European integration, productivity growth and real
convergence: Evidence from the new member states
Ali M. Kutan a,b,c,d
, Taner M. Yigit e,
*
a
Southern Illinois University, Edwardsville, United States
b
The Center for European Integration Studies (ZEI), Bonn, Germany
c
The Emerging Markets Group (EMG), London, United Kingdom
d
The William Davidson Institute (WDI), Michigan, United States
e
Bilkent University, Ankara, Turkey
1. Introduction
Following the breakdown of the former Soviet Union and its communist system in the early 1990s,
the countries of Central and Eastern Europe (CEE) have gone through an unprecedented economic and
political transformation. Following an initial and unexpected signiﬁcant decline in economic activity,
these countries have achieved signiﬁcant economic growth starting in the mid- to late 90s. One of the
Economic Systems 33 (2009) 127–137
A R T I C L E I N F O
Article history:
Received 19 January 2009
Received in revised form 22 March 2009
Accepted 25 March 2009
JEL classiﬁcation:
P2
F15
O1
J2
Keywords:
Productivity
Convergence
European Union
Transition economies
A B S T R A C T
We estimate the determinants of labor productivity growth in 8
new European Union (EU) member states that joined the Union in
2004. Our focus is on the impact of globalization and EU integration
efforts on labor productivity growth. Previous studies test the
impact of trade using either exports or trade openness. We also test
the impact of imports separately on labor productivity growth.
Using panel data for 1995–2006 period, we ﬁnd that globalization
has mixed effects. FDI and exports improve productivity, but
imports hurt it. Regarding domestic variables, we ﬁnd that human
capital is the most important source of labor productivity growth in
the new member states. There is also considerable adjustment of
labor productivity towards EU15 levels, indicating signiﬁcant
‘‘catching up’’ and hence real convergence. Policy implications of
the ﬁndings are also discussed.
ß 2009 Elsevier B.V. All rights reserved.
* Corresponding author.
E-mail address: tyigit@bilkent.edu.tr (T.M. Yigit).
Contents lists available at ScienceDirect
Economic Systems
journal homepage: www.elsevier.com/locate/ecosys
0939-3625/$ – see front matter ß 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecosys.2009.03.002
interesting features of this growth pattern has been the signiﬁcant increase in labor productivity.
According to the World Bank, during 1999–2004, the growth in labor productivity raised income per
capita in CEE countries more than that in employment and population (World Bank Report, 2008). At
the same time period, CEE labor productivity has grown at a rate greater than many other emerging
and developing countries, including Russia and Ukraine (Rada and Taylor, 2006).
In this paper, we study two related issues for 8 CEE countries that joined the European Union (EU)
in 2004, namely: the Czech Republic, Estonia, Hungary, Poland, Latvia, Lithuania, Slovakia and
Slovenia. We will refer to these countries as the CEE8 in the rest of the paper. First, we investigate the
determinants of labor productivity growth. Second, we test whether there have been some advances
in labor productivity level towards that of core EU (i.e., EU15). Given the signiﬁcant growth in their
labor productivity, it is important for policymakers and other stakeholders to understand what factors
have driven such growth. As the eventual goal of integration is the increase in standards of living to EU
levels, further improvements in labor productivity play an instrumental role in satisfying such an
economic (also historical and political) objective. This will facilitate the catching up (or real
convergence) of CEE8 countries to the income levels of the core EU members. Evidence on such real
convergence has important implications for the design of an optimal policy towards the entire euro
zone. Within the new and candidate countries, signiﬁcant progress toward per capita income
convergence is seen as key in securing the political assent of the population to both transition and
membership in the EU. For the EU countries, such income growth is important if excessive population
movements from the new members to the old are to be avoided and if the EU’s budget is not to be
strained by transfers to lagging economies. In other words, real convergence has signiﬁcant ﬁscal and
monetary policy implications (Kutan and Yigit, 2007).
In a broader sense, the main objective of this paper is to better understand the implications of
integration and globalization on labor productivity. We also account for the impact of domestic
variables, namely, human capital, research development expenditures and domestic investment,
which are all closely related to domestic policy-making. Given our objective, we believe that
examining the case of CEE8 countries provides a unique study, as the integration efforts have been
associated with signiﬁcant internationalization activities as well. As former communist economies,
they not only had to integrate their economies quickly with the West to survive the hardship of the
former economic system, but also assumed the task of reaching the EU standards. The experience of
these new EU members may provide important lessons for the candidate countries as well as other
emerging economies.
Another important feature of our paper is that we test the individual effects of exports and imports
on labor productivity separately. Recent literature has mainly focused on exports (Melitz, 2003; de
Loecker, 2007) and FDI (Borensztein et al., 1998; Xu, 2000; Javorcik, 2004) to explain productivity
changes. There are only a few available studies that investigate the direct impact of imports on
productivity and the cross-country evidence on the issue is mixed (Blalock and Veloso, 2007; Kasahara
and Rodrique, 2008). To our best knowledge, there is no aggregate country-level evidence regarding
the import-labor productivity link for CEE8 countries.1
In the next section, we explain our theoretical framework. Section 3 further motivates the study by
discussing the evolution of potential factors that might have affected the labor productivity growth in
CEE8 countries. We also refer to some related studies that examine the determinants of labor
productivity. In Section 3, we describe the data and discuss the estimation and the ﬁndings. The last
section concludes with policy implications of the ﬁndings.
2. Theoretical framework
We borrow our theoretical framework from Bernard and Jones (1996a,b) and Cameron et al. (2005),
who show that productivity growth of countries (or industries) is the result of domestic innovation or
1
The only study that we have found is a recent unpublished work by Halpern et al. (2006), who employ a panel data of
Hungarian ﬁrms to test the impact of imports on productivity. The paper argues that imports inﬂuence labor productivity
through a quality and a variety channel and provides empirical evidence supporting that.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137128
technology transfers from frontier countries.
Dln ðAitÞ ¼ ki þ gi ln
AFt
Ait
 
(1)
In Eq. (1), AFt and Ait represent the technical efﬁciency or productivity of the frontier and country i,
respectively, while ki is the country speciﬁc innovation and gi is the rate of technology transfer from
the frontier. We select EU15 countries as the relevant technological frontier for the CEE8 countries to
speciﬁcally observe whether the size of the gap enhances the rate of technological spillovers to these
recent new EU members from their more productive counterparts. As in Cameron et al., we assume
that both ki and gi are time varying and determined by R&D, international trade (imports) and human
capital
kit ¼ b1i þ dZitÀ1 git ¼ b2i þ mZitÀ1 (2)
where Zi is the vector of the above mentioned variables.2
While R&D raises the rate of innovation,
human capital proxies for absorptive capacity of technology, and ﬁnally, trade controls for the rate of
technological transfer. In our study, we also include exports and FDI as other international channels of
technological spillovers and domestic capital formation to account for the productivity impact of the
varying utilization of physical capital over the business cycle.
Regarding R&D expenditures as a driver of knowledge and innovation, Coe and Helpman (1995)
ﬁnd a signiﬁcant link between productivity and R&D capital stocks in EU countries. Engelbrecht (1997)
extends Coe and Helpman’s study by including a human capital variable to capture innovation, which
may not be accounted by only R&D capital stock. They ﬁnd that, besides R&D, human capital plays a
unique role in affecting economic growth. Using data for several OECD countries, Grifﬁth et al. (2004)
conﬁrm the importance of R&D and human capital in the growth process. Their results also indicate
that R&D is important not only in innovation but also in technological catch-up.
FDI can improve labor productivity by serving as a supplement to domestic investment. Much of
the FDI that came into transition economies has been used to purchase existing ﬁrms rather than to
ﬁnance new Greenﬁeld investments (Brada et al., 2006); however, even FDI used for mergers and
acquisitions has a positive effect on domestic capital formation (Sˇohinger and Harrison, 2004) because
investors do contribute additional capital to their acquisitions. Moreover, as Hunya (1996) shows in
the case of Hungary, foreign ﬁrms have higher proﬁts and reinvest a much higher share of it than do
domestically owned ﬁrms, thus increasing capital formation in the future. Another beneﬁt of FDI is
that it brings in new technology and managerial skills (Brada et al., 2006). Thus, foreign-owned ﬁrms
are likely to be more productive (Hunya, 1996; Sgard, 2001) and to use more advanced technologies
(Voicu, 2004). Moreover, there are likely to be important spillovers of these technologies and
managerial skills from foreign-owned ﬁrms to the domestic economy.3
Javorcik (2004) ﬁnds evidence
for the existence of backward linkages between the downstream suppliers and the MNEs in Lithuania.
Borensztein et al. (1998) and Xu (2000) argue that FDI’s growth impact goes beyond its inﬂuence on
total investment; it also depends on the stock of human capital in that country, suggesting that the
beneﬁts of higher FDI are realized not only through capital accumulation, but also through efﬁciency
gains. All these factors associated with FDI are expected to improve the labor productivity in CES
economies.
Melitz (2003) demonstrates that greater trade openness (especially exports) raises industry
productivity via selection and production re-allocation effects. His model shows how trade will induce
only the more productive ﬁrms to enter the export market and force the least productive ﬁrms to exit.
He also emphasizes that the industry’s exposure to trade will lead to inter-ﬁrm reallocations towards
higher productivity. De Loecker (2007) ﬁnds further evidence for this idea using Slovenian
manufacturing ﬁrms in his analysis of the changes in ﬁrm productivity when they start exporting.
Controlling for the self-selection into export markets, he ﬁnds that export entrants become more
2
The time period subscripts of the explanatory variables rely on their speed of transmission and will be discussed in the data
section.
3
Fan (2002) provides a useful survey of the literature on technology spillovers in transition economies. Aitken and Harrison
(1999) and Blomstrom and Persson (1983) provide useful case studies.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137 129
productive once they start exporting and that the productivity gap between exporters and their
domestic counterparts increases further over time.
Theory also indicates that imports have a positive impact on labor productivity through technology
spillovers. However, the empirical evidence on the impact of imports on productivity is scant and mixed.
For example, Kasahara and Rodrique (2008), Blalock and Veloso (2007), Fernandes (2007) and Halpern
et al. (2006) report positive effects of imports on productivity for plants or ﬁrms in Chile, Indonesia,
Colombia, and Hungary, respectively. On the other hand, Keller and Yeaple (2003) ﬁnd a negative impact
for US multinationals and Muendler (2004) reports no impact on productivity for Brazilian plants.
Overall, imports may have a positive or negative impact on productivity, depending upon (i) the
composition of imports (i.e., high-tech or consumption goods), (ii) the relative cost of imports with
respect to local substitutes and (iii) the relative usage of imported intermediaries in production with
respect to domestic intermediaries (Kasahara and Rodrique, 2008, and Blalock and Veloso, 2007).
Besides country speciﬁc innovation variables (ki) we discussed above, the theory outlined above
also emphasizes the rate of technology transfer from the frontier (gi) as a determinant of productivity
growth.
Based on the discussion above, the testable equation becomes
Dln ðAitÞ ¼ b1i þ dZitÀ1 þ b2i ln
AFt
Ait
 
þ mZitÀ1 Á ln
AFt
Ait
 
þ eit (3)
Again Zt vector includes globalization variables (exports, imports, and FDI) and domestic innovation
variables (human capital, domestic capital formation and R&D expenditures). The distance variable, a
proxy for the rate of technology transfer from the frontier, is measured by the absolute value of the log
ratio of productivity of country i to the productivity of EU15. Hence, as the productivity gap between
country i and the frontier EU15 widens, this ratio grows smaller, making the absolute value of the log a
larger number. With larger numbers, or a larger gap, the productivity growth of country i is expected
to be faster, indicating a positive coefﬁcient. Our a priori expectations are toward ﬁnding a positive
coefﬁcient for all of the ‘‘innovation’’ variables in the model as well (with the possibility of a negative
coefﬁcient for imports).
3. Evolution of labor productivity in CEE countries and globalization factors
Fig. 1 shows EBRD data on labor productivity changes in industry for CEE8 countries. Although
there have been periods of productivity slowdowns in early years in some countries due to events such
as ﬁnancial crises (i.e., the Czech Republic in 1997) and other country-speciﬁc events, the overall trend
is positive. The biggest productivity increase took place in the three Baltic States. The net cumulative
productivity change during 1995–2006 period was 108.6, 103.5, and 92.7 percent in Estonia, Lithuania
and Latvia, respectively. Hungary and Poland are the next biggest productivity gainers in the region
with a net cumulative gain of 74.6 and 67.8 percent, respectively. The other three CEE8 countries,
Slovakia, the Czech Republic and Slovenia, had the lowest net cumulative productivity growth of 48.7,
Fig. 1. Productivity changes (industry).
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137130
44.6 and 40.4 percent, respectively. However, all the CEE8 countries outperformed the net cumulative
productivity gains in EU 15, which was 24.2 percent during the same time period.
One of the most obvious reasons for growing labor productivity in the CEE8 countries is the
signiﬁcant amount of FDI ﬂows (Fig. 2). Estonia, which has the biggest increase in her labor
productivity, and Hungary, another signiﬁcant productivity gainer, also received the highest average
FDI ﬂows with respect to GDP during our sample period, which was about 8.1 and 7.4 percent,
respectively. Estonia’s success is attributed to its potential market growth, the ease of access it affords
to neighboring markets, the revision of FDI-related laws to create a transparent and nondiscriminatory
legal regime, and an expansive privatization process (OECD, 2001). Unlike Estonia,
Hungary was an early leader in FDI inﬂows, in part because of its more sophisticated economic
relations with the West before the transition, which led many foreign investors to view Hungary as a
country that had the infrastructure and economic savvy to accept foreign investments. Another reason
for Hungary’s early lead was its privatization strategy, which made sales of state-owned ﬁrms to
foreign investors the preferred path to privatization (Brada et al., 2006). The Czech Republic attracted
the next highest level of FDI inﬂows (5.71 percent of GDP), followed by Latvia and Slovakia (4.8 percent
each). The remaining three CEE8 countries received relatively smaller level of average FDI inﬂows than
the others during 1995–2006, namely, Lithuania (3.6 percent), Poland (3.5 percent) and Slovenia (1.9
percent). During the same period, EU15 countries received average level FDI inﬂows of about 2.3
percent of their respective GDP. The observation that those CEE8 countries with a relatively smaller
share of FDI in GDP still exhibited large productivity gains suggests that there are other forces in play
affecting labor productivity, such as international trade.
Figs. 3 and 4 show the evolution of exports and imports of CEE8 countries during 1995–2006.
Although there was some volatility in the early 1990s, there has been a stable and upward trend in
Fig. 2. FDI inﬂows to CEE8 countries.
Fig. 3. CEE8 exports.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137 131
both exports and imports especially since 1999. In regards to exports, the Czech Republic, Poland,
Slovakia and Slovenia have achieved the most signiﬁcant growth in exports over time, reaching 70, 40,
85, and 67 percent of GDP in 2006, respectively. The corresponding export/GDP ratios in 1995 were 50,
23, 57, and 51 percent, respectively. Estonia, Hungary and Latvia did not have as much exports growth
as these countries did, as their export ratios stayed relatively stable. On the imports side, we see a
similar trend as well. In 1995, Poland had the smallest imports to GDP ratio (21 percent), but it
increased signiﬁcantly over time, reaching 41 percent in 2006. Estonia had the largest imports ratio of
78 percent in 1995 and reached almost 90 percent in 2006. Other countries also exhibited a large
increase in imports over time. Overall, there was a signiﬁcant growth in international trade ﬂows
during 1995–2006 period.
Fig. 5 shows the distance parameter as a measure of the rate of technology transfer from the
frontier (EU15). The visible downward trend in all of the new member countries points toward the
decline in the productivity gap and positive spillovers from the frontier to the less productive
countries. Figs. 6–8 show the trends in our domestic innovation variables, namely, R&D expenditures,
education and capital formation, respectively.
4. Methodology, data and results
The sample period for the estimations is selected to be between 1995 and 2006. The productivity
data is obtained from the ‘‘Transition Reports’’ of the EBRD (change in labor productivity of industry),
while the rest of the data comes from AMECO of the European Commission’s Directorate General for
Economic and Financial Affairs and the EuroStat. The indicators are divided between domestic (R&D
Fig. 4. CEE8 imports.
Fig. 5. Productivity gap between EU15 and CEE8 economies.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137132
expenditure, investment and secondary education) and international (productivity gap, imports,
exports and FDI) variables. They are calculated as a percentage of GDP except for Education (% of
population) and for Productivity Gap (referred to as Distance in the regressions) as the absolute value
of the log ratio of productivity of country i to the productivity of EU15. Table 1 provides descriptive
statistics.
The estimations are carried out using a ﬁxed effects panel estimation correcting for potential
heteroskedasticity in the cross sectional dimension. The time lag for the explanatory variables are
Fig. 6. Research and development expenditures.
Fig. 7. Human capital.
Fig. 8. Capital formation.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137 133
chosen with respect to (i) the transmission period expected a priori4
and (ii) the larger contribution of
the variables to the explanatory power of the model. Collinearity problems are ﬁxed (when faced) by
regressing the correlated variables on each other and using the residual of that regression. Examples
for such corrections are the distance-R&D pair and the interactive terms. The results are displayed in
Table 2. Analyzing the descriptive statistics in Table 1 shows that the variation in the distance and R&D
variables are very small compared to other variables in the model. Consequently, we address the high
level of correlation between the two variables ($0.9) by regressing distance on all other explanatory
variables and using the auxiliary regression residual as the proxy for this variable. The ﬁrst column in
Table 2 shows the estimation of Eq. (3) without the interactive terms. The results show that the
implications of our theory are mostly true. The rate of innovation in the CEE8 economies seems to be
positively affected by the absorptive capacities (both domestic and international factors) of these
economies.
Among the domestic factors, the R&D expenditure of the CEE economies does not have any impact
on productivity changes. However, secondary school enrollment ratio has a very signiﬁcant effect on
productivity changes, implying that higher education levels improve the environment for
technological innovations. We also include ﬁxed capital formation to account for the productivity
impact of the varying utilization of physical capital over the business cycle. Although its coefﬁcient
seems to be insigniﬁcant, it contributes to the overall explanatory power very signiﬁcantly, hence we
opt to include it in our regressions. Its positive coefﬁcient indicates that increases in the utilisation of
physical capital over the business cycle lead to a rise in productivity.
The international factors in Table 2 also seem to play quite important roles as the determinants of
productivity. Initially, the distance variable, i.e., the productivity gap, has a signiﬁcant and positive
sign, which implies that widening of the gap to the frontier leads to increases in productivity of the CEE
countries. Second and as expected, the exports variable has a positive signiﬁcant coefﬁcient, validating
the Melitz argument of increasing productivity through selection into the export market. However,
imports seem to have a negative effect, likely due to a combination of high-cost of foreign
intermediaries relative to domestic intermediaries, a relatively higher usage of the latter in domestic
production and other relevant factors. FDI on the other hand has a signiﬁcant and positive effect on
productivity, conﬁrming the claims of technological spillovers or labor force training.
The other columns add the interactive terms in the regression equation one by one. Again to resolve
the problems of using the interactive term with its individual components (Ozer-Balli and Sorensen,
2008), we use the residuals from an auxiliary regression of the joint term on the individual parts. Both
the signs and the explanatory power of the model show that these terms are not too relevant to
increases in the industry productivity. The interactive term for R&D especially is problematic (again
probably due to low variation) and moves the R&D parameter wildly, hence is kept out of the rest of
Table 1
Descriptive statistics: mean (Variance).
Prod. growth Distance R&D Education Import Export FDI
CZE 3.7 (59.2) 1.28 (0.01) 1.2 (0.03) 86.3 (11.5) 62.6 (43.4) 60.8 (78.0) 5.7 (11.3)
EST 9.0 (58.1) 2.05 (0.06) 0.6 (0.15) 85.7 (14.3) 74.2 (38.7) 74.8 (25.8) 8.0 (24.0)
HUN 6.2 (12.7) 1.60 (0.01) 0.8 (0.02) 69.8 (39.7) 63.0 (116.4) 22.2 (1.2) 7.3 (12.9)
LAT 7.7 (38.9) 2.28 (0.04) 0.4 (0.01) 81.9 (8.8) 54.2 (36.0) 43.7 (7.1) 4.5 (3.5)
LIT 8.6 (47.9) 2.43 (0.05) 0.6 (0.01) 83.9 (11.5) 58.8 (33.2) 50.4 (31.7) 3.5 (3.8)
POL 5.6 (12.6) 1.71 (0.02) 0.6 (0.00) 79.6 (18.2) 31.9 (36.6) 29.1 (39.5) 3.4 (1.1)
SLA 4.0 (23.9) 1.81 (0.01) 0.7 (0.04) 83.1 (23.1) 73.3 (96.6) 67.8 (107.4) 4.5 (22.2)
SLE 3.3 (32.8) 0.74 (0.01) 1.5 (0.01) 74.9 (23.1) 57.0 (23.8) 55.7 (27.3) 1.9 (3.5)
EU15 1.9 (0.01) 61.3 (15.5) 31.6 (11.8) 35.6 (11.7) 2.3 (2.8)
Productivity growth refers to labor productivity at the industry level. Distance represents the last period’s productivity gap of
country i (in absolute value of logs) to EU15. All variables except distance are measured in percentages (of GDP or of population).
4
Variables like education and imports are expected to have a lagging effect on technology while exports and FDI are expected
to have synchronous effects. Hence, we use one lagged variables of imports, education and R&D expenditures, and
contemporaneous variables of exports, FDI and investment.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137134
the regressions. These results show that the technology transfer rate is not affected by domestic and
international variables accounted for in our model. We need to be cautious in interpreting these
results, especially with the interactive terms due to declining degrees of freedom and little variation in
the distance variable.
We run robustness checks especially for endogeneity.5
For endogeneity, we run Wu (1973) test to
ﬁnd that when we use the contemporaneous values for the independent variables, we do not run into
endogeneity problems. The second test we run is dynamic panel data estimation due to the possibility
of industry growth being very closely related with the distance variable constructed by using output
per labor. These tests also show that the standard errors of the coefﬁcient estimates increase but their
signs do not change.
5. Conclusion, policy implications and suggestions for further research
We have examined the role of innovation and the rate of technology transfer in explaining
productivity at the industry level in eight Central and Eastern European countries that joined the
Table 2
Productivity growth estimates.
(1) (2) (3) (4) (5) (6) (7)
C À54.05**
À53.11**
À53.81**
À51.44**
À62.68**
À61.81**
À55.43
(À3.18) (À3.03) (À3.15) (À2.8) (À3.11) (À2.91) (À3.36)
Distance(-1) 9.69**
9.15*
10.38*
9.98*
9.57*
9.47 8.62
(2.04) (1.86) (1.88) (1.76) (1.68) (1.64) (1.84)
R&D(-1) 0.30 À1.22 0.45 0.36 0.31 0.33 0.55
(0.05) (À0.2) À0.08 (0.06) (0.05) (0.06) (0.1)
Education(-1) 0.73**
0.76**
0.73**
0.70**
0.96**
0.95**
0.83
(3.83) (3.74) (3.75) (3.39) (3.53) (3.38) (4.28)
Imports(-1) À0.58**
À0.57**
À0.58**
À0.57**
À0.58**
À0.58**
À0.58
(À6.21) (À5.91) (À6.20) (À6.12) (À5.91) (À5.60) (À6.54)
Exports 0.50**
0.47**
0.51**
0.51**
0.40**
0.40**
0.43
(3.55) (3.08) (3.30) (3.21) (2.39) (2.37) (2.93)
FDI 0.35**
0.38**
0.34**
0.33**
0.40**
0.40**
0.36
(2.73) (2.77) (2.58) (2.35) (2.66) (2.55) (2.86)
Investment 0.25 0.23 0.24 0.24 0.09 0.08 0.16
(1.34) (1.19) (1.20) (1.19) (0.39) (0.37) (0.79)
D*RD 11.06
(0.74)
D*Education 0.14 0.13 0.04 0.05
(0.26) (0.23) (0.08) (0.09)
D*Imports À0.09 0.29 0.28
(À0.36) (0.86) (0.80)
D*Exports À0.51 À0.51 À0.34
(À1.54) (À1.49) (À1.47)
D*FDI À0.08
(À0.12)
Adj. R2
0.411 0.389 0.406 0.397 0.382 0.389 0.444
N 78 78 78 78 78 78 78
Note: t-statistics are reported inside the parentheses. Distance represents the last period’s productivity gap of country i (in
absolute value of logs) to EU15. All variables except distance are measured in percentages (of GDP or of population).
5
Heteroskedasticity is addressed with the cross sectional weights, and autocorrelation tests are very unreliable due to the
shortness of the data series.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137 135
Union since 2004. Innovation variables are divided into global (FDI, exports and imports) and domestic
(education, R&D and investment) factors to determine whether globalization and integration factors
have contributed to labor productivity more than domestic ones. Technology transfer rate is shown to
be related to the distance in productivity levels between individual CEE8 countries and EU15 and is
also used as a proxy for convergence in technical efﬁciency.
Regarding international innovation variables, we have found that all factors play a signiﬁcant
role in explaining labor productivity. However, FDI and exports are found to increase productivity
growth, while imports reduce it. Given scant studies on the impact of imports on productivity
and mixed available empirical evidence, our ﬁndings may shed some light on this highly
debatable issue now. Our evidence is at the aggregate level and some policy can be deduced from
aggregate impacts, but further research at the micro level is certainly necessary to better
understand the imports-productivity link in CEE8 countries to help policymakers design policies
accordingly.
Regarding domestic innovation variables, we found that education and investment variables are
signiﬁcant and positive, while R&D is not. CEE8 countries have a highly educated and skilled labor
force and our results show that this factor has been instrumental in improving productivity. Relatively
small amount of R&D expenditures (about 1–2 percent of GDP) in CEE8 countries may explain why this
variable does not contribute much to labor productivity.
With respect to technological transfer, the evidence indicates a positive and statistically signiﬁcant
impact of distance from technological frontier on productivity growth, indicating signiﬁcant
technology transfer facilitating ‘‘catching up’’ by CEE8 countries towards EU15 productivity. However,
our international and domestic variables do not seem to help facilitate this technology transfer. We
attribute this unintuitive result to the relatively small number of observations we have.
Overall, our results suggest that globalization and integration factors affect labor productivity
growth directly through rates of innovation, if not indirectly through distance from the technological
frontier or technology transfer. An important policy implication of the results is that further policy
attempts geared towards designing innovation-encouraging policies would have signiﬁcant payoffs.
Research at the micro level on the potential sources of the effects of imports on productivity would be
useful. Our ﬁnding that variables typically used as determinants of productivity (such as R&D,
international trade, FDI) may not facilitate technology transfer suggests that there may be some
informal channels present, such as the role of informal economy. Further research using longer
samples and including more formal and some informal indicators of technological transfers from the
frontier is necessary to generalize the ﬁndings of this study. We hope that our results provide some
yardstick which can be used for comparison purposes.
References
Aitken, B.J., Harrison, A.E., 1999. Do domestic ﬁrms beneﬁt from direct foreign investment? Evidence from Venezuela. American
Economic Review 89 (3), 605–618.
Bernard, A., Jones, C., 1996a. Productivity across industries and countries: time series theory and evidence. Review of Economics
and Statistics 78, 135–146.
Bernard, A., Jones, C., 1996b. Comparing apples to oranges: productivity convergence and measurement across industries and
countries. American Economic Review 1, 216–238.
Blalock, G., Veloso, F.M., 2007. Imports, productivity and supply chain learning. World Development 35, 1134–1151.
Blomstrom, M., Persson, H., 1983. Foreign investment and spillover efﬁciency in an underdeveloped country: evidence from the
Mexican manufacturing industry. World Development 11, 493–501.
Brada, J.C., Kutan, A.M., Yigit, T.M., 2006. The effects of transition and political instability on foreign direct investment inﬂows:
Central Europe and the Balkans. Economics of Transition 14, 649–680.
Borensztein, E., De Gregorio, J., Lee, J.W., 1998. How does foreign direct investment affect economic growth? Journal of
International Economics 45, 115–135.
Cameron, G., Proudman, J., Redding, S., 2005. Technological convergence, R&D, trade and productivity growth. European
Economic Review 49, 775–807.
Coe, D.T., Helpman, E., 1995. International R & D spillovers. European Economic Review 39, 859–887.
de Loecker, J., 2007. Do exports generate higher productivity? Evidence from Slovenia. Journal of International Economics 73,
69–98.
Engelbrecht, H., 1997. International R & D spillovers, human capital and productivity in OECD economies: an empirical
investigation. European Economic Review 41, 1479–1488.
Fan, E.X., 2002. Technological spillovers from foreign direct investment–a survey. Asian Development Bank ERD Working Paper
33, Manila.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137136
Fernandes, A.M., 2007. Trade policy, trade volumes, and plant-level productivity in Colombian manufacturers. Journal of
International Economics 7, 52–71.
Grifﬁth, R., Redding, S., Reenen, J.V., 2004. Mapping the two faces of R & D: productivity growth in a panel of OECD industries.
Review of Economics and Statistics 86, 883–895.
Halpern, L., Koren, M., Szeidl, A., 2006. Imports and Productivity. Federal Reserve Bank of New York, Mimeo, New York.
Hunya, G., 1996. Foreign Direct Investment in Hungary: A Key Element of Economic Modernization. The Vienna Institute for
Comparative Economic Studies Research Report 226, Vienna.
Javorcik, B.S., 2004. Does foreign direct investment increase the productivity of domestic ﬁrms? In search of spillovers through
backward linkages. American Economic Review 94, 605–627.
Kasahara, H., Rodrique, J., 2008. Does the use of imported intermediates increase productivity? Plant-level evidence. Journal of
Development Economics 87 (1), 106–118.
Keller, W., Yeaple, S.R., 2003. Multinational enterprises, international trade, and productivity growth: ﬁrm level evidence from
the United States. NBER Working Paper 9504, Cambridge, MA.
Kutan, A.M., Yigit, T.M., 2007. European integration, productivity and convergence. European Economic Review 51, 1370–1395.
Melitz, M., 2003. The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71,
1695–1725.
Muendler, M.A., 2004. Trade, technology and productivity: a study of Brazilian manufacturers 1986–1998. CESifo Working
Paper 1148, Munich.
OECD, 2001. Review of foreign direct investment in Estonia. Available online at: http://www.investinestonia.com/pdf/OEC-
DReviewFDIEstonia.pdf.
Ozer-Balli, H., Sorensen, B., 2008. The Use of Interaction Effects in Econometrics. University of Houston, Mimeo.
Rada, C., Taylor, L., 2006. Developing and transition economies in the late 20th century: diverging growth rates, economic
structures, and sources of demand. The United Nations Department of Economic and Social Affairs (DESA) Working Paper
34, available at: http://www.un.org/esa/desa/papers/2006/wp34_2006.pdf.
Sgard, J., 2001. Direct foreign investments and productivity growth in Hungarian ﬁrms, 1992–1999. Centre d’Etudes Prospectives
et d’Informations Internationales Working Paper 19, Paris.
Sˇohinger, J., Harrison, G.W., 2004. The implications of foreign direct investment for development in transition economies.
Eastern European Economics 42, 56–74.
Voicu, I., 2004. Are foreign subsidiaries technologically superior to local ﬁrms? Evidence from Romania. Eastern European
Economics 42, 5–32.
World Bank, 2008. Unleashing prosperity: Productivity growth in Eastern Europe and the former Soviet Union. Available at:
http://go.worldbank.org/NS2LJ69070.
Wu, D., 1973. Alternative tests of independence between stochastic regressors and disturbances. Econometrica 41, 733–750.
Xu, B., 2000. Multinational enterprises, technology diffusion, and host country productivity growth. Journal of Development
Economics 62, 477–493.
A.M. Kutan, T.M. Yigit / Economic Systems 33 (2009) 127–137 137