Gender Monitoring
Since 2000, North Rhine-Westphalian State law requires institutions of higher education to establish Gender Equality Plans that set specific targets for the representation of women in leadership positions. In order to comply with this requirement, sex-disaggregated data collection was first established. RWTH has since continually developed the collection process and criteria. The Equal Opportunities Office publishes annual reports on the number of students enrolled in the degree programs at RWTH, how many degrees are awarded every year, and tracks their proportional representation through Ph.D. programmes, habilitations, assistant professorships and tenured professorships. Because RWTH is a technical university and representation and involvement of women in STEM fields is generally low in Germany, equal opportunity measures put women in STEM into focus. The development of the statistical representation of women in this field is therefore of specific interest and the Equal Opportunities Office publishes these data in a “STEM Ticker” (a short annual presentation of the current data and progress). Additional presentations are published for the different University status groups and leadership positions.
ACTORS AND STAKEHOLDERS
Setting the goals: University leadership, faculty leadership with consultation from the Equal Opportunities Officer
Ongoing data collection and monitoring: Human Resources department, Equal Opportunities Office, Vice Rectorate for Human
Resources Management and Development
AUTHOR’S REFLECTIONS
What would you do the same/differently another time?
What have you learnt? Do you see relevance for this in other contexts?
Data collection is essential to gender monitoring. We currently collect data on male and female
employees and students. For one, this does not represent gender diversity. We are now in the process of also collecting
data on non-binary employees and students. Since the number of persons who are recognized as non-binary according to
federal law is currently rather low, however, this poses additional challenges for data protection. So, while this data
will be collected, it will only be published if it poses no risks of accidental “outings”; i.e. if this data cannot
be traced back to a specific person.
Additionally, we have learned that “women” constitute a rather diverse group of people. Intersectionality has taught
us that our measures – while intended for all – might not reach and support every woman equally. Additional factors
such as race, age, (dis)ability, sexual orientation or religion may play a role in how effective a specific measure is
and for whom. A more distinct collection of data may therefore be preferable. Our current reporting system on a federal
and state level, however, does not yet support an intersectional approach.