Real Convergence, Steps from Adherence to Integration

Busu, Mihail; Gyorgy, Adina Article Real Convergence, Steps from Adherence to Integration Amfiteatru Economic Journal Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Busu, Mihail; Gyorgy, Adina (2016) : Real Convergence, Steps from Adherence to Integration, Amfiteatru Economic Journal, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 18, Iss. 42, pp. 303-316 This Version is available at: https://hdl.handle.net/10419/169003 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ European Integration: Challenges Faced at Macro and Micro Levels AE Vol. 18 • No. 42 • May 2016 303 REAL CONVERGENCE, STEPS FROM ADHERENCE TO INTEGRATION. COUNTRIES FROM CENTRAL AND EASTERN EUROPE Mihail Busu1 and Adina Gyorgy2 1) 2) The Bucharest University of Economic Studies, Romania Please cite this article as: Busu, M. and Gyorgy, A., 2016. Real Convergence, Steps from Adherence to Integration. Countries from Central and Eastern Europe. Amfiteatru Economic, 18(42), pp. 303-316 Abstract The macroeconomic model Solow-Swan shows that the marginal rates of capital, technology and labor force, with positive values, have the capacity to stimulate the economic growth of the emergent economies, as in the case of Romania, for the 2005-2014 period, facilitating the determination of specific correlations between macroeconomic indicators to analyze the convergence towards the average European Union (EU). The economic theory, highlighted in particular by the Solow-Swan model, was applied in the current paper, to determine the potential of real convergence. Moreover, the statistics for the period 2005-2014 facilitated the analysis of the growth and recession factors specific to economies of Central and Eastern Europe and placed Romania amongst the EU countries with emergent economies and potential of economic growth. The European Commission’s reports regarding the macroeconomic imbalance procedure draw attention on the same indicators presented in the article to address proper recommendations and support corrective measures. Making a step from nominal to real convergence, the latter derives naturally from the need to recoup the gaps in comparison with developed economies amongst countries of the Euro Area. Keywords: economic growth, emergent economies integration, real convergence, macroeconomic model. JEL Classification: O18, O31, L84 Introduction The adhesion of Romania to the European Union in 2007, as consigned in Luxemburg by the Treaty of adhesion on 25 April 2005, was the first step towards the integration of the Romanian economy and culture in the European Union, once the acquis communitaire was transposed in the key sectors.  Corresponding author, Mihail Busu - [email protected] AE Real Convergence, Steps from Adherence to Integration. Countries from Central and Eastern Europe 304 Amfiteatru Economic Integration, a complex process in evolution, means a complete harmonization of the economic indicators and thus, diminishing gaps between developed countries of EU and less developed from Central and East Europe, such as: Bulgaria, Croatia, Czech Republic, Greece, Hungary, Poland, Romania, Slovakia and Slovenia. The process of uniformization through reduction of gaps is defined by economic literature as real convergence. In the attempt to quantify the existent gaps for adjustment, the economic theory is built on the neoclassic model of economic growth, based on the macroeconomic indicator, i.e. gross domestic product per inhabitant. As well, the EU regional and cohesion policy indicate on the necessity to reduce the gaps between EU regions and recommends effective measures for redress so to support the undeveloped areas. The same indicator, gross domestic product per inhabitant, represents a basic criterion to classify the regions based on the degree of economic development. The determinant factors that influence the capacity to cover the gaps may vary. In the approach of Hoskisson et. al. (2000), the emergent economies have potential of growth due, in particular, to the liberalization process. This hypothesis is supported by Edwards (2001), in whose opinion the economic growth rates are more visible in case of open economies, driven by capital flows. The importance of direct investments is observed by Konings (2000) who determined a direct link between the level of direct investments and the economic growth rate. The economic theory, although comprises a variety of approaches, goes in the direction of capital transfer between economies as long as these are open or semi-open economies, if we refer to countries who passed or are currently passing through a liberalization process of certain economic sectors. Irrespective of the approach, the model Solow Swan explains empirically the process of convergence and identifies the relevant determinant factors. The concept of economic growth in the long run was introduced by Solow (1956) and Swan (1956) in a macroeconomic model to become a classic, inserting in growth patterns made by economists Harrod (1939) and Domar (1946), a relationship expressing population growth and a requirement on more efficient use of labor. These factors were considered exogenous before Solow-Swan’s model, which determined the subsequent development of the theory of general equilibrium (Barro and Sala-i-Martin, 1992). Koopmans (1951) and Cass (1965) enriched the neoclassical growth model introducing consumer optimization analysis, which allowed determination of endogenous saving rate. Poor countries tend to catch up with developed economies through positive economic growth rates, influenced by the potential of the human resource (Barro, 1991). In his paper, the author shows that the level of private investment is inversely proportional with the economic indicator represented by the current government spending. Economic convergence between the Central and Eastern European countries, focusing on the shift between accession and integration was frequently approached in the economic literature of the last decades. Therefore, the real convergence would seem to depend crucially on the ability of the countries to harness technological innovations, in particular through foreign direct investment (Martin et. al, 2001). Zaman and Goschin (2014) performed an analysis of the convergence of real wages in Romania, at a regional level, under the impact of the recent global financial and economic crisis. Regarding the high costs of adopting the single currency for the countries of Central and Eastern Europe, a gradual convergence is required for compliance with institutional criteria European Integration: Challenges Faced at Macro and Micro Levels AE Vol. 18 • No. 42 • May 2016 305 provided for the Maastricht Treaty (Boone and Maurel, 1998). In his work, Nuti (2002) shows that there is no economic justification for the negativistic attitude regarding the affiliation to the Eurozone for the Central and Eastern European countries. Regional and cohesion policy play a central role in ensuring solidarity in the Central and Eastern Europe (Funck and Pizzati, 2003). Behind the developments of the classical model of economic growth, the authors conceptualize the economic growth model to determine the main impact factors, then to buid on empirical data, in an attempt to define the position of Romania in the Central and Eastern European region. 1. Conceptual analysis of the Solow – Swan model In the economic literature, the reduction of gaps is explained by the paradigm Solow-Swan. In 1956, the economists Robert Solow and Trevor Swan developed the macroeconomic model of long term economic growth, based on certain variables, such as: accumulation of capital, evolution of number of employed population and variation of technologized labor force, the latter being an expression of technological progress. A Cobb-Douglas function stands at the basis of the model. The information released from the application of Solow-Swan model denotes a trend of homogeneity through capital flows from developed to less developed economies. When the rates of savings are similar, the trend is increased and more visible, supporting the process of real convergence. Although the model suffered changes following the improvements brought by Mankiw et. al. (1992), the convergence steps may be observed in countries with high degree of innovation, by connection between information tehnology and productivity (Brynjolfsson, 1993; Black & Lynch 2001; Aral et. al., 2007; Busu, 2012). The function of production is built in the model of economic growth, as shown below: Y(t) = K(t)α (A(t)L(t))1-α (1) Where: t - the time interval; α - represents the elasticity of production dependent on the capital, 0 < α < 1; Y(t) - a function type Cobb-Douglas representing the total output; A(t) - the technological labor force; L(t) - the labor force and A(t)L(t) – the effective technological labor. We presume that all production factors are fully engaged and the rates of growth for A(t) and L(t) are noted with n, respectively g. The initial values for technological labor, capital and labor are A(0), K(0) and L(0). { L(t) = L(0)·ent ; A(t) = A(0)·egt (2) AE Real Convergence, Steps from Adherence to Integration. Countries from Central and Eastern Europe 306 Amfiteatru Economic Under these conditions the effective labor at a given moment will be A(t)L(t), with an exponential rate of growth (n+g). The amortization capital rate is noted with δ, while consumption is noted with c·Y(t) and 0 < c < 1, where c represents the share of consumption and s=1-c represents the share of savings or of the net investments. We obtain the following: Ќ(t)= s·Y(t)- δ·K(t) (3) Ќ(t) represents the derivative of capital function dependent on time, so dK(t)/dt represents the share of unused capital remained for savings. In other words, it describes the net investment. Now we determine production based on effective labor force: y(t) = Y(t) / A(t)L(t) = k(t)α (4) The target is the dynamic of capital function, i.e. k per unit of effective labor. The behavior of one investor on the market may be integrated in the model Solow–Swan, as following: ḱ(t) = s·k(t)α – (n+g+δ) ·k(t) (5) where:  s·k(t)= s·y(t): savings per unit of effective labor;  (n+g+δ) ·k(t): threshold from which the investment becomes profitable. k(t) converges to k*, defined by s·k(t)α – (n+g+δ) ·k(t), i.e. the equilibrium level or the net investment threshold. k*=[s/(n+g+ δ)]1/(1-α) (6) If we presume the growth rates to be constant, the rate of economic growth per inhabitant is determined by the rate of technological progress. If K(t)/Y(t) = k(t)1-α, where k* represents the equilibrium level, then: K(t)/Y(t) = s/(n+g+δ) (7) As a result, in conditions of equilibrium, the capital factor depends only by savings rate, technological growth and amortization rate. If we derive production based on capital, we obtain: MPK = δY/δK = αA1-α/(K/L)1-α (8) It is noted that a lower value of the indicator K/L determines a higher rate of investment because (1-α) is below the unit. As an consequence, in open economies, capital flows will emerge from developed economies to those less developed, if the capital rate K/L overall reaches the productivity rate Y/L. 2. Empirical analysis of the econometric model The model would be estimated starting from the derivative of the production function with respect to the variable capital we got at eq. (8). European Integration: Challenges Faced at Macro and Micro Levels AE Vol. 18 • No. 42 • May 2016 307 MPK = δY/δK = αA1-α/(K/L)1-α For small percentage increases, the derivative of the production function can be approximated by the first difference. Thus, the above equation is equivalent to: ΔY/ΔK = αA1-α/(K/L)1-α (9) To linearize this exponential equation, we will logarithm both members of the equation: ln(ΔY/ΔK) = ln[αA1-α/(K/L)1-α] (10) equivalent with: ln(ΔY/ΔK) = lnα+(1-α)lnA-(1-α) ln(K/L) (11) To achieve the growth rate of /YL , the first order differential equation will be calculated for the previous equation and we will get: Δln(ΔY/ΔK) = ΔlnA+α[Δln(K/L)] (12) In this context, the base value Y/L will also be included in our model, in order to control states’ convergence effect with their low level of incomes that have rapid growth rates. Δln(ΔY/ΔK) = β0+ β1·Δln(A)+ β2·ln(Y/L)+ β3·[Δln(K/L)] (13) where: Y- GDP; L- active labor input; K- fixed capital input and A- number of people employed in tech fields. When this model is run with SPSS statistics software, we use the following:  Δln(ΔY/ΔK) the percentage increase of ln(ΔY/ΔK) and  Δln(K/L) the percentage increase of ln(K/L). 3. The concrete model for the Romanian economy between 2005 and 2014 The econometric model derived in (13) is a multilinear regression equation, describing the evolution of an endogenous variable in relation to the three major exogenous variables: y = β0+ β1·x1+ β2·x2+ β3·x3 +Ɛ (14) where:  y = Δln(ΔY/ΔK) – dependent variable;  x1 = Δln(A) – independent variable;  x2 = ln(Y/L) – independent variable;  x3 = Δln(K/L) – independent variable;  β0, β1, β2, β3 -model parameters and  Ɛ – residual variable. AE Real Convergence, Steps from Adherence to Integration. Countries from Central and Eastern Europe 308 Amfiteatru Economic The multilinear regression equation we have built, has been performed by means of the Least Squares Method. We have used this method for estimating the coefficients of the equation we have derived before. In other words, we want to estimate the derivative of the production function with respect to the independent factors: GDP, active labour, fixed capital and number of people employed in tech fields. Data has been extracted from the Eurostat website and they represent the corresponding values of Romania for the 2005- 2014 period. Overall, the regression equation is significant (Sig. = 0.048), with the coefficient of determination equal with 0.832 and a value of R-squared adjusted equal with 0.706, while Durbin Watson test validates the modeling approach itself (DW = 1.8). Correlation ratio value of 0.912 certifies that the endogenous variable is strongly correlated (0.912) detached from a cumulative determination of exogenous variables which argues for 83.2% of the variance in the dependent variable.(table no. 1) Table no. 1. The econometric model Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 0.912a 0.832 0.706 1.04598 1.878 Note: a. Predictors: (Co0nstant), X3, X2, X1 b. Dependent Variable: Y ANOVA model for the estimated econometric model indicates that this multilinear regression model is statistically significant (F = 6.596; Sig. = 0.048). (table no. 2) Table no. 2. ANOVAa model Model Sum of Squares df Mean Square F Sig. 1 Regression 21.646 3 7.215 6.595 .048b Residual 4.376 4 1.094 Total 26.022 7 Note: a. Dependent Variable: Y b. Predictors: (Constant), X3, X2, X1 From table no. 3 above we could see that the parameters of the model ane significant (p_value < 0.05 for all three independent variables). Moreover, the values of VIF (variance inflection of the estimated regression coefficients β1, β2 and β3 confirm the expectations on the convergence effect of the derivative of the production function with respect to the labour, fixed capital and the productivity rate. European Integration: Challenges Faced at Macro and Micro Levels AE Vol. 18 • No. 42 • May 2016 309 Table no. 3. Estimation of regression coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Tolerance VIF 1 (Constant) -1.541 .901 -1.711 .162 -4.042 .960 X1 1.674 1.226 1.225 1.365 .024 -1.730 -5.077 .582 1.915 X2 .697 1.362 -.455 -.512 .036 -4.479 -3.086 .573 1.881 X3 .022 .007 -.652 -3.050 .038 -.042 -.002 .920 1.087 The regression equation resulted through the econometric analysis, leads to the following equation: Δln(ΔY/ΔK) = -1,541+ 1,674·Δln(A) + 0,697·ln(Y/L) + 0,022·[Δln(K/L)] (15) Hence, we could conclude that the derivative of the production function with respect to the variable capital is proportional with the tech labor, productivity rate and fixed capital rate, respectively. Therefore, the Solow-Swan Model applied to Romania indicates a high potential economic growth determined by productivity, capital and tech labor. The statistical data presented in the following chapter allow an analysis of the Central and Eastern European economic framework evolution. 4. Results and discussion: convergence in Central and Eastern Europe Although the EU countries from Central and Eastern Europe may have in common certain characteristics specific to the communist regime, the International Economic Forum shows in its recent publication (2015) that Romania has a particular situation, also pointed by the European Commission in the country recommendations. This regards the scanty infrastructure, observed especially in the transport sector. If indicators like corruption, red tape, excessive taxation are common to other member states from EU, the poor infrastructure is specific to Romania and it is similar to other countries from a) Africa: Tanzania, Liberia and Uganda; b) Asia: Iran, Nepal and Lao; c) Latin America: Colombia and Bolivia (Liao et. al., 2009; Rozylowicz, 2006; Matsukawa & Habeck, 2007, Klimaszewski & Nyce, 2009; Busu et. al., 2015). Nevertheless, the ranking of countries published by the International Economic Forum places Romania above some countries from Central and Eastern Europe, like Bulgaria, Slovakia, Slovenia, Hungary, Croatia and Greece, given the swift rate of catching up with developed economies pointed out in the last years. Savoiu et. al. (2014) performed an analysis of the convergence degree between Romania and the average of the European Union countries, in terms of standardized economic indicators. Previously to the assessment of the country specific recommendations, we have presented below some evolving macroeconomic indicators to better understand the evolution in time of these economies. Each macro-economic indicator is analyzed in the context of the economic growth theory. The indicator GDP per capita showing the economic growth in Solow-Swan model highlights accurately the country-specific living standards in the region. It is noted that AE Real Convergence, Steps from Adherence to Integration. Countries from Central and Eastern Europe 310 Amfiteatru Economic Poland, Hungary, Bulgaria, Romania and Slovakia have recorded constant growth in the last 10 years. Slovenia is in the top of the countries from Eastern and Central Europe, followed by Greece and Bulgaria on the last place, preceded by Romania. The average in the European Union is well above the level of the best performing economies in the region (Slovenia compared to the EU-28). (figure no. 1) Figure no. 1. GDP/capita, in euro Source: based on processed data provided by Eurostat, 2015 In the Solow – Swan model, the productivity is a key factor to determine whether there is potential for catching up with the developed economies. Similarly, the growth potential of the tech workforce leads to GDP growth. Below, it is presented an evolution of this indicator. (figure no. 2) Figure no. 2. Real work productivity per capita, base year 2010 Source: based on processed data provided by Eurostat, 2015