Empirical evidence of Gender Diversity and financial performance

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The “Glass cliff” phenomenon

To compare the differences in performance prior to a female and a male appointment, a matched sample is conducted, in line with previous research (Adams et al., 2009; Elsaid & Ursel, 2017; Hennessey et al., 2014; Ryan & Haslam, 2005). The data collected for the test includes year of appointment, company name and name of appointee which are extracted from the company data of the study. In addition, the “Industry Classification Benchmark”code (ICB) is used to divide the companies into industries and collected from Nasdaq (2018).
The ICB-codes are divided into main categories classified as “Industry” and subcategories classified as “Supersector”. The selection of matched control samples should, if possible, only change one variable (Lyness & Thompson, 1997), which in this study is gender. Hence, a matched male sample is only compared to the same level of directorship, e.g. chairman to chairman, board of director to board of director and employee representative to employee representative. Although, chairmen are compared to deputy chairmen, if no chairman is found, and employee representatives are compared to deputy employee representatives if no employee representative is found.
Firstly, an investigation within the company who appoints a female director is made, to see if a male is appointed during the same time. This is the most similar match, due to the fact that the appointments of the female and male director are in the same financial situation. However, these matched appointments decrease the possibility to find evidence for the “glass cliff” phenomenon. Secondly, if no male appointee is found within the same company, a male appointee within the same year and “Supersector” is selected from the sample. Thirdly, if no male appointee is found within the “Supersector”, a further search within th Industry” is conducted during the same year. Finally, in some specific cases a search for a male appointee is made within companies connected to each other in terms of similar operational activities when no matched appointment is found within the “Industry”. If these requirements are not fulfilled for the male appointment, both appointments are excluded, as seen in Table 3.
To evaluate the financial performance preceding the appointment of the female and matched male directors, the four measurements are used. Each performance measurement is calculated by the difference of the year preceding the appointment (n-1) and two years preceding the appointment (n-2), divided by (n-2). For example, concerning ROE, if a female is appointed in 2016, ROE is examined as the difference between 2014 and 2015. A positive difference indicates an upturn in the development of the performance measurement and conversely for a negative value. This ensures that the performance measurements are only analyzed preceding the appointment. Hence, the first year examined for an appointment of a female director is therefore 2009. However, due to how the study is constructed, the performance measurements of 2016 do not have an impact on the “glass cliff”, although the study captures the effects from the financial crisis of 2008.

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Female directors impact on financial performance

The Pearson correlation analysis is conducted to examine the relationship between the selected financial performance measurements and female directors. As seen in Table 4, four correlation analyzes are made, one for each dependent variable against the independent variable. Abnormal values for ROE and EBIT-margin are excluded in order to receive more accurate correlations. As seen in Table 2 for minimum and maximum values, the study rejects values over 100 and under -100. Despite the improvements made, the standard deviation for both of these measurements (ROE= 17.76; EBITm= 24.64) are still higher compared to Tobin’s Q-test (3.26) and ROA (11.03). This is mainly because Tobin’s Q-test and ROA are based on total assets, with a high value for companies with a market capitalization over 1 billion €, in relation to the other variable, leading to lower values of the performance measurements and hence the standard deviation. Due to this, different number of observations are received for the dependent variables.
The results from Table 4, present a positive correlation between proportion of females and each of the performance measurements. However, the correlation coefficients are relatively close zero, which indicates that the strength of the relationship between the variables are weak. All the accounting-based measurements receive significant positive correlations, while the market-based measurement receive no significant correlation (p=0,283). Hence, the result is interpreted as no relationship between the two variables exists. Concerning the accounting-based measurements, the coefficient for ROA and proportion of females (r=0,084) is statistically significant at a 95% level. This indicates a 5% chance that no relationship between the two variables exists in the population. Both ROE (r= 0,100) and EBIT-margin (r=0,111) are statistically significant at a 99% level, indicating a 1% chance that no relationship between the two variables exists in the population.

1. Introduction
1.1 Background
1.2 Problem .
1.3 Purpose
2. Literature Review and Theoretical framework .
2.1 Gender Diversity
2.2 Empirical evidence of Gender Diversity and financial performance
2.3 Empirical evidence of the “Glass Cliff “
2.4 Performance measurements
3. Regulations of the Board of Directors in Sweden
4. Methodology and Method
4.1 Data Collection and sampling .
4.2 Performance measurements .
4.3 Female directors’ relation to financial performance
4.4 The “Glass cliff” phenomenon
5. Empirical results and Analysis
5.1 Female directors impact on financial performance .
5.2 Statistical data of the “glass cliff”
5.3 Annual specific data for gender diversity
5.4 Limitations and future research
6. Conclusion
7. References