Economic growth, exchange rate and remittance nexus: Evidence from Africa

Lawal, Adedoyin Isola et al. Article Economic growth, exchange rate and remittance nexus: Evidence from Africa Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Lawal, Adedoyin Isola et al. (2022) : Economic growth, exchange rate and remittance nexus: Evidence from Africa, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 15, Iss. 6, pp. 1-13, https://doi.org/10.3390/jrfm15060235 This Version is available at: https://hdl.handle.net/10419/274757 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/ Citation: Lawal, Adedoyin Isola, Afees Adebare Salisu, Abiola John Asaleye, Ezeikel Oseni, Bukola Bose Lawal-Adedoyin, Samuel Olatunde Dahunsi, Emmanuel Oluwasola Omoju, Abigail Oyeronke DickTonye, Elizabeth Bolatito Ogunwole, and Abiola Ayopo Babajide. 2022. Economic Growth, Exchange Rate and Remittance Nexus: Evidence from Africa. Journal of Risk and Financial Management 15: 235. https://doi.org/10.3390/ jrfm15060235 Academic Editor: Antonio Ribba Received: 16 November 2021 Accepted: 13 May 2022 Published: 26 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Risk and Financial Management Article Economic Growth, Exchange Rate and Remittance Nexus: Evidence from Africa Adedoyin Isola Lawal 1,*, Afees Adebare Salisu 2,3, Abiola John Asaleye 1, Ezeikel Oseni 4,5, Bukola Bose Lawal-Adedoyin 6,*, Samuel Olatunde Dahunsi 7, Emmanuel Oluwasola Omoju 8, Abigail Oyeronke DickTonye 1, Elizabeth Bolatito Ogunwole 1and Abiola Ayopo Babajide 9 1Department of Economics, Bowen University, Iwo 232101, Nigeria; [email protected] (A.J.A.); [email protected] (A.O.D.); [email protected] (E.B.O.) 2Centre for Econometric & Allied Research, University of Ibadan, Ibadan 200132, Nigeria; [email protected] 3Department of Economics, University of Pretoria, Pretoria 0083, South Africa 4Bank of Industry, Lagos 102273, Nigeria; [email protected] 5Department of Finance, University of Lagos, Lagos 101212, Nigeria 6Department of Accounting and Finance, Landmark University, Omu-Aran 251103, Nigeria 7Directorate of Research, Bowen University, Iwo 232101, Nigeria; [email protected] 8National Institute for Legislative and Democratic Studies (NILDS), Abuja 904101, Nigeria; [email protected] 9Department of Banking and Finance, Covenant University, Ota 112104, Nigeria; [email protected] *Correspondence: [email protected] (A.I.L.); [email protected] (B.B.L.-A.); Tel.: +234-803-523-3567 (A.I.L.) Abstract: AbstractsThis paper examined the nexus between economic growth and exchange rate, remittances, trade, and agricultural output based on data sourced from 1980 to 2018 for 10 selected African economies. We employed both the Dumitrescu and Hurlin time-domain Granger causality test and the Croux and Reusens frequency domain Granger causality test. Results from the timedomain test suggests that causality only exists between economic growth and both exchange rate and trade, with no significant relationship between economic growth and both remittances and agricultural output. When we employed frequency domain model in our analysis, the results suggested that there is a bi-directional temporary and permanent causality between economic growth and exchange rate, trade, agriculture, and remittances. Our results suggest the validity of both the J-Curve and Marshall–Lerner hypotheses in the studied economies. Our study offers some relevant policy implications. Keywords: economic growth; exchange rate; remittances; agricultural output; trade; Africa JEL Classification: E22; F40; F41 1. Introduction Ever since the fall of the Bretton Woods agreement in 1973, economists have devoted time to researching the impact of exchange-rate fluctuation on the economy (BahmaniOskooee et al. 2016b;Chi 2018;Fall 2019;Qureshi and Tsangarides 2012;Romelli et al. 2018;Tunc et al. 2018). A key feature of the post-Bretton era was the adoption of a floating exchange rate regimes by many economies, inducing unpredictable instability in bilateral exchange rates (Ayopo et al. 2015;Bahmani-Oskooee et al. 2016c;Fashina et al. 2018;Lawal et al. 2018;Lawal 2014;Pradhan et al. 2017). The impact of the fluctuation in exchange rate helps in economies characterized by a low state of financial development (BahmaniOskooee and Gelan 2018). Africa’s economies, when compared with economies in other regions such as North America, Europe, and South Asia, are largely developing frontiers, J. Risk Financial Manag. 2022,15, 235. https://doi.org/10.3390/jrfm15060235 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2022,15, 235 2 of 13 with the majority showing signs of potential growth (Lawal et al. 2016,2020;Salisu et al. 2020). The fluctuation in the exchange rate regime has the capacity to impede African growth. African economies are largely agrarians and serve as primary sources of raw material for developed economies, depending largely on the developed economies for importation of finished goods. This suggests that fluctuation in exchange rate regime may have severe effects on African economies by way of dampening the volume of international trade. For instance, risk-averse investors (importers and exporters) may shy away from undertaking high-risk trades (Asteriou et al. 2016;Baek 2013;Hayakawa et al. 2017;Hu and Oxley 2017). In another view, the risk-portfolio hypothesis opined that higher risk connotes higher returns, suggesting risk-induced exchange rate instability could motivate an increase in volume of trade (Nicita 2013;Santana-Gallego and Pérez-Rodríguez 2019; Sharma and Pal 2018;Sugiharti et al. 2020). In addition to agriculture and trade, another salient factor that influences African economic behavior is remittances. (Apergis and Cooray 2018) noted that remittance receipt into developing economies over the years has increased substantially from around USD 31.05 million in 1990 to over USD 581,640 million in the year 2015. The inflow of remittance to transfer-receiving economies is said to be the second-largest foreign exchange inflow after the foreign direct investment (FDI), and higher than overseas development aid (ODA). The beauty of remittance inflow is that it is a ‘bottom up’ source of development finance as it is received directly by households, unlike FDI and ODA, which are institutionally received (Apergis and Cooray 2018;Fromentin 2017;Hathroubi and Aloui 2016). As noted by Hien et al. (2019), chances are high that a huge number of remittances as a means of inflow to emerging economies can induce an upward movement in a country’s real exchange, with a consequent impact on economic growth and competitiveness. This condition is referred to as the Dutch disease. The agricultural sector is a factor in the sub-Saharan Africa SSA growth basket, as the continent is largely agrarian. Agriculture contributes more than 70% in terms of labor force and over 50% of African economies GDP. In addition to natural resources such as Oil, Gold and other solid minerals, agriculture serves as the main source of foreign earnings for most African economies (Adetutu and Ajayi 2020;Djurfeldt 2013;Dorosh and Thurlow 2018; Adom and Adams 2020;Olanipekun et al. 2019). (Caruso et al. 2021;Edwards 2020;Shastri 2021) calibrated the impact of COVID-19 to the discussion of the impact of remittances on economic growth for the Central American economies, twenty-two developing economies, and India, respectively. The results offer varying degrees of outcomes. For instance, while (Shastri 2021) noted that the impact of remittance is countercyclical in relation to economic growth and that it is decoupled from macroeconomic fundamentals for India, (Caruso et al. 2021), on the other hand, noted that remittance negatively affected economic growth in the Central American economies with varying degrees; for example, El Salvador and Nicaragua were mostly affected, while the impact was mild for Panama and Guatemala. Furthermore, the impact on poverty reduction in this region is heterogeneous. (Edwards 2020), on the other hand, noted that the growth rate of the economies studied was largely influenced by remittance, tourism, and migration. The responses of each of these variables to growth are often shaped by exchange-rate volatility. For instance, an increase in domestic currency by a way of appreciation could lead to a significant decrease in remittance received. This suggests that exchange rate fluctuation can alter remittance receipt, thereby affecting economic growth (Bayangos and Jansen 2011;Dutta and Sengupta 2018;Rabbi et al. 2013). The effect on trade and agriculture, for instance, can be seen from an overvalued currency view, which can impose adverse consequences for economic growth by discouraging export competitiveness and imposing upward pressure on the current account (Apergis and Cooray 2018). Evidence abounds to show that African markets adopt a number of measures, including adoption of overvalued exchange regime, foreign exchange allocation and quotas, current account transactions, restrictive exchange-rate rationing resulting from a persistently J. Risk Financial Manag. 2022,15, 235 3 of 13 weak external account, appreciable black-market windows, exchange-rate liberalization, among others, with the intention of mitigating against the impact of exchange-rate volatility in the aggregate economy. Given this scenario, it is important to understand the impact of exchange fluctuation on growth with a focus on remittance, agriculture, and trade, among others. The current study therefore employed panel frequency domain analysis and panel causality test that account for heterogeneous panels in the time domain estimation techniques to analyze quarterly data sourced from 1980 to 2018 on some selected SSA economies to examine the nature of the relationship between economic growth, exchange rate fluctuation, trade, agriculture, and remittance, among other things. This study contributes to literature in three folds: first, in terms of methodology by calibrating the impact of frequency domain to exchange rate, remittance, agriculture, trade and economic growth discussion; second, in terms of scope and coverage, we cover a more recent time than most of the existing studies; third, we are among the first studies that examines exchange rate-growth nexus with a focus on the impact of remittance, trade and agriculture which are the major drivers of SSA economies. Foreshadowing our results, evidence abound to show that there is a bidirectional temporary as well as permanent causality between economic growth and exchange rate and trade, agriculture, and remittance. The rest of the paper is structured as follows: Section 2provides the Literature Review; Section 3provides the Methodology, Section 4present the Results and Policy implication, while Section 5concludes the study. 2. Literature Review This study examined the impact of exchange-rate fluctuation on African economies from two strands of existing international trade and macroeconomics literature. They are: (i) The Marshall–Lerner theory; and (ii) The J-curve theory. We briefly discussed each of these theories below. 2.1. The Marshall–Lerner Theory This theory emanated from the work of Lerner, who opined that the impact of exchange rate on economic growth is best measured through the interplay of import tariffs/quotas and export subsidies (Boyd et al. 2001;Ide and Takayama 1991). The theory noted when the home economy employs tariffs and quotas to control imports and at the same time subsidize export, the value of homeland currency will appreciate as imports become dearer. A country can advance its balance of trade through the coordinated depreciation of its currency. Through this measure (depreciation of local currency), the prices of imported goods become expensive, leading to a fall in demand for imported goods. On the other hand, export becomes relatively cheap, provoking an upward shift in demand for homeland goods at the global market. Economists have since criticized this position, stressing its potential to generate undesired outcomes from the global community, as economies may react to homeland position by imposing their own tariffs and quotas against imported goods from the homeland economy. This reaction can spur upward movement in the global prices of goods and services, which could result in a fall in aggregate demand, alter economic growth, and increase unemployment. It could also promote trade wars between the homeland economy and her trading partners, as experienced in the recent US–China scenario. The Lerner (1946) proposition was modified by the Marshall–Lerner condition, which emphasized the use of exchange rate as a policy instrument to manipulate trade balances, based on the (Marshall 1923) theory of price elasticity of demand (Dong 2017;Eita 2013;Hsing 2010;Nielsen 1987;Sastre 2012). This condition emphasized employment of a strategic currency depreciation measure by the homeland economy to improve the trade balance in the long run. The M-L framework was extended by (Bahmani-Oskooee et al. 2016a), who calibrated the impact of an adjustment process preceding currency depreciation by the homeland economy. J. Risk Financial Manag. 2022,15, 235 4 of 13 2.2. The J-Curve Theory The J-curve theory centers on both the longand short-term impact of devaluation of currency on trade balance. The theory suggests that initially, a currency depreciation is succeeded by an increase in spending on import, given that homeland consumers pay more in their local currency for imported goods as prices had been determined at a higher price/rate in foreign currency before the homeland devalued her currency. On the other hand, the prices of exports become less expensive right after the homeland devalues her currency, since homeland firms are paid relatively less at the initial set prices. These seemingly contrary situations only exist in the short run, as the balance of trade—based on the fact that homeland demand for imported products—will decline, while demand for export of the homeland product will increase; see (Badinger 2019;Bahmani-Oskooee and Aftab 2018;Bahmani-Oskooee et al. 2018;Dogru et al. 2019;Hurley and Papanikolaou 2018; Ivanovski et al. 2020;Sensoy and Tabak 2016). The validity of these two theories has been tested empirically using datasets from a few economies with mixed results. (Dogru et al. 2019) examined the impact of the fluctuation in exchange rate on tourism trade balance between the US and Canada, Mexico, and the UK, based on monthly data sourced from January 1996 to June 2017.The study employed a combination of linear and nonlinear autoregressive distributed lag (ARDL) cointegration, as well as error-correction models, to examine the validity of the M-L hypothesis and the J-curve model in the studied economies, and observed that depreciation of the USD positively exerts on US trade balance with balance with each of the studied trading economies. The study further subjects the test to examination of sectorial impact, with a focus on the tourism subsector and observed that when the USD appreciates, it exerts a negative consequence on the US bilateral tourism basket with Canada and the UK, though it has little or no impact on the US-Mexico tourism trade balance. Theoretically, the results tilts in support of the validity of the M-L condition and rejects the possibility of the J-curve hypothesis for the studied economies. For some selected African economies, (Senadza and Diaba 2017) employed the pooled mean-group estimator of dynamic heterogeneous parcels technique to analyze data sourced from eleven sub-Saharan African markets, comprised of Ghana, Gambia, Kenya, Madagascar, Mauritius, Mozambique, Nigeria, Sierra Leone, Tanzania, Uganda, and Zambia from 1993 to 2014. The study noted that exchange-rate fluctuation has no significant impact on imports, though a significant negative relationship exists on exports, at least in the short run. In a related development, (Bahmani-Oskooee and Gelan 2018) examined the impact of real exchange-rate volatility on trade balance of twelve African economies based on quarterly data sourced from 1971 (Q1) to 2015 (Q4). The authors divided their work into different parts by measuring the distinct impact of real exchange-rate volatility on import and export, focusing on short-run and long-run analysis. The study employed the ARDL bound testing approach and observed that exchange-rate volatility affects trade flows of many of the studied economies in the short run, while the impact was reduced to only five out of the twelve studied economies in the long run. A major flaw of the study is that it adopts only aggregate trade flows as it fails to account for each country’s major trading partners. (Kodongo and Ojah 2011) investigated the link between fluctuation of the real exchange rate and the international portfolio flows of the four largest African economies, Egypt, Morocco, Nigeria, and South Africa, based on monthly data sourced from 1997 (M1) to 2009 (M12). The study divided the work into two distinct periods—a full sample period, and two subperiods based on volume and volatility of portfolio flows. The study employed Granger causality techniques as well as a vector auto regressions model, and observed that the relationship between portfolio flows and fluctuation on foreign exchange rate is essentially country dependent and varies depending on time. (Gkillas et al. 2018) calibrated the impact of changes in stock market behavior to the study of exchange-rate fluctuations in the African economies with a focus on Botswana, J. Risk Financial Manag. 2022,15, 235 5 of 13 Egypt, Kenya, Mauritius, and South Africa. The study employed a combination of an asymmetric threshold approach, error correction, and structure VAR to analyze daily data sourced from 1 January 2001 to 20 January 2018. The study noted that African foreign exchanges respond significantly to large-scale news, and that these markets react speedily to short-term positive/negative stock market news, especially after the global financial crisis. In another strand, (Hien et al. 2019) examined the nexus between real exchange-rate, remittances, and economic growth for 32 selected Asian economies. The study attempted to uncover the existence of Dutch disease on the studied economies resulting from the remittances–exchange rate relationship. The authors employed the system Generalized Methods of Moments (S-GMM) and the linear dynamic panel data (DPD) model to analyze data sourced from the year 2006 to 2016. The authors noted that a positive and significant relationship exists between remittance and exchange rate, such that a 1% increase in remittance inflows will lead to 0.103% appreciation of the exchange rate. The author noted that this relationship undermines the studied economies’ competitiveness, suggesting the existence of the Dutch disease. The study further noted that economies with a high remittance ratio to GDP are not affected by the Dutch disease, and that floating exchange rate dampens real exchange appreciation occasioned by capital flows. For the Philippines, (Mandelman 2013) estimated the impact of the interactions between monetary policy, exchange rate, and remittance on economic growth. The study employed a heterogeneous agent model that calibrated a rule-of-thumb household without access to financial markets to examine the possibility of remittances serving as countercyclical and insurance mechanisms against macroeconomic shocks. The study noted a pure deterministic framework, nominal fixed-exchange-rate regime impedes aggressive real exchange-rate appreciation and performs effectively for recipient households faced with upward trends for remittances. The author concluded that a flexible floating regime suitably mitigates against unanticipated shocks in a business cycle. (Sobiech 2019) calibrated the impact of financial development into a remittance-growth nexus by employing a newly created index of overall financial development, a dynamic factor model, and a battery of panel data estimates to analyze data sourced from 1970 to 2010 from 61 selected developing economies. The study noted that an inverse relationship exists between financial development and the impact of remittances on economic growth, such that the more an economy is financially developed, the smaller the effect of remittances on growth. The study further revealed that a positive relationship is noted between remittances and growth with economies with a low level of financial development. The study concluded that remittances is a pro-growth model, at least in the short run, but for growth to be sustained, economies should pursue financial development. This finding is in line with an early study of (Nguyen 2017) for Vietnam, who noted that the impact of remittances on growth through exchange-rate regime is only positive and significant in the short run. The author pointed out that a 10% increase in remittance will induce a 3% fall in the exchange rate, suggesting the appreciation of domestic currency, with a consequential implication on exports competitiveness. For Moldova, (Nikas and Blouchoutzi 2014) employed fixed-effect ordinary least square (OLS) and a Granger causality test to analysis data sourced from 1995 to 2010 on remittance inflows, exchange rate, and economic growth. The study noted that remittances either depreciate or have no significant impact on real exchange-rate. Taguchi 2017 opined that the ability of Bangladesh to channel remittance inflows to investment, which results into capital accumulation given that the shares of gross fixed capital formation to GDP and that of exports of goods and services increases overtime. The study also noted that remittance inflows were supported by the adoption of a supporting environment for the manufacturing industry, massive investment in infrastructures, establishment of industrial zones, and export-processing areas, all of which aid the impact of remittance on growth. J. Risk Financial Manag. 2022,15, 235 6 of 13 3. Data and Methodology This study employed quarterly data sourced from 1980 to 2018 on economic growth proxy by the real gross domestic product, trade openness, agricultural output, and remittances for 10 of the largest African economies. All the data, except that of agricultural output, were extracted from the World Development Indicators of the World Bank. The data on agricultural output were sourced from the Economic Research service of the US Department of Agriculture (USDA). 3.1. Methodology The current study followed (Aydin and Esen 2018;Aydin 2019;Cowan et al. 2014; Nasreen et al. 2020;Lawal et al. 2019;Ozcan and Ozturk 2019;Lawal et al. 2017,2018; Salisu et al. 2020;Zhang et al. 2016) to employ the (Dumitrescu and Hurlin 2012) panel causality test, as well as that of (Croux and Reusens 2013) frequency-based panel-frequency domain test to examine the nature of the relationship between economic growth and each exchange-rate fluctuation, trade remittance, and agriculture in the economies considered in the empirical analysis. Our choice of the DH test was informed based on three advantages it offers compared with existing methods; (i) it considers cross-section dependency, (ii) the time dimension as well as the size of the cross-section relativity is inconsequential, (iii) it achieves effective results in an unbalanced panel. We first tested the cross-sectional dependence of the variable by employing the (Pesaran 2004) LM test. Thereafter, we employed the panel unit root test using the cross-sectional augment version developed by (Pesaran and Yamagata 2008). This test accounts for the cross-section dependence of the series, and the Slope homogeneity across regions. Furthermore, we examined the causal relationship between the variable by employing our main techniques (Dumitrescu and Hurlin 2012) panel causality test, which does not require pretesting for panel unit root and cointegration analysis (Aydin 2019;Cowan et al. 2014); and the (Croux and Reusens 2013) panel frequency domain test. Our (Dumitrescu and Hurlin 2012) panel unit test can account for heterogeneity in the variables used. 3.1.1. Cross-Sectional Dependence Test SSA economies shared almost the same characteristics: (1) they are all developing economies, serve as sources of raw material to the developed world, are majorly agrarian, mainly low-income economies (except for South Africa), and remittance serves as a major contributor to foreign inflows of capital. There is a need to first conduct a cross-sectional dependence test. Thus, we employed the Pesaran (2004) LM test, expressed as follows: CDLM =v u u t 1 N(N−1) N−1 ∑ i=1 N ∑ j=i+1 T2 . ρIJ −1!(1) As established in the literature, when the cross-sectional size (N) is larger than time dimension (T), we employed the test as expressed in (2), such that CDLM =v u u t 2T N(N−1) N−1 ∑ i=1 N ∑ j=i−1 . ρij (2) Here, . ρij is the correlation between the errors, and the null hypothesis is as follows: H0:COVUit,UIJ=0, no cross-sectional dependence H0:COVUit,UIJ6=0, cross-sectional dependence As a rule, if the ρ -values are smaller than the significance value, we reject the null hypothesis; if otherwise, we cannot reject the null hypothesis. J. Risk Financial Manag. 2022,15, 235 7 of 13 3.1.2. Panel Unit Root Test To account for cross-sectional dependence in the data-generating set, we employed the CIPS panel Unit root test developed by (Pesaran and Yamagata 2008). The DF(CADF) (which is the cross-sectional augmented Dickey–Fuller Static model) is expressed as follows: ∆Yi,t=αi+βiYi,t−1+ciYt+εi,t(3) Here, Yt=1 N∑N i=1Yi,t,∆Yt=1 −N∑N i=1∆Yi,tand εi,trepresent the error term. The CIPS is as follows: CIPS =1 N N ∑ i=1 CADFI(4) We test the null hypothesis against the alternative, the rule say we reject the null hypothesis if the critical values in smaller than the test statistics, if otherwise, we cannot reject the null hypothesis. 3.1.3. Dumitrescu–Hurlin Causality Test This test is expressed as follows: Yi,t=αi+ k ∑ k=1 γi,t−k+ k ∑ k=1 β(k) iXi,t−k+εi,t(5) Here, the individual effect is represented by βi=β(1) i,β(2) i, . . . . . . β(k) i , αi is the individual fixed effects, γ(k) i is the lag parameter, K represents lag length, and β(k) i are the slope parameters. The hypothesis is as follows: H0:βI=0, ∀i=1, . . . , N H1:βI=0, ∀i=1, . . . , N1 βii 6=0, ∀=N1+1, N1+2, . . . , N (6) Under the DH mechanism, the null hypothesis states that no causal relationship exists in at least one cross-section unit. The null hypothesis is a homogeneous phenomenon, while the alternative hypothesis is a heterogeneous output. Under the DH, we reject the null hypothesis if the calculated probability values are smaller than the significance value; if otherwise, the null hypothesis cannot be rejected. To calculate the test statistics (Dumitrescu and Hurlin 2012) first estimated the individual Wald statistics for each of the cross-section units and used the average of estimated individual statistics to calculate the Wald test statistics for the panel. The Panel statistics are expressed as follows: WHnc N.T=1 N N ∑ i=1 WiT (7) Beyond employing time-dimension-based estimates, we employed a frequency-based technique. This was premised on the fact that, while time-domain estimation techniques evaluate the time series as a function of time, frequency-domain-based techniques focus on frequency divided into various frequency components, which allows the spectral density function to be investigated in the frequency-domain framework, thus revealing the periodic fluctuation in the series. Unlike time-domain models, which estimate a single test statistic over time, frequency-domain estimation techniques can examine the causal relationship at different frequencies, and thus offer a robust result across a different domain of the data-generating set. Moreso, in a situation where the relationship between variables occurs in more than one frequency, the time-domain-estimating techniques will be insufficient to effectively maximize the information in the original dataset (Lawal et al. 2017,2019). J. Risk Financial Manag. 2022,15, 235 8 of 13 3.1.4. Frequency Domain Panel Causality Test The study followed (Aydin 2019;Ozcan and Ozturk;Ozcan and Ozturk 2019) to employ the (Croux and Reusens 2013) frequency-domain panel causality test. The SUR model is as follows: Xi,t= p ∑ j=1 βi,jXi,t−j+ p ∑ j=1 γi,t−j+εi,ti=1, 2, 3, . . . , M(8) Here, the variables of country iat time tare represented by Xi,t and Yi,t , respectively. εi,t represents the error terms at time tof the country I; prepresents the lag length, while M represents the number of countries. The constraints on the null hypothesis are stated as follows: p ∑ j=1 γi,jcos(jw)=0, i=1, 2, . . . . . . M(9) s.t. p ∑ j=1 γi,jsin(jw)=0i=1, 2, . . . . . . M(10) To test these constraints, we employed the incremental R2measured test as follows: R2 1=R2−R2 ∗(11) where R2is the unrestricted and R2is the restricted McElroy R2value. R2 1>F(2M,M(T−2P), 1−α)2M M(T−2p)(1−R2)(12) Here, F(2M,M(T−2P),1−α) represents the α critical value of the Fdistribution with 2M and MT−2p degrees of freedom. We reject the null hypothesis if the incremental R 2 value is larger than the F-statistic value; if otherwise, we cannot reject the null hypothesis. 4. Results and Discussions In Table 1, we present the results of the cross-section dependence and unit root test. The first panel presents the result of the cross-sectional dependence based on the (Pesaran 2004) LM test. From the result, it can be deduced that cross-sectional dependence exists in the studied economies. It can also be deduced that the impact of shock from any of the economies studied exerts on others. The panel unit root test result shows that all the variables are stationary (1) in the first differences. In Table 2, we represent the result of the (Dumitrescu and Hurlin 2012) panel causality test. From the result, it can be deduced that bi-directional causality exists between economic growth and exchange rate and trade. The result on the relationship between exchange rate and remittance and agriculture shows that no causality exists, not even at any of the three lag-lengths. Our results are in line with the findings of (Badinger 2019;Eita 2013;Hsing 2010) who documented the existence of a significant relationship between exchange rate and trade. This suggests that economic growth can be spurred up through deliberate manipulation of domestic currency. Our results tilt towards supporting the existence of J-curve in Africa, as evidenced in the existence of the bi-directional relationship between economic growth and exchange rate and trade. Our results contradict the finding of (Bahmani-Oskooee and Halicioglu 2017;Bahmani-Oskooee et al. 2016a;Dong 2017) who noted that J-curve theory does not hold between United States and her trading partners, Turkey and Mexico.