Bank foundations, social capital and the growth of Italian provinces

ABSTRACT

The funding role of bank foundations in the Italian economy, especially to the non-profit sector, significantly increased over the last 25 years. This paper constructs a novel measure of social capital at the provincial level that explicitly takes into account the bank foundations’ sectors of intervention (such as education, public health, and art and culture), together with other traditional aspects of social capital, and then tests the impact of bank foundations on the economic growth of Italian provinces. The findings suggest that the contribution of bank foundations to social capital positively affects the economic growth of provinces.

KEYWORDS:

• bank foundations
• social capital
• economic growth

JEL:

• C13
• G23
• O47
• R11

ACKNOWLEDGEMENTS

The authors thank Giovanni Ferri, three anonymous referees, and the editor for helpful comments on an earlier version of the paper; and the Italian Association of Foundations and Savings Banks (ACRI) for providing the data on grant-making bank foundations activities.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

ORCID

Giorgio Calcagnini http://orcid.org/0000-0002-2501-4199

Germana Giombini http://orcid.org/0000-0002-1271-7389

Francesco Perugini http://orcid.org/0000-0001-7102-9829

Notes

1. For the UK case, see Cooke, Clifton, and Oleaga (2005).

2. For a detailed description of data and data sources, see Appendix A in the supplemental data online.

3. The traditional four macro-areas are the North-West, the North-East, the Centre and the South, with 24, 22, 20 and 36 provinces respectively in the present paper.

4. For further details on BFs, see Appendix A in the supplemental data online.

5. The remaining 3% cannot be assigned to a single province or region.

6. Coleman’s (1988) definition also includes vertical associations that are hierarchical relationships and unequal power distribution among members (Iyer et al., 2005).

7. For all the steps and results of the PCA analysis, see Appendix B in the supplemental data online.

8. See Appendix A in the supplemental data online.

9. This is computed combining the coefficient parameters of Grants and Grants².

10. We also add the dummy for Vibo Valentia, which is the province with an SC index = 0.

11. From the estimated γ₁ coefficient, it is possible to recover the speed of convergence according to the formula: β = –ln (1 + γ₁)/T.

12. See Appendix A in the supplemental data online.

13. In the panel specification, Grants is the ratio between BF grants and VA in each year, while the other regressors have the same usual meaning. Differently from cross-section model (1), in which province dummies were included to control for measurement problems related to the BFs’ grant-making activities, model (3) has among the regressors the variable Grants². Having checked that the two alternatives produce equivalent results, the choice to include Grants² instead of the dummy variables depends on the fact that the latter conflict with the time dummies. Also, area dummies are not included to allow for greater variability in the time dimension of the data.

14. The implied half-life is defined as the time necessary for a province to reduce the gap between per capita income and its steady-state value by half: .

15. For a survey on local development theories, see Capello (2011).

16. Governance is defined as the set of traditions and institutions that ensures the exercise of authority by a government. This definition includes measures of voice and accountability, political stability and absence of violence and terrorism, government effectiveness, regulatory quality, rule of law, control and corruption (Nifo & Vecchione, 2014).

17. The number of observations used to estimate model specifications (5) and (6) is 700 as data for Tour are available until 2009.