Vienna | 23-26 August 2016
Congress of the European Regional Science Association: two interventions of CAPP researchers on counterfactual analysis: Annalisa Caloffi presents a paper, with Federica Rossi and Margherita Russo, on innovation policies; Francesco Pagliacci presents a paper, with Margherita Russo, on the appropriate unit of investigation to analyze the effects of a natural disaster
ERSA is shortly having its annual congress in Vienna, from 23-26 August. Some 750 presentations scheduled. for Young and Senior Researchers as well as renowned scholars from all over the world will be present in Vienna. Commonly recognized as a unique platform for Regional Science academics to come together, Vienna end of August, will surely be the time and venue for researchers to present their own research, exchange with their colleagues how academics results can provide society with efficient and innovative answers on societal issues in the field of regional science
View the final programme : ERSA_congress_book_Vienna
Special Session Thu_4_Room TC.3.09 S_J.
Can Policy Transform Regions into Entrepreneurship and Innovation Hubs?
Chair: Amnon Frenkel
Annalisa Caloffi, Federica Rossi, Margherita Russo
The behavioural additionality of innovation policy. Evidence from a regional small-business programme
The last twenty years have witnessed the diffusion of regional innovation policies that attempt to foster innovation by encouraging interactions between organisations with different knowledge and competencies. In particular, many of these regional policy interventions have been aimed at SMEs, encouraging them to interact with suitable partners in order to strengthen their skills, knowledge and creative abilities (Asheim and Nauwelaers, 2003; Tödtling and Trippl, 2005; Caloffi et al., 2015; Rossi et al., 2016).
The effectiveness of these policies should be assessed on the basis of many aspects. In particular, their evaluation should focus not only on the tangible outputs produced by the supported firms (such as the number of patents filed, or of new products launched), or on the increase in R&D expenditure that the public intervention stimulated, but also on their behavioural effects (Buisseret et al., 1995; Georghiou, 2002). This concept refers to the ability of a policy to stimulate learning processes that result in changes in the behaviour of participating organisations during and/or after the projects implementation. In the case of policies that support interactions between regional organisations, key behavioural effects would include improvements in these organisations ability to engage in cooperation and networking (Falk, 2004, 2007; Autio et al., 2008; Clarysse et al., 2009).
The paper aims to contribute to the debate on the assessment of behavioural additionality effects of policy by investigating the network additionality effects of interventions aimed at supporting regional innovation networks. The evaluation focuses on medium-term network additionality effects, intended both as an increase in the number of interactions that participants have engaged in, as well as a change in their relational patterns, which suggest that the policy has allowed the agents to break the circuits of cognitive lock-in that can occur in mature systems (Carlsson and Jacobsson, 1997). Some contributions have tried to perform this evaluation using descriptive techniques (Falk et al., 2006). We adopt instead a causal inference approach, by using the tools of the program evaluation (Abadie and Imbens, 2006).
We adopt this approach in the evaluation of a set of innovation policy interventions implemented in the Italian region of Tuscany between 2002 and 2008. By using propensity score matching techniques on an original database collected by the authors, covering the whole programming period of regional policies, we analyse the behavioural effects of a set of similar interventions (4 programs divided in 9 waves, participated by 1,621 firms) funding collaborative R&D projects implemented by networks of heterogeneous agents (Russo and Rossi, 2009; Bellandi and Caloffi, 2010; Caloffi et al, 2014). The Tuscany region is characterized by several traditional industrial districts that have undergone a period of crisis, as well as by some clusters of advanced competencies in innovative sectors. The application of the network additionality framework to the evaluation of this type of policy allows us to unravel the different types of systemic effects generated by the policy interventions, and to verify the impact that they have produced on the different types of organisations involved in them.
Keywords: Behavioural additionality, networking additionality, innovation policy, policy evaluation, innovation networks.
JEL codes: O38, O32, D04
Special Session Wed_2_Room TC.3.09 S_S.
Counterfactual methods for regional policy evaluation
Francesco Pagliacci, Margherita Russo
Socio-economic effects of an earthquake: Does sub-regional counterfactual sampling matter in estimates? An empirical test on the Emilia-Romagna's 2012 earthquake
Estimates of macroeconomic effects of natural disaster have a long tradition in economic literature (Albala-Bertrand, 1993a; 1993b; Tol and Leek, 1999; Chang and Okuyama, 2004; Benson and Clay, 2004; Strömberg, 2007; UNISDR, 2009; Cuaresma, 2009; Cavallo and Noy, 2009; Cavallo et al., 2010; The United Nations and The World Bank, 2010). After the seminal contribution of Abadie et al. (2010) in identifying synthetic control groups, with DuPont and Noy (2012) a new strand has been opened in estimating long term effects of natural disaster at a sub-regional scale, at which the Japan case provides plenty of significant economic variables. Although, the same methodology has been applied in estimating the impact of earthquakes in Italy (Barone et al. 2013; Barone and Mocetti, 2014), the analysis has been limited to the regional scale. In our paper we provide a test bed for assessing the relevance of a sub-regional counterfactual evaluation of a natural disasters impact. By taking the Emilia-Romagna's 2012 earthquake as a case study, we propose a comprehensive framework to answer some critical questions arising in such analysis. Firstly, we address the problem of identifying the proper boundaries of the area affected by an earthquake. Secondly, through a cluster analysis we show the importance of intra area differences in terms of their socio-economic features. Thirdly, the identified clusters are adopted to perform a counterfactual analysis based on a pre- and post-earthquake difference-in-difference comparison of average data in clusters within and outside the affected area. Eventually, three frames to apply propensity score matching at municipality level are adopted, by taking the control group of municipalities (outside the affected area): within the same cluster (a), within the same region (b), in all the country (c). The four variables considered in the counterfactual analysis are: total population; foreigner population; total employment in manufacturing local units; employment in small and medium-sized manufacturing local units (0 to 49 employees). All the counterfactual tests largely show a similar result: socio-economic effects are heterogeneous across the affected area, where some clusters of municipalities perform better, in terms of increase of population and employment after the earthquake, against some others. This result sharply contrasts with the average results we observe by comparing the whole affected area with the non-affected one or with the entire region.
Keywords: cluster analysis, counterfactual analysis, Emilia-Romagna
JEL codes: C38, R11, R58