ERDF Funding Contests –The Experience of North-Rhine Westphalia

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ARL International Summer School 2015 “Winners and Losers: Why are the Effects of Regional Policy so Different?” 1 Prague, August 25^th, 2015

ERDF Funding Contests –

The Experience of North-Rhine Westphalia

Source: http://www.fotocommunity.de/pc/pc/display/25471306;http://www.dortmund.de/de/wirtschaft/start_ws/

2 Institute of Geography

Julian Kahl & Matthias Kiese ••••Urban and Regional Economics

ARL International Summer School 2015 “Winners and Losers: Why are the Effects of Regional Policy so Different?”

Prague, August 25^th, 2015

1) Introduction

2) A Regional Economic and Policy Snapshot of NRW 3) A Novel Policy Delivery System in NRW

4) RCE-Programme Performance

5) Conclusion

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1) Introduction

Regional policy was introduced to Western Europe in the post-war era

− Times of strong economic growth, fiscal expansion and low unemployment (cf. OECD 2009)

− Main aim = convergence: narrowing regional disparities by correcting market imperfections in least-favoured regions (LFS)

− Low performance of these policies was conducive to the emergence of novel theoretical and policy approaches

New paradigm for regional policy in the wake of the renewed Lisbon agenda: Cluster policies

− Paradigm shift in regional policy accentuating competitivenessobjective (renewed Lisbon strategy) (cf. Bachtler/Yuill 2008; Wink 2007)

− Efficient use of public resources is key: Mobilisation of endogenous potential in regional clusters

− Central theoretical underpinnings of the cluster concept remain vague and provide little guidance for the design of policy instruments (cf. Martin/Sunley 2003, Alecke/Untiedt 2005)

1) Introduction

Regional policy in NRW was traditionally regionalized promoting economic development in the structurally weak Ruhr Area

− Low performance in promoting growth

− Pronounced regional disparities (e.g. Huggins/Thomalla 1995; Danielzyk/Wood 2004)

New approach: Fostering growth in the highest-performing areas in all of NRW through competitive allocation of funds

Research questions

− Which factors explain the regional distribution of structural funds in NRW?

− How has this new approach performedin the programming period 2007-2013 of EU-structural funds in NRW?

o Participation / Target Precision:

• Does the programme reach the intended beneficiaries?

• Appropriate representation of private sector?

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1) Introduction

Working Hypothesis: High-performing regions will attract a disproportionate share of funds from competitions

− Lagging regions are characterized by deficiencies in economic structure, innovation inputs and outputs

o Traditional (low-tech) industries, dominance of large incumbents o R&D-investments, R&D-personnel

o Low patenting activity, low lack of new products

⇒ Innovation paradox (cf. Ouhgton et al. 2002)

− Empirical studies confirm growth-orientation of funding contests (cf.

Eickelpasch/Fritsch 2005)

Methodology / Data

Statistical analysis (descriptive, regression) of the list of beneficiaries (> 2,900 project participants as of Dec 31

^st

, 2013)

eight interviews with key stakeholders

(cf. Kahl 2011)

2) A Regional Economic and Policy Snapshot of NRW

Source: Bross/Walter 2000, IT.NRW 2014; MWEBWV 2012

NRW is the largest German federal state

− 17.4 million inhabitants = approx. 22% of German total population

− Approx. 22% (4%) of German (EU-27) GDP (€ 599.8bn in 2014)

Pronounced socio-economic disparities between parts of Ruhr Area and non-Ruhr area

− Average GDP per capita in 2014: 33,621 € in NRW

− Considerable standard deviation: 9,158.22

− EU GDP per capita in 2013: 25,700 €

− Euro-Zone GDP per capita in 2013: 28,600

€

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0,0 10.000,0 20.000,0 30.000,0 40.000,0 50.000,0 60.000,0 70.000,0 80.000,0

Düsseldorf Bonn Münster Köln Gütersloh Essen Leverkursen Mülheim an der Ruhr Siegen-Wittgenstein Krefeld Olpe Minden-Lübbecke Duisburg Rhein-Kreis Neuss Bielefeld Mettmann Remscheid Dortmund Hagen Märkischer Kreis Borken Hochsauerlandkreis Paderborn Wuppertal Städteregion Aachen Oberbergischer Kreis Bochum Soest Mönchengladbach Gelsenkirchen Herford Warendorf Rhein-Erft-Kreis Ennepe-Ruhr-Kreis Steinfurt Unna Hamm Solingen Lippe Rhein-Sieg-Kreis Wesel Düren Coesfeld Kleve Viersen Höxter Oberhausen Euskirchen Recklinghausen Rheinisch-Bergischer Kreis Herne Heinsberg Bottrop

GDP per Capita in 53 NUTS-3 Regions in NRW (in €, 2014)

Status in 2000-2006 Funding Period Objective 2

Phasing-out

2) A Regional Economic and Policy Snapshot of NRW

adata retrieved from IT.NRW 2014; GDP in NUTS-3 (2014); Population in NUTS-3 (2011)

Source: Own calculations based on IT.NRW 2014

The Ruhr Area: A cluster in decline

− Old-industrialised region with 5.1 m inhabitants

− > 750.000 jobs lost in coal and steel industries since 1960 (cf. Schlieper 1986; Weber 1990)

− Slow economic restructuring (sclerotic structures):strong lobbying groups hampering restructuring (cf. Grabher 1993)

o Active structural change and diversification pursued since 1970s o Set-up of R&D infrastructure o Low absorptive capacity: R&D

personnel, expenditure and patent application (Hartmann 2007)

o Dominant large enterprises, R&D only in large firms

2) A Regional Economic and Policy Snapshot of NRW

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Policy responses focused on cushioning the impact of the dramatic industrial decline in the Ruhr Area

− Subsidiesto avoid mass unemployment

− Diversificationof regional economy and knowledge base

o New sectors (e.g. environmental protection industry) (Rehfeld 1995)

o Introduction of universities (first courses taught at RUB in 1965)

2) A Regional Economic and Policy Snapshot of NRW

Since 1989 ERDF funding focused on the Ruhr Area

− Over € 1,607 m ERDF funding between 1989-2006

− 26.2% to revitalisation of brown-field sites, 22.9% to science and innovation infrastructure, 12.1% productive investments (Ridder/Untiedt 2010)

Low performance in terms of narrowing regional disparities and innovation performance (low absorptive capacities)

3) Novel Policy Delivery System

Key challenge for regional policy in 2007-2013: Reconciling equity and growth

− Mobilisation of endogenous resources to promote economic restructuring in old-industrialized regions

− Fostering economic growth in highest performing areas

Elements

− New selection procedure: Contests for funding

− Territorially open approach: Target project consortia in highest performing regions in state-wide contests

− New actors: Universities and ‘regional actors’ (local public authorities)

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16 state-wide clusters serve as thematic intervention areas to channel ERDF money

− Structural framework for the cross-departmental coordination and delivery of cluster policy

o Ministry of Economic Affairs o Ministry of Innovation and Science

o Ministry of Climate Protection and Agriculture o Ministry of Health

Healthcare Medical Research

Food Logistics Automotive NanoMicro+Materials

Biotechnology Mech.Engineering

Plastics Environmental Tech.

Chemicals Energy Industry Energy Research

ICT Media Culture Industry

Source: Hausberg/Stahl-Rolf 2009

3) Novel Policy Delivery System: Two-Tier Approach

Growth: State-wide contests for the distribution of funds to leverage funding

Cohesion: Part of the RCE-programme is not allocated via state-wide funding contests (e.g. some infrastructure projects)

RCE-Operational programme 2007-2013:

€ 1.2bn € ERDF

Priority 1 Priority 2 Priority 3 Entrepreneurship Innovation in a

knowledge based society

Sustainable urban and regional development

20% of funding 50% of funding 30% of funding

Source: own illustration based on MWME 2006

Aim:Acquisition of 51%

of funds in lagging regions (i.e. Ruhr Area)

Commitment of 30% of funding to lagging regions under priority 3

≠ blanket funding, administrative areas Competitive allocation of funding

3) Novel Policy Delivery System: Governance

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Two-Stage Approach P

roject tender

Network formation

Concept proposal

Evaluation by juries of independent experts

Full application Funding decision

Interregional competition Cooperation

Funding criteria Financial

incentives

3) Novel Policy Delivery System: Selection Mechanism

Pros Cons

• Quality of selected projects

• Quality of selection procedure

• Self-organised division of labour: pretence of knowledge

(Hayek 1975)

⇒ discovery procedure

(Hayek 1968)

• Mobilisation of innovation potential

• Mobilisation effects among

“losers”

• Learning effect for policy and administration

• High administrative costs in terms of time and resources for applicants and public authorities

• Discouragements and discrimination of “losers”

• Demands high degree of flexibility from

administration

⇒ Quality of selection procedure is decisive!

Cf. Eickelpasch/Fritsch 2005: 1275-1279

4) Funding Contests: General Appraisal

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Source: Own Calculations based on MWEIMH-NRW 2015

4) RCE-Programme Performance

Regional Distribution of RCE-Funds (2007-2013)

Alongside parts of the Ruhr Area, RCE-funds cluster in NUTS-3 regions in the South of NRW (Aachen) (see also MS)

Low amount of funds in rural areas (corresponding to lower population density) No indication of marginalisation of less

prosperous regions

4) RCE-Programme Performance: Regions & Beneficiaries

NUTS-3 Region Total Funds (€)

No. Project

Participants Small Firms (in %)

Medium-Sized Firms (in

%) Large Firms (in %)

Universities & Research Organisations (in %)

Semi-Public Organisations (in %)

Düsseldorf 155.346.756 145 1.3 3.8 7.9 5.4 81.5

Duisburg 77.077.120 119 3.4 0.3 3.9 53.8 38.4

Essen 133.901.028 123 2.5 0.6 4.0 8.7 84.2

Krefeld 8.081.600 30 2.7 0.0 37.6 56.0 3.7

Mönchengladbach 28.691.762 16 12.3 1.1 0.0 0.0 86.6

Mühlheim an der Ruhr 22.164.286 25 0.0 60.3 0.5 7.0 32.2

Oberhausen 21.197.327 27 3.3 0.6 40.2 0.0 55.9

Remscheid 5.583.307 6 0.0 0.0 5.9 3.5 86.9

Solingen 10.751.184 31 8.8 18.1 0.0 5.9 67.3

Wuppertal 31.213.744 64 16.1 3.9 4.2 33.0 41.8

Kleve 1.420.905 11 29.0 0.5 13.9 20.4 36.3

Mettmann 7.377.403 34 31.8 18.0 21.0 12.3 16.9

Rhein-Kreis Neuss 3.380.046 21 11.3 8.1 30.1 6.7 43.9

Viersen 5.234.986 12 8.3 0.9 2.8 0.0 88.0

Wesel 21.340.994 38 1.2 39.9 0.8 1.3 56.8

Bonn 31.252.784 58 10.1 0.5 2.0 56.3 31.0

Köln 106.906.965 167 10.8 2.3 1.3 50.0 35.4

Leverkusen 10.136.493 15 0.0 1.3 37.7 14.2 46.8

Düren 59.684.097 75 14.0 0.5 3.1 79.4 2.6

Rhein-Erft-Kreis 17.431.435 16 12.2 4.9 3.4 0.0 79.4

Euskirchen 40.056.694 13 0.1 0.0 1.6 0.0 98.3

Heinsberg 4.176.353 14 18.9 40.3 40.8 0.0 0.0

Oberbergischer Kreis 20.152.941 15 3.4 0.0 2.3 3.9 90.4

Rheinisch-Bergischer Kreis 4.304.984 13 48.1 0.0 23.8 0.0 28.1

Rhein-Sieg-Kreis 59.113.156 67 4.4 0.2 2.8 59.0 33.3

Städteregion Aachen 207.821.421 401 9.5 4.4 9.1 58.2 18.2

Bottrop 5.989.792 17 1.8 2.0 0.0 0.0 96.2

Gelsenkirchen 47.723.206 70 2.5 0.0 1.2 14.0 81.9

Münster 57.473.432 102 7.1 2.0 0.3 66.6 24.0

Borken 9.080.677 43 5.9 18.0 5.2 0.0 70.9

Coesfeld 4.677.247 18 15.1 36.3 3.3 0.0 41.8

Recklinghausen 48.945.227 67 8.8 0.2 1.5 0.0 88.7

Steinfurt 8.484.678 39 1.8 3.2 14.4 14.0 61.9

Warendorf 2.384.642 10 2.0 32.2 0.0 0.0 65.9

Bielefeld 27.008.614 66 1.6 0.0 2.3 34.1 60.8

Gütersloh 4.295.839 28 10.5 47.7 25.0 0.0 16.8

Herford 366.744 9 0.0 63.6 0.0 0.0 36.4

Höxter 6.488.566 22 2.7 0.0 0.0 0.0 97.3

Lippe 11.008.323 41 4.2 5.4 5.9 33.2 51.4

Minden-Lübbecke 10.020.196 28 0.1 13.0 12.3 7.8 66.8

Paderborn 25.778.681 72 10.9 5.6 10.1 58.7 11.3

Bochum 94.224.612 153 8.0 4.5 1.2 50.0 36.2

Dortmund 112.141.660 192 13.5 3.5 3.9 29.5 49.0

Hagen 8.291.037 30 28.6 0.0 2.4 46.5 22.5

Hamm 27.878.493 21 0.0 0.8 2.4 5.2 91.7

Herne 12.555.912 16 6.2 1.0 1.4 0.0 91.4

Ennepe-Ruhr-Kreis 10.332.665 39 12.6 4.4 5.1 12.5 65.4

Hochsauerlandkreis 25.205.816 48 0.8 0.0 4.4 0.0 85.0

Märkischer Kreis 41.499.330 59 2.6 2.9 23.7 15.6 55.1

Olpe 20.343.477 16 3.0 1.6 2.3 0.0 93.2

Siegen-Wittgenstein 12.447.140 32 16.2 26.4 7.0 33.9 15.7

Soest 22.132.139 33 10.8 3.1 24.5 32.0 29.5

Unna 16.476.287 34 12.3 0.9 2.2 28.4 56.1

No. (Share) Of Projects 2861 16.9% 8.6% 9.9% 33.7% 30.8%

Total (Share of) Funds 1.767.054.203 7.0% 4.2% 5.8% 30.5% 51.5%

Highly varied performance across regions

Overwhelming share of semi-Public organisations and research institutions (51.5%, 30.5%)

Low participation of private sector

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4) RCE-Programme Performance: Competitive Selection

Share of Project Participants from Funding Contests (2007-2013)

Strong clustering of project participants in selected NUTS-3 regions within and outside of Ruhr Area (Growth-Objective)

4) Key Metrics of 25 Major Funding Contests

Coefficient of Variation (distr.

funds in NUTS-3)

No. of Project

Participants Funds (€) Cluster Contest High-Tech Focus

InnoMet.NRW 7.16 32 12,137,740 1 1

Create.NRW 4.06 23 6,181,901 1 0

Digitales Design.NRW 3.92 19 2,123,368 1 0

WissensWirtschaft.NRW 3.54 38 7,154,285 0 0

Medien.NRW 3.43 42 9,924,991 1 0

StandortInnenstadt.NRW 3.35 18 6,234,598 0 0

Permed.NRW 3.32 28 10,583,623 1 1

Energieforschung.NRW 3.14 47 13,190,891 1 1

Automotive.Production.NRW 2.88 185 52,373,211 1 0

Elektromobil.NRW 2.77 122 62,396,985 1 0

Ernährung.NRW 2.67 56 13,353,564 1 0

Hightech.NRW 2.62 166 117,355,403 0 1

Med in NRW 2.55 103 39,343,539 1 1

Nano+Mikro.NRW 2.53 100 87,956,426 1 1

CheK.NRW 2.45 102 37,224,565 1 0

Transfer.NRW 2.23 109 18,083,607 0 0

IKT.NRW 2.17 96 23,832,227 1 1

Familie@Unternehmen.NRW 2.15 33 3,867,694 0 0

Bio.NRW 2.14 54 31,566,550 1 1

Energie.NRW 2.10 127 41,863,411 1 0

RegioCluster.NRW 2.03 35 12,606,990 0 0

Logistik.NRW 1.96 120 28,978,014 0 0

Gründung.NRW 1.90 40 7,940,533 0 0

Ressource.NRW 1.80 92 40,282,946 1 0

Erlebnis.NRW 1.51 111 86,663,668 0 0

Total (25 Contests) 773,220,730

Others (3 Contests) 33,938,932

Linear regression model shows that cluster contests and high-tech focus are positive and significant predictors of the regional concentration funds within funding contests.

High-tech focus is positively and

significantly associated with the share of universities & research organisations.

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4) Beneficiaries of 25 Major Funding Contests

Contests

No. Project

Participants Small Firms (%)

Medium-sized

Firms (%) Large Firms (%)

Universities & Research

Organisations (%) Semi-Public (%)

InnoMet.NRW 32 6.49 12.87 6.57 73.09 0.00

Create.NRW 23 53.85 0.00 0.00 18.00 28.15

Digitales Design.NRW 19 83.58 3.83 0.00 12.59 0.00

WissensWirtschaft.NRW 38 22.63 5.55 11.30 59.64 0.88

Medien.NRW 42 41.95 4.40 5.78 12.71 33.09

StandortInnenstadt.NRW 18 3.77 0.00 0.00 0.00 96.23

Permed.NRW 28 13.98 0.00 0.00 72.36 13.68

Energieforschung.NRW 47 13.59 0.40 17.22 62.68 3.65

Automotive.Production.NR

W 185 17.92 17.94 18.61 44.41 0.22

Elektromobil.NRW 122 8.64 9.08 9.66 69.69 2.77

Ernährung.NRW 56 11.22 1.36 3.21 64.04 20.17

Hightech.NRW 166 11.11 4.18 15.73 67.94 0.91

Med in NRW 103 9.75 1.74 5.58 55.93 19.81

Nano+Mikro.NRW 100 11.34 4.15 11.49 71.48 1.54

CheK.NRW 102 7.82 4.70 8.20 71.98 7.30

Transfer.NRW 109 0.00 0.00 0.00 100.00 0.00

IKT.NRW 96 29.68 8.20 12.30 37.99 6.80

Familie@Unternehmen 33 15.13 18.09 7.78 16.71 42.29

Bio.NRW 54 14.51 2.61 2.89 67.46 12.52

Energie.NRW 127 5.43 14.45 27.88 45.59 6.60

RegioCluster.NRW 35 4.48 0.00 0.00 48.18 47.34

Logistik.NRW 120 13.07 6.12 12.57 57.44 10.79

Gründung.NRW 40 0.99 0.00 0.00 45.32 53.70

Ressource.NRW 92 16.34 26.00 42.97 12.59 1.66

Erlebnis.NRW 111 3.78 0.00 0.00 7.19 86.19

Share of Project Participants 19.10 8.70 13.70 42.40 16.20

Weighted Share of Funds 11.64 6.54 11.80 52.27 16.71

Compared to the complete RCE-

programme, universities and research organisations play an even more important role, whereas semi-public organisations are less important.

The share of funding gained by SMEsis rather low.

Prague, August 25^th, 2015 Source: BMBF 2015

4) Universities in NRW

Univ. of applied sciences University

Univ. of music and the arts Others

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Aachen Dortmund Köln Münster Bochum

Total Funds in 25 Major Contests (€) 154,730,990 54,849,144 45,769,668 41,405,504 40,788,772

in % of 25 major Contests 20.01% 7.09% 5.92% 5.35% 5.28%

Automotive.Production 37.68% 2.68% 2.66% 0.00% 2.62%

Bio.NRW 3.47% 16.57% 4.38% 0.80% 8.55%

Check.NRW 24.07% 3.65% 0.45% 6.81% 0.00%

Create.NRW 3.24% 0.00% 50.12% 0.00% 0.00%

Dig_Design.NRW 0.00% 0.00% 45.57% 0.00% 6.95%

Elektromobil.NRW 28.72% 15.81% 2.97% 18.84% 8.82%

Energie.NRW 18.54% 6.66% 6.44% 1.04% 10.74%

Energie.Forschung.NRW 38.61% 11.31% 11.46% 0.00% 4.79%

Erlebnis.NRW 0.44% 0.02% 1.51% 1.18% 0.42%

Ernährung.NRW 0.00% 12.20% 1.76% 10.34% 0.00%

Gründung.NRW 12.00% 10.87% 4.16% 16.18% 5.75%

Hightech.NRW 28.41% 3.04% 11.57% 0.52% 7.15%

IKT.NRW 19.70% 17.65% 3.50% 10.13% 10.68%

Logistik.NRW 6.84% 16.68% 3.53% 2.60% 3.48%

Med_in.NRW 23.91% 5.31% 1.77% 2.86% 11.85%

Nano.Mikro.NRW 18.57% 10.13% 7.89% 15.13% 2.69%

Ressource.NRW 8.59% 4.56% 0.81% 0.00% 1.01%

RegioCluster.NRW 5.95% 3.44% 1.48% 11.07% 0.00%

Permed.NRW 36.53% 12.30% 25.83% 0.00% 1.25%

WissensWirtschaft.NRW 45.02% 8.56% 0.00% 3.46% 17.95%

Innomet.NRW 95.68% 0.00% 0.00% 0.00% 0.00%

Transfer.NRW 20.78% 10.62% 7.28% 14.81% 13.43%

Familie@Unternehmen.NRW 5.27% 10.09% 5.00% 5.44% 0.00%

StandortInnenstadt.nRW 0.00% 0.00% 0.00% 0.00% 4.10%

Medien.NRW 0.00% 1.01% 30.11% 0.00% 16.61%

4) Leading Regions in Funding Contests

The five leading regions account for 43.6% of all funds allocated via the major funding contests.

Regions with major universities Aachenoutperforms even the leading

regions considerably.

4) RCE-Programme Performance: Regression Model

Regional Funding Intensity (Funds in NUTS3 / Population in NUTS3) Small Firms

Research Institutes Large Firms

Semi-Public Organisations

Population Density Research Grants

GDP per Capita Objective 2 Area

Share of projects allocated to the different beneficiaries at the NUTS-3 level

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4) RCE-Programme Performance: Regression Results

Regional Funding Intensity (Funds in NUTS3 / Population in NUTS3) (+) Research Institutes*

(+) Semi-Public Organisations*

(+) Research Grants***

4) RCE-Programme Performance: Regression Results

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4) RCE-Programme Performance: Regression Results

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Funding criteria and level of public policy interference explain distribution of funds

− Highly specific and demanding funding criteria: Top-down nature; hightech-bias

− Dispersed actors in non-universities localities may lack the necessary prerequisites

− Lower transaction costs in high-tech-regions with technologically specialized universities (existing spin-offs, regional networks)

4) RCE-Programme Performance: Stakeholder’s perspectives

Timing of contests: Explains concentration of funds

4) RCE-Programme Performance: Stakeholder’s Perspectives

Call: Tender for projects Building of consortia

Submitting project outlines

Jury‘s decision Approval stage Formal decision

3 months 6 months 6 months

Duration of the procedure: first-mover advantages lost

− Short period of time available for building consortia

− Rather than 6 months, the approval stage took 417 days on average

(Burkert et al. 2013b, p. 10)

− Main cause for delay: Complex assessment for funding eligibility, esp.

compatibility with EU laws on state aid

− Administrative complexity = strain on SMEs

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4) ERFD-RCE Funding and Administration Architecture in NRW

Source: Translated from Burkert et al. 2013a, p. 28

Recipients

Monitoring Committee (Begleitausschuss)

Leading personalities from ministries, parliament, regions, associations and universities

Objective 2 Office (Ziel 2-Sekretariat)

Quality Management

NRW.Bank

Disbursing Authority NRW.Bank

Legal Appraisal NRW Ministry of

Finance Administrative Authority

NRW Ministry for the Economy, Energy, Manufacturing, SMEs and Crafts

Ministerial Divisions (Fachreferate) Intermediaries

Executive Intermediaries Approving agencies, e.g. district governments

(Bezirksregierungen), NRW.Bank, etc.

Why so complex?

• Legal environment (esp. EU laws on state aid)

• Economics of bureaucracy

(Williamson, Niskanen; cf. Kiese/Wrobel 2011)

Specific features of funding contests and regional science infrastructure explain regional distribution of funds

− Structurally weak sub-regions are not systematically disadvantaged

− Cluster contests, High-Tech-Orientation, Research Institutes** = Regional concentration in regions with high-performing universities

− Target precision / participation: Share of private sectorlow, esp. SMEs

− Timingand complexityof funding contests

− Lack of existing networks of small firms (spin-offs), universities and semi- public organisations in non-university sub-regions (policy blind spot?)

5) Conclusion

High administrative burden and time-consuming procedure

Established consortia of universities and large firms advantaged Universitieswelcome ERDF as another source of third party funding Legal and administrative simplificationneeded to increase share of

SMEs and to allow for the formation of new network ties, but needs law of ever-expanding bureaucracyto be overcome

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Recommendations

− Accommodate regional diversity to facilitate participation (Tödtling/Trippl 2005)

− More inclusive approach, less restrictive criteria supporting process of local self-organisation

− More flexible timing of contests

− Fostering absorption capacities in lagging regions (universities?)

− High-tech Focus: Rethinking innovation: social aspects?

5) Conclusion

Discussion: Key driver of economic restructuring is the entrepreneurial discovery process

− Bottom-up, trial and error, experiment-based process: Pro-active involvement of entrepreneurial actors necessary

− Entrepreneurial actors may include universities and quasi-public institutions particularly in regions where industry structures and entrepreneurial capabilities are weak (Foray et al. 2012)

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ERDF Funding Contests –The Experience of North-Rhine Westphalia