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Willful Ignorance and Reference-Dependence of Self-Image Concerns
Last registered on June 09, 2021

Pre-Trial

Trial Information
General Information
Title
Willful Ignorance and Reference-Dependence of Self-Image Concerns
RCT ID
AEARCTR-0007563
Initial registration date
June 09, 2021
Last updated
June 09, 2021 1:01 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Düsseldorf Institute for Competition Economics
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2021-06-16
End date
2022-01-01
Secondary IDs
Abstract
Based on theoretical predictions, I conduct a laboratory experiment to explore whether (a) self-image concerns are reference-dependent and (b) loss aversion applies to self-image concerns. I let subjects work on an IQ test, a self-image-relevant task, and induce an exogenous shift in complexity designed to put them at either gain or loss in self-image. Then, I elicit their willingness to acquire feedback. Considering potentially asymmetric belief updating, I analyze whether subjects who experience loss in self-image are more likely to acquire feedback compared to those with a loss in this domain.
External Link(s)
Registration Citation
Citation
Petrishcheva, Vasilisa. 2021. "Willful Ignorance and Reference-Dependence of Self-Image Concerns." AEA RCT Registry. June 09. https://doi.org/10.1257/rct.7563-1.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2021-06-16
Intervention End Date
2022-01-01
Primary Outcomes
Primary Outcomes (end points)
(1) Willingness to acquire self-image relevant feedback (intelligence domain),
(2) Beliefs about subjects’ own performance in the intelligence test (prior, 1st posterior, 2nd posterior),
(3) Performance in the intelligence test
Primary Outcomes (explanation)
Details provided in the “Experimental Design” section
Secondary Outcomes
Secondary Outcomes (end points)
Post-experimental questionnaire
Secondary Outcomes (explanation)
The post-experimental questionnaire contains main sociodemographic characteristics such as age, gender, the field of study, occupation, current GPA (or last degree GPA), high school GPA as well as average monthly budget and spending. Additionally, I ask subjects about their experience in the lab and collect independent measures of overconfidence, loss aversion in the monetary domain, risk aversion, and time preferences as well as independent measures of social and self-image concerns.
Experimental Design
Experimental Design
I analyze whether individuals react differently to gains and losses of their self-image: Are they more willing to avoid self-image-relevant information if they expect positive or negative updates about their self-image? I analyze whether subjects who expect positive feedback are more likely to acquire information than those who expect negative feedback.

My experimental setup includes three stages. In Stage 1, I elicit subjects’ prior beliefs about their performance in the upcoming intelligence test. I treat prior beliefs as a within-subject reference point in intelligence, a self-image-relevant domain. In Stage 2, I induce an exogenous shift in self-image. I put subjects’ self-image at either loss or gain by varying the task complexity. The second belief elicitation is necessary to see whether the treatment variation worked, i.e. whether subjects indeed expect losses and gains when I assume they do. I then ask subjects whether they are willing to acquire feedback about their performance and elicit willingness-to-pay/willingness-to-accept (WTP/WTA) to do so, as well as their beliefs about their performance. In Stage 3, I let subjects work on the remaining tasks and elicit their beliefs once again upon completion. First, I analyze whether belief updating is symmetric for those with gains and losses in self-image.

Additionally, I perform analyses both unconditionally and conditionally on belief updating. I test whether subjects who care about their self-image avoid ego-relevant feedback. Then, I analyze whether those who experience a loss in self-image are more willing to acquire feedback than those who experience gain. I also test whether subjects with marginal self-image losses have a disproportionately stronger willingness to acquire feedback than those with marginal gains in self-image.

IQ test

In this experiment, subjects work on Raven’s Progressive Matrices (RPMs), which are commonly used in experiments to measure fluid intelligence. They are picture puzzles with a missing piece. Among the available answers, subjects should choose the best logical fit to fill in the blank space. RPM tests commonly consist of five sets of matrices (A to E), with 12 matrices in each set. These sets progress in difficulty. Set A includes the easiest matrices; Set B is slightly harder, and so on. Set E contains the 12 hardest matrices. Based on the reference sample, which includes 413 observations (students) from a previous experiment (the same lab in 2014) who worked on a full set of the same RPM matrices, I expect student subjects would solve all the matrices in set A correctly. Hence, I will not use the 12 easiest matrices in this experiment. I will use 48 matrices from sets B to E.

Those 48 matrices are split into two parts: EASY and HARD. Matrices from sets B and C belong to the EASY part. Matrices from sets D and E form the HARD part. Both parts are progressive, i.e. they start with easy tasks and get more complicated over time. Matrices in the EASY and the HARD parts do not repeat or overlap. Subjects get one point if they solve a matrix correctly, and get zero points otherwise. Subjects have a time limit of 30 seconds per matrix, which ensures that their performance is comparable within the experiment as well as to the references sample where the same time limit was imposed.

Stage 1

After reading the instructions and answering control questions, subjects proceed to the first belief elicitation. I elicit their prior beliefs about their overall performance, i.e. a number of correctly solved matrices in both parts. By indicating the number of matrices they think they will solve correctly, they see the following phrase being autocompleted: “I think I will solve X out of 48 picture puzzles correctly. This means that I think I will perform better than Y% of previous participants”. I inform subjects that their performance is compared to the reference sample, which includes 413 observations (students) from a previous experiment (the same lab in 2014) who worked on a full set of the same RPM matrices. I incentivize the decision using the binarized scoring rule. Participants can earn one euro in each belief elicitation task. Importantly, with the binarized scoring rule, subjects still have a small probability to get paid for the belief elicitation task even if their guess and their actual performance differ a lot. Hence, their payoffs are not (directly) indicative of their performance.

I treat subjects’ prior beliefs about their performance in the IQ test as a within-subject reference point in intelligence. The procedure of belief elicitations is always the same. I always ask subjects about their beliefs about their overall performance. Payoffs of multiple belief elicitations are independent.

Stage 2

Subjects work on part 1 of the test. In treatment GAIN, part 1 is EASY, such that subjects, on average, solve more matrices than they expected and hence can expect positive feedback about their performance. In treatment LOSS, on the contrary, subjects work on HARD tasks, so they on average perform worse than expected. After participants complete 24 tasks in part 1, I elicit their beliefs again. Then, I elicit their willingness to pay to get feedback (which might be negative) using the Becker-DeGroot-Marschak mechanism.

Stage 3

Subjects work on the remaining 24 RPM tasks. It means that subjects from treatment GAIN work now on the HARD part, while those from treatment LOSS work on the EASY part. All participants in the experiment work on exactly the same 48 picture puzzles described above. Once subjects complete the task, I elicit beliefs about their performance again before they proceed to get (or not) their feedback. I display their feedback in the same format as belief elicitation, i.e. it says “You solved X out of 48 picture puzzles correctly. This means that you performed better than Y% of previous participants”.

Technical details

This experiment will be conducted online with subjects from the DICE Lab, University of Düsseldorf. Subjects receive a show-up fee of 3 Euro as well as a 5 Euro endowment at the beginning of the experiment which might be used for the feedback WTP/WTA. 5 Euro endowment assures that (a) in order to ensure (not) getting feedback, the stakes are rather high, but (b) subjects cannot make an absolute loss after their decision is realized. Additionally, subjects face three rounds of belief elicitations (in Stages 1, 2, and 3) which pay at most 1 Euro each. On top of that, some parts of the post-experimental questionnaire (namely, overconfidence and loss aversion elicitations) are also incentivized. Total earnings are only paid out upon completion of the experiment to prevent subjects from potentially dropping out.

Hypotheses

This experimental setup allows me to formally test the following hypotheses:

Hypothesis 1. (Willful ignorance) Individuals who care about their self-image may avoid feedback relevant to their self-image.
I analyze the share of subjects with negative willingness to pay to acquire feedback. I hypothesize that this share will be non-negligible.

Hypothesis 2. (Asymmetric belief updating) Individuals who care about their self-image may update their beliefs stronger if they experience an actual gain in a self-image relevant domain compared to an actual loss in a self-image relevant domain.
In line with motivated beliefs literature, I hypothesize that the absolute difference between prior beliefs and 1st posterior beliefs will be larger for subjects in GAIN than in LOSS.

Hypothesis 3. (Reference-dependence) Individuals who care about their self-image and on average expect the loss in their self-image are more willing to acquire information than those who on average expect the gain in their self-image of the same size.

Hypothesis 4. (Loss aversion) Individuals who care about their self-image and expect a marginal loss in their self-image are more willing to acquire information than those who expect a marginal gain in their self-image.

I will rely on Mann-Whitney U tests and regression analyses to test the hypotheses described above.
Experimental Design Details
Not available
Randomization Method
Access links will be randomly allocated. Subjects will not be aware of which treatment they are a part of.
Randomization Unit
Each participant of the laboratory experiment is considered as one independent observation.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
120 subjects
Sample size: planned number of observations
120 subjects
Sample size (or number of clusters) by treatment arms
I aim at a balanced design in which about 60 observations (50%) are assigned to treatment GAIN and about 60 observations to treatment LOSS (50%).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Gesellschaft für experimentelle Wirtschaftsforschung e.V. German Association for Experimental Economic Research e.V.
IRB Approval Date
2021-05-25
IRB Approval Number
No. 49nWIXIa