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
Motivation
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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?
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Question
Theory is ambivalent about the effect of public bank
ownership on technological progress:
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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)
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Contribution
Germany is used as laboratory
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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
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Model relationship bank selection
Determining local bank supply of sample firms
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Question
Why do firms not simply switch their main lender if a
certain ownership type is beneficial for their innovation
preferences?
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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
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Findings
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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
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Agenda
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Motivation
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Data and Descriptives
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Methodology
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Empirical Results
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Conclusion
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Data and Descriptives
Financials
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Bureau van Dyck‘ Amadeus dataset for German manufacturing firms
9,310 firms (32,839 firm-year observations) for 1993-2006
Innovation ability
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Patent filings from European patent office (EPO)
Citations to measure relative importance of patent filing
Lending relationship
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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
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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)
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Data and Descriptives
All firms
Private
Public
Difference
(p-value)
Observations 12,343
(firm-year)
7,444
4,899
2,545
Innovative
(mean)
0.342
0.384
0.278
0.106
(0.000)
Employees
(mean)
1,687
2,010
987
1,023
(0.000)
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Data and Descriptives
Number of Patents
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Private Banks
Government Banks
Private Banks
Young firms
Government Banks
Old firms
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Agenda
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Motivation
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Data and Descriptives
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Methodology
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Empirical Results
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Conclusion
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Selectivity bias
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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
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Methodology
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Firm i has a choice to innovate or use an existing
technology. Innovation decision of firm i:
yi  1 X i   Fi  ui
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(outcome equation)
yi innovation decision of firm i (1/0)
Fi ownership of main lender (1 if government bank is
main lender/ 0 if private bank is main lender)
X i vector of controls (firm and industry characteristics)
 coefficient of interest
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Methodology
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To control for selectivity bias introduce bivariate
probit model (Heckman 1978). A firm’s main lender
selection can be modeled as follows:
Fi  1 X i   Z i  vi
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(selection equation)
Z i is vector of instruments
Two binary decisions (4 states of the world)
Full maximum likelihood bivariate probit estimation
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Agenda
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Motivation
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Data and Descriptives
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Methodology
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Empirical Results
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Conclusion
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Results - Selection
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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
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Bank and firm location should not be
endogenously determined:
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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)
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Results - Innovation
Bivariate probit estimates:
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Results - Innovators and # of patents
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Results - Robustness tests
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Use a 10 km radius of distance around each firm
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Use alternative definitions of relationship lender
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Alternative estimation method (2 SCML)
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Use sample with firm with high tangibility ratio
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Agenda
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Motivation
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Data and Descriptives
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Methodology
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Empirical Results
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Conclusion
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Conclusion
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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
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Appendix
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Results - Selection
Selection equation for different samples sizes:
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Results - Innovation
Bivariate probit estimates – high tangible assets:
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Data and Descriptives
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Results
Robustness
Locate banks in a 10 km radius around each firm:
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Results
Robustness
Using alternative definitions of relationship lender:
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Results
Innovation and Firm Size
2 SCML estimates:
Dr. Rainer Haselmann Johannes Gutenberg University
Mainz – June 17th, 2009
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Related work
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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
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