Gender-disaggregated data

Gender-disaggregated data Karabi Baruah Ph.D Gender, HIV & Development Specialist For Training Course on “Gender Equitable development Projects” APMASS & WAP, AIT: 27th June 2012 Danang, Vietnam

Session Outline • Basic concepts and issues related to the collection, analysis and use of gender-disaggregated data • Common understanding of terms useful in gender disaggregated data • Approaches to collecting and analysing gender disaggregated data

GDD and Analysis • Gender-disaggregated datathe collection of information, from a sample group thatincludes both male and female participants, on the differentexperiences, needs, interests, and access to opportunitiesand resources of men and women so as to establish anaccurate picture of the local context. • Gender analysisthe examination of relationships between men and womenand the factors that create and influence differentialopportunities and constraints for men and women at thelocal, regional and global level. Source: Hovorka, IDRC, 1998.

Gender-disaggregated data (GDD) • Collection of data disaggregated by sex (strictly, this is sex-disaggregated data)* • However GDD is more than simply collecting data FROM women and men** • GDD requires a gender-sensitive data collection process to reveal hidden or untold information 4

Why the Need for Gender Disaggregated Data? • Capture real need, contribution, benefits • GDD needs to be accompanied by disaggregated data on other variables (age, race, etc) to reflect gender dynamics • To improve project effectiveness and sustainability (project is then more responsive). • Better information lead to better performance (fish harvest, income, etc) • The benefits are to both women and men

Gender disaggregated data • Not only about what men and women do, or their characteristics • Need data to understand differentiated impacts, vulnerabilities, opportunities* • Especially important for monitoring and evaluation

GDD at different phases • GDD in baseline (so we can measure changes from a gender perspective) • Difference in labor, income, control and access to resource, perceptions • GDD in process (both within project and on target population) • Difference in participation • GDD on outcome and impact • Focus on long-term effects of the project, measuring against baseline data

Common understanding of terms useful in gender disaggregated data

 Data: • Sex disaggregated data, and • Gender disaggregated data • Statistics • Indicator & Gender sensitive Indicators: • Qualitative & quantitative

Sex & Gender-disaggregated data (GDD) • Data: Unprocessed” information that can be quantified • Sex-disaggregated data: Collection of data disaggregated by sex (strictly by physical attributes ) • Gender Disaggregated Data: are Analytical indicators derived from sex-disaggregated data on socioeconomic attributes 10

STATISTICS: • “Processed” data from a sample • Numerical information answering the question, “how much”, “how many” that are usually presented in aggregate form as numbers or proportions in tables and graphs (Hedman, Perucci and Sundstrom 1996 • Quantitative descriptions of some aspect(s) of the study population (Fowler 1992) • Characteristics of the study sample (Blalock 1979) )

Gender- sensitive indicators • “An indicator is a pointer. It can be a measurement, a number, a fact, an opinion or a perception that points at a specific condition or situation, and measures changes in that condition or situation over time.” (CIDA) • “Gender-sensitive indicators have the special function of pointing out gender-related changes in society over time.” (ibid) • Gender-sensitive indicators should be developed alongside other indicators measuring progress or achievements • Who develops indicators? Need for a participatory approach 12

Examples of indicators Level of income generated from agricultural activities for both male- and female-controlled crops. Levels of women’s and men’s inputs, by socio-economic grouping, in terms of labor, tools, etc. Number (or %) of women and men in key decision-making positions, by socio-economic grouping. Average household expenditure of female/male headed households on education/health. Quantitative indicators Qualitative indicators • Respondent attitude towards [new project component], disaggregated by sex. • Level of satisfaction by women and men with degree of participation in project implementation. • Perception of change in gender equality within the community since the project started. • Feedback in relation to the usefulness of training sessions and gender training material.

Approaches to collecting and analysing gender disaggregated data

Qualitative & Quantitative Methods of GDD • Structural questionnaire surveys for quantitative GDD • Qualitative data collection : in-depth interviews, Survey & structured interviews, Focus group discussion; Narratives, case-studies, life stories etc…

Examples of format for quantitative questions • Question 1 (yes/no, several options, multiple choice) • Option a • Option b • Option c • Question 2 (no specific number: how many things/years/children/etc?; income? • _________ • Question 3 (Rating questions) • Do you agree? Strongly disagree Strongly agree     

Quantitative indicators may fail to capture gender • Examples: • an increased income may hide an increase in dependence towards a spouse; an equal sex-ratio may hide that an activity is not tailored to women’s needs

Qualitative indicators • “Qualitative indicators can be defined as people's judgments and perceptions about a subject, such as the confidence those people have in sewing machines as instruments of financial independence.” (CIDA) • Hence qualitative indicators are crucial to participatory methods, since they don’t measures ‘things’ or ‘numbers’ but people’s views.

How gender-sensitive are the survey questions? Question that don’t generate gender-disaggregated data (household income, or respondent income) Questions that only cover waged labor or cash-crop (since these will be male dominated) to measure livelihoods. Assume the respondent knows better than other family members (access to training, resources, decision-making). A husband and a wife may give a different view on their level of decision-making, or on domestic violence) Issues to avoid Issues to include • Questions designed to cover differentiated task • Who collects water (or fuelwood, fodder, foodstuff) in your household? How far do you [respondent] or this person have to travel to collect water? • Or different crop cycles • Plowing, planting/transplanting, weeding, picking, grinding, etc., which may better represent both men and women’s economic activity • Questions that ask about intra-household dynamics • Question on time-use (to pick up what specific questions don’t) • Informal work when asking about labor activity

Examples of bad and good data collection methods in term of gender In a household survey, use HH as respondents (most HH are men, responses will reflect their views) In-depth interviews with women are conducted by men interviewers (contextual: possible in some, not in others) Selection of respondents based on village leaders, or available list (leaders tend to be men, so are their networks; list often use designated HH) Depending on context, mixed Focus-group discussion where men talk and women remain silent (or men sit in the middle, women on the outside) Bad Good • Respondents are alternated between W and M, or both W and M (father/mother), (husband/wife) are chosen • In-depth interviews with women are conducted by women interviewers (opposite may also be true in some context, men should interview men) • Random selection with equal number of women and men, or separate selection methods in some contexts (may take into account division of labor; where are M and W) • Male and female only FGD. However, whenever possible mixed FGD can be very useful to show contrasting or common views

Qualitative analysis • “Qualitative analysis is used to understand social processes, why and how a particular situation that indicators measure came into being, and how this situation can be changed in the future. Qualitative analysis can and should be used at all stages of the project cycle, and should be used alongside quantitative and qualitative indicators.” (CIDA) • Gender M&E should use qualitative analysis to measure the ‘quality’ of a change and to understand barriers not revealed by quantitative analysis

Measuring changes in gender roles • Productive • Reproductive • Community • How have these three spheres been influenced by the project? • Are gender roles changing towards more gender equity • Can positive process indicators (no. of women participating) lead to change in gender roles outcome (distribution of reproductive work) • Qualitative indicators and analysis may explain obstacles towards more equitable gender roles (stereotypes, perceptions, etc) 22

Gender disaggregated data, especially collected through qualitative methods, require gender aware data collection tool designer and data collector 23

Which method to choose? • Is the method appropriate to the evaluation exercise(what kind of data needed, are we looking at numbers or processes, etc) • Can the method best measure what one wants to measure (assets vs perceptions) • Does the data generated need to be comparable? • Or are we looking forvisual representation of the evidence? • Is it feasible, within cost, scope and limitations

Focus-group discussion • Good technique to understand attitudes and behavior of a target group • Questions are usually open-ended • Answers can add details to motives, why no or yes, can be useful to understand data collected in a survey • One can judge if a certain behavior or attitude is shared by the group • However, one cannot extrapolate data to a general or other population (may not be representative) • Risk of having the group interviewer provide personal opinion that may affect results

In-depth Interviews • In-depth and semi-structure interviews are a less rigid method to acquire data than than structured interviews • Respondents are allowed to answer at length, sometimes bringing in related information that was not asked by the interviewer • Mostly open-ended questions though close-ended ones can also be added • Can use random sampling (probability sampling) if large sample to be generalizable, unless the research is focused on a specific and small target-group, or that respondents are hard to find. In this case purposive(only disabled people for instance) or convenience sampling, such as snow-ball techniques, both non-probability sampling, can be used. However you have to mention how bias can be introduce (such as too many old people, or rich, etc) or whether the sample acquired is representative of a cross-section of the population

Narratives, case-studies, life stories • Underutilized in M&E • Provide a broader view of one’s experience, including changes over time • Allow us to understand better social costs and benefits from a personal standpoint • Allow closeness with subject of research which may help the interviewer gather information that she or he wouldn’t find otherwise • However, may not be representative as every life is different. Can be cross-checked with other stories or triangulated with other forms of data collection

Collecting Sensitive information • Gender related information can be about difference and similarities, inequalities, control and in more extreme case about abuse and violence • They tend to require a level of trust that is hard to build from survey questions • Women may be dependent of their partners, influencing responses • In some cases, similarities with the data collector may help (the peer approach) • On the other hand, one may not want to divulge some information with a local • So there is a need to assess the potential benefits (trust, relations) with shortcomings (fear of being ostracized) • Hence GDD require an ethical data collection process

Gender-sensitive location Should conduct the interview/survey where respondents feel safe, comfortable and open-; Should consider: • Location • Timing • Distance

Conclusion • GDD requires both quantitative and qualitative methods, it is collected on the project (input and process) as well as on the community (output and outcome) • Quantitative measurements are often limited to capture the quality of change in gender outcomes, hence the need in gender M&E to use qualitative methods of assessment • GDD requires gender-sensitive survey design as well as trained data collectors

Group Work • How would you generate gender differentiated resource use pattern? • Community in which you are working has implemented a project on access to energy by providing solar lamps and bio-digesters to households; How would you collect data on women and men are affected by implementing the project. • In the community you are working you have established an eco-tourism project which brings about xxx number of visitors to the community every week. How would you collect gender sensitive indicators?.

Gender-disaggregated data