Testing of incentive mechanism to foster farmers coordination: a lab-in-the-field in Zimbabwe

Last registered on September 17, 2024

Pre-Trial

Trial Information

General Information

Title
Testing of incentive mechanism to foster farmers coordination: a lab-in-the-field in Zimbabwe
RCT ID
AEARCTR-0013895
Initial registration date
September 12, 2024

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
September 17, 2024, 11:54 AM EDT

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

Locations

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Primary Investigator

Affiliation
CIRAD

Other Primary Investigator(s)

PI Affiliation
CIRAD
PI Affiliation
CEEM
PI Affiliation
CIRAD
PI Affiliation
University of Zimbabwe
PI Affiliation
CIMMYT
PI Affiliation
CIRAD
PI Affiliation
CIRAD

Additional Trial Information

Status
On going
Start date
2024-04-01
End date
2024-10-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Transitioning towards a more sustainable agriculture is essential for ensuring future food production (Corbeels et al., 2020; Garnett et al., 2013; Mueller et al., 2012; Pretty et al., 2011). Sustainable cropping systems yield greater benefits when implemented on a larger scale: this necessitates coordination between farmers. Thus, a policy that fosters coordination among farmers can be economically justified, as it is likely to generate more public goods per dollar invested.

This study investigates the effectiveness and efficiency of different incentive mechanisms to promote farmers’ adoption of sustainable cropping systems in Zimbabwe. The analysis considers financial incentives as a potential policy tool to enhance both the adoption and the coordination among farmers. These mechanisms are tested in a lab-in-the-field setting in rural Zimbabwe with smallholder farmers.

Our research explores the effectiveness of two types of financial incentives: individual-based and collective-based incentives. The individual incentive offers subsidies based on the farmer individual level of adoption. In contrast, the collective incentive is contingent upon broader community participation; a subsidy is granted only if at least 50% of a community's plots are managed under sustainable agriculture. This structure aims to encourage community-wide coordination by linking immediate and concrete benefits to collective action. Our lab-in-the-field experiments provide insights into how these incentives influence farmers' decisions. The study aims to evaluate which incentive structure more effectively promotes the adoption of sustainable cropping systems. The results will contribute to a broader understanding of policy mechanisms that can support the transition towards environmentally sustainable and economically viable agricultural systems in resource-constrained settings.

References

Corbeels, M., Naudin, K., Whitbread, A. M., Kühne, R., & Letourmy, P. (2020). Limits of conservation agriculture to overcome low crop yields in sub-Saharan Africa. Nature Food, 1(7), 447-454. https://doi.org/10.1038/s43016-020-0114-x

Cox, J. C. (2004). How to identify trust and reciprocity. Games and Economic Behavior, 46(2), 260-281. https://doi.org/https://doi.org/10.1016/S0899-8256(03)00119-2

Crosetto, P., & Filippin, A. (2013). The "bomb" risk elicitation task. Journal of Risk and Uncertainty, 47(1), 31-65. http://www.jstor.org/stable/43550175

Garnett, T., Appleby, M. C., Balmford, A., Bateman, I. J., Benton, T. G., Bloomer, P., Burlingame, B., Dawkins, M., Dolan, L., Fraser, D., Herrero, M., Hoffmann, I., Smith, P., Thornton, P. K., Toulmin, C., Vermeulen, S. J., & Godfray, H. C. J. (2013). Sustainable intensification in agriculture: Premises and policies [Short survey]. Science, 341(6141), 33-34. https://doi.org/10.1126/science.1234485

Midler, E., Pascual, U., Drucker, A. G., Narloch, U., & Soto, J. L. (2015). Unraveling the effects of payments for ecosystem services on motivations for collective action. Ecological Economics, 120, 394-405. https://doi.org/https://doi.org/10.1016/j.ecolecon.2015.04.006

Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490(7419), 254-257. https://doi.org/10.1038/nature11420

Pretty, J., Toulmin, C., & Williams, S. (2011). Sustainable intensification in African agriculture. International Journal of Agricultural Sustainability, 9(1), 5-24. https://doi.org/10.3763/ijas.2010.0583



External Link(s)

Registration Citation

Citation
Affholder, François et al. 2024. "Testing of incentive mechanism to foster farmers coordination: a lab-in-the-field in Zimbabwe." AEA RCT Registry. September 17. https://doi.org/10.1257/rct.13895-1.0
Experimental Details

Interventions

Intervention(s)
1. Testing the incentive mechanism

We use an adapted version of Midler et al. (2015). A framed public good game with threshold is presented to farmers. Players are given four plots and grouped with three other players. Players must choose a cropping system between A and B. The contribution to the public good (PG) occurs when players allocate their plots to cropping system B. The individual per capita return for cropping system B is lower than for A. If 50% of the group’s plots are allocated to cropping system B, the threshold is reached and players have returns proportional to the total group contribution.

Baseline settings (T0): framed public good game with threshold, no incentives

Treatment 1 (T1): baseline settings + individual incentive
Players receive a bonus proportional to their contribution to the PG

Treatment 2 (T2): baseline settings + collective incentive
Players receive a bonus if the threshold is reached and proportional to the group’s contribution to the PG

Treatment 3 (T3): baseline settings + individual incentive + collective incentive
Combination of both treatment 1 and 2

Treatment 4 (T4): treatment 3 settings but the game is not framed in terms of “bonus”, essentially, the baseline settings with different parameters. This treatment is specifically designed to examine the impact of framing on players' decisions.

The game is repeated over 8 rounds. Group members are anonymous and randomly selected at the beginning of the game. Groups stay the same for the 8 rounds.

H1 – In the absence of incentives, players will play according to the Nash equilibrium, meaning they will not contribute to the PG.

H2 – The introduction of incentives leads to increased contributions to the public good, with player behavior aligning more closely with the coordination Nash Equilibrium .

H3 – Both individual and collective incentives are equally effective in motivating players to contribute to the public good.

H4 – The combination of individual and collective incentives results in an additive effect, further increasing contributions to the public good compared to when these incentives are applied separately.

H5 - Players will contribute more to the public good only when they are aware of the incentive (positive effect of framing on contributions).

2. Measuring trust and altruism

We implement the games developed by Cox (2004) which allows disentangle trust and altruism.

3. To measure risk attitude (BRET game)

To measure risk attitude we use a pen and paper version of the “bomb” elicitation task (Crosetto & Filippin, 2013).

4. Questionnaire

Following the three incentivized games, participants complete self-reported measures of trust and a socio-demographic questionnaire.
Intervention Start Date
2024-04-01
Intervention End Date
2024-10-31

Primary Outcomes

Primary Outcomes (end points)
Individual and group contribution to the PGG
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Risk measure from BRET game
Altruism, trust and trustworthiness measures
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Between design. Each experimental session is randomly assigned a treatment.
Experimental sessions are to take place in rural Zimbabwe with smallholder farmers

Experimental Design Details
Not available
Randomization Method
Treatment assignment at session level using Excel.
Randomization Unit
Experimental session
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
30 villages
Sample size: planned number of observations
1200
Sample size (or number of clusters) by treatment arms
240
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
CIMMYT Internal Research Ethics Committee
IRB Approval Date
2024-08-21
IRB Approval Number
IRB00012744