Frequently asked questions Source: Fach- und Führungskräfte pressmaster Fotolia equal pace is jointly performed by Co-funded by the PROGRESS Programme of the European Union The consortium is also financially supported by its associate partner the German Federal Ministry for Family Affairs, Senior Citizens, Women and Youth. 1
1. What about data protection in the equal pace web tool. How can you ensure that my uploaded data are secured? The uploaded data are only stored for the purpose of generating an outcome report (PDFformat) and are deleted afterwards. The exchange of the data occurs via https-standard (https://equal-pace.personalmarkt.de/). If you have any further questions, please contact our partner PMSG PersonalMarkt Services who is responsible for the technical implementation and support of the equal pace web tool: PMSG PersonalMarkt Services Frank Behrmann Hoheluftchaussee 18 20253 Hamburg / Germany Email: behrmann@personalmarkt.de 2. Which type of companies can use the equal pace web tool? What about technical restrictions when using it? Because of its flexibility, companies from all sectors in the selected countries are invited to use the equal pace web tool. It is open to public and private companies as well as to companies of various sectors, such as trade, finance, manufacturing or services. Minimum requirements: The equal pace web tool relies on statistical calculations, namely on regression techniques. For this reason, we recommend that companies should include data of at least 50 employees in any analysis. Moreover, we suggest considering a minimum of 20 males and 20 females. This means that small(er) companies may also use the equal pace web tool but we assume that the results are likely to be biased. PLEASE NOTE: To ensure a high quality of the results, please check if the minimum requirements are met. In general, the more observations are available for an analysis and the more men and women could be considered, the higher should be the quality of the results. Please also keep in mind, that the calculations to compile an outcome report take more time the larger the dataset is. 3. Why are two alternative variables provided with date of entry and tenure_2014 (in the test data set)? The variables "date of entry" and "tenure" are alternative information; you only need to prepare one of these variables for an analysis. For standard application, we recommend to use "date of entry" because it allows you to replicate your analysis for various years without additional preparation. If you wish to analyse the impact of career breaks (e.g. parental leave periods) on the gender pay gap, you are requested to use tenure (referring to the reference period you have chosen) instead of date of entry. In this test data set the variable "tenure_2014" refers to 2
the reference period of the year 2014 (from 01.01.2014 to 31.12.2014). In detail, tenure indicates the duration of an employee within the company (rounded down). Please see FAQ No. 5 to find out how to analyse the impact of career interruptions on the gender pay gap. PLEASE NOTE: If you use tenure when preparing your dataset, please ensure that the data are fitting to the reference period which you have chosen. 4. What is my reference period and which employees do I have to consider in an analysis? The reference period is the time period for which you wish to perform an analysis. You are requested to insert the reference period just before uploading your dataset by indicating the start date (as DD.MM.YYYY) of a yearly period. The reference period can be any (completed) timeframe in the past; e.g. if you insert 01.08.2010, the reference period is 01.08.2010 to 31.07.2011. This means that you can perform analyses for different time periods and by comparing the results you are able to reveal how the (adjusted) gender pay gap changes over time and how the impact of specific drivers on the gender pay gap has changed. Basically, you are requested to consider all employees in your company. In very large companies it could make sense to restrict the analysis to subsidiary companies or to focus on specified departments such as marketing, accounting, sales, human resources, etc. In addition, by comparing the results of different departments you may get valuable information about structural differences in the relevance of the drivers on the gender pay gap. Please ensure that the minimum requirements for using the equal pace web tool are met in any case (see FAQ No. 2). 5. How can I analyse the impact of career breaks on the (adjusted) gender pay gap? If you are interested in the impact of career breaks (e.g. parental leave and/or care for relatives) on the gender pay gap, this can be done in the equal pace web tool as follows. Please note that the analysis of career interruptions is optional and can only be performed in an indirect operation: (1) The first step is simply to generate an outcome report (PDF-format) without considering any career breaks. (2) The second step is to calculate the tenure of each employee (until the end of the reference period for which you will perform the analysis) minus the cumulative duration of his/her career breaks since the date of joining your company. The result should be put into a new column (e.g. tenure_breaks ) in your dataset (excel-sheet). (3) The third step is to upload your dataset once again and, afterwards, to use the same variables as before for the analysis except the column date of entry or tenure. Instead, you choose the column tenure_breaks and, finally, generate a new outcome report. (4) The last step is the interpretation. By comparing the results in both reports you can identify the impact of career breaks on the gender pay gap in your company. In particular, you can see how the results in the dashboard in the management 3
summary (page 9) change if you consider career breaks. E.g. one would expect that the impact of tenure on the gender pay gap rises because women tend to have on average longer durations of parental leave. In this case, the part of the gender pay gap which is explained by tenure (please see the tachometer Tenure ) should have been larger when considering employment breaks. PLEASE NOTE: Only the duration of career breaks since the employee s entry in the company could be considered! Therefore, you might underestimate the impact of career breaks if (many) employees start to work in your company after they have completed child care periods. 6. The total remuneration in our company consists of many salary components. Which of the salary components do I have to use for the analysis of the gender pay gap? As far as the data for each salary component meets the requirements as mentioned in FAQ No. 2, you can analyse each of them separately or any combination of them which you are interested in. Firstly, we recommend performing an analysis with the basic payments as starting point and as a reference for further comparisons. The corresponding outcome report will reveal the (key) drivers of a potentially existing gender pay gap according to the basic salaries (as the only variable in the field Total cash p.a. ). In addition, you might be interested in the impact of the drivers if e.g. bonus payments (variable bonus or premium ) are considered: - If the data meet the requirements as mentioned in FAQ No. 2, you can use them as the only (single) component in the field Total cash p.a.. After generating a new outcome report (everything else being equal), you will receive valuable information: E.g. you find out if a gender pay gap in bonus payments exists and what is the impact of the considered variables (e.g. education, job complexity, management level, etc.) on this pay gap. Furthermore you can compare the results to those of the initial analysis with the basic payments as the frame of reference: E.g. if bonus payments are more frequently paid to managers or supervisors and if less women are in such positions, one might expect that the results do not only reveal a gender pay gap in bonuses but also to show a higher impact of the variable management level on this pay gap. - If the data for bonus payments do not meet the requirements as mentioned in FAQ No. 2, we recommend to jointly analyse basic payments and bonus payments. In this case, please drag and drop both salary components into the field Total cash p.a. after you have uploaded your data. As a result, you get a new outcome report (everything else being equal) and find out what is the impact of the drivers of a (potentially existing) gender pay gap which now is represented by the difference in the sum of basic and bonus payments between women and men. You can compare the results to those of the initial analysis with the basic payments as the frame of reference: As a key result, you will receive new insights on how the impact of the drivers will change if total remuneration consists of both salary components. 4
If you are interested in the impact of the drivers on gender pay differences regarding one or more salary component(s) you can proceed as explained above. Therefore, you can select any salary component you wish to the basket Total cash p.a.. However, please ensure that the data requirements as mentioned in FAQ No. 2 are met. PLEASE NOTE: Before uploading your data, please check that all payments are adjusted to full-time data and are fitting to the reference period (yearly payments). 7. May I include data for fixed-term and part-time employees? Basically, you can consider fixed-term and part-time employees in an analysis. As the equal pace web tool requires full-time data for the reference period (which involves annual data), you are requested to convert the data for fixed-term and part-time employees to this default. 8. How to provide data for the variable educational attainment? We know from scientific research that education has a crucial impact on the remuneration of an employee. Basically, the income perspectives grow if educational attainment increases. Due to technical reasons, it is more convenient to include a typical (standardised) number of years in education 1. In economic research, this is a frequently used procedure because the number of years spent in educational programmes is assumed to be highly correlated with the educational attainment. Since the actual number of years in education is generally not available for every employee or could only be identified at unreasonable costs, a typical number of years spent in educational programmes is used. In equal pace, six items could be used to reassign each employee s educational attainment. For the equal pace version of the United Kingdom, we define the following categorisation: Item Definition (version for the United Kingdom) 1 Unknown or unfinished training or lower qualification levels as in [2] 2 3 4 5 General National Vocational Qualification Foundation Level or Intermediate Level or qualifications equivalent to National Vocational Qualification Levels 1 or 2, or equivalent degree General National Vocational Qualification Advanced Level or National Vocational Qualification Level 3 or General Certificate of Education A/AS (equivalent), or equivalent degree Bachelor s degree, Higher National Certificate or Diploma (HNC, HND), Diploma in Higher Education (incl. nurses training), or equivalent degree Master s degree, professional qualifications in various fields (e.g. accountancy, law, audit), postgraduate diploma and certificate, or equivalent degree 6 Doctorate, or equivalent degree 1 The assumed number of standardised years in education is 10 / 12 / 13 / 15.5 / 17 / 19 for the items 1 to 6. 5
9. In the equal pace web tool the potential work experience is used. Why don t you ask for the actual work experience? We focus on the potential work experience, which we define as follows: potential employment experience = age (years) standardised years in education 6 (pre school years) where age (years) is automatically calculated from date of birth and the standardised years in education is derived from the variable educational attainment (please see FAQ 8). The reason for using a potential number of employment years is that the actual employment experience is rarely available. Since the employment biography of an employee may start directly after schooling, training or graduation at universities and may include employment breaks, such as unemployment periods or parental leave, this information is not available if an employee enters your company at later years. Therefore, we consider the potential employment experience which is an (adequate) approximation for the actual employment experience and ensures data consistency for all employees. In addition, taking the potential employment experience also reduces the expenses of collecting data because only data for the age of an employee are needed. 10. I want to prepare the data for the variables job complexity and management level. How do I proceed? Firstly, we strongly recommend to use job descriptions, staff appointment schemes, information from collective bargaining agreements (if applicable) or other appropriate information about the job positions in your company to create a structural plan to categorise every job position along the variables job complexity and management level. Afterwards, you can use up to 6 items to classify each job position. The variable job complexity focuses on the functional career level which means that it describes the requirements or rather the complexity of the tasks to be performed in a specific workplace. The variable management level focuses on the executive career level indicating the responsibility or rather the hierarchical level of a specific workplace. Please also see the help_file.pdf which is available via https://equal-pace.personalmarkt.de/. PLEASE NOTE: Both variables are related to the workplace and they are not related to the employee s skills. As mentioned in the help_file.pdf, the qualification or the skills that are required for a specific position may support you to assign a specific job to one of the six items regarding the job complexity of a workplace. You do not need to use each of the six items for the classification for both variables. E.g. if four management levels adequately characterize the (hierarchical) profile of your company, you can use item 1 to item 4 for the assignment of the workplaces in your company to the variable management level. PLEASE NOTE: The assignment of up to six values to characterize a workplace regarding job complexity and management level is not equivalent to the assignment that results from 6
applying a relevant job evaluation scheme at your company. It is only used to characterise a workplace on the basis of two variables. 11. The unadjusted and the adjusted gender pay gap are displayed in the management summary of the outcome report. What is the difference between both gaps and how do I interpret the adjusted gender pay gap? Initially, the unadjusted gender pay gap (GPG) is displayed as unadjusted GPG = average payments of women average payments of men average payments of men Hence, the unadjusted GPG is negative if women receive lower payments than men and vice versa (see e.g. outcome report no. 1, p. 6). The adjusted gender pay gap is derived from an ordinary least squares regression. This calculation takes simultaneously into account the following person- and job-related characteristics (variables): - education - tenure - potential work experience - job complexity - management level - [gender] As result, the adjusted gender pay gap could be interpreted as the remaining difference in average payments between women and men if women and men do not differ regarding the characteristics mentioned above, such as education, tenure, etc. In other words, it is the unexplained GPG referring to this model. This also means that the remaining gender pay gap could possibly have been lower if additional person- or job-related characteristics had been considered. But why does the equal pace web tool not consider more variables? This is because the more information (variables) are employed in the statistical regression the more observations are needed for applying the tool adequately (see FAQ No. 2): E.g. the minimum number of employees for analyzing the gender pay gap would have to be increased. In addition, if more observations had been required this probably would have been reduced the incentives to use the tool. Therefore, the number of characteristics is restricted to the selection mentioned above which is assumed to be of high relevance for explaining the gender pay gap (according to scientific standards). Comparing the results of the unadjusted and the adjusted gender pay gap reveals the part of the gender pay gap which is attributed to the model s characteristics: This part equals the difference between the unadjusted GPG and the adjusted GPG. PLEASE NOTE: Due to (probably) missing relevant personnel or job characteristics, the remaining (unexplained) gender pay gap must not to be interpreted as unequal treatment of males and females. Moreover, empirical studies studies point out that further reasons can be relevant for explaining the adjusted gender pay gap; these include e.g. behavioral differences between men and women in individual salary negotiations, a different availability outside the normal business hours or at weekends (and its impact on salaries) or different preferences for relative remuneration systems, where own remuneration depends on the performance of 7
others. In this context, some studies indicate that these remuneration systems increase the performance incentives for men something more strongly than for women. 12. How do I interpret the tachometers in the management summary of the outcome report? Please take the following example into consideration (see outcome report, page 9): If you focus on the tachometer educational attainment : If the pointer is on the left it approximately displays the percentage points by which the gender pay gap would have been reduced if men and women (on average) had not differed in their educational attainment. Conversely, an indicator pointing to the right approximately displays the percentage points by which the gender pay gap would have been larger if men and women (on average) had not differed in their educational attainment. The corresponding figures are shown in the Table 2.3. PLEASE NOTE: Due to technical reasons, the calculations to estimate the individual impact of each characteristic in Illustration 2.4 and Table 2.3 have to be performed separately. Thus, the values displayed are only approximations and their sum is not (exactly) equal to the unadjusted gender pay gap. 13. What do I have to consider regarding the format specifications in my data set? date of birth, date of entry: This type of data have to be stored in the format DD.MM.YYYY. If the default in your data set differs and cannot be changed into this format in your csv-file, you are requested to re-assign the format of your data after uploading your data. E.g. if the format of your data is DD/MM/YYYY, you are requested to enter this data format in the upload process: Total Cash (basic payments and other salary components): Please ensure that all payments and salary components you wish to consider for an analysis are (rounded) to integers. You are requested to use no separators or decimals. E.g. if the basic salary of an employee in your company is 76,543.21, then please consider 76543 in the column basic payments in your csv-file. 8
PLEASE NOTE: All data in your csv-file have to be integers except date of birth, date of entry and gender. Therefore, please do NOT apply decimals and do NOT use thousands separators, e.g. comma or decimal point. *** 9