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Moderated conference on impact assessment of agricultural research: May 2014

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Tue, 27 May 2014 17:23:04 +0200
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I am Alice Bonou from Benin Republic (West Africa). I am an Agricultural Economist and am currently a PhD student in economics of climate change. Hello to everyone, and thanks for all of your contributions which I have read with interest.

First I would like to thank the organizers of this email conference. It is a great opportunity for us to share and learn from each other. I am only sorry that I joined the conference a bit late so I have just completed the reading of all posts.

My first contribution to this debate is to follow up Amadou Binta Ba (Messages 13), Matthieu Stigler (Message 15), Peter Midmore (Message 20), Atse M. Yapi (Message 23), Daniel Suryadarma (Message 64).

Self-selection could be based on observed characteristics, unobserved factors, or both. Propensity score matching (PSM) methods deal with the self-selection bias problem (Mendola, 2007) but assume that selection bias is based only on observed characteristics and unobserved characteristics do not have a significant effect on treatment. However, the PSM method fails to deal appropriately with the selection on unobservable problem which may be handled by the Double-difference methods (DD). However, like PSM, Double-difference methods do not deal appropriately with the problem of non-compliance.

Instrumental variable (IV) based methods are used in order to remove both overt and hidden biases and deal with the problem of endogenous treatment. I used this method by calculating the non-parametric Local Average Treatment Effect (LATE) in order to evaluate the impact of adoption of new high-yielding varieties (NERICA) of rice on its varietal diversity in Benin Republic (Bonou et al, 2013).

For my PhD research, I am trying to estimate the impact of 2012 flooding on the livelihood of farmers in the Niger Basin (micro-level Impact Evaluation). I have some concerns and would appreciate your help regarding them:

My first concern is which outcome of interest will be appropriate (yield, income, total expenditure, school expenditure, health expenditure, calorie intake, subjective wellbeing....)?
My second concern is the benchmark. Which farmer should I considered flooded? Is it the one who lost more than 50% of his farm by flooding?
My third concern is about the sample size. How many farmers (flooded and non-flooded) at least I need to run the Impact Evaluation model?
The last concern is that, till now I have not been able to find an appropriate instrumental variable which explains treatment status (flooded) but is redundant in explaining the outcomes (yield for instance) as I want to use Instrumental Variable (IV) methods.

ir. Alice BONOU-FANDOHAN, Msc
Ingénieur Agro-économiste
PhD student 
West African Science Service Center on Climate Change and Adapted Land Use (WASCAL)
University Cheikh Anta Diop 
Senegal
http://leabenin-fsauac.net/fr/lea-personnel/bonou-alice/ 
http://scholar.google.com/citations?user=jp3MhSsAAAAJ&hl=fr&oi=ao 
Email: alice.bonou (at) gmail.com

References:

Mendola, M. 2007. Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh. Food Policy 32: 372-393. http://impact.cgiar.org/agricultural-technology-adoption-and-poverty-reduction-propensity-score-matching-analysis-rural-bang 

Bonou, A., Diagne, A. and G. Biaou. 2013. Agricultural technology adoption and rice varietal diversity: A Local Average Treatment Effect (LATE) Approach for rural Benin. Contributed Paper presented at the 4th African Association of Agricultural Economists (AAAE) Conference, Hammamet, Tunisia, September 22-25, 2013.  http://ageconsearch.umn.edu/bitstream/158482/2/BONOUAAAEUpdate.pdf (400 KB).

[According to Gertler et al (2011, see Message 70): "An instrumental variable is a variable that helps identify the causal impact of a program when participation in the program is partly determined by the potential beneficiaries. A variable must have two characteristics to qualify as a good instrumental variable: (1) it must be correlated with program participation, and (2) it may not be correlated with outcomes Y (apart from through program participation) or with unobserved variables". The instrumental variables technique is also described in some of the references provided in the conference background document, for example, in Leeuw and Vaessen (2009), under their Section 4.2 (Quantitative methods addressing the attribution problem)...Moderator]. 

[To contribute to this conference, send your message to [log in to unmask] For further information, see http://www.fao.org/nr/research-extension-systems/res-home/news/detail/en/c/217706/. See the searchable message archive at https://listserv.fao.org/cgi-bin/wa?A0=Impact-L ].

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