Download - Innovation and growth in the lagging regions of Europe: R ... · Observations 1,451 1,451 995 995 R-squared 0.669 0.751 0.806 0.826 Number of NUTS2 176 176 170 170 Estimated as fixed

Innovation and Growth in the Lagging Regions of Europe

Neil Lee

London School of Economics

[email protected]

Introduction

• Innovation seen as vital for growth in Europe (Europa 2020) – Economic growth – Narrowing disparities

• Innovation highly uneven geographically

• “Lagging regions” may find it harder to translate inputs into

innovation

• Does the impact of innovation on economic growth differ in different regions?

• Is innovation a viable strategy for lagging regions?

Growth theories and innovation

• Neo-classical model

• Endogenous growth theory

• New Economic Geography

• Evolutionary economics

Theoretical work on innovation

• Camagni (1995) suggested that lagging regions lacked the ‘innovative milieu’ which helps create innovation

• Rodriguez-Pose (various) innovation ‘prone’ and averse societies

– ‘Social filter’ – conditions innovation / growth relationship

Empirical evidence (1)

• Crescenzi (2005)

– European regions, 1990 – 2003

– Innovation (index) impact different according to regional conditions (peripherality / education)

• Sterlacchini (2008)

– European regions, 1995 – 2002

– Innovation (R&D) only impacts on growth beyond threshold GDP

– Impact differs Northern / Southern Europe

• Antonelli et al. (2011)

– Total factor productivity (TFP) in European regions

– Show an ‘inverted u-shaped’

Empirical evidence (2)

• Firm level evidence

– Frenkel (2000) local environments important and access to innovative inputs important in helping firms succeed

– Beugelsdijk (2007) firm level factors more important than local context

– Lee and Rodriguez-Pose (under review) context can matter - ‘creative’ employment related to innovation activity in local firms

Gaps

• Evidence often dated e.g. Sterlacchini (2008) considers 1995 – 2002

• Much more evidence on drivers of innovation (e.g. Crescenzi and Rodríguez-Pose, 2013) than link innovation / growth

• Mostly focused on cross-sectional evidence

The data

• NUTS2 regions

• Eurostat Regio database

• Countries only included if 7 + regions (to allow calculation of ‘lagging’)

• Sample: unbalanced panel of 191 regions, 12 countries

Defining “lagging regions”

• Lagging regions –

– Less than 75% of national average GDP in the based year (1998)

– 52 lagging regions, from 191 (27%)

• Alternative measures –

– Structural funds

– Peripherality

Sample of regions by country

Country Not-lagging Lagging Total

Belgium 8 (73) 3 (27) 11

Czech Republic 6 (75) 2 (25) 8

Germany 25 (73) 9 (27) 34

Greece 9 (69) 4 (31) 13

Spain 14 (73) 5 (27) 19

France 16 (73) 6 (27) 22

Netherlands 9 (75) 3 (25) 12

Poland 11 (69) 5 (31) 16

Portugal 5 (71) 2 (29) 7

Romania 6 (75) 2 (25) 8

Sweden 6 (75) 2 (25) 8

UK 24 (73) 9 (27) 33

Total 139 (73) 52 (27) 191

Parenthesis give share of regions by country.

Defining innovation

• Patenting – Accounts for ‘technological frontier’ – But biased by sector – Only 6 percent of innovation active firms patent (Hall et al. 2013) – Variable: Total patents (linear interpolation)

• Research and development. – An input rather than an output – Biased by sector – Also can be used to ‘assimilate’ innovations from elsewhere – Variable: Total R&D expenditure (log) [not per GDP/ capita]

• Missing values filled with linear interpolation

The model

Yit = α + β1 Innovationit + β2 Employmentit + β3 HighSkillit +

β4 Manufacturingit + β5 Youthit + εit

• GPD pc – GDP per capita, natural log • Employment - Total employment (1,000s), natural log

• Manufacturing - Manufacturing employment as % of total

• Skilled workers - Share of those in employment with first and second

stage of tertiary education (

• Young workers - Share of labour force aged 15 - 24, natural log

Estimated as fixed effects panel regression.

Does innovation have different effects in lagging regions?

Panel estimation: Patenting & regional growth (1) (2) (3) (4) (5)

Dependent variable: GDP per capita, natural log

Patents per capita (ln) 0.0841*** 0.0493*** 0.0396*** 0.0558***

(0.0158) (0.0122) (0.0134) (0.0182)

Patents per capita (ln) * Non-lagging

0.0939***

(0.0225)

Patents per capita (ln) * Lagging

0.0664***

(0.0183)

Total employment (ln) -0.106 -0.255 -0.0553

(0.129) (0.317) (0.124)

Manufacturing (%) 0.0115* 0.0149** 0.0105

(0.00680) (0.00678) (0.00885)

Skilled workers (%) 0.242*** 0.322** 0.224**

(0.0728) (0.149) (0.0919)

Young workers (%) -0.379*** -0.275** -0.405***

(0.0593) (0.118) (0.0728) Constant 9.295*** 9.280*** 9.775*** 10.49*** 9.097***

(0.0655) (0.0740) (0.837) (2.066) (0.815)

Sample Full Full Full Lagging Non-lagging

Observations 1,451 1,451 1,451 398 1,053

R-squared 0.669 0.670 0.751 0.756 0.753

Number of NUTS2 176 176 176 46 130

Estimated as fixed effects panel regressions. All regressions include year dummies. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Panel estimation: R&D & regional growth (1) (2) (3) (4) (5)

Dependent variable: GDP per capita, natural log

R&D (ln) 0.229*** 0.173***

(0.0473) (0.0423)

R&D (ln) * Non-lagging

0.248*** 0.248*** 0.188***

(0.0535) (0.0535) (0.0481)

R&D (ln) * Lagging

0.195*** 0.195*** 0.147**

(0.0652) (0.0652) (0.0599)

Total employment (ln) -0.0741 -0.0744

(0.118) (0.118)

Manufacturing (%) 0.0139** 0.0138**

(0.00622) (0.00603)

Skilled workers (%) 0.171** 0.176**

(0.0724) (0.0713)

Young workers (%) -0.307*** -0.301***

(0.0573) (0.0568) Constant 8.493*** 8.427*** 8.808*** 8.427*** 8.603***

(0.241) (0.237) (0.694) (0.237) (0.692)

Sample Full Full Full Full

Observations 1,013 1,013 1,013 1,013 1,013

R-squared 0.745 0.747 0.783 0.747 0.784

Number of NUTS2 173 173 173 173 173


Removing ‘convergence’ countries (1) (2)

GDPPC GDPPC


0.0103*

(0.00605)


0.0159

(0.0113)


0.0255*

(0.0148)

R&D (ln) * Lagging

0.0417

(0.0276)

Constant 7.269*** 7.085***

(0.323) (0.365)

Controls Yes Yes

Observations 1,239 807

R-squared 0.878 0.877

Number of NUTS2 144 138 Estimated as fixed effects panel regressions. All regressions include year dummies and full controls. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Removing ‘convergence’ countries (1) (2)

GDPPC GDPPC


0.0103*

(0.00605)


0.0159

(0.0113)


0.0255*

(0.0148)

R&D (ln) * Lagging

0.0417

(0.0276)

Constant 7.269*** 7.085***

(0.323) (0.365)

Controls Yes Yes

Observations 1,239 807

R-squared 0.878 0.877

Number of NUTS2 144 138

Impact larger in non-lagging regions Impact in lagging regions larger, but higher standard errors

Estimated as fixed effects panel regressions. All regressions include year dummies and full controls. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Diminishing returns to innovation?

Diminishing returns to innovation (1) (2) (3) (4)

GDP per capita, natural log Patents per capita (ln) 0.0827*** 0.0520*** (0.0193) (0.0144) Patents per capita (ln)2 0.000823 -0.00171 (0.00380) (0.00317) R&D 0.610*** 0.517*** (0.0656) (0.0661) R&D2 -0.0465*** -0.0408*** (0.00673) (0.00620)

Controls No Yes No Yes Constant 9.640*** 9.733*** 8.097*** 7.289*** (0.0689) (0.818) (0.208) (0.577) Observations 1,451 1,451 995 995 R-squared 0.669 0.751 0.806 0.826 Number of NUTS2 176 176 170 170






Patenting is associated with growth, but little evidence of non-linearities





Strong evidence of diminishing returns to R&D

Link between R&D and patents

Impact of R&D on patenting (1)

Patents (ln)

R&D (ln) – Not lagging

0.312* (0.167)

R&D (ln) – Lagging 0.412*** (0.121)

Constant 2.040*** (0.717)

Obs 995

NUTS2 170

R2 0.4805


Some conclusions (1)

• Innovation has less of an impact on growth in lagging regions

• This applies for both an input (R&D) and an output (patents)

• However, difference is marginal - impact is still positive in (most) lagging regions

• But impact may be more variable


• Evidence of diminishing returns of R&D – Input into innovation, rather than output – But would be expected to help absorb innovation from elsewhere

• Less evidence of patenting – Output measure of innovation – Patents indicate position on frontier

• Quantile regression evidence (not shown) suggests even impact

• Tentative evidence R&D may lead to innovations in lagging regions (but no controls for ‘social filter’)


• Does not imply innovation has no impact on economic growth in lagging regions

• Actually, if value growth more in lagging regions trade-offs may be finer

• Highlights the equity versus efficiency trade-off

Some limitations

• Lagging regions variable – endogeneous?

• Lack of controls for convergence – GMM-SYS / removing eastern europe

• Interpolated values – but checks show unimportant