The Effectiveness of Regional, National and EU Support for Innovation

in the UK and Spain

Bettina Becker, ERC and Aston Business School Stephen Roper, ERC and Warwick Business School

Jim Love, ERC and Warwick Business School

ERC Research Paper No. 52, 2017

@ERC_UK

• Innovation and R&D increase growth and productivity at the firm, industry and country level (supported by large body of work)

• Social and private benefits (e.g. Mohnen, 1996; Ceh, 2009)

• Classic public goods problem means innovative firms are unable to appropriate the full value of these benefits

• Hence market failure justification for corrective public interventions (e.g. Arrow, 1962; Rigby & Ramlogan, 2013)

• UK: Government Green Paper on Industrial Strategy (January 2017)

Why support innovation?

• Labour market: Innovation in modern production processes can lead to greater inequality in opportunity and earnings (‘Second Machine Age’ by Brynjlofsson & McAfee)

• Support allocation mechanisms: Public innovation support, especially at national level, is often awarded through competitions

→ tends to reward the best projects and best firms → may increase gap in firms’ competitiveness and productivity (OECD, 2015; Czarnitzki & Ebersberger, 2010)

• ‘Distribution sensitive innovation policies’ could include regional support targeted at under-developed regions (Zehavi & Breznitz, 2017)

Though is innovation necessarily ‘a good thing’?

1. Examine simultaneously the effects of firms’ receipt of policy support from regional, national and EU sources

2. Consider policy effects at both the extensive and intensive margin, i.e. % of innovating firms and % of innovative sales

3. Provide insights into the innovation policy effects of two very different innovation support regimes: UK & Spain

4. Consider sample of innovators & non-innovators, thus avoiding selection bias; innovators-only as robustness check

Contribution

Contrasting institutional and policy structures → disparities in firms’ behaviour and performance (Royo, 2007; Hall & Soskice, 2001):• Public intervention in innovation more intensive in Spain than in the

UK (e.g. Mate-Sanchez-Val & Harris, 2014) Total R&D investment (% of GDP)

Source: OECD Science and Technology Indicators database

Research context: UK and Spain −Public sector vs market influences

Business investment in R&D (% of GDP)

Source: OECD Science and Technology Indicators database

Research context: UK and SpainPublic sector vs market influences

Government funding of business R&D (% of GDP)

Source: OECD Science and Technology Indicators database

Research context: UK and Spain −Public sector vs market influences

• UK: - Liberal market economy (Hassel, 2014) - IP either corrective, i.e. designed to address market

failures, or creative, i.e. designed to enable leading-edge innovation

Spain : - Mixed market economy (Molina & Rhodes, 2007) - IP either compensatory, i.e. offsetting competitive or

financial shocks, or creative

• Support is more regional in Spain and more national in the UK (especially since abolition of RDAs in 2010)

• Regulation facing firms is more intensive in Spain than in the UK

Research context: UK and Spain –Nature of economy, Regional vs national, Regulation

• UK and Spain contributions to EU Community Innovation Survey: UKIS and PITEC

• UKIS conducted every two years, PITEC annually, each with 3-year reference period

• Both apply the definitions and type of questions defined in the OECD Oslo Manual (2005)

• Sample period (panel, matching waves): 2004-2012

• Over 35,000 company returns in the UK and over 52,000 in Spain

Data

Innovation and policy variables  UK (N>36,706) Spain (N>41,072)

Mean Std. Dev. Mean Std. Dev.Innovation indicators Product or service innovation (0/1) 0.308 0.462 0.482 0.500Process innovation (0/1) 0.196 0.397 0.382 0.486Organisational innovation (0/1) 0.214 0.410 0.154 0.361Strategic innovation (0/1) 0.210 0.407 0.350 0.477Management innovation (0/1) 0.201 0.401 0.341 0.474Marketing innovation (0/1) 0.227 0.419 0.255 0.436Novelty of produce/service innovation indicator

New to market product or service innovation (0/1) 0.250 0.433 0.580 0.494Innovation market success indicators

% of innovative sales - new products  5.615 15.946 8.045 20.876% of innovative sales - new and improved products 9.641 22.695 19.259 32.908Policy support measures     Regional or local innovation support (0/1) 0.059 0.235 0.194 0.395National innovation support (0/1) 0.050 0.218 0.183 0.387EU innovation support (0/1) 0.017 0.128 0.051 0.221

1) Firm has in-house R&D capability (binary indicator) (Love & Roper, 2001 & 2005; Griffith, Redding & Van Reenen, 2003)

2) Firms’ innovation-related investments (in design, external R&D, training, external knowledge acquisition, market intelligence, machinery)

3) Employment: scale of plants’ resources

4) Strength of human capital (% of graduates in employment) (Leiponen, 2005; Freel, 2005; Hewitt-Dundas, 2006)

5) Exporter: market scale effects (binary indicator) (e.g. Love & Roper, 2013)

6) Extent or breadth of firms’ innovation co-operation: interactive knowledge search (count indicator: 0-7) (Laursen & Salter, 2006; Moon, 2011)

Control variables

• Binary or truncated nature of our dependent variables

• Multiple (binary) treatments potentially subject to selection bias

→ Two-stage approach:

1. Probability of receipt of regional, national or EU innovation support () (Aerts & Schmidt, 2008; Czarnitzki & Lopes-Bento, 2004):

– : Firms’ identifiable characteristics– : Firms’ demand for public support– : Availability of public support in each industry, region and sizeband

Estimation strategy

2. Standard innovation production function () (Leiponen & Byma, 2009; Leiponen, 2012):

– : Firm level control variables– : Firms’ R&D spending– : Firms’ breadth of innovation cooperation– : Quality of firms’ human capital– : Firms’ receipt of regional, national, EU support– 2-digit industry & time specific effects

→ Conditional mixed process (CMP) approach (Roodman, 2011)

Estimation strategy

Notes: +/- indicate statistically significant effect, *** p<0.01, ** p<0.05, * p<0.1. Parentheses indicate effect is not statistically significant

Probability of receiving innovation support:Overview of stage 1 results

  UK Spain

  Regional National EU Regional National EU

Log(employment) (+) + *** (+) + *** + *** + ***

Science & eng. grad. (%) + *** + *** + ***

Other graduates (%) (+) + * (+)

Superior educ. grad. (%) + *** + *** + ***

Exporting firm (0/1) + *** + *** + *** + *** + *** + ***

Economic risk barrier + *** (+) (+)

Innovation cost barrier + *** + *** (+) + *** (+) (-)

Cost of finance barrier (+) (-) (-) + *** + *** + ***

Availability  of  finance barr.

+ *** + *** + ** + *** + *** + **

Uncertain demand barrier (+) + *** + ** + *** + *** + ***

Penetration rate - regional + *** + ***

Penetration rate - national + *** + ***

Penetration rate - EU + *** + ***

Notes: +/- indicate statistically significant effect, *** p<0.01, ** p<0.05, * p<0.1. Parentheses indicate effect is not statistically significant

Effectiveness of innovation policy support:Overview of stage 2 results

  UK Spain

  Regional National EU Regional National EU

Probability of innovation

Product/service (+) + *** (-) + ** + *** (+)

Process + * (+) - ** (+) (+) (-)

Organisational + *** (-) (-) + *** (+) (+)

Strategic + *** (-) (+) (+) + * + ***

Managerial + *** (-) (-) + *** (+) + ***

Marketing  + * (-) - *** + *** (-) (+)

Novelty of innovation New-to-the-market innovation (+) + *** (-) - ** + ** + ***

Innovation market success

New only + *** (+) (-) (-) + *** + ***

New and improved + *** (+) (-) + ** + *** (+)

1. Regional support most influential for the probability of innovation for process change and organisational innovation types

2. National innovation support is associated with a higher probability of product or service innovation

3. In the UK only national support is important in increasing the novelty of product/service innovation. In Spain also EU support.

4. In the UK only regional support is associated with increased innovative sales. In Spain, innovative sales are influenced by regional, national and EU support measures.

Key findings

• Centralisation of delivery of innovation policy since 2012 is likely to:→Strengthen the focus on leading-edge, novel produce/service

innovation→May increase competitiveness and productivity of the best firms→May potentially also increase the gap in competitiveness between

high-productivity and low-productivity firms (OECD, 2015; Zehavi & Breznitz, 2017)

→Reduce availability of regional innovation support, which may weaken the support for broadly based innovation in process and organisations

• EU support had little impact on innovation

Policy implications UK

Thank you!

Work in progress: Comments welcome.

Bettina Becker, b.becker@aston.ac.ukStephen Roper, stephen.roper@wbs.ac.uk

Jim Love, jim.love@wbs.ac.uk

Control Variables

  UK (N>36,706) Spain (N>41,072)Mean Std. Dev. Mean Std. Dev.

In-house R&D (0/1) 0.327 0.469 0.497 0.500Design spend (0/1) 0.207 0.405 0.096 0.294External R&D (0/1) 0.127 0.333 0.246 0.431Training spend (0/1) 0.334 0.472 0.162 0.369Acquisition of external knowledge (0/1) 0.129 0.336 0.040 0.197Acquisition of market intelligence (0/1) 0.323 0.468 0.187 0.390Machinery spend (0/1) 0.473 0.499 0.191 0.393Log (employment) 3.788 1.798 4.140 1.711Science and engineering graduates (%) 6.129 15.635   Other graduates (%) 8.166 17.143   Superior education graduates (%)   26.284 28.995Exporting firm (0/1) 0.342 0.474 0.583 0.493Number of innovation partners (0-7) 0.799 1.669 0.935 1.587

• Competitive allocation mechanisms for public innovation support may influence the extent of additionality or social benefit:

– Uniform distribution of strong firms / projects across regions: → National & regional schemes likely available to the same pool of companies

– Uneven distribution of strong firms / projects across regions: → Competition for support likely stronger in some regions than others, hence potential misallocation of regional support

Benefits of support:Allocation mechanisms

• A: Firms’ private optimum without government support• B: Firms’ private optimum with national, region-specific, government support: GA > GB • C: Firms’ private optimum with regional, average-level, government support: GA > G > GB • MPB: marginal private benefit; MCC: marginal cost of capital; MSB: marginal social benefit (Haapanaen, Lenihan & Mariani, 2014)

Benefits of support:Equity considerations

MPBA

MSBA MCCA

MCCA-GA

MCCA-GMPBB

MSBB

MCCB

MCCB-GB

MCCB-G

Region A: High social benefit Region B: Low social benefit

AA

C

CB

B

• ‘Real world’: Firms within each region will differ markedly in terms of potential social benefits of their R&D

→ e.g. absorptive capacity (e.g. Hewitt-Dundas & Roper, 2011; Cornett, 2009; Becker, 2015; Roper & Love, 2006)

• “The comparatively greater need to spend on innovation in lagging regions and their relatively lower capacity to absorb public funds earmarked for the promotion of innovation” (Oughton, Landabaso & Morgan, 2002)

→ EU Structural Funds, targeted national support initiatives

Benefits of support:Regional innovation paradox