Convergence and regional productivity differences: Evidence from Greek prefectures

Transcription

1 Ann Reg Sci (2004) 38: DOI: /s Convergence and regional productivity differences: Evidence from Greek prefectures Dimitris K. Christopoulos 1, Efthymios G. Tsionas 2 1 Department of Economic and Regional Development, Panteion University, Athens, Greece ( christod@panteion.gr) 2 Department of Economics, Athens University of Economics and Business, Athens, Greece ( tsionas@aueb.gr) Received: April 2001/Accepted: May 2003 Abstract. We study the effect of technology gaps and capital deepening on the productivity growth of Greek prefectures over the period The empirical results show that Greek prefectures tended to converge over time, contrary to conventional wisdom. It is found that capital deepening plays a significant role in explaining productivity differences at the regional level. The empirical results are discussed in detail. JEL classifications: O18, R11, D24 1. Introduction During the last decade, many empirical studies have focused attention on the presence and persistence of regional disparities in the European Union, including Pagano (1993), Button and Pentecost (1995) and Sala-i-Martin (1996). A few comments on this issue would be helpful in setting the recently published results in perspective. The neoclassical prediction (Solow 1956; Cash 1965) is that in poorer regions, per capita output should grow faster than richer areas, which gives rise to the so called convergence hypothesis. Other things being equal, the premise of this hypothesis is that the potential for growth is greatest in those regions that are furthest behind. There are several factors at work behind convergence. Migration of labour with low human capital from poor to rich regions tends to increase wages in regions of departure and reduce then in regions of destination (Barro and Sala-i-Martin 1995). Furthermore, the impact trade, the diffusion of new technology and diseconomies of location might give rise to spill over effects which benefit poor regions (Bergstrom 1998). Thus, there are a priori reasons to expect that regions tend to converge over time. We would like to thank the anonymous referees for helpful comments and suggestions. Thanks also go to Professor Clive Richardson and M. Terrasi for his constructive comments which improved the quality of the paper substantially. All errors remain the responsibility of the authors.

2 388 D.K. Christopoulos, E.G. Tsionas To test the convergence hypothesis various alternatives have been proposed in the literature. We discuss briefly the main three concepts usually used in evaluating the pattern of regional disparities: (1) the speed of convergence (b-convergence); (2) the analysis of dispersion of per capita regional output (r-convergence); and (3) the examination of regional distributional dynamics over time by Markov chains. In the context of b-convergence researchers estimate the convergence equation or the so called Barro (1991) regression for a given cross section of regions within countries with growth rate of output per capita over some time period to be explained by-among other things-the initial level of output per capita. Convergence is implied if the coefficient of initial per capita output is negative and statistically significant. The second concept, r-convergence, is based on time series analysis and focuses on the evolution of dispersion of output per capita (Barro and Sala-i- Martin 1991,1992). However, both Sala-i-Martin (1996) and Quah (1996) pointed out that r-convergence is sufficient but not necessary for b-convergence. This implies that the absence of r-convergence cannot be taken as evidence against b-convergence. In this context, since the concepts of b and r- convergence are not able to discriminate properly between convergence and non-convergence, Quah (1993, 1996) introduces a third concept of convergence that permits the existence of convergence clubs. To this end he employed Markov chain methods to model growth dynamics. All the above tests have been applied to regional output series, but have given mixed results. In the context of European regions, Quah s model of polarization failed to confirm the convergence hypothesis (Button and Pentecost 1995; Quah 1996). On the other hand, Barro style regressions, (see, for example, Sala-i-Martin 1996; Armstrong 1995; and Neven and Goyette 1995) as well as measures of dispersion (Sala-i-Martin 1996) have found that European regions tend to converge over time. Studies of regional convergence in Greece have been conducted by Siriopoulos and Asteriou (1998) and Petrakos and Saratsis (2000). Both studies basing their analysis on output per capita, ran simple b-convergence regressions over the periods and respectively, reaching opposite conclusions. While Siriopoulos and Asteriou (1998) concluded that there had been no tendency for regional income differences to disappear, Petrakos and Saratsis (2000) argued for a decrease in Greek regional inequalities (i.e., convergence) in the 1980s. This disparity might be attributed to the data employed and to the different level of spatial disaggregation, NUTS II (regions) and NUTS III (prefectures). The common feature of all the above studies is that they either consider technological progress to be identical for all regions, or they do not take technology into account, by focusing exclusively on the output per capita. However, this is a brave hypothesis. According to the arguments advanced by endogenous growth theory (Romer 1993, 1994, 1996; Lucas 1988) countries or regions might not converge, even in a world of constant returns and exogenous growth as the neoclassical growth model predicts, if they differ in the way they allocate their resources over time or if they do not have access to the same technology. This means that differences in steady state result not only from differences in accumulation rates of capital as the traditional Solow model assume but also from differences in technology. Further, this leads to a world in which similar steady state outcomes are exceptions rather the rule (Bernard and Jones 1996a,

3 Convergence and regional productivity differences 389 p.1041). In this context, one way to resolve the regional convergence controversy in Greek regions is to look at the empirical issue in a different way. Thus, we take into account that productivity growth across regions differs mainly because of the presence of technological gaps or capital deepening effects, (Romer 1993; Bernard and Jones 1996a,b; Caree et al and Andre s and Bosca 2000). More specifically, this paper is based on the following premises. We test for convergence in the output labour ratio as the growth models suggest and not in output per capita. This distinction is very important since it can be expected that the integration process (factor mobility, trade integration, technological diffusion, and so on) and the measures adopted to improve the economic performance of the poorer regions including infrastructure investments and human capital endowment) favour on increase in productivity levels of lagging regions, but this is not immediately translated into convergence of per capita incomes. We allow technology to differ across regions. This hypothesis is critical in the convergence literature since not all regions have the same ability to adopt new technologies and use them in the most efficient manner. Regions that adopt faster are able to manage their technologies better. This further improves their relative situation. We take into account the role of returns to scale on regional productivity growth. The presence of increasing return activities may explain why some poor regions have been able to improve their relative position significantly. In this connection, the presence of scale economies may affect significantly the convergence process. Our analysis focuses on Greece during the period We believe that Greek regional data are best suited for an initial examination of the convergence hypothesis under the existence of a regional technological gap. Conventional wisdom has it that there is economic dualism in Greece: prefectures with high levels of economic growth and high rates of R&D expenditure exist alongside prefectures in which no growth appears to be taking place. On the other hand, according to Rodrı guez-pose (2001) national technological policies in Mediterranean countries have been directed mainly towards reducing the technological gap with respect to countries in the European core and only secondarily to reducing disparities within each country. If such differences were indeed meaningful then the theoretical neoclassical predictions would not be verified. This would justify another conventional wisdom, namely that the European South is not converging with the rest of Europe. This has important implications for the conduct of macroeconomic policy at the EU level and the future of cohesion policies through the structural funds of the Union. Although Greece at the national level might be diverging from the rest of the EU (possibly along with other Southern European regions) despite the fact that Greek regions are converging (to a common trend which, however, diverges from the European trend), this is a remote possibility. The reason is that growth performance in Greece has been remarkable during the last five years, with government commitment to the Maastricht convergence criteria and the adoption of the euro: Thus it is hard to argue that Greece is not in the process of convergence with the rest of the EU. In this line of thought,

4 390 D.K. Christopoulos, E.G. Tsionas regional convergence in Greece could be attributed to the impact of structural funds, and if these are cut in poor regions, regional divergence may emerge 1. The remainder of the paper is organised as follows: Sect. 2 describes the theoretical model, Sect. 3 presents the empirical results, Sect. 4 summarises and concludes with some policy implications. 2. The model To test the effect of technology gaps and capital deepening on productivity growth of Greek prefectures over time we employ the model proposed by Bairam and MacRae (1999) for testing convergence of agricultural productivity across 101 countries 2. Our productivity growth equation is derived from the estimation of a production function. We adopt the Cobb-Douglas production function: Y it ¼ A i ðtþkit a Lb it ; 0 < a < 1; 0 < b < 1; i ¼ 1; 2;...k; t ¼ 1; 2;...T: ð1þ where Y it, K it, L it stand for output, capital and labour in region i at time t, respectively. A i ðtþ is the level of technology in region i at time t and its growth rate is discussed below. Finally, we allow for the possibility of non-constant returns to scale by allowing ða þ bþ to take any value. Taking the natural logarithms of Eq. (1) and dividing it by the number of workers (L) expresses output, and the physical capital on a per worker basis. That is, ln y it ¼ ln A i ðtþþaln k it þðaþb 1Þln L it ð2þ where y it stands for regional output per worker, and k it ¼ K it =L it, the per worker stock of physical capital. We have increasing (decreasing) returns to scale if the coefficient of ln L it is positive (negative). Finally, constant returns to scale prevail if the coefficient of ln L it equals zero. Differentiating Eq. (2) with respect to time t, yields the non-constant returns to scale version of the Cobb-Douglas growth equation, namely: dy i ¼ w i þ aðdk i dl i Þþðaþb 1ÞdL i ð3þ where dk i dðlogðk i ÞÞ=dt and dl i dðlogðl i ÞÞ=dt denote the growth rates of capital and labour respectively and w i ¼ d logða i Þ=dt. It should be noted that since the coefficient of dl i could be positive or negative, the contribution of labour growth to overall productivity growth could be positive, negative or zero depending on the returns to scale. However, the role of capital deepening is always positive as long as a > 0. The capital deepening effect shows the effect of changes in the capital-labour ratio on regional growth. According to (3) this is the first component of regional growth. The other component is related to changes in the total amount of labour L i, and, finally, there is 1 This observation was brought to our attention by an anonymous referee. 2 Although Quah s idea of growth clubs would be an alternative to test for the convergence hypothesis, his model cannot be estimated in a straightforward way when the transition probabilities depend on the technological gap and scale economies that are central to the analysis of the present paper.

5 Convergence and regional productivity differences 391 exogenous growth at the rate w i. Higher values of k i are associated with more capital-intensive production that can be expected to yield higher growth since new technologies are to a large extent more capital-intensive than older technologies. Next, we test the following specifications for the rate of technical progress w i, w i ¼ l o ð4þ w i ¼ l o þ l 1 y i0 ð5þ w i ¼ l o þ l 1 y i0 þ l 2 yi0 2 ð6þ where y i0 ¼ Y io =L io is the labour productivity level for the initial year in region i. Equation (4) implies that w i is constant and identical for all regions while Eq. (5) assumes that the rate differs across regions due to the technological gap. It is expected that l 1 is less than zero which means that productivity growth across regions is inversely related to their initial productivity levels. Thus a negative (positive) value for l 1 indicates a tendency towards convergence (divergence). Finally, Eq. (6) relaxes the assumption of linear convergence/divergence and explicitly tests for non-linearities in w i. 3. Empirical results In order to obtain empirical evidence on regional convergence in Greece, the two versions (4) and (5) of model (3) are estimated. The geographical unit of our analysis is the prefecture (NUTS III), as in the study of Petrakos and Saratsis (2000), and in contrast to Siriopoulos and Asteriou (1998) who used regions (NUTS II). The increase in data variability is the main reason for this choice. Greece covers an area of 132,000 km 2, has a population of 11,000,000 and is divided into 51 prefectures. The main characteristic of the regional pattern in Greece is the existence of a great concentration of population, economic activities and infrastructure in the Greater Athens area and in the main urban centre of Northern Greece, Thessaloniki. Thus, economic and urban activity is largely developed along this axis. This model of development changed during the 1980s as a result of the emergence of new sectors of specialisation that favoured regions of Northern Greece (Thrace and Macedonia) and, to a lesser extent, the islands and some peripheral regions. The IMP and CSF programmes during the 1980s and 1990s improved this situation further. Next we proceed to the estimation of Eqs. (5) and (6). For the nature and the structure of the variables see the Appendix. The resulting parameter estimates are reported in the following Table One issue, raised by an anonymous referee, concerns the possibility of spatial correlation. We have done a Newey-West procedure for autocorrelation-and-heteroscedasticity-consistent estimator of standard errors but we did not notice important differences relative to the White heteroscedasticity-consistent standard errors. In other settings, however, the presence of spatial correlation would imply biased OLS standard errors, although OLS estimates are consistent.

6 392 D.K. Christopoulos, E.G. Tsionas Table 1. Estimated regional growth equations, Parameter Equation (5) Equation (6) l o 5.142*** [9.049] 7.070*** [5.104] l 1 )0.0879*** [3.497] )0.238*** [2.312] l [1.485] a 0.022*** [4.096] 0.023*** [3.328] a þ b 1 )0.085 [0.776] )0.049 [0.738] R a þ b RESET [2] (p ¼ 0:85) (p ¼ 0:001) Notes: (a) The results for model (5) and (6) are corrected for heteroscedasticity as the Breusch- Pagan statistic indicated the presence of non-homoscedastic error terms. White s consistent covariance matrix was used to correct heteroscedasticity (b) RESET Test tests the null hypothesis that there is no functional form specification. The test is obtained by testing the joint significance of the fitted values squared and in the third power. Figures in brackets are t-statistics; (***) indicates statistical significance at 1%level. From the results of Table 1 the following conclusions are drawn: 1. Equation (5) presented in the first column of Table 1 indicates convergence to the steady state at the rate of 8.8% for Greek prefectures over the period , as the relevant coefficient l 1 is negative and statistically significant at the 1% levels. 2. The coefficient of labour input growth (a þ b 1) is statistically insignificant. On the other hand, the capital-deepening coefficient (a) takes a value statistically different from zero. These results imply that differences in labour productivity growth are solely due to different rates of capital deepening. Other things being equal, only capital deepening rates play an important role in explaining productivity growth in Greece. 3. The results in column 2 indicate that convergence is linear as the coefficient of the quadratic term (l 2 ) is statistically insignificant. This means that, labour productivity growth rates across Greek prefectures are directly related to their initial productivity level. However, this equation fails the RESET test, which suggests some form of miss-specification. Thus the results of Eq. (5) are more reliable. Overall our results show that Greek prefectures tend to converge over time, and that capital deepening is mainly responsible for productivity differences at the prefecture level. Our results clearly show that we cannot assume constant productivity levels for each prefecture. Instead, productivity levels are in a process of convergence. According to Petrakos and Saratsis (2000), whose results are consistent with our findings, the decrease in regional dispersion in Greece coincided with a prolonged recession that hit the Greek economy in the 1980s. Terrasi (1999) reached a similar conclusion for Italy over the period and Chatterji and Dewhurst (1996) for the UK in In both studies, regional inequalities diminished in periods of limited expansion of the aggregate output. This finding confirms Williamson s (1965) well known inverted U hypothesis according to which regional convergence stops in periods of aggregate growth and accelerates in periods of limited expansion of national output. In the case of Greece, the low degree of

7 Convergence and regional productivity differences 393 Table 2. Dispersion of output composition (OutCom) and labour productivity (LabProd ) across Greek prefectures Years Agricultural Industry Services OutCom LabProd OutCom LabProd OutCom LabProd Note: OutCom and LabProd denote output composition and labour productivity respectively. spatial integration as well as the existence of a dual economic base might explain this situation very well. Finally, to gain more insights into the convergence process we investigate further which sectors have been the main contributors to the process 4. In this framework, we can distinguish two different sources of productivity convergence: (a) reduction in productivity inequalities within each sector, and (b) equalization of the sector output composition among regions. The composition of regional output is expected to affect the relative position of regions in the long run, even if they have similar population growth rates and investment rates (Bernard and Jones 1996a). In addition, sectoral composition acquires more significance when agricultural absorbs a sizeable share of regional GDP, due to the much lower productivity of this sector with respect to industry and services (Terrasi 2000). To deal with the movements in sectoral productivity across regions we need information similar to that used in Eq. (5) or (6). However, the lack of capital stock at regional level makes this procedure inapplicable. So we prefer to use an alternative concept, r-convergence, as the measure of dispersion of sectoral labour productivity. Although the dispersion index can be measured in several ways, we choose the standard deviation of the log of labour productivity as in Barro and Sala-i-Martin (1992). This index although, not in log form, is also used to analyse dispersion in sector composition across regions. This procedure is applied to three sectors of production: agriculture, industry and services. Table 2 presents cross-sectoral dispersions in output composition and labour productivity for selected years. According to these results the cross section standard deviation of sectoral structure (OutCom) increased for industry and services, while for agricultural, although it increased to some extent between 1971 and 1981, at the end of the period 1995 it had returned to the initial level Other things being equal, Greek prefectures tended to be less homogenous in terms of sectoral shares during the period However, the same process cannot inferred to the labour productivity index (Lab Pr od). Although industry s labour productivity dispersion index increased among regions (NUTS III), in services this index showed clear evidence of convergence. The existence of considerable touristic resources may explain to some extent this situation, (Petrakos and Saratsis 2000; Rodrı guez-pose 2001). 4 This analysis was proposed to us by an anonymous referee.

8 394 D.K. Christopoulos, E.G. Tsionas 4. Concluding remarks In this paper we have investigated productivity growth differences between Greek prefectures. We used a growth model in which productivity rates differ from prefecture to prefecture because of a technological gap. The results show that Greek prefectures tend to converge over time, and that capital deepening is the most important factor in explaining productivity differences. The decrease of regional dispersion in Greece coincided with a prolonged recession that hit Greek economy in the 1980s. This can be attributed to the absence of spatial integration and duality. The results also suggest that convergence in the service sector is the main factor responsible for overall convergence. The existence of considerable infrastructure in tourism may explain this finding, (Petrakos and Saratsis 2000; Rodrı guez-pose 2001). References Andrés J, Boscá J (2000), Technological differences and convergence in the OECD. Spanish Economic Review 2: Armstrong HA (1995) Convergence among regions of the EU, Papers in Regional Science 74: Bairam IE, McRae DS (1999) Testing the convergence hypothesis: a new approach. Economics Letters 64: Barro JR, Sala-i-Martin X (1992) Convergence. Journal of Political Economy 100: Barro JR, Sala-i-Martin X (1991) Convergence across states and regions. Brooking Papers in Economic Activity 1: Barro JR, (1991) Economic growth in a cross-section of countries. The Quarterly Journal of Economics 106: Bergstrom F (1998) Regional policy and convergence of real per capita income among Swedish countries. SSE/EFI Working Paper Series in Economics and Finance no. 284 Bernard BA, Jones IC (1996a) Technology and convergence. Economic Journal 106: Bernard BA, Jones IC (1996b) Productivity across industries and countries: time series and evidence. The Review of Economics and Statistics 78: Button K, Pentecost E (1995) Testing for convergence of the EU regional economies. Economic Inquiry 33: Caree AM, Klomp L, Thurik RA (2000) Productivity convergence in OECD manufacturing industries. Economics Letters 66: Cass D (1965) Optimum growth in an aggregate model of capital accumulation. The Review of Economics and Statistics 32: Chatterji M and Dewhurst JH (1996) Convergence clubs and relative economic performance in Great Britain: Regional Studies 30: Easterly W, Kremer M, Pritchett L, Summers LH (1993) Good policy or good luck? Country growth performance and temporary shocks. Journal of Monetary Economics 32: Institute of Regional Development (IRD) (1998) NUTS-III regional data for Greece. Panteion University, Athens, Greece King R, Levine R (1994) Capital fundamentalism, economic development and economic growth. Carnegie-Rochester Conference Series on Public Policy 40: Lucas R (1988) On the mechanics of economic development. Journal of Monetary Economics 22: 3 42 Miller MS, Upadhyay PM (2000) The effects of openness, trade orientation, and human capital on total factor productivity. Journal of Development Economics 63: Neven D, Gouyette C (1995) Regional convergence in the European Community. Journal of Common Market Studies 33: Pagano P (1993) On productivity convergence in the European Community countries: Giornali degli Economisti e Annali di Economia 52: Petrakos G, Saratsis Y (2000) Regional inequalities in Greece. Papers in Regional Science 79: 57 74

9 Convergence and regional productivity differences 395 Quah D (1993) Empirical cross-section dynamics in economic growth. European Economic Review 37: Quah D (1996) Regional convergence clusters across Europe. European Economic Review 40: Rodríguez-Pose A (2001) Is R&D investment in lagging areas of Europe worthwhile? Theory and empirical evidence. Papers in Regional Science 80: Romer P (1994) The origins of endogenous growth. Journal of Economic Perspectives 8: 3 22 Romer P (1993) Idea gaps and object gaps in Economic development. Journal of Monetary Economics 32: Romer P (1996) Increasing returns and long run growth. Journal of Political Economy 94: Sala-i-Martin X (1996) Regional cohesion: evidence and theories of regional growth and convergence. European Economic Review 40: Siriopoulos C, Asteriou D (1998) Testing convergence across the Greek regions. Regional Studies 32: Solow RM (1956) A contribution to the theory of economic growth. The Quarterly Journal of Economics LXX: Terrasi M (1999) Convergence and divergence across Italian regions. Annals of Regional Science 33: Terrasi M (2000) Regional convergence in Italy and Spain: a comparative study. In: Regional planning and development in Europe. Ashgate Publishing Limited Williamson JG (1965) Regional inequality and the process of national development: a description of the patterns. Economic Development and Cultural Change 13: July, Part 2, 3 45 Appendix Following the arguments proposed by Miller and Upadhyay (2000) we choose the steady-state method to calculate regional capital stock data. At the steady state, the capital output ratio ðk=y Þ is constant. Therefore the rates of change in capital and output are equal. Furthermore, dk t ¼ I t uk t or ðdk=k t ÞðK t =Y t Þ¼ðI t =Y Þ t ðuk t =Y t Þ ða1þ Since the steady state levels of output and capital ratio grow at the same rate, we have the following: ðdy t =Y t ÞðK t =Y t Þ¼ðI t =Y Þ t ðuk t =Y t Þ or ðg t þ uþðk t =Y t Þ¼ðI t =Y t Þ ða2þ Thus, solving for the steady-state capital output ratio gives the following: ðk t =Y t Þ ¼ðI t =Y t Þ =ðg t þ uþ ða3þ where * refers to steady-state value and u equals 4.5%. However, the steady-state growth rate of output (g ) does not equal the actual growth rate for any region. Rather, as assumed by King and Levine (1994), we use the following formula: g ¼ kg i þð1 kþg n ða4þ where g i is the period-average actual growth rate for the region i ¼ 1; 2, k, g n is the actual national growth rate and k ¼ 0:25, a measure of mean reversion in the growth rates, following Easterly et al. (1993). This produces the steady-state capital output ratio ðk t =Y t Þ. Finally, multiplying the steady-state regional capital output ratio by the average regional output for the 5-year period yields the average regional capital stock for the period.

10 396 D.K. Christopoulos, E.G. Tsionas The output data is Regional Gross Product. Labour is measured as the number of workers. All data were collected from the database of the Institute of Regional Development, Panteion University (IRD, 1998). Finally, the growth rates of output, capital and labour are computed as follows: dx dðlogðxþþ ¼ ðlog x i;95 log x i;71 Þ dt 25 Of course, the reader must be warned that estimating capital stock is never a risk-free enterprise. Unfortunately, we do not have data on current investment so the method presented here is basically the only option we have to obtain data for the capital stock under reasonable assumptions.