What Makes Pyramid Schemes Work? A Followup

Last registered on February 15, 2019

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Pre-Trial

Trial Information

General Information

Title

What Makes Pyramid Schemes Work? A Followup

RCT ID

AEARCTR-0003880

Initial registration date

February 15, 2019

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

February 15, 2019, 4:14 PM EST

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

Locations

Country

United States of America

Region

All

Primary Investigator

Name

Gonul Dogan

Affiliation

University of Cologne

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Other Primary Investigator(s)

PI Name

Priscilla Man

PI Affiliation

University of Queensland

Contact Investigator

PI Name

Kenan Kalaycı

PI Affiliation

University of Queensland

Contact Investigator

Additional Trial Information

Status

In development

Start date

2019-02-15

End date

2020-02-25

Keywords

Finance & Microfinance, Other

Additional Keywords

pyramid schemes, investment, complexity, group size

JEL code(s)

Secondary IDs

Abstract

Via an online experiment, we investigate why people invest in pyramid schemes. We focus on cognitive biases that could explain entry decisions.

External Link(s)

Registration Citation

Citation

Dogan, Gonul, Kenan Kalaycı and Priscilla Man. 2019. "What Makes Pyramid Schemes Work? A Followup." AEA RCT Registry. February 15. https://doi.org/10.1257/rct.3880-1.0

Former Citation

Dogan, Gonul, Kenan Kalaycı and Priscilla Man. 2019. "What Makes Pyramid Schemes Work? A Followup." AEA RCT Registry. February 15. https://www.socialscienceregistry.org/trials/3880/history/41622

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Experimental Details

Interventions

Intervention(s)

Our study consists of three treatments. We will first run a baseline treatment to get enough subjects to use in the matching procude of the complexity treatment. This baseline treatment will have a small number of subjects. In the first complexity treatment, we will reduce the complexity of decisionmaking in a large group by making subjects consider how they would behave in a smaller group. Further, we will study witihin subject consistency of investment behavior in a pyramid scheme to an analogous lottery. In the second complexity treatment, we will reduce complexity by making subjects realise the payoffs in a large group in which everyone invests. We will do this by asking subjects to consider what their earnings would be in different levels of the tree.

Intervention Start Date

2019-02-15

Intervention End Date

2019-02-26

Primary Outcomes

Primary Outcomes (end points)

Percentage of investors

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

investment decisions in the 8 person game, the number of attempts for correct answers in the second complexity treatment, their beliefs about the number of investors, the amount of giving in the dictator decision, the switching point in HoltLaury lottery, the switching point in the investment induced lottery, the lottery decision based on one's beliefs on the number of investors, whether a subject won the race game , the number of times the subject has won the race game, financial literacy, gender, years of schooling, trust and warranty decisions.

Secondary Outcomes (explanation)

Experimental Design

Subjects are endowed with $4 and have to decide whether to invest or keep their endowment. 200 participants make this decision, and this is common knowledge. Subjects' decisions are implemented based on a randomly drawn tree, and the payoffs realise.

Experimental Design Details

For each participant, we will draw a position in the randomly drawn tree. At the top of the tree, there are two persons, and each person is matched with two further people (followers). If a person decided to invest, and is part of an active branch of the tree, her earnings would be 2 times the number of first degree followers, 1 time the number of second degree followers, 0.5 times the number of third degree followers and so on. Participants get detailed instructions about this earnings structure and have to answer comprehension questions with example trees.

Randomization Method

MTurk randomisation device. On our end, we will randomly draw the tree via a computer program.

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

420

Sample size: planned number of observations

420

Sample size (or number of clusters) by treatment arms

20 for the baseline, 200 for each complexity treatment.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Based on our earlier experiment, about 58 percent invest in baseline. Based on Gpower software, with 80 percent power at 5 percent alpha, with 400 subjects in total (200 baseline, 200 complexity), we can detect an effect that gives at most 43.6 percent investors.

Supporting Documents and Materials

IRB