Corresponding author: Ololade Mistura Aromasodun
Academic editor: A. Panibratov
This paper examines the determinants of foreign direct investment (
Foreign investment is regarded as the central engine for growth. Attracting investment has become the main factor of industrial policy in many countries. Even countries that were formerly inaccessible to foreign investors, such as China, have acknowledged the economic benefits of foreign investment and opened their borders to it.
Regarding West Africa as an
Regardless of collective initiatives at the regional and continental levels to improve the flow of
An attempt at assisting policymakers in this regard has been made through various theoretical and, especially, empirical studies on determinants of
Given this ad hoc formulation and the fact that they use different institutional variables, it is difficult to determine the source of the qualitative and quantitative differences in their results. It would be enlightening for policymakers to know to what extent macroeconomic factors determine
The present study is an attempt directed at addressing all these issues, which the existing studies have failed to address. It examines the impact of macroeconomic determinants on
The origins of
We can broadly divide theories into two categories: macroeconomic theories and microeconomic theories of
Lipsey (2004) describes the macroeconomic view as a specific type of capital movement across national boundaries, from home nations to host countries, as reflected in balanceofpayments statistics. These flows generate a specific type of capital stock in host countries: the volume of the home country investment in organizations, generally businesses, controlled by a homecountry owner or in which a homecountry owner has a specified proportion of voting rights. Various macroeconomic theories are reviewed below.
This section covers studies on the determinants of foreign direct investment outside Africa and then proceeds to review the evidence from Africa. The section concludes with a discussion of the gaps in the empirical studies that this paper aims to fill.
One of the earliest studies is a paper written by
A further test on determinants of inward
Unlike the previous study, which used VAR as an estimating approach, Marcelo and Mario (2004) used an econometric model based on panel data analysis. In order to shed light on
In another study using the Granger causality test on data for the period 1969–2000 for three countries (Chile, Malaysia, and Thailand),
The results obtained by
Using a panel of 69 countries between 1981 and 2005,
Using the same estimation technique as in the previous study by
Gholami et al. (2006) analyze the influence of such factors as GDP, ICT, and openness on
Using a different estimation technique compared to that of Gholami et al. (2006),
Another study on
A study carried out by
In addition,
Using a panel dataset for the period from 1970 to 2010, Anyanwu & Nadege (2015) attempted to establish the determinants of
A large body of empirical literature has been generated to study the determinants of
There is a limited amount of research concerning institutional and sociopolitical determinants of
The use of a composite institutional quality index, which combines multiple indicators of institutional quality, is another novelty of our research. The majority of the articles in the literature focus on just one or a few institutional variables. In the literature, however, it is suggested that institutional variables are significantly linked to one another (Globerman & Shapiro, 2002). As a result, we use Principal Component Analysis to create a composite index by integrating multiple characteristics of institutions into one component (
Another unique contribution of the study is the use of the Africa Infrastructure Development Index (
The Institutional
A panel databased regression model to test for the actual effects of the postulated determinants of
In subsequent equations, each of the aforementioned seven governance indicators is added, one at a time, to the benchmark Equation 1. They are included one at a time, instead of two or more featuring simultaneously in an equation, to avoid multicollinearity problems in view of the fact that they are highly intercorrelated. By including these governance indicators, the resulting equations can only be estimated with post1995 (instead of post1969) data, as a series of governance indicators start from 1996, with each of the seven governance indicators appearing in an equation.
where:
The basic features of the variables are highlighted based on the results of the descriptive and correlation analyses of policy makers. The main inferential analyses is carried out in the form of a unit root and cointegration test to properly address the timeseries features of the data and provide a guide on the methods of estimating the regression equation to be adopted. The study conducts autocorrelation, heteroskedasticity, multicollinearity, normality of distribution of the residuals and stability tests and adopts remedial measures when a test shows there is a problem to ensure that the results obtained lead to reliable conclusions.
The study covers 16 West African countries (Benin, Burkina Faso, Cape Verde, Gambia, Ghana, Guinea, GuineaBissau, Ivory Coast, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo) from 1970 to 2019. The choice of West Africa is due to the fact that limited research was carried out on the region, while the period is chosen based on the availability of data from 1970 onward and also because 2019 is the most recent year of data available at the time of this study.
Foreign direct investment is computed as the % of GDP, the growth rate of real GDP is calculated as the first difference of annual GDP expressed as a percentage of real GDP in the preceding year. The urban population is computed as a percentage of the total population. Gross domestic product per capita is expressed as purchasing power parity, constant for 2010, calculated in US dollars. Trade openness index is computed as total trade, % of GDP, while financial development is expressed as domestic credit to the private sector, % of GDP. The inflation rate is measured in annual percent. The political right is measured in index.
The data is obtained from the World Bank database (online), except
This section presents and discusses the results of the various analyses conducted in the study. These include descriptive analysis results, unit root results, multicollinearity test, heteroscedasticity test, autocorrelation test, normality test, and the Panel
Starting with the descriptive analysis, Table
The mean and median of the variables both measure the central tendency. The result from Table
Descriptive statistics










% of GDP  470  3.67  1.71  8.83  5.16  –11.64  103 

Domestic credit to private sector % of GDP  449  14.56  12.31  11.47  0.93  0.4  65.74 

Annual %  468  4.07  4.38  4.81  1.10  –30.15  26.42 

Constant 2010 US Dollars  470  2561  2144  1369  0.64  931  7171 

% of the total population  480  39.11  39.72  11.16  0.28  13.81  66.19 

Total trade % of GDP  468  64.91  58.76  31.07  0.53  20.72  311.35 

Annual  427  7.41  4.36  10.96  2.51  –7.8  72.84 

176  16.58  14.46  9.30  0.64  3.65  50.43  

Institution Composite Index  335  –0.00  –0.15  1.00  –6.67  –2.13  3.09 

An index ranging between 1 and 7  480  4.13  4.00  1.80  0.45  1  7 

Total natural resources  455  228  228  131  0.58  1  455 

An index ranging between –2.5 and +2.5  336  –0.62  –0.57  0.40  –0.70  –2.02  0.34 

An index ranging between –2.5 and +2.5  336  –0.51  –0.35  0.82  –2.34  –2.44  1.22 

An index ranging between –2.5 and +2.5  335  –0.78  –0.80  0.47  –0.59  –1.88  0.37 

An index ranging between –2.5 and +2.5  336  –0.61  –0.69  0.52  –0.75  –1.7  1.14 

An index ranging between –2.5 and +2.5  336  –0.67  –0.67  0.55  –0.82  –2.01  1.04 

An index ranging between –2.5 and +2.5  336  –0.39  –0.37  0.60  –1.62  –1.55  1.00 
A higher standard deviation value indicates a greater spread in the data. The standard deviation for
The minimum is the smallest data value, while the maximum is the largest data value. Comparing both minimum and maximum values for all variables in Table
As seen from Table
Results of the ADF Unit Root tests







At Level  –4.065  0.000  I(0)  Stationary or I(0) 

At Level  1.408  0.920  I(1)  Unit root I(1) 
At First Difference  –8.815  0.000  I(0)  

At Level  –7.052  0.000  I(0)  Stationary or I(0) 

At Level  5.295  1.000  I(1)  Unit root I(1) 
At First Difference  –7.234  0.000  I(0)  

At Level  6.576  1.000  I(1)  Unit root I(1) 
At First Difference  –4.379  0.000  I(0)  

At Level  –1.206  0.113  I(1)  Unit root I(1) 
At First Difference  –11.027  0.000  I(0)  

At Level  –7.410  0.000  I(0)  Stationary or I(0) 

At Level  –2.520  0.005  I(0)  Stationary or I(0) 

At Level  –1.598  0.054  I(0)  Stationary or I(0) 

At Level  –6.627  0.000  I(0)  Stationary or I(0) 
The Kao cointegration test methodology is used to check for the longrun relationship of the dependent variables with their independent variables. The result of the test shows that the tstatistic value is 3.465 with a probability value of 0.0003, which is less than 0.05 significance level in Equation 1. Hence, the null hypothesis is rejected and it is concluded that there is a longrun relationship between the dependent and independent variables. This implies that the Panel
To present and analyze the estimates of Equations 1 to 8 concerning the determinants of
Panel
































0.045  2.03  0.042  0.341  1.46  0.145  0.271  2.45  0.014 

–0.001  –0.02  0.981  –6.811  –1.13  0.259  –0.080  –0.56  0.572  

0.011  1.49  0.137  –0.001  –0.04  0.972  –0.072  –3.17  0.002 

0.001  0.05  0.958  2.841  1.03  0.305  0.068  1.03  0.301  

0.001  2.76  0.006  –0.014  –1.12  0.264  0.000  0.15  0.882 

–0.498  –2.42  0.015  18.086  0.92  0.358  –1.320  –1.01  0.310  

–0.010  –0.82  0.412  –1.791  –1.03  0.304  0.000  0.01  0.996 

–0.002  –1.56  0.118  0.028  0.94  0.347  –0.014  –1.26  0.208  

0.019  1.91  0.056  0.116  4.25  0.000  0.077  1.10  0.270 

0.023  0.95  0.343  2.546  1.12  0.264  0.022  0.16  0.870  
Hausman (Pvalue)  0.179  0.898  Hausman (Pvalue)  0.962  0.997  
R2  0.296  R2  0.296  
F(Pvalue)  0.000  F(Pvalue)  0.000  
No of Countries  16  16  16  No of Countries  16  16  16  
No of Observation  693  693  693  No of Observation  693  693  693 
OLS estimates of the regression equations
Table 


Equation 2  Equation 3  Equation 4  Equation 5  Equation 6  Equation 7  Equation 8  
Variables  Coefficient  ZStatisticst  Pvalue  Coefficient  ZStatisticst  Pvalue  Coefficient  ZStatisticst  Pvalue  Coefficient  ZStatisticst  Pvalue  Coefficient  ZStatisticst  Pvalue  Coefficient  ZStatisticst  Pvalue  Coefficient  ZStatisticst  Pvalue 































–0.315  –2.26  0.024  –0.268  –1.92  0.055  –0.258  –1.86  0.062  –0.315  –2.26  0.024  –0.256  –1.86  0.063  –0.311  –2.24  0.027  –0.271  –1.93  0.053 

0.276  1.06  0.296  0.267  1.00  0.319  0.269  1.00  0.316  0.276  1.04  0.296  0.309  1.15  0.250  0.194  0.71  0.480  0.274  1.02  0.306 

–0.001  –0.76  0.447  –0.001  –0.77  0.444  –0.001  –0.78  0.434  –0.001  –0.76  0.447  –0.001  –0.68  0.499  –0.001  –1.61  0.109  –0.001  –0.55  0.579 

0.271  1.67  0.096  0.189  1.19  0.236  0.180  1.10  0.273  0.271  1.67  0.096  0.145  0.93  0.354  0.215  1.32  0.188  0.133  0.79  0.432 

0.231  3.78  0.000  0.256  4.21  0.000  0.252  4.11  0.000  0.231  3.78  0.000  0.240  3.90  0.000  0.276  4.44  0.000  0.255  4.19  0.000 

–0.096  –0.38  0.702  –0.147  –0.58  0.565  –1.166  –0.65  0.513  –0.096  –0.38  0.702  –0.214  –0.85  0.397  –0.348  –1.31  0.194  –0.209  –0.81  0.419 

–0.482  1.74  0.082  –0.260  –0.98  0.325  –0.210  –0.84  0.400  –0.481  –1.74  0.082  –0.108  –0.43  0.665  0.093  0.35  0.730  –0.115  –0.41  0.679 

–0.748  0.88  0.377  –1.429  –1.63  0.103  –1.495  –1.01  0.311  –0.748  –0.88  0.377  –2.100  –2.78  0.006  –2.708  –3.17  0.002  –1.978  –2.41  0.016 

0.054  0.31  0.757  0.209  0.18  0.861  0.035  0.20  0.841  0.053  0.31  0.757  0.025  0.14  0.887  0.053  0.29  0.776  0.015  0.08  0.933 

4.344  1.99  0.047  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –  – 

–  –  –  –2.459  –0.56  0.576  –  –  –  –  –  –  –  –  –  –  –  –  –  –  – 

–  –  –  –  –  –  0.821  0.18  0.859  –  –  –  –  –  –  –  –  –  –  –  – 

–  –  –  –  –  –  –  –  –  8.542  1.99  0.047  –  –  –  –  –  –  –  –  – 

–  –  –  –  –  –  –  –  –  –  –  –  –5.100  –1.25  0.212  –  –  –  –  –  – 

–  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –4.163  –1.92  0.057  –  –  – 

–  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –  –2.673  –0.58  0.560 
Hausman (PValue)  –  –  0.854  –  –  0.772  –  –  0.645  –  –  0.854  –  –  0.581  –  –  1.000  –  –  0.507 
LM (PValue)  –  –  1.000  –  –  1.000  –  –  1.000  –  –  1.000  –  –  1.000  –  –  1.000  –  –  1.000 
FWald (PValue)  –  –  0.000  –  –  0.000  –  –  0.000  –  –  0.000  –  –  0.000  –  –  0.000  –  –  0.000 
Overall Rsquared  0.316  –  –  0.298  –  –  0.297  –  –  0.316  –  –  0.304  –  –  0.299  –  –  0.298  –  – 
No of Countries  16  –  –  16  –  –  16  –  –  16  –  –  16  –  –  16  –  –  16  –  – 
No of Observation  153  –  –  153  –  –  153  –  –  153  –  –  153  –  –  153  –  –  153  –  – 
Concerning the test statistics for choosing between the pooled OLS, fixed and random effects methods of panel data estimation, the Hausman test results show that we do not reject the null hypothesis that RE is preferred to FE in Equations 2 to 8 because the pvalues are greater than 0.05 level of significance in all cases, being 0.936, 0.851, 0.780, 0.897, 0.214, 0.317 and 0.539. Further testing using the BreuschPagan LM method confirms that Pooled OLS is more appropriate than either of Fixed Effects and Random Effects estimation methods in Equations 2 to 8 as the test reports a probability value of 1.000, which, in essence, leads to the rejection of the LM test and confirms pooled OLS as the most suitable method. Accordingly, the evaluation of the results carried out below is based only on the Pooled OLS result for Equation 2 to 8.
After evaluating the overall diagnostic statistics of the equation, we now proceed to examine the performance of each of the explanatory variables based on three ‘S’ — size, sign, and statistical significance.
Based on the above methodology, the main findings and conclusions relevant to each finding are as follows:
The coefficients of financial development are negative in all cases, some of them are statistically significant and others insignificant, giving the overall impression that financial development has a negative effect on FDI flows to West Africa, which, in turn, slows down globalization processes in the region.
In all cases, the coefficients of the growth rate of GDP, though positive, are statistically insignificant in all equations, implying that GDP growth rate does not affect FDI flows to West Africa, which accelerates globalization processes in the region.
The coefficients of real GDP per capita are negative but statistically insignificant, implying that real GDP per capita does not affect FDI flows to the region.
The coefficients of the urban population are positive but statistically insignificant, implying that the urban population does not affect FDI flows.
In all cases, the coefficients of trade openness are positive and statistically significant, implying that trade openness has a positive effect on FDI flows to West Africa.
The coefficients of inflation, though negative, is statistically insignificant, implying that inflation does not affect FDI flows.
The coefficients of infrastructure are statistically insignificant in all cases, implying that infrastructure does not affect FDI flows to the region.
The coefficients of political rights are negative, some of them are significant and others insignificant, implying that there is no robust evidence concerning their effect on FDI flows to West Africa.
The coefficients of natural resources are positive but statistically insignificant, implying that natural resources do not affect FDI flows.
The coefficient of composite governance indicator and that of one component of it, which is the extent of control on corruption, are both positive and statistically significant, implying the existence of their expected positive effects on FDI flows to West Africa, which potentially increases globalization processes in the region. On the other hand, the coefficients of the other five components, which are the rule of law, absence of violence, voice and accountability, regulatory quality, and government effectiveness, are all statistically insignificant, implying that their impact on FDI flows is not noticeable.
From the foregoing it can be concluded that the evaluation of the factors that determine foreign direct investment and influence globalization processes in West Africa did not yield all the expected results. It is revealed that financial development has a negative effect on
Based on the findings of this study, as highlighted above, the following policy recommendations are made.
Based on the conclusion that
The positive effect of trade openness on
Authorities should also boost highquality anticorruption mechanisms to accelerate the globalization process through inbound