EDORA: European Development Opportunities for Rural Areas

Transcription

1 EDORA: European Development Opportunities for Rural Areas ESPON 2013 Programme RUFUS/TRUST Workshop, August 25 th 2010 Andrew Copus Centre for Remote and Rural Studies

2 The EDORA Consortium UHI Millennium Institute, Inverness NORDREGIO, Stockholm University of Newcastle University of Valencia University of Patras TEAGASC, Dublin University of Gloucester University of Ljubljana Von Thunen Institute, Braunschweig BABF, Vienna Dortmund University Polish Academy of Sciences Hungarian Academy of Sciences Higher Institute of Agronomy, Lisbon Scottish Agricultural College International Organization for Migration, Warsaw

3 The EDORA Project Objectives (According to the Specification) to describe the main processes of change which are resulting in the increasing differentiation of rural areas. to identify development opportunities and constraints for different kinds of rural areas to consider how such knowledge can be translated into guiding principles to support the development of appropriate cohesion policy.

4 The EDORA Approach A very wide-ranging task Rural data availability is strongly influenced by the agrarian rural development tradition. Being driven by the data availability risks slipping into well-trodden paths A hybrid deductive/inductive approach first establish territorial concepts and theory, use this to steer the empirical analysis. Final stage is to build a policy rationale from the conceptual and empirical findings

5 EDORA Project Structure Review of the Literature: - Rural Demography - Rural Employment - Rural Business Development - R-U Relationships Storylines - Cultural Heritage - Access to Services - Institutional Capacity - Farm Structural Change Proposed Indicators Storylines Database Exemplar Regions Storylines, Narratives Meta-Narratives Connexity Globalisation. Agricentric Urban- Rural Variables and Indicators Empirical Examples Narratives Structural Types (Intermediate and Predominantly Rural Areas only): Agrarian..... Consumption Countryside..... Diversified (Strong Secondary Sector) Diversified (Strong Market Services) Country Profiles EDORA Cube D-P Typology: IA, IR, PRA, PRR Typologies Accumulation - Depletion Depleting Below Average Above Average Accumulating Future Perspectives Key Future Drivers (Exogenous) S1 Typologies S3 S2 S4 Scenarios Implications Cohesion Policy Implications and Potential for Territorial Empirical Cooperation Generalisations Conceptual Empirical Policy

6 The EDORA Typology The role of the typology (project spec.): To review explanatory potential of the Dijkstra-Poelman version of the OECD typology. Explore potential to elaborate it; add structure and performance aspects to U-R dimension. Elaborated typology might then serve as a framework for analysis of recent trends, consideration of future perspectives, and policy implications. Underlying Aims: To correct/replace Stylised Fallacies (Hodge 2004) about rural areas as background to rural/regional policy debate. To provide a means of benchmarking for regional and national policymakers.

7 Rural or Non-Urban? EDORA typology cannot be a typology of Rural Areas two reasons: (a)rural areas do not function separately from adjacent urban areas they are connected by a dense web of interactions. (b)smallest practicable data units are NUTS 3(2), most of these contain sizable towns/cities. It is a typology of Intermediate and Predominantly Rural Regions. It covers EU27 (+NO, CH, TR )

8 more of a three-dimensional framework for analysis, rather than a onedimensional classification. The three dimensions are: Urban-Rural (remote/accessible) Economic structure (diversification). Accumulation Depletion (performance). The EDORA Cube (Patterns of Differentiation) D-P Typology: IA, IR, PRA, PRR Structural Types (Intermediate and Predominantly Rural Areas only): Agrarian..... Consumption Countryside..... Diversified (Strong Secondary Sector) Diversified (Strong Market Services) Accumulation - Depletion Depleting Below Average Above Average Accumulating

9 Methodology and Database -Simple step-wise multi-criteria methodology -The database used: 27 raw data variables (mainly from Eurostat REGIO) 12 (ratio) indicators used for Structural Typology 5 used for Accumulation-Depletion (Performance) Scores Variables used No. Short Name Description Ag1 PCPrimeE % Private Sector Employment in Primary Activities V14,V13 Ag2 PCPrimeG % Private Sector GVA from Primary Activities V10,V9 Ag3 AWUPEmp AWU as a % of Total Private Employment V17,V13 CC1 HotCat % of employmet in Hotels and Catering V19,V18 CC2 BPPC Bed Places per Capita V20,V1 CC3 NSRES Nights Spent by Residents per capita V22,V1 CC4 NSNON Nights Spent by Non-Residents per capita V23,V1 CC5 NSTOT Nights Spent (Total) per capita V24,V1 CC6 ANA Access to Natural Areas V21 CC7 PCOGA % of holdings with OGA V25 CC8 LT4ESU % of Holdings <4 ESU V26,V27 NR1 CEGKGR Ratio of GVA from NACE CE to GK V11,V12 AD1 NETMIG Net Migration (rate) V3,V1 AD2 GDPpercap GDP per Capita V7,V1 AD3 GDPCh Average annual change in GDP V8 AD4 TotEmpCh Avg. annual change Total Employ. V15 AD5 Unemp Unemployment Rate V16,V6

10 The Three Typologies Reykjavik Canarias Guadeloupe Martinique Réunion Helsinki Tallinn Oslo Stockholm Guyane Riga Madeira København Vilnius Minsk Dublin Acores London Warszawa Berlin Amsterdam Kyiv Bruxelles/Brussel Praha Luxembourg Reykjavik Paris Kishinev Wien Bratislava Budapest Canarias Bern Vaduz Ljubljana Zagreb Bucuresti Guadeloupe Martinique Beograd Réunion Sarajevo Sofiya Podgorica Roma Ankara Tallinn Oslo Tirana Madrid Helsinki Skopje Stockholm Guyane Lisboa Riga Athinai Madeira Nicosia København Vilnius El-Jazair Minsk Tounis Dublin Valletta Acores London Warszawa Berlin Amsterdam Performance Kyiv Bruxelles/Brussel Praha Luxembourg Paris Kishinev Wien Bratislava Budapest Urban-Rural Bern Vaduz Ljubljana Zagreb Bucuresti Beograd Reykjavik Sarajevo Sofiya Podgorica Roma Canarias Ankara Tirana Madrid Skopje Lisboa Guadeloupe Martinique Athinai Helsinki Nicosia El-Jazair Tallinn Oslo Tounis Stockholm Guyane Valletta Riga Madeira København Vilnius Structural Minsk Dublin Acores London Warszawa Berlin Amsterdam Kyiv Bruxelles/Brussel Praha Luxembourg Paris Kishinev Wien Bratislava Budapest Bern Vaduz Ljubljana Zagreb Bucuresti Beograd Sarajevo Sofiya Podgorica Roma Skopje Ankara Tirana Madrid Lisboa Athinai Nicosia El-Jazair Tounis Valletta Réunion

11 Structural Types (Intermediate and Predominantly Rural NUTS 3 Regions) Reykjavik No Data Canarias PU Regions Agrarian Guadeloupe Martinique Réunion Oslo Stockholm Helsinki Tallinn Consumption Countryside Diversified (Strong Secondary Sector) Guyane Riga Diversified (Strong Private Services Sector) Madeira Dublin København Vilnius Minsk London Amsterdam Berlin Warszawa Kyiv Acores Bruxelles/Brussel Paris Luxembourg Bern Vaduz Praha Ljubljana Zagreb Wien Bratislava Budapest Beograd Bucuresti Kishinev Lisboa Madrid Roma Sarajevo Podgorica Skopje Tirana Sofiya Ankara Athinai Nicosia El-Jazair Tounis Valletta

12 Performance (A-D) Types (Intermediate and Predominantly Rural NUTS 3 Regions) Reykjavik No Data Canarias PU Regions Depleting Guadeloupe Martinique Réunion Below Average Oslo Stockholm Helsinki Tallinn Guyane Above Average Accumulating Riga Madeira Dublin København Vilnius Minsk London Amsterdam Berlin Warszawa Kyiv Acores Bruxelles/Brussel Paris Luxembourg Bern Vaduz Praha Ljubljana Zagreb Wien Bratislava Budapest Beograd Bucuresti Kishinev Lisboa Madrid Roma Sarajevo Podgorica Skopje Tirana Sofiya Ankara Athinai Nicosia El-Jazair Tounis Valletta

13 Comparing D-P with EDORA Structural Typology Dijkstra-Poelman Types PRR REG IO NS EDORA Structural Types D(PS) Ag (NB %s relate to Intermediate+PR Total) D-P Typology Types uneven in size (e.g. Intermediate Remote very small - 23 regions 2% pop.) Intermediate Accessible dominates especially in terms of population and GDP (>60%). PRA IR IA TOTAL AREA D(Sec) CC Predominantly Rural regions (both accessible and remote) have much smaller share of population and GDP than of regions or area. PO PULATIO N EDORA Structural Typology Dominated by Consumption Countryside Agrarian type roughly a quarter of area, and population, but much smaller share of GDP. Diversified (Private Services) 27% of GDP, and 23% of population on 17% of area. GDP (PPS) Diversified (Secondary) 11% of population and GDP on 7% of area.

14 Testing the robustness of the typologies T tests of difference between types (in terms of performance scores). Structural Typology: - all combinations of types have significantly different performance (at 95%) except CC/DSec and CC/AG D-P typology only one combination (IA/PRA) has significantly different performance. A-D Index PU Ag CC Dsec DPServ PU Ag N/A 1.00 CC N/A DSec N/A DPServ N/A A-D Index PU IA IR PRA PRR PU IA N/A 1.00 IR N/A PRA N/A PRR N/A

15 Country and Macro-Region Profiles % 100 (a) Rurality/Accessibility (Dijkstra-Poelman) % 100 (a) Rurality/Accessibility (Dijkstra-Poelman) PU IA IR PRA PRR PU IA IR PRA PRR AT BE BG CY CZ DE DK EE ES FI FR GR HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK 0 EU27 EU15 NMS12 MED CWE Nordic % (b) Economic Structure AT BE BG CY CZ DE DK EE ES FI FR GR HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK D(PServe) D(Sec) CC Ag % (b) Economic Structure EU27 EU15 NMS12 MED CWE Nordic D(PServe) D(Sec) CC Ag % (c) Performance (Depleting-Accumulating) BE BE BG CY CZ DE DK EE ES FI FR GR HU IE IT LT LU LV MT NL PL PT RO SE SI SK UK Accum. Above Below Deplet. % (c) Performance (Depleting-Accumulating) EU27 EU15 NMS12 MED CWE Nordic Accum. Above Below Deplet.

16 Cross Tabulation of A-D (Performance) Types and D-P Types (% of Total Population) A-D T ype s Dijkstra -Poe lma n De ple ting Be low Ave ra ge Above Ave ra ge Acumula ting % in Positive T ype s Intermediate Accessible Intermediate Remote Predominantly Rural Accessible Predominantly Rural Remote All Inte rme dia te a nd R ura l % of the population of Intermediate Accessible regions live in regions which have A-D scores above the EU27 average. In the Intermediate Remote category over 75% of the population lives in regions with below average A-D scores. Over 30% live in Depleting regions. In both the Accessible and Remote PR categories about 60% live in regions with below average scores.

17 Cross Tabulation of A-D (Performance) Types and Structural Types (% of Total Population) A-D Types Structural Types Depleting Below Average Above Average Acumulating % in Positive Types Agrarian Consumption Countryside Diversified (Secondary) Diversified (market services) All Structural Types Two-thirds of the population of the Diversified (Market Services) category live in regions with above average A-D score. More than a quarter live in accumulating regions. In the Diversified (Secondary Sector) category a 55% live in regions with above average performance scores. In the Consumption Countryside group over 67% live in regions with above average scores. The Agrarian group has the most negative result almost 88% live in regions with below average scores. Almost half live in Depleting regions.

18 Comparing distributions of Population and GDP, by D-P and Structural Types (Location Quotients) Structura l T ype s Consumption Dive rsifie d Dive rsifie d (ma rke t All Structura l Dijkstra -Poe lma n Intermediate Accessible Agra ria n 0.55 Countryside 1.15 (Se conda ry) 1.06 se rvice s) 1.18 T ype s 1.07 Intermediate Remote Predominantly Rural Accessible Predominantly Rural Remote All Inte rme dia te a nd Rura l Comparison of share of population with share of GDP as a simple indicator of performance D-P Types: Intermediate Accessible regions usually have a larger share of GDP than of population (LQ>1) The three other D-P types show a mixed picture, but generally smaller LQ. EDORA Structural Types: Agrarian regions (regardless of D-P type) have a lower share of GDP than population (i.e. LQ<1). Consumption Countryside regions generally have larger share of GDP than population. Diversified (Strong Market Services) have high LQs except in Intermediate Remote Diversified (Strong Secondary Sector) show rather mixed pattern remote regions have smaller share of GDP than population. Accessible regions more positive situation.

19 What have we learned,.and where do we go from here? Typologies are most interesting when they are not an end in themselves but rather are tools. Then they can derive a clear rationale from the purpose for which they are designed Data availability (and the underlying generalisations/rationale) sets traps for inductive typologists. Still need more territorial indicators. Cross-tabulation of typologies (U-R, Structure, Performance) is a simple but fruitful form of analysis. Scale and MAUP issues are critical its very easy to envisage two NUTS 3 regions which have the same average indicators, but entirely different internal distributions. This is true of rurality, but more interesting in terms of economic structure and performance indicators. Rural structural change is a key issue in relation to recession effects, and recovery. Complex mixture of inertia and responses to new global divisions of labour. Very important to monitor this.

20 Thank you for your attention. Coordinator: