1. Workshop on Improving Gender Statistics in Rwanda Session 4 Mainstreaming Gender into Statistics Collection and Reporting Serena Lake Kivu Hotel, Rubavu District March 25-27, 2014

2. Session Objectives The main objectives of this session are to: Identify areas in the data collection process where gender bias and other gender-related measurement errors may occur and provide guidance on how to avoid them; Raise awareness of the importance and usefulness of different types of data sources for producing gender statistics; and Show how a gender perspective can be integrated into particular types of data collection and how the collected data can be improved for gender statistics purposes. Primary references: UNSD 2013, Integrating a Gender Perspective in Statistics, Chapter 3; UNECE and WBI 2010, Developing Gender Statistics: A Practical Tool, Chapter 3. 2

3. Gender statistics production process Data collection is an integral part of the process of producing gender statistics, as shown in the diagram below. Identify gender issues Identify data needed, assess data sources, address conceptual and measurement issues Undertake data collection Analyse and interpret the collected data Produce and disseminate statistics 3

4. I. Data collection stages Planning Design Development Collection of data Processing of data 4

5. Planning The objectives need to identify and reflect the gender topics and policy issues to be addressed by the collection.  Objectives are typically determined following consultations between data producers and data users.  Include analysts and statisticians with a gender focus in such consultations. Because the objectives usually take account of findings from reviews of previous data collections in the same program, a gender perspective should be incorporated in such reviews.  Analysis of surveys from previous census round can show, ̶whether there was sex-selective underreporting of some characteristics ̶whether errors were due to poorly phrased questions or instructions, proxy response, sex of the interviewers, shortcomings in interviewers’ qualifications or to coding or data entry mistakes User groups and advisory committees can provide valuable input when planning and developing collections.  Include stakeholders concerned with gender issues among their members. 5

6. Collection design Sample designs should: Cover all groups of population or economic units known to have distinct gender patterns; for example,  Agricultural censuses and surveys should include small holdings which are typically owned and worked by women Ensure that reliable statistics can be produced for both females and males in sufficient detail, Allow disaggregation by other characteristics as required by meaningful gender analysis The units of data collection should be as disaggregated as possible to reveal gender-based inequalities in household or holdings.  For example, sex-disaggregated data on property ownership can be collected at the individual level, while data on decision-making in agricultural activities can be collected at sub-holding level, for plots of land and types of livestock. The sample size should take account of gender statistics needs. For example, the sample size of a survey measuring status in employment should be large enough to allow data to be analysed separately for female and male employers or other categories of self-employed, and further disaggregated by age group, rural/urban areas and educational attainment. 6

7. Collection development Gender issues and gender-specific conceptual and measurement issues should be taken into account in – design of collection questionnaires and formulation of questions – preparation of collection manuals preparation, and – training of interviewers and supervisors Questionnaires should be field-tested to ensure both women and men understand the questions in the same way and to detect any potential gender bias in reporting. Collection manuals’ preparation should:  Include detailed information about each question, and  instructions and procedures to be followed when using the questionnaire  Use general language free of gender biases or other stereotypes related to the characteristics measured  Use examples that do not reinforce gender stereotypes 7

8. Collection development (continued) Practices to help to avoid gender bias include:  Make questions as simple as possible, unambiguous, and using everyday terms that all respondents can understand  Use probing questions to reduce gender-related under-reporting and assist response coding ̶E.g., prompt respondents to remember something they may have forgotten  Give the same importance to answers related mainly to women than to those related mainly to men, when potential answers are categorised and pre-coded  Avoid using keywords that apply exclusively to one sex (e.g., housewife, or fisherman) or language that reinforces gender stereotypes  Include short explanatory notes for the interviewer when needed 8

9. Data Collection Field staff should be selected on the basis of competence, with both women and men recruited. Training of interviewers and supervisors should equip them with the skills needed to handle, consistently, the gender-related issues likely to be encountered in the field. Training should address:  gender-related measurement issues and gender stereotypes;  gender concerns associated with topics in the collection; and  how data collected will address those concerns. Certain types of surveys may require more careful selection and more extensive training of interviewers. Sex of the interviewer can play an important part in obtaining certain types of sensitive information from respondents.  Women may be more likely to disclose information on reproductive health to women interviewers than men interviewers. 9

10. Data Processing It is important to avoid gender bias in data coding and data editing, including imputations for non-response and misreporting. Subject matter specialists with training in gender issues should be involved in formulating rules for these processes so that the coders or processing assumptions are not based on gender stereotypes. 10

11. II. Bringing a gender perspective into selected types of data collection The next part of this session illustrates the importance of each of the following types of collection for gender statistics. It also provides examples of practices that can improve the data collected from a gender perspective. Population censuses and surveys Population and housing censuses Labour force surveysTime use surveys Personal violence surveys Business censuses and surveys Agricultural censuses and surveys Administrative data collections 11

12. What data sources are used in producing gender statistics? Many data sources are used by countries to produce gender statistics. The main types are: –Population and housing censuses –Population sample surveys –Business censuses and surveys –Administrative records To construct some gender indicators, data from more than one source may need to be combined. Some data sources provide more sex-disaggregated or gender-relevant data than others—e.g., demographic and health, or education, surveys. However, most data sources could improve the collection, coverage and quality of gender statistics by integrating a gender perspective in their planning, design, development and data collection. Handout 2.1 has the data sources used for producing gender statistics in Rwanda (from the NISR website) 12

13. Quality of data sources used in producing gender statistics The quality of gender-relevant data from each source depends on many factors, including: – concepts, definitions and classifications used – collection design and coverage – the way questions are asked – the collection methods used Gender bias can be present in any type of data collection and arise at any stage of the statistical production process. It is important to understand the value and limitations of the particular sources available in a country for purposes of producing gender statistics. 13

14. How can a gender perspective be brought into data collection? For each individual about whom data is collected, sex should be recorded plus other characteristics (e.g., age, marriage status, number of children) important for gender analysis. All stages of the data collection process should be examined to:  ensure user needs for gender statistics are taken into account  avoid gender bias in data obtained 14

15. Assessing the existing data sources: suggested questions When reviewing the different types of data collection, think of: How adequate are the existing sources for gender statistics purposes? What are the main gaps or deficiencies in covering gender differences, and what action is being taken to address them? Are metadata provided for all statistics, including information on data quality? Where can it be found? Are the statistics consistent with international standards? Are there any notable departures that would affect international comparability? What are the main conceptual and measurement issues associated with existing sources? How have they been addressed? Does more need to be done? What improvements to data sources (including potential sources) are most needed from a gender perspective? Are there any plans to make these improvements? Are there any other developments expected to improve the quality and availability of gender statistics? 15

16. Population and housing censuses Population censuses are typically the largest statistical collections undertaken by a country and are conducted relatively infrequently (e.g., every 10 years). They obtain data on each person in the population, including their sex, age and other characteristics. Generally a range of other topics are also covered. For example: – female and male labour force participation; – current occupation; – paid and unpaid work; – income; – educational participation and attainment; – aspects of health and disability; – living arrangements. They provide: – a rich source of information for examining differences between females and males across many dimensions of life and in fine geographical detail. – information for studying families, households and population sub-groups from a gender perspective. – population benchmarks for constructing indicators and other analytic measures for studying gender issues. Handout 4.2 provides checklists for integrating a gender perspective into census questionnaires, manuals and training. 16

17. Population and housing censuses: importance for gender statistics Primary source of benchmark gender statistics on various topics, both for the whole population and for specific population groups. Reliable, and sometimes the only, source of statistics on small geographic areas and small population groups disaggregated by various characteristics required for gender analysis. Source of data for population denominators needed to calculate various gender indicators based on data provided by administrative records. Where civil registration systems have incomplete coverage, census data along with household surveys data are vital for providing gender statistics on fertility, mortality, marriages and migration. Rwanda censuses collect information disaggregated by sex. – Do they collect other gender-relevant information? – What gender-relevant information do they not collect and why? 17

18. Population sample surveys Household and other population surveys collect information directly from a representative sample of households or individuals. They can cover a very wide range of topics in depth. Data collected invariably includes sex and age of each individual in the sample. Such surveys are very flexible and may have: a multipurpose focus, with many discrete topics a more general social focus, with a range of topics for analysis of cross-cutting issues a primary focus on a particular topic or population group (e.g. labour force, education, literacy, health, disability, time use, domestic violence, migrants) attached sessions on separate topics (e.g. sessions on discrete topics may be attached to regular labour force surveys) The implications of sampling error (as well as non-sampling error) need to be considered when using data from these surveys to produce gender statistics. In Rwanda, the following surveys collect sex-disaggregated and gender-relevant data: DHS EICV Which other surveys collect gender-relevant data? 18

19. Labour force surveys: importance for gender statistics The surveys provide data on the structure and composition of the labour force, disaggregated by sex, age group and other individual characteristics. This information is essential for design and evaluation of government policies aimed at employment creation and equal opportunity in employment. Labor surveys can provide data on employment and unemployment trends for particular sub-groups, which are crucial for assessing the social affects of government employment policies or structural adjustment policies. Labour force data can be collected at the same time as data on other topics. The combined information can be useful for understanding gender differences in labour force participation. Recurrent surveys can show the extent to which seasonal variations in labour force participation differ between women and men Does Rwanda collect labour force surveys? How does Rwanda collect sex-disaggregated and gender-relevant data on labour force and employment? 19

20. Business censuses and surveys These collections obtain data from businesses and other organisations, as well as from the registers on which the collections are based. The focus of the collections may be: ̶particular industries or activities (e.g., manufacturing, agriculture, education services), or ̶economy-wide They can provide gender-relevant information if sex-disaggregated data are collected for individuals associated with the organisation. For example: ̶earnings of different categories of employees; ̶characteristics of owners or managers of businesses and agricultural holdings, including type, size and location; ̶numbers of students and staff in different fields at educational and research institutions. In Rwanda, the Civil Servant census, establishment/enterprise surveys, and Manpower surveys also collect sex-disaggregated data. 20

21. Agricultural censuses and surveys Agricultural censuses cover all agricultural holdings. They collect structural and operational data related to each holding. Agricultural surveys (a type of business survey) cover only a sample of agricultural holdings. They are usually conducted more often than censuses and can accommodate more detailed questions. The role of these collections in obtaining statistics on gender and agriculture should be considered within an integrated system of producing gender statistics. Examples of topics they can be cover are: size of holding ownership management economic activity production income employment agricultural practices 21

22. Improving gender coverage in agricultural censuses and surveys Information on the composition and organisation of farm labour can be provided by recording sex and other characteristics of the household members and hired labourers working on the agricultural holding. Gender differences in management of agricultural holdings can be provided by obtaining data on the characteristics of holders and sub-holders and combining the data with other information, such as size and types of crops or livestock. Agricultural censuses and surveys are important for gender statistics : They can identify gender differences in – ownership of agricultural assets, – access to agricultural services and credit, and – access and use of agricultural practices. For Rwanda, where a large majority of employed work as small-scale farmers women (76%, compared to 53% of men, EICV3), agricultural censuses and surveys can reveal much about women’s economic roles, and their time use and unpaid work (which contributes to their household’s wellbeing) 22

23. Specialized Surveys: Time use surveys Time use surveys collect data on how individuals allocate their time to specific activities over a specified period. They typically use diaries to collect time use data, together with household and individual questionnaires to collect demographic and socio-economic data, including sex and age of individuals. They may be conducted in stand-alone mode or as a session attached to another population survey (such as a labour force survey, a living standards survey, or a multi-topic survey). Many countries have conducted such surveys, either as part of a regular survey cycle or on a periodic or ad hoc basis. Ethiopia has recently completed a time use survey 23

24. Time use surveys: importance for gender statistics Time use survey data reveal the extent to which time allocation patterns differ between women and men. The data are essential for estimating participation of women and men in unpaid work and its value for the economy. The data from time use surveys can:  improve labour force survey estimates, for both women and men, on: ̶participation in all forms of work (e.g., through more extensive capturing of relevant non-market activities) ̶working time, work locations and scheduling of economic activities  provide valuable insights into work/family balance for women and men, including the division of labour within the family, and  contribute to understanding gender differences in: ̶investment of time in education and health ̶welfare and quality of life, including availability and use of leisure time and extent of time poverty ̶intra-household inequality in terms of the distribution of household chores among female and male members of a household How has Rwanda measured women’s and men’s time use? 24

25. Specialized surveys: Personal violence surveys Surveys on personal violence measure the extent, nature and consequences of all types of violence against individuals. The focus may be on violence against women, or more broadly on violence against women and men. The surveys may be conducted in stand-alone mode or as a session attached to another population survey (such as a health survey, or a crime victimisation survey). Many countries have conducted such surveys, either as part of a regular survey cycle or on a periodic or ad hoc basis. How does Rwanda collect data about personal violence? 25

26. Personal violence surveys: importance for gender statistics Can provide reliable measures of prevalence of violence against women and men, including physical, sexual, psychological and economic abuse. ̶Administrative sources of data on violence against individuals, especially women--are characterised by extensive underreporting. Groups most at risk of violence can be identified, as data typically include characteristics of those who experience violence and those who do not. Data on the characteristics of perpetrators as well as victims can be used to develop strategies of violence prevention and early intervention. Can provide representative and comprehensive data on the impact of violence, e.g., injuries, and the need for medical attention. Such surveys may be the only source of data on the proportion of victims seeking help and accessing support and other services. ̶This information is crucial for developing strategies to respond to the needs of victims, such as emergency housing, counselling etc. Can provide data on the extent of tolerance of violence in the wider community. 26

27. Administrative records: importance for gender statistics These records contain data routinely collected through administrative processes by sectoral or line ministries and other national and local public entities. Where records hold information on individuals, including their sex, they can be a valuable and cost effective source of gender statistics. A wide range of topics may be covered. – For example: school enrolments; registered unemployed; registered births, deaths and marriages; registered diseases; use of health services; provision of income support; arrests for criminal activity. They have the potential to provide more frequent, reliable and finely disaggregated data than sample surveys, and to provide insights into gender issues not well covered by census or survey data. But, their usefulness may be limited because their primary focus is administration not statistics. They may be problems for example, because – Their coverage, completeness of records, definitions, classifications and collection methods may be deficient for purposes of gender statistics, and – The details they hold may not be current. Handouts 2.1 and 4.1 have examples of administrative data for Rwanda with the topics they cover 27

28. Improving administrative records data from a gender perspective Statistical agencies should develop and implement strategies to ensure that custodians of relevant administrative records understand the importance of gender statistics and the potential of those records to enhance the statistics. Statistical agencies may need to negotiate with those responsible for relevant administrative records in order to: –improve the existing quality of administrative data for purposes of gender statistics; –ensure the data are updated regularly; or –gain access to gender-relevant data not currently used for statistical purposes. Inter-agency agreements and inter-agency steering committees can facilitate progress on actions arising from such negotiations and help to achieve timely completion. 28

29. Summary of main themes of presentation Many types of data collection are important for gender statistics. Data collectors should integrate a gender perspective into all stages of the collection process for all relevant collections. Gender bias and other gender-relevant measurement errors can occur in any type of data collection and at any stage of the collection process. Such measurement issues need to be identified as early as possible to ensure they are adequately addressed. There are many practices that can be adopted to avoid gender bias and improve reported data for gender statistics purposes. Improvements to reported data benefit all statistics sourced from the collection, not only gender statistics. 29

30. Exercise 4 1.Choose a census or survey that you are familiar with from the list in Handout 2.1: – Does it collect sex-disaggregated or gender-relevant statistics? What are they? – Which steps or activities could have been taken to better integrate a gender perspective and minimize gender problems or biases? 2.To what extent is a gender perspective integrated in the production of statistics in Rwanda? What is working well in this regard, and what needs more attention? – Please give specific examples of data collection instruments or processes that integrate a gender perspective 3.What data collection census or survey in Rwanda has the highest priority for improvement from a gender perspective? Why? What needs to be done and by whom to improve it? 30