Understanding Farmers' Information-Seeking Behaviour

Last registered on January 27, 2020

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Pre-Trial

Trial Information

General Information

Title

Understanding Farmers' Information-Seeking Behaviour

RCT ID

AEARCTR-0005340

Initial registration date

January 25, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

January 27, 2020, 11:42 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Ethiopia

Region

Primary Investigator

Name

Hiwot Mesfin

Affiliation

Maastricht University

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Other Primary Investigator(s)

PI Name

Francesco Cecchi

PI Affiliation

Department of Development Economics, Wageningen University, The Netherlands.

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PI Name

Eleonora Nillesen

PI Affiliation

UNU-MERIT, Maastricht University, The Netherlands

Contact Investigator

PI Name

Nyasha Triviai

PI Affiliation

UNICEF, Italy

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Additional Trial Information

Status

In development

Start date

2020-02-03

End date

2020-03-02

Keywords

Agriculture

Additional Keywords

Information-seeking behavior, Trust, Overconfidence

JEL code(s)

Secondary IDs

Abstract

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

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

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2020-02-03

Intervention End Date

2020-03-02

Primary Outcomes

Primary Outcomes (end points)

Information-seeking

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

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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