Data And Information On Gender Monitoring

Monitoring is a systematic, long-term observation. It verifies the achievement of objectives: detect changes over time to react to them, if necessary, or ensure compliance with specific set values. Monitoring is descriptive and aims to regularly record and analyze a project’s or a measure’s input, output and outcomes. At the same time, monitoring can serve as an early warning system for possible unexpected developments. 

Evaluations, on the other hand, are carried out selectively to gain knowledge about possible effects (ex-ante), the implementation of a program or measure (implementation), or effects for longer periods (outcome and impact). An evaluation goal can be a causal attribution of effects to measures. Monitoring is also distinguished from evaluation by its periodicity. While monitoring is regular and recurring and often creates time series, evaluation is usually carried out singularly at a specific point in time. Monitoring and evaluation are, therefore not synonymous and should ideally complement each other. However, quality assurance refers to any organizational and technical measures that create and maintain the defined quality of a product or service. Therefore, the goal is to maintain a certain standard regarding the quality of a product or service. Monitoring, evaluation and quality assurance share the defining feature that they have a descriptive and analytical character and imply concrete use. Thus, knowledge gained through data collection facilitates action decisions and helps develop measures.

Monitoring and controlling also have certain conceptual proximity. Controlling is the part of an organization responsible for the planning, management and control of all organizational areas. Controlling refers, among other things, to goal definition and monitoring of planned and actual developments. Controlling in business enterprises thus assumes monitoring tasks, among other things, but concentrates strongly on aspects relevant to the company (e.g. cost aspects). Therefore, monitoring goes beyond the scope of controlling.

Sources:

Filsinger, Dieter (2014): Monitoring und Evaluation. Perspektiven für die Integrationspolitik von Bund und Ländern. Expertise im Auftrag der Abteilung Wirtschafts- und Sozialpolitik, Gesprächskreis Migration und Integration der Friedrich-Ebert-Stiftung. (URL: 11039.pdf).

Krems, Burkhardt: Monitoring. In: Online-Verwaltungslexikon. (URL: https://olev.de/).

PHINEO gemeinnützige AG (2017): Wirkung lernen – der Online Kurs. (URL: https://www.wirkung-lernen.de/).

Stockmann, Reinhard (2002): Was ist eine gute Evaluation? Einführung zu Funktionen und Methoden von Evaluationsverfahren. CEval-Arbeitspapiere 9. Saarbrücken: Centrum für Evaluation. (URL: workpaper9.pdf).

Voigt, Kai-Ingo: Qualitätssicherung. In: Gabler Wirtschaftslexikon. (URL: https://wirtschaftslexikon.gabler.de/definition/qualitaetssicherung-44396).

Weber, Jürgen: Controlling. In: Gabler Wirtschaftslexikon. (URL: https://wirtschaftslexikon.gabler.de/definition/controlling-30235).

Gender monitoring is an administrative system for permanently observing and controlling how an organization achieves its gender equality goals. Measuring inequality is necessary to make structural discrimination visible and communicable. Gender monitoring aims to create awareness of gender inequalities and to achieve equality among people of different genders. The underrepresentation of a group can be an indication of structural discrimination. While data collection alone does not reduce discrimination or underrepresentation, it is a tool to support the development, implementation and review of (gender equality) measures. Gender equality measures aim at reducing inequality and equal opportunities for all members of society. Monitoring is thus an instrument on the way to gender equality.

Regular, systematic monitoring is necessary to identify gender inequalities and long-term changes in gender equality. The data basis and the construction of suitable indicators to measure gender equality are crucial. In this context, gender monitoring aims to go beyond gender-disaggregated data (so-called “sex counting” or head counting) to avoid reducing gender equality to quantifiable dimensions.

Gender monitoring creates a data basis using suitable indicators to deduce the needs for action for decision-makers. Based on the findings of gender monitoring, universities and research organizations may develop gender equality goals and measures and review their achievement. Gender monitoring is, therefore, never completed but follows a process character. Setting gender equality policy goals and developing gender monitoring go hand in hand.

Gender monitoring shows how universities and research organizations progress regarding gender equality. They may use monitoring to communicate and legitimize their success in gender equality internally and externally.

In all German federal states, universities are obliged to draw up a plan for the advancement of women or a gender equality plan (link in German). In some federal states (Berlin, Hamburg, Hesse, North Rhine-Westphalia, Saarland, and Thuringia), there is also an obligation to report on the gender equality plan (link in German). Whether or not there is a reporting obligation, the legal basis for drawing up a gender equality plan results in the need for regular and comprehensive gender (equality) monitoring at universities. According to the AV-Glei (link in German), the implementation agreement on gender equality, research organizations must also develop concrete targets for staffing ratios based on the cascade model and gender equality measures and evaluate these regularly.

Goals Of Gender Monitoring

• Measuring inequality
• Making underrepresentation/discrimination of specific population groups visible and communicable
• Identifying the need for action by decision-makers
• Developing measures based on gender equality data
• Equal opportunities for all members of society of any identity or identities

Phases Of Gender Equality Monitoring (cf. Ahyoud et al. 2018: 32)

1. The initial collection of gender equality data
2. Analysis of the situation of disadvantaged groups
3. Definition of goals of action in different fields
4. Development of gender equality measures both at the societal level and within organizations
5. Implementation of measures
6. Re-assessment/documentation of the extent to which discrimination has been reduced or continues to exist
7. Monitoring (and evaluation) of the effects of the measures

Sources:

Ahyoud, Nasiha; Aikins, Joshua Kwesi; Bartsch, Samera; Bechert, Naomi; Gyamerah, Daniel; Wagner, Lucienne (2018): Wer nicht gezählt wird, zählt nicht. Antidiskriminierungs- und Gleichstellungsdaten in der Einwanderungsgesellschaft - eine anwendungsorientierte Einführung. Vielfalt entscheidet - Diversity in Leadership. Published by Citizens for Europe: Berlin. (URL: https://cloud.citizensforeurope.org/index.php/s/7gkZjZfSHDpZTRp#pdfviewer).

Gyamerah, Daniel; Wagner, Lucienne (2017): intersektional. Zur gesellschaftspolitischen Bedeutung von Antidiskriminierungs- und Gleichstellungsdaten. Published by neue deutsche organisationen: Berlin. (URL: 01_ndo_GLEICHSTELLUNGSDATEN_Intersektional.pdf).

Kehr, Cordula (2020): Unsichtbare Frauen – Was Daten nicht erzählen. In: 54books, 23.02.2020. (URL: https://www.54books.de/unsichtbare-frauen-was-daten-nicht-erzaehlen/).

Nentwich, Julia C.; Offenberger, Ursula (2018): Kennzahlen als verräterische Verbündete. Eine übersetzungstheoretische Perspektive auf hochschulische Gleichstellungsreformen. In: Hark, Sabine; Hofbauer, Johanna (Ed.): Vermessene Räume, gespannte Beziehungen. Unternehmerische Universitäten und Geschlechterdynamiken. Berlin: Suhrkamp, pp. 283–308.

Wroblewski, Angela; Kelle, Udo; Reith, Florian (Ed.) (2017): Gleichstellung messbar machen. Grundlagen und Anwendungen von Gender- und Gleichstellungsindikatoren. Wiesbaden: Springer VS.

The development and selection of the indicators play a crucial role in gender monitoring. Thus, this section reflects the function of indicators and the principles guiding the selection of a specific indicator set.

Quantitative indicators aim to represent a complex state or development in a compressed numerical value. We cannot measure gender equality directly. Therefore, indicators represent the multidimensional construct of gender equality. They can serve to argue, discuss and communicate the status quo of gender equality to the public. The aspects of gender equality highlighted by the indicators determine how we perceive reality. This is why selecting suitable indicators for gender monitoring is so central.

Quantifying indicators on gender equality questions have a high persuasive power because they are often perceived as reflecting reality with scientific evidence and rationality. However, they construct reality at the same time. This implies that the numbers are subject to interpretation. The meaning attributed to the numbers is always subject to negotiation processes. The sole indicator does not explain anything, but placing it appropriately in a context is necessary. When indicators are selected, the chosen interpretive context should be discussed and reflected upon. For example, a university might compare a specific indicator in a time series of its own university, with other universities and research organizations (in Germany or the EU) or with the optimum value of “complete gender equality”.

Since the value of an indicator is not explanatory per se, reflection on the value and the interpretation context also has significant consequences for the implications and needs for action to derive from gender monitoring.

Typology Of Gender-Sensitive Indicators

1. Non-personal indicators refer to the design of policies, laws and projects.
2. Person-related indicators measure changes at the actors’ level consisting of three person-related indicators.
   • Gender-differentiated indicators measure data disaggregated by gender. You can use gender-differentiated indicators when changes affect all genders.
   • Gender-selective indicators measure changes we only expect in individuals of one gender.
   • For gender-open indicators, gender is not relevant in the measurement. These indicators relate to the measurement of a gender dimension without explicitly differentiating between different genders.

(cf. Neck; Erich 2017: 223ff./ Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) 2014: 19ff.).

Indicator Model “Gender Mainstreaming At Higher Education Institutions”

To comprehensively map the multidimensional construct of gender equality, indicators for measuring equality should start at different levels. To this end, Eckstein (2016/2017) developed the following indicator model (GM-University Indicator Model).

• In the model, Eckstein defines four dimensions of gender mainstreaming at higher education institutions:

1. Representation (e.g. percentages of women, graduation rates)
2. Resources (money, time and space)
3. Rights (legal equality, implementation of legal frameworks)
4. Realities (societal norms and values that create inequality between men and women)

• In addition, she defines the following three fields of action of higher education institutions:

1. Teaching & studies
2. Research & development
3. Institution & administration

A university should use indicators for all four dimensions of gender mainstreaming and each field of action. Ideally, the indicators target input, process, output, and outcome/impact, use quantitative and qualitative data, and face objective and subjective dimensions.

You can find good indicators for the different dimensions and fields of action in the section "Gender Monitoring In Practice” >> “Selected Indicators”.

Sources:

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) (2014): Gender lohnt sich! Arbeitshilfe zur Erstellung eines gender-sensiblen wirkungsorientierten Monitoringsystems (WoM System). (URL: https://gender-works.giz.de/wp-content/uploads/filebase/giz-2014-de-gender-sensibles-monitoring.pdf).

Eckstein, Kirstin (2016): Gleichstellungsindikatoren: Entwicklung und Einsatz von Gleichstellungsindikatoren an Universitäten. (URL: Gleichstellungsindikatoren.pdf).

Eckstein, Kirstin (2017): Gleichstellungsindikatoren an Universitäten – von der Berichterstattung zur Steuerung. In: Wroblewski, Angela; Kelle, Udo; Reith, Florian (Ed.): Gleichstellung messbar machen. Wiesbaden: Springer VS, pp. 149-179.

Leitner, Andrea (2017): Indikatoren für ein kommunales Gleichstellungsmonitoring – Wiener Gleichstellungsmonitor. In: Wroblewski, Angela; Kelle, Udo; Reith, Florian (Ed.): Gleichstellung messbar machen. Wiesbaden: Springer VS, pp. 191-209.

Neck, Karin; Erich, Alexander (2017): Wirkungsorientiertes Monitoring und Indikatoren als strategische Hebel zur Stärkung von Gender Mainstreaming in der Internationalen Zusammenarbeit. In: Wroblewski, Angela; Kelle, Udo; Reith, Florian (Ed.): Gleichstellung messbar machen. Wiesbaden: Springer VS, pp. 211-229.

Nentwich, Julia C.; Offenberger, Ursula (2018): Kennzahlen als verräterische Verbündete. Eine übersetzungstheoretische Perspektive auf hochschulische Gleichstellungsreformen. In: Hark, Sabine; Hofbauer, Johanna (Ed.): Vermessene Räume, gespannte Beziehungen. Unternehmerische Universitäten und Geschlechterdynamiken. Berlin: Suhrkamp, pp. 283–308.

Gender is not the only dimension of underrepresentation. Thus, monitoring needs an intersectional perspective. Intersectionality describes the intersection of several discrimination categories. Besides gender, ethnicity, race, class origin, religion, disability, age, sexual identity, and care responsibilities are relevant dimensions. Gender monitoring that focuses only on the dimension of gender lacks differentiation between different groups within gender. Monitoring that considers the gender groups as homogeneous refers only to the mean values of these groups. An intersectional perspective explores the entanglements and interdependencies of different shapes of oppression and disadvantage.

In the case of people who have several discrimination attributes, these attributes don’t simply add together. Instead, the linkage of discrimination develops its dynamics (“The whole is more than the sum of its parts”). For this reason, the statistical recording of intersectionality and mapping these specific experiences of discrimination is a challenge.

The concept of intersectionality broadens the perspective on discrimination/equality and emphasises the issue’s complexity. For example, the situation of black women is rarely addressed in the public discourse around women in leadership positions, even though this group can be affected by racial and sexist discrimination. Differentiated gender equality monitoring beyond gender-disaggregated data is the basis for intersectionally conceived gender equality at universities and research organizations.

Implementing intersectional approaches in gender monitoring faces, obtaining intersectional data is the first challenge. Data collection on, for example, ethnicity, class, or sexual orientation in higher education must follow organizational and data protection requirements. While higher education institutions usually record the gender of students and employees statistically by default, they don’t collect information on religion or social origin. Thus, universities and research organizations must work out monitoring and survey strategies, considering the framework conditions. A second challenge is deciding which dimensions of inequality are relevant and should be considered, considering feasibility and complexity.

Under Research Data On Gender Relations In Academia (link in German), CEWS presents an overview of inequality categories collected in higher education statistics and surveys. The summary also explains how the dimensions are operationalized. The webpage offers access to research data that allow intersectional perspectives while also providing suggestions for collecting intersectional data when monitoring gender at one’s institution.

Sources:

Ahyoud, Nasiha; Aikins, Joshua Kwesi; Bartsch, Samera; Bechert, Naomi; Gyamerah, Daniel; Wagner, Lucienne (2018): Wer nicht gezählt wird, zählt nicht. Antidiskriminierungs- und Gleichstellungsdaten in der Einwanderungsgesellschaft - eine anwendungsorientierte Einführung. Vielfalt entscheidet - Diversity in Leadership. Published by Citizens for Europe: Berlin. (URL: https://cloud.citizensforeurope.org/index.php/s/7gkZjZfSHDpZTRp#pdfviewer).

Gyamerah, Daniel; Wagner, Lucienne (2017): intersektional. Zur gesellschaftspolitischen Bedeutung von Antidiskriminierungs- und Gleichstellungsdaten. Published by neue deutsche organisationen: Berlin. (URL: 01_ndo_GLEICHSTELLUNGSDATEN_Intersektional.pdf).

Kehr, Cordula (2020): Unsichtbare Frauen – Was Daten nicht erzählen. In: 54books, 23/02/2020. (URL: https://www.54books.de/unsichtbare-frauen-was-daten-nicht-erzaehlen/).

Küppers, Carolin (2014): Intersektionalität. In: Gender Glossar (5 paragraphs). (URL: https://www.gender-glossar.de/post/intersektionalitaet).

Löther, Andrea (2023): Zahlen bitte: Diversity-Monitoring an Hochschulen (400 kB), Lecture (German) on 01/02/2023.

In 2018, following a ruling by Germany’s Federal Constitutional Court, the legislature amended section 22(3) of the civil status law: If the child cannot be assigned to either the female or male gender, the civil status case can also be entered in the birth register without such an indication or with the indication “diverse”. To be able to subsequently adjust the civil status to “diverse”, medical proof must be provided attesting to a so-called “variant of gender development”. With the medical attestation - heavily criticized by many inter* and trans* associations -, legislation intends to make the entry “diverse” exclusively dependent on physical sex characteristics. Exceptionally, an affidavit may be sufficient as an alternative. However, subjective gender identity should not play a role in changing civil status according to the legislation.

When creating the “Third Option”, the legislation thus explicitly excluded trans* gender or non-binary persons in the explanatory memorandum to the law. Trans* persons are people whose gender identity does not match or only partially matches the sex assigned at birth. Independently of the changed law, politics and academia discuss how gender goes beyond the binary “male” and “female”. Another question is how to record gender statistically beyond binarity. The Federal Constitutional Court points out that not solely physical sex characteristics determine a person’s affiliation to a particular gender, but essentially also the subjective gender identity. In this respect, it is unclear whether, in the long term, transgender people will also be able to adjust their civil status to “diverse”.

There has been some uncertainty about how to deal with gender diversity and the “third option” in surveys (in general) and gender monitoring. Gender monitoring data often do not include people who assign themselves to the gender option “diverse”; they don’t record them statistically recorded or assign them to men and women. For example, although the Federal Statistical Office wants to take the characteristic “diverse” into account in future evaluations and publications, it is currently randomly assigned to “male”/“female”. As a result, this group of people is rendered invisible. In some cases, the reason given for this data gap is the small number of persons.

Furthermore, gender equality data usually refer to biological sex. One cannot transfer the biological sex directly to the social gender or a person’s gender identity. Because of the lack of differentiation between these dimensions, the available data give limited information about the experiences of persons of different sexes and gender identities. Thus, gender monitoring faces the challenge of incorporating multiple gender dimensions. Survey research started to reflect on including social gender and biological sex in surveys. Lösch et al. 2020 propose to capture social gender using, for example, a scale from “exclusively feminine traits” to “exclusively masculine traits” or using broad response categories (feminine, masculine, androgynous, agender, gender fluid, genderqueer, etc.). Further considerations are necessary, especially to avoid the reproduction of stereotypes.

According to the current legal status in Germany and the state of research, surveys or other data collection formula must offer more than two options (“male” and “female”) when asking for the biological sex. There are the following options for recording gender. Which option is used also depends on the objective of the survey or recording:

• An additional option “diverse / non-binary” to include people who see themselves outside the binary gender norm
• free text or a semi-open response category “different, namely ____ “, which offers respondents the opportunity to specify their gender independently of predefined categories
• Scales or “sliders”
• Multiple items
  • Differentiation between perception by others and self-perception
  • Differentiation between the assignment at birth and the gender identity

In addition, surveys could offer the option of not identifying the gender (“n/a”). For transparency, gender monitoring should reflect the underlying understanding of gender.

Statistics Canada is one example of multidimensional monitoring, which records researchers at different qualification levels based on socio-structural characteristics. It reports a third gender option (diverse) and other inequality-relevant categories (age, sexual orientation, disability, minorities), enabling more differentiated and intersectional monitoring.

Sources:

Antidiskriminierungsstelle des Bundes (2019): Mann – Frau – Divers: Die “Dritte Option“ und das Allgemeine Gleichbehandlungsgesetz. (URL: https://www.antidiskriminierungsstelle.de/DE/ThemenUndForschung/Geschlecht/Dritte_Option/Dritte_Option_node.html).

Böge, Jula (2019): Wo sind die diversen Menschen? (URL: https://www.julaonline.de/wo-sind-die-diversen-menschen/).

Bundeskonferenz der Frauen- und Gleichstellungsbeauftragten an Hochschulen e.V. (bukof) (2020): Handlungsempfehlungen für Geschlechtervielfalt an Hochschulen: Erste Schritte. (URL: 20-05-19-bukof-Handlungsempfehlungen-Geschlechtervielfalt-an-Hochschulen.pdf).

Döring, Nicola (2013): Zur Operationalisierung von Geschlecht im Fragebogen: Probleme und Lösungsansätze aus Sicht von Mess-, Umfrage-, Gender-und Queer-theorie. In: GENDER–Zeitschrift für Geschlecht, Kultur und Gesellschaft, 5(2). (URL: https://www.ssoar.info/ssoar/handle/document/39660).

Kortendiek, Beate et al. (2022): Gender-Report 2022. Koordinations- und Forschungsstelle Netzwerk Frauen- und Geschlechterforschung NRW. Essen. (URL: https://www.genderreport-hochschulen.nrw.de/fileadmin/media/media-genderreport/download/Gender-Report_2022/genderreport_2022_langfassung_f_web.pdf).

Lodge, Cassian Lotte (2016): Gender and Pronouns in Online Forms. (URL: https://docs.google.com/document/d/15aYbXeCw7PUCm2O-dg7l7cmuCQdIIAdFXzYIusRKh2A/edit).

Lösch, Regina; Voss, Amanda; Pfleger, Elisabeth; Wischlitzki, Elisabeth (2020): Zur Operationalisierung des sozialen und biologischen Geschlechts. (URL: https://www.researchgate.net/publication/344417175_Zur_Operationalisierung_des_sozialen_und_biologischen_Geschlechts).

Löther, Andrea (2023): Zahlen bitte: Diversity-Monitoring an Hochschulen (400 kB), Lecture (German) on 01/02/2023.

Nichtbinär-Wiki (2018): Geschlechtsabfragen. (URL: https://nibi.space/geschlechtsabfragen).

Statistics Canada (2020): Table 37-10-0165-01 Selected population characteristics of postsecondary faculty and researchers by region, role, and employment status.

Statistisches Bundesamt (Destatis) (o.J.): Drittes Geschlecht. Informationen zum Umgang mit dem Dritten Geschlecht in der amtlichen Statistik. Wiesbaden. (URL: https://www.statistikportal.de/de/methoden/drittes-geschlecht).