Direct and Indirect Effects of FDI in Emerging European Markets:

A Survey and Meta-analysis

Jan Hanouseka, EvženKočendab and MathildeMaurelc

Abstract
We review a large body of literature dealing with the effects of Foreign Direct Investment
(FDI) on economies during their transformation from a command economic system toward a
market system. We report the results of a meta-analysis based on the literature on externalities
from FDI. The studies on emerging European markets covered in our survey report direct and
indirect FDI effects weakening over time, similarly as in other FDI destination countries. This
is imputable to a publication bias that is detected and to the fact that more sophisticated
methods and more controls can be used once a sufficient time span is available. Panel studies
are likely to find relatively lower spillover effects. The choice of the research design
(definition of firm performance and foreign firm presence) matters. More specific to the
sampled studies is the role played by forward and backward spillovers, which dominate other
channels in driving FDI externalities.

Keywords: FDI; productivity spillovers; economic transformation; emerging markets; meta-

analysis

JEL classification: C42, D62, F21, F23, O3

a CERGE-EI, Charles University and the Academy of Sciences, Prague, Czech Republic; Anglo-American
University, Prague; The William Davidson Institute, Michigan; and CEPR, London. E-mail:
jan.hanousek@cerge-ei.cz.

b CERGE-EI, Charles University and the Academy of Sciences, Prague, Czech Republic; Anglo-American
University, Prague; Osteuropa-Institut, Regensburg; CESifo, Munich; The William Davidson Institute,
Michigan; CEPR, London; and the Euro Area Business Cycle Network. E-mail: evzen.kocenda@cerge-ei.cz.

c CES, University of Paris I, 106-112 Blvd de l’Hopital, 75013 Paris, France. Chaire de Finance Internationale.
E-mail: mathilde.maurel@univ-paris1.fr.

For helpful comments we are thankful to Nauro Campos, Jarko Fidrmuc, Renáta Kosová, Nargiza Maksudova,
Brano Saxa, Anastasia Shamshur, the Editor Richard Frensch and three anonymous referees. While preparing
this paper Hanousek and Kočenda benefited from GACR grant No. 402/091595. The usual disclaimer applies.

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1. Introduction

In this paper we review the literature on direct and spillover effects related to the presence of
multinational companies (MNCs) and of the associated Foreign Direct Investment (FDI). We
focus on the European economies that have undergone a transformation from a command
system towards a market system. As suggested in Campos and Coricelli (2002), the
experience of these countries foster many insights that can be generalized to other emerging
economies. On a macroeconomic level, the transformation process produced a dramatically
higher degree of openness (Matkowski, 2004). From a microeconomic perspective, foreign
investors participated in numerous forms of privatization that resulted in new ownership
structures and that impacted economic performance in varied ways, as evidenced in Estrin et
al. (2009). Emerging European economies began benefiting from FDI that brought not only
financial capital but also international experience, know-how, and promoted integration into
international networks of production and trade. According to Barrel and Holland (2000),
countries that opened their economies more widely to FDI reaped more gains from the
transition process.
FDI investment into emerging European markets was not uniform. FDI had different
effects in different countries due tothe variety of policies for attracting FDI (Demekas et al.,
2007) and varying initial conditions (Bijsterbosch and Kolasa, 2010). At the beginning of the
transition process the countries exhibited a great degree of heterogeneity and opted for
different transition paths, all of which however involved opening up to international trade and
capital. The consequences of the different strategies show in the overall productivity levels.
One can disentangle whether higher competition on internal markets faced by domestic firms
when confronted with very powerful international companies caused the expected output in
terms of improved access to markets, effective transfers of technology and know-how and
bringing domestic firms closer to the production frontier. Were domestic firms too weak to
compete in this new context? Were they crowded out? A recent report by the World Bank
(2006) distinguishes two sets of countries. The first set contains the geographically delimited
Central and Eastern European countries (CEECs) and also includes Baltic and Balkan
countries. The second set contains the countries belonging to the Commonwealth of
Independent States (CIS). The CEECs, in a more or less radical way, implemented new
market institutions, opened up to trade and capital, and made the necessary institutional steps
for entering the EU. In contrast, the CIS countries opted for more gradualist approaches. The
heterogeneity of results allows an assessment of the impact of trade and production

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integration, the impact of research and development (R&D), absorption capacity and
institutions on FDI efficiency and externalities.
From the policy perspective, the transition experience points to two key FDI
contributions that are emphasized by policymakers: direct effects on the productivity of firms
receiving foreign investment and spillovers produced by foreign-owned firms and positively
affecting local firms. Being able to track these effects to see whether they are significant at the
empirical level is of great importance as the potential of these effects often serves as a basis to
argue for the need for state aid provisions to foreign investors. For that reason this paper
focuses on the direct and spillover effects due to the presence of MNCs and FDI. There are
also other channels of spillovers, like exposure to international trade and R&D activities (see
Damijan et al., 2003b and Uzagalieva et al., 2010). Further, while the majority of studies
analyze FDI impact using production functions, Kosová (2010) employs a model that
combines a dominant firm-competitive fringe framework and a model of firm and industry
dynamics. Her results (on Czech data) show evidence of both technology spillover and
crowding-out effects suggesting that crowding out, and thus the adjustment of domestic firms
to FDI inflows, is just a one-time static effect realized upon foreign entry.
In any event, the findings in the current FDI literature that target post-transformation
economies in Central and Eastern Europe and the CIS produce evidence that is frequently
inconclusive due to various biases. Therefore, we perform a meta-analysis to show that while
direct effects are on average present, there is some dispute over the evidence for spillover
effects. The research design matters: there seems to be a publication bias and later studies
report less evidence for spillover effects. Also, the specificity of the transition experience may
rely on the importance of forward and backward spillovers in driving positive externalities.
As emphasized by Kinoshita (2000) and Damijan et al. (2003a), among others, education and
R&D channels are less conclusive.
The remainder of this paper is structured as follows. Section 2 presents some stylized
facts. Section 3 covers the basic definitions, introduces the baseline specification that
constitutes the basis for the meta-analysis, and lists the major econometric problems
encountered in the analysis of spillovers. Section 4 presents the sample of studies through a
review of the literature and section 5 displays the results of the meta-analysis. Section 6
summarizes the main results and suggests some conclusions.

2. Overview of trends and stylized facts

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According to Stiglitz (2000, p. 1076), “the argument for foreign direct investment is
compelling. Such investment brings with it not only resources, but technology, access to
markets, and (hopefully) valuable training, an improvement in human capital. Foreign direct
investment is also not as volatile – and therefore as disruptive – as the short term flows.”
Therefore multinational companies and the associated FDI may be less controversial by
promoting growth in emerging and developing countries and by constituting a driving force of
globalization, despite the recent trend reversal due to the financial crisis. Up to the year 2007,
the year in which a record global FDI inflow of USD 1.9 trillion was reached, transition
countries were the second-most important destination market for FDI, the first being
emerging Asia. While it is sometimes argued that FDI is more efficient in fairly advanced and
large host countries (see Mayer-Foulkes and Nunnenkamp, 2009), all continents (even Africa
as shown in Smeets, 2008; p. 109) benefited from this general increase in FDI (see Table 1).
Strong economic growth in most emerging markets in recent decades was driven by high
commodity prices and the good performance of equity markets. However, the situation
changed dramatically after the global financial crises of late 2008. Global FDI declined in
2008 and 2009 by 14% and 44%, respectively (UNCTAD, 2009; p. xix).
For transition economies specifically we observe a continuous trend of increasing FDI
over the period 2000–2007 (see Figure 2). Similar to most developing countries, 2007 was a
record year with a total inflow into transition countries reaching USD 158.5 billion. Similar to
other regions in the world, they did not escape the negative consequences of the global
financial crisis starting in 2008. As reflected in Figure 2, FDI in percent of GDP ranged
between 4 and over 5% of GDP over the period 2004–2007, but this percentage drops to less
than 3% for 2008, the lowest figure in 2000–2008. The decline is limited by a strong
exchange rate to the dollar, competitive labor costs as compared with the EU-15 and EU
membership, even though this is often over-emphasized. However, there are negative factors
hampering FDI prospects in some countries: political instability, disputed borders, and weak
states in the transition Balkans.

2.1 FDI, trade and international production networks

Strong FDI inflows in transition countries were driven first by massive privatization,
reinvested earnings, a real estate boom, commodity investments in some CISs and a very
strong FDI influx into Russia. According to the World Bank (2006), these inflows induced
technological and organizational spillovers, which changed the economic landscape facing

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industries and firms. FDI has been a key agent in the transformation from planned towards
market economies, through the creation of international production and trade networks.
Production sharing and spillovers have been growing, therefore, mainly in CEECs,
while most CIS countries have been left out of the process. The countries being integrated
into the networks benefited from bigger amounts of FDI: Tajikistan received only USD 0.3
billion of inward FDI inflows by the end of 2009, while the corresponding figure for Estonia
is 4.3 times higher, standing at USD 1.3 billion (see Table 2).
The shift of CEECs from unskilled-labor-intensive exports (clothing and furniture) to
capital-intensive exports (automotive and information technology industries) was driven by
sizable inflows of FDI and can be attributed to a better integration of the recipient countries
into the EU15-based networks of production. Amongst CEECs, Central European countries
perform better than Eastern European countries, which lag behind, as shown by Lefilleur and
Maurel (2010).
With the progress from transformation, the transition countries became more open and
engaged in international trade. As a majority of MNCs engaging in FDI produce for export,
the openness has further strengthened. Commodities are produced by market-seeking
investors and then re-exported towards EU-15 markets.

2.2 FDI and institutions

According to the World Bank (2006), EU-15 investors constituted more than 80% of CEE’s
inward FDI, of which half was invested into services. As a result of this massive foreign
investment, the region attained rapid growth in productivity and exports, developed a
financial sector, upgraded infrastructure and skills, and speeded up structural reforms.
The World Bank (2006) reports an apparently strong link between FDI and market-
oriented, open-trade policy regimes: a well-developed trade facilitation system, modernized
service sectors (such as transport and communication infrastructure), and trade and financial
services are considered important determinants of FDI. Further, liberalizing services such as
banking, telecommunications and transport allows the growth of service exports. More
importantly, higher standards of governance (as implemented under EU enlargement, for
instance) or the adhesion to labor rights (see Busse, Nunnenkamp and Spatareanu, 2010)
attract bigger FDI inflows and better quality investment. It is also widely agreed that a higher
level of corruption hinders growth through its impact on FDI (see Hellman, Jones and
Kaufman, 2002).

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3. Measurement of FDI effects and econometric issues
The impact of FDI presence is stronger if it produces effects beyond the enterprises where
FDI take place. In other words, it is stronger if the FDI can be translated into direct as well as
indirect (spillover) effects. According to most of the empirical findings, the direct effects of
FDI are quite straightforward and are reflected by new capital, technology and know-how. 1
The impact of direct effects are mostly studied on productivity, usually measured as a change
in Total Factor Productivity (TFP) or the labor productivity of the firm entered by the foreign
investor. The indirect effects of FDI are externalities (i.e. spillovers) to domestic companies
and industries and we review its essential forms presently.
FDI spillovers can be divided into horizontal and vertical. Horizontal spillovers are
externalities to domestic companies at the intra-industrial level. The entry of a company
whose productivity is driven by FDI encourages other companies within the same sector to
catch up in terms of performance and competitiveness. An increase inefficiency can happen
by copying new technologies or by hiring trained workers and managers from foreign-owned
companies (Javorcik, 2004). In contrast, vertical spillovers occur at the inter-industry level, as
in the case of technology transfers to domestic suppliers or customers in the production chain.
Companies operating in other sectors than the foreign enterprise are affected by the FDI
presence if they are in direct business contact with it through the supply and provision of
services. In most cases foreign companies require higher standards from their suppliers and
customers, including domestic companies. The efficiency of these domestic companies
therefore increases.
A spillover effect can be negative but it should not be always attributed to the lack of
absorptive capacity of domestic firms in less developed countries. It is rather a finding of “no”
spillovers (i.e. a lack of spillovers) that is likely driven by missing absorptive capacity. A
negative FDI impact should be rather attributed to competitive effects out-weighting any
potential positive spillover effects. The larger the technology and human capital gap between
domestic and foreign firms, the less likely domestic firms are to gain from the spillovers. This
is called the “gap” problem in the literature (Abramovitz, 1989; Fagerberg, 1994;
Gorodnichenko et al., 2007). Positive spillovers are found therefore in more technologically
advanced sectors or in more industrialized countries. There are other explanations behind
negative spillovers. Foreign investors can pick the best local company, allowing that company

1
It is important to distinguish between “takeovers” and “greenfields”. Takeovers usually start by improving the
acquired company’s organization and management; new technologies may arrive much later. Moreover, they are
likely to use the existing network of suppliers and customers. In contrast, greenfields often bring in state-of-the-
art technologies immediately and may not use local markets at all (Stancik, 2009).

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to dominate the market and crowd out other firms. Alternatively, an investor can choose an
industry with weaker local companies (Stancik, 2009). In either case negative horizontal
spillovers can occur. If foreign investors operate in the exporting industry, they do not have to
care about local companies within the same sector as they can find good suppliers and
concentrate on exporting. This may result in positive horizontal and backward spillovers.
Spillovers from foreign firms to domestic firms often take the form of technology
transfer through FDI. Blomström and Kokko (1998) distinguish three main channels for
technology transfers through FDI. The first channel is competition. According to Blomström
(1992), the entrance of foreign enterprises contributes to progression on the industrial,
technological and managerial levels. Placed in a more competitive environment, firms export
more. Or in the opposite way, MNCs may induce crowding-out effects and unfair
competition, which generates harmful externalities to domestic firms. Aitken and Harrison
(1999) first pointed to a “market-stealing” effect as a reason for finding the negative impact
from FDI when searching for spillovers in Venezuela. Later, Haddad and Harrison (1993)
tested these unwanted effects and reported evidence of such negative horizontal spillovers.
The second channel is the demonstration of differences in technology between foreign
investors and host-country firms. MNCs enter the host country market and establish affiliates
that possess better technology compared to the local firms’ technology. The local firms watch
and imitate these affiliates in the same industry, thus becoming more productive. The third
channel is labor turnover. The host country's citizens employed by the foreign investor might
benefit from contact with the new technologies and production methods. The transfer of
human capital, knowledge, and skills toward the host country labor force enhances the
competitiveness of domestic firms. MNCs train the local labor force, which is cheaper than
importing skilled labor from their home countries, even though, in most cases, they cannot
prevent a high labor turnover (Gorg and Greenaway, 2004).
To quantify the above direct and indirect effects, most of the empirical studies employ
following baseline model:

, . (1)

In terms of notation, i denotes the firm and j denotes the corresponding sector in which firm i
operates. Yit is an indicator of the economic performance of firm i. The performance can be
labor productivity estimated as real value added per worker (sectoral version applied by
Barrel and Holland, 2000; firm-level version in Schoors and Van der Tol, 2002), revenue,
6

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employment, cost per unit of revenue (Frydman, Gray, Hessel and Rapaczynski, 1999), output
growth, TFP, etc. Further, Xit denotes the determinants of this performance, such as inputs,
capital and labor, human capital, institutions, EU membership agenda, infrastructure, etc.
Then, Git stands for factors that produce direct effects, i.e. foreign ownership, majority
foreign ownership, or R&D expenditures of the firm measured as a ratio to total sales. Finally,
Zjt stands for sectoral spillovers stemming from the presence of foreign firms, i.e. the
employment share in foreign-owned firms, foreign output or the value-added share, the share
of assets held by foreign firms, or the share of sales by foreign firms, all in a corresponding
sector j in which firm i operates.

Econometric issues
3.1. TFP measurement
TFP is employed as a measure of firm productivity in most of the studies. Its measurement
can be biased by a problem of simultaneity, arising from the fact that a firm may observe (part
of) its productivity before the choice of inputs is made. Such a firm can then adjust the inputs
according to the observed productivity, which in the case of OLS estimation results in a bias
due to the correlation between the error term and the regressors. To correct for this
simultaneity problem, the approach of Olley and Pakes (1996) is employed by Evenett and
Voicu (2001) and Javorcik (2004). A similar way to correct for simultaneity proposed by
Levinsohn and Petrin (2003) is employed by Javorcik and Spatareanu (2008). Damijan et al.
(2003b) use a system GMM estimator and Konings (2001) applies a difference GMM
estimator.

3.2. Estimation biases

When estimating the effect of foreign presence on the productivity of domestic firms various
biases may arise.
Aggregation bias: In some cases, no data at the firm level is available, which leads to
estimation at the aggregate level, i.e. by province or industry. An important assumption in the
FDI spillover literature is that foreign-invested firms have better technology that spills over to
domestic firms through various channels. Thus, it is assumed that firms with foreign
investment are more productive than firms without foreign partners. In studies that use
aggregate data, foreign firms are frequently not excluded from the aggregates. In this way the
estimates of spillover effects obtained from aggregate regressions are subject to an upward
aggregation bias. Aggregation bias can be avoided by excluding foreign firms from the

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aggregates or by using firm-level data (Hale and Long, 2007). The studies that form the basis
of our subsequent meta-analysis do not suffer from this aggregation bias as they all use firm-
level data. We mention this type of bias for the sake of complete exposition.
Selection bias: This issue can be divided into two categories: sample-selection bias
and self-selection bias. Estimating the model on a subsample of domestic firms or using
aggregates that exclude firms with foreign investment is not without flaws as sample-selection
bias is present. 2 On other hand, decisions of foreign investors about the choice of firms to
enter are unlikely to be random, meaning that a simple comparison of productivity between
firms owned by domestic and foreign owners involves a self-selection bias. Both issues are
interconnected to a degree. Consider for example that FDI takes place as a merger and
acquisition rather than a greenfield investment. Foreign firms choose to invest in domestic
firms that are more productive ex ante (i.e. the “cherry-picking” phenomenon), as opposed to
investing at random and making firms more productive ex post. In this way, firm-level cross-
section regressions that are limited to domestic firms yield estimates of productivity spillovers
of FDI that are biased downward if “cherry-picking” is present. The same is true for aggregate
analysis that excludes foreign firms from the aggregates. Zajc (2006) analyses a firm's
probability of exiting. He emphasizes that foreign entrants are more productive than the
average firm and they exit more frequently, particularly those entering in the form of
acquisitions. He shows that the least efficient firms experience a drop in their survival
probability upon the entry of a foreign firm, and that foreign firm entry stimulates a selection
process not only within the industry but also through backward linkages in upstream
supplying industries. Moreover, there is more evidence of vertical than horizontal
productivity spillovers from foreign firms. In this respect Zajc (2006) and Kosová (2010)
found exactly the same results, i.e. a negative impact from FDI entry on the survival of the
Czech firms but positive spillovers afterwards.
Sample-selection bias can be addressed by estimating a model of sample selection,
which allows for the selection of the firms into domestic and foreign categories to be
correlated with the firm’s productivity. Of course, this approach requires firm-level data,
including data on firms with foreign investment. The Heckman (1979) selection model can be
employed, using, for example, a maximum likelihood estimation of two simultaneous
equations. However, caution has to be adopted as the Heckman two-step methodology is
directly applicable only when working with cross-sectional data. Use of the Heckman sample-

2
Vahter (2004) shows the presence of sample selection bias in the Slovenian case, but not in the Estonian case.

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selection methodology with panel data might be difficultfor two reasons: (i) Calculating
Mills’ ratio while taking into account repeated observations per unit of analysis (i.e. panel)
would be very complicated and traditional software packages are not designed deal with such
a situation. 3 (ii) It is not possible to control for firm fixed effects in the traditional way via
data de-meaning that is often desired to estimate the second stage structural equation. The
first stage equation in the Heckman’s approach relies on probit estimation but there is no
“fixed effects probit”. In sum, trying to control for “sample-selection bias” by employing the
Heckman methodology might not deliver reliable results when working with data sets
containing a time dimension.
Self-selection bias or “cherry-picking” seems to be an even bigger problem than
sample-selection. For example, foreign firms may target more efficient domestic firms or
industries to enter, or the most efficient domestic firms are able to benefit from spillovers.
The need to control for such an unobserved firm efficiency level or self-selection can be
resolved by collecting panel data and controlling for firm fixed effects. The “cherry-picking”
phenomenon is recognized in most of the empirical papers. For instance in Evenett and Voicu
(2001)’s benchmark model all sectors are together and use a balanced panel (i.e. only firms
that occur in the data every year over the selected period are used), and Heckman’s two-step
estimation is employed to correct for selection bias. To explain the choice of investors, the
financial data of firms from the year preceding the beginning of the sample period are used.
The authors find that foreign investors tend to choose the largest and most successful firms.
Heckman’s two-step method is also used by Djankov and Hoekman (2000) who suggest that
investors are more interested in acquiring firms with higher initial productivity. In contrast,
Damijan et al. (2003b), using the same method, find that size is insignificant in all countries
and that labor productivity is significant in only two out of ten countries. They find capital-
and skill-intensive firms to be preferred in seven countries. In addition, they show that foreign
investors tend to enter industries that already have a high concentration of foreign owners.
Similar conclusions are suggested in Damijan et al. (2003a), who find that in Estonia and
Slovenia, the perspective for export plays an important role in the decisions of foreign
investors.
Endogeneity: This problem leads to an upward bias in the estimates of the productivity
spillover of FDI. The best way to address this problem is to estimate a fixed-effect or
difference-in-differences model with individual firm fixed effects (Hale and Long, 2007). One

3
Calculating Mills’ ratio based on, say, only the first or the last observation of the panel is incorrect.

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must also ensure that the panel includes a large enough time span because FDI do not vary
much over time. Alternatively, an instrumental variables approach can be used through
employing 2SLS or GMM. When an independent set of instruments is not available the
Arellano and Bond (1991) technique is used: this methodology is applicable only when
estimating the dynamic panel equation with fixed effects; i.e. when the lagged dependent
variables are included among regressors. 4 However, this method is not generally applicable,
as is 2SLS or GMM, to resolve traditional endogeneity (or simultaneity) problems (i.e. when
the lagged dependent variable is not among the regressors). An example can be found in
Halpern and Muraközy (2005) who address the endogeneity bias by employing the Arellano-
Bond (1991) dynamic panel data technique. They analyze horizontal and vertical spillovers
through FDI in Hungary using a panel of 24,000 firms. There are significant horizontal and
backward spillovers for domestic-owned firms that imply benefits from foreign competitors
and customers. In contrast, the effect of regional and county boundaries is not significant. The
authors further estimate the spatial structure of spillovers: for domestic firms the foreign
presence matters only over a small distance (25 km), while for foreign-owned firms, the
longer the distance, the stronger the spillover (50 and 100 km).
Downward bias in standard errors: Since the measure of FDI presence is, by
definition, an aggregate measure, one must deal with the potential correlation of standard
errors in firm-level regressions (Moulton, 1990). If the standard errors are calculated based on
the assumption of i.i.d. disturbances, they will be biased downward, mistakenly leading to a
conclusion that the estimates are statistically significant even if they are not. This problem can
be easily remedied by computing robust standard errors clustered on industry i (Hale and
Long, 2007).

4. Review of the literature

In this section, we describe the major findings, techniques and data used in papers that
estimate the importance of both direct and indirect effects of FDI in transition countries.

4.1 Review of the empirical literature: Direct and spillover FDI effects
Direct Effects

4
It should be noted that Arellano-Bond (1991) technique tends to suffer from serious biases when most of the
panel variation comes from the “fixed effect “ as opposed to “idiosyncratic-error” type of variation, or when
coefficient of the autoregressive component (i.e. lagged dependent variable) tends to be close to one as discussed
by Blundell and Bond (1998). Hence, implications of studies using Arellano-Bond (1991) technique should be
interpreted with a caution.

10

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In terms of the direct effects of FDI, the majority of empirical findings are conclusive that
foreign presence is associated with better performance. Some studies analyze direct FDI
effects in the context of a single country. For example, direct FDI effects in the Czech
Republic are studied in Djankov and Hoekman (2000), who report that benefits are larger
when investment comes in the form of FDI with a direct foreign control rather than FDI in the
form of a joint venture with a domestic company. In the Czech context, Evenett and Voicu
(2001) argue that the estimated positive effects of FDI on performance are in some cases
unrealistically high, and that the lack of suitable variables leads to an unsatisfactory
estimation of self-selection. Using a data set of Hungarian firms, Sgard (2001) shows that
firms with foreign ownership outperform domestic firms.
In terms of empirical analysis, most of the papers focus on more than one country, for
comparison and generalization purposes. For instance, Konings (2001) analyzes firm-level
data from Romania, Bulgaria and Poland, and confirms that firms with some foreign
investment perform better than firms without foreign participation. Damijan et al. (2003a) and
Damijan et al. (2003b) provide comparable estimates of the impact of FDI on productivity for
seven and ten CEE countries, respectively. Damijan et al. (2003a) control for selection bias
and distinguish between spillovers occurring through innovative and absorptive capacity and
spillovers occurring through trade. Their results suggest that spillover effects bring important
technology transfers in five out of eight countries—the Czech Republic, Estonia, Poland,
Romania and Slovenia—while the impact of FDI is not significant in Bulgaria, Hungary and
Slovakia. 5 The effect of majority foreign ownership turns out to be insignificant in all eight
countries. Damijan et al. (2003b) confirm that the effect of FDI is mixed: significant and
positive in Hungary, Estonia and Slovenia, and significant and negative in the Czech Republic
and Poland. It is interesting to note that the differences between these two papers may be
imputable to different estimation techniques, and to the fact that the specifications estimated
are slightly different.
Two studies focus on labor productivity instead of TFP as a measure of productivity.
Vahter (2004) examines the effect of foreign ownership on the ratio of sales and employees in
Estonia and Slovenia. Besides the finding that foreign-owned firms are more productive than
their domestic counterparts in both countries, the authors look at the differences between

5
However, this is attributed to poor data quality in case of the latter two countries.

11

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exporting and non-exporting firms. In Estonia, export-oriented foreign-owned firms are less
productive, while the opposite result holds for Slovenia. 6

Spillover Effects
Spillover effects are interpreted as a transfer of knowledge and technology from a foreign-
owned firm to local firms. The presence of spillovers is empirically studied either on the intra-
industry level (horizontal spillovers) or the inter-industry level (vertical spillovers). The
variable of interest is the concentration of foreign investors in the same industry (horizontal)
or in the upstream/downstream industry (vertical). A summary of the empirical findings on
FDI spillovers is reported in Table 3. Contrary to the direct effects of FDI on performance, the
indirect effects are not clear-cut: the results differ according to the country or period analyzed
and the econometric methodology.
The estimation of spillover effects requires special attention to the specifics of the FDI
transfer mechanisms, such as:
i. MNCs invest in more profitable firms (selection bias), an issue which has been
investigated with special emphasis in all transition countries. This bias can be
controlled for in panel data analysis (as discussed in Section 3.2).
ii. The crowding-out effect: foreign firms have a higher production technology and
lower marginal costs, and attract demand away from domestic firms. Productivity
decreases (at least in the short run) because of competition.
iii. FDI spillovers occur chiefly in sectors with high R&D (Blomström and Kokko,
1998) but they also occur in sectors with low R&D intensity, either in general or
limited to European emerging countries (Nicolini and Resmini, 2010).
iv. Negative spillovers dominate in the early period, when crowding-out effects
dominate competition and demonstration effects. Local firms lose market share
and skilled employees are captured by foreign-owned firms. Only later are positive
spillovers, if any, more likely to occur.
v. Foreign owners have an incentive to prevent the leakage of knowledge and
technology to local competitors (in the same industry), but they may profit from
improvements on the side of their suppliers (backward spillovers). Also, local

6
In the only surveyed study that uses industry-level data, Barrel and Holland (2000) examine the effect of
foreign ownership on labor productivity, e.g. the total employment in a sector relative to the real value-added in
the sector. The countries covered are the Czech Republic, Hungary and Poland. It is shown that the presence of
FDI is positively correlated with labor productivity. After controlling for FDI, private ownership does not
increase labor productivity. Their study could be understood as a one dealing with indirect effects as it is a
sectoral analysis. We mention this work here due to its accent on labor productivity.

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 firms may benefit from using better intermediate input produced by the foreign-
owned firm (forward spillovers). The idea of searching for positive vertical
spillovers instead of horizontal spillovers was applied by Javorcik (2004).

There are several studies that cover the spillover effects in multiple transition
countries. Damijan et al. (2003a) report no significant horizontal spillovers, except in
Romania, even after controlling for absorptive capacity. On the contrary, Damijan et al.
(2003b) suggest that horizontal spillovers to domestic firms are significant and positive
although relatively small in the Czech Republic, Poland, Romania and Slovakia (out of the ten
countries studied). The authors report significant and positive backward vertical spillovers to
local firms in the case of the Czech Republic, Poland and Slovenia, but not in the other seven
countries. Konings (2001) finds no spillovers in Bulgaria and Romania and significantly
negative spillovers in Poland. Vahter (2004) finds evidence of horizontal spillovers in
Slovenia, but no horizontal spillovers in Estonia, which is in line with Vahter (2005). Javorcik
and Spatareanu (2005) analyze firms’ perceptions in the Czech Republic and Latvia. In the
Czech Republic (Latvia) 48% (41%) of respondents believed that the entrance of foreign-
owned firms increased competition in the sector, while 29% (29%) indicated they lost market
share. Positive spillovers are reported in the Czech Republic (Latvia) by 25% (15%) of the
firms that adopted new technologies and 12% (9%) of the firms that observed marketing
techniques. In a recent detailed country study Ayyagari and Kosová (2010) find that a larger
foreign presence in the Czech Republic stimulates the entry of domestic firms within the same
industry (positive horizontal spillovers from FDI). They also find evidence of significant
vertical entry spillovers—FDI in downstream (upstream) industries initiates entry in upstream
(downstream) sectors. Vertical spillovers are found stronger than horizontal ones that are
driven by FDI from the EU countries.
Tytell and Yudaeva (2006) focuses on the four most populous countries of Eastern
Europe: Russia, Ukraine, Poland, and Romania. The authors demonstrate that positive
spillovers occur only in the case of export-oriented FDI and that they are driven by more
productive foreign companies. They report evidence of threshold effects: benefits are more
likely to materialize when a larger stock of foreign capital is accumulated. Also the absorptive
capacity of domestic firms plays a crucial role in reaping the benefits of FDI. Finally both
knowledge spillovers and an improvement in production technology occur predominantly in
the more educated and less corrupt regions.

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 Franco and Kozovska (2008) test the presence of traditional forward and backward
horizontal spillovers in Poland and Romania. The authors introduced the concept of regional
clusters and examined two hypotheses: (1) whether the forward spillover effect is greater for
firms in clusters compared to non-clustered firms and (2) whether the backward spillover
effect actually takes place and if clusters have any impact. For estimation procedures the
authors employed data on more than 7000 manufacturing firms and compared OLS in first
differences with dynamic GMM model specifications. The results support the evidence of
positive cluster effects, and in particular there are reverse spillover effects found both in
clusters and outside clusters. The implication of these results is that the presence of clusters is
a determinant of FDI localization decisions since there is a chance of reverse spillovers even
if the host country does not possess higher technological capacity.
In their recent paper Damijan, Rojec, Boris and Knell (2008) employ the largest data
set so far (more than 90,000 firms) in ten transition countries: Bulgaria, the Czech Republic,
Croatia, Estonia, Latvia, Lithuania, Poland, Romania, Slovenia and Ukraine. From a
methodological point of view the authors control for various sources of firm heterogeneity,
and provide a correction for selection and simultaneity. The results suggest that horizontal
spillovers have become increasingly important over the last decade and could become more
important than vertical spillovers. Firm heterogeneity (i.e. absorptive capacity, size,
productivity and technology levels) matters while firms with higher absorptive capacities are
capable to both compete with foreign affiliates in the same sector and benefit from the
increased upstream demand for intermediates generated by foreign affiliates. Finally, FDI
presence could affect smaller firms to a greater extent than larger firms; this impact, however,
may be in either direction.
Most of the papers analyzing spillover effects focus on a single country. For the Czech
Republic, Djankov and Hoekman (2000) reports evidence of significant and negative
horizontal spillovers for both FDI and joint ventures. Kinoshita (2000) finds that horizontal
spillovers are limited to local firms involved in R&D. Stančík (2007) employs firm-level
panel data from 1995 to 2003 and studies horizontal/vertical spillovers. The paper considers
lagged spillovers and pays attention to the endogeneity of FDI with respect to future industry
growth. The results indicate that domestic firms suffer the most from the presence of foreign
companies, and the effect is more acute in upstream sectors. Horizontal and vertical spillovers
are negative and present mainly in recent years while time sensitivity is revealed for
horizontal spillovers. In a later study Stančík (2009) extends his previous paper by
distinguishing two types of foreign investment: takeovers and greenfields. He finds that the

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impact through horizontal spillovers is mixed: positive from foreign takeovers and negative
from greenfields. Forward spillovers are positive and present mainly in recent years while
time sensitivity is revealed for both horizontal and vertical spillovers.
For Poland, Kolasa (2007) uses an unbalanced panel of firms’ balance sheets and
profit-and-loss statements for the period 1996–2003, with a total of 147,479 observations. The
results are manifold. There is a positive benefit for local firms from FDI in the same
downstream industries, and the absorptive capacity of domestic firms, measured by their
investment in R&D, matters. Finally, higher competition facilitates spillovers from FDI in
downstream industries. The main policy implication, in line with Blomström and Kokko
(2003), is to support policies aimed at strengthening the absorptive capacity of domestic
firms. Golejewska (2009) used unbalanced panel data for 103 manufacturing industries during
1993–2006. By estimating two-way fixed effect and two-way random effect panel data
models, he reports no significant positive productivity spillovers from FDI. This result can be
compared with the previous findings of Zukowska-Gagelmann (2002) for 1993–1997 and
Ciolek and Golejewska (2006) for 1993–1998 for all Polish manufacturing firms. Using the
same methodology they find significant negative productivity spillovers. The authors consider
that the following factors can explain the lack of positive spillovers: the firm’s size, the
sectoral distribution of FDI, the insufficient investment into R&D by local firms, and
heterogeneity across industries.
For Hungary, Sgard (2001) finds positive spillovers and shows that export-oriented
foreign-owned firms produce more spillovers, suggesting this could stem from the fact that
such firms do not compete with local firms. In addition, Sgard (2001) finds spillovers more
pronounced in regions close to the EU border. For Estonia, Sinani and Meyer (2004) indicate
that labor- and sales-intensive foreign-owned firms generate larger spillovers than their
equity-intensive counterparts. Also, small firms, non-exporting firms and outsider-owned
firms are more likely to benefit from the presence of a foreign-owned firm.
Romania is investigated by Javorcik and Spatareanu (2008), who find positive
horizontal spillovers generated by firms fully owned by a foreign owner, but not by firms
partially owned by foreigners. For vertical spillovers, the results suggest that firms partially
owned by foreigners generate positive backward spillovers, while firms fully owned by
foreigners generate negative backward spillovers. The latter is interpreted as the result of the
different behavior of joint-venture investors who, unlike investors entering fully-owned
greenfield investments, more often source intermediate inputs from local firms. The impact of
structural breaks and environmental changes is emphasized in Schoors and Merlevede (2007),

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who focus on the period 1998–2001, when Romania experienced substantial structural
changes. The authors separate out labor market effects from other effects in their
identification of intra-industry spillovers, while inter-industry spillovers are identified through
upstream, downstream, and supply-backward spillover effects. Schoors and Merlevede (2007)
employ dynamic input-output tables to construct spillover linkages not only for
manufacturing but for all industries. This is justified by the fact that the lion’s share of foreign
affiliates in Romania operate in the services sector. The results suggest that labor market
effects differ from other intra-industry effects and spillovers across industries dominate those
within industries. Supply-backward effects match the predictions of Findlay and the
absorptive capacity hypothesis while the firm-specific level of technology, firm size and
ownership structure affect spillovers.
For Lithuania, Javorcik (2004) finds no evidence of horizontal spillovers or vertical
spillovers through forward spillovers, however, there are significant and positive vertical
spillovers through backward spillovers. Those are generated only by firms partially owned by
a foreign investor. Evidence of FDI impact for Ukraine is provided by Lutz, Talavera and
Park (2008). The authors employed unpublished panel data from 1996–2000 to investigate the
effects of a regional and industry-wide foreign presence on export volumes of domestic firms.
The results suggest that FDI presence may have negative competition effects on domestic
firms while productivity may be increased by technology transfer or through training and
demonstration effects.

5. Meta-Analysis
As we showed in the preceding section there exists a considerable heterogeneity of empirical
findings and inconclusive evidence on FDI spillover effects. In this section we run a meta-
analysis, to summarize in a straightforward and quantitative way the main findings from this
literature. Meta-analysis could shed more light on this issue and distinguish the reasons for
such heterogeneity among publications, including publication bias, methodological issues,
data availability and FDI measurement.

5.1 Previous research

Meta-analysis has not been used frequently in economics, because unlike fields such as
psychology or medicine, economic research is usually not based on experimental data.
Although one cannot argue that the transition is exactly like an experiment, the wide
heterogeneity across transition countries in opening to foreign capital and learning from

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international experience created quasi-experimental conditions and outcomes to be analyzed.
Such a meta-analysis is performed in Fidrmuc and Korhonen (2006) who focus on the
business cycle correlation between the euro area and the CEECs.
In Table 3 we list the results of our literature search. In our meta-analysis we include
papers that satisfy a combination of three criteria: they analyze direct effects, they analyze
spillovers, and they cover emerging European markets (transition economies). We disregard
studies that analyze only direct effects and also studies that use data about
emerging/developing countries, but not transition countries. Altogether we cover 21 papers,
which is the same number of studies used by Görg and Strobl (2001).
In the seminal work of Görg and Strobl (2001), the most important finding is that the
research design is essential for a proper analysis of productivity spillovers. First, panel data
studies turn out to be better by allowing quantification of the development of firms'
productivity over a longer time span, rather than focusing on only one year in cross-sectional
data. Second, the definition of the foreign presence variable included in the studies affects the
results. Finally, they introduce a new methodology for detecting publication bias, and report
evidence of such a bias.
Among the limited meta-analysis papers on FDI, Meyer and Sinani (2009) investigate
the reasons for the results of mitigated FDI effects on local performance. The authors argue
that cross-country differences may be driven by the use of aggregate versus firm-level data
and cross-section versus panel data analysis, implying that the research design matters for the
results. They report that spillovers are not found for industrialized countries in the 1990s,
while transition economies may experience spillovers, though declining in recent years.
Wooster and Diebel (2006) focus on developing countries. They conclude that spillover
effects are more pronounced when studies measure the effect of FDI spillovers on output.
Interestingly, they find that spillover effects are more likely to be more pronounced for Asian
countries, and that spillover effects may be partly a product of model misspecification.
Havránek and Iršová (2010) meta-analyze the literature on intra-industry productivity
spillovers from FDI. Their findings suggest that cross-sectional and industry-level studies find
relatively strong spillover effects, while the choice of a proxy for FDI is important. Papers
published in leading academic journals tend to report rather insignificant results. Contrary to
previous studies no publication bias is detected. Finally, a meta-analysis by Smeets (2008)
reveals mixed evidence on the magnitude, direction, and even existence of knowledge
spillovers from FDI. The results suggest that studies accounting for individual spillover
channels find robust evidence of knowledge spillovers from FDI, and studies on the

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importance of mediating factors and FDI heterogeneity are less conclusive. Our meta-analysis
delivers new results from a compact set of studies that satisfy three criteria. In this respect we
offer new insights compared to studies that cover wide and heterogenous samples of papers.

5.2 Methodology
The sample in our meta-analysis consists of 21 papers, 10 of which are published in academic
journals, 6 are contributions to an edited volume (base category), and 5 are working papers
(see Table 3 for a list of the studies included). The number of selected papers is a result of
rather restrictive criteria: we aimed to eliminate studies employing a simple OLS
methodology that is unable to effectively account for firm heterogeneity and sample selection
bias. Instead, we use data from studies that attempt to resolve these problems by employing
instrumental variables (IV) or fixed effects techniques. 7 To increase the number of
observations, we follow the strategy used by Rose and Stanley (2005) in a meta-study about
the impact of memberships into currency unions on international trade. We use all the
estimates that are drawn from different specifications, variable definitions (horizontal/vertical
spillovers, backward/forward spillovers, interacted with R&D, FDI, etc.), or sub-samples. For
instance in Damijan, Knell, Majcen and Rojec (2003a), we have two models, eight countries,
and two definitions of the spillover effect, from which we obtain a total of 16 estimates.
Within the sample of studies we selected, some studies are concerned with measuring
productivity spillovers in a sample of several transition countries, while others focus on one
CEEC or CIS country separately. Most papers use panel data, except three (Schoors and Van
derTol, 2002; Yudaeva et al., 2003; Hellman, Jones and Kaufmann, 2002), which use cross-
sectional data. All observations are obtained from studies that use firm-level data.
In terms of the variable definitions, observations related to foreign presence are
defined as employment share, output or value added share or other related measures.
Performance is defined as labor productivity (output or value-added per worker), output
growth or TFP.
In our estimation strategy we follow the seminal work of Görg and Strobl (2001), and
run our meta-analysis on the sample of studies listed in Table 3. Our strategy is as follows.
For a sample of studies on productivity spillovers and direct effects of FDI in transition
countries, we collect the t-statistics on the two related foreign presence variables. We regress

7
The asymmetric composition of the papers in our meta-analysis rather disregards papers focusing on direct
effects as these papers mostly represent early studies that suffer from heterogeneity and sample-selection
problems, which is not the case of later studies focusing on spillover effects.

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the t-statistics on a number of study variable characteristics: sample size, variable definitions
used for foreign presence and definition of performance, and methodology used (cross section
or panel data).
Before we formally introduce our specification we outline several important issues
that we account for in our meta-analysis. From a purely statistical point of view it can be
expected that studies based on large number of observations would more likely yield
statistically significant spillover and direct effects when compared to studies that employ
small numbers of observations. This observation can be generalized that sample size affects t-
statistics proportionally to the square root of degrees of freedom. Therefore, in order to take
effect of the sample size into account, we include the square root of degrees of freedom in our
meta-analysis, similarly as Card and Krueger (1995) or Görg and Strobl (2001).
Another phenomenon found in the literature is that many effects (foreign presence,
ownership structure, privatization, etc.) are more visible in studies employing cross-section
rather than panel data. It is well known that unobserved heterogeneity and possible
misspecification of a model could bias estimates of a particular effect upward. Further, we are
aware that studies based on cross-section data do not properly capture (firm or industry)
heterogeneity or unobserved time invariant effects. The problems arise when time-invariant
firm effects that are not captured in the explanatory variables are correlated with the foreign
presence variable. In these cases the cross-sectional studies may produce biased and
inconsistent estimates of the foreign presence and spillover effects. To control for these
phenomena we intend to include a dummy variable for studies that employ a panel
(longitudinal) data structure.
A role of time is important from yet other perspective, that is whether earlier or later
data are used (average year) and the length of the data set (span). For example, Görg and
Strobl (2001) found that older studies using earlier cross-section and industry level data show
stronger results. This finding leads to two associated research questions: (1) is there any effect
of the average year of the study when panel data is used, and (2) is there any interaction
between the time dimension of a panel and the significance of the results? Both questions
arise from the nature of the foreign presence specifically in transition economies. One could
speculate that early investment would have a large impact that would change with time as
economy becomes more saturated. For this reason we intend to include a variable for the
average year of the data employed in a study as well as a variable measuring the length of
data in years to control for the time span of available data in the associated research design.

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 Finally, we intend to control for the so-called publication bias. Our data are taken from
papers published in academic journals as well as from unpublished manuscripts (including
working papers). It has been pointed out by several authors (see Card and Krueger, 1995;
Görg and Strobl, 2001, among others) that there may be a tendency among editors of
academic journals to publish papers producing statistically significant results. A meta-analysis
provides an opportunity to test for publication bias using the results available from the
literature. Such tests are usually based on the idea that, if there were no publication bias, the t-
statistic on the coefficient in question should be positively related to the size of the sample
used in the analysis (Begg and Berlin, 1988; Görg and Strobl, 2001). In our research design
we test for possible publication bias by including a specific dummy variable. 8
Based on the above discussion we estimate the following two specifications:

∑ , 1,2, … (2)

∑ , 1,2, … , (3)

where Yj (Zj) is the reported Students’s t-statistic of the foreign presence variable, which
measures the direct effect: the FDI dummy is equal to one if observation i is a foreign firm
(indirect effect: the proxy for the foreign presence measured by the foreign employment share
in the sector where firm i operates), in study j from a total of N studies. Further, Xjk are meta-
independent variables that describe the characteristics of the empirical studies to explain the
variation in the dependent variable across studies, as follows:
i. The average trend of the study period (if the study covers the period 1994–1998, this
variable will be equal to 2, that is, the mean of 1994–1998 minus 1994, which is the
earliest starting date of our meta sample);
ii. A panel data dummy that takes a value of 1 for studies that use panel data combining
cross-section and time dimensions of data;
iii. The length of the data span, included to control for the effect of the data span in panel
data over which the effect of FDI is analyzed;
iv. The square root of degrees of freedom to control for sample size effect;

8
We would like to acknowledge the suggestion of an anonymous referee to disentangle the absorptive capacity
hypothesis from the technology gap hypothesis. Unfortunately, we were not able to pursue this avenue as, in
general, the studies employed in our meta-analysis do not contain variables that would adequately capture low
and high R&D intensity in sectors.

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 v. A publication dummy that takes a value of 1 for studies that were published in
academic journals and zero otherwise, included to control for the so-called publication
bias;
vi. separate dummies for the definition of the foreign presence (employment share=1,
output/value added share=2, other=3);
vii. separate dummy variables for the definition of economic performance (output per
worker is equal to 1, output growth is equal to 2, other is equal to 3);
viii. dummy variables for the foreign presence variable interacted with human capital, FDI,
and bribes, which are supposed to capture whether spillover effects are stronger or
weaker when the foreign presence is higher;
ix. dummy variables for the nature of spillovers: vertical forward and vertical backward;
x. country dummies.

5.3 Results of meta-analysis

In the first step we used the test proposed in Görg and Strobl (2001), which consists of
regressing the log of the absolute value of the t-statistics on the square root of the degrees of
freedom. For the spillover effect the estimated coefficient is 0.483, and the associated
standard error is 0.048 (detailed results are available upon request). Therefore, we can reject
the hypothesis of the coefficient on the square root of the degrees of freedom being equal to
one. This result confirms our original prior of the publication bias being present. Publication
bias might occur due to various factors. In particular, published works may have a larger
amount of control parameters resulting in smaller and less significant final effect. In addition,
published studies are more likely to use a proper estimation technique (instrumental variables
or fixed effects) to derive results.
We display the results of our meta-analysis in Tables 4 and 5. In Table 4 we present
the direct effects of FDI, while in Table 5 we augment our evidence with spillover effects. In
order to control for other variables affecting the research design, we included a dummy
variable for published studies in our meta-analysis. The results support the general presence
of the publication bias. 9 In particular, we found a positive and significant coefficient for the
direct effect (Table 4), supporting the idea that journal editors tend to favor significant results.
On the contrary, the specification that includes spillover effects does not support the presence

9
The importance of the research design and the publication bias hold in other meta-analyses (see Wooster and
Diebel, 2006; Meyer and Sinani, 2009 and Havranek and Irsova, 2010). Moreover, the average year of the data
used in published papers in our sample is 1996. But for working papers the average year is 1997. Even more
recent is the data coverage for contributions in other sources that are characterized by an average year of 2002.

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of a publication bias for the foreign presence variable (Table 5). One can speculate that a
reason behind this departure could be caused by more stress being placed on indirect and
spillover effects. This would mean that the significance of the coefficient on foreign presence
could be of secondary importance.
Our results confirm that studies that use cross-section data tend to have, on average,
significantly higher t-ratios than panel studies. In other words, the effect of productivity
spillovers appears to be significantly higher in cross-sectional studies. The finding is
consistent with the results of Görg and Strobl (2001); in our case we control for even more
characteristics of the research design and our results remain economically meaningful
andstatistically significant. From Table 5, column 1, we see that when a study employs only
panel data the t-statistic is on average greatly reduced (by 7.1). This finding confirms an
extensive unobserved heterogeneity that results in biased estimates of a particular effect in
cross-section studies. Due to the fact that studies based on cross-section data do not properly
capture the above-mentioned unobserved time invariant effects, we will base our further
interpretations on studies that employ solely panel data.
The surveyed studies frequently discuss the question of time-related effects of the FDI.
Namely, what is the immediate effect of the investment and how long is this effect observed?
We account for these issues by including variables capturing the average year of the data used
in a study (variable average trend) and the length of the employed data (variable length).
First, one could expect that the higher overall effect found in early studies may not be due to
data quality. We do not find statistically significant coefficients associated with the average
year of employed data when country or regional dummies are included (Table 4 and 5).
However, when we do not consider country effects, the average trend variable becomes
significant. This result can be interpreted such that the timing of the FDI matters and is
country-specific. Second, we account for the fact that foreign-presence and spillover effects
could change with time and, therefore, we control for the time span in the associated research
design. The results provided in Tables 4 and 5 show that the effect of the time span is
consistently negative. This means that studies with a longer time span of data tend to present
significantly smaller effects of foreign presence and spillovers.
As we do not find other significant effects in terms of factors influencing foreign
presence (Table 4) we continue with our inferences based on the results of spillovers given in
Table 5. The choice of proxies for both performance and foreign presence is an important
factor behind the differences across studies. Foreign presence measured by value-added share
has a negative effect while the variable employment (measuring employment share) has a

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positive impact on the significance of the foreign presence. Performance measured as output
growth produces an insignificant coefficient but output per worker shows a negative and
significant effect. To see the importance of spillovers interacted with several important
characteristics we include in our meta-analysis dummy variables for the importance of foreign
presence (FDI) in a given sector, and institutional channels (human capital and bribes). The
results from Table 5 show that in our research design, in which we control for more specific
factors discussed earlier, the above interactions become insignificant.
We already discussed that local firms might benefit from using the better intermediate
input produced by the foreign-owned firms that materializes in forward spillovers. 10 To see
the importance of forward and backward spillovers, we include in our meta-analysis dummies
for both types. Our results in Table 5 show negative and significant forward spillover effects
while backward spillover effects are positive and significant. Negative forward spillovers
decrease the effect of foreign presence while positive backward spillovers increase the effect.
This is an important finding because it shows that multinational enterprises in transition
countries are more likely to encourage the transmission of technology spillovers to local
clients and suppliers than to local competitors. This echoes the results of Lefilleur and Maurel
(2010), who highlight the role of backward linkages in shaping the structure of production—
hence the structure of spillovers—across FDI origin and destination countries. 11
Further, Damijan et al. (2003b) emphasize that in several countries they analyzed
where significant vertical spillovers were detected, the impact of backward vertical spillovers
was found to be higher by a factor of 10 relative to horizontal spillovers. This result speaks in
favor of the larger importance of vertical versus horizontal spillovers from FDI and resonates
with the same finding we derived from our meta-analysis. Our results also reflect those in
Gorodnichenko et al. (2007), who extend the analysis of forward and backward spillovers to
include the concept of selling or buying from firms outside of the country, i.e. importing and
exporting. The idea is that vertical spillovers are concerned with linkages with foreign firms
not only within the host country alone, but also with foreign trade partners. In the same vein,
Frensch (2010) suggests that trade reforms have induced the fragmentation of European
production by reporting evidence of stronger extensive import margin effects of liberalization
for intermediate and capital goods compared to consumer goods. Thus, the effects of forward

10
On the other hand, foreign firms may also profit from improvements on the side of their local suppliers.
11
Specifically, Lefilleur and Maurel (2010) show that a 10% increase in access to suppliers (backward linkages)
based in the FDI recipient country increases FDI by about 2% in Central European countries and by 1% in
Eastern European countries.

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and backward spillovers presented in our meta-analysis are intuitively correct and reflect
similar results found in the surveyed literature.

6. Conclusion
This paper summarizes the broad range of empirical results on direct effects and spillover
effects of FDI drawn from 21 studies focusing on transition countries. Similar to other studies
dealing with developing, emerging and industrialized countries, we find that the research
design is crucial for a proper analysis of productivity spillovers. Aside from the overview of
the FDI literature related to performance in European transformation economies, we introduce
key econometric issues relevant to applied work. Various defects that plague empirical work
on FDI and that may result in biased estimates is discussed in detail.
The aggregated findings of earlier FDI literature is provided based on the results of our
meta-analysis. We provide evidence that there exists a publication bias for results on direct
effects while a publication bias is missing for specifications that include spillover effects. We
also show that studies based on cross-section data tend to have substantially higher t-ratios
than panel studies. Thus, the effect of productivity spillovers is higher in cross-sectional
studies since they do not properly account for unobserved heterogeneity that results in biased
estimates. Therefore, we base our further evidence on studies that employ solely panel data.
When time effects of the FDI are analyzed we show that the timing of the FDI matters and it
is country-specific. However, the initial effect of the foreign presence dissipates with time
since studies with a longer time span of data tend to present significantly smaller effects of
foreign presence and spillovers.
Finally, we aggregate our findings on how local firms benefit from intermediate inputs
from foreign firms (forward spillover) and how these may profit from the improvement of
domestic firms (backward spillover). We show negative and significant forward spillover
effects while backward spillover effects are positive and significant. Negative forward
spillover decreases the effect of the foreign presence while positive backward spillover
increases the effect. Thus, the importance of forward and backward spillovers is strongly
supported. This is a key result, which implies that local firms in transition countries
experience efficiency gains if they supply industries with a higher share of foreign firms or if
foreign firms sell to them. The policy implication is that FDI must be encouraged where
intersectoral spillovers are expected to materialize.

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Table 1: FDI by Region

Foreign direct investment, net (BoP, current USD)

East Asia Europe & Latin America & Middle East & South Sub-Saharan
& Pacific Central Asia Caribbean North Africa Asia Africa

1995 0.00 26.11 0.87 2.81 1.52

1996 10.34 40.61 1.18 3.26 2.85
1997 13.66 58.01 2.23 4.81 5.48
1998 15.94 65.06 1.87 3.45 4.12
1999 14.73 80.74 2.02 2.97 6.78
2000 14.78 70.70 3.32 3.84 5.95
2001 16.41 67.42 3.43 4.69 16.60
2002 12.50 50.08 3.47 4.98 9.87
2003 15.46 36.67 5.89 3.45 10.47
2004 62.40 38.09 48.57 5.05 5.34 7.76
2005 86.23 40.26 53.13 12.71 7.40 13.16
2006 73.27 28.89 23.80 13.10 4.62
2007 96.43 84.44
2008 101.61
Foreign direct investment, net inflows (BoP, current USD)

1995 50.80 9.44 30.18 0.82 2.93 4.55

1996 58.64 11.55 43.81 1.26 3.51 4.07
1997 62.22 18.14 65.70 3.58 4.90 8.59
1998 57.82 18.29 73.35 3.55 3.55 6.86
1999 50.40 18.00 87.85 2.61 3.08 9.46
2000 45.17 19.07 79.34 4.88 4.36 6.80
2001 48.92 19.75 72.03 4.05 6.14 14.20
2002 59.40 18.02 52.96 4.97 6.70 10.21
2003 56.77 28.61 42.20 7.86 5.38 12.98
2004 70.35 55.12 64.89 7.49 7.59 10.68
2005 104.36 61.59 70.85 16.12 9.98 16.98
2006 105.15 113.35 71.48 28.07 23.16 18.47
2007 175.34 151.52 107.27 28.91 29.93 28.73

Source : World Bank Development Indicators

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Table 2: Economic Performance and FDI in Transition Countries (CEE, Balkans, CIS)

Real GDP growth, % Inward FDI stocks, end-2007 Inward flows (USD bln)
2007 2008 2009 USD bln % of GDP USD p.c. 2007 2008 2009
Transition average 7.6 5.8 2.2 1,046 35.4 2,590 158.5 155.4 98.1
Balkans 6.2 6.6 2.2 141.0 41.8 2,660 31.0 29.1 17.7
Albania 6.0 6.0 4.0 2.5 22.3 790 0.5 0.5 0.35
Bosnia, Herzegovina 5.5 5.3 3.0 5.3 37.0 1,350 2.1 1.0 0.6
Bulgaria 6.2 6.3 1.9 30.8 77.8 4,010 9.0 6.7 4.0
Croatia 5.7 3.1 1.7 20.9 40.5 4,640 4.9 3.7 2.2
Macedonia 5.1 5.3 3.0 3.1 44.0 1,510 0.3 0.6 0.3
Montenegro 7.0 7.0 3.5 2.5 41.7 3,980 1.4 1.3 0.8
Romania 6.0 8.2 2.6 60.9 36.2 2,820 9.4 12.0 7.5
Serbia 7.5 6.0 1.0 15 8.1 2.020 3.4 3.3 2.0
Central Europe 6.1 4.4 1.8 429.3 49.9 6,520 43.1 35.6 23.3
Czech Rep. 6.6 4.2 2.3 101.1 57.8 9,870 9.3 6.5 3.5
Hungary 1.3 1.2 -1.5 97.4 70.4 9,730 6.1 4.4 3.2
Poland 6.7 5.0 2.4 176.1 41.5 4,610 23.0 21.0 15.0
Slovakia 10.4 6.8 3.0 40.7 54.2 7,480 3.3 2.4 1.2
Slovenia 6.8 4.2 2.0 14.0 29.8 6,980 1.5 1.3 0.4
EU members 6.3 4.8 2.3 563.4 48.8 5,530 68.3 59.5 37.4
Baltic countries 8.7 1.0 -2.6 42.4 48.7 5,990 6.8 5.2 2.6
Estonia 6.3 -2.0 -2.5 16.7 79.8 12,380 2.7 2.4 1.3
Latvia 10.3 5.9 -7.0 10.6 39.1 4,610 2.2 1.5 0.5
Lithuania 8.8 4.0 0.2 15.1 38.7 4,400 1.9 1.3 0.8
CIS 8.5 6.5 2.5 433.6 25.9 1,560 77.7 85.5 54.5
Armenia 13.7 9.0 4.5 2.5 27.3 830 0.7 0.5 0.4
Azerbaijan 25.0 13.1 6.9 6.6 22.4 780 -4.7 -0.5 0.5
Belarus 8.1 10.0 2.5 4.5 10.1 460 1.8 1.6 1.0
Georgia 12.4 5.0 4.5 5.4 53.0 1,210 1.7 1.2 0.6
Kazakhstan 8.5 3.5 3.0 43.6 41.9 2,860 10.3 6.5 4.0
Kyrgyzstan 8.2 6.0 3.5 0.9 23.9 170 0.2 0.2 0.2
Moldova 3.0 5.8 3.0 1.9 42.3 440 0.5 0.6 0.2
Russia 8.1 6.7 3.0 324.1 25.1 2,260 55.1 60.0 40.0
Tajikistan 7.8 5.8 2.5 1.1 28.2 150 0.4 0.5 0.3
Turkmenistan 6.0 5.0 4.0 3.0 32.3 600 1.2 1.1 0.8
Ukraine 7.7 4.5 -3.0 38.1 27.0 810 9.9 13.0 6.0
Uzbekistan 9.5 8.6 4.5 2.1 9.5 80 0.7 0.9 0.6
Source: Economist Intelligence Unit, December 2008

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Table 3: Firm-level Effects of FDI – Direct and Spillover Effects

Reference Country, period, Measure of FDI Estimation

(N observations) technique
Bosco (2001) Percentage of foreign FE, First
HU, 1993-1997, (N=587, 1053)
participation in the capital of a differences
firm.
Damijan, Majcen, BG, CZ, EST, HU, PL, RO, Presence of foreign owner Heckman & FE
Rojec, Knell (2003a) SK, SI, 1994-1998, (N=134- (none/minor/major)
2199)

Damijan, Majcen, BG, CZ, EST, HU, LT, LV, FDI dummy if foreign capital Heckman & sys-
Rojec, Knell (2003b) PL, RO, SK, SLO, 1995-1999, >10%, Majority foreign GMM
(N=398-5075) ownership if foreign capital
>50%
Djankov and CZ, 1992-1996, (N=513, 340, FDI dummy, Joint Venture OLS, RE
Hoekman (2000) 431) dummy
Evenett and Voicu CZ, 1995-1998, (N=3188) FDI dummy Heckman
(2001)
Frydman, Gray, CZ, HU, PL, 1990-1993, Dummy equal to 1 if the largest FE
Hessel and (N=513) shareholder is a foreign owner
Rapaczynski (1999)
Gorodnichenko, AL, BG, HR, CZ, EE, HU, KZ, Share of foreign ownership in First difference
Svejnar, Terrel LV, LT, PL, RO, RU, SK, SI, industry
(2007) UA
Hanousek, Kocenda, CZ, 1996-1999, (N=2168- Three types of foreign Diff. OLS, IV
Svejnar (2007) 2949) ownership: Majority Foreign,
Blocking Minority Foreign,
Legal Minority Foreign
Hellman, Jones and AL, AM, AZ, BY, BG, HR, FDI dummy OLS
Kaufmann (2002) CZ, EE, GE, HU, KZ, KG, LV,
LT, MD, PL, RO, RU, SK, SI,
UA, UZ, 1989-2000, (N=2685)

Javorcik (2004) LT, 1996-2000, (N=681- Foreign share, Forward, OLS, First
11630) Backward, Horizontal Difference,
Olley&Pakes

Javorcik and CZ, RO, 1998-2000, Vertical, Horizontal First Difference,

Spatareanu (2005) (N=71517, 7400) Olley&Pakes

Kinoshita (2000) CZ, 1995-1998, (N= 704) Foreign ownership dummy, OLS
employment share of foreign
firms to that of all firms in the
industry
(continued on next page)

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Table 3 (continued): Firm-level Effects of FDI – Direct and Spillover Effects

Reference Country, period, Measure of FDI Estimation

(N observations) technique

BG, PL, RO, 1993-1997 Fraction of shares held by a foreign First

(1994-1997 in case of RO), investor, Share of output accounted for differencing,
Konigs (2001)
(N=6361, 8580, 2854) by foreign firms in total output at the 2- GMM IV
digit NACE sector level
Sabirianova, CZ, RU, 1992-2000, Foreign ownership OLS,RE, FE,
Svejnar, Terrel (N=18434, 136769) 2SLS-RE
(2005)

Schoors and Van HU, 1997-1998, (N=819- Foreign participation (10%, 50%, 95%) OLS, IV
der Tol (2002) 1021)
Sgard (2001) HU, 1992-1999, Share of foreign equity in a firm, share OLS, First to
(N=33033) of foreign equity in a sector fourth
differences

Sinani and Meyer EE, 1994-1999, (N=455, Share of foreign firms’ in industry FE, GLS
(2004) 374, 334) employment, sales, and equity as proxies
for spillovers

Vahter (2004) EE, SI, 1996-2001 for EE, FDI dummy (majority FDI dummy in RE, FE,
1994-2000 for SI, case of Estonia), share of FDI in a sector Heckman
(N=6780)
Vahter (2005) EE, 1996-2001, (N=1915) FDI dummy (foreign share equal to at FE,
least 50%)
Vahter and EE, 1995-2002, (N=15226, FDI dummy (foreign share equal to at OLS, FE, RE
Masso (2006) 56143) least 50%)
Yudaeva et al RU, 1993-1997, FDI dummy (foreign share equal to at OLS, FE, IV
(2003) (N=11029) least 10% or as defined elsewhere)

Notes: Estimation techniques abbreviations denote Ordinary Least Squares (OLS), Instrumental Variable (IV),
Fixed Effects (FE) and Random Effects (RE). Country codes denote Bulgaria (BG), Czech Republic (CZ),
Estonia (EE), Hungary (HU), Latvia (LV), Lithuania (LT), Poland (PL), Romania (RO), Slovak Republic (SK),
Slovenia (SI), Turkey (TR), Austria (AT), Belgium (BE), Luxembourg (LU), Denmark (DK), Finland (FI),
France (FR), Germany (DE), Greece (GR), Ireland (IE), Italy (IT), Netherlands (NL), Portugal (PT), Spain (ES),
Sweden (SE), United Kingdom (UK), Norway (NO), Switzerland (CH), Albania (AL), Armenia (AM),
Azerbaijan (AZ), Belarus (BY), Croatia (HR), Georgia (GE), Kazakhstan (KZ), Kyrgyzstan (KZ), Moldova
(MD), Russia (RU), Ukraine (UA) and Uzbekistan (UZ)

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Table 4: Is the FDI Direct Effect Significant? A Meta Analysis with (and without) Country
and Region Dummies

(1) (2) (3)

-0.001 0.001 0.002
√(degrees of freedom)
(0.004) (0.005) (0.005)
0.292 -0.801a -0.708a
Average trend
(0.265) (0.274) (0.192)
2.930a 1.724a 1.984a
Published
(0.367) (0.391) (0.357)
-0.866a 0.316 0.323
Length
(0.273) (0.278) (0.268)
Performance measured by:
0.889 0.043 -0.018
Output per worker
(0.731) (0.980) (0.987)
-0.104 -0.385 -0.146
Output growth
(0.427) (0.600) (0.593)
Country dummies yes ⎯ ⎯
Regional dummies ⎯ yes ⎯
-580.237 1598.782a 1412.027a
Constant
(527.693) (546.558) (383.907)
Adjusted R2 0.662 0.310 0.299
Number of observations 140 140 140

Note: Columns (1)-(3) contain the results of the meta-analysis (dependent variable being the t-statistics of the
reporting foreign presence variable). Specifically, column (1) refers to the specifications using a country effect
(captured by the country dummy variables), column (2) shows the results for regional effects (captured by
regional dummies) and column (3) contains the results for the design without country and regional effects.
Country dummies used in column (1) are for the following countries: Estonia, Hungary, Lithuania, Latvia,
Poland, Romania, Russia, Slovenia, Slovakia, Ukraine, Czech Republic. Regional dummies used in column (2)
are for the following groups of countries: Balkan (Albania, Bosnia, Herzegovina, Bulgaria, Croatia, Macedonia,
Montenegro, Romania, Serbia), Visegrad 5 (Czech Republic, Hungary, Poland, Slovakia, Slovenia), Baltic
Countries (Estonia, Lithuania, Latvia) and CIS countries that covers the Commonwealth of Independent States as
listed in Table 2.
For the sake of space detailed results for country and regional effects are not presented here but are available
upon request.
The statistical significance of the coefficients is denoted as follows: a (1%), b (5%) and c (10%).

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Table 5: FDI Spillover Effect – A Meta Analysis with (and without) Country Dummies

(1) (2) (3) (4)

a a a
0.013 0.014 0.011 0.009a
√(degrees of freedom)
(0.004) (0.004) (0.004) (0.003)
-0.398c -0.285 -0.441 -0.165c
Average trend
(0.223) (0.217) (0.327) (0.089)
0.877 0.897 0.817 1.366c
Published
(0.917) (0.888) (0.874) (0.704)
-7.065a
Panel
(1.408)
-0.892a -0.746a -0.471a
Length
(0.242) (0.206) (0.166)
Spillover measured by share of:
1.972a 1.990a 1.979a 1.980a
Employment
(0.633) (0.613) (0.613) (0.606)
-1.776a -1.010 -0.985 -1.350b
Value added
(0.661) (0.744) (0.734) (0.667)
Type of Spillover:
-3.350a -4.286a -4.429a -4.257a
Vertical Forward
(0.729) (0.751) (0.760) (0.729)
1.894a 2.016a 2.002a 2.114a
Vertical Backward
(0.517) (0.509) (0.508) (0.502)
Spillover Interacted with:
-0.832 0.099 0.049 0.023
Human capital
(0.657) (0.675) (0.673) (0.668)
-0.819 -0.006 0.139 0.017
FDI
(0.842) (0.823) (0.812) (0.806)
1.104 1.326 1.465 1.321
Bribes
(1.391) (1.343) (1.344) (1.337)
Performance measured by:
-2.419b -0.913 -0.611 -0.863
Output per worker
(0.965) (0.970) (0.758) (0.698)
-1.610 -3.329c -1.451 -0.995
Output growth
(1.728) (1.708) (1.504) (1.465)
Country dummies yes yes ⎯ ⎯
Regional dummies ⎯ ⎯ yes ⎯
803.422c 572.900 884.785 331.670c
Constant
(446.663) (434.786) (655.174) (178.184)
Adjusted R2 0.105 0.135 0.134 0.133
Number of observations 933 906 906 906
Note: Columns (1)-(4) contain the results of the meta-analysis (dependent variable being the t-statistics of the
reporting foreign presence variable). Column (1) shows the results for the entire dataset, while columns (2)-(4)
refer only to the panel data study. We use the same set of regional and country dummies as in Table 4. For the
sake of the space detailed results for country (columns 1-2) and regional (column 3) effects are not presented
here but are available upon request. The statistical significance of the coefficients is denoted as follows: a (1%),
b (5%) and c (10%).

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Electronic copy available at: https://ssrn.com/abstract=2020268

 Figure 1: Global inward FDI flows

- in USD millions - in USD, percentage change

2,000 50
40
1,500 30
20
1,000 10
0
-1 0
500
-2 0
-3 0
0 -4 0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2 0 00 20 01 20 02 20 03 2 00 4 2 00 5 2 00 6 2 00 7 20 08 20 09

Source: Economist Intelligence Unit, December 2008

Figure 2: FDI inflows into Eastern Europe

- in USD millions - in % of GDP

6
16 0 0 0 0
14 0 0 0 0 5
12 0 0 0 0
4
10 0 0 0 0
8 00 00 3
6 00 00
2
4 00 00
2 00 00 1
0
2 00 0 2 001 2 002 20 03 200 4 200 5 2 00 6 2 007 20 08 20 09 0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Source: Economist Intelligence Unit, December 2008

37

Electronic copy available at: https://ssrn.com/abstract=2020268