External Debt Accumulation and Foreign Direct Investment Inflows in Sub-Saharan Africa: Analysing the Interaction Effects of Selected Macroeconomic Factors

Abstract

By pooling a sample of 25 countries spanning from 1990–2017, this study analyzed the relationship between external debt accumulation and foreign direct investment inflows in Sub-Saharan Africa. The study employed Panel fixed effects and the Generalized Method of Moments estimation technique in order to address the potential issue of endogeneity. The results of the fixed effects and GMM analysis revealed a significant negative relationship between external debt accumulation and FDI inflows in SSA region. However, when external debt was interacted with macroeconomic variables such as corporate tax, infrastructure development, economic growth and military expenditure, the result shows a positive interaction effect between external debt and infrastructure development on FDI suggesting that external debt can positively influence FDI if channeled into the provision of critical infrastructures. On the other hand, the interaction effect of corporate tax showed a negative relationship between external debt and FDI and provided strong support for the debt overhang theory by Krugman which holds true in SSA region. Finally, economic growth and military expenditure also showed a negative interaction effect.

Keywords

External debt accumulation foreign direct investment inflows Sub-Saharan Africa fixed effect panel GMM estimation

Introduction

Developing countries most especially countries in the Sub-Saharan African region are frequently faced with budget deficit, that is a situation where government total expenditure exceeds total revenue due to their inability to effectively mobilize sufficient domestic revenue to meet their expenditures and as such resort to borrowing to finance annual budget deficits (Azolibe, 2021). Ogunmuyiwa (2010) stated that when tax revenue is limited and government does not want to compromise macroeconomic stability by printing more money, then borrowing becomes the only available avenue that government can explore to finance infrastructure projects.

Public borrowing occurs in domestic or foreign markets but over-reliance on the domestic market can lead to financial instability and crowd out the private sector (Panizza et al., 2010), hence most countries depend on foreign borrowing for the provision of public goods. According to Atique and Malik (2012), external debt constitutes a greater share of the public debt structure in developing countries. Todaro and Smith (2006), argued that developing countries in their early stages of development need to borrow externally because of inadequate domestic resource mobilization which contributes in part to rising level of external debt and debt servicing.

The external debt accumulation of Sub-Saharan Africa is best understood when considered as an integral part of the global debt crisis that emerged in 1982 due to: excessive borrowing by developing countries coupled with liberal lending by foreign commercial banks in 1970s, the fall in commodity prices especially petroleum products in early 1980s and astronomic increase in international lending rate in 1982. Increase in foreign borrowing that preceded the debt crisis was triggered by the oil price shocks of 1973 and 1979, which resulted in acute current account deficits in most non-oil producing less developed countries. These countries resorted to foreign borrowing to overcome the problems raised by the internationally generated shocks to their balance of payments. At the same time, during the period following the oil price hike of November 1973, the international commercial banks were awash with “petro dollars” that they were anxious to recycle.

Huge level of external debt profile could pose serious challenge to an economy in terms of stimulating investment most especially foreign direct investment. Anyanwu (1994) for instance, explained that huge debt accumulation discourages private investment for fear of higher taxes to repay the debt in the future. High debt creates uncertainty, deterring investment and innovation, and has a negative impact on economic growth (Cordella et al., 2005; Reinhart et al., 2012; Reinhart & Rogoff, 2010).

External debts for SSA countries have been increasing recently and most scholars such as Krugman (1988), Ostadi and Ashja (2014) and Mugambi and Murunga (2017) indicated that external debt is bad for a country in terms of attracting FDI. According to them, higher external debt results to increased external debt service which discourages foreign investors. The level of external debt service enters into the objective function of foreign investors. This is because high external debt service translates to higher taxes in the economy which eats into their profits (Mugambi & Murunga, 2017). Apart from its contagion effect in increasing taxes, external debt also reduces the amount of resources that would have been made available for infrastructure development, military expenditure and increased investment that will spur economic growth. A reduction in the level of infrastructure development, military expenditure and economic growth may in turn repel foreign investors. Total external debt in SSA rose from a level of US$176.49 billion in 1990, to US$235.94 billion in 1995. During the same period, external debt as a percent of Gross National Income (GNI) rose from 61.15 percent to 74.02 percent respectively. Also, external debt rose from US$213.33 billion in 2000 to US$313.17 billion in 2011. Finally, external debt in SSA rose from US$313.17 in 2011 to US$535.12 billion in 2017 (World Bank, 2018). For the years under study (1990–2017), the highest external debt to GNI ratio of 80.99 percent was attained in 1994 (see Figure A1 and A2 in appendix 1).

Also, in recent times, FDI inflows to SSA have been struggling coupled with rapid increase in external debt. FDI inflows into the region dropped from US$42.98 billion in 2011 to US$25.47 billion in 2017. Within the same period, FDI as a percentage of GDP dropped from 2.80 percent to 1.70 percent (World Bank, 2018) (see also Figure A3 and A4 in appendix 1). The high levels of external debt stock in the region have discouraged the inflow of foreign direct investment for fear of high tax imposition and macroeconomic distortions in the debtor economy as means of defraying debt owed (FitzGerald & Alexander, 2000). Massive capital flight has occurred in Sub-Saharan Africa as a result high debt stocks. Collier et al. (2004) reported that with a dollar increase in the stock of debt leads to 3.2 cents of capital flight in SSA region. Similarly, Ndikumana and Boyce (2003) found that for every dollar of external borrowing by an SSA country in a given year, on average, roughly 80 cents left the country as capital flight. Thus as a result of economic implications of external debt, domestic resources that could have been otherwise invested into growth-stimulating projects, abscond to the developed economies for investment in order to avoid abnormal domestic risks.

A lot of cross country studies on external debt such as Mensah et al. (2018), Senadza et al. (2018), Butts (2009), Folorunso and Ayadi (2008), Chowdhury (1994), Fosu (1996), and Fosu (1999), have focused much on its impact on economic growth. The few empirical studies on the relationship between external debt and foreign direct investment such as Mugambi and Murunga (2017), Azam & Asmatullah (2011), and Ajisafe et al. (2006) are country-specific studies. While the only cross country study so far, Ostadi and Ashja (2014) examined the relationship between external debt and foreign direct investment in D8 member countries. However, given the rising external debt profile in sub-Saharan Africa, this study intend to complement the dearth in knowledge gap in the literature by using panel data set of 25 countries covering period of (1990–2017) to investigate the relationship between external debt accumulation and foreign direct investment inflows in Sub-Saharan Africa. The study is unique as it analyzes the interaction effects of selected macroeconomic factors such as corporate tax, infrastructure development, economic growth and military expenditure in the relationship between external debt accumulation and FDI inflows in SSA region. The choice of these selected countries (see Table A1 in appendix 1) is based on the fact that they are the high debt countries in Sub-Saharan Africa and also availability of data on the variables.

External Debt Accumulation in Sub-Saharan Africa

External debt problem in Sub-Saharan Africa started initially in the form of difficulty in servicing external loans in accordance with terms and conditions specified in the original loan contract. Drouin (1989) claimed that 27 out of 44 Sub-Saharan African countries had payments arrears hence debt financing and rescheduling were adopted to resolve the problem. Whilst this strategy seemed to relieve debtor countries of debt service burden in the short run, it led to continual postponement of debt into the future without finding the fundamental structural defect of their economies that causes the problem. This method persisted until 1990s where debt levels of majority of countries in the region were pronounced unsustainable.

International financial community has been providing assistance to debtor countries since the emergence of the debt crisis in an attempt to reduce their external indebtedness, reduce poverty, foster growth, and to achieve external viability (IMF, 1998). This assistance takes the form of lending to developing countries with high concessions, and provision of debt reliefs. This assistance has helped to some extent in reducing external indebtedness of countries but could not halt the increasing BOP deficits, fiscal imbalances, rate of external borrowing, and poverty in Sub-Saharan Africa.

According to the World Bank International Debt Statistics (2019), countries in Sub-Saharan Africa accumulated external debt at a faster pace than low- and middle-income countries in other regions in 2017: the combined external debt stock rose from $463 billion in 2016 to $535 billion representing an increase in 15.5 percent (see Table 2). Much of this increase was driven by a sharp rise in borrowing by two of the region's largest economies, Nigeria and South Africa, where the external debt stock rose 29 percent and 21 percent respectively. The combined external debt stock of the 30 Sub-Saharan African countries that benefitted from debt relief under the Heavily Indebted Poor Country (HIPC) and Multilateral Debt Relief (MDRI) initiatives rose by 11 percent in 2017, compared to 7 percent in 2016. In 2017, South Africa borrowed $176 billion externally with a debt to GNI ratio and debt to export ratio of 52.0 percent and 160.4 percent respectively, followed by Nigeria at $40 billion with a debt to GNI ratio and debt to export ratio of 11.0 percent and 76.9 percent respectively and Angola at $37 billion with a debt to GNI ratio and debt to export ratio of 31.6 percent and 103.8 percent respectively. The total debt stock was lower in some of the frontier markets than in middle-income countries, but the increase over the past few years was nonetheless considerable. For instance, Ethiopia's external debt stock rose more than 250 per cent, from $7.3 billion in 2010 to $26.5 billion in 2017. Kenya's pace of external debt accumulation was similar, with external debt stocks rising from $8.8 billion in 2010 to $26.4 billion in 2017 (nearly a 200 per cent rise). Ghana's public debt rise was close to 145 per cent between 2010 and 2017 (from $9 billion to $22 billion).

The increase in external debt accumulation raises concerns about debt sustainability in many African countries, especially as external debt stocks have risen much faster than economic growth owing to rising interest rates in international capital markets. While the average ratio of external debt to GNI in Africa declined from 119 per cent of GNI in 2003 to 32 per cent in 2012 before rising in 2013 and stabilizing at 46 per cent in 2017. Debt ratios are still very high in some countries, mostly low-income economies. At the end of 2017, eight countries in the region had an external debt-to-GNI ratio of over 60 percent, including six that had benefited from HIPC and MDRI: Gambia, Liberia, Mauritania, Mozambique, Sao Tome and Principe and Zambia. Debt ratios in 2017 were high in Mauritius (156 per cent), Cabo verde (104 percent), Mozambique (101 per cent), Mauritania (89 per cent), São Tomé and Príncipe (67 per cent), Gambia (66 percent), Zambia (65 per cent) and Liberia (61 percent). The lowest in terms of external debt to GNI ratio were Botswana (10 percent), Nigeria (11 percent), Congo DR (14.0 percent), Guinea (14.3 percent), Eswatini (14.7 percent) and Burundi (18 percent). These countries had external debt to GNI ratio of less than 20 percent. With the high share of external debt to GNI, debt servicing costs have increased. This is shown in Table 1:

Table 1.

External Debt Stock in Sub-Saharan Africa.

External Debt Stock
	Total Debt ($Millions)		Total Debt to GNI (%)		Total Debt to Export (%)
Year	2016	2017	2016	2017	2016	2017
Angola	35,365	37,201	39.3	31.6	123.5	103.8
Benin	2,316	2,897	27.0	31.3	103.5	114.5
Botswana	2,100	1,740	13.6	10.2	24.9	24.4
Burkina Faso	2,818	3,119	25.3	25.0	81.8	86.0
Burundi	603	613	20.1	17.7	289.1	245.5
Cabo Verde	1,543	1,762	97.9	104.4	204.5	204.5
Cameroon	8,186	10,396	25.9	30.3	126.9	154.3
Central African Republic	688	731	39.1	37.4	..	..
Chad	3,011	3,139	32.6	31.9	..	..
Comoros	159	166	25.6	25.3	137.8	124.6
Congo, Dem. Rep.	5,078	5,128	14.8	14.0	50.2	38.3
Congo, Rep.	3,830	4,456	51.9	56.1	83.1	..
Cote d’Ivoire	11,534	13,433	32.6	34.4	95.6	105.6
Eswatini	487	651	13.1	14.7	26.7	31.0
Eritrea	796	819	..	..	..	..
Ethiopia	23,794	26,562	32.7	33.2	401.9	397.6
Gabon	5,076	6,166	39.3	45.1	..	..
Gambia	516	650	55.2	65.9	210.2	247.5
Ghana	21,371	22,022	51.2	48.1	120.7	106.2
Guinea	1,381	1,489	15.9	14.3	55.2	31.7
Guinea-Bissau	295	328	24.4	24.3	94.5	87.8
Kenya	22,326	26,424	31.8	35.7	216.0	243.8
Lesotho	883	936	34.1	31.6	64.7	60.7
Liberia	952	1,137	52.3	61.3	239.8	252.9
Madagascar	2,976	3,376	31.0	30.3	88.5	82.2
Malawi	1,847	2,160	34.9	35.1	151.2	172.9
Mali	3,790	4,368	27.7	29.3	110.1	127.6
Mauritania	3,930	4,231	85.0	89.2	227.2	207.3
Mauritius	18,017	21,067	148.1	155.9	128.5	141.9
Mozambique	10,705	12,010	99.5	100.8	276.3	216.5
Niger	3,237	3,746	43.5	47.2	241.8	288.5
Nigeria	31,151	40,238	7.9	11.0	78.5	76.9
Rwanda	2,783	3,338	33.7	37.4	177.2	161.4
Sao Tome and Principe	248	262	69.8	66.6	241.2	276.3
Senegal	6,678	8,886	47.1	56.2	159.5	212.6
Sierra Leone	1,598	1,731	50.4	47.3	170.2	..
Somalia	2,865	2,958	42.6	40.3	..	..
South Africa	146,041	176,335	50.8	52.0	150.3	160.4
Sudan	21,120	21,754	24.4	20.3	455.2	387.3
Tanzania	16,192	18,242	34.5	35.4	172.9	205.6
Togo	1,176	1,631	26.0	32.8	63.4	105.9
Uganda	9,510	11,189	40.3	44.3	196.0	222.5
Zambia	15,529	16,309	75.8	65.2	207.3	179.4
Zimbabwe	8,844	9,330	59.6	59.1	206.6	188.6

Source: World Bank International Debt Statistics, 2019.

From the Table 2 Sub-Saharan Africa is the second highest in terms of total external debt to GNI ratio of 34.2 percent in 2017 after Europe and Central Asia which had an external debt to GNI ratio of 48.7 percent. A higher debt to GNI ratio slows down the economic growth of a country and the country typically has problem paying off its debt. On the other hand, it was the third highest in terms of total external debt to export ratio of 136.1 percent in 2017 after Latin America and Caribbean and Europe and Central Asia which had a total external debt to export ratio of 152.7 percent and 145.5 percent respectively.

Table 2.

External Debt Stock Across Regions of the World.

External Debt Stock
	Total Debt ($Millions)		Total Debt to GNI (%)		Total Debt to Export (%)
Year	2016	2017	2016	2017	2016	2017
Low & middle income	6,442,877	7,070,182	25.0	25.0	105.0	101.7
East Asia & Pacific	2,103,028	2,461,916	16.0	17.1	65.5	68.7
Europe & Central Asia	1,527,779	1,570,782	53.2	48.7	167.8	145.5
Latin America & Caribbean	1,461,992	1,501,590	35.9	33.3	166.3	152.7
Middle East & North Africa	263,351	294,225	21.4	25.2	105.4	100.1
South Asia	623,382	706,545	21.5	21.3	116.2	116.9
Sub-Saharan Africa	463,344	535,126	32.1	34.2	137.9	136.1
Low income	140,278	157,193	28.9	29.5	135.9	..
Middle income	6,302,599	6,912,989	24.9	24.9	104.5	101.2
Lower middle income	1,710,115	1,899,126	28.5	29.3	125.7	120.3
Upper middle income	4,592,484	5,013,863	23.8	23.6	98.3	95.4

Source: World Bank International Debt Statistics, 2019.

Drivers of External Debt Accumulation in Africa

The key drivers of external debt accumulation in Africa as discussed by Coulibaly et al. (2019) are:

Shocks of the global financial crisis and the 2014 terms of trade shock: The global financial crisis and the 2014 terms-of-trade shock hit economies around the world hard. The economic downturn, which ensued and the policy response contributed to a widespread accumulation of debt. Africa was not an exception. In the years leading up to the GFC, most countries in the region ran a primary fiscal surplus averaging about 3.5 percent of GDP between 2005 and 2008. Following the GFC, the surpluses turned into deficits, averaging 1.6 percent of GDP through 2013. The 2014 terms-of-trade shock further widened the deficit to 3.2 percent, on average, between 2014 and 2017, which accelerated debt accumulation.

Deficits in resource-intensive countries ballooned; these economies experienced a more rapid increase in debt (Figure 1 and 2). Among the region's eight oil-exporters, average debt increased by almost 40 percentage points from 21 to 59 percent of GDP, far higher than the 16 percentage points increase in the continent's non-oil exporting economies, which is still rapid. Five of 10 countries with the fastest debt accumulation were oil-exporting economies.

Figure 1.

Average primary deficit by country group (percent of GDP). Source: IMF World Economic Outlook Database, October 2018. Note: ‘Other countries’ include all countries in the sample excluding oil exporters.

Figure 2.

Average government debt by country group (percent of GDP). Source: IMF World Economic Outlook Database, October 2018; IMF Historical Public Debt Database. Note: ‘Other countries’ include all countries in the sample excluding oil exporters.

Low global interest rate environment: The changing financial landscape in the advanced economies pursuant to the GFC has facilitated the debt build-up in Africa. Following the GFC and the European debt crisis, the ensuing ultra-low interest rates prevailing in the U.S and other advanced economies markets led to the private market stepping up lending to frontier markets, including to several African countries, in a search for higher yielding assets. For their part, several African countries began in 2006 to access international capital markets, with the pace of issuance and number of countries entering the markets ramping up after 2012 (see Figure 3). In 2019, 16 countries have issued Eurobonds in international debt markets. These bonds were typically denominated in US dollars, unlike what their name implies (‘Europe’). The appetite for such bonds persists despite the debate about the possibility of a debt crisis, increase in spreads, and sovereign downgrades in some countries. The majority of the eurobonds are issued by six countries: Angola, Côte d’Ivoire, Ghana, Kenya, Nigeria, and Senegal. In the last two years, all six countries have issued bonds with 30 year maturities. Eurobond issuances in the context of a low interest rate environment and appetite for higher yields contributed to the increase in debt.

Figure 3.

International bond issuances (selected countries). Source: Bloomberg.

Large financing gaps, particularly for infrastructure: The rapid increase in debt for non-oil exporters highlights other contributing factors. Low domestic saving rates in face of growing financing needs for infrastructure and other economic programs led governments to contract debt from a variety of sources: international debt markets (Eurobond issuance, in particular), domestic markets, multilateral institutions, and Paris and non-Paris Club countries, with China being the major non-Paris Club player. The African Development Bank (AfDB) estimates Africa's annual financing needs for infrastructure at $130 billion to $170 billion and the financing gaps at $68 billion to $108 billion (AfDB, 2018).

The role of China in infrastructure development and financing is particularly prominent. Apart from emerging as the largest trading partner for Africa, China has been the most prominent external player in the construction of railways, roads, power plants, ports, and so on. Accompanying this growth in investment is its large-scale provision of debt financing. Between 2012 and 2016, African governments financed over 40 percent of their own infrastructure needs. Over this period, China financed 15 percent, much more than the 3 percent from multilateral development banks.

Poor governance: In a few countries, poor governance has contributed to the increase in debt. For example, in Mozambique, one of the countries currently in debt distress, the disclosure of hidden loans issued by public companies, but guaranteed by the government, led to the deterioration of debt sustainability. This hidden debt included borrowing by three companies that were created shortly before the borrowing took place and were all headed by the same CEO, who at that time was a senior officer in the country's security services. This example highlights the importance of strengthening governance around debt issuance, especially transparency.

Exchange rate depreciation: A portion of the debt accumulation can be attributed to balance sheet effects of exchange rate depreciations. A sizeable share of Africa's debt, including eurobonds, is denominated in foreign currency. As these issuances generally are not hedged against currency risks, the domestic currency value of unhedged debt increases with exchange rate depreciations like the ones that followed the GFC and the 2014 terms-of-trade shock.

More favorable debt dynamics: Most of the drivers of the increases in debt appear to be dissipating. Commodity prices have retracted their declines, economic growth is recovering, exchange rates have strengthened, and in many cases, fiscal consolidation is under way.

External Debt Accumulation and Foreign Direct Investment Inflows Nexus

External debt is one of the main macroeconomic indicators, which forms countries’ image in international market. It is one of the inward foreign direct investment flow determinants. External debt has a positive and or negative interaction effect on FDI through its influence on some macroeconomic factors such as corporate tax, infrastructure development, economic growth and military expenditure. A higher level of external debt accumulation could result to an increase in taxes and one of the ways through which a country can finance its rising debt profile is to increase taxes. Higher taxes lead to a reduction in foreign direct investment inflows due to increased cost of doing business (Cockcroft & Riddell, 1991) as it reduces return on investment and may be forced to move their investment to other countries with low external debt burden.

The debt overhang theory as explained by Krugman (1988) clearly demonstrates how accumulation of high external debt leads to low FDI inflows translating into low economic growth of a country. According to Krugman (1988), debt overhang refers to a situation where the existing external debt is very large. The theory suggests that foreign investors will be discouraged from investing in a country that has a large external debt since part of their proceeds would be used to service the debt through high taxation. On the other hand, the theory postulates that reducing debt obligation results to a rise in both domestic and foreign direct investment thus minimizing the chances of debt default.

Another way in which external debt accumulation could deter foreign direct investment inflows is based on the fact that it reduces the amount of resources that will be available for the provision of infrastructures such as good road network, constant power supply, health care and transport infrastructure. Good infrastructure has been perceived by most scholars (Asiedu, 2002; Babatunde, 2011; Bakar et al. 2012; Behname, 2012; Chakrabarti 2001; Essia & Onyema, 2012; Fung et al. 2005; Hakro & Omezzine, 2011; Rehman et al. 2011; Wheeler & Mody, 1992) as one of the major determinants of FDI in an economy. A high level of external debt will be interpreted by foreign investors that the economic strength of a country in terms of adequate infrastructure will be very weak as a country that has accumulated a huge amount of debt will focus more on the allocation of its scarce resources in servicing and repaying the debt and this will invariably reduce the amount of resources that will be available for the provision of critical infrastructures. Most foreign investors will perceive the future of such an economy to be quite bleak in terms of infrastructure development. Thus, for external debt to impact positively on FDI, such borrowed funds must be channeled into the provision of critical infrastructures, particularly productive infrastructures. Also, a huge external debt profile will deter FDI as it reduces government expenditure particularly military expenditure that is incurred to maintain a peaceful and conducive environment devoid of internal and external aggression for foreign investors to do business.

External debt accumulation could have a positive relationship with FDI if such debt is channeled into productive investment that will increase the size and attractiveness of the host country's market and also reduce the ratio of external debt to GDP rather than on consumption expenditure. Such productive investment will be able to generate enough revenue that will be used in servicing and repaying the debt. This will boost the economic strength of a country in the eyes of foreign investors and hence, attract more inflows of FDI.

A large number of literatures are available showing negative relationship between external debts and FDI. Nunnenkamp (1991) explained that higher debt burden creates constraints not only in terms of new private lending but also in terms of FDI inflows. Some of the studies found the relationship between debt and FDI inflow statistically significant with negative sign but a few did not find any significant relationship. Khattak et al. (2005), found a significant negative relationship between FDI and external debt over the period of 1970–2000. While on the other hand, Nnadozie (2000) found debt burden variable the most significant with unexpected sign. Yasmin et al. (2003) also used external debt as a determinant of FDI but found no meaningful relationship between external debt and FDI inflows into developing countries. Ostadi and Ashja (2014) evaluated the relationship between external debt and foreign direct investment in D-8 member countries over the period of 1995 to 2011 using panel data. The result shows that external debt has significant negative effect on foreign direct investment and increasing foreign debt has destroyed foreign investor's vision and created negative expectations of the future economy which together reduced investment in the country.

In Africa, Mugambi and Murunga (2017) investigated the effect of external debt service on foreign direct investment inflows in Kenya using time series data running from 1980 to 2014. The study estimated long run cointegating equation and the findings showed that external debt service has a negative impact on the country's foreign direct investments. Lawanson (2014) investigated how indebtedness and capital flight have affected the growth of 14 West African countries directly, and via investment and fiscal balance mechanisms, using data from 1970 to 2008. Two econometric specifications (linear and non-linear) were used, and evaluated with the fixed effects and GMM estimation techniques on the relationship between debt and growth. The hypothesis that external debt and capital flight affect growth was well-supported by the results. All debt variables and the capital flight variable have the expected signs and were statistically significant. The results revealed that debt appears to have a non-linear effect on growth.

Empirical Methodology

To discuss the relationship between external debt accumulation and foreign direct investment inflows in SSA, the study estimates the following empirical model using panel data covering period of 1990 to 2017:

FD I_{i t} = α 0 + α 1 EXT D_{i t} + β \sum C V_{i t}

(1)

Where: FDI is foreign direct investment, EXTD is external debt and ΣCVit is a vector of macroeconomic variables which are comprised of corporate tax, infrastructure development, Gross domestic product and military expenditure for the representative countries. Foreign direct investment is proxied by the ratio of foreign direct investment to GDP while external debt is proxied by the ratio of external debt to GNI. The final equation is written as follows.

FD I_{i t} = α_{1} + φ (FD I_{t - 1})_{i t} + β_{1} (EXTD)_{i t} + β_{2} (TAX)_{i t} + β_{3} (INFDEV)_{i t} + β_{4} (GDP)_{i t} + β_{5} (MEXP)_{i t} + ε_{i t}

(2)

Where; i represents specified economies, t denotes time, and the rest of the variables are defined as: FDI represents Foreign Direct Investment, FDI_t−1 denotes lag of foreign direct investment, EXTD denotes external debt, TAX represents corporate tax, INFDEV signifies infrastructure development, GDP means gross domestic product, MEXP means military expenditure, εit represents residual term (this term refers to other terms that affect foreign direct investment but are not captured in this model, it is assumed to be normally distributed), α is the unobserved country-specific effect, φ represents the coefficient of foreign direct investment lag one and β1–β5 represents vector of coefficient.

The models are estimated via panel data analysis on the unrestricted specification. Subscript ‘t’ stands for 28 years from 1990 to 2017 and ‘i’ stands for 25 countries.

For the purpose of this study, annual times series data spanning from 1990 to 2017 (28 observation) for 25 selected Sub-Saharan African countries was used. The selection of countries and length of study period are determined by the availability of data for all required variables. All variables were obtained from the World Bank (2018).

The study includes selected macroeconomic variables- corporate tax, infrastructure development, GDP and military expenditure in other to take into account of other determinant factors that may likely affect FDI inflows. In analyzing the data, these variables are interacted with external debt in order to determine whether they could alter or strengthen the apriori expectation. As for each macroeconomic variable, a brief explanation of the role of each variable in the model is given as follows:

Corporate Tax: Higher corporate tax rate eats deep into the profit of corporations and may likely discourage foreign investors from doing business in a particular country. Loree and Guisinger (1995), Gastanaga et al. (1998), and Wei (2000) in their study found that host country tax rate has negative relationship with foreign direct investment. However, on the level of significant relationship, there seems to be mixed empirical results as for instance, Kemsley (1998) and Billington (1999) have found the host country tax rate to be a significant factor in determining FDI inflows while Wheeler and Mody (1992) and Lipsey (1999) have found the tax rate of the host country to be insignificant. Corporate tax is measured in this study as taxes on income, profits and capital gains (% of total taxes) and is defined as taxes levied on the actual or presumptive net income of individuals, on the profits of corporations and enterprises, and on capital gains, whether realized or not, on land, securities, and other assets (World Bank, 2018).

Infrastructure Development: Good infrastructure such as transport, road network, electricity reduces the cost of doing business in the host country and thus, will attract greater inflows of FDI. Jordaan (2004) claimed that good quality and well-developed infrastructure increases the productivity potential of investments in a country and therefore stimulates FDI flows towards the country. Several empirical studies such as Bakar et al. (2012), Chakrabarti et al. (2012), Behname (2012), Fung et al. (2005), Hakro and Omezzine (2011), Rehman et al. (2011), Asiedu (2002), Essia and Onyema (2012), Babatunde (2011) and Wheeler and Mody (1992) have all found a positive relationship between infrastructure development and FDI. For the purpose of this study, the number of fixed telephone subscriptions per 100 people is used as a proxy for infrastructure and is expected to have positive sign (Asiedu, 2002; Onyeiwu & Shrestha, 2004).

Gross Domestic Product (GDP): GDP is the market value of all goods and services produced in a country over a period of one year and are one of the primary indicators used to gauge the economic performance of a country. Asiedu (2013) is of the view that GDP serves as an indicator to potential investors of the existence of profitable investment opportunities, as well as the attractiveness of the host country's market. Lim (1983) maintained that a rapidly growing economy provides relatively better opportunities for making profits than the ones growing slowly or not growing at all. Pärletun (2008), Ang (2008), Tsai, (1994), Lunn (1980), Schneider and Frey (1985) and Culem (1988) found a significantly positive effect of growth on FDI. For the purpose of this study, the GDP growth rate will be used as a measure of the attractiveness of the host country's market.

Military Expenditure: Government military expenditure is bound to safeguard the country from both internal and external aggression that will be conducive enough for foreign investors to do business. Thus, it is expected that a positive relationship should exist between military expenditure and FDI. For the purpose of this study, the ratio of military expenditure to GDP is used to measure the host country's peacefulness. Studies on the determinants of FDI inflows have not controlled for military expenditure.

Data Analysis Technique

The study employed the dynamic panel data Generalized Method of Moments (GMM) estimation technique as shown by equation 2. This estimation technique was developed by Holtz-Eakin et al. (1990) and Arellano and Bond (1991) and subsequently advanced by Blundell and Bond (1998). The study used the dynamic panel data estimator to deal with endogeneity bias and economy-specific effects. Endogeneity broadly refers to situations in which an explanatory variable is correlated with the error term. In establishing the link between external debt together with a set of macroeconomic variables such as corporate tax, infrastructure development, GDP and military expenditure and FDI, reverse causality becomes an issue since previous empirical literatures have also established that causality could run from FDI to GDP, external debt to GDP, military expenditure to GDP, infrastructure development to GDP, external debt to infrastructure development and lastly external debt to military expenditure. Thus, resolving the problems of causality dynamics becomes crucial to the analysis of the hypothesized link and justifies the decision to use the system GMM. By the application of the dynamic panel data GMM estimation approach, the study inclined to change the model into first difference and applying the lags of the independent variables as instrumental variables. This is to help deal with simultaneity bias as well as country-specific consequence (Arellano & Bond, 1991). The study also compares estimates from the GMM to that of the panel fixed effects (FEs) and random effects (REs) models. The Hausman specification test is used to compare estimates from the RE with that of the FE. This will ensure a more robust finding (Table 3).

Table 3.

Summary of the Variables Measurement and Their Expected Sign.

Variables	Measurements	Expected sign	Researchers who have employed them
EXTD	Ratio of external debt to GNI	Negative (-)	Khattak et al. (2005), Yasmin et al. (2003), Ostadi and Ashja (2014), Nunnenkamp (1991), Mugambi and Murunga (2017).
TAX	Taxes on income, profits and capital gains (% of total taxes)	Negative (-)	Loree and Guisinger (1995), Gastanaga et al. (1998), Wei (2000), Kemsley (1998), Billington (1999), Wheeler and Mody (1992), Lipsey (1999)
INFDEV	Fixed telephone subscriptions per 100 people	Positive (+)	Bakar et al. (2012), Chakrabarti et al. (2012), Behname (2012), Fung et al. (2005), Hakro and Omezzine (2011), Rehman et al. (2011), Asiedu (2002), Essia and Onyema (2012), Babatunde (2011) and Wheeler and Mody (1992).
GDP	Gross domestic product at growth rate	Positive (+)	Pärletun (2008), Ang (2008), Tsai, (1994), Lunn (1980), Schneider and Frey (1985), and Culem (1988).
MEXP	Ratio of military expenditure to GDP	Positive (+)	No Researcher

Empirical Analysis and Discussion of Results

Descriptive Statistics

The descriptive statistics in Table 4 shows that there are 700 observations for each variable. The mean value of the dependent variable (FDI) is 3.853684 and the standard deviation is 12.42866. FDI shows a large disparity between the maximum (254) and minimum (−8.59). The mean value of the variable of interest (external debt) is 89.77483 while the standard deviation is 127.0033. The macroeconomic variables- tax, infrastructure development, GDP and military expenditure have a mean value of 10.57579, 2.136314, 3.820886 and 1.940486 respectively with a standard deviation of 16.85040, 5.127191, 5.074586 and 2.898528 respectively.

Table 4.

Summary of Variables Descriptive Statistics.

Variables	No of Obs	Mean	Std. Deviation	Minimum	Maximum
FDI	700	3.853684	12.42866	−8.590000	254.0000
EXTD	700	89.77483	127.0033	0.000000	138.770
TAX	700	10.57579	16.85040	0.000000	71.70883
INFDEV	700	2.136314	5.127191	0.000000	32.65000
GDP	700	3.820886	5.074586	−36.04000	26.85000
MEXP	700	1.940486	2.898528	0.000000	29.73000

Table 5 shows the correlation matrix for all variables used in the model. The existence of high correlation between certain variables may lead to the issue of multicollinearity. The result indicates that the correlations among explanatory variables are moderate and weak. Gujarati et al. (2012) stated that if the correlation coefficient between two variables is in excess of 0.8, multicollinearity is a problem. Multicollinearity is a phenomenon in statistics when two or more independent variables within a stated model are confirmed to portray a great height of correlation with each other. When this happens, the estimated coefficient of the variables may be caused to vary intermittently when the model or data are modified. A variable that is meant to be significant becomes insignificant due to the problem of multicollinearity. An achievable technique to circumvent multicollinearity is to run a priori correlation analysis of the variables that will be included in a model and removing any variable noticed to possess a great degree of correlation coefficient from the model.

Table 5.

Correlation Matrix.

Correlation	FDI	EXTD	TAX	INFDEV	GDP	MEXP
FDI	1.000000
EXTD	−0.016567	1.000000
TAX	−0.139789	−0.167304	1.000000
INFDEV	0.040226	−0.119841	0.234712	1.000000
GDP	0.069350	−0.218769	0.112394	0.006122	1.000000
MEXP	−0.055167	0.308907	−0.080561	−0.111253	−0.140050	1.000000

However, this study found no evidence of high or exact multicollinearity as all correlation coefficient of the independent variables of the model did not exceed the 0.8 bench mark. Therefore, all variables can be included in the empirical model.

Panel Fixed Effects and GMM Estimation

Table 6 presents the results of the estimation for foreign direct investment model under the fixed effects and GMM. From the analysis, the Chi-Sq Statistic (89.597246) of the Hausman Test result is significant at 1% level. Therefore, the null hypothesis that random effects is appropriate is rejected. Thus, the study concludes that the fixed effects is more appropriate.

Table 6.

Results of Panel Fixed Effects and GMM Estimations.

	Fixed Effects Model		GMM Model
Variable	Coefficient	t-Statistic	Coefficient	t-Statistic
C	6.675123***(1.158666)	5.761040
FDI(−1)			0.126532**(0.057966)	2.182862
EXTD	−0.021984***(0.005278)	−4.165365	−0.022332***(0.006713)	−3.326658
TAX	−0.144448***(0.038948)	−3.708713	−0.140022(0.097235)	−1.440036
INFDEV	0.176269(0.404044)	0.436262	0.885810**(0.447998)	1.977263
GDP	0.284794***(0.108677)	2.620552	0.319368***(0.079888)	3.997712
MEXP	−1.211730***(0.257410)	−4.707397	0.075593(0.635397)	0.118969
FDI Against Interaction Variables Between External Debt And Corporate Tax
EXTD*TAX	−3.05E-05(0.000298)	-0.102327	-0.001635(0.002205)	-0.741373
FDI Against Interaction Variables Between External Debt And Infrastructure Development
EXTD*INFDEV	0.000732(0.003767)	0.194320	0.004194(0.012309)	0.340704
FDI against Interaction Variables between External Debt and Economic Growth
EXTD*GDP	-0.001344***(0.000288)	-4.663979	-0.001517***(0.000255)	-5.949879
FDI against Interaction Variables between External Debt and Military Expenditure
EXTD*MEXP	0.000789*(0.000431)	1.830025	-0.002006(0.003898)	-0.514670
Hausman Test	89.597246***
R-squared	0.182188
Adjusted R-squared	0.144235
J-statistics			13.316
Prob(J -statistics)			0.715
AR(1)			0.468
AR(2)			0.848
Number of Observation	700		650
Number of Cross sections	25		25

Note: ***, ** and * denote significant at 1%, 5% and 10% level respectively. Standard errors are in bracket.

From the panel data estimation result in Table 6, the GMM and the fixed effects models yielded the same result in terms of the relationship between external debt and foreign direct investment inflows. But on the other hand, it showed conflicting result on the relationship between the macroeconomic variables (infrastructure development and military expenditure) and FDI inflows and also between the interaction variables (external debt and military expenditure) and FDI inflows. However, since the GMM is superior to the fixed effects model in terms of addressing the potential issue of endogeneity, the interpretation of the result is based on the GMM model and it shows that external debt accumulation has a negative and significant influence on FDI inflows in Sub-Saharan Africa. The coefficient shows that a one percent accumulation of external debt results to 2.2 percent reduction in the amount of FDI received in SSA region. This shows that higher level of external debt accumulation discourages foreigners from doing business in SSA region due to external debt burden on higher taxes within the region. This relationship is found to be significant at one percent level. This shows that external debt accumulation is a major factor that determines the amount of FDI inflows in SSA region. The outcome is in line with the findings of previous scholars such as Ostadi and Ashja (2014) whose result shows that external debt has significant negative effect on foreign direct investment and increasing foreign debt has destroyed foreign investor's vision and created negative expectations of the future economy which together reduced investment in the country. Also, Khattak et al. (2005), Mugambi and Murunga (2017) found out that external debt has a negative impact on a country's foreign direct investments. However, it did not agree with the findings of Yasmin et al. (2003) that used external debt as a determinant of FDI but found no significant relationship between external debt and FDI inflows into developing countries.

For the macroeconomic variables, Infrastructure development has a significant positive relationship with FDI inflows. This was in line with past empirical studies such as Bakar et al. (2012), Chakrabarti (2001), Behname (2012), Fung et al. (2005), Hakro and Omezzine (2011), Rehman et al. (2011), Asiedu (2002), Essia and Onyema (2012), Babatunde (2011) and Wheeler and Mody (1992) who found a positive relationship between infrastructure development and FDI. Also, corporate tax shows a negative and insignificant relationship with FDI. The negative relationship of the estimated coefficient of tax agrees with the findings of Loree and Guisinger (1995), Gastanaga et al. (1998), and Wei (2000) while the insignificant relationship corroborates the findings of Wheeler and Mody (1992) and Lipsey (1999). Furthermore, gross domestic product which measures the size and attractiveness of the host country's market was equally found to be positive and significant. This positive relationship is in tandem to the findings of previous scholars such as Pärletun (2008), Ang (2008), Tsai, (1994), Lunn (1980), Schneider and Frey (1985) and Culem (1988) who found a positive effect of growth on FDI. Finally, military expenditure exacts an insignificant positive relationship with FDI inflows signifying that the impact of the expenditure is not felt on the economy as most countries within the region are still facing security challenges and hence deterring FDI inflows into the region.

Shifting to the interaction effect between external debt and the selected macroeconomic variables, the result shows that the interaction variables of external debt and infrastructure development are positive on FDI and concurs with the view that countries that invest a huge proportion of their external debt on the provision of good infrastructure positively influences the impact of external debt on FDI. On the other hand, the result suggests that corporate tax rate has a negative interaction effect on the relationship between external debt and FDI inflows. One possible explanation of this is that huge external debt profile puts enormous pressure on the government to increase taxes in order to generate enough revenue that will be used in servicing and repaying the debt and this higher tax rate eats deep into the profit of corporation and will in turn discourage foreign investors from doing business in a particular country. This authenticates the debt overhang theory by Krugman (1988) which suggests that foreign investors will be discouraged from investing in a country that has a large external debt since part of their proceeds would be used to service the debt through high taxation. Also, the result shows that the interaction effect between external debt and economic growth on FDI is negative in SSA region. This implies that higher external debt accumulation reduces the amount of resources that are necessary to drive economic growth and hence, deters FDI. And lastly, the result shows that military expenditure has a negative interaction influence on the relationship between external debt and FDI as external debt reduces the level of funds meant for military expenditure that will in turn create a peaceful and conducive environment necessary for foreign investors to do business.

An insignificant probability (J -statistics) value of 0.715 for the estimation of the relationship between external debt and FDI, means that there is no overriding identity and that the instruments used are efficient and do not correlate with the error term. Likewise, an insignificant AR(1) and AR(2) figure of 0.468 and 0.848 respectively for the estimation of the relationship between external debt and FDI implies that there is no serial or autocorrelation.

Conclusion and Policy Implications

This study analyzes the relationship between external debt accumulation and foreign direct investment inflows in Sub-Saharan Africa during the period of 1990 and 2017. The study contributes to empirical literature as it analyzed the interaction effects of selected macroeconomic factors such as corporate tax, infrastructure development, economic growth and military expenditure in the relationship between external debt accumulation and FDI inflows in SSA region. The study employs panel fixed effects and system GMM estimation method and finds a negative and statistical significance relationship between external debt accumulation and foreign direct investment inflows in Sub-Saharan Africa. A one percent rise in external debt led to a 2.2 percent reduction in the amount of FDI received in SSA region.

However, when external debt was interacted with variables such as corporate tax, infrastructure development, economic growth and military expenditure, the result behaved differently as it shows a positive interaction effect between external debt and infrastructure development on FDI suggesting that if SSA region can focus more on channeling their external debt into the provision of critical infrastructure, it has the capacity to increase FDI inflows. That of corporate tax seems not to behave differently as it shows a negative interaction effect and provides strong support for the debt overhang theory by Krugman (1988) which holds true in SSA region. Furthermore, the interaction effect of economic growth and military expenditure also shows a negative relationship between external debt and FDI in SSA region. Hence, the study can conclude that the relatively low level of foreign direct investment inflows in Sub-Saharan Africa is largely due to their huge external debt profile which has led to an increase in taxes, endangered the economic strength of the region in the eyes of foreign investors and also reduced the level of military expenditure that is necessary to create a peaceful and conducive environment that is devoid of internal and external aggression for foreigners to do business.

External debt accumulation in Sub-Saharan Africa may be attributed to a situation where a significant proportion of borrowed funds are channeled into recurrent expenditures which produces little or no resources towards future servicing of the debt hence governments continually resort to further borrowing and also raising taxes for debt service and hence discouraging foreign direct investment inflows. In other words, external debt is mostly diverted into unproductive projects. One way of ensuring that external debt does not increase taxes and endanger the economic strength of a country in the eyes of foreign investors is to invest a significant proportion of the borrowed funds into productive capital projects and infrastructures. This productive capital projects and infrastructures will in turn generate enough revenue that will be used in servicing and repaying the debt so that Government will not fall back on raising taxes as a last resort of repaying the debt that will invariably discourage foreign investors. This measure will help boost the economic strength of the host country for greater inflows of FDI.

Table A1.

List of Selected Countries in Sub-Saharan African Region.

S/N	Country	S/N	Country	S/N	Country
1	Botswana	10	Guinea	19	Mozambique
2	Burkina Faso	11	Guinea Bissau	20	Nigeria
3	Burundi	12	Kenya	21	Sierra Leone
4	Central African Republic	13	Liberia	22	South Africa
5	Democratic Republic of Congo	14	Madagascar	23	Tanzania
6	Ethiopia	15	Malawi	24	Togo
7	Gabon	16	Mali	25	Uganda
8	Gambia	17	Mauritania
9	Ghana	18	Mauritius

Footnotes

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External Debt Accumulation and Foreign Direct Investment Inflows in Sub-Saharan Africa: Analysing the Interaction Effects of Selected Macroeconomic Factors

Abstract

Keywords

Introduction

External Debt Accumulation in Sub-Saharan Africa

Drivers of External Debt Accumulation in Africa

External Debt Accumulation and Foreign Direct Investment Inflows Nexus

Empirical Methodology

Data Analysis Technique

Empirical Analysis and Discussion of Results

Descriptive Statistics

Panel Fixed Effects and GMM Estimation

Conclusion and Policy Implications

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Appendix 1

References