1. A MONITORING AND EVALUATION FRAMEWORK TO BENCHMARK THE PERFORMANCE OF WOMEN IN SET Presentation at Women in ICT Workshop 31 January 2006

2. About the M&E framework Brief: to develop a monitoring and evaluation framework for women in SET that should support planning and resourcing of the National System of Innovation Designed to provide a comprehensive national profile of women in SET in South Africa, that will tell us:  how many women are potentially available to participate in the NSI;  how women are distributed horizontally and vertically within the NSI;  how women are supported to participate in the NSI,  what recognition women get as scientists  and what women’s contributions are to scientific output.

3. South African Monitoring and Evaluation Framework: Constructs 1. SET Potential6. Scientific Recognition 2. SET Labour Force7. Scientific Agenda Setting 3. R&D Workforce8. Scientific Output 4. Fairness and Success in Funding 9. Scientific Collaboration and Networking 5. Rank and Status

4. South African Monitoring and Evaluation Framework: Descriptions 1.SET Potential  Leakages in the pipeline  Distribution across study fields  Size and potential of SET and R&D pool 2.SET Labour Force  SET human resource capacity  Horizontal distribution  Absorption of graduates 3.R&D Workforce  R&D human resource capacity  Horizontal distribution  Absorption of graduates

5. South African Monitoring and Evaluation Framework: Descriptions 4.Fairness and Success in Funding  Access to Funding 5.Rank and Status  Vertical distribution 6.Scientific Recognition  Recognition by peers 7.Scientific Agenda Setting  Representation on scientific boards and councils 8.Scientific Output  Authorships and citation ratings 9.Scientific Collaboration and Networking  Co-authorships, collaborative projects and conference attendance

6. Where does ICT fit in? Broad field of study that can be compared to participation in Natural Sciences and Engineering, Health Sciences and Social Sciences and Humanities SET occupational field that can be compared across sectors and occupational levels Sectors include Higher Education, Government/Science Council, Business/Industry and Not-for-profit Participation indicators include gender, race, age, nationality and qualification level

7. What study, research and occupational fields are included in ICT? R&D SurveyHEMISInstitute for Science Information (ISI) Information systemsCode systemsComputer science, artificial intelligence HardwareCommunication technologyComputer science, cybernetics SoftwareCyberneticsComputer science, hardware & architecture Current information technologyInnovative communicationComputer science, information systems CommunicationApplications in Computer Sc. & Data Processing Computer science, interdisciplinary applications Security systemComputer Ops. and Operations Control Computer science, software engineering Computer Hardware SystemsComputer science, theory & methods Computer Hardware Information and Data Base Systems Numerical Computations Programming Languages Programming Systems Software Methodology Theory of Computation Computer Engineering and Technology

8. Monitoring-for-policy questions (1) 1.SET Potential  How do the gender and race profiles of students compare at each level of study?  Are there differences between men and women students in “drop-out” level? If so, are these differences related to qualification level?  Are women students starting and completing postgraduate studies at a later age than men?  Are women students overly clustered in broad fields of study and under-represented in others?  Are there certain fields of study that attract more foreign students than others?

9. 2.SET Labour Force  What proportion of the total labour force is made up of SET workers?  What proportion of SET workers are female?  Are SET graduates moving into SET occupations?  Are women SET workers overly represented in certain occupations and under-represented in others? 3. R&D Workforce  What proportion of the total labour force is made up of R&D workers?  What proportion of R&D workers are female?  Are female researchers overly represented in certain sectors and under- represented in others?  Are certain sectors attracting more foreign R&D workers than others? Monitoring-for-policy questions (2)

10. 4.Fairness and success in funding  Are there gender and race differences in applying for funding?  Are there gender and race differences in the awarding of funds?  Are there gender and race differences in the monetary value of funds awarded?  Do foreign researchers have differential access to certain funding sources? 5. Rank and employment  Are there gender and race differences between the lower ranks and higher ranks in Higher Education?  How is gender and race distributed across different scientific fields and between the lower ranks and higher ranks?  Are there gender and race differences in the appointment of permanent researchers across sectors?  Are there gender and race differences in the promotion patterns of researchers across sectors? Monitoring-for-policy questions (3)

11. 6.Scientific Agenda Setting  What is the representation of women on scientific boards and councils?  What proportion of executive and senior managers across sectors are women? 7. Scientific Recognition  What proportion of reviewers for national and international funding agencies are South African women?  What proportion of reviewers for scientific journals are South African women?  What is the representation of women scientists in national academies?  Are there differences in citation ratings for South African researchers by gender and by field? Monitoring-for-policy questions (4)

12. 8.Scientific Output  What is the contribution of women scientists to scientific output in the system?  Are there differential patterns of scientific production by field and gender? 9. Scientific Collaboration and Networking  Are there gender and race differences in the undertaking of collaborative research projects?  What is the proportion of female co-authored articles?  What proportion of papers presented at international conferences is by female researchers?  What proportion of academics taking overseas sabbaticals is female? Monitoring-for-policy questions (5)

13. The Monitoring and Evaluation Framework  Constructs  Indicator categories and sub-categories  Data tables  Indicators

14. Example of an indicator category with its indicator subcategories 14. Share of female students enrolled for a doctoral degree or equivalent 14.1. Students enrolled for a doctoral degree or equivalent, by gender and by race 14.1.1. Students enrolled for a doctoral degree or equivalent in Social Sciences and Humanities, by gender and by race 14.1.2. Students enrolled for a doctoral degree or equivalent in ICT, by gender and by race 14.1.3. Students enrolled for a doctoral degree or equivalent in Natural Sciences and Engineering, by gender and by race 14.2. Students enrolled for a doctoral degree or equivalent, by gender and by nationality 14.2.1. Students enrolled for a doctoral degree or equivalent in Social Sciences and Humanities, by gender and by nationality 14.2.2. Students enrolled for a doctoral degree or equivalent in ICT, by gender and by nationality 14.2.3. Students enrolled for a doctoral degree or equivalent in Natural Sciences and Engineering, by gender and by nationality 14.3. Students enrolled for a doctoral degree or equivalent, by gender and by science field 14.4. Mean age of women and men enrolled for a doctoral degree or equivalent 14.4.1. Mean age of students enrolled for a doctoral degree or equivalent, by gender and by race 14.4.1.1. Mean age of students enrolled for a doctoral degree or equivalent in Social Sciences and Humanities, by gender and by race 14.4.1.2. Mean age of students enrolled for a doctoral degree or equivalent in ICT, by gender and by race 14.4.1.3. Mean age of students enrolled for a doctoral degree or equivalent in Natural Sciences and Engineering, by gender and by race 14.4.2. Mean age of students enrolled for a doctoral degree or equivalent, by gender and by science field Green = Indicator category Red = Indicator subcategory 1 Blue = Indicator subcategory 2 Brown = Indicator subcategory 3

15. Gender x RaceSocial Sciences & Humanities ICTNatural Sciences & Engineering Total WomenAfrican Coloured Indian White Totalwt MenAfrican Coloured Indian White Totalmt Total women & mengt Example of a data table

16. Gender x RaceSocial Sciences & Humanities ICTNatural Sciences & Engineering Total WomenAfrican Coloured Indian White Totalwt MenAfrican Coloured Indian White Totalmt Total women & mengt How to derive the indicators (1) Indicator category 14. Share of female students enrolled for a doctoral degree or equivalent Women as % of students enrolled for a doctoral degree or equivalent

17. How to derive the indicators (2) Indicator subcategory 1 14.1. Students enrolled for a doctoral degree or equivalent, by gender and by race Gender x RaceSocial Sciences & Humanities ICTNatural Sciences & Engineering Total WomenAfrican Coloured Indian White Totalwt MenAfrican Coloured Indian White Totalmt Total women & mengt Set 1 African women as % of students enrolled … African men as % of students enrolled … Coloured women as % of students enrolled … Coloured men as % of students enrolled … Indian women as % of students enrolled … Indian men as % of students enrolled … White women as % of students enrolled … White men as % of students enrolled … … for a doctoral degree or equivalent Set 1

18. How to derive the indicators (3) Indicator subcategory 1 14.1. Students enrolled for a doctoral degree or equivalent, by gender and by race Gender x RaceSocial Sciences & Humanities ICTNatural Sciences & Engineering Total WomenAfrican Coloured Indian White Totalwt MenAfrican Coloured Indian White Totalmt Total women & mengt Set 2 African women as % of African students enrolled … Coloured women as % of Coloured students enrolled … Indian women as % of Indian students enrolled … White women as % of White students enrolled … … for a doctoral degree or equivalent Set 2

19. How to derive the indicators (4) Indicator subcategory 1 14.1. Students enrolled for a doctoral degree or equivalent, by gender and by race Gender x RaceSocial Sciences & Humanities ICTNatural Sciences & Engineering Total WomenAfrican Coloured Indian White Totalwt MenAfrican Coloured Indian White Totalmt Total women & mengt Set 3 African women as % of women enrolled … Coloured women as % of women enrolled … Indian women as % of women enrolled … White women as % of women enrolled … African men as % of men enrolled … Coloured men as % of men enrolled … Indian men as % of men enrolled … White men as % of men enrolled … … for a doctoral degree or equivalent Set 3

20. How to derive the indicators (5) Indicator subcategory 2 14.1.3. Students enrolled for a doctoral degree or equivalent in ICT, by gender and by race Gender x RaceSocial Sciences & Humanities ICTNatural Sciences & Engineering Total WomenAfrican Coloured Indian White Totalwt MenAfrican Coloured Indian White Totalmt Total women & mengt Set 1 African women as % of students enrolled … African men as % of students enrolled … Coloured women as % of students enrolled … Coloured men as % of students enrolled … Indian women as % of students enrolled … Indian men as % of students enrolled … White women as % of students enrolled … White men as % of students enrolled … … for a doctoral degree or equivalent in ICT Set 1

21. Application of the Framework Three factors to consider:  Purpose of monitoring and evaluation  Data availability  Audience They influence:  Selection of indicators  Frequency of data collection  Form of reporting

22. Application of the Framework Data availability A. Routinely collected data that are readily accessible  The data are either available in the public domain or can easily be obtained from the data collection agency in the desired format. B. Routinely collected data that are not readily accessible  Special requests and negotiations are required to solve issues of data ownership and/or to arrange for data permutations as the available data are not in the desired format. C. Data not routinely collected  Procedures for collecting this data can be introduced requiring different degrees of effort/investment of time and money.

23. Application of the Framework Application Scenarios (1) Scenario A: System monitoring scenario  Annual reporting on the system  Routinely collected data  Selected indicator categories Scenario B: Sector monitoring scenario  3 sectors: HE; Gov/SETI; Business/industry  Inform sector level policies and interventions  Three-year cycle, with one sector report per year  Invest time and resources to collect data  Adapt indicators for sectors

24. Application of the Framework Application Scenarios (2) Scenario C: International benchmarking scenario  International comparisons  Every three years  Selected indicator categories Scenario D: System review scenario  Comprehensive review of the system  Every six years  Inform all stakeholders of all aspects of the NSI  Include all constructs and main indicator categories

25. Summary YEAR 1YEAR 2YEAR 3YEAR 4YEAR 5YEAR 6 Scenario ASystem monitoring Scenario BHE Sector monitoring Govt Sector monitoring Industry Sector monitoring HE Sector monitoring Govt Sector monitoring Industry Sector monitoring Scenario CInternational benchmarking Scenario DSystem review

26. Conclusion  The M&E framework is a dynamic measuring instrument that expands or contracts in terms of constructs, indicator categories and indicators, depending on the purpose it is to serve.  Although the four application scenarios are complimentary activities, the implementation of these scenarios would have to be based on careful consideration of time and resources (financial and human).

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