Measuring Impact — The billion person question

A post on Nextbilliion.net today announced that the World Business Council for Sustainable Development (WBCSD) just launched a new framework to measure social impact of a business or organization. While the name of the framework, Measuring Impact Framework, is not the catchiest in name, it is attempting to do something that has been a challenge so far within the field of social entrepreneurship. We have written many posts to this issue in the past, you can check them out here.

The goal of developing an effective measurement system is a topic of much research and debate and numerous models have come prior to this one an a number more are likely to follow. A post on Socialedge from last year listed the following:

•    Balanced Scorecard Methodology (New Profit Inc.)
•    The Acumen-Mckinsey Scorecard (Acumen Fund)
•    Social Return Assessment Scorecard (Pacific Community Ventures)
•    AtKisson Compass Assessment for Investors (AtKisson)
•    Poverty and Social Impact Analysis (World Bank)
•    OASIS: Ongoing Assessment of Social Impacts (REDF)

Makes you wonder whether or not we need a metric to measure the effectiveness of these metrics. But in all seriousness, quantification of social impact is a complex and at times impossible task, effectively guaranteeing that none of these indicators on their own will ever fully get the picture. The most important thing, however, is to recognize that while each may come from a slightly different angle and methodology, the actual organizations and ventures that sift to the top should be relatively the same. Those in the middle are likely to differ as subtle differences in how each index weights various factors may cause significant movement among organizations that are scored closely together. However, like many rankings and indexes, those organizations that are consistently on the top of multiple metrics are there for significant reasons that should be relatively immune to methodological differences.

It is then when we can either know that the methods are being accurate (or all suffer from an identical fundamental flaw) and then when they in complement will have the greatest utility.

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