Tag Archives: need

Don’t split target areas, but some programs, like HRSA’s Rural Health Network Development (RHND) Program, encourage cherry picking

In developing a grant proposal, one of the first issues is choosing the target area (or area of focus); the needs assessment is a key component of most grant proposals—but you can’t write the needs assessment without defining the target area. Without a target area, it’s not possible to craft data into the logic argument at is at the center of all needs assessments.

To make the needs assessment as tight and compelling as possible, we recommend that the target area be contiguous, if at all possible. Still, there are times when it is a good idea to split target areas—or it’s even required by the RFP.

Some federal programs, like YouthBuild, have highly structured, specific data requirements for such items as poverty level, high school graduation rate, youth unemployment rates, etc., with minimum thresholds for getting a certain number of points. Programs like YouthBuild mean that cherry picking zip codes or Census tracts can lead to a higher threshold score.

Many federal grant programs are aimed at “rural” target areas, although different federal agencies may use different definitions of what constitutes “rural”—or they provide little guidance as to what “rural” means. For example, HRSA just issued the FY ’20 NOFOs (Notice of Funding Opportunities—HRSA-speak for RFP) for the Rural Health Network Development Planning Program and the Rural Health Network Development Program.

Applicants for RHNDP and RHND must be a “Rural Health Network Development Program.” But, “If the applicant organization’s headquarters are located in a metropolitan or urban county, that also serves or has branches in a non-metropolitan or rural county, the applicant organization is not eligible solely because of the rural areas they serve, and must meet all other eligibility requirements.” Say what? And, applicants must also use the HRSA Tool to determine rural eligibility, based on “county or street address.” This being a HRSA tool, what HRSA thinks is rural may not match what anybody living there thinks. Residents of what has historically been a farm-trade small town might be surprised to learn that HRSA thinks they’re city folks, because the county seat population is slightly above a certain threshold, or expanding ex-urban development has been close enough to skew datasets from rural to nominally suburban or even urban.

Thus, while a contiguous target area is preferred, for NHNDP and RHND, you may find yourself in the data orchard picking cherries.

In most other cases, always try to avoid describing a target composed of the Towering Oaks neighborhood on the west side of Owatonna and the Scrubby Pines neighborhood on the east side, separated by the newly gentrified downtown in between. If you have a split target area, the needs assessment is going to be unnecessarily complex and may confuse the grant reviewers. You’ll find yourself writing something like, “the 2017 flood devastated the west side, which is very low-income community of color, while the Twinkie factory has brought new jobs to the east side, which is a white, working class neighborhood.” The data tables will be hard to structure and even harder to summarize in a way that makes it seem like the end of the world (always the goal in writing needs assessments).

Try to choose target area boundaries that conform to Census designations (e.g., Census tracts, Zip Codes, cities, etc.). Avoid target area boundaries like a school district enrollment area or a health district, which generally don’t conform to Census and other common data sets.

Good needs assessments tell stories: Data is cheap and everyone has it

If you only include data in your needs assessment, you don’t stand out from dozens or hundreds of other needs assessments funders read for any given RFP competition. Good needs assessments tell stories: Data is cheap and everyone has it, and almost any data can be massaged to make a given target area look bad. Most people also don’t understand statistics, which makes it pretty easily to manipulate data. Even grant reviewers who do understand statistics rarely have the time to deeply evaluate the claims made in a given proposal.*

Man is The Storytelling Animal, to borrow the title of Jonathan Gottschall’s book. Few people dislike stories and many of those who dislike stories are not neurologically normal (Oliver Sacks writes movingly of such people in his memoir On the Move). The number of people who think primarily statistically and in data terms is small, and chances are they don’t read social and human service proposals. Your reviewer is likely among the vast majority of people who like stories, whether they want to like stories or not. You should cater in your proposal to the human taste for stories.

We’re grant writers, and we tell stories in proposals for the reasons articulated here and other posts. Nonetheless, a small number of clients—probably under 5%—don’t like this method (or don’t like our stories) and tell us to take out the binding narrative and just recite data. We advise against this, but we’re like lawyers in that we tell our clients what we think is best and then do what our clients tell us to do.

RFPs sometimes ask for specific data, and, if they do, you should obviously include that data. But if you have any room to tell a story, you should tell a story about the project area and target population. Each project area is different from any other project area in ways that “20% of the project area is under 200% of the Federal Poverty Line (FPL)” does not capture. A story about urban poverty is different from a story about recent immigration or a story about the plant closing in a rural area.

In addition, think about the reviewers’ job: they read proposal after proposal. Every proposal is likely to cite similar data indicating the proposed service area has problems. How is the reviewer supposed to decide that one area with a 25% poverty rate is more deserving than some other area with a 23% poverty rate?

Good writers will know how to weave data in story, but bad writers often don’t know they’re bad writers. A good writer will also make the needs assessment internally consistent with the rest of the proposal (we’ve written before “On the Importance of Internal Consistency in Grant Proposals“). Most people think taste is entirely subjective, for bad reasons that Paul Graham knocks down in this excellent essay. Knowing whether you’re a good writer is tough because you have to know good writing to know you’re a bad writer—which means that, paradoxically, bad writers are incapable of knowing they’re bad writers (as noted in the first sentence of this paragraph).

In everyday life, people generally counter stories with other stories, rather than data, and one way to lose friends and alienate people is to tell stories that move against the narrative that someone wants to present. That’s how powerful stories are. For example, “you” could point out that Americans commonly spend more money on pets than people in the bottom billion spend on themselves. If you hear someone contemplating or executing a four- or five-figure expenditure on a surgery for their dog or cat, ruminate on how many people across the world can’t afford any surgery. The number of people who will calmly think, “Gee, it’s telling that I value the life of an animal close at hand more than a human at some remove” is quite small relative to the people who say or think, “the person saying this to me is a jerk.”

As you might imagine, I have some firsthand investigative experience in matters from the preceding paragraph. Many people acquire pets for emotional closeness and to signal their kindness and caring to others. The latter motive is drastically undercut when people are consciously reminded that many humans don’t have the resources Americans pour into animals (consider a heartrending line from “The Long Road From Sudan to America:” “Tell me, what is the work of dogs in this country?”).

Perhaps comparing expenditures on dogs versus expenditures on humans is not precisely “thinking statistically,” but it is illustrative about the importance of stories and the danger of counter-stories that disrupt the stories we desperately want to tell about ourselves. Reviewers want stories. They read plenty of data, much of it dubiously sourced and contextualized, and you should give them data too. But data without context is like bread instead of a sandwich. Make the reviewer a sandwich. She’ll appreciate it, especially given the stale diet of bread that is most grant proposals.


* Some science and technical proposals are different, but this general point is true of social and human services.