ImpactMojo now carries three flagship courses, six foundational decks, three Studios, three Fundamentals pages, two deep dives, six reading companions, a timeline, a game, a challenge and a stack of printable handouts that are all, in one way or another, about gender. They were built over eighteen months, in no particular order, and nobody has ever listed them in one place. This is that list, with what each thing is actually for.
The organising question, if there is one, is the gap between two activities that get the same name. Teaching gender theory is one thing. Measuring whether anything changed is another. Most of what follows sits on one side or the other, and the newest addition exists because the middle was empty.
Start here, depending on what you need
Three different starting points, and they are genuinely different rather than three doors to the same room.
If you need the theory, start with Gender Studies: Feminisms, Power & Social Change. Sixteen modules on feminist thought and the political economy of gender, grounded in South Asia rather than translated into it. It is the course that argues about what gender is before anyone reaches for an indicator.
If you need to measure something, start with Gender-Sensitive Monitoring, Evaluation & Learning, which went live this week. Thirteen modules on the machinery: indicators that can register a change in who decides rather than who attends, empowerment measured the way Kabeer defined it, unpaid care read off India's own Time Use Survey with its denominators intact, violence measured under protocols that put the respondent ahead of the dataset, and intersectional analysis honest about what a sample of 1,200 households can and cannot support.
If your question is about exclusion more broadly, Nothing About Us Without Us runs the same measurement problems along the disability axis, where the data is thinner and the categories are contested in different ways. Reading it alongside the gender material is more useful than reading either alone, because it makes visible which problems belong to gender and which belong to measuring any marginalised category at all.
The division of labour, stated plainly. The Gender Studies flagship teaches the frameworks. The MEL flagship teaches the general system. Gender-Sensitive MEL is the part that falls between them, and it was empty for a year: deciding what a change in gender relations would look like in data, building an instrument that could detect it, collecting it without harming the person who answers, and reporting a finding that a steering committee would rather not receive.
One number, to show why the measurement course exists
India's Time Use Survey 2024 asked a national sample what they did with their day. For people aged 15 to 59, on all unpaid activities, it reports two averages side by side.
Per participant — averaging over the people who did any unpaid work at all — women spent 389 minutes and men 120. That is a ratio of 3.2 to one.
Per person — averaging over everybody, counting those who did none as zero — women spent 366 minutes and men 62. That is 5.9 to one.
Same survey, same year, same activity, same age band. The ratio nearly doubles depending on which denominator you quote, and the difference between the two numbers is entirely the participation rate: 94.2 per cent of women did some unpaid work on the reference day, against 51.5 per cent of men. Conditioning on participation quietly deletes the half of men who did nothing, which is the finding.
Both figures are correct. Neither is the number. Deciding which one your indicator means, and saying so where a reader can see it, is roughly the whole job — and it is the kind of thing no amount of feminist theory will teach you and no generic MEL course will think to mention.
The foundational decks
Six 100-slide primers, free, no sign-in. These are where to send someone who needs the vocabulary by Thursday.
- Gender Mainstreaming 101 — what mainstreaming was supposed to mean, what it became in practice, and how to tell the two apart in a project document.
- Feminist Research 101 — whose questions get asked, who counts as a credible source, and what reflexivity looks like when written honestly rather than performed.
- Data Feminism 101 — power in data collection, classification and display, applied to datasets South Asian practitioners actually use.
- Care Economy 101 — the accounting decision that keeps unpaid care outside GDP, and what recognising it would cost.
- Women's Economic Empowerment 101 — the evidence on what raises women's incomes and what only appears to.
- Safeguarding & PSEA 101 — the duty that applies to a research team spending three weeks in a village, not only to service delivery.
Things you use rather than read
The Studios are browser tools with a guided workflow; nothing is uploaded and nothing is stored.
- Gender Analysis Studio — apply Moser, Longwe and the social relations approach to a programme you are actually working on, and see which one produces a question you cannot already answer.
- Data Feminism Studio — interrogate a dataset for what it counts, what it omits and whose categories it uses.
- Disability-Inclusive MEL Studio — the closest sibling to the new flagship, and worth running even if disability is not your subject.
Three Fundamentals pages carry the frameworks as interactive diagrams rather than prose:
- Practical and Strategic Gender Needs — Moser's distinction, which is the single most useful and most misused idea in this field.
- Kabeer's Empowerment Framework — resources, agency, achievements, and where each one breaks as a measure.
- Who Counts — the prior question underneath every disaggregation decision.
And two vocabularies, both searchable and filterable: the Gender & MEL Lexicon (58 terms, from practical gender needs to the per-participant denominator) and the Gender Studies Lexicon.
The evidence, worked through
Two deep dives, each on a question where the popular answer is wrong.
India's Female Labour-Force Puzzle asks why women's measured workforce participation fell while the economy grew, and takes seriously the possibility that a large part of the fall is a measurement artefact rather than a behavioural change. The Care Economy works through what it would mean to count care as production, and what follows for budgets if you did.
Six reading companions turn dense sources into something navigable:
- Women in India
- Dalit Feminist Discourse — read this one before writing anything about intersectionality in an Indian context.
- Dhar, Jain and Jayachandran on changing gender attitudes — a rare case of an attitudes intervention measured properly.
- Gender and Child Budgeting in India
- Subnational Gender Budgeting
- Social Safety Nets and Women's Agency
Practice, and things to print
The Gender and GESI Audit challenge hands you a real programme design, a demographic profile and a mid-term review, and asks you to audit them. It is the closest thing here to the work itself.
The Gender Equity Game is quicker and blunter, and works well as a workshop opener. The Gender and Work in India timeline is the one to project when somebody claims a trend began in 2014.
For handouts to print and put on a table: the Gender Studies handout and its quick reference card, Data Feminism in South Asia, and the Care Economy handout.
For the underlying numbers, the NFHS state explorer and the NFHS explorer are where most of the Indian gender statistics quoted across this site come from, and they let you check them rather than take our word for it.
What is missing
Three gaps worth naming, since a roundup that only lists what exists is an advertisement.
There is no course on gender-based violence programming. The measurement of violence is covered inside Gender-Sensitive MEL, under the WHO safety protocols, but the design and delivery of prevention and response is not, and it is a large field to leave to one module.
There is nothing substantial on gender and climate, which is now one of the most heavily funded areas in the sector and one of the thinnest in terms of usable measurement guidance.
And almost all of this is in English. That is a real limit on who can use it, and it is the open item that has been open longest.
If you use any of this and it fails you somewhere — a framework that does not fit your context, a figure that does not reconcile with your own data, a module that assumes something it should not — that is worth telling us. The material improves mostly when somebody reports where it broke.