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Warm-Glow versus Cold-Prickle Revisited: First Evidence from the Field
Last registered on June 10, 2019

Pre-Trial

Trial Information
General Information
Title
Warm-Glow versus Cold-Prickle Revisited: First Evidence from the Field
RCT ID
AEARCTR-0004283
Initial registration date
June 06, 2019
Last updated
June 10, 2019 10:04 PM EDT
Location(s)

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Primary Investigator
Affiliation
RWI - Leibniz Institute for Economic Research
Other Primary Investigator(s)
PI Affiliation
University of Mannheim
PI Affiliation
University of Alabama, NBER, RWI
PI Affiliation
RWI and Ruhr Graduate School in Economics
Additional Trial Information
Status
In development
Start date
2019-06-07
End date
2020-07-01
Secondary IDs
Abstract
In his seminal paper, Andreoni (1995) has shown in a laboratory experiment that framing the contribution to a public good as a positive externality leads to larger contributions to the public good than framing non-contribution as a negative externality. He refers to the motivation to contribute under the positive externality frame as the “Warm-Glow from doing good”, while he refers to the motivation to contribute under the negative externality frame as the avoidance of the “Cold-Prickle from doing bad”. In this paper, we test the external validity of the Andreoni (1995) findings in the field.
External Link(s)
Registration Citation
Citation
Andor, Mark A. et al. 2019. "Warm-Glow versus Cold-Prickle Revisited: First Evidence from the Field ." AEA RCT Registry. June 10. https://doi.org/10.1257/rct.4283-1.0.
Former Citation
Andor, Mark A. et al. 2019. "Warm-Glow versus Cold-Prickle Revisited: First Evidence from the Field ." AEA RCT Registry. June 10. https://www.socialscienceregistry.org/trials/4283/history/47860.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-06-07
Intervention End Date
2019-06-30
Primary Outcomes
Primary Outcomes (end points)
y_i: Amount of money donated to Atmosfair (in Euro)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
See Pre Analysis Plan
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
individuals
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
6000 individuals
Sample size: planned number of observations
6000 individuals
Sample size (or number of clusters) by treatment arms
1500 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We base our power analysis on data from an experiment by Löschel et al. (2013). They find the standard deviation of voluntary purchases of carbon offsets amounts to 849 kg of CO2. We set the significance level to 0.05 and the power level to 0.8. As we split our sample of around 6,000 respondents into four treatment groups, there are 1,500 respondents in each treatment group. Given this parametrization, the minimum detectable treatment effect (in a two-sided test) amounts to the tenth of the standard deviation, which corresponds to some 85 kg of CO2 offsets or, in monetary terms, a contribution of about €2.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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