On the Determinants of Foreign Capital Flows - DAAD partnership ...

On the Determinants of Foreign Capital Flows:
Evidence from an African economy
Beghum N, Sannassee R V, Seetanah B, and Lamport M J
University of Mauritius
Corresponding author: m.lamport@uom.ac.mu
Abstract
This paper supplements the relatively scarce evidences from the African continent and
investigates the determinants of FDI using dynamic time series analysis for the period 1976-
2009. Results from the analysis shows that, consistent with theoretical underpinnings gross
domestic product, domestic investment, real exchange rate, productivity and openness are all
important in explaining the FDI flows in Mauritius. The interesting findings resides in the
existence of long-run and short-run relationships as implied by the statistical significance of
the coefficients of differenced explanatory variables and the lagged error correction term
(ECT) respectively. Causality analysis indicates the presence of varying relationships and
impulse response and variance decomposition analysis validates the overall results as well.
1. INTRODUCTION
In an ongoing globalized era, foreign direct investment (FDI) has and is contributing to the
success of various economies with a bundle of productive assets like technology, growth
prospects and employment amongst so many. With the exception of a few economies, the
vast majority of the fast growing countries have relied a great deal on FDI to jump-start and
sustain their rapid economic revolution 1 . The Economic Report on Africa (United Nations
Economic Commission for Africa) states that FDI is the key to solving the continent’s
economic issues. This belief of attracting FDI as the solution to bridging the resource gap
1 Countries which have achieved rapid growth with little reliance on FDI are mainly Japan and Korea with
their high entrepreneurial spirit, high level of man power, appropriate investment regime and efficient
burocracy.
1

has been reinforced by the experience of a small number of fast growing East Asian newly
industrialized countries 2 .
In Mauritius, FDI has played a critical role in the industrial diversification, employment
creation, export development and growth of the country. In fact FDI was estimated to have
contributed more than 25 % of the Mauritian Gross Domestic Product in 2008 (World
Factbook). Thus, drawing foreign investment flows is profitable and measures have to be
implemented for the country to act as a platform of attractiveness towards such inflows. To
implement these measures, it is necessary to know the driving forces of FDI, how they are
related to each other and why are countries on the global race to lure such investment.
The rationale behind this paper is to estimate the domestic determinants of foreign flows and
their relationship towards FDI attraction in Mauritius along with the existing relationship
between them. The determinants of FDI are diverse and cannot be attached to one single
factor. Social, legal, political as well as economic factors are all necessary ingredients
towards successfully attracting FDI.
However, the issue is that not all countries can nurture the same kind and same type of FDI
from multinationals (MNCs) with the same type of determinants and it is important for a
country like Mauritius which is searching for worldwide recognition to be able to place itself
in this competition for better future prospects. Moreover, for the study of the causality
effects between the determinants of FDI, Mauritius provides a good case study as the island
is one of the most successful countries in Africa which has been registering a relatively good
inflow of FDI since the 1980’s (Seetanah 2009).
The remaining sections of the paper are organized as follows: Section 2 will provide an
overview of the Mauritian Economy especially in terms of foreign flows it attracted over the
years and the problems faced. Section 3 will critically review the literature in terms of both
2 United Conference on Trade and Development(2005), economic Development in Africa: Rethinking the Role
Of Foreign Direct Investment (United Nations :New York and Geneva)
2

the theoretical framework and empirical studies. Section 4 will discuss the research
methodology. Section 5 will present and discuss the findings and section 6 will conclude the
paper.
2. OVERVIEW OF THE MAURITIAN ECONOMY
The success of the Mauritian economy in terms of investment resulted mainly from the
choice of proper development strategies and incentives focusing on attracting foreign
counterparts. In the early 70’s, FDI has played a small but fundamental role in the Mauritian
economy. With government implementing the first Export Processing Zone (EPZ) in Africa,
many Asian investors were attracted to the island to implement textile and garment
manufacturing in order to benefit from preferential access to European and American
markets.
It is only from the mid-1980s that FDI started coming to Mauritius considerably, mostly in
the tourism sector and EPZ. In the 80’s and early 90’s, FDI was given a better position in
terms of bringing advantage to the island whereby the Mauritian government laid much
emphasis on fiscal incentives in terms of preferential tax rates and other sorts of benefits like
tax holidays or free repatriation of profits. Mauritius was now well entered in the league of
drawing outward investors as foreign firms began to establish operations in lower-wage sites
in the region and it was important to develop new and resulting policies to induce investors
to the island.
Source: Bank of Mauritius database (2007 data includes for the first three-quarters only)
3

Figure 2.1 FDI by sector from 1990 to 2007
In 2000 where investment had to be given a good impetus, the Board of Investment (BOI)
was established under the Investment Promotion Act (IPA) 2000. It is the leading agency of
the Government responsible for promoting and facilitating investment in Mauritius. This has
been the year where there has been a boost in FDI flows in the country as shown in the
figure above mainly due to France Telecom's purchase of a 40% share of Mauritius Telecom
as part of their strategic alliance. From 2000 to 2007, the country has tried its best to attract
such global flows and as from 2005, it can be noted that the attempt has been a success due
to a change in policy which is addressed below.
The Mauritian Government’s policy since 2005 has been to open the economy and
streamline administrative procedures for people to come, work, and live in the country. The
Business Facilitation Act 2006 not only banned trade licenses but also allowed businesses to
start operations within three days of incorporation. Moreover, residence and work permits
for foreign investors, professionals and entrepreneurs have been combined into an
occupation permit which is now processed within three working days.
With globalization reaching its peak in this century, most governments are actively
competing for FDI because of its positive economic advantages. Dunning (2001) claimed
that “FDI flows remain one of the most dynamic constituents of the global economy and in
3
coming years, it is expected that they will considerably outpace the growth of both trade
and world GDP”. Hence, the Mauritian government is doing its best to posit the country as
an ideal springboard for investment. 4 Hence, one of the main aims of government will be to
do its best to implement more and more incentives on what actually attract FDI in Mauritius.
3. LITERATURE REVIEW
3 World Bank 2009 Doing Business Survey
4 Qfinance Newsletter 2009, Mauritius; Ease of Doing Business 2009
4

3.1 MOTIVES BEHIND FDI
Literature on FDI suggests differing reasons for firms to invest across national boundaries.
In reality, it is difficult to isolate the different motives for such investment, as one motive
may overlap another. The major motives stipulated by Basu and Srinivasan (2002) are
• Natural-resource-seeking investment, which aims at exploiting natural resource
endowments of countries.
• Market-seeking investment, where companies intend to access new markets that are
attractive as a result of their growth and size and are common in times of implementation of
import substitution strategies. Investors will be attracted to a country that has a large but
fast growing market (Campos and Kinoshita 2004).
• Efficiency-seeking investment mostly export promoting, where advantage of special
features are sought in certain areas such as costs of labour, skills, and the quality and
efficiency of infrastructure.
• Strategic asset-seeking investment, whereby investors locate in such places to be able to
take advantages in terms of mostly research and development
3.2 THEORIES AND APPROACHES TOWARDS FDI
There are already well-established theories explaining why FDI takes place and what its
potential determining factors are, but the most influential ones are those based on the Capital
Market hypothesis, the industrial organization explanations, internalization theories and the
eclectic approach. One of the oldest theories is the Capital Arbitrage Theory based on the
fact that the main propeller of FDI is interest rates. In general, foreigners will go to countries
that pay a higher return. But, with globalization capital markets have grown across the globe
and it is argued that such theory is inconsistent in explaining cross border investment of
multinationals. When market imperfections such as risk, uncertainty or transaction risks are
5

included in the model, the movement of capital flows internationally is no longer responsive
to interest rates (Hymer 1960-1969).
Following Hymer, much literature has contributed to theories to FDI. For instance, the
internalization approach (1976) underlines the fact that the influence of imperfections in
the capital market provides a strong impetus for firms to internalize operations. Buckley
and Casson (1991) claimed that if firms keep operations internal through wholly-owned
subsidiaries rather than using foreign investment instruments in terms of licensing, patenting
or trademarks, they are more apt to minimize transaction costs, hence increasing productive
efficiency.
All such writers added more and more determinants to FDI as a whole. But the need to
integrate location specific factors to internalization variables to explain capital flows seemed
to be important. In this line of thought, Dunning’s (2004) OLI paradigm explains that
multinational firms mainly responsible for bringing FDI in host countries enjoy three
distinct types of advantages, namely, ownership, locational and internalization benefits.
Ownership advantages may be in terms of primarily, monopolistic gains through the
possession of scarce natural resources or patent rights, technology–knowledge gains or even,
economies of large size. Locational benefits will include economic, political, social and
cultural advantages amongst others whilst internalization advantages will bring about the
right choice of mode of entry in order to operate efficiently in the host economy.
However over the years, economic, social and political factors, cultural aspects as well as
government policies have all turned out to be equally significant in establishing a proper
environment to attract FDI. The Dynamic macroeconomic FDI theory suggests that FDI is
a long term function of multinationals’ strategies. Time plays an important role and timing
of investment depends on the macroeconomic environment at that particular period in the
host country as well as its degree of openness and rate of economic growth. Risk
perceptions also lie as a determinant (Sanjaya Lall 1997).
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Closely related, FDI can be taken as a tool of exchange rate risk reduction whereby the
importance of exchange rates in attracting FDI is underlined. The more favorable the
exchange rate, the more attractive will be the country in terms of return gained and
repatriation of profits. Not much known and talked about is the Gravity approach (Mauro
2000) to FDI where the closer two countries are (geographically, economically or culturally)
the higher will be the FDI flows between these countries.
As a concluding note from UNCTAD (1997), it can be noted that the success of Latin
American countries and other developing nations, as is Mauritius, in attracting FDI was
attributed mainly to the following among many reasons:
(i) liberalizing investment (the granting of national treatment to foreign investors,
elimination or reduction of restrictions on profit remittance or other financial
flows and the opening of sectors previously closed to FDI);
(ii) establishing investor promotion agencies (IPAs);
3.3 EMPIRICAL STUDIES
A number of previous studies have examined the determinants of foreign direct investment.
Considering primarily some pull factors, it is noted that they all depend on a long list of
domestic policies and characteristics of potential host countries. Some of the various
indicators have been summarized by Sanjaya Lall (1997):
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Figure 3.1 Host Country Determinants of FDI- A summary
No unanimously accepted single factor determines investment flow. Literature is supplied
with various data on the full range of factors that are likely to propel the flow of foreign
direct investment worldwide. In making decisions to invest abroad, firms are subject to a
constellation of economic, geographic, political, social and cultural issues. While it is
“difficult to determine the exact quantity and quality of FDI determinants that should be
present in a location for it to attract a given level of inflows, it is nevertheless clear that a
critical minimum of these determinants must be present before FDI inflows begin to occur”
(Ngowi, 2001).
The factors influencing the flow of FDI as per Lall’s table thus range from the size of
markets to labour skills, infrastructure and stability to the availability of resources. Factors
most appropriate to the Mauritian economy are laid below:
• Market size and growth have showed to be most prominent determinants,
particularly for market seeking investment flows. The market size hypothesis readily comes
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with the idea that inward FDI is a function of the market size of a country. Thus, an
economy with a large market size is important for FDI as it provides potential for local sales,
greater profitability and relatively diverse resources, which make local sourcing more
feasible (Pfefferman and Madarassy 1992). Studies by Edwards (1990) and Asiedu
(2004) on the other hand have shown that there is no significant impact of growth or market
size on FDI inflows which is rather surprising. To investigate into the matter, Loree and
Guisinger (1996) find that market size and growth does impact but it only differs under
different conditions.
• Openness, namely the sum of exports and imports to Gross Domestic Product
(GDP) of an economy also plays a role to induce FDI inflows. The more open an economy,
the more likely it will pursue an appropriate trade and exchange rate regime to attract more
FDI. The most common used measure of openness is the share of trade in GDP (Asiedu
2004). Thus, such positive relationship implies that countries that wish to attract more FDI
should increase trade. In addition, as pointed by Brecher and Findlay (1983) in exploring
the openness or restrictiveness of trade policies, investors generally like to invest in
countries which have regional trade integration or where there are greater investment
provisions in their trade agreements. Aizenman and Noy (2004) studied the effects of
foreign investment flows and trade openness, and results demonstrated that both variables
are closely linked.
• Labor productivity also reside as a major FDI attraction in considering locational
factors, especially when investment is export oriented (Mody and Srinivasan, 1998).
Lower labour cost reduces the cost of production and cheap labour does attract overseas
firms. In fact, recent empirical work by Hanson, Mataloni and Slaughter (2001) gives
credit to multinationals for their concentration of production in low-wage countries
Nowadays, however technologically intensive activities are more popular, thus there is
rather demand for qualified human capital (Pigato, 2000). A recent study in China by
Buckley et al (2003) found that productivity does cause FDI but the other way round is
limited to some companies only and depend on other factors like economic climate and
labor force.
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• A good exchange rate policy among various other governmental policies does foster
investment inflows. In a country where the currency is appreciating, positive relationship
with FDI is expected and vice-versa 5 . Dunning (1977) held that in the 1960’s, the increase
in offshore investment was due to the shortage of dollars internationally. Singh in 1983
explored a further aspect of exchange rate on FDI issues where he linked the fact that
exchange rate influences value of repatriated profits. Blonigen (1997) presents another way
in which fluctuations in the exchange rate level may affect inward FDI for a host country in
terms of prices of assets especially for asset-seeking investment. Other empirical studies
confirmed this strong impact of exchange rate on FDI. Yoshimura and Kiyota (2003)
studied the impacts of exchange rate on Japan’s FDI for different periods and their work
exposed that such appreciation of the home currency vis-à-vis the host currency encouraged
FDI from the home country to the host country. Moreover, an important point when
considering exchange rates and FDI is to acknowledge the fact that they may have a bidirectional
relationship- In countries with fixed or quasi fixed regimes, this is said to be true
while for large freely floating economies, causality is said to flow from exchange rate to FDI
(Liargovas and Kosteletou 2000).
• Domestic Investment measured in the form of the aggregation of private/public
investment and FDI is said to have a two-way relationship. Domestic firms, mainly private
ones respond to FDI inflows by increasing their investment to face competition from the
foreign counterparts (De Mello, 1996). As public investment is usually used as a proxy for
physical and human infrastructure, it suggests that the better the infrastructure, the more
profitable would be the FDI and its beneficial effects. Coughlin et al (1991) reached the
conclusion that more extensive transportation infrastructures were associated with increased FDI
through their study. Harrison and Revenga (1995) clearly include domestic investment as
an explanatory variable of FDI but they find that compared with the size of the local market
and openness to trade, domestic investment has no impact on FDI. As for causality,
5 New Economist: FDI flows, Is China crowding out the rest of Asia?
10

literature itself is ambiguous on the matter but lately as per the results of Apergis et al
(2006), causality between domestic and inward FI is said to be from bilateral in the long run.
When estimating the determinants of FDI, it is thus imperative to investigate the possibility
of endogenous variables. To solve the latter issue, two approaches can be used- from the
studies of Li and Lui (2005, p395), the first one being to test for causality and the second
one is to resort to a simultaneous equation system technique within which FDI will include,
many variables such as growth, human capital, infrastructure or even exchange rate
fluctuation.
3.4 FISCAL INCENTIVES AS OPPOSED TO QUANTITATIVE VARIABLES
Despite the fact that only quantitative factors are being considered, it is necessary to
acknowledge that qualitative factors in terms of fiscal or investment incentives do also exist.
“Investment incentives include direct instruments (grants or subsidies, tax holidays,
investment tax credits, depreciation allowances etc.) used in order to reduce the fixed costs
of making an investment and indirect instruments, such as trade tariffs and quotas and
foreign exchange restrictions, that affect the decision to invest”(Pigato 2000). Many papers
(McLure 1999, Shah 1995, Dunning 1993) have demonstrated that incentives rarely attract
additional investment since they bring about a high fiscal cost along with the fact that they
create distortions in the allocation of resources.
The SADC Report (2004) underlined the fact that if there is no good investment climate in a
country, then even a zero-tax regime would not attract investment. The report concluded that
for African countries “taxes and regulations rank far behind other determinants”. Wells and
Allen (2001) grouped two arguments against tax incentives stating that:
i) Fiscal incentives may not be the first best mechanism for attracting FDI as they
merely create a net transfer from tax payers to investors.
ii) The costs exceed the benefits that are provided by the investments.
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In addition, Dunning’s underlying theory of FDI does suggest that pull factors lie as the
foreground to injecting investment in an economy in terms of the OLI paradigm. Thus, fiscal
incentives may be thought as secondary - as confirmed by Morisset (2003.) The work of
Krever (2005) reiterates the latter fact by admitting that tax ranks low in the long list of
factors considered by investors since they will go for a two stage process whereby they will
first start by “screening countries based on fundamental determinants” and only those
economies which satisfy the first stage requirements will go through the later one of being
“evaluated in terms of tax rates, grants or other incentives as considered.”
Attracting FDI is very demanding, especially in this globalized era. Policy-makers have
therefore become increasingly interested in understanding the motivating factors underlying
FDI. The driving force is “improving investment incentives for foreign investors” (Zitta &
Powers, 2003). Although a number of investigations have been conducted in this area
(Blomström & Kokko, 1998), not enough is known about the specific motivations that
influence particular investment flows.
4. METHODOLOGY AND ANALYSIS
4.1 MODEL SPECIFICATION
Based on literature review, no concrete conclusion can be reached to which variable has the
greatest and positive impact on FDI since each and every country differs. The main
objective is to determine and analyze the most important factors that affect inward foreign
investment in Mauritius and for this purpose; the following model shall be used:
FDI = f (GDP, OPEN, INV, PRO, RER) Equ1
Where FDI is inward foreign Direct Investment, OPEN is openness, PRO is productivity,
RER is Real Exchange Rate, CTAX s the corporate tax rate and INV is Domestic
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Investment. For the purpose of estimation however, this equation shall be represented by a
logarithmic reduced form equation to capture all proportional effects of the data:
LFDIt = f (LGDPt, LOPENt, LINVt, LPROt, LRERt ) Equ2
Econometrically speaking, the following equation will be formed for the purpose of
regression and VAR modeling,
LFDIt = β0 + β1LGDPt + β2LOPENt + β3LINVt + β4LPROt + β5LRERt + β5LCTAXt + εt
Equ3
Where β is an unknown parameter which shall capture the effects of each variable with β0
being a constant (the intercept term) and εt, the random error term. The subscript “t” is
added to the variables to show that these are time series data. The period of study is 1976-
2009.
4.2 DEFINITION OF VARIABLES
Dependent variable
The dependent variable, FDI, is measured as the foreign direct investment inflow in terms of
US $ and is a widely used measure (Goodspeed et al, 2006). The main source has been
from the BOM and confirmation of the data comes from the BOI and the UNCTAD
Statistics.
The table below is based on appropriate research studies and works 6 which have been laid
down with respect to the similarities of the conditions in Mauritius in order to arrive at the
expected results.
Independent Variables
Table 4.1 Summary of independent variables and their respective expected results
Variables Calculation Researchers’ Opinion DataSource
Mauritius
6 Traced back to Chapter 3: Literature Review for works and results of researchers
13
Expected
Results

GDP
OPEN
INV
PRO
RER
Nominal Gross
Domestic Product
per Capita in US $
Million
(Nominal Exports+
Nominal Imports)
/Nominal GDP
Taken as an index
Domestic
Investment = Private
Investment + Public
Investment
Labor Productivity
Index
Real Exchange Rate
1 US $ = Rs ….
5. ANALYSIS AND FINDINGS
5.1 DATA ANALYSIS
Pfefferman and Madarassy (1992)positive
Chowdhury and Mavrotas (2005)unilateral
from GDP to FDI or
bilateral depending on countries
Asiedu (2004)- positive
Aizenman and Noy (2004) –long run
Bidirectional Causality
De Mello(1996)- Positive
Apergis et al (2006) – Long run
bidirectional relationship
Mody and Srinivasan, (1998)positive
Buckley et al (2003)- unidirectional
with productivity causing FDI
New Economist: Appreciation-
Positive, vice versa. Liargovas and
Kosteletou (2000) –freely floating
unidirectional-ER to FDI; Managed
& fixed-bilateral
In-depth analysis of FDI data in the Mauritian Context
14
Central
Statistical
Office
Penn World
Table6.3
Bank Of
Mauritius
Central
Statistical
Office
Bank Of
Mauritius
- Positive
- Bidirectional
Causality
- Positive
- Long Run
Bidirectional
Causality
- Positive
- Long run
Bidirectional
Causality
-Positive
Unidirectional
Productivity
causes FDI
- Negative
- Bidirectional
Causality

0 100 200 300 400
FDI US
1970 1980 1990 2000 2010
Year
With the EPZ in Figure fulfilling 5.1 its FDI role Inflows from its from setting 1976-2009 up in 1970, in US the $ Million level of FDI inflows from
1976 till 1990 has been quite stable with an trend upwards in the mid 80’s till 1990 where
FDI reached about $41.02 million. However, from 1991 Mauritius entered in the league for
outward investors where firms wanted to operate in lower wage sites, thus the country had to
struggle to keep its foreign inflows from declining. Such an attempt had been successful with
FDI stabilising from 1991 till 1996 where it started to adopt an upward trend.
In 1998, FDI unfortunately dropped down to some $ 12.02 million with a rise in exchange
rate and a fall in both GDP and domestic investment. 2000 was a marking year where the
BOI was set up and France Telecom's purchased 40% share of Mauritius Telecom as part of
their strategic alliance. The great downturn in 2001 was a return to normal FDI flows in the
country after the exceptional surge occurring in 2000. (UN Conference on Trade and
Development 2002). As from 2004, an upward trend started with considerable FDI inflows
coming from USA with the purchase of equity by the US firm Covance Laboratories Ltd.
From then onwards, investment flows picked up year by year with the flourishing tourism
15
*1
*2
*4
*3
*5 *6

sector- Integrated Resort Scheme in 2005, the liberalization of the telecommunications
sector in 2006- Outremer Telecom and Mahanagar Telephone Mauritius Ltd investing in the
country and more recently in 2007, China’s considerable investment for the Tianli project or
in 2008-2009, the health sector booming with investment by India with Apollo an Indian
firm signing a joint venture with BAI Mauritius. Data statistics on the above can be seen
below:
Figure5.2 Sectored distribution of FDI in Mauritius from 2001-2008
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Figure 5.3 Geographical distribution of FDI in Mauritius from 2001-2008
Overall from 2004 till today, there has been an increase in the number of offshore
companies offering financial services, thus a considerable increase in terms of inflows
coming from the “financial intermediation” and business sectors.
5.2 ECONOMETRIC ANALYSIS
MULTIPLE REGRESSION
For the purpose of the multiple regression, Equ3 from Chapter 4 will be used as follows:
LFDIt = β0 + β1LGDPt + β2LOPENt + β3LINVt + β4LPROt + β5LRERt + εt
, which is derived from the model that FDI = f (GDP, OPEN, INV, PRO, RER).
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Below is the summary statistics for the logarithmic form of the variables in the model. 7
Table 5.2 Summary statistics of variables
Variable Mean Std. Dev. Min Max
LFDI 16.58686 1.711117 13.41712 19.76725
LGDP 7.852768 .9124908 6.04834 9.388511
LOPEN .2495567 .1863881 -.0833816 .5247285
LINV 6.476609 .8007153 5.236655 7.739868
LPRO 4.249344 .4749508 2.99072 4.865224
LRER 2.801668 .5136263 1.818564 3.460095
By these preliminary data, it is very difficult to pronounce oneself on the relationship of FDI
and its determinants.
Unit Root Test and Order of Integration
Using the Augmented Dickey Fuller and Phillip Perron tests, we could not reject the null
hypothesis at 1%, 5% or 10% critical level suggesting that the variables are not stationary.
Hence, their first differenced form was then subjected to the same ADF test and with the test
statistic being greater than the critical levels in absolute terms, all variables were stationary
with Ho being rejected for H1. This also suggested that FDI and its determinants are all
integrated of order 1.
Since all of our variables are integrated of the same order, a VAR model can be used to test
for the regression as well as for the causality presented afterwards. The following regression
equation will now be used with variables at their first level differenced form:
LDFDIt = β0 + β1LDGDPt + β2LDOPENt + β3LDINVt + β4LDPROt + β5LDRERt + εt Equ 7
Regression Results
Hence, it is better to look at the regression results which are presented next:
7
All the variables being always positive for the case of Mauritius over the period of study was not a concern
when converted into logarithm.
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Dependent variable: LDFDI
Table 5.4 Summary results of Multiple Regression
Variables Coefficient Standard Errors T-Ratio P-Value Significant Beta Coeffs
LDGDP 1.265571 0.997528 2.01 0.049 8 Yes, Positive 0.6775634
LDOPEN 4.052001 0.663381 3.67 0.017 Yes, Positive 1.1815245
LDINV 1.895862 0.419843 3.43 0.028 Yes, Positive 0.8648651
LDPRO 2.335129 0.790041 3.54 0.023 Yes, Positive 0.9665672
LDRER -4.089821 0.064277 -3.87 0.006 Yes, Negative -1.5048924
Intercept 3.702278 0.035929 4.04 0.01 Yes -
Prob > F = 0.0000
R-squared = 0.8827
Adj R-squared = 0.8497
For the significance of the model, the p-value of the F-test is observed and with a p-value of
zero to four decimal places, the model is statistically significant. As per the tests for model
specification, the link test (_hat 0.017 and hat_sq: 0.283) and the Ovtest (Prob > F = 0.5844)
have both demonstrated significant results. Thus, one can say that the group of variables
LDRER, LDPRO, LGDP, LDINV and LDOPEN can be used to reliably predict LDFDI
(the dependent variable). The R-squared is 0.8827, when adjusted being about 0.8497
meaning that approximately 84% of the variability of LDFDI is accounted for by the
variables in the model as the adjusted R-squared attempts to yield a more honest value.
The “coeff” column indicates the amount of change one could expect in LDFDI given a
one-unit change in the value of that variable, given that all other variables in the model a
held constant. Expressed in terms of the variables used in this example, the regression
equation is:
LDFDI =3.702278 + 1.265571* LDGDP + 4.052001*LDOPEN + 1.895862*LDINV +
2.335129*LDPRO - 4.08982*LDRER + ut
8 Ldgdp with a p-value of 0.049 is taken as significant when reduced to 2 decimal places.
19
If less than or
equal to 5%,

These estimates show the relationship between the independent and the dependent variables,
which are as expected from Chapter3. But they do not enable comparison in between
variables.
Hence, the beta coefficients are studied to compare the relative strength of the various
predictors within the model since they are all measured in standard deviations. It can be
noted that the strongest relationship is between LDFDI and LRER where with one standard
deviation increase in LRER leading to a 1.505 decrease in predicted LDFDI, with other
variables held constant or preferably, in other words, a one-unit decrease in LRER will lead
to a 4.09 decrease in the predicted LDFDI. Other variables can also be interpreted in the
same way.
Tests for Reliability of Regression Results
Variable VIF 1/VIF Durbin-Watson statistic (original) 1.813348
LDGDP 4.22 0.236967 Durbin-Watson statistic (transformed) 1.983255
LDINV 4.08 0.245098
LDPRO 3.38 0.295858
As a rule of thumb, the Durbin Watson statistic must lie
LDRER 2.37 0.421941 somewhat near 2. With a transformed value of 1.983 there is
LDOPEN 1.67 0.598802
Mean VIF 3.14
hence proof that serial correlation does not exist.
Multicollinearity measures the correlation between independent variables. To check for the
latter the variance inflation factor (VIF) is interpreted. The model presented above shows
no worrisome VIFs. They are all under 10 while the tolerance factor exceeds 0.1
Figure 5.10 Kdensity against normal density plots
20
Normality of residuals is required
for valid hypothesis testing, thus
assuring that the p-values for the ttests
and f-tests are valid. As per the
kernel density estimate, the blue
line representing the residuals is
deemed to be more or less normal.

Inference from Multiple Regression
From the regression model above, it is noted that the variables GDP per capita, openness,
domestic investment, productivity and RER do have an impact on the level of inward FDI in
Mauritius 9 as expected by the different studies referred to in the literature review. From
theoretical and empirical evidence, Mauritius also follows the same trend as other similar
countries with real exchange rate having a negative impact towards FDI when there is an
appreciation of the foreign currency vis-à-vis the rupee while other determinants such as
GDP per capita, openness, productivity and domestic investment being positively related
with inward foreign flows.
In the Mauritian economy, it is real exchange rate which has the greatest impact on FDI
followed closely by openness. GDP per capita on the other hand is of the least concern to
FDI between all these variables explaining to some extent the policies of the Mauritian
Government in giving much more importance towards opening the country to trade rather
than concentrating on growth; Mauritius has been upgraded from 24 th to 17 th in the ‘trade
across borders’ area in 2009/2010 10 .
In addition, it should also be noted that productivity and domestic investment also have their
say in FDI attractiveness. This suggests that investors abroad are pulled towards the highly
productive labor force of the country, which is being offered at a comparative cost 11 as
purported by Hanson, Mataloni and Slaughter (2001). In terms of domestic investment,
there is hence evidence that the economic and financial environment in terms of public and
private dealings also affect the investors’ perception to come and fund the country. With
FDI and domestic investment sharing a positive relationship, it can be inferred that the risk
9
All variables taken in logarithmic form
10
Ease of Doing Business 2010 – 183 countries
11
Low unit cost of labor compared to neighboring countries with similar productivity level; Reference: CSO
statistics
21

factor is being reduced with high domestic inflows, thus foreign investors are apt to invest in
such a country.
Nevertheless, it is impossible to pronounce which one of these variables causes FDI since
relationship between factors does not mean causality of one variable on another. To be more
effective in policy making, it is hence imperative to look at the causality aspect of each of
these determinants on FDI made possible through the cointegration and VAR/VECM model.
The latter are also very useful on addressing the issues of dynamics and endogeneity in FDI
modeling.
COINTEGRATION AND VAR/VECM
Since variables have already been for tested before for stationarity, their first differenced
form shall be used for the VAR model to check for cointegration and further, causality.
Using the Johansen test for Cointegration confirmed the existence of co integration and a
VECM is this employed to better capture and predict results about causality. The ECT (from
Johansen procedure) is used together with current and past differenced values of FDI
determinants to get speed of adjustment.
Causality Relationships
Table 5.7 Error Correction Representations of the model
Variables LFDI LGDP LOPEN LINV LPRO LRER ECT
LFDI - Yes
14.78
(0.0020)
LGDP Yes
10.90
(0.0123)
Yes
4.23
(0.0081)
- No
2.02
(0.5678)
Yes
12.67
(0.0054)
No
7.15 (0.0672)
22
No
1.30 (0.7283)
No
1.35 (0.7172)
Yes
22.63
(0.0000)
Yes
14.52
(0.0023)
-0.856474
t-stat : -5.95
(0.000) 12
-.5508932
t-stat : -3.75
(0.001)
12 R-squared 0.8848 Adjusted R-squared 0.8291 F-Statistic 6.3241(0.000) Durbin Watson test 1.9876

LOPEN Yes
25.02
(0.0000)
LINV
No
2.56
(0.4637)
LPRO Yes
22.03
(0.0001)
LRER Yes
8.74
(0.0329)
Yes
16.09
(0.011)
Yes
12.81
(0.0051)
Yes
15.20
(0.0017)
Yes
12.87
(0.0049)
- Yes
17.94
(0.0005)
No
1.03
(0.7945)
No
3.19
(0.3630)
No
2.15
(0.5424)
Discussion of results from the VECM
23
No
5.67 (0.1290)
- No
1.73 (0.5874)
Yes
19.72
(0.0002)
No
7.81 (0.0583)
Yes
22.53
(0.0001)
Yes
12.17
(0.0068)
- Yes
27.41
(0.0000)
Yes
8.81 (0.0319)
-.4684196
t-stat : -2.03
(0.048)
-.7097322
t-stat : -4.52
(0.000)
-.1362406
t-stat : -1.20
(0.077)
- -.2592053
t-stat : -2.20
(0.037)
The variables in the first column are taken as the independent variables namely as those
causing the other variables in the first row- being as LFDI causing LGDP, LRER, LOPEN,
LPRO or still LINV when the first row is concerned. If a variable is causing the other, a
“yes” is written along with its chi-square and p-value (p-values given in brackets which are
less than 0.005 are interpreted as significant). As a matter of fact, for the purpose of this
study, only the highlighted values are in our main interests. Other values can also be
interpreted in the same way to know if for example LGDP is causing LFDI or as follows.
The coefficient of determination R 2 and its adjusted coefficient are sufficiently high,
implying that approximately 83% (most honest value from adjusted R 2) of the total variation
in FDI flows in Mauritius is specified by the macro-economic determinants of FDI flows as
confirmed previously by the multiple regression. The Durbin-Watson test on the other hand
being close to 2 suggests that residuals are uncorrelated with their lagged values implying no
first-order serial correlation problem among the residuals.
As per the table above, it can be noted that the first differences of LFDI causes the variables
like LGDP, LINV, LOPEN and LRER (in their first differences) suggesting that the past

value of LFDI has an impact on the past value of LGDP, LINV, LOPEN or LRER. The
change in LFDI on the other hand does not cause productivity to change.
After interpretation of results with LFDI as independent term, it is imperative to know if
bilateral, unilateral or no causality at all is present in data. From the above, it can be inferred
that the past values of LGDP, LOPEN, LPRO and LRER are all causing the change in LFDI.
LINV on contrary does not cause the past values of foreign inflows.
The ECT represents the proportion by which a long-run disequilibrium in FDI can be
corrected each year and it is statistically significant at 5%level. This coefficient adds force to
the interpretation to the long run relationships between the variables. With the coefficient
estimated at -0.856 to three decimal places, p-value being less than 0.05 and it carrying a
negative sign, a highly significant long run association implying a very high speed of
adjustment of LFDI after a shock is noted. Approximately, 86% of the disequilibria of the
previous year’s shock converge back to the long run equilibrium in the current year- more
simply; it takes about 1 year and 2 months to correct disequilibrium in Mauritian FDI flows.
Summarizing interpretations above, such a result can be deduced for the short-run:
1. LFDI and LGDP: Bidirectional Causality
2. LFDI and LOPEN: Bidirectional Causality
3. LFDI and LINV: Unilateral causality with LFDI causing LINV
4. LFDI and LPRO: Unilateral causality with LPRO causing LFDI
5. LFDI and LRER: Bidirectional causality
The actual results coincide with the expected results in the research methodology except for
domestic investment where it was expected that there would be bidirectional causality.
However, this deviation will be shortly explained in the interpretation part.
24

ESTIMATION OF THE LONG RUN RELATIONSHIP
The beta coefficients are studied to compare the relative strength and relationship of the
various predictors within the model in the long run. It can be noted that the strongest
relationship is between LFDI and LRER where with one standard deviation increase in
LRER leading to a 2.923 decrease in predicted LFDI, with other variables held constant or
preferably, while LGDP has the lowest impact with its beta being the lowest, hence reenforcing
the short run results of the previous multiple regression.
Inference of causality aspect on the Mauritian economy
With respect to the above findings, the causality between variables can greatly help policy
making in Mauritius. Knowing about the short run and long run relationships between
variables enable those who are drafting incentives in attracting FDI to know which measures
will promote such type of investment.
Since LGDP and LFDI have bidirectional causality, the Mauritian government can go for
both attractiveness of investment while promoting economic growth in the country. Trade
liberalization has become an important concept for developing nations such as Mauritius and
it is important to note that when opening the country more towards trade, there are better
prospects for investors from abroad to invest in the island. As for real exchange rate, its
stability will surely promote inward foreign investment. With other relationships depicted in
table 5.7, it can also be noted that encouraging productivity will promote growth which in
turn impact on the level of FDI.
Nevertheless, the fact that domestic investment and foreign inflows present only unidirectional
causality can be explained by the fact that in Mauritius, domestic investment and
foreign investment are taken as substitutes. When foreign inflows are considerable in the
country, private investors tend to respond to FDI inflows by increasing their investment as
25

well to be able to face competition from their foreign counterparts as stipulated by De Mello
(1996) before.
The Mauritian government should work on a short term basis with expectations that the
benefits will also flow on a longer time frame as purported by the VECM and ECT above.
Hence, the interconnectedness of variables enable a compromise of incentives which the
government can adopt in order to maintain its economic objectives of economic
sustainability, investment promotion or still the level of international trade.
The impulse response and variance decomposition analysis confirms to a large extent the
above results suggesting some robust findings.
6. CONCLUSION AND RECOMMENDATIONS
In the light of the above study, an overview of Mauritius as a platform of attractiveness
towards FDI has been underlined with much emphasis placed on each of the quantitative
factors affecting inward foreign direct investment through data and econometric analysis.
The Johansen technique of cointegration has been used in a vector error correction model to
examine data on foreign direct investment, gross domestic product, domestic investment,
real exchange rate, productivity and openness over the period 1976 till 2009. In general, all
the estimated parameter coefficients carry the expected signs and are statistically significant
at the conventional five percentage level when testing for the existence of any type of
relationship between variables. The most important findings however resided as the
existence of long-run and short-run relationships as implied by the statistical significance of
the coefficients of differenced explanatory variables and the lagged error correction term
(ECT) respectively. Some demonstrated bidirectional causality, others unilateral or no
causality at all in the short and longer run. These results can be of pretty importance to
policy makers in Mauritius in order for them to take good decisions and follow a course of
action to lead the Mauritian economy towards success.
26

From the findings and analysis, it is imperative to discuss the recommendations which can
be brought about to the Mauritian economy in order for the country to lure more. As it is, the
main implications came from are the surprising interconnectedness of domestic and foreign
investment along with the relationship of productivity and FDI. These two areas have
unfortunately never been the basis of foundation of any measures or incentives to make
Mauritius a better platform. Thus, it would be thus interesting for the BOI to try to
implement incentives which tend to promote foreign investment as increases will also
induce domestic investment and such benefits will be more apparent in the long run which
will finally uphold the economy as a whole.
Concerning productivity, this area is still of exploratory stage. Since in the short run,
productivity is said to encourage FDI, it would be interesting if this could be used to lure
investors to the country in promising them to get better workers at cheaper costs. The
problem of unemployment will also be catered for while increasing standard of living of
people and decreasing poverty level. This convergence of macroeconomic objectives can be
well achieved though the right proportion of FDI determinants.
On a concluding note, an important area which the government should not neglect is trying
to attract FDI from other economies which have never dealt with Mauritius before by
implementing measures which will cater for the investment profile of the financier of the
foreign market. This will bring the country to an international recognition and promote
sectors such as the tourism industry or enhance communication in between economies.
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