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Geographical Organization of Banking Systems and Innovation Diffusion

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The Changing Geography of Banking and Finance

Abstract

The empirical literature is largely supportive of the importance of financial constraints and identifies local banking development and relationship lending as possible determinants of firms’ propensity to innovate. In this chapter, we argue that the spatial organization of banking systems and the distance of local branches from banks’ decisional centers are major factors influencing the effectiveness in collecting and processing soft information on local innovative firms. We provide evidence showing that, while branch density and the length of credit relationships have a positive causal effect on innovation when considered singularly, after controlling for the functional distance between the banking system and the local economy they lose statistical significance in favor of the latter. In this perspective, our results suggest that the geographical organization of banks and the spatial distribution of their headquarters represent key variables for local development.

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Notes

  1. 1.

    See Hall (2005) for a review

  2. 2.

    Following Guiso et al. (2004), Herrera and Minetti (2007) measure the financial development of Italian regions as the (estimated) relative ease of local households of accessing credit.

  3. 3.

    Others have suggested the alternative labels of organizational and hierarchical distance (Jimenez et al. 2009, Mistrulli and Casolaro 2008).

  4. 4.

    Notable exceptions were Chick and Dow (1988), Martin (1989, 1994), Dow (1994, 1999) and Alessandrini and Zazzaro (1999) who forcefully argued that the costs of bank consolidation and agglomeration of decisional centers would not be temporary and could trigger vicious circles entrapping peripheral areas and local firms in low growth equilibria.

  5. 5.

    Complementary reviews of this literature are presented by Udell (2009: Chapter 2, this volume) and by Cerqueiro et al. (2009: Chapter 4, this volume).

  6. 6.

    According to the data drawn from the statistical database (BIP on-line) of the Bank of Italy available at http://www.bancaditalia.it/statistiche, the number of ATMs in operation grew from 25,546 in 1997 to 43,809 at the end of 2007 and, over the same time period, the number of online retail (corporate) consumers increased from 65 (251) thousand to almost 12 (1.8) million, and the number of phone banking consumers increased from one to 11 million

  7. 7.

    In 2007, the consolidation process experienced a further acceleration. The two largest operations gave birth to two big players, Intesa-San Paolo and Unicredit Group, with a national market share equal, respectively, to 20.2 and 17.3% and with a significant presence in Europe (Bank of Italy 2008). However, consolidation also affected the world of cooperative banks (Banche Popolari), with the creation of two very large mutual bank groups, Banco Popolare and UBI Banca, both with a large geographical spread with a network of 2,000 branches each.

  8. 8.

    At the end of 2005, the number of banks in the South was equal to 147, 110 of which were mutual banks, and 21 were affiliated to banking groups headquartered in the Center-North (Table 5.1).

  9. 9.

    Our data do not allow us to disentangle how much decisional autonomy a chartered bank loses when it enters a banking group. In what follows, we assume that the ultimate control of local branches of affiliated banks is in the hands of the lead bank of the holding company. For robustness, we reproduce our empirical analysis building F–DISTANCE indicators on the opposite assumption that the ultimate control on lending decisions is taken by the chartered bank. Estimation results remain substantially unaltered and are available on request from the authors. Finally, Italy is currently divided into 107 provinces, which are grouped into 20 administrative regions. However, since some provinces were recently constituted, we use the old classification of 95 provinces.

  10. 10.

    Participation at referenda as indicator of social capital has been introduced by Putnam (1995) and employed in the banking literature by Guiso et al. (2004), Alessandrini, Calcagnini and Zazzaro (2008), and Alessandrini, Presbitero and Zazzaro (2009), amongst others.

  11. 11.

    More precisely, in 2006, F–DISTANCE measured in terms of social capital (kilometers) in the South was 2.4 (1.6) times greater than in the Center-North, compared to a ratio of 1.9 (1.5) in 1990.

  12. 12.

    Considering F–DISTANCE_SC, the provinces of Aosta, Imperia and Trieste are examples of the former phenomenon, while Chieti, Matera, Sassari, Cosenza, and Ragusa are amongst the southern provinces with levels of functional distance below the average.

  13. 13.

    This specification for the length of the bank relationship is proposed by Gambini and Zazzaro (2009), while Ferri and Rotondi (2006) simply subtract 3 from the number of years of the bank relationship as reported by firms.

  14. 14.

    A detailed description of these and all other variables used in the empirical analysis is reported in Appendix.

  15. 15.

    There are 20 Italian administrative regions, but to avoid the very low number of observations in three regions, Valle d’Aosta, Molise, and Basilicata are considered together with, respectively, Piemonte, Abruzzo, and Calabria.

  16. 16.

    Since data on bank branches in 1936 are classified by bank institutional type, we consider the “Istituti di Credito di Diritto Pubblico” and the “Banche di Interesse Nazionale” to be large banks.

  17. 17.

    For the sake of brevity, we do not report the results of the first-stage regressions, but they are available on request from the authors.

  18. 18.

    In order to save space, we report only results for the IV estimates, while the probit estimates are available upon request.

  19. 19.

    When functional distance is measured by social capital, however, the full specification does not provide significant results either for distance or for the length of a bank relationship.

  20. 20.

    This finding could be interpreted as another piece of evidence in favor of less need of secrecy for product innovation.

  21. 21.

    Holloway and Wheeler (1991), Meijer (1993), Pike (2006).

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Authors and Affiliations

  1. Dipartimento di Economia, Università Politecnica delle Marche, Piazza Martelli 8, 60121 Ancona, Italy

    Pietro Alessandrini

  2. Depertment of the World Bank on debt issues and has been a visitor at CEP., Universitá Politecnica delle Marche in Ancona and a member of the Money and Finance Research group., Marche

    Andrea F. Presbitero

  3. Università Politecnica delle Marche in Ancona, a member of the Money and Finance Research group, and a research associate with CFEPSR (Portici, Italy), Italy

    Alberto Zazzaro

Authors
  1. Pietro Alessandrini
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Correspondence to Pietro Alessandrini .

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    Appendix: List of variables

    Appendix: List of variables

    • INNOVATION, by firm, is a dichotomous variable which is equal to one if the firm introduced a process and/or product innovation in the 3-year period covered by each survey. Source: Unicredit’s Surveys.

    • PROCESS, by firm, is a dichotomous variable which is equal to one if the firm introduced a process innovation in the 3-year period covered by each survey. Source: Unicredit’s Surveys.

    • PRODUCT, by firm, is a dichotomous variable which is equal to one if the firm introduced a product innovation in the 3-year period covered by each survey. Source: Unicredit’s Surveys.

    • F–DISTANCE_KM, by province, is a measure of functional distance, computed as the ratio of branches weighted by the logarithm of 1 plus the kilometric distance between the province of the branch and that where the parent bank is headquartered, over total branches in province j (see Section 5.4 for details). Source: authors’ calculations on Bank of Italy data.

    • F–DISTANCE_SC, by province, is a measure of functional distance, computed as the ratio of branches weighted by the logarithm of 1 plus the difference in social capital (computed as the average voter turnout at the 21 referenda held in Italy in 1993, 1995 and 2001 as published by the Home Department) between the province of the branch and that where the parent bank is headquartered, over total branches in province j (see Section 5.4 and equation (5.1) for details). Source: authors’ calculations on Bank of Italy data.

    • BRANCHES, by province, is an indicator of branch density, computed as the number of bank branches in province j per 10,000 inhabitants (see Section 4 or details). Source: authors’ calculations on Bank of Italy and ISTAT data.

    • RELATIONSHIP, by firm, is the natural logarithm of one plus the length in years of the credit relationship between the firm and its main bank. (see Section 5.4 and equation (5.2) for details). Source: Unicredit’s Surveys.

    • VALUE ADDED, by province, is the logarithm of the real value-added. Source: ISTAT.

    • EMPLOYEES, by firm, is the number of workers, divided into six categories: 11–20, 21–50, 51–100, 101–250, 251–500, and more than 500, where the first class is taken as reference category. Source: Unicredit’s Surveys.

    • AGE, by firm, is the age, in years since the foundation of the firm. Source: Unicredit’s Surveys.

    • CORPORATION, by firm, is a dummy equal to one if the firm is a corporation. Source: Unicredit’s Surveys.

    • R&D, by firm, is the ratio between the expenditures in research and development per-employee, deflated by the ISTAT’s price index by industrial sectors. Source: authors’ calculations on ISTAT data and Unicredit’s Surveys.

    • EXPORT, by firm, is a dummy equal to one if the firm exports a share of its sales. Source: Unicredit’s Surveys.

    • ISO9000, by firm, is a dummy equal to one if the firm is ISO9000 certified. Source: Unicredit’s Surveys.

    • CONSORTIUM, by firm, is a dummy equal to one if the firm if the firm belongs to one or more credit, export and/or research consortium. Source: Unicredit’s Surveys.

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    Alessandrini, P., Presbitero, A.F., Zazzaro, A. (2009). Geographical Organization of Banking Systems and Innovation Diffusion. In: Zazzaro, A., Fratianni, M., Alessandrini, P. (eds) The Changing Geography of Banking and Finance. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-98078-2_5

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