Do Matching Contributions incentivize informal workers to participate in retirement saving plans? A Randomized Evaluation in Peru

Last registered on June 04, 2020

Pre-Trial

Trial Information

General Information

Title
Do Matching Contributions incentivize informal workers to participate in retirement saving plans? A Randomized Evaluation in Peru
RCT ID
AEARCTR-0003395
Initial registration date
November 18, 2019
Last updated
June 04, 2020, 3:26 PM EDT

Locations

Region

Primary Investigator

Affiliation
Universidad del Pacífico

Other Primary Investigator(s)

PI Affiliation
Universidad del Pacífico
PI Affiliation
University of Maryland

Additional Trial Information

Status
On going
Start date
2017-12-05
End date
2021-03-31
Secondary IDs
Abstract
This field experiment analyzes whether a Matching Defined Contribution (MDC) scheme is an effective incentive to increase enrollment and contributions in retirement saving plans. Our intervention involves transferring funds into workers’ pension funds, conditional on their own contributions. The research proposes three pension plans: 100%, 50%, and 0% MDC (subject to a maximum amount) for six months. These plans are offered to informal workers from micro firms located in the city of Lima, Peru, who were not previously enrolled in the pension system.
External Link(s)

Registration Citation

Citation
Bernal, Noelia, Sebastian Galiani and Oswaldo Molina. 2020. "Do Matching Contributions incentivize informal workers to participate in retirement saving plans? A Randomized Evaluation in Peru ." AEA RCT Registry. June 04. https://doi.org/10.1257/rct.3395-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The MDC scheme is applied between three different groups of workers, two of them receive a monthly matching incentive of 100% (full match) or 50% (partial match), and a third one, which is the control group, receives 0% (no match). We match 100% of the contribution or 50% of it up to a monthly maximum amount of USD 42 and six months after enrollment. All groups receive detailed information about the importance of saving for retirement, the peruvian pension system and the benefits of enrolling to the Private Pension System. Since all three groups receive the same kind of information, we are able to estimate the effect of the matching incentive.
Intervention Start Date
2018-04-24
Intervention End Date
2020-09-14

Primary Outcomes

Primary Outcomes (end points)
1) Enrollment to the Pension Fund
2) Contribution to the Pension Fund.
Primary Outcomes (explanation)
1) Enrollment: This is a dummy variable which indicates whether the worker was enrolled to the pension fund. This enrollment can occur right after the sales agent gives the worker the information and explains the saving plan and incentives, or later.
2) Contribution: We have two definitions for this outcome. The first is a dummy variable that indicates if the worker contributed at least once during the treatment period, while the other is a continuous variable indicating the total amount of contributions made by the worker.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our unit of analysis are the workers from the micro firms located in Lima. The unit of randomization is the micro firm, since it was the only mean we could use to reach the worker. These firms need to be registered in two administrative databases. After visiting the firms, we found that an important number of them could not be located or were closed, so we excluded them from the analysis. We also had to keep only workers who were not previously enrolled in the pension system. Therefore, our final sample comprises 3,038 workers from 2,770 micro firms and it is representative of the informal workers from micro firms located in Lima, who were not previously enrolled in any pension system. Treatment assignment is as follows: 912 workers in the control group, 1,063 in the 50% matching group and 1,063 in the 100% matching group.
Experimental Design Details
We applied some criteria of selection to our administrative database of micro firms before randomizing. The unit of randomization is the firm, since there is a very high probability of contamination between workers from the same firm. The randomization method involves dividing the sample into groups (strata) of similar observable characteristics, and then randomize observations preserving the similarities within observations from the same strata. Micro firms were stratified by sales level, geographic location and economic sector.

29,251 micro firms were selected from the process of randomization. We verified the addresses of these firms and visited them. An important number were excluded from the experiment due to different reasons (non-located, closed, etc.). We kept the 6,504 micro firms which could effectively be located and surveyed. In spite of the fact that we collect information on 7,789 workers from the baseline survey, not all the individuals were part of the design of the experiment. Our design focuses on exploring the effects of MDC on enrollment and saving of workers who do not belong to any pension system. Therefore, we had to filter 2,783 workers already affiliated to a pension system and keep only 5,006 workers to visit and offer to be part of the experiment.

From the 5,006 workers, 1,968 did not agree to participate in the experiment after being randomized to the arms of the study, but before they knew their treatment status. Since this happens before treatment status was communicated, we also exclude these individuals from the analysis. Our final sample comprises 3,038 workers from 2,770 micro firms and it is representative of the informal workers from micro firms located in Lima, who were not previously enrolled in any pension system.
Randomization Method
Stratified Randomization done in office by a computer.
Randomization Unit
Firm
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
2,770 micro firms
Sample size: planned number of observations
3,038 workers
Sample size (or number of clusters) by treatment arms
835 micro firms control, and 968 micro firms in the 50% matching group and 967 micro firms in the 100% matching group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We expect that our intervention will have a Minimum Detectable Effect of 0.03612 percentage points on the probability to contribute. This MDE is associated with an overall percentage variance of 14% between treatment and control groups. We assume a significance level of 0.05 and a power of 0.8.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Human Subjects Committee for Innovations for Poverty Action IRB
IRB Approval Date
2017-11-21
IRB Approval Number
14370
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan Matching Contributions

MD5: 93d3ac0a8fbb55ead7700ea29507c4e1

SHA1: effdb5ff0943b3a9466d558d569915d3a40d6d3a

Uploaded At: June 03, 2020

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