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Understanding Farmers' Information-Seeking Behaviour
Last registered on January 27, 2020


Trial Information
General Information
Understanding Farmers' Information-Seeking Behaviour
Initial registration date
January 25, 2020
Last updated
January 27, 2020 11:42 AM EST
Primary Investigator
Maastricht University
Other Primary Investigator(s)
PI Affiliation
Department of Development Economics, Wageningen University, The Netherlands.
PI Affiliation
UNU-MERIT, Maastricht University, The Netherlands
PI Affiliation
Additional Trial Information
In development
Start date
End date
Secondary IDs
We design a lab-in-the-field experiment to understand farmers’ information-seeking behavior by taking Ethiopia as a case study. Specifically, we aim to understand the role of overconfidence and trust in farmers’ information-seeking behavior. The study will thus contribute to a deeper understanding of the low uptake of agricultural technologies among farmers in sub-Saharan Africa.
External Link(s)
Registration Citation
Cecchi , Francesco et al. 2020. "Understanding Farmers' Information-Seeking Behaviour." AEA RCT Registry. January 27. https://doi.org/10.1257/rct.5340-1.0.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We design an incentivized "exam" based experiment with an option to seek help from a designated advisor. Advisors are made to have varying skills by providing training on the topics covered in the "exam" to a random set of advisors. Furthermore, we also elicit farmers' performance expectations using questions and incentives. We also use the "investment" and "staircase" methods to elicit risk preferences as well as a survey to obtain demographic, behavioral and socio-economic information.
Experimental Design Details
To understand the effect of trust and overconfidence on farmers’ information-seeking behavior, we design a lab-in-the-field experiment where we ask farmers to solve questions on farming-related topics with the option to get help from advisors. For every correctly solved question, farmers earn five birr (birr is Ethiopian currency where 1 USD is exchanged for about 29 birr in November 2019). If farmers decide to outsource any of the questions, they incur a cost of one birr. Furthermore, we repeat the same exercise by using the Raven’s test. After completing the two exam-based experiments, participants play a risk preference game where they decide whether to invest in a lottery with a 50% chance of doubling their investment and 50% chance of halving it. Lastly, they complete an exit survey on demographic, behavioral and socioeconomic aspects.

The treatment arms are as follows. The first treatment is the advisor type, where we randomly assign farmers to an extension agent or peer farmer advisor. The second treatment is the “Skill” treatment where we vary the skill of the advisor by providing training to a random set of advisors. The third treatment is what we call the “skill nudge”. In this treatment, we randomly vary how we frame the skill of the trained advisors to the farmers. Consequently, we have a “high-skill nudge” where we emphasize how the training enhances the advisor’s fitness to solve the questions, and “low-skill nudge” where we indicate the possibility that advisors, may be unable to solve the questions correctly. Moreover, we also have a control group where farmers can solve the questions only by themselves.

To measure overconfidence, we elicit farmers’ expectations by asking them how many questions they expect to solve correctly, once before they see the questions and again, after they complete solving the questions but prior to getting feedback on their performance. For farmers with the outsourcing option, we also ask their individual and combined (with advisor) expected performance before they see the questions and their expected combined performance after they see the questions.
Randomization Method
Randomization will be done using excel random number generator.
Randomization Unit
The randomization units are individual farmers.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
760 farmers
Sample size: planned number of observations
760 farmers
Sample size (or number of clusters) by treatment arms
The sample size consists of 120 farmers in the control group; and, 80 farmers in the advisor skill treatment group, and 40 farmers in the skill-nudge treatments in each of the two advisor categories--agricultural extension agent and peer farmer advisors.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The expected detectable effect size is 25%. The unit of the outcome variable is the number of times the farmer seeks advice from her advisor. We use a standard deviation of 1. Using the Stata power calculator, this gives us a power of 99.7% in the "skill" treatment and 91% in the "skill-nudge" treatment.
Supporting Documents and Materials

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Request Information
IRB Name
Ethics Review Committee Inner City faculties (ERCIC)
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)