(2012), Herrera and Sánchez-Gonzáles (2012) (output additionality found for small firms, but not for medium-sized firms) and Foreman-Peck (2013). Two studies found insignificant treatment effects, those are Aerts and Czarnitzki (2004) and Cerulli and Potí (2008). Finally, a partial crowding out is reported in Marino and Parrota (2010). However, whether negative or positive, the programme effects are small. Catozzella and Vivarelli (2011) estimate the impact of public support on innovative productivity - the ratio of innovative sales to innovative expenditures - for Italy and report an average treatment on the treated (ATT) effect of -4.95 percentage points. Similarly, Garcia and Mohnen (2010) explore the impact of public support on both product innovation and innovative sales in Austrian firms. Their results vary depending on the source of funding: EU support has no effect; but central government support has a positive effect on both product innovation and innovative sales. Only one specifically focuses on output additionality in SMEs. That is the study by Foreman-Peck (2013), who uses the 2004 Community Innovation Survey dataset to investigate the impact of public support on technological innovations in UK SMEs using the Nearest Neighbour matching estimator. The results report a positive and significant treatment effect on SME innovation for both firms receiving R&D tax credits and those supported by non-tax public support. Interestingly, empirical findings suggest a differentiated additionality effect of R&D tax credits: almost 30% in medium sized firms; only 15% in small firms. For non-tax public support, the results are reversed; the ATT for small firms is twice that for medium-sized firms. Comparison between public policy evaluations is hampered not only by heterogeneous outcome variables but also by the lack of a common methodology. Best practice evaluation methodology is characterised by the use of a control or at least a comparison group as – – the platform to address potential endogeneity (Garcia-Quevedo, 2004; Lööf and Heshmati, 2007, p.83). To address the ubiquity of selection bias, most studies apply matching estimators (Gonzales and Pazo, 2008; Hussinger, 2008). The drawback of this method is that unobserved
(2012), Herrera and Sánchez-Gonzáles (2012) (output additionality found for small firms, but not for medium-sized firms) and Foreman-Peck (2013). Two studies found insignificant treatment effects, those are Aerts and Czarnitzki (2004) and Cerulli and Potí (2008). Finally, a partial crowding out is reported in Marino and Parrota (2010). However, whether negative or positive, the programme effects are small. Catozzella and Vivarelli (2011) estimate the impact of public support on innovative productivity - the ratio of innovative sales to innovative expenditures - for Italy and report an average treatment on the treated (ATT) effect of -4.95 percentage points. Similarly, Garcia and Mohnen (2010) explore the impact of public support on both product innovation and innovative sales in Austrian firms. Their results vary depending on the source of funding: EU support has no effect; but central government support has a positive effect on both product innovation and innovative sales. Only one specifically focuses on output additionality in SMEs. That is the study by Foreman-Peck (2013), who uses the 2004 Community Innovation Survey dataset to investigate the impact of public support on technological innovations in UK SMEs using the Nearest Neighbour matching estimator. The results report a positive and significant treatment effect on SME innovation for both firms receiving R&D tax credits and those supported by non-tax public support. Interestingly, empirical findings suggest a differentiated additionality effect of R&D tax credits: almost 30% in medium sized firms; only 15% in small firms. For non-tax public support, the results are reversed; the ATT for small firms is twice that for medium-sized firms. Comparison between public policy evaluations is hampered not only by heterogeneous outcome variables but also by the lack of a common methodology. Best practice evaluation methodology is characterised by the use of a control or – at least – a comparison group as the platform to address potential endogeneity (Garcia-Quevedo, 2004; Lööf and Heshmati, 2007, p.83). To address the ubiquity of selection bias, most studies apply matching estimators (Gonzales and Pazo, 2008; Hussinger, 2008). The drawback of this method is that unobserved
Radicic%20et%20al_2015_EPC_Accepted%20Manuscript%20for%20repository%20uploading.docx