.9% was
locally,
consumed
and
7.91
was
stored,
according
to
AGRICULTURAL STATISTICS 1990 (USDA).
This research will use Cameroon cotton exports as a case
study to assess if individual industries in the Franc Zone
countries trading sectors were affected by the long lasting
constancy of the FF-FCFA exchange rate during a time period
when the FF was fluctuating in value.
By examining Cameroon on a case study approach it is
hoped that the results and findings will be generalizable to
other FZ countries and provide decision makers with more
information about the benefits and/or costs associated with
the Franc Zone.
The choice of Cameroon was guided by the desire to match
the researcher's interests with the research topic.
choice
of
the
commodity
(cotton)
was
dictated
by
The
its
4
importance to many developing economies, especially in Africa.
Further,
the number and variety of
countries involved in
cotton and/or textile trade makes the topic a fertile ground
for further research.
1.3 RESEARCH OBJECTIVE:
The main objective of this thesis
is
to assess the
effects of the fixed FCFA-French Franc nominal exchange rate
on Cameroon cotton exports during the time period 1975-1990.
Specifically,
we
will
Cameroon'S
assess
cotton
export
responses to the CFA Franc fluctuations.
1.4 THESIS ORGANIZATION
Chapter
two
provides
a
description
of
cotton
international trade and exchange rate evolution in cotton
exporting countries. This will produce a classification to be
used in subsequent sections.
Chapter three presents the
theoretical approach to the questions being investigated and
the literature review.
The empirical analysis and results are
presented in chapters four and five,
respectively.
six summarizes and concludes the study.
Chapter
5
CHAPTER
II
WORLD COTTON TRADE AND EXCHANGE RATE EVOLUTION IN
SELECTED COTTON EXPORTING COUNTRIES.
The purpose of this chapter is to provide a description
of the international cotton trade, and exchange rates trends
facing cotton exporting countries. The information will be
used for setting up the model in chapter III and chapter IV.
2.1 COTTON TRADE.
The world net flow of cotton during the last two decades
is summarized
in the following graph:
6
2.1.1. World net flow of cotton
OCEANIA
EUROPE
U.s_s .p..
ASIA
U.S .A.
OIIER ARICA
AFRICA
-8000
-6000
-4000
-2000
0
2000
REGIONAL AVERAGE NET EXPORT
4000
6000
Figure 1: World cotton yearly average net flow. In thousands
of bales.
Source:
Agricultural Statistics (USDA).
Cotton: world statistics, 1988.
The figures show that during the last two decades, Europe
and Asia were net importers of cotton while the rest of the
world's
regions
were net
exporters.
For
the
group of
importing countries their import share for the time period
under consideration was computed.
The results are summarized
7
2.1.2 Main cotton importing countriesin
figure 2.
soviet tinion
United States-
Greece
Aus triali a ____
other isia
.
Ijiia
cn.i.na _-.
rey $vriaPakita_
sraei Iran._
Afgaiustan
Hozaithiqie =
TanzaXia
EavDt
uan
fa1aiqi -
Jngo.La....
Togo
Senqal -
N19_
issauCote d'Ivoire
Guine4
Burktna
Caine
çn-
al African RepuL_
ionduras =
Peru..
ParaqiiayCo1u2flb4._
Brazil ___
ArcrentiaGute1a1a..
El Salvad.ore.....
Nicaragua
ferico
I
I
20
25
I
0
5
10
15
30
Figure 2: Main Cotton Importing Countries
Figure 2
shows that during the last 20 years, Japan
(20.91), the Republic of Korea (8.31), and Taiwan (6.81) were
respectively first second and third largess cotton importers.
As a group they accounted f or 36% of the overall imports.
Italy (6.71), (FR) Germany (6.4%), Hong kong (5.71) and France
(5.6%)
were
fourth,
fifth,
sixth,
and
seventh
largest
8
importers respectively. Each of these seven countries presents
an import share higher than 5%. As a group, their import share
It is noticeable that the main cotton
amounts to 24.4%.
importers are located in Asia and Europe.
2.1.3 Main cotton exporting countries
Other .frica_.......
South Africa_...
)Tigeria_
Ethiopia
Other Asia
Taiwan_
Philipinee -
!Zorea, Rep of_
apan_
india.
!c1g kong_
uria_
Other Europe_
ugolav.a
T3nited ingdom
Switzerland
sweden
Portugal
Neth$rlafld8
Italy_
GerzlanyF Fed Rep of
France
Finland.
Au9 t r ia_....
cestern lieziiphere..._.
diile_..
Canada 0
5
15
10
Import share
20
25
(%)
Figure 3: Main Cotton Exporting Countries
Figure 3 presents the main cotton exporting countries,
and their export share. The United States (27.5%), the former
9
Soviet Union (17.1%), and Pakistan (6.1%) respectively rank
second and third as major cotton exporters.
account
for 50.7%
of
the world exports.
Together they
Following
in
decreasing share order are Thrkey (4.3%), Egypt (4.3%), Sudan
(3.8%),
China
(3.3%),
Mexico
(3.2%)
and Brazil
(2.7%).
Cameroon ranks near the bottom, accounting only for 0.5% of
the world cotton exports.
2.1.4
Cotton
classification.
Cotton quality is traditionally described in terms of
grade, staple length, and micronaire reading. These determine
to a large extent the processing efficiency and the market
value (Perkins, Ethridge, Bragg 1984).
More explanation about
the classification of cotton is provided by these authors:
the fineness
tiMicronaire reading is a measure of
used
in cotton
cotton fiber and it is widely
of
is a
Grade
marketing and manufacturing (...)
color,
three
factors:
composite assessment of
Any departure from bright
leaf, and preparation.
white color indicates a deterioration in quality.
characteristic refers to the amount of
The leaf
foreign matter in the product (...) Preparation
the ginned
describes the relative neppiness of
roughness
in
lint and the degree of smoothness or
Neps
are
small
tangled
knots
the ginning process.
of fiber caused by mechanical processing. Neps in
lint are undesirable because they will appear as
defects in the yarn and fabrics (...) Length of
staple is an important factor of quality because it
is related to yield and processing quality. Other
factors held constant, cotton with longer staple
provide higher yields and greater returns to
farmers. longer staple cotton process at higher
10
efficiencies in the mill and produce higher quality
finer yarns". P????
In
a
study
about
market
integration
in
cotton
international trade, Monke and Todd (1984) showed that cotton
with staple lengths ranging from short to long form a "highly
integrated market."
But the evidence is not as clear for
extra-long staple lengths, which show unstable linkage with
shorter staple overtime.
According to the same authors,
"evidence does not suggest a differentiated market by country
after controlling for staple length."
For the remaining part
of this thesis we will consider that extra-long staple cotton
and shorter staple belong to two separate markets.
2.1.5 Classification of
of
countries according to the type
cotton exported.
Following Monke and Todd's results cotton exporting
countries may be divided in two groups: extra-long staple
exporters and shorter staple exporters.
The two groups may
overlap since some countries produce both types of cotton.
The main extra-long staple cotton exporters are Egypt, India,
Israel,
Peru,
the People's Republic of
China, Sudan, the
former USSR, and the United States (Cotton and Wool situation
and Outlook report May 1991).
We will assume that the
remaining exporting countries belong to shorter staple cotton
II
exporting group.
types of
cotton.
The United States produces and exports both
Based on the available data, it is not
clear which other countries belong to both groups.
2.1.6 Cotton price across types and country of origin.
This section examines how prices vary across types (grade
and staple length) of cotton and exporting countries. This is
to provide an idea of the
prices.
magnitude of the difference in
Figure 4 below provides a description of C.I.F. North
Europe cotton prices
1990/1991 season.
by and country of
origin for the
1990/1991 is the most exhaustive season in
terms of prices data.
12
RIAN ?IXII
PEPJJ/DtA (Gi)
PERU/ThNGTjI (GRADE 3)
sUDAN/G5 (aT)
stANfBAPM
EGYPr/DENDERIz (GYZA 69f8fl
EG9T/(GYZA 75
EG!PT/ (GYZA 77
FGYPr/1a.TCAjFI (GYZA 70)
EGPT (GYZA 76)
EGYPT (GYZA 45) -________
AtJSIT.TM((NIDD)
AFRICAN FRANC ZO(IDD)
ANTh/ARZA)
I$E/TPE 329
J-34/SG
INOIMI
BRID
PJIIAN/sI0w/PUNJAB
PSTiJPtINThB (1505)
USSR/TII (SI()
UsSR/VTCfI (IDD)
KNIDD)
'1RrrfIZMIR
JPiYIADANA
ANTINEfc-1/2)
PGUt(XIDfl)
BRAZILIAN T'IPE 5/8
cEN'ZOAL AR(XIDD)
XEXICO(XIDD
u$/CAL/AC/s''
US/CMJAZ/ (MIDO)
USI!.IS/ NID)
U$IOILI±(sIA)
US/C.L/TX(MIDD)
I
0
50
100
150
200
300
250
Prices (U.S. cents per pind)
Figure 4: Europe CIF prices, 1990/1991.
Cotton; World Statistics. October 1988.
Source:
It is apparent that the prices for higher grades of
cotton are greater than those of lower grades.
are positively correlated with grades.
Staple lengths
The highest priced
varieties are the Egyptian Gyza and Menoufi, and the american
pima.
These correspond to extra-long staple type of cotton.
13
2.1.7. Marketing of cotton.
Between the
farm and the mills
including
functions
ginning,
numerous marketing
storage,
compression,
The
merchandising, and transport are performed on cotton.
numerous dimensions of fiber quality makes the marketing
system for cotton a complex one (Perkins, Ethridge and Bragg
1984).
According to these authors, there are 45 official
grade designations, 23 official staple designations,
and 7
micronaire groups making over 7000 possible distinct quality
combination.
On the demand side cotton appears also more
complicated than other agricultural products as it is
in
competition with such non-agricultural products as man made
fibers.
In the United States, the main participants in the cotton
marketing system are cotton producers, cotton gins, cotton
compresses and warehouses, merchants and shippers and textile
mills.
In the United States, merchant shippers perform all
the functions involved in moving cotton from the producer to
the mill.
They become owners of the cotton from the time it
is purchased from farmers, brokers and/or cooperatives to the
time it is sold to mills or trading firms (Perkins Ethridge
and Bragg 1984).
There are also Cooperative
Marketing
Associations which act as shippers on behalf of their farmermembers.
14
In other countries, the handling of cotton from the farm
to domestic or foreign mills is similar to the steps described
above, but the actors may be different.
for
Zone,
example,
since
In the African Franc
early
the
government
1970s,
corporations have been responsible for the marketing, research
and extension, and loan distribution activities in the cotton
sector.
At this time it is not possible to provide specifics
about other countries' cotton marketing systems, but it seems
reasonable to assume that in developing countries as well as
in countries where the State or the society is the owner of
the
means
of
production,
the
government
exercises
considerable control over the marketing system.
a
In those
instances, it is common that their objectives include price
stabilization and/or export
2.1.8.
revenue maximization.
The Multi Fiber Arrangements.
The world's cotton market is influenced by a variety of
policy interventions and agreements one of which is the Multi
Fiber Arrangement (MFA).
The MFA was initiated from the Short-Term Arrangement
Regarding
International
Trade
in
Cotton Textiles
(STA),
negotiated between the United Stated and Japan in 1961.
It
became a Long-Term Agreement (LTA) in 1962 and lasted (through
15
extensions) until the beginning of the MFA in 1974.
"Through
three successive renegotiations of the MFA, it has grown to
encompass
countries.
a
wider
successively
range
of
products
and
The spread of this restrictions has been part of
a wider growth in product specific-trade restrictions used by
developed countries to against developing countries in the
1970s and the 1980s" (Trela and Whalley 1990 p.13)
According to these authors, the MFA I lasted from January
1974 to December 1977; the MFA II from 1978 to December 1981;
the MFA III from January 1982 to July 1986; the MFA IV from
August 1986 to July 1991.
From the same source the MFA like
the trade restriction which preceded it in 1960, was intended
to provide temporary protection for domestic industries in
developed countries.
This was supposed to allow developed
countries to adjust to foreign competition while at the same
time giving exporters gradual access to developed countries
markets (Trela and Whalley 1990).
The MFA directly impacts
final demand for textiles which presumably in turn influences
the (derived) demand
for textile inputs, including cotton.
Thus agreements between textile manufacturing countries may
have direct and significant effects on developing countries
attempting to export cotton.
16
2.2 EXCHANGE RATE EVOLUTION IN SELECTED COUNTRIES.
2.2.1 Selection criteria.
The countries of interest here are the main cotton net
exporters.
Countries having the CFA in common are grouped
under the title CFA Zone.
with CFA countries.
(thirty years).
France is included for comparison
The time period considered is 1961-1990
All exchange rates1 are measured with respect
to the Special Drawing Rights (SDR).
Table 1 describes exchange rate increases over the 20
years period and the yearly average increase rate in selected
countries. Countries are sorted in decreasing order of yearly
average
exchange
rate
increase.
The
data
source
is
International Financial Statistics 1991.
In this section, the exchange rate is defined as the
domestic currency price of the Special Drawing Rights (SDR).
Thus, an increase in this price means a depreciation of the
domestic currency.
17
Table 1: Exchange rates evolution in selected countries.
COUNTRIES
RANK
TOTAL INCREASE
YEARLY
BETWEEN 1970
AVERAGE
AND 1990
RATE OF
(in
CHANGE
)
(%-)
1
Argentina
1.75x1012
225.16
2
Nicaragua
1.37x10'°
155.13
3
Brazil
2.02x109
131.88
4
Turkey
30,700
33.18
5
Mexico
30,400
33.12
6
Tanzania
3600
19.80
7
Colombia
3590
19.78
8
Sudan
1650
15.40
9
Paraguay
1220
13.79
10
Pakistan
518
9.54
11
Guatemala
509
9.45
12
Zimbabwe
365
7.99
13
Egypt
343
7.72
14
India
217
5.93
15
China
164
4.97
16
U.S.A.
35.7
1.54
17
CFA Zone
33
1.44
18
U.S.S.R.
NA
NA
18
Table 1 shows that the currencies of all other exporting
countries depreciated faster than the CFA Franc during the
time period 1970-1990.
U.S.S.R.
No information was available for the
The range of currency depreciation varies from 3.3
in the Franc Zone to 8.73x10'° % in Argentina.
Therefore, the
nominal gains from exports in terms of domestic currencies
must have been lower in CFA countries than in export competing
countries without impact on the world market if we assume
perfect competition in cotton international trade.
The following two graphs compare the CFA Franc and the
French Franc evolution.
19
60
ci
-.1
40
30
0
20
8
C)
10
0
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
Figure 5: CFA Franc to French Franc market exchange rates ratio.
International Financial Statistics 1991.
Source:
Figure 5 shows that over the 1961-1990 (thirty years)
time period, the CFA Franc and the French Franc maintained a
constant 50 to 1 parity.
As a result of this Itrigidityll, the
CFA Franc followed the fluctuations of the French Franc as
shown in figure 6 where the CFA curve and the FF curve are
coincident.
While this rigidity may have provided the Franc
Zone with a relatively stable
currency,
and possibly a
relatively low inflation, its trade effects are questionable.
20
170
160150140dP
130120-
11010390
80
i
62
i
I
64
66
68
Figure 6:
Source:
I
I
I
I
70
U
1
I
72
74
I
I
76
Years
CFA zone
*
78
80
82
84
86
88
90
French franc
Base year 1970.
Exchange rate index.
International Financial Statistics 1991.
2.3 CONCLUSION:
The world cotton market is one in which a few countries
(the United States and the former Soviet Union on the supply
side, with Japan, Korea and Taiwan on the demand side) account
for the bulk of
differentiated,
the
trade.
The cotton market may be
not by country of origin,
length: the Extra Long Staple (ELS)
but by staple
cotton market, and the
21
shorter staple cotton market. Besides the international trade
of cotton may be affected by the Multi-Fiber Arrangements.
Observation of market exchange rate trends show that over the
1960-1990 time period,
the currencies of cotton exporting
countries depreciated faster than the CFA Franc.
Although
this means a relative stability of the CFA Franc, its trade
effects are not known.
22
CHAPTER III
THEORETICAL CONSIDERATIONS 2ND LITERATURE REVIEW.
Again, the objective is to assess how Cameroon's cotton
exports were affected by the fixed CFA-French Franc fixed
exchange rate, since all nominal change in the CFA Franc are
due to change in the French Franc.
The purpose of this
chapter is to theoretically (graphically and analytically)
define the problem under investigation and to position this
research with respect to other relevant studies.
No attempt
is made to describe or analyze the macroeconomics of exchange
rates.
Exchange
rates
will
be
treated
as
exogenously
determined.
3.1 THEORETICAL CONSIDERATIONS.
3.1.1 Graphical approach.
ASSUMPTIONS
Al.
Since the yearly average export share of each of the
selected countries is too small
(5%. or less) to affect the
world market of cotton, these countries are price takers in
the world market.
23
In each country, the domestic market for cotton is small
and for Cameroon and Tanzania, characterized by government
monopoly.
We will therefore assume that trading companies,
whether public or private, are monopolists in their respective
domestic markets.
Trading companies countries are revenue
maximizers.
The agents' decision rule in order to maximize their
revenue is to allocate their traded quantity of cotton in the
domestic and the world market such that their marginal revenue
from each markets is equal; MRd = MR. Were this not the case,
the agent could make more revenue by allocating
additional
units of the product to the market with the higher marginal
revenue.
The following three panels graph constructed under the
above assumptions describes the problem under study.
24
Panel 1
P
Panel 2
ES
Panel 3
EP
ES
Pw
EP
Q
010
WC -WC
Q
World market
World market
from Cameroon's
point of view
Caxneroon's
domestic
market
Figure 7: Graphical Theoretical Approach
The first panel describes the world market equilibrium.
S,, is the world cotton supply.
D
is the world total demand.
Together, they determine the world price P,, and the world
total quantity traded Q.
Panel
2
represents the world market considered from
Cameroon's (as a price taker's) point of view.
the world price P
as given.
Cameroon takes
The world price and Cameroon's
excess supply curve (ES0) determine the quantity supplied to
the world market Q.
ES0
is derived by subtracting the
domestic marginal revenue MRc from the domestic supply S (W(t-l))
25
S
Panel 3 represents Cameroon's domestic market.
is Cameroon's total supply of
D0
is Cameroon's
MR is the corresponding marginal revenue
domestic demand.
curve.
cotton2.
(P1)
Together,
and the domestic
the world price EP
marginal revenue curve determine the quantity (Qd) supplied to
the domestic market.
Given Qdl the agent (who is a monopolist
in the domestic market) sets the price at
d
which corresponds
to the consumer's maximum willingness to pay for the quantity
Qd.
The issue is to find out how Cameroon's allocation of
cotton between the domestic and the world market will respond
to an exogenous change in the exchange rate.
An appreciation3 in the CFA Franc
appreciation of
the French franc)
will
(following say an
cause
a
counter
clockwise movement of the excess supply curve from ESc to ESc'
thus reducing Cameroon's supply to the world market as the
price received in CFA francs will be lower for any given Pp,.
The fall in the quantity supplied to the world market is
described by the movement from
of
Q
to
Q'.
2The quantity of cotton supplied each year is a function
the previous years' cotton world price.
3Throughout the thesis a depreciation (appreciation) is
defined as an increase (decrease) in the in the number of
units of domestic country's currency per US dollar (domestic
currency price of the US dollar).
26
The devaluation itself is a fall in the CFA price of the
US dollar, which means a fall in the agent's marginal revenue
from the world market.
At this point, the marginal revenue
from the domestic market at quantity Qd becomes higher than
its world market counterpart, so the agent could increase
total revenue by
market.
allocating more cotton to the domestic
This increases the domestic quantity supplied from
to Qd' and lowers the domestic price from P4 to
d-
Qd
There is
no effect on the world market, other than a reallocation from
one supplier to another.
In the same way it
can be shown that a CFA Franc
depreciation will reverse the above changes in quantities
supplied to different markets.
In conclusion, an appreciation (depreciation) of the CFA
Franc will reduce (increase) the supply to the world market
and increase (reduce) the supply to the domestic market.
3.1.2 Theoretical approach.
All the assumptions stated in the previous section are
retained.
In this section, we will derive a theoretical
export model which will be used for assessing the effects of
exchange rate changes on Cameroon exports.
In this model, a
27
(set
of)
including
functional relationship(s)
exports,
among trade variables
are derived from the firm's assumed
In mathematical terms,
revenue maximizing behavior.
the
firm's revenue maximizing objective can be translated as:
Max R(PW,E) = QdP(Qd) + QWPWE
(1)
QdI Q
s.t.
with
d = a - bQd
+
= Qm (Pf,
Qm(f' P) = Qf(Pf, P)
Qf(Pf,P,)
-
(2)
(3)
P,)
Q, and
= c + dPf - eP
with d,e > 0
(4)
(5)
Where:
R(.) is the firm's total revenue,
Qd
is the quantity supplied to the domestic market,
Q
is the quantity supplied to the world market,
Qm
the quantity marketed.
Q
the supply response as a function of own prices,
and production alternatives prices in the previous
year;
Q,
d
(Qf corresponds to S on the graph).
is the domestic cotton price.
is the prices on the domestic market,
P
is the price on the world market,
Pf
is the cotton price paid to cotton farmers in year
t-l.
is the domestic prices of production alternatives
in year t-l.
28
E
is the exchange rate expressed as units of domestic
country's currency per US dollar,
words,
domestic
the
or
currency price
in other
of
US
the
dollar.
The endogenous variables are:
d'
Qd,
Q.
The exogenous
variables are: E and P,, P5.
We assume for simplicity that the change in stocks is
negligible so that:
and Qm('s) = Qf(P,P5)= S,
= 0
Under this condition, substituting
(2)
and (3)
(7)
in
(1)
yields the following unconstrained maximization problem:
MaxR
E) =
(aQd- bQd2) +
PWEQf (Pf, P5)
-
(8)
PWEQd
Qd
First Order condition (FOC):
i.e.
(a
- 2bQd
)
dR/dQd = 0
- PE = 0
(9)
(10)
Solving the first order condition for Q, we get:
Q*(PEPfP)
= a/2b -
Q*(PEPfP)
= Qm - a/2b + (PE)/2b
=
(C +
(PE)/2b
dPf -
eP,)
and consequently
- a/2b + (PE)/2b
(11)
(12)
Grouping like terms in equation 11, we get:
Q5W(PW,E,Pf,PS)
=
(c-a/2b) + dPf - eP, + (PE)/2b
(13)
which is the final theoretical expression of the export
function from which refutable hypotheses will be derived.
29
d2R/Qd2 < 0
Second order condition (SOC):
i.e. -2b < 0
(14)
b>0
which requires,
(15)
and states that the domestic demand curve is downward sloping
in own price.
3.2 REFUTABLE HYPOTHESIS
Q*,,
With the optimal solutions Qd and
the FOC holds as an
identity, meaning that whatever the value of the parameters
(exogenous variables E and P), the agents will adjust the
allocation of cotton in the domestic and world markets so as
to maximize total revenue4.
In other words, agents respond to
changes in trading conditions as necessary to stay at the
revenue maximizing point. Mathematically, these responses are
measured by the first derivative of optimal quantity supplied
with respect to the parameters.
Since the focus of this study
is on the role of the pegged exchange rate in determining
exports supply, our theoretical model is equation 12 from
which the following refutable hypothesis is derived.
4E.
Silberberg,
Mathematical Analysis,
The
Structure
1990 p.243
of
Economics:
A
30
3.2.1 The Refutable Hypothesis
Taking the partial derivative of the export function
(equation 12) with respect to E, we get:
dQ/dE = (P/2b)
(16)
âQ*W/öE > 0 since b > 0 by the second order condition.
It
describes
the
effect
of
a
country's
currency
depreciation on its cotton exports.
Interpretation:
everything
else
a depreciation (an appreciation)
of a
Ceteris
remaining the same)
paribus
(i.e.
country's (say Cameroon's) currency will cause an increase (a
reduction) of her cotton export.
This interpretation is consistent with the conclusion of
the graphical approach.
3.3 LITERATURE REVIEW.
Since the collapse of the Bretton Woods system in the
early 1970s, many studies have been published about the trade
and other economic effects of exchange rate volatility.
This
review of literature will focus on publications pertaining to
trade effects of exchange rate variations, and studies about
the CFA Zone.
31
3.3.1 Trade effects of variations in exchange rates.
In an article titled "The Recent Decline in Agricultural
Exports: Is Exchange Rate the Culprit ?" (Federal Reserve Bank
of St. Louis 1984), Batten and Belongia attempt to show that
appreciation of the US dollar was not the primary cause of
decline in the US agricultural exports.
Their analysis
explored the fundamental differences between nominal and real
movement in exchange rates.
Using a reduced form of demand
for U.S. exports they investigated the effects of variables
other than exchange rates on exports.
They found evidence
that "real exchange rate were related negatively to exports
but their impact was dominated by the level of real GNP in
importing nations." (p.14)
R.
Kurnar
and
R.
Dhawan
(World Development
1991),
empirically estimated the impact of exchange rate uncertainty
on Pakistan's exports to the developed world during 1974-85.
They found evidence that exports were adversely affected by
bilateral exchange rate variability.
In contrast to Batten
and Belongia, they found nominal exchange rate rather than
real exchange rates variability to be significant.
Their
results also suggested evidence of third country exchange rate
effects.
32
M. E. Kumcu and E. Kumcu (Journal of Business Research
1991) studied the impact of exchange rate policy as an export
promotion
and
performance.
liberalization
on
tool
They concluded that
Turkey's
export
"failure to keep relative
domestic prices of exportable products competitive through
realistic
rates
exchange
limits
the
success
of
export
promotion programs, and hence the performance of exports."
(p.129)
SE. Grigsby and C.A. Arnade (AJAE 1986) theoretically
examined how Argentina's exchange rate policy influenced
domestic
and world grain prices,
(U.S.)
and Argentina's
The analysis found that "Argentina's
competitive position.
distorted exchange rates" can affect their competitiveness on
the world grain market.
3.3.2 Studies about the CFA Zone.
S. Devarajan and J. De Melo (World Development vol. 15
1987)
conducted an evaluation of
objective was
to
the CFA Zone.
investigate whether or not
Their
the pegged
exchange rate regime had interfered with member countries'
economic growth.
To address this question, they compared CFA
Zone countries GNP growth rates with other similar countries
during
1960-1982.
They found that
"CFA countries grew
33
significantly faster than comparator subsaharan countries but
usually slower, and often significantly so than the whole
Breaking the sample
sample of developing countries." (p.483)
in two time periods before and after 1973,
they found a
stronger result from the later period.
In an article titled "The Decline of The Franc Zone:
Monetary Politics in Francophone Africa"
(J1Th.S,
July 1991)
Nicolas Van De Wale investigates how the Franc Zone's regime
of fixed exchange rates guaranteed by France affected the
political economy of its member countries, and its implication
for the current "period of austerity and adjustment." (p.385)
In
the process he provides
insight
about
the CFA Zone
evolution:
that
FZ
suggested
data
the
1980,
the
"By
arrangements had provided Government with salutary
fiscal and monetary discipline. After 1985, the
situation worsened considerably in the FZ. While
other African countries gave into the IMF pressures
monetary
and
fiscal
exerted
stringent
and
discipline to adjust to changes in international
avoided
in
the
Zone
countries
environment,
adjustment and continued to live beyond their means
Fortuitous events like the
and accumulated debts.
discovery of oil in several BEAC (Central African
States Bank) countries and the sharp appreciation
of the dollar in the early 1983-84 disguised the
Since 1987, it has become
burgeoning crisis.
In
both
1988
and 1989, the Zone as a hole
evident.
recorded negative growth. In some countries these
Cameroon, for example
two years were disastrous.
witnessed real GDP growth rate of negative 15.7 and
11.47 percent." (p.392)
34
In an explanation of the Zone's economic crisis in the
1980's after it seemed to have avoided "the worst of the rest
of Africa's crisis" (p.393) the author argues that one of the
factors
is
the
fixed
CFA
franc-French
franc
exchange
aggravated by the "devaluation and introduction of floating
exchange rates regimes"
(p.393)
in neighboring and export
competiting countries.
R. Medhora's paper and S. Devarajan and J. De Melo's
research results pertain to time periods prior to 1982.
They
found no evidence that the fixed nominal exchange rate was a
problem for the CFA Zone African member countries during that
time period.
According to Nicolas Van De Wale, this was because the
French franc was weak during the decades of the 1960's and
1970's.
In the 1980's the French franc strengthened while the
prices of the main african exports declined.
of
these two
The combination
factors undermined the CFA Zone
countries
economies.
However, Van De Wale's analysis is based on no formal
mathematical
or
statistical
model,
substantiated with intensive literature.
although
it
is
The present thesis
research represents an effort to investigate the question
using such techniques.
Unlike the studies mentioned above,
35
the focus is on exports of a single commodity in one CFA Zone
country.
36
CHAPTER IV
EMPIRICAL STUDY
To add robustness to the empirical analysis, the export
model developed in the previous chapter is estimated for three
cotton exporting countries: Tanzania, Turkey and Cameroon.
The choice of Cameroon was explained in chapter I.
Four main
factors determined the choice of Tanzania and Turkey.
The availability of the necessary data.
The type of cotton exported. They all export types of
cotton other than the extra long staple (ELS) cotton. As
seen
above,
staple
shorter
cotton
constitute
and
integrated market. Therefore a disturbance in the supply
of (or demand for) one is likely to affect the demand for
(or the supply of
The
)
magnitude
the other.
of
changes
in
exchange
rates
(Countries with the highest depreciation rates were
preferred provided that they fulfilled the above criteria
1 and 2).
4.
Finally countries selected must satisfy one of the
following qualifications: CFA Zone country, African non-
37
CFA-Zone country, or non-African country.
It was not
possible to select any CFA-Zone country for comparison
because their export price data were not available.
The
a country would have been useful in
presence of such
shedding more light on the differences between the CFAZone trade determinants and its member-countries-specific
trade determinants.
following
The
two
graphs
present
a
more
detailed
description of exchange rates evolution over time for selected
countries.
35
30
'p
25
20
15
10
5
0
62
64
66
68
70
72
74
76
78
80
82
Yeare
CF
zone -'k---- Turkey
Figure 8: Turkey's Exchange Rates Trend
84
86
88
90
38
4000
3500
3000
2500
2000
].500
1000
00
I
62
64
liii
66
68
I
70
72
I
I
I
76
74
I
78
I
90
I
I
82
I
I
I
84
86
88
I
I
90
Years
--- CFZ zone
-
Thnzania
Figure 9: Tanzania's Exchange Rates Trend
It appears from the above graphs that Turkey and Tanzania
had a relatively stable exchange rate until 1979 and 1983
respectively, then experienced a steady currency depreciation.
4.1 THE MODELS.
In chapter III section 3.1.2, we derived the theoretical
export model below (equation 12).
39
Q*(PEPP)
= (c-a/2b) + dPf - eP + (PE)/2b
(12)
which may be written in estimable form as:
Q
where:
P
(P
+ a1P
=
E, P)
+ a2E1 + a3P
(17)
+
is the ratio of the domestic cotton farm gate
price to the domestic prices of production
alternatives in the previous year.
is
taken
in
order
to
preclude
This ratio
possible
multicolinearity between P1 and P.
is a random error term.
j =1
for Cameroon,
for Tanzania and t
j =2
=
for Thrkey,
1,2,3.. .T
j
= 3
(number of
observations per country).
All other variables and parameters are as
defined above.
The
coefficients
in
the
estimable
model
bear
the
following relationships to those in equation (12).
a0 = c-a/2b
a1 = 1/2b
and
(18)
(19)
40
It will not be possible to recover the domestic demand
intercept (a), unless the value of domestic supply intercept
(C) is known.
Conversion to a non-linear relationship between
the farm level supply price and export quantity through
inclusion of the price ratio P precludes recovery of d and e.
Domestic prices for cotton and production substitutes
were not available for Turkey and Tanzania. As a result their
yearly cotton production (approximated by the quantity traded)
figures were substituted for the supply component in their
respective country equations.
For Turkey and Tanzania, the
estimable equation is therefore:
(P, E) =
+
+
2JEJt
+
3jQmj
(20)
Et
where 03j the coefficient of Qmj is constrained to be equal to
one,
since
identity: Qd
Qmj
is
just an accounting variable
(from the
(21)
= Qm(f's)
This makes their country model very short run.
Thus, as
described on the following graph (fig 10), cotton supply is a
continuous function of lagged own and substitutes prices for
Cameroon and fixed each year for Turkey and Tanzania.
41
Cotton farm
gate price
Cotton farm
gate price
S.
1
9ff
Supply curve
Caiteroon's case
Supply in year I (il. .T)
case of Turkey
and Tanzania
Figure 10: Cotton Supply Curves
4.2
LINK BETWEEN THE THEORETICAL AND THE EMPIRICAL
MODELS
With the exception of the error term,
the equation
estimated empirically is the excess supply for cotton which,
analytically, is the country's cotton supply curve less the
domestic marginal revenue curve.
Before estimation,
two
alterations of the linear functional form depicted in figure
2 and in the derived equations were made:
42
For Cameroon, the ratio of the domestic price of cotton to
the price of domestic production substitutes is used in order
to avoid multicolinearity and reduce the number of parameters
to be estimated.
Thus oe
is the slope coefficient for a ratio
of prices and the effect on Q
of a change in either price
will not be recoverable.
As pointed out by R. G. Chambers and R. E. Just (1979),
the demand response to exchange rates changes may be different
from the demand response resulting from changes in world
prices.
In order to account for a possible differential
export response to change in world price versus change in
exchange rate,
the (PE) term in the theoretical model is
split to separate the price effect from the exchange rate
effect in
our empirical model.
4.3 VARIABLES SPECIFICATION
The above model was estimated using only real values of
prices and exchange rates.
This assumes that the agents are
not subject to money illusion, and that trade depends on the
real exchange rate as opposed to the nominal exchange rate.
Nominal exchange rates measure the relative prices of two
monies whereas the real exchange rate approximates a country's
relative competitiveness (S. Edwards 1988, p.3). According to
43
D.S. Batten and M.T. Belongia
(1984),
nominal changes in
exchange rates have no long-run effects on trade.
"Only real
changes in exchange rates influence trade flows." (p.S.)
Since production responds to price changes with a time
lag,
it is assumed that exports are a function of lagged
domestic cotton and production substitutes prices.
Exports
are assumed to be a function of current prices and current
exchange rates, as the agent's problem given the world price,
the exchange rate, and the total supply of Cotton is to
allocate the commodity between the domestic and the world
market.
4.4 THE METHOD.
Following the model formulation developed in previous
sections, the Seemingly Unrelated Regression (SUR) method will
be used to estimate the system of equations.
The StIR method
is used in order to take in consideration possible cross
equation correlation.
44
4.4.1 SUR Specification and caveats5
In compact (matrix) form6, equation (17) and (20) can be
written for each country as:
= X)3
Q
where:
+
Q
is a T by 1 vector of cotton exports
X
is
a T by
6
matrix of observations
(dummy
variable included)
f3
is a 6 by 1 vector of parameters to be estimated
e
is a T by 1 vector of random errors
As a system, the three equations may be written in block
matrix form as:
(o1I
(x
0
IQw2I0
X2
to
0
QW3)
0 't
(i3
(E
011p21+Ic2I
X3)
3J
u3J
(25)
(G.G. Judge 1982 p.446)
Or in more compact form:
Q=X3 e
(26)
5me following development is written with reference to
William H Greene in ECONOMETRIC ANALYSIS (1990 pp. 510-520),
and G.G. Judge (INTRODUCTION TO THE THEORY AND PRACTICE OF
ECONOMETRICS, 1988 pp 426-427).
6W.H. Greene, Econometric Analysis 1990, p.510
45
7The SUR specification of the error term is:
E[e]
= 0
all disturbances have zero mean
(27)
Var()
(28)
=
=
Given an equation, the disturbance variance is constant
over time.
E[ee]
=
Given a time period, disturbances in different equations
are correlated.
E[ej
= 0 for t
and j,k = 1,2,3
(29)
Disturbances in different time period are uncorrelated
whether they are in the same equation or not.
Therefore, in matrix form the error terms in equation
j
and equation k are such that:
E[EJk'J
=
(30)
7'rhese error terms specifications are taken from G. G.
OF
D PRACTICE
THEORY
THE
TO
Judge
(INTRODUCTION
ECONOMETRICS, 1982)
46
and for the entire system:
(7111 0121 0131
E[eI']
=
(7211 (7221
(7231
0311 (3321
0331
1011
with E =
l2
(31)
=
13
(32)
21 022 023
032 033
and I a T by T identity matrix
The E matrix is the matrix of cross equation error term
The traditional SUR method assumes that the E
covariances.
matrix is the same for all observations.
take
in
account
possible
It thus does not
heteroskedasticity
across
observations.
According to W.
H. Greene,
(1990 pp.474,519) the SUR
specification can be modified to account for autocorrelation.
In this case and assuming first order autocorrealtion, the
identity matrix in equation 31 above will be replaced by 9
autocorrelation matrices Q with the form:
47
I
a Ujj
1
p
p,
p
1
p
p
1
1J
.
.
.
.
.
.
.
T-1
T-2
T-3
Pi
P
P
where
1
(34)
= Cov[uk,uJt)
jt = PjEjt1 +
(35)
with u uncorrelated across observations.
Since the base SUR specification does not account for
autocorrelation and heteroskedasticity, our procedure will
include a test
for,
and if necessary correction of non-
spherical disturbances prior to the SUR estimation.
4.5 DATA DESCRIPTION.
The data used to run the model came from various sources.
These are annual data pertaining to the time period 1975-1990.
Turkey's and Tanzania's cotton prices and exports were
taken from the COTTON:
WORLD STATISTICS bulletin of
International Cotton Advisory Committee
(ICAC).
the
The unit
prices faced by Cameroon in the world market were derived from
48
the values and quantities of her cotton exports, provided by
!tDirection de la Statistique et de la Comptabilite Nationale"
(DSCN
I
Carneroon).
The exchange rates data were taken from
the INTERNATIONAL FINANCIAL STATISTICS (International Monetary
Fund 1991).
The consumer price index (CPI) data were taken
from the WORLD TABLES (World Bank 1991).
Cameroon's domestic prices were provided by the "Office
Cerealier" (Cameroon).
Real prices were calculated as a ratio of nominal price
to the country's
(CPI).
Real exchange rates
(RER)
were
calculated using its PPP (purchasing power parity) definition
(RER = E*(Pw/Pd) where P and P, are respectively the domestic
and world prices).
For the RER calculation,
d
and P
were
approximated respectively by the domestic CPI, and the exports
weighted average
(EWA)
foreign
CPI.
The
EWA CPI
was
considered in order to best reflect the real parity faced by
each country in the cotton exports business.
49
CHAPTER V
ESTIMATION JD RESULTS
The following are the estimation results.
In order to
meet the seemingly unrelated regression (SUR) assumptions we
systematically tested for and if necessary, corrected nonspherical
disturbances
in
individual
estimating the systems of equations.
equations prior to
All prices and exchange
rates were converted to real values to remove the effect of
inflation.
The equations are estimated using annual data from
1975 to 1990.
Since we are interested in the effect of exchange rate
variations on Cameroon's cotton exports, the emphasis will be
on assessing the main refutable hypothesis
Therefore,
(chapter III).
particular attention is paid to the sign and
significance of the exchange rate variable slope coefficients.
The t-values are presented in parentheses with one star
(*) and two (**) stars meaning significant at the 5% and 10%
levels respectively. The variables are as defined in chapters
III and IV.
We will first present the heteroskedasticity correction
method. The autocorrelation correction method is standard and
50
explicitly described in econometric text books.
Correction of
heteroskedasticity is less straight forward in the sense that
it requires the identification of a functional relationship
between the error terms and the variables in the model.
following section discusses
this
functional
The
relationship
problem.
5.1 HETEROSKEDASTICITY CORRECTION METHOD
The forms of heteroskedasticity encountered in this study
can be generalized in the following equation.
(36)
= f(X)
where:
c(E)
is
squared in the Breuch-Pagan test
() is the logarithm of e squared in the Harvey test
() is the absolute value of
X
in the Glejser test
is represents all the explanatory variables in the
model.
f
is
the
functional
form
to
be
specified
or
approximated.
Our correction for heteroskedasticity did not explicitly
identify the f function.
Assuming that f is at least three
times continuous and differentiable over the set and range of
all explanatory variables, a Taylor approximation of the f
51
function without the cross-product terms was used in place of
the f function.
This is equivalent to writing equation 36 as
a third degree polynomial function of all the right hand side
variables, without the cross product terms.
Subsequently, the fitted values from the estimation of
equation 36 were used as weights in the correction for
heteroskedasticity.
As shown in the following two sections,
this procedure considerably improved the primary8 results.
5.2 CANEROON'S FARM GATE PRICE TO WORLD PRICE REL1TIONSHIP
One of the implicit assumptions of the model is that farm
prices at time period t
(Pft),
and therefore the domestic
cotton supply at time period t+19, are functions of the world
cotton prices in the previous time period
In
an
attempt
to
assess
this
assumption,
the
relationship between Cameroon's domestic farm prices and the
world prices was estimated.
8
The following two equations
Not corrected for non-spherical disturbances
9Domestic cotton supply is presumed to be a function of
the previous year's price relative to the price of alternative
crops.
52
respectively correspond to the primary and the correcte&°
estimation.
Pft = 0 . 178 + 0
(541)
(37)
w(t-l)
(2.22)*
df = 13
R2 = 0.27
Durbin Watson Statistic = 0.827
The 10
significance level indeterminate region for the
Durbin
Watson
statistic
positive
for
first
order
autocorrelatiori is (1.077; 1.361)
Heteroskedasticity tests'1
Breuch-Pagan test:
3.998 with 1 degree of freedom
Harvey test:
0.975 with 1 degree of freedom
Glejser test:
5.720 with 1 degree of freedom
10% significance level critical value: 2.71
The above Durbin-Watson test, Glejser and Breuch-Pagan
tests
indicate
that
there
are
positive
first
order
autocorelation and heteroskedasticity in the above equation.
'°Corrected for non-spherical disturbances
'1A11 heteroskedasicity tests are CHI-square distributed
53
The correction for autocorrelation and heteroskedasticity
gave the following result.
(38)
Pft = 1. 160 + 0 . 16 1PW(t1)
(6.36)
(1.69)
df=13
R2=0.76
Durbin Watson statistic = 1.434
The 10% significance level indeterminate region for the
Durbin
Watson
Statistic
for
positive
first
order
autocorrelation is (1.077; 1.361)
Heteroskedasticity tests
Breuch-Pagan test:
0.101 with 1 degree of freedom
Harvey test:
0.047 with 1 degree of freedom
Glejser test:
0.106 with 1 degree of freedom
10% significance level critical value: 2.71
Equation 37 shows no statistically significant first
order autocorrelation or heteroskedasticity.
It presents a
good fit.
This
result
suggests
that
there
is
a
positive
and
statistically significant relationship between Cameroon' s farm
54
cotton prices at time period t and the world market cotton
prices at time period t-l.
5.3 THE ESTIMATED INDIVIDUAL EQUATIONS
5.3.1 Individual country equations before STiR.
In this
section,
we successively present individual
equations before correction for non spherical disturbances
(primary equation), and the associated corrected equation.
The corrections are
executed in order to meet the
STiR
assumptions.
Cameroon: primary equation.
= 38266 - 26.514E1 - 11289P
(_2.98)*
(2.83)* (-1.12)
R-squared
0.67
+ 2O08.8P(1)
(0.96)
(39)
df = 11
Durbin Watson Statistic = 2.74
lO
significance level
Durbin
Watson
indeterminate region for the
statistic
for
autocorrelation: (2.25; 3.186)
negative
first
order
55
Heteroskedasticity tests:
Breuch-Pagan test:
5.439 with 3 degrees of freedom
Harvey test:
3.527 with 3 degrees of freedom
Glejser test:
5.473 with 3 degrees of freedom
10% significance level critical value:
6.25
There is no statistically significant heteroskedasticity
in equation 39.
The autocorrelation test is inconclusive.
Correction for possible autocorrelation was conducted and the
following result obtained.
Cameroon: corrected equation.
Q,,
= 22024 + 11.02E - 7414.3P
(2.67)*
(1.90)** (0.77)
R-squared = 0.79
(40)
+ 5152.7P(t1)
(3.48)*
df = 11
Durbin Watson Statistic = 2.17
10%
significance level
Durbin
Watson
indeterminate region for the
statistic
for
autocorrelation: (2.25; 3.186)
negative
first
order
56
Heteroskedasticity tests:
Breuch-Pagan test:
5.439 with 3 degrees of freedom
Harvey test:
3.527 with 3 degrees of freedom
Glejser test:
5.473 with 3 degrees of freedom
10% significance level critical value:
6.25
The above tests suggest no statistically significant
heteroskedasticity or autocorrelation.
associated with
equation
will
40
The transformed data
be used
for
SUR
the
estimation.
Tanzania: primary equation.
15298 + 28l.44E + 1874.6P
(1.99)** (3.40)*
(2.52)
+ lQm
(41)
(1020)*
df = 12
R-squared = 0.48
Durbin Watson Statistic = 2.59
10% significance level
Durbin
Watson
indeterminate region for the
statistic
for
negative
first
order
autocorrelation: (2.272; 3.143)
Heteroskedasticity tests:
Breuch-Pagan test:
1.362 with 3 degrees of freedom
57
Harvey test:
0.246 with 3 degrees of freedom
Glejser test:
0.641 with 3 degrees of freedom
1O
significance level critical value:
6.25
There is no statistically significant heteroskedasticity
in equation 41.
The autocorrelation test is inconclusive.
Correction for possible autocorrelation was conducted and the
following result obtained.
Tanzania: corrected equation.
Q
= 19441 + 303.53E + 1774.4P
(2.31)* (4.53)
(2.81)*
+ lQm
(42)
(2.9x105)*
df = 12
R-squared = 0.63
Durbin Watson Statistic = 1.96
1O
significance level
Durbin
Watson
indeterminate region for the
statistic
for
positive
first
order
autocorrelation: (0.814; 1.750)
Heteroskedasticity tests:
Breuch-Pagan test:
5.439 with 3 degrees of freedom
Harvey test:
3.527 with 3 degrees of freedom
Glejser test:
5.473 with 3 degrees of freedom
58
10% significance level critical value:
6.25
The above tests suggest no statistically significant
heteroskedasticity or autocorrelation.
The transformed data
associated with the equation 42 will be used for the SUR
estimation.
Turkey: primary equation
Q
= 1.3x105 - 34.99E + 2706.9P
(0.92)
(-0.15)
(1.79)**
+ lQm
(1.9x105)
df = 12
R-squared = 0.25
IDurbin Watson Statistic = 3.05
10% significance level
Durbin
Watson
indeterminate region for the
statistic
for
negative
first
order
autocorrelation: (2.272; 3.143)
Heteroskedasticity tests:
Breuch-Pagan test:
6.330 with 3 degrees of freedom
Harvey test:
4.889 with 3 degrees of freedom
Glejser test:
9.596 with 3 degrees of freedom
10% significance level critical value:
6.25
59
The Glejser test
and Breuch- Pagan test
The autocorrelation test
43.
the
significant heteroskedasticity in
presence of statistically
equation
reveal
inconclusive.
is
Correction for heteroskedasticity and possible autocorrelation
was conducted and the following result obtained.
Turkey: corrected equation
Q
2.125 + 235.13E + 7328.8P
=
(_5.36)* (26.93)*
(6.96)*
(44)
+ lQm
(1.9x106)*
df = 12
R-squared = 0.99
Durbin Watson Statistic = 2.22
101 significance level
Durbin
Watson
indeterminate region for the
statistic
for
negative
first
order
autocorrelation: (2.250; 3.186)
Heteroskedasticity tests:
Breuch-Pagan test:
0.394 3 degrees of freedom
Harvey test:
0.632 with 3 degrees of freedom
Glejser test:
0.310 with 3 degrees of freedom
10% significance level critical value:
6.25
The above tests suggest no statistically significant
heteroskedasticity or autocorrelation.
The transformed data
60
associated with the equation 44 will be used for the SUR
estimation.
Since the error terms in all equations approximately
satisfy the spherical assumptions required by the SUR, the
system was estimated maintaining the restriction on the values
of the coefficient for Qm in the equations for Turkey and
Tanzania.
The following results were obtained.
5.4 THE SYSTEM ESTIMA.TION
The results below were obtained after 17 iterations using
a 0.000001 convergence tolerance
estimates
of
the
(difference between the
coefficient
same
in
two
iterations).
Cameroon
= 36395 - 0.836E
(7.22)*
(-0.13)
-
1O133P
(4.80)*
+
3514P1)
(3.85)
Tanzania
Q
= 21670 + 278.82E + 1714.6P
(3.05)
(5.01)*
(3.06)*
+ lQm
(5.2x105)*
TurkeY
=
2.11 + 233.65E + 7358.7P1 + lQm
(7.5x107)*
(5.69)* (37.60)* (8.29)*
consecutive
61
System R2 = 0.999
Likelihood ratio test for diagonal covariance matrix: 30.95
with 3 degrees of freedom.
The system R2 suggests a good fit.
The likelihood ratio
test f or a diagonal covariance matrix suggests at the 95
confidence level that the system covariance matrix is not
diagonal, thus justifying the use of SUR as the estimation
method.
5.5 THE RESULTS
The refutable hyoothesis
Our objective is to assess the impact of exchange rate
changes
on
Cameroon's
cotton
exports.
Our
refutable
hypothesis is that everything else remaining the same,
a
depreciation (an appreciation) of a country's currency will
cause an increase (a reduction) in her exports.
The evidence suggests that Cameroon's cotton exports were
not significantly affected by her currency's fluctuations.
The
estimated
coefficient
on
the
significantly different from zero.
exchange
rate
is
not
Thus the CFA franc
62
depreciation from 1980 to 1985 and appreciation from 1985 to
1990 are not shown to have stimulated or impeded Cameroon's
cotton exports in a systematic way which can be captured in
the
present
quantitative
analysis.
The
exchange
rate
coefficient was not significantly different from zero in each
of the three versions of Cameroon's export equation.
Thus for
some reason, Cameroon cotton exports do not appear to be
directly responsive to real exchange rates.
This may be due
regulating trade.
to government interventions in
Additional results
Cameroon's domestic price ratio coefficient is positive
and significant, suggesting that an increases in Caineroon's
farm level cotton price relative to the price of production
substitutes led to an increase in her cotton exports; this is
in line with a priori expectations. The associated elasticity
at the mean is 0.30, suggesting that a 1
increase in the
ratio of the farm level price to the price of production
alternatives would induce a 0.3 percent increase in cotton
exports.
Cameroon's world price
significant.
This
expectations.
It
sign
suggests
coefficient
is
contrary
that
the
is
to
negative
and
theoretical
revenue maximizing
63
assumption is possibly not correct for Cameroon.
Looking at
the data (figure 11 and figure 12 below) it is clear that it
is reflecting the increase in Cameroon cotton exports over the
estimation period while the real price of cotton fell.
64
1.8
1.4
1.2
-4
I.
4.
O.6
0.4
i
1975
1977
1979
i
4
I
1983
1985
4
1981
Inn
I
I
1987
1
4
1989
Figure 11: Cameroon's Cotton Real World Prices Trend
65
40000
35000 -
r
30000 -
25000
- 20000
0
k
15000
10000
5000
1975
1977
1979
1981
1983
1985
1987
Figure 12: Cameroon's Cotton Export Trend
1989
66
In contrast to Cameroon's results, all of Tanzania's and
Turkey's estimated coefficients are significantly different
from zero and consistent in sign with expectations.
countries' cotton
Both
exports were significantly affected by
their associated real world prices and real exchange rates.
Turkey and Tanzania's exports are price and exchange rate
In both the price and exchange rate components,
inelastic.
Turkey's
export
Tanzania's.
response
was
found
to
be
higher
than
For both countries the exchange rate elasticity
at the mean of the observations is
price elasticity at the same point.
larger than the world
Thus their export
response to exchange rate changes is larger than their export
response to changes in world prices.
67
CHAPTER VI
CONCLUS ION
6.1 STJNMARY AND CONCLUSION
Since its creation in 1948, that is about twelve years
before
Cameroon's
independence
in
1960,
the
CFA
maintained a 50 to 1 parity with the French Franc (FF).
Franc
The
objective in this research was to assess how Caxneroon cotton
exports were affected by the CFA-French Franc fixed exchange
rate given the fluctuations of the French Franc the 1975-1990
time interval.
To that end we developed a simple model of
export supply of cotton and estimated it as a seemingly
unrelated regression system for Cameroon Turkey and Tanzania.
The
latter
two
countries
were
included
for
comparison
purposes.
First and foremost, the estimated equation for Cameroon
did not reveal any statistically significant relationship
between the exchange rate and cotton export levels.
The
estimated coefficient for the real U.S. dollar price of cotton
exports was statistically significant but negative, indicating
that decreases in this price appears to have led to higher
export volumes. The estimated coefficient for the lagged real
farm price of cotton relative to the price for alternative
68
was
crops
positive,
as
expected,
and
statistically
significant.
An investigation of the relationship between Cameroon's
farm prices of cotton and her lagged export price revealed a
significant and positive link between both prices, indicating
that Cameroon government is setting farm prices with reference
to the previous year's world price level.
6.2 POLICY IMPLICATIONS.
Utilizing the available data, we were not successful in
establishing a
causal
relationship between the level
of
Cameroon real exchange rate and the level of her cotton
exports.
Thus the research supports neither the position that
the pegged currency helps export performance nor the position
that
it
causes harm.
One explanation
f or the apparent
neutrality of the exchange rate is the prominent role that
France plays as an importer of Cameroonian cotton.
Over the
sixteen year study period France absorbed 25.6% of Cameroon
cotton exports.
This is a relatively large share compared to
the average share of
Cameroon's cotton.
the fifty eight other importers of
Linking of the economies through such
trading relationships is one of the expected outcomes of the
monetary alignment. Discussions regarding the desirability of
69
such a strong linkage to France's economy may be fruitful,
particularly given the changes taking place in Europe.
The
results presented here for the cotton market supports neither
the argument that the tie is harmfull or the counter argument
that it is beneficial.
The negative coefficient associated with the world cotton
price suggests that the export authority may have a goal other
than revenue maximization.
One possibility is that there may
be a target for foreign exchange earnings for the purchase of
imports. Under these circumstances, lower world prices create
and incentive to export more'2, rather than less as would be
expected under our optimization assumptions.
Government
policies such as export taxes may also have influence the
behaviour of cotton exporters.
One of the implications of the relative farm price
coefficient is
that it does appear that the Cameroonian
government can affect the quantity supplied of cotton by
setting the price paid to farmers.
In particular, at the mean
of the series, a one per cent increase in the relative price
can be expected to lead to a 0.30 percent increase in the
quantity marketed in the subsequent years.
Such an action
'2Under the objective of foreign exchange maximization,
all the cotton produced would be exported so long as the
marginal revenue remains greater than or equal to zero.
includes the case where the world price is falling.
prices are, of course, considered positive.
This
All
70
will result in a greater or lesser volume available for
exports.
On the basis of the above results alone, there seems to
be no reason for Cameroon to reject the prevailing CFA-FF
parity.
That the fixed exchange rate does make the difference
is suggested by the fixed peg currency makes a difference is
suggested by the striking contrast between the results for
caineroon and those for the two comparison countries.
Yet,
even had the exchange rate been shown to significantly affect
cotton exports as it does in Turkey and Tanzania, it would be
difficult to make a recommendation since Cameroon cotton
export does notO represent the entire economic activity in the
country.
Clearly,
a more exhaustive study needs to be
conducted prior to recommending devaluation, withdrawal from
the CFA zone or any kind of other monetary measure.
Given
the
potential
economy-wide
cost
instability and an incoherent monetary policy,
advisable
monetary
of
it may be
to maintain the current parity until
exhaustive
assessment
maintaining
the
disadvantageous.
of
parity
costs
unchanged
and
benefits
proves
it
to
from
be
Clearly, our results are not sufficient for
suggesting a modification
arrangements.
the
such an
of
the prevailing
Franc
Zone
7]-
The estimated equations f or Turkey and Tanzania stand in
strong contrast to that of Cameroon.
Both the exchange rate
coefficient and the real US dollar export price coefficient
were statistically significant and had the expected sign.
They suggest that the dramatic currency depreciation of the
1980's did affect Turkey's and Tanzania's exports.
Since no
domestic price data were available, we were unable to evaluate
direct or indirect effects on cotton production of exchange
rate changes via any linkages to the price received by
farmers.
6.3 COMPARISON WITH PREVIOUS STUDIES
Comparing the GDP growth rate of CFA-Zone countries to
that of other developing countries, S. Devarajan and J. De
Melo (World Development vol 15 1987) found that CFA countries'
growth was
faster than
that
of
non-CFA-Zone
subsaharan
countries, but slower than that of other developing countries.
The above quoted study is different from ours in many
respects:
It extends to the Franc Zone and beyond, whereas
ours focuses mainly on Cameroon.
The above paper studies the
CFA Zone through GDP growth while our research uses cotton
exports.
The time period of interest in the above quoted
paper is 1960-1982 whereas the present work studies time
72
period 1975-1990.
Thus the earlier study focuses on a time
period when the French Franc was stable, unlike this research
In spite of these differences, our results may be considered
in line with S. Devarajan and J. De Melo's results since we
found no evidence that Cameroon cotton exports were harmed by
the CFA Franc fluctuations of the 1980s.
In a study of the effects of exchange rates on U.S.
agricultural
exports,
Batten
and
Belongia
(1984)
found
evidence that real exchange rates were related negatively to
exports, but their impact was dominated by the level of real
GNP in the
developed nations.
Our study finds that real
exchange rates have not directly affected Cameroon's cotton
eports.
We can not tell how real GNP in developed nations
affect Carneroon's exports since unlike Batten and Belongia, we
did not approch exports from the demand side.
In contrast to Batten and Belongia,
R.
Kumar and R.
Dhawan (1991) find nominal rather than real exchange rates to
be statistically significant in affecting exports.
study assumes,
following S.
Edwards
(1988)
Since our
and Batten and
Belongia (1984), that exports are affected by real exchange
rates rather than by nominal exchange rates,
we can not
position our result with respect to Kumar and Dhawan's.
possible that
a
It is
switch from real to nominal would have
qualitative qualitatively affected the outcome.
73
6.4 LIMITS OF THE STUDY
As the international trade of cotton and that of other
components of Cameroon exports may be different, the above
result is not generalizable to all Cameroon exports.
Likewise
the above results may not be extended to other Franc-Zone
members since it was not possible to include any of them in
the model.
The validity of the above result is therefore
limited to Cameroon's cotton trade.
No data about domestic
trade policy (export tax or subsidy) were available.
Cotton
and her production substitutes domestic prices for Tanzania
and Turkey were also unavailable.
This made their respective
equations very short run (no supply response ).
6.5 RESEARCH RECOMMENDATION.
The following research recommendations aim at improving
the present work and producing information about the Franc
Zone's cost and benefits to member countries.
The thesis research could be improved by including all
relevant domestic supply response and the domestic trade
74
policy
(export
subsidies and/or taxes) variables.
requires, of course, that
This
the necessary data be available.
Cameroon's cotton export evolution was not found to be
consistent with the assumed revenue maximizing behavior.
Since her domestic consumption is small,
the bulk of her
cotton trade revenue comes from sales in the world market.
Discarding the revenue maximizing behavior assumption is
equivalent to ruling out the possibility of a foreign exchange
maximization objective.
of a target however.
It does not rule out the possibility
More exhaustive institutional research
may be required in order to determine the country's objective
in cotton trade.
In order to derive stronger policy recommendations, the
present
study
should be extended
or
repeated
to
cover
Cameroon's main exports, and clearly determine the benefits
and the cost, the beneficiaries and the losers of the present
monetary system.
Depending on the success of the above studies they should
be extended to other Franc-Zone countries in order to assess
the benefits and costs to each member country.
As the main
international development institutions and the CFA- Zone member
countries discuss the possibility of the CFA devaluation on a
more frequent base, such a study may be useful for possible
75
negotiation.
Otherwise we believe that it is always important
to periodically assess the effectiveness of structures assumed
beneficial and generally believed to be so.
Another
possibly
useful
assessment
for
eventual
negotiations in the near future may be that of France's cost
and benefits in supporting the Franc Zone.
76
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Perkings,
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The World Bank, "World Tables" 1991.
Trela I. and Whalley. J. "Unraveling the Threads of the MFA".
The Uruguay Round, Textile Trade and the developing
Eliminating the Multi-fibre Arrangement in
countries.
Carl
B Hamilton editor. A World Bank
the 1990s.
Publication
(1990)
Uma L, Van De Wale, Gbetibouo, "Cotton in Africa an analysis
of difference in performance" MADIA Discussion paper
(1989)
United Nations Statistical Papers (various years).
United States Department
Statistics" (1990)
Van
of
Agriculture,
"Agricultural
De Wale. N. "The Decline of The Franc Zone: Monetary
Politics in Africa" The Journal of The Royal African
Society, Vol 90, No 360, pp 383-405, July 1991.
78
APPENDIX
ECONOMIC DATA
79
SECTION 1
CANEROON'S DATA SET
TABLE 2: Prices, exchange rates and consumer prices index.
WORLD PRICES
OF COTTON
(US cents/lb)
EXCHANGE RATES
(CFA/$US)
DOMESTIC PRICES
OF PRODUCTION
SUBSTITUTES (US
cents/tb)
DOMESTIC
PRICES
OF COTTON
(US CENTS/tb)
CPI(1988
= 100)
YEARS
NOMINAL
REAL
NOMINAL
REAL
NOMINAL
REAL
NOMINAL
REAL
1975
39.11
1.39
214.31
252.39
7.30
0.26
9.10
0.32
28.20
1976
55.08
1.72
238.95
352.61
7.57
0.24
8.16
0.25
32.10
1977
58.27
1.66
245.68
355.49
6.85
0.19
10.15
0.29
35.20
1978
61.46
1.52
225.66
317.31
10.71
0.27
13.06
0.32
40.40
1979
67.95
1.49
212.72
267.15
8.85
0.19
13.86
0.30
45.50
1980
76.33
1.57
211.28
282.69
10.60
0.22
16.10
0.33
48.50
1981
74.85
1.41
271.73
390.91
8.73
0.16
13.35
0.25
53.10
1982
64.65
1.10
328.61
378.82
12.81
0.22
13.80
0.23
58.80
1983
73.80
1.11
381.06
451.69
7.50
0.11
12.50
0.19
66.60
1984
61.63
0.79
436.96
457.39
9.04
012
12.14
0.16
77.70
1985
49.45
0.57
449.26
461.73
11.78
0.14
14.64
0.17
86.50
1986
49.61
0.57
346.30
373.80
10.03
0.11
20.30
0.23
87.60
1987
57.19
0.61
300.54
294.03
9.66
0.10
23.39
0.25
94.40
1988
72.80
0.73
297.85
297.85
7.16
0.07
23.60
0.24
100.00
1989
58.26
0.54
319.01
318.56
6.11
0.06
23.46
0.22
108.60
1990
79.14
0.73
272.26
352.63
4.03
0.04
27.82
0.26
108.60
SourciiThe nominaL world prices are unit prices calculated from export quantities and vaLues,
taken pubLished in the "United Nations Statistica Papers."
The nominal exchange rates are taken from the "Financial Statistic" (InternationaL
Monetary Fund, 1991)
Cameroon's domestic prices were obtained from the Office CereaLier (Cameroon).
The consumer price index data used in the caLcuLation of real prices and exchange rtes
were obtained from " The World Tables 1991" The WorLd Bank.
80
Cameroon's exports by destination.
TABLE 3:
Source: Cotton: WorLd Statistics (International Cotton Advisory Coninitee, 1992)
1975
1976
1977
1978
1979
Albania
Algeria
Argentina
Austria
Australia
1980
200
Bang I adesh
Belgiun
Brazil
Bulgaria
Canada
Chile
China(P.R.)
China(Taiwan)
Czechoslovakia
Ethiopia
Finland
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
60
5537
520
80
3231
6164
7487
2184
210
1000
1020
300
543
1716
8229
54
14
2000
2000
4936
4719
600
1125
I ndi a
Indonesia
Iraq
Ireland
IsraeL
ItaLy
Japan
Kenya
Korea
Kuwait
Lebanon
Malaysia
Malta
1250
470
300
2713
1331
1803
3097
3486
3910
50
300
704
Mauri tius
Mexico
Morocco
Netherlands
Nigeria
Philippines
Poland
Portugal
450
500
450
1520
1016
75
720
450
29
58
15037
13077
Romani a
Singapore
South Africa
Somali
Spain
Sri lanka
sweden
Switzerland
Thai Land
Tunisia
Turkey
United Kingdom
USSR
Yugoslavia
Zaire
Zambia
Other
TOTAL
9356
9092
30
76
13493
22257
81
TABLE 3(CONTINUED): Cameroon's exports by destination
1981
1982
1983
1984
1985
1986
150
520
220
340
120
400
300
2710
1230
400
1560
6490
6210
7440
6030
4140
1630
2770
3440
3240
1740
300
200
A lbani a
Algeria
Argentina
Austria
Australia
Bangladesh
Belgiun
Brazil
Bulgaria
Canada
Chile
China(P.R.)
China(Taiwan)
Czechoslovakia
Ethiopia
FinLand
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
5075
I ndi a
Indonesia
Iraq
Ireland
Israel
Italy
Japan
Kenya
Korea
Kuwait
Lebanon
Malaysia
Malta
Mauritius
Mexico
Morocco
NetherLands
Nigeria
Philippines
Poland
PortugaL
Romania
Singapore
South Africa
SomaLi
Spain
Sri Lanka
sweden
Switzerland
Thailand
Tunisia
Turkey
United Kingdom
USSR
Yugoslavia
Zaire
Zambia
Other
TOTAL
180
7040
460
1790
350
2970
300
300
1230
570
4430
1730
2210
690
3000
1140
3590
50
200
2570
600
6650
1750
600
180
300
200
150
1250
130
14495
18770
17870
80
100
460
440
250
830
1110
1440
1240
17860
27940
21500
82
TABLE 3 (Continued): Cameroon's exports by destination
Albania
Algeria
Argentina
Austria
Australia
Bangladesh
Belgiun
Brazil
Bulgaria
Canada
Chile
China(P.R.)
China(Taiwan)
Czechoslovakia
Ethiopia
Finland
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Ireland
Israel
ItaLy
Japan
Kenya
Korea
Kuwait
Lebanon
1987
1988
1989
1990
440
760
520
100
100
1780
1610
1100
1100
12100
5030
3360
3500
920
9840
5990
11520
2000
270
600
260
3160
1660
270
1360
2000
2100
50
1220
790
400
120
350
Ma lays i a
Malta
Mauri tius
Mexico
Morocco
Netherlands
Nigeria
Philippines
Poland
Portugal
Romania
Singapore
South Africa
Somali
Spain
Sri lanka
490
100
100
sweden
Switzerland
280
570
1100
1530
850
1110
1600
1360
1120
Thai land
Tunisia
Turkey
United Kingdom
USSR
Yugoslavia
Zaire
Zambia
Other
1470
230
1780
940
20
TOTAL
28900
20060
37980
25250
1200
83
SECTION 2:
TURKEY'S DATA SET
TABLE 4 TURKEY'S PRICES, EXCHANGE RATES AND CONSUMER PRICE INDEX.
YEARS
EXPORTS
(METRIC
TONS)
WORLD PRICES (US
cents/tb)
EXCHANGE RATES
(domestic
currency per US
NOMINAL
REAL
NOMINAL
REAL
I
CPI
PRODUCTION
OF COTTON
(METRIC
TONS)
1975
126920
54.70
45.58
14.44
510.08
1.20
599
1976
470860
61.65
41.10
16.05
508.78
1.50
480
1977
126230
88.49
52.05
18.00
532.22
1.70
475
1978
265260
66.22
30.10
24.28
526.69
2.20
575
1979
209440
78.24
24.45
31.08
471.70
3.20
477
1980
134370
90.25
18.05
76.04
715.45
5.00
477
1981
222080
96.65
9.12
111.22
677.29
7.80
500
1982
162450
77.55
4.10
162.55
642.61
10.60
488
1983
137250
83.44
3.35
225.46
612.85
18.90
489
1984
108580
92.74
2.51
366.68
637.30
24.90
522
1985
155240
74.58
1.39
521.98
750.64
36.90
580
1986
68140
54.13
0.75
674.51
827.20
53.50
518
1987
111470
64.87
0.65
857.21
636.66
72.00
518
1988
40280
86.21
0.49
1422.35
736.86
100.00
537
1989
146260
66.77
0.22
2121.68
571.24
175.40
650
1990
44570
90.45
0.21
2608.64
351.17
297.50
621
Sources:- The nominal world prices were obtained from Cotton: WorLd Statistics (International
Advisory Convnitee, various versions).
The nominal exchange rates are taken from the "FinanciaL Statistic" (InternationaL
Monetary Fund, 1991)
The consijner price index data used in the calculation of reaL prices and exchange rtes
were obtained from " The WorLd TabLes 1991" The WorLd Bank.
84
TABLE 5: TURKEY'S EXPORTS BY DESTINATION (in metric tons)
Source: Cotton: World Statistics (International Cotton Advisory Coninitee, various versions.)
Albania
Algeria
Argentina
Austria
BangLadesh
BeLgiun
1975
1976
1977
1978
1979
0
0
0
0
1990
20
0
0
0
0
2950
16800
20
0
8220
5830
24760
0
10060
0
0
0
390
0
0
2990
1990
9820
0
0
0
0
0
5530
20270
4640
3790
8370
17350
Brazi I
Bulgaria
Canada
ChiLe
China(P.R.)
China(Taiwan)
Czechoslovakia
FinLand
France
Germany 0. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Ireland
IsraeL
ItaLy
Japan
Kenya
Korea
Kuwait
Lebanon
Malaysia
Malta
Morocco
NetherLands
PoLand
Portugal
Runania
Singapore
South Africa
SomaLi
Spain
Sri lanka
sweden
Switzerland
Thailand
Tunisia
United Kingdom
USSR
YugosLavia
Other
TOTAL
40
0
40480
0
9540
650
13380
540
3400
0
2540
0
6500
5030
0
0
2780
2190
0
0
0
20
11170
650
20
36080
13920
300
4360
0
0
0
0
0
0
0
20900
6220
390
1630
0
0
0
520
0
1210
280
2470
0
16960
3450
2620
2730
890
800
0
200
130
1710
930
3400
4900
16410
0
0
0
0
4620
13900
1970
3380
500
7160
0
110
0
0
110
0
0
0
0
20940
8630
300
20010
127950
11710
1840
52640
28230
5120
1130
23050
55570
3010
500
31290
1000
10390
950
4490
0
11900
1240
5000
112290
412050
114900
0
90
0
170
6790
1210
110
24740
2210
300
1130
690
410
5250
4790
23330
1600
690
200
1990
5680
16040
16130
85
TABLE 5 (continued): TURKEY'S EXPORTS BY DESTINATION
Albania
1983
1984
2820
506
200
220
6310
1605
1170
11820
14503
16730
14880
11210
12250
22660
5632
7700
8860
12120
1980
1981
2990
1960
0
6310
1982
1985
AL ger ía
Argentina
Austria
Bangladesh
Betgiun
Brazil
Bulgaria
Canada
Chile
0
1730
2180
0
0
Chiria(P.R.)
China(Taiwan)
CzechosLovakia
FinLand
France
Germany D R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Ireland
IsraeL
Italy
Japan
0
2520
1410
0
8650
440
0
0
0
0
0
9710
15020
4350
110
Kenya
Korea
Kuwait
Lebanon
Malaysia
MaLta
Morocco
NetherLands
Poland
Portugal
Runania
Singapore
South Africa
Somali
Spain
Sri lanka
sweden
SwitzerLand
Thailand
Tunisia
United Kingdom
USSR
YugosLavia
Other
0
0
500
5160
5010
2320
TOTAL
110040
187460
126960
20
0
1520
0
40
0
0
0
440
4050
9840
0
3710
300
220
3150
4960
8810
110
5660
7680
6310
6021
10403
0
1960
5870
5990
0
0
2670
0
18560
110
1520
15830
61209
50
59760
28370
13720
10380
3480
1431
8603
1200
16850
1240
15500
11577
105050
122090
86
TABLE 5 (continued): TURKEY'S EXPORTS BY DESTINATION
1986
ALbania
Algeria
Argentina
Austria
Bangladesh
Belgiun
Brazil
Bulgaria
Canada
ChiLe
China(P.R.)
China(Taiwan)
Czechoslovakia
FinLand
France
Germany D. R.
Germany F.R
Greece
Hong Kong
Hungary
1987
1988
1989
1990
430
50
70
70
2450
170
550
1760
4380
210
7230
2550
28770
1080
3070
3360
9990
15120
810
720
I ndi a
Indonesia
Iraq
Ireland
IsraeL
Italy
Japan
Kenya
Korea
Kuwait
Lebanon
MaLaysia
11240
33260
6040
200
110
18440
920
240
6950
40
Ma L ta
Morocco
NetherLands
Poland
Portugal
Runania
Singapore
South Africa
6220
3630
5510
3100
10
Spain
Sri Lanka
sweden
Switzerland
Thailand
Tunisia
United Kingdom
USSR
Yugoslavia
Other
440
5910
TOTAL
9810
9680
5040
13480
790
12780
20
20
1390
11220
12110
60
Somali
4270
600
70
710
150
8440
17780
1360
4000
8070
980
11590
4710
4790
1390
1010
10240
1250
57110
109050
36860
140940
37310
20910
330
4480
87
SECTION 3 TANZANIA'S DATA SET
TABLE 6: PRICES, EXCHANGE RATES AND CONSUMER PRICE INDEX
YEARS
EXPORTS
(METRIC
TONS)
WORLD PRICES
(US Cents/tb)
EXCHANGE RATES
(domestic
currency per US
doLlar)
NOMINAL
REAL
REAL
NOMINAL
CPI
PRODUCTION
(metric
tons)
1975
52310
58
9
48
7
6
72
1976
39380
75
10
47
8
8
42
1977
51055
88
10
43
8
8
67
1978
29140
84
9
40
8
9
50
1979
38380
88
8
38
8
11
56
1980
49730
92
8
34
8
12
61
1981
43530
103
7
32
8
16
59
1982
37750
88
5
31
9
20
45
1983
25060
88
3
31
11
25
43
1984
36040
95
3
36
15
32
54
1985
19200
78
2
36
17
43
49
1986
26560
56
1
50
33
58
37
1987
48070
67
1
78
64
77
71
1988
38650
88
1
99
99
100
70
1989
61950
70
1
116
143
131
70
1990
47480
87
1
140
195
166
71
Sources:- The nominal world prices were obtained from Cotton: WorLd Statistics (InternationaL
Advisory Coninitee, various versions.
The nominal exchange rates are taken from the "Financial Statistic" (InternationaL
Monetary Fund, 1991)
The consuner price index data used in the caLculation of real prices and exchange rtes
were obtained from " The WorLd Tables 1991" The WorLd Bank.
88
TABLE 7: TANZANIA'S EXPORTS BY DESTINATION
Source: Cotton: World Statistics (International Cotton Advisory Coimnitee, various versions.)
1975
Albania
Algeria
Argentina
Australia
Bangladesh
Belgiun
Brazil
Bulgaria
Canada
Chile
China(P.R.)
China(Taiwan)
Czechoslovakia
Ethiopia
Finland
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Ireland
Israel
ItaLy
Japan
Kenya
Korea
Kuwait
Lebanon
Malaysia
Malta
Mauritius
Morocco
Netherlands
PhiLippines
PoLand
Portugal
Runania
Singapore
South Africa
SomaLi
Spain
Sri Lanka
sweden
Switzerland
ThaiLand
Tunisia
Turkey
United Kingdom
USSR
YugosLavia
Zaire
Zambia
Other
TOTAL
1976
1977
1978
1979
1980
370
460
1450
200
690
1410
40
2710
110
6030
4570
2340
950
4860
6330
1080
10260
650
1000
3560
430
910
4050
17280
9740
18490
8200
590
950
12970
390
8280
560
2190
1340
2360
1110
460
1520
3360
9170
4470
6790
260
820
2340
480
2450
4230
1240
1110
1040
130
500
390
40
910
2060
200
40
1800
850
1280
1520
2320
890
1190
1520
280
5100
610
540
540
300
300
1390
1240
690
1340
1260
540
200
70
390
15
200
980
260
1300
780
610
1000
980
1370
2040
52310
39380
51055
29340
38380
49730
89
TABLE 7 (continued): TANZANIA'S EXPORTS BY DESTINATION
1981
Albania
Algeria
Argentina
Australia
Bangladesh
1982
1983
Malta
Mauritius
Morocco
Netherlands
Philippines
Poland
Portugal
Runania
Singapore
South Africa
1985
870
1120
Betgiian
Brazil
Bulgaria
Canada
Chile
China(P.R.)
China(Taiwan)
CzechosLovakia
Ethiopia
Finland
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Ireland
Israel
Italy
Japan
Kenya
Korea
Kuwait
Lebanon
Malaysia
1984
440
3920
1630
440
1260
2460
2430
20
3D
16760
4530
20
7460
440
1740
540
520
90
1820
2170
220
1740
2870
1230
3530
1630
330
1550
2040
1580
2180
1320
4390
1310
650
1570
2180
880
200
820
Somali
Spain
Sri lanka
sweden
Switzerland
Thailand
Tunisia
Turkey
United Kingdom
USSR
YugosLavia
Zaire
760
240
50
1310
870
1440
160
440
5960
3310
570
Zania
Other
37750
2470
6180
4320
TOTAL
37750
25060
36040
19200
10730
90
TABLE 7 (continued): TANZANIA'S EXPORTS BY DESTINATION
1986
1987
1990
1988
1989
690
190
12770
3820
12570
Albania
ALger i a
Argentina
AustraLia
Bangladesh
Belghsn
280
Brazi
Bulgaria
Canada
Chile
China(P.R.)
China(Taiwan)
CzechosLovakia
Ethiopia
FinLand
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
1730
50
680
2320
1340
1010
1900
8450
2720
14370
970
1010
420
1760
4110
2750
2150
530
1570
2110
390
1310
2640
500
1580
140
9210
9390
7730
11450
1210
3650
1700
4690
1820
2300
2010
I ndi a
Indonesia
Iraq
IreLand
IsraeL
Italy
Japan
Kenya
Korea
Kuwait
Lebanon
MaLaysia
Malta
Mauritius
Morocco
NetherLands
Philippines
Poland
Portugal
250
3150
50
Riinania
Singapore
South Africa
SomaLi
Spain
Sri Lanka
sweden
Switzerland
Thailand
Tunisia
Turkey
United Kingdom
USSR
Yugoslavia
Zaire
Zambia
Other
TOTAL
1310
100
250
710
570
600
630
570
26560
120
1320
410
48070
840
3000
680
800
11270
7610
7540
38650
61950
37480
Source: International Cotton Advisory Coninittee
91
SECTION
IMPORTING COUNTRIES' CONSUMER PRICE INDEX
4:
TABLE B IMPORTING COUNTRIES' CONSUMER PRICE INDEX (1988=100).
source: World Tables 1991 (The World Bank)
1975
Albania
Algeria
Argentina
Austria
Australia
BangLadesh
Betgiun
Brazil
BuLgaria
Canada
Chile
China(P.R.)
1976
1977
30.7
33.4
2.7E-05 7.8E-05 0.000425
54.5
59.1
63.4
34.3
39
29.8
32.8
26.3
32
50.8
55.5
45.1
0.0164 0.0233
0.0127
28.1
1978
1979
37.4
43.8
0.00117 0.00324
69.3
47.2
66.9
43.8
34.3
59.4
0.0334
36.1
62.1
0.0464
53.5
14.8
63.5
49.1
10.5
64
38
37.2
36.4
48.8
42.5
39.9
56.9
47.9
43.7
65
63.9
11.3
38.8
44.8
42.2
22.8
67.7
73.3
16.3
27.1
70.7
14.6
41.4
49
41.2
32.5
75.2
18.4
46.2
53.3
45.8
39
22.4
0.0164
27.1
0.0228
22.3
62.9
26.6
30.3
55
32
0.03
26
68.8
29.6
34.9
57.8
36.3
0.0404
30.4
74.4
34
38.4
63.6
39.1
62.9
58.5
29.2
0.8
35.2
61.1
64.5
58.8
32.9
67.6
64.7
36
1.2
70.9
67.7
39
43
47
73.9
29.7
38.2
0.371
61.7
42.3
1.8
61.9
45.5
35.6
31.6
32.6
5.1
Ch i na(Tai wan)
CzechosLovakia
Ethiopia
FinLand
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Irland
Israel.
ItaLy
Japan
Kenya
Korea (Rep of)
Kuwait
Lebanon
MaLaysia
Malta
Mauritius
Mexico
Morocco
Netherlands
Nigeria
Philippines
PoLand
PortugaL
Romania
Singapore
South Africa
SomaLi
Spain
Sri lanka
sweden
Switzerland
Thailand
Tunisia
Turkey
19
56.3
United Kingdom
USSR
Yugoslavia
Zaire
Zambia
22.3
24.2
50.7
60.2
53.7
25.4
0.7
32.7
55.5
12.9
19.7
12.2
12.8
39.9
46.5
44.6
1
38.2
66.7
21.4
23
50.9
44.6
36
71
0.0608
34.1
77.6
39.8
64
69.1
1.4
12.8
24.4
25.3
13.6
16.3
70.5
23.9
72.8
26.6
3.7
24.6
30.7
4.1
31.1
36.7
34.9
40.1
44.7
73.8
49.1
73.6
53.1
57.3
44.7
47.1
39.1
2.2
45.3
3.2
49
1.7
0.8
10.7
1.9
1.3
12.8
2.2
9
17.2
21.1
12.4
10.9
70
19
2.7
18.3
28.5
33.1
66.2
45
71.9
21.5
3.3
21.4
30.3
36.3
70.7
47.4
36.3
39.8
1.2
1.5
71.9
49.4
41.9
1.7
27
33.5
1.2
1.5
0.3
8.2
0.4
9
43.7
51.6
47.6
13
30.6
27.1
14.7
20
76.2
29.3
4.6
2
14.9
92
TABLE 8 (continued): IMPORTING COUNTRIES' CONSUMER PRICE INDEX (1988=100)
ALbania
Algeria
Argentina
Austria
AustraLia
Bangladesh
Betgiun
Brazil
Bulgaria
Canada
Chile
China(P.R.)
Chtha(Taiwan)
Czechoslovakia
Ethiopia
FinLand
France
Germany 0. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Irland
Israel
Italy
Japan
Kenya
Korea (Rep of)
Kuwait
Lebanon
Malaysia
MaLta
Mauritius
Mexico
Morocco
Netherlands
Nigeria
Philippines
PoLand
Portugal
1980
1981
1982
1983
1984
48.9
0.0084
71.9
51.5
41.4
64.9
0.0708
53.5
61.3
0.0345
81.6
62.2
54.6
74.5
0.266
65.4
0.0913
86
69.2
61.4
70.5
0.405
88.9
76.2
67.2
87.2
1.27
58.4
19.7
65.3
64.3
26.6
80.1
35
70.1
72.3
31.9
71.9
75.5
55.5
52.8
78.9
61.9
59.8
83.7
69.4
67.8
88.6
76
75.8
88
82.4
78.3
21.9
51.6
58
48.6
82.6
27.3
59.3
63.4
54.2
54.4
87.8
34
67.5
66.3
61.3
92.4
41.1
74.6
70.9
66.1
66.9
95.5
49.4
82
75.5
73.9
74.8
52.4
0.25
47.3
63.1
73.9
1.2
80.4
43
52
74
86.7
48.9
67
91
81.6
2.94
74.6
95.2
73.4
73.5
72.6
78.4
84
63.4
2.2
56
82
36.5
37.7
46.1
44.3
0.11
39.1
44.7
1.7
51.2
77
33.2
31.9
15.8
24.8
0.0169
76.4
56.7
47
69.2
0.129
79.1
17.3
28.9
61.1
0.54
55.8
81
0.526
73.4
65
93.5
84.4
44.6
74.8
83.1
54.7
81.3
84.9
65.9
87.1
91.5
90.1
86
93.7
72.6
2.8
63
87.6
44.1
42.6
20.9
34.7
91
99.1
94.4
98.3
85.4
8.9
73.9
95.3
58.5
51.7
51.3
53.2
93.1
43.5
13
56.2
57.5
67.1
84.5
96.8
49.9
80.9
4.4
69.6
92.7
475
47
42
42.5
95.8
Romani a
Singapore
South Africa
SomaLi
Spain
Sri lanka
sweden
Switzerland
Thailand
Tunisia
Turkey
United Kingdom
USSR
YugosLavia
Zaire
Zambia
79.3
33.2
5.7
42.4
38.6
52.7
76.3
63
50.7
5
55.6
2.7
4
16.3
86.1
37.7
9
49.1
48.8
59.9
79.3
75.4
55.8
10.6
65.6
3.5
5.8
18.2
16
97.9
56
21.8
84.9
60.8
64.3
63.7
72.9
89.3
89.4
69.7
14.5
73.4
79.9
72.6
79.4
91.9
92.7
75.3
24.9
83.4
4.9
7.9
20.6
6.4
10.8
23.4
9
19
28
18.9
72.1
93
TABLE 8 (continued): IMPORTING COUNTRIES' CONSUMER PRICE INDEX (1988=100)
Albania
Algeria
Argentina
Austria
Australia
BangLadesh
Belgiun
BraziL
BuLgaria
Canada
ChiLe
China(P.R.)
China(Taiwan)
Czechoslovakia
Ethiopia
Finland
France
Germany D. R.
Germany F.R.
Greece
Hong Kong
Hungary
India
Indonesia
Iraq
Irtand
IsraeL
ItaLy
Japan
Kenya
Korea (Rep of)
Kuwait
Lebanon
Malaysia
Malta
Mauritius
Mexico
Morocco
Netherlands
Nigeria
PhiLippines
PoLand
Portugal
Romania
Singapore
South Africa
SomaLi
Spain
Sri Lanka
sweden
SwitzerLand
Thailand
Tunisia
Turkey
United Kingdom
USSR
Yugoslavia
Zaire
Zania
1985
1986
1987
1988
1989
1990
75
82.8
22.7
97
84.5
82.2
97.2
93.1
43.2
98.6
92.2
91.3
98.5
30.3
100
100
100
100
100
100
100
105.9
443
101.9
107.2
109.3
101.2
782.3
14085
104.5
115.3
120.3
104.3
10850.3
95.8
83.4
91.9
100
100
100
104
100
100
100
107.1
105.1
100
100
100
100
100
100
101.3
113.5
107.4
116.3
109.4
108
104.1
129.1
100
100
100
100
100
100
100
102.2
116.3
105
100.7
108.3
107.1
101.5
106.3
139.8
111.6
103
118.9
113.3
104.9
100
100
100
100
100
100
100
100
100
100
102
100.9
109.2
104.8
101.8
123
214.2
102.4
100.7
138.3
108.8
160.2
109.6
257
105.6
101.5
112.8
181.9
104.8
114
105.8
101.9
103.9
106.4
175.4
104.9
103.9
129.4
96
100
100
100
100
100
100
100
100
100
100
100
45.3
52.5
69.9
100
100
100
294.1
187.7
155.6
2.9
94
79.2
74.3
92.7
3.8
12.4
88.5
53.4
76.8
69.8
85.9
95.4
88.2
89.3
113.6
93.4
94.4
102.5
96.1
97.7
99.8
85.9
82.6
99.9
69.8
92
87.8
84.5
86.5
88.6
13.92
82.6
97.4
80.9
92.2
97
93.4
56.33
90.2
99.4
91.5
94.4
98.4
97
83.46
95.5
98.1
98.4
97.6
97.9
23.2
89.6
100.6
99.1
86.1
90.7
96.3
79.9
58.5
88.9
82
80.1
97.8
91.7
14.7
83.1
98.4
81.6
77.7
59
68.6
92
95.6
67.9
81.9
100.4
62.4
41.7
80.3
84.7
85.7
94.6
93.6
81.7
36.9
87.5
100.9
72.6
57.5
87.3
86
13.9
28.9
33.6
23.9
35.8
92.1
97.8
95.8
88.2
53.5
92.8
46.1
96.8
95
92.4
91.9
91.5
100
95.1
97
99.3
99.6
99.5
43.1
97.4
100.7
91.4
99.5
86.1
78
95
92.8
95.9
98.6
97.6
93.3
72
114.7
120.7
102.7
109
134.2
140.4
115.5
112.1
106.3
117.8
136
116.1
115
1018
194.8
120.3
562.5
123.4
111.9
127.2
112.6
105.1
109.4
114.2
297.5
113.1
3940.5
372.9