Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
The Impact of Exports on Economic Growth in Botswana
1980-2013
Letlhogonolo M. Mpatane and Ireen Choga
The objective of this paper was to determine the impact of exports on economic
growth in Botswana using data from 1980 to 2013. The study tests for stationarity in
variables (GDP, exports, exchange rate, degree of trade openness and terms of
trade) using the Augmented Dicky-Fuller test (ADF). Cointegration test is done using
the Johansen (1991, 1995) methodology. The Vector Error Correction Model is run
to measure the correction from disequilibrium of previous periods. Exports and real
exchange rate have been found to have a positive long-run relationship with
economic growth. The estimate of the speed of adjustment coefficient found in this
study has revealed that about 68 percent of the variation in GDP from its equilibrium
level is corrected within one year. The results are favourably comparable to those in
the literature and are also supported by previous studies.
Keywords: Exports, Economic Growth, Vector Autoregression, Botswana.
1. Introduction
Botswana is a landlocked country located in Southern Africa and shares its boarders
with Zimbabwe, Zambia, South Africa and Namibia. Botswana got its independence
on the 30th September 1966. According to Beaulier (2003), Botswana was one the
poorest countries when it gained its independence from the United Kingdom in 1966
and it has since transformed itself and has become one of the fastest growing
economies in the world. According to Sentsho (2003), before Botswana got its
independence, its main export was mainly beef and its trade was dominated by game
meat and game skins. Botswana continued to export beef and relied on beef export
for its economic growth until after its independence when it started discovering
minerals.
In 1967, diamond was discovered at Orapa and started its first production in 1971
while the Jwaneng pipe was discovered in 1972 and began with production in 1982.
Several discoveries of high grade copper were made at Thakadu, Makala and
Bushman in the Matsitama Schist Belt in the 20th century with the most recent
exploration work done by Bamagwato Concessions Limited (BCL) between 1957 and
1974 and Falconbridge between 1977 and 1989. Other minerals such as nickel, matte
and coal were also discovered.
______________________
Letlhogonolo M. Mpatane, School of Economic & Decision Sciences, North West University,
South Africa, Email: lettieatty@gmail.com
Dr Ireen Choga, School of Economic & Decision Sciences, North West University, South Africa,
Email: Ireen.Choga@nwu.ac.za
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
Up to about 1990, Botswana used to export 90% of its products to the world market
beyond Africa, mainly to Europe and America. Botswana forms a customs union with
South Africa Namibia, Zimbabwe and Zambia.
Botswana experienced rapid economic growth rate for more than three decades which
is associated with its minerals more especially diamond. According to (Limi, 2006),
rich resource-countries tend to grow less rapidly than scares resources-countries
which he referred to as the resource curse. In this case, Botswana contradicts the
resources curse as it managed to transform diamond into growth and development.
This was supported by Sentsho (2003) who stated that if exports from primary
resources are managed properly they can lead to a sustained economic growth.
Another factor apart from diamond that made it possible for Botswana to have a high
economic growth over the period is the country’s exchange rate policy. Botswana used
the South African Rand before and after it gained its independence in 1966. It was a
member of Rand Monetary Area (RMA) until1976 when it terminated its membership
and formulated its own central bank with Pula as its currency (Harvey 1996; Ahmed,
2006). This resulted from the fact that the membership of RMA did not give Botswana
monetary and exchange rate independence as the policies were managed by the
South African Reserve Bank. The establishment of a reserve bank by Botswana gave
it the opportunity to formulate its own monetary and exchange rate policies that best
suit its exports, economic growth and development.
The paper is organised as follows: Following this introduction (Section 1), Section 2
provides a brief overview of exports and economic growth in Botswana over the period
1980-2013. This will be followed by Section 3 which provides review of related
theoretical and empirical literature. After literature review follows presentation of
methodological framework and sources and data in Section 4. Section 5 will cover
estimates of the regression and analysis of results. Lastly policy implications,
recommendations and conclusion will be presented in Section 6.
2. An Overview of Exports and Economic Growth in Botswana
Botswana adopted the Export-Led Strategy during the colonial era (1885-1966) when
it was Bechuanaland Protectorate (Sentsho 2003). Export-Led Strategy is an
economic development strategy in which export expansion play a central role in a
country’s economic growth. Statistical evidence has shown that exports have played
a very important role in the economic growth of Botswana by contributing more than
50% towards the country’s GDP.
Historical economic activity shows that at independence, 40% of the country’s GDP
and 90% of employment were mainly from the agricultural sector. As more minerals
were discovered, the agricultural sector contributions declined over the years to a point
where it contributed 4% and 6% by the mid-1990s (Beaulier (2003). The mining sector,
especially the diamond sector has been the main contributing factor to the country’s
economic activity since the early 1990s to date.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
The previous efforts to diversify have been slow and largely unsuccessful. The NDP10
has identified tourism, international financial services, energy (including regional
power export), agriculture and manufacturing as the main potential sectors for
diversification. Botswana also has copper, nickel, coal, clay, soda ash, salt and small
quantities of gold. This will provide
initial diversification from diamonds to other minerals and eventually to non-minerals
(ADF, 2009).
The global financial and economic crisis of 2008-2009 hit Botswana very hard. Global
diamond sales fell sharply and led to a negative real growth rate. The temporary
closure of the diamond mines in the first half of 2009 caused the economy to fall into
a recession with a growth rate of minus 7.8 percent in 2009as well as budget and
balance of payments deficit (De Beers Family of Companies, 2009). The non-mining
sector on the other hand continued to grow steadily at 9.7 percent and 7.5 percent in
2011 and 2012 respectively. The economy recovered in 2010 as a recovery on
diamond production helped to lift overall GDP by 8.6 percent for that year. The GDP
then fell to 6.1 percent in 2011. In 2012, 8.1 percent contraction in the mining output
lead to a 3% increase in growth.
Expotrs percentages
Figure 1: Exports Contribution to GDP in Percentages
80
70
60
50
40
30
20
10
0
Exports Contribution to GDP in percentages
Years
Exports as percentage of GDP
Source: World Bank 2014
The export of goods and services as a percentage of GDP in figure 1 shows that
exports have been the main contributor to Botswana`s economic growth. The share of
exports towards GDP rose from 1982 up until 1985 when exports contributed 70
percent of the GDP. In 1982 the Jwaneng Diamond mine started its production. This
mine produces the largest output in the country and as a result it led to an increase in
total exports. The total export share to GDP then declined to 47 percent in 1993. This
was because of a reduction in the sales of Copper Nickel matte and beef. It then
fluctuated between 49 and 53 percent in 2008. It then fell to 37 percent in 2009 due to
the recession and then inclined to 55 percent in 2013.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
3. A Review of Supporting Literature
This study is supported by several economic theories which include among others
Smith, (1776), the Theory of Absolute Advantage, David Ricardo (1817), the Theory
of Comparative Advantage, Heckscher (1919) and Ohlin (1924), Heckscher-Ohlin
Model (HOM). The Heckscher-Ohlin Model used multiple factors of production. The
authors put more emphasis on the roles of labour, capital and land in agriculture and
industry trying to show how their inclusion shapes a country pattern of specialisation
and trade. However, the modern presentations of this model consider only two factors
of production labour and capital. Ohlin (1933) disputed that countries differ in their
respective features of labour and capital and that for both goods to be produced they
needed different composition of capital and labour.
A wide range of empirical studies on the impact of exports on economic growth have
been carried out world-wide by different authors and various results were obtained.
Due to methods used and data employed, some studies were not able to give
supporting facts fulfilling this relationship while others gave evidence that indeed there
was a causal linkage between exports and economic growth. However, very few
studies have been conducted to explain the exports behaviour in Botswana. Research
conducted using time series approach includes the work of Jordaan and Eita (2007)
and Abual-Foul (2004). Studies that focused on the impact of exports and economic
growth in grouped countries were also discussed and they include Njikam (2003) and
Sinoha-Lopete (2004). Although a large gap of literature exists in Botswana, few
researchers have conducted a research on the impact of exports and economic growth
in Botswana. The researchers include among others Jordaan and Eita (2009) and
Sentsho (2002). The next section discusses the methodology used for this study.
4. Methodology used in the Study
To estimate the impact of exports on economic growth in Botswana, this study uses
Vector Autoregression (VAR) model. . According to Gujarati and Porter (2009), the
VAR methodology superficially resembles simultaneous equation modelling where
several endogenous variables are considered. The Augmented Dickey-Fuller (ADF)
statistic is used to test the stationarity and non-stationarity of the variables and their
order of integration. The univariate characteristics is used to show whether the
variables are stationary or non-stationary. If the variables are I(1), the next step is to
test whether they are cointegrated. The Johansen (1991, 1995) cointegration test is
employed followed by vector error correction model (VECM) which is used to estimate
the long run equation and the existence of error correction. Diagnostic checks are also
performed to test for normality (Jarque-Bera), heteroskedesticity (White test) and
serial correlation (Lagrange Multiplier). Lastly stability test is done to check if VAR
satisfies stability condition of our model.
5. Model Specification
The study adopts and modifies Artha, Irfan and Komal (2012) model which explains
economic growth as a function of exports and terms of trade. However, this study adds
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
exchange rate and degree of trade openness which may have a great influence on
export-economic growth relationship in Botswana.
Artha, Irfan and Komal (2012) model was formulated as:
Y  f ( X , TOT ……………………………………………………………….1
Where Y was Real GDP, X Real Exports and TOT as Terms of Trade.
This model has been modified to:
Y  f ( X , RER , DTOP , TOT ) ……………………………………………………2
Real exchange rate (RER ) and degree of trade openness (DTOP ) have been added
to the model.
The study uses annual time series data covering the period1980 to 2013. Data on
exports, GDP and DTOP was obtained from Bank of Botswana Annual reports (1980
– 2013) while data on TOT was obtained from the World Bank. All data series were
tested for stationary as a way to avoid making conclusion based on statistically
spurious results. The tests were done using Augmented Dicky-Fuller Test and the
results are presented in Table 1.
Table 1: Unit root/Stationarity tests results
Augmented DickeyTest
Fuller
Order
of Variable
Constant
Constant
None
integration
and Trend
Level
LRGDP
-3.789***
-1.849
6.867
1st Diff
DLRGDP
-7.514***
-9.429***
-1.223
Level
LRX
-1.3639
-2.174
4.1677
1st Diff
Level
DLRX
LRER
-6.102***
-2.303
-6.493***
-1.669
-3.708***
0.474
1st Diff
DLRER
-4.774***
-5.0616***
Level
1st Diff
LDTOP
DLDTOP
-2.3299
-6.282***
-2.4627
-6.367***
Level
1st Diff
TOT
DTOT
-2.8729*
-3.98***
-2.674
-4.074***
8.8049***
-2.1644**
6.3515***
-0.0287
4.0228***
Conclusion
Stationary
Stationary
Nonstationary
Stationary
Nonstationary
Stationary
stationary
stationary
*represents a stationary variable at 10% level of significance
**represents a stationary variable at 5%, 10% level of significance
***represents a stationary variable at 1%, 5%, 10% level of significance
L represents Logarithms of variables
D represents differenced variables
Table 1 show that LRGDP was stationary both at levels and 1 st difference. All the
variables became stationary at 1st difference. The null hypothesis of a unit root is
rejected in favour of the stationary alternative in each case if the test statistic is more
negative than the critical value. Therefore, a rejection of the null hypothesis means
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
that the series do not have a unit root. The results from the ADF test are in line with
those from the graphical analysis. We therefore conclude that the all the variables are
I(1) stationary. This means that the variables are integrated of the same order which
lead to a cointegration test.
Table 2: Johansen cointegration tests results in lag interval of 1
Ho
Eigenvalue
Trace
Critical
Max-Eigen
Critical
Rank=P
Statistics
value
statistics
value
0.05
0.05
P=0*
0.740256
95.73236
69.81889
43.13785
33.87687
P≤1*
0.638561
52.59451
47.85613
32.56516
27.58434
P≤2
0.373220
20.02935
29.79707
14.94913
21.13162
P≤3
0.133967
5.080222
15.49471
4.602645
14.26460
P≤4
0.014813
0.477577
3.841466
0.477577
3.841466
Trace test indicates 2 cointegrating equation at the 0.05 level
Max-eigenvalue indicates 2 cointegrating equations at the 0.05 level.
*denotes rejection of the hypothesis at the 0.05 level
Table 2 shows that the trace test and the max-eigenvalue test indicate 2 cointegrating
equations at 0.05 levels which mean that we reject the hypothesis zero and one in
favour of at least two cointegration vectors. The trace statistics and max-eigenvalue
values are greater than the critical values at the 0.05 level with the probability less
than five percent. The presence of cointegration between variables suggests a longrun relationship between them. Since cointegration of variables has been established,
the vector error correction model of the VAR is now employed. The next section
establishes the short-run and long-run dynamics of the model.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
Cointegrating
Eq
Table 3 Vector Error Correction Estimates
cointEq1
Log_RGDP(-1)
1.000000
Log_RX(-1)
-1.069646
(0.00885)
[-120.869]
-0.103945
(0.01799)
[-5.77766]
0.535533
(0.06241)
[8.58149
1.213456
(0.10752)
[11.2857]
-0.715721
Log_RER(-1)
Log_DTOP(-1)
TOT(-2)
c
Error Correction
CointEq1
D(LOG_RG
DP)
-0.687292
(0.22277)
[-3.08518]
R-squared
Adj. R-squared
0.770788
0.630715
D(LOG_RX
)
-0.036639
(0.52967)
[-0.06917]
D(LOG_RE
R)
-0.069403
(0.61636)
[-0.11260]
D(LOG_DTOP
)
0.108106
(0.41290)
[0.26182]
0.487996
0.306090
0.203809
0.175104
-0.117965
-0.282753
D(TOT(-1))
-0.592301
(0.08002)
[-7.40173]
0.84448
4
0.74944
6
The Error Correction model shows the long-run and short-run dynamics to be
estimated in a single step. The error correction term is expected to be negative and
for this model it is -0.687292 which means that it is statistically significant. This also
indicates that the dynamics adjust to long-run equilibrium. The speed of adjustment is
68.72%. This is the speed at which our dependent variable GDP returns to equilibrium
after a change in an independent variable in this case exports.
Our model shows that there is a positive long-run relationship between real exports
and real GDP and real exchange rate and real GDP. Both the R-squared and adjusted
R-squared are more than 50% which is statistically significant and shows the
goodness of fit to the model. The Adjusted R-squared is 0.630715 which is very good.
Adjusted R2 is a measure of the closeness of fit in the regression model.
After establishing the short-run and long-run dynamics of this model, the next step is
to do diagnostic tests.
Diagnostic tests are very important in the analysis because if there is a problem in the
residuals from the estimation of the model, it means that the model is inefficient such
that the parameter estimates from such a model may be biased. To test whether our
model is a reasonable fit to the data the diagnostic tests were carried out and they
included checking for serial correlation, normality and heteroskedasticity. The tests
carried out showed the model is reasonably well specified. The diagnostic test
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
revealed that the residuals are normally distributed, are not serially correlated and that
they are homoscedastic. The results obtained from the diagnostic tests are shown in
table 4 below.
Table 4: Diagnostic checks results
Test
Null Hypothesis
t-Statistic
Probability
Langrage Multiplier (LM)
No serial correlation
19.28
0.783
White (CH-sq)
No conditional heteroskedesticity
342.64
0.304
Jarque-Bera (JB)
There is a normal distribution
2.059
0.357
Table 5 Stability Test
Roots of Characteristic Polynomial
Endogenous variables: LOG_RGDP
LOG_RX LOG_RER LOG_DTOP
TOT
Exogenous variables: C
Lag specification: 1 2
Date: 11/07/14 Time: 14:05
Root
Modulus
0.989394
0.989394
0.752706
0.565616 0.384167i
0.565616 +
0.384167i
0.752706
0.682565
0.132981 0.565928i
0.132981 +
0.565928i
0.682565
-0.556105
0.556105
-0.256013
0.256013
0.124723
0.124723
0.683744
0.683744
0.581342
0.581342
No root lies outside the unit circle.
VAR satisfies the stability condition.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
Figure 2 Stability test
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Table 5 and figure 2 above show that no root lies outside the unit circle. VAR satisfies
the stability condition of our model.
6. Policy Implications, Recommendations and Conclusions
In determining the impact of exports on economic growth in Botswana, the results
obtained revealed that there is a long-run positive relationship between exports and
economic growth and between real exchange rate and economic growth. The policy
implication of this positive relationship between exports and economic growth in
Botswana is that an expansion of exports will lead to an increase in economic growth.
This also implies that policy makers in Botswana should continue to promote and
implement policies aimed at expanding exports in order to accelerate economic growth
and development.
The diversification of exports sector away from primary commodities especially
diamond should highly be taken into consideration. Botswana can also expand its
limited domestic market by exporting more in order to increase economic growth. The
exchange rate on the other hand must be kept stable in order to maintain good
economic performance as movements on the exchange rate might have negative
impacts on the export sector and economic growth.
The main objective of this study was to determine the impact of exports on economic
growth in Botswana using annual data for the 1980 to 2013. Time series techniques
were used to test for the causal relationship between exports and GDP in Botswana.
The unit root tests (ADF), a cointegration test (johansen procedure), vector error
correction model and diagnostic test were applied.
The results of the unit root tests indicated that all variables (LRGDP, LX, LREX, TOT,
LDTOP) were stationary in first difference I(1). Since all the variables were stationary
and cointegrated of the same order, the Johansen cointegration test was applied and
there was evidence of two cointegrating vectors. Long-run exists between exports and
economic growth and between real exchange rate and economic growth in Botswana.
The Vector Error Correction Model was then conducted. The VECM provided the
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia
ISBN: 978-1-922069-89-4
parameter estimates for the long-run relationship. Real Exports and real exchange
rate have a positive long-run relationship with real GDP.
The speed of adjustment coefficient was -0.687292. This measures the speed of
adjustment in real GDP following a shock in the system. The estimate of the parameter
revealed in this study indicates that about 68 percent of the variations in real GDP
from its equilibrium are corrected within a year. The results are favourably comparable
to those in the literature.
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