1. Network analysis as a method of evaluating support of enterprise networks in ERDF projects Tamás Lahdelma (Urban Research TA, Finland) tamas.lahdelma@kaupunkitutkimusta.fi

2. A pilot study on the impacts of networking and clustering projects funded by the European Regional Development Fund (ERDF). The study is a part of the ongoing evaluation of the innovation and networking theme of ERDF programmes. The main objective is to develop methods for the impact evaluation of networking projects. Empirical study combining network analysis and statistical methods. All results are preliminary and strong conclusions cannot be drawn yet. Background I

3. Support to enterprise networks has an important role in the strategies and implementation of ERDF programmes. Networking and clustering projects are typically organized by regional business development organizations, regional councils or local research organizations. Their objectives vary from strictly focused R&D themes to very generally defined business development themes. So far, there is only weak evidence on the effects of networking projects on the competitiveness and business success of participating enterprises as well as on regional development. Background II

4. The present monitoring systems do not include indicators on networking. Earlier evaluations haven’t been able to show causal links between inputs and results. A simple correlation analysis between the growth of enterprises and the receiving of public subsidies can be misleading about the actual effects of the support. Econometric studies of public support to enterprises do not consider the effects of networks. – Econometric models are based on the assumption of independent firms reacting only to consumer demand. Background III

5. Data Analysis of selected networking projects in Central Finland region financed from the ERDF programme of Western Finland during 2008- 2011. Networks were mapped according to the project data base of the programme authority. Firm-level data on network activity was collected by a web-based survey. – Contains data on cooperation relations with all the other firms in the project. – Also the firms’ assessments on the benefits of the project. Survey data is linked with firm-level business indicators. – Turnover, personnel, profit; level & growth, years 2006-2010. – Based on annual balance sheets of firms (must be delivered to public authorities). – Data gathered from publicly available data bases.

6. Firms participating in ERDF projects compared to a reference group Selected business indicators before and during/after the project were compared between the firms participating in the projects and a reference group (all SMEs of manufacturing and business services in Central Finland region). It was found that firms participating in the projects are faster growing, more productive and more profitable than the firms of the reference group on average, both before the project and during/after the project. This indicates that the projects attract more successful firms but does not prove that participating in networking projects has an effect on growth rate, productivity or profit.

7. Identifying the network position of firms I Centrality – two different structural approaches. – Degree centrality: a firm is central to the extent that it is connected to connected others in the network. – Betweenness centrality: a firm is in a central position to the extent that it falls on the paths between all other pairs of actors in the network. Brokerage – focus on the roles that firms play in connecting groups based on industry. Is a firm acting as a mediator in relations among groups? – Brokering within the same group as the firm itself. – Brokering a relation between two members of the same group, but the firm is not itself a member of that group. – A firm is situated on the boundaries of two groups. – Brokering a relation between two groups, and is not part of either.

8. Identifying cohesive sub-groups within the network. – Attuned to multiple group membership. “Entrepreneurship in the network context is driven by the intersection of cohesive groups where actors have familiar access to diverse resources.” Identifying the network position of firms II

9.  = Metal & machinery industry  = Wood & other industry  = Business services Position of firms in the project network

10. Testing the statistical significance of network positions Ordinary Least Square regression was used to study whether there are network characteristics that can explain employment or productivity growth or firm’s assessment on the benefits from participating in the project. – Productivity growth was measured as the growth of gross profit margin (turnover minus purchase of inputs) per employee. – The level of productivity during the whole time frame, including the years prior to the project. – Employment growth was measured by the relative change in the number of the firm’s employees. – The achievement of targets related to the project. Firm size, time of entering the project and branch of industry as controlling variables. The level of productivity prior to the project is included as a controlling variable in the model explaining productivity growth.

11. Key findings I Betweenness centrality predicts significantly higher productivity growth. – A positional advantage in terms of information flow. – Centrality in the sense of being well connected to other firms does not predict productivity growth, but firms occupying a mediating position between other firms show significantly higher productivity growth rates. High productivity prior to the project predicts significantly slower productivity growth (declining marginal productivity). Betweenness centrality doesn’t explain the level of productivity during the whole period. – Firms with multiple group membership have significantly higher productivity.

12. Key findings II Firms occupying mediating positions among groups based on industry have a more positive view on the benefits of the project. Betweenness centrality predicts finding new customers. Firms whose branch of industry is “wood and other industry” are dissatisfied with the achievements of targets related to development activities.

13. Conclusions I The results are preliminary and they should be interpreted with caution as there are a number of limitations to the generalizability of our findings. More extensive data is needed to make broader conclusions. Adding a temporal dimension to our analysis would make it possible to study network formation from its inception. Preliminary results indicate that network characteristics can predict firm level business indicators and firms’ assessments on the benefits of participating in the projects. The study also shows that the cost effective gathering of data representing the cooperation relations of enterprises can be challenging.

14. Conclusions II Data on networks makes it possible to identify patterns that are not visible to the participants on the ground and allows rigorous impact evaluation. The greater emphasis on innovations and knowledge in the next programming period requires also methods to monitor and evaluate results. More emphasis should be put on results and impacts of Structural Funds. This requires a research-oriented approach and the development of data gathering methods.

15. Conclusions III According to the Comission’s note on monitoring and evaluation each project could be required to have one or more outcome indicators. Data on networks could serve as one of the project-level outcome indicators for projects emphasising networking. The cost-effective gathering of data on networks would be possible as part of a specific business survey. Network analysis extends the range of evaluation methods.