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Applied Economics
ISSN: 0003-6846 (Print) 1466-4283 (Online) Journal homepage: http://www.tandfonline.com/loi/raec20
Exchange rate volatility–economic growth nexus in
Uganda
Lorna Katusiime, Frank W. Agbola & Abul Shamsuddin
To cite this article: Lorna Katusiime, Frank W. Agbola & Abul Shamsuddin (2016) Exchange
rate volatility–economic growth nexus in Uganda, Applied Economics, 48:26, 2428-2442, DOI:
10.1080/00036846.2015.1122732
To link to this article: http://dx.doi.org/10.1080/00036846.2015.1122732
Published online: 17 Dec 2015.
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Date: 14 March 2016, At: 20:29
APPLIED ECONOMICS, 2016
VOL. 48, NO. 26, 2428–2442
http://dx.doi.org/10.1080/00036846.2015.1122732
Exchange rate volatility–economic growth nexus in Uganda
Lorna Katusiime, Frank W. Agbola and Abul Shamsuddin
Newcastle Business School, University of Newcastle, Callaghan, NSW, Australia
ABSTRACT
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The global financial crisis has disrupted trade and capital flows in most developing economies,
resulting in an increased volatility of exchange rates. We develop an autoregressive distributed
lag model to investigate the effect of exchange rate volatility on economic growth in Uganda.
Using data spanning the period 1960–2011, we find that exchange rate volatility positively affects
economic growth in Uganda in both the short run and the long run. However, in the short run,
political instability negatively moderates the exchange rate volatility–economic growth nexus.
These results are robust to alternative specifications of the economic growth model.
I. Introduction
The effect of exchange rate volatility on economic
growth has gained considerable attention following
the breakdown of the Bretton Woods system. In the
present environment of financial deregulation, globalization and crises, the importance placed on
exchange rate dynamics is unlikely to wane.
Moreover, national economic prosperity is increasingly linked to the ability to compete successfully in
the global economy. Consequently, exchange rate
volatility remains a major concern for national governments operating in a global economy. This is
particularly relevant for developing economies
because of their fragile financial systems and high
vulnerability to external shocks (Aghion et al., 2009;
Tumusiime-Mutebile 2012).
Arguably, excessive exchange rate volatility
increases uncertainty, which may adversely affect
economic growth. While a plethora of theoretical
and empirical studies have investigated the
impact of exchange rate volatility on economic
growth (Aghion et al., 2009; Arratibel et al.
2011; Schnabl 2008, 2009), the empirical findings
are mixed. Notably, the exchange rate literature
does not provide a direct link between exchange
rate volatility and economic growth. Instead, the
debate is framed within the context of economic
growth outcomes under different exchange rate
regimes. Proponents of a free market economy
CONTACT Lorna Katusiime
© 2015 Taylor & Francis
[email protected]
KEYWORDS
Exchange rate volatility;
economic growth; Uganda
JEL CLASSIFICATION
C32; E44; F31; F43
(Edwards and Levy Yeyati 2005; Friedman 1953;
Hoffmann 2007) argue that a flexible exchange
rate regime allows the domestic economy to
adjust to volatile real shocks with minimum output losses. Nonetheless, such an exchange rate
regime may be accompanied by excessive
exchange rate volatility, leading to poor macroeconomic performance. In contrast, a fixed
exchange rate regime can be conducive to macroeconomic stability, which in turn can promote
international trade and investment, and ultimately economic growth (Frankel and Rose
2002). However, a fixed exchange rate regime
may often encourage protectionist behaviour and
thereby lead to inefficient allocation of resources
(Obstfeld and Rogoff 1995).
The empirical literature provides mixed results
on the effect of exchange rate volatility on economic growth. For example, some empirical studies have found that exchange rate volatility has
no impact on economic growth (Bleaney and
Greenaway 1998), while others have asserted
that an increase in exchange rate volatility
reduces economic growth (Arratibel et al. 2011;
Boar 2010; Schnabl 2008, 2009). It is important to
note that studies by Frankel (1999) and Husain,
Mody, and Rogoff (2005) have provided empirical
evidence to show that the macroeconomic performance of an economy under different exchange
rate regimes is influenced by country-specific
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APPLIED ECONOMICS
factors. For instance, Aghion et al. (2009) found
that the effect of real exchange rate volatility on
economic growth is moderated by the level of
financial development of the country.
Evidence for the effect of exchange rate volatility
on economic growth in Africa is very sparse and the
findings are mixed. For instance, in a recent panel
study, Adewuyi and Akpokodje (2013) examined the
effect of exchange rate volatility on macroeconomic
activity in African countries. They provided evidence
that exchange rate volatility has a significant positive
effect on economic growth. However, their study
also found differences in the impact of exchange
rate volatility on growth across country groups. In
view of the mixed empirical evidence, this study
examines the effect of exchange rate volatility on
economic growth in Uganda – a developing economy that has received little attention in the extant
literature.
Like other small open economies, Uganda’s
growth trajectory is sensitive to exchange rate
volatility
and
global
economic
trends
(Kasekende, Atingi-Ego, and Sebudde 2004). In
the wake of the global financial crisis (GFC),
Uganda has experienced increased exchange rate
volatility arising from global shocks, balance of
payments deficits and speculative attacks on its
currency (Bank of Uganda 2011). This macroeconomic instability is threatening to undermine economic growth gains achieved prior to the GFC.
For instance, economic growth declined from an
average of 5% during the period 2005–2008 to an
average of 2.3% for the period 2009–2012 (The
World Bank 2013). Although an understanding of
the exchange rate volatility–economic growth
nexus is important for developing effective
macroeconomic and exchange rate policies, no
previous empirical study explicitly investigated
the impact of exchange rate volatility on economic growth in Uganda.
The objective of this study is to empirically investigate the exchange rate volatility–economic growth
nexus in Uganda. In our investigation, we control
for the effects of fundamental determinants of economic growth as identified in the extant literature,
such as domestic investment, human capital, trade
openness, financial development and inflation (for a
review, see Barro and Lee [1994]; and Durlauf,
Kourtellos, and Tan [2008]). We also test the
2429
hypothesis that the impact of exchange rate volatility
on economic growth is affected by the political
instability of the early 1970s to the mid-1980s and
in recent times.
The rest of this article is organized as follows.
Section II provides an overview of the literature
on exchange rate volatility and economic growth,
highlighting the mixed theoretical predictions and
empirical evidence and the importance of other
fundamentals of growth. Section III describes the
methodology employed in the analyses by
describing the models and estimation technique
used, namely, the autoregressive distributed lag
(ARDL) bounds testing approach introduced by
Pesaran, Shin, and Smith (2001). Our measure of
exchange rate volatility is generated using a
Generalized
Autoregressive
Conditional
Heteroscedasticity (GARCH) model, the standard
measure of exchange rate volatility in the extant
literature. Section IV presents the empirical
results. Section V draws some conclusions and
makes policy recommendations for managing
exchange rate volatility in order to maintain a
stable economic growth path for Uganda.
II. Exchange rate volatility and economic
growth: an overview
The two main theoretical foundations underlying
empirical studies of economic growth, namely,
the neoclassical growth theory pioneered by
Solow (1956) and the endogenous growth theory
popularized by Romer (1986) and Lucas (1988).
The neoclassical growth theory posits that shortrun steady growth is generated through exogenous technical progress. Early theoretical work
investigating the linkage between exchange rate
volatility and economic growth has relied on
classical growth theories (Baxter and Stockman
1989). In contrast, endogenous growth theory is
based on the argument that steady growth can be
generated endogenously. In other words, this
growth trajectory could occur without any exogenous technical progress but rather through
external capital accumulation, human capital
development or through existing productive
designs. Technological innovation makes it possible to introduce new and superior products and
processes, and this consequently increases
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2430
L. KATUSIIME ET AL.
productivity and thus economic growth. Based on
endogenous growth theory, it could be argued
that technological progress is achieved through
the implementation of effective economic policies
that ensure macroeconomic stability and promote
increased investment and productivity.
Since the advent of endogenous growth theory,
there have been advances and extensions of the
model proposed by Romer (1986) and Lucas
(1988), whose recent empirical work has focused
on analysis within the endogenous growth theoretical framework. The endogenous growth model is
typically augmented with a variable representing
exchange rate regime or volatility. Proponents of
the flexible exchange rate regime (Edwards and
Levy Yeyati 2005; Friedman 1953; Hoffmann 2007)
argue that this regime permits an economy to adjust
in response to external shocks with minimum output
losses. Consequently, these real external shocks have
differing impacts on the domestic and foreign economy. In a flexible exchange regime with sticky prices
and wages, the exchange rate tends to adjust to
correct the discrepancy between domestic and foreign prices in the presence of external shocks. This
has the effect of countering the adverse influences on
output.
However, a flexible exchange rate regime can be
accompanied by excessive exchange rate volatility,
which may be detrimental to macroeconomic stability and performance. A fixed exchange rate regime
reduces exchange rate uncertainty, which in turn
promotes macroeconomic stability and increases
international trade – key drivers of economic growth
(Frankel and Rose 2002). Nevertheless, as argued by
Obstfeld and Rogoff (1995), a fixed exchange rate
regime induces protectionist and noncompetitive
behaviour. For instance, fixed exchange rate regimes
may encourage speculative capital flows, moral
hazards and overinvestment in the domestic economy because of the implicit or explicit guarantee of
stable exchange rates making economic agents disregard potential exchange rate risks (Schnabl 2009).
This topic of optimal exchange rate policy continues
to generate debate.
In recent times, alternative approaches have
emerged exploiting the indirect links between
exchange rate volatility and growth. This literature
argues that exchange rate volatility can negatively
influence some key determinants of economic
growth, such as investment and trade. Excessive
exchange rate volatility may deter or delay investments, particularly when investment decisions are
irreversible and adjustment costs to exchange rate
volatility are high (Goldberg and Kolstad 1994). A
number of empirical studies provide evidence of a
negative impact of exchange rate volatility on investment (Aghion et al., 2009; Arratibel et al. 2011).
However, other studies have either found no effect
of exchange rate volatility on investment (Bleaney
and Greenaway 1998) or a positive effect on investment (Goldberg and Kolstad 1994). An increase in
exchange rate volatility may reduce international
trade as market participants direct their resources
to less risky economic activities (Clark 1973).
However, the higher risk resulting from exchange
rate volatility may provide new opportunities to
market participants and thereby increase trade. In
general, the literature does not suggest an unequivocal link between exchange rate volatility and trade
(McKenzie 1999).
In the context of the theoretical ambiguity regarding the effect of exchange rate volatility on economic
growth, several studies attempt to empirically
address this issue, but provide mixed results.
Bleaney and Greenaway (1998) find exchange rate
volatility is irrelevant in determining economic
growth, whereas other studies find that increased
exchange rate volatility leads to lower growth
(Arratibel et al. 2011; Boar 2010; Schnabl 2008,
2009). Evidence of a positive exchange rate volatility–economic growth relationship is also provided
by some (e.g., Mahmood and Ali 2011). However,
their findings may be due to the omission of other
determinants of economic growth. The mixed results
on the relationship between exchange rate volatility
and growth may be attributed to the role of countryspecific factors, including the level of financial development, human capital, physical capital and institutional settings (Schnabl 2008; Frankel 1999; Husain,
Mody, and Rogoff 2005).
Importantly, evidence for the effect of exchange
rate volatility on economic growth in Africa is very
sparse and the findings we do have are ambiguous.
Using a sample of sub-Saharan African countries,
Ghura and Grennes (1993) and Bleaney and
Greenaway (2001) find exchange rate volatility has
no significant effect on economic growth.
Conversely, Adewuyi and Akpokodje (2013) did
APPLIED ECONOMICS
find a significant positive effect of exchange rate
volatility on economic growth for a panel of
African countries during the period 1986–2011.
Exchange rate volatility exerted more significant
effects in non-francophone countries, including
Uganda, compared to francophone countries. Given
the inconclusiveness of findings on the effect of
exchange rate volatility on economic growth, this
study aims to provide new empirical evidence in
the context of Uganda, a developing country that
has experienced major institutional and economic
policy reforms and recurring economic instability.
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Uganda’s economy
Like many developing countries, Uganda has undergone a major economic transformation to develop
market-based institutions (Brownbridge, Harvey,
and Gockel 1998; Whitworth and Williamson 2010;
Katusiime 2015). Prior to the early 1990s, Uganda
operated under a system of direct controls on prices
and flows of goods and capital. Until 1992, the Bank
of Uganda (BOU) controlled the level and structure
of interest rates and for most of the 1970s and 1980s
nominal interest rates were held below the rate of
inflation. For instance, inflation averaged 103% during the period 1981–1990 while nominal lending and
time deposit rates averaged 31% and 24%, respectively. The negative real interest rates discouraged
the Ugandan public from holding deposits with
commercial banks while financial repression and
severe mismanagement led to a decline in the quality
of financial institutions. In addition, governmentowned institutions dominated most banking in
Uganda whereby of the 290 commercial bank
branches operating in the country in 1970, only 84
remained by 1987. Of these, 58 branches were operated by government-owned banks. Lending practices, although administered through domestically
owned banks, were highly influenced by government
and predominantly focused on promoting agriculture. A case in point is the rural farmers’ scheme of
the Uganda Commercial Bank. The number of parastatals also increased as the government nationalized
previously foreign-owned enterprises. Due to its
involvement in domestic production, the government became heavily involved in setting prices for
goods such as beer, salt, sugar and soap among
others. Further, under the foreign exchange control
2431
act of 1964, the public was forbidden to hold foreign
currency. There was an acute shortage of foreign
exchange during this period and exporters/importers
of commodities were required to deal directly with
the central bank which operated under various fixed
exchange regimes that were at odds with market
conditions. As a result of the controls in the goods
and financial markets, parallel/black markets for
goods and financial services developed.
The transition from a highly regulated economy to a market-based one can be traced back to
the late 1980s and early 1990s when after more
than a decade of political instability and economic decline, the government implemented
macroeconomic reforms aimed at returning the
economy onto a sustainable growth trajectory
with assistance from international donors, including the International Monetary Fund and the
World Bank (Kasekende, Atingi-Ego, and
Sebudde 2004; Whitworth and Williamson 2010;
Katusiime, Shamsuddin, and Agbola 2015a).
These reform efforts focused mainly on introducing market reforms and commitment to prudent
macroeconomic management (Kuteesa et al.
2009). Among the key reforms was the introduction of a floating exchange rate regime in 1993 as
part of extensive policies aimed at eliminating
market controls, thus resulting in the liberalization of interest rates, current and capital accounts
as well as the privatization of state-owned enterprises (Whitworth and Williamson 2010;
Katusiime, Shamsuddin, and Agbola 2015b). The
impact of these reforms has been a substantial
reduction in poverty and high and sustained economic growth, earning the country recognition as
one of the fastest growing economies on the
African continent. This in turn has exerted a
strong influence on development thinking and
international aid architecture in other developing
countries in Africa (Kuteesa et al. 2009).
III. Methodology
Model specification
The effect of exchange rate volatility on economic
growth is examined using an ARDL bounds testing
method proposed by Pesaran, Shin, and Smith
(2001). The ARDL approach is less restrictive,
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2432
L. KATUSIIME ET AL.
applicable to variables of any order of integration
and yields unbiased parameter estimates. Unlike a
vector autoregressive model, the ARDL approach
does not require all the variables to be integrated
of the same order. The bounds test is applicable
whether variables have mixed orders of integration
(Bahmani-Oskooee, Bolhassani, and Hegerty 2010;
Morley 2006) and as such this approach does not
require pretesting the order of integration of the
variables, especially when the computed Wald or
F-statistic falls outside the critical value bounds.
Therefore, this approach eliminates the uncertainty associated with low power of unit root
tests in pretesting the order of integration (2001).
The ARDL model also takes into account endogeneity (Harris and Sollis 2003; Pesaran and Shin
1998) and performs relatively well in small samples (Narayan and Smyth 2005; Pesaran and Shin
1999). A general ARDL relationship may be specified as follows:
ϕðL; pÞyt ¼ βi ðL; qi Þxit þ α0 zt þ εt
(1)
where L is the lag operator; ϕðL; pÞ ¼ 1 ϕ1 L ϕ2 L2 ϕ3 L3 ϕp Lp
and βi ðL; qi Þ ¼ βi0 þ
βi1 L þ βi2 L2 þ þ βiq Lqi and z is a vector of deterministic variables comprising the intercept, and exogenous variables with fixed lags; yt is the dependent
variable; xit represents explanatory variables in the
cointegrating vector; p and qi are the lag lengths; α0
represents coefficient on the deterministic variables
and ε is the error term. The error correction representation of Equation 1 can be expressed as follows:
Δyt ¼
k
X
βi0 Δxit þα0 Δzt i¼1
^qi 1
k X
X
^p1
X
θj Δytj
j¼1
(2)
βij Δxi;tj θð1; ^pÞECTt1 þεt
i¼1 j¼1
where Δ is the first difference operator; the error
correction term (ECT) is given by ECTt ¼
_0
Pp
Pk
yt i¼1 θi xit Ψ zt and θð1; ^pÞ ¼ 1 i¼1 θ
measures the quantitative significance of the ECT; θj
and βij are the parameters representing the model’s
speed of convergence to equilibrium.
The specific form of our base model for economic
growth (Model 1) can be expressed as follows:
ΔLRGDPCt ¼ α0 þ
n1
X
α1k ΔLRGDPCðtkÞ
k¼1
þ
n2
X
k¼1
þ
n4
X
α2k ΔLGKðtkÞ þ
n3
X
α4k ΔLVOLðtkÞ þ
k¼0
þ
n6
X
k¼0
α3k ΔLHK
k¼1
n5
X
α5k ΔLOPENðtkÞ
k¼0
α6k ΔLPSCðtkÞ þ
n7
X
α7k ΔINFðtkÞ
k¼0
þγ0 LRGDPCðt1Þ þγ1 LGKðt1Þ
þγ2 LHKðt1Þ þγ3 LVOLðt1Þ þεt
(3)
where L denotes natural logarithm, RGDPC denotes
economic growth and is derived as real GDP per
capita, GK denotes physical capital and is measured
as gross capital formation to GDP ratio, HK denotes
human capital and is measured by the human capital
index, VOL denotes exchange rate volatility and is
derived using a GARCH (1, 1) model as presented in
Equation 6, OPEN is trade openness and is derived
as the sum of total value of exports and imports to
GDP ratio, PSC denotes financial development and
is measured by private sector credit to GDP ratio
and INF denotes domestic inflation and derived
using the GDP deflator.
The capital, both human and physical, and technological progress are widely regarded as fundamental determinants of economic growth. An increase in
investment represents an increase in the stock of
physical capital and is expected to result in higher
economic growth. Further increases in human capital, often assumed to occur by increasing the population’s years of schooling, promote economic
growth by increasing the productivity of labour.
The important role of technological innovation is
due to the ability to introduce new and superior
products and processes, including institutions and
policies which enhance the productivity of factor
inputs. It is impossible to include all the other potential variables identified in the literature to capture
the effects of technological innovation, not least
because data do not exist for some potentially
important variables for the period of analysis covered in this study. Thus our empirical model investigates the impact of exchange rate volatility on
growth, controlling for variables suggested by the
theoretical literature based on data availability.
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APPLIED ECONOMICS
According to endogenous growth theory, the
effects of trade openness on economic growth are
ambiguous and depend on the magnitude and dominance of the different channels through which
openness impacts on growth. This includes, for
example, facilitating the transmission of technologies, allowing for specialization and access to markets (Eriṣ and Ulaṣan 2013). In endogenous growth
models, the effect of inflation on economic growth is
nonlinear, whereby the relationship between growth
and inflation may be positive at low inflation rates
and negative at higher inflation rates (LópezVillavicencio and Mignon 2011). Low inflation
rates preserve business optimism and thus increase
investment which boosts economic growth while
high inflation rates discourage investment and consequently economic growth. A sound and welldeveloped financial system is essential for growth.
The degree of financial development of an economy
may influence economic growth through its effects
on capital accumulation, particularly through facilitating savings mobilization and risk management.
Thus, it is expected that α2k > 0; γ1 > 0; α3k > 0;
γ2 > 0; α5k > 0; α6k > 0 and α7k < 0.
An increase in exchange rate volatility may
increase exposure of domestic firms to transaction,
translation and economic risks. However, while
exchange rate volatility may lead to variability in
international competitiveness of domestic firms, its
ultimate effect on economic growth depends on the
size of the nontradable sector, the extent to which
investments are irreversible in the tradable sector
and whether or not institutional settings are conducive to take advantage of exchange rate volatility.
Since the relationship between exchange rate volatility and growth is a priori indeterminate, it is
expected that α4k > or < 0 and γ3 > or < 0.
Two augmented versions of Equation 3 are also
estimated. First, an interaction term, the product of
exchange rate volatility and a dummy variable for
political instability, is added to Equation 3 to obtain
Model 2, which allows us to determine whether the
effect of exchange rate volatility on growth is moderated by domestic political instability. We expect
political instability to negatively influence the relationship between growth and exchange rate volatility. This is because political instability increases
uncertainty and risk, discourages physical and
human capital accumulation and engenders less
2433
efficient resource allocation by firms and governments (López-Villavicencio and Mignon 2011).
Second, Equation 3 is augmented by including the
real trade balance (referred to as Model 3). An
improvement in trade balance is expected to positively influence economic growth. This is because an
improved trade balance increases inflow of foreign
currency which stimulates enterprises and economic
growth. Conversely, a trade deficit may reduce
growth due to a decline in reserves and high interest
rates which discourage investment. The positive
impact of a surplus/deficit may also be intensified
through multiplier effects on consumption and
investment. In addition, where a deficit is unsustainable, it may lead to a currency crisis which may also
further dampen growth.
In Equation 3, αik represents the short-run effect
and γik represents the long-run effect, which are
normalized by α0 . The joint F-statistic proposed by
Pesaran, Shin, and Smith (2001) is used to test for
cointegration. The null hypothesis of no cointegration is tested as follows:
H0 : γ 0 ¼ γ 1 ¼ γ 2 ¼ γ 3 ¼ 0
(4a)
H1 : γ0 Þγ1 Þγ2 Þγ3 Þ0
(4b)
The null hypothesis is rejected if the computed
F-statistic is greater than the upper level of the
bound. The null hypothesis cannot be rejected if
the computed F-statistic lies below the lower
bound. The test is regarded as being inconclusive if
the F-statistic falls within the band.
Data and measurement of key variables
The data used for analysis are compiled from the
World Bank, the BOU and the Penn World
Table 8.0 (Feenstra, Inklaar, and Timmer 2013).
The analysis is based on annual data for the period
1960–2011. The sample consists of 52 yearly observations. The choice of the sample period and data
frequency is guided by data availability. Details of
the data and sources are summarized in Table 1
while Table 2 provides a summary of descriptive
statistics and the results of unit root tests.
In order to identify the effect of exchange rate
volatility on macroeconomic performance, it is
important to identify a suitable measure of exchange
2434
L. KATUSIIME ET AL.
Table 1. Data description and sources.
Variable
Definition
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LRGDPC
Natural logarithm of real GDP
per capita at constant 2005
USD
LGK
Natural logarithm of gross
capital formation (as % of
GDP).
LHK
Natural logarithm of index of
human capital per person,
based on years of schooling
(Barro and Lee 2013) and
returns to education
(Psacharopoulos 1994).
LVOL
GARCH measure of exchange
rate volatility and derived
using natural log of monthly
nominal exchange rate
LOPEN
Natural logarithm of trade
openness – derived as the
total value of sum of exports
and imports of goods and
services (as % of GDP)
LPSC
A measure of financial sector
development, calculated as
natural logarithm of
domestic credit to the private
sector (as % of GDP)
INF
Inflation rate, measured in
terms of GDP deflator in
percentage (2005 = 100); for
the period 1983–2011
obtained from WDI and that
for the period 1960–1982
obtained from Bigsten and
Kayizzi-Mugerwa (1999)
LTB
Natural logarithm of trade
balance – derived as real
external trade balance on
goods and services (as %
of GDP)
LVOL × POLS The interaction term is the
product of natural logarithm of
exchange rate volatility (VOL)
and political instability dummy
(POLS). The dummy variable
for political instability takes a
value of 1 during the political
instability of 1971–1986 and 0
otherwise
Source
Penn World Table 8.0 and
World Bank WDI databases
World Bank WDI database
Penn World Table 8.0
Bank of Uganda
World Bank WDI database
World Bank WDI database
World Bank WDI database
and Bigsten and KayizziMugerwa (1999)
World Bank WDI database
and Penn World Table 8.0
Own calculations
Table 2. Summary statistics.
Variable
LRGDPC
LGK
LHK
LVOL
LOPEN
LPSC
INF
LTB
Mean
6.66
2.55
0.40
0.03
3.58
1.81
35.71
−0.35
SD
0.23
0.41
0.18
0.10
0.30
0.49
54.19
0.40
Maximum
7.13
3.20
0.68
0.70
4.08
2.88
216.00
0.26
Minimum
6.28
1.72
0.16
0.00
2.83
0.96
−11.00
−1.10
Observations
52
52
52
52
52
52
52
52
McKenzie [1999]). In general, the suitability of any
measure adopted is informed by the scope of the
analysis.
Two types of exchange rate volatility measures
stand out in the literature: ex ante and ex post
volatility. The ex ante measures of volatility, also
known as implied volatility, are based on market
estimates of future volatility while ex post measures
use observed historical price information to calculate
volatility. The most common measure of ex ante
exchange rate volatility which captures market
expectations of how volatile prices will be in the
future is derived from traded foreign exchange
option prices (Bonser-Neal and Tanner 1996).
Arguably, given the absence of ‘options on US dollar
(USD)/Uganda shilling’, it is not possible to calculate
the implied volatility of the ‘Ugandan foreign
exchange rate’. Ex post exchange rate volatility can
be measured in terms of either realized volatility
(standard deviation of historical exchange rate
returns) or conditional volatility from a GARCH
model. The realized standard deviation of exchange
rate volatility is calculated from the moving subsamples of exchange rates.
Another challenge in the literature is choosing an
appropriate exchange rate variable to represent the
uncertainty component of the exchange rate.
Throughout the floating period, nominal and real
exchange rates appear to have moved together.
This co-movement is the result of the stickiness of
domestic prices. Thus, the distinction between real
or nominal measures of exchange rate volatility is
unlikely to significantly change the assessment of the
effects of exchange rate volatility (McKenzie 1999).
Financial time series such as exchange rate have
certain stylized characteristics such as volatility clustering and leptokurtic properties that the OLS estimator is unable to adequately capture. In this study,
the conditional volatility of the nominal exchange
rate is calculated from a GARCH (1, 1) model of the
following form:
Rt ¼ μ0 þ μ1 ðPOLSÞ þ εt
where εt jΩt1 ,N ð0; ht Þ
(5)
rate volatility. The main contentions in the debate
concerning the measurement of volatility arise from
the lack of a definitive theoretical approach to quantifying exchange rate risk. As a result, various techniques are adopted in the literature (for a review, see
ht ¼ θ0 þ θ1 ε2t1 þ θ2 ht1
(6)
h i
where Rt ¼ ln PPt1t , and where Pt denotes the
nominal Uganda shilling/USD exchange rate in
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APPLIED ECONOMICS
period t; μ0 is the expected exchange rate return in a
period of political stability; POLS is a dummy variable for political instability; μ0 þ μ1 is the expected
exchange rate return in times of political instability;
ht is the conditional variance of exchange rate
returns and θ0 ; θ1 and θ2 are GARCH model parameters. The annual estimate of exchange rate volatility is derived as an average of the monthly
GARCH exchange rate volatility estimates.1 We
apply White’s heteroscedasticity test to examine the
presence of heteroscedasticity. The White test statistic of 9.861 with a p-value of 0.002 indicates the
presence of heteroscedasticity and thus justifies the
use of a GARCH model for modelling exchange rate
volatility.
The nominal exchange rate data used are taken
from the BOU database. Before 1980, the Uganda
shilling was pegged to major currencies including
the USD, pound and special drawing rights before
1980 (Atingi-Ego and Sebudde 2004). In the latter
half of the 1970s, the domestic economy deteriorated
and there was a corresponding prolonged shortage
of foreign currency, which gave rise to a parallel
unofficial market with an overvalued Uganda shilling. In the decade 1980–1990, various exchange rate
regimes were implemented with the aim of restoring
macroeconomic stability. These were an independent float, a dual exchange rate regime, auction
system, adjustable independent peg and the discretionary crawl. The early 1990s saw the emergence of
the bureaux market, which helped to narrow the gap
between the official exchange rate and the bureau
rate (Kasekende and Ssemogerere 1994; Whitworth
and Williamson 2010). In 1993, Uganda adopted a
flexible exchange rate regime, and since then the
Uganda shilling has persistently depreciated against
the USD. The study uses data spanning the period
1960–2013 and includes the pre-float and floating
exchange rate regime eras. This is justified by the
frequent regime changes in the pre-float period.
In order to construct a continuous inflation series,
it was necessary to link the old GDP deflator series
obtained from Bigsten and Kayizzi-Mugerwa’s
(1999) for the period 1960–1982 with the current
index for the period 1983–2011 obtained from the
World Development Indicators (WDI). We create a
1
2435
new series of inflation based on the GDP deflator
with 2005 = 100 as the base year. This was achieved
by splicing the old series on to the new series at a
common point of time via a link factor. The index
was then rescaled to the new base year of 2005.
The measure of economic growth used in this
study is calculated using real GDP data from the
Penn World tables and population data from the
World Bank database as the natural log of real
GDP per capita at constant 2005 prices (in million
2005 USD). The interaction term combines the natural logarithm of exchange rate volatility and a political instability dummy, where by the dummy
variable for political instability takes on the value
of 1 during the political instability (1971–1986) and
zero if otherwise.
IV. Results and discussion
Unit root and cointegration tests results
The ARDL model does not require testing of the
orders of integration of variables. Nevertheless, for
bounds testing the dependent variable should be I(1)
and the regressors should be I(0), I(1) or fractionally
integrated (Pesaran, Shin, and Smith 2001). Table 3
reports the Augmented Dickey–Fuller Test (ADF)
(Dickey and Fuller 1979) and Phillip–Perron (PP)
(Phillips and Perron 1988) unit root test results for
all the variables employed in the analyses. The results
show that there is a mixture of I(1) and I(0) variables
employed in the analyses. Since the dependent
Table 3. Unit root test results.
ADF
Variables
LRGDPC
LGK
LHK
LVOL
LOPEN
LPSC
INF
LTB
Levels
−0.52
−1.06
−1.47
−5.82***
−1.76
0.11
−2.73
−2.26
1% Level
−3.568
5% Level
−2.921
PP
First difference
−3.73***
−9.05***
−1.85
−10.86***
−6.74***
−3.44**
−7.72***
−8.90***
Levels
−0.07
−1.06
0.85
−5.82***
−1.82
−0.25
−2.75*
−2.07
ADF
First difference
−3.77***
−9.12***
−1.85
−37.06***
−6.93***
−7.68***
−7.97***
−10.30***
PP
10% Level
−2.599
1% Level
−3.565
5% Level
−2.92
10% Level
−2.598
***, ** and * denote the series is stationary at the 1%, 5% and 10%
significance level, respectively.
The results based on a measure of realized volatility (LVOL) where the annual volatility is obtained as the sum of log squared monthly returns are fairly
similar and hence are not reported here. They are available from the corresponding author on request.
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2436
L. KATUSIIME ET AL.
variable is I(1) and none of the independent variables
appears to be integrated at an order higher than one,
we use the ARDL technique (see Bahmani-Oskooee,
Bolhassani, and Hegerty 2010; Morley 2006).
The Pesaran bounds test for cointegration was
carried out using the econometric software Microfit
5.0 based on Equation 3, referred to as Model 1. The
results are reported in Table 4. Given the small
sample size, the study draws on asymptotic critical
values provided by Narayan (2005) for small sample
sizes of 30–80 observations to assess the statistical
significance of the test statistics. Since the study is
based on annual data, we follow Pesaran and
Pesaran (2010) to estimate the conditional ARDL
model in Equation 3 with a maximum lag length of
1. The F-test statistic of 7.2 is greater than the upper
bounds of the critical values suggested by Narayan
(2005) at the 5% significance level. Thus, the null
hypothesis of no cointegration is rejected at the 5%
significance level, providing evidence of a long-run
equilibrium relationship between economic growth
and explanatory variables.
Table 4. ARDL bounds cointegration test results.
Dependent variablea
Model 1
LRGDPC
LGK
LHK
LVOL
Model 2
LRGDPC
LGK
LHK
LVOL
Model 3
LRGDPC
LGK
LHK
LVOL
F-statistic for Case II Intercept
no Trend F(4,136)b
Conclusion
7.23
1.98
0.62
3.80
Cointegration
No cointegration
No cointegration
No cointegration
6.85
1.30
0.58
1.94
Cointegration
No cointegration
No cointegration
No cointegration
6.99
1.97
0.61
4.14
Cointegration
No cointegration
No cointegration
No cointegration
Notes:
a
The cointegrating vector includes real GDP per capita (LRGDPC), gross
capital formation as a percent of GDP (LGK), human capital (LHK) and
exchange rate volatility (LVOL), while trade openness (LOPEN), financial
sector development (LPSC) and inflation (INF) are excluded from the
cointegrating vector, but included in the short-run dynamic models. In
Model 2, the interaction term (LVOL × POLS) is also included in the shortrun dynamics while the real trade balance (LTB) is included in the shortrun dynamics of Model 3. The F-test statistic indicates which variable
should be normalized when a long-run relationship exists between the
lagged level variables in the cointegrating vector. For each model, four
alternative cointegrating relationships are examined with different
dependent variables.
b
The relevant critical values are obtained from Table B.1 Case II: Intercept
no Trend when k = 3. They are 3.22 and 4.38 for the lower and upper
bound, respectively, at 95% significance level. Narayan (2005) provides a
set of critical values for small sample sizes ranging from 30 to 80
observations. When N = 55, the critical values are 3.41 and 4.62 for the
lower and upper bound, respectively, at 95% significance level.
We extend the analysis in Model 2 by including
an interaction term in Equation 3 to capture the
combined effects of political instability and
exchange rate volatility and in Model 3 by including the real trade balance. We perform the bounds
test for cointegration to confirm the presence of a
cointegrating relationship in Models 2 and 3. The
results, presented in Table 4, also yield conclusive
evidence of cointegration as indicated by the estimated F-statistics of 6.8 and 7.0 for Model 2 and
Model 3, respectively. These are higher than the
critical values reported by Narayan (2005) at the
5% significance level. Therefore, a cointegrating
relationship exists between economic growth and
explanatory variables.
Given the conclusive evidence of cointegration
for Models 1–3, we proceed to estimate their longand short-run dynamics, applying the Schwarz
Bayesian criterion (SBC) for selecting the optimal
lag length. The results are presented in Table 5.
The F-tests for the presence of a long-run level
relationship between economic growth and the
explanatory variables in the models are presented
in Panel B of Table 5 under model diagnostics. In
addition, critical values of those test statistics at
the 95% confidence level are presented. These critical values are applicable even when intercept
dummies are included in the model as regressors
(Pesaran and Pesaran 2010). The results indicate
conclusive evidence of cointegration for both
Model 1 and Model 2. The calculated F-statistics
of 12.1 and 16.6 for Model 1 and Model 2, respectively, are greater than their respective simulated
critical values at 95% confidence level. Thus, the
null hypothesis of no long-run equilibrium relationship between economic growth and the explanatory long-run variables in Models 1 and 2 is
rejected at the 5% significance level. In contrast,
for Model 3 the F-statistic of 5.8 lies between the
critical values of 4.9 and 6.1 at the 95% confidence
level, yielding an inconclusive cointegration test
result.
Discussion of results
Table 5 reports the results for alternative ARDL
models of economic growth. In view of the significant ECT, we report the results for Models 1–3
in line with Kremers, Ericsson, and Dolado (1992).
APPLIED ECONOMICS
2437
Table 5. ARDL model results.
Model 1
Regressors
ARDL (1,0,0,1)
Long run
1.512***
(3.79)
0.730***
(10.86)
0.071***
(2.96)
5.598***
(25.78)
Panel A
Intercept
LRGDPC (−1)
GK
ΔGK
HK
ΔHK
LVOL
ΔLVOL
LVOL(−1)
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LOPEN
ΔLOPEN
LPSC
ΔLPSC
INF
ΔINF
Model 2
Short run
0.264**
(2.15)
ARDL (1,0,0,1)
Long run
1.521***
(4.02)
0.730***
(11.42)
0.037
(1.39)
5.629***
(27.54)
0.139
(1.25)
0.071***
(2.96)
0.038
(0.80)
0.139
(0.87)
0.080
(1.65)
0.103***
(3.21)
−0.0002**
(−2.11)
0.275**
(2.56)
LVOL POLS
5.635***
(27.18)
0.087**
(2.25)
−0.006
(−0.31)
0.057
(1.05)
−0.024
(−1.18)
0.105***
(3.43)
0.103***
(3.21)
−0.0002**
(−2.11)
ΔLVOL POLS
0.052
(1.07)
0.502*
(1.85)
0.052
(1.07)
0.093**
(2.21)
−0.030
(−1.35)
−0.006
(−0.31)
0.388***
(8.06)
−0.0004***
(−3.19)
−0.001**
(−2.47)
−0.281**
(−2.35)
−1.039**
(−2.06)
0.103***
(3.19)
0.105***
(3.43)
−0.0004***
(−3.19)
−0.0002**
(−2.14)
0.018
(0.71)
ΔLTB
−0.270***
(−4.02)
−0.104
(−1.31)
−0.030
(−1.35)
0.357***
(6.01)
−0.001*
(−1.85)
0.103***
(3.19)
−0.0002**
(−2.14)
−0.281**
(−2.35)
LTB
ECT(−1)
0.199
(1.19)
0.057
(1.05)
1.339**
(2.37)
−0.024
(−0.31)
Short run
0.285**
(2.33)
0.275**
(2.56)
0.382***
(7.50)
−0.001*
(−1.81)
1.628***
(3.76)
0.711***
(9.80)
0.082***
(2.86)
0.080
(1.65)
0.485*
(1.70)
−0.089
(−1.11)
Long run
0.082***
(2.86)
0.294*
(1.85)
0.045
(0.95)
0.086**
(2.12)
−0.024
(−1.18)
ARDL (1,0,0,1)
0.037
(1.39)
0.038
(0.80)
0.045
(0.95)
Model 3
Short run
0.064
(0.75)
0.018
(0.71)
−0.289***
(−3.98)
−0.270***
(−4.23)
PANEL B
Model diagnostics
F-statistics5
95% Lower bound
95% Upper bound
Adjusted R2
SE of regression
SBC
Durbin–Watson statistic
Residual diagnostics
Serial correlation1
Functional form 2
Normality3
Heteroscedasticity4
F-statistics
12.10
4.34
5.79
0.99
0.03
97.95
2.17
0.61
2.147
0.361
2.551
1.102
443.953
[0.143]
[0.548]
[0.279]
[0.294]
[0.000]
16.58
4.31
5.78
0.99
0.03
99.21
1.78
0.64
0.404
2.363
1.805
0.052
437.810
[0.525]
[0.124]
[0.406]
[0.820]
[0.000]
5.85
4.87
6.05
0.99
0.03
96.30
1.62
0.60
1.863
0.554
2.142
1.508
390.080
[0.172]
[0.457]
[0.343]
[0.219]
[0.000]
Notes: The values in parentheses are t-ratios while probabilities are brackets.
*, ** and *** denote statistical significance at 10%, 5% and 1% significance levels, respectively.
The critical value bounds are computed by stochastic simulations using 2000 replications.
1
Breusch-Godfrey Lagrange multiplier test of residual serial correlation.
2
Ramsey’s RESET test for omitted variables/functional form.
3
Jarque–Bera normality test based on a test of skewness and kurtosis of residuals.
4
White’s test for heteroscedasticity based on the regression of squared residuals on squared fitted values.
5
If the F-statistic lies between the bounds, the test is inconclusive. If it is above the upper bound, the null hypothesis of no level effect is rejected. If it is
below the lower bound, the null hypothesis of no level effect can’t be rejected.
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2438
L. KATUSIIME ET AL.
Model 1 corresponds to specification of Equation
3, Model 2 is Model 1 augmented with the interaction term for exchange rate volatility and political instability and Model 3 is Model 1 augmented
with the real trade balance. The results for all
three models are qualitatively similar. In addition,
the signs of the coefficients and magnitudes are
similar. For instance in all models, both the shortand the long-run coefficients have the expected
signs with the exception of the trade openness
variable. The signs of the short- and long-run
coefficients are similar, but the short-run coefficients are less than long-run coefficients in absolute magnitude across all models.
In all three models, exchange rate volatility positively affects long-run economic growth and also
positively impacts on short-run economic growth
in Model 2. In addition, financial sector development positively affects economic growth in the
short and long run in all three models. Further, in
the short run, economic growth in Models 1 and 3 is
explained by gross capital formation. The coefficient
of ECT in all three models is negative and significant
at the 1% level. For example, the coefficient of ECT
is −0.27 in Model 1, implying that 27% of any
deviation from equilibrium is corrected within a
year.
Table 5 reports the short- and long-run elasticities for Models 1–3. In line with the predictions
of economic growth theories, the coefficient on
gross capital formation (as a percentage of GDP)
in Models 1 and 3 carries the expected positive
sign and is statistically significant at the 5% level
in both the short run and the long run. The corresponding elasticity coefficient indicates that, in
the short run, a 1% rise in the investment-to-GDP
ratio results in an increase in economic growth of
about 0.07 percentage points in the short run,
while a 1% increase in the investment-to-GDP
ratio increases economic growth by approximately
0.26 percentage points in the long run. Our results
are qualitatively similar to those of Gylfason and
Herbertsson (2001), Baldacci et al. (2008), Oketch
(2006), Arratibel et al. (2011) and Herrerias and
Orts (2011). Importantly, Model 2, which is augmented by the exchange rate and political instability interaction term, yields a coefficient on gross
capital formation as a percentage of GDP that
carries the expected sign. It is, however, statistically
insignificant in both the short-run and the longrun model specifications.
The coefficient of the measure of human capital
carries the expected sign in all models in both the
short-run and the long-run horizons, but the effect is
statistically significant at the 10% level only in the
long run for Model 2. The result indicates that a
higher stock of human capital leads to higher economic growth. In particular, a 1% increase in the
level of human capital results in an increase in economic growth of approximately 0.3 percentage
points. This finding is similar to the findings of
Baldacci et al. (2008), Aghion et al. (2009) and
Herrerias and Orts (2011) who found a positive
and statistically significant effect of education on
economic growth.
In general, exchange rate volatility has a positive
effect on economic growth. In the short run, the
elasticity of economic growth with respect to
exchange rate volatility is only significant in Model
2 at the 5% level, while in the long run, exchange
rate volatility exerts a statistically significant effect
on economic growth in all models. In the short run,
a 1% increase in exchange rate volatility results in
0.3% increase in economic growth, while in the long
run, a 1% increase in the level of exchange rate
volatility increases economic growth by approximately 0.5–1.3 percentage points. Our finding is
similar to that of Adewuyi and Akpokodje (2013)
for African countries, but contrasts with the negative
relationship between exchange rate volatility and
economic growth as reported by Ghura and
Grennes (1993), Schnabl (2008, 2009) and Arratibel
et al. (2011). The positive association between
exchange rate volatility and economic growth may
be due to the flexible exchange rate regime introduced in Uganda in 1993. Arguably, the flexible
exchange rate regime insulates the economy against
economic shocks resulting in lower output losses
(Edwards and Levy Yeyati 2005; Friedman 1953;
Hoffmann 2007).
Table 5 shows that the effect of exchange rate
volatility on economic growth is negatively moderated by political instability but the impact is only
marginal. The elasticity of exchange rate volatility
during periods of political instability is calculated
as the sum of the coefficient on foreign exchange
volatility and the coefficient on the interaction variable. In times of political instability, a 1% increase in
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APPLIED ECONOMICS
exchange rate volatility increases economic growth
by only 0.3 (= 1.339 – 1.039) percentage points in
the long run. The corresponding short-run elasticity
is only −0.006 (= 0.275 – 0.281) during an episode of
political instability. This finding is consistent with
the findings of Guillaumont, Guillaumont
Jeanneney, and Brun (1999), Gyimah-Brempong
and Corley (2005) and Aisen and Veiga (2013) who
provide evidence that political instability has an
adverse influence on economic growth. In addition,
Guillaumont, Guillaumont Jeanneney, and Brun
(1999) argue that political instability is often accompanied by ‘bad’ economic policies in African
countries.
The coefficient on trade openness variable has
an unexpected sign but is statistically insignificant
at the 5% level in all models. While a number of
studies have found that trade openness has a positive effect on economic growth (Adewuyi and
Akpokodje 2013; Aghion et al., 2009; Arratibel
et al. 2011), other studies have found no effect at
all (Eriṣ and Ulaṣan 2013) or a negative effect
(Montalbano 2011). The findings of this study
suggest that trade openness may not have stimulated productivity growth and therefore economic
growth in Uganda.
The level of financial development has a positive and statistically significant effect on economic growth in both the short and the long
run. This finding holds in all models. In the
short run, a 1% increase in financial development
is associated with a 0.1% rise in economic
growth, while in the long run, it leads to a 0.4%
rise in economic growth. These results are similar
to those of Aghion et al. (2009) and Huang and
Lin (2009).
Table 5 shows a negative and statistically significant effect of inflation rate on economic
growth. In the short run, a 1% increase in inflation
reduces economic growth by approximately 0.01
percentage points, while in the long run, a 1%
increase in the inflation rate results in a decline
in economic growth of between 0.03 and 0.05
percentage points. This implies that price instability endangers economic growth in Uganda. Our
results are comparable to those of Gylfason and
Herbertsson (2001), Schnabl (2009), Gillman and
Harris (2010) and Eriṣ and Ulaṣan (2013).
Inflation acts as a tax on physical and human
2439
capital, thus decreasing the rate of return on capital and ultimately lowering economic growth
(Gylfason and Herbertsson 2001). From Table 5,
we find a positive association between the real
trade balance and economic growth in both the
short and the long run, but these effects are statistically insignificant.
The coefficient of ECT is negative and statistically significant at the 5% level in all models.
Thus, the results demonstrate that Uganda’s economic growth trajectory moves towards a longrun steady state, although the speed of adjustment is very slow. It is estimated to be approximately 27–29 percentage points per year with the
full adjustment to equilibrium expected to take
about 3–4 years.
V. Conclusion and policy implications
Both the theoretical predictions and the empirical
evidence on the effect of exchange rate volatility
on economic growth are mixed. In addition, there
is a paucity of research on the experience of
African countries. This study provides new empirical evidence on the effect of exchange rate volatility on economic growth in a developing country
in transition, Uganda, within the ARDL cointegration theoretic framework. We find a long-run
equilibrium relationship between exchange rate
volatility and economic growth. Our empirical
results suggest that exchange rate volatility has
positive short- and long-run effects on economic
growth. This finding implies that the flexible
exchange rate regime which is accompanied by
increased volatility may have served as a buffer,
and thereby outweighing the adverse impacts of
exchange rate volatility on economic growth in
Uganda. We find that during periods of political
instability exchange rate volatility appears to exert
an adverse effect on economic growth largely due
to the increased uncertainty over policy decisions
and enforcement in the short run, although the
effect is reversed in the long run. In addition,
capital stock, human capital and the level of financial development positively affect economic
growth. Uganda’s government should promote
financial market development and formation of
human and physical capital to accelerate economic
growth.
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2440
L. KATUSIIME ET AL.
The empirical findings reaffirm the need for
further structural and institutional reforms in the
final sector to further promote financial sector development given its growth enhancing capability. Given
the negative effect of inflation on economic growth,
the Uganda government should pursue prudent
macroeconomic policies to curb inflation in order
to anchor inflationary expectations downwards. The
evidence that trade openness and real trade balance
have no effect on economic growth suggests the need
to appraise trade policies with the aim of improving
the country’s tradable sector competitiveness.
Finally, the evidence of a positive exchange rate
volatility–economic growth nexus implies that
Uganda’s flexible exchange rate regime has been
effective in stabilizing external shocks on the domestic economy. Given that exchange rate volatility
exerts a negative effect on economic growth during
episodes of political instability, there is the utmost
need for safeguarding political stability to ensure a
stable and sustainable economic growth trajectory in
Uganda.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by the AusAID [grant number
ADS1102456].
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