Salience and Relative Skewness
Last registered on April 08, 2019


Trial Information
General Information
Salience and Relative Skewness
Initial registration date
November 10, 2018
Last updated
April 08, 2019 12:06 AM EDT
Primary Investigator
University of Cologne
Other Primary Investigator(s)
PI Affiliation
Additional Trial Information
Start date
End date
Secondary IDs
It is a robust finding in the literature on choice under risk that people like right-skewed, but avoid left-skewed risks (e.g., Golec and Tamarkin,1998, Sydnor, 2010, or Ebert, 2015). We conduct a laboratory experiment in order to test whether, beside this preference for absolute skewness, there is also a preference for relative skewness. By changing the correlation structure of lotteries we can manipulate how skewed these lotteries are relative to each other without affecting their absolute skewness. We thereby test for key predictions of salience theory of choice under risk (Bordalo et al., 2012) that allow us to distinguish it from cumulative prospect theory (Tversky and Kahneman, 1992).
External Link(s)
Registration Citation
Dertwinkel-Kalt, Markus and Mats Köster. 2019. "Salience and Relative Skewness." AEA RCT Registry. April 08.
Experimental Details
In a within-subjects design, we study in how far the choice between a right-skewed and a left-skewed lottery (with the same expected value and the same variance) depends on the correlation of these lotteries and how the correlation interacts with the level of absolute skewness.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Proportion of preference reversals from the right-skewed lottery under negative correlation to the left-skewed lottery under positive correlation.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Each individual makes twelve binary decisions in random order. In each decision the subject chooses between two lotteries that form a Mao pair. A Mao pair is uniquely defined by its expected value (E), its variance (V), its absolute skewness (S), and its correlation structure. One of the lotteries is right-skewed and the other one is left-skewed. We use six different Mao pairs, three of which are rather skewed (i.e., S = 2.7) with E=36 and V=144, with E=72 and V=576, and with E=108 and V=1296, while the remaining three are rather symmetric (i.e, S = 0.6) again with E=36 and V=144, with E=72 and V=576, and with E=108 and V=1296. For each Mao pair subjects choose between the right-skewed and the left-skewed lottery both under perfectly negative correlation and under maximal positive correlation.
Experimental Design Details
Randomization Method
Randomization is done by the computer.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
110 individuals
Sample size: planned number of observations
660 paired choices
Sample size (or number of clusters) by treatment arms
330 paired choices for symmetric Mao pairs, 330 paired choices for skewed Mao pairs
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Intervention Completion Date
December 13, 2018, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
December 13, 2018, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
113 individuals
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
678 paired choices
Final Sample Size (or Number of Clusters) by Treatment Arms
339 paired choices for symmetric Mao pairs, 339 paired choices for skewed Mao pairs
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers
Whether people seek or avoid risks on gambling, insurance, asset, or labor markets crucially depends on the skewness of the underlying probability distribution. In fact, people typically seek positively skewed risks and avoid negatively skewed risks. We show that salience theory of choice under risk can explain this preference for positive skewness, because unlikely, but outstanding payoffs attract attention. In contrast to alternative models, however, salience theory predicts that choices under risk not only depend on the absolute skewness of the available options, but also on how skewed these options appear to be relative to each other. We exploit this fact to derive novel, experimentally testable predictions that are unique to the salience model and that we find support for in two laboratory experiments. We thereby argue that skewness preferences—typically attributed to cumulative prospect theory—are more naturally accommodated by salience theory.
Dertwinkel-Kalt, Markus and Köster, Mats (2019). "Salience and Skewness Preferences," DICE Discussion Paper No. 310.