Move beyond sex-disaggregation to substantive gender impact assessment. Choose frameworks, operationalise intersectionality, and design reporting that does not flatten gender to a checkbox. Walk out with a GIA design brief.
4 modules~3 hoursInteractiveIndia-context
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Your Capstone
Drafted GIA Design Brief
Walk in with a programme. Walk out with a gender impact assessment design brief -- framework selection with justification, intersectionality operationalisation, measurement plan, and reporting structure. Built automatically from your module answers.
Module 1 . ~25 min
Beyond sex-disaggregation -- what GIA actually measures
Sex-disaggregated data tells you how many women participated. Gender impact assessment tells you whether the programme changed gender relations. These are fundamentally different questions, and most evaluations stop at the first one.
The three layers of gender analysis
Layer 1: Access and participation -- who participated, in what numbers? This is what sex-disaggregation captures. Necessary but insufficient. A programme can have 50% women participants and still reinforce existing power structures.
Layer 2: Agency and decision-making -- did the programme change who makes decisions? About money, mobility, reproductive health, children's education? NFHS-5 (2019-21) shows that only 32% of married women in Bihar participate in all three household decisions (own health, major purchases, visiting family).
Layer 3: Structural change -- did the programme shift norms, institutions, or policies? This is the hardest to measure and the most important. Examples: changes in dowry expectations, acceptance of women working outside the home, women's participation in gram sabha.
Why Indian evaluations stall at Layer 1
Three structural reasons:
Funder reporting requirements focus on sex-disaggregated numbers because they are easy to report and compare. NITI Aayog's Aspirational Districts programme, for instance, tracks women beneficiaries as a count, not as an agency measure.
Time horizons -- agency and norm change take 3-5 years to manifest. Most evaluations are commissioned at 18 months.
Measurement difficulty -- validated instruments for agency and norms in Indian contexts are scarce. The WEAI was designed for Bangladesh and adapted for South Asia, but it measures agricultural empowerment specifically, not general agency.
Worked example
Mahila Samakhya ran women's collectives in Uttar Pradesh for over 25 years. External evaluations consistently reported high participation numbers. But ICRW's 2018 deep-dive found that participation alone did not predict changes in intra-household decision-making. What mattered was the duration of membership (3+ years) and whether the collective engaged in public action (gram sabha participation, PDS monitoring). The evaluation had to measure collective-level and household-level agency separately.
Your GIA Scoping Sheet
Fill these for your programme. Your answers save automatically and flow into the final capstone.
Is this an 18-month midterm or a 5-year endline? This determines which layers are measurable.
Saved
Self-check
A funder asks you to evaluate the "gender impact" of a livelihoods programme. Their reporting template has one column: "% women beneficiaries." What is the most useful first response?
Fill the column and submit
Refuse the template and design a full GIA
Fill the column AND propose 2-3 additional indicators measuring agency or decision-making change
Explain that gender impact cannot be measured in one number
Correct. Meet the funder where they are (fill the column), then expand the frame. Proposing 2-3 concrete, measurable agency indicators alongside participation data is more persuasive than a lecture on gender theory.
Module 2 . ~30 min
Framework choice: WEAI vs Pro-WEAI vs GEM vs custom
Choosing a GIA framework is not an academic exercise. The framework determines what you measure, how long fieldwork takes, what skills your enumerators need, and what kind of findings you can report. Choose wrong and you spend Rs 20 lakh measuring the wrong thing.
Framework comparison
Framework
Measures
Best for
India validation
Fieldwork burden
WEAI
5 domains of agricultural empowerment + Gender Parity Index
Agriculture/livelihoods with explicit women's empowerment theory
Strong (IFPRI South Asia)
High: 45-60 min survey per couple
Pro-WEAI
12 indicators across 3 domains; adds intrinsic agency, self-efficacy
Programme-specific dimensions drawn from validated sub-scales
When no single framework matches your ToC
Depends on sub-scale selection
Variable
Decision rules
Agriculture/livelihoods programme? Start with WEAI. If the programme also addresses psychological empowerment or self-efficacy, consider Pro-WEAI but budget for the fieldwork burden.
BCC or adolescent programme? GEM Scale is lighter and measures attitudes/norms directly. Add behavioural observation or case studies for behaviour change evidence.
Multi-sector programme? Custom composite is often necessary. Pull sub-scales from validated instruments. Document your construct validity reasoning.
Government scheme evaluation? Align to whatever the scheme's results framework specifies (often NFHS indicators), then add framework-specific depth.
The adaptation trap
Adapting a framework is not picking the items you like. It means testing whether the items measure the same construct in your context. The WEAI "input in productive decisions" item assumes women make agricultural decisions. In Jharkhand's mining-affected tribal communities, the relevant decisions may be about forest produce or wage labour instead. Cognitive pretesting in your specific context is mandatory, not optional.
Your Framework Selection
Choose and justify. These flow into your capstone.
e.g., "Qualitative life-history interviews with 30 women" or "GEM Scale alongside WEAI"
Saved
Self-check
You are evaluating an adolescent girls' life-skills programme in Rajasthan. The programme teaches negotiation, menstrual health, and financial literacy. Which framework is most appropriate as your primary measure?
WEAI -- measures agricultural empowerment across 5 domains
GEM Scale -- measures gender-equitable attitudes, adapted for adolescents by Population Council India
Pro-WEAI -- 60-90 min survey designed for adult women in agricultural households
NFHS household decision-making module
Correct. GEM Scale measures exactly what this programme targets: gender-equitable attitudes. It has been adapted for Indian adolescents by Population Council. WEAI and Pro-WEAI are designed for agricultural contexts and adult women; NFHS decision-making items are for married women.
Module 3 . ~30 min
Intersectionality in practice
Gender does not operate in isolation. A Dalit woman in rural Bihar, an Adivasi woman in Jharkhand's mining belt, and an upper-caste woman in urban Pune experience the same livelihood programme differently. Intersectionality is not a theoretical add-on -- it is a design requirement.
The practical problem
Intersectional analysis requires sufficient sample sizes across intersecting categories. If your sample is 300 women, and you want to disaggregate by caste (SC/ST/OBC/General) and geography (rural/urban), you need 300 / 8 = ~37 per cell. With 20% attrition, you need 45 per cell. That is 360 women minimum. Most GIA budgets do not account for this.
Three operational approaches
Stratified sampling -- oversample from marginalised intersections. If your population is 15% SC women, sample 25% SC women to ensure analytical power. Weight back during analysis. Budget this explicitly.
Qualitative depth on critical intersections -- where quantitative cell sizes are too small, conduct deep qualitative work. 15-20 life-history interviews with SC/ST women can reveal mechanisms that the survey misses entirely.
Intra-household analysis -- the unit of analysis matters. Individual-level data (woman respondent) tells a different story than household-level data. WEAI interviews both the woman and the primary male decision-maker for this reason.
Indian intersections to consider
Axis
Why it matters for GIA
Data source
Caste
SC/ST women face compounded discrimination in credit access, labour markets, and political participation
Self-reported; verify with community records
Religion
Muslim women in India have lower labour force participation (PLFS 2022-23: 15.6% vs 32.8% Hindu women)
Self-reported
Disability
Women with disabilities are systematically excluded from most programmes; 2011 Census undercounts significantly
Washington Group Short Set questions
Age/marital status
Adolescent girls, young married women, and widows face distinct constraints
Demographic section
Geography
Urban/rural; plain/hill/tribal/coastal -- distinct livelihood structures and gender norms
Sampling frame
The ethics of naming
Intersectional analysis can identify marginalised sub-groups in small communities. In a 500-person village, reporting that "SC women who are also single mothers experienced increased harassment" can identify individuals. Aggregate to the block or district level for sensitive findings. Discuss this in your ethics protocol explicitly.
Your Intersectionality Plan
Design the intersectional analysis. These flow into your capstone.
e.g., "caste (SC/ST/OBC/General), geography (rural/peri-urban), age group (18-25, 26-40, 40+)"
Will you interview male household members? Use couple-level analysis?
Saved
Self-check
Your GIA sample is 200 women from 3 districts. The programme team wants disaggregation by caste (4 categories), geography (rural/urban), and disability status. How many cells does this create, and is 200 sufficient?
8 cells; 200 is sufficient (25 per cell)
16 cells; 200 is far too small (12.5 per cell) -- reduce axes or increase sample
12 cells; 200 is borderline
4 cells; 200 is plenty
Correct. 4 caste categories x 2 geography x 2 disability = 16 cells. At 200, that is 12.5 per cell -- far below the minimum 30 needed for quantitative analysis. Either increase the sample to 500+, drop an axis, or use qualitative methods for the thinnest cells.
Module 4 . ~25 min
Reporting that does not flatten gender to a checkbox
The most rigorous GIA is wasted if the report reduces findings to "the programme benefited women." Three reporting failures are endemic.
Failure 1: The aggregation trap
Reporting average effects hides the distribution. If empowerment scores improved for OBC women (+0.3 SD) but declined for SC women (-0.1 SD), the average effect (+0.15 SD) is misleading. Always report sub-group effects for your pre-specified intersections.
Failure 2: The binary framing
"The programme is gender-positive" or "gender-negative" is never the right frame. Programmes are simultaneously empowering on some dimensions and reinforcing on others. A microfinance programme may improve women's income (empowering) while increasing their workload without reducing domestic labour (reinforcing).
Failure 3: Disconnecting quant from qual
The most powerful GIA reports use qualitative data to explain quantitative patterns. If the WEAI score improved in one domain but not another, the life-history interviews explain why. Report them together, not in separate chapters.
Reporting structure for GIA
1-page executive summary with gender-specific headline findings and one recommendation per finding
Gender context section -- baseline gender landscape, norms, constraints. This grounds the findings.
Findings by domain (not by method) -- for each empowerment domain, present quant + qual together
Intersectional findings -- dedicated sub-section showing how effects vary across intersections
Unintended effects -- did the programme create new burdens, risks, or backlash for women?
Recommendations -- actionable, specific, addressed to named stakeholders
Worked example
PRADAN's SHG programme in Jharkhand: The GIA report found that economic empowerment (measured by WEAI production domain) improved significantly. But qualitative interviews revealed that women's increased farm work came with no reduction in domestic labour -- they were working 14-hour days. The report framed this as "economic gains with time-poverty costs" and recommended integrating time-use analysis into future evaluations. The funder changed the programme design to include household-level negotiation sessions.
Your Reporting Plan
Design the report structure. These flow into your capstone.
e.g., increased workload, backlash, time poverty, new dependencies
"This GIA will tell us ___ and will NOT tell us ___."
Saved
Self-check
Your GIA finds: WEAI score improved on average (+0.2 SD). But disaggregation shows SC women declined (-0.05 SD) while OBC women gained (+0.35 SD). How should the executive summary frame this?
"Empowerment gains concentrated among OBC women; SC women saw no improvement -- programme design needs caste-differentiated strategies"
"Average effect positive but not statistically significant for all sub-groups"
Correct. The honest frame names who benefited, who did not, and what the programme should do about it. Averaging over the distribution hides the most important finding -- that the programme may be widening caste gaps in empowerment.
Capstone
Your GIA Design Brief
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Gender Impact Assessment Design Brief
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