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

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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?

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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)

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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

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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?

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 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

Motivation

Data and Descriptives

Methodology

Empirical Results

Conclusion

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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)

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Data and Descriptives

All firms Private Public Difference(p-value)

Observations(firm-year)

12,343 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

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Private Banks Government Banks Private Banks Government Banks

Young firms Old firms

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Agenda

Motivation

Data and Descriptives

Methodology

Empirical Results

Conclusion

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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

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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

iiii uFXy 1

iy

iF

iX

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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

iiii vZXF 1

iZ

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Agenda

Motivation

Data and Descriptives

Methodology

Empirical Results

Conclusion

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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 lender2.) 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)

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Results - InnovationBivariate probit estimates:

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Results - Innovators and # of patents

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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

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Agenda

Motivation

Data and Descriptives

Methodology

Empirical Results

Conclusion

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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

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

Appendix

Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009 24

Results - SelectionSelection equation for different samples sizes:

Dr. Rainer Haselmann Johannes Gutenberg University Mainz – June 17th, 2009 25

Results - InnovationBivariate 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