Examining Agriculture from a Regional Perspective: Implications for the Common Agricultural Policy Stilianos Alexiadis1, Nikolaos Hasanagas2 and Ladias Christos3 1 Ministry of Rural Development & Foods, Department of Agricultural Policy & Documentation, Division of Agricultural Statistics, e-mail: ax5u010@minagric.gr 2 Aristotle University of Thessaloniki, Department of Forestry & Natural Environment 3 University of Central Greece, Department of Regional Economic Development Abstract. Regional convergence is one of the major goals of the European Union. In this paper, the intention is to augment the existing literature on regional convergence across the NUTS-2 regions of EU-27 in terms of agricultural labour productivity during the period 1995-2004. A low annual rate of absolute convergence is estimated for the NUTS-2 regions over the period 1995-2004. The rate of regional convergence exhibits a considerable variation across different territorial divisions of the European Union. The implications of these results are discussed in the context of the Common Agricultural Policy and respective recommendations are issued. Keywords: Agriculture, European Union, CAP 1. Introduction Recent years have witnessed a growing number of attempts to assess regional convergence using extensive datasets, such as the regions of the European Union (EU). This focus of interest is not entirely unexpected given the concern about regional convergence or what the European Commission calls ‘regional cohesion’. As Button and Pentecost (1999) point out ‘[…] if the growth rates of regions deviate significantly this, it is feared, can generate instabilities. Those in the poorer regions feel resentment at the prosperity of others’ (p. 2). In this literature industrial sites are mainly considered from a planning or environmental point of view, thereby largely neglecting the economic perspective Nevertheless, in the so far literature regional convergence is mainly considered from a aggregate point of view, i.e. for the economy a whole1, neglecting the agricultural sector2, especially at the regional level. The findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the official position, policies or views of the Ministry of Rural Development and Foods and/or the Greek Government. 1 It is not difficult to document studies on regional convergence across Europe (e.g. Button and Pentecost, 1995; Neven and Gouyette, 1995; Álvarez-Garcia et al., 2004; Ezcurra et al., 2005). Fewer studies refer specific sectors, explicitly, usually manufacturing (Pascual and Westermann, 2002; Gugler and Pfaffermayr, 2004) or services (e.g. Button and Pentecost, 1993). 2 Some notable exemptions are the studies by Soares and Ronco (2000), Bivand and Branstad (2003, 2005). 647 Regional convergence in terms of the agricultural sector is a key issue, especially in connection with the Common Agricultural Policy (CAP). The second pillar of the CAP (‘rural development’) and the agricultural and rural sections of the Structural Fund Programs of the European Regional Policy attempt to promote a ‘regionalisation’ of agricultural policies. As regions in the EU take more political and administrative responsibilities, the ‘regionalisation’ of CAP incurs opportunities and challenges for regions. However, Trouvé and Berriet-Solliec (2010) point out the risk that this regionalisation might increase inequalities across regions. Therefore, a clear and precise knowledge of the existing convergence pattern across the European regions is essential for an effective reform of the CAP. This paper attempts to shed some further light on that issue. We should emphasise at the outset that the approach used in this paper is mainly quantitative. However, it is hoped that this paper will be able to isolate some interesting views on the issue of convergence in RALP across Europe. The rest of this paper is structured in the following manner. Section 2 is devoted to an overview of agriculture in Europe. Two of the most commonly used measures of regional convergence are discussed in Section 3. Section 4 presents the econometric results. In the concluding section we offer a possible explanation for the results we obtain and suggest that might afford an interesting policy conclusion. 2. Agriculture in the European Union Europe faces probably the worst recession since World War II. The current economic crisis has wiped out years of economic and social progress and exposed structural weaknesses in Europe’s economy. More than 80 million people are at risk of poverty; 19 million of them are children while 8% of labour force does not earn enough to make it above the poverty threshold3. Unemployment, budget deficits4 and divergent growth patterns result to accumulation of government debts and put uncertainty and unpredictability for the single currency (euro). The GDP in the EU- 27 has fall by 4% in 2009, industrial production has dropped back to the levels of the 1990s and 23 million people (10% of active population) are unemployed 5. According to EUROSTAT (2010), employment rate rose from an average of 65.4% in 2007 to only 65.9% in 2008. The Lisbon employment target (70%) is set to be achieved in 20106. However, in 2008, only 94 NUTS-2 regions, out of 271 regions, had already achieved this target for 2010, while 50 regions were still 10 percentage points below the overall employment target. Relatively low employment rates were 3 Poverty threshold is defined as 60% of the average income in each Member State of the EU. 4 Budget deficits were 7% of the GDP, on average (the target of 3% of GDP is set to be achieved by 2013) and debt levels at over of 80% of the GDP. 5 Only two-thirds of labour force in the EU is currently employed, compared to over 70% in the US and Japan. 6 It is questionable, however, if, under the present circumstances, the target of the employment 75% of the population aged 20-64 set by Europe 2020 would be achieved. 648 recorded in the south of Spain, the south of Italy, Greece, Poland, Slovakia, Hungary, Bulgaria and Romania, whereas a relatively high employment rate characterises the regions of Netherlands, United Kingdom, Denmark, Sweden and Finland. Europe faces a moment of transformation and three factors can be taken into consideration: globalisation, energy consumption and climate change. Globalisation creates more opportunities for producers and entrepreneurs, who are in a position of enjoying larger markets and higher competitions. Consumers will benefit from higher living standards through lower prices and a wider choice of goods. A general increase in economic activity and trade will enhance labour demanded and real wages for skilled labour create employment and increase economic growth. Globalisation is driving scientific and technological progress, making the European dimension ever more important in boosting knowledge, mobility, competitiveness and innovation. The opening up of huge new markets creates vast opportunities for Europeans, but it will at the same time test Europe’s capacity to further adjust to structural change and manage the social consequences of that change. The dissemination of innovation and know-how will also increase productivity. However, globalisation might also bring structural adjustment. Increasing competition can put additional pressure on local firms and, indirectly, on wages, especially for low-skilled labour. Regions are enlarging their area of influence, sometimes globally. Several regions in the EU should restructure their economic base and promote continuous innovation (in products, management and processes), as well as human and social capital – to face the challenge of globalisation. Nonetheless, the benefits of globalisation remain concentrated in a limited number of regions with advanced urban centres. Globalisation is likely to increase regional imbalances within Europe. Most regions located in the Southern and Eastern parts of the EU, stretching from Latvia, Eastern Slovakia, Hungary, Bulgaria and Romania to Greece, Italy, Spain and Portugal, appear to be much more exposed to the challenges of globalisation. This vulnerability is predominantly due to the relatively large share of low value added activities in these regions and weaknesses in workforce qualifications, which may lead to difficulties in attracting investment and creating or maintaining jobs. The EU is characterised by a growing external energy dependency, especially in the fossil energy sources (oil, gas, coal) and in nuclear energy sources (uranium) 7. Agriculture and industry, especially Small-Medium Enterprises (SMEs), have been hit hard by the economic crisis and all sectors adjusting their production processes and products to a low-carbon economy. Energy prices appear to have become ever more volatile with extreme price peaks. Peripheral regions located in Eastern and southern Member States appear to be more vulnerable. Energy consumed directed by agriculture is related to the use of machinery, such as tractors, and the heating of livestock stables and greenhouses. There is also the indirect energy use for the production of agrochemicals, farm machinery and buildings while considerable 7 In 2005, 53% of energy consumption in the EU was covered by imports. 649 amounts of natural gas are used for the production of inorganic nitrogen fertilisers. Although the use of machinery and mineral fertilisers results to increases in agricultural productivity and food supply, nevertheless it contributes to the depletion of non-renewable energy sources and to global warming (CO2 emissions from fossil fuel consumption). The total consumption of energy by agriculture in the EU-27 has decreased by 7% since 2005; from 29,939 kilo tonnes of oil equivalent to 27,826 in 2007 (EUROSTAT, 2010a). The share of agriculture in final energy consumption by all sectors, in the EU-27 on average has been steadily declining, from 2.7% in 2000 to 2.4% in 2007. Nevertheless, this share exhibits considerable variations across the EU-27 countries (8.1% in the Netherlands and 0.6% in the United Kingdom). This index, however, does not reveal anything about the intensity of energy use by agriculture and depends on the size of agricultural sector, the energy use and size of the remaining sectors. Therefore, a more appropriate indicator would be the final energy consumption of all energy products by agriculture in kilograms of oil equivalent per hectare of utilised agricultural area. According to EUROSTAT (2010), the average energy consumption in the EU-27 is 161 kilograms of oil equivalent per hectare. The highest energy consumption per hectare is recorded for the Netherlands (2,166 kilograms of oil equivalent) due to the high intensity of production in heated greenhouses, the most energy consuming type of crop production. Climate change will, in the long-run, lead to an increase in average annual temperatures, alter rainfall quantities and patterns, and raise the sea level and the risk of coastal erosion. In Southern regions, climate change is projected to worsen existing conditions through declining precipitation and drought. More than 170 million people (about one third of the EU population) live in regions most affected by climate change. Regions subject to the highest pressure are generally located in the South and East of Europe, Spain, Italy, and several southern parts of France Greece, Bulgaria, Malta, Hungary and Romania. Although agriculture is of particular importance for the low-income Southern regions, nevertheless these are characterised by a low capacity for adoption to climate change. The Alpine areas with reliable snowfall will decrease and the industry will have to shift its focus to summer holidays, whereas Mediterranean regions might suffer from temperatures above the heat comfort zone and loss of biodiversity. In the energy sector, climate change will lead to changing patterns of energy demand and to greater fluctuations in energy production and demand, particularly in regions with a high share of renewable energy8 and varying availability of water for cooling of large-scale heating power plants. These effects will impact on regional growth potential in affected regions and create disparities with those regions that are less affected by climate change. Changing weather conditions will have a negative impact on 8 The share of renewable energy resources in consumer’s energy consumption exhibits considerable variation across the EU countries. The highest percentage is recorded for Sweden (about 40% in 2005), due to geothermal and hydro energy production, while the lowest are found in the UK, Luxembourg and Malta. Increasing tendencies are evident in Latvia, Lithuania, Romania and Estonia. 650 human health and well-being in several areas9. In this respect, the Mediterranean regions will suffer the most from worsening conditions, while Northern, Western and Eastern European regions will see a less serious deterioration or even a temporary improvement in conditions. Changes in temperature and precipitation will also lead to changing agricultural yields and production methods with distinct patterns throughout Europe. In fisheries, climate change will place an even greater strain on marine ecosystems subject to over fishing. This is likely to intensify the existing social and environmental disparities between the EU regions, especially in terms of regional agricultural labour productivity (RALP). The Treaty of Rome expresses a commitment to “ensure a fair standard of living for the agricultural community, particularly by increasing the individual earnings of persons engaged in agriculture” while increased productivity in agriculture is one of the main goals of the Common Agricultural Policy (CAP); a policy which still dominates the EU budget10. Even a swift glance at the various publications of EUROSTAT (1999, 2007) reveals that this activity follows a declining tendency. For instance, total employment in agriculture has fallen from 16.3 million in 1970 to 7.9 million in 1994. In 2005 the share of agriculture, hunting, forestry and fisheries in Europe’s (EU-25) total employment was just 4.9% while in this share EU-15 was 3.7%. An employment share more than 10% is recorded for five countries (Greece, Latvia, Lithuania, Austria and Poland). In EU-15, throughout a period of ten years (1995- 2005), the labour input11 in agriculture has declined by an average rate of 2% annually while for the EU-25 countries, this share was about 2.5% (Table 1). This decline in agriculture is accompanied with an increase of labour employed in sectors related to services. To be more specific, in 2005 the share of economic activities in total employment of EU-25 was 67.6% in services, 27.5% in industry and 4.9% in agriculture. A similar tendency is observed for the share of agriculture in Gross Value Added (GVA) (Table 2). In 2005, about 2% of the EU-25 GVA is produced by sectors related to agriculture. The share of these sectors in the New Member States (NMS) is relatively higher compared to that of the EU-12 and EU-15. Nevertheless, there examples of EU-15 countries in which the share of agriculture is higher than NMS (Greece and Poland with shares 5.2% and 4.8%, respectively). In 2005 the share of agriculture in the total GVA of EU-26 was less than 1.8%. Nevertheless, agriculture does not seem to be evenly distributed across the EU countries. For 9 The increasing number of heat-related deaths, the limited availability and quality of drinking water, constitute examples of such negative impacts. 10 For a more detailed of the CAP see Fennell (1979, 1997), Grant (1997), Scott (1995), among others. 11 Labour input is measured in terms of Annual Works Units (AWUs), defined as full-time equivalent employment (total hours worked) divided by the average annual number of hours worked in full-time jobs within an economic territory. It covers all persons providing salaried and non-salaried labour input to the agricultural industry. 651 example, France, the largest agricultural producer in the EU-12, contributes 19.1% in total agricultural output, followed by Italy (14.7%) and Spain (12.2%)12. Table 1. Labour Input in Agriculture 1995 2000 2005 1995-2000 2000-2005 AWU (1,000 persons) Annual Change (in %) EU-25 : 10,540 9,310 : -2.5 EU15 7,209 6,529 5,797 -2 -2.3 Belgium 84 75 71 -2.3 -1.2 Czech Republic : 166 157 : -1.1 Denmark 90 76 65 -3.3 -2.9 Germany 792 685 583 -2.9 -3.2 Estonia 70 65 38 -1.7 -10.2 Greece 645 586 610 -1.9 0.8 Spain 1,102 1,101 989 -0.02 -2.1 France 1,137 1,028 943 -2 -1.7 Ireland 232 172 167 -5.8 -0.5 Italy 1,463 1,383 1,159 -1.1 -3.5 Cyprus : 24 22 : -1.7 Latvia : 149 136 : -1.7 Lithuania : 187 151 : -4.1 Luxembourg 5 4 4 -2.6 -1.4 Hungary 780 676 521 -2.8 -5.1 Malta 5 4 4 -0.4 -0.8 Netherlands 221 220 197 -0.1 -2.2 Austria 198 175 169 -2.4 -0.7 Poland : 2,495 2,292 : -1.7 Portugal 619 503 370 -4.1 -5.9 Slovenia 111 104 91 -1.3 -2.6 Slovak Republic 203 143 101 -6.8 -6.6 Finland 141 111 96 -4.6 -2.8 Sweden 90 77 76 -3.3 -0.2 United Kingdom 391 334 299 -3.1 -2.2 Bulgaria : 771 626 : -4.1 Romania : 3,645 2,515 : -7.2 : Not Available. Source: EUROSTAT (2007) 12 Depending on the specific year, Germany after unification is classified as the second power in agriculture in the EU-12. 652 Table 2. Gross Value Added in Agriculture (% of the total economy) 1995 2000 2002 2003 2004 2005 EU-25 2.8 2.3 2.2 2.1 2.1 1.9 EU-15 2.7 2.2 2.1 2 2 1.8 Belgium 1.5 1.5 1.4 1.1 1.1 1.1 Czech Republic 5 3.9 3.3 3.1 3.3 2.9 Denmark 3.5 2.6 2.2 2 1.9 1.5 Germany 1.3 1.3 1.1 1.1 1.2 1 Estonia 8 4.9 4.2 3.7 3.8 3.7 Greece 9.9 7.3 7 6.7 5.7 5.2 Spain 4.5 4.4 4 4 3.8 3.3 France : 2.8 2.7 2.5 2.5 2.2 Ireland 7 3.4 2.6 2.5 2.5 : Italy 3.3 2.8 2.6 2.5 2.5 2.3 Cyprus 5.1 3.6 3.7 3.4 3 2.9 Latvia 9.1 4.6 4.6 4.1 4.4 4.1 Lithuania 11.4 7.9 7 6.4 5.8 5.7 Luxembourg 1 0.7 0.6 0.6 0.5 0.4 Hungary 6.7 5.4 4.7 4.3 4.8 4.3 Malta : 2.3 2.5 2.5 2.5 2.5 Netherlands 3.5 2.6 2.3 2.3 2.2 2.2 Austria 2.7 2.1 2 1.9 1.9 1.6 Poland 8 5 4.5 4.4 5.1 4.8 Portugal 5.7 3.8 3.3 3.4 3.3 2.8 Slovenia 4.2 3.2 3.2 2.6 2.7 2.5 Slovak Republic 5.9 4.5 5.1 4.5 4.5 4.3 Finland 4.3 3.5 3.3 3.2 3.1 2.9 Sweden 2.7 1.9 1.8 1.8 1.8 1.2 United Kingdom 1.8 1 0.9 1 0.9 0.9 Bulgaria : 13.9 12.1 11.6 10.9 9.3 Romania : 12.4 12.6 13.0 14.3 10.1 : Not Available. Source: EUROSTAT (2007) Agriculture accounts for about 20%, on average, of the working population in Greece and only 2% in Belgium and the UK. In 1988 as an illustration, the percentage employed in agriculture ranged from 45.9% in the region of Central Greece down to 0.2% in the Brussels-Gewest region and 0.3% in Bremen. In terms of RALP, about 46% of the EU-27 regions are below the European average with the majority of them located in Southern Mediterranean and Eastern Europe. Northern regions, especially in the UK and Netherlands, characterised by a cost effective agricultural sector, display a level of labour productivity two times higher than regions located in Southern and Eastern countries, which are generally characterised by relatively high shares of labour force employed in agriculture. A 653 rather stable distribution of crop-specialist, livestock-specialist and mixed farming holdings is detected between 2003 and 2007. About 40% of agricultural holdings in the EU-27 are specialized13 in cropping (filed crops, horticulture and permanent crops), 22% in livestock (grazing livestock, granivores, i.e. animals mainly feeding on cereals, such as pigs and poultry) and 38% on mixed farming holdings. Regions in the Mediterranean (especially in Greece, Italy, Portugal and Spain) and in Scandinavian countries are highly specialized in crops while livestock farming is the dominant activity in the agricultural sector of several regions in Ireland, the UK, Germany and the Benelux countries. On the other hand, mixed farming is found in most regions of the New Member States (NMS). Considerable variations are also detected in the regional distribution of input expenditure. On average, input expenditure is rather low in the regions of Portugal (less than 190 euros per hectare) while the average input expenditure in the western coastal regions is in the range between 630 and 1,040 euros per hectare. From what has been said in this section, it is obvious that there are considerable differences in agriculture across the EU-27. Clearly, this implies that rate of convergence might differ across the European regions. It becomes of crucial importance, therefore, to determine an appropriate framework for examining the trends in regional convergence. The following section presents a contextual review of two of the most commonly used measures of regional convergence. 3. The Empirical Framework In the context of regional convergence, the term ‘region’ refers either to areas determined according to similarities in geographical characteristics or areas corresponding to administrative divisions, which may be arbitrary. The relevant literature makes extensive use of two alternative notions; -convergence and absolute -convergence. Conceptually, -convergence is based upon the cross-sectional dispersion in per- capita GDP and is defined as a decreasing tendency in the dispersion of per-capita GDP. Typically, -convergence is measured by standard deviation ( i ,,tt ) (Dalgaard and Vastrup, 2001): 2 1 n y i,t log i . (1) n i1 y 1 n where log y log y i . n i1 13 The terms ‘specialisation’ is used to describe the trend towards a single dominant activity in farm income. An agricultural holding is characterised by EUROSTAT as specialised if a particular activity provides a Standard Cross Margin (SGM), i.e. the difference between gross production and costs, at least two-thirds of the total SGM of the holding. 654 -convergence is signified when ii,, T i ,0 or more generally, when i, i ,t 0 , as t T , where T is a terminal time. Absolute -convergence requires that regions with relatively low initial labour productivity grow faster that those with relatively high labour productivity. Consider a distribution of regional labour productivity, i.e. Yi,0 Ymin,0 , L , Ymax,0 and the associated rates of growth, i.e. g i,T g min,T ,L, g max,T . Absolute convergence occurs when g i ,T g min,T as Yi , 0 Ymax max, 0 , as shown in Figure 1: g i,T g max,T g min,T Ymin,0 Ymax,0 Yi,0 Fig. 1. Catch-up between ‘Poor’ and ‘Rich’ Regions Assume that regional growth ( g i ,T ) over a given time period ( T 0,K, t ) is a function of the initial level of labour productivity ( Yi , 0 ). This assumption can be expressed as follows (Goddard and Wilson, 2001): g i ,T f (Yi , 0 ) . (2) Assume further that labour productivity ( Yi ,T ) grows as follows, g Yi ,T e i ,T Yi ,0 . (3) Taking logarithms and solving equation (2) for g i ,T yields: g i, T y i ,t y i ,0 . (4) Hence, the test for regional convergence is formulated in terms of the following dynamic regression equation: g i ,T a byi ,0 . (5) In equation (5), the parameter b , the ‘convergence coefficient’, reflects the partial correlation between the growth rate and the initial level of labour productivity ( f gi ,,TT yi , 0 ). Absolute convergence requires that b [ 1 0] while b [0 [0 1] indicates that g i ,T g max,T as y i , 0 y max max, 0 . In the latter case high- productivity regions grow faster than low-productivity regions increasing the existing gap between them. If b 0 implies that g i ,T a , i.e. regions grow at a 655 given rate which can be considered as an indication of an autonomous growth rate that maintains productivity differences across regions. There is, of course, the case when b 1 , which Romer (1996) describes as ‘perfect convergence’. Similarly, the condition b 1 can be conceived as ‘perfect divergence’. In this context, it is possible (and necessary given the concerns of this paper) to construct a precise measure of the speed at which regions converge. Following Barro and Sala-i-Martin (1995) the convergence coefficient can be expressed as follows: b (1 e T ) . (6) Equation (6) can be written as follows: 1 e T (b 1) 1 e T . (7) (b 1) Solving equation (7) for it is possible to obtain an expression for the speed at which regions approach the steady-state value of labour productivity. Thus, the average rate of convergence over a time period is given by the following ratio: ln ln(b 1) . (8) T Given that b [ 1 0] signifies convergence, then [0 1] . A value of [0 0 indicates absence of absolute convergence while 1 indicates a rate leading to perfect convergence. It follows, therefore, that a higher corresponds to more rapid convergence. Estimating equation (4) using various data sets, Sala-i-Martin (1996a) estimates a ‘surprisingly’ similar rate of convergence across both regional and national economies, and forms the ‘mnemonic rule’ that ‘economies converge at a speed of about two percent per year.’ (p. 1326). Barro and Sala-i-Martin (1995) argue that even if absolute -convergence holds, the dispersion of per-capita income does not necessarily tend to decline over time and - convergence can occur simultaneously with absence of -convergence. In this respect -convergence is a stricter criterion than -convergence. Friedman (1992) argues that -convergence is a weak criterion due to the fact that is a regression to the mean. Carree and Klomp (1997) offer a solution to this problem using the following ratio: ˆ i2,1 / ˆ i ,T 2 1 S i ,T N . (9) 2 1 (1 ˆ i ) 2 where N is the number of observations. The hypothesis of convergence is accepted if Si ,T 0. Having outlined the main features of the regional convergence model, this paper will proceed to evaluate the pattern of regional convergence across the NUTS-2 regions of the EU-27. 656 3. Convergence in RALP across the EU-27 regions Agricultural productivity can be approximated in various ways. In this paper we exploit data on GVA per worker since this measure is a major component of differences in the economic performance of regions and a direct outcome of the various factors that determine regional ‘competitiveness’ (Martin, 2001). The regional groupings used in this paper are those delineated by EUROSTAT and refer to 310 NUTS-2 regions14. The EU uses NUTS-2 regions as ‘targets’ for convergence and defined as the ‘geographical level at which the persistence or disappearance of unacceptable inequalities should be measured’ (Boldrin and Canova, 2001, p. 212). Despite considerable objections for the use of NUTS-2 regions as the appropriate level at which convergence should be measured, the NUTS-2 regions are sufficient small to capture sub-national variations (Fischer and Stirböck, 2006). The time period extends from 1995 to 2004; a time period that might be considered as somehow short. However, Durlauf and Quah (1999) point out that convergence-regressions, such as equation (4), are valid for shorter time periods as well, since they are based on an approximation around the ‘steady-state’ and supposed to capture the dynamics toward the ‘steady-state’. The values of standard deviation for the initial and the terminal years of the analysis (0.9 and 0.88, respectively) seem to confirm the hypothesis of σ- convergence across the NUTS-2 regions of the EU-27. Additional support is provided by the S i ,T ratio, which is estimated to be positive (0.27). Figure 2 summarises the potential for absolute convergence between 1995 and 2004. Essentially, this figure is a scatterplot which shows the average annual growth rate against the initial level of labour productivity. 14 A list of the NUTS-2 regions used in this paper is provided in Appendix. Due to data limitations, previous studies on regional convergence across the EU-27 regions used to treat countries, such as Denmark, Lithuania and Slovenia as NUTS-2 regions. In this paper, the empirical analysis is enhanced using data for the NUTS-2 regions of the aforementioned countries. 657 4 2 Average Growth Rates, 1995-2004 (in %) 0 0 0.5 1 1.5 2 2.5 3 3.5 4 -2 -4 -6 Labour Productivity, 1995 (in natural logarithms) Fig. 2. Absolute -convergence in RALP, EU-27 regions, 1995-2004 Casual inspection of the data in Figure 2 provides some indication of an inverse relationship between the average annual growth rate and initial level of RALP. Nevertheless, this property does not appear to be uniform across all the NUTS-2 regions of the EU-27. As Figure 2 makes visible, this property seems to be constrained in a certain group of regions with a relatively high initial level of RALP. Several regions, on the other hand, appear to diverge, in the sense that relatively low initial levels of labour productivity are associated with relatively low rates of growth and vice versa. The presence of absolute convergence (or divergence), however, cannot be confirmed by visual inspection alone. A formal test for absolute convergence can be expressed in terms of the following regression equation: g i ,T a b1 y i,t i . 0 (10) where i is the random error-term, t 0 19 1995 and T 10 . Equation (8) is estimated using Ordinary Least Squares (hereafter OLS), for the NUTS-2 regions of EU-27 while separate regressions are carried out for the regional divisions of EU-12, EU-15 and the NMS15. The results are set out in Table 3 and show that the convergence coefficient ( b1 ) to be negative and statistically significant at the 95% level in the case of the NUTS-2 regions of the EU-27. Table 3 also shows the average rate of convergence, implied by equation (8). 15 These are Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia, Slovak Republic, Romania and Bulgaria. 658 Table 3: Regional Convergence in Agriculture EU-27 EU-15 EU-12 NMS Depended Variable: g i ,T OLS a 0.2678 0.4689 0.6313 0.1037 b1 -0.0437 -0.1084 -0.1601 0.0665 Implied (in %) 0.4471 1.1473 1.7451 -0.6441 Notes: ** indicates statistical significance at 95% level of confidence while * indicates significance at 90% level. The presence of absolute convergence in the form of a negative relationship between the rate of growth and initial level of labour productivity is suggested by this evidence, and the NUTS-2 regions of the EU-27 have, on average, shown a tendency to converge over the period 1995-2004, albeit at a relatively slow rate of 0.45% per annum. Given this slow rate of convergence, it would take a very long time for all the EU-27 regions to reach a common level of labour productivity, as predicted by the absolute convergence model. Analysis for the NUTS-2 regions of the EU-12 and EU-15 shows that the regions of EU-12 exhibit a relatively high average rate of convergence compare to that estimated for the regions of the EU-15 (1.75% and 1.14%, respectively). On the other hand, the property of absolute convergence does not appear to characterise the regions of the new and ascending countries. As the results imply, these regions actually diverge at a rate almost equal to 0.6% per annum. There is a positive relationship between the rate of growth and initial level of labour productivity, suggesting that in these countries initially high-productivity regions grow at expanse of initially low-productivity regions. Estimating equation (10) separately for each EU-27 country16, yields the results in Table 417. It is clear that the property of regional convergence is restricted mainly in the EU-15 with the Netherlands to exhibit the highest rate (8.2% per annum). The results also indicate that only 4 NMS (Czech Republic, Hungary, Slovenia and Romania) are able to converge. 16 Luxembourg, Cyprus and Malta are considered as single NUTS-2 regions and had to be excluded. 17 For brevity, only the coefficients and the rates of convergence are shown. 659 Table 4. Regional Convergence in Agriculture: Country Analysis b1 Implied (in %) Belgium -0.1906 2.1149 Denmark -0.0821 0.8563 Germany -0.2614 3.0304 Ireland -0.3763 4.7207 Greece -0.0231 0.2337 Spain -0.2643 3.0695 France 0.0370 -0.3629 Italy -0.3559 4.3995 Netherlands -0.5580 8.1634 Portugal 0.1263 -1.1891 United Kingdom -0.3656 4.5509 Austria -0.0427 0.4359 Sweden 0.0014 -0.0136 Finland -0.3840 4.8450 Bulgaria 0.4640 -3.8119 Czech Republic -0.3659 4.5552 Estonia 0.0742 -0.7155 Latvia 0.0874 -0.8375 Lithuania 0.0180 -0.1787 Hungary -0.2063 2.3100 Poland 0.0857 -0.8224 Slovenia -0.0403 0.4109 Slovakia 0.0893 -0.8556 Romania -0.1154 1.2261 The results in Table 4 illustrate several points. The existence of different rates of convergence in different levels of territorial disaggregation is, perhaps, not unexpected. The EU cannot be characterised as a static entity and its spatial composition has changed considerably since its early days. The EU is, as Button and Pentecost (1999) aptly call, ‘a fluctuating geographical area’ (p. 45). Successive enlargements of the EU have brought into the union regions with low levels of labour productivity in agriculture, a fact which has obviously brought additional difficulties in the process of regional convergence in EU. With a larger number of regions the patterns of convergence can, of course, become more complex with some groups of regions converging while others diverge and where outlying or peripheral regions can distort the overall pattern. This dissimilarity in the rates of convergence implies considerable ‘within’ countries variations in growth rates. Almost all countries exhibited standard 660 deviations in growth rates lower than the international standard deviations, as shown in Table 5. In contrast, there is a greater variability of internal regional growth rates for most of the NMS. This provides some support to the argument that inter-regional disparities tend to increase during the initial stages of development 18. Table 5. Growth Differentials in RALP Standard Deviation Minimum Maximum Range EU-27 1.1600 -5.5438 4.4418 9.9856 EU-12 0.8767 -3.2910 3.7840 7.0750 EU-15 0.8827 -3.2910 3.7840 7.0750 NMS 1.4947 -5.5438 4.4418 9.9856 Belgium 0.3166 -0.4763 0.5586 1.0349 Denmark 0.4876 -0.9124 1.1736 2.0860 Germany 0.4686 -5.5438 1.6563 7.2001 Ireland 0.0804 -0.1032 0.4247 0.5278 Greece 0.1877 0.2776 0.9490 0.6714 Spain 0.9298 -0.2660 2.8402 3.1062 France 0.0976 -0.1588 0.2802 0.4390 Italy 1.0460 -0.0549 3.4988 3.5536 Netherlands 0.4223 -0.6232 0.9216 1.5447 Portugal 2.4485 -3.2910 3.4944 6.7854 United Kingdom 0.8991 -0.5414 3.7840 4.3254 Austria 1.2673 -0.6871 3.6386 4.3257 Sweden 0.3912 -0.1615 1.1474 1.3089 Finland 0.8193 -0.8705 1.3497 2.2202 Bulgaria 0.5822 0.9866 2.5918 1.6052 Czech Republic 0.9766 -0.2465 2.2682 2.5147 Estonia 0.8103 1.3843 3.6861 2.3018 Latvia 0.9433 -0.4826 3.3903 3.8729 Lithuania 1.1302 -0.4826 3.1648 3.6474 Hungary 0.4209 0.4952 1.9558 1.4606 Poland 1.6595 -2.2358 3.3587 5.5945 Slovenia 0.7852 1.6173 4.4418 2.8245 Slovakia 0.2527 0.3445 0.9958 0.6513 Romania 0.9620 0.3445 2.9877 2.6432 The empirical results, reported in this section might be considered, to a certain extent, as descriptive. In particular, there is a critical question that an answer should be provided. What do these empirical results imply about the effectiveness of the 18 This idea is put forward by Williamson (1965). 661 CAP in regional agricultural convergence? It seems that this policy had little effect in promoting regional convergence in agriculture. CAP can be seen as a mechanism able to rectify regional imbalances, although historically has been managed by national and European authorities. Overall, CAP policies seem to have little success in promoting regional convergence or the effects of these policies are slow in restoring regional imbalances. This can be attributed, possibly, to two factors. A first factor is related to the absence of an explicit regional perspective in designing and implementing CAP. Future agricultural policies should aim towards countries with ‘slow-converging’ regions, i.e. regions in which intervention is more urgent compare to regions belonging to others groups. A second factor refers to ‘inferior’ responses of regions in low-paths. Indeed, several such regions, especially in the Mediterranean area, had limited experience in incorporating CAP initiatives in their production structures. It might be argued that CAP benefits were rather an ‘additional’ income to the produces in these regions, rather than as an opportunity for improvement. 4. Concluding Remarks In the case of the EU, and although an increasing number of empirical studies have paid attention to issues of economic convergence, the empirical assessment of agricultural productivity convergence has not so far received the due attention. In this paper some new empirical work has been set in the context of an expanding empirical literature that has concerned itself with question of regional convergence. To be more precise, the hypothesis of convergence in terms of agricultural labour productivity is tested empirically using data for the NUTS-2 regions of the European Union over the period 1995-2004. Taken as a whole, we think that these results are important for the ongoing European policy debate about regional convergence. What is clarified by the econometric results is that the European regions exhibit a slow tendency of convergence in terms of agricultural labour productivity. Convergence appears to be considerably faster within the EU-12 and EU-15 regions. In terms of implications for public policy, especially regional policy, this paper raises a number of pertinent issues. Firstly, regional assistance should, to a substantial extent, be diverted towards those regions that exhibit a relatively low rate of convergence. Secondly, the greater part of effort and assistance should be directed to improve the underlying structural conditions of slow-converging regions and thereby generate an economic environment that more closely resembles the combination of characteristics found in the fast-converging regions, such as product-mix, adoption of new techniques and innovations in agriculture and so forth. While the empirical results are serious in the own right, they must be placed in perspective. There is a little pretence that the forgoing analysis provides an exhaustive account of all the factors that affect the process of regional convergence in terms of agriculture productivity. For example, additional complications arise from the multidimensional nature of the institutional and political structure of the CAP; a policy with spatial implications. Nevertheless, the CAP has been designed 662 and managed at the national level. The variations in the rates of convergence in terms of regional convergence in agricultural productivity reported in this paper suggest that an explicit regional dimension should be taken in the next CAP reform, anticipated in 2013. The challenge for policy makers and practitioners at different administrative levels is to appreciate the heterogeneous territorial context in Europe and get inspiration for including an explicit spatial dimension in further policy development. Examination of the interaction between the political and spatial dimensions of CAP to individual regions remains an important area for future research. References 1. Álvarez-Garcia, S., Prieto-Rodriguez, J. and Salas, R. (2004) The evolution of income inequality in the European Union during the period 1993-1996. Applied Economics, 36, p. 1399-408. 2. Barro, R. and Sala-i-Martin, X. (1995) Economic Growth. MIT Press. 3. Bivand R. and Brunstad R. 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Economic Development and Cultural Change, 13, p. 3-45. 664 APPENDIX: The NUTS-2 Regions of EU-27 Number of Number of Country Regions Region Country Regions Region 1 be10 Région de Bruxelles 1 pt11 Norte 2 be21 Prov. Antwerpen 2 pt15 Algarve 3 be22 Prov. Limburg (B) 3 pt16 Centro (PT) 4 be23 Prov. Oost-Vlaanderen Portugal 4 pt17 Lisboa 5 be24 Prov. Vlaams Brabant 5 pt18 Alentejo Belgium 6 be25 Prov. West-Vlaanderen 6 pt20 Região Autónoma dos Açores (PT) 7 be31 Prov. Brabant Wallon 7 pt30 Região Autónoma da Madeira (PT) 8 be32 Prov. Hainaut 1 ukc1 Tees Valley and Durham 9 be33 Prov. Liège 2 ukc2 Northumberland, Tyne and Wear 10 be34 Prov. Luxembourg (B) 3 ukd1 Cumbria 11 be35 Prov. Namur 4 ukd2 Cheshire 1 dk001 København og Frederiksberg Kommuner 5 ukd3 Greater Manchester 2 dk002 Københavns amt 6 ukd4 Lancashire 3 dk003 Frederiksborg amt 7 ukd5 Merseyside 4 dk004 Roskilde amt 8 uke1 East Riding and North Lincolnshire 5 dk005 Vestsjællands amt 9 uke2 North Yorkshire 6 dk006 Storstrøms amt 10 uke3 South Yorkshire 7 dk007 Bornholms amt 11 uke4 West Yorkshire Denmark 8 dk008 Fyns amt 12 ukf1 Derbyshire and Nottinghamshire 9 dk009 Sønderjyllands amt 13 ukf2 Leicestershire, Rutland and Northants 10 dk00a Ribe amt 14 ukf3 Lincolnshire 11 dk00b Vejle amt 15 ukg1 Herefordshire, Worcestershire and Warks 12 dk00c Ringkøbing amt 16 ukg2 Shropshire and Staffordshire 13 dk00d Århus amt 17 ukg3 West Midlands 14 dk00e Viborg amt 18 ukh1 East Anglia United 15 dk00f Nordjyllands amt 19 ukh2 Bedfordshire, Hertfordshire Kingdom 1 de11 Stuttgart 20 ukh3 Essex 2 de12 Karlsruhe 21 uki1 Inner London 3 de13 Freiburg 22 uki2 Outer London 4 de14 Tübingen 23 ukj1 Berkshire, Bucks and Oxfordshire 5 de21 Oberbayern 24 ukj2 Surrey, East and West Sussex 6 de22 Niederbayern 25 ukj3 Hampshire and Isle of Wight 7 de23 Oberpfalz 26 ukj4 Kent 8 de24 Oberfranken 27 ukk1 Gloucestershire, Wiltshire and North Somerset 9 de25 Mittelfranken 28 ukk2 Dorset and Somerset 10 de26 Unterfranken 29 ukk3 Cornwall and Isles of Scilly 11 de27 Schwaben 30 ukk4 Devon 12 de30 Berlin 31 ukl1 West Wales and The Valleys 13 de41 Brandenburg - Nordost 32 ukl2 East Wales 14 de42 Brandenburg - Südwest 33 ukm1 North Eastern Scotland 15 de50 Bremen 34 ukm2 Eastern Scotland 16 de60 Hamburg 35 ukm3 South Western Scotland 17 de71 Darmstadt 36 ukm4 Highlands and Islands 18 de72 Gießen 37 ukn0 Northern Ireland 19 de73 Kassel 1 fi13 Itä-Suomi 20 de80 Mecklenburg-Vorpommern 2 fi18 Etelä-Suomi Germany 21 de91 Braunschweig Finland 3 fi19 Länsi-Suomi 22 de92 Hannover 4 fi1a Pohjois-Suomi 23 de93 Lüneburg 5 fi20 Åland 24 de94 Weser-Ems 1 se01 Stockholm 25 dea1 Düsseldorf 2 se02 Östra Mellansverige 26 dea2 Köln 3 se04 Sydsverige 27 dea3 Münster 4 se06 Norra Mellansverige Sweden 28 dea4 Detmold 5 se07 Mellersta Norrland 29 dea5 Arnsberg 6 se08 Övre Norrland 30 deb1 Koblenz 7 se09 Småland med öarna 31 deb2 Trier 8 se0a Västsverige 32 deb3 Rheinhessen-Pfalz Austria 1 at11 Burgenland 33 dec0 Saarland 2 at12 Niederösterreich 34 ded1 Chemnitz 3 at13 Wien 35 ded2 Dresden 4 at21 Kärnten 36 ded3 Leipzig 5 at22 Steiermark 37 dee1 Dessau 6 at31 Oberösterreich 38 dee2 Halle 7 at32 Salzburg 39 dee3 Magdeburg 8 at33 Tirol 40 def0 Schleswig-Holstein 9 at34 Vorarlberg 41 deg0 Thüringen Bulgaria 1 bg31 Severozapaden 1 gr11 Anatoliki Makedonia, Thraki 2 bg32 Severen tsentralen 2 gr12 Kentriki Makedonia 3 bg33 Severoiztochen 3 gr13 Dytiki Makedonia 4 bg34 Yugoiztochen 4 gr14 Thessalia 5 bg41 Yugozapaden 5 gr21 Ipeiros 6 bg42 Yuzhen tsentralen 6 gr22 Ionia Nisia Czech 1 cz01 Praha Greece 7 gr23 Dytiki Ellada Republic 2 cz02 Strední Cechy 8 gr24 Sterea Ellada 3 cz03 Jihozápad 9 gr25 Peloponnisos 4 cz04 Severozápad 10 gr30 Attiki 5 cz05 Severovýchod 11 gr41 Voreio Aigaio 6 cz06 Jihovýchod 12 gr42 Notio Aigaio 7 cz07 Strední Morava 13 gr43 Kriti 8 cz08 Moravskoslezsko 1 es11 Galicia Cyprus 1 cy00 Cyprus 2 es12 Principado de Asturias 1 hu10 Közép-Magyarország 3 es13 Cantabria 2 hu21 Közép-Dunántúl 4 es21 Pais Vasco 3 hu22 Nyugat-Dunántúl 5 es22 Comunidad Foral de Navarra Hungary 4 hu23 Dél-Dunántúl 6 es23 La Rioja 5 hu31 Észak-Magyarország 7 es24 Aragón 6 hu32 Észak-Alföld 8 es30 Comunidad de Madrid 7 hu33 Dél-Alföld 9 es41 Castilla y León Malta 1 mt00 Malta Spain 10 es42 Castilla-la Mancha 1 pl11 Lódzkie 11 es43 Extremadura 2 pl12 Mazowieckie 12 es51 Cataluña 3 pl21 Malopolskie 13 es52 Comunidad Valenciana 4 pl22 Slaskie 14 es61 Andalucia 5 pl31 Lubelskie 15 es62 Región de Murcia 6 pl32 Podkarpackie 16 es63 Ciudad Autónoma de Ceuta (ES) 7 pl33 Swietokrzyskie 17 es64 Ciudad Autónoma de Melilla (ES) 8 pl34 Podlaskie Poland 18 es70 Canarias (ES) 9 pl41 Wielkopolskie 1 fr10 Île de France 10 pl42 Zachodniopomorskie 2 fr21 Champagne-Ardenne 11 pl43 Lubuskie 3 fr22 Picardie 12 pl51 Dolnoslaskie 4 fr23 Haute-Normandie 13 pl52 Opolskie 5 fr24 Centre 14 pl61 Kujawsko-Pomorskie 6 fr25 Basse-Normandie 15 pl62 Warminsko-Mazurskie 7 fr26 Bourgogne 16 pl63 Pomorskie 8 fr30 Nord - Pas-de-Calais 1 sk01 Bratislavský kraj 9 fr41 Lorraine 2 sk02 Západné Slovensko Slovakia 10 fr42 Alsace 3 sk03 Stredné Slovensko 11 fr43 Franche-Comté 4 sk04 Východné Slovensko 12 fr51 Pays de la Loire Estonia 1 ee001 Põhja-Eesti 13 fr52 Bretagne 2 ee004 Lääne-Eesti France 14 fr53 Poitou-Charentes 3 ee006 Kesk-Eesti 15 fr61 Aquitaine 4 ee007 Kirde-Eesti 16 fr62 Midi-Pyrénées 5 ee008 Lõuna-Eesti 17 fr63 Limousin 1 lv003 Kurzeme 18 fr71 Rhône-Alpes 2 lv005 Latgale 19 fr72 Auvergne 3 lv006 Riga Latvia 20 fr81 Languedoc-Roussillon 4 lv007 Pieriga 21 fr82 Provence-Alpes-Côte d'Azur 5 lv008 Vidzeme 22 fr83 Corse 6 lv009 Zemgale 23 fr91 Guadeloupe (FR) 1 lt001 Alytaus (Apskritis) 24 fr92 Martinique (FR) 2 lt002 Kauno (Apskritis) 25 fr93 Guyane (FR) 3 lt003 Klaipedos (Apskritis) 26 fr94 Reunion (FR) 4 lt004 Marijampoles (Apskritis) 1 itc1 Piemonte 5 lt005 Panevezio (Apskritis) Lithuania 2 itc2 Valle d'Aosta/Vallée d'Aoste 6 lt006 Siauliu (Apskritis) 3 itc3 Liguria 7 lt007 Taurages (Apskritis) 4 itc4 Lombardia 8 lt008 Telsiu (Apskritis) 5 itd1 Provincia Autonoma Bolzano-Bozen 9 lt009 Utenos (Apskritis) 6 itd2 Provincia Autonoma Trento 10 lt00a Vilniaus (Apskritis) 7 itd3 Veneto 1 si001 Pomurska 8 itd4 Friuli-Venezia Giulia 2 si002 Podravska 9 itd5 Emilia-Romagna 3 si003 Koroska 10 ite1 Toscana 4 si004 Savinjska Italy 11 ite2 Umbria 5 si005 Zasavska 12 ite3 Marche 6 si006 Spodnjeposavska Slovenia 13 ite4 Lazio 7 si009 Gorenjska 14 itf1 Abruzzo 8 si00a Notranjsko-kraska 15 itf2 Molise 9 si00b Goriska 16 itf3 Campania 10 si00c Obalno-kraska 17 itf4 Puglia 11 si00d Jugovzhodna Slovenija 18 itf5 Basilicata 12 si00e Osrednjeslovenska 19 itf6 Calabria 1 ro11 Nord-Vest 20 itg1 Sicilia 2 ro12 Centru 21 itg2 Sardegna 3 ro21 Nord-Est 1 nl11 Groningen 4 ro22 Sud-Est Romania 2 nl12 Friesland 5 ro31 Sud - Muntenia 3 nl13 Drenthe 6 ro32 Bucuresti - Ilfov 4 nl21 Overijssel 7 ro41 Sud-Vest Oltenia 5 nl22 Gelderland 8 ro42 Vest 6 nl23 Flevoland 1 ie01 Border, Midlands and Western Netherlands Ireland 7 nl31 Utrecht 2 ie02 Southern and Eastern 8 nl32 Noord-Holland Luxemburg 1 lu00 Luxembourg (Grand-Duché) 9 nl33 Zuid-Holland 10 nl34 Zeeland 11 nl41 Noord-Brabant 12 nl42 Limburg (NL) 665