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Workshop on Gender Statistics

Workshop on Gender Statistics. Tashkent 11-15 July 2005. Summary and Lessons Learnt. The role of official statistics in providing data for gender analysis was recognized as well as the complementary research work needed to further explore gender issues and promote gender advocacy

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Workshop on Gender Statistics

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  1. Workshop on Gender Statistics Tashkent 11-15 July 2005

  2. Summary and Lessons Learnt • The role of official statistics in providing data for gender analysis was recognized as well as the complementary research work needed to further explore gender issues and promote gender advocacy • Gender statistics is not about women statistics. It provides evidence to study the different role of BOTH women and men in the society

  3. Summary and Lessons Learnt • Gender statistics deals with all statistics related to individuals. Gender should be mainstreamed in the production, dissemination and analysis of ALL fields of official statistics (including business and agriculture) • The Population and Housing Census, Household surveys, and Administrative Records can provide relevant data for gender analysis. Each source has advantages and disadvantages and it is important to understand why different sources can produce different data • Uzbekistan reported the experience of using extensively administrative records and the survey on income and expenditures • It is encouraging that in Tajikistan the first Labour Force Survey was carried out in 2004 and data were recently released • The experience of Moldova in collecting data on emigrants was reviewed as an interesting experience

  4. Summary and Lessons Learnt • The availability of gender statistics can be improved by: • Increasing the use of existing data • Improving the gender-sensitiveness of the current data sources (making sure that sex is included in all data collections related to individuals and reviewing the content of data collections to include more gender-sensitive topics) • Improving the dissemination of sex-disaggregated data (sex as the principal classification even when other classifications are used in the presentation of the data) • The quality of Gender statistics is important. Bad data gives the wrong message. Quality relates to relevance and methods used to collect the data. With the data collected through interviews, it is important that the interviewers and the questions’ wording are not gender-biased • The increased use of international standards improves the quality of gender statistics

  5. Summary and Lessons Learnt • Crime data collected in police records or court records does not give proper information on the prevalence of women who experience violence. Statistics on violence against women can be improved by: • Carrying out specialized surveys or including an ad-hoc module in on–going surveys (with caution!) • Exploring the possibility of using service-based statistics (health centers, shelters, social services) • Improving the collection of data from victims (the experience of Kyrgyzstan is a good example on how administrative records can be used to collect data on victims) • It is difficult to measure domestic violence particularly because it is not visible as a crime. Government laws (such as the one approved in 2004 by Kyrgyzstan) can help in making this type of violence more visible

  6. Summary and Lessons Learnt It is not relevant to present sex-disaggregated data based on the concept of head of household. To report data available only at household level (ex: income), it is relevant to make gender comparisons based on households composed only by one person or one adult for which the analysis by sex makes sense.

  7. Summary and Lessons Learnt • The international definition of Employment includes the concept of work made for income (cash or kind) or unpaid production of goods for own-consumption. • Services provided for the households are excluded • Often women engaged in the production of goods for own consumption are underreported as employed persons. There is the need to improve the reporting of women, particularly in agriculture • Many women are engaged in informal employment and it is important to improve the measurement of informal employment for women and men

  8. Summary and Lessons Learnt • Looking at gender and labour market, it is important to collect data and make gender analysis in the following areas: • Segregation of women and men particularly in different occupations, industries, and status in employment • Gender pay gap • Activity status by family composition • It is important that data on the engagement of women and men in the unpaid production of services is studied • Time-use surveys are the main source of data • Time-use surveys require the use of diaries where members of the household can report their use of time with a relatively short intervals of time (10-15 minutes) • Data from time-use surveys can provide useful information on: • the unequal roles that women and men have in the household • The different burden carried by women and men in paid and unpaid work that ultimately affect the empowerment of women • The contribution of women and men to the overall national production of goods and services

  9. Summary and Lessons Learnt • The data relevant for gender analysis in agriculture are based on the concept of a head of an agriculture holding and his/her characteristics. • The definition of a head of a holding is based on the management of the agriculture holding • Women are underreported as a head of a holding and data based on sex of a head of a holding may give biased pictures • There is a need to better identify the different roles of women and men in agriculture holdings

  10. Summary and Lessons Learnt • Data need to be presented in a user-friendly way • Analytical tools (tables, graphs, diagrams, and maps) can help get a better understanding of statistics by users • Analytical tools should be clear and simple • They should present one finding or concept at a time • The gender differences should be clearly and simply presented • When data are reported by sex and another variable, different percentages can be calculated: • To look at the sex distribution of the other variable (percentage of men and women for each category) • To look at the percentage distribution within the category of the other variable both for women and then for men

  11. Summary and Lessons Learnt • Data, graphics, diagrams and maps should be presented with meaningful explanations • The message should be presented with short and simple statements using a style “like a journalist” • The statements should be • easy to understand • relevant and interesting • targeted to the needs of the users

  12. Summary and Lessons Learnt • During the workshop’s exercises, we have practiced: • To read tables and highlight differences between women and men • To create graphics and diagrams to present the main message related to gender differences • To write a simple story about gender differentials within one topic (employment, agriculture), comparing countries and comparing the same country overtime

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