The role of mechanistic explanations in technology adoption across contexts: Evidence from Uganda

Last registered on December 20, 2023

Pre-Trial

Trial Information

General Information

Title
The role of mechanistic explanations in technology adoption across contexts: Evidence from Uganda
RCT ID
AEARCTR-0012560
Initial registration date
December 12, 2023

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
December 20, 2023, 9:52 AM EST

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

Locations

Region

Primary Investigator

Affiliation
Stanford

Other Primary Investigator(s)

PI Affiliation
University of Chicago
PI Affiliation
University of Chicago
PI Affiliation
Stanford University
PI Affiliation
CGIAR
PI Affiliation
International Institute for Tropical Agriculture - Uganda
PI Affiliation
Matrice360
PI Affiliation
Agriworks Uganda

Additional Trial Information

Status
In development
Start date
2023-12-13
End date
2024-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
It is long documented that small-scale farmers in Uganda under-adopt technologies like inorganic fertilizers despite their productivity advantages. A critical obstacle arises in how farmers learn about these technologies. Farmers frequently witness others’ impressive results with an agricultural technology, yet remain unconvinced that it will work as well for themselves. Indeed, simply copying others’ fertilizer practices may fail to replicate their success, as African soil and weather conditions vary quickly over small distances and affect fertilizer results. We consider a novel intervention in Eastern Uganda for helping farmers translate others’ fertilizer results to their own context, even if underlying conditions differ. In particular, we study whether providing mechanistic explanations of *why* fertilizers produce a particular result helps farmers deduce what, if anything, would change in a result for themselves, and tailor an observed practice accordingly.
External Link(s)

Registration Citation

Citation
Davies, Ben et al. 2023. "The role of mechanistic explanations in technology adoption across contexts: Evidence from Uganda." AEA RCT Registry. December 20. https://doi.org/10.1257/rct.12560-1.0
Experimental Details

Interventions

Intervention(s)
We pursue a lab-in-the-field RCT design with our partner organization, Agriworks, who provide an on-demand irrigation service in Eastern Uganda. We invite tomato farmers irrigating with Agriworks, and who grow in different soils, to a fertilizer demonstration predetermined soil context. In collaboration with extension officers, who will present the information accompanying the demonstration, we randomize the provision of mechanistic explanations to causally identify whether, and how much, mechanistic explanations help farmers translate the demonstration information to their own context.
Intervention Start Date
2023-12-13
Intervention End Date
2024-04-30

Primary Outcomes

Primary Outcomes (end points)
Fertilizer knowledge and practices for each soil type, Farmer beliefs about returns to fertilizer on soil types, WTP for fertilizer information, WTP for fertilizer application on their plot
Primary Outcomes (explanation)
We will use both incentivized and unincentivized elicitations of farmers' beliefs about agricultural production, in particular their mental models of fertilizers and agriculture. The incentivized measures will be obtained from giving farmers a small endowment, which they can use to pay to reveal advice from agronomist-simulated vignettes. In addition to structured survey responses, we will also collect unstructured qualitative data, for example free responses transcribing farmers' mechanistic descriptions of why a particular (simulated) agronomic result occurred.

Secondary Outcomes

Secondary Outcomes (end points)
Heterogeneity by soil type, demographic characteristics (education, wealth), comprehension of demonstration
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will recruit 200 tomato farmers from the smallholder region of Mbale in Eastern Uganda, evenly distributed between farmers who grow in two soil contexts: ``black soils" and "grey soils". These soil contexts are archetypes familiar to farmers, and correspond to a particular ensemble of textural, mineral, organic matter, and drainage properties that impacts fertilizer results. We collaborated with agricultural scientists at the International Institute for Tropical Agriculture (IITA) to identify and develop a vocabulary around the most common soil archetypes in the region. The eligible pool of farmers are those who are registered with Agriworks and have hired their services at least once in the prior year. Agriworks possesses approximate knowledge of registered farmers plot locations and soil types. We will randomly invite farmers from the eligible pool stratified by soil type. We will confirm soil types by phone when we invite farmers to attend demonstrations to achieve a balanced distribution of soil contexts.

Farmers will be randomly invited to attend two treatment arms: 1. Mechanistic demonstration, 2. Non-mechanistic demonstration. One-half of eligible farmers will be invited to a demonstration that includes mechanistic explanations (arm 1), and one-half will be invited to a demonstration that includes a training on fertilizers that is void of mechanistic explanations and only provides a recipe for fertilizer application without information on *why* and *how* fertilizers work (arm 2).

By stratifying on soil type, half of farmers will attend a demo where tomatoes are grown in a soil context that matches the soil type of their own plot (“matched farmers”). The other half will attend a demo where soil type does not match their context (“mismatched farmers”). The goal is to set up a situation where mismatched farmers will have to translate the demonstration information to their context in order to use it. Our logic is that matched farmers can more easily copy the fertilizer ‘recipe’ shared during demos. By contrast, the mismatched farmers will have to learn from the mechanistic explanations to translate the fertilizer recipe catered to soil type A to one that fits their soil type B.

Of 200 farmers invited to demonstrations, we expect that 75% will come to the first demonstration. We will then administer the baseline, the first of three surveys that we formulate and administer with the help of the experienced local research firm Matrice360. This baseline elicits basic demographic information, social networks, farming experience, current and past practices, plot characteristics, and risk aversion. The endline survey will take immediately after the demonstration and we are planning a short phone survey to ask which practices they implemented during the prior season.
Experimental Design Details
Randomization Method
Randomization in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
18 villages
Sample size: planned number of observations
126 farmers
Sample size (or number of clusters) by treatment arms
63 farmers in arm 1, 63 farmers in arm 2
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Administrative Panel on Human Subjects in Non-Medical Research, Stanford University
IRB Approval Date
2023-10-27
IRB Approval Number
72226

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials