Understanding Paternalism

Last registered on August 18, 2022

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Pre-Trial

Trial Information

General Information

Title

Understanding Paternalism

RCT ID

AEARCTR-0004630

Initial registration date

August 28, 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

August 29, 2019, 8:38 AM EDT

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

Last updated

August 18, 2022, 10:53 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Henning Hermes

Affiliation

ifo Institute

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Alexander W. Cappelen

PI Affiliation

NHH Bergen

Contact Investigator

PI Name

Björn Bartling

PI Affiliation

University of Zurich

Contact Investigator

PI Name

Marit Skivenes

PI Affiliation

University of Bergen

Contact Investigator

PI Name

Bertil Tungodden

PI Affiliation

NHH Bergen

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2019-08-12

End date

2020-01-31

Keywords

Governance, Other

Additional Keywords

Paternalism, well-being, freedom of choice, autonomy, decision-making

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We study a decision situation in which a person ("the paternalist") is given the opportunity to intervene in order to prevent another person ("the paternalee") from making a choice that will not reflect his or her true preferences, i.e., prevent the person from making a mistake. In a between-subject design we vary the mode of intervention available to the paternalist: (i) to restrict the paternalee's freedom of choice or (ii) to provide the paternalee with information. We also vary the reason why the paternalee is making the mistake: (i) because they have made a mistake in their own calculations or (ii) because they have been given incorrect information. We fix the paternalists' beliefs about the effectiveness of the interventions, with both interventions being fully effective. We conduct an online experiment in which n = 8000 paternalists, recruited from the general population of the U.S., make real decisions for paternalees recruited through an online labor market.

In a second step, we try to disentangle the motives of freedom of choice and resentment by using an additional treatment with n = 6000 paternalist, again recruited from the general U.S. population.

External Link(s)

Registration Citation

Citation

Bartling, Björn et al. 2022. "Understanding Paternalism." AEA RCT Registry. August 18. https://doi.org/10.1257/rct.4630-2.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2019-08-12

Intervention End Date

2020-01-31

Primary Outcomes

Primary Outcomes (end points)

We measure the decision of whether participants in our experiment (the paternalists) decide to intervene into the choice environment of a real online worker (the paternalee) or not.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

In a between-subject design we vary the mode of intervention available to the paternalist: (i) to restrict the paternalee's freedom of choice or (ii) to provide the paternalee with information. We also vary the reason why the paternalee is making the mistake: (i) because they have made a mistake in their own calculations or (ii) because they have been given incorrect information. We fix the paternalists' beliefs about the effectiveness of the interventions, with both interventions being fully effective.

Experimental Design Details

Randomization Method

Software-based randomization (Qualtrics)

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

8000 individuals

Sample size: planned number of observations

8000 individuals

Sample size (or number of clusters) by treatment arms

2000 individuals per treatment arm

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

Sample size was determined in order to have 80% power to detect a difference of 5 percentage points with a baseline intervention rate of 70% (or 30%) and alpha = .01. Necessary sample size per treatment cell amounts to n = 2049 (power analysis conducted using Stata 15 SE).

Supporting Documents and Materials

IRB