Effect of social information on farmers' irrigation decisions.
Last registered on July 13, 2017

Pre-Trial

Trial Information
General Information
Title
Effect of social information on farmers' irrigation decisions.
RCT ID
AEARCTR-0002283
Initial registration date
July 07, 2017
Last updated
July 13, 2017 3:11 AM EDT
Location(s)

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Primary Investigator
Affiliation
INRA
Other Primary Investigator(s)
PI Affiliation
INRA-LAMETA
PI Affiliation
INRA-TSE
PI Affiliation
INRA-TSE
Additional Trial Information
Status
On going
Start date
2017-06-01
End date
2017-12-01
Secondary IDs
Abstract
Social information has been shown to influence a diversity of human decisions : household energy and water consumption, charity giving, student alcohol use... These behavior changes however entail limited monetary costs or even reduce costs for the decision maker. Does the effect of social information stand in the context of costly decisions?
Irrigation is a key input to ensure optimal crop yield in many contexts throughout the world. Yet, the reduction of water use by farmers is a key challenge considering the current and expected modification of rainfall patterns and the resulting reduction of water resources available for irrigation. However, reducing water use may potentially incur significant costs for farmers by reducing yields. In this experiment, we intend to test whether providing social information may contribute to the reduction farmers' water use for irrigation. The potential effect of peer behavior relies on the assumption that farmers have upward biased beliefs on the behavior of others. Correcting these biases may incline farmers to reduce water use for two reasons: i) to conform to the social norm and/or ii) because they may be reassured by the fact that others are reasonable and are not over-consuming water.
Our experiment takes the opportunity of the installation of irrigation smart-meters in 3 watersheds in the South West of France. These smart-meters communicate daily to the Irrigation Scheme Manager (ISM) the amount of water used by farmers. The principle of the experiment will therefore be to test the effect of providing weekly social information on water use coming from these smart-meters on individual farmers' irrigation decisions.
External Link(s)
Registration Citation
Citation
COENT, Philippe et al. 2017. "Effect of social information on farmers' irrigation decisions.." AEA RCT Registry. July 13. https://www.socialscienceregistry.org/trials/2283/history/19389
Experimental Details
Interventions
Intervention(s)
The principle of the intervention is to provide information coming from smart-meters to farmers on i) their level of water use and ii) the level of water use of other farmers of the same watershed. This information will be provided weekly via mobile texts by the Irrigation Scheme Manager. Farmers without smart-meters will only receive information on the water use of others. Water use in cubic meters is not an appropriate indicator because smart-meters can be associated to a different amount of land and different type of crops. Each smart-meter is however associated with a water quota. We therefore considered that the relevant and comparable indicator of water use is the percentage of attainment of the water quota. It will be used in the communication with farmers and as the indicator of water ised/
Intervention Start Date
2017-07-10
Intervention End Date
2017-09-30
Outcomes
Outcomes (end points)
As mentioned in the "intervention" section, the main indicator of water use in this experiment is the percentage of attainment of the water quota (%Quota). The outcome which will be examined is the weekly evolution of % Quota (for farmers with smart-meters) as well as the % Quota at the end of the intervention.
Outcomes (explanation)
Experimental Design
Experimental Design
In this experiment different RCTs are carried out. In both RCTs, treatment and control group receive weekly texts by mobile.
The first RCT is carried out with farmers that are equipped with a smart-meter. The control group receives a standard message requesting farmers to limit their water use. The treated group receives weekly information on his personal attainment of the irrigation quota (individual %Quota) and the average attainment of the quota of farmers of his watershed (social information on % Quota).
The second RCT is carried out with farmers that are equipped with standard water-meters. The control group receives a standard message requesting farmers to limit their water use. The treated group receives weekly information only on the average attainment of the quota of farmers of his watershed (social information on % Quota coming from farmers equipped with smart-meters).
Experimental Design Details
Not available
Randomization Method
In both RCTs, the sample is stratified by farmer type (collective or individual structure), watershed, size of the quota and percentage of attainment of the quota in 2016. Treatments are randomly assigned in each strata (28 in the first RCT and 23 in the second one) in office with a computer.
Randomization Unit
Individual farmer randomization is used in both RCTs.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
RCT with smart meters: 200 farmers. RCT with traditional water-meters: 261 farmers. There is no cluster in this study.
Sample size: planned number of observations
RCT with smart metters: 200 farmers. RCT with traditional water-meters: 261 farmers
Sample size (or number of clusters) by treatment arms
RCT with smart metters: 200 farmers. RCT with traditional water-meters: 261 farmers. There is no cluster in this study.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The regression with fixed effects on strata reveals a standard error of .0339 for the nudge. The minimal detectable effect on the total water consumption is therefore 0.0952, which represents 28% of the average water consumption (in % of the quota). This power may be improved considering that weekly consumption will be subsequently analyzed.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number