1
Feminist share prices, a report on empowered returns;
Does the effect of a newly announced CEO’s sex influence the firm’s
short-term performance?
By Sjoerd Läubli, 11000961 Bachelor Thesis Finance & Organisation
Thesis Supervisor: S. D. Jagau 26 June 2018
Abstract:
Board diversification and female representation in the corporate world in general is the subject of many discussions worldwide. This paper tries to analyse if gender bias can be found during the announcement period of the Chief Executive Officer succession. The database is comprised of firms, indexed in the S&P500, with CEO succession announcements that took place after 2010. An empirical study using simple linear regression has been performed, where a relationship between the dummy variable for a newly appointed CEO’s sex and a firms excess returns on the market was found. The results of this study indicate a positive effect of announcing a Female CEO on the short-term performance of a company.
2 Statement of Originality
This document is written by Student Sjoerd Läubli who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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CONTENT
INTRODUCTION ... 4
LITERATURE REVIEW & HYPOTHESES ... 6
RESEARCH METHODOLOGY ... 8
Regression Model ... 8
Dependent Variables ... 8
Independent Variables ... 9
Control Variables & Bias Control ... 9
Peer Group Establishment & Data Gathering ... 10
Robust Regression Analysis ... 13
Stata Progress ... 13 RESULTS ... 14 Linearity ... 14 Normality ... 14 Multicollinearity ... 15 Descriptive Statistics ... 15
Regression Results & Discussion ... 16
CONCLUSION & DISCUSSION ... 20
Conclusion ... 20 Discussion ... 20 BIBLIOGRAPHY ... 22 APPENDICES ... 29 Appendix 1 ... 29 Appendix 2 ... 30 Appendix 3 ... 31 Appendix 4 ... 32 Appendix 5 ... 33
4
Introduction
Four years after the instalment of a 40% female board members quota for large Norwegian companies in 2008, the European commission proposed a similar regulation which also contained a quota of at least 40% female board members for large companies in the entire European Union (The Economist, 2018) (European Commission, 2012). This regulation still has not been approved, however, in 2015 Germany introduced a quota for large German companies with more than 2000 employees to have at least 30% female board members; ensuring female representation in the corporate field (Smale & Miller, 2015). The debate on the instalment of gender quota in corporate law and the consequences for firms involved in both the USA and Europe has been ongoing until today (Stalet, 2016) (Boffey, 2017). Female representation in the corporate environment is growing. Between 2007 and 2017 the
percentage of S&P500 companies board seats occupied by women rose from 16% to 22% (Lublin, 2018). The amount of female Chief Executive officers (CEO’s) rose from 12 in 2007 to 23 in January 2017 (Lublin, 2017) (Catalyst, 2018). Although these percentages are low, the United States is actually one of the leading countries when it comes to female CEO representation. A study published by Heidrick and Struggles on CEO and board practises in 2016 in France, Germany, Switzerland, the UK and the US shows that the United States has the highest percentage of female CEO’s, of 8% (Heidrick & Struggles, 2017).
Although the effect on firm performance of appointing female board members and CEO appointments in general have been researched multiple times, the effect of the succession from a male to a female CEO has not yet been studied thoroughly. This raises the question if the sex of a CEO is a relevant variable for firm performance in the short run and thus, if gender bias can be found in the progress of CEO succession. This thesis will consider the following research question:
“Does the sex of a newly selected CEO influence the firm performance in the short run during the announcement period for firms, indexed in the S&P500, after 2010?”
Note that the terminology of sex and gender are different. In this paper gender refers to behaviour and capabilities expected of or demonstrated by an individual that comply with the expectations regarding their sex. The sex of an individual refers to the anatomy of a person’s reproductive system (Muehlenhard & Peterson, 2011, pp. 795-799). This thesis will only employ an individual’s sex as a differentiating factor between male and female CEOs.
The Chief Executive Officer could be regarded as the single most important position of a company. A CEO must be able to analyse the company in order to accurately allocate responsibilities and recognize causalities. The CEO’s role is unique due to its broad
5 monitoring and controlling, where as other positions in the company tend to have a more narrowed focus. Because of this the Chief Executive Officer is usually held accountable for changes in the company’s performance (Lafley e.a., 2009). Therefore, analysing the effect of a newly announced CEO’s sex on firm performance is crucial for understanding both a company’s responsibility for gender bias and its possible consequences.
Research into gender bias occurring during CEO successions is part of
understanding the drivers behind the inequality between women and men in the corporate field. This paper will not delve further into the ethical discussion on gender equality and women’s rights and representation in the corporate field. However, from a logical point of view it makes sense to limit emotional decision making in the firm. To ensure that a company is at its highest level of profitability, resources must be allocated to the best of the
managements and top executives abilities. This includes the resource of human capital, which includes the appointment of a new CEO. Emotional or societal influences, like gender bias, on a firm’s human capital allocation can deviate the firm from using their resources as efficiently as possible. An obvious example would be the selection of a future male CEO with lesser qualifications and capabilities over a future female CEO, due to the short term effects of the new CEO’s sex on firm performance. Analysing if bias can be found obviously does not automatically negate the existence and effects found, however, it may give a crucial insight into the current situation of the corporate field and an incentive for boards to consider the motive behind governmental decisions.
This paper contains five chapters; Chapter two will consist of a summary and review of all the studied literature that is relevant to this research, after which the hypothesis will be given. The third chapter will include both the theoretical framework, where the models, terminology and statistics used are explained, and the research methods applied to gather data and achieve results. After this the results of these methods will be presented and discussed. In the final chapter a short summary will be given and a conclusion formed. A discussion on improvements and further research will also be included in this chapter.
6
Literature review
In this chapter an outlay of previous research related to the topic is given. Starting with multiple papers written on board diversification, after which CEO succession effects and a control variable will be reviewed.
Several studies have been done on the diversification of boards. In 2005 Farrel & Hersch published a paper on the drivers of board diversification and the characteristics of the firm in which a woman is most likely to be appointed a board member. The research
suggests that, even though more women serve on well performing firms, the most important driver is an internal or external demand for diversification. Thus, the reasoning behind this decision is not performance based (Farrel & Hersch, 2005). Multiple effects of a diversified board on the company have been found. A paper in 2009 concluded that diversification leads to better attendance rates of board members, an increase in effort allocation and higher willingness to join committees from the board (Adams & Ferreira, 2009). Dobbin & Jung (2011) concluded that the decision to hire female board members usually has a neutral or negative impact on firm performance. This could either be caused by decreased monitoring and controlling capabilities of the board itself or because of institutional investors’ gender bias. This would mean that the performance expectations of a female board member differ from the expectations of a male board member’s performance (Dobbin & Jung, 2011). Based on data from Norwegian companies after the implementation of the women quota, hiring female board members because of regulations or as a reaction to demands for diversity can also have a negative impact on firm performance due to the lack of experience of female candidates (Ahern & Dittmar, 2012). However, in 2014 Gregory-Smith, Main & O’Reilly found that although diversified boards behave differently, the bottom line performance of a firm does not change.
The effects of diversification in boards on companies has been researched many times with contradictory results. On the other hand the amount of papers on the effect of appointing a female Chief Executive Officer (CEO) is limited. In 1987 Beatty & Zajac concluded that Chief Executive Officer succession announcements in general have a negative impact on the firm’s value, measured in stock prices. This is supported by a paper in 2017 that found a negative correlation of CEO succession with the company’s stock prices in the short run (Schepker, Kim, Patel, Thatcher & Campion, 2017). The nature of this
relationship between CEO succession and firm performance had already been researched in 2010 by Chang, Dasgupta & Hilary, who concluded that the firm’s performance is mainly influenced by the expectations of the new CEO’s abilities. This would support the notion that short run effects of a CEO’s sex on firm performance could be caused by gender bias. In other words, the investors have different expectations of male and female CEO’s behaviour and capabilities and this would also keep in line with Dobbin & Jung’s findings on the effects
7 of appointing female board members. An actual difference in behaviour was found by Eahab & Ursel (2011) who concluded that female Chief Executive Officers tend to be more risk averse than their male counterparts and therefore are associated with a decrease in risky investments.
Another variable that should be considered when researching the effect of a newly appointed CEO on firm performance is whether the executive was chosen from within the company or its industry, as insider successions tend to have a better market responds in the short run (Jalal & Prezas, 2012).
Based on the Literature studied, a negative correlation between a female CEO announcement and firm performance is expected, as both diversification of boards and CEO succession is negatively correlated to short term stock returns. The two alternative
hypotheses of this paper are:
i. Hypothesis 1: A significant negative effect of a female CEO succession
announcement on firm performance measured in a percentage change of returns, of two months prior and after the announcement, will be found.
Therefore, the dummy for a female CEO succession announcement
(dummy_FemSucc) will have a negative coefficient that significantly differs from zero. ii. Hypothesis 2: A significant difference can be found between companies with a
newly announced female CEO and companies with a newly announced male CEO, in the firm performance of two months after the announcement.
Therefore, the coefficient of the dummy for a newly announced female CEO (dummy_FemCEO) will also negatively differ from zero.
The null hypothesis for both alternative hypotheses is that the sex of the CEO announced does not have an effect on short term performance, meaning that the coefficients for both will not significantly differ from zero.
8
Research Methodology:
In this chapter the theoretical framework is given. First the model used is presented and the variables explained. Thereafter the control variable and different actions to eliminate bias are introduced. Following this the research methods applied for the peer group establishment and data gathering are stated. Finally, the two kinds of regression analyses and the Stata procedures are given.
i. Regression Model:
The regression model used in this paper is a simple linear regression model, with different specifications for both hypotheses. The basic model is:
𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖 = 𝛼𝑖+ 𝛽1∗ 𝐷𝐹𝑒𝑚𝑎𝑙𝑒+ 𝛽2∗ 𝐷𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟+ 𝜀𝑖
To be able to analyse the two hypotheses Firm Performance is specified in two ways. Both are based on the same data, however, for the first hypothesis the data is remodelled into a ratio. The specific models for the hypotheses are:
- Hypothesis 1:
𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑅𝑒𝑡𝑢𝑟𝑛𝑠 𝑅𝑎𝑡𝑖𝑜𝑖 = 𝛼𝑖+ 𝛽1∗ 𝐷𝐹𝑒𝑚𝑎𝑙𝑒+ 𝛽2∗ 𝐷𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟+ 𝜀𝑖
In this model 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑅𝑒𝑡𝑢𝑟𝑛𝑠 𝑅𝑎𝑡𝑖𝑜𝑖 (ratio_change) is the dependent variable,
𝐷𝐹𝑒𝑚𝑎𝑙𝑒 (dummy_FemSucc) is the independent dummy variable for a female CEO
succession announcement and 𝐷𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟 (dummy_Outsider) is the dummy control variable for
an Outsider CEO succession announcement.
- Hypothesis 2:
𝐷𝑎𝑖𝑙𝑦 𝐸𝑥𝑐𝑒𝑠𝑠 𝑅𝑒𝑡𝑢𝑟𝑛𝑠𝑖= 𝛼𝑖+ 𝛽1∗ 𝐷𝐹𝑒𝑚𝑎𝑙𝑒+ 𝛽2∗ 𝐷𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟+ 𝜀𝑖
In this second model 𝐷𝑎𝑖𝑙𝑦 𝐸𝑥𝑐𝑒𝑠𝑠 𝑅𝑒𝑡𝑢𝑟𝑛𝑠𝑖 (r_daily) is the dependent variable, 𝐷𝐹𝑒𝑚𝑎𝑙𝑒 (dummy_FemCEO) is the independent dummy variable for an announced female CEO
succession and 𝐷𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟 (dummy_Outsider) is the dummy control variable for an announced
Outsider CEO succession.
ii. Dependent Variables:
The dependent variable is different for each hypothesis. In the first hypothesis, in order to take both the succession effect and influence of a CEO’s sex into account, a ratio of the
9 average daily returns was used as the dependent variable. For each company the average return before and the average return after were calculated, after which the ratio was computed by the standard method of: 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑎𝑖𝑙𝑦 𝑅𝑒𝑡𝑢𝑟𝑛𝑎𝑓𝑡𝑒𝑟 − 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑎𝑖𝑙𝑦 𝑅𝑒𝑡𝑢𝑟𝑛𝑏𝑒𝑓𝑜𝑟𝑒
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑎𝑖𝑙𝑦 𝑅𝑒𝑡𝑢𝑟𝑛𝑏𝑒𝑓𝑜𝑟𝑒 . By
using the ratio, it is possible to analyse the difference in returns caused by the succession and the influence of the dummies, dummy_FemCEO and dummy_Outsider, in the same regression.
For the second hypothesis the unchanged daily excess returns on the market of each company can be used as the dependent variable. Because the influence of succession itself is not included into the model. For this regression only the daily excess returns after the succession announcement are used, therefore, only testing whether a company with a newly announced female CEO does better than a company with a newly announced male CEO after the market is informed of the planned succession.
iii. Independent Variables:
The independent variable of the model is a dummy which indicates the sex of an announced CEO. For a female CEO announcement 𝐷𝐹𝑒𝑚𝑎𝑙𝑒 = 1, for a male CEO announcement
𝐷𝐹𝑒𝑚𝑎𝑙𝑒 = 0.
For the first hypothesis model the dummy, dummy_FemSucc, is applied to all the company’s ratios. For the second hypothesis model the dummy, dummy_FemCEO, is
applied to all the daily excess returns of each company after the succession announcement.
iv. Control Variables & Bias Control:
The control variable used in both regressions is the dummy variable (dummy_Outsider) indicating the announcement of a succession with an Outsider CEO. An Outsider succession is defined as a CEO succession were the candidate is not promoted from within the company but appointed from outside the company and sometimes even from outside the industry (Jalal & Prezas, 2012). When a CEO is selected from within the company dummy_Outsider = 0 and for an Outsider succession announcement dummy_Outsider = 1.
As individual returns would also be affected by different systematic risks of the market, the dependent variable is based on the excess returns, limiting the differences between the companies to idiosyncratic risk (Berk & DeMarzo, 2014). This would undermine the effect of changes in the market’s economy over time and therefore, render the use of a control variable for time in the model unnecessary.
Other factors that might be of influence on the dependent variable Firm Performance could be significant differences in both bias and average excess returns between firms. For example, a sample with more male to male CEO successions announcements of hedge fund
10 companies could give a skewed image of returns between the two peer groups. Also, the bias might differ, due to more male dominated industries. As the sample for male to female CEO successions is relatively small, it makes sense to match the industries of each company in this peer group with two companies within the same industry with male to male CEO announcements. Thereby undermining the industry effects on both the possible gender bias and the excess returns. Thus, each company with an announced female CEO is “matched” with two companies with an announced male CEO. A secondary principle of this matching was the level of competition between companies, where the biggest competitors within a company’s industry were prioritised and their CEO successions were examined first.
The final restriction on the CEO announcements used in the sample is the omission of appointments caused by the forced departure of the previous CEO. As Lafley e.a. (2009) pointed out the CEO is often held accountable for a change in the performance of the company. Therefore, a CEO might be forced to leave due to a company’s decreased performance. On top of that public scandals regarding the CEO’s decisions may also influence the company’s performance, while causing a forced departure at the same time (Lafley e.a., 2009).
v. Peer Group Establishment and Data Gathering:
As no CEO succession database could be accessed, necessary information was gathered manually. Firstly, a list of Female CEO’s in the S&P500 by Catalyst was the basis for the male to female CEO successions peer group. Thereafter four main information sources were used to attain information on the successions. The announcement date is used as the event date for succession, as it is likely that the market will respond as soon as the announcement is made (Jalal & Prezas, 2012, p. 409). Dates and general info on the succession were gathered from either the Wall Street Journal, PR Newswire or a press release by the
company. When the necessary time frame and reasons for succession were established, all executive profiles were regarded in the online Bloomberg database to establish if the newly appointed CEO was promoted from within the company or not.
The peer group for male to female CEO announcements contains 22 companies. After applying the matching principles, gathering information using the same sources, the male to male CEO succession announcements peer group contains 44 companies. The total sample for this research contains 66 companies in 14 different industries.
The excess returns of each company are obtained from CRSP, a partner of Wharton Research Data Services. CRSP offers a direct download of a company’s excess return on the S&P500 index, which in this paper is used as the market index. Daily excess returns of two months prior and two months after the announcement were obtained for each company, providing between 81 and 88 datapoints per firm. The data was collected in excel and lined
11 by the announcement date. The change in average excess return ratios were also calculated in excel. All companies’ information on the announced CEO’s sex and Insider or Outsider appointment was then compiled into columns for both the excess returns after
announcement and the ratios, creating the basis for the dummy regressions in Stata.
Table 1; The peer group of Companies who announced a succession of a female CEO between 2010 and 2018. Information Sources can be found in the bibliography.
Peer Group 1; Male to Female Succession Announcements
Company Industry Announced CEO Event Date
1 Campbell Soup Packaged Food & Meats Denise Morrison 23-6-2011 2 The Hershley Company Packaged Food & Meats Michele Buck 21-12-2016
3 General Motors Automobile Manuf. Mary Barra 15-1-2014
4 Lockheed Martin Aerospace & Defence Marilyn Hewson 9-11-2012 5 General Dynamics Aerospace & Defence Phebe Novakovic 7-6-2012
6 Duke Energy Utilities Lunn Good 18-6-2013
7 Occidental Petroleum Utilities Vicky Hollub 5-5-2015
8 Alliant Energy Utilities Patricia Kampling 20-1-2012
9 American Water Works Utilities Susan Story 17-12-2013
10 PG&E Utilities Heisha Williams 14-11-2016
11 CMS Energy Utilities Patti Poppe 26-1-2016
12 Sempra Energy Utilities Debra Reed 20-1-2010
13 Keycorp Banking Beth Mooney 18-11-2010
14 Ross Stores Apparel Retail Barbara Rentler 7-5-2014
15 Mattel Toy Manuf. and Retail Margaret Georgiadis 17-1-2017
16 Staples Suplies Manuf. and Retail Shira Goodman 31-5-2016
17 Ulta Beauty Specialty Stores Mary Dillon 24-6-2013
18 The Progressive Company Insurance Tricia Griffith 13-5-2016
19 HCP Health Care REITs Lauralee Martin 3-10-2013
20 Anthem Managed Healthcare Gail Boudreaux 6-11-2017
21 Mylan Pharmaceuticals Heather Bresch 26-8-2011
12
Table 2; The peer group of Companies who announced a succession of a male CEO between 2010 and 2018. Information Sources can be found in the bibliography.
Peer Group 2; Male to Male Succession Announcements
Company Industry Announced CEO Event Date
1 J. M. Smucker Packaged Food & Meats Richard Smucker 3-8-2011
2 McCormick & Co. Packaged Food & Meats Lawrence Kurzius 1-12-2015
3 Conagra Packaged Food & Meats Sean Conolly 13-2-2015
4 Hormel Foods Packaged Food & Meats James Snee 6-4-2015
5 Advanced Auto Parts Automobile Manuf. Tom Greco 4-4-2016
6 Ford Motors Automobile Manuf. Mark Fields 1-6-2014
7 United Technology Aerospace & Defence Gregory Hayes 24-11-2014
8 Raytheon Aerospace & Defence Thomas Kennedy 7-6-2012
9 Arconic Aerospace & Defence Chip Blankenship 23-10-2017
10 Boeing Aerospace & Defence Dennis Muilenburg 23-6-2015
11 Ameren Utilities Warner Baxter 18-2-2014
12 American Electric Power Utilities Nick Akins 25-10-2011
13 Eversource Energy Utilities Jim Jidge 6-4-2016
14 Nisource Energy Utilities Joe Hamrock 21-3-2012
15 Valero Energy Utilities Joe Gorder 3-3-2014
16 PPL Corp. Utilities James Miller 17-11-2011
17 WEC Energy Utilities Allen Leverett 28-1-2016
18 AES Utilities Andrés Gluski 1-10-2011
19 Exxon Mobile Utilities Darren Woods 1-1-2017
20 Centerpoint Energy Utilities Scott Prochazka 29-10-2013
21 Xcel Energy Utilities Ben Fowke 24-8-2011
22 First Energy Utilities John Chambers 6-3-2014
23 NRG Utilities Mauricio Gutierrez 3-12-2015
24 Consolidated Energy Utilities John McAvoy 19-9-2013
25 PNC Bank Banking William Demchak 14-2-2013
26 Fifth Third Bancorp Banking Greg Carmichael 8-7-2015
27 J.C.Penney Apparel Retail Marvin Ellison 1-9-2015
28 Footlocker Apparel Retail Richard Johnson 4-1-2014
29 Dollar General Variety Manuf. and Retail Todd Vasos 28-5-2015
30 Church & Dwight Variety Manuf. and Retail Mathhew Farrel 7-5-2015
31 Clorox Household products Manuf. Benno Dorer 20-11-2014
32 Home Depot Supllies Manuf. and Retail Greg Menear 21-8-2014
33 Tractor Supply Company Specialty Stores Gregory Sandfort 27-9-2012
34 Macy's Specialty Stores Jeffrey Genette 23-6-2016
35 American International Group Insurance Peter Hanco 10-6-2014
36 The Travelers Company Insurance Alan Schnitzer 4-8-2015
37 Welltower Health Care REITs Thomas DeRosa 14-1-2013
38 Boston Properties Health Care REITs Owen Thomas 11-3-2013
39 UnitedHealth Managed Healthcare David Wichmann 17-8-2017
40 Humana Managed Healthcare Bruce Broussard 7-11-2011
41 Eli Lilly Pharmaceuticals David Ricks 27-7-2016
42 Merck & Co. Pharmaceuticals Kenneth Frazier 6-8-2011
43 Intel Information Technology David Krzanich 2-5-2013
13 vi. Robust Regression Analysis
Using an Ordinary Least Squares (OLS) analysis to establish the coefficient of an independent variable is problematic when the data sample includes outliers or influential observations. Outliers can be checked by creating a scatter plot and observing if largely deviating data are present. Influential observations are detected in the same way (Keller, 2012, pp. 650-651).
As the scatter plot of the Change in Average Returns Ratios data shows at least one definite Outlier, namely the ratio of the 40th company of the graph. Applying the standard
OLS regression would give a biased result. A robust regression analysis sets barriers for the value of data points and eliminates outliers and influential observations, ensuring the fit of the OLS line to all the data in the sample (Keller, 2012, 651-652). The data in the scatter plot of the excess returns on the S&P500 after the announcement is not that easily analysed. Therefore, to ensure a non-biased result, robust regression analysis are run on the data in Stata for both hypothesis model 1 and hypothesis model 2. Scatter plots can be found in the appendix 1, standard regression results can be found in appendix 4 and 5.
vii. Stata process
In Stata the two regressions models are tested. For both models two dummies are generated. For the first regression the two dummies are dummy_FemSucc and
dummy_Outsider, and for the second regression the dummies generated are dummy_FemCEO and dummy_Outsider.
Hereafter, the independent variables are regressed with their dummies as the dependent variables together and again without the control variable (dummy_Outsider). Robust and standard regression are performed for both regressions. In total eight regressions are run.
The barrier used is an α of 5%. As it is not known if the effect might be positive or negative, the test is two-sided. The coefficients are considered significantly different from zero when the given p-value is smaller than 2.5%, (𝛼=0.05
14
Results
In this chapter the results will be presented and discussed. Firstly the OLS are tested on the data. Secondly the descriptive statistics from the robust and standard regressions will be given per dependent variable. Thirdly the results of the regressions will be discussed, and significance analysed. Results that are significant for the chosen level of alpha are
underlined in the tables.
i. Linearity
Linearity is analysed by creating a scatter plot with a trend line. However, with only a dummy variable as regressor, linearity cannot be checked thoroughly, as the plot only takes the mean of each peer group into account. As the independent variable can only take on two values, we assume linearity for the OLS regression.
The scatter plots can be viewed in the appendix 1.
ii. Normal distribution
A test for the normal distribution assumption checks to see if the OLS regression is an efficient tool to analyse the data. However, both datasets have an amount of datapoints where the normal distribution of the data is not a necessity for using an ordinary least squares analysis (𝑛 > 30).
To check if the data is normally distributed one can use either a graphical or a numerical method. Two examples of the graphical method are a histogram or a Q-Q plot. Both show the distribution shape of the data sets. The numerical methods include a Jarque-Bera test on the residuals, where the skewness of the residuals of the dependent variable is analysed (Jarque & Bera, 1980). The histograms show the data to have a distribution that is strongly similar to a normal distribution. However, a Jarque-Bera test shows that the data actually are not normally distributed. The shape of the histogram suggests that the data might be shaped more like a t-distribution.
However, Lumley, Diehr, Emerson & Chen (2002) state in their research that “The t-test and linear regression compare the mean of an outcome variable for different subjects. While these are valid even in very small samples if the outcome variable is normally
distributed, their major usefulness comes from the fact that in large samples they are valid for any distribution” (p. 151).
The choice of not using an alternative method, such as implementing a maximum likelihood model in Stata, will be further discussed in the discussion.
15 iii. Multicollinearity
To make sure the independent variables are not correlated with each other to a degree that would cause biased results, correlation matrixes for both sets of dummies have been created. Multicollinearity exists if the two dummy variables have a correlation that exceeds 0.8 (Farrer & Glauber, 1967). As can be seen in the two matrixes, for both models,
multicollinearity does not exist.
The Correlation Matrixes can be found in the appendix 3.
iv. Autocorrelation & Heteroskedacity
Using a robust regression with robust standard errors undermines the effect of
heteroskedacity and autocorrelation. Therefore, no tests need to be computed to make sure the data is not autocorrelated and is homoscedastic (Keller, 2012, pp. 646-652).
v. Descriptive Statistics
Table 3; The number of observations is 66 for the first model. Both the dependent and the two independent dummy variables are included, representing the mean values, standard deviations, minimum and maximum sample values. The Outlier has not been omitted.
Table 4; The number of observations is 2979 for the second model. Both the dependent and the two independent dummy variables are included, representing the mean values, standard deviations, minimum and maximum
sample values.
Table 3 gives the summary statistics for the model of the first hypothesis. The mean of the dependent variable (0.5516323) implies an average increase of returns after the succession of 55%. The means of the dummies give a portion of 33.3 % female CEO announcements, exactly 1
3 𝑟𝑑
) and 16.7% outsider CEO announcements (exactly 1
6 𝑡ℎ
). The maximum given in the table is an increase in return of 2242.2%, this would be regarded as an Outlier.
The fourth table gives the summary statistics for the second hypothesis model. The mean of the dependent variable (0.0004337) shows an average daily excess return above
Variable Obs Mean Std. Dev. Min Max
ratio_change 66 .5516323 5.266664 -14.9622 22.42169
dummy_FemSucc 66 .3283582 .4731602 0 1
dummy_Outsider 66 .1641791 .3732338 0 1
Variable Obs Mean Std. Dev. Min Max
r_daily 2,797 .0004337 .0106029 -.066634 .047407
dummy_FemCEO 2,797 .3324991 .471193 0 1
16 the market of 0.043% after the succession announcement. The percentages for the dummies slightly differ from the first table. This is caused by the different amounts of daily returns per company due to the succession announcement having taken place on different dates for each firm. However, the differences do not exceed 0.3%, thus the portions of female CEO and outsider CEO announcements would still be close to respectively 1
3 𝑟𝑑 and 1 6 𝑡ℎ of the sample. The minimum value of -6.7% deviates a lot from the average daily return, this again advocates the use of robust regressions.
vi. Regression Results and Discussion
The results will be discussed for each Hypothesis model separately. For both hypothesis models the robust regressions gave significantly different outcomes. Therefore, only the robust regression results will be discussed. The standard regression results for hypothesis models 1 and 2 can be found in appendix 4 and 5 respectively. Underlined values are significant at an alpha of 2.5%.
Hypothesis 1;
Table 5; The robust regression results with dependent variable Change in Average Returns Ratios
(ratio_change). Independent dummy variables for female CEO announcement and Outsider CEO announcement.
Table 6; The robust regression results with dependent variable Change in Average Returns Ratios
(ratio_change). Independent dummy variable for female CEO announcements. The control variable is omitted.
Robust Regression Hypothesis 1
Number of Obs. = 66 F(2, 63) = 4.29 Prob > F = 0.018
ratio_change Coef. Std. Err. t P>t (95% Conf. Interval) dummy_FemSucc 1.121 0.406 2.760 0.008 0.309 1.932 dummy_Outsider 0.463 0.514 0.900 0.371 -0.564 1.490
_cons -0.592 0.248 -2.390 0.020 -1.089 -0.096
Robust Regression Hypothesis 1, no control var.
Number of Obs. = 66 F(2, 64) = 7.85 Prob > F = 0.007
ratio_change Coef. Std. Err. t P>t (95% Conf. Interval) dummy_FemSucc 1.137 0.406 2.800 0.007 0.326 1.948
17 Table 5 and 6 show the results of the regressions run for the first Hypothesis model, where the Change in Average Returns Ratios is the dependent variable. As shown in the table, the dummy variable for a female CEO announcement has a coefficient of 1.121 with the control variable and 1.137 without the control variable. This describes a higher average of returns for companies with an announced female CEO between approximately 112% and 114%,
compared to companies with an announced male CEO. Both times the coefficient is of significant influence (𝑝 = 0.008 < 0.025 and 𝑝 = 0.007 < 0.025). Therefore, there is a definite effect of the sex of an announced CEO on the ratios. However, the coefficient has a positive influence on the returns ratios. This is the opposite to what was expected in the hypothesis. In Table 3 the control variable does not have a significant effect on the independent variable (𝑝 = 0.371 > 0.025). This will be discussed in the final discussion of the results.
Hypothesis 2;
Table 7; The robust regression results with dependent variable Daily Excess Returns on the S&P500 (r_daily). Independent dummy variables for female CEO announcement and Outsider CEO announcement.
Table 8; The robust regression results with dependent variable Daily Excess Returns on the S&P500 (r_daily). Independent dummy variable for female CEO announcements. The control variable is omitted.
As the first regression model also took into account the returns of a company before
announcement, the second regression gives a narrower image of a company’s performance after the announcement only. The dummy for a company with an announced female CEO has a coefficient of 0.0008 with the control variable and 0.008 without the control variable.
Robust Regression Hypothesis 2
Number of Obs. = 2,797 F(2, 63) = 3.36 Prob > F = .0347
r_daily Coef. Std. Err. t P>t (95% Conf. Interval) dummy_FemCEO .0008 .0003 2.56 .011 .00019 .00146 dummy_Outsider -.0002 .0004 -.52 .603 -.00101 .00058
_cons .0006 .0002 2.92 .004 .00019 .00096
Robust Regression Hypothesis 2, no control var.
Number of Obs. = 2,797 F(2, 64) = 6.40 Prob > F = .0115
r_daily Coef. Std. Err. t P>t (95% Conf. Interval)
dummy_FemCEO .0008 .0003 2.53 .011 .00018 .00145
18 This means that companies with an announced female CEO have higher daily excess
returns of approximately 0.08% than companies with an announced male CEO.
Again, the control variable itself does not have a significant coefficient as 𝑝 = 0.603 > 0.025. In this case adding the control variable does not even change the significance level of the independent variable. The coefficient for an announced female CEO is significantly different from zero, suggesting a definite effect of the sex of an announced CEO on the short-term performance of a company.
General Results;
For both hypothesis the coefficients deciding the influence of an announced CEO’s sex are significantly different from zero and positive. Thus, a definite advantage of female CEO succession announcement on the company’s short-term performance is implied. This result is contradictory to the literature studied about the effect of board diversification and CEO succession in general.
The control variable for an outsider CEO succession announcement is not significant, and thus cannot be regarded as different from zero. This result is contradictory to the findings of Jalal & Prezas (2012), who found a definite negative influence of outsider CEO succession on a company’s returns.
No evidence is found for both the first and second hypothesis. However, the null-hypothesis, of there being no influence of a CEO’s sex, is also no longer supported, as we have found significantly positive coefficients.
One theory that could explain the results found has already been introduced in the literature review. Farrel & Hersch (2005) suggest that board diversification is driven by an internal or external demand for female representation on the board. The same could be true for the appointment of female CEO’s. If the demand for diversification is external and largely constituted by the company’s investors, meeting this demand would logically institute a rise of investments into the company, which increases its value in the short run. If the demand is driven by the shareholders or potential future shareholders of the company, appointing a female CEO would increase the demand for the firm’s shares, which leads to an increase in share price and thus, a positive return.
Another theory that would explain these unexpected results could be the glass-cliff effect. This theory describes the tendency of firms and individuals to select a female leader in times of crisis, and a male leader in times of prosperity (Ryan & Haslam, 2005). Ryan and Haslam (2005) discovered that firms that appointed female board members had performed significantly worse in the previous five months than companies who appointed male board members and therefore, concluded that female representation is most likely to occur in times of bad performance. This theory has since been applied to many different positions and fields
19 (Bruckmüller & Branscombe, 2010). If women are more regularly appointed to companies that have been performing badly, the chance of a female CEO achieving positive returns in the short run is more likely. This would explain the positive gender bias found.
There are many more theories that could drive these results, further research must be conducted to analyse which one could be of the largest explanatory value.
20
Conclusion and Discussion
The final chapter is separated into two parts. First a summary of the research topic and results is provided, from which a conclusion is drawn. Second a discussion on this research and suggestions for further research are given.
i. Conclusion
This thesis tried to find an answer to the question if the sex of a newly announced CEO influences the short-term performance of companies, indexed in the S&P500, after 2010. Two hypotheses were formed to answer this question. The first hypothesis regarded the effect of a CEO’s sex on the difference in performance of each company before and after the succession announcement. The second hypothesis analysed the difference in performance between firms with an announced female or an announced male CEO succession. The expectations for both hypotheses, based on literature studied, was a negative effect of a female CEO succession announcement on the firm’s short-term performance. Robust regressions were run with a dummy variable for the CEO’s sex.
The coefficients found for hypothesis 1 and hypothesis 2 were significantly different from zero. However, both coefficients have a positive value, indicating a positive gender bias for announcing a female CEO. These results are contradictory to the expectations contrived from the studied literature, and no evidence could be found for hypothesis 1 and 2.
Nevertheless, the null-hypotheses of there being no effect of an announced CEO’s sex on firm performance are also rejected.
Two theories are presented which could explain the results found. First a positive gender bias for women could be driven by the fulfilling of an external demand for
diversification or female representation. The second theory described the glass-cliff effect, where companies have the tendency to appoint women to leadership roles in times of crisis. Thereby, enhancing the likelihood of a positive change in short term performance for firms with a female CEO.
In conclusion a positive gender bias for the announcement of a female CEO has been found for companies in the S&P500. In the next paragraph multiple ways to enhance and expand this research will be discussed.
ii. Discussion
The use of an alternative testing method is easier said than done. The process of, for example, implementing a maximum likelihood model in Stata is quite complicated and therefore, both time consuming and more sensitive to mistakes. With the OLS regression having still a lot of explanatory power, the internal validity of the coefficients is still intact. Of course, one could argue that testing multiple models, to see which one has the best fit, could
21 enhance the validity of the results. This could be a good objective for further research into this topic.
As the amount of female CEO successions in the S&P500 is still very limited, this research can only be considered an indication of the relationship in reality. By filtering the successions used in the database the internal validity was improved. However, this has a negative effect on the external validity, as CEO succession in industries where no female CEO announcements were made were left out of the regression analyses. In order to extend this external validity, research could be done by getting access to a Chief Executive Officer succession database and regressing the female CEO announcements on male CEO
announcements of all industries in the S&P500. One could even go to the extent of including both female and male CEO succession of companies listed in multiple market indexes from different regions, for example the indexes of the largest companies on the Asian or European stock exchanges.
The control dummy variable for outsider CEO succession was not significantly different from zero for all the regression in this thesis. This may also be due to the relatively small portion of outsider CEO announcements included in the database, as Jalal & Prezas (2012) did find a large and significant coefficient when testing this variable on a larger database. Excluding the variable from the results of this thesis would therefore have been a dubious decision, even though it is common to omit non-significant regressors.
To gain more insight into the effects of a CEO’s sex on firm performance one could also compute the excess returns over both the short and the long term, to analyse if the bias found is time-resistant. The literature studied on diversified board suggests no change in the bottom line performance of a company in the long run, this could be tested for the CEO position too.
A final suggestion for future research could be to analyse if there is a correlation between the measure of diversification of a company’s board and the appointment of a female CEO, as one of the main responsibilities of a board is the selection and appointment of a new Chief Executive Officer.
22
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Appendices
I.
Appendix 1; Scatter Plots:
-20,00 -15,00 -10,00 -5,00 0,00 5,00 10,00 15,00 20,00 25,00 -1,5 -1 -0,5 0 0,5 1 1,5 2 2,5 Rat ios Dummy Value (0-1)
Scatter Plot, with Linear prognosis;
Change in Average Returns Ratios
00.000 00.000 00.000 00.000 00.000 00.000 00.000 00.000 -1,5 -1 -0,5 0 0,5 1 1,5 2 2,5 Exce ss Re tu rn s Dummy Value (0-1)
Scatter Plot, with Trend line prognosis;
Excess Returns on the S&P500.
30
II.
Appendix 2; Histograms & Jarque-Bera Test results:
The outcome of the Jarque-Bera tests, where the null-hypothesis implies normally distributed data.
Skewness/Kurtosis tests for Normality; Ratios ---joint--- Variable Obs. Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
66 0.0000 0.0000 32.19 0.0000
Skewness/Kurtosis tests for Normality; Exc. Ret. ---joint--- Variable Obs. Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
31 III.
Appendix 3; Correlation Matrixes:
Both correlation matrixes show a correlation between the dummies of less then 0.8. Thus no multicollinearity is found.
Hyp. 1 dummy_FemSucc dummy_Outsider
dummy_FemSucc 1.0000 -
dummy_Outsider .0333 1.0000
Hyp. 2 dummy_FemCEO dummy_Outsider
dummy_FemCEO 1.0000 -
32 IV.
Appendix 4; Standard Regressions Hypothesis 1:
The standard OLS regressions give non-significant coefficients for both dummies. These results deviate from the results from the robust regression analysis, which is caused by a combination of outliers, heteroskedacity and autocorrelation.
Standard Regression Hypothesis 1
Source SS df MS Number of obs = 66
Model 42.326 2 21.163 F(2, 63)= = 0.760 Residual 1760.627 63 27.946 Prob > F = 0.473 Total 1802.954 65 27.738 R-squared = 0.024 Adj. R-squared = -0.008 Root MSE = 5.286
ratio_change Coef. Std.Err. t P>t (95% Conf. Interval)
dummy_FemSucc 1.670 1.370 1.220 0.225 -1.059 4.415
dummy_Outsider 0.336 1.747 0.190 0.848 -3.155 3.827
_cons -0.061 0.844 -0.070 0.942 -1.748 1.626
Standard Regression Hypothesis 1, no control var.
Source SS df MS Number of obs = 66
Model 41.292 1 41.292 F(1, 64)= = 1.500 Residual 1761.661 64 27.526 Prob > F = 0.225 Total 1802.954 65 27.738 R-squared = 0.023 Adj. R-squared = 0.008 Root MSE = 5.247
ratio_change Coef. Std.Err. t P>t (95% Conf. Interval)
dummy_FemSucc 1.678 1.370 1.220 0.225 -1.059 4.415
33 V.
Appendix 5; Standard Regressions Hypothesis 2:
The standard OLS regressions give non-significant coefficients for both dummies of an alpha of 2.5%. However, at a barrier with an alpha of 5 % the dummy for an announced female CEO would be significantly different from zero and positive. These results deviate from the results from the robust regression analysis, which is caused by a combination of outliers, heteroskedacity and autocorrelation.
Standard Regression Hypothesis 1
Source SS df MS Number of obs = 2,797
Model .00048 2 .00024 F(2, 63)= = 2.12 Residual .31386 2,794 .00011 Prob > F = .1203 Total .31433 2,796 .00011 R-squared = 0.0015 Adj. R-squared = 0.0008 Root MSE = .0106
r_daily Coef. Std.Err. t P>t (95% Conf. Interval)
dummy_FemCEO .0009 .0004 2.04 .041 .00003 .00170
dummy_Outsider .0001 .0005 .20 .841 -.00094 .00116
_cons .0001 .0003 .49 .627 -.00038 .00064
Standard Regression Hypothesis 2, no control var.
Source SS df MS Number of obs = 2797
Model .00048 1 .00047 F(1, 2975)= = 4.20 Residual .31385 2,795 .00011 Prob > F = .0405 Total .31433 2,796 .00011 R-squared = .0015 Adj. R-squared = .000 Root MSE = .0106
r_daily Coef. Std.Err. t P>t (95% Conf. Interval)
dummy_FemCEO .0008 .0004 2.05 .041 .00004 .00171
