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Is Foreign Direct Investment a Catalyst of Economic

Growth? Theory and Empirics.

Monika Tarsalewska

Abstract

The paper studies the influence of foreign direct investment on economic growth. To

study this relation was used the endogenous growth model with the expanding product variety. Into the model was introduced a new variable which captures the impact of FDI on the rate of economic growth. The estimating equation, derived from the theoretical model, is examined for two groups of countries – OECD and CEE. I implemented OLS method and panel estimation with the use of instrumental variables. The estimation results show that FDI has a positive impact on economic growth in the OECD countries. In the CEEC group the relation is not very clear.

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Introduction

The aim of this paper is to study the impact of foreign direct investment (FDI) on the

rate of economic growth. FDI can affect growth in two ways, directly, by increasing the

amount of physical capital by bringing new inputs and technologies to the recipient economy,

and indirectly by human capital augmentation, via technology or knowledge transfers.

Therefore, through both channels, FDI is a crucial factor of technology diffusion in the host

country. The benefits for the host country might be diverse. The activity of multinational

firms may increase income and reduce poverty. However, it may be also associated with

negative spillovers such as destructive competition for domestic firms. It is not

straightforward for the receiving countries which strategy to chose. Therefore, this subject

deserves more attention.

FDI is expected to augment the existing stock of knowledge by improving the quality of

human capital through labour training, skill acquisition and through the alternative

management practices and organisational arrangements. The volume and type of FDI and its

impact on the host economy may depend on country-specific characteristics, such as the host

economy’s trade regime, legislation, political stability, and its scale factors such as balance of

payments constraints, the size of domestic market for goods produced via FDI etc.

There exists a wide body of empirical literature which tries to explain the FDI and GDP

relationship. The human capital seams to be an important factor that allows the host country to

benefit from positive knowledge spillovers that result from the presence of FDI. Some empirical

studies suggest that in order to capture the positive effects of FDI the host country must have

reached appropriate level of development. Borensztein et al, (1998)1 point out that FDI raises

growth only in countries where the labour force has achieved a minimum level of education.

More recently, Wang and Wong (2004)2 suggest that the greenfield investment had a positive

impact on growth while M&A negative. These results supported the view that by the M&A

investment is only the transfer of ownership and control while greenfield investment could be

an important vehicle for the transfer of technology and knowledge spillovers in the host

economy and boosts growth. Carkovic and Levine (2002)3 re-examined the relationship

1 Borensztein, E., De Gregorio, J., Lee, J.-W., 1998. How does foreign direct investment affect economic growth,

Journal of International Economics 45, 115– 135. 2 Wang, Miao Grace and Wong, Man Chiu Sunny, What Drives Economic Growth? The Case of Cross-Border

M&A and Greenfield FDI Activities, (November 30, 2004). Available at SSRN: http://ssrn.com/abstract=627663 3 Carkovic Maria and Levine Ross, Does Foreign Direct Investment Accelerate Economic Growth? University of

Minnesota, June, 2002

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between economic growth and FDI, however they did not find any positive effects of FDI on

economic growth. In contrast to previously presented studies their results suggest that FDI is

growth enhancing only in host countries with the low educational attainment. Moreover, they

pointed out that the exogenous component of FDI did not exert a robust, independent

influence on economic growth. The Carkovic-Levine (2002) paper argues that the estimates

of such macro-economic studies have to be viewed sceptically, since they do not control for

the ‘simultaneity bias’ nor for country-specific effects nor for lagged dependent variables in

growth regressions.

Another strand in the empirical literature investigates the relationship between FDI and

growth underlining the importance of well developed financial markets in the recipient

country. In the previous century Schumpeter (1912)4 recognized the importance of well-

developed financial intermediaries in enhancing technological improvement, capital

accumulation, and economic growth. The Shumpeterian tradition has been reviewed in the

recent empirical studies that devote a lot of attention to the interaction of financial markets in

searching for the influence of FDI on economic growth. Alfaro, Chanda, Kalemli-Ozcan, and

Sayek (2002)5 find that FDI promotes economic growth in economies with sufficiently

developed financial markets. In fact, FDI heavily relies on capital imported from abroad,

although for domestic firms to believe to gain from the spillover effect from foreign firms it is

necessary to invest in their own development. In the similar vain, to the empirical literature

contributes also paper of Hermes and Lensink (2003)6 that finds the positive impact of FDI in

the presence of well developed financial markets.

Other studies point out that it is important to evaluate also the country characteristics as

political stability, business environment, law regulations and other factors influencing the

overall country specification. Among macroeconomic variables connected with the political

stability, the factor that affects the influence of FDI on growth is also the trade policy.

Balasubramanyam, et al. (1996)7 stress that trade openness is a crucial factor in obtaining the

growth-effects of FDI. Busse and Groizard (2006)8 argued that countries that want to benefit

4 Schumpeter, J.A. Theorie der Wirtschaftlichen Entwicklung (The Theory of Economic Development). Leipzig:

Dunker and Humblot, 1912 (translated by Redvers Opie, Cambridge, Massachusetts: Harvard University Press, 1934).

5 Alfaro L., Areendam, C., Sebnem, K. and Selin, S. FDI and economic growth: The role of local financial

markets. Working Paper 01-083. Cambridge MA: Harvard Business School 2001. 6 Hermes, Niels and Robert Lensink (2003), Foreign Direct Investment, Financial Development and Economic

Growth, Journal of Development Studies, Vol. 40, No. 1, pp. 142-163. 7 Balasubramanyam V. N. and Salisu, M. (1991),Export Promotion, Import Substitution and Direct Foreign

Investment in Less Developed Countries, The Economic Journal, Vol. 106, No. 434. (Jan., 1996), pp. 92-105. 8 Matthias Busse and José Luis Groizard, FDI, regulations and growth, World Bank Policy Research Working

Paper 3882, April 2006

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in the presence of multinationals need sound and safe business environment in the form of

properly established government institutions and clear regulation. Bengoa and Calvo (2003)

pointed out that the economic freedom is an important indicator in the decision making

process of foreign investors. To reconcile the effects of FDI on the host country Alfaro

(2003)9, took into account the fact that multinational decisions might be sector specific,

studied the potential effects of FDI across economic sectors. Her results indicated that FDI

had a positive and strongly significant influence on growth only in manufacturing industry. In

services it had ambiguous impact and a clearly negative impact in primary sector.

The survey of literature indicates that the impact of FDI on growth in host countries is

still controversial topic. The variety of results may be due to the different samples and the

methods applied by different authors or the positive effects may be limited by local conditions

of receiving countries. Nonetheless, given the shortcomings of other studies it is still a wide

area of investigation.

However, whether FDI is a vehicle of output growth and technological progress seems

to be a less troublesome hypothesis in theory than in practice. There exists a problem of the

simultaneity bias and omitted variables. In the empirical studies these problems have been

investigated in the more recent literature, although the problem of sensivity of estimates to the

instruments chosen to reduce the simultaneity bias in growth equations has not been

accounted for to the same degree and still deserves a more detailed analysis.

In the paper we try to analyse the subject first starting from theoretical study of FDI and

GDP relationship. The impact of FDI on the rate of economic growth was incorporated into

the Barro and Sala-i-Martin (1997)10 model of endogenous growth. This model combines the

assumption of neoclassical convergence theories with the technology inflow from abroad. The

introduction of a new variable, capturing the impact of FDI, in the Barro and Sala-i-Martin

(1997) model changes the imitation cost function and leads to a different steady-state. This

new variable affects not only the steady-state level of income but also the rate of growth along

the transition path to the new steady-state. The empirically testable hypothesis concerning the

determinants of economic growth was obtained from the model. The main research

hypothesis says that in addition to traditional factors such as initial per capita income also FDI

affects the rate of economic growth in recipient countries. This hypothesis is tested

empirically using panel data for two groups: OECD and CEE countries. Due to various

9 Laura Alfaro, Foreign Direct Investment and Growth: Does the sector matter? Harvard Busines School, 2003 10 Barro Robert J., Xavier Sala-i-Martin, Technological Diffusion, Convergence and Growth, 1997, Jurnal of

Economic Growth, 2, 1-27

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problems which occur in the growth regression the research hypothesis is tested using

different estimation methods. In particular, was implemented the instrumental variable

method with the use of various instruments to address the endogeneity problem in the growth

regression.

The paper has the structure as follows. The following section presents a theoretical

model and the research hypothesis. In the third section are described the variables which were

used in the regression and the data used in the empirical analysis. The fourth section discusses

the econometric issues and the potential problems which occur in the growth regressions, and

presents the results obtained from the estimations and tests. Summary of the main results and

final remarks are presented in the concluding section.

The leader-follower model

The aim of this section is to provide the theoretical link between FDI and the rate of

economic growth. The endogenous growth model proposed by Barro and Sala-i-Martin

(1997)11was extended to incorporate the role of multinational firms in the knowledge

diffusion process.

Although recent growth theories endogenize the rate of technological progress, they

tend to lose the prediction of conditional convergence. Unlike most endogenous growth

models Barro and Sala-i-Martin (1997) model combines some elements of endogenous

growth with the convergence implications of the neoclassical growth model. In their model, in

the long run, growth depends on the expanding number of products developed in a few

leading economies. The key assumption is that the imitation is typically cheaper than the

invention. Therefore, most countries prefer to imitate than to innovate. Furthermore, the

relatively low cost of imitation implies that the typical follower grows relatively fast and

tends to catch up with the technological leaders. As the range of copiable goods decreases, the

cost of imitation tends to rise and the follower’s growth rate tends to fall. Therefore, the

pattern of conditional convergence12 that emerges in this model depends on the diffusion of

technology. This similarity with the neoclassical model applies because the increasing cost of

imitation is analogous to the diminishing returns to capital. However, their model does not

specify channels via which knowledge diffusion takes place, and in particular says nothing

11 Robert J. Barro, Xavier Sala-i-Martin, Technological Diffusion, Convergence and Growth, 1997, Jurnal of

Economic Growth, 2, 1-27 12 Conditional convergence – initial position y(0) is conditioned on the steady state position y*.

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about the role of multinational firms in this process. Therefore, the model which addresses

this issue is derived.

The setup of the model for country 1 is similar to that described in Barro and Sala-i-

Martin (1997) while in the imitation process of country 2 is incorporated a new variable,

which captures the impact of FDI on the economic growth.

I assume that the product-specific technologies in each sector are more advanced in the

developed country than in the less developed country. Therefore, domestic firms may gain

from MNCs presence and lower the costs of research because it is easier and cheaper to

imitate than to innovate. Therefore, foreign direct investment is the main channel of

technological progress in this framework. An increase in the quantity of supplied products

depends on the adaptation of technology available in more advanced countries that permits to

introduce into host market brand new goods. The process of implementation of imitated

products in country 2 is costly and requires a cost of research and adaptation of new

technology - )(2 tν - the costs of imitation however it is less expensive than cost of innovation.

The growth rate per capita in the imitating country along the transition path to the

steady-state is found as a solution of the system of two autonomous (depending only on time)

differential equations. After log-linearization around steady-state (the linear approximation)

we have a general first order linear system with constant coefficients. After, some calculation

we could derive a final equation with introduced variable for FDI, which looks as follows:

+⋅⋅−−⋅⋅−−−⋅⋅−+⋅+⋅−= −−−−−

11210201 lnln)1/1()1(ln)1/1()1(2

ησβαβσαβσββγγ βββββ ttttt

y eAeAeyeye

)/ln(ln/1ln/1ln 2122 NMeLeLeetttt ⋅⋅+⋅⋅⋅−⋅⋅⋅+⋅⋅ −−−− ββββ εσββσβσησβ (1)

The above equation allows to obtain the main research hypothesis saying that

multinationals firms positively affect the rate of economic growth per capita in the follower

economy having controlled for a number of other variables that include: the GDP growth per

capita of leader economy, the initial GDP level of the follower country, the overall

productivity level of the economy, the cost of imitation and the size of population.

The impact of the growth of GDP of the leader economy should be positive. The impact

of the initial GDP level of the follower country should be negative. The impact of cost of

innovation should be positive. The impact of the overall level of productivity should be

positive and the impact of the population size should be positive.

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Variables description and data sources

This section describes the data which were used in the empirical analysis, specifically

describes the subgroups of countries, the measures of FDI, GDP and other explanatory

variables used in the growth regressions.

The first group for estimation is the unbalanced sample, covering period from 1981 to

2003 for Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France,

Germany, Greece, Hungary, Iceland, Ireland, Italy, Netherlands, New Zeeland, Norway,

Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Kingdom.

The sample choice was determined by data availability. Moreover, the group is quite

homogenous therefore, I could obtain reliable results.

I would also like to approximate and predict the influence of FDI in the Central-East

European countries, where in last years a considerable inflow of foreign investment was

noted. The time series of FDI for central-east European countries (new participants of

European Union) are quite short, therefore I decided to make estimation also for this group

using panel data estimations. I collected data for Czech Republic, Estonia, Hungary, Latvia,

Lithuania, Poland, Slovakia, Slovenia, Belarus, Ukraine, Bulgaria, Croatia, Macedonia and

Romania. The time span for the Central and Eastern European countries covers years from

1988 to 2004.

The GDP per capita, to be comparable between countries, is expressed in constant US

dollars and in purchasing power parity.

The FDI variable is taken from the UNCTAD database. Following the previous

empirical studies, the FDI variable is measured as the inflow of foreign capital into the host

country. According to these studies positive externalities are associated with increased capital

flows that are accompanied by the new knowledge bought by multinational firms to the host

economy, therefore the FDI inflow seems to be a better measure of positive externalities than

the FDI stock.

In addition to FDI which is the main explanatory variable in the estimating equation we

need to control for other factors that may affect the rate of growth in the host economy.

According to the theoretical model I include also the variable which indicates how the

rate of growth in the leading economy influences the rate of growth of the follower economy.

I assume that the leader economy is the US economy and following the model I added the rate

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of growth of GDP per capita in the US for each year expressed in purchasing power parity

terms.

Furthermore, I incorporated also the variable which shows the initial level of GDP per

capita for the host country. To assure comparability between countries I chose the variable

which is expressed in the purchasing power parity. To proxy the variable which indicates the

human capital quality I chose the number of technicians in R&D sector (per million people).

The variable which describes the quality of institutions in the host country is the inflation rate,

which shows the stability of the economy. Moreover, as suggested by the model I added to the

regression population of the recipient economy as a measure of the size of the host economy

to approximate the scale effect. The summary statistics of the variables used in the empirical

study are reported in the Table 1.

Table 1. Summary statistics

Variable Source Explanation

Supposed

Influence

on GDP

growth

per capita

Number

of

observati

ons

Mean Std. Dev. Min Max

GDPPPP WDI

Gross domestic product per capita growth

(constant US $ 2000) in purchasing power parity

549 0.0252816 0.0272365 -0.1424372 0.1464279

LFDI UNCT

AD FDI inflows (in

logarithms) + 528 7.565536 1.952224 -0.812458 12.19742

USAPPP WDI GDP per capita in

purchasing power parity of USA

+ 575 0.0308132 0.0192748 -0.0299853 0.0634384

INITIALPPP

WDI

GDP per capita (constant 2000 US$) in 1980 purchasing power

parity, growth in previous period

- 545 16325.73 8199.565 1896.669 40125.93

LHUMAN

WDI Technicians in R&D

(per million people, in logarithms)

+ 361 10.64221 1.336808 6.612041 13.1545

LPOP WDI Population (in

logarithms) + 575 16.30131 1.244519 12.35017 18.22881

LINFATION

WDI Inflation as a political

instability proxy - 535 1.548 1.160562 -4.073972 6.319654

FREE Frasesr Institut

e

Index of economic freedom

+ 193 6.888012 0.9887241 3.285546 8.482761

INDUSTRY

WDI The size of industry

production + 200 0.3648397 10.539 -49.56306 18.39807

Source: Own study

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Econometric theory

This chapter explains the econometric methods used to test empirically the theoretical

hypothesis that foreign direct investment positively influences the rate of economic growth

and presents the estimation results.

In order to test for the link between these two variables I start from the simple OLS

regression and then I apply more advanced panel data methods. The basic motivation is to

explain the long-run growth through FDI inflows into developed and transition economies.

However, analysing this issue is more complicated, because there is a complex relationship

between the rate of economic growth and FDI. The causality between these two variables

might run in both directions. For instance, the inflow of FDI might occur due to attractive

growth prospects for foreign investors and the growth might arise due to the inflow of FDI.

For that reason we have a causality problem in the model and FDI may be in principle an

endogenous variable. This implies that OLS regressions estimates may be biased. Therefore, I

will try to use the instrumental variable (IV) estimation to address this problem.

First, I start from the theory which stands behind the estimation of growth equation

estimated in this paper. The general empirical specification, which results from theory, is as

follows:

ititiit vXy ++= ββ 0 (2)

where yit is explained variable for country i and at time t, Xit – specific variables, vit is a

stochastic normally distributed uncorrelated with xit error term and 0β , iβ coefficients to be

estimated.

When we estimate the relationship between growth and investment, the marginal effect

of the inflow of foreign direct investment could be correlated with aspects of the economic

environment that should be also included in the regression. Therefore, we should take into

account that vit might include a factor ui - a time-constant individual unobserved component,

such as the country size, language, race or other individual characteristics, which are

correlated with growth and invariable through time. Thus we have:

itiit uv ε+= (3)

The bias might influence the accuracy of estimated coefficients. Therefore, the random-

effects model might give biased results for coefficients of x’s. In a such case, it would be

appropriate to relax the assumption that the individual effects are not correlated with x’s and

implement the fixed-effect model, which allows to assume that ui is arbitrarily correlated with

x’s. The idea of estimating the fixed effect is first to transform the equation 3, to obtain the

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averaging estimation and then subtract the obtained equation from the original equation. In

this way the constant specific effect is removed from the original equation.

We also have to deal with the time-constant omitted variables, which might be

correlated with explanatory variables and are not included in the model. Thus, to estimate

partial effects consistently, in the presence of time-constant omitted variables, it would be

better to use the fixed-effects method. Therefore, the fixed-effects analysis is more robust than

the random-effects model.

As it is well known, in the case of panel estimation we do not have a problem with non-

constant variance because the coefficient estimates remain unbiased, however, the OLS is

inefficient and the estimates of the standard errors are biased.

A lot of criticism in growth regressions is made on the basis of endogeneity of right-

hand side variables explaining growth due to the influence on the consistency of estimates.

The most popular way to solve the problem is to implement the instrumental variables method

and replace an endogenous variable by its instrument.

To overcome the problem of endogeneity I decided to implement the instrumental

variable method (IV). It provides a solution by replacing the endogenous variable by the

variable which is not included in the equation, although it is correlated with the endogenous

variable and uncorrelated with the error term.

The idea of this method is to find a set of variables - instruments that are both correlated

with the explanatory variables in the equation, and uncorrelated with the disturbances. These

instruments are used to eliminate the correlation between explanatory variables and explained

variable. The method takes into account two steeps. First, 2SLS finds the proportions of the

endogenous and exogenous variables that can be used as the instruments. On this stage is

estimated an OLS regression for each variable in the model on the set of instruments. Second,

is taken a regression of the original equation, with all of the variables replaced by the fitted

values from the first-stage regressions.

In the wide body of literature authors present different methods. For example Barro and

Lee (1994)13 used 5-year lagged explanatory variables as instruments, Caselli, Esquivel, and

Lefort (1996)14 employ a generalized method of moments (GMM) estimator to analyse a

panel variant of the standard cross-country growth regression. I perceived to be appropriate to

implement as an instrument the lagged values of FDI. FDI lagged as an instrument steams

13 Barro and Lee, Sources of economic growth, JME, 1994 14 Caselli,Francesco, Gerardo Esquivel y Fernando Lefort, Reopening the Convergence Debate: A New Look at

Cross-Country Growth Empirics, Journal of Economics Growth,1,363-389, 1996

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from the fact that multinationals enterprises are much more likely to invest in countries which

already have considerable FDI inflows. Therefore, the successes of other plants in abroad are

strong encouragement for future investment.

Furthermore, I consider also use of other variables as instruments of FDI such as index

of economic freedom or the industry production. There are two ways of measuring economic

freedom. The first one is proposed by the Fraser Institute15, and the second one by the

Heritage Foundation. Both include such variables as: the degree of openness, government

intervention, distortion in the economy and the level of corruption. Both indices give similar

results, although the Fraser index covers a larger period. Appendix 7 presents the average of

the economic freedom index on years 1981-2003 for the sample. The higher the value of this

index the higher the level of economic freedom. Thus, it is clearly visible that it has lower

value for the Central and East European Countries such as Czech Republic, Hungary, Poland,

Slovak Republic, and also Turkey that are lower that the value obtained for Greece.

I claimed that the fixed effect method would be more appropriate as an estimation

method, although the practical issue which allows us to distinguish between the random or

fixed effect methods in the case of panel estimates is implementation of the Hausman (1978)

test, which is widely used in econometrics.

Properties of the dataset

Prior to running growth regressions it is necessary to investigate the time series

properties of the dataset by testing for unit roots and cointegration. The literature suggests that

the standard tests such as the Dickey-Fuller (DF), the augmented Dickey-Fuller (ADF) and

the Phillips-Perron (PP) tests fail to meet the expectations of researchers who are testing for

unit root vs. alternative stationary hypothesis on the panel data and that the panel-based unit

root tests are better than unit root tests based on individual time series. EViews computes the

following five types of panel unit root tests: Levin, Lin and Chu (2002), Breitung (2000), Im,

Pesaran and Shin (2003), Fisher-type tests using ADF and PP tests (Maddala and Wu (1999)

and Choi (2001), and Hadri (1999).

Due to the short time series I decided to assess the stationarity of the data on the basis of

the tests which take into account this problem such as: Levin, Lin and Chu, Breitung, and PP-

Fisher, with the null hypothesis of nonstationarity. The Levin, Lin and Chu test is the most

15 Gwartney, James and Robert Lawson (2006). Economic Freedom of the World: 2006 Annual Report,

www.freetheworld.com

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frequently used method for the panel data unit root. Maddala and Wu (1999)16 compared the

Levin, Lin and Chu approach with the alternative established by Fisher (1932). They find out

whether there is no cross-sectional correlation in the error terms the Fisher test is more

powerful. Second, in the case of heteroscedasticity and serial correlation in errors terms

Fisher is also better. Moreover, in the case of a mixture of stationary and non-stationary

variables the Fisher test also performs better. The Breitung test corrects for the problem of

interdependence between observations over cross sections, which is popular in the case of

country level data17.

P-values and test statistics for panel unit root test are presented in the Appendix 8 and

Appendix 9.

In the case of the OECD group tests show clearly that GDP growth per capita are stationary

according to Breitung, Fisher-type PP and Levin, Lin and Chu tests. The Breitung and Fisher-

type PP shows that FDI is stationary for the whole group as well individually. According to

Levin, Lin and Chu approach the FDI variable seams to have a common unit root process. In

the case of the CEEC group, according to implemented tests, GDP growth per capita does not

have a unit root. However, results are not so much clear for FDI; two of three tests indicate

the stationarity of the variable. However, the Levin, Lin and Chu test also indicates unit root

process.

The not clear performance of the tests might arise also from the property that I do not

have very long time series. However, two of three test confirm the stationarity hypothesis,

therefore I can estimate the data with Ordinary Least Squares or through panel regressions18.

Ordinary least squares estimation results

I start the empirical research with estimating the theoretical relationship via ordinary least

squares (OLS). While this method is inconsistent in this case and is likely to yield biased results,

it is useful to see the signs of the partial effects. Therefore, I estimate the data with the

ordinary least squares and in Table 3 are presented the results of the benchmark regressions.

16 Maddala G. S. & Shaowen Wu, A Comparative Study of Unit Root Tests with Panel Data and a New Simple

Test, Oxford Bulletin of Economics and Statistics, Volume 61, Issue s1: 631-652 17 Can Tongur, Johan Lyhagen, A study of power and size properties of some panel unit root tests, 2005, Uppsala

University 18 However, if that the time series properties of the data were not stationary in levels the null hypothesis that the

variables are cointegrated should be tested. If there exists a cointegrating vector we could analyse the relationship between FDI and the economic growth a through Vector Error Correction model (Panel VEC). However, this sophisticated analysis is beyond the scope of this paper.

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Table 2. The impact of FDI on the GDP growth per capita (OLS regression)

OECD countries CEE countries

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept .0545521 (1.71)*

.0700632 (2.23)**

.0129967 (0.26)

.043216 (1.46)*

.0548263 (1.51)

.0687068 (2.24)**

.0460781 (1.39)

.0561622 (1.33)

FDI .0022063 (1.94)**

.0027965 (1.84)**

.0100837 (2.90)***

.0007657 (0.23)

.0032244 (1.95)**

.0030578 (2.07)**

.002263 (0.62)

.0009486 (0.19)

USAppp .2668017 (3.34)***

.2480764 (3.14)***

.6423378 (2.32)**

.2461902 (3.26)***

.262039 (3.27)***

.1610936 (1.87)*

.0684188 (0.22)

.0689494 (0.22)

Human -.0031968

(0.342) -.0037494

(-1.08) -.0184658 (-3.02)***

.0004569 (0.11)

-.0082012 (-1.89)**

-.0048774 (-1.44)*

3.19e-06 (0.33)

1.27e-06 (0.12)

Pop -.0007531

(-0.21) -.0015721

(-0.45) .0072341

(1.29) -.001728 (-0.55)

.0019961 (0.44)

-.0007123 (-0.21)

-8.96e-10 (-2.93)***

-8.39e-10 (-2.47)***

Initialppp -3.75e-07

(-1.18) -3.70e-07

(-1.19) -1.60e-07

(-0.33) -4.83e-07

(-1.69) -2.80e-07

(-0.88) -3.19e-07

(-1.05) -2.39e-06

(-1.20) 1.27e-06

(0.12)

Inflation -.0001744

(-1.13)

-.000127 (-3.83)***

-.0001275 (-3.84)***

Dummy variable No No No No No Yes No No

Number of observations

343 339 121 321 329 329 111 111

R-sq 0.0538 0.0743 0.0231 0.0531 0.0910 0.1344 0.1864 0.1854

F test 3.84 4.20 3.44 3.18 4.45 4.43 3.97 3.91

Prob > F 0.0022 0.0010 0.0063 0.0081 0.0002 0.0000 0.0013 0.0015

Notes:

T statistics in parenthesis

***significant at 1% level, ** - significant at 5% level, *significant at 10% level

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The estimation results presented in the column (1) were obtained by the simple OLS

method. Estimates in columns (2) - (6) were obtained using a two stage least squares (2SLS)

method. In column (2), the instrument for FDI is its one-year lagged value. In the column (3) I

used as an instrument for FDI the economic freedom index. In column (4) I used as an

instrument the variable called - industry. In column (5), like in column (1), the instrument for

FDI is its one-year lagged value but to check the sensivity of the model I added also inflation

as an economic stability indicator.

We can observe that in all regressions for the OECD countries FDI has a positive

impact on GDP, which in almost all cases is statistically significant. The impact of the leader

economy appeared with positive sign and it is statistically significant. The estimated

parameters on the of human capital variable does not give clear results. It could be the reason

that it is very difficult to find an appropriate proxy variable for the cost of human capital.

Furthermore, the initial income, as predicted by the theory, has in each regression a negative

impact on growth, however not statistically significant. Population has insignificant negative

influence. The negative influence of inflation in the specification presented in column (5)

could be explained by the fact that a high inflation rate puts at risk competitiveness and hence

exports19, and may be a symptom of existence of distortions in the markets, lack of fiscal

discipline or poor macroeconomic stability (as argued by Fischer, 1993).

In column (6) are presented regression results which include also dummy variables for

the transformation period. It increased the explanation of the model to about 13 % but also

shows that some fixed effects were important for the economic growth in that period. From

this estimation we could infer that there exist some fixed effects before the transformation that

have a positive impact on growth. It could be due to the fact that in the period of central

planning growth was artificially increased, and statistics have not been reliable. Aftermath, in

the period of transformation, economies had been in recessions. Thus, it could explain the

negative influence of dummy variables.

The R-squared statistic as we can observe is quite low, however this result should be

viewed sceptically. By definition it is the proportion of squared error that is explained by the

model and it should indicate goodness of fit of the model. However, the model could contain

a large random component that is difficult to specify. Therefore, this variable is recommended

to be of secondary interest and the more important issue is to have a correctly specified

model.

19 Prices in the host country rise what causes that the goods are less completive with the foreign, thus export

decreases.

15

Due to the fact that I want take a look at CEECs I made also analysis of these countries.

The structural break due to the fall of communism it very visible on the graph (Appendix 10).

In the column (7) is simple OLS regression on the sample of CEE countries and in the

column (8) IV OLS on the same group of countries estimated with the use of the one-year

lagged value of FDI as an instrument for FDI. In spite of insignificance, the pattern in FDI

variable is the same like in case of OECD countries. With the predicted influence

significantly appeared inflation and population.

Panel data estimation results

Basic results of panel data analysis are presented in Table 4. Regressions are made on

the panel data to explore the dimension of the OECD and CEEC groups. Two main

techniques have been implemented to analyse the data econometrically, group A is estimated

by random effect method and group B is estimated by fixed effect method.

In the columns (1a) – (2b) the fixed and random panel data regression for the OECD

group are presented. The Hausman test indicates the importance of controlling for random

effects and a better fit of the random effect model. The result shows the significant influence

of FDI on economic growth. The positive impact on growth has obviously the growth rate of

the leader economy. The parameter on the human capital variable shows ambiguous results.

The scale effect has a negative and statistically significant effect on the rate of economic

growth. The initial level of GDP in each country and the inflation variable also display

negative signs and are statistically significant in host economies.

Columns (3a) – (4b) of the Table 4 present the results of the panel regressions with the

fixed and random method for the CEECs. The influence of FDI on the rate of growth

appeared positive and significantly significant in the column (3a), however in the following

estimations do not confirm this pattern. These estimations indicate that there are some

country-specific fixed effects that have a crucial influence on economic growth. It is quite

obvious that due to the transformation process there are country-specific effects that were

important in the beginning of this period. The importance of FDI could arise in the later

period therefore it is quite difficult to extract the importance of foreign capital inflow to these

countries on their rates of economic growth.

However, these results are not conclusive because of the endogeneity problem that

exists in the estimated equations. Therefore, I move to the more appropriate analysis with the

16

Table 3. The impact of FDI on the GDP growth per capita (panel estimation)

OECD countries CEE countries

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b)

Intercept .090052

(2.35)**

1.653814 (1.94)**

.0843466

(2.13)***

2.151928 (2.53)**

.4170299 (2.57)**

17.20013

(4.37)***

.4089957 (2.55)**

15.00733 (3.38)***

Lfdi .0037363

(3.50)***

.003659 (2.28)**

.0029313

(2.43)**

.0030293 (1.83)*

.0076651 (1.75)*

-.0008698

(-0.17)

.0050272 (1.11)

.0000963 (0.02)

Usappp .2870322

(4.07)***

.2917939 (4.14)***

.2493256

(3.44)***

.2388137 (3.31)***

-.0560583 (-0.18)

.1623423

(0.63)

.1221109 (0.41)

.2072653 (0.79)

Lhuman .0019819

(0.50)

.01212 (1.35)

-.0034686

(-0.79)

.0010382 (0.11)

-.0158149 (-0.87)

-.1005597

(-3.57)***

-.0186921 (-1.05)

-.0935949 (-3.22)***

Lpop -.0070454

(-1.70)*

-.1094989 (-2.01)**

-.0020348

(-0.44)

-.1319824 (-2.43)**

-.0188403 (-3.12)***

-1.040493

(-4.12)***

-.0148653 (-2.38)**

-.9037231 (-3.19)***

Initialppp -7.35e-07 –

(2.06)**

9.83e-08 (0.10)

-8.70e-07

(-2.38)**

3.44e-07 (0.34)

-2.27e-06 (-0.84)

1.32e-06

(0.16)

-3.39e-06 (-1.25)

-1.64e-06 (-0.19)

Llinflation -.0060788

(-2.99)***

-.0080647 (-3.60)***

-.0083242 (-2.69)***

-.0038833 (-1.06)

Hausman test

0.4915

(3.41)

0.4915 (3.41)

0.1601

(7.93)

0.1601 (7.93)

0.0000 (60.03)

0.0000

(60.03) Not conclusive20 Not conclusive21

Type of estimation

Random

effects Fixed effects

Random

effects Fixed effects Random effects Fixed effects Random effects Fixed effects

Number of observations

334 334 332 332 111 111 110 110

Notes: Z statistics in parenthesis ***significant at 1% level, ** - significant at 5% level, *significant at 10% level Hausman Test - H0: difference in coefficients not systematic (thus individual effects not correlated with the explanatory variables)

20 Model fitted on these data fails to meet the asymptotic assumptions of the Hausman test, it could not satisfy assumption and cause such distortion due to the little sample properties 21 Model fitted on these data fails to meet the asymptotic assumptions of the Hausman test, it could not satisfy assumption and cause such distortion due to the little sample properties

17

implementation of the instrumental variable approach which is presented in the following

section.

Panel data results with the use of instrumental variable method

These estimations presented in the Table 5 correct for the causality problem and are

performed by the Instrumented Variable (IV) method due to the endogeneity of variables

GDP and FDI.

In columns (1), (2) and (4), according to the Hausman test, the specification of the

model should be estimated by the random effects method, which yields more reliable

estimates of the model coefficients. The fixed effect method in these cases is also consistent

but not fully efficient. Therefore, in this case the fixed effect method is less efficient and the

better choice is the use of random effect method. Only, in the column (3) the use of fixed

effect method is more appropriate.

In columns (1a) and (1b), IV estimation results are presented, where the instrument for

FDI is its one-year lagged value.

In the columns (2a) and (2b), I decided to instrument endogenous variable by the

indicator of economic freedom. This instrument is correlated with FDI, because multinational

firms will choose the country in which there is established a high degree of economic

freedom. This estimation is also more efficient in the case of the random effect method.

In the columns (3a) and (3b) FDI is instrumented by the variable which indicates the

industry level in the particular country. I decided to use this variable due to its variability in

time and the fact that the level of industry means the capacity of the market and therefore, it

could be the determinant in the decision process of foreign investors. The Hausman test

indicates that it is better to perform the fixed effect estimation. In this case is appropriate to

relax the assumption that the individual effects are not correlated with regressors and

implement the fixed-effect estimation.

In the columns (4a) and (4b), where the instrument is one-year lagged value of FDI, I

include also inflation. In this case more reliable results are obtained by the random effects

method.

In the columns (5a) – (6b) I made regressions on the group of CEE countries with the

use of one-year lagged value for FDI. The pattern of FDI partial effect in case of regressions

with the random effects remains similar, however it is recommended by the Hausman test the

usage of the fixed effect method on this dataset. Therefore, the impact of FDI on the rate of

18

Table 4. Impact of FDI on GDP growth per capita (panel estimation, IV method )

Notes:

Z statistics in parenthesis

***significant at 1% level, ** - significant at 5% level, *significant at 10% level

Hausman Test - H0: difference in coefficients not systematic (thus individual effects not correlated with the explanatory variables)

OECD countries CEE countries

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b) (6a) (6b)

Intercept

.08025

16

(1.68)*

2.367384 (2.55)**

.0129967

(0.26)

3.976068 (2.06)**

.0451853 (1.27)

2.116365

(2.11)** .0764264

(1.54)

2.762593 (3.00)***

.4477535 (2.54)**

17.67505

(4.18)*** .4540539 (2.66)***

15.81205

(3.36)***

Lfdi .0040669

(2.28)**

.0038547 (0.98)

.0100837

(2.90)***

.0311921 (2.09)**

.0005377 (0.14)

.0182375

(2.86)***

.0044092

(2.17)**

.0051783 (1.37)

.0131241 (1.64)*

-.0065034

(-0.35)

.0014534 (0.16)

-.0124851

(-0.69)

Usappp .2379025

(3.22)***

.2373128 (3.23)***

.6423378

(2.32)**

1.043109 (0.020)***

.2406926 (3.20)***

.2618414

(3.21)***

.222779

(2.89)***

.2110076 (2.76)***

-.0722929 (-0.24)

.2013413

(0.70)

.1517576 (0.51)

.2955507

(1.00)

Lhuman -.0018846

(-0.38)

.0139428 (1.40)

-.0184658

(-3.02)**

-.0117177 (-0.47)

.0010295 (0.21)

-.0027274

(-0.24)

-.0074403

(-1.40)

.0026339 (0.26)

-.0148675 (-0.69)

-.108155

(-2.92)***

-.0288637 (-1.34)

-.1134821

(-2.80)***

Lpop -.0039101

(-0.77)

-.1540041 (-2.61)**

.0072341

(1.29)

-.2436239 (-1.95)**

-.0020763 (-0.58)

-.1284437

(-2.02)**

.0001717

(0.03)

-.1710522 (-2.92)***

-.0232104 (-2.71)***

-1.065438

(-4.01)***

-.011654 (-1.25)

-.9419592

(-3.17)***

Initialppp -4.60e-07

(-1.05)

5.42e-07 (0.36)

-1.60e-07

(-0.33)

-6.18e-06 (-1.37)

-4.96e-07 (-1.52)*

-5.66e-06

(-2.33)**

-5.12e-07

(-1.15)

4.17e-07 (0.30)

-3.64e-06 (-0.97)

4.43e-06

(0.34)

-2.12e-06 (-0.57)

6.08e-06

(0.43)

Llinflation -.0039312

(-1.67)*

-.0051957 (-2.05)**

-.0094536 (-2.71)**

-.0042535

(-1.12)

Hausman test

0.1173

(7.38)

0.1173 (7.38)

0.2111

(5.84)

0.2111 (5.84)

0.0037 (15.51)

0.0037

(15.51)

0.1040

(9.13)

0.1040 (9.13)

0.0000 (60.03)

0.0000

(60.03)

0.0133 (14.39)

0.0133

(14.39)

Type of estimation

Random

effects Fixed effects

Random

effects Fixed effects

Random effects

Fixed

effects

Random

effects

Fixed effects

Random effects

Fixed

effects

Random effects

Fixed effects

Number of observations

334 334 116 116 316 316 334 334 111 111 110 110

19

economic growth also remains ambiguous. There are some fixed effects which have

unimportant influence on the economy.

Therefore, the main conclusion is that FDI has a low although positive impact on

economic growth in the OECD countries. In the case of CEE countries this impact is not so

clear. However, the impossibility of observing this impact may be due to short time series for

these countries and increasing importance in the recent years. Moreover, the performed

estimation method for the OECD countries is the random effects estimation, while for the

CEE countries a better technique is the use of fixed effects.

Conclusion

The contribution of this master thesis to the literature of the effects of foreign direct

investment inflow on the rate of economic growth is both theoretical and empirical. To

understand better the effects of FDI influence on the economic growth I extended the

endogenous growth model proposed by Barro and Sala-i-Martin (1997) by including foreign

direct investment into the analysis.

The FDI variable changed the dynamic behaviour of the model and changed the position

of the steady-state. The approximation of the behaviour around steady-state and solving the

dynamic system allowed me to derive the main research hypothesis, concerning the FDI

influence on the rate of economic growth per capita.

Then I tested my research hypothesis via various econometric methods such as OLS

and panel estimation with the implementation of the instrumental variables. Moreover, I made

tests for the appropriateness of the estimation method. I validated the estimations for the two

groups of countries: the OECD with long time series, and I also tried to estimate the influence

of FDI on the rate of economic growth of the CEEC group. The results for the OECD

countries generally confirm the positive influence of FDI on the GDP per capita growth

derived from the theory. However, the results for CEECs were not conclusive probably due to

the problem of data imperfections. Nonetheless, the estimation results suggest that openness

to foreign capital inflows can play an important role in the process of economic development.

However, we have to bear in mind that in the light of the macroeconometric evidence

there still exists the endogeneity problem. In particular, the issue of finding appropriate

instruments for FDI that are correlated with this variable and are not correlated with the

explained variable.

20

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24

Appendix 1. The Fraser Economic Freedom Index

Average 1981-2003

AUSTRALIA 7,5

AUSTRIA 7,1

BELGIUM 7,2

CANADA 7,7

CZECH REPUBLIC 4,1

DENMARK 7,1

FINLAND 7,3

FRANCE 6,6

GERMANY 7,3

GREECE 6,2

HUNGARY 6,0

ICELAND 6,8

IRELAND 7,4

ITALY 6,4

NETHERLANDS 7,5

NEW ZEALAND 7,6

NORWAY 6,9

POLAND 4,6

PORTUGAL 6,7

SLOVAK REPUBLIC 3,8

SPAIN 6,7

SWEDEN 6,9

SWITZERLAND 8,0

TURKEY 5,2

UNITED KINGDOM 7,7

Source: Own calculations based on the economic freedom index of the Fraser Institute

25

Appendix 2. Panel Unit Root Tests on the group of OECD countries

Variables Method

Null: Unit root (assumes common unit root process)

Statistic Prob.** Cross-sections Obs

Levin, Lin & Chu t* 3.21018 0.9993 25 472

Breitung t-stat -1.21961 0.1113 25 447

Null: Unit root (assumes individual unit root process)

PP - Fisher Chi-square 119.093 0.0000 25 519

FDI

Variables

Method

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -7.39842 0.0000 25 505

Breitung t-stat -4.56324 0.0000 25 480

GDP

Null: Unit root (assumes individual unit root process)

PP - Fisher Chi-square 156.121 0.0000 25 526

** Probabilities for Fisher tests are computed using an asympotic Chi

-square distribution. All other tests assume asymptotic normality.

26

Appendix 3. Panel Unit Root Tests on the group of CEE countries

Variables Method

Null: Unit root (assumes common unit root process)

Statistic Prob.** Cross-

sections Obs

Levin, Lin & Chu t* 0.00080 0.5003 14 176

Breitung t-stat

-3.38504 0.0004 14 162

Null: Unit root (assumes individual unit root process)

PP - Fisher Chi-square 28.6514 0.4304 14 189

FDI

Variables

Method

Null: Unit root (assumes common unit root process)

Levin,

Lin & Chu t* -

8.18166 0.0000 14 189

Breitung

t-stat -

2.56931 0.0051 14 175

GDP

Null: Unit root (assumes individual unit root process)

PP - Fisher Chi-square 71.8581 0.0000 14 197

** Probabilities for Fisher tests are computed using an asympotic Chi

-square distribution. All other tests assume asymptotic normality.