Evaluating the Impact of the Predicted Fall of Foreign Direct Investment on Sub-Sahara African Countries' Economy

CONTENT

1. INTRODUCTION

2. LITERATURE REVIEW

3. DATA METHODOLOGY
3.1. Data
3.2. Data analysis

4. EMPIRICAL RESULTS AND DISCUSSION
4.1. DESCRIPTIVE STATISTICS
4.2. Correlation and regression results
4.3. Direct and indirect effects analysis

5. CONCLUSION AND POLICY IMPLICATIONS

6. REFERENCE

Abstract

The impact of the predicted fall of foreign direct investment (FDI) for Africa of a 25-40 percent is not well known. The decrease in FDI may affect all components of the equation of GDP such as household consumption (HHC), government expenses (GEXP), gross capital formation (GCF), export (EXP), and import (IMP). The purpose of this study is to analyze the relationship between FDI-GDP-HHC-GEXP-GCF-EXP-IMP, and to compare the impact of FDI on GDP in SSA countries whose FDI is under and greater than median. Annual data 2018 were collected on one hand; on the other hand, data from 1995 to 2018 were also collected. Spearman correlation, robust regression, robust mediation, and bootstrap methods were used to analyze data collected. Strong and statistically significant correlation between FDI-GDP-HHC-GEXP-GCF-EXP-IMP was found. Positive and significant direct and indirect effects of FDI on GDP were identified. It was found that GDP is highly influenced by FDI in countries whose mean is under median. Additionally, some countries ’GDP are strongly related to FDI while in others FDI has no great influence on GDP. Due to such correlation, the consequences of the fall of FDI will be dramatic for SSA countries in contrast of the predicted fall of GDP from 3.2 per cent to -2.8 per cent for Africa. Gross domestic product, household consumption, government expenses, gross capital formation, will be reduced, and export and import will fall. The more a country’s economy is linked to FDI, greater will be the consequences of fall of FDI on GDP.

Keywords: Foreign Direct Investment, Household Consumption , Government Expenses , Gross Capital Formation, Export, Import.

1. INTRODUCTION

The paper investigates what may be the impact of the predicted fall of FDI on GDP in SSA. The United Nations Conference on Trade and Development (2019) indicated that FDI flows to Africa increased by 11 percent to $46 billion, despite declines in many of the larger recipient countries. The increasing of FDI flows was justified by continued resource-seeking inflows, some diversified investments, and a recovery in South Africa after several years of low-level inflows. However, the predictions of 2020 regarding FDI flows are dramatic. Compared to 2019, global flows of FDI are expected to decrease by up 40 percent from their value $1.5 trillion in 2020 as a result of the COVID-19 pandemic (United Nations Conference on Trade and Development, 2020). This organization predicted a fall of FDI to reach -45 to -30 in Europe, North America -35 to -20, -40 to -25 in Africa, -45 to -30 in Asia, Latin America and the Caribbean -55 to – 40, and in transition economies -45 to -30. The prediction for Africa of a 25-40 percent decline is based on GDP growth projections as well as a range of investment-specific factors. The same organization indicated a decline in GDP growth for Africa from 3.2 per cent to -2.8 percent.

While the existing literature has provided numerous studies on the positive effects of FDI on economic growth in host-country, very little is known about how the decline in FDI affects all component of GDP. The prediction made by the United Nations Conference on Trade and Development (2019) does not tell which country will be more affected by the fall of FDI than other. The prediction does not consider the relationship between the components of the equation of GDP such as HHC, GEXP, GCF, EXP, and IMP.

Studies have found positive relationship between FDI and GDP. Steve (2014) used system generalized method of moment (SYS-GMM) estimators, and found that FDI inflows had a significant impact on economic growth in the African region during the period 1980 to 2009. Tsatsaridis (2017) used ordinary least squares regressions and cointegration tests to analyze 43 countries in the region of Sub-Saharan Africa for the years between 1996 to 2016. He concluded on a statistically significant impact of foreign direct investment inflows on the growth of gross domestic product in the region. Tamilselvan and Manikandan (2015) used simple regression between foreign direct investment (FDI) and gross domestic production (GDP) for 23 years, and found FDI has a positive impact on GDP in India. Abbas, Akbar, Nasir, Aman, and Naseem (2011) using multiple regression models found a positive and significant relationship between GDP and FDI in South Asian Association for Regional Co-Operation.

The consequences of the fall in FDI are still unknown as the pandemic of COVID’19 continues to destabilize the world. FDI is linked to many other variables that contribute to the increase in GDP. The decrease of FDI may impact negatively those variables that are positively related to FDI. The more a country’s economy is linked to FDI, greater negative impact will be. There is a belief in policymakers and scholars that FDI contribute positively to economic growth in a host country. However, empirical evidence indicates mixed result, showing a positive, neutral, or even negative relationship of FDI with growth (Tamar & Luca, 2019).

To test the impact of the fall of FDI on GDP, the study uses the equation of GDP stated by Topi (2011). GDP = C + I + G + (X – M) where C is consumption of final goods and services by the households, I is investment in things such as plants, equipment and software, G is the government expenditures on goods and services, X is exports, and M is imports. Casi and Resmini (2011) indicated that the indirect effects of FDI are able to potentially affect all variables included in the production function, and that increases the impact of FDI.

The primary goal of this study is to investigate the relationship between FDI and GDP through import (IMP), export (EXP), gross capital formation (GCF), and household consumption (HHC), and government expenses (GEXP). Specifically, the study tends to address the following sub-objectives:

- Analyzing the relationship between FDI-GDP-HHC-GEXP-GCF-EXP-IMP.
- Analyzing direct, and indirect impact of FDI on GDP for 40 SSA all together,
- Investigating the impact of FDI on GDP in SSA countries whose FDI is under median on one hand, and SSA countries whose FDI is greater than median, on the other hand,
- Finally, comparing the impact of FDI on GDP in 12 SSA with highest and lowest FDI.

2. LITERATURE REVIEW

The aim of this litterature review is to show the results regarding the impact of FDI on economic growth. For instance, the Organization for Economic Co-operation and Development (“OECD”, 2002) indicated FDI triggers technology spillovers, assists human capital formation, contributes to international trade integration, helps improve environmental and social conditions, helps create a more competitive business environment, leading to more socially responsible corporate policies and enhances enterprise development. In addition, all of these contribute to higher economic growth, which is the most potent tool for alleviating poverty in developing countries. The organization also indicated some factors that limit foreign direct investment in Africa like macroeconomic instability; loss of assets due to non-enforceability of contracts; and physical destruction caused by armed conflicts; transparent judicial system, sustainability of national economic policies, poor quality of public services and closed trade regimes. Steve (2014) used system generalized method of moment (SYS-GMM) estimators, and found that FDI inflows had a significant impact on economic growth in the African region during the period 1980 to 2009. Tsatsaridis (2017) used ordinary least squares regressions and cointegration tests to analyze 43 countries in the region of Sub-Saharan Africa for the years between 1996 and 2016. He concluded on a statistically significant impact of FDI on the growth of gross domestic product in the region. Abbas, Akbar, Nasir, Aman and Naseem (2011) using multiple regression models found a positive and significant relationship between GDP and FDI in South Asian Association for Regional Co-Operation. After examining descriptive statistics, correlation and regression model, Nadeem, Naveed, Zeeshan and Sonia (2013) found a positive relationship between FDI and GDP in Pakistan. Tamilselvan and Manikandan (2015) used simple regression between foreign direct investment (FDI) and gross domestic production (GDP) for 23 years, and found FDI has a positive impact on GDP in India.

Besides the positive impact of FDI on GDP, negative impact of FDI on GDP was also found by some studies. For instance, Igor (2015) concluded that FDI have negative influence on domestic investment in the republic of Croatia with time lag. Umeora (2013) concluded that FDI did not increase GDP, instead increased inflation, and had negative effect on exchange rate in Nigeria.

Countries made effort to make economic environment more attractive for foreign investors. Therefore, among the incentives there are some rights and advantages included in investment law, the creation of investment promotion agencies, etc. The purpose of such instrument is to promote and facilitate investment. Rwanda up dated its investment law of 2005 in 2015 to insert more incentives for investors. Rwanda investment law (2015) indicates that a foreign investor may invest and purchase shares in an investment enterprise in Rwanda and shall be given equal treatment with Rwandan investors with regard to incentives and investment facilitation. The same law indicates conditions under which an international company can have a preferential corporate income tax rate of zero per cent. The same law promotes exportations by offering a preferential corporate income tax rate of 15 per cent under certain circumstances. Investing an equivalent of at least USD 50,000,000 and contributing at least 30 per cent of this investment in form of equity in the sectors specified may be exempted to paying income tax for a maximum of seven years. Microfinance institution can have five years of exemption of paying income tax. There is an exemption of customs tax for products used in export processing zones. The Rwanda law of investment offers opportunity of a flat accelerated depreciation rate of 50 percent for the first year for new or used assets under some circumstances. As result, FDI was US $ 7 960 000 in 2005, increased to US $ 30 643 966 in 2006, 82 283 165 in 2007, and reached US $ 250 504 802,92 in 2010, (World Bank 2020).

3. DATA METHODOLOGY

3.1. Data

To test the impact of FDI on GDP, IMP, EXP, GCF, HHC, and GEXP, data for annual 2018 were collected from World Bank. To compare the impact on FDI on GDP in SSA with highest and lowest FDI, data related to GDP and FDI were collected from 1995 to 2018 for selected countries. The group considered with FDI greater than median is South Africa, Congo Republic, Ethiopia, Ghana, Mozambique, Nigeria, Kenya, Uganda, Congo Democratic Republic, Côte d'Ivoire, Gabon, Cameroon, Zimbabwe, Chad, Senegal, Madagascar, Burkina Faso, Mali, Niger.The group considered with FDI less than median includes: Mauritius,Guinea, Rwanda, Botswana, Sierra Leone, Benin, Namibia, Liberia, Cabo Verde, Togo, Mauritania, Seychelles, Lesotho, Gambia, Eswatini, Guinea-Bissau, Central African Republic, Comoros, and Burundi. Countries used to compare the impact of FDI on GDP for countries with lowest FDI are Burundi, Rwanda, Angola, Eswatini, Burkina Faso, and Comoros. Countries used to compare the impact of FDI on GDP for countries with highest FDI are Congo Republic, Ethiopia, Nigeria, Ghana, Mozambique, and South Africa.

3.2. Data analysis

Data were collected to achieve the purpose of this study. The primary goal is to investigate the relationship between FDI and GDP through import (IMP), export (EXP), gross capital formation (GCF), and household consumption (HHC), and the government expenses (GEXP). Specifically, the study tends to address the following sub-objectives:

- Analyzing the relationship between FDI-GDP-HHC-GEXP-GCF-EXP-IMP.
- Analyzing direct, and indirect impact of FDI on GDP for 40 SSA all together,
- Investigating the impact of FDI on GDP in SSA countries whose FDI is under median on one hand, and SSA countries whose FDI is greater than median, on the other hand,
- Finally, comparing the impact of FDI on GDP in 12 SSA with highest and lowest FDI.

To find relationship between FDI and GDP, GEP, GCF, EP, IMP, HHC pairwise Spearman correlation analysis was first performed. To investigate direct and indirect effects of FDI on GDP via HHC, GEXP, GCF, EXP, and IMP, robust mediation, and bootstrap procedure were used. Robust regression was performed to compare countries with FDI greater, and less than median. The choice of this method is based on the fact that robust methods have the advantage of down weighting deviating observations while estimating the model, yields reliable results even when the data deviate from normality assumptions (Andreas, Nüfer & Patrick 2018). According to Alma (2011) robust regression is generally used when data are contaminated with outliers, and is able to detect outliers and to provide reliable results in the presence of outliers. Data normal distribution was tested by using Skewness, and Shapiro-Wilk normality test. All the analyses were performed using R software and JASP. To qualify the nature of the indirect effect, the variance accounted for (VAF) representing the share of the indirect effect in the total effect is used. Accordingly, if VAF is less than 20%, the indirect effect is not significant. Therefore, there is no mediation. If VAF is between 20% and 80%, there is partial mediation. A VAF value of greater than 80% implies full mediation (Hadi, Abdullah & Sentosa, 2016). Data were normalized to get better coefficients.

4. EMPIRICAL RESULTS AND DISCUSSION

4.1. DESCRIPTIVE STATISTICS

To analyze central tendency of the data collected, mean, and median are calculated. As data are skewed and not normally distributed, the median gives a better idea of the central tendency, as the mean can be skewed by outliers. The descriptive statistics show a great difference between mean and media. For instance, the GDP’s mean is $ 38 690 355 784 while the median is $13 385 156 232. This indicates that 50 percent of the countries analyzed have GDP under the median. Concluding based on the mean will mislead the understanding of the central tendency. The difference between countries regarding GDP is great with high difference between the country with the maximum GDP and the country with the minimum. The standard deviation shows a great variability in the collected data. This indicates inconsistency of the collected data. The skewness is greater than 0.5 indicating the lack of normal distribution. The normality tests performed confirm that data are not normally distributed. The violation of normality assumption implies choosing a technique that is able to deal with not normally distributed data. The choice of robust regression, bootstrap, and robust mediation is resulting from not normally distributed data.

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