FDI, Information Technology and Economic Growth in the MENA Region

M. Kabir Hassan, Ph.D. Endowed Professor of Finance and Economic Development

And Associate Chair Department of Economics and Finance

University of New Orleans New Orleans, LA 70148

Phone: 504-280-6163 Fax: 504-280-6397

Email: mhassan@uno.edu

10th ERF Paper

FDI, Information Technology and Economic Growth in the MENA Region Using a panel data of 95 countries and 8 MENA countries over 1980-2001 period, this paper examines the important factors that contribute to FDI and economic growth in the world and compares them with those of MENA countries. FDI brings host countries capital, productive facilities, new technology and modern management know-how. Information and communication technology (ICT) is essential to growth, necessary to develop a country’s productive capacity in all sectors of the economy, and links a country with the global economy and ensures competitiveness. This paper finds both growth and FDI are related to a host of macroeconomic, ICT and globalization variables. This paper provides policy recommendations for improving FDI inflows with special focus on trade facilitation, export processing zones, investment promotion agencies, export performance requirements and WTO rules and incentives. Key Words: FDI, MENA, economic growth, ICT and globalization

FDI, Information Technology and Economic Growth in the MENA Region

1. Introduction

Growth performances vary across countries and regions. The determinants of

growth are not unique for all countries that contribute to such variations. The growth

pattern is linked to characteristics of countries such as economic base, population growth,

unemployment rate, and investment in physical and human capital, flow of foreign

investment, industrial growth, inflation, and development of financial institutions. Recent

literature includes military expenditures, foreign aid in the context of developing

countries (Benoit, 1973, 1978; Ball, 1983; Joerding, 1986; Chowdhury, 1991; Looney,

1991; Madden and Haslehurst, 1995; Kollias and Makrydakis. 1999). The growth pattern

in the MENA region has special characteristics: heavy reliance on oil; weak economic

base; high population growth and unemployment rates; dominance of the state in the

economic sector; low level of integration with the world; underdeveloped financial and

capital market; underdeveloped institutions; and low rates of returns on human and

physical capital (Hassan and Bashir, 2002; Makdisi, Fattah and Liman, 2002).

Information and communication technology (ICT) is essential to growth,

necessary to develop a country’s productive capacity in all sectors of the economy, and

links a country with the global economy and ensures competitiveness. It is seminal to

invention, innovation, and wealth creation. In this rapidly evolving environment, Middle

East and North African and other developing countries face opportunity costs if they

delay greater access to and use of information infrastructure and information technology.

ICT contributes to poverty reduction by increasing productivity and providing new

opportunities, offers opportunities for global integration while retaining the identity of

traditional societies, enhance the effectiveness, efficiency and transparency of the public

sector.

Since the 1980s country barriers to foreign investment have given way to

countries actively seeking FDI instead of discouraging it. Governments now compete

with each other to win more investment from foreign companies. In this context, it is

important to understand what factors attract FDI, a topic much studied in international

business, and to understand the increasingly important role of communications and

technology infrastructure in this global competition for FDI. In order to successfully

restructure their economies to lure foreign investors and ultimately to get and sustain

competitive advantages, policy makers need to better understand what makes a market

attractive to foreign companies. This paper is in line with previous studies investigating

how country factor conditions affect the amount of FDI. It provides a special

contribution to FDI theories by focusing on technological, particularly communications,

infrastructure, an area of increasing importance in global competition, and by comparing

MENA economies to developed ones. The purpose of this study is to investigate the

differences between the world and the MENA countries in market-factor determinants of

FDI inflows.

FDI has been seen as an effective channel to transfer technology and foster

growth in developing countries. Within the framework of the neo-classical models à la

Solow (1956), the impact of the FDI on the growth rate of output was constrained by the

existence of diminishing returns in the physical capital. Therefore, FDI could only exert a

level effect on the output per capita, but not a rate effect. In other words, it was unable to

alter the growth rate of output in the long run. Therefore, a group of economists did

consider FDI as a drive engine of growth by mainstream economics. In the context of the

New Theory of Economic Growth, however, FDI may affect not only the level of output

per capita but also its rate of growth. This literature has developed various arguments that

explain why FDI may potentially enhance the growth rate of per capita income in the host

country. It is argued that FDI facilitates the use and exploitation of local raw materials,

introduces modern techniques of management, eases the access to new technologies,

foreign inflows allow financing current account deficits, finance flows in form of FDI do

not generate repayment of principal and interests (as opposed to external debt), increases

the stock of human capital via on the job training, and stimulates the investment in R&D.

This paper examines the relation between foreign direct investment (FDI) and

economic growth in the context of a multivariate economic growth framework for 95

countries from developed and developing countries over the period from 1980 through

2001 with special reference to 8 MENA countries. The 8 MENA countries covered in this

study are Egypt, Iran, Jordan, Saudi Arabia, Morocco, Tunisia, Turkey and Yemen. The

central theme of this paper is to ascertain whether FDI enhance or retards economic

growth with special reference to information technology.

Single-equation and simultaneous-equation models have been estimated, and

more recently, there has been the application of techniques that investigate causal links.

Studies have varied from extensive cross-country analyses, which failed to reach any

consensus, to detailed case studies of individual countries. Dunne (1996) provides an

extensive survey. The contribution of this study is that it goes beyond the standard

“Granger causality” econometric techniques used in previous econometric work, and uses

a panel data approach within the framework of economic growth model. Therefore, our

approach is more comprehensive than others.

The rest of the paper is divided into six sections. Section 2 provides a summary of

important literature on FDI, economic growth and development. Section 3 provides a

theoretical model on the relationship between FDI and economic growth. Section 4

describes the data, methodology and hypotheses. Section 5 explains the results. Section

analyzes the policy measures, and section 7 concludes the paper.

2. Literature Review

Empirical tests based on neoclassical theory, and they conclude that different

saving rates and population growth rates might affect different countries’ steady-state

levels of per capita income (Solow, 1956). Recent literature based on endogenous growth

models (Romer, 1986; Lucas, 1988) provides evidence of initial economic conditions, in

other words, conditional income convergence across countries. The convergence

hypothesis is "conditional" because it depends upon various factors in each country,

among which the central ones are: the rate of savings, the growth rate of population, the

marginal productivity of labor. The lower the initial level of income the greater the

opportunity of catching up through higher rates of capital accumulation and diffusion of

technology. The relationship between the per capita GDP growth and the initial level of

DPD per capita after controlling for other variables such as government policies and

character of national population supports this convergence hypothesis.

In literature, human capital has also been to be a leading factor in explaining

economic growth. Barro (1991), based on his study on 98 countries, concludes that the

growth rate of real per capita GDP is positively related to initial human capital. Ben

Habib and Spiegel (1994) have found evidence that human capital affect Total Factor

Productivity Growth through its impact on the capacity of a country to innovate and

capability of using and adapting foreign technology. Sach and Warner (1997) also noted

that a rapid increase in human capital development results in rapid transitional growth.

Becker et al (1990) state that higher rates of investment in human and physical capital

lead to higher per capita growth. This is because well developed human capital leads to

an improvement in productivity, and an increase in the growth rate and investment ratio.

According to Barro (1997) investment in human capital in the form of secondary and

higher education are highly significant in their effects on potential rates of growth. The

better and more highly trained the work force, the more productive it will be in helping to

enhance the rate of annual real output in a society. At the same time, the lower the rate of

population increase relative to the rate of growth in the capital supply, the more capital

can be invested per worker to increase the average output of each member of the work

force.

Globalization or the degree of openness to the global economy is another

important factor in recent years. Gallup et al. (1998) conclude that open economies are

generally in a better position to import new technologies and new ideas from the rest of

the world. In addition they are likely to have a greater division of labor and production

processes that are more consistent with their comparative advantages, which enable them

to grow faster.

Macroeconomics factors play an important role for the sustainability of growth.

Fischer (1993) has shown that growth is negatively related with inflation, large budget

deficits, and distorted foreign exchange market. The use of black market premium rate s a

measure of distortions in the foreign exchange market has been widely disputed. It has

also been argued that the possibility that the omission of inflation as independent variable

might conceal the true effect of interest rates on saving. Their empirical work on saving

shows that, the inclusion of inflation along with real interest rate as independent variables

increases the magnitude of real interest rate coefficient. Fischer presents cross-sectional

and panel-regressions showing that growth is negatively associated with inflation. Levine

and Renelt (1992) state that high growth countries are also lower inflation countries.

Using statistical data for 64 countries, Barro (1997) finds that high and variable

rates of price inflation have significant negative effects on economic growth. For

example, a 10 percent increase in the average annual rate of inflation can reduce the

growth rate in real per capita GDP by 0.3 percent to 0.4 percent per year. Over a 30-year

period, this would mean a country's real output would be 6 percent to 9 percent less than

it might have been without such an inflation policy by government. The rate of inflation

experienced by these countries was not significantly influenced by whether or not they

had "independent" central banks, i.e., central banks supposedly insulated from direct

political manipulation by their governments. Thus, the frequently heard arguments that

all that is needed for monetary stability is to ensure that the central bankers are free from

the immediate control of the government does not stand the test of the statistical

evidence. Fischer and Modigliani (1978) argue that such negative relationship exists

because firms and workers devote productive resources to deal with inflation. They

further note that inflation uncertainty reduces efficiency by discouraging long-term

contracts and increasing relative price variability.

Levine and Zervos (1993) attempt to measure the role of government in

economics activity by suing the ration of government consumption to GDP, and they

found a negative relationship between government consumption and growth. Barro

(1991) states that growth is inversely related to the share of government consumption in

GDP, but insignificantly related to the share of public investment. Clark (1993) shows

that inflation in the U.S.A. increases the productivity growth, thereby GDP growth. On

the other hand, Grimes (1990) uses time-series data from 21 countries for the period of

1961-1987, and estimates that a 1 percentage point reduction in inflation increases out put

growth by 0.1 percentage point. Barro (1995), using data for 100 countries for the period

of 1960-1990 shows that if a number of country characteristics are held constant, then the

regression results suggest that an increase in average inflation of 10 percent per annum

reduces the growth rate of real GDP by 0.2-0.3 percent per annum, and the ratio of

investment to GDP by 0.4-0.6 percent.

The importance of investment in accelerating economic growth is discussed

widely in literature. The most recent empirical work by Mankiw, Romer and Weil (1992)

shows that using cross-country data the ratio of investment to GDP is an important

determinants of growth which indirectly indicates the importance of saving. However, the

findings on the determinants of the foreign direct investment (FDI) in the form of

multinational enterprises are not clear-cut. Asiedu (2002) finds that not only is there a

variation in the factors counted to be important but different studies yield conflicting

results with respect to the same factor. GDP per capita is found to have a positive

relationship with FDI in Schneider and Frey (1995), Tsai (1994) and Lipsey (1999),

while a negative relationship is reported in Edwards (1990). In his review, Asiedu (2002)

identifies two variables those have unambiguous positive effect on FDI: the quality of

infrastructure (Wheeler and Mody, 1992; Kumar, 1994), Loree and Guisinger, 1995)

openness to international trade (Edward, 1990; Gastanga et al, 1998). Asiedu also finds

that infrastructure development, measured by telephone per 1,000 population, does not

have a significant impact on FDI to Africa but encourages FDI to other countries.

3. A Theoretical model on the Relationship between FDI and Economic Growth

According to Hermes and Lensink (1999), there are different channels through

which positive externalities associated with FDI can occur : i) competition channel:

increased competition leads to increased productivity; efficiency and investment in

human and/or physical capital, and also increased competition may lead to changes in the

industrial structure towards more competitiveness and more export-oriented activities; ii)

training channel : increased training of labor and management; iii) linkages channel :

foreign investment is often accompanied by technology transfer; such transfers may take

place through transactions with foreign firms; iv) demonstration channel: domestic firms

imitate the more advanced technologies used by foreign firms.

To sum up, economic theory identifies a number of channels through which FDI

may exert an impact on economic growth. These effects may be direct as well as indirect.

a. Direct impact: FDI flows can promote growth if they lead to an increase in the

investment rate. b. Indirect impact: FDI flows can promote growth if they lead to

investments that are associated with positive spillovers, which may enhance the

productivity of labor and capital in the recipient economy.

The linkage between FDI and growth is not without criticism. FDI may not lead

to growth rate because MNCs tend to operate in imperfectly competitive sectors (with

high barriers to entry or a high degree of concentration). As a result, FDI may crowd out

domestic savings and investment. Moreover, FDI may have a negative impact on the

external balance because profit repatriation will tend to affect the capital account

negatively. Other critics argue that FDI is often associated with enclave investment,

sweatshop employment, income inequality and high external dependency. These

arguments are not related to growth performances however. If production based on FDI is

geared for the internal domestic consumption as opposed to exports, the current account

may be adversely affected and host governments may lose control of production.

(Durham 2000). All these qualifications regarding the potential positive impact of FDI on

growth point to the importance of enabling conditions: in the absence of certain

conditions, the negative effects may outweigh the positive impacts.

Romer (1990), Grossman and Helpman (1991), Barro and Sala-i-Martin (1998),

Borensztein, De Gregorrio and Lee (1998) and Cunado and Sanchez Robels (2001)

contribute in developing models to predict the impact of FDI on growth. Let, we have

two sectors in an economy, which differ in their levels of productivity. Sector 1 is more

advanced technologically, and is encompassed by foreign firms producing intermediate

goods. Sector 2 is composed of local enterprises. Technological progress is driven by an

increase in the number of intermediate goods available.

Preferences: Individuals maximize an intertemporal utility function of the form-

0

(0) ( )tt tU e u c L dtρ

∞−= ∫ (1)

where ρ is the discount rate, ct is per capita consumption in time t and Lt is for the size of

the family. The instantaneous utility function is of the CRRA type:

1 1( )1

cu cσ

σ

− −=

− (2)

where σ the intertemporal elasticity of substitution between periods.

Technology: Let Y is a consumption good produced by two sectors, and sold in

the competitive markets. Total output in the economy can be written as:

Y = Y1 + Y2 (3)

And the production function for each of the sector can be written as:

Y1 = A1 Hα K1-α 0 1α< < (4)

Where H is the human capital endowment, and K is the stock of physical capital, and is

defined as:

1

FDIN

ii

K x=

= ∑ (5)

where xi is the intermediate goods when i indexes different varieties of intermediate

good, and NFDI is the number of varieties of intermediate goods by sector 1 (where firms

of foreign ownership work). The intermediate goods enter in the production function in

an additive and separate fashion (Romer, 1990), and the stock of physical capital in the

LDC is encompassed by NFDI intermediate goods. We can substitute K in equation (4)

and taking into account that in equilibrium the price and quantity of each intermediate

good is the same, x :

Y1 = A1 Hα NFDI x 1-α (6)

The production function of sector 2 can be written as:

2 2 2Y A Lβ= (7)

If we add the following restrictions on the parameters:

1 2(1 )A Aα β− >

Then we it follows that efficiency in sector 2 is a fraction of that prevailing in sector 1:

2 1

1A Aεε

=<

The fixed cost can be written as:

( )FDIF f N= where 0FDIF

N∂

<∂

The presence of such negative relationship characterizes some kind of monopolistic rents

for sector 1. The existence of F warrants the presence of increasing returns in sector 1 and

therefore the presence of extra profits. When they are positively related, that is,

0FDIF

N∂

>∂

One of the predictions of the model is the convergence hypothesis: those a country in

which technological gap is larger would grow faster.

4. Data, Research Methodology and Hypothesis

Data for this analysis are derived from World Development Indicators, International

Financial Statistics, the World Telecommunication Development Report, and the

UNESCO database. The analysis was based on data from a cross section of 95 countries

over time from 1980 to 2001. Data of all variables are yearly.

4.1 Data Definition The following is a description on the variable we used in this study, and how the data

has been constructed for each variable:

FDI inflows are net inflows of investment to acquire a lasting management interest

(10 percent or more of voting stock) in an enterprise operating in an economy other than

the home country of the investor. The measure is the sum of equity capital, reinvestment

of earnings, other long-term capital, and short-term capital as shown in the balance of

payments.

ICT infrastructure, or information and communications technology infrastructure, is a

composite variable composed of indicators such as the density of Internet hosts and the

number of computers, telephone mainlines, fax machines, TV sets, radios, users of

mobile phones, and subscribers to newspapers. The reliability alpha among the sub-

factors was .89. All the sub-factors were standardized and then combined to make the

ICT-infrastructure variable.

Globalization is calculated as the sum of exports and imports divided by GDP. This

ratio of trade to GDP provides a measure of the degree of economic openness. Human

capital means percentage of relevant group participating in secondary education.

Secondary education completes the provision of basic education that began at the primary

level, and aims at laying the foundations for lifelong learning and human development,

by offering more subject- or skill-oriented instruction using more specialized teacher.

Population growth rate is the exponential change of population each year.

Gross domestic investment consists of outlays on additions to the fixed assets of the

economy plus net changes in the level of inventories. It is coded as percentage of GDP.

Also indicated by percentage of GDP, Government’s Expenditure denotes Central

government’s total expenditure including nonrepayable current and capital expenditure.

FDI inflows are net inflows of investment to acquire a lasting management

interest (10 percent or more of voting stock) in an enterprise operating in an economy

other than the home country of the investor. The measure is the sum of equity capital,

reinvestment of earnings, other long-term capital, and short-term capital as shown in the

balance of payments.

Inflation rate indicates GDP implicit deflator measuring the average annual rate

of price change in the economy. Exchange rate is a measure of each country’s currency

stability. Larger numbers indicate a weaker currency.

4.2 Methodology

We apply a pool estimation technique, for which the estimation equation can be

written, in general, as /

it it it i iy xα β ε= + +

where ity is the dependent variable, and itx and iβ are k-vectors of non-constant

regressiors and parameters for i=1,2,3……,N cross-sectional units. In our study, we have

95 countries as cross-sectional units so that we have actually 95 cross-sectional equations

for our data set. The residual covariance matrix for this set of equations is given by

( )

/ / /1 1 1 2 1

/ // 2 1 2 2

1 1

/ /1

............................

.............

N

N N N

E E

ε ε ε ε ε ε

ε ε ε εε ε

ε ε ε ε

Ω = =

L L

L

Thus the pool specification treats the model as a system of equation. However, an OLS

estimation of this model might give us biased estimates of parameters when residuals are

heteroskedastic and contemporaneously correlated.

Our hypothesis explaining economic growth and FDI, based on a review of the

theoretical and empirical literature and on the ideas we presented above in our theory

section, are represented by the equation below:

(GDP GROWTH)it = β0 + β1(Y0)+ β2(GI)it + β3(ICT)it + β4(HC)it + β5(PG)it +

β6(GDI)it + β7(GE)it + β8(FDI)it + β9(ER)it + β10(IR)it + + β11 (ME) it + εit

(1)

(FDI)it = β0 + β1(Y0)+ β2(GI)it + β3(ICT)it + β4(HC)it + β5(PG)it + β6(GDI)it +

β7(GE)it + β8(GDP GROWTH)it + β9(ER)it + β10(IR)it + + β11 (ME) it + εit

(2)

Yo is the initial GDP per capita, FDI is net FDI inflows, ICT is information and

communications technology infrastructure, GI is globalization index (an indicator of

market openness), GDP is gross domestic product, GDI is gross domestic investment, PG

is population growth, GE is government expenditure, FDI is foreign direct investment,

ER is exchange rate, IR is inflation rate, ME is military expenditure, and ε is error, i

represents each sampled country, and t represents each year. We estimate the parameters

of equation (1) and (2) separately by applying two estimation techniques:

4.2.1 Pooled Cross-section regression

In order to avoid possible cross-sectional heteroskedasticity and contemporaneous

correlation in residuals, we apply generalized least square (GLS) which use cross-

sectional weights of residuals to calculate the variance of the residuals:

( )

21

2/ 2

1 1

2

0 0

0 0............................

0 ..............

T

T

N T

I

IE E

I

σ

σε ε

σ

Ω = =

LL LL

LL LL

K

In such pool regression model, the intercept terms are restricted to be identical, that is,

there is no country-specific variation so that itα α= .

4.2.2. Fixed Effect Panel Model

Panel regression is an efficient technique when there is a large number of cross

sectional units with diverse qualitative variations. In our study, we have 95 countries,

some of which are developed, some underdeveloped or developing from different

continents. In such situation, an unrestricted intercept term is more plausible. The fixed

effects estimator allows itα to vary across cross-section units so that we get different

constants for different countries. In other words, it iα α= and ( ) 0i iE α ε ≠ . We also

estimate the parameters in fixed effect model using GLS method. All estimates are

adjusted for White heteroskedasticity- consistent standard errors & covariance.

4.3. Hypotheses Development Based on theoretical and empirical literature, we develop and test the following hypotheses:

H1: ICT infrastructure has positive impact on FDI and economic growth.

H2: Globalization has positive impact on FDI and economic growth.

H3: human capital has positive impact on both FDI and economic growth. .

H4: Gross Domestic investment has positive impact on FDI and economic growth.

H5: Government expenditure has negative impact on both economic growth, but its impact on FDI is ambiguous. H6: The impact of inflation on FDI and economic growth is ambiguous.

H7: The impact of exchange rate fluctuation on FDI and economic growth is negative.

H8: The impact of military expenditure on FDI and economic growth is ambiguous. 5. Analysis of Empirical Results

In the pooled cross-section regression of GDP growth we find foreign direct

investment (FDI) and gross domestic investment, are statistically significant at 0.1

percent level. Domestic and foreign investments positively influence the GDP growth as

expected. On the other hand, globalization has a significant negative impact on such

growth. In the fixed effect model, the globalization factor become insignificant, and the

impact of government expenditure become significantly negative at 1 percent level.

Among the infrastructure variables, only population growth, PG have highly

significant positive impact on GDP growth; but this significance goes away when

country-specific variations are allowed in fixed effect model. The other two variables,

contrary to our belief, information and communications technology infrastructure, ICT,

and human capital, HC negatively affect the growth. However, such negative effect is

only significant in case of ICT.

We also find significant negative effects of inflation rate, IT, exchange rate, ER,

and military expenditure, ME. On the other hand, per capita income, GC has a very small

but a strong positive effect on GDP wroth. In the fixed effect model, such effect of per

capita income negative, but insignificant. And the inflation rate no longer bears any

significance. Overall, when the country-specific variations are allowed the fixed effect

estimation is selective in picking up variables in explaining the GDP growth without

loosing goodness-of-fit measure of adjusted R2.

When we compare the results of all countries in our sample, we find that the GDP

growth rate in MENA countries is poorly explained with these factors. Only exchange

rate is significant in pooled regression. While the impact of this variable is still negative,

but bears no statistical significant in the fixed-effect model.

When we regress foreign direct investment against a set of explanatory variables,

we find no significant effect of GDP growth on FDI in either model. The GDI and GE

both have opposite impact on FDI in two estimations. In fixed effect model GDI has a

positive impact, and GE has a negative impact on FDI, and both of them are significant at

0.01 percent level. The globalization exerts a positive and significant effect on FDI

inflow in pooled cross-section mode, which is reversed in fixed effect model.

We also find information technology proxy contributes positively to the FDI

flows. We, however, find unexpectedly negative effects of human capital and population

growth variable on the FDI inflows.

Among the economics factors, we find that the inflation rate and exchange rate

have negative impact on FDI flows in pooled cross-section model, which are reversed in

fixed effect model and still significant at 0.01 percent level. This signifies that when

country-specific variations are allowed, domestic currency devaluation exerts positive

influence on FDI inflows. The negative and significant coefficient of income per capita

supports the income convergence hypothesis among countries. On the other hand,

military expenditure has positive significant impact on FDI.

Unlike the results of all countries, GDI in MENA countries has a marginally

significant negative impact on FDI; but that significance goes away in the fixed-effect

model. The government expenditure affects FDI flows negatively. On the other hand, we

find a strong negative impact of population growth, the significance of which is no more

prevail in the fixed-effect model. Similarly, globalization has no significant impact on

FDI flows except in the pooled model. None of the economic factors are significant in

explaining FDI in MENA countries.

Overall, the fixed effect model explains the variations better than the pooled

cross-section model as reflected in the adjusted R2 value, which is 93% in the former, a

jump from 48% in pooled model. The F-value is also very high in the fixed effect model.

6. Policy Implications

Exports are important to countries because they generate foreign-exchange

earnings, allow firms in developing countries to enter activities they could not have

undertaken in their own country, help them build new productive capacity, and can serve

as a monitoring device for economic performance. This is why policies to attract export-

oriented foreign direct investment (FDI) are also important. When developing these

policies, there are four key factors to consider.

First, countries need to realize the need to begin targeted efforts to promote

export-oriented FDI. The advantage of a targeted approach is to attract investments that

can help meet the country’s development objectives. The targeted approach can be

successful if the country understands their relative competitiveness for specific activities,

and the corporate strategies affecting their choice of location. Countries can identify their

production niches through which they can link up with global value chains. This requires

more than just investment promotion agency (IPA)s; it also needs a coordinated effort

with the country’s government. The targeted effort must also be in tune with the

industrial policy and overall development strategy of the country and the needs of the

targeted export activity. It is also important to ensure that not all countries aim at

exporting the same products, which would cause an oversupply and a subsequent

deterioration of the terms of trade.

The second factor countries need to consider is that access to key destination

markets is a necessary, but not sufficient, condition for attracting export oriented

activities. Many countries have relied on preferential arrangements such as the European

Union and its association agreements, NAFTA, and EU-MED initiatives. Policy makers

need to be aware of any opportunities arising from these agreements, and also understand

their limitations. These preferences do not provide a sufficient or sustainable basis for

developing competitive export industries and hence, need to be seen as a temporary

window of opportunity that provides enough time to strengthen the country’s advantages.

Third, when policy makers define the measures to be included in a package for

investors, they should measure their effectiveness and also ensure that they comply with

the international regulatory framework, specifically World Trade Organization (WTO)

rules. Special attention to export subsidies should be given. The Agreement on

Subsidies and Countervailing Measures (the SCM Agreement) stipulates that all export

subsidies be phased out by the end of 2002, except by LDCs and other countries listed in

Annex VII of the agreement. Those countries given an extension are not allowed to

create new subsidies and are required to develop post policy responses. This is

particularly important to countries that use export processing zones (EPZs) because the

export subsidies are an integral part of the incentive package offered in the zone.

However, countries will still be allowed to exempt exports in these zones from indirect

taxes (such as sales taxes), border taxes, and import charges. Duty drawbacks and duty

exemptions will still be permitted. The advantages of the EPZ in the form of a well

functioning infrastructure and streamlined administrative procedures will be unaffected.

As a result, several countries are turning their EPZs into industrial parks or science parks,

which act as a catalyst for cluster development.

The risk of intense competition for export oriented FDI may be a race to the

bottom (in social and environmental standards) and a race to the top (in incentives).

EPZs should not be judged solely on their capacity to attract FDI or increase exports and

foreign exchange earnings. They should also be assessed by the extent to which they

help meet the country’s broader social and economic objectives. Countries that pursue

more integrated policy approaches by encouraging tripartite representation on EPZ

committees, guaranteeing workers’ rights, and upgrading skills and work conditions,

have attracted higher quality FDI. Regarding the incentives race to the top, while export

subsidies are usually prohibited, other locational incentives are still widely used.

Increasing competition for export oriented FDI risks accelerating the incentives race

among competing locations and calls for further international cooperation in addressing

this issue. Developing countries are at a disadvantage in the race due to the resources

available for public support to private investment. A reduction of investment subsidies in

these countries should help their governments to allocate more money for skill

development and infrastructure improvements which will attract more export-oriented

activities.

The fourth factor countries need to consider when developing their policies is

encouraging linkages between foreign affiliates and local suppliers. Export-oriented

foreign affiliates often import all or most of the input requirements and assemble the

product in the host country and then export their output. Linkages with foreign affiliates

are a key channel for the diffusion of skills, knowledge and technology among domestic

firms. Policy measures have shifted towards the use of instruments that address market

failures and contribute to building local capabilities; examples include information

provision and matchmaking, encouraging foreign affiliates to participate in programs

aimed at upgrading domestic suppliers’ technological capabilities, promoting the

establishment of supplier associations or clubs, and the joint provision of training.

Industrial cluster formation can be spontaneous, but they are increasingly formed

by strategic government intervention. Three kinds of effort are essential for cluster

development involving inward FDI. The first is investment and business promotion in a

targeted manner. Policy makers need to under the competitive needs of different

industries to avoid encouraging investments in the wrong kinds of clusters. There is also

a need in FDI based cluster development for close cooperation between IPAs and related

government institutions. The second is institution building. Agglomeration tendencies

can be encouraged by the development of EPZs and industrial parks that often specialize

in one or more industries. Institutions such as metrology, standards, testing and quality

assurance provide the infrastructure of modern industrial activity and are important to

competitiveness. The third element focuses on the training and upgrading of human

resources. Although this is usually addressed in the education system, it may be

necessary to make specific and targeted efforts to ensure a sufficient supply of qualified

people. This may involve foreign affiliates in developing specialized training centers.

Another approach is to attract internationally mobile skills to complement the local skills

base.

Ultimately, the success of attracting and upgrading export-oriented FDI depends

on the country’s ability to develop its domestic resources. Some of the most successful

countries in boosting export competitiveness have taken a two pronged approach:

developing domestic capabilities while targeting foreign resources. This strategy

includes the following: the establishment of a top agency to oversee FDI targeting, in

line with the country’s broader development and industrial strategies; an evolving

package of targeted incentives to encourage TNCs to invest in strategic activities;

harnessing the presence of TNCs and partnerships with foreign governments to develop

and upgrade a highly flexible and responsive human resource system; the development of

a world class infrastructure; the establishment of capital investment funds to partner with

foreign companies in strategic investments; and targeted support for the development of

domestic enterprises, and for suppliers and clusters.

The need for countries to improve themselves and the attractiveness of their

advantages is difficult for policy makers and requires sophisticated and comprehensive

policies. Export competitiveness can promote country development, but the need of

developing countries to preserve sufficient policy space to pursue their development

objectives needs to be recognized. The extent to which the countries profit from new

opportunities created by international production systems depends on their own actions;

countries can help in several ways. This includes assistance for the development of

institutional capacity, dissemination of information about export-oriented investment

opportunities in low cost locations, and the dismantling of barriers to exports from

developing countries. However, there are several causes of concern that may jeopardize

the prospects for poor countries to exploit their comparative advantages fully. These

include the use the anti-dumping weapon, increased tariffs on certain products, and the

expanding use of targeted subsidies in developed countries.

7. Summary and Conclusion

Our results indicate that the set of variables that affect GDP growth and FDI are not

same. We find insignificant impact of globalization on GDP growth; but it affects FDI

flow negative. In case of impact of gross domestic investment and government

expenditures on GDP growth and FDI, we find a positive impact of gross domestic

investment on GDP growth, and a similar impact on FDI. The result shows a pattern of

complementarities between it and FDI flow since higher FDI flow exerts positive

influence on GDP growth. On the other hand government expenditure exert a negative

influence on the flow of FDI.

Among the infrastructure variables, we do not find any significant effect of ICT

infrastructure and its utilization on GDP growth. ICT, but it has a significant positive

impact on FDI. Infrastructure and its utilization matter, because they are important for

attracting and retaining investment. A consideration regarding emerging markets is that

FDI inflows may sometimes be highly controlled by governmental or public bodies, but

our finding tells us that this has not significantly deterred investment. In explaining GDP

growth population growth and human capital do not have significant Impact; but both

factors affect FDI negatively.

In our growth equation, military expenditure has significant negative impact, where

it has positive impact in the FDI equation. It is believed that developing countries gain

more from defense spending vis-à-vis the developed countries, as benefits are more

widespread across the economy in these countries. We find that the impact of defense on

FDI is positive as it may bring overall stability in the economy by providing security

against all external threats and aggression. However, the ultimate impact of military

spending on GDP growth turns out to be negative. However, It is imperative that we

carefully assess various supply-side (spin-offs from technology or infrastructure) and

demand-side (resource diversion) factors. The growth concepts are not usually kept in

mind while making expenses for military purposes. Sometimes military expenditure and

GDP comparisons are misleading. The proportion of national resources allocated to

defense reflects the perceptions of national elite and decision making circle, which is

largely founded on the security milieu in which a country finds itself. It simply does not

represent any comprehensive plan of sound investment where mass benefits exist.

We find negative significant effects of inflation and exchange rates on GDP growth,

where as they are significantly positive. This means exchange rate devaluation exerts

growth reducing impact even though our conventional idea indicates that such

devaluation should encourage exports. On the hand, a positive impact of devaluation on

FDI implies an export-competitiveness that encourages more foreign investment. The

negative and significant coefficient of income per capita supports the income

convergence hypothesis among countries.

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TABLE 1

Summary of Important Research on Economic Growth Effect on growth

Author(s) Panel of Counties Studied

Independent variables Sign (+/-)

Significance (Yes/No)

Real per capita GDP - Secondary-school enrollment + Primary-school enrollment +

Real govt. consumption/real GDP - Number of revolutions or coups

per year +

Number of assassinations per million population per year

+

R. J. Barro (1991)

98 (1960-1985)

Magnitude of deviation of 1960 PPP for investment deflator from

the sample means

Inflation rate - Yes Budget surplus / deficit + Yes

Terms of trade + Yes Exchange rate premium + Yes

Per capita GDP - Yes Tariff protection - No Human capital + No

S. Fischer (1993)

101 (1960-1989)

Average ratio of liquid liabilities to GDP

- No

GDP - Male secondary and higher

education +

Life expectancy + Male Schooling -

Fertility - Government consumption ratio -

Rule of law index + Terms of trade change +

Democracy index +

R. J. Barro (1995)

100 (1960-1990)

Inflation rate - Real GDP per worker - Yes

Log average growth rate of working-age population + sum of

rates of technological progress and depreciation

- Yes M. Knight, N. Loayza

and D. Villanueva

(1992)

81 (1960-1985)

Log average ratio of real investment to real GDP

+ Yes

Log ration of human capital investment to GDP

+ Yes

“Closedness” of the economy - Yes

Public infrastructure to GDP + No Real GDP per worker - Yes

Log average growth rate of working-age population + sum of

rates of technological progress and depreciation

- Yes

Log average ratio of real investment to real GDP

+ Yes

Log ration of human capital investment to GDP

+ Yes

“Closedness” of the economy - Yes

M. Knight, N. Loayza

and D. Villanueva

(1992)

59 Developing countries

(1960-1985)

Public infrastructure to GDP + Yes Real per capita GDP -

Total government spending to GDP

-

Total investment to GDP + Social security to GDP +

Government consumption (excluding defense and

education) to GDP

-

X. Sala-i-Martin

75 (1970-1985)

Government investment to GDP -

Table 2 Regression Estimates of Pooled Cross-Section

and Panel Fixed Effect Models (GLS), All Countries (95) (Dependent Variable: GG, GDP Growth Rate)

Explanatory Variables

Pooled Cross-Section

GLS (N = 95)

Panel: Fixed Effect

GLS (N = 95)

Globalization GI (Globalization Index)

Infrastructure ICT (ICT Configuration) HC (Human Capital) PG (Population Growth)

National Investment GDI (Gross Domestic Investment) GE (Government Expenditure) FDI (FDI Inflows)

Economic Factors IR (Inflation Rate) ER (Exchange Rate) ME (Military Expenditure) GC (Per Capita Income)

Constant

-0.009** (-2.168) -0.351** (-2.129) -0.001 (-0.018) 2.753*** (6.906) 0.196*** (3.003) 0.097 (1.500) 0.0001***

(16.053) -0.011** (-2.999) -0.001** (-2.266) -0.921*** (-4.108) 0.000*** (3.680) 2.085 (0.851)

-0.083 (-0.944) 1.095*** (2.643) -0.025 (-0.591) 0.297 (0.814) 0.398*** (4.402) -0.222** (-1.642) 0.000*** (10.063) 0.003 (0.828) -0.002*** (-3.634) -2.609*** (4.594) -0.001 (-0.687) -

R2 Adjusted R2 F-Value

0.99 0.97

0.99 0.96

t-statistics in parentheses * p < .05** p < .01

Table 3 Regression Estimates of Pooled Cross-Section

And Panel Fixed Effect Models (GLS), All Countries (Dependent Variable: Foreign Direct Investment)

Explanatory Variables

Pooled Cross-Section

GLS (N = 95)

Panel: Fixed Effect

GLS (N = 95)

Globalization GI (Globalization Index)

Infrastructure ICT (ICT Configuration) HC (Human Capital) PG (Population Growth)

National Investment GDI (Gross Domestic Investment) GE (Government Expenditure) GG (GDP Growth)

Economic Factors IR (Inflation Rate) ER (Exchange Rate) ME (Military Expenditure) GC (Per Capita Income)

Constant

17799*** (7.537) -1.02E+08 (-0.953) -23290*** (-3.823) 3.43E+08 (1.499) -14182 (-0.790) 83139** (2.237) -11833 (-0.450) -15002*** (-5.984) -23073*** (-2.743) 74186 (0.668) -202130*** (-5.417) 9.97E+08 (1.804)

-110437** (-2.058) 8.35E+08*** (6.271) -816010* (-1.911) -700211*** (-5.871) 1.49EE+08***(7.541) -2.31E+08*** (-10.487) 189908 (1.362) 164670*** (4.809) 75699*** (2.729) 1.69E+08** (1.948) -728411*** (-8.928) _

R2 Adjusted R2 F-Value

0.50 0.48 8.29

0.99 0.93 9.04E+30

t-statistics in parentheses * p < .05; ** p < .01; *** p < .001

Table 4 Regression Estimates for MENA Countries Dependent variable: GDP growth rate, GG

Explanatory Variables Pooled

OLS Fixed-effect

Model Globalization

GI (Globalization Index)

39.572 (1.427)

-125.297 (-0.865)

Infrastructure ICT (ICT Configuration)

1.899

(0.273)

-9.327

(-0.916) HC (Human Capital) 0.042

(0.740) 0.179

(0.370) PG (Population Growth) -1.783

(-1.205) 10.944 (1.742)

National Investment GDI (Gross Domestic Investment)

0.008

(0.065)

-0.034

(-0.208) GE (Government Expenditure) 0.233

(1.361) 0.172

(0.785) FDI (FDI Flow) -0.001

(-0.266) -0.003

(-0.630) Economic Factors

IR (Inflation Rate)

0.069 (1.447)

-0.445

(-0.979) ER (Exchange Rate) -0.000**

(-2.463) 0.000

(-0.633) Constant -6.787

(-0.537) -

Adjusted R2 0.02 0.12 F-Value 0.981 1.469

Table 5

Regression Estimates MENA Countries Dependent variable: FDI Inflows, FDI

Explanatory Variables Pooled

OLS Fixed-effect

Model Globalization

GI (Globalization Index)

3869.997** (2.266)

8849.902 (1.1963)

Infrastructure ICT (ICT Configuration)

527.821 (0.950)

50.382 (0.070)

HC (Human Capital) 4.770 (1.170)

31.958* (1.686)

PG (Population Growth) -310.300*** (-3.577)

56.482 (0.212)

National Investment GDI (Gross Domestic Investment)

-15.540* (-1.895)

-6.634

(-0.908) GE (Government Expenditure) 11.524

(1.208) -26.589***

(-3.479) Economic Factors

GG (GDP Growth Rate)

-4.700 (-0.270)

-8.943

(-0.527) IR (Inflation Rate)

2.476 (0.904)

-4.968 (-0.321)

ER (Exchange Rate) -0.001 (-0.481)

0.000 (-0.212)

Constant 907.639 (1.121)

-

Adjusted R2 0.398 0.661 F-Value 3.505*** 10.189***