1. “Assessing the impact of public funds on private R&D. A comparative analysis between state and regional subsidies ” Sergio Afcha and Jose Garcia-Quevedo, University of Barcelona

2.  Motivation: increasing importance of regional innovation policies  Objectives: evaluation of public policies  Data and descriptive statistics  Methodology and results  Conclusions and future lines of research

3.  In the last two decades increasing interest has been putting in the relationship between geographical location and innovation (Storper, 1997; Audretsch&Feldman, 1999).  Emergence of different concepts about industrial agglomeration: Clusters, Industrial districts, Innovative Millieu, competitive regions etc. Has contributed to the awareness of regional dimension is a suitable level to promote innovation activity.

4.  More active role of regional actors (Government, Institutions, firms)  Regional governments: specific regional policies in order to promote regional innovation systems.  However... There is few evidence about: Evaluation studies in innovation policy at regional level, and comparisons between regional and central government interventions

5. This work compares central and regional policies promoting R&D activities: I. Analyzing determinants (firms characteristics) of receiving subsidies, both at regional and central level. II. Quantifying the effect of central and regional R&D subsidies on firms’ Innovation effort (financial additionality).

6.  Data: Survey on Business Strategy, ESEE (Encuesta de estrategias empresariales/FUNEP).  Period: 1998-2005.  Sample: Innovative manufacturing firms. R&D >0.

7. Non subsidized firmsSubsidized firms at regional level Subsidized firms at central level Year 250 employees or less More than 250 employees 250 employees or less More than 250 employees 250 employees or less More than 250 employees 19981.591.003.412.384.702.19 19991.581.103.802.195.612.93 20001.950.853.611.314.871.64 20011.891.103.513.404.033.70 20021.500.924.192.405.282.96 20031.911.113.751.994.743.10 20041.590.992.032.703.794.14 20051.771.315.162.495.443.10 Total1.711.043.912.314.92 2.87 Innovative Effort

8. CentralRegional Public Subsidies state/regionalCoef.Std. Err.ZCoef.Std. Err.Z COOPERATION Joint Ventures 0.210.121.77*0.180.131.36 Coop. Univ. and Tech. Centers.0.410.104.05***0.500.114.38*** Coop. with Customers-0.110.09-1.230.020.100.26 Coop. With Competitors 0.180.131.32-0.230.15-1.37 Coop. with Providers0.230.092.43***-0.060.10-0.58 Part. EU projects0.720.272.60***-0.050.27-0.21 RRHH Recruitments of Univ. Graduates and Engineers0.230.092.42***0.330.103.10*** Recruits personnel with R&D experience. -0.080.08-0.96-0.090.09-0.98 INNOVATION Innov. New functions -0.000.10-0.070.050.100.54 Innov. New materials 0.11 1.00-0.050.12-0.42 Innov. New components 0.110.120.91-0.050.13-0.43 Innov. New design -0.110.11-1.050.150.111.36 Innovation Indicators 0.030.080.40-0.020.09-0.29 Tech. Export-Tech. Imports 50.74e-0740.60e-060.1240.12e-0750.99e-060.07 Total Nº of patents0.040.022.42***-0.010.02-0.63 Capital Participation in Innovative firms -0.060.11-0.540.150.121.23 PUBLIC FINANCES State/Regional subsidies0.580.115.20***0.580.105.57*** Others subsidies-0.050.22-0.230.260.221.20 Innovative effort t-1 0.020.011.86*0.0000.010.08 Public Subsidies t-1 10.480.0816.64***10.710.1017.03*** FIRMS CHARACTERISTICS Age 0.00080.0010.50-0.000.00-1.41 <250 emp. -0.260.10-2.54***0.180.101.69* Industry Med-High 0.240.092.70***-0.090.09-0.94 Nº of competitors 0.04 1.13-0.010.04-0.33 % Foreign Capital -0.0010.0009702-1.38-0.000.00-0.03 REGIONALS DUMMIES Catatonia 0.010.100.17 --- Madrid 0.180.121.49 --- Basque Country -0.040.13-0.36 --- Times dummiesIncluded Nºof Obs.=2213 LR chi2(38)=787.42 Prob>chi2=0.0000 Pseudo R2=0.41 LR chi2(38) =566.65 Prob> chi2=0.000 Pseudo R2=0.3669

9.  If coordination works, relevant variables should not be exactly the same in the Probit regressions.  Coincidence in significant variables could be indicating duplication of goals at different levels of government.

10. Central SubsidiesRegional Subsidies SignVariables COOPERATION Equal sign  Cooperative agreements with Universities and Technological centers (+)  Cooperative agreements with Universities and Technological centers (+) Significant in only one regression  Joint ventures (+)  Cooperation with providers (vertical cooperation) (+)  Participation in UE Projects (+) HUMAN RESOURCES Equal sign  Recent recruitment of University graduates and engineers (+) R&D Significant in only one regression  Total patents (+) FINANCIAL Significant in only one regression  Innovative Effort in t -1 (+) Equal sign  Subsidies from another level of government (regional)(+)  Central subsidies in t-1(+)  Subsidies from another level of government (central) (+)  Regional subsidies in t-1 (+) FIRMS CHARACTERISTICS Different sign  More than 250 employees (-)  Less than 250 employees (+) Significant in only one regression  Medium–High Tech. Industry (+)

11.  Non parametrical Technique: Propensity score Matching. Nearest neighbor matching algorithm.  Purpose: Establish a valid control group in order to compare innovative effort performed by subsidized and non subsidized firms.  Control Group: Innovative firms without subsidies.

12. CENTRALREGIONAL ATT 1 (Z-Value)0.56 (1.7)*0.66 (1.08) ATT 2 (Z-Value)0.87(3.59)***0.53(1.38)

13.  Although some common patterns, evidence of differences (firm characteristics) in the participation in regional and national innovation policy.  Crowding out effect is rejected for central subsidies. Evidence of additionality (Herrera&Heijs, 2007; Fernandez&Pazó, 2008;)  ATT for regional subsidies are not statistically significant.  Regional effects should be estimated by region in order to take in account regional differences.  Regional innovation policy: Need to analyse other additional effects (behavioral)