Loss Aversion and Agricultural Investment: Experimental Evidence from Smallholder Farmers in Kenya

Last registered on November 21, 2025
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Pre-Trial

Trial Information

General Information

Title
Loss Aversion and Agricultural Investment: Experimental Evidence from Smallholder Farmers in Kenya
RCT ID
AEARCTR-0017074
Initial registration date
November 17, 2025

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
November 21, 2025, 7:59 AM EST

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

Locations

Country
Kenya
Region
Africa

Primary Investigator

Name
Madison Levine
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Sarah Janzen
PI Affiliation
University of Illinois Urbana Champaign
Contact Investigator
PI Name
Berber Kramer
PI Affiliation
International Food Policy Research Institute
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PI Name
Kelvin Shikuku
PI Affiliation
International Livestock Research Institute
Contact Investigator

Additional Trial Information

Status
On going
Start date
2025-05-19
End date
2026-05-19
Keywords
Agriculture, Behavior, Finance & Microfinance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This study examines how loss aversion influences smallholder farmers' investment decisions in the presence of innovative tools such as loans, index insurance, and emergency credit. Using a lab-in-the-field experiment with 210 farmers in two of Kenya’s semi-arid counties, we investigate how behavioral biases influence investment in productivity-enhancing inputs with and without the availability of risk-mitigating products. We hypothesize that farmers perceive upfront costs as certain losses, discouraging adoption even when long-term benefits outweigh costs. Additionally, we assess whether emergency credit, which removes upfront payment requirements, mitigates this reluctance and encourages investment. Participants engage in experimental games under risk and uncertainty, and make repeated agricultural investment choices across varying financial and climatic conditions. We also elicit behavioral parameters of risk aversion, loss aversion, and inverse probability weighting. This approach allows us to observe how different innovative tools and preferences influence decision-making.
External Link(s)

Registration Citation

Citation
Janzen, Sarah et al. 2025. "Loss Aversion and Agricultural Investment: Experimental Evidence from Smallholder Farmers in Kenya." AEA RCT Registry. November 21. https://doi.org/10.1257/rct.17074-1.0
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Experimental Details

Interventions

Intervention(s)
The intervention consists of a lab-in-the-field experiment designed to study how behavioral preferences, particularly loss aversion, shape smallholder farmers’ agricultural investment decisions under risk.
Participants play two digital, incentive-compatible games:

1. Financial Investment Game (FIG) – a multi-round simulation that mimics real-world farming. Farmers decide how much to invest in fertilizer and whether to adopt three risk-management tools: drought-tolerant (DT) seeds, index insurance, and emergency credit (EC). Early rounds offer these tools for free; later rounds use a Becker–DeGroot–Marschak (BDM) auction to elicit willingness-to-pay.

2. Multiple Price Listing (MPL) Risk and Loss Aversion Game – a series of incentivized lotteries used to estimate each farmer’s risk- and loss-aversion parameters recovered through maximum likelihood estimations.

The intervention combines experimental exposure to financial tools with elicitation of behavioral parameters in a controlled but realistic environment.
Intervention Start Date
2025-05-19
Intervention End Date
2025-06-09

Primary Outcomes

Primary Outcomes (end points)
1. Fertilizer investment per round – total amount invested (continuous outcome)
2. Tool uptake – binary indicator of whether the participant adopted drought tolerant seeds (DT), insurance, or emergency credit (EC).
3. Willingness to pay (WTP) – stated maximum bid for each tool via BDM mechanism.
4. Loss aversion and risk aversion parameters – elicited through the MPL lotteries.
Primary Outcomes (explanation)
1. Fertilizer investment and tool uptake are directly observed within the Financial Investment Game.
2. WTP values are generated from the BDM mechanism, where a randomly drawn strike price (25 % or 50 % subsidy) determines purchase outcomes.
3, Behavioral parameters are estimated structurally from TCN lottery choices.
4, Main hypotheses: (i) greater loss aversion reduces investment and WTP for tools with up-front costs; (ii) EC is less sensitive to loss aversion since it requires no up-front payment.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This lab-in-the-field experiment examines how exposure to different risk-management tools affects farmers’ fertilizer investment decisions and how these effects interact with individual loss aversion. Each participant plays both the Financial Investment Game and the MPL lotteries task in a tablet-based setting. Modules introducing DT seeds, insurance, and EC are randomly ordered within sessions to prevent learning biases. Each session includes 12–16 farmers across 17 villages drawn from Machakos and Makueni counties in Kenya.
Experimental Design Details
Not available
Randomization Method
From this study population, eligible smallholder farmers were randomly selected using a proportional sampling method across Machakos and Makueni counties.

Within the experiment, randomization occurs entirely within the digital game application. Each participant receives:

1. A randomized order of financial-tool modules (drought-tolerant seeds, index insurance, emergency credit) to prevent ordering effects.
2. A randomized strike price in the BDM auctions (either a 25% or 50% subsidy).
3. Randomized weather outcomes drawn by the software according to fixed probabilities explained to participants in advance.

All randomization is performed automatically by computer using the built-in random number generator in the experimental software, ensuring transparency and independence from enumerators.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable (no clustering).
However, the study sample consists of approximately 250 individual farmers, organized into sessions of 12–16 participants across ~17 sessions. Each farmer makes repeated decisions across multiple rounds.
Sample size: planned number of observations
The design is within-subject, meaning every participant encounters all three tool treatments (DT seeds, index insurance, and EC) in randomized order. Target sample size is 250.
Sample size (or number of clusters) by treatment arms
Because treatment assignment occurs within the game rather than across groups, there are no separate arms in the traditional sense. All 250 participants experience all three financial tools in randomized sequence, and therefore contributes multiple observations across all treatment conditions.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Simulation-based power analysis (alpha = 0.05, N = 250) shows >= 80 % power to detect 10 percentage-point differences in fertilizer investment or tool uptake between conditions. For example, detecting an increase from 30 % to 40 % investment yields 93 % power, and from 40 % to 50 % yields 90 % power. The design is thus adequately powered to identify moderate behavioral effects in individual-level decisions and WTP estimates.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Illinois Urbana Champaign
IRB Approval Date
2025-03-04
IRB Approval Number
IRB24-2189
IRB Name
International Food Policy Research Institute
IRB Approval Date
2025-04-14
IRB Approval Number
00007490
IRB Name
International Livestock Research Institute
IRB Approval Date
2025-01-31
IRB Approval Number
ILRI-IREC2025-02
Analysis Plan