ISSN 1392-1258. ekonomika 2015 Vol. 94(1)
THE EVALUATION OF OUTPUT CONVERGENCE
IN SEVERAL CENTRAL AND EASTERN
EUROPEAN COUNTRIES
Simionescu M.*
Institute for Economic Forecasting of the Romanian Academy
from Bucharest, Romania
Abstract. The main objective of this study is to check the convergence in output for six countries from Central-Eastern Europe that are also members of the European Union. A slow convergence was obtained only for
Greece during 2003–2012, for the rest of the countries (Bulgaria, Croatia, Hungary, Poland and Romania) the
divergence being observed. The regression coefficients were estimated using bootstrap simulations in order
to solve the problem of a small data set. However, the graphical representations suggested a convergence for
Bulgaria and Romania, the assumption proved also by the application of the Augmented Dickey Fuller unit root
test. There is no evidence of the convergence of each country towards Greece, this country having a specific
evolution of its GDP with higher values than the rest of the countries.
Key words: convergence, GDP per capita, ADF tests, co-integration, steady state
Introduction
The accession of eight Central and Eastern European countries (CEEC) in 2004 was an
important event in the enlargement of the European Union (EU). Then, Bulgaria and
Romania joined the European community in 2007, and they increased the number of
the former Communist Bloc countries among the EU members. After the transition to
market economy and consistent transformations in the legal and political systems, these
countries have to catch up with the economies of Western Europe. Therefore, the economic convergence was an important task for all these countries, and common structural
and monetary policies were required. The increase of the GDP per capita convergence
was required along the road to the EU accession. On the other hand, the Maastricht
Treaty imposed criteria for achieving nominal and real convergence before entering the
European Monetary Union (EMU). So, the problem of economic convergence is essential for CEEC.
* Corresponding author:
Institute for Economic Forecasting, Romanian Academy, 13, Calea 13 Septembrie, District 5, Bucharest, 050711,
Romania.
E-mail: [email protected]
42
The main aim of this study is to establish if there is a GDP per capita convergence
for some countries of the Central-Eastern Europe that are also members of the European
Union. Different econometric methods were applied to study the long-term behavior
of GDP per capita. The results showed that only Greece’s output per capita converges
towards a steady state level, but the rest of the countries (Bulgaria, Croatia, Hungary,
Poland, and Romania) do not converge to steady state and to the average change of
GDP per capita from Greece. Given that Bulgaria, Croatia, Hungary, Poland and Romania have common roots and their economies experienced very similar challenges over
time, they were included in the analysis. The regional convergence of the CEE countries
can be considered an intermediary step of the CEE direct participation in the European
Monetary Union.
The economic convergence is defined by taking into consideration that poorer countries
have to advance faster than richer ones when various countries are at points relative to
their balanced growth paths and when structural differences among the countries are considered. The rate of convergence permits the calculation of the speed of convergence of an
economy towards its steady state. The economic divergence reflects the situation when the
poor countries are not able to reduce the gap between them and the rich countries.
A consistent part of the literature regarding the economic convergence in the EU considered the development gap between the European Union and the Balkans. For example, Ouardighi and Kapetanovic (2009) proved that the income convergence was really
higher during the 2000s for the EU-27. Most of the economic convergence was observed
in the second half of the 1990s for the Balkan countries.
The rest of the paper is organized as follows. After a brief literature review, the methodology framework is presented. The empirical study for some Central-Eastern countries is described, and conclusions are drawn in the last section.
Section I. Literature review
For testing the common trends and the convergence hypothesis, some multivariate methods were used by Bernard and Durlauf (1995), showing the output convergence of 15
OECD countries. The tests are applied for trends that are linear independent stochastic,
the spectral density matrix being studied has null frequency. If all the countries satisfy
the convergence assumption, the rank of this matrix for output deviations from the reference country is null. Using the Johansen (1991) test for co-integration, the convergence
assumption supposes the presence of p-1 co-integrating vectors. According to Evans and
Karras (1996), the convergence of N economies is ensured if the logarithm of output yit
is not stationary, but the deviation from the average (yit – yt̵ )) is stationary.
Estrin and Urga (1997) tested the convergence of GDP in transition countries from
Central and Eastern Europe in the period from 1970 to 1995, obtaining little evidence for
43
convergence using the co-integration analysis, time-varying coefficients procedure and
the unit root approach. The same poor evidence for convergence and post-communist
countries was obtained by Estrin, Urga and Lazarova (2001) for 1970–1998.
Bijsterbosch and Kolasa (2010) made an industry-level investigation for studying
the FDI and productivity convergence in Central- Eastern Europe. The authors have obtained a strong convergence in productivity for the countries from this region.
Azomahou, El ouardighi, Nguyen-Van and Cuong Pham (2011) proposed a semi-parametric partially linear model to assess the convergence among the EU countries, showing
that there is no convergence for members with a high income. Beyaert and García-Solanes
(2014) measured the impact of economic conditions on long-term economic convergence.
The convergence in terms of GDP/capita is different from that of the business cycle during
1953–2010. Cuaresma, Havettová and Lábaj (2013) evaluated the income convergence dynamics and proposed some forecast models for European countries. The authors predicted
that the human capital investment will determine income convergence.
Palan and Schmiedeberg (2010) tested the structural convergence in terms of the unemployment rate for Western European countries, observing divergence for technologyintensive manufacturing industries. Le Pen (2011) utilized the pair-wise convergence of
Pesaran for the GDP per capita of some European regions.
Crespo-Cuaresma and Fernández-Amador (2013) determined the convergence patterns for the European area business cycles. In the middle of the 80s there was an obvious business cycle divergence while in the 90s the convergence was persistent.
Kutan and Yigit (2009) used a panel data approach for 8 new countries in the EU and
stated that the productivity growth was determined by human capital in the period from
1995 to 2006. Monfort, Cuestas and Ordóñez (2013) utilized a cluster analysis, putting
into evidence the existence of two convergence clubs in the EU-14, the Eastern European countries being divided into two groups. Andreano, Laureti and Postiglione (2013)
assessed the economic growth of North African and Middle Eastern countries using the
conditional beta-convergence. Iancu (2009) assessed the real convergence using the sigma approach in the EU members considering three groups: EU-10, EU-15, and EU-25,
the results showing an increase of the divergence in the period from 1995 to 2006.
The novelty of our research is brought by the application of the methodology for assessing the economic convergence for several CEEC countries and on the time period
that was not analyzed before in the literature. We chose six particular countries from
Central and Eastern Europe with similar trends of GDP per capita (Bulgaria, Croatia,
Hungary, Poland, Romania, and Greece). Except Greece, the other countries are recent
members of the European Union (Croatia from 2013, Bulgaria and Romania from 2007,
Poland and Hungary from 2004). However, the mentioned period included the economic
crisis that strongly affected Greece’s GDP per capita.
44
economic crisis that strongly affected Greece GDP per capita.
Section II. Methodology
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46 Section III. The assessment of output convergence
Section III. The assessment of output convergence
In this study, we have used the data for annual GDP per capita of six countries from Central-Eastern Europe that are member of the European Union: Bulgaria, Croatia, Greece,
Hungary, Poland, and Romania. The data series are provided by the Eurostat (GDP per
capita in PPS) covering the period from 2003 to 2012. The data series length is small,
and the coefficients are estimated using bootstrap simulations. 10 000 replications are
made by re-sampling the error terms.
4.6
4.4
4.2
4.0
3.8
3.6
3.4
03
04
05
06
07
BG
CR
GREECE
08
09
10
11
12
HUN
POL
ROM
FIG. 1. The evolution of the logarithm of GDP per capita in the six European countries
Source: author’s graph.
In Table 1, the values of the AIC indicator for different models of squared demanded
GDP per capita for the six countries are displayed. From this table, we select the models that are the best for each country, the polynomials having different degrees for the
selected countries. For Bulgaria, Croatia, and Romania the degree of the polynomials is
2, for Hungary and Poland 3, and for Greece 5. These econometric results conduct us
to conclude that from the economic point of view Bulgaria, Romania, and Croatia have
similar trends of GDP per capita in the analyzed period. On the other hand, Greece is
placed in a separate cluster with the levels of GDP per capita and the trend that are different as compared to the other countries in the group of analysis.
The results of the Jarque–Bera test for each best model show us that there is not
enough evidence to reject the normality hypothesis for the errors’ distribution.
47
TABLE 1. The value of Akaike Information Criterion for different models of squared demeaned GDP
per capita for the six countries
Model wit = fk(t) + uit
fk(t) = θ0 + θ1t + θ2t2 + ... + θk–1tk–1 + θktk
Country
K
Bulgaria
Croatia
Greece
Hungary
Poland
Romania
1
-1.3852
-3.141
-2.2806
-4.0576
-1.4324
-0.5752
2
-4.7870
-3.8168
-2.8738
-5.0103
-4.5771
-2.9570
3
-3.2848
-3.5196
-3.3208
-5.1761
-4.9154
-1.9386
4
-2.6191
-3.3484
-3.7724
-5.0832
-3.7065
-1.4407
5
-2.2724
-3.2549
-4.1788
-4.9052
-3.0658
-1.1720
Source: author’s calculations.
TABLE 2. Estimates and average slopes and the p-value associated to t-ratios for testing the convergence in the six countries
Country
Bulgaria
Croatia
Greece
Hungary
Poland
Romania
Average slope for output gap
0.0377
0.0121
-2.04E-05
7.42E-05
0.00031
0.055
p-value associated with t-ratio
0.0000
0.0102
0.0001
0.0023
0.0000
0.0000
Source: author’s calculations.
A negative average slope was registered only for Greece, only for this country being
available the evidence of convergence. For the rest of the countries, the positive average
slopes indicate a divergence. So, Greece tends to an economic convergence in the output
per capita, the fact that is not met yet in other CEEC countries like Bulgaria, Croatia,
Hungary, Poland, and Romania.
The estimates of the average slopes can be interpreted as the average convergence
rate for each country towards the average level of the six countries. The convergence
rate for Greece is very low (0.0024%), while for Bulgaria, Croatia, Hungary, Poland, and
Romania the divergence rates are 3.761%, 1.209%, 0.0074%, 0.0314%, and 5.496%. For
Hungary and Poland the divergence rate is very low.
According to the cluster analysis based on k-means, there is an evidence of ascending
values in the period from 2003 and 2007. The crisis period (2008–2012) is characterized
by the descending values of the output for all the six countries.
The following table reports the results of the Augmented Dickey–Fuller test for unit
roots of the GDP per capita gap from Greece. These models include only a constant without trend. According to Bernard and Durlauf (1995), the rejection of the null hypothesis
of the unit root supposes convergence.
48
5
4,5
Gr
4
3,5
Bg
3
Cr
Hun
Pol
Rom
2,5
average (log gdp /cap))
2
1,5
1
0,5
0
0
2
4
6
8
FIG. 2. The distribution of the average of the logarithm of GDP per capita for the
six countries
Density
Source: author’s graph.
FIG. 3. The Kernel density estimate for the output average of the six countries
during 2003-2012
Source: author’s graph.
The results of ADF tests show that there is no evidence of convergence for any country towards Greece’s output. The graphical representation also shows higher values for
Greece. According to the graph, three convergence clubs might be identified (the cluster
of the most recent members of the EU – Croatia, Bulgaria and Romania, the cluster represented by Poland and Hungary and the cluster with a former country like Greece). The
ADF test is applied again for each group of two countries for which the output might
converge.
The ADF test for a comparison between Romania and Bulgaria indicates divergence
between the two countries even if the graph shows a close value of the output for Bul49
garia and Romania. It should not be surprising that the ADF test gives a misleading
impression. The value of AR(1), which is very close to unit, implies the non-rejection of
the null hypothesis of the unit root. The model with trend has alleviated this conclusion,
suggesting the rejection of the null hypothesis. For the rest of the groups of the countries,
the ADF tests did not suggest the evidence of convergence.
TABLE 3. Augmented Dickey–Fuller (ADF) tests for the GDP per capita gap from Greece
Country
Value of ADF statistics
1.037353
0.050255
0.192226
1.424165
0.285654
Bulgaria
Croatia
Hungary
Poland
Romania
Conclusion
Do not reject the hypothesis of unit root
Do not reject the hypothesis of unit root
Do not reject the hypothesis of unit root
Do not reject the hypothesis of unit root
Do not reject the hypothesis of unit root
The models include the intercept but not the trend. The critical value for the ADF test is–3.3350 for the 5%
level of significance.
Source: author’s calculations.
TABLE 4. Augmented Dickey–Fuller (ADF) tests for the GDP per capita gap for groups of countries
Cluster
Value of ADF statistic
(only intercept is included)
Value of ADF statistic
(intercept and trend are
included)
Bulgaria–Romania
-1.351395
-0.034982
Poland–Hungary
0.708173
-3.560869
Poland–Croatia
-0.540680
-2.223739
Hungary–Croatia
-2.322237
-2.018181
Final conclusion
Reject the hypothesis of unit
root
Do not reject the hypothesis
of unit root
Do not reject the hypothesis
of unit root
Do not reject the hypothesis
of unit root
The critical value for ADF test with intercept is –3.3350 and for the model with trend and intercept –4.1961
at the 5% level of significance.
Source: author’s calculations.
The limit of this approach is that the rejection of the unit root does not necessarily suppose the convergence. A significant trend must be positive. Even if it is positive
and the error is stationary, the convergence is not surely ensured because the long-run
predictions of the output gap will not converge to zero. So, a relative convergence was
observed in the pairs of countries like Poland–Hungary, Poland–Croatia and HungaryCroatia. This indicates that countries like Poland, Hungary, and Croatia made efforts to
increase their GDP per capita level.
50
Section IV. Conclusions
In this research, we have checked if there is a convergence in the GDP per capita for six
individual economies from Central-Eastern Europe towards a common steady state level.
A slow convergence was obtained only for Greece during 2003–2012, for the rest of the
countries (Bulgaria, Croatia, Hungary, Poland, and Romania) the divergence being present.
However, the graphical representations suggested a convergence for Bulgaria and
Romania, the assumption was proved also by the application of the Augmented Dickey Fuller unit root test. There is no evidence of convergence of each country towards
Greece, this country having a specific evolution of GDP with higher values than the rest
of the countries.
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