CC BY
71
УДК 339.923
Чаба Чаки,
почетный профессор университета Корвинуса в Будапеште,
член редакционного совета «Международного сельскохозяйственного журнала»,
Аттила Ямбор,
профессор университета Корвинуса в Будапеште, Венгрия
Csaba Csaki is professor emeritus and Attila Jambor is associate professor at Corvinus university of Budapest, Hungary
10 ЛЕТ ЧЛЕНСТВА В EC В СЕЛЬСКОМ ХОЗЯЙСТВЕ: УРОКИ НОВЫХ ЧЛЕНОВ ЕВРОПЕЙСКОГО СОЮЗА
10 YEARS OF EU MEMBERSHIP IN AGRICULTURE: LESSONS FROM THE NEW MEMBER STATES
В 2004 и 2007 гг. 12 стран Центральной и Восточной Европы присоединились к Европейскому союзу, благодаря чему процесс перехода от бывшей социалистической системы к рыночной форме хозяйства формально подошел к концу. Несмотря на длительную подготовку стран, вступление в Европейский союз было как шаг в неизвестную территорию. Ожидаемые последствия расширения в сельском хозяйстве в 15 странах ЕС и в странах-кандидатах были одной из самых обсуждаемых тем. Десятая годовщина присоединения — это хорошая возможность для анализа и оценки развития сельского хозяйства новых государств-членов ЕС в свете последних имеющихся данных. Результаты исследования свидетельствуют о том, что Польша и страны Балтии могут рассматриваться как лидеры вступления в ЕС в сельском хозяйстве, в то время как Румыния, Болгария и Словения использовали свой потенциал в наименьшей степени. Результаты также показывают, что сосредоточение на высокой добавленной стоимости сельскохозяйственной продукцией оказалось хорошей стратегией для достижения развития в аграрной сфере, а те страны, которые были сосредоточены на производстве аграрно-продовольственного сырья, оказались отстающими. Эти результаты могут также быть полезными и для других стран, испытывающих аналогичные проблемы интеграции, как и новые государства-члены Европейского союза.
S u m m a r y
In 2004 and 2007, 12 countries of Central and Eastern Europe acceded to the European Union, by which the transition process from the former socialist system to a market based agriculture formally came to an end. Despite the long-lasting preparations of countries, accession to the European Union was somehow a step into unknown territory. The expected impacts of enlargement in agriculture both in EU15 and in the candidate countries have been one of the most debated areas. The tenth anniversary of accession is a good opportunity for stocktaking and assessing the agricultural developments of the New Member States (NMS) in light of the latest data available. Results suggest that Poland and the Baltic countries can be treated as the winners of EU accession in agriculture, while Romania, Bulgaria and Slovenia proved to have used their potentials to the least. Results also suggest that focusing on high value added agri-food products proved to be a good strategy to reach development in the agriculture sector, while those countries concentrating on the production of agri-food raw materials turned out to be lagged behind. These results might also be useful for other countries experiencing similar integration issues like the NMS.
Ключевые слова: членство в ЕС, сельское хозяйство, эффективность, новые государства-члены. Keywords: EU membership, agriculture, efficiency, new member states.
1. Introduction
In 2004 and 2007, 12 countries of Central and Eastern Europe acceded to the European Union, by which the transition process from the former socialist system to a market based agriculture formally came to an end. Despite the long-lasting preparations of countries, accession to the European Union was somehow a step into unknown territory. The expected impacts of enlargement in agriculture both in EU15 and in the candidate countries have been one of the most debated areas. The tenth anniversary provides a good opportunity for stock taking and analysing the winners of accession in the agricultural sector during the previous decade. Despite the apparent importance of the topic, there is a limited number of research dealing with the impacts of EU accession on NMS agricultural sector and even less on quantifying these effects. Which countries used the possibilities provided by the common market to the most? Which countries lacked behind? What are the reasons behind these changes? These are the questions the article aims to answer.
In order to achieve its aim, the paper is structured as follows. Section 2 presents a brief literature review on the topic, while Section 3 summarizes the method used for conducting the analyses. Section 4 analyses changes in agricultural performance and identify the winners of accession, while Section 5 seeks to identify some reasons behind different performances, while the last section concludes.
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2. Literature review
Research on the lessons of EU accession on New Member States' agriculture is a relatively new but expanding field in the literature. Many books around the millennium have quantitatively estimated the impact of EU enlargement in agriculture on EU expenditures, on agricultural protection levels, on commodity markets and trade (see e.g. Tangermann and Banse 2000, Hartell and Swinnen 2000).
Hertel et al. (1997) were among the first to conduct a sectoral and economy-wide analysis of integrating NMS into the EU by using the GTAP model and found that accession would result in very substantial increases of both crop and livestock production in the NMS, while net budgetary consequences of integration for agricultural expenditure would be quite modest. Bchir et al. (2003) investigated the impact of EU enlargement on Member States with a CGE approach and analysed three scenarios. On the whole, they provisioned that EU accession would provoke huge swings on relative prices and big fluctuation in the real exchange rate, raising serious concerns for agriculture. They also forecasted that the impact of accession on EU15 members would be negligible, whereas NMS would face huge and not always beneficial consequences.
A few years after accession, Gorton et al. (2006) analysed the international competitiveness of Hungarian agriculture by calculating domestic resource cost (DRC) ratios and making estimations
for 2007 and 2013. They projected that EU enlargement will have a negative impact on the international competitiveness of Hungarian agriculture by increasing land and labour prices. Similar estimations were conducted by Erjavec (2006), forecasting that the newly accessed countries will gain from higher prices and budgetary support, indicating real improvements in most agricultural sectors on recent production levels. Ivanova et al. (2007) analysed Bulgarian agriculture following EU accession by the AGMEMOD model and found that accession would have a very positive effect on the crop sector in Bulgaria, whereas the effect is the opposite on the livestock sector.
A large amount of literature is also dedicated to the analysis of trade impacts after 2004. Bojnec and Ferto (2008) analysed the agri-food trade competitiveness with the EU-15 of the newly accessed Member States and concluded that trade has increased as a result of enlargement, though there have been 'catching-up' difficulties for some countries in terms of price and quality competition, more so in higher value-added processed products. Artan and Lubos (2011) analysed the agrarian trade transformation in the Visegrad Countries and found that the value and volume of export and import operations increased significantly. Ambroziak (2012) investigated the relationship between FDI and in-tra-industry trade (IIT) in the Visegrad countries and found that FDI stimulated not only vertical IIT
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in the region but also horizontal IIT. He found that differences in country size and income were positively related to IIT as is FDI, while distance and IIT showed a negative relationship. Bojnec and Ferto (2015) analysed the price and quality competitiveness as well as comparative advantage in EU countries agri-food trade and found that new and old member states have become more similar in successful agri-food competitiveness and comparative advantages.
Policy-oriented analysis of the lessons of accession can be found in Mollers et al. (2011) who investigated the changes in agricultural structures and rural livelihoods in the NMS and reached several agricultural policy conclusions, especially regarding the ongoing debate of the Common Agricultural Policy. Gorton et al. (2009) analysed why the CAP does not fully fit the region and identified several reasons valid for the NMS. Csaki and Jambor (2013) analysed the impacts of EU accession on NMS agriculture and concluded that EU accession has had an overall positive impact, although member states capitalised their possibilities in a different manner. Kiss (2011) echoed the above conclusion and added that accession has created an incentive to NMS agriculture but also had negative effects due to tough competition in the enlarged market. So-mai and Hegedus (2015) investigated the speed of changes in NMS agri-food sector after accession and concluded that Poland and the Baltic countries showed the best performances regarding overall development. Szabo and Grznar (2015) analysed the Slovakian position in EU agriculture and ranked it in the last in their sample due to low input of fixed assets, intermediate product, livestock units, but also a lower volume of the provided subsidies than the advanced countries.
3. Methodology
In line with the aim of the chapter, an innovative tool (the agricultural performance index) is used to analyse the post-accession agricultural performance of the NMS. The agricultural performance index is similar to those generally applied by international organisations to measure and compare economic performance of a group of countries (e.g. Global Competitiveness Index, Environmental Performance Index, etc.). Just like in the associated reports, past performance is ranked through different indicators and then aggregated into one. A similar approach is applied here as 15 different agriculture-related indicators is captured and then aggregated to get the agricultural performance index. Except for Csaki (2004) who used a similar logic to assess the status of transition, this approach has not been used to the agri-food sector so far.
The paper analyses agricultural performance of NMS in 1999-2013. This period is subdivided into three equal periods (1999-2003, 2004-2008, 20092013) to better assess the impacts of EU accession. An average for all sub-periods is calculated for each of the 15 indicators and then averages of the first and last periods are compared. In order to manage negative results (i.e. negative changes in specific indicators in time), the value of the smallest average, pertaining to a country, is added to all countries' respective changes (changes from 1999-2003 to 2009-2013) and then final scores by country are given in percentage of the highest value. This method enables us to give 100 points to the best performing country (i.e. the country with the highest posi-
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tive change for an indicator) and continuously less to those performing worse. As countries are ranked on the basis of their own performance, initial differences among countries do not play a role. The list of the 15 indicators selected is given in Appendix 1.
As a major source, the paper uses the Eurostat database but FAO and World Bank datasets are also used in some cases. Note that Cyprus and Malta are excluded from the analysis because of the marginal importance of the agricultural sector in their economies compared to other NMS. Croatia is also excluded on the basis that her 2013 accession does not allow any impact analysis considering the timeframe of the sample. We are also aware that the 2007 accession of Bulgaria and Romania slightly changes the interpretation of our results, though we still think that the performance of these countries are comparable to other NMS based on historical and geographical reasons.
4. Agricultural performance indices
The first indicator describing the performance of agriculture is gross value added at real prices. There are very significant differences in this regard
among NMS (Figure 1). On the one hand, Slovenia had a gross value added of 759 euro per hectare on average in 2009-2013, while Latvia could only reach 90 euro per hectare at the same time. What is more important, only Estonia, Lithuania and Poland could increase gross value added in agriculture after accession, while huge falls are observable in the others (including Bulgaria's sharply decreasing performance of 44% from the first to the last period analysed).
It is evident from Figure 1 that Lithuania became the first in agricultural gross value added performance (showed the highest increase from 1999-2003 to 2009-2013), thereby received a score of 100. On the other end, Bulgaria showed the biggest fall here and got zero points (see first column of Table 1).
Agricultural performance can also be measured by sector. Indices 2-7 actually capture country performances by their diverging sector outputs. For instance, Lithuania doubled her cereals output from 1999-2003 to 2009-2013 (from 262 million to 539 million euro), thereby obtaining 100 points for the second index (see second column of Table 1).
BGGG 7GGG 6GGG 5GGG 4GGG 3GGG 2GGG 1GGG G
5G%
4G%
3G%
2G%
1G%
G%
-1G%
-2G%
-3G%
-4G%
-5G%
■ 1999-2003 ■ 2004-2008 12009-2013 □ Change (2009-2013/1999-2003)
Figure 1. Changes in agricultural gross value added in real terms in the NMS, 1999-2013 (euro/ha and percentage)
Source: Own composition based on Eurostat (2015) data
Table l
Summary of agricultural performances in NMS
Country/ Index I1 I2 I3 I4 I5 IS I7 IS I9 I10 I11 I12 I13 I14 I15
Bulgaria 0 44 73 6 0 0 28 6 0 7 17 33 53 76 56
Czech Republic 37 25 26 27 96 31 48 47 38 21 18 28 28 59 29
Estonia 67 77 100 3 73 82 84 100 39 100 100 0 100 100 35
Hungary 37 41 36 38 38 45 23 31 37 17 3 62 19 25 45
Latvia Lithuania Poland 22 100 98 82 100 48 73 69 53 0 79 100 63 28 100 67 78 100 85 79 100 55 52 63 9 89 100 15 58 46 78 41 17 12 53 92 57 45 30 69 81 56 38 33 81
Romania 17 0 32 49 59 13 0 0 14 18 35 100 17 0 89
Slovakia 7 25 25 23 44 14 23 35 13 25 32 62 0 27 100
Slovenia 27 7 0 88 64 43 43 5 23 0 0 57 3 52 0
Note: The detailed list of indices can be found in Appendix 1. Source: Own composition
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1000 900 800 700 600 500 400 300 200 100 0
70% 50% 30% 10% -10% -30% -50%
yy A/ </ y s s s
cf
■ 1999-2003
2004-2008 12009-2013 DChange (2009-2013/1999-2003)
Figure 2. Changes in agricultural gross value added per hectare in real terms in the NMS, 1999-2013
(euro/ha and percentage)
Source: Own composition based on Eurostat (2015) data
For the same index, Romania got zero points as her respective change for the same period was the lowest (-20%). Similarly, Estonia increased her industrial crop output to the most in the period analysed (+173%), while Slovenia actually showed a decrease in this regard (-19%) — thus Estonia got 100 points and Slovenia zero (check the third column of Table 1).
Another common way to analyse agricultural performances is to check real farm incomes
Table 2
The agricultural performance index of the NMS
Country/Index Total Score Rank
Poland 1084 1
Estonia 1060 2
Lithuania 985 3
Latvia 725 4
Czech Republic 558 5
Hungary 497 6
Slovakia 455 7
Romania 443 8
Slovenia 412 9
Bulgaria 399 10
Source: Own composition
growth (Index 8). Although farm income per capita increased in each and every country in the region, Estonia experienced the biggest increase of farm income per capita after accession (222%), while farmers' income increased the least in Romania (+16%).
Another group of indicators measures agricultural productivity. The first such indicator is gross value added per hectare that measures land productivity (Index 9). Contrary to Figure 1, it is evident that gross value added per hectare was the highest in Slovenia in all periods analysed, while the lowest in Latvia (Figure 2). However, in terms of changes, Poland could increase her per hectare output by 59% from the first to the last period, while the respective change for Bulgaria was -37%). Thereby Poland got 100 points for Index 9 and Bulgaria got zero.
Agricultural productivity can also be measured per worker (Index 10). Results suggest that Estonia actually more than doubled her gross value added per worker, while Slovenia even experienced some decrease with respect to this index.
The remaining indices capture agricultural productivity by sector. As evident from Table 1, Estonia leads the line here in most cases, while relatively low values can be seen for the Czech Republic and Hungary.
Changes in factors of production in the NMS, 1999-2013
The agricultural performance index is calculated by summing up the 15 indices. There exists a huge competition among NMS regarding their final ranks (Table 2). Poland became the first, preceding Estonia and Lithuania — all obtained scores around 1000. Latvia reached the fourth position, while the Czech Republic got to the fifth. On the other hand, Hungary, Slovakia, Slovenia, Romania and Bulgaria lagged behind. Note that their score does not even reach 50% of the winners. On the whole, Poland and the Baltic countries seem to have gained the most with EU-accession in agriculture while countries with scores below 500 have used their possibilities of EU accession the least in the agricultural sector.
We are aware that our approach has many limitations. First, it is evident that the selection of indices can alter the final performance of the countries. Second, ranks can also change by the selection of new periods to compare. Third, we are not certain whether these changes would anyway have happened or they are an effect of EU accession. Fourth, there might be some correlations between the selected indicators which can over represent the performances. However, we believe that our selection of 15 different indices shows trends close to reality.
5. Possible reasons behind
There can be many external reasons behind the different performances described above. First of all, these countries have different initial conditions. Different distribution of agricultural land quality and quantity together with the differences in agricultural labour and capital endowment definitely had an impact.
As evident from Table 3, Poland and Romania had the biggest agricultural land, labour and capital endowment in the NMS. However, only Estonia and Latvia could increase their agricultural land area from 1999-2003 to 2009-2013, while agricultural labour decreased in each and every NMS. On the other end, agricultural capital increased in all countries but Bulgaria, Hungary, Slovakia and Slovenia. It can be observed from Table 3 that mainly those countries, where changes in factors of production were better than the regional average, performed better.
Besides initial conditions, another factor behind different country performances lies in farm structures (Figure 3).
Table 3
Country Utilised Agricultural Area (1000 ha) Agricultural labour (1000 AWU) Gross fixed ag. capital (million euro)
1999-2003 2009-2013 Change 1999-2003 2009-2013 Change 1999-2003 2009-2013 Change
Bulgaria 5482 5058 -8% 770 377 -51% 160 122 -24%
Czech Republic 4038 3524 -13% 165 108 -34% 340 462 36%
Estonia 881 950 8% 57 25 -56% 76 138 82%
Hungary 6169 5428 -12% 654 440 -33% 911 725 -20%
Latvia 1763 1833 4% 146 87 -41% 101 156 54%
Lithuania 3066 2800 -9% 194 145 -26% 211 308 46%
Poland 17543 14789 -16% 2414 1979 -18% 696 901 29%
Romania 14802 13897 -6% 3175 1692 -47% 694 799 15%
Slovakia 2315 1928 -17% 136 62 -54% 153 125 -18%
Slovenia 507 474 -7% 104 80 -23% 211 193 -9%
NMS total 56566 50680 -10% 7815 4995 -36% 3553 3928 11%
Source: Own composition based on Eurostat (2015) and FAO (2015)
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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
■ Less than 5 ha ■ 5-29.99 ha
30-99.99 ha
100 ha or more
Figure 3. Share of farms by UAA in the NMS in 2010 (%)
Source: Own composition based on Eurostat (2015) data
Slovenia
Slovakia
Romania
Bulgaria ,00
Poland
1999-2003
Czech Republic
Estonia
Hungary
Lithuania 2004-2008
Latvia
2009-2013
Figure 4. Annual GDP growth in the NMS, 1999-2013 (%)
Source: Own composition based on World Bank (2015) data
On one hand, the majority of land was cultivated by small farms only in Latvia, Lithuania, Poland, Romania and Slovenia. In Poland and Slovenia, small scale farms dominated agriculture during the socialist period and they have not been changed much after 1990 (Csaki and Jambor, 2013). On the other hand, large farms ruled land use in the other five countries. Values of Czech Republic and Slovakia (around 90% for large farms) show an extreme dominance of large scale farming. However, medium-scale farming is missing in most cases. These land use patterns stayed relatively stable if comparing these results to pre-accession levels. Concerning the impact of farm structures on post-accession performances, it is evident that in Poland and Slovenia small scale agriculture proved to be beneficial, while the dominance of large scale farming seemed to have detrimental impacts on country performances except for Estonia.
Differently implemented land and farm consolidation policies had also diverse effects on postaccession country performance. Restrictive pre-accession land policies and the lack of land and farm consolidation (e.g. in Hungary) has negatively influenced the capacity to take advantage of the enlarged markets by significantly constraining the flow of capital outside the agricultural sector (Ci-aian et al. 2010). Conversely, liberal land policies (e.g. in Baltic countries) helped the agricultural sector to obtain more resources and utilise the possibilities created by the accession better. In other words, those countries with restrictive land policies, as also suggested by Swinnen and Vranken (2010), performed worse.
The magnitude of privatisation in the agri-food sector and the type of foreign ownership also affected post-accession performances. After the collapse of the Soviet markets there was a massive privatisation of the agri-food sector in the majority of NMS. Those countries giving ownership of food processing companies to local farmers (e.g. Czech Republic, Poland) performed better, while the rapid rise of foreign ownership together with fast privatisation resulted in worse performances in the long run (e.g. Bulgaria, Hungary, Romania).
The ways in which the countries used EU-fund-ed pre-accession programmes such as SAPARD, ISPA and PHARE was also important. Those who focused on competitiveness enhancement and production improvement were better in realising the benefits after accession. On the contrary, delays
in creating the required institutions as well as the initial disturbances of implementation resulted in the loss of some EU funds in a number of countries (Csaki-Jambor, 2013).
The diversity of the macro environment also had an impact (Figure 4). Annual average GDP growth in the NMS was the highest in Latvia for the first two periods and Poland for the third, while the lowest in Bulgaria, Hungary and Slovenia in the three respective periods. Note that it was only Estonia and Poland whose annual GDP growth remained positive in the third period when the effects of the 2008 economic crisis was the biggest.
Volatility and transparency of agricultural policies were probably the most important reasons behind different performances. Changing agricultural policies, usually taking a u-turn after elections, were very much against the long-term growth of the agri-food sector. Those countries with reliable and transparent policies (e.g. Poland) could reach better results than those with fire-brigade agri-food policy making during the past decade (e.g. Hungary). The consistency of agri-food policy making is also reflected in the existence of long-term agriculture and rural development strategies of which the majority in the region was in lack (Potori et al. 2013).
The focus of total payments on agriculture also determined agri-food performances. Before acces-
sion, payments in favour of competitiveness enhancement definitely proved to be beneficial. On one hand, those countries, where agricultural subsidies to farmers remained at a lower level (e.g. Poland), have gained much with the accession which has provided visible incentives for production and led to an increase of agri-food trade balance. On the other hand, those countries providing initially high and uneven price and market support (e.g. Bulgaria, Romania, Hungary) were considered to lose with accession as it has brought hardly any price increase. Agricultural policy aimed to enhance competitiveness was a failure and resulted in a situation where the majority of farmers were not prepared for the accession (Csaki-Jambor, 2013, Popp-Jambor, 2015).
Regarding the focus of total payments on agriculture, a different picture appears after accession. Interestingly, those countries that spent less than the regional average on value added generally performed better (Figure 5). On one hand, Bulgaria, Romania and Slovakia spent more than a quarter of their axis 1 funds to agricultural value added growth which, from 10 years hindsight, was a mistake. The reason probably lies in the low effectiveness of these payments — value added does not necessarily mean enhanced competitiveness if the product structure is mis-selected.
■ Generation change
■ Adding value to agricultural and forestry products
■ Modernisation of agricultural holdings
Figure 5. Distribution of the most important first axis payments in the programming period 2007-2013 by NMS (percentage)
Source: Own composition based on RDR (2013)
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The other side of the story is that countries, which invested in agriculture for enhancing generation change (by spending on young farmers and early retirement) generally performed better. Poland actually spent 43% while Lithuania 24% of their respective axis 1 payments to fostering generational change which proved to be beneficial.
6. Conclusions
The article analysed the post-accession agri-food performance of NMS on the occasion of the 10th anniversary of EU accession. By selecting 15 indices measuring agricultural performance, it turned out that Poland and the Baltic countries were the winners of EU accession while Romania, Bulgaria and Slovenia proved to have used their potentials to the least. The second part of the article identified some possible external reasons behind changes. It turned out that post-accession performance in the agri-food sector differed to a great extent. Although all countries gained with EU membership, NMS used their possibilities to a different extent.
Acknowledgements
This research was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences and by the Hungarian Scientific Research Fund Project № 112394, «10 years of Accession: Lessons from the agri-food sector of the New Member States».
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Appendix 1
Definition of indices
Indicator Name Definition Unit of Measurement
I1 Gross Value Added Gross Value Added at real prices for agriculture. million euro
I2 Cereals Output The total output of the cereals sector at real prices. million euro
I3 Industrial Crop Output The total output of the industrial crops sector at real prices. million euro
I4 Fruits Output The total output of the fruits sector at real prices. million euro
I5 Vegetables Output The total output of the vegetables sector at real prices. million euro
I6 Meat Output The total output of the meat sector at real prices. million euro
I7 Milk Output The total output of the milk sector at real prices. million euro
I8 Farm Income Indicator A: Index of the real income of factors in agriculture per annual work unit 1999=100
I9 Land Productivity Gross Value Added divided by Utilised Agricultural Area euro/ha
I10 Labour Productivity Gross Value Added divided by Annual Working Units euro/capita
I11 Cereal Yields Harvested production per unit of harvested area for crop products. tonnes per ha
I12 Fruit Yields Harvested production per unit of harvested area for fruit products. tonnes per ha
I13 Vegetables Yields Harvested production per unit of harvested area for vegetable products. tonnes per ha
I14 Milk Yields Milk given by a cow per year. tonnes per animal
I15 Poultry Yields The size of the animal when slaughtered. kilograms per animal
Source: Own composition
INTERNATIONAL AGRICULTURAL JOURNAL № 6 / 2016
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