=Paper=
{{Paper
|id=Vol-1152/paper56
|storemode=property
|title=Examining Agriculture from a Regional Perspective: Implications for the Common Agricultural Policy
|pdfUrl=https://ceur-ws.org/Vol-1152/paper56.pdf
|volume=Vol-1152
|dblpUrl=https://dblp.org/rec/conf/haicta/AlexiadisHL11
}}
==Examining Agriculture from a Regional Perspective: Implications for the Common Agricultural Policy==
https://ceur-ws.org/Vol-1152/paper56.pdf
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. (2003) Regional growth in Western-Europe: An
empirical exploration of interactions with agriculture and agricultural policy.
In European Regional Growth, ed. B. Fingleton, p. 351–73. Springer: Berlin.
4. Bivand, R. and Brunstad R. (2005) Further explorations of interactions
between agricultural policy and regional growth in Western Europe:
approaches to non-stationarity in spatial econometrics. Paper presented on the
45th Congress of European Regional Science Association, Amsterdam, 23-27
August 2005.
5. Boldrin, M. and Canova, F. (2001) Inequality and convergence in Europe’s
regions: reconsidering European regional policies. Economic Policy, 32, 207-
53.
6. Button, K. and Pentecost, E. (1993) Regional service sector convergence.
Regional Studies, 27, p. 623-36.
7. Button, K. and Pentecost, E. (1995) Testing for convergence of the EU
regional economies. Economic Inquiry, 33, p. 664-71.
8. Button, K. and Pentecost, E. (1999) Regional economic performance within
the European Union. Edward Elgar Publishing.
9. Carree, M. and Klomp, L. (1997) Testing the convergence hypothesis: A
Comment. Review of Economics and Statistics, 79, 683-6.
10. Dalgaard, C. and Vastrup, J. (2001) On the measurement of -convergence.
Economics Letters, 70, 283-7.
11. Durlauf, S. and Quah, D. (1999) The new empirics of economic growth. In
Handbook of Macroeconomics, 1, J. Taylor, and Woodford, M. eds. p. 235-
308. Amsterdam: Elsevier.
12. EUROSTAT (1999) Agriculture in the European Union. Luxemburg.
13. EUROSTAT (2007) Agricultural statistics: 1995-2005. Luxemburg.
14. EUROSTAT (2010). Eurostat regional yearbook. Luxemburg.
15. EUROSTAT (2010a) Agricultural Statistics: 2009-2009. Luxemburg.
16. Fennell, R. (1979) The common agricultural policy of the European
Community. Granada.
663
17. Fennell, R. (1997) The common agricultural policy: continuity and change.
Oxford: Clarendon Press.
18. Fischer, M. and Stirböck, C. (2006) Pan-European regional income growth
and club-convergence. Annals of Regional Science, 40, p. 693-721.
19. Friedman, M. (1992) Do old fallacies ever die? Journal of Economic
Literature, 30, 2129-32.
20. Goddard, J. and Wilson, J. (2001) Cross-sectional and panel estimation of
convergence. Economic Letters, 70, p. 327-33.
21. Grant, W. (1997) The common agricultural policy. Palgrave, Macmillan.
22. Gugler, K. and Pfaffermayr, M. (2004) Convergence and productivity in
European manufacturing. German Economic Review, 5, p. 61-79.
23. Martin, R. (2001), ‘EMU versus the Regions? Regional Convergence and
Divergence in Euroland’, Journal of Economic Geography, 1, p. 51-80.
24. Neven D. and Gouyette, C. (1995) Regional convergence in the European
Community. Journal of Common Market Studies, 33, p. 47-65.
25. Pascual, A. and Westermann, F. (2002) Productivity convergence in European
manufacturing. Review of International Economics, 10, p. 313-23.
26. Romer, D. (1996) Advanced Macroeconomics. McGraw-Hill.
27. Sala-i-Martin, X. (1996) Regional cohesion: evidence and theories of regional
growth and convergence. European Economic Review, 40, p. 1325-52.
28. Sala-i-Martin, X. (1996a) The classical approach to convergence analysis. The
Economic Journal, 106, p. 1019-36.
29. Scott, J. (1995) Development dilemmas in the European Community.
Buckingham: Open University Press.
30. Soares, F. and Ronco, R. (2000) Agricultural income and productivity in the
European Union: convergence or divergence among members?. International
Centre for Economic Research, Working Paper No 20/2000.
31. Trouvé, A. and Berriet-Solliec, M. (2010) Regionalisation in European
Agricultural Policy; Institutional Actualities, Issues and Prospects. Regional
Studies, 44 (8), p. 1005-17.
32. Williamson, J. (1965) Regional inequalities and the process of national
development. 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