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The impact of a gamified financial education course to mitigate the effects of myopic behavior. Evidence from a Randomized Controlled Trial.
Last registered on December 14, 2020


Trial Information
General Information
The impact of a gamified financial education course to mitigate the effects of myopic behavior. Evidence from a Randomized Controlled Trial.
Initial registration date
December 11, 2020
Last updated
December 14, 2020 10:33 AM EST
Primary Investigator
KU Leuven
Other Primary Investigator(s)
PI Affiliation
KU Leuven
Additional Trial Information
In development
Start date
End date
Secondary IDs
Financial illiteracy has been linked to many poor financial decisions, which led many countries to adopt financial education programs to improve financial well-being. Although financial knowledge can be greatly improved with regular financial education programs, the gains to better financial behavior and better decisions are limited. A possible explanation is the existence of behavioral biases and heuristics such as myopia, which can undermine good decisions through automated thought processes. Myopia can affect decisions, for instance, through the underestimation of risks and a higher short-term preference. This study proposes a randomized controlled trial experiment to test if a gamified financial education course material directed to increase the awareness of myopia can improve (i) the degree of financial literacy and (ii) reduce the degree of myopic behavior.
External Link(s)
Registration Citation
De Witte, Kristof and FRANCISCO PITTHAN. 2020. "The impact of a gamified financial education course to mitigate the effects of myopic behavior. Evidence from a Randomized Controlled Trial.." AEA RCT Registry. December 14. https://doi.org/10.1257/rct.6879-1.0.
Sponsors & Partners
Experimental Details
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 myopia).
- Treatment group 1: In addition to the content received by the control group, students receive materials about estimation of risks as well.
- Treatment group 2: In addition to the content received by the treatment group 1, students receive materials about short-term preference as well.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The main outcomes are:
(1) Financial literacy by a test based on twelve questions. The questions measure financial knowledge, attitude and behavior.
(2) Myopic behavior by a test based on eleven questions. The questions measure the (under)estimation of risks and the degree of short-term preference.
(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)
For the outcome variable of financial literacy, we will follow a methodology close to the approach of Atkinson & Messy (2011), Maldonado et al. (2019) and OECD (2013) to both elaborate the questions and the score of the outcome variable. Answers that show good financial literacy in terms of financial knowledge, attitude and behavior will get a score of 1 (in some questions more than one option could be interpreted as good financial literacy), and 0 if related to a low financial literacy degree. In the end, to estimate the outcome variable, the score will be summed as a ratio of the total questions answered.

Part of the questions for myopic behavior were inspired in Jacobs & Matthews (2012), while others were new to reflect other components of myopia. The myopic score was estimated in a similar way to the financial literacy one, with answers indicating a high myopic behavior getting a score of 1.

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.

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.
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, myopic behavior 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 will be randomized at school level. All students and teachers in the same school will be 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?
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), our work will aim to reach similar number of participating schools (e.g. 60).

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.
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
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents