ABSTRACT
This paper investigates whether territorial characteristics and, in particular, regional administrative capacity influence the effects of European Union (EU) Cohesion Policy support to firms. A novel two-step methodology is applied. First, the effects of Cohesion Policy on employment growth of supported manufacturing firms are estimated separately for the regions of six different EU countries. Second, potential territorial factors influencing these effects are explored using meta-analysis techniques. The empirical results point to a significant relationship between firm-level policy effects and territorial capital, especially mixed-materiality assets, as well as administrative capacity as proxied by citizen engagement and administrative efficiency.
ACKNOWLEDGEMENTS
The authors thank the special issue editors and three anonymous referees for their comments. The participants at conferences and seminars, including the 58th ERSA Congress, Cork, Ireland, 2018, VI Regional Modelling Workshop, Seville, Spain, 2018, RSA Winter Conference, London, UK, 2019, and CPNET workshop on administrative capacity, Delft, the Netherlands, 2019, provided valuable inputs to earlier versions of this work. The authors bear sole responsibility for any error or omission.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
1. To ensure comparability and robustness of the ATTs, we set a threshold for the minimum number of treated firms per region. For each of the 62 regions included in the analysis, information is available on at least 200 firms that received support in the period 2007–13. These regions are all at the NUTS-2 level, except for France, where NUTS-1 regions are used instead to maintain the threshold for treated firms. Firms with consolidated accounts reported in the AMADEUS database are not considered in the estimation. Bachtrögler et al. (Citation2020) has more details on data availability and the methodological approach.
2. Estimating the effects of CP support on the growth of value added of supported firms as another indicator of firm performance leads to similar results (Bachtrögler et al., Citation2020).
3. The common support restriction applies, that is, all treated observations are matched with at least one untreated observation. The distribution of the propensity score was tested to check the homogeneity of the distribution of treated and untreated observations.
4. NUTS-1 for France, as explained in note 1.
5. The estimation results are robust when considering statistical significance at the 95% level.
6. For a detailed discussion of the elements of territorial capital, see Camagni (Citation2009).
7. Based on data availability and excluding institutional quality, seven out of nine combinations of rivalry and materiality are covered in the dataset; the only missing combination is an indicator for mixed materiality and mixed rivalry (cooperation networks in Camagni, Citation2009).
8. For reasons of space, see Fratesi and Perucca (Citation2014) for a detailed discussion of empirical measures of territorial capital.
9. PCA loadings and scores are reported in Appendix A in the supplemental data online.
10. A map showing the spatial variation of these effects is provided in Appendix B in the supplemental data online.
11. An additional set of regressions was run including regional economic growth to ensure that the results on policy effects do not depend on overall regional performance. The inclusion of regional growth in the estimators is not significant and does not change the results for the other regressors.