1. Availability and Quality of Data Angela Me UNECE Statistics Division

2. Issues in looking at the quality of the data For users: do not leave the issue of quality and availability only to statisticians. There are issues that are not too “statistically technical” that affect the Message and need to be addressed

3. Issues in looking at the availability of the data Do the available data provide evidence for gender analysis?  Under-use of existing data  Gender is not properly considered in the existing sources  There are no sources available  Miss-use of existing data

4. Under-use of existing data: ex. Wages Activity branchMaleFemale Transport/ Communication 2345015670 Education2067018970 Health2769022840 Public administration 1546013250 No total pay gap presented (needed for advocacy) No pay gap included in the gender publication despite the data are available

5. Under-use of existing data: ex. Wages In gender statistics publications do you include data on: Employment by occupation Employment by status in employment (self-employers) Employment by family composition These data are available if a census and/or a labour force survey was carried out and usually show large gender disparities

6. Gender not properly considered  Sex is not included in the data collection  Business registers, disease reporting, voting registers  Sex is not included in the dissemination of the data  Issues that reflect an unequal participation of women an men are not properly collected  Quality of work, informal employment, leading positions

7. Data collection not available  Surveys on gender attitudes  Surveys on time-use  Surveys on reproductive health  Surveys on violence against women

8. Miss-use of data: Example of Monetary Poverty Data on income poverty are based on household income or consumption Difficult to disaggregate by sex (transfers within households are unknown) It is not relevant to use the concept of head of household: it is only a statistical concept that does not reflect the income distribution in households

9. Miss-use of data: Example of Monetary Poverty Data based on income of the head of households (HH) give a biased picture Women who declare as HH are usually: the most educated The ones that do not live with a partner Statistics of men HH are usually based on the largest proportion of households and women HH are a not representing minority

10. Miss-use of data: Example of Monetary Poverty To be measured considering income or consumption by type of household  One-single-person households by sex  One-single-parent households by sex  One-income-earning-person households by sex  Others…

11. Issues in looking at the availability of the data How to improve?  Inquiry about all the data available  Try to influence the existing sources to make them more gender-sensitive  Advocate for the development of new gender- sensitive data collection (within the national statistical masterplan)  Avoid the miss-use of data

12. Issues in looking at the quality of the data Why data quality is important?  Wrong data give wrong messages  Wrong messages lead to wrong political interventions or no intervention  Advocacy needs to be backed up by solid data to be credible in the long run

13. Issues in looking at the quality of the data Some of gender related issues  Inadequate definitions and concepts  Man-biased data collection (question wording)  Gender-biased responses  Gender-biased enumerators

14. Inadequate definitions and concepts Data collection is based on:  Households or farm and not on individual  The concept of the head of economic activity  Classifications are men-oriented (ex: occupation -ISCO)  Concept definitions (ex: in some countries employment may include women in long maternity leave)

15. Biased question wording Example: Do you work? “Work”=interpreted as formal work People engaged in informal activities are undercounted. Usually women are engaged more than men in informal sector (particularly agriculture) and therefore are undercounted

16. Biased question wording More women-sensitive….. o Are you engaged in any work paid in money or in kind? o Do you sell products on the street or at the market? o Are you engaged in agriculture activities to produce goods for the household consumption?

17. Gender biased responses  Male respondents may fail to report women  Respondents may not understand the content of the questionnaire  Respondent give wrong answers to meet social norms

18. Gender-biased enumerators  Enumerators may introduce his/her personal view (norm) in the interview  Poor training  Social pressure  Lack of interest  Enumerators may establish poor relationship  Not gender-correct language  Body language

19. Gender-biased enumerators

20. Issues in looking at the quality of the data Different sources may provide different data

21. Entrepreneurship An issue of definition and data availability

22. Entrepreneurship: definitions Own-account workers Employers Owners Managers Self-employed Members of Ex. Boards

23. Entrepreneurship Labour Force Surveys (LFS) self-employed managers employers own-account workers Enterprise Surveys employers managers owners members of ex. boards access to credit Registers (Business, taxes, …) SME owners access to credit

24. Entrepreneurship Different sources  different perspectives  different concepts and definitions  Data from different sources may not be comparable

25. Entrepreneurship: Data Availability