Modern Economy, 2011, 2, 538-545
doi:10.4236/me.2011.24059 Published Online September 2011 (
Copyright © 2011 SciRes. ME
Effects of Exchange Rate Volatility on Trade in Some
Selected Sub-Saharan African Countries
David Olayungbo, Olalekan Yinusa, Anthony Akinlo
Department of economics, obafemi Awolow o Uni versi t y , Ile-Ife, Nigeria
Received April 2, 2011; revised June 2, 2011; accepted June 15, 2011
The paper investigates the impact of exchange rate volatility on trade in 40 selected sub-Saharan African
countries for the period 1986-2005. The study employs a gravity model with pooled ordinary least square
(POLS) allowing for fixed effect and panel Generalized Method of Moments (GMM) techniques. The results
of the analysis show that the net effect of exchange rate volatility on aggregate trade was positive using the
two approaches. In the way the results show that there is not much difference between the impact of ex-
change rate volatility on primary and manufactured trade as well as between ECOWAS and non-ECOWAS
countries. However, the results should be interpreted with caution as the history of exchange rate volatility is
still relatively young compared with the developed countries.
Keywords: Exchange Rate Volatility, Trade, Sub-Saharan Africa
1. Introduction
Foreign exchange rate for sub-Saharan African countries
have been highly volatile following introduction of the
structural adjustment reforms since early 1980s. A cen-
tral question has been the effect of such high exchange
rate volatility on the growth of foreign trade. In the de-
veloped and other industrialized economies, several
studies have provided empirical evidence on the rela-
tionship between exchange rate volatility and trade. In
general, most of these studies have concluded that ex-
change rate volatility deters the growth of foreign trade.
However, little is known about the extent this conclusion
may be true for sub-Saharan African countries consider-
ing their peculiar characteristics including low exports
volume and dominance of primary commodities in the
aggregate exports. Essentially, studies of the experience
with sub-Saharan African countries have been very few,
due mainly to the unavailability of sufficient time series
This article examines the impact of exchange rate
volatility on trade for forty sub-Saharan African coun-
tries. The paper adds to existing literature in many ways.
One, the paper focuses specifically on sub-Saharan Afri-
can countries as against many others that merely only
included few African countries as part of larger sample.
Moreover, this study covers more Sub-Saharan African
countries compared with few existing studies in Sub Sa-
haran countries. Also, we examine the differential impact
of exchange of rate volatility on both primary and manu-
factured exports in the sub region2. Finally, we examine
the impact of regional grouping on the relationship be-
tween exchange rate volatility and trade in the region.
Essentially, this study is important for two main rea-
sons. One, the effect of the exchange rate volatility on
trade has significant impact on the reforms in the
sub-region. If exchange rate volatility adversely affects
trade, the export expansion programme will be jeopard-
ized. Moreover, the intended effect of the current trade
liberalization policy being implemented in the sub region
may be dammed thereby precipitating a balance of pay-
ment crisis.
The paper is organized as follows. In Section 2, we
discuss an overview of exchange rate management and
trade development in the region, in Section 3, we equally
examine the specification of the model. Data sources and
variable definitions are described in Section 4. In Section
1Few recent studies that focus exclusively on African Countries data
include [1], [2] and [3]. Out of the forty five countries that make up the
sub-Saharan African countries only five countries such as Eritrea,
Somalia, United Republic of Tanzania, Guinea Bissau and Comoros
are excluded from our sample due to unavailability of data.
2Indeed, it has been observed in the literature that total trade aggregate
tends to hide substantial variation across sectors. There is the need to
look at some of the components of the aggregate trade.
5, we discuss the empirical results for the forty selected
sub Saharan African countries. Conclusions are drawn in
the last section.
2. An Overview of Exchange Rate
Management and Trade Development in
Sub-Saharan Africa
The trade policy of most sub-Saharan countries in the
late 1960 s to early 1970 s had been export promotion
policy. The export of sub-Saharan African countries has
been basically primary products and raw materials such
as vegetable oils, palm oil, palm nut, kernels and ground-
nuts. During this time, the growth of export of sub-Sa-
haran countries started from 3.1% in the late 1960s
(UNCTAD 2004). The export performance of the region
started declining in 1970 from 3.9 percent to 3.4 percent
in 1979. The oil-price shocks, the slow growth in the
world trade in primary commodities, institutional weak-
ness, political instability, civil war, trade restriction, tar-
iff barriers and persistent rise in price of imported manu-
factured goods were factors identified in the economic
literature responsible for low export in sub-Saharan Af-
rica [4]. During this period, the exchange rate policy of
these countries were fixed and pegged to the U.S. dollars
which is a fixed exchange rate system. By 1980, the
share of non-fuel exports had reduced from 18 percent in
1970 to about 9 percent, while the growth of import ex-
panded by 5.8 percent (World Development Indicator,
As a result of this import dependent tendency, coupled
with overvaluation of the exchange rates, most of the
countries in the region in the 1980s had to shift from
export-promotion policy to import-substitution strategy
to bring their economies back to the growth path. In or-
der to further correct the distortions in the economy,
most of the countries in sub-Saharan region adopted lib-
eralized policy of exchange rates after massive loss of
world market share. Nigeria for instance in 1986 adopted
Structural Adjustment Programme (SAP) with liberalized
exchange rate. The South African Reserve Bank’s
(SARB) flexible exchange rate regime resulted in vola-
tility of the Rand in 1997 [5]. Moreover, between 1987
and 1998, the average quarterly depreciation of the Gha-
naian cedi was 6.59%. The real effective appreciation of
the naira also in the 1980s eroded Nigeria’s competi-
tiveness, and growth of trade slowed remarkably during
those periods. By 1990, sub- Saharan share of world
trade had fallen to 1.2 percent compared to Asian world
share of 19.81 percent in the same year (UNCTAD
With the pursuit of trade liberalization in the 1990 s,
the U.S., through the Uruguay Round Agreement Act
(1994), devised measures to improve trade relation with
sub-Saharan Africa. In 2000, the U.S imports from
sub-Saharan region were petroleum products, followed
by non-ferrous metal, apparel and clothing and iron and
steel. While major U.S exports to the region were aircraft
and parts, mining machinery, wheat, general industrial
machinery and road vehicles. Likewise, sub-Saharan
export of primary products to Europe improved to 44
percent, while that of manufactured and energy products
was 22 and 34 percent respectively. The trade liberaliza-
tion also opens way for Asian-African trade relation with
sub-Saharan countries’ total trade as a percentage of
GDP reaching 71.75 percent in 2006. It equally paid off
with Mauritius’ manufactured exports of 19.13 percent
expressed as a percentage of GDP in 2006 [6]. However,
the overall world trade share of African countries was on
the decline (see Table 1).
3. Model Specification
In specifying the model, the study adopts the gravity
approach as employed by [7]3. In a gravity model, the
volume of trade between two countries increases with the
product of their gross domestic products (GDP) and de-
creases with their geographical distance. This implies
that high-income countries trade more than low-income
countries. Also, more proximate countries have lower
real exchange rate volatility and trade more than distant
countries. The gravity model has been widely used in
empirical work in international economics. The theoreti-
cal foundation of the gravity model assumes monopolis-
tic competition, identical and homothetic preferences
across countries. It relies heavily on the concept of in-
tra-industry trade as postulated by the new trade theories
such as the product-differentiation model and the tech-
nological-gap models.
Besides the distance, the empirical specification of the
gravity model often includes a number of dummy vari-
ables to control for different factors augmenting or re-
ducing trade; such as land areas, similarity, language,
Table 1. Sub-Saharan Africa’s share in world trade (%)
Period Figure
1970-1976 3.1 %
1970-1979 5.2%
1980-1989 3.2%
1990-1999 1.1%
2000-2006 1.2%
Source: Inte rnational Financial Standard (2006).
3To conserve space, no theoretical discussions on the relationship be-
tween higher exchange rate volatility and foreign trade are presented
here. Several studies in the developed and industrialized countries have
rovided detailed discussions on the theoretical and empirical evidence
of the relationship between the two variables. See [8] and [9] among
Copyright © 2011 SciRes. ME
geographical position, historical links, and preferential
trading arrangements.
The gravity model is of the following form:
ijtit ijt
where 10
0, 2
T, ijt is a measure of bi-
lateral total trade between sub-Saharan African countries
i and trading countries j at time t, A is the constant term,
and 2
are coefficients, ijt is the distance be-
tween sub-Saharan African countries i and trading coun-
tries j at time t and it is the income of sub-Saharan
African countries i at time t.
Taking logs of the gravity model, we derive an equa-
tion for country i and j at time t as:
12ijtitijt it
trade agdpd
  (2)
where ijt is a measure of bilateral trade between
sub-Saharan African countries i and trading countries j at
time t, a becomes the intercept of the gravity model,
dp d
, a proxy for income it
Y, is the gross domestic
product of sub-Saharan African countries i, ijt is the
distance between sub-Saharan African countries i and
trading countries j, 1
and 2
are the coefficients, it
is the error term of sub-Saharan African countries i at
time t.
To capture the effect of population as a determinant of
trade the model becomes:
123ijtitijtit it
trade agdpdpop
  (3)
where it represents population of sub-Saharan Af-
rican countries i at time t.
Incorporating exchange rate volatility and real ex-
change rate to capture exchange rate risk, the model be-
123ijtit ijtit
trade agdpdpop
45it it it
 
  (4)
The variable it
and it are both the measure of
exchange rate volatility and the real exchange rate of
sub-Saharan African countries i respectively at time t.
Assuming fixed-effect to account for time-varying
factors the model becomes:
12ijtiit ijt
trade agdpd
5ititit it
pop reer
 
 (5)
where i
stands for time-varying effects such as coun-
try’s size, economic power etc. However, the result is not
reported due to space constraint. A priori, the signs of the
coefficients are as follows: 10
, 0
, 30
4or 0
 5or0,
We disaggregate Equation (5) to incorporate the like-
lihood differential effect of exchange rate volatility on
primary and manufactured products, the model therefore
ijtimitm ijt
Magdp d
 
 
mit mit mitit
pop reer
 
ijtipitp ijt
itp itpitit
pop reer
P agdpd
 
 
 
Equations (5), (6) and (7) are the estimated equations.
Where ijt
is a measure of manufacturing trade be-
tween sub-Saharan African countries i and trading coun-
tries j at time t, ijt is a measure of primary product
trade between sub-Saharan African countries i and trad-
ing countries j at time t, and
a are constant terms,
and 15
are coefficients, while m
are time-varying effects for the manufacturing
and primary product trade respectively, we expect the
sign of the coefficients to be as before, and errors across
equations are assumed independent,
. In estimating the models, we used the
Pooled ordinary least Square technique (POLS). How-
ever, for robustness check we equally used the General-
ized Method of Moments (GMM) method of estimation.
The results of our estimations are presented in the section
5 of the paper.
4. Data Sources and Variable Measurements
4.1. Data Source
Data for the study were obtained from World Bank,
World Bank Development Indicator (WDI) CD-ROM
2007) and Commodity Trade Statistics (COMTRADE)
database available at
4.2. Variable Measurements
Operational definition and measurement of variables is
as presented below:
Trade (trade): Trade is the volume of aggregate sum
of import and export series sourced from World Devel-
opment Indicator (WDI 2007) published by World Bank,
prmtrade denotes primary trade also sourced from
Commodity Trade Stastistics Database (COMTRADE)
available at published by United Na-
tions (UN). The primary products are classified based on
SITC rev.3 (Standard International Trade Classification
revision 3) commodity code, such as Food and Live
Animals, Beverages and Tobacco, Cotton and Textiles,
Crude Materials, Inedible, Minerals fuels, Ores and Met-
als, Animal and Vegetable oils. The mantrade variable is
manufactured trade also available at COMTRADE with
SITC rev.3 commodity classification as Equipment,
Utensils, Appliances and Machines. Trade variable de-
Copyright © 2011 SciRes. ME
notes the volume of aggregate bilateral trade among se-
lected trading sub-Saharan African Countries. The vol-
ume of the aggregate bilateral trade is constructed as
bilateral trade value deflated by the relative price index
(import and export prices of trading countries).
Gross Domestic Product (gdp): This is the produc-
tive capacity of an economy. The real domestic product
is the nominal value of the GDP deflated by the con-
sumer price index.
Real Effective Exchange Rate (REER): Exchange
rate is a relative price that measures the worth of a do-
mestic currency in terms of another currency. It relates
the purchasing power of a domestic currency, in terms of
the goods and services it can purchase, vis-à-vis a trading
partners’ currency over a given period.
where n= Bilateral trade weighted real exchange
rate = Nominal exchange rate.
= Wholesale price index for importing country i.
WPI = Wholesale price index for exporting country j.
Exchange rate volat ility (exvol): Exchange rate vola-
tility is a measure that intends to capture the uncertainty
faced by both exporters and importers due to unpredict-
able fluctuations in the exchange rates. Clearly, this is an
unobservable variable and thus its measure is a matter of
serious contention. This study follows recent literature
and uses the measures derived from the GARCH (1, 1)
model as measures of exchange rate volatility. Following
[10] and [11], the conditional volatility of exchange rate
was extracted and modeled via a state space representa-
tion of the form:
tt t
 NID, (9)
0,/ /1
 
t is the exchange rate. The term z
is a scale factor
and subsumes the effect of a constant in the regression of
t, π, is a parameter, t
is a disturbance term that is
uncorrelated with t
is an iid (0, 1) are random distur-
bances symmetrically distributed about zero. The ht
equation is a transition equation in autoregressive form
where the absolute value of π is less than unity to ensure
that the process in Equation (8) is stationary [10]. These
equations generate the conditional volatility of exchange
Population (pop): This is a measure of a country size.
This is another determinant of trade. It is expected that
countries with higher population trade more. Therefore,
positive relationship should exist between population and
Distance (d): This is a measure of distance between
trading countries. In the literature some studies used
transport cost as a proxy while some represented distance
by air distances between capital cities. Tariffs, import
and export taxes, and taxes on international trade can
also be used. Taxes on international trade include import
duties, export duties, profits of export or import monopo-
lies, exchange profits, and exchange taxes, World De-
velopment Indicator (2007). This study makes use of
taxes on international trade as proxy for distance due to
data availability and the bilateral model adopted. All
variables are expressed in log-form except exchange rate
volatility (exvol).
5. Empirical Results
The first step in our analysis is to perform a panel unit
root tests to overcome the heterogeneity biases that are
common characteristics of panel data analysis. Specifi-
cally, we used Levin, Lin & Chu, Im, Pesaran and Shin
W-stat, ADF-Fisher Chi-square and PP-Fisher Chi-
square tests. These tests assume individual unit root
process to allow for heterogeneity across cross-sectional
units. As a check, Hadri Z-stat test is reported as well,
which imposes the same unit root process across coun-
tries. The full sample exhibits stationarity for all the
variables at first difference. The results are contained in
Table 2.
From the panel unit root tests (see Table 2), taxes and
mantrade variable are stationary at levels, while other
variables i.e pop, gdp, exvol, prmtrade and trade are sta-
tionary at first difference. Given the unit root properties
of the variables, we proceed to conduct our estimation
using both POLS and GMM techniques.
Table 3 presents the results for the pooled OLS with
fixed effects for total trade over the period 1986-2005
(see column 1). The model performs well empirically,
yielding precise and generally reasonable estimates. The
R2, which measures the goodness of fit is relatively high
and the F- statistics is significant. The results for total
trade as shown in column 1 of Table 3 show that ex-
change rate volatility positively related to trade. The co-
efficient is only significant at 10 percent. This simply
suggests that volatility of the exchange rate enhances
aggregate trade in the sub-Saharan African countries.
This possibly suggests that traders are risk takers who
see increase in volatility as opportunity for profit. This
finding is consistent with [12]. The coefficient of tax is
negative but barely significant at 20 percent. The results
show that a 10 percent increase in tax would reduce trade
by 0.8 percent. This means that higher tax tend to in-
crease trade costs, which depresses exports. The coeffi-
cients of population and gross domestic product have
positive signs and they are both significant. Which
means that trade responds positively with increase in
population and GDP.
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Table 2. Panel unit root tests-individual effects, full sample.
Variables Level LLC p-v IPS p-v ADF p-v PP p-v Hadri Zp-v
Trade 0 4.62 0.96 5.86* 0.01 64.95* 0.89 56.3 0.98 8.57* 0.00
1 –17.78* 0.00 –16.63* 0.00 422.83* 0.00 741.15* 0.00 6.22* 0.00
Exvol 0 –3.99* 0.00 1.41 0.92 65.06 0.89 103.88** 0.04 15.98*0.00
1 –13.63* 0.00 –12.98* 0.00 317.96* 0.00 366.56* 0.00 5.93* 0.01
Prmtrade 0 –1.27*** 0.1 0.31 0.62 99.26*** 0.07 100.53*** 0.06 –1.7 0.96
1 –19.57* 0.00 –16.88* 0.00 425.28* 0.00 916.37* 0.00 14.01*0.00
Mantrade 0 –3.24* 0.00 –3.24* 0.00 131.64* 0.00 104.03** 0.04 12.01*0.00
Gdp 0 –0.94 0.17 1.27 0.9 106.24** 0.03 92.13 0.17 14.28*0.00
1 –8.54* 0.00 –9.66* 0.00 272.93* 0.00 331.37* 0.00 5.71* 0.00
Taxes 0 –5.78* 0.00 –7.51* 0.00 211.36* 0.00 222.57* 0.00 5.60* 0.00
Pop 0 0.76 0.78 12.14 0.75 53.73* 0.01 50.32* 0.01 12.02*0.00
1 5.32* 0.01 –2.58* 0.01 164.94* 0.00 406.05* 0.00 6.52* 0.00
Notes: The null hypothesis (Ho) is that there is no unit root, (H1) some do not have a unit process. Significance levels are denoted by *: 1%, **: 5%, ***: 10%:
and indicate rejection of the null hypothesis. 0 and 1 represent level and first difference respectively. Probabilities for Fisher tests are computed using an as-
ymptotic Chi-square distribution. All other tests assume asymptotic normality. LLC denotes Levin, Lin and Chin, IPS denotes Im Pesaran Shin W-Stat, ADF
indicates Augmented Dickey Fuller test, PP denotes Phillip Peron, Hadri Z Stat is also reported, and P-V indicates Probability Value.
Table 3. POOL-OLS with Fixed Effects Full- sample period
Dependent Variables: Trade
Constant 11.3982 4.1469 3.1322
(5.38) (2.24) (2.46)
Tax –0.0841 0.053668 0.0085
(–1.69) (1.21) (0.29)
Population (LOG(POP) 0.3246 0.0642 0.0537
(2.58) 0.59 (0.72)
Gross Domestic Product
(LOG (GDP) 0.7507 –0.1329 –0.0276
(5.34) (–1.13) (–0.28)
Real Effective Exchange Rate
(EXCH) 0.0E+01 –0.0001 0.0E+04
(0.96) (–5.44) (–0.29)
Exchange Rate Volatility
(EXVOL) 0.0397 0.0190 0.1017
(1.90) (1.18) (6.69)
Summary Statistics
R-Square 0.7789 0.5416 0.6623
Durbin-Watson Statistic 1.85 1.13 1.17
F-Statistic 60.29 20.17 33.52
Prob(F-statistic) 0.0000 0.0000 0.0000
AIC 2.2924 1.2439 1.0083
SC 2.5564 1.5085 1.2726
Cross sections included 40 40 40
Obsevations 798 796 797
N.B.: t-statistics in parenthesis.
In order to get a better picture of the relationships be-
tween exchange rate volatility and trade we look at the
two of the components of the aggregate trade namely
primary trade and manufactured trade. The results are as
shown in columns (2) and (3) of Table 3 . Essentially, the
results obtained for these two categories of trade are not
significantly different from those of aggregate trade.
However, tax variable has positive sign for both primary
and manufactured trade as against the negative sign ob-
tained for aggregate trade. But the coefficient of taxes is
not significant in both cases. Hence, firm conclusion
cannot be based on it. The real effective exchange rate
variable has the expected negative sign on both primary
and manufactured trade but significant only in the case of
the latter. The coefficient of gross domestic product is
negative for both categories of trade. However, the coef-
ficient is not significant. The exchange rate volatility
variable has positive effect on both primary and manu-
factured trade but only significant for the former. The
results show that a 10 percent increase in exchange rate
volatility would increase manufactured trade by 1.0 per-
cent. The corresponding figure for primary trade is 0.2
Next we address the question that do members of trade
union affect the impact of exchange rate volatility on
trade? To address this question, we divide the selected
sub-Saharan African countries into ECOWAS and
non-ECOWAS countries4. The results for ECOWAS
countries are shown in Table 4. Columns 1, 2 and 3 are
results for aggregate trade, primary and manufactured
trade respectively. For aggregate trade, the exchange rate
volatility coefficient is positive and barely significant
with t-statistics of 1.64. 10 percent rise in exchange rate
volatility would increase aggregate trade by 0.9 percent.
Tax variable is negative as expected but not significant.
The coefficient gross domestic product (GDP) is positive
and significant at 1 percent.
With respect to primary and manufactured trade in the
ECOWAS countries, the results show that exchange rate
volatility has significant positive effect on the two cate-
gories of trade. For manufactured trade, a 10 percent
increase in exchange rate volatility will lead to 0.2 per-
cent increase in manufactured trade in the ECOWAS sub
region. The corresponding figure for primary trade is 1.8
percent. The results is in Table 4 show that GDP is sig-
4This exercise is important for two main reasons. One, it will help to
ascertain the assertion that members of the same regional grouping
tend to trade more even in the face of high exchange rate volatility than
non members. Two, such division tends to reinforce the homogenous
nature of these countries thereby obviating the problem often associ-
ated with pooling countries with different underlying time series prop-
erty. 16 countries actually make up Ecowas, but 15 countries are used
in this paper while the remaining 25 countries represent non-Ecowas.
nificantly positively related to manufactured trade. The
reverse is the case with primary trade though the coeffi-
cient is not significant. Real effective exchange rate has a
significant negative effect on primary trade while the
coefficient is positive for manufactured trade though not
In the case of non-ECOWAS countries, the results for
aggregate, primary and manufactured trade are as shown
in columns 1, 2 and 3 of Table 4 respectively. The re-
sults in Table 4 for aggregate trade show that the ex-
change rate volatility variable is significantly positively
related to aggregate trade for non-ECOWAS countries.
The results indicate that a 10 percent increase in ex-
change rate volatility will lead to 0.3 percent increase in
aggregate trade in non-ECOWAS sub region.
The coefficient of tax is negative and significant. This
is means that an increase in taxes will lead to reduction
in aggregate trade in non ECOWAS sub region. Popula-
tion and gross domestic product both have significant
positive impact on aggregate trade in the non-ECOWAS
sub region.
With respect to primary and manufactured trade, the
results show that tax variable has positive impact on both
primary and manufactured trade though the coefficient is
only significant in the case of primary trade. In the same
way, population variable is positively related to both
primary and manufactured trade but only significant in
the latter. The coefficient of GDP is negative and sig-
nificant for both manufactured and primary trade. Real
effective exchange rate variable has negative impact on
the two categories of trade but only significant for pri-
mary trade. Finally, the coefficient of exchange rate is
positive for both primary and manufactured trade. The
variable is only significant in the case of manufactured
Further Consideration
The basic assumption behind Pooled Ordinary least
Square (POLS) results presented above is the exogeneity
of explanatory variables. However, when this assumption
is relaxed, the POLS breaks down. Therefore, relaxing
the assumption requires that we use another approach
capable of correcting biases introduced by including the
lagged dependent variable on the right hand side of the
equation. Therefore, a Generalized Method of Moments
(GMM) estimator in [13] approach was used to obtain
consistent estimates. Such panel techniques allow one to
control for endogeneity or simultaneity of some of the
explanatory variable in particular GMM estimators, as
well as for potential biases due to correlation between the
explanatory variables and the regression residual. More-
over, the use of GMM estimation technique provides the
robustness check for for the results obtained through the
pooled OLS technique. The panel GMM with fixed ef-
fects is performed on aggregate trade, primary product
and manufacturing product trade5. The results are pre-
sented in Table 5.
Columns 1, 2 and 3 of Table 5 show the GMM results
for aggregate trade, primary and manufactured trade re-
spectively. Overall, the results from Generalized Method
of Moments (GMM) perform better considering the
j-statistics, instrument rank, significant t-statistics, and
the coefficients. With respect to aggregate trade from
Table 5 column 1, the coefficient of exchange rate vola-
tility is positive and significant. The results show that a
10 percent increase in exchange rate volatility would
increase trade by 0.6 percent. In the same way, the coef-
ficients of population and gross domestic product are
positive and significant. A 10 percent increase in GDP
would lead to 6 percent increase in aggregate trade. Tax
variable is negative and significant as expected. The re-
sults indicate that a 10 percent increase in taxes would
reduce aggregate trade in sub-Saharan Africa by 2 per-
As regards primary and manufactured trade, the results
show that exchange rate volatility has significant nega-
tive effect on primary trade while it has significant posi-
tive effect on manufactured trade. The results indicate
that increase in population would lead to increase in pri-
mary trade. The reverse is the case with manufactured
trade though the coefficient is not significant. The coef-
ficient of gross domestic product is negative and signifi-
cant for both primary and manufactured trade. The coef-
ficient of tax is positive and significant for both primary
and manufactured trade. A similar panel study carried
out by [3] between 1972-1987 on sub-Saharan Africa
reported a negative effects of exchange rate volatility on
trade. However, the estimation period was a period of
fixed exchange rate regime and this might have biased
the result. A Study conducted also by [2] analyzed the
effects of bilateral exchange rate movements in terms of
real effective exchange rate misalignment and volatility
on the growth of non-oil exports in Nigeria over the
1960-1990 periods. The findings of the study showed
that exporters in Nigeria are less risk averse and would
readily substitute other activities for exporting should
adverse movement in real exchange rate occur. Apart
from a single country study, the conclusion may be as a
5However, the reliability of the GMM estimator depends very much on
the reliability of the instruments. The validity of the instrument was
evaluated using the popular Sargan test [14]. The Sargan test is a test
on over-identifying restrictions by comparing both the j-statistic and
instrument rank. It is asymptotically distributed as χ2 and tests the null
hypothesis of validity of the (over-identifying) instruments. P-values
report the probability of incorrectly rejecting the null hypothesis, so
that a P-value above 0.05 implies that the probability of incorrectly
rejecting the null hypothesis above 0.05. In which case, a higher
P-value makes it more likely that the instruments are invalid. Our
P-values are generally lower than 5% with the value of 0.03, which
means that instruments used are valid.
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Table 4. POOL-OLS with Fixed Effects Full-sample period
1986-2005 (NON ECOWAS COUNTRIES) 25 countri es .
Dependent Variables: trade
Constant 10.9604 4.6812 1.9147
(3.65) (1.95) (1.48)
Tax –0.1010 0.0817 0.0034
(–3.40) (1.53) (0.11)
Population( LOG (POP) 0.4526 0.0907 0.1943
(3.34) (0.61) (2.37)
Gross Domestic Product (LOG
(GDP) 0.5232 –0.2951 –0.1681
(3.49) (–3.67) (–1.98)
Real Effective Exchange Rate
(EXCH) 0.0E+06 –0.0E+09 –0.0E+01
(2.70) (–6.28) (–0.10)
Exchange Rate Volatility
(EXVOL) 0.0338 0.0079 0.1044
(8.22) (1.08) (2.73)
Summary Statistics
R-Square 0.9472 0.5068 0.6571
Durbin-Watson Statistic 0.64 1.14 1.24
F-Statistic 311.62 18.74 34.15
Prob (F-statistic) 0.0000 0.0000 0.0000
AIC 0.5950 1.3901 1.3404
SC 0.8485 1.6441 1.5940
Cross sections included 25 25 25
Observations 520 519 520
Table 5. Panel generalized method of moments fixed effects (first
difference) 1988-2005 (A ll coun tries).
Variables: Trade
LOG (TRADE (–1)),
0.0520 0.2135 0.1343
(1.07) (53.3) (71.3)
EXVOL 0.0568 –0.0341 0.1570
(3.75) (–5.73) (128.85)
LOG (POP) 0.4270 0.0880 –0.0274
(2.83) (2.75) (–0.41)
LOG (GDP) 0.5890 –0.6537 –0.8362
(12.4) (-34.9) (–24.04)
LOG (TAX) –0.2257 0.0930 0.0789
(–7.04) (26.4) (17.4)
j-statistic 35.70211 35.84797 38.02933
Instrument rank 42.00000 41.00000 40.00000
result of the data span not extending beyond 1990.
6. Concluding Remarks
The purpose of this study is to examine the impact of
exchange rate volatility on trade in sub-Saharan African
countries. Therefore, we begin by specifying gravity
model that incorporates exchange rate volatility as argu-
ment. We then estimate the model using pooled OLS and
GMM techniques for the period 1986-2005. Essentially,
the results for aggregate trade show that exchange rate
volatility tends to enhance trade in the sub-Saharan Af-
rican region. This suggests that traders in the sub-region
perceive increase in volatility as opportunity for profit
making and thus ready to export more in the face of in-
creased exchange rate volatility. The evidence reported
here suggests that there is not much difference between
the impact of exchange rate volatility on primary and
manufactured trade as well as between ECOWAS and
non-ECOWAS countries.
However, the results should be interpreted with cau-
tion because the history of exchange rate volatility is still
very short in Sub-Saharan African countries compared to
the developed countries. Therefore, its impact on the
macro variables in these economies might not yet be
7. References
[1] G. O. Akpokodje, “Exchange Rate Volatility and Exter-
nal Trade Performance of Selected African Countries,
1973-2003,” A Ph. D Thesis Submitted to University of
Ibadan, Nigeria, 2007.
[2] O. Ogun, “Real Exchange Rate Movements Export
Growth in Nigeria, 1960-1990,” AERC Research Paper
82, Nairobi, 1998.
[3] D. Ghura and T. Grennes, “The Real Exchange Rate and
Macroeconomic Performance in Sub-Saharan Africa,”
Journal of Development Economic, Vol. 42, No. 2, 1993,
pp. 155-174. doi:10.1016/0304-3878(93)90077-Z
[4] A. Subramanian, “The Mauritian Success Story and its
Lessons,” Wider Research Paper 2009/36, UNU-WIDER,
Helsinki, 2009.
[5] I. Bah and H. A. Amusa, “Real Exchange Rate Volatility
and Foreign Trade: Evidence from South Africa’s Export
to the United States,” The African Finance Journal, Vol.
5, No. 2, 2003, pp. 1-20.
[6] World Bank, “World Development Indicators,” World
Bank, Washinton DC, 2006.
[7] E. Helpman, “Imperfect Competition and Inter National
Trade: Evidence from Fourteen Indus Trial Countries,” In:
M. Spence and H. Hazard, Eds., International Competi-
tiveness, Cambridge, Ballinger, 1987. pp. 197-220.
[8] M. D. Mckenzie, “The Impact of Exchange Rate Volatil-
ity on International Trade Flows,” Journal of Economic
Surveys, Vol. 13, No. 1, 1999, pp. 71-106.
[9] R. Anderton and F. Skudelny, “Exchange Rate Volatility
and Euro Area Imports,” European Central Bank Work-
ing Paper No. 64, 2001.
[10] N. S. Ndung’u, “Liberalization of the Foreign Exchange
Market and Short Term Capital Flows Problem,” AERC
Research Paper 109, Nairobi, 2001.
[11] D.O. Yinusa, “Exchange Rate Variability, Currency Sub-
stitution and Monetary Policy in Nigeria (1986-2001),”
An Unpublished Ph.D Thesis, Depart Economics,
Obafemi Awolowo University, Ile-Ife, Nigeria, 2005.
[12] A. Asseery and D. A. Peel, “The Effect of Exchange Rate
Volatility on Export-Some New Estimates,” Economic
Copyright © 2011 SciRes. ME
Copyright © 2011 SciRes. ME
Letters, Vol. 37, 1991, pp. 173-177.
[13] M. Arellano and S. R. Bond, “Some Tests of Specifica-
tion for Panel Data: Monte Carlo Evidence and an Appli-
cation to Employment Equations,” Review of Economic
Studies, Vol. 58, No. 2, 1991, pp. 277-297.
[14] J. D. Sargan, “The Estimation of Economic Relationships
Using Instrumental Variables,” Econometrica, Vol. 26,
No. 3, 1958, pp. 393-415.