External debt and current account adjustments: The role of trade openness

Ibhagui, Oyakhilome Wallace Article External debt and current account adjustments: The role of trade openness Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ibhagui, Oyakhilome Wallace (2018) : External debt and current account adjustments: The role of trade openness, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 6, Iss. 1, pp. 1-42, https://doi.org/10.1080/23322039.2018.1446247 This Version is available at: https://hdl.handle.net/10419/194775 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 ECONOMIC METHODOLOGY, PHILOSOPHY & HISTORY | RESEARCH ARTICLE External debt and current account adjustments: The role of trade openness Oyakhilome Wallace Ibhagui 1 * Abstract:In this paper, we examine the various links among external debt, trade openness and current account in Sub-Saharan Africa, utilizing an approach that highlights current account from the perspective of saving and investment. We explore whether external debt aids the subsequent adjustment process of current account deficits in SSA. We also examine the role of openness in the adjustment process. Empirical analysis using large panel data samples of Sub-Saharan African countries between 1985 and 2013 shows that external debt mostly sets the tone for the subsequent adjustment of current account deficits in SSA. However, the current account deficits of countries with high openness expand significantly from increases in external debt. The results are robust to different time periods and econometric estimation techniques, the inclusion of other current account determinants as control variables and consideration of endogeneity. Subjects: Statistics & Probability; Economics; Finance Keywords: external debt; trade openness; current account balance; endogeneity JEL classifications: F30; F32; F40 *Corresponding author: Oyakhilome Wallace Ibhagui, University of Kent, Canterbury, UK E-mail: [email protected] Reviewing editor: Duncan Watson, University of East Anglia, UK Additional information is available at the end of the article ABOUT THE AUTHORS Oyakhilome Wallace Ibhagui am rounding off the correction of my PhD thesis at the University of Kent where I registered as a student in the fall of 2014. I am a student member of the macroeconomic research group of the School of Economics, University of Kent.My research activities involve doing empirical and theoretical research in international finance and financial economics. In international finance, I look at exchange rate, capital flows and their impact on African economies. In financial economies, I analyse the drivers of different asset classes such as equities and fixed income and the interrelationships of global financial markets. The research reported in this paper relates to providing a view onthe ongoing debate regarding whetherAfrica countries should expand their external debt levels, the effect on the adjustments in current account and the role of trade openness in the adjustment process. The contribution of this research is primarily to provide an empiricalbased perspective to the African countries that are looking to become more or less open to international trade and the global economy. PUBLIC INTEREST STATEMENT In the last few decades, current account, defined as therecord of a nation’s transactions with other countries has attracted considerable interest. This paper investigates the relationship among external debt, trade openness and current account in Sub- Saharan Africa. Specifically, this study examines the role external debt and openness plays in the adjustment process and behaviour of current account deficits in SSA. To address these issues, the study employed large panel data samples of SSA countries between 1985 and 2013. The empirical analysis utilized fixed effects, generalized methods of moments, pooled mean group and dynamic fixed effects models. Several findings emerge from our analysis. First, it is shown that external debt plays a significant role in the behaviour and adjustment process of current account deficits. Second, for countries with high degree of openness, current account deficits expand considerably from increases in external debt. The use of different econometric estimation techniques, time periods and the inclusion of other control variables did not invalidate these results. Received: 09 October 2017 Accepted: 11 February 2018 First Published: 01 March 2018 © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 42 Page 2 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 1. Introduction The past decades were marked by efforts to arrive at an understanding of the macroeconomic factors that determine current account balances, both in developed and developing countries. Arriving at this understanding is crucial because it provides a clear idea of helpful strategies for effective policy-making. Over time, several potential current account determinants have been studied in the literature in a bid to uncover the specific macroeconomic variables that determine the behaviour of current account. A number of theoretical models on the behaviour of current account have thus resulted from these studies (see Buiter, 1981; Glick & Rogoff, 1995; Sachs, 1981). However, majority of the models provide predictions of current account determinants which, when tested, yield inconsistent magnitude and direction of the relationships that current account bears with the identified determinants, Calderon, Chong, and Loayza (1999). This inconsistency in the theoretical models forced researchers to gravitate towards empirical analysis for definitive answers. The earliest empirical studies on current account determinants focused more on developed economies and much less on the developing economies of Africa, Asia and South America due to data constraints. Moreover, until recently, previous empirical studies mainly emphasized the analysis of the responses of current account balances to shocks in a specific macroeconomic determinant. This emphasis can be seen in several studies that deals with terms of trade shocks or fiscal policy shocks on current account balances using econometric techniques as in Marquez and McNeilly (1988), Rose and Yellen (1989) and Marquez (1990). The problem has also been studied in the context of real business cycle (RBC) models for both developed and developing countries as in Backus et al. (1994), Mendoza (1995) and Senhadji (1998). Also, the problem has been evaluated with impulse-response functions using dynamic stochastic general equilibrium (DSGE) models as in Leiderman and Razin (1991) and Frenkel, Razin, and Yuen (1996) and with techniques such as VAR and panel data analysis as in Glick and Rogoff (1995). Despite the significant contributions made by these studies to the existing current account literature, comprehensive cross-country empirical studies on current account determinants are still limited. Even where available, results often conflict and diverge. Furthermore, studies on current account adjustments and the factors that accelerate these adjustments are still relatively scarce. In addition to the limited literature on the drivers of current account in SSA, we believe research on current account adjustments in SSA is pressingly required and worth doing because the state of current account is a major indicator used to gauge the health, external position and future behaviour of SSA economies; it aids the decision-making process of policy-makers and partly forms the basis of the outlook and sovereign ratings ascribed to SSA countries. In a bid to empirically ascertain current account determinants, Debelle and Faruqee (1996) use a panel of 21 industrial countries over 1971–1993 and an expanded cross-sectional data-set that includes 34 industrial and developing countries to explain long-run variations and short-run dynamics of current account. They find that relative income, government debt and demographic factors play a significant role on the long-run variation of current account in the cross section, whereas reverse is the case for fiscal surplus, terms of trade and capital controls. Their investigation of the short-run effects further revealed that real exchange rate, business cycle and terms of trade are significant short-run determinants of current account. Calderon et al. (1999) provide a generalized characterization of the empirical linkages between current account deficits and an expanded set of macroeconomic variables for a panel of 44 developing countries over the period 1966–1995. Their main findings are that current account deficits in developing countries are persistent, albeit moderately. However, their results for the effects of external debt on current account are not robust and do not yield a statistically significant coefficient in the cross-country analysis. This highlights the divide on the effects of external debt on current account balances. Chinn and Prasad (2003) adopt a structural approach, which includes the roles of the fundamental macroeconomic determinants of savings and investment to investigate the medium-term determinants of current account using data samples for 18 industrial and 71 developing countries covering the period 1971–1995. They find that the initial stock of net foreign assets and government budget balances each has positive effects on current account balances. Shortly after the ASEAN financial Page 3 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 crises of 1997, the current account surpluses in affected Asian countries have grown in leaps and bounds, largely as a war chest to prevent the situation that triggered the crisis from reoccurring and as a hedge or safety-net against sudden reversals. Chinn and Ito (2007, 2008) provide an empirical explanation for the surge in current account surpluses in some Asian countries, mainly from deficits in 1997 to surpluses in subsequent years. They propose that standard current account determinants as in Chinn and Prasad (2003) cannot explain the surge in current account surpluses. Given this, they introduce indicators of financial development and legal environment likely to affect savings, investment and economic growth. Their results show that the interaction of legal environment with financial development plays a significant role in explaining capital outflows from Asia. Thus, their results suggest that lack of investment opportunities, rather than excess savings is responsible for the surge in current account surpluses in Asia, following the 1997 ASEAN financial crises and this leads them to reject the savings glut hypothesis. Calderon, Chong, and Zanforlin (2007) analyse the behaviour of current account deficits in developing countries. With respect to African countries, they find that there is not much persistence in current account deficits as is the case in the full sample of developing countries. However, their results show that external debt does not have a significant impact on current account deficits. This again, nicely summarizes the problem that exists in the literature: whereas some studies provide a strong basis for expecting external debt to impact current account deficits significantly, others show that the empirical evidence is fragile, to say the least. This ambiguous effect of external debt on current account is one of the areas explored in this paper and forms part of the motivation for this research. Although the highlighted previous studies have looked at the determinants of current account balances, none of them investigated or analysed the adjustments of current account balances in Sub-Saharan Africa. While it may seem natural to argue that high external debt shrinks surpluses and worsens current account deficits, a country’s capacity to understand its current account adjustment process and curtail the unfavourable impact of external debt on its current account might be enhanced or limited by its degree of openness. In an effort to examine the effects of external debt on current account adjustment in Sub-Saharan Africa, our research takes its cue from a lack of theoretical foundation to justify if, why and how external debt is involved in current account adjustment and also the little or no emphasis on the role openness plays in the adjustment process. In particular, we reproduce Bulut (2011) theoretical model that provides some guidance on how external debt functions in the current account adjustment process; we also emphasize how high openness to trade, despite its economic benefits might hinder the current account adjustment tendencies of external debt. The preceding arguments illustrate the role of openness in unlocking the current account adjustment properties of external debt. Despite this role of openness, the literature on current account adjustment appears to have ignored its indirect importance altogether. Figure 1 which shows data on external debt and trade openness provides some motivation for our view on openness. The aims of Figure 1 is to show that openness and external debt can be related so that relationships involving external debt could be plausibly altered or enhanced by openness. We use external debt as a share of GDP alongside a standard measure of openness, as used in the literature for the period 1985–2013. As the scatter plot in Figure 1 suggests, there is a positive relationship between the two variables. However, it is also apparent that a wide variation exists in both variables given their interaction with one another. Indeed, if the extent of openness plays an important role in influencing the effects of external debt on current account in SSA, one can expect countries with the same levels of external debt but different degree of openness to in fact have very different outcomes in terms of the adjustments of their current account balances. Our empirical analysis of current account adjustments is based on the saving–investment approach. In this paper, we argue that external debt is a significant reason why high capital mobility has not influenced current account in recent years. Our position is that previously high external debt is responsible for the narrowing of current account imbalances in sub-Saharan Africa. External debt Page 4 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 results in current account adjustments and increases correlation between savings and investment because high external debt forces SSA countries to look inwards and prune down the accumulation of further debt to finance domestic investments, and instead rely increasingly on domestic savings to finance domestic investment leading investment to depend on, and hence correlate with domestic savings. We thus show theoretically and empirically that external debt has a role to play in the behaviour and adjustments of current account in SSA. In the empirical analysis, a negative coefficient on external debt in current account regressions implies that external debt reduces current account imbalances in SSA either via a reduction in investment or an increased dependence of investment on domestic savings. This narrows the saving–investment gap, reduces the current account deficits and gradually results in current account adjustments. Our approach, which follows Bulut (2011), suggests that running high current account deficits increases the effective interest rate for countries, thus, SSA countries with high external debt face a positive spread over world real interest rate making it cost-ineffective for heavily indebted SSA countries reputed for low credit rating to run consistent current account deficits due to high costs associated with the accumulated debt to finance the deficits. We argue that the high costs lead to a decline in external debt accumulation which either slowdowns investment or increases correlation between investment and saving, resulting in a decline in current account deficits and causing current account deficits to gradually adjust upwards from the negative terrain towards the origin. This adjustment comes with a decrease in the persistence of current account deficits as foreign investors reduce their inflows for fear of debt default. However, the persistence begins to rise again as soon as considerable adjustments have been achieved and favourable domestic conditions—lower debt levels following perhaps deleveraging or debt forgiveness and better growth prospects—prompt foreign investors to view SSA markets as less risky, more attractive or less prone to a default. This leads to an increase in foreign inflows making it possible for SSA countries to finance deficits, fund new investments and thus run benign and expansive current account deficits. In our theoretical framework, our approach assumes a small open economy and that SSA countries can borrow externally to fund shortfalls in total income. Finances available to the representative agent thus come from total income generated from domestic goods and services and external debt. Furthermore, we assume that external debt, in addition to the usual interest costs, incurs transaction-related convex adjustment costs. The representative agent uses the external debt and total income to cover all expenses—interest and non-interest costs, including consumption and investment related costs. In this regard, our work differs from existing research which provides no theoretical motivation on current account determinants in Sub-Saharan Africa. Figure 1. External Debt and Openness (1985–2013). Note: Countries in this plot are the 30 SSA countries for which data are available. They constitute the data samples whose summary statistics are presented in Tables 2a and 2b. In accordance with the literature, openness enters into the model in logs. H denotes External Debt while C denotes openness. A more detailed graphical illustration of each variable is provided in Appendix 1. MWI KEN MRT BDI SDN TGO ZAM ETH BEN MDG BWA RWA NGA MUS SIE GHA LST CMR GMB CPVD COG SYC GAB MWI GHA GAB ETH MUS SDN CPVD ZAM SYC BEN BDI GIN BWA LST KEN MRT SIE CMR GMB RWA TGO MDG COG NGA 0100 200 300 400 H 1.2 1.4 1.6 1.8 2 2.2 C Page 5 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 Summarily, in this paper, we tackle two main problems. First, we investigate one implication of our theoretical model which predicts that external debt adjusts current account deficits in SSA. Second, we examine whether the external debt of more open SSA countries significantly reverses the current account adjustment process. To do this, we interact openness with external debt and study the impact of the resulting variable on current account adjustments. In specifying our empirical model, we draw on current account determinants implied in our theoretical model and we also follow Calderon et al. (1999, 2002), Bulut (2011) and to some extent, Chinn and Prasad (2003). This ensures our regressions include a comprehensive, but not exhaustive list of control variables identified in the literature as current account determinants. Our major results are in two fold. First, we find that external debt mostly plays an important role in the adjustment process of current account deficits in Sub-Saharan Africa. Second, openness to trade, despite its benefits, reverses results significantly. In particular, countries with high openness experience current account deficit expansions following a rise in external debt. We find that this result holds true for different time periods and after controlling for other current account determinants and also after addressing concerns regarding joint endogeneity of explanatory variables and after using different techniques of estimation inclusive of fixed effects, generalized methods of moments, pooled mean group and dynamic fixed effects models. The rest of the paper is organized as follows—theoretical background that introduces the convex costs of external debt into the incomplete small open economy models are provided in Section 2; data samples are defined in Section 3; empirical results are presented and discussed in Section 4 while Section 5 presents the conclusion. 2. The model The model presented in this section fully follows Bulut (2011). Although we utilize this model as a guidance for understanding and interpreting our results, our objective in this paper is not to test the implications or predictions of the variables in the model or any other model for that matter as no existing single theoretical model can capture the entire range of variables and relationships that constitute our focus in this paper. Instead, we are primarily more keen on providing an empirical characterization of the current account adjustment process in SSA which could set the stage for building more structured and testable models of current account adjustments to aid subsequent theoretical, empirical and policy related work. Consider an incomplete small open economy (SOE) that produces goods and has a representative infinitely-lived household that consumes goods according to established preferences represented by a utility function U( Ct ) defined on consumption Ct . The economy generates income Yt from domestic production and finances consumption Ct and investment It with no government intervention in the decision-making. Suppose the economy can borrow with minimal restrictions via issuing bonds in the global financial markets. The economy has an external debt stock or issued bonds outstanding whose cumulative value at current time t is Bt . The non-time varying interest on the outstanding debt is r, so that the interest cost of servicing the debt becomes rBt . In instances, where the income Yt does not fully finance all of consumption Ct , investment It , and interest cost rBt on outstanding debt, the economy approaches the global market and issues bonds which raise the value of its external debt stock to Bt + 1 by the start of the next period. Suppose, in addition, the economy now attracts a convex external debt holding cost ( 𝜔 2 ) B2 t+ 1 on its external debt which is interpreted as the cost of holding external debt or convex external debt holding cost, where ω > 0 shows credit worthiness effects and represents external debt holding cost parameter, then the total expenditure the economy incurs is the sum of expenditures on consumption, investment, interest on debt and convex cost. This is given by If income Yt generated from production fully covers all costs, then Y t≥Ct+It+rBt+ ( 𝜔 2 ) B2 t+ 1 and we are done. However, in our case, there is need to borrow because the income generated does not cover the costs in full, so Yt < φ or 𝜙 = Yt + 𝛿 , for some 𝛿>0 , where δ is the additional cost not (1.1) 𝜙 =Ct+It+rBt+ (𝜔 2) B2 t+ 1 Page 6 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 covered by Yt . We argue that δ is financed by the flow of debt which increases the external stock from Bt to Bt + 1. Thus, δ must equal B t +1 − Bt , and the intertemporal budget constraint of the representative agent who borrows internationally becomes where C t+It+rBt+ ( 𝜔 2 ) B2 t+1−Yt> 0 The infinitely-lived household receives utility from consumption Ct . The lifetime utility function is expressed as The intertemporal maximization problem of the infinitely-lived household is to choose a consumption path that maximizes lifetime expected utility. Thus, the household solves Following Bulut (2011), we assume there is an aggregate production function F that represents constant returns to scale technology F( AKt ) =AF(Kt ) with the standard capital accumulation Kt, is homogenous to degree one, with given labour and total factor productivity A > 0 parameters and zero depreciation δ = 0 of capital stock. Thus, Under this assumption, the intertemporal maximization problem for 0 < β < 1 becomes Now suppose the infinitely-lived household continues to derive utility from consumption Ct but now experiences disutility from the given labour L supplied within the economy. To introduce openness into the model, we take a cue from Lane and Milesi-Ferretti (2004) and assume the lifetime objective function of the infinitely-lived household, which is to be maximized, is modelled as and that the aggregate consumption index Ct is a composite of traded CTt and nontraded CNt goods, defined as where 𝜌 measures the constant elasticity of substitution between traded (CTt) and nontraded (CNt) goods, and 𝜇 is the share of tradable goods in the domestic consumption basket, labour is mainly supplied to the nontraded sector, σ is the constant relative risk aversion parameter and β is the household’s discount factor. All parameters are positive and the last term in Equation (1.8) captures the disutility, in terms of reduced leisure of supplying labour. Specifically, the last term is the disutility of work effort, where ϕ > 0 represents the inverse of the Frisch elasticity of labour supply with (1.2) 𝛿 =Bt+1−Bt=𝜙−Yt=Ct+It+rBt+ (𝜔 2) B2 t+1−Y t (1.3) ∞ ∑ t=0 𝛽tU ( Ct ) ,0<𝛽< 1 max Ct E0 ∞ ∑ t=0 𝛽tU ( Ct ) ,0<𝛽< 1 (1.4) s.t Ct=Bt+1+Yt−It−(1+r)Bt− (𝜔 2) B2 t+ 1 (1.5) Yt =AF ( K t) ,K t+1 =K t +(1−𝛿)I t (1.6) max Ct E0 ∞ ∑ t=0 𝛽tU ( AF ( Kt ) +Bt+1−Kt+1+Kt−(1+r)Bt− ( 𝜔 2 ) B2 t+1 ) (1.7) E 0 ∞ ∑ t=0 𝛽t [ 𝜎 𝜎−1C 𝜎−1 𝜎 t−𝜗 1+𝜑 L1+𝜑 t ] ,0<𝛽<1 and 𝜎,𝜗,𝜑> 0 (1.8) C t= [ 𝜇 1 𝜌C 𝜌−1 𝜌 Tt +(1−𝜇) 1 𝜌C 𝜌−1 𝜌 Nt ]𝜌 𝜌−1 ,𝜌> 0 Page 7 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 respect to real wage. Meanwhile, the price index corresponding to the consumption index is the consumption price index Pt given by where PNt is the price of nontradable goods. From (1.8) and (1.9), the demand for traded and nontraded goods as a function of the consumption index is given by 2.1. Optimality conditions under the assumptions of no economic uncertainty To simplify the model and option tractable optimality conditions, we first assume the economy faces no uncertainty, which makes for a deterministic case. Then we obtain the optimality conditions under this assumption. To achieve this, we derive the capital, bond and consumption Euler equations associated with the optimization problem using the value function approach which relies on Bellman dynamic optimization. Capital-Euler Equation The Bellman equation, or value function, associated with the optimization problem in (1.3)–(1.6) can be written as Under the assumption of no uncertainty in the economy, we have Bond-Euler Equation Differentiating the right and left hand side of the value function with respect to Bt+1 and Bt respectively gives the bond equation and envelope condition as Plugging the envelope condition into the bond equation yields the Bond-Euler equation We assume the economy has a perfect foresight, so variables are deterministic. This implies 𝔼 t [ U �( Ct +1)] = U�( Ct +1) . Thus, the Bond-Euler equation, in the current period, becomes (1.9) P t= [ 𝜇+(1−𝜇)P1−𝜌 Nt ]1 1−𝜌 , (2.0) C Tt =𝜇 ( 1 P t)−𝜌 Ct,CNt =(1−𝜇) (P Nt P t)−𝜌 Ct , (2.1) V( Kt,Bt ) =max { Bt +1 ,Kt +1}{ U ( Ct ) +𝛽𝔼t [ V(Kt+1,Bt+1) ]} (2.2) { U� ( Ct ) =𝛽 ( AF� ( Kt+1 ) +1 ) U� ( Ct+1 ) U� ( Ct−1 ) =𝛽 ( AF� ( Kt ) +1 ) U� ( Ct ) (2.3) ( 1−𝜔Bt+1 )( AF� ( Kt+1 ) +1 ) U� ( Ct+1 ) +𝔼t [𝜕V(K t+1 ,B t+1 ) 𝜕B t+1] = 0 (2.4) 𝜕V ( Kt+1,Bt+1 ) 𝜕B t+1 =− (1+r)U� ( Ct+1 ) AF �(Kt+1)+1=(1+r)𝔼t [ U �( Ct+1 )] ( 1−𝜔B t+ 1 ) U� ( C t+ 1 ) AF � ( Kt ) +1= (1+r) ( 1−𝜔Bt ) (2.5) Page 8 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 and In the Bond-Euler equation, AF′( Kt ) represents the marginal productivity of domestic physical capital which essentially equates effective domestic interest rate. When external holding cost ω is zero, so that decisions on household investment and holdings of domestic physical capital is dictated by the exogenous world interest rate, then we have that AF�( Kt ) +1=1+ r or AF�( Kt ) =r which refers to the standard steady state. In this case, the effective domestic real interest rate equates the prevailing world real interest rate. Thus, Equation (2.6) implies that, with non-zero external holding cost, the economy deviates from steady state, and externally indebted SSA countries face an effective interest rate higher than the prevailing world real interest rate. As countries increase external debt, the value of the external convex cost ωBt increases and so 1 − ωBt shrinks. For a given level of world interest rate r, this increases the domestic effective interest rate AF′( Kt ) , and raises the overall cost of capital. Thus, the domestic effective interest rate increases as countries become increasingly indebted externally and incur non-zero convex costs of external debt, since 𝜕 AF �( Kt ) 𝜕B t > 0 . As effective interest rate increases, it becomes more and more expensive to service external debt and this decreases the demand for external debt. A decrease in demand for external debt, ceteris paribus, reduces investment and consumption incentives. For a given level of output, the decline in consumption increases savings. Together with a reduction in investment, the increase in savings adjusts current account deficits upwards, towards the origin, from the negative domain, thereby narrowing the current account deficits and achieving some degree of balance or adjustment. The empirical analysis tests the implications of the predictions of this model. From (2.2) and (2.6), we have which represents how the introduction of external convex holding costs alters the relationship between current and future marginal utility of consumption. For convenience, and as a first pass, we follow the literature and assume, without recourse to the two sectors of the economy, a logarithmic utility function U( Ct ) =ln C t , i.e. limit of (2.7) as σ → 1, and a constant discount factor, 𝛽(1+r)=1, so that the representative agent maximizes a logarithmic utility function and the desire to borrow and lend in the steady state is ruled out. Thus, in the case where variables are deterministic, the Euler equation in (2.7) becomes and where ΔCt = ln Ct , ln 𝛽(1+r)=0 and ln ( 1−𝜔Bt ) =−𝜔B t for 0 < ( 1−𝜔Bt ) ≤ 1 . Equation (2.8) generates some sort of consumption tilting effect for externally indebted SSA countries. It predicts consumption growth increases intertemporarily when external debt increases. The optimality conditions characterizing our theoretical model are summarized below as (2.6) 𝜕 AF �( Kt ) 𝜕Bt =𝜔(1+r) ( 1−𝜔B t) 2>0, 𝜔> 0 (2.7) {( 1−𝜔Bt+1 ) U �( Ct ) =𝛽(1+r)U �( Ct+1 ) ( 1−𝜔B t) U� ( C t−1) =𝛽(1+r)U� ( C t) C t C t−1 =𝛽(1+r) ( 1−𝜔B t) (2.8) ΔCt =𝜔 Bt Page 15 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 Bulut (2011) argues that the final effect of high debt on current account balance takes time, such as one period before, to be realized. This informs why we have included the lagged value of external debt in our regression. According to our regression, we find no such significance of lagged external debt on current account. Instead, we find that the contemporaneous impact of external debt on current account deficits completely captures all the external debt effects, given that the coefficient of lagged external debt is not only small and economically meaningless but is also insignificant. We next look at the openness channel through which external debt adjusts current account deficits. The regressions in Table 3 examine the adjustment properties of external debt on current account balance through the trade openness channel. We interact external debt with openness and use the resulting variable as a regressor to test for the significance of trade openness in the current account adjustment process associated with external debt. To ensure the interactive term between external debt and openness does not proxy for either external debt or trade openness, both variables were included in the regression independently. Thus, we perform the following regression: As shown in Table 3, the Hausman test continues to signify that fixed effects technique is the more appropriate estimation for the regression. This yields an interactive term that turns out positive and significant in all columns and for all time periods for results obtained via fixed effects and reported in columns (1F), (2F) and (3F). Each regression uses a slightly different time period and hence, data samples differ slightly in observation from one regression to another. Column (1F)/(1R) uses the full sample, i.e., 1980–2013, column (2F)/(2R) uses partial samples, i.e. 1985–2008, while column (3F)/ (3R) uses partial samples spanning 1990–2013. Our main result in this section is that the interactive term is significant at the 5% level for the entire range of time periods and control variables used. External debt is now conditionally significant in all time periods, albeit at varying levels of significance, and continues to adjust current account deficits in SSA. The significance of the interactive term may in part be due to its capturing an indirect burden of external debt on current account balance in Sub-Saharan Africa—external debt could trigger current account adjustments, but the adjustment process could be impaired in the presence of high openness, leading instead to a widening of current account deficits in Sub-Saharan Africa. Interestingly, the coefficient of external debt displays considerable variation in its level of significance even within the same sample of countries and control variables as the time period changes— clearly supporting our decision for looking at a range of different time periods rather than just one time period. Table 2b also reports (a) the joint significance test of openness with the interaction term and (b) the joint significance test of external debt with the interaction term. For all of the time periods considered, the tests confirm the importance of trade openness and external debt as well as the control variables. In particular, the hypothesis that the coefficient of external debt and the interaction term is zero is rejected, further supporting the finding that external debt is an important factor in the adjustment process and, in the presence of high openness, the adjustment of current account deficits could well be reversed. Also, the hypothesis that the coefficient of openness and the interaction term is zero is rejected. Both rejections are at the 5% level. The rejections would appear to be stronger for the period 1985–2008 given that the coefficients of the interactive terms in these regressions also report the highest t-statistics compared with the counterparts in the other columns. In all of these, the interaction between external debt and openness remains robust. (3.2) CA it = ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ 𝛿i+𝜗1EDit +𝜗2EDit−1+𝜗3 � EDit ×TOit � + 𝜗5TOit +𝜗5CONTROLS +𝜀it Page 16 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 3.4. Endogeneity issues In the empirical analysis performed thus far, there has been no discussion on the possibility of problems arising from joint endogeneity. Empirically, it is likely and plausible that the current account determinants considered are jointly endogenous in the sense of being correlated with the error term and the presence of reverse causality. This potentially could lead to overstatements of the effects of each of external debt and openness as well as their interaction on current account balance. Therefore, following Calderon et al. (2001), we specify a dynamic panel regression model that (1) allows for joint endogeneity of variables; (2) includes an unobserved country-specific factor that correlates with the hypothesized current account determinants and (3) contains lagged values of the Table 3. Current account balance and external debt: The role of trade openness: fixed effects (F) and random effects (R) *Indicate both statistical significance at 10% significance level and p<0.1. **Indicate both statistical significance at 5% significance level and p<0.05. ***Indicate both statistical significance at 1% significance level and p<0.01. (1)F (2)F (3)F (1)R (2)R (3)R Period 1985–2013 1985–2008 1990–2013 1985–2013 1985–2008 1990–2013 Observations 741 635 628 741 635 628 ED/GDP −0.007* −0.0060** −0.0081* −0.0065 −0.0432* −0.008* (1.73) (2.25) (1.78) (1.60) (1.76) (1.75) ED/GDP × Openness 0.00446** 0.0365** 0.00506** 0.0045** 0.0319** 0.0548** (2.04) (2.47) (2.06) (2.04) (2.30) (2.19) l.ED/GDP 0.004 −0.0020 0.0053 0.0045 −.00366 0.005864 (1.06) (0.51) −1.19 (1.12) (0.91) (1.31) l.ED/GDP × Openness −0.0022 0.0011 −0.0028 −0.0024 .0021341 −0.0032 (0.52) (1.20) (1.12) (1.00) (1.35) Rel. income −0.241** −0.4394*** −0.3412** 0.0422 0.0482 0.0705 (2.09) (2.95) (2.39) (0.74) (0.80) (1.20) REER −0.2842** −0.3120** −0.3800** −0.327** −0.3051** −0.4836*** (2.09) (2.25) (2.29) (2.72) (2.35) (3.12) Openness −0.4991** −0.7044*** −0.5856** −0.4834*** −0.5694*** −0.5701*** (2.59) (3.18) (2.44) (2.89) (3.03) (2.92) (0.42) (0.80) (0.59) (0.11) (0.83) (0.01) Domestic growth −0.026 0.0022 0.00273 0.0021 0.0079 0.0018 (1.10) (0.84) (0.95) (0.86) (0.29) (0.61) Int. aid flows −0.1401 −0.0928 0.0203 −0.4057*** −0.4079** −0.4669019** (1.07) (0.64) (0.12) (3.52) (3.33) (3.18) Terms of trade 0.0073 0.1344 −0.0676*** −0.02935 0.0607 −0.0958 (0.06) (0.94) (4.42) (0.25) (0.45) (0.67) Government consumption 0.02160*** 0.0236*** 0.0263*** 0.0138*** 0.01265** 0.0142*** (4.03) (3.52) (4.13) (2.98) (2.34) (2.77) Age dependency −0.0044 0.0184 0.0105 −0.0126 −0.0083 −0.0001 (0.17) (0.50) (0.30) (0.76) (0.46) (0.04) World interest rate 0.0055 0.0028 0.0046 0.01597** 0.0109 0.0142* (0.65) (0.29) (0.49) (2.10) (1.19) (1.66) R 2 0.0139 0.0465 0.0313 0.1925 0.2014 0.1962 F-stat [p-value] 0.0000 0.0005 0.0002 0.0000 Hausman [p-value] 0.0000 0.0000 0.0000 0.0000 Page 17 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 dependent variable, being lagged current account. In this instance, the Generalized Method of Moments (GMM) estimator for dynamic models of panel data is employed for the econometric analysis. This estimation technique not only allows for the use of instruments to deal with issues of endogeneity of explanatory variables and unwanted correlations of error terms and differenced lagged dependent variables, it also aids in analysing persistence of the dependent variable as well as in estimating long- and short-run effects of specific variables on the dependent variable. Furthermore, in this paper, it provides a means to ascertain whether results obtained thus far are robust to different estimation techniques. For identification, we follow Anderson and Hsiao (1982), Arellano and Bond (1991) and use as instruments the lags of potential endogenous and exogenous regressors because they are expected to satisfy the exclusion restriction hypothesis—they are correlated with the potential endogenous regressors but uncorrelated with the error term, so that their impact on current account, wherever they occur, can only operate through the variables that they are instrumenting. The validity of these instruments is further accentuated by the fact that the requirement of no second order serial correlation in the error term of the differenced equation, as in Arellano and Bover (1995), is satisfied. We specify two dynamic panel regression models—one without the interaction term, wherein the openness (TO) variable is contained in the set of controls, and the other with an interaction term in which the openness variable is spelt out. Both regressions are specified below as where CAit − 1 is the lagged current account variable, αi is the fixed effect while 𝜀it is the idiosyncratic error term. The regressors are as defined in the previous section. Long-run effects of regressors on current account is then calculated as 𝜃 1−𝜃 ,i∈ ℕ . Following Calderon et al. (1999, 2002), Arellano and Bond (1991) and Arellano and Bover (1995), we employ a Generalized Method of Moments (GMM) procedure to generate consistent and efficient estimates of the coefficients of our variables of interest. The consistency of GMM estimator relies on the validity of lagged values of the explanatory variables as appropriate instruments in the current account regressions presented in (3.3 and 3.4). In order to address this issue of instrument validity, two core specification tests are considered. The first is the Sargan test of over-identifying restrictions, which tests the overall validity of instruments. The null hypothesis for this test is that the instruments are valid and over-identifying restrictions exist. Thus, failure to reject the null hypothesis (large p-values) gives support to the model and implies that the number of instruments used in the estimation is appropriate for the model. The second test is a test for serial autocorrelation. The null hypothesis in this case is that the error term is not serially correlated. Non-rejection of the null hypothesis (high p-values) implies that serial correlation does not exist, and this holds true whether in first, second or third order. When the test fails to reject the null hypothesis of the absence of second-order serial correlation, we conclude that the original error term is serially uncorrelated and continue with the GMM estimation. Tables 4a and 4b report the GMM results obtained through this procedure, as shown below. As shown in Table 4a, we find that after controlling for endogeneity, external debt becomes even more strongly significant in most of the sample periods and we continue to find a negative relationship between external debt positions and current account deficits in SSA. The effect of world interest rate on current account deficits displays the expected ambiguity in direction. In particular, we find that world real interest rate bears a positive relationship with current account deficits in the second sample period, 1985– 2008, but the relationship becomes negative when the sample period is altered to 1990–2013. The negative relationship suggests that an increase in world interest rate impacts savings and investment in a way that leads to a decline in current account deficits; a positive relationship implies current account deficits expanded following a rise in world real interest rate. (3.3) CAit =𝛼 i +𝜗 1CAit−1 +𝜗 2EDit + EDit−1 +𝜗 3CONTROLS +𝜀 it (3.4) CAit =𝛼 i +𝜗 1 CA it−1 +𝜗 2 ED it +ED it−1 +𝜗 3( ED it ×TO it) +𝜗 4 TO it +𝜗 5 CONTROLS +𝜀 it Page 18 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 In all sample periods, we find that the significant relationship between real effective exchange rate (REER) and current account deficits is not consistent with the predictions of the Mundell-Fleming model. Our results suggest that a fall in the real effective exchange rate has the effect of expanding current account deficits. In particular, a 10 per cent depreciation of the real exchange rate leads to an average increase in current account deficits of 1.95 percentage points across the three sample periods. Thus, we continue to obtain some evidence in support of the J-curve hypothesis. For terms of trade, we find a positive and significant relationship between terms of trade and current account deficits in most sample periods, a result which again is at variance with the Harberger–Laursen– Metzler effect which proposes that terms of trade bear a negative relationship with current account deficits. Where significant, our results suggest that a 10 percentage point increase in terms of trade heightens current account deficits by about 1.56 percentage points. The hypothesis of stages of development continues to receive support even after controlling for endogeneity. The results in Table 4a show that relative income has a negative and significant effect on current account deficits. That is, a country’s current account deficits decrease as the country becomes relatively developed and its per capita income approaches that of more developed economies. This finding is significant, economically and statistically, across all time periods of samples considered which gives complete support to the stages of development hypothesis is SSA. Table 4a. Current account balance adjustment and external debt—GMM *Indicate statistical significance at 10% significance level; p<0.1. **Indicate statistical significance at 5% significance level; p<0.05. ***Indicate statistical significance at 1% significance level; p<0.0.01. (1) (2) (3) Period 1985–2013 1985–2008 1990–2013 Observations 687 580 571 l.CA 0.7662*** 0.0119*** 0.7080*** (114.92) (3.23) (103.02) ED/GDP −0.0003*** −0.0008*** −0.0001 (3.24) (7.45) (0.55) Relative income −0.1054*** −0.2305*** −0.0758** (3.93) (7.15) (1.99) REER −0.1834*** −0.2170*** −0.1839*** (5.34) (5.20) (3.66) Openness −0.2712*** −0.3073*** −0.3147*** (6.36) (10.80) (5.49) Domestic growth 0.0029*** 0.0029*** 0.0036*** (4.12) (3.01) (3.65) Int. aid flows 0.0299 0.1213** −0.0164 (1.25) (2.35) (0.56) Terms of trade 0.1015** 0.04187 0.2075*** (2.18) (0.83) (4.02) Government consumption 0.0026*** 0.0051*** 0.001 (2.69) (10.85) (0.89) Age dependency −0.0148 −0.0022 −0.0144 (1.62) (0.13) (1.37) World real interest rate −0.0016 0.0051*** −0.0044** (1.11) (3.63) (2.58) LR impact of ED on CA −0.0013 −0.001 −0.0003 LR impact of Openness on CA −1.1791 −0.3110 −1.0490 Page 19 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 As before, we assess the relevance of demographics on current account deficits using age dependency ratio. Despite obtaining consistently negative estimated coefficients in all sample periods as shown in Table 4a, we find that these coefficients are not statistically significant. Thus, we conclude that changes in demographics do not significantly accelerate changes in current account deficits, though their effects on savings are well-established in the literature. Again, the degree of Table 4b. Current account balance and external debt: The role of trade openness—GMM Notes: In columns (1)–(3), the variables which are taken as current account determinants are instrumented and this reduces the sample size in relation to the previous sample size which ignored the possibility of joint endogeneity of the independent variables. Lagged external debt, as external debt variable, remains insignificant in all preceding regressions, so we drop it henceforth. *Indicate both statistical significance at 10% significance level and p<0.1. **Indicate both statistical significance at 5% significance level and p<0.05. ***Indicate both statistical significance at 1% significance level and p<0.01. (1) (2) (3) Period 1985–2013 1985–2008 1990–2013 Observations 687 580 571 l.CA 0.7270*** 0.8931*** 0.6819*** (14.62) (12.94) (110.21) ED/GDP −0.0070*** −0.0041** −0.0052** (3.43) (2.88) (2.84) ED/GDP × Openness 0.0038*** 0.0021** 0.0031*** (3.55) (2.78) (3.23) Rel. income −0.1659*** −0.2187*** −0.1712*** (4.01) (4.25) (2.22) REER −0.2060** −0.0707 −0.1713*** (2.09) (0.44) (3.12) Openness −0.5507*** −0.4795** −0.4748*** (4.15) (3.26) (3.62) Domestic growth 0.0035 0.0019 0.0040* (1.87) (1.52) (2.81) Int. aid flows 0.0681 0.1162* −0.0343 (1.18) (1.87) (0.07) Terms of trade 0.1165 0.09295* 0.2230 (1.45) (1.94) (1.45) Government consumption 0.0063** 0.0082** 0.0788*** (2.78) (2.41) (4.24) Age dependency 0.0132 0.0083 −0.0126 (0.52) (0.22) (0.62) World real interest rate −0.0321 0.0005 −0.0327 (1.54) (0.21) (1.27) LR impact of ED on CA 0.0096 −0.0051 −0.0081 LR impact of openness on CA −0.7575 −0.5390 −0.696 LR impact of ED/GDP × Openness on CA 0.0052 0.0020 0.0045 Wald test 0.0000 0.0000 0.0000 1st-order serial correlation 0.0829 0.1693 0.0967 2nd-order serial correlation 0.2509 0.2198 0.2252 Sargan test >0.10 >0.10 >0.10 Page 20 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 openness appears to be associated with reduced current account deficits among SSA countries and this tends to suggest that SSA countries are largely consumption rather than investment-based, and a significant amount of their imports are consumer goods for consumption rather than investment or capital goods. Meanwhile, for income per capita growth, we find that increases lead to an enlargement in current account deficits and this relationship is significant across all time periods. Again, the results indicate that although increases in growth may be associated with a rise in savings, it appears the correlation of growth with investment is somewhat larger, leading to an expansion in current account deficits. The coefficient of growth is robust since it is positive and significant across all sample periods. Although significant, the size of this estimated coefficient seems to be unchanged even after controlling for endogeneity. Thus, the strong positive relationship between these variables is consistent with the observation that SSA countries that recorded relatively high growth rates over the last decades have generally demanded investment capital from other economies. Table 4b shows results obtained when external debt is interacted with openness and the interaction term included as a regressor. As expected, the coefficient of lagged current account deficit (as a fraction of GDP) is positive and highly significant (at 5% level) in each of the three sample periods, and demonstrates moderate persistence, with an estimated median persistence of about 0.68 across the three sample periods, slightly higher than that obtained in previous studies. The median size of this coefficient reveals moderate persistence of transitory shocks, implying that the half-life of these shocks on the current account deficit is about 1.79 years. Thus, despite occurrences of current account reversals in some SSA countries, in general current account deficits in SSA is moderately persistent. Compared to Calderon et al. (2001), our results suggest that the level of persistence is somewhat higher in SSA, and this likely points to more benign levels of external debt in SSA, following the HIPC and MDRI debt forgiveness programmes. The benign debt levels supported growth in the region in the last decades, decreased the risk profiles of affected countries and increased their attractiveness to foreign investors. This combination has spurred some capital inflows into the region to maintain moderate persistence of current account deficits. To be clear, SSA countries have become less precariously indebted externally and have enjoyed a significant amount of economic growth in the last decades. The decline in external debt over the years through the various debt support programmes has encouraged the inflows of funds which make it possible to fund the deficits, thus the moderate increase in persistence. During years of high debt, heavily indebted countries’ growth and external debt position could not justify persistence in current account deficits as foreign investors gradually held back their funds. This made it difficult to increasingly finance the deficit, which led to a reduction in the persistence of deficit. This explains why our median coefficient of current account persistence is about six times larger than that obtained in Calderon et al. (2001) in their analysis of the determinants of current account deficits in SSA for the period 1975–1995, a period where the levels of external debt in most African countries were at all-time high levels while the region’s economic growth was at an all-time low and persistence of current account deficit was much lower given the unwillingness foreign investors to export capital to the region, resulting in low persistence in current account deficits. For domestic growth, when estimation is done using GMM, we find that exogenous increases in domestic growth enlarge current account deficits and this conclusion holds across all samples and time periods. This improves on the fixed effects estimation which finds no significant relationship between growth and current account deficits. The positive and significant coefficient is consistent with a situation where domestic absorption rises faster than exports. Again, although an increase in growth may well spur exports and be associated with a rise in savings, the results suggest that the correlation of growth with investment is somewhat larger, which in turn triggers an expansion in current account deficits, based on the savings-investment framework. Moreover, where significant, the size of the estimated coefficient of domestic growth now appears larger when we control for endogeneity. Neglecting the possibility of endogeneity as in fixed effects, the coefficient shrinks and Page 21 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 is insignificant in all the three time periods. As noted in Calderon et al. (2001), a smaller growth coefficient may result from negative reverse causation; this negative causation is corrected via GMM estimators. Although we find a significant relationship between real exchange rate and current account deficits, contrary to predictions of the standard open economy Mundell-Flemings model we find that the coefficient of REER is negative. That is, a fall in the real effective exchange rate (i.e. a depreciation of the domestic currency) expands current account deficits where significant and according to the GMM estimation, a 1 per cent depreciation of the real exchange rate increases current account deficit by about 0.20 percentage points. A possible explanation for this is that the demand for SSA exports is exchange rate inelastic and foreign demand is weakly responsive to changes in domestic exchange rate. Thus, depreciation worsens the current account deficits in SSA and we obtain some evidence in support of the J-curve hypothesis as it applies to yearly data. This is consistent with the fact that most SSA countries that export usually export globally traded commodities whose prices in the international market are entirely expressed in a standard currency, i.e. the US$, different from the domestic currency. So, a depreciation of the domestic currency will not change the price of the commodities expressed in US$, except of course it will make it more expensive for those who hold this domestic currency to purchase commodities as they would have to pay more due to the currency depreciation, thus raising import costs and increasing current account deficits. The exchange rate depreciation does not necessarily affect the price that other buyers of the commodities would pay in the international market. It is the appreciation or depreciation of the standard currency in which the commodities are traded that determines the relative cost of commodities to buyers and hence the quantity they would demand of the commodities. This is what partly determines the degree of elasticity of demand for the commodities—the appreciation or depreciation in the currency in which they are traded. In most of the regressions performed, we find that international aid flows, represented as the ratio of effective assistance to GDP and age dependency ratio do not have significant effects on current account deficits in SSA. Terms of trade, on the other hand, appears to bear a negative and significant relationship with current account deficits only for the sample period 1990–2013 and this is consistent with the Harberger–Laursen–Metzler effect. The results in the case of fixed effects suggest that a 10 percentage point increase in terms of trade reduces current account deficit by 0.67 percentage point. When, however, the possibility of endogeneity is controlled for, the result either changes in direction or the significance vanishes for most of the time period considered. In particular, results in Table 3 show that for the period 1985–2008, a 10 percentage point increase in terms of trade significantly increases current account deficit by 0.90 percentage point while the results are insignificant for other time periods. Contrary to results obtained in the case of developing countries, we find statistically insignificant relationship between world real interest rate and current account deficits in SSA, with varying direction of coefficients across the three samples. This implies that there is no significant empirical evidence that an increase in world real interest rate lessens current account deficits in SSA or that reductions in international real interest rate widen SSA demand for international capital which leads to an expansion in current account deficits. Among the most consistent results is the hypothesis of the relative stages of development. In all regressions, we continue to get evidence for the relative stages of development hypothesis, even after accounting for endogeneity. Thus, we find evidence that the size of the current account deficits of SSA countries is likely to decline as SSA countries become increasingly developed and narrow the wide gap that exists between them and developed economies. On a more important note, external debt continues to bear a negative relationship with current account deficits, providing an evidence that external debt accelerates current account adjustments in SSA. Moreover, the interaction between external debt and openness continues to bear a positive relationship with current account deficits, which implies that high openness hamstrings or dampens the current account adjustments potential of external debt. The result shows that the interaction term worsens current account deficits by 0.21 percentage of GDP in the short-run with this figure increasing to 0.31 percentage of GDP in the long-run. Meanwhile, the correlation tests show that error terms are serially uncorrelated Page 22 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 while the Sargan test shows that instruments are valid. All columns suggest that the coefficients of the interactive term remain positive and significant and results are similar, in many ways, to the fixed effects results, especially for the interaction term, except that external debt has now become significant, albeit conditionally. All of the columns also report the test statistic for no over identifying restrictions to confirm the validity of the instruments. Columns (1)-(3) control for the joint endogeneity of the indicators of current account determinants. In all of these, the results continue to support the finding that external debt yields adjustments in current account deficits and high openness significantly reverses the current account adjustment process initiated by external debt. The coefficients and levels of significance, however, changed considerably in values compared with the earlier fixed effects results in Table 2b. At this juncture, it is imperative to state that while GMM is the favoured method in the empirical literature for addressing endogeneity issues, it is not the only technique that deals with endogeneity. There are other instrumental variables estimators that are also suitable to tackle endogeneity. In this section, we re-estimate the preceding model using IV-2SLS and limited information maximum likelihood (LIML) estimators to further confirm the robustness of our main finding. GMM, IV-2SLS and LIML estimators all provide consistent estimates when endogenous variables are present among regressors, but of the three endogeneity-consistent estimators, GMM often has the dual advantages of consistency and efficiency under homoscedasticity and heteroscedasticity. IV-2SLS which, to a considerable extent, is an offshoot of GMM is consistent and efficient, without an external robust correction, only in the presence of homoscedasticity, something that cannot always be guaranteed in SSA data, while LIML has no mean and variance in finite samples, has moment issues, and thus often not efficient, and its quantiles to some extent deviate substantially from the true value of the parameter of interest. Meanwhile, we also test whether endogeneity is an issue in our empirical analysis. As with the GMM estimation, and since it is not always easy to obtain external instruments that are convincing and acceptable, our identification strategy is such that we utilize lagged values as appropriate instruments for the potential endogenous regressors. The results obtained for each estimator are detailed in Tables 5a and 5b. The purpose of these re-estimations in our empirical analysis is to examine whether our central finding that the current account deficits of SSA countries with high openness expand significantly from increases in external debt, are robust to different specifications and estimators that are endogeneity-consistent. As a first pass in the re-estimation, we assess whether our earlier results are invariant to different time periods and estimation technique adopted. To do this, we use two additional estimators: IV-2SLS estimator and LIML. Then, we ask whether or not addressing the effects of endogeneity is necessary in our empirical analysis. This we do by carrying out formal tests of exogeneity of our independent variables. As before, we interact external debt with openness and use this as the regressor whose behaviour we want to test its robustness. We also include separately in the regression each variable in the interaction term to ensure that the interaction term does not proxy for external debt or openness. Tables 5a and 5b presents results obtained from IV-2SLS and LIML respectively. As seen in both tables, the interaction term continues to be significant in the three sample periods, albeit at a lower level compared to previous estimations, even when most variables have lost their significance. These results thus provide an overwhelming support that confirms the robustness of our main finding. This relationship between the interaction term and current account is one of the few consistent results to have emerged from the myriad of regressions performed in this paper and surprisingly one that had been overlooked in the literature. In the nadir rows of Tables 5a and 5b, we report results of over identifying restrictions and exogeneity of explanatory variables to confirm the validity of our instruments and justify the use of estimation techniques that address endogeneity. The results continue to side with the finding that instruments utilized are valid and, more importantly, not all explanatory variables in our regressions are exogenous, confirming that the use of endogeneity-consistent estimators is very well justified. Page 23 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 One could argue that the reason the interaction term remains “stubbornly” significant and sign invariant in the above analysis is because the moderately large time dimensions in our panels and the likelihood that the panel vectors are integrated have not been addressed. To address these Table 5a. Current account balance and external debt: The role of trade openness—IV-2SLS Notes: In columns (1)–(3), the endogeneity test is a core and regression tests which confirm all of the variables are not exogenous and at least one variable is endogenous and it is appropriate to address endogeneity issues, as we have done. For the robust regression F test, the value of X are 684, 581 and 574 for sample 1985–2013, 1985–2008 and 1990–2013 respectively. We have dropped the variable growth which remains insignificant in all these regressions. Standard errors are robust standard errors to accommodate any presence of heteroscedasticity as one can never count on homoscedasticity. *Indicate both statistical significance at 10% significance level and p<0.1. **Indicate both statistical significance at 5% significance level and p<0.05. ***Indicate both statistical significance at 1% significance level and p<0.01. (1) (2) (3) Period 1985–2013 1985–2008 1990–2013 Observations 702 599 571 l.CA 0.8012*** 1.000*** 0.7762*** (5.73) (13.07) (5.41) ED/GDP −0.0173* −0.0168** −0.0145* (1.92) (2.00) (1.72) ED/GDP × Openness 0.0092* 0.0088** 0.0075* (1.94) (2.01) (1.73) Rel. income 0.0096 −0.0243 −0.0073 (0.40) (1.29) (0.31) REER 0.2623 −0.0620 −0.1079 (0.62) (0.79) (1.02) Openness −0.9216 −0.9647** −0.8490 (1.61) (2.18) (1.88) Int.aid flows 0.0129 0.0972 −0.0265 (0.08) (1.19) (0.66) Terms of trade 0.0157 0.1111 −0.0478 (0.22) (1.02) (0.97) Government consumption −0.0003 −0.0001 −0.0018 (0.09) (0.04) (0.66) Age dependency 0.0021 0.0112* 0.0959 (0.23) (1.87) (1.29) World real interest rate 0.0005 0.0030 0.0057 (0.07) (0.68) (1.07) R 2 0.7189 0.7949 0.7544 Wald test 0.0000 0.0000 0.0000 Endogeneity test 𝜒2(6) 14.9398 14.7109 23.1222 p-value 0.0207 0.0226 0.0008 Endogeneity test F(6, X) 2.9811 4.088 4.874 p-value 0.007 0.0005 0.0001 Sargan test 0.3252 0.3804 0.6284 Bassman test 0.3294 0.3879 0.6348 Score test 0.3788 0.5210 0.7359 Page 24 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 issues, we turn to the PMG/MG estimation techniques in the next section. We adopt these techniques because it is well know that in instances where panel vectors are integrated, and the time dimension has observations large enough for each country within the panel to be studied separately, then the pooled mean group (PMG) and the mean group (MG) estimators due to Pesaran, Shin, and Smith (1999) and Pesaran and Smith (1995) are two appropriate estimation techniques for estimating model coefficients. Table 5b. Current account balance and external debt: The role of trade openness—LIML Note: In columns (1)–(3), the null hypothesis for the over identifying restrictions is that there is over identifying restrictions and the instruments used are valid. A P-value greater than 0.10 means the null cannot be rejection which implies the model is well specified. This conclusion is well supported by both Anderson-Rubin and Basmann tests for over identifying restrictions presented in the table. For the Basmann F test, the value of X are 689, 585 and 578 for sample 1985–2013, 1985–2008 and 1990–2013, respectively. We have dropped the variable growth which remains insignificant in all these regressions. *Indicate both statistical significance at 10% significance level and p<0.1. **Indicate both statistical significance at 5% significance level and p<0.05. ***Indicate both statistical significance at 1% significance level and p<0.01. (1) (2) (3) Period 1985–2013 1985–2008 1990–2013 Observations 702 599 592 l.CA 0.8031*** 0.9923*** 0.7749*** (5.72) (12.35) (5.40) ED/GDP −0.0192* −0.0199* −0.0159* (1.79) (1.82) (1.64) ED/GDP × Openness 0.0102* 0.0104* 0.0083* (1.81) (1.82) (1.65) Rel. income 0.0101 −0.0283 −0.0098 (0.40) (1.24) (0.39) REER 0.3340 −0.0771 −0.1122 (0.70) (0.81) (1.00) Openness −1.0298 −1.1206** −0.9209 (1.53) (1.98) (1.80) Int.aid flows 0.0395 0.1184 −0.0097 (0.22) (1.30) (0.07) Terms of trade 0.0202 0.1420 −0.04497 (0.26) (1.06) (0.84) Government consumption −0.0001 −0.0005 −0.0015 (0.04) (0.24) (0.72) Age dependency 0.0019 0.0124* 0.0103 (0.20) (1.83) (1.30) World real interest rate −0.0004 0.0026 0.0061 (0.04) (0.54) (1.08) R 2 0.6948 0.7629 0.7352 Wald test 0.0000 0.0000 0.0000 Over identifying restrictions tests Anderson-Rubin test 𝜒2(2) 0.9320 1.8104 0.9132 p-value 0.3343 0.4045 0.6334 Basmann test F(2, X) 0.9147 0.8840 0.4458 p-value 0.3392 0.4137 0.6405 Page 31 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 justification of our empirical specifications as well as the broad variety of econometric techniques adopted, distinguishes this paper from existing research. We have focused in particular on how external debt enhances current account adjustment and the role of openness in the current account adjustment process in SSA. Contained in the paper is also a study on the empirical relationship between current account and some variables proposed in the literature as determinants of current account balance. This provides an opportunity for us to obtain a number of stylized facts on the effects of a variety of economic variables on current account deficits for a sample of SSA countries selected based on data availability. By controlling for the possibility of joint endogeneity of regressors in the spirit of Calderon et al. (2001) and employing two additional estimation techniques—PMG and DFE—our final empirical evidence in line with our theoretical model suggests that external debt aids in the current account adjustment process in SSA. However, after interacting external debt with openness, we find that high openness reverses the current account adjustment process of external debt. That is, external debt significantly expands current account deficits when openness is high. Our results are robust to the inclusion of lagged levels of external debt, different time periods, the addition of other determinants of current account, consideration of endogeneity and the use of different estimation techniques inclusive of the pooled mean group and dynamic fixed effects methods of estimation. Our results suggest that openness plays a role in the current account adjustment process of external debt. To the best of our knowledge, this has not been shown before and thus constitutes an important contribution. Thus, we have provided empirical evidence, part-supported by theory, that previous high external debt aided the resulting subsequent adjustment of current account deficits in SSA and that high openness, when interacted with external debt, reverses the current account adjustment process. In other words, external debt expands current account deficits when countries have high openness to trade, with the direction of trade tilting more towards imports. The results in this paper suggest that SSA countries should put the right openness policies in place before amassing large external debtcapital to finance projects that would improve local and external conditions. These two policies need not be incompatible. Better domestic policies on openness to trade that not only encourage trade, but also emphasize the right kind of trades that unlock the benefits of external debt on current account should be pursued. We do not rule out the possibility that our work can be helpful for constructing more formal theoretical models of current account determinants. We leave this as an area to be explored for future research. Table 9. Coefficients of the interactive term for different methods of estimation Note: The random effects (RE) and mean group (MG) estimators are not reported here because the Hausman test performed in each case suggests the use of fixed effects (FE) and pooled mean group (PMG) respectively. *p<0.1. **p<0.05. ***p<0.01. 1985–2013 1985–2008 1990–2013 Fixed effects (FE) 0.0045** 0.0037** 0.0051** Generalized method of moments (GMM) 0.0038*** 0.0021*** 0.0031*** IV-two stage least squares (IV-2SLS) 0.0092* 0.0088** 0.0075* Limited info maximum likelihood (LIML) 0.0102* 0.0104* 0.0083* Pooled mean group (PMG) 0.0026*** 0.0027*** 0.0021*** Dynamic fixed effects (DFE) 0.0014** 0.0012* 0.0015* Average 0.0053 0.0048 0.0046 Median 0.0038 0.0027 0.0031 Page 32 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 Funding The author received no direct funding for this research. Author details Oyakhilome Wallace Ibhagui 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-6986-6422 1 School of Economics, University of Kent, Canterbury, UK. Citation information Cite this article as: External debt and current account adjustments: The role of trade openness, Oyakhilome Wallace Ibhagui, Cogent Economics & Finance(2018), 6: 1446247. References Anderson, T., & Hsiao, C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18, 67–82. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58, 277–297. https://doi.org/10.2307/2297968 Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51. https://doi.org/10.1016/0304-4076(94)01642-D Backus, D. K., Kahoe, P. J., & Kydland, F. E. (1994 March). 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Page 33 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 Appendix 1 0200 400 600 0 50100150200 20 40 60 80 0 10 20 30 0 50 100 150 0 500 20 40 60 80 0 50 100 150 0 50 100 0 50 100 150 0 50 100 150 50 100 150 200 0 50 100 150 30 40 50 60 70 0 50 100 150 50100 150200250 050 100 0 50 100150200 010 20 30 40 0 100 200 300 050 100 150 50100150200250 0 100 200 10 15 20 25 0200 0 50 100 150 0 50 100 150 100200300400 20 30 40 0100200300400 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 AGO BDI BEN BWA CMR COG CPVD ETH GAB GHA GIN GMB KEN LST MDG MRT MUS MWI NAM NGA RWA SDN SIE SWA SYC TAZ TGO WSM ZAF ZAM H Year Graphs by Country Page 34 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 -20-10 0 1020 -10 -5 0 5 10 -5 0 5 10 -10 0 10 20 -10 -5 0 5 10 -5 0 5 10 0 5 10 15 20 -10 0 10 20 -20 -10 0 10 5 10 15 0246 -5 0 510 0 2 4 6 8 0 2 4 6 8 -20 -10 0 10 -40 -20 0 20 0 5 10 -10 0 10 20 0510 15 -10 010 20 30 -50 0 50 -10 010 20 -20 0 20 40 0 5 10 15 20 -5 0 5 10 02 4 6 8 -20-10 0 10 20 0 5 10 15 -2 0 2 4 6 -10 -5 0 5 10 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 AGO BDI BEN BWA CMR COG CPVD ETH GAB GHA GIN GMB KEN LST MDG MRT MUS MWI NAM NGA RWA SDN SIE SWA SYC TAZ TGO WSM ZAF ZAM F Year Graphs by Country Page 35 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 -3.5 -3 -2.5 -2 -4.2 -4 -3.8 -3.6 -3.8 -3.6-3.4 -3.2 -3.8 -3.6-3.4-3.2 -3 -3.1 -3 -2.9 -2.8 -2.7 -3.8 -3.6 -3.4-3.2 -3 -5 -4.5 -4 -3.2 -3 -2.8 -2.6 -2.4 -3.4 -3.2 -3 -2.8 -3.5 -3 -2.5 -3.7-3.6-3.5-3.4 -4.6 -4.4 -4.2 -4 -3.2 -3 -2.8 -2.6 -2.4 -4.05-4-3.95-3.9 -3.85 -3.5 -3.4 -3.3-3.2-3.1 -4 -3.8 -3.6 -3.4 -3.8-3.6-3.4-3.2 -3 -4 -3.8 -3.6 -3.4 -3.6 -3.4-3.2 -3 -3 -2.5 -2 -1.5 -4 -3.5 -3 -3.2-3-2.8 -2.6 -2.4 -4.2-4-3.8 -3.6-3.4 -4.4 -4.2-4 -3.8 -3.6 -4.6 -4.4 -4.2 -4 -3.5 -3 -2.5 -4 -3.5 -6 -5.5 -5 -4.5 -2 -1.8 -1.6 -1.4 -3.6 -3.4 -3.2-3-2.8 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 AGO BDI BEN BWA CMR COG CPVD ETH GAB GHA GIN GMB KEN LST MDG MRT MUS MWI NAM NGA RWA SDN SIE SWA SYC TAZ TGO WSM ZAF ZAM A Year Graphs by Country Page 36 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 1.8 2 2.2 2.4 1.31.41.51.61.7 1.6 1.651.7 1.751.8 1.9 1.95 22.052.1 1.5 1.6 1.7 1.8 1.8 2 2.2 1.8 1.9 22.1 1 1.21.41.61.8 1.9 2 2.1 1.4 1.6 1.8 2 1.61.71.8 1.9 2 1.71.81.9 22.1 1.71.751.81.85 2.152.22.252.32.35 1.4 1.6 1.8 2 1.8 2 2.2 22.05 2.1 2.15 1.7 1.8 1.9 2 1.9 1.95 22.052.1 1.4 1.6 1.8 2 1.2 1.4 1.6 1.8 11.2 1.4 1.6 1.4 1.6 1.8 2 2 2.1 2.2 2.3 1.8 22.2 2.4 1.51.61.71.81.9 1.7 1.8 1.9 2 1.75 1.8 1.85 1.9 1.6 1.7 1.8 1.9 1.751.81.85 1.9 1.95 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 AGO BDI BEN BWA CMR COG CPVD ETH GAB GHA GIN GMB KEN LST MDG MRT MUS MWI NAM NGA RWA SDN SIE SWA SYC TAZ TGO WSM ZAF ZAM C Year Graphs by Country Page 37 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 -1 -.5 0 .5 -.4-.3-.2-.1 0 -.5 0 -.5 0.5 -.6 -.4 -.2 0 -1 -.5 0 .5 -.8-.6-.4-.2 0 -.6 -.4 -.2 0 -.5 0.5 -.6 -.4 -.2 0 -.6 -.4 -.2 0 -.4 -.2 0 -.8-.6-.4-.2 0 -1.5 -1 -.5 -.6 -.4 -.2 0 -.6-.4-.2 0 .2 -.8-.6-.4-.2 0 -.6 -.4-.2 0 -.6-.4-.2 0 .2 -.5 0 .5 -.8-.6-.4-.2 0 -.6 -.4 -.2 0 -.6 -.4 -.2 0 -6 -4 -2 0 -2-1.5-1-.5 0 -.8-.6-.4-.2 0 -.6 -.4 -.2 0 -.8-.6-.4-.2 0 -.8-.6 -.4 -.2 0 -.6-.4-.2 0.2 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 AGO BDI BEN BWA CMR COG CPVD ETH GAB GHA GIN GMB KEN LST MDG MRT MUS MWI NAM NGA RWA SDN SIE SWA SYC TAZ TGO WSM ZAF ZAM E Linear prediction Year Graphs by Country Page 38 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 1.5 22.5 1.8 2 2.22.4 1.9 2 2.1 1.91.95 2 2.05 1.81.9 22.12.2 1.6 1.82 2.22.4 1.941.96 1.98 2 2.02 2 2.22.42.6 22.12.22.32.4 2 2.22.42.6 1.5 2 2.5 3 2 2.2 2.4 1.7 1.81.9 22.1 1.8 2 2.22.4 1.8 2 2.2 2.4 1.8 2 2.2 2.4 1.81.9 2 2.1 1.8 2 2.2 2.4 1.9 2 2.12.2 1.5 2 2.5 3 1.6 1.8 22.2 2.4 1.8 2 2.2 2.4 2.6 1.5 2 2.5 3 1.9 1.95 22.05 1.81.9 22.1 2.2 2 2.22.42.62.8 1.41.61.822.2 1.8 2 2.22.4 1.8 1.9 2 2.1 1.5 2 2.5 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 AGO BDI BEN BWA CMR COG CPVD ETH GAB GHA GIN GMB KEN LST MDG MRT MUS MWI NAM NGA RWA SDN SIE SWA SYC TAZ TGO WSM ZAF ZAM BL Year Graphs by Country Page 39 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 Appendix 2 Sub-Saharan African countries in the sample Code Country SSA Region GDP/ capita (US$) Code Country SSA Region GDP/capita (US$) AGO Angola Southern Africa 3,679 MRT Mauritania SSA/Maghreb 1,084 BDI Burundi East Africa 199 MUS Mauritius East Africa 1,084 BEN Benin West Africa 682 MWI Malawi Southern Africa 1,084 BWA Botswana Southern Africa 6,051 NAM Namibia Southern Africa 4,595 COM Comoros East Africa 741 NGA Nigeria West Africa 1,802 COG Congo, Rep Central Africa 2,633 RWA Rwanda East Africa 498 CPVD Cape Verde West Africa 3,147 SDN Sudan North/East Africa 1,261 ETH Ethiopia East Africa 334 SIE Sierra Leone West Africa 491 GAB Gabon Central Africa 9,030 SWA Swaziland Southern Africa 2,711 GHA Ghana West Africa 1,194 SYC Seychelles East Africa 12,105 GIN Guinea West Africa 426 TAZ Tanzania East Africa 671 GMB Gambia West Africa 495 TGO Togo West Africa 499 KEN Kenya East Africa 503 WSM Sao Tome Principe West Africa 1,198 LST Lesotho Southern Africa 919 ZAF South Africa Southern Africa 6,391 MDG Madagascar Southern Africa 392 ZAM Zambia Southern Africa 1,285 Note: The GDP/capita in US$ is the 10 year average real per capita income of each of the countries in the sample. Appendix 3 Description data, variables and sources Variable Sources Description Current account balance (CA) World Bank and IMF IFS Current account deficit as a percentage of GDP Real effective Zsolt/World Bank Exchange Rate REER represents the multilateral real exchange rate, in logarithm Terms of trade (TOT) World Bank Terms of trade is calculated as the ratio of export prices to import prices (base 2000 = 100), in logarithm International aid World Bank International aid is the ratio of the effective development assistance (EDA) to GDP. It measures the aggregate aid flows combining total grants and the grant component of all official loans External debt (ED) World Bank External debt is measured as the ratio of a country’s total external debt to GDP World real interest rate World Bank Taken as the real interest rate of the US—which is the annualized nominal interest rate less average annual inflation rate Trade openness World Bank Calculated as the sum of exports and imports as a fraction of GDP, in logarithm Domestic relative income level World Bank Ratio of domestic output to the US output, expressed in logarithm Domestic growth dependency ratio World Bank Yearly percentage growth in per capita GDP Note: Definitions used are from the World Bank. Page 40 of 42 Ibhagui, Cogent Economics & Finance (2018), 6: 1446247 https://doi.org/10.1080/23322039.2018.1446247 Appendix 4 The pooled mean group and mean group estimator Here, we explain in some detail the PMG/MG estimation technique which is suitable in instances where T is large so much so that regression analysis can be separately implemented across time for each of the countries i=1, …,N . In our set up, we have T=29, N=30 , lending support to either MG/PMG model. In order words, this estimation model has been implemented because our dynamic panel data is such that the cross-sectional observations (N) are below the number of time-series observations (T). This specification implies some departure from the assumptions of slope homogeneity, meaning each country’s long run coefficients are not forced or constrained to equate. An additional requirement for this specification is that the model to be estimated constitutes variables that are non-stationarity. This can be a major concern when suitable methods of handling nonstationary variables are non-existent. Pesaran et al. (1997) and Pesaran et al. (1999) address this downside by proposing techniques to estimate nonstationary dynamic panels that allow slope parameters to be heterogeneous across groups. These techniques are the aforementioned meangroup (MG) and pooled mean-group (PMG) estimators. The MG estimator due to Pesaran and Smith (1995) involves estimating N time-series regressions and averaging the coefficients; the PMG estimator (see Pesaran et al., 1997, 1999), on the other hand, combines pooling and averaging of coefficients. We provide a brief formulation of these approaches below as has been used in our scenario. Suppose a general panel regression specification as where Xit is a vector of K regressors. The generalized autoregressive distributive lag (ARDL) ( p,q1…,qk ) dynamic panel specification associated with this equation can be written as where i=1, 2, …,N is the number of countries, t=1, 2, …,T is the number of time periods and Xit is a k×1 vector of explanatory variables. 𝛿ij are the k×1 coefficient vectors, τij are scalars while μi is the usual country-specific effect and T is sufficiently large to ensure the model can be fitted for each country. If the variables are I(1) and cointegrated, then the error term is a I(0) process for all i. This feature implies an error correction model in which the short-run dynamics are influenced by the deviation from equilibrium. Thus, the error correction equation associated with the generalized autoregressive distributive lag is where and 1 ≤ j ≤ p − 1 and 1 ≤ j ≤ q − 1. (1) y it =𝜇 i +𝛿 � X it +𝜀 it, (2) yit = p ∑ j=1 𝜏ijyi,t−j+ q ∑ j=0 𝛿� ijXi,t−j+𝜇i+𝜀it , (3) yit =𝜑i ( yi,t−1−𝜃 � iXit ) + p − 1 ∑ j=1 𝜏∗ ij Δyi,t−1+ q − 1 ∑ j=0 𝛿 �∗ ij ΔXi,t−j+𝜇i+𝜀it , (4) 𝜑 i=− � 1− p � j=1 𝜏ij � ,𝜃i= ∑q j=0𝛿ij 1− ∑ k𝜏ik ,𝜏∗ ij =− p � m=j+1 𝜏im,𝛿∗ ij = q � m=j+1 𝛿 im