PUBLIC GOODS PROVISION, WELFARE POLICIES, AND GOVERNMENT SIZE

Last registered on April 10, 2022

View Trial History

Pre-Trial

Trial Information

General Information

Title

PUBLIC GOODS PROVISION, WELFARE POLICIES, AND GOVERNMENT SIZE

RCT ID

AEARCTR-0008995

Initial registration date

February 18, 2022

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 22, 2022, 1:32 PM EST

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

Last updated

April 10, 2022, 9:39 PM 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

Daiki Kishishita

Affiliation

Tokyo University of Science

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Tomoko Matsumoto

PI Affiliation

Tokyo University of Science

Contact Investigator

PI Name

Atsushi Yamagishi

PI Affiliation

Princeton University

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2022-02-18

End date

2022-02-28

Keywords

Electoral, Welfare

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

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

Abstract

This study examines how people's perceptions on the governmental roles of providing public goods and providing welfare policies affect their policy preferences for the government size. To this end, we conduct an online survey experiment where either the information regarding the governmental role of providing public goods or the information regarding the governmental role of helping the poor is randomly assigned.

External Link(s)

Registration Citation

Citation

Kishishita, Daiki, Tomoko Matsumoto and Atsushi Yamagishi. 2022. "PUBLIC GOODS PROVISION, WELFARE POLICIES, AND GOVERNMENT SIZE." AEA RCT Registry. April 10. https://doi.org/10.1257/rct.8995-1.1

Sponsors & Partners

Interventions

Intervention(s)

We randomly provide either information that emphasizes the governmental role of providing public goods or information that emphasizes the governmental role of helping the poor.

Intervention Start Date

2022-02-18

Intervention End Date

2022-02-28

Primary Outcomes

Primary Outcomes (end points)

Policy preferences for taxation and public spending

Primary Outcomes (explanation)

Policy preferences for taxation and public spending. Outcomes include preferences for the government size (e.g., whether one agrees to increase tax rates), preferences for the desirable tax system (e.g., progressive vs. uniform vs. lump-sum), and preferences for public spending (e.g., whether it should be used for helping the poor).

Secondary Outcomes

Secondary Outcomes (end points)

Manipulation check: the perception on the governmental role of providing public goods/helping the poor.
Another outcome: level of political trust

Secondary Outcomes (explanation)

Experimental Design

We conduct an online survey experiment where the information emphasizing the governmental role of providing public goods/helping the poor is randomly assigned.

Experimental Design Details

We conduct an online survey experiment where the information emphasizing the governmental role of providing public goods/helping the poor is randomly assigned. We look at the treatment effect of this information on preferences for taxation and providing public goods.
Specifically, we have four arms:
1. Treatment group who receives the information emphasizing the governmental role of providing public goods
2. Control group for the comparison with 1
3. Treatment group who receives the information emphasizing the governmental role of helping the poor
4. Control group for the comparison with 3

Randomization Method

Randomization is done by a computer.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

one country

Sample size: planned number of observations

2000

Sample size (or number of clusters) by treatment arms

500 individuals for each arm.

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

Significance level: 5%, Power: 80% For a binary outcome where the average of the control is 0.5, the minimum detectable effect size is around 0.1 (10%).

Supporting Documents and Materials

IRB