agriregionieuropa Mapping changes on agricultural and rural areas: an ex-post evaluation of the EU membership for Hungary Monasterolo, I., Pagliacci, F University of Bologna (IT) 122 nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 17 th – 18 th, 2011, Ancona (Italy) associazioneAlessandroBartola studi e ricerche di economia e di politica agraria Centro Studi Sulle Politiche Economiche, Rurali e Ambientali Università Politecnica delle Marche

agriregionieuropa  This analysis moves from the evolution of EU agri&RD scenario in the last decade, characterized by: – progresses in policy evaluation for rural areas with introduction of many indicators but.. a shared definition of rurality still missing in the most lagging behind areas, unsatisfactory results for growth and development path – preliminary CAP & Cohesion Policy results in NMSs: designed for the EU-15, don’t address regional specificities, hindering development and convergence Importance of timing and targeting policies: > regional inequality in NMSs, driven by persisting backwardness of agri. & rural areas, where the highest percentage of poverty is located – CAP budget reform for the next programming period , and future possible enlargement to Western Balkans Inside the EU Agri&RD enlargement scenario

agriregionieuropa The missing link between goals and policies  Evaluation of the policies and programmes introduced evidenced: – lack of institutional planning and implementing abilities – insufficient targeting of policies and payments  These outcomes were also heavily influenced by – limited knowledge of transition Countries – the unclear areas definition – delays in adaptation to statistical requirements (already in evaluation of Acquis Communitaire chapters) – persisting limited statistics availability and accessibility  More targeted (and efficient) policies for agri&rural areas require a deeper knowledge of their structural and dynamic characteristics

agriregionieuropa Aim of the paper & methodology  Ex-post analysis of EU membership for Hungary (attention on agri&rural areas) through multivariate statistics – Principal components analysis – Principal components analysis (PCA) on Hungarian Counties (NUTS III) at 2003 (year before EU accession) & 2007 (after first programming period for EU-8) to highlight main structural features and their changes – Cluster analysis – Cluster analysis (CA) at 2003 & 2007 to map Counties (NUTS III) according to their main characteristics  Evaluation Single Area Payment Scheme (SAPS) distribution: correlations with factors able to determine applications success/failure – info on the applications at County level (NUTS III) provided by the Hungarian Paying Agency

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Hungary as a case study  Relevance of rural areas on total land (96%, OECD methodology)  Historical socio-economic role of agriculture – ha, of which 83% used for agriculture, 6 million ha arable land – 1 million people agri. Population  Main agri areas: Western Transdanubia, Northern&Southern Great Plains  Production concentrates in 3 sectors: – arable crops (60% of Gross Agricultural Output; maize and wheat); horticulture; animal breeding (pigs, poultry breeders and milk producers).  All sectors influenced by agricultural transition, achieving different results: while crop cultivations increased notably, horticulture and animal breeding (especially pigs) dropped

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Applying PCA on pre-accession Hungary  42 variables (NUTS III), 19 Counties plus Budapest  Indicators able to catch: – dynamics – dynamics of evolving areas (structural, socio-demographic, agricultural features) – EU membership effect – EU membership effect (instruments provided by Cohesion policy and CAP, new CAP vision); data qualityavailability: – accounting for data quality and availability: secondary data provided by the Census 2001, sub regional data provided by the Hungarian Statistical Office (KSH), Eurostat database  Variables listed in 4 groups to better understand results: – economic and productive structure (attention to employment structure); – structural indicators for agriculture (productive features of the sector); – socio-demographic structure, to monitor the evolution of the population in its age structure, cultural aspects and accessibility; – economic dynamism (structural components in agri. and employment structure)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)  7 principal components, explaining 86% of the original variance (VARIMAX rotation) – PC1 economic development (29%). Areas with high population density, higher than national average GDP, net earnings, university education, employment in services, value of industrial production. Confirmed by the negative values, associated to dependency ratio, employment in primary sector, role of primary sector on GDP, unemployment rate. – PC2 social and industrial decline (17.8%). Unemployment rate, recipients of social support, employment in public administration, dependency ratio. Negative: values for employment rate (especially secondary sector), role of the secondary sector on GDP, labour productivity, population change. – PC3 demographic structure (12.1%). Positive values for youth index, generational change, younger farmers, small farms (< 5 ha), recipients of social support and population change. – PC4 economic backwardness (9.7%). Unemployment, recipients of social support, occupation in PA, part time agriculture. Negative values associated to agri. sector. – PC5 agricultural development (8 %). Good agri. performance, role of primary sector on GDP, high land price, prevalence of cereals and maize among cultivations, touristic vocation. – PC6 area productivity (5.4%). Positive labour productivity, in-migration rate, infrastructures, employment in secondary sector, presence of older farmers. Negative values: land price, average farm size, occupation in primary. – PC7 touristic attractiveness (3.7%). Beds in hotels, infrastructural endowment, employment rate, employment in services, agricultural productivity

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Application of cluster analysis to 2003  6 clusters (k-means method, after comparison with hierarchical method) – I. capital town Budapest : high economic development (GDP p.c. and net earning respectively 3 times and 39% higher than nat. average); declining secondary sector in favour of tertiary and PA – II. Baranya, Somogy and Tolna; rural areas vocated for agri., which plays important role on employment and GDP (42% and 36% over nat. average) but ongoing industrial decline ; naturalistic&folkloristic attractions, touristic accommodations – III. Heves and Nograd, most backward Counties, in Northern Hungary. Structural problems in economic (declining heavy industry), social, agri, demographic sectors, unsolved and increased in transition period (value industrial production twice lower than nat. average, GDP 22%) – IV. Fejér, Komárom-Esztergom, Győr-Moson-Sopron, Vas, Zala (We&Ce Transdanubia). Good productive performance, advanced economic structure, FDI makes this areas one of the most developed, thanks to manufacturing (machine industry, textiles and foods) service sector and agriculture – V. Eastern Hungarian border (4 Counties). Traditional farming area: agri-food industry & foreign companies not able to reverse the difficult structural changes (high unemployment rate, low agricultural productivity) – VI. 3 Counties in Southern Great Plain Region plus Vezprém and Jász- Nagykun-Szolnok. Touristic attractions (here “Puszta”, Balaton) but lower ec. &agri. development (GDP p.c. 70% of the nat. average, earnings 76%); lower unempl. but high rate of part time in agri

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Catching enlargement effect: PCA on 2007  Same process repeated for PC, differing from the previous ones both in the values and in the interpretation: – PC1 economic development (30%). Positive values identify areas with high population density, GDP and net earnings in comparison with nat. average, occupation and GDP in services sector, hospital beds, university students. – PC2 social disease (19%). Positive values for unemployment rate; distribution of social support and employment in PA; dependency ratio. Negative values for the employment rate; population change and GDP. – PC3 industrial decline (10.8%). Negative values for occupation and GDP in secondary sector; employment rate; value of industrial production. Positive values for occupation in PA, recipients of social support. – PC4 age structure (8.8%). Positive values for youth index & generational change; farmers <35 years old; presence of small farms &part time in agriculture; distribution of social support. – PC5 agricultural development (7.2%). Positive values for land price & average farm size; presence of cereals, maize, livestock; employment and GDP in primary sector – PC6 touristic attraction (6.2%). Positive values for number of hotel/pension beds; presence of forested area; land price. – PC7 agricultural productivity (3.8%). Positive values for agri. value added, labour productivity in agri., contribution of primary sector on GDP, maize.  From 2003, we notice an accentuation of the decline recorded by the industrial sector, and an increased role of agriculture for the Country.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) 5 clusters in 2007 – I. again composed by Budapest, confirming leading role in economic development, even if at a lower pace (GDP p.c. > 6% but < net earnings by 4%) – II. Vezprém, Zala and Pest: touristic potential (touristic accommodations 30% over the average, thanks to Zala), positive age structure; effects of industrial decline (< employment in industry & its role on GDP, also driven by Zala) – III. 7 Counties, Baranya, Heves, Nógrád, Jász-Nagykun-Szolnok, Bács-Kiskun, Békés, Csongrád. Lagging economy, age structure & agri development, industrial decline. This result coherent with 2003 findings and even worse (GDP p.c. by 26%) – IV. Fejér, Vas, Tolna, Komárom-Esztergom and Győr-Moson- Sopron. Eco&social situation worsened: GDP p.c. (+ 35% in average), employment in agri. +30% and its role on GDP + 10% – V. 4 Counties: Somogy, Borsod-Abaúj-Zemplén, Hajdú-Bihar, Szabolcs Szatmár-Bereg, mainly on Eastern border (with the exception of Somogy). Relevance of component of social disease &age structure, with high unemployment rate (12%, 50% increase) and low GDP p.c., underling the persisting problems of bordering areas.

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) SAPS analysis at County level  SAPS (1 billion Euros) + RD support + top-up payments (paid from the national budget as an integration of SAPS, till 30%)  From previous surveys findings on Hungarian farmers’ applications, we looked at correlation between: – Number applications received, payments received (SAPS+TOPUP), farmers’ age, farm size, farm location in LFA (County level)  We found that: – Land size and area of provenience affect quality and quantity of demands: Lowest number applic. from farms < 0.3 ha and mostly refused (i.e. Heves, lagging behind County in 2003 and 2007) Most of applications & successful (>100ha) from Fejér, Pest (better off Counties) – no significant correlation between number of applications (and payments) and farm location in LFA – negative correlation between applications presented, payments received, farmers’ age (> 55)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Conclusion  Comparing Hungary in 2003/2007: divergence between objectives of EU Membership and results obtained, especially in worse off agri&rural areas  Move to agriculture in some Counties, as in Zala and Győr- Moson-Sopron without increase in economic performance and living conditions  Increased marginalization of lagging behind Counties (Nógrád, Szabolcs-Szatmár-Bereg), confirming the existence of winning and loosing regions from the enlargement: – winners: Budapest (attract initiatives in tertiary) – loosers: Eastern peripheries (due to lack of ability in attracting investments, low agri productivity after land reform)  CAP introduction accompanied by inequality issue in SAPS payments (to bigger farms in wealthier areas)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) THANKS FOR YOUR ATTENTION!!