The probability weighting of decision-making under heuristics – Are availability heuristics and gambler’s fallacy subject to the possibility effect of prospect theory?

Last registered on October 01, 2021

Pre-Trial

Trial Information

General Information

Title
The probability weighting of decision-making under heuristics – Are availability heuristics and gambler’s fallacy subject to the possibility effect of prospect theory?
RCT ID
AEARCTR-0008298
Initial registration date
September 27, 2021
Last updated
October 01, 2021, 7:27 AM EDT

Locations

Region

Primary Investigator

Affiliation
KU Leuven

Other Primary Investigator(s)

PI Affiliation
KU Leuven

Additional Trial Information

Status
In development
Start date
2022-01-10
End date
2022-05-27
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Availability heuristics and gambler's fallacy affect considerably the decision-making of individuals, which is particularly true regarding financial decisions. Those two heuristics are seen to affect the probability weighting (and sometimes also its estimation) of an event/information by its frequency or on how easy it comes to mind. Although those heuristics are usually analysed in comparison to the expected utility theory, this paper analyses them using the prospect theory. More specifically, it analyses if different degrees of availability heuristics and gambler's fallacy are more or less affected by the possibility effect (i.e. overweight of small probabilities just seen as "possible" in contrast to "impossible"). Moreover, this paper analysis a causal link of financial education treatments mitigating those heuristics and a possible spill over to the possibility effect using a randomized controlled trial with Belgian secondary schools.
External Link(s)

Registration Citation

Citation
De Witte, Kristof and Francisco Pitthan. 2021. "The probability weighting of decision-making under heuristics – Are availability heuristics and gambler’s fallacy subject to the possibility effect of prospect theory?." AEA RCT Registry. October 01. https://doi.org/10.1257/rct.8298-1.1
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Experimental Details

Interventions

Intervention(s)
Schools are assigned to the following three experimental conditions:
- Baseline group: Students do not receive any treatment.
- Control group: Students receive a regular form of the financial education course material (without components related to heuristics).
- Treatment group: In addition to the content received by the control group, students receive materials about the definition and the dangers of the availability heuristics and the gambler’s fallacy related with financial decisions. On what they are and on how they can affect our financial decisions and lead us to undesired outcomes.
Intervention Start Date
2022-01-24
Intervention End Date
2022-04-01

Primary Outcomes

Primary Outcomes (end points)
(1) Financial literacy by a test based on twelve questions. The questions measure financial knowledge, attitude and behaviour.
(2) Availability heuristics and gambler’s fallacy behaviour by a test based on ten questions. The questions measure how the frequency of events (i.e. events that either come easily or hardly to mind) affect the decisions of students.
(3) The degree of possibility effect is estimated using a set of nine questions. They measure the preference between different possible financial decisions, in which the expected value is the same, but the probabilities and total possible loss/gain is different. This will be tested with multiple probabilities for both gains and losses of different sizes.
Primary Outcomes (explanation)
The study will use three main outcome variables. The first of them is the financial literacy score, which will be based on the “Big-Three” to attest for financial knowledge (including degree of certainty by answer), with extra questions for financial knowledge, financial attitude and financial behaviour from slightly modified questions of the full OECD (2013) questionnaire, Atkinson & Messy (2011), Maldonado et al. (2019) and from Iterbeke et al. (2020).
For the second outcome variable we will develop a behavioural test for heuristics divided into two segments. First an availability heuristic use test, which asks students for preference among financial alternatives and likelihood of different outcomes at different degrees of availability (i.e. events that come easily to mind). Second, a gambler’s fallacy heuristics test based on the opposite of the availability heuristics, when individuals put higher probabilities to events that did not happened recently. Part of the questions for heuristics are based on Camerer (1989).
The last outcome variable, the degree of possibility effect is analysed in a set of different nine questions individually, which vary in terms of probabilities, economic size and type of financial decision (investment or insurance). The questions are partially inspired by Kahneman & Tversky (1979) and Tversky & Kahneman (1992), but considering its applications to financial decisions.
Besides the tests of the main outcome variables, pre-existing conditions of students (e.g. grades in math and language, situation of family, absence rate) and school (e.g. private/public, region, performance in past standardized tests, funding by student) will also be collected.
References:
Atkinson, A., & Messy, F. A. (2011). Assessing financial literacy in 12 countries: an OECD/INFE international pilot exercise. Journal of Pension Economics & Finance, 10(4), 657-665.
Camerer, C., & Kunreuther, H. (1989). Experimental markets for insurance. Journal of Risk and Uncertainty, 2(3), 265-299.
Iterbeke, K., De Witte, K., Declercq, K., & Schelfhout, W. (2020). The effect of ability matching and differentiated instruction in financial literacy education. Evidence from two randomised control trials. Economics of Education Review, 78, 101949.
Jacobs, A. M., & Matthews, J. S. (2012). Why do citizens discount the future? Public opinion and the timing of policy consequences. British Journal of Political Science, 903-935.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
Maldonado, J. E., De Witte, K., & Declercq, K. (2019). The effects of parental involvement in homework. Two randomised controlled trials in financial education. FEB Research Report Department of Economics DPS19. 14.
OECD (2013), PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy, OECD Publishing.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Schools that registered for participation were randomized to the aforementioned three experimental conditions right-after completion of the pre-test. We assessed the level of financial literacy, heuristics and the degree of possibility effect of all students before as well as after followed the course. Students assigned to the control and baseline groups completed the same tests at the same time as students in the treatment groups. The students in the baseline group complete the tests even though they will not receive any intervention between the tests. A second post-test will also be applied after a waiting period.
Experimental Design Details
Not available
Randomization Method
Schools will be randomly assigned to the different experimental conditions by a random number generator in STATA after completion of pre-tests.
Randomization Unit
The treatment was randomized at school level. All students and teachers in the same school were assigned to the same experimental group. In this way, all teachers in the same school received the same teaching material and instructions in order to minimize the possibility of spill-over effects and contamination of the different experimental group intra-cluster.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Given previous randomized controlled trial studies with Flemish schools in Belgium (e.g. Iterbeke et al., 2020; Maldonado et al., 2019; Pitthan & De Witte, 2021), our work will aim to reach similar number of participating schools (e.g. between 40 and 60).

References:
Iterbeke, K., De Witte, K., Declercq, K., & Schelfhout, W. (2020). The effect of ability matching and differentiated instruction in financial literacy education. Evidence from two randomised control trials. Economics of Education Review, 78, 101949.
Maldonado, J. E., De Witte, K., & Declercq, K. (2019). The effects of parental involvement in homework. Two randomised controlled trials in financial education. FEB Research Report Department of Economics DPS19. 14.
Pitthan, F. & De Witte, K. (2021). A behavioural-mediated mechanism of financial education. On the effectiveness of behavioural-based course materials to improve financial literacy directly and indirectly by better awareness to the myopic bias. Working paper.

Sample size: planned number of observations
With an average number of participating students per school of 45, we plan to have around 2475 students.
Sample size (or number of clusters) by treatment arms
Baseline group = 340 pupils, 12 schools
Control group = 675 pupils, 15 schools
Treatment group = 810 pupils, 18 schools
Average number of schools per condition = 15
Average number of pupils per condition = 675
Average number of pupils per school = 45
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The computation is based on List et al. (2011) and accounts for intra-cluster correlation in the calculation of the minimal detectable effect size. In our planned experimental setting, we expect 15 schools in each experimental condition, with on average 45 students by school. Details of the calculation: According to List et al. (2011), in a clustered design, the minimum number of observations in each experimental group can be computed as follows: n=2(t_(α/2)+t_β)²(σ/δ)²(1+(m-1)ρ) This implies that the minimum detectable effect size is equal to: δ=σ/√(n/(2(t_(α/2)+t_β)²(1+(m-1)ρ))) Or the minimum detectable effect size expressed as a fraction of a standard deviation is equal to: δ/σ=1/√(n/(2(t_(α/2)+t_β)²(1+(m-1)ρ))) Following the standards of the literature and the rules of thumb from List et al. (2011), we can set a significance level of 0.05 and power to 0.80, which would lead us to t_(α/2)=1.96 and t_β=0.84 (from normal tables). Replacing in the formula (with the planned sample size and assuming intra-correlation of cluster equal to 0.1) we get the minimal detectable effect size of: δ/σ=1/√(675/(2(1.645+0.84)²(1+(45-1)0.1)))=0.31 Reference List, J., Sadoff, S. and Wagner, M. (2011), So you want to run an experiment, now what? Some simple rules of thumb for optimal experimental design, Experimental Economics 14, 439-457
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