Data management and analysis with gender sensitivity
When gender data gaps persist, it is difficult to monitor the gender-based inequalities that affect women and girls as well as progress in these areas. These gaps will persist unless gender is mainstreamed into international statistical strategies and gender data collection and analysis are prioritised.
Our ability to monitor actions from a gender equality perspective is constrained by three main challenges:
2. women and girls experience multiple and intersecting inequalities, which are difficult to measure;
3. the available and quality of data across countries is very variable.
There are two broad or systematic ways to analyse data with a gender lens:
1. Disaggregation of data: Analyse differences according to sex (and preferably age too). Disaggregated output and outcome indicators by sex such as the number/percentage of malnourished/admissions of children in nutrition programs by sex provide a big picture overview of which gender is more affected by a circumstance.
This approach can be applied more systematically and is quite generalised in data collection and analysis tools. It does not, however, offer reasons for such differences in a given population or group.
Global underweight trends in adults over 18 by sex between 2000 and 2015
Source: Global Nutrition Report
2. Including a gender perspective in the data management and study design process: Ensuring that NIPN policy questions include a gender perspective will enable further analysis. For instance, more information can be captured on the linkages between gender and nutrition outcomes (for example, prioritising question on how the household decision-making processes influences nutrition outcomes).
However, this approach can be more challenging due to the following reasons:
a) according to Kabeer10: ‘gender issues’ cover three dimensions: resources (material, human and institutional), agency (decision-making process) and achievements (well-being outcome), each measured by several, non-standardised, quantitative indicators; and
b) there are complex and multiple gender-related issues affecting or favouring undernutrition and other forms of malnutrition and these are often very context-specific.
Considering these factors, measuring gender issues solely with quantitative indicators can be challenging. It is therefore crucial to include and integrate qualitative information (including feedback loops) in the data analysis to capture a more holistic overview of gender issues in a given context. A good example of the power of qualitative methods in analysing gender-based needs is available11 12.
GTA and Data Management
Most NIPN platforms have conducted a nutrition data mapping exercise. To better anchor GTA, NIPN countries might review whether this exercise took account of gender-related indicators and whether datasets can provide information that describe gender inequalities as well as disaggregation of the data.
“Data 2X” is an initiative13 to “make gender data central to global efforts to achieve gender equality”. The initiative has conducted an analysis of data gaps on gender relevant indicators for most of the NIPN countries looking at national and international datasets. It identified a list of 104 ‘gender-relevant indicators’14 (combining those of UN Women and the SDGs). The indicators are mainly related to the health, economy and education domains. Although some may not be applicable for a NIPN analysis, the list offers a useful view of gender issues in a particular context.
According to the Data 2X studies, more work is needed to obtain data on gender-based needs, constrains and inequities. Interestingly, there is more gender-relevant information on the health sector than elsewhere due to specific issues affecting women such as pregnancy, anaemia and menstruation. Health data is thus better informed and more systematically disaggregated by sex and age.
The EU-Rome-based agencies’ joint Programme on Gender Transformative Approaches for Food Security and Nutrition (JP GTA) has developed guidance on how to formulate indicators to measure changes in gendered social norms in the context of food security and nutrition15. However, there is no standard or validated set of social norms indicators, and there is a general lack of clear and practical guidance or examples of social norms indicators for these sectors.
NIPN platforms should evaluate and identify if gender-related information and indicators are available at national and sub-national level. Did the NIPN data mapping exercise look at the common gender-relevant indicators listed by Data2X? What indicators are endorsed by your country government to measure progress in gender inequalities? Are there any gaps? Can NIPN platforms complete the data analysis including gender-relevant information and data?
NIPN teams are recommended to describe, analyse and communicate the gaps in gender data (missing indicators or inability to disaggregate by sex) to inform decision makers.
Availability of data (104 gender relevant indicators) in 15 Sub-Saharan African Countries. Copied from Data2X, Africa report16
10 Kabeer, N. (1999). "Resources, Agency, Achievements: Reflections on the Measurement of Women’s Empowerment." Development and Change 30(3): 435-464.
11 Muraya, K. W., C. Jones, J. A. Berkley and S. Molyneux (2017). "“If it’s issues to do with nutrition…I can decide…”: gendered decision-making in joining community-based child nutrition interventions within rural coastal Kenya." Health Policy and Planning 32(suppl_5): v31-v39
12 Action Against Hunger: https://linknca.org/etudes.htm
13 Data 2X initiative: https://data2x.org/what-we-do/
14 “Bridging the Gap:Mapping Gender Data Availability in Africa” TECHNICAL REPORT. MARCH 2019. DATA2X. Link: https://data2x.org/resource-center/bridging-the-gap-mapping-gender-data-availability-in-africa/
15 https://www.fao.org/documents/card/en/c/cc0673en
16 Bridging-the-Gap-Technical-Report-Web-Ready.pdf (data2x.org)