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Personalizing Information to Improve Pension Savings
Last registered on May 04, 2017

Pre-Trial

Trial Information
General Information
Title
Personalizing Information to Improve Pension Savings
RCT ID
AEARCTR-0000367
Initial registration date
May 07, 2014
Last updated
May 04, 2017 9:37 AM EDT
Location(s)
Primary Investigator
Affiliation
Pontificia Universidad Catolica de Chile
Other Primary Investigator(s)
PI Affiliation
Superintendencia de Pensiones
PI Affiliation
Pontificia Universidad Catolica de Chile
PI Affiliation
Pontificia Universidad Catolica de Chile
PI Affiliation
Universidad Adolfo Ibanez
Additional Trial Information
Status
Completed
Start date
2014-06-15
End date
2016-02-28
Secondary IDs
Abstract
This study explores how providing simulations to poor individual in a defined contribution setting (Chile) that identify the impact of formalizing their employment or delaying their retirement age may improve their pension saving and, through that, their life-time welfare. Individuals who approach a self-service module in government offices that many low-income individuals need to visit regularly to obtain benefits will be randomly allocated to receiving some personalized simulations regarding how some change in contribution behavior can affect their potential pension wealth while others will simply be given the generic information about how one can improve their pension wealth. We will study the impact of the provision of information on savings accumulated within the pension fund through administrative data provided by the pension funds supervising agency over the following 18 months, which will also provide information on labor supplied in the formal market. We postulate that most low-income individuals do not realize the impact that non-formal work may have on their eventual pension wealth and that our pension simulator will give them the information necessary to make them gain a better understanding. We further hypothesize that this new knowledge will be used to alter some labor supply decisions of the individuals.
External Link(s)
Registration Citation
Citation
Fuentes, Olga et al. 2017. "Personalizing Information to Improve Pension Savings." AEA RCT Registry. May 04. https://doi.org/10.1257/rct.367-4.0.
Former Citation
Fuentes, Olga et al. 2017. "Personalizing Information to Improve Pension Savings." AEA RCT Registry. May 04. https://www.socialscienceregistry.org/trials/367/history/17285.
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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2014-06-15
Intervention End Date
2014-12-15
Primary Outcomes
Primary Outcomes (end points)
Pension wealth, labor supply in formal market, income, perception of pension funds, financial knowledge
Primary Outcomes (explanation)
Our main hypothesis is that upon improving their knowledge, the individuals will change their labor supply and savings behavior in response to the new information they have received. These will be our final outcomes, which we will be able to measure directly using the frequency and amount of contributions to the pension funds, as measured by the administrative data of the Superintendencia de Pensiones. In particular, we will measure increase in formalization of employment (through which one starts making contributions to the system) and delayed retirement age.

Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
While one group will receive publicly available, generic information on how to improve pensions (control group), a second group will receive a simulation session in which they will be able to estimate the effect of changes in their contribution behavior on their expected pensions, based on their actual saving balances and contributions (treatment group).
Experimental Design Details
Randomization Method
Individuals will be assigned to treatment or control group through an algorithm based on their national identification number (RUT), which they will be asked to provide at the beginning of the session.
Randomization Unit
Individual level
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
At least 2,200
Sample size: planned number of observations
At least 2,200
Sample size (or number of clusters) by treatment arms
At least 1,100
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
0.1 s.d.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
February 28, 2015, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
February 28, 2016, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
N/A
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
2,604 individuals participated in the intervention from August 2014 to February 2015 (with 92.7% affiliated with the system by the time that the intervention was conducted)
Final Sample Size (or Number of Clusters) by Treatment Arms
Not specified
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
No
Reports and Papers
Preliminary Reports
Relevant Papers
Abstract
We randomly offer to workers in Chile personalized versus generalized information about
their pension savings and forecasted pension income. Personalized information increased the
probability and amounts of voluntary contributions after one year without crowding-out other
forms of savings. Personalization appears to be very important: individuals who overestimated
their pension at the time of the intervention saved more. Thus, a person’s inability to
understand how the pension system affects them may partially explain low pension savings.
Despite the significant response to the intervention, its temporary nature and size suggest that
information should be combined with other elements to increase its efficiency.
Citation
January 2017