Academy of Economic Studies
Doctoral School of Finance and Banking
Foreign Direct Investments –
A Force Driving to Economic
Growth.
Evidence from Eastern European Countries
Supervisor:
Professor Moisă Altăr, PhD
Bucharest, 2010
MSc Student:
Oana Simona Caraman
Contents








Introduction
Literature Review
Model
Data and Methodology
Empirical Results
Conclusions
Suggestions for Further Research
References
1. Introduction

In this paper we intend to call into question the existing of a direct
and positive impact of foreign direct investments on economic
growth. We will resort to a panel data approach in order to capture
the continuously evolving country-specific differences, thus
eliminating many of the difficulties encountered in other types of
estimations.

We will focus on the economy of seven Eastern European
countries, namely: Romania, Bulgaria, Hungary, Poland, Moldova,
Czech Republic and Slovak Republic, for the period 1993-2008,
considering, by applying the methodology of panel cointegration
and causality, the presence of heterogeneity in the estimated
parameters and dynamics across countries.
2. Literature Review
2.1. Theoretical background

Neoclassical growth model (Solow, R (1957)) - FDI is
conceived as an addition to the capital stock of the target
economy. Considering this, we could state that the influence of
FDI on growth is similar to that of domestic capital: given the
diminishing returns on capital, FDI has just a temporary impact
on the target country’s growth rate.

Endogeneous growth model (Romer, P. (1986); Lucas,
R. (1988)) - underlines the role of science and technology,
human capital and externalities in economic development. FDI
impacts economic growth by acting as an engine of
technological diffusion coming from the developed world and
being directed towards the target country.
2.2. Empirical studies

Positive and significant correlation between FDI and economic
growth (Bende-Nabende and Ford (1998); Soto (2000); Lu and Liu
(2005))

Positive results, but conditional on home country’s levels of human
capital, infrastructure, financial market development, and so on
(Borensztein, De Gregorio, and Lee (1998); Olofsdotter (1998);
Nair-Reichert and Weinhold (2001); Bengoa and Sanchez-Robles
(2003) ; Lai, Peng and Bao (2006); Kinoshita and Lu (2006))

Insignificant or no relationship between foreign direct investments
and economic development (De Mello (1999); (Bende-Nabende
Ford, Santoso and Sen (2003); Laureti and Postiglione (2005);
Carkovic and Levine (2005); Onaran and Stockhammer (2008);
Lee and Chang (2009))
3. Empirical Model
GDPit    1FDI it   2 DI it  3TGit   4 INFit   it
where: εit is stochastic error term and β1, β2, β3, β4 are the
parameters to be estimated
and
GDP - gross domestic product per capita
FDI - net overall inflows of foreign direct investments
DI - domestic investments
TG - technological gap
INF - Infrastructure
4. Data
All data used in this paper were obtained from the World Development
Indicators 2009 of the World Bank.

GDP – gross domestic product per capita expressed in US dollars –
absolute values

FDI – net foreign direct investments inflows expressed in US dollars –
absolute values

DI – domestic investments expressed in US dollars – absolute values

TG – technological gap rendered as an economic gap, according to Li
and Liu (2004), as:
TGit 

GDPUSAt  GDPit
GDPit
INF – infrastructure reflected by appealing to PCA based on road
density, energy consumption and telephone lines.
In order to standardize our data we have used some variables in natural
logarithm (l_GDP, l_FDI and l_DI).
L_GDP
9.2
25
8.8
24
8.4
23
8.0
22
7.6
21
7.2
20
6.8
19
6.4
18
6.0
17
5.6
16
_RO - 93
_RO - 96
_RO - 99
_RO - 02
_RO - 05
_RO - 08
_BL - 95
_BL - 98
_BL - 01
_BL - 04
_BL - 07
_HU - 94
_HU - 97
_HU - 00
_HU - 03
_HU - 06
_PO - 93
_PO - 96
_PO - 99
_PO - 02
_PO - 05
_PO - 08
_MO - 95
_MO - 98
_MO - 01
_MO - 04
_MO - 07
_CH - 94
_CH - 97
_CH - 00
_CH - 03
_CH - 06
_SL - 93
_SL - 96
_SL - 99
_SL - 02
_SL - 05
_SL - 08
_RO - 93
_RO - 96
_RO - 99
_RO - 02
_RO - 05
_RO - 08
_BL - 95
_BL - 98
_BL - 01
_BL - 04
_BL - 07
_HU - 94
_HU - 97
_HU - 00
_HU - 03
_HU - 06
_PO - 93
_PO - 96
_PO - 99
_PO - 02
_PO - 05
_PO - 08
_MO - 95
_MO - 98
_MO - 01
_MO - 04
_MO - 07
_CH - 94
_CH - 97
_CH - 00
_CH - 03
_CH - 06
_SL - 93
_SL - 96
_SL - 99
_SL - 02
_SL - 05
_SL - 08
5. Empirical Analysis and Results
5.1. Basic information
L_FDI


For GDP, the highest ascension is to be attributed to Slovak
Republic and the lowest to Moldova.
Country
GDP- yearly avg. increase rate
RO
3.76 %
BL
3.44 %
HU
3.35 %
PO
4.47 %
MO
0.65 %
CH
2.98 %
SL
4.82 %
For FDI increase, top position comes to Bulgaria (as revealed by the
graphs), the lowest position belonging to Poland.
Country
FDI - yearly avg. increase rate
RO
21.90 %
BL
27.22 %
HU
15.01%
PO
6.54 %
MO
18.58 %
CH
9.13 %
SL
11.10 %
Hereinafter is presented the correlation between the variables
considered in this paper, that is l_GDP, l_FDI, l_DI, TG and INF.
Variables
L_GDP
L_FDI
L_DI
TG
INF
L_GDP
1.000000
0.783851
0.890490
-0.927451
0.134417
L_FDI
0.783851
1.000000
0.848868
-0.754339
0.214613
L_DI
0.890490
0.848868
1.000000
-0.879736
0.123664
TG
-0.927451
-0.754339
-0.879736
1.000000
-0.021078
INF
0.134417
0.214613
0.123664
-0.021078
1.000000
_RO - 93
_RO - 96
_RO - 99
_RO - 02
_RO - 05
_RO - 08
_BL - 95
_BL - 98
_BL - 01
_BL - 04
_BL - 07
_HU - 94
_HU - 97
_HU - 00
_HU - 03
_HU - 06
_PO - 93
_PO - 96
_PO - 99
_PO - 02
_PO - 05
_PO - 08
_MO - 95
_MO - 98
_MO - 01
_MO - 04
_MO - 07
_CH - 94
_CH - 97
_CH - 00
_CH - 03
_CH - 06
_SL - 93
_SL - 96
_SL - 99
_SL - 02
_SL - 05
_SL - 08
A more interesting graphic clearly rendering the relationship between
GDP and FDI is obtained by grouping in a single graph the gross
domestic product and the foreign direct investments series, by stacking
cross-sections.
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
L_FDI
L_GDP
5.2. Series Stationarity
We have started by performing a panel unit root test – Im, Pesaran,
Shin (IPS) which specifies a separate ADF regression for each cross
section:
i
yit  yit 1   ij yit  j  X 'it    it
j 1
where the null hypothesis (the series contains a unit root I(1)) might
be rendered as follows:
H 0 : i  0
for
i  1,2,...N
while the alternative hypothesis (some cross-sections do not have
unit root) shall be:
 i  0
H1 : 
 i  0
for
i  1,2,...N1
for
i  N11 , N1 2 ,...N
Variables
IPS panel unit root test
Level
1st difference
l_GDP
3.91016
(1.0000)
-1.55736
(0.0597)*
l_FDI
0.30892
(0.6213)
-4.29183
(0.0000)***
l_DI
0.95980
(0.8314)
-3.34402
(0.0004)***
TG
2.67458
(0.9963)
-1.41654
(0.0783)*
INF
0.79350
(0.7863)
-3.14637
(0.0008)***
P-values are in parenthesis. *, ** and *** show significance at 10%, 5% respectively 1% level.
The Null hypothesis is that series are non stationary.
The hypothesis that the variables contain a unit root cannot be
rejected. When first difference is used, unit root non-stationarity is
rejected at the 1%, respectively 10% significance level, resulting in all
series being I(1).
5.3. Parameter estimation
After having analyzed the series stationarity, we have proceeded to
the analysis of the parameter significance while resorting to the
following estimation methods:

Ordinary Least Squares (OLS)

Generalized Method of Moments (GMM)
Considering the specific features characterizing each country, it is not
quite suitable to use panel estimation methods with none effects. For
this reason, we also resort to fixed effects (FE) and random effects
(RE) estimates for both OLS and GMM methods, followed by a
Hausman test which may help us in selecting the most appropriate
model.
5.3.1. Fixed effects model
Suppose we have the following equation:
yit    xit  uit
In order to see how the fixed effects model works, we can decompose
the disturbance term, uit, into an individual specific effect, λi
(encapsulating all of the variables that affect yit cross-sectionally but
without varying over time) and the ‘remainder disturbance’, vit, which
varies over time and entities (capturing everything that is left
unexplained about yit).
uit  i  vit
Therefore we can rewrite the initial equation and obtain:
yit    xit  i  vit
5.3.2. Random effects model
Under the random effects model, the intercepts for each cross-sectional
unit are assumed to arise from a common intercept α (the same for all
cross-sectional units and over time), plus a random variable ηi that
varies cross-sectionally but is constant over time, where ηi measures
the random deviation of each cross-section’s intercept term from the
intercept term α.
Unlike the fixed effects model, the random effects one does not capture
the heterogeneity in the cross-sectional dimension by means of dummy
variables but via the ηi terms.
5.3.3. Hausman test
The generally accepted way of choosing between fixed and random
effects is running a Hausman test.
The Hausman test checks a more efficient model against a less
efficient but consistent model to make sure that the more efficient
model also gives consistent results.
H 0:
H 1:
both estimators are consistent, but the random effect estimator
is more efficient (has smaller asymptotic variance) than the
fixed effect one.
one or both of these estimators is/are inconsistent.
If we accept the null hypothesis, the random effects model shall prevail.
OLS and GMM Estimation with no effects
Dependent variable: d_ l_GDP
Variables
OLS estimation
GMM estimation
d_l_FDI
0.002612
(0.0027)***
0.006296
(0.0001)***
d_l_DI
0.013771
(0.0007)***
0.018168
(0.0097)***
d_TG
-0.886651
(0.0000)***
-0.814641
(0.0000)***
d_INF
0.000793
(0.0409)**
0.002267
(0.0020)***
c
0.031741
(0.0000)***
0.027988
(0.0000)***
P-values are in parenthesis. ** and *** show significance at 5%, respectively 1% level.
OLS and GMM Estimation with fixed effects
Dependent variable: d_ l_GDP
Variables
OLS estimation
GMM estimation
d_l_FDI
0.002660
(0.0004)***
0.004884
(0.0002)***
d_l_DI
0.014097
(0.0000)***
0.008697
(0.0859)*
d_TG
-0.893748
(0.0000)***
-0.815785
(0.0000)***
d_INF
0.000984
(0.0068)***
0.001465
(0.0213)**
c
0.030455
(0.0000)***
0.029294
(0.0000)***
P-values are in parenthesis. *, ** and *** show significance at 10%, 5% and 1% level.
OLS and GMM Estimation with random effects
Dependent variable: d_ l_GDP
Variables
OLS estimation
GMM estimation
d_l_FDI
0.005167
(0.0892)*
0.005134
(0.0000)***
d_l_DI
0.020164
(0.0037)***
0.012825
(0.0000)***
d_TG
-0.846806
(0.0000)***
-0.832185
(0.0000)***
d_INF
0.001368
(0.0849)*
0.001103
(0.0000)***
0.029734
(0.0000)***
0.027972
(0.0000)***
c
P-values are in parenthesis. * and *** show significance at 10%, respectively 1% level.

As we have just seen, foreign direct investments, direct
investments and infrastructure are significant and exert a positive
influence on the gross domestic product in each and every case,
while higher the technological gap between a leading country and
country i determines, as expected, lower gross domestic product
per capita.

As it can be seen from the tables above, the results are highly
similar and significant for both OLS and GMM estimation, no
matter if none, fixed or random effects are used, therefore
indicating the robustness of such results.
Yet, we have tried to see whether the fixed or random effects models
are more appropriate for our analysis, resorting for this end to the
Hausman test.
OLS estimation
Cross-section random
Hausman test
2.092040
(0.7188)
GMM estimation
Cross-section random
Hausman test
1.713709
(0.7882)
As p-value indicates us that in both cases the null hypothesis is to be
accepted, we assume that the random effect model is both consistent
and more efficient and it shall prevail.
5.4. Panel Cointegration Testing
Given that all series considered are I(1), we have tested the
cointegration relationship, by appealing to Pedroni cointegration test,
which has extended the framework of Engel-Granger in order to test
cointegration in panel data into two steps:

It starts with computing the residual from the regression equation:
yit    1i X 1it   2i X 2it  ...   ni X nit   it
If the series are cointegrated, this term should be a stationary
variable. Thus, stationarity is achieved by testing whether ρit is unity in:

 it  i it 1  vit
The null hypothesis, associated with Pedroni's test procedure is:
H 0 : i  1
for
i  1,2,...N
The alternative hypothesis for between dimension would be:
H1 : i  1
for
i  1,2,...N
While for the within dimension would be:
H1 : i    1 for
i  1,2,...N
Pedroni has developed seven tests for cointegration in panel data,
where there is more than one independent variable in the regression
equation:

four such tests are based on within dimension statistics (panel vstat, panel rho-stat, panel pp-stat and panel adf-stat)

three on between dimension statistics (group rho-stat, group ppstat and group adf-stat)
The non-parametric and parametric tests (panel pp-stat and grouppp stat, panel adf-stat and group adf-stat) are deemed to be more
powerful for smaller time dimensions (Bonham and Gangnes
(2007); Salotti (2008)).
Statistic
Probability
Weighted statistic
Probability
Panel v-stat
-1.990607
(0.9767)
-2.174568
(0.9852)
Panel rho-stat
0.706167
(0.7600)
0.819825
(0.7938)
Panel pp-stat
-1.686893
(0.0458)**
-1.437406
(0.0753)*
Panel adf-stat
-2.685765
(0.0036)***
-2.669599
(0.0038)***
Regressors: l_GDP, l_FDI, l_DI, TG, INF
P-values are in parenthesis. * , ** and *** show significance at 10%, 5% and 1% level.
Statistic
Probability
Group rho-stat
1.653956
(0.9509)
Group pp-stat
-2.829460
(0.0023)***
Group adf-stat
-5.161905
(0.0000)***
Regressors: l_GDP, l_FDI, l_DI, TG, INF
P-values are in parenthesis. *** shows significance at 1% level.

Given that our time series observations are restricted to 16 years
(1993-2008), we shall consider the non parametric and parametric
results - panel pp-stat and group pp-stat, respectively panel adfstat and group adf-stat.

The conclusion drawn is that, for a significance level of 10%, 5%
respectively 1%, the null hypothesis of no cointegration is to be
rejected, resulting in a cointegration relationship of the variables
concerned.
5.5. Granger Causality

The approach of Granger (1969) relating to whether x causes y is to
see how much of the current y may be explained by the past values of
y and subsequently to see whether, by adding lagged values to x, we
succeed in improving the explanation of y.

Granger causality runs, for all possible pairs of (x,y) series in the
group, bi-variate regressions of the form:
yt   0  1 yt 1  ...   j yt  j  1 xt 1  ...   j xt  j   t
xt   0  1 xt 1  ...   j xt  j  1 yt 1  ...   j yt  j  vt

The null hypothesis is, for the first regression, that x does not Granger
– cause y and, for the second regression, that y does not Granger –
cause x, meaning:
1   2  ...   j  0
l _ GDPit   0   i1l _ GDPi (t 1)  i1l _ FDI it  i 2l _ FDI i (t 1)   it
l _ FDI it   0   i1l _ FDI i (t 1)  i1l _ FDI it  i 2l _ FDI i (t 1)  vit
Null hypothesis:
F-statistic
Probability
l_FDI does not Granger cause l_GDP
7.97217
(0.0057)***
l_GDP does not Granger cause l_FDI
5.25510
(0.0239)**
P-values are in parenthesis. ** and *** show significance at 5%, respectively 1% level.
As revealed above, at a significance level of 1%, respectively 5%,
there is a bi-directional causality between GDP and FDI.
6. Conclusions
In this paper we have examined, by using the panel
cointegration and causality approach, the relationship existing
between foreign direct investments and economic growth for
seven Eastern European countries, drawing the following
conclusions.

There is a positive impact of FDI on economic growth.

The analyzed variables are cointegrated, witnessing for a
long-run relationship.

The causality between FDI and GDP per capita is bidirectional.
7. Suggestions for further research

Our regression could be extended by introducing also the
schooling variable (SCH), therefore reflecting the level of
education of the target country, and thus its absorptive
capacity.

Interaction terms such as FDI*TG*INF, FDI*TG*SCH and
FDI*TG*INF*SCH, meaning the technological spillover of FDI
conditional on infrastructure, on educational level, respectively
on both infrastructure and educational level could be used in
order to render the indirect impact of FDI on GDP.
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Appendix
_RO
_BL
_HU
24
24
28
20
20
24
16
16
20
12
12
16
8
8
12
4
4
1994
1996
1998
2000
2002
2004
2006
2008
8
1994
1996
1998
2000
_PO
2002
2004
2006
2008
1994
1996
1998
_MO
24
20
2000
2002
2004
2006
2008
2002
2004
2006
2008
_CH
20
24
16
20
12
16
8
12
16
12
8
4
4
1994
1996
1998
2000
2002
2004
2006
2008
8
1994
1996
1998
2000
2002
2004
2006
2008
_SL
24
20
16
12
8
1994
1996
1998
2000
L_GDP
2002
2004
L_FDI
2006
2008
1994
1996
1998
2000
35
30
25
20
15
10
5
93
94
95
96
97
98
_RO-L_GDP
_BL-L_FDI
_PO-L_GDP
_MO-L_FDI
_SL-L_GDP
99
00
01
02
_RO-L_FDI
_HU-L_GDP
_PO-L_FDI
_CH-L_GDP
_SL-L_FDI
03
04
05
06
07
_BL-L_GDP
_HU-L_FDI
_MO-L_GDP
_CH-L_FDI
08