Social Security and Redistribution

Last registered on November 17, 2022

Pre-Trial

Trial Information

General Information

Title
Social Security and Redistribution
RCT ID
AEARCTR-0010406
Initial registration date
November 14, 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
November 17, 2022, 3:48 PM EST

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

Locations

Region

Primary Investigator

Affiliation
Tokyo University of Science

Other Primary Investigator(s)

PI Affiliation
Tokyo University of Science

Additional Trial Information

Status
In development
Start date
2022-11-16
End date
2022-11-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Previous literature has examined the relationship between people's future risk and their support for redistribution. However, people's risk
varies according to their incomes, jobs, and family background. Hence, these discussion faces the difficulty to increase universal support regardless of people's socioeconomic background. One way to gain wide support for redistribution is to let people realize their own benefits from government policies regardless of their socioeconomic positions. Some welfare goods are given to the whole people although many people do not realize the fact. For example, although currently youth and middle-age people do not receive benefits from pension and medicare programs, all of them will gain benefits from the program when they become elder in the end.
Motivated by this background, this study aims to answer the following question: Does realizing such future benefits increase support for the social security system? Even though realizing their own future benefits, some may concern that they may not be able to receive benefits due to the government's bad financial management. Hence, we also examine the effect of fiscal unsustainability.
External Link(s)

Registration Citation

Citation
Kishishita, Daiki and Tomoko Matsumoto. 2022. "Social Security and Redistribution." AEA RCT Registry. November 17. https://doi.org/10.1257/rct.10406-1.0
Experimental Details

Interventions

Intervention(s)
We randomly provide information that emphasizes the benefit of social security programs.
Intervention Start Date
2022-11-16
Intervention End Date
2022-11-30

Primary Outcomes

Primary Outcomes (end points)
In the survey, we ask (i) preferences for the health insurance system (e.g.,whether one agrees to increase tax rates to maintain the health insurance system) and (ii) how much one wants to rely on the private health insurance. These are the main outcomes of our study.
Primary Outcomes (explanation)
In the survey, we ask (i) preferences for the health insurance system (e.g.,whether one agrees to increase tax rates to maintain the health insurance system) and (ii) how much one wants to rely on the private health insurance. These are the main outcomes of our study.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conduct an online survey experiment where the information about the number of benefits the elderly receive from the Japanese medical insurance system is randomly assigned.
Experimental Design Details
We conduct an online survey experiment where the information about the number of benefits the elderly receive from the Japanese medical insurance system is randomly assigned. The aim of this study is to identify the effect of our treatment on policy preferences for the social security system, especially the health insurance system. Our focus is the effect of correcting the underestimation about the benefits. For this purpose, we focus on the respondents who initially underestimated the number of benefits. That is, we focus on those whose estimation is lower than the true value.

Randomization Method
Randomization is done by a computer.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
one country
Sample size: planned number of observations
3,000 individuals
Sample size (or number of clusters) by treatment arms
For each arm, 500 individuals (we have six arms).
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 0.55, the minimum detectable effect size is around 0.8 (8%).
IRB

Institutional Review Boards (IRBs)

IRB Name
Tokyo University of Science
IRB Approval Date
2022-10-14
IRB Approval Number
22026
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials