Automated grant making

Top executives in large corporations tend to be busy, and don’t have time to make every decision themselves. A technique used by management consultants to help organizations make decisions consistent with those of their top executives without necessarily having to involve those executives in every decision is to model an executive’s values and risk tolerances.

In profit seeking businesses, generally these decisions revolve around how much money (measured in time and assets) a company is willing to risk, and at what level of risk, to receive a certain monetary pay out.

This same concept can be applied to philanthropic giving. By modeling a grant making entities’ social values and their assessment of each applicant’s ability to deliver results, one could quickly and consistently evaluate an arbitrary number of grant applications.

Valuing outcomes in terms of other outcomes

To illustrate this point let’s take the example of two funders, Funder A and Funder B, who each receive two applications from Applicant X and Applicant Y. Applicant X runs a food program plans feed 150 people for a month. Applicant Y runs a housing program that plans to house 20 people for a month. Applicant X is requesting a grant of $26,100 and Applicant Y is requesting $18,000. The following table summarizes this setup.

So, which is better? The answers depends on how we value the intended outcomes. The following table demonstrates how Funder A and Funder B values feeding a person for a month in terms of a person housed for a month.

The idea of measuring one outcome in terms of another is an essential concept to this modeling process. Reading the below table, Funder A values feeding 5 people in a month the same as housing one person for a month. This ratio of 5 people fed to one person housed is Funder A’s point of indifference. That is, according to Funder A, feeding 5 people for a month has the same social value as housing one person for a month.

Funder B puts considerably more value on housing relative to food than Funder A. For Funder B, you have to feed 15 people for a month to equal housing one person for a month. Therefore, we can not only see that funder B values housing more than Funder A, but we see that Funder A values housing 3 times as much, allowing us a way to quantify the subjective value differences between these funders.

Using the relative values of persons fed and persons housed for each of the funders and the number of people each applicant plans to feed and house, we can evaluate each proposal using the value system of each funder. Since we are using “Persons housed for a month” as a base outcome that we compare food outcomes to, Funder A and Funder B both assign the food program, Applicant Y, a value of “20”, which is simply 1 times the number of people Applicant Y plans to feed, 20.

In order to evaluate the outcomes of Applicant X, which is a housing program, in terms of the food outcome used by Applicant Y, we divide the number of people Applicant Y plans to feed, 150, by the number of people each funder believes must be fed to equal one person housed. For example, because Funder A values 5 people being fed the same as 1 person being housed, we divide 150 by 5, which leads Funder A to assign Applicant X a value of 30. Using the same logic, Funder B assigns applicant X a value of 10, reflecting Funder B’s preference for housing over food relative to Funder A’s preferences.

Expected social value calculations

In the above table, we see that Funder A prefers Applicant X’s application and Funder B prefers Applicant Y. But what if we don’t necessarily believe Applicant X and Applicant Y will be able to help as many people as they plan to? We can adjust our model to account for each funder’s confidence in each applicant’s ability to achieve their intended results.

The above matrix shows each funders’ confidence in each applicants’ ability to deliver their intended outcomes. For example, Funder A is only 40% sure applicant X will deliver its intended outcome of feeding 150 people for a month.

Using these subject funder probabilities we can calculate the expected value of the number of people each applicant will help according to the funders’ assessments of the applicants’ capacities.

By accounting for funders’ confidence in each applicant, we now see that both funders prefer Applicant Y, whereas Funder A preferred Applicant X before applying the probabilities in Table 4, as shown by the preferences depicted in Table 3.

Finally, we can factor in the cost of each applicants’ grant request by dividing the grant amount by the expected social value in terms of people housed.

Accounting for cost, the decisions made by Funder A and Funder B do not change from Table 5, with both funders preferring applicant Y’s application. However, whereas Funder A has a modest preference for Applicant Y in Table 5, accounting for the dollar amount per social value metric makes funding Applicant Y a clear decision for both funders A and B.

Applying this approach

All the calculations in this example are simple, and this approach scales to any number of data points. The real trick is picking a base indicator to value other indicators against. In this case, we used the number of people housed in a month, but really any indicator can be used.

While this example focuses on grant making, the same idea can be used for any type of social investment decisions. By modeling an organization or grant making institutions’ values, decisions can not only be made more quickly, but the decision making criteria is made transparent, which helps drive intelligent discussions about whether investments are being made consistently, and whether those decisions are designed to maximize social impact.

As the social sector continues to debate how to better incorporate metrics in our work – we have to move away from simple summary statistics and outputs enumerations to more sophisticated uses of data that directly aid decision making. Automating some aspects of grant making is a logical application of data driven methodologies in the philanthropic sector.