Convergence across EU Members and the Consequences for the Czech Republic

Transcription

1 Mgr. Patrik Bauer Phone: Private address: Podolská 56, Praha 4 Podolí, 14700, Czech Republic University: IES FSV UK, Opletalova 1606, Praha 1, 11001, Czech Republic Convergence across EU Members and the Consequences for the Czech Republic Abstract The difference in GDP per capita between the Czech Republic and the EU average is considerable. Because the Czech Republic s aim in the long run is convergence to the EU average, the essential question is whether the CR would converge after its expected entry into the EU. In this paper convergence based on neoclassical growth models is analysed. Theoretical and empirical results of convergence are deduced and the main determinants, by which the value of GDP per capita in steady state is influenced, are specified. Then the hypothesis of absolute convergence (β convergence) in the EU in the period of is tested by an econometric model. The convergence among the EU members was observable, but the speed of convergence was slow. The EU enlargement of the CEE transition countries is unique, however, the growth effects of economic integration with the EU can be illustrated, primarily by the experience of s enlargements. Thus the experience of the entries of Ireland, Greece, Spain and Portugal into the EU is examined and the possible consequences of the Czech Republic s entry to the convergence are derived. The main result is that EU accession should have the positive impact to the GDP per capita of the Czech Republic and should support convergence to the EU average. But convergence will not be the automatic phenomenon and the real benefits will depend on the Czech Republic itself. 1

2 1. Introduction After the Velvet Revolution of 1989 the Czech government declared the most important political and economic issue European Union (EU) accession. Although the Czech Republic supposed to become the EU member in a few years, nowadays EU accession remains the most important political and economic aim. Considering the Czech Republic as a country belonging to European structures, the economic indicators of the Czech Republic have to be compared with the economic indicators of EU members. Adopting the GDP per capita (in PPP) as an indicator for comparing living standards, the immense difference between EU average and the Czech Republic has to be admitted (in the year 2000 GDP per capita of the CR was only 59% of EU average 1 and 91% of the poorest EU member, Greece, while in the 1955 it was 350% of Greece and 230% of Spain 2 ) If the Czech Republic aspire to converge to the EU average after the entry into the EU, the crucial question is whether there occurs the convergence between the EU members (if there is higher economic growth in countries where GDP per capita in PPP is lower), if need be how fast is this convergence. Empirical results indicate the occurrence of convergence between the countries (or regions) with similar institutional environments 3. If these environments differ significantly then the empirical results prognosticate slow divergence. From the experiences of past European integration (during the s) can be derived that the convergence after the entry into the EU can be substantial (the cases of Ireland, Spain and Portugal), however, convergence is not automatic (in the case of Greece occurred slow divergence). In this paper the hypothesis of absolute convergence (β convergence) across EU members is tested and the past European integration is analysed. Then possible consequences for the countries seeking EU accession (especially the CR) are predicted. The paper is divided into the three parts. In the first part the models of economic growth and questions of convergence are analysed. In the second part the empirical results of convergence are summarised and the econometric model for the absolute convergence hypothesis testing is arranged. In the third part the past European integration is analysed and the consequences for countries seeking EU accession are discussed. 1 See Ekonom 47/ See World Bank (1999). 3 Institutional environment means in this paper tastes, technologies, political institutions, policies and so on. 2

3 2. Models of economic growth and convergence In 1960s the basic neoclassical growth model was formed (by Robert Solow, thus the model is called the Solow growth model). By the integration of some factors that were in this model exogenous, the models of endogenous growth were formulated. As basic Solow growth model 4 claims, GDP per capita in steady state of a particular economy depends on the amount of capital per capita and the technology 5. The basic model is specified by the endogenous growth models 6, however, the main factors, by which the value of GDP per capita in steady state is influenced, are: amount of physical capital per capita amount of human capital per capita technology (the other factors) Questions of convergence Convergence is predicted by the neoclassical growth models under specific circumstances (the absolute convergence hypothesis (β convergence) is accepted). As is declared by the absolute convergence hypothesis, within two countries (or regions) with similar institutional conditions the country (region) with lower GDP per capita will tend to grow faster. As is stated by World Bank (1999: 33): A less strict version of convergence, which assumes that all countries are not equal and in fact differ in many aspects is the so-called conditional convergence, also known as σ convergence. This type of convergence implies declining cross-sectional dispersion of per capita income across units [...] The basic difference is that while absolute convergence relates to the relationship with initial level of income and subsequent growth rates, conditional convergence implies that each country has its own steady state level of income and will grow faster the farther away it is from this level. Growth is thus affected by a number of things, including the policy framework a country chooses to implement. In neoclassical growth models, an increase in GDP per capita is a consequence of an increase in the amount of capital (physical or human) per capita or the technology development. 4 See for example Romer (1996). 5 In the model the technology is considered as a residual value it is called Solow residual (it includes the other factors by which the GDP per capita in steady state is influenced). 6 Primarily Research and development models and Human capital models see Romer (1996). 3

4 Convergence depends on assumptions of the models. Under the assumption of production function with diminishing returns to capital an economy with lower amount of capital per capita will tend to grow faster than an economy with higher amount of capital per capita (thus an economy with lower GDP per capita will tend to grow faster than an economy with the higher GDP per capita and convergence will occur). The speed of convergence depends on the rate of free movement of production factors that should flow from richer to poorer economies (because of higher rate of return in poorer economies). Another way in which convergence can occur is the technology development. In theory there is assumed that there are no barriers to adopt the best technology (including the institutional environment) in every economy, what can be the problem in practise. According to the assumptions of the models the technology development is in the long run the main source of the GDP per capita growth. As the long-run result there are established the same best technologies and the same amount of capital per capita in every economy. Then there are the same GDP per capita in steady state in every economy. Under the another assumptions the results are different. For example under the assumption of increasing returns to capital there will occur divergence instead of convergence. 3. Empirical analysis of convergence In recent years, there have been a large number of empirical studies aimed at the problem of convergence 7. As Barro and Martin (1995: 413) claim: We can interpret the results as consistent with the neoclassical growth model [...] if regions within a country have roughly similat tastes, technologies, and political institutions. This relative homogeneity generates similar steady-state positions. [...] One surprising result is the similarity of the speed of β convergence across data sets. The estimates of β are around 2-3 percent per year in the various contexts. This slow speed of convergence implies that it takes years to eliminate one-half of an initial gap in per capita incomes. As European Commission (2000a: 180) declares: 7 See for example Barro (1994), Barro, Martin (1995), Barro (1997), European Commission (2000a). 4

5 Neven and Gouyette (1994) investigated β convergence for all NUTS II level EU regions for the period in terms of per capita income relative to the EU average. Absolute convergence was found to be very weak (0,5% per annum) The absolute convergence (b convergence) hypothesis across EU members The absolute convergence hypothesis across EU members will be tested by using a simple econometric model. The data for 14 EU members will be used to estimate the coefficients of the model (all members except of Luxembourg 8 ). The data sources are European Commission (2000a) and calculations of the author. As is assumed by the absolute convergence hypothesis, an economy with the smaller value of GDP per capita will tend to grow faster than an economy with the higher value of GDP per capita. To test this hypothesis GDP per capita in 1960 is chosen as an explanatory variable and average GDP per capita growth (during the period ) as a dependent variable. Thus the coefficients of the following equation are estimated: g = β0 + β1 * log GDP_60 + u, where: g...average GDP per capita growth (during the period , per annum) log GDP_60...log GDP per capita in 1960 (PPS of Eurostat) Results 9 : B St. Err. of B t(13) p-level R 2 =0,81 Intercpt 0,1288 0,0141 9,1209 0,0000 Log GDP_60-0,0340 0,0048-7,0954 0, Some anomalies were exhibited by the Luxembourg economy, thus it was omitted from the analysis. There will not be strong distortion in results because GDP of Luxembourg is only 0,2% of the EU and population is 0,12% of the EU. 9 For verification of basic assumptions see Appendix. 5

6 g=0,129-0,034*log GDP_60+eps 0,046 0,042 IRL Average GDP per capita growth 0,038 0,034 0,030 0,026 0,022 P GR E FIN I AB F NL D DK S UK 0,018 2,55 2,65 2,75 2,85 2,95 3,05 3,15 Log GDP_60 The explanatory variable GDP_60 is statistically significant at 99% level and the coefficient of determination is relatively high. Because B1 is negative the hypothesis of absolute convergence would be accepted (for the 40 years long period ). Convergence across EU economies occurred during the past 40 years. How fast was this convergence? As can be stated from results, the higher was the gap between the GDP per capita of the particular economy in 1960 and the EU average, the faster was the convergence. But the speed was generally slow, per annum was closed less than 1% of the existing gap. The main shortage of the model (except of small data sample) is the fact that no all 14 economies were EU members during the whole 40 years long period (just 7 countries were EU members in the past 40 years). While the countries which joined the EU in 1995 (Austria, Finland, Sweden) are not so interesting examples for the recent EU Candidate Countries (especially the CEE transition countries), the experiences of countries which joined the EU during the s are more relevant. In the next chapter there is the analysis of this past European integration, it means the EU accession of Ireland (joined the EU in 1973), Greece (1981), Spain and Portugal (both 1986). 4. The experience of Ireland, Greece, Spain and Portugal with the European integration However EU enlargement of CEE transition countries is unique, the growth effects of economic integration with the EU can be illustrated by the experience from s enlargements. 6

7 All Ireland, Greece, Spain and Portugal were in a better position than recent CEE candidate countries because there existed a functioning market economy. By the time they joined the EU their GDP per capita was at 59% of EU average for Ireland, 69% for Greece, 70% for Spain and 54% for Portugal, what is more or less similar with CEE candidate countries. Because sufficient time has passed since these countries joined the EU, the growth effects of the economic integration can be analysed. Some facts are summarised in table 1. Table 1. GDP per capita convergence of Ireland, Greece, Spain and Portugal Index GDP per capita (EU-15 average=100) Ireland Greece Spain Portugal Source: World Bank (1999), European Commission (2000a), calculations of the author Remark: Index measures the improvement of particular country relative to the EU-15 average between the year of accession and 2000 (for example 202% for Ireland means growth from 59% to 119%). As is shown in the table 1, the convergence occurred in three cases of four, in the case of Greece slow divergence occurred. The speed of convergence was slow, except of Ireland in 1990s. Approximately 2% of the gap between the particular country and EU average was closed per annum. In the case of Ireland in 1990s its GDP per capita growth was accelerated by institutional environment reforms. GDP per capita in steady state was probably increased by these reforms and because of conditional convergence, Ireland did not only converge to the EU average, but exceeded this level significantly. Thus from EU membership profited mainly Ireland, while Greece did not utilize the potential of EU membership. The results are probably determined by the institutional environment. Then convergence is not natural phenomenon, but it is conditioned by the institutional environment Benefits of EU accession for the Czech Republic In chapter 2 the neoclassical growth models were analysed and the main factors by which the GDP per capita in steady state is determined were described. How will EU accession influence these factors and consequently the GDP per capita in steady state? 7

8 Amount of physical capital per capita Ireland, Spain and Portugal experienced after the entry the increase in investment, while Greece instead experienced a consumption boom. According to World Bank (1999: 27): The investment boom was driven primarily by the reduced political risk, the restructuring of the capital stock in view of new production patterns, and the introduction of new technologies accompanied by increased FDI. The similar positive effect of the entry into the EU to investment can be reached for the Czech Republic. Amount of human capital per capita the Czech Republic should experience the increase in amount of human capital per capita as the consequence of joining the European educational system, adoption the higher standards in education and introduction of new technologies, demanding more human capital. Technology the entry into the EU should cause the convergence of technology (institutional environment, technologies of production, and so on) to the European standards. Important will be also the integration of the Czech companies into European production structures and the inflow of FDI. Thus EU accession should have the positive impact to the GDP per capita of the Czech Republic and should support convergence to the EU average. But convergence will not be the automatic phenomenon and the real benefits will depend on the Czech Republic itself Conclusions In this paper convergence based on neoclassical growth models was analysed. Theoretical and empirical results of convergence were inferred and the main determinants of the GDP per capita value in steady state were specified (the amount of physical capital per capita, the amount of human capital per capita and technology (the other factors)). By the econometric model was accepted the hypothesis of absolute convergence (β convergence) in the EU in the period of The convergence among the EU members was observable, but the speed of convergence was slow (only 1% of the gap between the particular country and EU average was closed every year). The EU enlargement of the CEE transition countries is unique, however, the growth effects of economic integration with the EU can be illustrated, primarily by the experience of 8

9 s enlargements. Thus the experience of the entries of Ireland, Greece, Spain and Portugal into the EU was examined and the possible consequences of the Czech Republic s entry to the convergence were derived. Convergence occurred in three of four cases (Ireland, Spain and Portugal), in the case of Greece slow divergence arose. The speed of convergence was slow, except of Ireland in 1990s. Approximately 2% of the gap between the particular country and EU average was closed per annum. In the case of Ireland in 1990s its GDP per capita growth was accelerated by institutional environment reforms. GDP per capita in steady state was probably increased by these reforms and because of conditional convergence, Ireland did not only converge to the EU average, but exceeded this level significantly. Thus from EU membership profited mainly Ireland, while Greece did not utilise the potential of EU membership. The results were probably determined by the institutional environment. Then convergence is not automatic phenomenon, but it is conditioned by the institutional environment. EU accession of Ireland, Greece, Spain and Portugal influenced the main determinants of the GDP per capita value in steady state. Ireland, Spain and Portugal experienced after the entry the increase in investment, while Greece instead undergone a consumption boom. The technology was influenced mainly by the FDI inflow (especially in the case of Ireland). The main result is that EU accession should have the positive impact to the GDP per capita of the Czech Republic and should support convergence to the EU average. But convergence will not be the automatic phenomenon and the real benefits will depend on the Czech Republic itself. 10 As was investigated, the positive impact of EU accession on growth is conditioned by the institutional environment. 9

10 Appendix Verification of Basic Assumptions a) Normality To test for normality, a test based on the values of moments of distribution can be used (See Kmenta (1997), p. 266). index of symmetry...a 3 =1,2587 index of kurtosis...a 4 =5,7591 stat=n(a 2 3 /6+(a 4-3) 2 /24) χ 2 2 stat=8,138 Tabulated value of χ 2 2 at 5% (1%) level of significance is 5,99 (9,21). At 5% (1%) level, the hypothesis of normality would (would not) be rejected. b) Homoskedasticity To test for homoskedasticity, White test can be used (See Víšek (1997), p. 87). White d=2,64473 White d χ 2 2 Tabulated value of χ 2 2 at 5% (1%) level of significance is 5,99 (9,21). At 5% (1%) level, the hypothesis of homoskedasticity would not be rejected. c) Independence of disturbances To test for the absence of autocorrelation, Durbin-Watson test is usually used (See Durbin and Watson (1952)). d=1,803 Tabulated value is du=1,350 (du=1,054) at 5% (1%) level of significance. Thus du<d< (4-dU) at 5% (1%) level and the hypothesis of no autoregression would not be rejected at 5% (1%) level of significance. 10

11 References Baldwin, R., Seghezza, E. (1996): Growth and European Integration: Towards an Empirical Assessment. Centre for Economic Policy Research, No Barbone, L., Zalduendo, J. (1998): EU Accession of Central and Eastern Europe: Bridging the Income Gap. Policy Research Working Paper No. 1721, World Bank Barro, R. (1997): Determinants of Economic Growth, A Cross-Country Empirical Study. The MIT Press, Cambridge, Massachusetts Barro, R. (1994): Economic Growth and Convergence. ICEG No. 46 Barro, R., Sala-i-Martin, X. (1995): Economic Growth. McGraw-Hill Inc., New York Cahlík, T. (2000): Èeská republika po vstupu do EU. IES FSV UK, Praha Cahlík, T. (1998): Makroekonomie. Karolinum, Praha Dornbusch, R., Fischer, S. (1994): Makroekonomie. SPN, Praha Durbin, J., Watson, G. S. (1952): Testing for serial correlation in least squares regression. Biometrika 37, Ekonom 47/2000: Pravidelná zpráva Evropské komise o ÈR za rok European Commission (2000a): European Economy No. 71 (The EU Economy: 1999 Review). European Commission (2000b): 2000 Regular Report on the Czech Republic s Progress Towards Accession. Hušek, R. (1999): Ekonometrická analýza. Ekopress Kmenta, J. (1997): Elements of Econometrics. The University of Michigan Press Romer, D. (1996): Advanced Macroeconomics. McGraw-Hill Víšek, J.A. (1997): Ekonometrie I. Karolinum, Praha World Bank (1999): CR Toward EU Accession. 11