Representative Evidence on Exchange Asymmetries
Last registered on March 22, 2019


Trial Information
General Information
Representative Evidence on Exchange Asymmetries
Initial registration date
March 08, 2019
Last updated
March 22, 2019 2:52 PM EDT

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Primary Investigator
University of Heidelberg
Other Primary Investigator(s)
PI Affiliation
WZB Berlin
Additional Trial Information
On going
Start date
End date
Secondary IDs
The endowment effect describes the tendency of individuals to place greater value on objects they own than on the same or a similar object they do not own. This drives a wedge between the willingness to pay and willingness to accept for an object, or leads individuals to trade a randomly assigned object significantly less often than predicted by standard theory, resulting in an exchange asymmetry. In this project, we investigate the prevalence of exchange asymmetries in the general population and test a leading theoretical explanation.
External Link(s)
Registration Citation
Fehr, Dietmar and Dorothea Kuebler. 2019. "Representative Evidence on Exchange Asymmetries." AEA RCT Registry. March 22.
Experimental Details
We implement our study in the SOEP Innovation Sample (SOEP IS, Richter and Schupp, 2015). The SOEP IS is a representative longitudinal survey of German households. Participating households receive monetary incentives for completing the surveys and, in addition, receive a small item in the beginning of a survey as appreciation for the time (household gift). We use this unique feature of the survey for our experiment and implement a modified version of the exchange paradigm introduced by Knetsch (1989).
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
(i) the share of respondents who keep their assigned item and
(ii) the total trading rates.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Loss aversion
Secondary Outcomes (explanation)
We measure loss aversion with two hypothetical choice lists. Specifically, we elicit respondents’ loss aversion with two separate questions before and after the trading possibility. The first question consists of six hypothetical lotteries that involve an equal chance of a gain or a loss. For each lottery, respondents have to indicate whether they would accept or reject the lottery. The number of rejected lotteries is an indicator for individuals’ loss aversion. We ask this question before the trading possibility in the household survey. After the trading possibility and as part of the personal questionnaire, we use two additional questions to measure loss aversion. That is, we elicit the hypothetical minimum gain X to accept a fair gamble where a respondent has an equal chance to hypothetically win X or lose 25 Euro in one question and win X or lose 100 Euro in the other.
Experimental Design
Experimental Design
Our design is based on the exchange paradigm introduced by Knetsch (1989). That is, we randomly endow households with one of two equally valued items. At the end of the household survey, interviewers offer respondents the opportunity to trade the endowed item for the alternative item. After completing trades (if any), the interviewers continue with personal questionnaires for all household members.
Experimental Design Details
Not available
Randomization Method
Randomization through survey software
Randomization Unit
First we randomize households to one of the three conditions described above and then we randomize the assignment of the two items.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
about 3,300
Sample size: planned number of observations
about 3,300. The exact number depends on panel stability.
Sample size (or number of clusters) by treatment arms
10% of the total sample participate in the choice condition
45% in the treatment without information about the trading possibility (baseline condition)
45% in the treatment with information about the trading possibility (expectation condition)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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