The Promises and Challenges of Gender Data
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Authors: Mayra Buvinic and Eleanor Carey Presented by Agnes Quisumbing
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The Promises and Challenges of Gender Data
- 1. The Promises and Challenges of Gender Data Authors: Mayra Buvinic and Eleanor Carey Presenter: Agnes Quisumbing
- 2. Good Data Form the Backbone of Effective Policy • This chapter examines the state of gender data on rural women and girls in SSA in three ATOR focus areas: • Assets, income and work • Social and political empowerment • Food security and nutrition
- 3. Chapter Outline 1. Outlines benefits of improved data, offers guiding principles, and explores methodological issues 2. Selects 32 indicators from SDG and related frameworks to measure the three ATOR outcomes • Assesses availability and quality of these data in 15 SSA countries using research from Open Data Watch (ODW)
- 4. Benefits of Improved Data 1. Problems of substandard data are particularly prevalent in SSA and impede full accounting of women’s work 2. Poor data hamper the ability to formulate effective policies to increase women’s productivity in agriculture and improve food security and nutrition 3. Better understanding and measuring rural women’s and girls’ poverty is the first step to effective anti-poverty policy solutions
- 5. Guiding Principles for Gender Data Collection 1. Because women’s economic and social roles (especially in rural economies) are interdependent, data need to be generated on both economic and social outcomes, and measures need to track their interdependence 2. Because women’s individual and household choices are linked, data should be generated and analyzed at both the individual and household levels
- 6. Good Gender Evidence • Is high quality (data are reliable, valid, representative and free of bias) • Has good coverage across countries, and is produced at regular intervals • Is comparable across countries (concepts, definitions, measures) • Has desirable complexity and granularity • Is parsimonious and policy relevant
- 7. Methodological Issues •Income: challenging to capture– sporadic, variable, difficult to disentangle from household income •Assets: preferable to income as measure of women’s economic status– but better as a medium- and long- term indicator, not short-term •Work: previous methodologies failed to capture much of rural women’s work; conceptual advancements made with 19th ICLS •Empowerment: includes an objective outcome dimension and a subjective sense of agency; it is difficult to measure accurately
- 8. Current Data Availability: Bridging the Gap Assessment • ODW assessed data for 104 indicators across 15 SSA countries from 2010-2018 • Authors selected 32 indicators that best measured three key outcomes for rural women and girls
- 9. Current Data Availability: Bridging the Gap Assessment 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Sex disaggregated at international level Availability at international level Sex disaggregated at national level Availability at national level Availability and Sex Disaggregation Scores for 15 SSA Countries Assets, income, work (20 indicators) Empowerment (6 indicators) Food security and nutrition (6 indicators)
- 10. Current Data Availability: Bridging the Gap Assessment • On average, around 70% of all indicators have some data available • Assets, income and work show the lowest availability across countries • Women’s empowerment and food security/nutrition show high availability • Sex disaggregation is an issue in both national and international databases
- 11. Current Data Availability: Bridging the Gap Assessment • 15% of assets and 11% of income indicators are sex disaggregated in international data bases, and 35% and 18% in national • Political indicators are more available at the international level (100% vs. 73%), and social indicators at the national (75% vs. 61%) • Food security and nutrition perform best, but there are still gaps in sex disaggregation
- 12. Current Data Availability: Bridging the Gap Assessment Availability varies significantly between countries both at the national and international levels
- 13. Discussion •Rural women and girls in SSA are a key group to target– but generating good evidence is challenging • Assessing availability of data a basic first step • ODW’s assessment revealed promising results, but shows that sex disaggregation is still a challenge
- 14. Recommendations •Recommendations to improve coverage, comparability, granularity, and policy relevance: 1. Collect data at the individual and household levels 2. Invest in efforts to better combine and harmonize data sources to achieve multiple disaggregations 3. Support the widespread implementation of new guidelines and technical assistance to countries in areas that will improve measurement on rural women and girls.
- 15. Recommendations 4. Undertake work to agree on methodology for tier III indicators and devise indicators that are better at capturing objective and subjective measures of empowerment. 5. Prioritize disaggregating data by sex for indicators on income and assets. 6. Emphasize secondary analysis of data. 7. Build connections to decision makers to improve the potential for uptake and impact.
Editor's Notes
- Accounting for all of women’s work Measurement of rural women’s work has improved notably since attention was first called to it in 1970s But measurement issues arising from tradition of not counting unpaid work in systems of national accounts linger Gender gap in productivity has not budged (O’Sullivan et. Al 2014; World Bank 2012). Problems of substandard data well documented in SSA; for example, in Uganda the use of insufficient screening questions to define ‘activity’ led to significant undercounting (close to 10% of the labor force) of subsistence workers, the majority women (Fox and Pimhidzai 2013). And poor data hamper our ability to determine women’s exact contributions to agriculture—so it’s then difficult to discern the needed improvement to productivity required to tackle food insecurity, or even to design effective policy responses to observed gender inequalities in farming (Doss et al. 2015). Better understanding and measuring rural women’s and girls’ poverty—including economic, social, psychological, and political correlates—is the first necessary step to effective policy solutions.
- Two main principles should guide the collection of data on rural women and girls: 1. Women’s economic and social roles, especially in rural economies, are interdependent: Data need to be generated on both economic and social outcomes, and measures need to track their interdependence 2. Women’s individual experiences are difficult to separate from that of the household Especially true for women because of strong interdependence between their family and economic roles Ideally, therefore, data on rural women and girls should be generated and analyzed at both the individual and household levels
- Good evidence on women and girls must be of high quality—that is, based on data that are reliable, valid, representative, and free of gender biases. Good evidence also: Has good coverage across countries, and is produced at regular intervals Is comparable across countries (concepts, definitions, measures) Has desirable complexity, where data from different domains in women’s lives can be cross-referenced and cross-tabulated, and granularity, where the data can be disaggregated into smaller units by race and ethnicity, age, geographical location, etc. Is parsimonious and policy relevant, that is, able to reflect the reality of women and girls’ lives with a minimum amount of information/ indicators (all based on Buvinic, First-Nichols, and Koolwal, 2014). These principles and qualities of good data should be the basis of measurement on rural women—but executing these principles is challenging.
- 1) Income, assets, and work Income: rural women’s income is particularly challenging to capture because it may be more sporadic, variable, and difficult to disentangle from household income than rural men’s income. Another challenge is to identify who the main owner or manager of the firm/farm/plots is when the enterprise or farm is jointly owned. Assets: assets—or resources that individuals, families, or other groups control to produce economic or social value—are preferable to income as a measure of rural women’s economic status, because they are less sensitive to recall bias. But assets are less sensitive than income measures to detecting short-term variations, so they are better medium- and long-term indicators of wealth (Knowles 2014; UNSD 2019). Advancements to data collection on assets have included careful consideration of who should be interviewed, and identifying which people are involved in activities as owners, managers, workers, and decision makers (Doss 2013; World Bank 2015). Work: definitions and methodologies in household surveys have often failed to capture much of rural women’s work—for example, by overlooking unpaid work, or because women participating in agricultural production often list homemaking as their occupation (World Bank, 2015). The new definitions of work and employment, agreed by the International Conference of Labor Statisticians (19th ICLS) in 2013, have changed the conceptualization of work—both paid and unpaid activities are now considered work. When fully implemented, these new definitions should improve the measurement of rural women’s work; uptake has been slow, but there are signs of accelerating implementation in the coming years. 2) Women’s empowerment There are problems with measuring both subjective and, surprisingly, objective indicators across domains; for example, in the social domain, prevalence and incidence data on gender-based violence can be difficult to obtain.
- Bridging the Gap Indicator Assessment ODW assessed the availability and quality of data for 104 indicators across 15 SSA countries in both international and national databases, from 2010 to 2018. For this chapter the authors selected the 32 indicators that best measured the three key outcomes of interest for rural women and girls in the 15 SSA countries. Four indicators measure assets, six measure income (and expenditures), 10 measure paid and unpaid work, and six each measure social and political empowerment and food security and nutrition. See Figure 12.1 Used the assessment to measure how available the indicators are and, second, whether the available indicators are sex-disaggregated for the 15 SSA countries.
- This chart presents availability and sex-disaggregation scores by domain, in national and international databases (percentages), for the 15 SSA countries. (Findings discussed next slide)
- Findings: on average, around 70 percent of all indicators have some data available across international and national databases Assets, income, and work show the lowest total availability across countries The lower scores for assets, income, and work are largely because asset and income indicators are available at international and national levels but are not sex disaggregated (Table 12.1, b and d) Across domains, availability of data (without considering sex-disaggregated) is lower at the national level (71%) than at the international level (85%), thus dragging the average of total availability downward and suggesting that the international level is performing better in terms of producing headline indicators (Table 12.1, a and c) But interestingly, when considering the availability of sex-disaggregated asset and income indicators, national data sources score somewhat better than international data sources, although sex disaggregation remains a significant challenge.
- Overall these findings are promising in that they indicate that, on average, around three-fourths of all indicators have some data available across the 15 countries in SSA. Their availability, however, drops by more than 20 percentage points when considering sex disaggregation at the international level and by 11 percentage points at the national level. So, need investments in sex disaggregation of currently available indicators, and stronger feedback loops between international- and national-level data collection.
- Rural women and girls in SSA are a key group to target in the drive to leave no one behind—but generating good evidence at the individual and household levels that acknowledges the interdependence between economic and social aspects of their lives is challenging, for conceptual and practical reasons. Assessing availability is a basic first step. ODW’s assessment yielded promising results for the 15 countries, but revealed that sex disaggregation is still a major challenge.
- Recommendations to improve coverage, comparability, granularity, and policy relevance: 1. Collect data at the individual and household level, where possible and appropriate. 2. Invest in efforts to better combine and harmonize data sources to achieve disaggregations required to generate insights on rural women and girls. 3. Support the widespread implementation of new guidelines and technical assistance to countries in areas that will improve measurement on rural women and girls.
- 4. Undertake work to agree on methodology for tier III indicators and devise indicators that are better at capturing objective and subjective measures of empowerment. 5. Prioritize disaggregating data by sex for indicators on income and assets. 6. Emphasize secondary analysis of data, in addition to primary data collection, especially because of availability in particular domains. 7. Data producers require support to build connections to decision makers to improve the potential for uptake and impact. Understanding the relevant policy questions will be crucial to guide data producers in where to focus their efforts, while a reciprocal understanding on the part of decision makers of the possibilities and limits of data will help bring the realities of rural women into sharper focus—and, hopefully, lead to real change.