Valuation uncertainty: explaining the WTA-WTP gap and their lack of correlation

Last registered on September 03, 2021


Trial Information

General Information

Valuation uncertainty: explaining the WTA-WTP gap and their lack of correlation
Initial registration date
September 02, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 03, 2021, 5:27 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

Radboud University

Other Primary Investigator(s)

PI Affiliation
Radboud University
PI Affiliation
National University of Singapore

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Putting an exact value on assets is often difficult since it involves a complicated process of trading off different attributes. This difficulty can lead to valuation uncertainty; people consider a range of possible values for an asset instead of one exact value. Valuation uncertainty has important scientific, practical, and societal implications. It is, however, difficult to measure. Using a representative sample of a Dutch household panel, we propose a novel, incentivized measure of a valuation uncertainty interval in which individuals have valuation difficulty. In this measure subjects are allowed to randomize between options when they are not fully confident about their preference. We then use valuation uncertainty to organize a number of seemingly independent anomalies, in particular the WTA-WTP gap and the lack of correlation between the two. Furthermore, we link valuation uncertainty to individual traits and life outcomes.
External Link(s)

Registration Citation

Arts, Sara, Jianying Qiu and Songfa Zhong. 2021. "Valuation uncertainty: explaining the WTA-WTP gap and their lack of correlation." AEA RCT Registry. September 03.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
- Simple lottery:
-- Willingness to Accept
-- Willingness to Pay
-- Valuation Uncertainty interval

- Complex lottery
-- Willingness to Accept
-- Willingness to Pay
-- Valuation Uncertainty interval
Primary Outcomes (explanation)
Each of the outcomes described above will be elicited directly in the experiment. For a more detailed description of the elicitation method, see the experimental design section below.

Secondary Outcomes

Secondary Outcomes (end points)
- Cognitive ability
- Desire for control
- Demographic and real-life outcome variables
Secondary Outcomes (explanation)
- Cognitive ability is measured through 6 matrix reasoning questions from the ICAR.
- Desire for control is measured by a subset of the desirability of control scale.
- Demographic variables and real-life outcome variables (such as wealth and income) are obtained from the background data of the panel.

Experimental Design

Experimental Design
The experiment consists of five treatments, which allow for testing the effect of the experimental setup. The main treatment will be described here. The difference between the treatments is described below in the clustering description.

Subjects face questions on a simple lottery (50% chance of gaining 900 points and 50% chance of gaining 100 points), which is similar to the lottery used in Chapman et al. (2019), and a complex lottery (20% chance of gaining 975 points, a 30% change of gaining 850 points, a 30% chance of gaining 150 points, and a 20% chance of gaining 25 points). As a control subjects are also asked to answer the question for a 'sure' lottery (100% chance of gaining 500 points).

Subjects go through 3 main sections. First of all, the valuation uncertainty elicitation. The method is based on a standard multiple price list (MPL), in which subjects make a choice between a lottery and a sequence of certain amounts (Holt & Laury, 2002). We extend the method by introducing a third option: the option to receive the lottery or the certain amount randomly. The sure amounts for which subjects choose the option of random assignment define valuation uncertainty interval. The lower bound of the interval is defined by the maximum certain amount at which subjects choose the lottery only, and the upper bound is defined by the minimum certain amount at which subjects choose the certain amount only. These two bounds can serve as “the extremes that define an incomplete preference relation, connecting with the existing approach to modelling the endowment effect that does not rely on loss aversion” (Chapman et al., 2019, p. 37).
Since going through the whole list is tedious, we facilitate subjects’ decision making by asking them to indicate only their switching points from the lottery to the random assignment, and from the random assignment to the certain amount, their preferred choices will be automatically be filled in.
The second and third sections of the experiment will elicit the subjects' Willingness To Accept and Willingness To Pay for each lottery according to the standard approach (Chapman et al., 2019). In these measures subjects will again face a MPL, but this time with the traditional two options. The questions are framed as selling (WTA) and buying (WTP) the lottery for a certain amount. Again, subjects only need to select their switching point, and the table will fill automatically. Finally, in between the different sections subjects will be asked to answer some questions about their cognitive ability (IQ questions), and the desirability of control scale (Burger & Cooper, 1979; Gebhardt & Brosschot, 2002).
Experimental Design Details
Randomization Method
Randomization is done by a computer when the survey is distributed.
Randomization Unit
Subjects are randomly assigned to one of the treatments described below.
Within these treatments we randomize between the order of the sections [WTA-WTP-VU, WTP-WTA-VU, VU-WTA-WTP, VU-WTP-WTA] the order of the lotteries [simple-complex OR complex-simple], the order of the values in the MPL [ascending OR descending].
In the single question treatment the order of the sections is fixed (VU-Neutral frame) and only the order of the lotteries and the order of the values in the MPL are randomized.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
We have 5 different treatments with slightly different methods of elicitating valuation uncertainty.
1. Main treatment (as described above)
2. Cost treatment (there is a small cost for choosing the lottery or the sure amount of points).
3. Benefit treatment (there is a small benefit for choosing the lottery or the sure amount of points).
4. Reduced lottery treatment (instead of 'letting the computer decide' the valuation uncertainty option is described as a reduced lottery of 50% chance of getting the lottery).
5. Single question treatment (instead of a multiple price list with 2 switching points, subjects make a single decision per screen between the lottery, letting the computer decide, or the sure amount of points; instead of the WTA and WTP questions subject select a switching point for both lotteries in a neutral frame).
Sample size: planned number of observations
2400 subjects
Sample size (or number of clusters) by treatment arms
Depending on response rate, but we are aiming for:
1. Main treatment: N = 1200
2. Cost treatment: N = 200
3. Benefit treatment: N = 200
4. Reduced lottery treatment: N = 200
5. Single question treatment: N = 600
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

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Analysis Plan

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Is the intervention completed?
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Data Publication

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Program Files

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Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials