Trying to understand the diversity of research taking place in Africa, Asia and Latin America is a near impossible task to say the least. How do you take thirty-seven different projects, in Africa alone, spanning across the continent, with a dynamic range of researchers both local and international, and try to synthesize and understand the nature of the work they are doing?
To assist 3ie with increasing the policy influence of their grantees, the Policy Influence Monitoring (PIM) Consortium was created consisting of CEPA, CIPPEC, CommsConsult and ODI. Before we could even begin to support the grantees we needed to figure out the easiest way to understand what they were doing, their understanding of the policy context of the country they were operating in, their communication strategies, monitoring systems and overall capacity to reach their relevant policymakers.
The mapping matrix was created in an attempt to systematically synthesize information while tracking areas of interest when trying to achieve policy influence. In theory it seemed simple enough. Create an excel spreadsheet with different sub-headings and populate it with policy relevant information extracted through simply reading all the documentation submitted by the grantees. Documents such as their application, policy influence plan and any other reports. This was supposed to enable us to track the policymakers the Impact Evaluation (IE) team was trying to inform and influence, the communication strategies that would be employed and methods of monitoring and evaluating the policy influence process. The type of M&E indicators that allow the IE team to monitor their own policy influence i.e. feedback from policymakers, number of requests for more information, number of media clippings, number of hits to their websites and any other relevant pieces of information that indicate policy traction.
Easy enough right? Maybe…maybe not! The first challenge, as most of us know, is that policy influence is not a linear process. There are many factors that contribute to the successful adoption of evidence based recommendations in the policy arena. Factors that include, but are certainly not limited to, the policy relevance of the project, the understanding of the policy context and political climate, the ability of the IE team to draw the attention of the relevant stakeholders and of course a dash of good fortune.
Trying to extract this information in a nuanced yet simplistic way proved to be a challenge. We ended up with twenty-two categories of information, giving us a whole bunch of data that is not easy to read across and that still left us feeling we needed to know more about these grantees.
The problem is that there is only so much that documents can tell us about the broad range of activities being carried out by these Impact Evaluation teams. So although we can begin to see their ambitions and potential to influence policy, we need to dig deeper and analyze each variable to strategically map a policy influence plan.
The mapping matrix left us with seemingly daunting gaps of information that seemed impossible to fill. Questions about team capacity and dynamics were difficult to answer, as well as gaining an understanding of the political climate. Issues surrounding theories of change and tracking policy impact remained unclear. However, in the end these gaps allowed us to begin the process of supporting and shaping policy influence strategies to increase the chances of successful policy engagement.It allows us to see the grantees with policy influence lens while highlighting the dynamics of each research team.
Nothing beats face-to-face interaction, but mapping is a good place to start.