Maximizing the Value of Philanthropic Efforts through Planned Partnerships between the U.S. Government and Private Foundations. The Hewlett Foundations Expected Return Metric


In 2007, the William and Flora Hewlett Foundation undertook to increase the rigor of its grantmaking. Two primary approaches were developed: an Outcomes-Driven Grantmaking (ODG) strategy, and an expected return (ER) metric. The ODG process creates a strategic program plan. The plan sets measurable goals for specified outcomes, defines the programs scope, establishes logic models that lay out how programs are expected to affect outcomes, and determines how to allocate resources to achieve outcome targets. The ER is a quantitative metric used to evaluate and compare clusters of potential investments in grants with similar purposes. It is created by estimating the potential benefits of a particular grant, its likelihood of success, and its costs. Both ER analysis and an ODG process require a clear definition of goals in terms of explicit outcomes that can be measured and used to evaluate potential program investments and estimate their likely success.

Though too imprecise to serve as the decisive factor in funding allocations, ER calculations have provided a structure for thinking about potential benefits and investment risk, according to Hewlett Foundation respondents. In their perception the ER fosters a common language for assessing tradeoffs, which is more important than the actual calculation of expected return. The ODG process and use of the metric forces program staff to be explicit about their program goals and assumptions and to clearly identify anticipated outcomes. With explicit goals and outcome measures established, the tradeoffs between different grant-making strategies have become more evident, providing a more objective framework for discussing trade-offs.

The possibility of applying a tool like ER in other settings is thought-provoking. Hewlett respondents were cautious about suggesting that ER could be used in other settings, particularly in government. They felt that appropriate use of the tool necessarily involves flexibility and the freedom to make subjective judgments, which they surmised might be limited in government settings. Still, the investment attitude, explicit assumptions, and common language fostered by such a metric could prove useful in many contexts in both the public and private sectors.

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