FDI and ECONOMIC GROWTH: Granger Causality Tests in Panel Data
Model-Comparative results in the case of European Union countries EU
(European Union countries) and ASEAN Association of South East Asian
Nations
EEFS2008-CONFERENCE
June 2009-Warsaw-Poland
Argiro Moudatsou 1
Assistant Professor, Technological Educational Institute of Crete
Management and Economics School
Tourism Department
Dimitrios Kyrkilis2
Associate Professor, University of Macedonia, Department of Balkan, Slavic, and
Oriental Studies
Abstract
This study represents an attempt to address the causal-order between inward FDI and
economic growth using a panel data set for two different Economic Associations that
is EU (European Union) and ASEAN (Association of South Eastern Asian Nations)
over the period 1970-2003
The inflows of FDI to developed host countries raise the question of how these
inflows affect their economies and what is the interaction between FDI and growth.
While there is considerable evidence on the link between FDI and Economic Growth ,
the causality between them has not been investigated in a reasonable procedure.
Three possible cases are investigated in this paper 1) Growth-driven FDI, is the case
when the growth of the host country attracts FDI 2) FDI-led growth , is the case when
the FDI improves the rate of growth of the host country and 3) the two way causal
link between them.
Empirical results obtained from heterogeneous panel analysis indicate the following
Regarding the EU countries the results
support the hypothesis of GDP -FDI
causality (growth driven FDI) in the panel
Regarding the ASEAN there is evidence that there is a two ways causality between
GDP per capita and FDI in the cases of Indonesia and Thailand while in the cases of
Singapore and the Philippines FDI is host country GDP growth motivated.
JEL Classification, F21, F23,O52 Keywords: Foreign Direct Investment, Economic
Growth, Cointegration, Error-correction models and Causality.
1
Postal address: Markou Avgeri 3, 71202 Heraklion Crete- Greece, Tel 30 2810 284682 , e-mail:
moudatsou@sdo.teicrete.gr
2
Egnatia 156, P.O. Box 1591, Thessaloniki, Greece 540 06, Tel. +30 2310 891473, e-mail:
kyrkilis@uom.gr
1
1. Introduction
The relationship between foreign direct investment (FDI) and economic growth is a
well-studied subject in the development economics literature, both theoretically and
empirically. Recently, renewed interest in growth determinants and the considerable
research on externality-led growth, with the advent of endogenous growth theories
(Barro, 1991; Barro and Sala-i-Martin, 1995), made it more plausible to include FDI
as one of the determinants of long run economic growth.
The notion of an investment -development path puts forward the idea that the outward
and inward FDI position of a country is systematically related to its economic
development relatively to the rest of the world. It suggests that countries tend to go
through five different stages of development and that these stages can functionally be
classified according to the propensity of outward and/or inward investments (Dunning
and Narula 1994, World Investment Report 1995,. pp237, UN). This propensity, in
turn, rests on the extent and pattern of the ownership -specific advantages of each
country’s indigenous firms, its location advantages and the extent to which indigenous
and foreign firms choose to utilise their ownership-specific advantages jointly with
the country’s location-bound endowments through an internalization of the crossborder markets for these advantages.
Existing empirical evidence, in contrast with more settled theoretical evidence, shows
mixed results about the relationship between FDI and economic growth of the host
countries. Several reasons may be advanced to explain such disparity of empirical
results. To mention a few, first, tests are traditionally conducted using data sets
usually belonging to heterogeneous groups of countries while empirical studies use a
variety of theoretical models. Second, empirical studies have usually implemented a
number of different econometric techniques in testing theoretical models. However,
this disparity in results does not preclude the need for further investigation of the
subject as long as it is clearly indicated that the analysis and the obtained results are
not necessarily generalized to other cases.
2
In this paper the aim is to test for the causal relationship between FDI flows and
economic growth for two different groups of countries. The first group is comprised
by developing countries, which are members of the Association of South East Asian
Nations (ASEAN) and the second group contains a number of fifteen developed
countries, which are members of the European Union (EU-15). The idea is to compare
the empirically identified relationship between these two different country groups.
Inspired by previous results about the impact of FDI on growth the paper seeks to
identify systematic patterns in the size of the long run impact of FDI on GDP and/or
the opposite, and, in addition to relate the direction of impact on the economic and
technological conditions of host countries3 as they differentiate between developed
and developing countries. Initially, it is investigated the existence of co integration
between GDP growth and FDI flows using the heterogeneous panel co integration test
developed by Pedroni (1997, 1999), which allows for cross-sectional interdependency
among different individual effects. Next, an Error Correction Model is applied in
order to detect the causality between the two variables.
2. Recent literature
During the last decade a number of interesting studies of the role of foreign direct
investment in stimulating economic growth has appeared. De Mello (1997) lists two main
channels through which FDI may be growth enhancing. First, FDI can encourage the
adoption of new technologies in the production process through technological spillovers.
Second, FDI may stimulate knowledge transfers, both in terms of labour training and skill
acquisition and by introducing alternative management practices and better organizational
arrangements. A survey by OECD (2002) underpins these observations and documents
that 11 out of 14 studies have found FDI to contribute positively to income growth and
factor productivity. Both de Mello and OECD stress one key insight from all studies
reviewed: the way in which FDI affects growth is likely to depend on the economic and
technological conditions in the host country. In particular, it appears that developing
countries have to reach a certain level of development, in education and/or infrastructure,
before they are able to capture potential benefits associated with FDI. Hence, FDI seems
to have more limited growth impact in technologically less advanced countries.
3
See De Mello (1997)
3
Four studies, relying on a variety of cross-country regressions, have looked into the
necessary conditions for identifying a positive impact of FDI on economic growth.
Interestingly, they stress different, though closely related, aspects of development. First,
Blomström et al.(1994) argue that FDI has a positive growth-effect when a country is
sufficiently rich in terms of per capita income. Second, Balasubramanyam et al. (1996)
emphasize trade openness as being crucial for acquiring the potential growth impact of
FDI. Third, Borenztein etal. (1998) find that FDI raises growth, but only in countries
where the labour force has achieved a certain level of education. Finally, Alfaro et al.
(2004) draw attention to financial markets as they find that FDI promotes economic
growth in economies with sufficiently developed financial markets. However, when
Carkovic and Levine (2002) estimate the effects of FDI on growth after controlling for
the potential biases induced by endogeneity, country-specific effects, and the omission of
initial income as a regressor, they find, using this changed specification that the results of
these four papers break down. Carkovic and Levine conclude that FDI has no impact on
long run growth.
Another strand of the literature has focused more directly on the causal relationships
between FDI and growth and, at least, six studies have tested for Granger causality
between the two series using different samples and estimation techniques. Zhang (2001)
looks at 11 countries on a country-by-country basis, dividing the countries according to
the time series properties of the data. Tests for long run causality based on an error
correction model, indicate a strong Granger-causal relationship between FDI and GDPgrowth. For six counties where there is no co integration relationship between the log of
FDI and growth, only one country exhibited Granger causality from FDI to growth.
Chowdhury and Mavrotas (2003) take a slightly different route by testing for Granger
causality using the Toda and Yamamoto (1995) specification, thereby overcoming
possible pre-testing problems in relation to test for co integration between series.2 Using
data from 1969 to 2000, they find that FDI does not Granger cause GDP in Chile,
whereas there is a bi-directional causality between GDP and FDI in Malaysia and
Thailand. De Mello (1999) looks at causation from FDI to growth in 32 countries of
which 17 are non-OECD countries. First he focuses on the time series aspects of FDI on
growth, finding that the long run effect of FDI on growth is heterogeneous across
countries. Second, de Mello complements his time-series analysis by providing evidence
from panel data estimations. In the non-OECD sample he finds no causation from FDI to
4
growth based on fixed effects regressions with country specific intercepts, and a negative
short run impact of FDI on GDP using the mean group estimator.
Nair-Reichert and Weinhold (2001) test causality for cross country panels, using data
from 1971 to 1995 for 24 countries. Like de Mello, they emphasize heterogeneity as a
serious issue and, therefore, use what they refer to as the mixed fixed and random (MFR)
coefficient approach in order to test the impact of FDI on growth. The MFR approach
allows for heterogeneity of the long run coefficients, thereby avoiding the biases
emerging from imposing homogeneity on coefficients of lagged dependent variables.
They find that FDI on average has a significant impact on growth, although the
relationship is highly heterogeneous across countries.
Choe (2003) uses the traditional panel data causality testing method developed by HoltzEakin et al. (1988) in a data set of 80 countries. His results points towards bi-directional
causality between FDI and growth, but he finds the causal impact of FDI on growth to be
weak. Basu et al. (2003) addresses the question of the two-way link between growth and
FDI. Allowing for country specific co integrating vectors as well as individual country
and time fixed effects they find a co integrated relationship between FDI and growth
using a panel of 23 countries. Basu et al. emphasise trade openness as a crucial
determinant for the impact of FDI on growth, as they find two-way causality between FDI
and growth in open economies, both in the short and the long run, whereas the long run
causality is unidirectional from growth to FDI in relatively closed economies.
Hansen and Rand (2004) using a sample of 31 developing countries and using
estimators for heterogeneous panel data, found a bi-directional causality between
FDI/GDP and the level of GDP. They interpret this result as evidence in favour of
hypothesis that FDI has an impact on GDP via knowledge transfers and adoption of
new technology. MAhmoud Al-Iriani and Fatima Al-Shami (2007) testing for the
relationship between FDI and growth in the six countries comprising the Gulf
Cooperation and using heterogeneous panel analysis methods indicate a bi-directional
causality. Their results support the endogenous growth hypothesis for this group of
countries.
Summing up, the main message to take from this selective survey is that there seems
to be a strong relationship between FDI and growth. Although the relationship is
5
highly heterogeneous across countries, the studies generally agree that FDI, on
average, has an impact on growth in the Granger causal sense. The main exception
from this general conclusion is Carkovic and Levine (2002).
3. Empirical Methodology, Variables, and the Data Set
In this paper we estimate the causality between FDI and economic growth in two
country groups, namely the EU-15 group, i.e. Austria, Belgium, Cyprus, Denmark,
Finland, France, Germany, Greece, Ireland, Italy, Malta, Netherlands, Portugal, Spain,
Sweden, and United Kingdom; and the ASEAN group, i.e. Indonesia, Singapore,
Philippines, and Thailand. The remaining ASEAN member countries, i.e. Malaysia,
Brunei, Lao, Myanmar, Cambodia and Vietnam are not considered because there are
numerous missing observations the period under investigation, i.e. 1970 – 2003.
The variable economic growth is approximated by the growth of the GDP per capita
of country i at a particular time t. The variable FDI is approximated by the ratio of
FDI inflows to country I at a time t OVER the Gross Fixed Capital Formation in
country i at a time t.
The data used are annual and they are sourced in the International Monetary Fund
(IMF) and PENNTABLES databases.
The estimation is conducted performing the following tests.
First, the order of
integration of the GDP and FDI time series is tested using the Johansen’s approach.
Then, after correcting the time series for stationarity the heterogeneous panel Pedroni
(1997, 1999) co integration test is performed for the economic growth and FDI
variables. The Pedroni test allows for cross-sectional independency among different
individual effects. Second, in order to detect the direction of causality between the
two variables the technique of Error Correction Mechanism is applied.
3.1. Heterogeneous Panel Cointegration
The concept of co integration was first introduced into the literature by Granger
(1980). Co integration implies the existence of a long-run relationship between
economic variables. The principle of testing for co integration is to test whether two
or more integrated variables deviate significantly from a certain relationship (Abadir
and Taylor, 1999). In other words, if the variables are co integrated, they move
6
together over time so that short-term disturbances will be corrected in the long-term.
This means that if, in the long-run, two or more series move closely together, the
difference between them is constant. Otherwise, if two series are not cointegrated,
they may wander arbitrarily far away from each other (Dickey et. al., 1991).
Further, Granger (1981) showed that when the series becomes stationary only after
being differenced once (integrated of order one), they might have linear combinations
that are stationary without differencing. In the literature, such series are called “co
integrated”. If integration of order one is implied, the next step is to use co integration
analysis in order to establish whether there exists a long-run relationship among the
set of the integrated variables in question. Earlier tests of co integration include the
simple two-step test by Engle and Granger (EG hereafter) (1987). However, the EG
method suffers from a number of problems. Alternatively, Engle and Yoo (1987) (EY,
hereafter) 3-step procedure have been widely recognized as dealing with most of these
problems. Nevertheless, a problem remains which is that both EG and EY methods
cannot deal with the case where more than one co integrating relationship is possible.
Hence, Johansen’s Vector Auto Regression (VAR) test of integration (Johansen,
1988) uses a ‘systems’ approach to co integration that allows determination of up to r
linearly independent co integrating vectors (r  g-1, where g is the number of
variables tested for co integration). The Johansen’s procedure is useful in conducting
individual co integration tests, but does not deal with co integration test in panel
settings.
Recognizing the shortcomings of traditional procedures, this study utilized the two
types of the heterogeneous panel co integration test developed by Pedroni (1997,
1999) which, in addition to using panel data thereby overcoming the problem of small
samples, allows different individual cross-section effects by allowing for
heterogeneity in the intercepts and slopes of the co integrating equation.
Pedroni’s method includes a number of different statistics for the test of the null of no
co integration in heterogeneous panels4. The first group of tests is termed “within
dimension”. It includes the panel-v, panel rho(r), which is similar to the Phillips, and
Perron (1988) test, panel non-parametric (pp) and panel parametric (adf) statistics.
4
Interested readers may refer to Pedroni (2004) for details and mathematical representations of the
tests.
7
The panel non-parametric statistic and the panel parametric statistic are analogous to
the single-equation ADF-test. The other group of tests is called “between
dimensions”. It is comparable to the group mean panel tests of Im et al. (1997). The
“between dimensions” tests include four tests: group-rho, group-pp, and group-adf
statistics.
The seven of Pedroni’s tests are based on the estimated residuals from the following
long run model:
m
yit  i    ji x jit   it
j 1
Where  it   i  i ( t 1)  wit are the estimated residuals from the panel regression.
The null hypothesis tested is whether  i unity is. The seven statistics are normally
distributed. The statistics can be compared to appropriate critical values, and if critical
values are exceeded then the null hypothesis of no-cointegration is rejected implying
that a long run relationship between the variables does exist.
3.2. Causality Tets
Pedroni’s heterogeneous panel cointegration method tests only for the existence of
long run relationships. The tests indicate the presence or absence of long run links
between the variables, but do not indicate the direction of causality when the variables
are co integrated.
Having detecting the number of co integrated equations (Johansen’s procedure) we
used an error correction model (ECM) for a country by country analysis.
(Cointegration necessitates that the variables to be integrated are of the same order).
If the variables in the model contain unit roots, the Error Correction Model (ECM) is
used to examine the long-run or co integrating relationships between the time series as
well as the existence and the direction of causality between the variables.
The estimated bi-variate ECM for each country takes the following form:
ΔGit = α0 + ∑ α1i ΔGit-1 + ∑ α2iΔFDit-1+φECTit-1 +u1it
8
(1)
(i=1…n1)
(i=1…n2)
ΔFDit= b0 + ∑ b1i ΔFDit-1 + ∑ b2iΔGit-1+φECTit-1 +u2it
(i=1…n1)
(2)
(i=1…n2)
Where Δ is the difference operator, Gt is the GDP per capita, FDt is the FDI as
percentage to gross fixed capital formation, ECTit-1 is the error correction term
derived from the long- run co integrating relationship, u1t and u2t are the white noise
error terms t denotes the years and n1, n2 are the lag orders of α’s and b’s respectively.
The VECM results distinguish between short-run and long-run Granger causality. The
coefficients of the lagged error correction term show that there is a long-run causal
relationship between economic growth and FDI. It also indicates that FDI and
economic growth are adjusting to their long-run equilibrium relationships. The
coefficients (and the magnitudes) of the ECM indicate the speed of adjustment to the
long-run equilibrium relationship.
If φ is statistically significant in the first equation, but not significant in the second
then we say that FDI Granger causes GDP, if the opposite happens we say that GDP
Ggranger causes FDI. If φ is significant in both equations we say that there is a bidirectional relationship.
4. Results
4.1 Unit-root tests for stationarity
The first stage in testing for cointegration between a set of variables is to determine
the order of integration of individual time series that is, to determine how many times
a variable require inducing stationarity. “If a set of variables are co integrated then
there always exists an error correcting formulation of the dynamic model and vice
versa (Granger and Engle, 1985). So we run first the ADF tests for stationarity which
are performed under three hypotheses: The series are stationary at levels (no unit
root), at differencing once (one unit root) at differencing twice (two unit roots). The
series of economic growth and FDI for almost all countries in both samples have one
unit root so we take the first differences and then run cointegration tests.
9
In the case of the EU-group and in particular for the cases of Austria, Cyprus,
Denmark, Finland, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden,
United Kingdom. There were found two co integrated equations while in the cases of
Greece, Ireland, and Malta it was found on co integrated equation, and none co
integrated equation for Belgium, and Spain. In the case of the ASEAN group two
co integrated equation were found for all countries of the group.
4.2 Panel cointegration tests (PEDRONI)
We take the first difference of the series in both samples and then we run the
Pedroni’s test for the panel cointegration.
Table 1. Pedroni’s Heterogeneous Panel Cointegration Test Results
ASEAN-group
Test Statistics
Value
panel v-stat
0.19778
panel rho-stat
-7.77843
panel pp-stat
-11.80177
panel adf-stat
-9.37884
group rho-stat
-5.27172
group pp-stat
-11.31416
group adf-stat
-9.50582
All except the first one sre significant at 5%
level
Nsecs = 4, TPeriods = (unbalanced), no. regressors = 1
All reported values are distributed N (0, 1) under null of unit root or no cointegration.
Panel stats are weighted by long run variances
10
Table 2. Pedroni’s Heterogeneous Panel Co integration Test Results
EU group
Test Statistics
Value
panel v-stat
-0.08211
-14.49312
panel rho-stat
panel pp-stat
-26.13542
panel adf-stat
-16.91834
group rho-stat
-11.22026
group pp-stat
-28.26462
group adf-stat
-17.96417
All except the first one ore significant at
5% level
Nsecs = 13, TPeriods = (unbalanced), no. regressors = 1
All reported values are distributed N (0, 1) under null of unit root or no cointegration.
Panel stats are weighted by long run variances
The Pedroni’s tests indicate that there is a long-run relationship between FDI and
GDP for both Associations. We proceed to test for causality direction using Error
Correction model for each country (see the equations 1,2 in section 5 of this paper)
4.3 Causality through ECM models
The results from the error correction equations are reported below in Table 3:
11
Table 3. ECM Estimates of Causality between FDI and GDP
EU group
AIRTSUA
SUFSIR
DRAMASD
PUALAAD
PSAASR
IRSMAAU
ISRRSR
USRLAAD
UTALU
MALTA
ARTARSLAADR
FASTIIAL
RERDRA
IDF sescac PDU
sescac PDU IDF
IDF sescac PDU
PDU sescac IDF
PDU sescac IDF
IDF sescac PDU
IDF sescac PDU
IDF sescac PDU
IDF sescac PDU
IDF sescac PDU
yaseaetuen
IDF sescac PDU
DF sescac PDUI
UADAARRUA
TAAULAAD
RUAIAFASR
FAULUFUARR
Sescetuen uy ouen uaaseuuyc
Sescetuen uy ouen uaaseuuyc
IDF sescac PDU
IDF sescac PDU
ASEAN group
5. Conclusions
The findings confirm the strong and positive relationship between economic growth
of the host country and FDI inflows in both developed and developing countries.
However, this relationship runs from economic growth to FDI in the case of the
developed countries while the picture is mixed in the case of the developing countries.
In the ASEAN group both directions causality is detected in the cases of Indonesia
and Thailand compared with a conventional economic growth induced FDI inflow in
the cases of Singapore and the Phillipines. Economic growth is rather promoted by
technological spillovers and diffusion than any other mechanism advancing GDP. In
the case of developed economies host countries are already technologically advanced
and there is little scope for any impact of technologies transferred from the investing
foreign companies. On the contrary, in the case of developing nations there is a
12
technology gap between them and the FDI source countries, which are rather
developed ones, and there is scope for technology diffusion subject to the ability of
the host country to absorb and utilize the transferred technologies.
This ability
depends on the institutions, human capital, and other infrastructure available in the
country. The quality and quantity of these factors are both positively related to the
ability of adopted the imported technologies.
13
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