The exchange rate, income, trade openness and the trade balance: Longitudinal panel analysis for selected SSA countries

Abille, Adamu Braimah; Meçik, Oytun Article The exchange rate, income, trade openness and the trade balance: Longitudinal panel analysis for selected SSA countries International Trade, Politics and Development (ITPD) Provided in Cooperation with: Department of International Commerce, Finance, and Investment, Kyung Hee University Suggested Citation: Abille, Adamu Braimah; Meçik, Oytun (2023) : The exchange rate, income, trade openness and the trade balance: Longitudinal panel analysis for selected SSA countries, International Trade, Politics and Development (ITPD), ISSN 2632-122X, Emerald, Leeds, Vol. 7, Iss. 2, pp. 138-153, https://doi.org/10.1108/ITPD-04-2023-0007 This Version is available at: https://hdl.handle.net/10419/319582 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/ The exchange rate, income, trade openness and the trade balance: longitudinal panel analysis for selected SSA countries Adamu Braimah Abille Institute of Economic Research, Slovak Academy of Sciences, Bratislava, Slovakia, and Oytun Meçik Department of Economics, Eskis ¸ehir Osmangazi University, Eskisehir, Turkey Abstract Purpose –Motivated by recent rapid exchange rate depreciations, shrank economic growth, high inflation, and persistent trade deficits, this study examines the trade balance (TB) in the face of the recentdynamics of the stated macroeconomic factors, which are also important determinants of the TB. The symmetric test of the J-curve phenomenon for the selected Sub-Saharan African (SSA) countries is revisited in this regard. The study uses panel data from 1970 to 2020 for ten of these countries for the longitudinal panel analysis with the TB as the dependent variable and the real exchange rate, foreign and domestic national incomes, and trade openness as the set of independent variables. Design/methodology/approach –Because the underlying data set involves a heterogeneous panel of relatively short N and long T, the pooled mean group (PMG) and mean group (MG) heterogeneous panel models are employed based on the Hausman test for parameter consistency in heterogeneous panels. Findings –The findings largely support the domestic income growth–TB worsening and the foreign income growth–TB improvement hypotheses. Trade openness is found to mostly augment the TB performance of the countries. The results also validated the J-curve effect for only 3/10 and 2/10 countries in the PMG and MG models, respectively. The divergence for most of the countries is attributed to possible import compression and institutional structure of SSA countries. Practical implications –Given the favorable effects of trade openness on the TB performance of SSA countries, it is recommended that SSA countries place much emphasis on import-substitution industrialization and value addition to their natural resources as well as investment-driven growth policies to improve the competitiveness of their exports and reverse the chronic deficits in their TBs. Originality/value –This paper is unique for invoking heterogeneous panel models to analyze the TB in light of recent dynamics of its determinants, as well as providing an update on the symmetric test of the J-curve phenomenon for the selected SSA countries. Keywords Macro-factors, Trade balance, Longitudinal panel analysis, SSA Paper type Research paper 1. Introduction Exchange rate stability is one of the most important economic policy issues that attracts greater attention among economists. This is because just like inflation and interest rates, the exchange rate affects the activities of economic agents such as households, investors, and governments. One of the most critical roles of the exchange rate is the fact that it determines ITPD 7,2 138 © Adamu Braimah Abille and Oytun Meçik. Published in International Trade, Politics and Development. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (forboth commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons. org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2586-3932.htm Received 1 April 2023 Revised 22 May 2023 Accepted 19 June 2023 International Trade, Politics and Development Vol. 7 No. 2, 2023 pp. 138-153 Emerald Publishing Limited e-ISSN: 2632-122X p-ISSN: 2586-3932 DOI 10.1108/ITPD-04-2023-0007 to a large extent the balance of trade position of a country. Most especially in this era of globalization and trade liberalization among countries, the issue of the exchange rate is widely debated among researchers and economic actors. One of such discussion is the J-curve phenomenon based on the Marshal Lerner condition, which states that devaluation/ depreciation only improves trade balance (TB) if and only if the sum of the price elasticities of demand for imports and exports is greater than one in absolute terms (Hussain and Haque, 2014). Without recourse to the time scale, economic theory predicts that a depreciation/ devaluation of a country’s currency should ultimately improve the TB as it increases import prices and decreases export prices (Bahmani-oskooee and Gelan, 2012a,b). Since the transition from a fixed to a floating exchange rate regime in the late 1970s to the early 1980s, African countries have had to contend with rapidly depreciating currencies (Ahmad and Pentecost, 2020). The weak currency phenomenon is a feature of the African economy so much such that countries such as Zimbabwe have in recent times replaced the Zimbabwean dollar with the United States dollar. At the same time, most of these African countries have had to contend with balance of payment problems over the years. Most researchers have attributed the adverse balance of payment situation in Africa to underindustrialization most especially export-oriented industrialization (Mendes et al., 2014;Abdel- Salam, 1966;Chong and Zanforlin, 2007). From the point of view of economic theory, net export is an integral component of the national economy, and therefore, works that seek to improve the balance of payment and or current account position of African countries must be given greater attention at this point in time. It is important to allude at this point that several works have been done in Africa regarding the relationship between devaluation/depreciation and the TB. For instance see Bahmani-Oskooee and Gelan (2012a,b),Anning et al. (2015), Adeniyi et al. (2011), among others. In fact, recent literature has even moved from a symmetric to an asymmetric investigation of the J-curve phenomenon in Africa. As examples of asymmetric investigations of the J-curve in the literature, one can refer to Mwito et al. (2021), Bahmani-Oskooee and Arize (2020),Arize (2019), etc. Alas, these numerous works (whether symmetric or asymmetric) have not produced conclusive evidence in support or otherwise of the J-curve phenomenon, especially for African countries. Consequently, the import of the J- curve theory for exchange rate policy formulation remains vague for these countries. The literature has attributed the inconclusive evidence for or against the existence of the J-curve to the following. First, the assumption underlying the J-curve that there exists short-run inelastic response of import volumes to import prices may not be a tenable assumption at least for the countries in the panel and for the time period under consideration. In this regard, it is possible that immediate import compression following exchange rate depreciation/devaluation is the dominant force characterizing the TB in these countries. This could explain the finding of less evidence in support of the falling part of the J-curve. The second reason that could justify these findings is the argument offered by Nelson and Plosser (1982) to the effect that the earlier works that support the existence of the J-curve could have been spurious because the methods used then could not deal with the unit root properties of the underlying variables, a situation which modern studies have found to be typical of most economic data. Yet, efficient exchange rate, trade, and income policy formulation is critical for African countries because these countries not only have to grapple with significant fiscal slippages and escalating public debts but are also very characteristic of long-term chronic deficits on their balance of payments. Consequently, the African continent, despite being rich in natural resources, continues to face losses in terms of the benefits of international trade. The corollary of which is low economic growth and a deterioration in the living standards of the people (Safaeimanesh and Jenkins, 2021). Panel analysis of the trade balance in SSA 139 On account of the inconclusive outcome in the literature and the apparent exchange rate shock, contraction in economic growth and decreased trade volumes in most African countries largely occasioned by the coronavirus disease 2019 (Covid-19) pandemic, the current study aims to analyze the effects of these important macroeconomic factors on the TB positions of a selected few of these countries in a longitudinal panel framework. The symmetric test of the J-curve will be revisited in this regard. With almost 5 decades of country-level data coverage on the key variables, the longitudinal analysis is conducted using the panel ARDL method through the MG and the PMG models based on the outcome of the stationarity tests. This framework uses a relatively large N but longer T to enable a panel analysis that takes the dimension of a time-series analysis and thus gives more meaning to the short- and long-run differentiation of the phenomenon under consideration (Pesaran et al., 1999). Unlike the time-series version of the ARDL model, the panel ARDL model can be estimated using different techniques. These include the pooled mean group (PMG), mean group (MG) and dynamic fixed effects (DFE) estimation techniques. The PMG method assumes long-run homogeneity and short-run heterogeneity in the impacts of the independent variables on the dependent variable. The MG method assumes both short-run and long-run heterogeneities in the model. On the specific subject matter, the current study is a panel with small N and long T as opposed to the generalized method of moments (GMM) example of Hussain and Haque (2014), which used large N and small T as per the dictates of the GMM models to investigate the J- curve in Africa. Therefore, in addition to focusing on the balance of payment positions of the countries in response to recent dynamics in macroeconomic factors, the current study also provides insights into determining whether the selected African countries are stuck in the short-run phase of the Marshall–Lerner condition. The condition has contributed to a negative portrayal of their balance of payment during the period under consideration. The rest of the study is organized as follows. The second section deals with the literature on the subject matter mostly those related to the Marshall–Lerner condition. The third section deals with the methods and source of the data. The fourth section deals with the estimation strategies. The fifth section entails the empirical findings and the discussions, and the sixth section concludes with some policy recommendations. 2. The literature Developed independently by Marshall (1949) in his classical work entitled “The pure theory of foreign trade: the pure theory of domestic values”and Abba Lerner (1903–1982), the Marshal–Lerner Condition establishes the circumstances under which depreciation and or devaluation improves a country’s balance of payment or current account. This condition dictates that, in absolute terms, the price elasticities of imports and exports must sum to more than unity in order for depreciation/devaluation to improve the balance of payment position of a country (Shea, 1979). What this means is that import and export elasticities must be the overriding consideration for a successful devaluation policy in a particular country. This perhaps explains why some countries are successful at devaluation and others are not. The TB, being the absolute difference between the absolute values of a country’s export and import, the standard economic theory postulates that, following a currency depreciation/ devaluation, export prices fall leading to an increase in the volume of export and import prices rise, leading to a decline in the volume of imports (Sohmen, 1958). Since the exchange rate has to do with the price of a country’s currency in terms of foreign currencies, it is only natural that there have to be commensurate adjustments in the volumes of imports and exports, which are indicators of international relations for depreciation or devaluation to be beneficial. Mathematically, the intuition behind the Marshall–Lerner condition is presented as follows: ITPD 7,2 140 TB ¼XeM (1) Where TB is the trade balance, Xis the value of exports, Mis the value of imports, and e is the exchange rate, which is the price of imports in terms of foreign currency say the dollar. Following simple rules of differentiation, we take the derivative of the TB with respect to the exchange rate to arrive at equation (2). dTB de ¼dX de edM de Mde de (2) Simplifying equation (2) by way of economic intuitive manipulations, we obtain equation (3), which is an expression of the derivative of the TB with respect to the exchange rate in terms of export and import elasticities. dTB de ¼MdX de : e XX eM dM de : e M1(3) Equation (3) brings us closer to establishing the critical role of the exchange rate in determining the balance of payment position of a country. Replacing the elasticity expressions with their contracted denotations, we arrive at equation (4). dTB de ¼M ε x X eM  ε m1(4) Adding and subtracting eM to the numerator of the first term of equation (4) gives: dTB de ¼M ε x XeM eM þ ε xeM eM  ε m1(5) If X−eM ¼0equation (5) simplifies to give equation (6), which is a key expression for analyzing the balance of payment position of a country. dTB de ¼Mð ε x ε m1Þ(6) From equation (6), when the absolute price elasticities of exports and imports sum to more than 1, TB rises which is the Marshall–Lerner condition. If net exports are positive, i.e. X-M > 0, then exchange rate depreciation improves the TB irrespective of whether the sum of the price elasticities of exports and imports is greater, less, or equal to unity in absolute terms. On the other hand, if net exports are negative, i.e. X-M < 0, then the sum of the price elasticities of imports and exports must necessarily sum to more than 1 before exchange rate depreciation/ devaluation impacts positively on the TB. This is so because the initial harmful price effect in this instance is big and the corresponding quantity change has to be bigger to be commensurate with the price effect. On the empirical front, several works have been done to ascertain the existence of the J-curve in so many countries. Whereas the following works found evidence to support the J-curve: Tripti and Bandyopadhyay (2016) for India and the South Asian Association for Regional Cooperation (SAARC), Adeniyi et al. (2011) for Nigeria, Kyophilavong et al. (2013) for Laos, Hussain and Haque (2014) and Siklar and Kecili (2018) for Turkey, Mwito et al. (2020) for Kenya, Amusa and Fadiran (2019) for South Africa and Lira and Lal and Lowinger (2000). The following works found no evidence to support the existence of the J-curve: Khatoon and Mahbubur (2009) for Bangladesh, Awan et al. (2012) for Pakistan, Serdar and Panel analysis of the trade balance in SSA 141 Hakan (2017) for Brazil and USA, Bahmani-oskooee and Gelan (2012a,b) for Africa, Yılmaz et al. (2017) and Horata (2019) for Turkey, Anning et al. (2015) and Canipe (2012) for Ghana, Trabelsi and Jelassi (2016)) for Tunisia. 3. Methods and sources of data This study employs panel data from 1970 to 2020 to analyze the impact of exchange rate depreciation/devaluation on the TB of the selected Sub-Saharan African (SSA) countries. These countries are selected based on the ready availability of longitudinal data for the variables of interest and the volatile exchange rate environment of these countries especially at the onset of the COVID-19 pandemic. The natural logarithm of the ratio of exports (X)to imports (M) i.e. ln(X/M) constitutes the dependent variable and the regressors are the official exchange rates of the countries relative to the dollar, the natural logarithm of foreign (China) and domestic gross domestic products and trade as a percentage of the gross domestic products of the countries. Data on all variables are sourced from the World Bank’s World Development Indicators (WDI) database. Foreign and domestic gross domestic products as well as trade openness are included as regressors because they are important driving factors of the TB of countries. The gross domestic product of China is used to represent foreign national income in this study because China is currently considered the largest trading partner of most SSA countries with estimated trade of over 15%. 4. Estimation strategy To analyze the short-run and long-run effects of real exchange rate including other independent variables on the TB of the various countries in the panel, this study adopts the panel autoregressive distributed lag (ARDL) model proposed by Pesaran and Smith (1995) and Pesaran et al. (1999). The functional form of which is specified in equation (7): Δyit ¼θiyit−1λ0 iXitþX p−1 j¼1 f i;jΔyi;t−jþX q−1 j¼0 α 0 i;jΔXi;t−jþwiþ μ i;t(7) Equation (7) is re-parameterized to incorporate the variables of interest to arrive at the operational model for estimation as specified in equation (8): ΔTBit ¼2 6 6 4 TBit λ1tradeit TBit λ2fgdpit TBit λ3dgdpit TBit λ4exchit 3 7 7 5 þX p−1 j¼1 f i;jΔTBi;t−jþX q−1 j¼0 2 6 6 4 α 1iΔtradei;t−j α 2iΔfgdpi;t−j α 3iΔdgdpi;t−j α 4iΔexchi;t−j 3 7 7 5 þwiþ μ i;t(8) Where TB it is the TB of country i at time t calculated as the natural logarithm of the ratio of the value of a country’s export to the value of imports, trade 5trade openness, fgdp 5foreign income, dgdp 5domestic income and exch 5real exchange rate of each country relative to the US dollar. wiis the cross-country heterogeneity, and μ it is the panel idiosyncratic error term, which is assumed to be iid, i.e. μ it ≈Nð0; σ 2Þ. 5. Empirical results and discussions First-generation tests of unit root are adopted for this study based on the outcome of Pesaran’s tests for cross-sectional dependence among the countries in the panel considering the variables as a group. The results of Pesaran’s test for cross-sectional independence are reported in Table 1. From the table, it can be observed that the test statistic of Pesaran’s test of cross-sectional independence is 1.440 with an associated p-value of 0.1497, which is higher ITPD 7,2 142 than even the 10% level of significance leading to the acceptance of the underlying null hypothesis of cross-sectional independence. The absence of cross-sectional dependence among the African countries in this regard is attributed to the fact that Africa is the region with the least intra-regional trade estimated at just above 13% as against the estimated 60%, 40% and 30% intra-regional trade for Europe, North America, and Association of Southern East Asian Nations (ASEAN), respectively. This is a disturbing phenomenon, and it can only be hoped that the introduction of the African Continental Free Trade Area (AFCFTA), which has since been ratified by over 31 AU member countries, would be a game changer that reverses this narrative and improve intra-regional trade in Africa. Because the panel ARDL is the panel version of the time-series ARDL, it is critical to ensure that none of the variables under consideration is integrated of order two, i.e. I(2). Against this backdrop, the empirical section of this study begins with a unit root test on all the variables using both the Im–Pesaran–Shin (IPS) test, proposed by Pesaran et al. (1997), and Levin–Lin–Chu (LLC) test of a unit root in panel data proposed by Levin et al. (2002). The null hypothesis underlying both tests is that the series has a unit root. It is also imperative to note that both tests are first-generation tests of a unit root in panel data applicable when there is cross-sectional independence among the subjects in the panel (Barbieri, 2009). The results of the IPS and the LLC tests for unit roots are reported in Table 2. As it is obvious from Table 3, the null hypothesis of the presence of unit root is rejected for all the variables after the first difference for both IPS and LLC. This implies that all the variables to be used for the estimation are at most integrated of order one, i.e. I (1). This is an indication that estimating the underlying model with the panel ARDL model will not produce spurious results. Unlike the time-series version of the ARDL model, the panel ARDL model can be estimated using different techniques. These include the PMG, MG and DFE estimation techniques. The PMG method assumes long-run homogeneity and short-run heterogeneity in the impacts of the independent variables on the dependent variable. The MG method assumes both shortrun and long-run heterogeneities in the model. Although the DFE method is similar in spirit to the PMG model, the former imposes prior equality restrictions on the slope coefficients and error variances across all the cross-sectional units in the panel. Although all three models used for panel ARDL estimation have their underlying assumptions, the choice of which model to specify at any given point in time depends highly on the outcome of the Hausman test proposed by Hausman (1978). Between the PMG and the MG, the null hypothesis underlying the Hausman test is that the PMG is appropriate and between the MG and the DFE, favors the MG model. The results of the Hausman tests for deciding between the PMG and MG are reported in Table 3. From Table 3, it can be seen that at the 5% level, the null hypothesis of PMG model cannot be rejected but at the 10% level, the null hypothesis of PMG has been rejected in favor of the MG model. This conclusion has led to the estimation of both PMG and MG models as reported in Tables 4 and 5 respectively. Before reporting the results for the PMG and the MG models, we consider it important to report the descriptive statistics of the variables. This is reported in Table 4. From the table, the following observations can be made: Summary of test Test statistic Pesaran’s test of cross-sectional independence 1.440 (0.1497) Source(s): Authors’construct Table 1. Test of cross-sectional independence Panel analysis of the trade balance in SSA 143 Im–Pesaran–Shin Levin–Lin–Chu Variables I (0) I (1) I(0) I(1) I.C. I.C.& Tr I.C. I.C. & Tr I.C. I.C.& Tr I.C. I.C. & Tr LNTB 2.274** 1.946** 13.743*** 12.510*** 2.304** 3.087*** 12.152*** 9.968*** EXCH 4.013 1.120 10.247*** 9.490*** 3.407 0.259 9.716*** 9.145*** TRADE 0.565 0.143 12.120*** 10.522*** 0.580 0.518 10.761*** 8.658*** LNFGDP 0.349 1.541 8.886*** 7.202*** 5.926*** 5.417 40.190*** 4.296*** LNDGDP 2.129 0.723 10.594*** 9.314*** 1.713* 2.129*** 10.122*** 8.552*** Note(s): *, ** and *** denote the absence of unit root at 10%, 5 and 1% significance levels, respectively Source(s): Authors’construct Table 2. Results of the Im– Pesaran–Shin and Levin-Lin-Chu unit root tests ITPD 7,2 144 (1) For the 10 Sub-Sahara African countries included in the study, each variable consists of 450 observations for a total of 2,250 observations. (2) The average TB for all the countries is a deficit of 0.250, which reflects the persistent balance of payment deficit phenomenon in Africa (Osakwe, 2007;Høst- Madsen, 1967), which partly motivated this study. The average higher units of the selected countries’currencies that is required for a unit of the US dollar is indicative of the widespread currency depreciation phenomenon in Africa. For economies that depend heavily on imports, exchange rate depreciation is directly related to the price level because the depreciation though is expected to promote exports and increases the average price of imported goods and services (Meniago and Eita, 2017)(Meniago and Eita, 2017). Of particular interest is the huge deviation from the mean of the exchange rate of the African countries included in the panel. (3) Another variable of interest is the trade openness which has a mean value of 65.64%, a standard deviation of 23.75%, minimum and maximum values of 6.32 and 131.49%, respectively. As observed in Oloyede et al. (2021), the countries in the Economic Community of West African States (ECOWAS) and the South African Development Community (SADC) have their economies positively impacted by trade openness although the findings are not necessarily significant. The results of the PMG model are reported in Table 4 and from the table, it can be seen that there is a long-run positive effect of the exchange rate on the TB of all the countries in the panel. This finding is consistent with the predictions of the J-curve analysis, which holds that the TB or the current account position of a country improves in the long-term following exchange rate depreciation. The finding is also consistent with the findings of Mwito et al. (2020) for Kenya and Amusa and Fadiran (2019) for South Africa in their time-series analysis. However, the results also indicates that there exist short-run positive effects of the exchange rate on the TB of seven of the countries in the panel (Ghana, Kenya, South Africa, Senegal, Ivory Coast, Gabon and Botswana) whereas for three of the countries (Cameroon, Burkina Faso and Gambia), exchange rate depreciation leads to a short-run deterioration in their TBs. For the three countries, the short-run adverse effects of exchange rate depreciation, coupled with the long-run positive effects of exchange rate depreciation on their TBs, indicate the presence of the J-curve effect in these countries. Specifically, this effect can be observed in Test summary χ 2statistic Cross-section random 8.71* (0.069) Note(s): *p< 0.05, **p< 0.01, ***p< 0.001. p-values in parentheses Source(s): Authors’construct Variable Observation Mean Std. dev Min Max TB 450 0.250 0.571 2.269 0.919 Rexch 450 230.24 238.91 0.0001 732.40 Trade 450 65.64 23.75 6.32 131.49 DGDP 450 2.88eþ10 6.45eþ10 1.12eþ08 4.16eþ11 FGDP 450 3.20eþ12 3.31eþ12 2.45eþ11 1.15eþ13 Source(s): Authors’construct Table 3. Hausman test Table 4. Descriptive statistics of the variables Panel analysis of the trade balance in SSA 145 Kyophilavong, P., Shahbaz, M. and Uddin, G.S. (2013), “Does J-curve phenomenon exist in case of Laos? An ARDL approach”,Economic Modeling, Elsevier, Vol. 35 C, pp. 833-839. Lal, A. and Lowinger, T. (2000), “The J-curve: evidence from east Asia”,Journal of Economic Integration, Vol. 17 No. 2, pp. 397-415, doi: 10.11130/jei.2002.17.2.397. Levin, A., Lin, C.-F. and James Chu, C.-S. 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Yılmaz, S., € Ozayt€ urk, _ I. and Oransay, G. (2017), “Testing the hypothesis of J curve for Turkish economy”,Chinese Business Review, Vol. 16 No. 9, pp. 419-428, doi: 10.17265/1537-1506/2017. 09.0013. Further reading Owczarczuk, M. (2013), “Government incentives and fdi inflow into r&d - the case of visegrad countries”,Entrepreneurial Business and Economics Review, Vol. 1 No. 2, pp. 73-86, doi: 10. 15678/EBER.2013.010207. Smith, A. (1776), An Inquiry into the Nature and Causes of the Wealth of Nations, in Strahan, W. and Cadell, T. (Eds), London. Corresponding author Adamu Braimah Abille can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Panel analysis of the trade balance in SSA 153