Mariusz Próchniak

Determinants of economic growth in Central and Eastern Europe: the global crisis perspective

This article presents an empirical analysis of economic growth determinants in the 10 Central and Eastern European (CEE) countries Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia. The analysis covers the period 1993–2009. All the calculations are performed on five three-year subperiods and one two-year subperiod: 1993–95, 1996–98, 1999–2001, 2002–04, 2005–07 and 2008–09. The analysis is composed of three steps: data selection, correlation analysis and regression analysis. The correlation analysis is based on the coefficient of partial correlation to exclude the impact of the global crisis. In the regression analysis we build 10 alternative variants of empirical models of economic growth. Our results suggest that the most important economic growth determinants in the CEE countries are investment rate (including FDI), human capital measured by the education level of the labour force, financial sector development, good fiscal stance (low budget deficit and low public debt), economic structure (high services share in GDP), low interest rates and low inflation, population structure (high share of working-age population), development of information technology and communications, high private sector share in GDP and favourable institutional environment (economic freedom, progress in market and structural reforms). Our study indicates that the CEE countries have developed in line with the hypothesis of income level convergence (both in absolute and conditional terms). This means that less developed CEE economies grew on average faster than more developed ones.

Economic Convergence Between the CEE-8 and the European Union

This paper analyzes the real economic convergence between the new EU entrants in Central and Eastern Europe (CEE-8) and the EU core (EU-12 or EU-15). It includes new estimates on the speed of income-level convergence and new findings about the conformity of cyclical changes. The results offer further evidence of continuous progress in real economic convergence between Central and Eastern Europe and the European Union.

Real beta and sigma convergence in 27 transition countries, 1990-2005

The aim of this study is a detailed analysis of real economic convergence in 27 former socialist (or transition) countries. We focus on two concepts of convergence: absolute (unconditional) beta convergence and sigma convergence. The time frame of our study is 1990–2005. We provide a broad empirical picture of convergence. First, we analyse the catching-up process in the whole group of 27 countries as well as in several narrower sub-groups. Second, we carry out our calculations for the entire period 1990–2005 as well as for shorter sub-periods.

EU enlargement and real economic convergence

This article presents an analysis of economic implications of the major EU enlargement in 2004. The research is based on sigma (σ) and beta (β) convergence of per capita GDP among the 10 countries which joined the European Union in 2004. Our results confirm the existence of both types of convergence in the second half of the 1990s and the 2000s. Generally, the poorer new EU member states grew faster than the richer new EU member states. As a result, the income gap between these two groups of countries has narrowed although it still remains quite large. The convergence occurred at the rate of 4.2% during the period 1992–2006 and 7.0% and 9.6% during the sub-periods 1995–2006 and 2002–06 respectively.

Real β-Convergence of Transition Countries

The aim of the paper is to analyze whether there is and, possibly, how strong the gross domestic product (GDP) β-convergence is in a group that includes most postcommunist countries, which, we believe, are a relatively homogeneous group compared to all countries. We use country-level panel data from the 1960-2009 period. In order to verify the β-convergence hypothesis, we use the Bayesian averaging of classical estimates (BACE) approach proposed by Sala-i-Martin et al. (2004). We estimate a convergence model without imposing an a priori assumption regarding the exact set of growth factors used as control variables in the regression. We use twenty potential growth factors (and an initial GDP level as an additional factor) and additional dummy variables (for Central and Eastern European and Commonwealth of Independent States countries) to further reduce the heterogeneity of the considered population. Our research allows us to robustly confirm the existence of β-convergence at a rate of 1.5 percent to 2 percent per annum. Using the BACE approach, we also identify the most relevant growth factors to be included in the β-convergence model. Our results are not controversial but in some respects differ from those present in the mainstream literature.

On the Stability of the Catching-Up Process Among Old and New EU Member States

This paper analyzes the time stability of the real economic convergence in the European Union. As a robustness check, the calculations are carried out for two groups of countries: EU27 during 1993-2010 and EU15 during 1972-2010. The analysis is based on three-year intervals, as contrasted with an analysis by Prýchniak and Witkowski (2013a) that is based on annual data. In addition, the paper examines the strength and stability of impact of selected economic growth determinants. In order to verify the research hypotheses, Bayesian model averaging is applied to Blundell and Bonds generalized method of moments system estimator. It turns out that the EU27 countries converged at a rather constant over time rate of about 6 percent per annum while the EU15 countries converged at about 3 percent per annum; this is an enormous difference compared to the widely cited 2 percent speed of convergence in the mainstream literature.

On The Use Of Panel Stationarity Tests In Convergence Analysis: Empirical Evidence For The Eu Countries

The study examines the concept of stochastic convergence in the EU28 countries over the 1994–2013 period. The convergence of individual countries’ GDP per capita towards the EU28 average per capita income level and the pair-wise convergence between the GDP of individual countries are both analyzed. Additionally, we introduce our own concept of conditional stochastic convergence which is based on adjusted GDP per capita series in order to account for the impact of other growth factors on GDP. The analysis is based on time series techniques. To assess stationarity, ADF tests are used. The study shows that the process of stochastic convergence in the EU countries is not as widespread as the cross-sectional studies on b or s convergence indicate. Even if we extend the analysis to examine conditional stochastic convergence, the number of converging economies or pairs of countries rises, but not as much as it could be expected from the cross-sectional studies.

Transition Countries: Economic Situation and the Progress of Market Reforms

The aim of this study is to assess current economic situation of 28 post-socialist countries in Europe and Asia undergoing systemic transformation towards an open market economy, their macroeconomic policies and the progress of structural reforms. We focus on the situation prevailing in 2008 and the changes against the previous year, though some estimates regarding future prospects are also included. The focus in this analysis is on the impact of the global economic crisis on macroeconomic performance in the countries of the region. We also present the most recent projections of the basic macroeconomic indicators for 2009 and forecasts for 2010 and 2014, assuming a gradual recovery of world economy from the current crisis.

An Attempt to Assess the Quantitative Impact of Institutions on Economic Growth and Economic Development

Abstract This study aims at assessing to what extent institutional environment is responsible for worldwide differences in economic growth and economic development. To answer this question, we use an innovative approach based on a new concept of the institutions-augmented Solow model which is then estimated empirically using regression equations. The analysis covers 180 countries during the 1993-2012 period. The empirical analysis confirms a large positive impact of the quality of institutional environment on the level of economic development. The positive link has been evidenced for all five institutional indicators: two indices of economic freedom (Heritage Foundation and Fraser Institute), the governance indicator (World Bank), the democracy index (Freedom House), and the EBRD transition indicator for post-socialist countries. Differences in physical capital, human capital, and institutional environment explain about 70-75% of the worldwide differences in economic development. The institutions-augmented Solow model, however, performs slightly poorer in explaining differences in the rates of economic growth: only one institutional variable (index of economic freedom) has a statistically significant impact on economic growth. In terms of originality, this paper extends the theoretical analysis of the Solow model by including institutions, on the one hand, and shows a comprehensive empirical analysis of the impact of various institutional indicators on both the level of development and the pace of economic growth, on the other. The results bring important policy implications.

The application of Bayesian model averaging in assessing the impact of the regulatory framework on economic growth

The paper aims to assess the impact of regulations (measured by the Fraser Institute index of economic freedom) on economic growth in the world as well as in EU and post-socialist countries. The method of the analysis is based on growth regressions where economic freedom is included in the set of explanatory variables, along with some other control factors. The dependent variable is gross domestic product per capita growth rate. In order to be robust to the selection of explanatory variables, the paper uses Bayesian model pooling applied to Blundell and Bonds generalized method of moments system estimator. Other contributions are: the use of ‘overlapping’ panel data in which subsequent observations cover observations from partly overlapping periods and the inclusion of nonlinearities. The results show that the level of and the change in economic freedom both reveal a positive and nonlinear relationship with economic growth. The same applies to most of the component indicators.