FACTORS INFLUENCING FOREIGN DIRECT INVESTMENT IN ECONOMIC ... · development positively influences FDI inflows in ECOWAS countries excluding Nigeria ... such as economic growth,

INTERNATIONAL ECONOMICS & FINANCE JOURNALVol. 6, No. 2, July-December (2011) : 187-207

* Department of Economics, University of Botswana, [email protected]; [email protected]

FACTORS INFLUENCING FOREIGN DIRECT INVESTMENTIN ECONOMIC COMMUNITY OF WEST AFRICAN

STATES (ECOWAS)

C. Njoku, F. N. Okurut1 and M. Bakwena

Abstract: This paper assesses the impact of some selected macroeconomics variables on ForeignDirect Investment (FDI) inflows in Economic Community of West African States (ECOWAS) usingan unbalanced panel data of all ECOWAS member states from 1975 to 2007. To achieve this, Panelunit root test, Kao- residual Cointegration test and Hausman test were conducted before using apanel dynamic ordinary least square estimation under fixed effect specification. Infrastructuraldevelopment positively influences FDI inflows in ECOWAS countries excluding Nigeria and boththe lagged and leaded trade openness have positive influence on FDI inflows in ECOWAS countriesand statistically significant. On the other hand, Lagged and leaded of financial development havenegative influence on FDI inflows in ECOWAS and are statistically significant. The policiesincorporating national goals should be put in place in order to properly screen the sustainability ofFDI applications before issuing license of operation to FDI investors.

Jel Classification: F21 International Investment, Long Term Capital Movements

Key Words: Foreign Direct Investment Africa

1. INTRODUCTION

African countries are keen to realize high rate of economic development as well as attractmore capital investment from abroad. Foreign direct investment (FDI) is an example ofcapital investment2. Studies have shown that FDI has the potential of transferringtechnology, capital and knowledge to the host countries (Addison and Mavrotas 2004;Dunning and Hamdani, 1997). Therefore, FDI inflows are of paramount importance notonly in addressing the capital shortage issue but also in achieving some of MillenniumDevelopment Goals (MDG’s) (Ajayi, 2009).

Africa continent received just 2.5 per cent of FDI capital injection in the world; WestAfrica received 0.6 per cent of it, and 33.1 per cent of FDI inflows in Africa over the periodof this paper. It is obvious that African countries should put more effort in attracting FDIbecause of its contributions to the economy. As much as FDI inflows in the world haveincreased in value over the years, from $26.6 billion in 1975 to $1.4 trillion as it peak in2000, decline to $56.5 billion in 2003, and increases to $2.1trillion in 2007, correspondingFDI inflows to Africa decreased from 3.4 per cent in 1975 to 0.7 per cent in 2000, and 3.0per cent in 2007. In spite of political and economic instability in Western Africa region, theregion is the second recipients of FDI inflows in Africa after Northern Africa. One might

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188 International Economics and Finance Journal

ask what is/are behind Western Africa success and why is FDI inflows to Africa continentvery insignificant?

According to Calvo et al. (1993: 108–51), the total FDI inflows in a country isstimulated largely by push factors, such as economic growth, return on investment andinterest rates in industrial countries. Apart from the push factors, there are other factorswhich are known as pull factors that decide the allocation of the available resource toLDCs (Collins, 2002).

In addition to push and pull factors, investor’s motives are of paramount importance inchoosing destination and types of investment. The reason is that foreign direct investorsare neither Non-government organization nor philanthropists, but are profit maximizingagents. Some of their motives are correlated with the type of investment. For example, anatural-resource-seeking investor aims to exploit the natural resource endowments of thatcountry. This investor will mostly channel his/her funds to companies extracting oil (inNigeria, and Cote d’Ivoire), gold (in Ghana) and diamond (in Botswana), etc. Other typesof investor are; Market-seeking investors who aim at taking advantage of new markets interms of its size and/or growth. Efficiency-seeking investors take advantage of specialfeatures such as the costs of labor, the skills of the labor force, and the quality andefficiency of infrastructure. Finally, strategic-asset-seeking FDI investors locate a placewhere they can take advantage of what is readily available in terms of research anddevelopment and other benefits. Unfortunately, strategic-asset seeking FDI investors arenot common in Sub-Sahara Africa except South Africa (Basu and Srinivasan, 2002).Therefore, one of the tenets of this paper was to understand the behavior of someselected macroeconomics variables contribution to FDI inflows in Western Africa, inparticular ECOWAS member states. This paper has provided an empirical explanation ofthe impact of some selected macroeconomics variables associated with investors’motive, the pull and push factors on FDI inflows to L‘Union Economique et MonétaireOuestafricaine (UEMOA) and non-UEMOA members of ECOWAS. The paper isorganized as follows; section two contains an overview of ECOWAS member stateseconomy, while section three review the theoretical and empirical literature from otherrelated studies, from which the paper derive factors having a potential impact on FDIinflows in ECOWAS. The methodology, econometric specification and estimationstrategy are presented in section four. Empirical results and their interpretation arediscussed in section five, while section six summaries, present policy discussion andsome suggestions for extensions.

2. OVERVIEW OF ECOWAS MEMBER STATES ECONOMY

ECOWAS is in Western Africa. It is a regional institute of 15 West African nations formedin 1975. There were 16 nations in the organization until Mauritania withdrew membershipfrom ECOWAS in 2002. The major purpose of forming ECOWAS was to realize economicintegration, socio-political interactions and shared development in order to form a unifiedeconomic zone in Western Africa3.

Factors Influencing Foreign Direct Investment in Economic Community of West African... 189

ECOWAS members are further divided into L‘Union Economique et MonétaireOuestafricaine (UEMOA) and non-UEMOA members. UEMOA was enacted in 1994,which was previously two separate organizations: the Communauté Economique del’Afrique de l’Ouest (CEAO) as a kind of francophone ECOWAS and the CFA Franc zonemonetary union4. Guinea is the remaining francophone country in the sub-region, which isnot a UEMOA member. Guinean government has continually distancing itself fromFrance. Guinea uses its own currency. Another monetary union that is coming up fromECOWAS member states is West Africa Monetary Zone (WAMZ5).

2.1. Demographic Characteristic of ECOWAS Member States

Eventually, all the countries in ECOWAS had their independence in 1960s with exceptionof Ghana, Guinea, Cape Verde, Guinea Bissau, and Liberia. These countries have hadmilitary personnel attempt to take over the government at least once. For example, Nigeriahave had more coups d’état among ECOWAS member states and 6 out of 15 attempted,plotted, alleged had succeeded. Nigeria total area is less than that of Mali and Niger but itspopulation is greater than the entire population of ECOWAS member states exceptNigeria. Majorities of ECOWAS population are between the ages of 15-64 years. Most ofECOWAS member states population lives in rural area except Cape Verde, Gambia, andLiberia. Like other parts of the continent, ECOWAS member states continue to face manydevelopment problems. These include low literacy rates, high unemployment rates andhigh adult prevalence H IV/AIDS, which constitute a heavy burden for the ECOWAScountries, and hence deter their development. For example, literacy rate in Burkina Faso,Niger, and Guinea is below 30.0 per cent as compared to Togo, Cape Verde, Ghana,Liberia, and Nigeria that are above 50.0 per cent. Unemployment rate, people living belowtwo dollars daily, and HIV/AIDs adult prevalence among ECOWAS member states aregenerally high (2010 Factbook).

2.2. Socio-economic Characteristics of ECOWAS Member States

In general, industrial sector has less developed in ECOWAS member states than othersectors. This can be seemed from its contribution to GDP. But in some countriesagriculture contributes more to their GDP and employed most of their labor forcepopulation. For instance, Agricultural sector contributes more (76.9 per cent) to LiberiaGDP than other sectors as compared to Cape Verde, which services contribute more (74.3per cent) to its GDP than other sectors. Major export commodities among ECOWASmember states are agricultural products and to some like Cote d’Ivoire and Nigeria ispetroleum which contributes much to their economy. Secondary economic activities inCote d’Ivoire and Nigeria are oil exploration and refining, followed by natural gasdistribution. Other industries among ECOWAS member states include power generation,bus and truck assembly, construction materials, ship building and repair, food items,beverages, and timber products. Foodstuff, capital equipment, vehicle/parts, and fuel arethe main imports of ECOWAS member states. There is an intra-trade between ECOWAS


member states. France, Germany, USA, Netherlands, China, Japan, Belgium, and Ukraineare some inter- trade partners of ECOWAS member states (World Bank worlddevelopment indicator).

Nigeria is the biggest player in ECOWAS economy. Nigeria gross domestic productsby purchasing power parity is more than three times of the entire ECOWAS member statesexcept Nigeria. Nigeria consumption of electricity is more than five times of the entireECOWAS member states. Cote d’Ivoire is the only country in ECOWAS member statesthat has current account surplus and the second oil producer order than Nigeria. Eightcountries among ECOWAS member states have adopted CFA franc as their new currencyand the rest are using their individual currency (2010 Factbook).

2.3. FDI Inflows in ECOWAS Countries

The highest recipient of FDI inflows among ECOWAS member states from 1975 to 2007 isNigeria. Nigeria received about 72.1 per cent of the total inflows of FDI in ECOWAScountries, followed by Cote d’Ivoire that received about 6.6 per cent and the least recipientwas Guinea Bissau (0.1 per cent) of FDI inflows among ECOWAS countries (WorldInvestment Report UNCTAD (2009)).

3. LITERATURE REVIEW

3.1. Review of the Theoretical Literature

There are a number of theories explaining FDI in developing countries. FDI theories aremainly based on imperfect market conditions and imperfect capital market. Some of thesetheories consider the effect of non-economic factors on FDI while others explain theemergence of Multinational Corporations (MNCs) exclusively among developingcountries. For instance, neo-classical economic theory assumes a free capital markets anddiminishing returns. According to trade theory, capital should flow from capital abundantcountries (developed countries) to capital scarce countries (developing countries) until themarginal returns to capital in both countries are equal. Such flows can contributeimmensely to closing domestic savings gap in developing countries (Mundell, 1957). Theimplication is that, it is more profitable to invest capital where it is scarce than where it isabundant. It holds on the assumption that there should be no outflows of FDI fromdeveloping countries.

Apart from trade theory, Dunning’s eclectic paradigm explained in detail someimportant variables essential in attracting FDI inflows in the developing countries.Eclectic paradigm is toward the view point of the firm. Dunning’s eclectic paradigm whichis the combination of the imperfect market-based theories of FDI, that is, industrialorganization theory, internalization theory and location theory. It postulates that, at anygiven time, the stock of foreign assets owned by a multinational firm is determined by acombination of firm specific or ownership advantage, the extent of location boundendowments, and the extent to which these advantages are marketed within the various


units of the firm. He further argued that the reason why FDI inflows are greater in onecountry but not in another is the country’s locational advantage. (Dunning, 1980, 1993).

Another theory for FDI is development theory, according to the theory, FDI contributeto growth in the real income of the host country in the following ways; FDI supplementslow domestic savings in the process of capital accumulation. It implies that, FDI serves tostimulate domestic investment and the entire investment in the host country (Ajayi, 2006).The theory further argued that FDI creates externalities through technology transfer andspillovers (Carkovic and Levine, 2005). That is, FDI brings new knowledge andinvestments in physical infrastructure like roads and factories. It implies that, FDIimprove overall economic growth through promoting competition in the domestic inputmarket leading to the domestic firms to adopt more efficient methods in their productionprocess (Adams, 2009). On the contrary, dependency theorists argue that reliance onforeign investment is likely to have a negative effect on economic growth and theallocation of income. Bornschier and Chase-Dunn (1985) argued that foreign investmentgenerates an industrial make up that is monopolistic in nature. They further claim thatcountries that are wholly dependent on FDI will experience stagnation, unemploymentand increasing inequality. Other factors which can cause the FDI to have a negativeeffect on growth in Africa are lack of competition and distorted regulatory and incentiveframework (UNCTAD report, 2007). Based on the foreign direct investment theoriesreviewed so far, it is improper to accept any of the above theory to explain the impact ofselected macroeconomics variable on foreign direct investment inflows to ECOWAScountries. This is because the above theories was not able to combine both the investors’motives related factors and that of the host countries related factors detrimental toforeign direct investment inflows. This is where the integrative theory was adopted bythis paper.

The integrative theory gives a clearer view of the impact of some selectedmacroeconomics variables on FDI by analyzing it from the perspectives of host countriesas well as investors. It incorporated the effect of the short-run, contemporaneous and thelong-run effect of the factors on FDI into its theory. For instance, eclectic paradigm, whichis the combination of the firm and internalization theories, and industrial organizationtheory tackle FDI determinants from the viewpoint of the firm. The neoclassical andperfect market theories examine FDI from the perspective of free trade. The developmentand dependency theories shed light on the perspective of the host nation while integrativetheory integrates neoclassical, developments, dependency and eclectic factors which arecritical in attracting FDI inflows into a country in its theory.

According to integrative theory, foreign direct inflows are a function of the hostcountry factors, the firms’ factors and the foreign direct investors’ related factors.Integrative theories account for the multiplicity of heterogeneous variables involved inattracting the FDI inflows in ECOWAS countries. It also forms the paper theoreticalbackground of its assessment of selected macroeconomics variables impact on foreigndirect investment among ECOWAS countries. Integrative theory is presented as;


FDI inflows = F (factors related to the host country, firm specific factors and investorsfactors related to their motives).

The interactive theoretical framework gives the bases for adopting dynamic ordinary leastsquare (DOLS) model by this paper to assess the impact of the selected macroeconomicsvariables on foreign direct inflows in ECOWAS countries. The model captured all therelevant factors identified by interactive theory.

3.2. Review of Empirical Studies

Neoclassical economists have argued that FDI influences economic growth by increasingthe amount of capital per person. It does not influence long-run economic growth due todiminishing returns to capital. A study conducted in East Asia have shown that FDI has apositive effect on less advanced economies’ output than advanced economy (Bende-Nanende et al. 2002). However, there was no consensus on the positive impact of FDI ongrowth. For instance, Globeram (1979) paper on some selected developing countriesstipulated that FDI has a significant effect on economic growth of the countries studied viaits positive effect on domestic firms. Eighteen years later, Regnanet (1997) conducted astudy on the same countries which he found the same result as Globeram. On the contrary,a study conducted in Asia countries by Aitken et al. (1997) found a negative relationshipbetween FDI and economic growth. Other found inconclusive result. Another variable thatmay help to stimulate economy growth of a country via FDI inflow is the level of financialdevelopment.

Financial development can make it easier for aid recipient countries to assess aid fromforeign aid donors (Nkusu and Sayek, 2004). Furthermore, Beck et al. (2002) studiesshows that countries with an effective financial sector have a comparative advantage inmanufacturing industries. Hermes and Lensink (2003) argued that financial developmenthas a positive effect on FDI. Their reasons advanced for the result were; a developedfinancial system mobilizes savings efficiently and as such may increase the amount ofresources available to undertake investment. Financial development also speeds upadoption of new technologies by minimizing the risk associated with it. With a welldeveloped financial system, foreign investors are able to deduce how much they canborrow for innovative activities and are able to make investment financing decision aheadof time. It also increases liquidity and, thus, facilitates trading of financial instruments andtiming and settlement of such trades (Easterly and Levine, 1997). This will also lead togreater FDI inflows as the projects can be undertaken with lesser time being spent insettling the trades. Nabamita and Sanjukta (2008) conducted a study on developingcountries and their findings were that there was a positive relationship between financialdevelopment and FDI inflows after a threshold level of financial development is reached.Beyond that level the impact becomes negative.

In theory, openness impact on FDI inflows to an economy defers based on theinvestor’s motivation for engaging in FDI activities (Brainard, 1997; Markusen andMaskus, 2002; Navaretti and Venables, 2004). The more open an economy becomes the


better for non-market seeking investors who would like to use the destination as an exportbase. On the contrary, market seeking investor who’s their target market is the hostcountries would prefer less openness. There are many empirical studies that includeopenness as one of the factors that affect FDI. Chakrabarti (2001) study on thedeterminants of FDI inflows to developing countries, he found that openness is animportant determinant of FDI and is positively related to FDI inflows. Moosa and Cardak(2006) conducted a similar study as Chakrabarti’s. They found that export as a per centageof GDP positively affects FDI inflows. Openness is found to be positively and significantlyrelated to FDI inflows in developing countries (Lucas, 1990). Globerman and Shapiro(2002), papers concluded that openness is statistically insignificant and does not affectFDI inflows. Some other researchers found an inclusive result with respect to FDI, such asGoodspeed et al. (2006). They found a positive and significant relation with FDI inflows inone model and insignificant in other specifications of the empirical model. When testingthe vertical, horizontal and knowledge capital models, Markusen and Maskus (2002)concluded that trade precincts might be less significant as a motivation for horizontaltariff-jumping investments in developing countries. This means that a greater degree ofopenness will have less of an effect on market-seeking investments in developing countriesin comparison to developed countries. Morisset (2000) used panel data of 29 Africancountries over the period 1990-1997 to study the main determinants of FDI in Africa.Morisset study stipulated that GDP growth rate and openness are positively related to FDIin Africa.

Return on investment is a critical factor for rational investors. Foreign direct investorswill go to countries that pay a higher return on capital. According to Asiedu (2001),measuring rate of return in developing countries is a difficult process. The reason is thatthe capital market in developing countries is under developed. The measurement whichhas been adopted for this variable is to use the inverse of real GDP per capita to measurethe return on capital. The empirical result of the relationship between real GDP per capitaand FDI is diverse. This is based on the argument that a higher GDP per capita impliesbetter prospects for FDI in the host country. One of the ways in which FDI positively affecteconomic growth is through release of binding constraint on domestic savings in theeconomy. FDI does it through the process of Capital accumulation. Given that domesticsavings in Africa is very low, which may result to low investment rate and hence lead tosluggish economic development (Ajayi, 2006).

A study conducted by Khan and Bamou (2006) on some selected developing countries.They found that FDI has positive effect on domestic savings through foreign savings.Other studies results are consistent with those of Khan and Bamou (Asante, 2006,Abedian, 2003). Less emphasize has been put on trying to see effect of an improvingdomestic savings on FDI. Other variables which influence FDI inflows to a country areinflation, political instability, natural resources and market sizes.

Asiedu (2003) used panel data consisting of 22 African countries for the period 1984–2000 to examine empirically, the impact of several variables including natural resource


endowment, macroeconomic instability, corruption, effectiveness of the legal system andpolitical instability on FDI flows. He also found a negative relation between FDI andinflation and positive effect with natural resource endowment. Eibadawi and Mwega(1997) used panel data in their econometric analysis of the determinants of FDI flows inAfrica. They argue that market size is relatively not important in explaining FDI flows inAfrica.

4. METHODOLOGY

4.1. Types and Sources of Data

Secondary annual data were used in this paper. The data were sourced from United NationsConference on Trade and Development (UNCTAD) data base, World Bank Developmentindicators database and Conflict Trends in Africa (Marshall) database. The periodconsidered for the paper was 1975-2007. The time period was adopted so as to include allECOWAS member states effect and excludes the effect of the recent financial crisis on thefindings.

4.2. Model Specification

The paper used the following variables in the model specification to assess the impact ofeconomic growth (EG), inflation (INF), financial development (FD), openness (OPN),domestic savings (DS), infrastructure development (INFRD), government size (GS),political Instability (PI), market size (MZ), return on investment (RI), Labor forceparticipation (LF) and domestic investment (DI) on Foreign Direct Investment (FDI) inECOWAS countries.

The Dynamic Ordinary Least Square Estimates for Heterogeneous Panel

.'

i

i

q

it i it ij it j itj q

FDI X C X V+=−

= α + β + ∆ +∑ (1)

FDI = Foreign direct investment inflows, i represent the individual country, t is the time, Xrepresent the number of explanatory variables included in the model, q

i and –q

iare

different lead and lag respectively, ∆ is the difference, and itV is the error term, α, β and Care coefficients.

4.3. Techniques for Data Analysis

This paper first tested for stationarity of the variables that was included in the model. Thevariables that were non-stationary at level were tested for the existence of a co-integratingrelationship. The cointegration test was conducted in order to check for a long run relationbetween the non-stationary variables. Before estimating panel DOLS, Hausman test wasconducted to determine whether to use a fixed or random effect specification. The next


step was to use a Dynamic ordinary least square (DOLS) regression to estimate the impactof the selected macroeconomics variables on FDI inflows in ECOWAS countries. This wasdone after conducting stationarity test.

5. EMPIRICAL RESULTS AND INTERPRETATIONS

5.1. Panel Unit Root Test

The panel unit root tests were carried out to test for the stationarity of the variables used inthe model specification. The tests are necessary in order to avoid spurious results. Thispaper focus on two type of first generation6 panel unit root test such as Levin, Lin and Chu(LLC) (2002) and Fisher-Type test using Augmented Dickey Fuller ADF (Maddala andWu, (1999)). The reasons why the paper has chosen LLC and ADF over Im, Pesaran andShin (IPS) (2003) is that; LLC generalize the Quah’s model which allows for heterogeneityof individual deterministic effects (constant and/or linear time trend) and heterogeneousserial correlation structure of the error terms assuming homogeneous first orderautoregressive parameters. They assume that both N and T tend to infinity but T increase ata faster rate, such that N/T tends to zero. ADF is similar to IPS but both LLC and Fisher-ADF allows for an unbalanced data in the test while IPS does not allow for unbalanceddata. Therefore, the tests are suitable for this paper because it is based on unbalanced dataspecifications.

Levin, Lin and Chu (2002) and ADF- Fisher Chi-square were proposed by Madala andWu (1999) and they both have the same null hypothesis of unit root is present against italternatives.

Table 1ECOWAS Member States Panel Unit Root

Variables Intercept Intercept and trend

LLC ADF-Fishers LLC ADF-FishersChi-square Chi-square

Levels 1st diff Levels 1st diff Levels 1st diff Levels 1st diff

FDI 2.90825 -7.32978* 41.6313 199.871* 2.90825 -7.32978* 41.6313 199.871*EG -9.04224* ASL 240.492* ASL -8.00156* ASL 178.964* ASLOPN -0.61415 -7.54076* 31.3583 202.669* 0.13314 -6.01976* 0.2994 162.380*RI -0.46799 -8.50649* 18.0344 -12.2953* -0.57596 -7.04053* 38.5449 172.060*PI -5.81922* ASL 83.5261* ASL -7.34566* ASL 97.2629* ASLINF -7.39749* ASL 96.0524* ASL -6.54017* ASL 76.3318* ASLINFRD 0.74238 -8.36499* 32.2739 159.226* 1.71856 -6.91814* 28.1081 121.159*DS -0.68679 -10.4327* 44.3827** ASL -0.38568 -7.87177* 45.1458** ASLGS -1.50682 -10.8440* 42.7718 199.602* -1.7340** ASL 46.2286** ASLDI -0.09715 -9.49561* 43.1234 228.708* 0.61244 -8.01928* 31.6263 198.158*LnMS 0.00594 -10.0891* 10.4927 178.233* 0.92652 -9.27201* 30.3032 152.173*FD 0.59657 -9.09735* 27.5759 142.875* -0.78129 -7.76449* 36.3643 110.878*

Probabilities are computed assuming asymptotic normality. Critical values 1% *and Critical values at 5%**ASL represented Already Stationary at Level


Table 1 shows panel unit test using LLC and ADF-Fisher Chi-square test statistic forboth intercept and intercept and trend. The stationarity tests of the 12 variables used in themodel specification for ECOWAS were conducted. Both the intercept and the interceptand the trend panel unit root test were tabulated. The table also shows that both the two teststatistic used for testing the presence of unit root test presented a consistent result. It isevidence from the table that only 3 out of the 12 variables were stationary at levels whenonly intercept was included in the LLC panel unit root test. The stationary variables atlevels were Economic Growth (EG), inflation (INF) and Political instability (PI) and therest of the variable became stationary after first differencing. The same variables werestationary with ADF-Fisher chi-square test including domestic savings (DS) and the rest ofthe variable became stationary after first differencing. But when intercept and trend areincluding in the specification, LLC test included government size (GS) together with theother three became stationary at levels and the ADF-Fisher Chi-square included GS withfour variables in the only intercept specification.

Table 2Panel Unit Root for ECOWAS Member States Except Nigeria

Variables Intercept Intercept and trend



FDI 3.06259 -7.04779* 40.5952 182.262* 1.30428 -4.58847* 40.3113 146.548*

EG -8.70432* ASL 198.679* ASL -7.32793* ASL 168.658* ASL

OPN -0.69071 -7.14479* 30.4338 189.862* 0.00976 -5.73502* 32.4506 153.125*

RI -0.41473 -8.12408* 16.3637 191.722* -0.77336 -6.26223* 38.4922 160.032*

PI -5.00633* ASL 73.0001* ASL -6.84620* ASL 90.0405* ASL

INF -7.05269* ASL 88.7995* ASL -6.2327* ASL 71.8365* ASL

INFRD 0.81483 -8.24375* 31.0972 152.507* 1.2001 -6.68082* 28.0549 115.795*

DS -0.67863 -10.2032* 39.8308 231.166* -0.61422 -7.86572* 41.8807** ASL

GS -1.35351 -10.1691* 40.6494 183.339* -1.5500 -8.82187* 43.5676** ASL

DI 0.81632 -8.4329* 39.0797 208.582* 1.27655 -6.96234* 29.4520 179.714*

LnMS -0.21128 -10.2198* 10.4718 173.392* 0.7557 -9.4611* 29.6148 148.819*

FD 0.4862 -9.0447* 25.0938 136.459* -0.9985 -7.7156* 34.9440 107.645*

Probabilities are computed assuming asymptotic normality. Critical values 1% * and Critical values at 5%**

Table 2 shows panel unit root test for 12 variable used for ECOWAS member statesmodel specification except Nigeria7; It was found that when included only intercept, EG, PIand INF were stationary at levels with both LLC test and ADF-Fisher Chi-square test whilethe rest of the variables became stationary after first differencing. It differs from ADF-Fisher Chi-square test when intercept and trend are included in the test, 2 additionalvariables (Domestic Savings and Government Size) were stationary making a total of 5variables stationary at levels.


Table 3Panel Unit Roots for UEMOA in ECOWAS

Variables Intercept Intercept and Trend



FDI 0.86592 -3.3781* 19.7800 92.7312* 1.3255 -1.1353 16.0738 71.6919*

EG -8.8399* ASL 113.204* ASL -7.9964* ASL 95.7818* ASL

OPN -0.9406 -6.3517* 20.2368 94.0980* -0.6181 -4.8107* 18.5783 71.2757*

RI -0.6684 -8.3941* 5.9555 109.192* -0.5547 -7.2121* 22.0931 92.8753*

PI -3.0577* ASL 52.2073* ASL -2.5006* ASL 45.9119* ASL

INF -6.3157* ASL 53.7140* ASL -5.9198* ASL 43.3453* ASL

INFRD 1.4846 -5.2783* 10.6269 68.3375* 0.90403 -3.8468* 16.6600 48.7138*

DS -1.3098 -8.2121* 19.8128 128.1720* -1.5478 -6.7062* 23.8340** ASL

GS -1.7252** ASL 27.9052** ASL -0.8093 -6.2225* 21.4164 87.5755*

DI 1.3962 -6.4204* 19.8570 105.1870* 0.7855 -5.9725* 13.8731 89.7752*

LnMS 1.3980 -8.4105* 2.7735 104.4000* 0.3876 -7.9260* 17.1668 90.5189*

FD -0.3492 -8.1831* 9.8354 79.8479* -0.3945 -7.5424* 17.1474 58.8189*

Probabilities are computed assuming asymptotic normality. Critical values 1% *, Critical values at 5%**

Table 3 explained the nature of the variables used in UEMOA model specification.There were 12 variables included in panel unit root test. LLC test shows that 4 out of the 12variables were stationary at levels with only intercept in the test equation. The variableswhich were stationary at levels with LLC test are EG, PI, INF, and GS. The same variableswere stationary with ADF-Fisher Chi-square test with only intercept. The rest of thevariables in both LLC and ADF-Fisher Chi-square test became stationary after firstdifferencing. When intercept and trend are included in the test equation, EG, PI, INF andLF became stationary at levels with LLC test while the rest of the variables becamestationary after first differencing. On the other hand, with ADF-Fisher Chi-square test, 5out of 12 variables became stationary at levels and the rest became stationary after firstdifferencing.

Table 4 discusses the outcome of panel unit test for all the variables used in proposedWAMZ members’ model specification. LLC test and ADF-Fisher Chi-square test withintercept only in the test equation shows that 3 out of 12 variables were stationary at levelsat significant level of less than one per cent. These variables are EG, PI and INF, and thenthe rest became stationary after first differencing. When intercept and trend are included inthe test equation the same variables were stationary at levels in addition to GS in both LLCtest and ADF-Fisher Chi-square test. The rest of the variables became stationary after firstdifferencing.

After testing for the stationary test, it became proper for the paper to test for theexistence of long run relationship between the variables that were not stationary at level.


However, the DOLS model adopted included the long run and short run effect but in theadvert of non significant lags and lead variable the long run relationship disappears.Therefore, panel Cointegration becomes a necessary test that was conducted because theprobability of getting an insignificant lag and lead variables is certain.

5.2. Panel Cointegration Test

The paper employed Kao residuals panel co-integration tests to investigate the long-runrelationship between some selected macroeconomics variables and foreign directinvestment inflows. The use of panel co-integration allows one to determine the long-runstructure of the selected macroeconomics variables in a dynamic setting. Panel co-integration test avoids both the problems involved in using static co-integration testing andthe problem of the sensitivity of Cointegration tests to low-powered stationarity testsinvolved in time series. These innovative panel data techniques allow for heterogeneity incoefficients and dynamics across countries. It also permits one to test directly for theexistence of long-run equilibrium functions (Apergis et al., 2006).

Kao residual panel Cointegration test allows more variables in the test than Pedroni(1999) panel Cointegration which allows a maximum of seven variables. Kao (1999)presented two tests for the null hypothesis of no Cointegration in panel data which are theDickey Fuller (DF) and ADF type tests. The study used a non-parametric test of Kao ADFt-statistic and its associated probability. The null hypothesis of this test is that of no

Table 4Panel Unit Roots for Proposed WAMZ Members in ECOWAS Countries

Variables Intercept Intercept and Trend

LLC ADF-Fishers Chi-square LLC ADF-Fishers Chi-square


FDI 4.11650 -3.98080* 8.55151 66.0923* 2.32235 -3.29735* 9.15889 56.3409*

EG -5.69633* ASL 51.7129* ASL -5.90802* ASL 51.7952* ASL

OPN 0.35344 -4.89702* 6.90711 59.4928* 0.00360 -3.41626* 9.00848 44.3305*

RI 0.44807 -4.20300* 7.51939 44.4880* -0.66588 -3.72223* 12.8854 41.2003*

PI -4.46803* ASL 20.5601* ASL -6.33213* ASL 33.6891* ASL

INF -3.50730* ASL 26.4462* ASL -2.93875* ASL 21.5623** ASL

INFRD -1.33104 -5.00842* 9.00596 56.4784* 1.36303 -4.31341* 3.85439 44.8215*

LF 1.6509 -0.3697 3.9800 19.7614** -0.5351 0.5074 10.1414 23.7704*

DS 0.03811 -4.34265* 15.8006 67.1526* 1.77917 -2.59194* 10.7371 50.1762*

HC 0.51483 -3.77311* 11.6048 66.4495* 1.49598 -2.03570** 10.4418 51.1075*

GS -0.28883 -6.86555* 5.68327 66.4848* -2.30104* ASL 19.4102** ASL

DI -0.94590 -7.10526* 12.7481 79.5340* -0.90115 -5.88319* 9.60320 66.6567*

LnMS -0.80581 -4.20403* 2.78487 34.3815* 1.61813 -3.78321* 7.31030 27.5065*

FD 3.37182 -2.43352* 4.29494 31.1493* 0.48551 -1.97934** 7.02777 29.0292*



Cointegration and null hypothesis is rejected if the associated probability is between zeroand five per cent level of significant. Table 5.31 shows the panels Kao ResidualCointegration test for variables associated to ECOWAS model, ECOWAS without Nigeriamodel, WAMZ model and UEMOA model respectively.

Tables 5 below shows that ADF t-statistic is statistically significant for all thecountries; It then implies that the null hypothesis of no Cointegration was rejected. It alsoindicated that there is a long run relationship between the independent variable of thedifferent models and their associated foreign direct investment inflows in ECOWAScountries. The variables included in the Cointegration test were those variables which wasnot stationary at levels

Table 5Kao Residual Cointegration Panel Test

ECOWAS ECOWAS with WAMZ UEMOACountries Nigeria

t-Statistic Prob. t-Statistic Prob. t-Statistic Prob. t-statistic Prob.

ADF -7.8308 0.0000 -7.566 0.0000 -4.7462 0.0000 -2.9814 0.0014Residual variance 0.0066 0.007 0.00033 0.00013 HAC variance 0.0038 0.004 0.00026 0.0009


5.3. Empirical Results and Discussions

The paper used Dynamic Ordinary Least Square (DOLS) to answer all the studyobjectives. The DOLS allows for the inclusion of both variables that were stationary atlevels and those that became stationary after differencing in the model. The lag and lead ofexplanatory variables that were not stationary at level were included in the model afterdifferencing and insignificant lag and lead variables were excluded in the finalpresentation of the tables below.

The results in model 1 and 2 of table 6 below indicated that EG, GS, INF, PI, lagged FDand Lead FD have negative effect on FDI inflows in ECOWAS countries. Their effect on FDIinflows were statistically insignificant except EG, lagged of FD, lagged of RI and lead of RI.For instance an increase in EG by one per cent reduces FDI inflows to ECOWAS by 0.0014units and 0.0015 units in model 1 and 2 respectively. The reduction became more (0.0019units) when Nigeria is excluded in model 3 and 4. It means that as the economies of thisregion grow, they will pay less attention to foreign investors and more to domestic locallyowned firm. This attitude towards the foreign investors will result in the investors assuminga low return on investment and in turn reduce the FDI inflows to this region. The result showsthat domestic investment has a positive effect on FDI inflows to ECOWAS countries, but wasstatistically insignificant. Inflation is a cost to capital. The higher the inflation the less likelyto receive FDI inflows and the reverse is partly true. Inflation has negative effect on FDIinflows into ECOWAS although the effect was not statistical significant.


Table 6Impact of Some Selected Macroeconomics Variables on FDI Inflows in ECOWAS

Variable Model with Nigeria Model without Nigeria

Model 1 Model 2 Model 3 Model 4

Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic

DI 0.0119 0.2213 0.0472 0.7242 0.04219 0.6840 0.0581 0.7340

DS 0.0133 0.2926 0.0108 0.2048 0.02331 0.4512 0.0175 0.2728

EG -0.0014* -3.2239 -0.0015* -2.8308 -0.00188* -3.8369 -0.0019* -3.2680

FD 0.0240 1.9648 0.0266 1.9425 0.02223 1.7459 0.0273 1.8764

GS -0.1542 -1.7126 -0.1887 -1.4771 -0.08160 -0.7866 -0.2157 -1.5101

INF -0.00004 -1.5155 -0.0001 -1.7970 -0.00004 -1.5028 -0.0001 -1.9061

INFRDD 0.0097 1.6109 - - 0.01395** 2.0970 - -

LnMS - - 0.0073 0.7361 - - 0.0029 0.2744

OPN 0.0389 1.8177 0.0357 1.2541 0.01551 0.6185 0.0333 1.0049

PI -0.0038 -0.7284 -0.0033 -0.5246 -0.00673 -1.1860 -0.0066 -0.9310

RI 6.7017 1.5498 9.7141 1.9351 8.03266 1.7570 9.5007 1.7617

C -0.0584 -1.3911 -0.1495 -0.7416 -0.08637 -1.8885 -0.0576 -0.2767

D(FD(-1)) -0.1309* -7.1996 -0.1380* -7.0274 -0.13340* -7.1041 -0.1394* -6.7728

D(OPN-)) 0.0778** 2.1761 0.1265* 2.9871 0.10347** 2.5416 0.1628* 3.3171

D(FD(1)) -0.0995* -4.8319 -0.0930* -4.0227 -0.10969* -5.0800 -0.0989* -4.0954

D(OPN(1)) 0.0854** 2.3925 0.1043** 2.4723 - - 0.1020** 2.0334

D(RI(1)) - - -31.0192** -2.2857 - - -28.4127** -2.0007

R-squared 0.7 0.7 0.7 0.7

F-statistic 18.9* 16.2* 18.8* 16.0*

Durbin- 1.9 2.0 2.0 2.0Watson stat


Theory has shown that government spending crowd out private investment in aneconomy. The result is consistent with this school of thought. Government size has anegative insignificant effect on FDI inflows to ECOWAS countries. The sign of thecoefficient is in support to the earlier proposition that too much government intervention inthe economy may be detrimental to FDI inflows. Interestingly, the sign of the coefficient iscontrary to Harrison and Revenga (1995) argument that the size of government ispositively correlated with FDI flows. These conflicting results are more likely to beassociated with outcome of differences in estimation methods than of sample differences.Since Harrison and Revenga (1995) do not allow for fixed effects in their model, theirestimates are potentially biased and thus might be unreliable. The coefficient associatedwith the contemporaneous market size variable has an insignificant positive effect on FDIinflows in model 2and 4. This result was consistent to what the study had early expectedand other studies as well.


ECOWAS member states have been associated with political instability zone whichmight distort their overall infrastructure development (INFRD) in this region. Model 1 and2 shows that DS, INFRD, contemporaneous financial development (FD), leaded FD,return on investment (RI), contemporaneous openness (OPN), and leaded (OPN) have apositive impact on FDI inflows in this region. But the positives statistically significantvariables are; long-run openness, long-run financial development and long-run return oninvestment. Although the contemporaneous financial development had a positive effect onFDI inflows in model 1 and 2 respectively and both were statistically insignificant. But thelong-run and short-run financial development has negative effect on FDI inflows intoECOWAS countries which are statistically significant. However, some studies have shownthat financial development has a positive impact on FDI inflows (Balasubramanyam,Salisu and Sapsford, 1996; Alfaro et al., 2003; Hermes and Lensink, 2003). A developedfinancial system mobilizes savings efficiently which, in turn, expand the amount ofresources available to finance investment. FD speeds up adoption of new technologies byminimizing the risk associated with it. With developed financial system, the foreign firmsare able to judge how much they can borrow for innovative activities and are able to makeex-ante planning about their investments. Financial development also increases liquidityand, thus, facilitates trading of financial instruments and timing and settlement of suchtrades (Levine, 1997). This will also lead to greater FDI inflows as the projects can beundertaken with lesser time being spent in settling the trades. It therefore implies that anincrease in the level of FD will increase the FDI inflows to that region. However, the resultshows that the lagged and lead FD has a negative impact on FDI inflows to ECOWAScountries. This effect is evidence with the nature of the FDI investors that are attracted toECOWAS countries. The pattern is likely to be associated to natural resource seekinginvestors.

According to theory, the effect of openness on FDI inflows to an economy varies basedon the investors’ motivation (Dunning, 1993; Brainard, 1997; Navaretti and Venables,2004). Openness has a different impact on FDI inflows based on the motives of theinvestor. If the investor is targeting the host country markets, less openness is appropriatebut for an investor that is targeting the regional market will prefer high level of opennesssuch is the case in the study. The study shows that the contemporaneous OPN has apositive impact on FDI inflows in model 1 and 2 but which were not statistical significantwhile the short run and long run effect are positive negative and were statistical significantat five per cent. For instance, although contemporaneous openness has a positive effect onFDI inflows to ECOWAS which are not statistical significant but both the long-run andshort-run effect of FDI inflows into ECOWAS countries was positive and statisticallysignificant. In the model that excluded Nigeria from the sample has a little more impact onFDI inflows meaning that the nature of OPN was of a great importance to foreign directinvestors. Furthermore, effect of contemporaneous return on investment on FDI inflowswas negative but in the long run decreases the FDI inflows to ECOWAS by 31.0192 unitsand were statistical significant in model 2. This is an evident that FDI investors repatriatemost of their profit than reinvesting it back to ECOWAS economy.


Model 3 and 4 followed the same criteria for model 1 and 2. The model was run byexcluding the impact of Nigeria contribution to the region output. According to theempirical results from model 3 and 4, economic growth (EG), government size (GS),inflation (INF), political instability (PI), lagged and lead financial development (FD) andlead return on investment (RI) have negative impact on FDI inflows into ECOWAS regionexcluding Nigeria. For instance, an increase in EG by one per cent decreases FDI inflowsin this region by 0.0019 units and an increase in GS by one per cent decreases the inflowsof FDI into ECOWAS countries excluding Nigeria. On other hand, contemporaneousdomestic investment (DI), domestic savings (DS), financial development (FD),Infrastructural development (INFRDD), market size (MS) in natural log, openness, andreturn on investment have positive on FDI inflows but were statistical insignificant exceptinfrastructural development. For instance, an increase in infrastructure development by aunit increases the inflows of FDI into ECOWAS without Nigeria by 0.0140 units, whichwere statistically significant. There is positive effect of short run and long run openness onFDI inflows, which were statistically significant while the long run return on investmenthas a negative effect on FDI inflows in model 4 and was statistically significant. That is, anincrease in return on investment by one per cent will reduce the FDI inflows in model by28.413 units.

The findings in table 7 below show that Domestic Investment, Economic Growth,Financial Development, Inflation, and Return on Investment have negative effect on FDIinflows in UEMOA while the same variables have a reverse effect on FDI inflows toproposed WAMZ member states. For instance, an increase in the level of contemporaneousFinancial Development by a unit reduces the FDI inflows to UEMOA by 0.067 units andincreases FDI inflows to proposed WAMZ by 0.102 units, which are statisticallysignificant in both proposed WAMZ members and UEMOA. But the long run FinancialDevelopment increases the FDI inflows to UEMOA countries by 0.122 units. From thefindings, domestic savings has a positive effect on FDI inflows to UEMOA and negativeeffect on FDI inflows to proposed WAMZ. The contemporaneous Labor forceparticipation rate has a positive effect to FDI inflows to UEMOA and negative effect toFDI inflows to proposed WAMZ. But in the short run, an increase of Labor forceparticipation rate by a per cent increases FDI inflows to both UEMOA and proposedWAMZ by 0.010 units and 0.044 units respectively.

Inflation has a negative effect on FDI inflows to UEMOA and positive effect toWAMZ member states but were statistical insignificant. The findings highlighted theimportance of the market size to the inflows of FDI to both zone. According to the findings,although market size has a positive effect on FDI inflows to UEMOA, which was notstatistical significant as opposed to a per cent increase in market size increases the FDIinflows to WAMZ by 0.0495 units which was statistically significant. The level ofopenness has a positive effect to FDI inflows to UEMOA and a negative effect to WAMZ.But its effect to FDI inflows to proposed WAMZ was statistical significant. Politicalinstability has a negative effect on FDI inflows to both UEMOA and WAMZ but the effect


are not statistical significant while an increase of RI by one unit reduces the FDI inflows toUEMOA by 5.4194 units and statistical significant and increases the FDI inflows toWAMZ but was statistical insignificant.

In summary, market size has positive significant effect on FDI inflows to proposedWAMZ members as opposed to UEMOA members. The findings iterated proposed WAMZcountries have more open economy than the UEMOA members. The paper shows thatfinancial development plays a role in the attraction of FDI inflows in WAMZ as opposed toUEMOA member states. Finally the increase in value of FDI inflows in value to theproposed WAMZ members might be associated to the size of its market, the level of tradeopenness, the short run effect of the labor force participation rate and negative effect ofreturn on investment on FDI inflows to UEMOA.

6. CONCLUSION

This paper assesses the impact of selected macroeconomics variables on foreign directinvestment inflows in the ECOWAS countries. It covers the period from 1975 to 2007 andemploys an unbalanced panel dynamic ordinary least square method of analysis. Atheoretical review is first undertaken to elucidate the relationship between the selectedmacroeconomics variables used in the study and foreign direct investment inflows. The

Table 7Comparative Statics Analysis between UEMOA and WAMZ countries

UEMOA members except Guinea Bissau Proposed WAMZ members

Model 5 Model 6

Variable Coefficient t-Statistic Coefficient t-Statistic

DI -0.00736 -0.19833 0.0778 1.8366DS 0.03426 1.25000 -0.0387 -1.3869EG -0.00028 -1.29247 0.0002 0.4576FD -0.06661** -2.34990 0.1021** 2.4142GS 0.00020 0.00446 0.1522 1.7900INF -0.00004 -0.33339 0.0002 2.5682INFRDD 0.00364 0.89379 -0.0097 -1.6983LF 0.00068 1.23579 -0.0110 -1.9835LnMS 0.00327 0.83322 0.0495* 4.8539OPN 0.01544 1.10735 -0.0520** -2.0495PI -0.00278 -1.38906 -0.0037 -1.0114RI -5.41939** -2.09758 7.0203 1.0063C -0.09276 -1.25420 -0.3096 -0.6059D(LF(-1)) 0.01013* 3.06372 0.0438* 3.1785D(FD(1)) 0.12146** 2.37883D(DS(1)) -0.0752* -2.8749R-squared 0.5 0.7F-statistic 3.4* 6.1*Durbin-Watson stat 1.5 1.8



selection of the macroeconomics variables were based on the push and pull factors and aswell as some macroeconomics variables that investors use as a yard stick in measuringprofit destination for investment.

Africa has been able to attract about 2.5 per cent of global FDI inflows, Western Africa0.6 per cent and Western Africa from Africa share is 33.1 per cent. However, Africa’s shareof World output is low, given that the FDI inflows to the world are increasing and WesternAfrica is the second largest recipient of FDI inflows in Africa. FDI inflows have thepotential to help the ECOWAS member states meet the Millennium Development Goals(MDGs).

Economic growth, short-run and long-run financial development have a negativesignificant effect on FDI inflows to ECOWAS countries while the short-run and the long-run trade openness have a positive effect on FDI inflows to the region. The contemporaryfinancial development has a negative influence on FDI inflows to UEMOA and a positiveimpact on FDI inflows to proposed WAMZ. The labor force participation rate has apositive significant influence on FDI inflows to both UEMOA and proposed WAMZmember states. However, a FDI inflow decreases to UEMOA members as a result of anincrease in the level of OPN but increases to WAMZ under the same condition. It impliesthat FDI investors attracted to UEAMOA may be associated with market seeking investorswhile the investors attracted to WAMZ members are both the resource seeking and non-market seeking investors. This inference can be further deduced from the relationshipbetween the FD and INF to both UEMOA and WAMZ zones. In general FDI investors inECOWAS countries are both market seeking and natural-resource seeking investors. Thisconclusion is based on the relationship between DS, FD, INFRDD, OPN and RI with FDIinflows in ECOWAS countries.

The screening of investment applications and granting differential enticement todifferent foreign firms is of paramount importance to ECOWAS region in order to attractsustainable foreign investors. China for instance permits repatriation of profits only out ofnet foreign exchange earnings (Adams, 2009b). ECOWAS counties should contract anindependent consultant to assess foreign investor business applications based on thecountry specific nation objectives before issuing them license of operation. Policy makersin ECOWAS region should adopt china’s strategy towards foreign direct investorsespecially towards natural resource-seeking foreign investors. The consultant should beindependent of government influence while carrying their mandate so that to minimize theeffect of corruption in duties. The consultant should also be insured by a reputableinsurance company in the country and will be obliged to pay a heavy consequence if foundviolating the contract. The paper recommends that policy makers in the ECOWAScountries should focus on promotional resources to attract some types of foreign directinvestment which are willing to convert the primary resource to finished product andregulate others. Policies should be aimed at putting in place an ideal model based on thenational goal of the country to screen foreign direct investment applications so as toascertain their productive level. The paper strongly recommends that policies aimed at


ensuring proper and coordinated manpower planning and training should be put in place toensure same quality of labor force in the West African region and Africa. There should alsobe a policy that insures that any project that is providing infrastructural facilities should beindependent inspected before and after completion by all stakeholders especially the user.Central bank or the banking authorities should apply a stricter regulation to banking sectorespecially commercial banks regarding their daily transaction. This will ensure an efficientand reliable financial system that is less prone to ambiguity and increase the likelihood ofabsorptive capacity to the ECOWAS region to take advantage of the technologicalspillovers of foreign direct investment.

Finally, this paper recommends policies such as high tariff on exportation of rawmaterials and high tariff on importation of goods that are produced with the raw materialsfound within ECOWAS region. This will discourage exportation of primary goods and inthe long run boast the manufacturing industry.

The major limitation was the problem of unavailability of some important variablesfrom the selected countries which were detrimental to FDI inflows to the host country.Other limitations are insufficient funds which have restricted the chance of directlyassessing the data from the individual country data base and the problem associated withusing different sources of secondary data. This problem may result in either overestimationor underestimation of the variables used in the model estimation. The current paper hasmade a substantial contribution to the literature by using panel rather than the commonlyused cross-country studies. However, future studies should focus more comparativeanalysis on individual countries in UEMOA and WAMZ member states. And even moreimportantly, which type of FDI was attracted and into which sectors need to be promoted.This is the authors’ next research agenda.

Notes1. FDI is an investment made to get hold of a long-term management interest (at least 10.0 percent of voting

stock) in a business enterprise operating in a country other than that of the investor’s country ofresidency (World Bank, 1996).

2. The ECOWAS members are Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, Gambia, Ghana, Guinea,Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo.

3. UEMOA has eight member states, which are Benin, Burkina Faso, Côte d’Ivoire, Mali, Niger, Senegal,and Togo. Guinea-Bissau is first non-francophone member that joined in 1997.

4. It consists of five ECOWAS members, which are Gambia, Ghana, Guinea, Nigeria and Sierra Leone.WAMZ members are working towards a launch of its currency (Eco) by 2015 (ECOWAS handbook,2009).

5. A first generation of models has analyzed the properties of panel-based unit root tests under theassumption that the data is independent and identically distributed (i.i.d.) across individuals.

6. The exclusion of Nigeria in this test was as a result of conducting the impact of the proposed selectedmacroeconomics variables on foreign direct Investment inflows. The review of literature stipulated thatNigeria is the biggest and largest player in ECOWAS economy which makes it necessary to access theperformances on those variables on FDI inflows in ECOWAS countries except Nigeria.


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