Best practices for the collection, monitoring, and publication of gender-disaggregated data

Research performing and funding institutions need to collect fine-tuned data, especially gender-disaggregated data, which is sub-categorized into a typically binary distinction of male and female, to uncover gender inequalities in their processes. This type of data must be collected regularly to determine how potential imbalances evolve over time and to establish a baseline. The development and success of specific measures to reduce inequalities and their impact can be demonstrated and determined using the regularly collected gender-related data. Data collection and evaluation can help to determine the impact, quality and significance of an internally applied policy or activity by providing credible and useful information. It enables the organisations to take account of lessons learnt that can be used in future decision-making processes.

The different steps of collecting, monitoring, and publishing gender-disaggregated data

• Evaluate your current data collection workflow within your organisation, research performing organization and research funding organizations, and in collaboration with the department of human resources.
• If any form of employee data does not yet exist in your organisation, serious efforts must be made to set up and maintain a consistent workflow and data management in-house.
• Check which data are already monitored and which data are currently not being recorded for each employee of the organisation (including employees with both short- and long-term contracts).
• Always make sure that your data recording is in line with local GDPR regulations. Please consult with your GDPR officer in-house.
• As soon your employee database is available, you carry out a statistical analysis to receive quantitative results on, among others, gender-disaggregated data as a proxy for your organisation’s current gender equality and diversity index.
• While employee data needs to be collected annually, a complete synopsis of your analysis would be published regularly internally and externally at a recommended frequency of every 3 years minimum. A short summary and success stories on actions taken to improve gender equality can be published in the annual progress reports.
• Based on the outcome of the data analysis, organisations should define explicit objectives for gender equality, either linked to national objectives or beyond. These objectives must be explicit, measurable, monitored and reported back on at a pre-defined time.
• Depending on the set objectives, actions must be undertaken to drive progress and meet these objectives in the given timeframe. If an objective is not met, additional actions are needed, and the topic prioritized.

Types of data to be collected, monitored, and published

The type of data collected will vary according to the type of institution. Some data will be the same for research performing organization, university, and research funding organizations. Please find below all data points that are suggested for collection for a most complete overview of your organisation’s structure and gender balance.

As certain socio-demographic data can be specific per institution, these best practices can be implemented by the human resources departments with the help of the Gender Working Group to homogenize data collection, monitoring and analysis most effectively among the different institutions.

If there is a decision to collect more detailed information about employees’ profiles (role descriptions etc.), be aware to collect these data sensibly and re-evaluate potential introduction of bias based on those skills. Brief your personnel on what standardized skills (social and professional) should be added to reduce any potential bias or inequalities.

General socio-demographic data that must be collected for each administrative and scientific employee per research performing and funding organisation are:

• Personal information (Name, Nationality, Gender, Age)
• Professional information (Job position, Date of Employment, Educational degree, Employment contract type, Working time, Salary)
• Scientific staff specific (Scientific field, Academic position, Academic age)
• Career progression (Career breaks due to leaves, Promotion details, Training details)