Abstract
In this study, we examine the relationship between the proportion of women in top management positions at banks and these institutions’ financial performance. Using prudential data from supervisory reporting for all credit institutions in the Grand Duchy of Luxembourg from 1999 to 2013, we find a positive association between female management and firm performance. The economic effect is substantial: a 10 % increase in women in top management positions improves the bank’s future return on equity by more than 3 % p.a. Moreover, we show that this positive relationship is (i) almost twice as large during the global financial crisis than in stable market conditions and (ii) non-linear, with banks having 20–40 % female management being the most successful.
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Notes
Exploiting exogenous retirements of board members due to death or illness, Schmid and Urban (2015) find that firms benefit from a corporate culture that fosters the promotion of women.
A plausible reason might be that women are more risk averse than men (Eckel and Grossman 2008) which is beneficial to firm performance during crisis periods. This idea is also consistent with recent research showing that stronger risk management-related corporate governance mechanisms improve bank performance mostly (or even only) in periods of financial crisis (Aebi et al. 2012; Ellul and Yerramilli 2013).
Most studies from outside the USA cover Scandinavia (e.g., Sweden, Norway, and Denmark). This is not surprising since these countries were the first to introduce quotas for female representation on management boards.
The observation period of 15 years from 1999 to 2013 serves two purposes. First, measuring bank performance throughout several years enables us to observe performance during different states of the economy and provides more consistent results in comparison to very short sample periods (Adams and Ragunathan 2012; Smith et al. 2006). Second, the impact of strategic decision-making on organizational performance typically requires several years to observe. Thus, a multi-year interval allows observing and evaluating diverse candidates’ potential contributions to strategic decision-making. We are consequently able to provide a remarkably robust analysis of the financial impact of female representation in bank management.
Winsorization does not affect our results. We obtain very similar results when we do not winsorize the dependent variable or employ different cutoff points.
Interestingly, the inclusion of financial strength reduces the impact of FMS on future RoE by approximately 30 %. Hence, a potential channel through which female representation creates value is the management of the Tier 1 capital ratio. However, this channel cannot fully explain the value of female management—the positive relationship between FMS and future RoE is still statistically significant at the 5 % significance level.
We do not find evidence of a significant relationship between any squared terms of the control variables and future bank performance (results are available upon request). We investigate the possible non-linear association between FMS and future RoE separately in Sect. 4.4.
For the last two decades, the role and the impact of the board of directors have been a special focus of corporate governance research (Berger et al. 2014). Corporate governance literature reports a positive correlation between women’s presence on the board of directors and financial performance (Adams and Ferreira 2009; Hartarska 2005; Hartarska and Mersland 2012; Hartarska and Nadolnyak 2012). Yet there are very few studies on the financial impact of the gender composition of a bank’s top management team, i.e., the managers charged with the day-to-day running of the bank such as the Chief Executive Officer, the Chief Financial Officer, the Chief Operating Officer, the Chief Risk Officer, the Chief Compliance Officer, the Chief Internal Auditor, and the executives of other subdivisions (Adams and Ragunathan 2012; Beck et al. 2013; Berger et al. 2014; Hartarska et al. 2013).
Therefore, in the same vein as Berger et al. (2014), our empirical analysis suggests that it is not only a bank’s corporate governance structure that influences financial performance, but also the gender diversity of the management team in a given credit institution.
We compute the value-weighted average RoE in quarter \(t+1\) by weighting each financial institution by its total assets in quarter t.
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Acknowledgments
The authors thank Claude Reiser, Conseiller de Direction 1\(^{\grave{e}re}\) Classe, for valuable research assistance and the anonymous referee for detailed comments and suggestions that were very helpful in improving the paper.
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Appendix: Definitions and data sources of main variables
Appendix: Definitions and data sources of main variables
This table briefly defines the key variables and firm characteristics used in the empirical analysis. Our data were obtained from the Commission de Surveillance du Secteur Financier (CSSF) and includes firm-level data from all credit institutions in Luxembourg. EST: indicates that the variable is estimated or computed based on original variables from the respective data sources
Variable name | Description | Source |
|---|---|---|
Panel A: Dependent variables | ||
RoE | RoE of bank i in quarter t defined as the net income at the end of quarter t divided by bank i’s equity capital at the end of quarter \(t-1\). To deal with the impact of outliers in our analysis, we winsorize RoE at the 1 % level | CSSF |
RoA | RoA of bank i in quarter t defined as the net income at the end of quarter t divided by bank i’s total assets at the end of quarter \(t-1\). To deal with the impact of outliers in our analysis, we winsorize RoA at the 1 % level | CSSF |
Panel B: Key variable and independent, time-varying firm characteristics | ||
FMS | Female Management Share (FMS) of bank i in quarter t defined as the proportion of women among all managers, including senior executives as well as members of the board | CSSF |
Firm size | Firm size of bank i in quarter t calculated as the natural logarithm of the winsorized total assets (in million of EUR) at the end of quarter \(t-1\) | CSSF, EST |
log CD | Client Deposits (log CD) of bank i in quarter t computed as the natural logarithm of the winsorized client deposits (in million of EUR) at the end of quarter \(t-1\) | CSSF, EST |
log SEPC | Total Staff Expenditures per Capita (log SEPC) of bank i in quarter t calculated as the natural logarithm of the winsorized personnel expenditures, including remuneration and social security contributions, as well as expenses for pension plans (in EUR) at the end of quarter \(t-1\) | CSSF, EST |
log SEPM | Total Staff Expenditures per Manager (log SEPM) of bank i in quarter t calculated as the natural logarithm of the winsorized manager expenditures, including remuneration and social security contributions, as well as expenses for pension plans (in EUR) at the end of quarter \(t-1\) | CSSF, EST |
Vola RoE | Volatility of RoE of bank i in quarter t calculated as the firm’s historical standard deviation of RoE over the past 12 quarters. We require a firm to have at least six non-missing RoE observations in the past 12 quarters | CSSF, EST |
Vola RoA | Volatility of RoA of bank i in quarter t calculated as the firm’s historical standard deviation of RoA over the past 12 quarters. We require a firm to have at least six valid RoA observations in the past 12 quarters | CSSF, EST |
Activity | Bank activity of bank i in quarter t calculated as the winsorized client deposits (in million of EUR) divided by the winsorized total assets (in million of EUR) following Demirgüc-Kunt and Huizinga (2010) | CSSF, EST |
Financial Strength | Financial Strength defined as the Tier 1 capital ratio of bank i in quarter t, which is the bank’s core equity capital divided by its total risk-weighted assets | CSSF |
Panel C: Independent, time-invariant firm characteristics | ||
log Bank Age | log Bank Age of bank i measured as the natural logarithm of the number of years that the credit institution has provided services in Luxembourg (= log(2014 - starting year of entity)). If the bank drops out of our dataset before 2014, we use the delisting year (instead of 2014) in our computation instead | CSSF, EST |
Dummy SL | Dummy SL of bank i representing a dummy variable that takes on the value 0 if the credit institution under review is a branch of a foreign bank; the variable takes on the value 1 if the credit institution is a Luxembourg parent company or a Luxembourg subsidiary of a foreign bank. The sample includes 183 subsidiaries and 81 foreign branches | CSSF, EST |
Dummy CO | Dummy CO of bank i representing a dummy variable taking on the value 0 if the bank’s country of origin is Luxembourg and 1 otherwise (indicating all foreign banks). 10 Credit institutions are banks originating from Luxembourg (representing 4 % of the sample); the country of origin most frequently represented in Luxembourg is Germany with 68 banks (26 % of the sample), followed by France with 27 banks (10 %), Italy with 24 banks (9 %), Switzerland with 23 banks (9 %) and Belgium with 15 banks (6 %) | CSSF, EST |
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Reinert, R.M., Weigert, F. & Winnefeld, C.H. Does female management influence firm performance? Evidence from Luxembourg banks. Financ Mark Portf Manag 30, 113–136 (2016). https://doi.org/10.1007/s11408-016-0266-8
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DOI: https://doi.org/10.1007/s11408-016-0266-8