Ali M. Kutan

M. Taner Yigit

English

Research Papers in Economics

 
 
 
Convergence of Candidate Countries to the European Union 
 
By 
 
Ali M. Kutan* and Taner M. Yigit** 
Abstract 
 
Ten European Union (EU) candidate countries are scheduled to join the Union by 
2004. A key requirement to join the Economic and Monetary Union is real and 
financial convergence to EU standards. Using recent panel unit root techniques, we 
find strong evidence of price level convergence, but not real convergence. Thus, an 
early peg to the Euro and a quick adoption of the Euro as a national currency is 
feasible for the candidates, but the benefits of joining the Euro zone are as yet 
limited.  
 
 
* Kutan - Southern Illinois University Edwardsville, the William Davidson Institute 
(WDI), Michigan, and the Center for European Integration Studies (ZEI), Bonn.  
 
 
** Yigit - Bilkent University, Ankara, Turkey.   2
 
     
I.  Introduction 
A key goal of transition economies of Central and Eastern Europe and the Baltic 
States to join the European Economic and Monetary Union (EMU), after entering the 
European Union (EU). Most recently, ten candidate countries are scheduled to join the 
Union by early 2004. These countries include Cyprus, the Czech Republic, Estonia, 
Hungary, Latvia, Lithuania, Malta, Poland, the Slovak Republic, and Slovenia. To the 
extent that countries display similar economic performance over time and gear policies 
towards EU standards, we expect more real and financial convergence in macroeconomic 
fundamentals towards the EU as the impact of initial conditions decline over time. The 
signing of the Copenhagen and Maastricht Treaties indicates that Europe supports a 
parallel approach in that both real and nominal convergence is emphasized 
simultaneously. This paper tests whether such convergence is achieved between the ten 
candidate countries and the EU.  
There are limited studies that investigate the issue of convergence of candidate 
economies to EU standards (Brada and Kutan, 2001; Korhonen and Fidrmuc, 2001; 
Richards and Tersman, 1996; Backé et al., 2002). While most of these papers do not 
provide comprehensive evidence on convergence either in terms of country coverage or 
the type of convergence (real versus nominal) to the EU standards
1, one needs to note 
their reliance on time series data methodology with fairly small datasets. Some studies 
cover leading transition economies, while others focus only on selected candidate 
                                                 
1 Kočenda (2001) and Kutan and Yigit (2003) focus on real and economic convergence, but only among 
transition economies, not convergence of these economies to those of the EU.   3
countries. On the other hand, some work focuses on nominal convergence, while others 
study only real convergence of transition economies to EU standards. 
    In this paper, we execute a comprehensive study that covers all the 10 candidate 
countries and examine both nominal and real economic convergence.  We also employ a 
set of panel approaches that not only allows us to test convergence much more reliably 
than by just using time series evidence, but also help us maintain the assumption of cross 
country differences. In the next section, we describe our panel methodologies.  Section III 
explains our data and reports the empirical findings.  Section IV discusses the policy 
implications of our findings and concludes the paper. 
 
II.  Methodology 
In the past decade, a wide variety of empirical work on neoclassical growth model 
was undertaken. One branch of these studies has utilized time series methodology to test 
for the key proposition of convergence hypothesis. Based on mostly unit root tests, these 
papers focus on capturing the persistence of shocks relative to per capita incomes.
2 Such 
(stochastic) convergence applies, if per capita income disparities between economies 
follow a mean-stationary process, i.e. relative per capita income shocks lead to transitory 
deviations from any tendency toward convergence.  
          Recently,  panel  unit  root  tests have been adopted to address the low power 
associated with univariate tests, increasing the power by the square root of the cross 
sectional units when testing for convergence. Some (certainly not all) tests include Quah 
(1992), Levin, Lin and Chu (2002), Maddala and Wu (1999), Hadri (2000), and Im, 
                                                 
2 Earlier papers concentrated on the notions of β convergence (where poor countries grow faster than rich 
ones) and σ convergence (where income variance between poor and rich countries is diminishing). Our 
Deleted: s of  4
Pesaran and Shin (IPS, 2003). In this paper, we focus on the last two due to their superior 
size and power properties and their permission for heterogeneity of cross sectional units 
within the panel.  
The Im, Pesaran and Shin (2003) test improves the power of the univariate 
Augmented Dickey-Fuller procedures  
  () () () ,, 1 1 , , ,
1
p
it t i i it t ik it k t k it
k
yy y y y y u δρ φ −− − −
=
∆− = + − + ∆ − + ∑  (1) 
where  , it t yy −  is the income disparity from mean output (or benchmark economy) of i = 
1,..,N countries at time t. Their method pools N separate independent ADF regressions, in 
which values of ρi less than 1 indicate that disparity from the mean is decreasing with 
time. Testing  0 :0 i H ρ =  (the null of unit root) for all i against  :0 Ai H ρ <  for at least one 
i, they show that the limiting distribution for their t-statistic is given as:  
  ( ) ()
2 N0 , 1
ADF ADF
ADF
t
N
µ
σ
−
→  (2) 
 
where the moments  ADF µ and 
2
ADF σ  are obtained from Monte Carlo simulations, and  ADF t  
is the average estimated ADF t-statistics from the sample. Their simulations show that the 
power of the tests should increase by  N  when compared with univariate models.  
An important assumption of the IPS technique is the iid error structure. When this 
assumption is violated and residuals are contemporaneously correlated, Maddala and Wu 
(1999) and Strauss and Yigit (2003) show that this technique will suffer from significant 
size distortions, which do not disappear by simple demeaning. Therefore, for the 
                                                                                                                                                 
analysis concentrates on stochastic convergence, which does not necessitate each country to converge to the 
same steady state.   5
remaining part of the paper we make size adjustments by deriving new critical values for 
the IPS test.  
Next, we utilize a test that has the null of stationarity rather than that of the unit 
root. Since it is well known that panel tests with the null of unit roots might not be very 
powerful against relevant alternatives, we utilize the Hadri (1999, 2000) stationarity test 
to check for real and nominal convergence. Building on a model 
 
1
it it it
it it it
yr
rr u
ε
−
= +
= +
 (3)   
and testing 
22
12 0 :0 ... uu H σσ == =  vs. the alternative 
2 0 ui σ >  for some i, they show that 
their one-sided LM statistic  
 
2 1
2
1
1
1
ˆ
T
N it t
i i
S
T LM
N ε σ
=
=
⎛⎞
⎜⎟
= ⎜⎟
⎜⎟
⎝⎠
∑
∑  (4) 
where  it S  is the partial sum of residuals, 
1 ˆ
t
it ij j S ε
= =∑ , asymptotically has a normal 
distribution. Their simulations show good size and power of this test, especially when T 
is above 50. Monte Carlo simulations not reported in our paper show that the Hadri test is 
less prone to size distortions that are caused by contemporaneous correlation. 
 
III.  Data and Results 
  We use monthly data from 1993:01 to 2001:12 to test for convergence in annual 
growth rates in monthly output (industrial production), price (PPI and CPI), and nominal 
and real interest rate spreads
3 series for the 10 countries chosen as the first round 
                                                 
3 Spread is measured by the difference between lending and deposit rates. Real spread is constructed by 
subtracting inflation from the nominal spread.   6
candidates for EU membership
4. Germany is used as the benchmark economy 
representing the EU standards. The data used in estimations are obtained from 
International Financial Statistics of the IMF. Descriptive statistics are reported in Table 
1. 
In this paper, real convergence is measured by the industrial production variable. 
Analysis of nominal convergence starts with tests of monetary policy convergence. 
Although some studies use monetary aggregates (e.g., Brada and Kutan, 2001), we use 
interest rate spreads to measure monetary convergence because changes in nominal 
lending and interest rates directly affect demand and time deposits and thus the 
composition of money supply.  Hence, changes in monetary aggregates reflect more 
policy outcomes than actual implementation of the policy.  Another part of the tests for 
nominal convergence is the analysis of the CPI and PPI. They not only reflect monetary 
policy outcomes, but also the trade linkages between sample countries.  
Table 2 reports the IPS results and the progress made by the candidate economies 
as to their convergence to EU economic standards. As mentioned above, we generated 
new critical values using Monte Carlo simulations to correct for the size distortions that 
arise due to cross-correlation between the candidate countries (Table 3). We use the 
correlation matrix of the sample countries and 3000 iterations to derive the critical values 
for the mean and variance of the IPS t-bar statistic. Then we use these critical values to 
derive the z-statistic of the average (Table 2).  
Looking at the results for the industrial production, we observe significant real 
convergence. When we look at the findings for price level convergence, we seem to have 
                                                 
4 Again, these are Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, the 
Slovak Republic, and Slovenia.   7
strong convergence in both the CPI (at 99%) and the PPI (at 95%). Assuming that PPI 
linkages reflect more trade relationships, while CPI linkages capture more monetary 
policy outcomes, the finding of convergence in both the PPI than the CPI indicates that 
trade linkages have been as strong as monetary policy linkages. In other words, the 
candidate countries have adhered to EU monetary policy and targeted stronger trade 
relationship with the EU. The former is further confirmed by significant convergence in 
monetary policy, given by interest rate spreads.  
Examination of Hadri test results (Table 4) tells quite a different story, however. 
Concentrating on the Newey-West (1994) and Andrews-Monahan (1992) estimates of 
long run variance, as suggested by the author, we note that only the CPI and PPI price 
series fail to reject stationarity. The interpretation of our conflicting results likely lies in 
the averaging nature of both statistics. Since both statistics average individual statistics 
across  N, the outliers in each test that are ‘very’ stationary (for IPS) or ‘very’ non-
stationary (for Hadri) are the determining factors. More importantly, the results indicate 
that inferences on convergence are sensitive to econometric methodology employed.  
Comparing the IPS and Hadri tests results, we find strong evidence of price level 
convergence. However, it is hard to make more definite inferences about monetary policy 
or real convergence as both tests provide conflicting results.  Because both the CPI and 
PPI not only reflect monetary policy outcomes, but also the trade linkages between 
sample countries, finding no monetary policy convergence indicates that the candidate 
countries pursued strong trade linkages with the EU. Indeed, data show growing trade 
relations between the EU and candidate countries during 1990s and early 2000s.   8
 
IV. Policy Implications and Conclusion 
 
  We have tested real and monetary stochastic convergence of the ten EU candidate 
economies to the EU standards, using macroeconomic data from January 1993 to 
December 2001. Evidence indicates that the EU candidates have made significant 
progress in price level convergence with respect to EU, suggesting that these countries 
are satisfying the Maastricht conditions only partially. Strong price level convergence 
suggests significant trade linkages between the EU and candidate countries. Evidence on 
monetary policy convergence and real convergence is not as strong as the price level 
convergence, however.  
Lack of real economic convergence suggests that candidate countries’ supply and 
demand shocks may not be closely correlated with those of the EU members, indicating 
high costs of following Euro-zone wide policies. Thus, an early adoption of the Euro as a 
national currency would not be welfare enhancing for the candidate countries if such high 
costs due to reduced policy autonomy more than outweigh the benefits of reduced 
transactions costs by adopting the Euro. Hence, the accession countries need to evaluate 
the timing of their commitments to the Euro, and this may delay entering the Euro zone. 
The growing trade linkage should help the accession countries promote further real 
convergence over time. The candidate countries should therefore continue to foster trade 
relationships with the EU. On the methodology side, our results indicate the sensitivity of 
convergence inferences to different econometric specifications. Given the fact there is no 
commonly used methods of estimating convergence, this issue needs further scrutiny.    9
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Autocorrelation Consistent Covariance Matrix Estimator”, Econometrica, 1992, 
Vol. 60, 953-966. 
 
Backè, Peter, Fidrmuc, Jarko, Reininger, Thomas and Schardax, Franz.  “Price Dynamics 
in Central and Eastern European EU Accession Countries”, Working Paper No. 
61, Oesterreichische Nationalbank, February 2002. 
 
Brada, Josef C. and Kutan, Ali M.  “The Convergence of Monetary Policy between  
Candidate Countries and the European Union”, Economic Systems, Vol. 25, No.  
3, September 2001, pp. 215-231. 
 
Hadri, K. “Testing for Stationarity in Heterogeneous Panel Data” Econometrics Journal, 
2000, Vol. 3(2); pp. 148-161. 
 
Hadri, K. “Testing for Stationarity in Heterogenous Panel Data with Serially Correlated 
Errors” University of Liverpool Research Papers in Economics, Finance and 
Accounting, 1999/04. 
 
Im, K. S., Pesaran, M. H. and Y. Shin. “Testing for Unit Roots in Heterogeneous Panels” 
Journal of Econometrics July 2003, 115(1); pp. 53-74. 
 
Kutan, Ali and Taner Yigit. “Nominal and Real Stochastic Convergence of Transition 
Economies, 2003, forthcoming in Journal of Comparative Economics. 
 
Kočenda, Evžen.  “Macroeconomic Convergence in Transition Economies”, Journal of  
Comparative Economics, Vol. 29, No. 1, March 2001, pp. 1-23. 
 
Korhonen, Iikka and Fidrmuc, Jarko. “Similarity of Supply and Demand Shocks between 
the Euro Area and the Accession Countries”, Focus on Transition, No.2, 2001, 
pp. 26-42.  
 
Levin, Andrew; Chien-Fu Lin; and Chia-Shang J. Chu. “Unit Root Tests in Panel Data: 
Asymptotic and Finite-Sample Properties” Journal of Econometrics, May 2002, 
108(1); pp. 1-24. 
 
Maddala, G. S.and S. Wu, “A Comparative Study of Unit Root Tests with Panel Data and 
a New Simple Test” Oxford Bulletin of Economics and Statistics, Special Issue 
Nov. 1999, v. 61, iss. 0, pp. 631-52 
 
Newey, W.K. & K.D. West. “Automatic lag selection in covariance matrix estimation”, 
Review of Economic Studies, 1994, Vol. 61, 631-653. 
   10
Quah, D. “International Patterns of Growth: Persistence in Cross-Country Disparities”, 
unpublished manuscript, London School of Economics, 1992. 
 
Richards, Anthony J. and Tersman, Gunnar H.R.  “Growth, Nontradables, and Price
  Convergence in the Baltics”, Journal of Comparative Economics, Vol. 23, No. 2, 
October 1996, pp. 121-145. 
 
Strauss, J. and T. M. Yigit. “Shortfalls of Panel Unit Root Testing” Economics Letters, 
Vol 81(3), December 2003, Pages 309-313. 
 
 
 
 
  
Table 1: Descriptive Statistics of Percentage Growth Rates: Averages (Standard deviations) 
  Industrial 
Production  PPI CPI  Nominal 
Spread  Real Spread 
Cyprus  0.02 (36.0)  2.97 (4.8)  3.13 (1.7)  2.31 (0.5)  -0.83 (1.7) 
Czech  4.96 (8.6)  4.08 (2.1)  7.29 (3.0)  1.83(0.1)  1.00(0.7) 
Estonia  4.25 (15.7)  11.86 (10.8)  11.86 (10.8)  2.25(0.4)  1.04(0.8) 
Hungary  9.59 (7.3)  14.40 (6.7)  16.05 (5.6)  1.29(0.1)  0.33(0.1) 
Latvia  7.21 (20.0)  12.45 (10.4)  4.50 (6.1)  2.44(0.5)  2.22(1.2) 
Lithuania  -2.06 (22.4)  31.11 (47.3)  34.68 (59.9)  1.69(0.5)  4.71(13.6) 
Malta  NA  NA  3.00 (1.1)  3.21 (0.36)  0.20 (1.0) 
Poland  5.06 (9.2)  13.31 (7.8)  16.25 (7.7)  1.23(0.2)  0.39(0.3) 
Slovak   3.61 (4.9)  8.80 (3.2)  6.12 (3.4)  1.55(0.2)  0.74(0.3) 
Slovenia  2.96 (4.2)  8.02 (4.6)  10.42 (3.7)  1.54(0.1)  0.82(0.2) 
Note: NA means data not available. 
   12
 
 
Table 2: IPS Results for the First-Round Countries 
  Industrial 
Production
♠  PPI
♠ CPI  Nominal 
Spread.  Real Spread
♠ 
  Coeff. Coeff.  Coeff.  Coeff.  Coeff. 
Cyprus  0.44 0.74  0.72  0.84  0.76 
Czech  0.72 0.89  0.92  0.83  0.90 
Estonia  0.79 0.91  0.84  0.77  0.75 
Hungary  0.53 0.97  0.96  0.83  0.94 
Latvia  0.77 0.88  0.76  0.51  0.62 
Lithuania  0.83 0.92  0.84  0.50  0.85 
Malta  NA NA  0.77 0.94  0.75 
Poland  0.87 0.90  0.90  0.89  0.95 
Slovak  0.61 0.80  0.91  0.63  0.79 
Slovenia  0.76 0.95  0.95  0.89  0.85 
t-ave.  -2.73 -2.77  -3.53  -3.08 -4.64 
z-stat.  4.30
*** -2.25
** -5.92
***  -3.84
*** -13.36
*** 
Note: NA means data not available. 
** (
***) indicates significance at 95% (99%) confidence level. Z-
statistics are derived using the critical values reported in Table 3. 
♠ A trend term was included in the regression. 
 
   13
 
 
 
 
 
Table 3: Moments of the IPS t-bar statistic, simulated under the consideration of cross-
correlations within groups 
 
Industrial 
Production  PPI
♠ CPI
♠  Nominal 
Spread
♠ 
Real 
Spread
♠  
Mean  -1.51 -2.19  -2.17  -2.17  -0.16 
Variance  0.72 0.59  0.53  0.56  1.12 
♠ A trend term was included in the simulations to derive the above critical values.   14
 
 
Table 4: Hadri (2000) stationarity  test results on convergence to the EU. 
  N  T  LM with A-M  LM with N-W 
CPI  10 98  2.44
** 0.07 
Industrial Prod.
 ♠  9 102  5.48
** 4.28
** 
PPI
♠  9 102  -0.38  -1.01 
Nominal Spread  10 104  3.72
** 2.98
** 
Real Spread
♠  10 105  2.30
** 2.58
** 
Note: LM with A-M refers to the LM statistic obtained using the Andrews_& Monahan (1992) long run 
variance estimator while N-W refers to Newey-West (1994). We reject the null at 5% if the one-sided 
statistic is greater than 1.645. Sample sizes vary due to the truncation necessary to supply a balanced 
panel.  
♠ Stationarity was tested around a deterministic trend. 

A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test”

An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator”,

International Patterns of Growth: Persistence in Cross-Country Disparities”,

Macroeconomic Convergence in Transition Economies”,

Nominal and Real Stochastic Convergence of Transition Economies,

Nontradables, and Price Convergence in the Baltics”,

Testing for Stationarity in Heterogeneous Panel Data”

Testing for Stationarity in Heterogenous Panel Data with Serially Correlated Errors”

Testing for Unit Roots in Heterogeneous Panels”

The Convergence of Monetary Policy between Candidate Countries and the European Union”,

Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties”

Convergence of Candidate Countries to the European Union

http://www.bilkent.edu.tr/~economics/papers/04-03%20DP_Yigit&Kutan.pdf

Convergence of Candidate Countries to the European Union

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