The art of writing grant recommendations

How do you summarise a nuanced social challenge, a multi-faceted organisation, and pages of analysis in 300 words?

Every foundation operates differently and this post might not be relevant to many of them. This is for those people who work within a foundation and are responsible for sharing their own recommendations with their Directors. These staff typically get to know an organisation deeply over a few weeks, arrive at a position regarding their suitability for partnership and, all things going well, then draft a recommendation to share internally.

I’m not convinced that there is one way to do this, nor even a best way. Originally, I adopted principles from my management consulting days by using statement-like headings followed by short paragraphs as justification. This gets you some of the way, but there remains a need to prioritise information that won’t distract from the critical points.

So below I have attempted to pull out the most critical pieces of information. I’ve broken these into three parts: Model; Partnership; and Expected Value. In the spirit of keeping things simple, I’ll provide a brief explanation of each heading, followed immediately by a fictional example. I have almost certainly missed key points or included others that aren’t so useful — I’d love feedback!

Model

Problem to solve: Clear and specific description of the issue being addressed.

“[Target pop] in [geography] experience [magnitude] lower foundational literacy and numeracy scores than their peers at a regional and national level.”

Organisation’s solution: Simple description of the intervention that is delivered.

“[Org] distributes locally-relevant content to specially trained teachers to increase hours of learning per week.”

Predicted mechanism of change: Description of the causal mechanism that allows the intervention to adjust the experience of the audience.

“[Org] predicts that locally-relevant content increases engagement to allow for more hours of practice. Meanwhile, the teacher training empowers teachers to dedicate more time to foundational learning to subsidise what might not be happening at home.”

Critical assumptions: The most important assumptions that would need to be true for the causal mechanism to work.

“1. Content that is engaging and builds in difficulty.
2. Teacher training in [x] months.
3. Scalable distribution channel.”

Partnership

What’s required: The top 1–3 priorities for the organisation in the next 24 months.

“Test whether intervention works in >2 new geographies with >500 young students, and increase the robustness of outcomes data.”

Role of funder: Why and how the foundation is uniquely placed to add value.

“Provide security for R&D to expand with no restrictions.”

Alignment with funder: How the intervention would help achieve the foundation’s goals.

“Method of distribution has potential to scale, reaching more students faster.”

Success for this partnership: Clear and specific description of desired impact by the end of the partnership. Typically, success is a certain outcome reliant on several outputs (I have used OKRs below as one method to keep this approach specific) or proxies/indicators to measure a particular change among the target audience.

“O: Support 500 young students to increase literacy and numeracy scores [using x measure] by [magnitude] within 24 months.
KR: Equip 150 teachers in x locations to deliver materials within 3 months.
KR: Have 500 young students commence learning with materials by [date].
KR: Achieve 80% retention of students and teachers in [x] hours of learning per week by [date].
KR: Measure a x% increase in literacy and numeracy scores of control group by [date].”

Expected Value

Likelihood of success: An estimate of the probability that the success measures will be achieved.

List the most likely reasons the intervention may fail. For each, estimate the probability of occurring. Calculate 1 — (sum of probabilities). Take an average across the team.

Magnitude of success: An estimate of how broad and deep impact will be.

“We expect between 50–70% of target audience will consistently engage with learning materials. Of those, we expect 70–90% will experience at least a [magnitude] increase in learning score.”

Best case for not investing: Strongman the other side — why shouldn’t you back this?

“Evidence to date lacks a counter-factual, so there is limited insight to value added.”