ESPON Seminar, Rome, December 4th 2014Workshop 2.B

Regional Resilience and PovertyProfessor Mark Shucksmith, Newcastle University UK.

ESPON TiPSE The Territorial Dimension of Poverty and Social Exclusion in Europe

The TIPSE project is the first comprehensive and systematic attempt to map (NUTS3) regional patterns of Poverty and Social Exclusion across Europe to inform policy decisions.

The research team comprises 7 partners from 5 EU Member States:• Nordregio, Stockholm (Petri Kahila, project coordinator)• James Hutton Institute, Aberdeen (Andrew Copus, project leader)• Newcastle University, Newcastle upon Tyne• Institute of Economics, Hungarian Academy of Sciences, Budapest• ILS – Research Institute for Regional and Urban Devt, Dortmund• EKKE – National Centre for Social Research, Athens

• UHI Millenium Institute, Inverness

Introduction

ARoP – At Risk of Poverty Rate (Eurostat)

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Acores

Guyane

Madeira

Réunion

Canarias

MartiniqueGuadeloupe

Zagreb

Valletta

Budapest

Bratislava

Roma

Riga

Oslo

Bern

Wien

Kyiv

Vaduz

Paris

Praha

Minsk

Tounis

Lisboa

Athina

Skopje Ankara

MadridTirana

Sofiya

London

Berlin

Dublin

Tallinn

Nicosia

Beograd

Vilnius

Kishinev

Sarajevo

Helsinki

Warszawa

Podgorica

El-Jazair

Stockholm

Reykjavik

København

Bucuresti

Amsterdam

Luxembourg

Bruxelles/Brussel

Ljubljana

Percentage population in householdswith <60% of the national medianequivalised disposable income.

2.7 - 9.9

10.0 - 14.9

15.0 - 19.9

20.0 - 29.9

30.0 - 42.3

Source: Eurostat Regio database Table ilc_li41

Year: 2012, except; BE (2011), DE, EL, NL, (2010), FR, UK, (2009) TR, (2006), PT (2005).

Region Level: NUTS 0 - EE, CY, LV, LT, LU, MT, IS, HR, TRNUTS 1 - BE, EL, HU, PL, UKNUTS 2 - BG, CZ, DK, DE, IE, ES, FR, IT, NL, AT, PT, RO, SI, SK, NO, CH

ARoP – At Risk of Poverty Rate (TiPSE - NUTS 3)

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Acores

Guyane

Madeira

Réunion

Canarias

MartiniqueGuadeloupe

Zagreb

Valletta

Budapest

Bratislava

Roma

Riga

Oslo

Bern

Wien

Kyiv

Vaduz

Paris

Praha

Minsk

Tounis

Lisboa

Athina

Skopje Ankara

MadridTirana

Sofiya

London

Berlin

Dublin

Tallinn

Nicosia

Beograd

Vilnius

Kishinev

Sarajevo

Helsinki

Warszawa

Podgorica

El-Jazair

Stockholm

Reykjavik

København

Bucuresti

Amsterdam

Luxembourg

Bruxelles/Brussel

Ljubljana

Percentage population in householdswith <60% of the national medianequivalised disposable income.

No Data

< 9.9

10.0 - 14.9

15.0 - 19.9

20.0 - 29.9

30.0 - 63.4

Sources:

BE, DE, EL, ES, IT, AT, PT, TR, CH - ESPON TiPSE project.

DK, SE, FI, NO, IE, NL, FR, UK, HR - National Statistical Institutes

LV, HU, RO, SI, SK - World Bank.

BG, CZ, EE, CY, LT, LU, MT, PL, LI - Eurostat Regio Database (NUTS 0-2)

Income poverty rates are;

•higher in urban areas in 4 countries (the centre)

•higher in rural/intermediate areas in 11 countries (Med. and East)

•No clear U-R difference in 5 countries (mostly NW)

What can we learn from this map?

o Regression across ESPON countries, EU15, EU12, Welfare regime groups, P&SE clusters.

o Some key results:

• Unemployment (total, youth and long-term) is closely associated with ARoP throughout Europe

• Profiles of relationships in EU15 and EU12 are slightly different:

• EU15 – labour market characteristics, elementary occupations.

• EU12 – accessibility, primary sector, education & skills, productivity.

• GDP per capita

• strong negative correlation in EU12 (i.e regional performance drives poverty rates), BUT

• weak relationship in EU15 (intra-regional distributional effects more important than inter-regional variation in overall performance)

What socio-economic indicators seem to be associated with income poverty?

1. 28 different poverty lines…?

2. EU-SILC – sample size issue:• regression modelling is a problematic short term fix• longer term solution is to develop EU-wide register data.

Eurostat “Register Hub”?

3. Income is only one aspect of poverty, living costs also very substantiallyHousing cost adjustment is not enough (urban bias)

ARoP – Can we improve on it?

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Acores

Guyane

Madeira

Réunion

Canarias

MartiniqueGuadeloupe

Zagreb

Valletta

Budapest

Bratislava

Roma

Riga

Oslo

Bern

Wien

Kyiv

Vaduz

Paris

Praha

Minsk

Tounis

Lisboa

Athina

Skopje Ankara

MadridTirana

Sofiya

London

Berlin

Dublin

Tallinn

Nicosia

Beograd

Vilnius

Kishinev

Sarajevo

Helsinki

Warszawa

Podgorica

El-Jazair

Stockholm

Reykjavik

København

Bucuresti

Amsterdam

Luxembourg

Bruxelles/Brussel

Ljubljana

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Acores

Guyane

Madeira

Réunion

Canarias

MartiniqueGuadeloupe

Zagreb

Valletta

Budapest

Bratislava

Roma

Riga

Oslo

Bern

Wien

Kyiv

Vaduz

Paris

Praha

Minsk

Tounis

Lisboa

Athina

Skopje Ankara

MadridTirana

Sofiya

London

Berlin

Dublin

Tallinn

Nicosia

Beograd

Vilnius

Kishinev

Sarajevo

Helsinki

Warszawa

Podgorica

El-Jazair

Stockholm

Reykjavik

København

Bucuresti

Amsterdam

Luxembourg

Bruxelles/Brussel

Ljubljana

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Acores

Guyane

Madeira

Réunion

Canarias

MartiniqueGuadeloupe

Zagreb

Valletta

Budapest

Bratislava

Roma

Riga

Oslo

Bern

Wien

Kyiv

Vaduz

Paris

Praha

Minsk

Tounis

Lisboa

Athina

Skopje Ankara

MadridTirana

Sofiya

London

Berlin

Dublin

Tallinn

Nicosia

Beograd

Vilnius

Kishinev

Sarajevo

Helsinki

Warszawa

Podgorica

El-Jazair

Stockholm

Reykjavik

København

Bucuresti

Amsterdam

Luxembourg

Bruxelles/Brussel

Ljubljana

National Quintiles

Median = 100

NationalMedians

Social Exclusion is relational, multi-dimensional and dynamic.

In TiPSE we identified four broad domains of social exclusion:• Earning a living (income; employment)• Access to Services (health; education; housing; transport ; communications)• Social Environment (Age; Ethnic Composition; Migrants; Crime and Safety)• Political participation (Citizenship; Voice)

Such a complex multi-faceted concept cannot be mapped by a single indicator. Moreover social exclusion is a set of processes rather than static characteristics. We identified proxy indicators which reflect the risk of different kinds of exclusion.

From Poverty to Social Exclusion…?

Mapping SE Domains…

Mapping SE Domains…

EU Social Policy and the ‘Domains’ of SE• EU Social Policy emphasises the ‘earning a living’ domain with less attention given to the other domains of ‘access to basic services’, ‘social environment’ and ‘political participation’.• All EU policies should contribute to economic, social and territorial cohesion but it is not clear how social strategies seek to address territorial cohesion below the MS level. Social groups at risk of P&SE are targeted but not regions at risk. As a result, many EU policies are ‘territorially blind’.• The targeting of regions for allocation of ESIF is according to GDP/head but this is poorly correlated with measures of P&SE

Policy Analysis - EU

Welfare Regimes:• Society-based• Individual• State-based• Familial• Transitional

Governance Issues:• Lack of data• Subsidiarity• Coordination• Empowerment

Policy Analysis – Member States

Conceptualising P&SE•P&SE are closely related but nevertheless distinct.•The 5 aspects of P&SE are differentially addressed in both data assembled and in policy development:

• At risk of poverty rate• Earning a living• Access to services• Social environment• Political participation

Recommendation:• Use broad conceptualisation of P&SE in all policy arenas and provide appropriate data to support this… • We would commend the ’domains’ of P&SE developed in TIPSE as a useful tool in this respect.

Policy Implications

Issues of Scale:• The territorial risk of P&SE varies according to scale.• Different clusters are associated with high incidence of P&SE from different TIPSE domains - as also are different scales.• Identification of P&SE in social and political domains, and at sub-national level, is underplayed by social OMC.

Recommendations:•P&SE data should be collected and analysed regularly at least at NUTS2 (ideally NUTS3)•Elaborate geography of P&SE.•Assess value of other methods•Consider producing a report on territorial aspects of P&SE

Policy Implications

Geographies of P&SE:Why do these territorial differences emerge?•Rural – urban differences:

• Poverty risk worse in rural areas in poorer countries, but not rich.

• SE domains differ by R-U.

•Regions of immigration.• Border regions• Parts of cities

•Macro-level effects• N-S and E-W splits in EU• Regional splits within countries

Recommendations:•Use TIPSE insights to build territorial cohesion dimension into all policies, eg CAP, URBACT.•Recognise that geographies of P&SE reflect processes at several scales; and these call for policy at multiple levels

Policy Implications

Effects of the Crisis:Not a major focus of TIPSE, but…• Most Mediterranean states, Baltic states, Ireland and some East Central European countries were worst hit.• Widespread austerity measures of MSs led to cuts in state services which reinforced P&SE, especially in remoter areas.• Intra-Europe migration patterns changed because of the Crisis• In some regions the outmigration of young people has made the challenge of an ageing population more acute.• The capacity of family-based support structures in southern Europe appears further stretched by the Crisis

Policy Implications

Recommendations:• National Reform Programmes should include Regional Chapters• CLLD/LEADER type approach should be encouraged as a means of alleviating P&SE at local level.• In resource & policy targeting, high risk of P&SE should be considered as well as GDP/head.• Differential living costs should also be reflected in indicators.• Common issues could usefully be discussed among similar regions• Consider how best to collate and disseminate case studies

Monitoring recommendations:• Broaden set of data expressing multidimensionality of SE • Data accessible at as low NUTS level as possible• Encourage mutual learning among monitoring bodies, through secondments and exchange of experiences• Making existing data (indicators) accessible in a more user-friendly manner.

Policy Implications