Can heuristics also mediate the effect of financial education to financial literacy? On the effectiveness of financial education to reduce the negative effects of availability heuristics and gambler’s fallacy while improving financial literacy.

Last registered on September 30, 2021

Pre-Trial

Trial Information

General Information

Title
Can heuristics also mediate the effect of financial education to financial literacy? On the effectiveness of financial education to reduce the negative effects of availability heuristics and gambler’s fallacy while improving financial literacy.
RCT ID
AEARCTR-0007956
Initial registration date
September 27, 2021
Last updated
September 30, 2021, 11:25 PM EDT

Locations

Region

Primary Investigator

Affiliation
KU Leuven

Other Primary Investigator(s)

PI Affiliation
KU Leuven

Additional Trial Information

Status
In development
Start date
2021-10-15
End date
2022-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Many behavioural and cognitive biases have been linked with poorer financial decisions, which literature suggests as a reason for limited impact of financial education programs and smaller levels of financial literacy. Although behavioural heuristics can sometimes help and simplify decision making, it can also lead to sub-optimal and undesired outcomes. This research project develops a randomized controlled trial to test different financial education interventions to reduce the negative effect of two heuristics (availability and gambler’s fallacy) to the financial decision-making of secondary school students of Belgium. With this, it aims to verify if financial education based on heuristics can have an indirect effect to financial literacy mediated by better awareness to heuristics. Besides a baseline condition that will not receive an intervention, this work proposes three types of cumulative intervention: (i) regular financial education; (ii) additional material to increase awareness to heuristics; (iii) complementary material about the dangers of heuristics.
External Link(s)

Registration Citation

Citation
De Witte, Kristof and Francisco Pitthan. 2021. "Can heuristics also mediate the effect of financial education to financial literacy? On the effectiveness of financial education to reduce the negative effects of availability heuristics and gambler’s fallacy while improving financial literacy. ." AEA RCT Registry. September 30. https://doi.org/10.1257/rct.7956-1.0
Experimental Details

Interventions

Intervention(s)
Schools are assigned to the following four 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 1: In addition to the content received by the control group, students receive materials about the definition of the availability heuristics, the gambler’s fallacy and the possibility effect related with financial decisions.
- Treatment group 2: In addition to the content received by the treatment group 1, students receive materials about the dangers of the availability heuristics, the gambler’s fallacy and the possibility effect, on how they can affect our financial decisions and lead us to undesired outcomes.
Intervention Start Date
2021-10-27
Intervention End Date
2021-11-27

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 financial knowledge obtained through a test of the main-part of the course material (the one common to the control and treatment groups) based on six questions. The questions are related to pensions, insurance and investment products.
Primary Outcomes (explanation)
First, the financial literacy test 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).
Secondly, we should develop a behavioural test for the use of heuristics divided into two segments: (i) 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); (ii) 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).
Finally, the outcome variable related to the knowledge of the basic course material will be evaluated by a simple multiple-choice test with only one right answer in each question, with the total score being normalized to one. Those questions are developed with the help of teachers of financial education for secondary school students.
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.
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.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Schools that registered for participation were randomized to the aforementioned four experimental conditions right-after completion of the pre-test. We assessed the level of financial literacy, heuristics and knowledge of the course material content 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 2700 students.
Sample size (or number of clusters) by treatment arms
Baseline group = 450 pupils, 10 schools
Control group = 630 pupils, 14 schools
Treatment group 1 = 810 pupils, 18 schools
Treatment group 2 = 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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