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**Real economic convergence in the CEE countries**

ACADEMY OF ECONOMIC STUDIES, BUCHAREST DOCTORAL SCHOOL OF FINANCE AND BANKING Real economic convergence in the CEE countries MSc Student: Iancu Guda Supervisor: Professor Moisă Altăr

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**Real economic convergence in the CEE countries**

Contents : Abstract Review of the Literature 3. Data and Methodology 4. Estimation results 5. Conclusions and remarks 6. References

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ABSTRACT This study aims to assess the economic convergence of the European countries, and especially the convergence of the 12 CEE countries that acceded European Union after 2000 (Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia and Slovakia EU members starting with 2004, Bulgaria and Romania EU members starting with 2007 ). Convergence among the group is studied, as well as their convergence with the EU 15. Time series, panel data and SVAR models are used to assess the economic growth, approximate the period of real convergence of Romania to the EU, as well as to estimate the σ- and β-convergence , and the main shortcomings of the last indicator. The objectives of the present study are : • To know whether the selected 12 EU countries are converging in absolute beta sense independently and jointly • To test for conditional convergence across countries • To measure the speed of absolute and conditional convergence to assess the required time to fill the gap in the economic development • To study the underling factors that support and accelerate real convergence.

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**REVIEW OF THE LITERATURE**

Under the convergence hypothesis, countries starting with a low per capita income should have a higher growth rate. Thus, an inverse relationship between output growth and initial output is interpreted as evidence in favor of the convergence hypothesis. The theoretical literature on economic growth has gone through several phases, and the answers to the above questions depend on the specification of the respective growth model : neoclassical (exogenous) growth model (Solow-Swan 1956) - the economy converges towards a steady state due to diminishing returns to investment in physical capital - Assuming a constant population, the long-run growth rate is solely determined by the rate of technological change, which is assumed to be exogenous. - economic policy changes will only have a temporary effect on economic activity - convergence of per capita output across countries with a similar productivity level, savings rate, depreciation rate, productivity growth and population growth (*) Endogenous growth : - Determine the long-run growth rate within the model ( endogenous growth models ) -Incorporation of R&D theories and imperfect competition ( Romer 1987,1990 ), Grossman and Helpman ( 1993 ) -In these new framework the long term growth rate depends on governmental action, such as taxation, maintenance of law and order, provisions and infrastructure services, protection of intellectual property rights, regulation of international trade -The new research also includes models of the diffusion of technology (*)Viewed in the same simple way, the catch-up process would be self-limiting because as a follower catches up, the possibility of making large leaps becomes smaller and smaller (Abramovitz, MOSES, (1986))

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**REVIEW OF THE LITERATURE**

Neoclassical theory of economic growth – Models of exogenous growth Y = F(K,L) production function – is neoclassical if the following three properties are satisfied : ; and ; F(ʎK,ʎL) = ʎ F(K,L) for all ʎ > 0 Inada Conditions : and Solow and Swan Model (1956) Y = F(K,L) = L*F(K/L,1) = L*f(k) => y=f(k) Differential Equation of the Solow-Swan model : Exogenous variable : population growth rate, rate of technological progress Constant variables : savings rate, capital depreciation rate

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**REVIEW OF THE LITERATURE**

The above case covers the so-called conditional convergence, that is the alternative implying that all economies with differences in the initial stock of capital per capita have the same saving rates (s), similar technologies (the same parameters A and δ), as well as the same population (labour) growth rates (n). Unless such requirements are met, the equilibrium points of the rich countries differ from those of the poor countries, and the convergence cannot take place.

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**REVIEW OF THE LITERATURE**

New methodological approaches to convergence and its determinants The new theory is focused in finding out the real causes and mechanisms of the long-term disparities (through cross-section analysis or long time series), by correlating the growth rate of production and income per capita at national or/and regional level with several economic, social and political variables that could be either the engine or the brake of economic growth. Usually, conditioning variables such as education attainment, government spending, political instability, and the growth rate of the terms of trade are included in such an output regression equation to control for effects of other growth factors (Barro and Sala-i-Martin, 1995). There are authors who conducted empiric research on convergence using the modified and augmented dynamic neoclassical model that involved the human capital and technological progress besides the physical capital. For example, Mankiw, Romer and Weil (1992), and Islam (1995) revealed, by the new variants of models, that the economies with an initially low level of the income tended to increase faster than those with initially high level of the income after they had introduced in the model the saving rate and the population growth rate, as control variables.

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**REVIEW OF THE LITERATURE**

New econometric testing of the new calculation tools and models, such as : the β and σ indicators (Barro and Sala-i-Martin, 1991) the augmentent dynamic neoclassical model : Mankiw, Romer, Weil, 1992 ( with human and fix capital ) // Islam, 1995 // Bassanini, Scarpetta, 2001). Conclude convergence proof after they had introduced in the model the saving rate and the population growth rate, as control variables. Barro, Sala-i-Martin, Blanchard and Hall (1991) considered variables like capital mobility, labour migration Fischer Stanley, Ratna Sahay and Carlos Vegh. 1998, growth regression in panel data with variables like GDP, inflation, enrolment, investments, government spending ( also Robert J. Barro 1991) Ross Levine and David Renelt 1992 – Extreme bound analyses to test the robustness, include variables like trade and investments Sergio Rebelo 1997 – determinants of economic growth ; Easterly, W., Rebelo, S., 1993 fiscal policy influence over growth perspective Robert E. Hall and Charles I. Jones – regression the study the differences of GDP levels among countries the stochastic convergence model (Lee Kevin, M Hashem Pesaran, Ron Smith ; 1997) Blanchard, O.J., Perotti, R., – VAR models testing governments spending and taxation influence over economic growth Bart Verspagen 1994 and Ben David : trend calculation for series of per capita income relative to group average

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**REVIEW OF THE LITERATURE**

The counter-reaction literature The appropriateness of the cross-country regression approach is challenged by, for example, Quah (1993), Bernard and Durlauf (1996), and Evans (1997) Quah (1993) shows that a negative correlation between output growth and initial output is consistent with a stable variance in cross-country output. Durlauf, 1996; Quah, 1996 – time series models to negate convergence and promote the presence of convergence clubs and the polarisation of the countries in rich and poor ones. They argue that the initial-output regression approach tends to reject the null hypothesis of no convergence too often in the presence of multiple output equilibrium Bernard and Durlauf (1995), using the unit root and cointegration techniques, detect the presence of multiple integrated processes driving the output data of the OECD countries Using a panel unit root test, Evans (1998) shows that convergence occurs within a group of developed countries and different growth patterns are observed among countries with different literacy rates

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**DATA AND METHODOLOGY I Time series I.1 Verspagen (1994)**

This test assumes the following relation between per capita income relative to the group average in different periods t and t - 1. where Yt is defined as (Q/P)/(ΣQ/CP), and Q denotes RGDP, P denotes population (in thousands), and Σ indicates the sum in some group of countries The assumed relationship allows for : • converging (if ψ < l) • diverging (if ψ > l) • stable (if ψ= 1) differences in per capita income Data are comprised from Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 6.3, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, August for the 12 CEE countries and UE15 average values

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DATA AND METHODOLOGY In order to estimate the value of ψ for different periods of time, the next steps are followed: For each ‘case’ (defined as a combination of countries, years, and reference group), a pooled cross-country time series dataset is set up. In turn, ψ is estimated by OLS for the subset for periods t - 2 to t + 2. This means that the number of observations in each OLS estimate is five times the number of countries in the analysis. The resulting estimate of ψ is attributed to t, and plotted in a graph together with the estimates for other periods. The estimated coefficient plus/minus two times the estimated standard error is also plotted, so that a (reasonably wide) confidence interval is established. Whenever this confidence interval is completely below (above) unity, convergence (divergence) is said to be observed. Whenever the confidence interval embraces the unit line, no particular trend is found. The objectives are: • To test for convergence differences in per capita income within the 12 CEE countries group – to this purpose the series of RGDP/cap ( Real GDP per capita in 2000 values as base year) are comprised with annual frequency between The series are divided in subset of periods t - 2 to t + 2. Hence, we obtain a final 11 observation for each country. The pooled data comprises 60 observations (12 cross country and 5 time series ) of country individual RGDP/cap difference from groups average. • To test for convergence differences in per capita income between the CEE countries and UE15 average value. The time series is extended to range, thus including 34 annual observations ( after extracting subsets of 5 years each ). Due to data limitation, only 6 CEE countries are included ( Bulgaria, Cyprus, Hungary, Malta, Poland, Romania ). Hence, the polled data comprises 30 observations ( 6 cross section and 5 time series ) of country individual RGDP/cap difference from UE15 average.

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DATA AND METHODOLOGY I.2 . Convergence tests for pairs of countries ( methodology used in M. Hashem Pesaran, 2007) A time-series based approach to investigate output convergence has been proposed by Bernard and Durlauf (1996) and Quah (1992). According to Bernard and Durlauf (1996), there is output convergence between two countries if the long-run forecasts of their real per capita outputs are the same. If pair-wise convergence holds for all the pairs of countries under consideration, then there is convergence of all the countries simultaneously. Specifically, let Yi,t be the (logarithm of the) country i’s real per capita output at time t and Y*,t is the output variable of the benchmark country. The no-convergence hypothesis is stated as : H0 : = I(1), i = 1,…,N World Table Version 6.3, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, August 2009 for the 12 CEE countries and UE15 average values. Values for annual Real GDP /capita (in 2000 values as base year) are found for the following series: • for Bulgaria, Cyprus, Hungary, Malta, Poland, Romania and UE15 • for Slovak Republic • for Czech Republic, Estonia, Slovenia • for Lithuania and Latvia In testing for: •Convergence within the 12 CEE countries group, ADF stationary test are applied for N(N-1)/2 possible pairs of log per-capita output gaps across N economies. •Convergence between the 12 CEE countries and UE15 average real GDP/capita, stationary is tested for 12 output pairs differentials.

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DATA AND METHODOLOGY II. Panel Data ( methodology also used in Islam 1995 // Crespo-Cuaresma J, Dimitz MA, Ritzberger-Grunwald 2002 // Matkowski Z, Prochniak M 2004 // Rajasalu Teet 2003 ) Pooled data LSDV (Least Square Dummy Variable ) – fixed effects for cross and time series ( intercept / slope variation) ECM ( Error Components Model ) or Random Effects Variables : real GDP per capita growth rate (difference of consecutive log values ) LN of Real GDP per capita Trade ( export + import ) % of GDP Gross Capital Formation % of GDP Total Government Expenditure % of GDP Total Government Income % of GDP Saving rate % of GDP Enrollment ( secondary + tertiary ISCED Levels ) Data series for 12 EU members acceded after 2000 ( Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia and Slovakia EU members starting with 2004, Bulgaria and Romania EU members starting with 2007 ), period Source : Eurostat Observations : 12 Countries, 15 years ( 1995 – 2009 ), 5 time intervals ( 3 years each ) = 60 panel data observations (12 Cross countries and 5 interval time series )

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**DATA AND METHODOLOGY Argumentation:**

• Why not include data ? The beginning of the transition of the former socialist countries caused a massive fall of output which rivaled, and in several countries even surpassed, the dimension of the United States recession during the Great Depression. After a few years, the fall in output stopped, and a few countries, mostly Central and Eastern European ones (CEE), started a process of economic growth. The success of reforms and the sustained economic growth spurred a whole line of research on the determinants of economic growth and on the long run economic growth perspectives of the CEE countries. • Why 3 years for each period ? The standard approach for reducing the effects of structural changes and business cycles, is to divide the series and average data on 5 years ( or even 10 if longer data available ) for each interval. Due to data limitation ( total range of data compiled 15 years ), a 5 years interval would have reduced the degree of freedom

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**DATA AND METHODOLOGY II. Panel Data**

The above equations are estimated to test for : Absolute Convergence Absolute convergence speed The fixed effects LSDV Random effects

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DATA AND METHODOLOGY III. Structural VAR ( methodology used in Sarah M. Lein, Miguel A. Leo´n-Ledesma, Carolin Nerlich, 2008) Variables : real GDP per capita growth rate (difference of consecutive log values ) Trade ( export + import ) % of GDP Gross Capital Formation % of GDP empirically assess their relevance using a Structural VAR method for Lithuania and Romania in trying to find out the real causes and mechanisms of the long-term disparities Data is compiled from : • EUROSTAT , with quarterly frequency between 1995 Q1 : 2009 Q4, comprising 60 observations for each variable for Lithuania • Nations Institute of Statistics ( Romania’s official webpage) with quarterly frequency between 2000 Q1 : 2009 Q4, comprising 40 observation for each variable for Romania. Observation: All series are seasonally adjusted using the Census X12 seasonal adjustment procedures

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**ESTIMATION RESULTS Scatter Plot Evidence:**

Country 1995 Real GDP per capita, EUR Real Per Capita Average Growth Rate (1995:2009) Bulgaria 1,700.00 3.41% Czech Republic 5,500.00 2.62% Estonia 3,100.00 5.76% Cyprus 12,900.00 1.84% Latvia 2,600.00 5.65% Lithuania 2,700.00 5.54% Hungary 4,100.00 3.05% Malta 10,700.00 0.53% Poland 3,700.00 4.59% Romania 1,939.48 Slovenia 8,700.00 3.31% Slovakia 3,500.00 4.35% Scatter Plot Evidence: Romania and Bulgaria, although having the lowest Real GDP per capita in the group for the starting year, fail to converge in terms of absolute β convergence ( as in Barro and Sala-i-Martin, 1991) Average growth rate for Romania and Bulgaria in bellow group’s average Better convergence results ( adjusted R squared, F statistic ) for the group when excluding Romania and Bulgaria (Panel Data)

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ESTIMATION RESULTS Year European Union (15 countries) Bulgaria Czech Republic Estonia Cyprus Latvia Lithuania Hungary Malta Poland Romania Slovenia Slovakia 1995 100% 8.33% 26.96% 15.20% 63.24% 12.75% 13.24% 20.10% 52.45% 18.14% 9.51% 42.65% 17.16% 1996 7.25% 28.02% 15.94% 62.32% 13.53% 14.01% 19.81% 52.17% 19.32% 9.73% 43.48% 17.87% 1997 7.08% 26.89% 17.45% 61.32% 14.15% 14.62% 20.28% 50.47% 8.96% 44.81% 18.40% 1998 6.88% 26.15% 18.35% 61.93% 14.68% 15.60% 21.10% 49.08% 20.64% 8.31% 44.95% 1999 7.14% 25.89% 18.30% 62.50% 14.73% 15.18% 21.43% 48.21% 20.98% 7.92% 46.43% 17.86% 2000 7.33% 25.86% 19.40% 15.52% 15.09% 21.55% 46.55% 21.12% 7.76% 17.67% 2001 7.66% 26.38% 20.43% 63.40% 16.60% 16.17% 22.13% 45.11% 20.85% 8.09% 47.23% 2002 8.02% 26.58% 22.36% 63.71% 17.72% 17.30% 23.21% 45.57% 8.86% 48.52% 18.57% 2003 8.40% 27.31% 23.95% 63.45% 18.91% 44.96% 21.85% 9.24% 49.58% 19.33% 2004 9.09% 28.10% 25.21% 63.64% 20.25% 24.79% 44.21% 22.73% 9.92% 50.83% 2005 9.39% 29.39% 27.35% 63.67% 22.45% 21.63% 25.71% 44.90% 23.27% 10.20% 52.24% 21.22% 2006 9.96% 30.68% 29.48% 63.35% 24.70% 22.71% 25.90% 45.02% 23.90% 10.76% 53.78% 22.31% 2007 10.16% 31.64% 30.86% 64.45% 26.56% 24.61% 25.78% 45.31% 25.00% 11.33% 56.25% 24.22% 2008 10.94% 32.03% 29.69% 66.02% 25.39% 46.09% 12.16% 58.20% 2009 10.90% 32.20% 27.05% 68.08% 22.95% 25.41% 47.13% 28.34% 11.85% 55.74% 25.82% Gap reduction 2.57% 5.23% 4.85% 9.72% 5.31% -5.32% 10.21% 2.34% 13.09% 8.66% Weights calculated based on Real GDP per capita levels, compared with EU15, SOURCE : calculation based on series from EUROSTAT Countries bellow the average rhythm of real DGP/capita recovery : Bulgaria, Czech Republic, Cyprus, Hungary, Malta, Romania, Slovakia Countries over the average rhythm of real DGP/capita recovery: Estonia, Latvia, Lithuania, Poland, Slovenia

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**ESTIMATION RESULTS - VERSPAGEN Test**

Convergence among 12 CEE Countries Convergence between 6 CEE Countries and UE15 Average For convergence within the 12 CEE group, the solid line is below unity, indicating convergence. Moreover, for all years considered, the trend is significant ( the confidence interval is completely below unity ). For the convergence between the six CEE countries and UE15, a mixed pattern is observed. In most cases, the solid line is below unity, indicating convergence. However, there are no years for which this trend is significant. This indicates that although there is a weak trend for convergence, the growth behavior of the individual countries is so erratic that the overall trend is insignificant.

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**Convergence tests for pairs of countries - The Augmented Dickey-Fuller test**

Real GDP differences for pairs of countries result in nonstationary series (based on the ADF test), only for 22.72% they are I(0), hence rejecting convergence The result is consistent with the result obtained by M. Hashem Pesaran (2007), who found ~25% I(0) series when running similar convergence tests to output series in the Penn World Tables over 1950–2000 (“A pair-wise approach to testing for output and growth convergence”, 2006, Journal Of Econometrics)

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**Estimation results σ -convergence**

Year Dispersion 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Barro and Sala-i-Martin (1992) introduced the notion of σ -convergence. σ -convergence is said to be present if the dispersion of income per capita across countries display a tendency to decline through time

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**Panel Data – Absolute β Convergence – All 12 countries**

Pooled Data : All Coefficients Constant across Time and Individuals LSDV: Fixed Effects The Time Effect For all 3 models, sign of Beta Convergence ( as in Barro and Sala-i-Martin (1992) ), negative relation between Real GDP per capita growth rates and initial level of Real GDP / capita at the beginning of each subperiod. LSDV: Fixed Effects The Individual Effect Judged after the value of adjusted R squared, the time variant model seems to be the most appropriate, although some problems arise with the statistical significance of the estimated coefficients for DY2 and DY3. Restricted F test are also employed and favor for The Time Effects model This could mean that the production function shifts over time because of factors such as technological changes, changes in government regulatory and/or tax policies, and external effects such as structural changes in transitional economies. Individual country effects are also statistically significant, with one exception ( country code 10 meaning Romania )

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**Panel Data – Absolute β Convergence – All 12 countries**

Hausman Test interpretation The null is that the two estimation methods are both validated and that therefore they should yield coefficients that are "similar". The alternative hypothesis is that the fixed effects estimation is validated and the random effects estimation is not. If this is the case, then we would expect to see differences between the two sets of coefficients. This is because the random effects estimator makes an assumption (the random effects are orthogonal to the regressors) that the fixed effects estimator does not. If this assumption is wrong, the random effects estimator will be inconsistent, but the fixed effects estimator is unaffected. Hence, if the assumption is wrong, this will be reflected in a difference between the two set of coefficients. The bigger the difference (the less similar are the two sets of coefficients), the bigger the Hausman statistic. A large and significant Hausman statistic means a large and significant difference, and so the rejection of the null that the two methods are valid in favor of the alternative hypothesis that one is valid (fixed effects) and one isn't (random effects).

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**Panel Data – Absolute β Convergence – 10 countries (without RO & BG)**

Pooled Data : All Coefficients Constant across Time and Individuals LSDV: Fixed Effects The Time Effect The elimination of RO and BG series : strengthen the estimator for LN_GDP0, which is statistically significant for all models = > stronger Beta Convergence process among the group of 10 countries Increased the value of adjusted R-squared λ, speed of convergence, increases for all the corresponding models ( pooled data to 2.87% , time effect model to 2.05% and the LSDV to 9.61% ) For all four models (pooled, LSDV, time effect and random effect), sign of Beta Convergence ( as in Barro and Sala-i-Martin (1992) ), negative relation between Real GDP per capita growth rates and initial level of Real GDP / capita at the beginning of each sub period. LSDV: Fixed Effects The Individual Effect

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**Panel Data – Absolute β Convergence – 10 countries (without RO & BG)**

The Random Effects The Hausman test employed also indicates the fixed effects model against the random effects model. In line with the arguments of Hsiao (2003) and Baltagi (2005) the choice of fixed effects option is more appropriate when the research focuses on a specific set of N countries, which are not drawn randomly from a large population, and the outcomes of the study are viewed as conditional on this set of countries. This situation precisely reflects our convergence study where the choice of country matters for answers we search for. This choice was confirmed during estimations with Hausman’s test favoring the fixed effects specification

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**Panel Data –Conditional β Convergence – All 12 countries**

Pooled Data : All Coefficients Constant across Time and Individuals LSDV: Fixed Effects The Time Effect For all 3 models, sign of Conditional Beta Convergence ( as in Barro and Sala-i-Martin (1992) ), negative relation between Real GDP per capita growth rates and initial level of Real GDP / capita at the beginning of each sub period. LSDV: Fixed Effects The Individual Effect Judged after the value of adjusted R squared, F statistic, Darbin-W stat and the statistical significance of the estimated coefficients, the individual effect (time invariant) model seems to be the most appropriate Restricted F test are also employed and favor for The LSDV model Variable sign as expected : real GDP growth rate negatively correlated with initial level of real GDP per capita and government spending / positively correlated with trade, gross capital formation, government income, saving rate and enrollment ( although the latter two are statistically insignificant) Individual country effects are also statistically significant, with no exception

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Structural VAR The identification problem (the recovering of the structural innovations from the reduced form) is solved by employing Choleski decomposition. Hence, the system is exactly identified by imposing 3 [n*(n-1)/2, with n=3, the number of variables] short term restrictions on A matrix. The Choleski ordering is : real GDP per capita growth rates(GDP), trade openness (Trade) and gross capital formation(GCF) GDP TRADE GCF 1 a b c The contemporaneous relation between the variables is described as follows : the variables on each line are influenced, during each quarter, by variables on each column. “0” is for lack of contemporaneous influence ( imposed restrictions ), “1” signify the existence of contemporaneous influence. The diagonal is one as each variable is influenced by it’s own innovations

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**Structural VAR Hence, the influences between the variables are :**

•Real GDP per capita growth is influenced by contemporaneous and lagged values of trade openness and gross capital formation. The argument for this is the panel data test of gradually inclusion of variables that finally favor for trade openness and gross capital formation (as proxy for investment rate) as being the most important determinants for growth enhancing for the 12 CEE countries •Trade openness is influenced by contemporaneous and lagged values of gross capital formation and only by lagged values of real per capita GDP growth. The intuition for this is that higher ( lagged and contemporaneous ) investment rates ( approximated by gross capital formation ) will eventually increase the productivity and generate higher competitiveness for the domestic products. Empirical results in this regard are also found by Monica Ioana Pop Silaghi (2009) “Exports-Economic growth causality: evidence from CEE countries” when testing the causality between trade and economic growth (with the result that the first variable influences that latter ) •Gross capital formation is influenced by lagged values of trade openness and per capita GDP growth. Economic growth is seen as revenue for sustaining the investments and trade intensification would eventually increase the competition between foreign and local companies. The local firms, the hold the competition and protect their market share, would have to optimize their products quality by increasing productivity and investments. For the final results to be relevant the econometric model must pass over the diagnostic tests. The main tests and results are briefly summarized bellow

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**Structural VAR Lag Selection**

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**Structural VAR – Stability test**

VAR is stable ( stationary ) if innovations over the variables within the system diminishes along the time. Stability is confirmed if the unit roots have absolute values less than unity (this entails that matrix An tends to zero as n increases to infinity, meaning that the VAR is not explosive). The unit roots values are compiled bellow. As observed, all values are less than unity, thus confirming the stability of VAR

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**Structural VAR Structural Factorization**

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**Structural VAR - Impulse Response**

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**Structural VAR - Impulse Response**

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**Structural VAR – Variance Decomposition & Granger causality tests**

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**Conclusions, Methodology limitations and future research**

Final conclusions can be summarized bellow: •Compared with the cross-country analysis, the time-series approach yields less convincing findings for the convergence hypothesis •Strong evidence of σ convergence, as the dispersion for the 12 CEE countries real per capita GDP levels tends to decrease over time •CEE countries converge between themselves in terms of absolute convergence. Although strong evidence is found in this direction, the estimated speed of absolute convergence is low, being around 1.30% for the 12 CEE countries and 2.87% after eliminating the series for Romania and Bulgaria. •High speed of convergence is obtained when controlling the steady state. Unfortunately, we are looking at conditional convergence. That is, groups of countries and separate countries are converging fast to their respective steady state of income level, and nothing can guarantee that it is a good steady state. •Individual country specific growth patterns are found within the 12 CEE group •Panel data test results indicate that the production function for the 12 CEE countries shifted over time because of factors such as technological changes, changes in government regulatory and/or tax policies, and external effects such as structural changes in transitional economies. •trade openness and gross capital formation (as proxy for investment rate) are found as being the most important determinants for growth enhancing for the 12 CEE countries. Generally, the lack of conclusive evidence from the data on the rate of catch-up makes it advisable for transition countries to opt for growth-enhancing policies rather than concentrate their efforts on nominal convergence with Maastricht criteria.

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**Conclusions, Methodology limitations and future research**

For research in future, conditional convergence can be tested using cross sectional average data on pertinent growth factors like corruption perception indices, rule of law index, social capital and trust variables, formal and informal rules governing the society, among others. It will be interesting to find out the speed of conditional convergence by including such variables in the per capita growth equation. Another possible source of the growth bonus are the changes in the institutional framework due to European integration. Whereas the completion of the internal market or, in other words, the openness of the countries is covered more or less by the trade variable, there are other developments which could also play a role. Examples are the legal and the institutional framework of the financial sector, the scale and the nature of foreign direct investment, transport infrastructure and the efficiency of public administration. As to other possible future extensions of this research, one can suggest a complementary study of factors behind the recent growth performance in transition economies ( especially for range ). It might determine the sustainability of growth in transition economies. This will help to answer the major convergence question as to how long it will take for transition countries to close the income gap with the EU. Secondly, one can resort to a number of case studies related to the accession experiences of countries such as Greece, Spain and Portugal. These countries started with large income disparities with the EU average. The study of what happened to them before and after accession may shed light on what may happen to the present and future candidates

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