Everyday econometricians: Selection neglect and overoptimism when learning from others

Last registered on November 29, 2023


Trial Information

General Information

Everyday econometricians: Selection neglect and overoptimism when learning from others
Initial registration date
November 14, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 29, 2023, 9:56 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
WZB Berlin Social Science Center
PI Affiliation
University College London and Paris School of Economics

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
This is an ex-post registration of Experiment 1, conducted for the paper entitled "Everyday econometricians: Selection neglect and overoptimism when learning from others."

The paper reports the results from two sets of experiments. Both sets of experiments study whether individuals neglect selection effects when learning from data generated by others' decisions.

Experiment 2 was pre-registered in a separate registration file (https://www.socialscienceregistry.org/trials/10536).
External Link(s)

Registration Citation

Barron, Kai, Steffen Huck and Philippe Jehiel. 2023. "Everyday econometricians: Selection neglect and overoptimism when learning from others." AEA RCT Registry. November 29. https://doi.org/10.1257/rct.12503-1.0
Experimental Details


In Experiment 1: We have 5 treatment conditions.

Scenario 1: Selected: Participants observe selected data when learning from others' decisions - the only observe decisions that are implemented.

Scenario 2: Correlated. Participants again observe selected data when learning from others' decisions. However, the information that learners have is perfectly correlated with past decision-makers.

Scenario 3: Externality. Participants observe selected data when learning from others' decisions, but the degree of selection is exogenously increased by introducing more informed investors.

Scenario 4: Control. Participants observe non-selected data when learning from others' decisions

Scenario 5:Selected NoDGP. Similar to the selection treatment, except participants are provided with less information about the data-generating process.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Investment propensity, the threshold for investing
Primary Outcomes (explanation)
The propensity to invest is constructed by calculating the average number of times an individual chooses to invest within a specific round - e.g., by counting the number of attribute values for which the individual chooses to invest. We also calculate the propensity to invest over all twenty rounds and over just the last five rounds.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design is described in detail in the paper (https://bibliothek.wzb.eu/pdf/2019/ii19-301.pdf and https://shs.hal.science/halshs-04154345/document).
Experimental Design Details
Randomization Method
The randomization is done by a computer programme (oTree).
Randomization Unit
We have multiple layers of randomization. Our treatments in Experiment 1 were assigned at the experimental session level. Individuals within the session are then randomly allocated into groups of 2 or 3. Then, in each round, there is randomization in terms of the attributes of the investments that participants choose whether to invest or not invest in. Within each group, these investments are used to compile the databases of the participants to facilitate learning.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Each of the five treatments has 48 participants, but due to the experimental design, the number of clusters differs. The cluster is defined by the number of participants who interact within a group. Therefore, in the treatments with groups of 3, we have 16 clusters, in the treatment with groups of 2, we have 24 clusters, while in the treatments where there is no interaction, we have 48 clusters.

16 clusters: Selected, Correlated, Selected NoDGP
24 clusters: Externality
48 clusters: Control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
WZB Research Ethics Committee
IRB Approval Date
IRB Approval Number


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials