Board Composition and Bank Risk Taking

1. Board Composition and Bank Risk Taking Allen N. Berger UNIVERSITY OF SOUTH CAROLINA, WHARTON FINANCIAL INSTITUTIONS CENTER, AND CENTER – TILBURG UNIVERSITY Thomas Kick DEUTSCHE BUNDESBANK Klaus Schaeck BANGOR UNIVERSITY Conference “Corporate Governance of Financial Institutions” Amsterdam November 2012

2. Introduction– Hypotheses – Data and Methodology –Results – Conclusion • The recent financial crisis highlights the importance of risk taking by banks and the larger consequences of excessive risk taking for society as a whole. • Much of the blame for the crisis and suggested remedies have focused on corporate governance arrangements. • While others have argued for and studied restrictions on inside ownership and executive compensation, we take a different approach and look at executive board composition. • While there are studies of board composition elsewhere, we focus on banks, where risk taking is of first-order importance.

3. Board Composition and Risk Taking Introduction– Hypotheses – Data and Methodology –Results – Conclusion 3 Research Question: How does executive board composition affect bank risk taking?

4. Board Composition and Risk Taking Introduction– Hypotheses – Data and Methodology –Results – Conclusion 4 • Paper has a (management) team perspective • Management board forms a team that interacts dynamically. • Composition of the “team” is crucial in shaping outcomes. (e.g., Holmstrom (1982, BJE); Bolton and Dewatripont (2005, MIT press)) • Composition: Socioeconomic characteristics • age • gender • education • We study a country with two-tier board system (Germany) • Executive board Direct responsibility for the management of the company. Runs the corporation, makes decisions and reports to the supervisory board. • Supervisory board Monitors the management, i.e. gives consent to certain transactions. Appoints and dismisses members of the executive board on behalf of the shareholders. • We focus on the composition of the executive board This generalizes directly to other nations with two-tier board systems (of which there are many) The results may generalize to executive members of the board in one-tier board countries

5. Introduction– Hypotheses – Data and Methodology –Results – Conclusion 5 • Focus on banks and risk taking • Policy relevance. • Impact of governance arrangements in banking on wider society, as illustrated by the recent financial crisis. Hau and Thum (2009, EP); Illueca, Norden, and Udell (2012, AEA Paper) • Literature generally excludes regulated industries. Bertrand and Schoar (2003, QJE); Fich (2005, JB); Farrell and Hersch (2006, JCF) • Adds to the emerging evidence on governance arrangements in banking. Fahlenbrach and Stulz (2011, JFE); Adams and Mehran (2012, JFI).

6. Board Composition and Risk Taking Introduction – Hypotheses– Data and Methodology –Results – Conclusion 6 • Age • Perception of young managers’ overconfidence and old managers’ wisdom • Expect strong effect of experience on risk taking behavior • Theory and literature also suggest inverse association between age and risk taking • Knowledge of risky situations and ability to control risk increases with age Grable et al. (2009, JBER), Russo and Schoemaker (1992, SMR) , Gervais and Odean (2001, RFS) • Empirical evidence points towards a negative relationship Campbell (2001, JF), Sahm (2007, FRB) and Grable et al. (2009, JBER) Age hypothesis HI: Risk taking decreases in executive board age

7. Board Composition and Risk Taking Introduction – Hypotheses– Data and Methodology –Results – Conclusion 7 • Gender • Perception of risk loving male and risk averse female managers • Anecdotal evidence on women being more prudent in financial decision making (e.g., microfinance) • Important in light of recently passed, proposed (and then scrapped) minimum quotas for women on boards and on executive teams in Europe • Theory and literature is ambiguous • Growing debate on the effect of gender on economic outcomes e.g. Croson and Gneezy (2009, JEL) , Schubert, Brown, Glyser, and Brachinger (1999 AER P&P) • Investment literature suggests women are more risk averse e.g. Barber and Odean (2001, QJE), Niederle and Vesterlund (2007, QJE), Goel and Thakor (2008, JF) • Governance: female directors are less risk averse and reduce performance Adams and Funk (2012, MS); Ahern and Dittmar (2012, QJE) • Little evidence on the effect in banking except for loan officers Agarwal and Wang (2009, Working Paper) and Beck, Behr, and Güttler(forthcoming,RoF)

8. Board Composition and Risk Taking Introduction – Hypotheses– Data and Methodology –Results – Conclusion 8 Gender Hypotheses HII a: A higher representation of female executives reduces risk taking HII b: A higher representation of female executives increases risk taking

9. Board Composition and Risk Taking Introduction – Hypotheses– Data and Methodology –Results – Conclusion 9 • Education • Education affects knowledge of and perception of risk e.g. risk management techniques taught in business schools International efforts to encourage banks’ appointment of knowledgeable directors: Basel Committee on Banking Supervision (2006) • Stereotype of MBA graduates’ aggressive management style • Theory and literature is ambiguous • More risk taking suggested for household decisions Carducci and Wong (1998, JBP); Grable (2002); Christiansen, Joensen, and Rangvid (2008, RoF) • MBA literature is ambiguous More sophisticated project valuation techniques, Graham and Harvey (2001, JFE) But aggressive style and more leverage, Bertrand and Schoar (2003, QJE) • Negative relationship suggested for corporate outcomes Financing and acquisition policies: Güner, Malmendier, and Tate (2008, JFE) Failure of financial institutions: Fich and Fernandes (2009, Working Paper)

10. Board Composition and Risk Taking Introduction – Hypotheses– Data and Methodology –Results – Conclusion 10 Education hypotheses HIII a: Better educated executives engage in less risk taking HIII b: Better educated executives engage in greater risk taking

11. Board Composition and Risk Taking Introduction – Hypotheses – Data and Methodology –Results – Conclusion 11 • Data • Proprietary data from the Deutsche Bundesbank • Complete and detailed information on executives’ age, gender, and education • Time horizon 1994-2010 • 19,750 bank-year observations for 3,525 banks • Estimation setup • Difference in difference matching estimation – exploits exogenous variation via mandatory retirements • Matching criteria size (+/- 20 percent of the treatment bank’s size in terms of total assets) performance (+/- 20 percent of the treatment bank’s ROE) board size held constant bank type (public banks, cooperative banks, and private banks) time period (year) • Sample excludes ‘bad’ banks (banks with interventions, capital support, distress merger banks) • Final sample: 10,719 bank-year observations for 2,490 banks • Risk measure • RWA/Total Assets – risk on and off the balance sheet • Used in regulatory treatment and in empirical literature

12. Introduction – Hypotheses – Data and Methodology –Results – Conclusion 12 • Methodology and Identification • Difference-in-difference estimator Yi : Measure of risk taking for bank iin year t Post: Dummy for Post change period in board composition Treated: Dummy if the bank experienced a Board change Xit: Control variables Focus: Interaction term: Post × Treated captures the effect of an executive board change due to retirements in the period after the change takes place. The slope coefficient of the interaction term therefore allows isolating effect of the change in executive board composition on the risk measure. We consider at most one executive board change of each type per bank. We examine the seven-year time window surrounding the executive board change and consider the three years prior to, the three years following, and the actual year of the executive board change.

13. Introduction – Hypotheses – Data and Methodology –Results – Conclusion 13 • Controls • Total assets Capacity to absorb risk, too-big-to-fail • Charter value (core deposits/Total assets) Affects risk taking, e.g. Dell’Ariccia and Marquez (2006, JF) • Capital adequacy ratio Affects moral hazard and monitoring incentivesMorrison and White (2005, AER),Allen, Carletti, and Marquez (2011, RFS) • Merger dummy May reflect changes in risk attitudes • Powerful CEO (tenure) Influential CEOs affect risk takingAdams, Almeida, and Ferreira (2005, RFS) • Executive board size Expect less risk taking for large boardsSah and Stiglitz (1986, JPE) • Share of customer loans and off balance sheet items in total assets Control for bank balance sheet composition and business model

14. Introduction – Hypotheses – Data and Methodology –Results – Conclusion 14 • Growth of total assets (deflated) Banks that experience substantial growth msy run a higher degree of risk • GDP growth (county) Control for macroeconomic environment, booms coincide with increased risk-taking e.g. Dell’AricciaandMarquez (2006, JF) • Population (log, state) Controls for market size • Interest rate spread Spread between 10 and 1 year bonds • Time trend Mitigateserialcorrelationconcerns Bertrand, Duflo, andMullainathan (2004, QJE)

15. Introduction – Hypotheses – Data and Methodology – Results – Conclusion Descriptivestatistics

16. Introduction – Hypotheses – Data and Methodology – Results – Conclusion Average Age: Share offemales: Share ofPhDs:

17. Introduction – Hypotheses – Data and Methodology–Results–Conclusion Main Results • Data are consistent with hypothesis HIII a • Increasing representation of highly-educated executives (PhD degree) decrease bank risk • Supports idea of better risk management techniques or more conservatism among highly educated executives •  Data are consistent with hypothesis HI • Executive board changes that cause a decrease of average age raise a bank’s risk profile significantly • Supports theory: Executives have different attitudes towards risk as they age. • Data are consistent with hypothesis HII b • Executive board changes that increase the representation of female executives also increase risk taking • We can not rule out male executives could change behavior following arrival of female executives!

18. Introduction – Hypotheses – Data and Methodology–Results–Conclusion Exploringthemechanisms • Age heterogeneity, captured by executive board age range: • Finding is not significant. • Better educated executive board members adjust the liability composition towards more stable funding sources. • Better educated executive board members diversify income streams. • New female executive board members are less experienced (Ahern and Dittmar, 2012, QJE)– could explain why risk taking increases. • Female executives appointed to boards of banks with significantly higher capital ratio before the change, so may have greater ability to absorb risk.

19. Introduction – Hypotheses – Data and Methodology–Results–Conclusion • Robustnesstests • Exclusion of all loss-making banks from the estimations • Reason: poorly performing banks may have incentives to restructure executive boards in specific ways to restore profitability (Schaeck et al., 2012, RoF) • Alternative risk measure: Herfindahl Hirschman Index of loan concentration • Reports the degree of concentration in banks’ loan portfolio  indicator of risk exposure • Alternative matching procedures • Adding of capital adequacy ratios as matching criterion • Narrowing of matching band for all criteria: between 90% and 110% • Additional regressions where • Merged banks are omitted • Poorly capitalized banks are omitted • Placebo test • Idea: pretense of a board change at a point in time when it in fact did not occur • Redefinition of Treatment dummy to unity two years prior to the actual executive board change Main results are robust to alternative specifications.

20. Introduction – Hypotheses – Data and Methodology– Results –Conclusion • Conclusion • Socioeconomic characteristics of executive board composition matter for banks’ risk-taking. • Decreases in executive board age and more female representation are associated with more risk taking, and more education is associated with less risk taking. Female results may in part reflect differences in experience and in initial conditions. • Robustness of empirical results to different specifications • Future work • Better disentangling of age, gender, and education effects • Testing for career concerns and overconfidence • Alternative control groups