Self-confidence, entitlement, and the POUM Hypothesis: an experimental study

There are two treatments, respectively called “Random treatment” and “Effort treatment”. The two treatments differ in how the transition matrix is modified during the experiment. The transition matrix is a table that summarizes the probability with which every participant reaches a given level of income given a starting level of income. In this experiment, it is endogenously modified by individuals’ performance or luck. In the “Random treatment”, the transition matrix is modified according to the results of a lottery in which all subjects participate, whilst in the “Effort treatment”, it is modified according to the results of an effort test.

2023-07-06

2023-09-28

The key outcome variable of interest in this experiment is the redistribution rates participants choose.

Individuals choose redistribution rates in a range between 0 and 100, where 0 means no redistribution and 100 means full redistribution.

Self-confidence and optimism measures, a risk-aversion measure, political and religious orientation, and previous and current school information.

Self-confidence/optimism measures are derived from individuals’ expectations about their performance in a test or over a lottery relative to actual results. I collect absolute self-confidence/optimism, the difference between expected and actual results, and relative self-confidence/optimism, the difference between the expected and actual percentage of subjects that performed worse than oneself.
The risk-aversion measure is derived through a test, which is the Bomb Risk Elicitation Task (Crosetto and Filippin, 2013).
Political and religious views and previous and current school information are collected through a questionnaire.

The experiment uses a between-subjects design, in which participants, who are recruited online, are equally and randomly divided across the experimental sessions.
The experiment is divided into two parts. In both parts, I collect subjects’ preferences over redistribution. In the first part of the experiment, subjects choose redistribution rates with no future mobility. At this moment, they do not know that mobility is possible in the second part.
The second part allows future mobility according to the probabilities given by the transition matrix, which is modified by an uncertain value “p”, initially unknown. It is made clear to subjects how the value of “p” is determined before the beginning of the second part. In the second part of the experiment, redistribution choices are made twice: not knowing the real transition matrix that will be applied to their initial income (i.e., with the value “p” unknown), and knowing it. Then, choices in the second part are different as the self-confidence effect fades away when the true value of “p” becomes known.
Before redistribution rates are chosen in the second part, I collect individuals’ expectations regarding the results of the effort test or the lottery (depending on the treatment). These expectations will be used to determine self-confidence (in the “Effort treatment”) and optimism (in the “Random treatment”) measures. At the end of the experiment. a questionnaire is proposed to collect individual characteristics.

Before this main part of the experiment begins, subjects completed the Bomb Risk Elicitation Task (BRET), proposed by Crosetto and Filippin (2012) to measure risk attitudes.
In both treatments, the experiment is divided into two parts. Each part is preceded by the corresponding instructions, which are read aloud by the experimenters, and a comprehension test, highlighting the main concepts of each part.
In both parts of the experiment, subjects made redistribution rate choices. I use the linear taxation scheme discussed in Melzer and Richards (1981). Subjects’ decisions affect only their own earnings, whilst they are not affected by others’ decisions. During the experiment, subjects do not know their initial income. They choose a redistribution rate for each possible income level, knowing that only the choice made correspondingly to their initial income can be used to compute earnings at the end of the experiment. As subjects do not know their real initial income, each choice has the same probability to be used.
As above-mentioned, the experiment is divided into two parts. In the first part, subjects make choices with no future mobility, whilst in the second part, subjects make choices with future mobility. In the second part, choices are made twice: not knowing the real transition matrix that will be applied to their initial income (i.e., with the value “p” unknown), and knowing it.
In both treatments, choices in the second part are preceded by the effort test, and individuals’ expectations are collected. In the “Effort treatment”, subjects give expectations about the test results before completing the slider task, knowing the test that is used and how it works, and after, to collect information about ex-ante and ex-post self-confidence. They are asked to give their expected number of correct answers and the percentage of participants they expect to have a lower score than their own. In the “Random treatment”, subjects give their expectations about the lottery results. They are asked to give the interval in which they expect their number to lie and the percentage of participants they expect to have received a lower drawn number than their own.
At the end of the experiment, subjects have expressed three redistribution sets, each of which is composed of three redistribution rates (i.e., one rate for each possible initial income in each set). A set of rates is randomly drawn. If the drawn set is the first one, the redistribution rate chosen in the first set correspondingly to the real initial income is applied to the initial income (given that in the first part mobility is not considered). If the drawn set is the second (third) one, the redistribution rate chosen in the second (third) set correspondingly to the real initial income is applied to the final income, which is the income after the application of the transition matrix (given that in the second part mobility is considered).

Randomization by a computer, using the sofware z-Tree.

Individual

No

The treatment is not clustered.

192 students

96 students will be in the first treatment and 96 students will be in the second treatment.

In the power analysis, I use a power of 80%, 192 subjects, a standard deviation of 40, and given that subjects are equally divided into the two treatments. This is under the assumption that the standard deviation is slightly higher than what has been found in similar research investigating redistribution preferences. This is a conservative scenario, as the standard deviation should not differ too much with respect to previous studies. I detected a minimum detectable effect size of 16.17.