Randomized Field Experiment on Determinants of Self-employed Worker's Participation in Public Pension

Last registered on May 01, 2018

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Pre-Trial

Trial Information

General Information

Title

Randomized Field Experiment on Determinants of Self-employed Worker's Participation in Public Pension

RCT ID

AEARCTR-0002944

Initial registration date

April 28, 2018

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

May 01, 2018, 10:24 AM EDT

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

Locations

Country

Mongolia

Region

East Asia

Primary Investigator

Name

Tomoaki Tanaka

Affiliation

Japan International Cooperation Agency (JICA)

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Other Primary Investigator(s)

PI Name

Yasuyuki Sawada

PI Affiliation

Asian Development Bank

Contact Investigator

PI Name

Junichi Yamasaki

PI Affiliation

Kobe University

Contact Investigator

Additional Trial Information

Status

On going

Start date

2016-04-11

End date

2021-06-30

Keywords

Finance & Microfinance, Welfare

Additional Keywords

Public pension

JEL code(s)

H55, D83, I38

Secondary IDs

Abstract

This study aims to investigate the reasons why the take-up rate of the public pension for self-employed workers such as herders in Mongolia is low and find effective policies to increase the take-up rate. We distribute different types of leaflets to local community (bagh) meeting participants with conducting a questionnaire survey to study their impact on take-up rate. We target 690 baghs in eight provinces, stratified at the province level and herder's population share within provinces. We will analyze the impact on take-up and contribution using administrative records.

External Link(s)

Registration Citation

Citation

Sawada, Yasuyuki, Tomoaki Tanaka and Junichi Yamasaki. 2018. "Randomized Field Experiment on Determinants of Self-employed Worker's Participation in Public Pension." AEA RCT Registry. May 01. https://doi.org/10.1257/rct.2944-1.0

Former Citation

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Experimental Details

Interventions

Intervention(s)

We randomly distribute different kinds of explanatory material about the social insurance. In collaboration with the Social Insurance General Office of Mongolia, we create four sets of leaflets. All of the leaflets include general information about the public pension, along with the following additional information:
(Control) General information about the public pension only,
(Treatment 1) Survivors and disability pension, supplementary to the public pension,
(Treatment 2) Japanese official assistance for the public pension administration for consolidating the trust, and
(Treatment 3) Mobile banking payment for reducing the transaction cost.

Intervention Start Date

2017-03-27

Intervention End Date

2017-10-06

Primary Outcomes

Primary Outcomes (end points)

Our main outcomes are (1) the voluntary social insurance take-up, (2) the payment of the social insurance contributions based on administrative records.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We distribute different types of leaflets to local community (bagh) meeting participants with conducting a questionnaire survey to study which information can increase take-up. We target 690 baghs in eight provinces, stratified at the province level and herders' population share within provinces from March to October in 2017. When distributing the leaflets, we conduct a survey at the same time to collect basic information such as age and time and risk preferences. We will analyze the impact on take-up and contribution using administrative records covering the data before and after the distribution, which will be available in May/2018.

Experimental Design Details

Randomization Method

We conducted randomization in office by a computer.

Randomization Unit

The randomization was conducted at the local community level.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

690 local communities

Sample size: planned number of observations

Approximately 38,000 individuals

Sample size (or number of clusters) by treatment arms

173 local communities control (general information only), 168 local communities treatment 1 (survivors and disability pension), 170 local communities treatment 2 (Japanese official assistance), 179 local communities treatment 3 (mobile banking)

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

Supporting Documents and Materials

IRB