196-ogunleye

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EXCHANGE RATE VOLATILITY AND FOREIGN DIRECT INVESTMENT IN SUB-SAHARAN AFRICA: EVIDENCE FROM

NIGERIA AND SOUTH AFRICA*

Eric Kehinde OGUNLEYE African Center for Economic Transformation

50, Liberation Road Ridge Residential Area

PMB CT 4, Cantonments Accra, Ghana

Email: [email protected] Phone: +233 24 1111 494

Abstract

Foreign Direct Investment (FDI) plays a very significant role in financing growth and development in sub-Saharan African (SSA) countries. However, exchange rate volatility is being increasingly recognized as a disincentive to the choice of the region as FDI destination because it adds to the list of risks inherent in the region. Thus, the share of SSA in global FDI has been consistently low vis--vis other developing regions. While several studies have investigated this relationship for developed countries and other developing regions, very little have been done for the SSA countries. This study contributes to the literature by investigating the relationship between exchange rate volatility and FDI in SSA with particular focus on Nigeria and South Africa. Our investigation reveals that there is endogeneity between exchange rate volatility and FDI inflows in both countries. Thus, the system two-stage least squares methodology is adopted. The exchange rate volatility variable was obtained using the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model. It is found that exchange rate volatility has deleterious effect on FDI inflows, with FDI inflows aggravating exchange rate volatility in both countries. Exchange rate volatility is driven largely by inflation, nominal and foreign reserves shocks in both countries. Exchange rate and FDI policy coordination, with a view to minimizing the harmful effect of exchange rate volatility and FDI on each other is, therefore, a challenge that fiscal and monetary authorities must face.

Key words: Exchange rate volatility, Foreign direct investment, Two-Stage Least Squares, Nigeria, South Africa.

* This study is an excerpt from a PhD Dissertation Exchange Rate Volatility and Foreign Direct Investment Inflows in Selected Sub-Sahara African Countries, 1970-2005. The PhD is supported by the Collaborative PhD Programme Scholarship offered by the African Economic Research Consortium and also benefited from the UNU-WIDER PhD Research Internship and WTO Doctoral Support Programmes under the respective supervision of Augustin Fosu and Marc Bacchetta. The financial supports of these institutions are gratefully acknowledged. I am also highly indebted to Prof. Festus Egwaikhide, the Chair of my thesis committee, for his painstaking readings and very insightful comments.

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1. Introduction

Foreign direct investment (FDI) is an increasingly important channel for resource flows

between the industrial and developing sub-Saharan African (SSA) countries, on the one hand, and

among the developing SSA countries themselves, on the other. Several real and potential benefits

discernible from these flows include technological spillovers, job creation, improved managerial

skills and productivity (MacDougall, 1960; and Blomstrm and Kokko, 1997). Given the capital-

deficient nature of SSA countries and the benefits accruable from these activities, FDI is essential

for growth and development in the region. In fact, it has been argued that low and volatile FDI is

part of the challenges to the persistent poverty, high inequality and underdevelopment of the region

(Naud and Krugell, 2007).

There is an expansive literature indicating that real exchange rate volatility has a direct,

deleterious effect on FDI inflows (see, for instance, Bnassy-Qur et al, 2001; Kiyota and Urata,

2004; and Ruiz, 2005). Exchange rate volatility generates air of uncertainty as the variance of

expected profits rises and its net present value falls. This could cause investors to hesitate about

committing significant resources to FDI, thus serving as a serious disincentive for FDI in SSA and

compounding the existing political and economic risks.

Despite the fact that literature on FDI is well established and the issue of exchange rate

volatility and FDI is extensive, such literature on SSA countries are very sparse. This study evaluates

the relationship between exchange rate volatility and FDI in Nigeria and South Africa. These

countries are singled out for analysis in the SSA due to the observed similarity on the relationship

between exchange rate volatility and FDI. In an analysis done elsewhere, it is found that it is only in

these two countries that endogeneity is established between exchange rate volatility and FDI among

several countries analysed.

This study differs from previous research in several respects. First, the study endogenises

exchange rate volatility as a determinant of FDI. Thus, system two-stage least squares methodology

is employed. Earlier studies model the relationship between exchange rate volatility and FDI by

assuming exchange rate volatility is exogenous. Second, rather than using the measures of

unconditional volatility, such as different versions of standard deviation, this study focuses on the

conditional volatility employing the GARCH technique. This has been adjudged to be a superior

measure of uncertainty in the international finance literature (Crowley and Lee, 2003).

The paper is structured into six sections. Following this introduction, section 2 presents a

review of the existing literature on the relationship between exchange rate volatility and FDI in SSA.

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Section 3 provides background information on FDI and exchange rate volatility in Nigeria and

South Africa. Section 4 sheds light on the data and methodology employed for the empirical

analysis. Section 5 reports the estimation results while section 6 concludes.

2. Literature Review

Studies on the relationship between exchange rate and exchange rate volatility, on the one hand, and FDI, on the other, for SSA countries are very scanty1

An attempt was made by Bleaney and Greenaway (2001) to examine the impact of the level

and volatility of real effective exchange rate on investment and growth for fourteen SSA countries

. Mowatt and Zulu (1999) in a study

of the South African investment in the Southern and Eastern African region, identified exchange

rate as one of the major barriers to FDI in Zimbabwe, Botswana and Mozambique. Similarly, in a

survey of the southern African countries, Jenkins and Thomas (2002) found that about 25 per cent

of the total firms surveyed identified exchange rate risk as an important determinant of FDI in the

sub-region. However, these studies did not analyse the relationship and the extent to which

exchange rate volatility constrains FDI in these countries.

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Alaba (2003) is one of the very few studies that have attempted to bridge the gap on the

exchange rate volatility-FDI nexus for SSA countries. The study aimed at determining the magnitude

and direction of the effects of exchange rate movement and its volatility on FDI flows to agriculture

and manufacturing sectors in Nigeria. Employing the GARCH measure of volatility, the error

correction methodology was used for the empirical investigation in testing the effects of both the

official and parallel market exchange rates on FDI flows to agriculture and manufacturing. While the

results show that the official market exchange rate movement significantly reduces FDI inflows to

agriculture, the same is, however, insignificant for the manufacturing FDI. For the volatility

coefficients, official market exchange rate volatility was not found to be significant for FDI inflows

.

The study found that exchange rate volatility has a strong negative effect on investment. However,

the focus of the study was on total investment, not FDI.

1 It is a well established fact in the literature that there is a significant relationship between exchange rate volatility and FDI (see, for instance, Cushman, 1985; Cushman, 1988; Froot and Stein, 1991; Goldberg and Kolstad, 1995; Goldberg, 1997; Goldberg and Klein, 1997; Urata and Kawai, 2000; Bnassy-Qur et al, 2001; Chakrabarti and Scholnick, 2002; Brzozowski, 2003; Crowley and Lee, 2003; Kiyota and Urata, 2004; and Razafimahefa and Hamori, 2005.

2 These countries are Botswana, Burkina Faso, Cameroon, Cte dIvoire, The Gambia, Ghana, Kenya, Malawi, Mauritius, Niger, Senegal, Tanzania, Togo and Zimbabwe.

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to both manufacturing and agriculture. Conversely, the estimated parallel market exchange rate

coefficients suggest that both systematic movement of the exchange rate and its volatility are

significant for flow of FDI to both agriculture and manufacturing in Nigeria with the parallel market

rates, yielding both negative and positive signs for exchange rate volatility in the two sectors. The

emerging conclusion was that while exchange rate volatility attracted investment in agriculture, it

rather deterred FDI in the manufacturing sector, thus suggesting ambiguity on the effects of

exchange rate movements and its volatility on FDI inflows.

In a recent series of country-specific studies commissioned by the African Economic

Research Consortium (AERC), although Ajayi (2004), Khan and Bamou (2005) and Mwega and

Ngugi (2005) recognised the possible effect of exchange rate volatility on FDI, they did not explicitly

examine the relationship empirically.

Ogunleye (2008b) did an extensive work aimed at providing a comprehensive analysis of the

exchange rate volatility-FDI nexus in SSA by examining nine countries in the region, with the

countries cutting across exchange different exchange rate and FDI policies and arrangements. Both

country-specific time-series and panel model estimation techniques were employed. The study found

that exchange rate volatility generally constrains FDI inflows to SSA. This is equally established for

both the CFA and non-CFA group of countries, though with varying degrees.

This brief literature review shows that the existence of a significant linkage between

exchange rate volatility and FDI in both developed and developing countries are well established in

the literature. However, there are very few studies that have attempted to interrogate whether there

is any relationship between these phenomena based on SSA countries experience. A few studies that

have attempted this enquiry either focused only on levels of exchange rate, public investment or are

restricted to a single country analysis, without considering the possible endogeneity between

exchange rate volatility and FDI.

2. Background Before undertaking an econometric analysis of the impact of exchange rate volatility on FDI in Nigeria and South Africa, the nature and pattern of FDI and exchange rate movements in both

countries are examined.

a. Foreign Direct Investment Nigeria is one of the SSA countries that have attracted the most FDI targeted at the region. Consistently, FDI inflows to the country have been very high compared to most other countries. In

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1970, the country attracted a total FDI inflow amounting to $205 million, second only to South

Africa. FDI inflow has been relatively stable and has grown steadily over the years. For instance,

while the country recorded a mean annual FDI inflow of $319 million in the 1970s, the figure

increased to $434 in the 1980s, with a further rise to $1.5 billion in the 1990s. The annual FDI

inflow that was only $205 million in 1970 increased to $3.4 billion in 2005 with mean annual inflows

of about $1 billion between 1970 and 2005 (see Table 1).

Table 1: FDI Profile in Nigeria, 1970-2005 Year FDI

Inflows (Million $)

FDI Outflows

(Million $)

FDI Stock (Million

$)

FDI Inflow Per Capita ($)

FDI Stock Per

Capita ($)

FDI Inflow as

% of GDP

FDI Stock as

% of GDP

FDI Stock as

% of GFKF

1970-79 319.62 N/A N/A 5.27 N/A 0.63 N/A N/A

1980-89 434.00 88.04 4 426 5.00 53.75 0.63 5.19 54.44

1990-99 1 494.06 317.06 15 527 13.89 141.55 3.05 31.46 407.44

2000-05 2 054.85 177.19 28 573 15.27 213.99 2.54 36.58 600.49

2000 1 309.67 168.94 23 786 10.50 190.64 1.94 35.31 654.13

2001 1 277.42 93.88 25 064 9.98 195.75 2.01 39.51 568.15

2002 2 040.18 172.16 27 104 15.53 206.37 3.08 40.93 695.88

2003 2 171.39 167.32 29 275 16.13 217.40 2.77 37.32 613.99

2004 2 127.09 260.76 31 402 15.41 227.55 2.42 35.75 566.96

2005 3 403.34 200.09 34 806 24.08 246.23 3.00 30.68 503.84

1970-2005 966.83 201.05 14 268 9.25 124.50 1.62 22.54 316.22

Source: UNCTAD Foreign Direct Investment Database, October 2007.

In spite of the high and rising inflow of FDI into the economy, the share of FDI inflows in

GDP and inflows per capita are very low. Throughout 1970s and 1980s, mean annual FDI ratio to

GDP was less than 1%. A slight improvement was experienced, however, with the share increasing

beyond less than a single digit in recent times to reach 3% in 2005. Viewed per head, FDI inflows

was relatively high in Nigeria compared to most countries in the region. With a mean annual value

of a single digit during the 1970s and 1980s, FDI inflows per head increased to $24 in 2005.

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A possible explanation for these phenomena is the nature and structure of FDI inflows in

Nigeria. First is the fact that FDI inflows have been concentrated on the oil sector. Presented in

Figure 1 is a graphical view of the relative shares of oil and non-oil FDI in total FDI inflows in

Nigeria. Initially, FDI was concentrated in the non-oil sector even long after the discovery of oil in

commercial quantity. In 1970, for instance, non-oil FDI was N93.6 million, representing about 73%

of total FDI inflows. This trend was generally maintained until about 1985 except for some few

years. However, from 1986, onwards, this trend was completely reversed. By 1987, the total FDI

inflows to the oil sector was N

2.3 billion, representing 94% of total FDI inflows for the year.

Henceforth, the gap between oil and non-oil FDI has widened considerably.

Source: Based on data obtained from the Central Bank of Nigerias Statistical Bulletin, Vol 17, 2006. Another very important stylized fact about FDI in Nigeria is the nature of the oil sector. The

oil sector is an enclave without sufficient forward and backward linkages with other sectors of the

economy. Despite this fact, FDI consistently represented a great percentage of total GDP. With a

modest mean annual average of 5.2% in the 1980s, the total share of FDI stock in GDP rose to

about 41% in 2002, with a mean annual average of 22.5% throughout the entire period of 1980 to

2005.

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The percentage share of FDI stock in domestic capital formation in Nigeria was

overwhelming and represented one of the highest in SSA. From a very low average annual level of

54.4% in the 1980s, beginning from the 1990s, the share of FDI stock in domestic capital formation

became very monumental, rising to 407% during this decade, with a further rise to a peak level of

about 700% in 2002. This demonstrates the indispensable role of FDI in augmenting low domestic

savings in SSA countries. FDI stock per head was also expectedly high in Nigeria. With a per capita

FDI stock of $246 in 2005, the country had one of the highest FDI stock per capita in SSA, second

only to South Africa.

South Africa is one of the most important choice destinations of FDI in SSA. With a total

FDI inflow of $333.6 million in 1970, the country received the highest inward FDI among the

countries selected for this study and most probably for the whole SSA. Although this inflow was

subject to volatility such that reverse inflow were experienced consecutively between 1977 and 1980

and between 1985 and 1990 with the exception of 1988. The major determinant of the volatile

nature of FDI in this country was the apartheid policy that prevailed in the country until 1994.

During this period several sanctions were imposed on the country by the United Nations and the

international community which included financial flows. However, with the end of apartheid which

ushered in a new government in 1994, FDI inflows rose substantially from a total of $10 million in

1993 to about $380 million in 1994. The figure leapt by more than a factor of four to record $1.2

billion the following year. From then on, FDI inflow has been on a steady stride, attaining an

unprecedented level of about $6.4 billion in 2005. This figure almost doubled Nigerias total inflow

in the same year (see Table 2).

By 2005, South Africa had the highest FDI inflow per capita among the countries selected

for this study with an annual average of $133. This contrasts sharply with most countries in the

region that generally have only single digits. Generally, FDI inflow to South Africa was one of the

highest by the SSA regional standards. However, the share of FDI inflow in GDP was one of the

lowest in the region being consistently less than 1% between 1970 and 2004 except in very few

years. In recent times, it was only in 1997, 1999, 2001 and 2005 that the country could record more

than 1% share of FDI in GDP with privatisation transactions being the primary motive (Gelb,

2005). It is noteworthy, however, that stock of FDI was a major source of domestic capital

formation in the economy, accounting for an unprecedented 216% of domestic capital and

consistently about 200% thereafter.

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Table 2: Performance of FDI in South Africa, 1970-2005 Year FDI

Inflows (Million $)

FDI Outflows (Million

$)

FDI Stock (Million

$)

FDI Inflow Per Capita ($)

FDI Stock Per

Capita ($)

FDI Inflow as

% of GDP

FDI Stock as

% of GDP

FDI Stock as

% of GFKF

1970-79 92.22 70.68 N/A 4.23 N/A 0.49 N/A N/A

1980-89 14.16 221.08 11 576 0.61 363.82 0.00 14.82 63.51

1990-99 850.31 1 295.84 16 579 19.80 395.69 0.59 12.33 76.87

2000-05 2 724.27 -220.52 48 296 58.13 999.64 1.76 28.51 182.02

2000 887.90 270.80 43 462 19.56 956.91 0.67 32.69 215.89

2001 6 788.70 -3 180.1 30 568 147.53 666.20 5.73 25.88 171.91

2002 756.70 -398.90 29 611 16.24 634.36 0.68 26.65 177.38

2003 733.70 565.10 45 714 15.58 968.22 0.44 27.44 173.40

2004 799.20 1 352.10 63 064 16.81 1 325.02 0.37 29.34 182.08

2005 6379.40 67.90 77 361 133.07 1 447.10 2.67 29.05 171.44

1970-2005 719.79 404.25 21 974 16.53 522.80 0.60 17.02 95.99

Source: UNCTAD Foreign Direct Investment Database, October 2007.

South Africa attracts FDI across a broad range of sectors. This implies that the large FDI

inflow into the economy is not driven by resource-seeking motive. Rather, it is as a result of the

confidence foreign investors have in the economy, given its macroeconomic stability, policy

certainty, sufficient fiscal incentives targeted at foreign investors, developed business infrastructure,

and large size of the economy (Gelb, 2005). Hence, the country was ranked 18th in the A.T.

Kearneys 2007 FDI Confidence Index, the only African country that featured in the report. In fact,

South Africa came ahead of major FDI destinations such as Central Asia, South Korea and Poland,

among others.

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Source: Based on Information Provided in Page and Willem te Velde (2004).

Another interesting feature of FDI in South Africa is the countrys leadership role in south-

south FDI in SSA. It is indeed the only country that has consistent data on FDI outflows among the

sample countries. The country recorded a mean annual outward FDI amounting to $70.7 million in

the 1970s with a significant rise to $221 million in the 1980s. By 1997, the figure had reached about

$2.4 billion. In 1999, South Africa accounted for about 49% of the inward FDI stock in Botswana,

of which 60% was through De Beers Diamonds subsidiary located in Luxembourg (UNCTAD,

2003). In 2003, 25% of total SADC FDI was from South Africa (African Development Bank, 2003).

In 2004, 7% of total South African FDI was directed at other African countries. While this figure

may appear little, the weight of the countrys FDI is more felt within the Southern African region,

accounting for 86% and 80% of total FDI inflows to Lesotho and Malawi, respectively (Page and

Willem te Velde, 2004). See Figure 2 for details.

b. Exchange Rate

The real exchange rate in Nigeria has been principally influenced by external shocks resulting

from the vagaries of world price of agricultural commodities and oil price, both major sources of

Nigerian exports and foreign exchange earnings. In the early 1970s, when the economy depended on

agricultural exports, real exchange rate volatility was less pronounced given the fact that these

products were subjected to less volatility and there were more trading partners currencies involved

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in the calculation of the countrys real exchange rate. Hence, between 1970 and 1977, the currency

experienced very minimal annual change in real exchange rate amounting to 0.14 % (see Figure 3).

However, with increased dependence on oil, the country experienced severe terms of trade shock

resulting from the global oil price shocks. Between 1978 and 1985, annual percentage change in real

exchange rate increased to almost 10%.

Another very important factor that determined the movement in real exchange rate during

this period was nominal shock resulting from fiscal deficits (Iyoha and Oriakhi, 2002). The oil

windfalls resulted in excessive fiscal expenditure in ambitious development projects (Ogunleye,

2008a). When the windfall ended, the government resorted to financing its expenditures through

money creation. This expansionary monetary policy exerted upward pressure on inflation, thus

further aggravating sharp movements in real exchange rate movements.

Beginning from 1986, the adoption of the SAP became a contributory factor in shaping the

dynamics of real exchange rate in Nigeria. One of the cardinal points of this policy was floating

nominal exchange rate policy. As the Naira was allowed to float, the nominal exchange rate

movements became more pronounced, thus contributing to sharper movements in exchange rate

during this period. Indeed, between 1986 and 1992, the mean annual change in real exchange rate in

the country had risen to about 25%. It appears, however, that the economy is gradually grappling

with this problem as the real exchange rate has become less volatile in recent times, with a reduction

in mean annual real exchange rate to 4.5% in between 2000 and 2006. Favorable terms of trade, less

fiscal dominance, effective monetary policy induced by more independent and transparent central

bank, and well managed nominal exchange rate policy are some of the factors behind this benign

condition.

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Source: Based on IMFs International Financial Statistics, July 2007.

Source: Based on IMFs International Financial Statistics, July 2007.

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The South African rand experienced relatively high percentage changes in real exchange rate

between 1970 and mid-1980s (see Figure 4). In 1979, the change was no less than 25%. This became

significant in 1995 and more particularly pronounced in 2001. From the 1980s, the prices for South

Africas main commodity exports experienced a steady decline until late 1990s. Also, during this

period, the real interest rate differential between the country and her trading partners rose

considerably. In recent times, the volatility experienced has reduced considerably. The decline in the

real interest rate differential and improvement in productivity are possible explanations for this trend

(Aron et al, 2000).

Several attempts have been made to explain the peculiar factors shaping the nature and

dynamics of real exchange rate movements in South Africa. These include the terms of trade, the

price of gold, the extent of trade protection, the magnitude of official reserves, long-run capital

flows, government expenditure, commodity prices, productivity and real interest rate differentials

vis--vis trading partners, the size of the fiscal balance, the extent of trade openness, and the

countrys net foreign assets which include the central banks open position in the forward market

(see Aron et al, 2000; Aron et al , 2003 ).

4. Data, Model and Methodology

The observation period spans between 1970 and 2005, with both countries operating flexible

exchange rate system for most part of this period. FDI is measured as the annual FDI inflows to

each country. This represents total FDI flows from all sources to all sectors of the host economy,

and is measured in real term as the percentage share of FDI in GDP. This data is collected from the

United Nations Conference on Trade and Developments FDI database.

Exchange rate is measured as a unit of domestic currency vis--vis a unit of the US dollar.

This is measured in real term as the real effective exchange rate (REER). This is the most

appropriate measure for real exchange rate for a study of this nature given its ability to capture and

measure the international competitiveness of countries. Moreover, it has been weighted by the level

of trade and investment between each country and the rest of the world (Kiyota and Urata, 2004).

This has the advantage of eliminating the bias of the sample towards actual investors when bilateral

exchange rates are used and account as much as possible for potential investors in the sense that

investors, actual or potential, are more likely to come from countries which are already in trading

relationship with these countries.

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Real exchange rate volatility is generated using the GARCH methodology. Several measures

of volatility have been employed in the literature. These can be broadly divided into (1) those that

use various modifications of standard deviations and (2) the ones that use different versions of the

ARCH and GARCH techniques. One of the major criticisms of the different variants of standard

deviation as a measure of exchange rate volatility is that they ignore the stochastic process generating

the exchange rates. They are unconditional measures of volatility that ignore relevant information on

the random process generating the exchange rate (Engle, 1982). This method is also arbitrary in

choosing the order of the moving average and noted for underestimating the effects of volatility on

decisions (Pagan and Ullah, 1988).

Furthermore, standard deviation measure of volatility is characterized by skewed

distribution. Exchange rates are typified by volatility clustering, implying that future exchange rate

changes are not independent of the past and current changes. To correct for these apparent

deficiencies, the ARCH was introduced by Engle (1982) and later modified by Bollerslev (1986) as

the Generalised Autoregressive Conditional Heteroscedasticity (GARCH). Ever since, different

variants of the ARCH and GARCH models have emerged. One of the asserted superiority of the

ARCH and its variants over the standard deviation measures is their ability to distinguish between

predictable and unpredictable elements in the real exchange rate formation process, and are,

therefore, not prone to overstating volatility (Arize, et al, 2000; and Darrat and Hakim 2000).

There is the possibility that while exchange rate volatility influences FDI, it is also possible

that the relationship runs in the opposite direction with FDI inflows significantly inducing exchange

rate volatility. The implication of this is that exchange rate volatility may be endogenous while

assuming exogeneity, thus making the model suffer from endogeneity bias. To ascertain the presence

or absence of endogeneity in the model, the Hausman test for endogeneity, originally proposed by

Hausman (1978), is applied. The test is performed based on Davidson and MacKinnon (1989, 1993).

To test this hypothesis, there is need to find a set of appropriate instrumental variables that are

correlated with the variable considered to be endogenous, in this case exchange rate volatility, but

uncorrelated with the error term of FDI. The process of searching, identifying and choosing the

appropriate instruments for this test is very crucial and challenging since the choice has significant

influence on the results. The first step in the test is to regress the specific endogenous variable,

exchange rate volatility, on all the exogenous variables and instruments and obtain the residuals. The

second stage involves re-estimating the original model, the FDI model, including the residual series

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as an additional regressor. If the estimates of the original model are consistent, then the coefficient

of the residuals series should not be significantly different from zero.

In Nigeria, the instrumental variables were nominal, inflation and reserves shocks while

South Africa had inflation and nominal shocks as appropriate instruments. It is worth mentioning

here that the shocks identified and applied are generated with the same GARCH technique. The

high and statistically significant impact of these shocks on exchange rate volatility in both countries

affirms the position that these shocks are appropriate instruments in the respective countries.

The FDI-exchange rate volatility nexus is investigated within the context of a simultaneous

equation whereby both variables are jointly determined:

( ) ( ) ( ) ( ) ( ) ( ) ( )

, , , , , ,t tt t t tt tFDI Ex Exd Vol Dvol R Infr K+ + =

(1)

( ) ( ) ( ) ( ) ( )

, , ,Re ,t t t tt tVol FDI Inf Nom s Tot+ + + + + =

(2)

FDI is the annual FDI inflows to each country. This represents total FDI flows from all

sources to the host economy, and is measures in real term as the percentage share of FDI in GDP.

Ex represents changes in the levels of real exchange rate measured as the real effective exchange

rate. This is the most appropriate measure for real exchange rate for a study of this nature given its

ability to capture and measure the international competitiveness of countries. Moreover, it has been

weighted by the level of trade and investment between each country and the rest of the world

(Kiyota and Urata, 2004). This has the advantage of eliminating the bias of the sample towards

actual investors when bilateral exchange rates are used and account as much as possible for potential

investors in the sense that investors, actual or potential, are more likely to come from countries

which are already in trading relationship with these countries. Exd is the standard deviation of the

monthly real exchange rate. This variable captures the foreign investors attitude to risk as a

determining factor in investing in SSA.

Real exchange rate volatility is denoted byVol . This volatility variable is generated using the

GARCH methodology. Dvol is the demand volatility. This captures the market size as well as

economic uncertainty in the individual economies as a determining motive for FDI. R is the real

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interest rate in the host economy which captures the host countrys return on investment as an

attracting factor for FDI. Infr is infrastructure represented by the total electricity provision in each

country. This variable is measured as the total electricity production less electricity power losses

during transmission and distribution. K stands for the capital control dummy which takes the value

one for the period of capital control and zero otherwise. Similarly in equation (2) Inf , Nom , Re s

and Tot are inflation, nominal, foreign reserves and terms of trade shocks, respectively. The signs

on top of each variable in the model indicate the a priori expectations of the estimated coefficient.

5. The Estimation Results

The results of the estimated results employing the OLS technique are presented in Table 3.

South Africa are singled out for discussion given their peculiarities of the existence of endogeneity

which necessitated a different estimation technique. The Hausman test for endogeneity for both

countries could not reject the hypothesis of no endogeneity (see Table 4). One very important

discovery emanates from this. It corroborates earlier assertion that FDI can only induce exchange

rate volatility in economies with large FDI inflows (Kosteletou and Liargovas, 2000). This calls for a

re-specification of the model to correct for the observed bias, using a more appropriate technique.

To do this, the model was re-specified as a system and estimated using the two-stage least squares

methodology.

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Table 3: Effects of Exchange Rate Volatility on FDI in Nigeria and South Africa (OLS Estimation) Nigeria South Africa Constant -0.525

[0.252] 7.331

[0.516] Exchange Rate Volatility -0.082

[-1.984] * 0.018

[0.083] Exchange Rate Volatility(-1) -0.020

[-0.604] -0.021

[-1.127] Risk Attitude -0.044

[1.296] -0.214[-1.759]

*

Real Exchange Rate Movement 0.035[2.676]

** -0.157 [-0.291]

Income Volatility -0.033[-2.335]

** -0.024 [-0.191]

Infrastructure 0.076[2.476]

** 0.035 [1.061]

Real Interest Rate 0.016 [0.420]

0.194 [0.379]

Capital Control Dummy 0.065 [0.317]

1.457[2.767]

***

F-Statistic 15.308*** 2.168* R2 0.825 0.400 S.E.R. 0.369 0.995 Durbin-Watson Statistic 2.047 2.121 Notes: The values in square brackets [ ] are the t-statistic, and ***, ** and * imply statistical significance at the 1 %, 5 %, and 10 %, respectively.

Table 4: Results of Hausman Test for Endogeneity Country

Instrument

Residual Nigeria Inflation and Reserves shocks 0.3086

[3.159]**

South Africa Inflation and nominal shocks -4.5868 [-2.127]**

The estimated results employing the system two-stage least squares are presented in Table 5.

The explanatory power of the models for both countries is very high for both the FDI and exchange

rate volatility models, except the exchange rate volatility model for South Africa. Specifically, about

84% and 78% of the variations in FDI and exchange rate volatility are accounted for by the

respective explanatory variables for Nigeria while the respective values are 51% and 39% for South

Africa. The Durbin-Watson statistic indicates that all the models are free from autocorrelation.

16

Table 5: Two Stage Least Squares Estimation Results on the Effects of Exchange Rate Volatility on FDI in Nigeria and South Africa.

Nigeria South Africa Dependent Variable: FDI Dependent Variable:

Volatility Dependent Variable: FDI Dependent Variable:

Volatility Variable Coefficient Variable Coefficient Variable Coefficient Variable Coefficient Constant -5.509

[-1.553] Constant 14.165

[9.291]*** Constant -16.429

[-2.579]** Constant -11.614

[-0.956] Exchange Rate Volatility

-0.331 [-1.780]*

FDI 1.742 [6.566]***

Exchange Rate Volatility

0.114 [1.569]

FDI 0.481 [0.902]

Exchange Rate Volatility(-1)

-0.027 [-0.655]

Inflation Shock

0.151 [0.487]

Exchange Rate Volatility(-1)

-0.113 [-1.939]*

Inflation Shock

-0.506 [-0.876]

Risk Attitude -0.062 [1.349]

Nominal Shock

0.487 [2.602]**

Risk Attitude -0.056 [-0.646]

Nominal Shock

-0.303 [-1.759]*

Real Exchange Rate Movement

0.018 [0.417]

Reserves Shock

-0.098 [-0.344]

Real Exchange Rate Movement

-0.049 [-0.337]

Reserves Shock

0.638 [2.274]**

Income Volatility

-0.033 [-2.093]*

Terms of Trade Shock

0.257 [0.961]

Income Volatility

0.029 [0.955]

Terms of Trade Shock

0.458 [1.744]*

Infrastructure 0.288 [1.911]*

Infrastructure 0.6254 [2.439]**

Capital Control Dummy

0.3006 [0.768]

Capital Control Dummy

0.1438 [0.611]

Real Interest Rate

-0.031 [0.646]

Real Interest Rate

-0.005 [-0.034]

R2 0.838 R2 0.782 R2 0.5111 R2 0.3918 S.E.R. 0.127 S.E.R. 0.909 S.E.R. 0.2279 S.E.R. 0.2109 D-W Statistic 2.253 D-W

Statistic 1.773 D-W Statistic 2.7202 D-W

Statistic 2.0688

Notes: The values in brackets [ ] are the t-statistic, and ***, ** and * implies statistical significance at the 1%, 5%, and 10%, respectively.

In the FDI equation for Nigeria, all the explanatory variables have the theoretically expected

signs, except the real interest rate. Similarly, FDI has the expected sign in the exchange rate volatility

model. A very intriguing finding is made here. Both exchange rate volatility and FDI exert

statistically significant effect on each other in both models. This further strengthens the conclusion

that exchange rate volatility is endogenous in the model for this economy. Hence, while a rise in

17

exchange rate volatility induces a reduction in FDI inflows, large FDI inflows engender a rise in

exchange rate volatility. This suggests that causation runs both ways. It implies that the pattern of

FDI inflows has very significant effect on the exchange rates. At the same time, exchange rate

behaviour influences FDI inflows. This finding agrees with Kosteletou and Liargovas (2000) that

found that exchange rate and FDI are interdependent in economies with relatively high FDI inflows.

Turning to South Africa, all the explanatory variables have the theoretically-expected signs

except exchange rate movement. Again, it is established that exchange rate volatility has deleterious

effect on FDI inflows. Contrary to expectations, exchange rate depreciation retards FDI inflows

while appreciation attracts it. This is in consonance with Campa (1993). The statistically significant

negative effect of income volatility on FDI inflows makes sense. South Africa is one of the

economies in SSA whose FDI is more of market-seeking than resource-seeking. Most of the

products made with the aid of FDI are meant for domestic consumption. Therefore, foreign

investors are very interested in, and influenced by the domestic economic climate.

Furthermore, FDI inflows have positive effect on exchange rate volatility. Although, this

effect is not statistically significant, it suggests that a rise in FDI inflows will induce larger volatility

of the exchange rate in the South African Rand. The sound exchange rate and monetary policies of

the South African Reserve Bank may have been the antidote dousing the negative effects of large

FDI inflows on exchange rate. For example, large FDI, especially outflows require the intervention

of the monetary authority for the purpose of managing any potential impact on the foreign exchange

rates. Also, nominal, reserves and terms of trade shocks are found to have significant effects on

exchange rate volatility in the country. This is in conformity with the earlier findings by Savvides

(1992) and Canales-Kriljenko and Habermeier (2004) that these variables are important determinants

of exchange rate volatility in open economies.

6. Concluding Remarks

The endogeneity of exchange rate volatility in investigating the relationship between

exchange rate volatility and FDI inflows informs the use of Two-stage Least Squares methodology.

It was found that in Nigeria there is a statistically significant relationship between the variables with

exchange rate volatility retarding FDI inflows and FDI inflows increasing exchange rate volatility.

However, this relationship appears weak for South Africa as significant impact of exchange rate

volatility on FDI is established at the first lag while the impact of FDI inflows on exchange rate

18

volatility is not significant. The possible reason for this is the sound capital flows management policy

of the South African reserve Bank.

There is need for policy cohesion and coordination on exchange rate and FDI management,

especially in these countries given the endogeneity between them. This is especially true for Nigeria

where the management of FDI inflows is not as sound as that of South Africa. It implies that FDI

policies can have very significant effect on the exchange rates. At the same time, exchange rate

policies can also stimulate or stifle FDI. Hence, in formulating either policy, the other one must be

factored in. This will help improve policy performance and its ultimate impact on improving FDI

inflows and reducing exchange rate volatility. This calls for concerted efforts and coordination

among the different institutions in charge of exchange rate and FDI.

The instrumental variable models reveal that inflation, nominal and reserves shocks are the

prominent sources of exchange rate volatility in both countries. Hence, sound macroeconomic and

exchange rate policies will help put these shocks under effective control and dampen exchange rate

volatility. Of course, this will ultimately minimise the deleterious effect of exchange rate volatility on

FDI. This is a challenge for both the fiscal and monetary authorities in these countries.

In conclusion, an important proposal for further research is discernible from this study. It is

suggested that the FDI-exchange rate volatility nexus be pursued in sectoral context in these

countries. This will improve our understanding of the nature and pattern of influence between these

variables across sectors.

19

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