Ee Ning Foo, World
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Measuring Nonprofits: A New Framework for Social Impact

By: Ee Ning Foo

How should everyday citizens choose which non-profit organisations to support, especially with the variety of problems they work on? More specifically, what kind of metric would be good enough to provide a reasonable comparison of these problems?

As you hand over the dollar bill to the volunteer with the tin can, have you ever wondered where that dollar is going? Is it actually going to the beneficiaries of the organisation?  Maybe not. A study by The Tampa Bay Times collected data from almost 6,000 US charities and found that 50 of the worst-performing charities allocated less than 4% of funds donated in direct aid to their causes. Three measures are typically used to assess the efficiency of a nonprofit’s funds: the overhead ratio, administrative expense ratio and fundraising ratio. While these measures show the surface-level efficiency of donations, the actual social impact of those donations are not measured. A quick scan through online reports of nonprofit organisations shows that the only common metric used to measure their outcomes is the number of people or organisations helped. Nonprofits which seek to alleviate poverty may have statistics related to the number of people lifted out of poverty and the average increase in income, while those in education usually stick to the literacy rate and number of students kept in schools. Evidently, the metrics for social impact vary widely depending on the sector, making it difficult for donors to compare nonprofit organisations across sectors. Moreover, the average adoption of outcome measurement by human service nonprofits is only 74%, which is less than ideal.

Effective Altruism (EA), first coined and spearheaded by renowned psychologist Peter Singer, advocates for a heightened awareness of the efficiency of charities. EA is important because more efficient charities are able to produce greater social impact. Thus the main driver of this movement is the large difference between the impact of certain nonprofits compared with others using the same amount of resources. For example, a paper by Dr. Toby Ord, the founder of Giving What We Can, a non-profit dedicated to alleviating poverty in the developing world, shows the number of healthy years of life saved per $1000 donated to different prevention and treatment methods in combating HIV and AIDS.

As shown above, the positive effect of education is more than two times higher than that of the next-best alternative, condom distribution, which makes education a clear winner in terms of effectiveness. As seen from the graph, the interventions are ordered in stages from bottom up, with education being the earliest intervention and surgical treatment the latest. Hence it can be seen that as interventions to particular problems escalate in later stages, the effectiveness of resources used decreases. This trend reflects the concept of diminishing marginal returns, which is one of the factors that EA consider in evaluating nonprofits that conventional metrics do not. Thus, nonprofit organisations need to constantly re-evaluate their impact to ensure the best use of their funds. How then, should everyday citizens choose organisations to support, especially with the variety of problems nonprofits work on and what kind of metrics would be good enough to provide a reasonable comparison of these problems?

The EA movement uses three standards to evaluate the effectiveness of funds donated: extent of negligence, scale and solvability.

First, the level of negligence is used as a yardstick because of diminishing returns and the ‘value of information’, as research economist Robert Wiblin says, that can be gained from helping a previously neglected area. Organisations are accorded a “crowdedness score” based on the direct annual spending on the problem, the number of full time staff working on it and the number of active supporters of work. The greatest weightage is given to the category which uses the most amount of resources.

Second, the scale of a problem is measured because the potential impact of solving a large-scale problem could result in a dramatic improvement in global well-being. Scale is broken down into two parts: the far-reaching consequences of the problem and its extent of negative impact. Irresponsible nuclear proliferation, for example, could produce devastating results for humanity. While this yardstick is value-based and hence subjective, four benchmarks are used to produce a score: risk of human extinction, global economic output, income of the world’s poorest and years of healthy lives saved.

Third, the solvability of a problem is necessary in practical analyses. A problem with a low probability of being solved would be a riskier choice for resource allocation as its results are unlikely to justify that choice. Models are used to evaluate the potential and likelihood of an improvement due to the intervention, with new information constantly updated. The three standards are combined to produce a final score which is used to compare the desirability of nonprofit organisations relative to each other.

While many loopholes and immeasurables exist in this framework of social impact analysis, it nonetheless provides a benchmark for rethinking the roles of nonprofits and their work in improving social outcomes.

Works Cited

Image Source:

Glazer, A. (2016, April 1). The profit-maximizing non-profit. Oxford Economic Papers, 68(2), 301-315. Retrieved from

Ecer, S., Magro, M., & Sarpça, S. (2017, February 1). The Relationship Between Nonprofits’ Revenue Composition and Their Economic-Financial Efficiency. Nonprofit and Voluntary Sector Quarterly, 46(1), 141–155. Retrieved from

Lee, C., & Clerkin, R. M. (2017). The Adoption of Outcome Measurement in Human Service Nonprofits. Journal of Public and Nonprofit Affairs, 3(2), 111-134. Retrieved from

Wiblin, R. (2017, February). How to compare different global problems in terms of impact. 80,000 Hours. Retrieved from

(2018, June 2). Can “effective altruism” maximise the bang for each charitable buck? The Economist. Retrieved from



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