This article analyzes through a panel data analysis covering the period 1994 to 2016, the effects of the 2008 financial crisis on the determinants of Foreign Direct Investment in the West African Economic and Monetary Union (WAEMU). From the analysis, it turns out that this crisis has strongly impacted FDI’s determinants in the union. In fact, as consequence, it created a much more infatuation of resource-seeking investors in spite of market-seeking investors. This result shows that those countries can still attract investors even in time of crisis if they have tremendous natural resources, which makes the protection of these resources very important. In addition, we can notice that after the crisis, the stability in the region became an important decisional variable for foreign investors. That points out the importance of promoting a good political governance. Furthermore, the study has also shown that the financial crisis has been a stepping corner for the emergence of a third type of investors in the region (efficiency seeking investors).
Economic growth is a central question for every country. Despite the fact there are several contradictions among economists about how to grow the economy (classical theories, endogenous growth theories), they all agree that foreign capital plays an important role in the process of growing the economy. This external source of funding is relevant for both developed and least developed countries. More specifically for least developed countries, the very low amount of their national savings to finance investments is a typical economic problem. In fact, they constantly need foreign capital to prime their economic development. Those capitals can be either direct or indirect investments, or both.
Before 1980, it was possible for those countries to easily get loans from international institutions. However, since 1980, this mechanism was no longer possible, so that they have to make some reforms and more specifically about their investment policies in order to attract more stable and lasting capital inflows. A good source of foreign capital that is not risky regarding the country national debt is without any doubt the Foreign Direct Investment. It then became for many countries an alternative source of funding since the inaccessibility to international institutions funds.
For Agiomirgianakis et al. [
Several studies have shown the potential factors that could be seen as attractive by investors. For instance, the ADB1 (2004), stated that rapid technological progress, emergence of globally integrated production and marketing networks, existence of bilateral investment treaties, recommendations from multilateral development banks, and positive evidence from developing countries that have opened their doors to FDI are factors that have substantially accelerated FDI’s direction.
From 1970 to 2007, the FDI flows in the world have increased dramatically from $13.3 billion to $2.1 trillion, followed by a decline two years later ($1.1 trillion in 2009) mainly due to the global financial crisis. However, Africa as a region has not benefited from the FDI boom. In fact, the FDI inflows to the continent represent a low share of global FDI and are on the downward trend. On the same period, Africa’s share of global FDI inflows declined from 9.5 percent to 5.3 percent (World Bank, 2010).
FDI represents a potential solution to the continent’s growth and development challenges. It provides the needed capital for investment, brings with it, employment, managerial skills, technology and at the end accelerates growth and development. The role of FDI is quite critical in Africa given the fact that poverty levels are generally high while domestic savings and incomes remain extremely low as income is mainly channeled to consumption expenditure. These factors coupled with the unpredictability of foreign aid flows, the low share of Africa in world trade and the high volatility of short-term capital flows call for the need to attract different forms of FDI inflows.
The West African Economic and Monetary Union (WAEMU) which is composed of eight least developed countries, sharing almost the same national language (French) and the same currency (CFA), is not on the sidelines of this reality. Prior to its creation, the countries members of this union experienced low growth, economic instability (high inflation), high external debt, couple of factors that seems to not play in their favor in attracting foreign capital. One of the main purposes in creating such union was to boost their economic performance by taking common actions and make it easier for foreign capital to flow in the region. But there is a big gap between the current state and the expectations.
In addition, the world has been shaken by a financial crisis in 2008. This crisis negatively impacted the world economy. Since the investors of the WAEMU mainly come from developed countries which have been severely affected by the crisis, it is paramount to pay attention on the consequences of the crisis on the Union’s FDI and its determinants. Instinctively, we can assume that the pre-crisis and the post-crisis FDI’s determinants will change considerably.
On the light of what have been mentioned above, the central question of our research is: what have been the effects of the 2008 financial crisis on the determinants of Foreign Direct Investments in the West African Economic and Monetary Union (WAEMU)?
With respect to this interrogation, the main objective of this article is to identify the determinants of FDI before and after the 2008 financial crisis, and to explain the gaps.
The main contribution of this article is twofold. First, studies have been already conducted to identify the determinants of FDI in the WAEMU, but to our knowledge, no research has tackled the effect of the financial crisis on those determinants. Secondly, the adoption of the panel data approach allows to better capture the effect of the crisis since countries do have specifities.
The remaining part of this article is organized as follows. A literature review that allow to capture not only the theoretical aspects of FDI, but also the results of past empirical studies. It is followed by the presentation of materials and methods that have been used, notably the sources of our data, the choice of variables, the presentation and justification of the model. After that, we present our empirical results and discuss them by confrontation with prior studies. In fine, the research ends up with a conclusion and eventually the proposition of economic policies. The main limitation of this article is the difficulty to find information about some variables which could possibly influence FDI.
Despite the growing importance of Foreign Direct Investments in economic, there is no unified theoretical framework for understanding the underlying determinants of their flows. Nonetheless, the magnitude of FDI flows around the world has attracted particular attention from many least developed countries which have invested heavily in empirical studies to better understand the phenomenon and certainly to have better control over it. This review tackles both FDI theories, but also confronts prior empirical results.
Various and complementary theories have been put forward by authors to explain the flows of FDI in a specific location. In fact, the terminology FDI is not very recent in the literature. According to the neoclassical, the flows of FDI correspond to an adaptation of firms to the conditions of national and international markets in terms of factor costs resulting from factors endowments. According to their view, capital should therefore flow from countries where they are more abundant to those where they are relatively scarcer, because in those countries, yields of new investments should be higher.
Another theory on FDI was based on the Heckscher-Ohlin (1933) model to which two other models from MacDougall [
An imperfect market refers to any economic market that does not meet the rigorous standards of a hypothetical perfectly (or “purely”) competitive market, as established by Marshellian partial equilibrium models. It arises whenever individual buyers and sellers can influence prices and production, or otherwise when perfect information is not known to all market actors. Hymer [
Dunning [
The Ownership advantages are related to the firm intrinsic capacity which can be measured in terms of its assets, patents, technologies or other types of advantages that are unique to the company [
A large number of variables have been identified in the literature to explain FDI’s inflow. Among them, some are just based on theories of FDI whereas other have been used for their instinctive explanatory power. Broadly speaking, most of the studies conducted carry out a few number of African countries and concentrated on the sub Saharan Africa but do not tackle the case of the WAEMU zone.
By using data compiled from 32 African countries over the period 1970 to 1999, Asiedu [
Onyeiwu and Shrestha [
For Krugell [
Suliman and Mollick [
Some authors also found that labor cost is an important determinant to FDI for developing countries. In fact, all other factors remaining unchanged, a low cost of labor would be profitable for the MNC since it reduces the production cost. Krugell [
Finally, political instability is also associated with a low FDI inflow. Sichei and Kinyondo [
Beyond economic, social and political factors, financial crisis also impacts FDI for both home and host countries. Dornean et al. [
The impacts of the crisis of FDI are known. However, its effects on FDI’s determinants have not been studied yet. This article has the merit to be the first one in tackling the effects of the crisis of FDI’s determinants in the WAEMU.
This study is based on data collected from eight least developed countries, all members of the WAEMU. The data cover the period 1994 to 2016. The choice of this time frame is due to the fact that the union has been created in 1994 and all our independents variables are lagged by one year. In addition, we could not get the data for 2017 at the time we were conducting the research.
The data on FDI inflow come from the United Nation Conference on Trade and Development (UNCTAD) database. The information related to inflation, GDP, GDP growth, foreign aids have been collected from the World Bank. Finally, data concerning natural resources, trade openness, labor cost, business environment, political stability, credit to private companies are from the Global Economy database. The resort to many databases is explained by the fact that all data could not be obtained from only one database.
The dependent variable in our model is the Foreign Direct Investment inward, and refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent3 or more of the ordinary shares of voting stocks is the criterion for determining the existence of a direct investment relationship. Data are in current U.S dollars.
Prior empirical researchers have identified the market size as an important parameter for investors [
For Mottaleb and Kalirajan [
Generally, least developed countries have comparative advantage in labor and have tremendous natural resources. In the frame of our research, because of the difficulties to obtain data on wages, we followed Asiedu [
For the variable “natural resources”, it corresponds to income from total natural resources as a percentage of GDP (Global Economy) which reflects the importance of natural resources in the host country. Mathematically, it is the sum of oil rents, natural gas rents, coal rents (soft and hard), mineral rents and forest rents.
Another important factor to investors is the business environment in the host country. Because of its explanatory power to FDI’s inflow, the business environment is considered in the model, and measured by the Business Freedom Index which is based on 10 indicators, using data from the World Bank’s Doing Business study: starting a business-procedures (number), time (days), cost (% of income per capita), and minimum capital (% of income per capita); Obtaining a license―procedures (number), time (days), and cost (% of income per capita); Closing a business―time (years), cost (% of estate), and recovery rate (cents on the dollar).
Moreover, the access to credit represents a source of diversification of financial risks for companies, and can then impel investors. It refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of non-equity securities, trade credits and other accounts receivable, that establish a claim for repayment. This variable is measured as a percentage of GDP.
The non-respect of the constitution (terms limit) could be seen by foreign investors as a stepping corner to political instability in the medium or long run. This dummy variable is added to the model to see whether the president tenure affects foreign capital inflow. It takes the value 1 if a president has been in the power for more than two terms4 and 0 otherwise.
The summary statistics shows that the mean value of FDI in the WAEMU is about $178 million with a very high variation ($210 millions). We can also notice that in average, economic growth and inflation are low in the region (4.08% and 4.48% respectively). Nonetheless, these numbers hide some disparities because the maximum value of economic growth is a two-digit number (15.37%) and the inflation is amounted to 50%. In addition, we can see that for a large number of variables, the skewness is close to zero, which is a characteristic of a normal distribution. However, the Jarque-Bera probabilities tell us that our data are not normally distribution, which could have some incidence on our econometrics tests (
Several of the previous studies adopted the panel estimation method [
Variables | Mean | Median | Min | Max | Std. Dev | Skewness | Kurtosis | Jarque-Bera |
---|---|---|---|---|---|---|---|---|
FDIt | 178 M | 0.78 B | 19.14 M | 1.07 B | 210 M | 1.7 | 6.02 | 150 (***) |
ln GDPt−1 | 22.12 | 22.23 | 19.14 | 24.28 | 1.10 | −0.55 | 3.06 | 9.04 (***) |
RGDPt −1 | 4.08 | 4.17 | −28.09 | 15.37 | 4.08 | −2.810 | 25.683 | 3640.91 (***) |
TROPNt−1 | 62.22 | 59.93 | 30.51 | 125.03 | 17.99 | 0.61 | 3.09 | 11.11 (***) |
ln WAGEt−1 | −6.14 | −6.18 | −7.34 | −5.07 | 0.54 | −0.00 | 2.15 | 5.19 (*) |
NATRESt−1 | 9.35 | 8.58 | 2.46 | 31.62 | 5.11 | 1.16 | 4.75 | 61.83 (***) |
BUSENVt−1 | 49.59 | 52 | 23 | 70 | 11.06 | 0.01 | 2.50 | 1.80 |
FORAIDSt−1 | 540 M | 440 M | 46.09 M | 2.89 B | 420 M | 1.70 | 8.51 | 306 (***) |
POLSTABt−1 | 3.76 | 4 | 2 | 6 | 1.09 | −0.11 | 2.16 | 5.40 (*) |
ECOSTABt−1 | 4.48 | 2.48 | −3.5 | 50 | 8.82 | 3.34 | 14.92 | 1363 (***) |
CREDPRIVt−1 | 14.01 | 13.53 | 00 | 37.71 | 7.34 | 0.69 | 3.76 | 18.24 (***) |
DUMMYPRESt | 0.29 | 0 | 0 | 1 | 0.45 | 0.88 | 1.78 | 33.69 (***) |
of the explanatory variables is based on both the correlation of the variations “within” (temporal) and “between” (spatial). But with respect to the coefficient of a variable as well as its significance, the results do not allow us to dissociate the spatial dimension from the temporal dimension. The fact that an explanatory variable is strongly correlated with FDI on the temporal dimension does not mean this variable is a determinant of the spatial distribution. Therefore, it has disadvantages because if a study is to identify only the determinants of the spatial distribution of FDI, this “advantage” becomes a disadvantage.
In addition, the method of fixed panel estimation does not allow the introduction of variable that does not vary across time (such as distance). However, we consider that the panel estimation is a good method for our research because we seek to identify the determinants of FDI in space and time in the WAEMU zone, and our model does not contain any fixed explanatory variable. Therefore, in our study, we will carry out analysis on panel data.
Since our study is based on row data, we first take the natural log of all the independent variables which are not express in terms of rate or index, namely GDP and Wage. Doing so, the coefficients we obtain can be interpreted as semi-elasticity. Also, it takes a certain time between the changes in the explanatory variables and the decision to invest. That is why all the independent variables are lagged by one period. The general model to be estimated can then be written as:
F D I i t = β 0 + β 1 ln G D P i t − 1 + β 2 R G D P G i t − 1 + β 3 T R O P N i t − 1 + β 4 ln W A G E i t − 1 + β 5 N A T R E S i t − 1 + β 6 B U S E N V i t − 1 + β 7 F O R A I D S i t − 1 + β 8 P O L S T A B i t − 1 + β 9 E C O S T A B i t − 1 + β 10 C R E D P R I V i t − 1 + β 11 D U M M Y P R E S i t + ε i t .
where lnGDP is the natural log of national wealth lagged by one period. RGDP the growth rate of GDP. TROPN and lnWAGE are respectively variable to capture the effect of trade openness and labor cost (salary). NATRES represents Natural resources, BUSENV is a variable to identify the impact of the business environment. FORAIDS is the contribution of foreign aids, POLSTAB and ECOSTAB respectively represent political stability and economic stability. CREDPRIV corresponds to the ratio credit to private companies as percentage of GDP. Finally, DUMMYPRES is a dummy variable to see the effect of the president tenure on FDI.
Given that our work focuses on panel data, the first test that needs to be done is the test of specification of homogeneity or heterogeneity. Economically, this test serves to see if the theoretical model studied is perfectly identical for all the countries in the sample, or if there are specificities among countries. Econometrically, this means to check the equality of the coefficients of the model studied in the individual dimension. The Hausman test allows to check whether to use the fixed effect or the random effect model. Because the number of independents variables (11) is larger than the number of cross-section, we split the general model into two equations with respect to the economic dimension and the business environment dimension. Yet, all other econometric tests will be carried out following the general model.
Equation (1): Economic variables
F D I i t = β 0 + β 1 ln G D P i t − 1 + β 2 R G D P G i t − 1 + β 3 E C O S T A B i t − 1 + β 4 C R E D P R I V i t − 1 + β 5 T R O P N i t − 1 + β 6 ln W A G E i t − 1 + β 7 N A T R E S i t − 1 + ε i t .
Equation (2): Business environmental variables
F D I i t = β 0 + β 1 P O L S T A B i t − 1 + β 2 BUSENVit − 1 + β 3 F O R A I D S i t − 1 + β 4 D U M M Y P R E S i t + ε i t .
The test shows (
Our econometrics results show that over the horizon 1994-2016, FDI inflows are influenced by many variables. In fact, the fixed effect model for cross-sections and times allows to identify only two variables that are not significant (trade openness and business environment). However, when ignoring the time effect, to these two variables mentioned above, is added economic growth, natural resources and the cost of labor.
The significance of the coefficient of lnGDP shows that FDI is strongly sensitive to the market size, and its sign indicates that there is a positive linkage between the evolution of national wealth and investor’s attraction. In the case of our research, a 1% increase in the market size leads to increase FDI inflow by 11%. Under the hypothesis that GDP measures the market size, we can conclude that a large number of investors in the region are market seeking investors. This result corroborates the eclectic paradigm and the empirical results of Jordaan [
Paradoxically, economic growth in the union has a negative and significant effect on FDI. That can be explained by the fact that the growth in the region is not self-preserved. In fact, it varies as time goes by. This is captured by investors as an instability of growth, which does not allow to conduct some forecasting. Furthermore, this growth is weak for least developed countries (4% on average). This number might be interesting for developed countries, but very weak for countries that aspire to development.
The variable “natural resources” impacts negatively and significantly FDI inflow in the region. This result is the consequence of a weak contribution of natural resources to the GDP in the union. In fact, this contribution varies between 2.46% to 31.62% with a median value of 8.58%. Since the union’s GDP is weak, these percentages indicate that the exploitation of natural resources does not generate high income, which does not make the union attractive for resource seeking investors.
Test Summary | Chi-Sq. Statistic | Chi-Sq. d.f. | Prob. |
---|---|---|---|
Cross-section random (Equation (1)) | 49.876912 | 7 | 0.0000 |
Cross-section random (Equation (2)) | 21.418726 | 4 | 0.0003 |
The political stability (measured by inflation) and the cost of labor (measured by the real GDP per capita) play positively on FDI. These results are against the expectation. In theory, a low inflation and a cheap labor force must affect positively FDI. The evidence from our research state otherwise. A 1% increase in these variables leads to increase FDI respectively by 0.04% for inflation and 9.22% for wage. Although significant, the impact of inflation is marginal. Concerning the labor cost, its positive sign implies that an improvement in employee’s remuneration increases their motivation, and, as consequence, arises their productivity. This is then captured positively by foreign investors.
The effect of the variable “polstab” on FDI must be interpreted carefully. In fact, since it is a bounded index, a high value given to this variable is source of instability. Our results corroborate prior studies and support that instability in the union impedes FDI inflow. This result is however contrary to ODI’s (1997) conclusion which supports that political instability is not a negative factor to FDI when the country is endowed with tremendous natural resources.
Against all expectation, the variable “credit to privates” does not give the expected sign. It influences negatively and significantly FDI. This result can be understood as the consequences of difficulties to access credit. In the case of the WAEMU, the average contribution of credit to private sector is about 14% with a maximum of 37%5, whereas this ratio is more than 80% in developed countries such as US, France …
Finally, the president tenure strongly discourages foreign investors. The research supports that after two terms, the reelection of the president reduces FDI inflow by more than 50%. This result is in fact an anticipation by investors to the political destabilization in the future which creates a bad business environment (
As the 2008 financial crisis has shaken the financial world, and since investments in Africa mainly come from developed countries which have been negatively affected, it is fundamental to analyze its impact on FDI in the goal of proposing some economic policies.
The regression shows that before and after the crisis, FDIs were positively sensitive to the national wealth. However, this variable only influences significantly FDI after the crisis. Reversely, natural resources were not significant before the crisis, but FDI became sensitive to this variable after the crisis. These results show that one of the consequences of the crisis has been an effect of substitution of determinants. In fact, the crisis chased away market seeking investors, and called resource seeking investors. A possible explanation is that post crisis, the traditional sectors of opportunities were no longer attractive except natural resources (Goal, Iron, Manganese) for which the exploitation could generates substantial income for investors. The variable “political stability” is also negatively linked to FDI in the WAEMU, but significant only after the crisis. That shows how careful investors of the region became in committed their funds after crisis. However, the president tenure identified as a potential precursor to political destabilization has no impact on FDI post crisis (
In a new regression, we check the robustness of our estimates to see whether they vary according to the proxy used. In that purpose, we use three new variables (economic globalization index, Political globalization index and social globalization index) as proxy to see their effect on FDI. In addition, trade openness is now measured by the part of import to the GDP (TROPN2) since the union is a big importer; and by export to GDP (TROPN3).
The economic globalization has two dimensions: actual economic flows and restrictions to trade and capital. The sub-index on actual economic flows includes data on trade, FDI, and portfolio investment. The sub-index on restrictions takes into account hidden import barriers, mean tariff rates, taxes on international trade (as a share of current revenue), and an index of capital controls. The degree of political globalization is determined by the number of embassies and high commissions in a country, the number of international organizations to which the country is a member, the number of UN peace missions a country participated in, and the number of treaties signed between two or more states. And finally, social globalization has three dimensions: personal contacts, information flows, and cultural proximity. The sub-index on personal contacts includes international telecom traffic, degree of tourism, transfers, foreign population, and number of international letters. The sub-index on information flows includes number of internet users, share of households with a television set, and trade in newspapers. The sub-index on cultural proximity includes trade in books and number of McDonald’s restaurants and Ikea located in a country.
The estimation shows (
Concerning the three global indices, only the economic globalization index is not statistically significant. The political globalization index impacts positively and significantly FDI post crisis. This result corroborates those obtained in
For the social globalization index, it impacts significantly FDI at 1% level, but differently before and after the crisis. Considered to be a determinant of FDI on the pre-crisis horizon, this variable is an impediment factor after crisis.
Variables | Coefficients (two ways fixed effect) | Coefficients (no time fixed effect) |
---|---|---|
C | −177.95450*** | −47.52627*** |
lnGDP | 11.56300*** | 3.635699*** |
RGDPG | −0.049434** | −0.025207 |
TROPN | 0.008292 | 0.007275 |
FORAIDS | −6.53E-10** | −6.53E-10** |
NATRES | −0.073002** | −0.033008 |
ECOSTAB | 0.040989*** | 0.028819*** |
POLSTAB | −0.333901** | −0.277575** |
lnWAGE | 9.226611*** | 1.949635 |
CREDPRIV | −0.077747*** | −0.051719** |
BUSENV | −0.000958 | −0.014306 |
DUMMYPRES | −0.627491** | −0.879148*** |
R-squared | 0.827270 | 0.786957 |
Adjusted R-squared | 0.7769970 | 0.762217 |
***; **; * are respectively 1%, 5%, and 10% significance level.
Variables | Before the crisis | After the crisis | ||
---|---|---|---|---|
C | −44.84296 ** | 6.717168 | ||
lnGDP | 3.506417** | 1.154081 | ||
RGDPG | −0.030018 | −0.011740 | ||
TROPN | −0.008136 | 0.016534 | ||
FORAIDS | −7.26E-10 | −2.61E-10 | ||
NATRES | −0.055699 | 0.089324* | ||
ECOSTAB | 0.034968*** | −0.016007 | ||
POLSTAB | −0.221910 | −0.386290* | ||
lnWAGE | 1.729501 | 2.174893 | ||
CREDPRIV | −0.071595** | −0.028483 | ||
BUSENV | −0.011727 | 0.011317 | ||
DUMMYPRES | −1.329757*** | 0.859500 | ||
R-squared | 0.791014 | 0.810581 | ||
Adjusted R-squared | 0.715871 | 0.753396 | ||
***; **; * are respectively 1%, 5%, and 10% significance level.
Variables | pre-crisis Coefficients | post-crisis Coefficients | |
---|---|---|---|
C | 16.98781 (0.0000)*** | 14.18263 (0.0000)*** | |
ECOGLOBINDEX | −0.057868 (0.0034)*** | 0.004988 (0.7493) | |
SOCGLOBINDEX | 0.047000 (0.0815)* | −0.117397 (0.0000)*** | |
POLGLOBINDEX | 0.013708 (0.1874) | 0.075978 (0.0000)*** | |
TROPN2 | −0.004448 (0.8449) | 0.047943 (0.0002)*** | |
TROPN3 | 0.493711 (0.0000)*** | 0.138085 (0.0003)*** | |
R-squared | 0.476643 | 0.761745 | |
Adjusted R-squared | 0.453485 | 0.733381 |
***; **; * are respectively 1%, 5%, and 10% significance level.
Over the course of the last 22 years, the West African Economic and Monetary Union created in the goal of economic integration has put forward many policies to attract foreign investors. That was one of the goals of the union. But to this day, it is clear that on this point (attract FDI), the union has failed because the inflow of foreign capital remains low. The main objective of this article was to figure out what have been the effects of the 2008 financial crisis on WAEMU FDI’s determinants. In that purpose, our research has used data from World Bank, the United Nation Conference on Trade and Development and the Global Economic databases.
Because of the interest for both temporal and spatial dimension, we have used panel approach. From the research, it turns out that the crisis has strongly impacted many determinants in the union. More specifically, it leads to chase out market seeking investors, and appealed resource seeking investors. In addition, our robustness tests underlined the presence of a third type of investors in the region: the efficiency seeking investors. Furthermore, the three indices show a variability of coefficients with respect to the proxy used. In fact, the effect of the crisis is not the same when economic stability is measured by global economic index as inflation. However, concerning political stability, the proxy used does not change the conclusion: political stability influenced positively FDI after the crisis.
With respect to the variables that have been impacted by the crisis, our research implies the following policies.
First of all, a better management of natural resources is paramount, because the crisis has shown that despite a small market size, a country can still attract FDI if it has tremendous natural resources. For instance, the average contribution of natural resources to GDP in Burkina Faso was 8.85% before the crisis with a corresponding FDI inflow amounted to 46.64 million. However, on the post crisis period, when the GDP stagnates ($11 billions) and the contribution of natural resources has doubled, the average amount of FDI inflow has been multiplied by 5 (249.39 millions).
The same information shows that the average contribution of natural resources is marginal for Benin and in a downward trend. As a consequence, the volume of FDI in this country has dropped just after the crisis from 169 million in 2008 (134 million in 2009, 161 million in 2011) to 160 million in 2016. So policies makers should pay more attention to this variable. In addition, the post crisis period has also been characterized by much more precaution of investors with respect to the stability of area they invest. That implies if the WAEMU wants to attract more and more foreign capital, they have to promote democracy, a good political governance, and reinforce liberties (associational, organizational, freedom of speech and belief) because these factors are generally the roots of political instability. Furthermore, the union should better its degree of openness to the global market, because the possibility for investors to export their products beyond the borders of the country they are constitutes an important decisional factor.
Artige and Nicolini [
The authors declare no conflicts of interest regarding the publication of this paper.
Zoungrana, T.D. and tanToé, D.L. (2018) Effects of the 2008 Financial Crisis on the Determinants of Foreign Direct Investments in the West African Economic and Monetary Union (WAEMU): A Panel Data Approach. Modern Economy, 9, 1729-1746. https://doi.org/10.4236/me.2018.911109