Predicting, tracking and evaluating results across the project cycle: GIF’s approach to results measurement




Poster session 4 Saturday: Evidence implementation and evaluation


Saturday 16 September 2017 - 12:30 to 14:00


All authors in correct order:

Chomitz K1
1 Global Innovation Fund, United Kingdom
Presenting author and contact person

Presenting author:

Ken Chomitz

Contact person:

Abstract text
Background:The Global Innovation Fund (GIF) is an evidence-based investor in public and private innovations that can benefit people living on less than $5/day. It seeks to maximiSe the benefits received by those people. It uses evidence as a screen for investment decisions; supports evidence-based feedback to improve implementation; and generates rigorous evidence to guide decisions on scale-up and replication.
GIF and other funders face two challenges in impact assessment. First, finding a universal metric of impact. Appraising proposals across disparate sectors, GIF needs to decide which are the most impactful to support. Ex post, it seeks a concise way of aggregating the benefits it has created. Traditional universal metrics – ‘people reached’ or dollars disbursed – fail to capture the depth of impact.

Second, funders are accountable for achieving results from their funding. But typical accountability cycles are shorter than the time needed to bring innovations from pilot to fruition at scale. And both funders and investees could use continuous feedback on results to improve outcomes.

Objectives:To create a system that concisely summarises impact across different classes of outcomes; that supports and motivates selection and management of a portfolio of projects for maximum impact; and, that combines rigour and ease of application.

Methods:The system has two components: 1) Rigorous ex post application of project economic analysis, supported by projects with built-in impact evaluation. 2) Ex ante, and regularly updated, order of magnitude impact estimates incorporating people impacted; depth of impact (encompassing economic and non-economic welfare); and probability of success.

Results:The paper discusses challenges in implementing the system, including bias avoidance, and insights gained.

Conclusions:We describe a workable system with broad applicability to investment finance and impact investing.