Social Comparisons and Groundwater Use
Last registered on October 28, 2019


Trial Information
General Information
Social Comparisons and Groundwater Use
Initial registration date
October 16, 2019
Last updated
October 28, 2019 11:09 AM EDT
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
Colorado State University
PI Affiliation
Johns Hopkins University
Additional Trial Information
On going
Start date
End date
Secondary IDs
USDA-NIFA: 2017-67024-26278
A large literature examines the effect of social comparisons in nudging individuals towards conservation and pro-social behavior. This study builds on this literature by analyzing the salience of social comparisons in determining resource use behavior in the novel context of groundwater use among agricultural producers. We utilize a randomized controlled trail to measure how the provision of information comparing an individual’s past annual groundwater use to that of other groundwater users in their district affects the individual’s future groundwater use.
External Link(s)
Registration Citation
Hrozencik, Robert, Paul Ferraro and Jordan F. Suter. 2019. "Social Comparisons and Groundwater Use." AEA RCT Registry. October 28.
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Experimental Details
We provide a social comparison mailer to a randomly selected group of agricultural producers in an area of eastern Colorado overlying the High Plains Aquifer. The mailer graphically compares their 2018 groundwater use to other groundwater wells within their groundwater management district.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
well-level groundwater use during the 2019 growing season
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We randomly assigned eligible agricultural producers in the study area to either a treatment or control group. Eligibility was based on ownership of one or more wells in the study area. The treatment group of producers received a mailer during the beginning of the 2019 growing season which compared 2018 groundwater use of up to three wells that they own to groundwater wells within their groundwater management district (GWMD). The mailer graphically provided treated producers with information comparing the groundwater use of up to three of the wells that they own to well-level mean groundwater use and the groundwater use of the 20th percentile well in the GWMD. The mailer also indicated the percentile of the GWMD groundwater use distribution for each of their wells, using the following language “Comparing your 2018 water use to other wells in the YY GWMD, your well(s) recorded use higher than X% of wells.”

Many agricultural producers in our study area operate multiple wells. The assignment to treatment or control groups occurred at the producer level, not the well level. Producers receiving the treatment mailers were provided with well-level comparison information, but due to size constraints on the mailer, recipients were provided comparisons for a maximum of three wells. For producers that own or operate more than three wells, we limit the well-level comparison information to the three wells that utilized the most water in 2018. We also exclude wells that rank less than the 5th percentile among wells within their comparison group. Finally, some producers operate and own wells within multiple GWMDs. For these producers we determine their comparison group based on the GWMD the majority of their wells are located within.

To assign agricultural producers to treatment or control we follow a block randomization methodology wherein we block at the GWMD level and on past water use compared to their comparison group’s average . Specifically, we generate a variable that indicates whether a producer’s 2018 water use averaged across all their wells exceeded the median well-level water use within their comparison group. This blocking protocol resulted in 14 mutually-exclusive blocking groups. Assignment of producers to treatment or control was conducted using R’s randomizr package, which assigned 487 producers into the treatment group and 489 into the control group.
Experimental Design Details
Randomization Method
We randomize assignment using the R package randomizr
Randomization Unit
Individual (well owner / agricultural producer)
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
We did not utilize clustering in our methods
Sample size: planned number of observations
976 eligible groundwater well owners / producers in the study area, 487 of these eligible well owners / producers were randomly placed within the treatment group and received information comparing their groundwater use to that of other wells in their GWMD. Some well owners in our study area rent their land and associated wells to other producers while others farm their own land. Our research design elected to send mailers only to well owners (both producers and non-producers) given a lack of reliable data regarding which producers rent specific agricultural land parcels. The eligibility criteria for well owners / producers is defined based on the uniqueness of the mailing address. The contact database provided by the State of Colorado provides a contact address for 2,558 wells in the study area. However, many agricultural operations utilize multiple wells in their operation. As such, we limit our analysis to 976 unique agricultural operations in one of seven GWMDs, which we define by unique mailing addresses.
Sample size (or number of clusters) by treatment arms
487 individuals (groundwater well owners / agricultural producers) were placed in the treatment group, 489 were placed in the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Given our sample design and stratification of assignment, our power analysis without covariate control reveals a minimum detectable effect size of 5%, with power = 0.8 and alpha = 0.05, to distinguish groundwater use differences between treatment and control wells. However, with the inclusion of covariate control in the power analysis, specifically groundwater management district specific effects lagged well-level pumping, we find a minimum detectable effects size of 0.03, with power = 0.8 and alpha = 0.05. We use data on past well-level pumping to conduct the power analysis.
Supporting Documents and Materials
Document Name
Example Mailer
Document Type
Document Description
Note that this example mailer was generated using the author's (Aaron Hrozencik) contact information and the reported groundwater use and well identifiers are not associated with an agricultural producer or well owner in the study area.
Example Mailer

MD5: ae7d2460e337d8158aa952a7eaf8a5c3

SHA1: a50c7bb62a063787ab69e449e9e87ddac0d98951

Uploaded At: October 16, 2019

Document Name
Power Analysis
Document Type
Document Description
Power Analysis

MD5: 8904c99c4daa27111c99bfe4b84fae44

SHA1: 7c44ba6e02d2deac35144d297e761298c3815e84

Uploaded At: October 16, 2019

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
Analysis Plan

MD5: 23dfcb1f8242924a6c705b0e0fe237e0

SHA1: 44f9f39f3f351515a5cb3634d6adc8354722bcca

Uploaded At: October 28, 2019

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