Why the influence of agricultural policy research is probably greater than we think


by Thom Jayne, Tom Reardon, Mywish Maredia, David Tschirley | December 11, 2018

It is commonly understood that agricultural and food security policy research can influence policy only through direct engagement with policy makers. Another common view is that good policy analysis must be “demand driven” and follow these measurable steps:

  1. engage with policy makers to learn what their priority policy challenges are;
  2. researchers respond to policy makers’ priority issues and undertake “demand led” analysis to address them;
  3. research findings feed into policy engagement activities, providing new information that policy makers would consider, discuss with other policy analysts and stakeholders, which eventually leads to…..
  4. policy impact!

Some demand-led research has indeed generated policy impact in this way – and it serves an important purpose.

But arguably, agricultural and food security policy research that has had the greatest influence on policies did not follow this linear approach at all. Many of the world’s most impactful ideas were “supply driven,” generated by scholars and diffused out via networks of other scholars, research institutes, development agencies, social media, conventional news agencies, civil society, the private sector and ultimately governments. By the time these ideas get to governments, they are often considered well accepted facts, which makes it difficult to attribute the policy change that eventually occurs over time to any specific research.

We believe there are at least three categories of impactful policy-oriented research that development organizations should be promoting, even though it may be difficult to track the immediate impact of such research on a policy change:

Category 1 – discovery: The first is research that anticipates the major challenges coming down the pike and alerts governments about the need to respond proactively rather than reactively. This category of research also includes discovering under-appreciated facts about the ways that agri-food systems work – in some cases profoundly altering policy makers’ view of what the challenges are and how they should be addressed.  Examples include studies on the rapid change in retail food systems in developing countries, the rapid rise of commercialized African investor farmers, and the employment effects resulting from the swift transformation in African dietary patterns.

None of this was well understood 10 years ago, and very few African policy makers were asking how they should be responding to these developments.  Now they are.

It is difficult to track the impact of this kind of research because it tends to follow a diffusion process. But indirectly, such analysis may have a huge impact if other scholars pick up on these points and spread similar messages that get assimilated into mainstream views. Last year’s radical ideas become this year’s conventional wisdom.

Category 2 - reactive: The second type of policy research is short-term fire-fighting analysis in response to specific government requests, following the linear approach stated above. Governments tend to greatly appreciate this work because it provides quick response to the issues that they themselves identify. So it is important. But it may result in policy analysis dealing primarily with short-term issues du jour that have a very short shelf-life rather than the more fundamental challenges that often are not even on the radar screen within many Ministries of Agriculture. This is especially the case when senior government positions are filled by political appointees who do not understand well the agricultural sectors of their own countries.

Category 3 – addressing known barriers: The third type of policy analysis is geared toward overcoming entrenched barriers to policy change. Researchers may believe that policy reforms in a specific area (e.g., removing trade barriers) could bring important benefits, and that more rigorous analysis is needed to convincingly measure the potential benefits and costs, and propose concrete steps for governments to achieve those benefits (i.e., propose implementation options). However, this type of supply-driven research is rarely invited by governments.

There is a need for all three categories of policy analysis noted above. Each play an important and synergistic role in generating policy impact. Short-term analysis that responds to specific government requests cannot be evidence-based unless the database is there to provide the evidence. This means that long-term supply driven analysis which involves extensive data generation is necessary to enable short-term demand-led analysis to be effective.

Another major ingredient is cross-country pollination of ideas through research networks, enabling long-term foresighting and discovery analysis in one country to spread through networks represented by local researchers in other countries. That is one of the reasons why it is valuable to promote solid links among local research organizations in developing countries (south-south interaction) and between north-south research networks. The second and third categories of policy analysis often get identified in the first place by supply-driven analysis (e.g., on resilience, climate impacts, the existence of market/trade barriers needing reform). It is often difficult to point to attribution of policy impact from Category 1 discovery and foresighting analysis but many if not most meaningful policy change often depends on it.

Bottom line: First, there is an important role for both demand-led and researcher-led policy research. Second, the ability to provide evidence-based policy guidance in response to government priorities requires long-term efforts to collect and analyze data – that’s where evidence-based policies come from. Third, research impacts on policy typically take time to realize and are difficult to measure because of the way that new knowledge gets diffused through myriad networks before it gets to policy makers.

This post originally appeared on AgriLinks. The authors are with the Feed the Future Innovation Lab for Food Security Policy, and professors at Michigan State University, Department of Agricultural, Food and Resource Economics. Thom Jayne is co-leader of PIM's Flagship 2: Economywide Factors Affecting Agricultural Growth and Rural Transformation and member of the Program's Management Committee. Mywish Maredia is co-leader of PIM's Cluster 1.2: Science Policy and Innovation Systems for Sustainable Intensification under Flagship 1: Technological Innovation and Sustainable Intensification.