For the ninth year in a row, hkp/// group has examined top executive compensation in a global context. This article gives insights on the methodology for gender pay gap analyses as well as on the
results of this year’s special analysis: compensation differences between female and male managers in selected countries world-wide.
The gender pay gap – as well as the methodology behind its calculation – has been widely discussed in the media. But questions have been raised about the reliability, quality and accessibility of the data, as well as the validity of the corresponding results. Indeed, it is very difficult to obtain a comprehensive data set – a data set that includes information on compensation, job value, degree of part-time employment, education level, number of children or duration of parental leave. That lack of comprehensive data makes analysis of the gender pay gap rather challenging and may, in fact, lead to contradictory results and misleading conclusions.
This is a challenge companies face even when it comes to analyzing their own internal data. Because the availability of quality data is so limited, it becomes even more important to apply a robust analytical framework to the information that is available in order to draw robust conclusions.
The unique hkp/// group approach of collecting and matching compensation data for its compensation surveys provides for an extremely high level of consistency and comparability of data across companies and countries. This enables us to conduct differentiated fair pay analyses, especially regarding compensation differentials attributable to gender.
hkp/// group Gender Pay Gap Methodology
This paper analyzes pay differentials between male and female incumbents in top and middle management roles in selected countries. hkp/// group employs a regression analysis, often used in scientific applications and especially effective in analyzing differences in compensation.
Accordingly, target direct compensation is expressed as a linear function of explanatory variables, such as gender, age, job family, job value and company and their corresponding coefficients. If the coefficient for the variable gender is positive and statistically significant, indicating that random effects can most probably be excluded, there is statistically valid evidence of a gender pay gap.
Using this regression analysis, hkp/// group is able not only to calculate differences in reported compensation between female and male managers, but also to deliver deeper explanations on several factors which might drive these differences. In order to examine possible reasons behind the gender pay gap, a stepwise regression analysis is conducted, considering additional variables in each step of the analysis.
An example for the stepwise calculation of the gender pay gap is shown in Fig. 1. Focusing on German data, the difference in target direct compensation between males and females appears to be 15% when accounting for gender as the sole explanatory variable (unadjusted gender pay gap).
Fig. 1: Gender pay gap five step analysis – each step adds a new explanatory variable
In this scenario, we do not consider any other influencing factors such as age or job content between male and female incumbents. However, one must assume that there is more behind this 15% difference in compensation in favor of males than gender alone. So, other explanatory variables are added to the model to isolate potential additional causes of the “gender pay gap”. For example, when considering age as an additional driver of pay differentials (males are on average older than females in the dataset) the difference in compensation between male and female managers attributable to gender alone is reduced by almost half to 8.3%. Of course, age might be interpreted as a proxy for length of service. So, one might conclude that experience brought to the job has a strong impact on compensation.
By using the hkp/// group approach to fair pay analyses, we are not only able to calculate the compensation differences between female and male managers, but also to deliver explanations on additional factors which drive this difference in pay.
Jennifer S. Schulz, Senior Manager hkp/// group
In the next step, the hkp/// group model also considers that males tend to work in higher-paid job families (such as finance and management board) than their female colleagues. This consideration further reduces the “gender” difference in compensation from 8.3% to 8.1%.
Not surprisingly, job value, or ‘job grade’, also has a substantial impact on compensation levels. hkp/// group uses its own grading system, hkp/// Executive Levels, (and translates and integrates all other common grading approaches) in order to make different jobs comparable among different companies on the basis of their value to the company. Including job value into the model reduces the gender-based differences in target direct compensation even further to 3.4%.
In the final step of the regression analysis, we cover company-specific differences. This step allows us to define the gender pay gap within the same company and eliminate company-specific characteristics such as industry or differences in compensation policies and market positioning. Taking into account all of the above-mentioned factors, the analysis results in an adjusted gender pay gap of 2.5% for Germany.
The data set represents an executive population, and as such the analysis is not representative of the totality of the working population of Germany. Nor does it claim to cover all possible factors. But it quantifies the impact of different factors on compensation and allows for a statistically valid comparative analysis across countries.
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