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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. Submit your article to this journal Article views: 70 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=raec20 Download by: [203.128.244.130] 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 Downloaded by [203.128.244.130] at 20:29 14 March 2016 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 Downloaded by [203.128.244.130] at 20:29 14 March 2016 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 Downloaded by [203.128.244.130] at 20:29 14 March 2016 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. Downloaded by [203.128.244.130] at 20:29 14 March 2016 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, Downloaded by [203.128.244.130] at 20:29 14 March 2016 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. Downloaded by [203.128.244.130] at 20:29 14 March 2016 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 Downloaded by [203.128.244.130] at 20:29 14 March 2016 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 Downloaded by [203.128.244.130] at 20:29 14 March 2016 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. Downloaded by [203.128.244.130] at 20:29 14 March 2016 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) Downloaded by [203.128.244.130] at 20:29 14 March 2016 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. Downloaded by [203.128.244.130] at 20:29 14 March 2016 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 Downloaded by [203.128.244.130] at 20:29 14 March 2016 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. Downloaded by [203.128.244.130] at 20:29 14 March 2016 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. 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