Making the global SDG indicators relevant for local actors: measuring the indirect impact, or “ripple effect” of sustainable development initiatives

Meghann Jones & Kaitlin Love, Ipsos Sustainable Development Research Center, Washington DC

In our recent paper on making the SDG indicators relevant to local actors tasked with achieving the SDGs, we discussed how a theory of change approach can be used to link program-level metrics up to national and global indicators. This follow-up digs a little deeper into understanding the impact of programs beyond the direct impacts envisaged by program leads and articulated explicitly in the program theory of change. Such thinking can help those responsible for sustainable development programming build a picture of the broadest potential impact of their initiatives.

For example, a program that focuses on water stewardship will be primarily focused on metrics such as increased access to potable water, the lowering of pollutants in community water sources, or efficiencies gained in water use on smallholder farms. But what are the indirect impacts of this program for communities and individuals?

This indirect impact is what we call the “multiplier”, or “ripple” effect. In the context of speaking to the global SDG indicators, where the primary impact of the water stewardship example might align with SDG6 (ensure availability and sustainable management of water for all), there may be “ripple effects” where such a program is showing impact in SDG2 (zero hunger), SDG3 (good health and wellbeing) and SDG5 (gender equality).

Studying this phenomenon has become a staple of Ipsos’ evaluation work, to the extent that we have an ever-expanding toolkit for “ripple effect research”.  Like with all other evaluative tasks, the Ipsos team’s preferred starting point is a theory of change. A theory of change that builds in indirect impacts is likely to be significantly more complicated than one focusing on the primary hypothesis for an intervention.

The potential indicators, metrics, and measurement strategies that flow from a theory of change that incorporates indirect impacts are more numerous and complex than when focusing just on direct impacts for two reasons: (1) the more removed an observed impact is from a program, the more challenging it is to attribute the impact to the program, and (2) implementing metrics that apply beyond the direct beneficiaries of a program is significantly more resource intensive than studies that focus solely on direct beneficiaries.

In its “ripple effect research”, the Ipsos team has used multiple approaches to understanding the indirect impacts of the programs it evaluates. For example, in the context of mentoring programs, where mentees are encouraged to become mentors themselves we have used social network analysis to understand the nature/structure of the ripple effect. In the water example, the Ipsos team is using exploratory and observational research to study the impact of water stewardship programs on women’s empowerment: when women in a community are free from water collection activities and looking after children made ill from contaminated water, are they more economically productive, and in turn, more influential in household and community decision-making?

Like much impact measurement work, ripple effect research can be complex and resource intensive, and those responsible for programming may never be able to quantify the full impact of their initiatives. However, exploring the potential ripple effects for a program is helpful in understanding the impact of particular types of policies and programs, and how they contribute achieving multiple SDGs at the local, national and global levels.

Meghann Jones is a Senior Vice President with Ipsos and is the US lead for the Ipsos Sustainable Development Research Center, based in Washington DC.

Kaitlin Love is a Director with the Ipsos Sustainable Development Research Center, and is based in Washington DC.

Ipsos is a member of the Global Partnership for Sustainable Development Data.