This is Hailemichael Taye, again. In my earlier email (Message 63), I discussed the challenges of impact evaluation (IE) in agricultural research and extension from defining IE itself to methodological/conceptual limitations in terms of attributing impact to research and extension (R&E) interventions. One of the challenges emanates from the complex and dynamic nature of agricultural research and extension interventions and processes. Now, it is widely understood that agricultural R&E is not a linear process where research develops the innovations, extension disseminates and farmers will adopt and use it. Rather, the innovation development to dissemination to adoption to impact continuum is characterized by complex and dynamic processes and multi-institutional and plural actors with interweaved roles and contributions. This makes attributing impact to research and extension difficult. It is important to note that even if attribution is nice for impact evaluation, as it helps us to know whether or not the intervention has made a difference, it should not be taken for granted that it works for every intervention. As I mentioned in my earlier email, the characteristic of the intervention highly dictates the type of impact evaluation.
Because of their inherent characteristics, using attribution analysis in research and extension interventions would lead us false claims. That is why a number of impact evaluations (using attribution analysis) on agricultural extension programs have reported exaggerated rate of returns. Hence, we need to incline to analyze the contribution of research and extension interventions to outcome indicators. Contribution analysis is an approach for assessing causal questions and inferring causality in evaluations. It offers a step-by-step approach designed to arrive at conclusions about the contribution of an intervention to particular outcomes. The essential value of contribution analysis is that it offers an approach designed to reduce uncertainty about the contribution the intervention is making to the observed results through an increased understanding of why the observed results have occurred (or not!) and the roles played by the intervention and other internal and external factors.
There are various literatures on how to conduct contrition analysis including the steps, tools and instruments. Please see Mayne (2001, 2008).
Hailemichael Taye
Results Based Monitoring and Evaluation Expert
Livestock and Irrigation Value chains for Ethiopian Smallholders (LIVES)
International Livestock Research Institute
Box 5689,
Addis Ababa,
Ethiopia
www.ilri.org
Tel: +251 11 617 2417
Email: h.taye (at) cgiar.org
Skype: hailaat1
References:
- Mayne, J. (2001). Addressing attribution through contribution analysis: Using performance measures sensibly. Canadian Journal of Program Evaluation 16:, 1-24. http://betterevaluation.org/sites/default/files/WKSHP%20Perrin%20-%20Mayne%202001%20%28article%29.pdf (170 KB).
Mayne, J. (2008). Contribution analysis: An approach to exploring cause and effect. ILAC Brief No. 16. Institutional Learning and Change (ILAC) Initiative. http://www.cgiar-ilac.org/files/ILAC_Brief16_Contribution_Analysis_0.pdf (130 KB).
[The contribution analysis approach was previously mentioned in the conference background document and in Message 61 (by Dominique Barjolle)...Moderator].
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