Real Effects of Bank Governance: Bank Ownership and Firm Level Innovation

1. Real Effects of Bank Governance: Bank Ownership and Firm Level Innovation Rainer Haselmann Katharina Marsch Beatrice Weder di Mauro 15th Dubrovnik Economic Conference June 24 - June 27, 2009

2. Motivation • High government involvement in banking sector since financial crisis • Financial intermediaries select entrepreneurs – choice affects rate of technological progress (King and Levine 1993 QJE; Levine and Zervos AER 1998) • Banking development stimulates the introduction of innovations (Benfratello et al. JFE 2008) • Are public or private financial intermediaries better in selecting innovative projects?

3. Question • Theory is ambivalent about the effect of public bank ownership on technological progress: • Public banks might foster innovation because of market failures (e.g. asymmetric information/moral hazard/positive externalities) • Government bankers’ incentives can result in a misallocation of financial resources (e.g. politicians follow personal goals; government banks want to secure employment – La Porta et al. JF 2002; Sapienza JFE 2004)

4. Contribution • Germany is used as laboratory • Industrialized country (e.g. Khwaja and Mian QJE 2005: Pakistan) • German financial sector is bank-based • Large public banking sector • Innovative economy (innovative SMEs) • Unique dataset • Methodology • Model relationship bank selection • Determining local bank supply of sample firms

5. Question • Why do firms not simply switch their main lender if a certain ownership type is beneficial for their innovation preferences? • Asymmetric information and moral hazard are large in the process of innovation financing (Carpenter and Petersen EJ 2002) • Main lender (relationship bank) collects information on borrower to moderate asymmetric information and moral hazard problem (Diamond REStud 1984) • Hold-up problem is especially important for information opaque projects such as innovation financing

6. Findings • A firm’s probability to innovate is affected by the ownership of its main lender • A firm’s probability to innovate is about 13 percent higher if the main lender is a private compared to a government banker (after controlling for firm specific characteristics and selectivity bias) • The ownership of the main lender affects the probability to innovate to a larger extend for smaller firms • Innovators with a private main lender (as compared to a government main lender) produce more innovations

7. Agenda • Motivation • Data and Descriptives • Methodology • Empirical Results • Conclusion

8. Data and Descriptives • Financials • Bureau van Dyck‘ Amadeus dataset for German manufacturing firms • 9,310 firms (32,839 firm-year observations) for 1993-2006 • Innovation ability • Patent filings from European patent office (EPO) • Citations to measure relative importance of patent filing • Lending relationship • Credit registry from the Deutsche Bundesbank (Mio-Evidenz) • Every lending relationship exceeding 1.5 M Euros in a quarter • Remaining sample ~ 6,500 firms • Supply of local bank branches • Address of all bank branches (Banken-Verlag Medien GmbH) • Geocoding of addresses • Great-Circle-Distance of 3 km (~28 km2) and 10 km (~ 314 km2)

9. Data and Descriptives

10. Data and Descriptives

11. Agenda • Motivation • Data and Descriptives • Methodology • Empirical Results • Conclusion

12. Selectivity bias • Firms may choose a certain type of bank depending on their innovation ability • Idea: Instrument for firms’ main lender selection by determining supply of local bank branches • Assumption: Geographic distance is an important determinant for the choice of main lender (Degryse and Ongena JF 2005, Peterson and Rajan JF 2002) • Private banks do not have branches in all regions – regional principle for public banks

14. Methodology • Firm i has a choice to innovate or use an existing technology. Innovation decision of firm i: (outcome equation) • innovation decision of firm i (1/0) • ownership of main lender (1 if government bank is main lender/ 0 if private bank is main lender) • vector of controls (firm and industry characteristics) • coefficient of interest

15. Methodology • To control for selectivity bias introduce bivariate probit model (Heckman 1978). A firm’s main lender selection can be modeled as follows: (selection equation) • is vector of instruments • Two binary decisions (4 states of the world) • Full maximum likelihood bivariate probit estimation

16. Agenda • Motivation • Data and Descriptives • Methodology • Empirical Results • Conclusion

17. Results - Selection • Two conditions need to be met for our instrument to be valid: 1.) Instrument has to be important determinant of firm‘s choice of a main lender 2.) Instrument must not be a determinant of firm‘s decision to innovate • Bank and firm location should not be endogenously determined: • Regional principle: Rural areas tend to be overbanked by public banks • Moving for manufacturing firms is costly especially for small firms and those with a high proportion of fixed assets (high tangibility)

18. Results - Innovation Bivariate probit estimates:

19. Results - Innovators and # of patents

20. Results - Robustness tests • Use a 10 km radius of distance around each firm • Use alternative definitions of relationship lender • Alternative estimation method (2 SCML) • Use sample with firm with high tangibility ratio

21. Agenda • Motivation • Data and Descriptives • Methodology • Empirical Results • Conclusion

22. Conclusion • Providing external finance is key mechanism through which banks affect economic growth • Probability of a firm to innovate is about 13 percent higher if the main lender is a private compared to a government bank • Public bankers’ incentives are manifold which is adverse impact on selecting innovative projects • Government ownership of banks might comes at the cost of lower innovation in the long run

23. Appendix Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

24. Results - Selection Selection equation for different samples sizes: Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

25. Results - Innovation Bivariate probit estimates – high tangible assets: Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

26. Data and Descriptives Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

27. Results Robustness Locate banks in a 10 km radius around each firm: Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

28. Results Robustness Using alternative definitions of relationship lender: Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

29. Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

30. Results Innovation and Firm Size 2 SCML estimates: Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009

31. Related work • Herrera and Minetti JFE (2007) find that relationship finance (duration of credit relationship) promotes innovation finance • Benfratello, Schiantarelli, and Sembenelli JFE (2008) show that local banking development matters for the probability of corporate innovations • Atanassov, Nanda, and Seru (2005) show that large firms actually prefer market based finance over relationship lending Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009