Regional stock exchange development and economic growth in the countries of the West African economic and Monetary Union (WAEMU)

Zonon, Babatounde Ifred Paterne Article Regional stock exchange development and economic growth in the countries of the West African economic and Monetary Union (WAEMU) Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Zonon, Babatounde Ifred Paterne (2021) : Regional stock exchange development and economic growth in the countries of the West African economic and Monetary Union (WAEMU), Economies, ISSN 2227-7099, MDPI, Basel, Vol. 9, Iss. 4, pp. 1-19, https://doi.org/10.3390/economies9040181 This Version is available at: https://hdl.handle.net/10419/257339 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/ economies Article Regional Stock Exchange Development and Economic Growth in the Countries of the West African Economic and Monetary Union (WAEMU) Babatounde Ifred Paterne Zonon   Citation: Zonon, Babatounde Ifred Paterne. 2021. Regional Stock Exchange Development and Economic Growth in the Countries of the West African Economic and Monetary Union (WAEMU). Economies 9: 181. https://doi.org/ 10.3390/economies9040181 Academic Editor: Robert Czudaj Received: 10 October 2021 Accepted: 8 November 2021 Published: 17 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the author. 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/). School of Economics and Management, Southwest Jiaotong University, Jiuli Campus, Chengdu 610031, China; [email protected] Abstract: This study used panel data covering 27 years to investigate the causality between regional stock exchange development and economic growth in the West African Economic and Monetary Union (WAEMU) countries. We performed a homogeneous Granger non-causality with an autoregressive distributed lag model (ARDL) and Markov-switching analysis, using six indicators for the stock and financial market and six for control. The results showed a close economic relationship between WAEMU countries and causality from the regional stock exchange, which supports the supply leading hypothesis. The causality was confirmed in the short and long run, depending on the variable. The causal relationships that support the demand-driven hypothesis were recorded from the economic growth for four market measurements. Keywords: BRVM; WAEMU; regional stock exchange; economic growth; developing countries JEL Classification: E44; G10; O43 1. Introduction Financial market conditions are usually linked to a country’s political and economic conditions. Developed countries have an established market or trade in well-developed markets, whereas emerging financial markets belong to developing countries. Further, pre-emerging markets, or frontier markets, are those belonging to less developed countries. Almost every developed country has many financial services provided by a multitude of institutions operating in a diversified and complex network, which together make up the financial system. However, in developing countries, the financial system offers shorter ranges or lowerquality services. Today, in developing countries, development of the financial system is considered an excellent way to support economic growth and includes the development of financial institutions, financial markets, etc. Rao and Cooray (2011) found that for countries with lower incomes, but not for those with higher ones, there is a close relationship between future economic growth and stock exchange activity. Most low-income countries are still at an early stage of development. With a low Gross Domestic Product (GDP) per capita, and a small population, these frontier market countries (Shum 2015) usually do not even have a domestic financial market. However, they can depend on or be influenced by stock exchange activities. Among these countries, some have established communities, such as the West African Economic and Monetary Union (WAEMU). Categorised as a frontier market (Morgan Stanley Capital International (MSCI) 2021), the WAEMU possesses a financial market known as the Regional Securities Exchange or the Bourse Régionale des Valeurs Mobilières (BRVM). As the saying goes, “United, we are stronger.” WAEMU countries are neither financially stable nor economically strong enough to have different exchanges, and so by pooling resources they can safely navigate internationally. Economies 2021,9, 181. https://doi.org/10.3390/economies9040181 https://www.mdpi.com/journal/economies Economies 2021,9, 181 2 of 19 The BRVM, the common exchange of the eight states (Benin, Burkina-Faso, Cote d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, Togo) of the WAEMU, is one of the very few exchanges worldwide that is regionally integrated and serves as the central exchange for a group of regionally related countries, as noted by Ganti (2019). Despite many challenges, it should be a pioneer for the further integration of the stock exchanges in Africa (James 2018). The eight member states of WAEMU are all developing countries, which are still considered low-income countries; hence, the impact of the regional stock exchange on their economy becomes important. Another factor that should not be overlooked is what WAEMU countries have in common: they are all post-colonial states that are still under monetary control. They use a currency called the CFA franc (Franc of the African Financial Community). Assane and Malamud (2010) pointed out that the constraints of monetary unions affect the financial depth of the CFA countries and hurt economic growth. Koddenbrock and Sylla (2019) later found that the CFA caused extreme external repression of financial and monetary policies because of its dependence on the euro and the US dollar. The reason is that France prints the CFA for the CFA countries and is, therefore, able to control the financial and monetary regulations, the money supply, the credit allocation, the banking activities, and the economic and budgetary policies of these nations. Moreover, as suggested by Tadesse (2018), this control breeds corruption and illegal diversion of public aid between France and its former colonies. For instance, conditional French public aid has forced these African states to spend the money destined for aid on French equipment, goods or contracts with French firms, especially construction and public work firms. Internally, the design of the CFA franc strengthens the constraints that are imposed on all bank–firm relations and central bank policies in the Global South and makes it more difficult to pursue growth strategies for the benefit of the broader population. The CFA franc is a heavy economic burden on WAEMU countries. To support these observations, Sow et al. (2020) explained that various financial development policies have been unsuccessful from independence (1960) to the present. As a result, economic development is slow, not to mention the existence of lasting economic precarity. WAEMU countries are facing an unprecedented economic dilemma stemming from their common currency, whereas, in contrast, most of the lowand high-income countries studied have independent currencies. Given the interest in financial markets, the specifics of developing markets, and WAEMU countries, this study attempts to assess the existence and nature of the relationship between BRVM development and economic growth in WAEMU countries. To this end, a panel Granger non-causality procedure is conducted for the eight WAEMU countries, followed with an Autoregressive Distributed Lag model (ARDL) and a Markovswitching analysis. The contribution of this paper to the empirical literature on stock exchange development and economic growth covers various aspects. First, it examines the WAEMU regional stock exchange, which has not been the subject of many studies up to now. Second, awareness is created concerning whether WAEMU countries should use the BRVM as an economy booster given their current level of development. Third, given the analysis process, cross-sectional dependence is considered for the causality approach, in order to avoid inconsistency and bias in the empirical results. To the best of our knowledge, no such attempt to include the cross-sectional hypothesis in the literature on stock exchange development and economic growth in the WAEMU countries has been made up to date. Fourth, financial development is known to be of a multidimensional nature. Thus, to capture the numerous aspects of the market in the economic development process, we utilize six indicators. Finally, the dataset contains eight countries for a period of 27 years, to abide by the well-known rule of thumb in economics which stipulates that a large dataset is necessary for a relevant panel data analysis. Economies 2021,9, 181 3 of 19 The rest of this paper is divided into four sections: the literature review is outlined in Section 1. Section 2presents the data, describes the variables and the empirical strategy. Section 3presents the analysis and results. The conclusion is given in the last part. 2. Review of Literature Long-run securities, such as bonds and shares, are among the services provided by capital markets. Several authors, such as Ewah et al. (2009) and Oke and Adeusi (2012), stated in their contributions to the literature that the capital market (comprising bond and equity markets) is the market in which mediumto long-term financing can be obtained. The purpose is to develop companies that, as a result, promote a nation’s economic growth. Therefore, the existence of the capital market seems to be an economic booster. However, such an expected positive effect should differentiate between countries to avoid scientific bias, as economic realities differ from country to country. Seven and Yetkiner (2016), who examined 146 middleand high-income countries from 1991 to 2011, found a significant positive correlation between economic growth and stock exchange development. However, almost a decade earlier, Ben Ben Naceur and Samir (2007) had contradicting views on developments in financial markets and economic growth in middle-income countries. By examining a sample of 11 countries in the MENA (the Middle East and North Africa) region, they reported that the development of financial systems could harm economic growth. Later, Stephen and Enisse (2012) pointed out that financial booms rarely encourage growth and suggested re-examining the relationship between real growth and finance. Yu et al. (2012) were more specific, arguing that the positive relationship between growth and finance found in many studies is a long-run relationship. Regarding underdeveloped countries, they mentioned that these countries may face slower economic growth in the short run, although the stock exchange is developing, mainly because of political instability and poorly enforced legal systems. It can be observed here that the positive effect of stock exchange developments on economic growth, especially in the middleand low-income countries, is shadowed by the negative impact of political instability and poor enforcement. Thus, depending on the size and direction of the changes, the positive effect may prevail, or the countries may face a slowdown in their economy in the short or long run. With underdeveloped or low-income countries, however, some authors, such as Haque (2013), who examined the countries of the South Asian Association for Regional Corporation (SAARC), could show that stock exchange developments had no significant influence on economic growth. Rioja and Valev (2014) supported these findings. Using a large cross-country panel, they observed a significant positive impact on stock exchanges and capital accumulation in banks for high-income countries; however, banks have not contributed to productivity growth or capital accumulation in low-income countries. Here, the stock exchange development seems to have a neutral effect on economic growth, and thus we can conclude that capital markets are not among the best channels for economic development in developing countries. This idea is supported by the findings of Ewah et al. (2009). Using multiple regression and ordinary least squares (OLS) on 44 years of data, they conducted a study on Nigeria and showed that the Nigerian capital market, although endowed with the ability to induce growth, has not contributed significantly to Nigeria’s economic growth because of low market capitalisation, small market size, low transaction volume, illiquidity, and few listed companies, etc. Francis and Ofori (2015), working with data on 101 countries from 1980 to 2009, showed that political instability in some underdeveloped countries can hamper the development of stock exchanges and justify their ineffectiveness regarding economic growth because the stock exchange development is positively influenced by policy scores. Karim and Chaudhary (2017) also pointed out that while stock exchange developments contribute to South Asia’s economic growth to some extent, their effect is negligible. A year later, Pan and Mishra (2018) concluded that there is no significant impact on economic Economies 2021,9, 181 4 of 19 growth in economies where the stock exchange plays a minimal role (large economies like China). The preceding lines reveal two major pieces of information. Firstly, the positive or negative impact of the stock exchange on economic growth is to be observed in the long or short run, depending on the income level and political characteristics of the country. Secondly, in most cases, the development of the stock exchange plays an economically neutral role and can even be a hindrance. Instead, some authors have looked only for a connection and have concentrated on analysing the causal relationship between economic growth and stock exchange developments. In Nigeria, Adamu and Sanni (2005) used regression analysis and the Granger causality test to assess the role of the stock exchange in economic growth in Nigeria. A one-way causality between market capitalisation and GDP and a two-way causality between market turnover and GDP growth was discovered. A significantly positive relationship between turnover ratios and GDP growth was also observed. This suggests that market turnover and turnover ratios are essential stock exchange proxies that impact economic growth. In Nigeria too, Kolapo and Adaramola (2012) carried out a Granger causality and Johansen cointegration test. They discovered a long-run positive influence of the capital market on economic growth. Demirguc-Kunt et al. (2012) also showed that the development of banks and stock exchanges shows a parallel relationship with the economic growth of countries. Therefore, even if no direct causality is shown, it is evident that the development of stock exchanges and economic growth advance as a pair. Ikikii and Nzomoi (2013) confirmed this by examining the effects of the development of the stock exchange on economic growth in Kenya. Using quarterly time-series data, they found that stock exchange developments had a positive impact on economic growth. Mittal (2014) also found strong indications of an existing causality in his search for a causal relationship between economic growth and the stock exchange in the newly industrialised countries (NIC). He suggested that the governments of these countries should better direct monetary and fiscal policies towards promoting the growth of the financial sector. Later, Milka (2021) carried out a similar analysis in Serbia. With the Toda-Yamamoto-Dolado-Lütkepohl approach for the Granger causality test, the vector autoregression model, the forecast error variance decomposition, and the impulse response function, he demonstrated the existence of a unidirectional Granger causality from stock exchange development to economic growth. Similarly, Maku (2020) used an autoregressive distributed lag model (ARDL) bound test and examined the relationship between the development of stock exchanges and economic growth in Nigeria. The empirical results confirmed the existence of a long-run relationship between stock exchange development and growth. Ezeibekwe (2021) used a vector error correction model and concluded that using the ratio of market capitalisation to GDP as a proxy for stock exchange development does not contribute significantly to economic growth in Nigeria in the long run. This finding implies that the Nigerian economy is still at a stage of development where the stock exchange cannot play a crucial role in economic development. It is evident that there are still contradictory views regarding the relationship between stock exchange development and economic growth, even though the causal effect of the stock exchange on the economy, especially in underdeveloped countries, has been confirmed by most studies. This hypothesis of the causal effect of stock exchange development on the real economy belongs to supply leading theory. This theory suggests that the accumulation of financial assets improves economic growth; therefore, the development of the financial markets will lead to positive economic growth (McKinnon 1973;Shaw 1973). As observed in some studies, the results sometimes suggest an economy to stock exchange causality or a reverse relationship between the stock exchange and economic growth. This gives rise to another hypothesis, expressed as the demand-driven hypothesis, by Friedman and Schwartz (1963). They suggested that Economies 2021,9, 181 5 of 19 economic growth brings with it the emergence and establishment of financial centres. Simply put, the growth of the real economy endogenously determines financial development. These two theories are part of others drawn from decades of research into the finance– growth nexus. Fink et al. (2006) pointed out that the nexus between the real economy and the financial market can be summarised in five forms: supply leading, demand-driven, no causal relationship, interdependence, and negative causality from finance to growth. One reason for the ambiguity in the existing literature about the financial growth nexus issue could be the proxies used for the stock exchange and the real economy. Using one or two proxies is too restrictive to capture different aspects of the market. As often used, gross domestic product (GDP) also measures an annual global or individual level of production instead of the actual economic growth trend. Another reason may be the geographical limitations and economic diversity of countries. Most studies relate to specific countries and rarely to groups of countries with a financial market. Even if different countries are included in panels, they are often heterogeneous. Most of the countries examined so far have also been economically independent with a local currency, unlike WAEMU countries, which still use a common currency, often referred to as colonial money. Given the peculiarities of the WAEMU context, this paper seeks to determine whether WAEMU countries will benefit from having a financial market. We also test demand-driven and supply-driven hypotheses 3. Research Data, Materials, and Methods 3.1. Data Source and Type The first steps of the BRVM can be traced back to 14 November 1973 when the treaty establishing the WAEMU was signed. Members included Burkina Faso, Benin, Côte d’Ivoire, Niger, Mali, Togo, and Senegal. In 1997, Guinea-Bissau joined, and to date there are eight member countries. On 17 December 1993, the WAEMU Council of Ministers set up a regional financial market and commissioned the West African Central Bank (WACB), known as Banque Centrale des États de l’Afrique de l’Ouest (BCEAO) in French, to manage the project. Introducing the market regime in 1997 gave investors more confidence and helped stimulate business. With e-commerce and the transition to daily basic trading (1999–2001), a steady increase has been observed since 2000. As Proshare (2010) reported, many years of reforms caused significant changes in 2011 and 2013, and saw a sharp decline in operations, with the market recovering and booming in 2012. Since then, the BRVM has experienced a stable evolution. Pan and Mishra (2018) found that most time-series research used a short sampling period because of data limitations. This problem is more pronounced in developing countries where obtaining data is quite a challenge. This study also deals with this problem, even though the focus is on panel data. While updated time-series data can be obtained for most of the companies listed on the BRVM, countries updated market data remain a challenge. The data used in this study were obtained for each WAEMU country from the World Bank (Global Financial Development Database, World Development Indicators Database, and World Governance Indicators Database) and the International Monetary Fund (Financial Development Index Database). The first data collected covered from 1971 to 2020. Data were then filtered variably. For some variables, data were not available as early as 1989, and the last update for some variables was before 2016. Therefore, after filtering, the final data set contained observations from 1989 to 2015. 3.2. Stock Exchange and Other Variables The model used was, like that of Mohtadi and Agarwal (2007), a modified and improved version of the well-known model by Levine and Zervos (1998). The first specificity of this improved model is to overcome the measurement and consistency problem associated with the use of two different data sources by Levine and Zervos (1998). Although the present study uses different data sources, they are filtered and combined into one single Economies 2021,9, 181 6 of 19 final dataset for analysis. Moreover, the data sources are interconnected, which guarantees the consistency of our data. Second, this model uses several measures of stock exchange development to maximize the use of information extracted from the data, as opposed to a single composite measure. It enables the economic growth in year t to be estimated as a function of the stock exchange development in year t-1. This study also used several proxies of the stock exchange development to optimally employ the information obtained from the data instead of a single composite measure. However, some financial indices were added individually to represent the development of both the financial market and of the stock market. The purpose was to capture different aspects of the market and create more space for relevant results to be achieved. The PSE (primary school enrolment) variable was also added to the model, along with CC (corruption control) and OP (oil prices). WAEMU countries face many challenges in primary and secondary education. In a 2015 education report, the Africa-America Institute (AAI) mentions that many African countries cannot keep up with rising enrolments, the results of which are that the learning outcomes have been negatively impacted (Africa-America Institute (AAI) 2015). Therefore, governments need to invest in educational innovation to improve the quality of education in schools. The variables were the following: - Stock Market Capitalisation (SMC): This results from the market capitalisation of listed companies divided by GDP. - Shares Traded Total Value (STTV): This represents the value of the shares traded and the total market capitalisation above GDP. - Stock Market Turnover Ratio (SMTR): This is measured as the market capitalisation of listed companies (MCLC) divided by the total market capitalisation (TMC). Three variables for financial development were taken from the financial development index database and used in this study, as follows: - Financial Markets Access Index (FMA): This represents the level or degree of accessibility of the market by individuals, companies, and countries to raise funds, invest in the market, etc. - Financial Markets Depth Index (FMD): This represents the depth of the market or its ability to take large orders without significantly impacting the prices of the securities. - Financial Development Index (FD): This represents the level of development of the market. Information for the remaining variables was collected from the World Development Indicators database: - Growth: This measure represents the annual growth rate per capita and was used as a dependent variable. - Foreign Direct Investment (FDI): Since FDI is considered a determinant of economic growth, it is used as a control variable. In this study, the variable was the value of net FDI divided by GDP. - Investment (INV): Investments are defined as real investments divided by GDP. - Primary School Enrolment (PSE): PSE was represented as a percentage of the total population. - Corruption Control (CC): CC captures the perception of the extent to which public power is exercised for private gain. The values were estimates (in units of a standard normal distribution) of the country’s score on CC. - Oil Prices (OP): OP represents the pump prices of the best-selling grade of gasoline. - Inflation (INF): INF shows the rate of change in prices in the economy. This variable was used because of the special situation that WAEMU countries have (sharing a common colonial currency, namely the CFA franc). WAEMU member countries were once French colonies. After their independence, among other agreements, they have kept a currency that is printed and entirely dependent on France, their former colonist. Thus, the CFA franc is called colonial currency. Some studies have analysed the parity of the fixed exchange rate between the French franc (FF) and the CFA franc (FCFA), Economies 2021,9, 181 7 of 19 as this exchange rate does not reflect the economic fundamentals of the countries it should serve (Des Adom 2012). Given also the increasing globalisation of financial markets, raw materials, and volatile oil prices, the CFA franc faces challenges, such as significant changes in export prices and a prolonged genuine appreciation of the currency (Gulde and Tsangarides 2008). 3.3. Model Specification According to Mohtadi and Agarwal (2007), the original approach enables control for countries as a block and each country specifically. It is divided into two models: the first model tests whether the stock exchange affects economic growth, and the second examines their relationship. However, our approach was slightly different in that we only checked for the entire region. This is because the WAEMU states have a unique stock exchange that pools the countries’ financial market investments. Checking for country-specific observations would also have meant a small sample of observations over 27 years. As a result, a country-specific process in WAEMU countries could have resulted in biases. We only used one model and directly investigated the relationship between regional financial market development (with an emphasis on stock exchange development) and economic growth in WAEMU countries. Here, the growth (as a dependent variable) at time t was a function of the other variables at time t −1. Our analysis was based on the following panel data model: yit =βj+∑k j=1βjXji(t−1)+ci+δt−1+εi(t−1)(1) where i represents the unit of observation, t indicates the time, j is the observed explanatory variables, t is the time trend, and δ signifies the implicit assumption of a constant rate of change. y stands for the dependent variable and X for the independent variable (k is up to 12 in this study). c represents the unobserved effect, and ε is the error term. β are the individual intercepts or slope coefficients that can differ across the states. We reinforced the model with a step-by-step analysis process that compensated for our dropping of model 1 by Mohtadi and Agarwal (2007) and made the results more robust. For the analysis process, the steps recommended by Menegaki (2019) to implement an ARDL were followed. This procedure was chosen because it is a step-by-step sequence that guarantees control of model misspecifications and more relevant results. The approach of Mohtadi and Agarwal (2007) has long since been proven efficient, but the economic case of the WAEMU requires more precision. With the additional variables in the model (both stock and financial market) and considering the improvement of econometric measurements in the scientific literature, traditional analysis procedures (regression, correlation tests, etc.) may not be enough. Therefore, it was applied in the ARDL procedure. The ARDL is a step-by-step procedure in which the next test to apply depends on the results from the previous test; thus, the final results are more precise. The first stage of the analysis was a cross-sectional dependency test, the second stage was a stationarity test, the third stage was a cointegration test (for non-stationarity), and the last step was a causality test. After these steps, an actual ARLD regression with structural break control was implemented to check the robustness. The control of structural breaks was important to our analysis because of many global events (such as the Afghanistan and Iraq wars, the Asian financial crisis, the attacks on the World Trade Centre, and the 2008 Global Financial Crisis), which occurred during our sample period. We completed our analysis process by performing a dynamic regression (Markov-switching regression). 4. Data Analysis, Results, and Discussion The details of the data for each variable are shown in Table A1. Negative values can be observed for Growth, CC, INV, FDI, and INF in WAEMU countries. Figures A1 and A2 in the Appendix ATable A1 give a visual representation of the evolution of economic growth along with the stock and financial market variables in each of the WAEMU countries. A Economies 2021,9, 181 8 of 19 quick look at the figures does not seem to show that growth for any WAEMU country followed the same trend as other variables. However, higher stock market capitalisation (SMC) appears to be associated with higher economic growth. We had an overall observation of 216 per variable with T = 27 and N = 8. Where T is small and N is large (the common situation when analysing panel data), a Pesaran test, a Friedman test, or a Frees test (Friedman 1937;Frees 1995,2004;Pesaran 2004) are better adapted, but if T > N, the Lagrange Multiplier (LM) test by Breusch and Pagan (1980) is well suited to test the cross-sectional dependency (CD) (see Equation (2)). LMBP =T∑N−1 i=1∑N j=i+1ˆ P2 i(2) where ˆ P2 i is the estimated correlation coefficient between the residuals derived from the panel model estimate. Under H0, there is an asymptotic chi-square distribution (chi2) concerning the LM statistic with a degree of freedom of N (N − 1)/2. i, j, and T are derived from the panel model equation, with t = 1, 2, ..., T. As shown in Table A2, the results suggest the rejection of the null hypothesis; there was a correlation between the panels’ attributes. This means that any shock in one of the WAEMU countries will be transmitted to others. In line with Pesaran and Yamagata (2008), we also checked the nature of our panels. The slope homogeneity null hypothesis H0 is β i = β for each is compared to the heterogeneity hypothesis H1: βi6=βj for pair-wise slopes non-zero fraction for i 6=j. Pesaran and Yamagata (2008) developed standardised dispersion statistics that cover a broader spectrum of analysis. In contrast to Swamy’s (1970) model, which is only limited to models where N is relatively smaller than T, it takes the Pesaran and Yamagata model into account and extends it to wider panels. The model is represented as: e ∆=√N N−1es−k √2k !(3) with es being a modified version of Swamy’s (1970) slope homogeneity test. es=∑N i=1.. βi−ˆ βWFEx0 iMγxi eσ2 i.. βi−ˆ βWFE(4) where .. βi represents the pooled OLS estimator, ˆ βWFE is the pooled estimator of the weighted fixed effect, Mγis an identity matrix, and eσ2 iis the estimator of σ2 i. In addition, the e ∆ test’s small sample properties can be improved with normally distributed errors by using the following variance and mean bias-adjusted version: e ∆. adj =√N    N−1es−Eez. it rvarez. it   (5) with Eez. it=k, and varez. it=2k(T−k−1)/(T+1). The analysis result (Table A2) did not reject the null hypothesis of homogeneous coefficients. Therefore, in the WAEMU area, there will be replication in other countries of every significant economic relationship or change in one country. From the previous analysis, we could have gone straight to a bootstrap panel Granger causality according to Kónya (2006); however, this requires cross-sectional dependency and cross-border heterogeneity, which was not the case for us. Following the analysis, four tests—Hadri Lagrange, Levin Lin Chu, Im-Pesaran-Shin, and Fisher—(Hadri 2000;Levin et al. 2002;Im et al. 2003) were carried out to check the stationarity; this provided information about the degree of integration for each variable. Economies 2021,9, 181 15 of 19 Table A4. Cointegration test. Form Test Statistic p-Value Conclusion Kao Modified Dickey-Fuller −6.8663 0.0000 Cointegrated Dickey-Fuller −8.5123 0.0000 Cointegrated Augmented Dickey-Fuller −6.0538 0.0000 Cointegrated Unadjusted modified Dickey-Fuller −17.1662 0.0000 Cointegrated Unadjusted Dickey-Fuller −11.0471 0.0000 Cointegrated Pedroni Modified Phillips-Perron 0.6275 0.2652 Not Cointegrated Phillips-Perron −6.6608 0.0000 Cointegrated Augmented Dickey-Fuller −7.0636 0.0000 Cointegrated Note: The tests presented in this table have Ho of no cointegration. Ha is that all panels are cointegrated. Table A5. Dynamic ordinary least squares (DOLS) regression. Variables SMC STTV SMTR FMA FMD FD CC OP INV FDI PSE INF Constant Coefficients 12.5360 −313.3423 0.6370 708.5800 53.2830 1.2000 15.1240 −0.2080 1.6970 −0.0560 0.0940 −0.0460 3.6000 z-values (2.66) *** (−3.00) *** (1.80) * (1.76) * (1.73) * (1.67) * (3.17) ** (−0.10) (1.63) * (−0.33) (2.28) ** (−1.84) * (2.19) ** Note: This table reports the results of the DOLS regression for the cointegrated variables. The z-values are shown in parentheses below the coefficients. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. Table A6. Causality test. Variables SMC STTV SMTR FMA FMD FD CC OP INV FDI PSE INF Test 1 Coefficients 10.2097 −262.9142 3.5925 1447.8210 69.6771 88.3132 15.6401 −3.9260 −0.4106 −0.4898 −0.0374 −0.1458 z-values (2.49) ** (−2.21) ** (2.75) *** (2.87) ** (2.02) ** (2.23) ** (9.67) ** (−2.16) ** (−0.98) (−2.12) ** (−2.78) ** (−2.96) ** Conclusion C C C C C C C C NC C C C Test 2 Coefficients 0.0418 0.0007 −0.0395 0.4843 −0.0484 −0.0009 0.0192 0.6036 −0.0464 −0.0660 1.9810 0.5468 z-values (3.38) ** (2.29) ** (−2.19) ** (0.28) (−0.96) (−5.25) ** (3.49) ** (2.10) * (−2.79) ** (−2.08) ** (4.12) (2.95) Conclusion C C C NC NC C C C C C C C Note: The test’s null hypothesis is that the independent variable does not cause the dependent variable. “C” means causes, and “NC” means does not cause. In test 1, the dependent variable is Growth. In test 2, the direction of causality is reversed, with Growth being the independent variable. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. Table A7. Estimated short-term coefficients for ARDL model. ARDL Model (1,0,0,0,0,0,4) Stock and financial market variables Variables SMC STTV SMTR FMA FMD FD Constant Coefficients 0.0198 11.4781 0.7832 110.5114 8.9475 18.27 1.5005 t-values (0.03) (1.66) * (2.48) ** (0.23) (0.26) (0.85) (1.13) LM test aDurb. test bHet. test cNorm. dCusum test Cus. Sq. Single test 1.5010 1.4430 9.9500 49.8200 0.4126 e0.8478 f13.3596 g (0.2206) (0.2297) (0.0016) (0.0498) Control variables Variables CC OP INV FDI PSE INF Constant Coefficients −2.6005 −0.9851 0.2131 −0.1153 0.0119 −0.0527 3.9363 t-values (−3.23) *** (−1.65) * (0.65) (−1.91) * (1.04) (−1.97) (6.70) *** LM test aDurbin test bHet. test cNorm. dCusum test Cus. Sq. Single test 0.0670 0.0640 30.5400 23.7700 0.3432 e0.5521 f5.6293 g (0.7964) (0.8004) (0.0000) (0.6430) Note: The dependent variable is Growth. a Breusch-Godfrey Lagrange Multiplier (LM) test for autocorrelation; the p-values is in parentheses. b Durbin’s alternative test for autocorrelation; the p-values is in parentheses. c Breusch-Pagan/Cook-Weisberg test for heteroscedasticity; the p-values is in parentheses. d White test for homoscedasticity; the p-values is in parentheses. Cusum, Cusum Square, and Single tests are all for the detection of structural breaks. e,f the test statistics are lower than the critical value at 1, 5, and 10%, so we confirm the null hypothesis of no structural break. g p-values are all > 0.050; thus, we confirm the null hypothesis of no structural break. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. Economies 2021,9, 181 16 of 19 Table A8. Markov-switching dynamic regression. Variables SMC STTV SMTR FMA FMD FD CC OP INV FDI PSE INF Constant Regime 1 (High Volatility), Coefficients −7.7008 280.4843 1.1925 5609.459 −45.0576 181.4329 −12.4264 −9.4715 −1.7978 −1.1427 −0.0501 0.1035 −19.0943 z-values (−4.22) *** (2.60) *** (1.82) * (3.53) *** (−0.40) (3.78) *** (−4.95) *** (−5.38) *** (−1.23) (−1.73) * (−1.11) (0.57) (−3.24) *** Regime 2 (Low Volatility), Coefficients 0.8345 −0.09028 0.6175 −38.9273 15.4718 −4.1333 0.673 0.2382 0.0535 −0.1035 0.0189 0.0018 3.7029 z-values (2.12) ** (−1.88) * (2.71) *** (−0.12) (1.69) * (−0.52) (1.07) (0.53) (0.23) (−1.17) (2.31) ** (0.10) (4.13) *** Note: The dependent variable is Growth. *, ** and *** indicate significance at the 10%, 5% and 1% level, respectively. Economies 2021, 9, x FOR PEER REVIEW 16 of 19 (0.7964) (0.8004) (0.0000) (0.6430) Note: The dependent variable is Growth. a Breusch-Godfrey Lagrange Multiplier (LM) test for autocorrelation; the p-values is in parentheses. b Durbin’s alternative test for autocorrelation; the p-values is in parentheses. c Breusch-Pagan/CookWeisberg test for heteroscedasticity; the p-values is in parentheses. d White test for homoscedasticity; the p-values is in parentheses. Cusum, Cusum Square, and Single tests are all for the detection of structural breaks. e,f the test statistics are lower than the critical value at 1, 5, and 10%, so we confirm the null hypothesis of no structural break. g p-values are all > 0.050; thus, we confirm the null hypothesis of no structural break. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. Table A8. Markov-switching dynamic regression. Variables SMC STTV SMTR FMA FMD FD CC OP INV FDI PSE INF Constant Regime 1 (High Volatility), Coefficients −7.7008 280.4843 1.1925 5609.459 −45.0576 181.4329 −12.4264 −9.4715 −1.7978 −1.1427 −0.0501 0.1035 −19.0943 z-values (−4.22) *** (2.60) *** (1.82) * (3.53) *** (−0.40) (3.78) *** (−4.95) *** (−5.38) *** (−1.23) (−1.73) * (−1.11) (0.57) (−3.24) *** Regime 2 (Low Volatility), Coefficients 0.8345 −0.09028 0.6175 −38.9273 15.4718 −4.1333 0.673 0.2382 0.0535 −0.1035 0.0189 0.0018 3.7029 z-values (2.12) ** (−1.88) * (2.71) *** (−0.12) (1.69) * (−0.52) (1.07) (0.53) (0.23) (−1.17) (2.31) ** (0.10) (4.13) *** Note: The dependent variable is Growth. *, ** and *** indicate significance at the 10%, 5% and 1% level, respectively. Figure A1. Economic growth and selected stock and financial market variables’ evolution from 1989 to 2015 in Benin, Burkina Faso, Cote d’Ivoire, and Guinea Bissau. Source: Prepared by authors, based on collected data. Figure A1. 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