Financial technology and behavioral barriers to saving for retirement

Last registered on October 05, 2021

Pre-Trial

Trial Information

General Information

Title
Financial technology and behavioral barriers to saving for retirement
RCT ID
AEARCTR-0008316
Initial registration date
October 01, 2021
Last updated
October 05, 2021, 11:27 AM EDT

Locations

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Primary Investigator

Affiliation
ETH Zurich

Other Primary Investigator(s)

PI Affiliation
ETH Zurich
PI Affiliation
ETH Zurich

Additional Trial Information

Status
In development
Start date
2021-10-04
End date
2022-08-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
A large Swiss pension fund is introducing a new digital pension application to help its insureds plan for retirement. The platform allows individuals to access personal information related to their occupational retirement account. Moreover, the digital pension application allows the insureds to initiate voluntary contributions to their retirement saving plan. In collaboration with the pension fund, we randomize different invitation letters across insureds to test (i) which aspects of the new platform are most important for the insureds and (ii) which behavioral barriers are relevant in the context of adopting and utelizing the digital pension application. We explore whether the different invitation letters affect the probability that individuals access the online platform, its usage, and retirement contribution behavior.
External Link(s)

Registration Citation

Citation
Daminato, Claudio, Massimo Filippini and Fabio Haufler. 2021. "Financial technology and behavioral barriers to saving for retirement." AEA RCT Registry. October 05. https://doi.org/10.1257/rct.8316-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-10-04
Intervention End Date
2021-12-31

Primary Outcomes

Primary Outcomes (end points)
Key outcome variables are the insureds' registration status on the portal, user behavior on the portal, and voluntary retirement contribution behavior.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Individuals insured with a pension fund in Switzerland will gain access for the first time to a newly introduced digital pension application. The pension fund sends different invitation letters (via e-mail) to all of its insureds in several consecutive waves. Each individual receives the invitation in its preferred language (German, French, Italian or English). The treatment status (corresponding to different letters) is randomized at the individual level for the six largest firms / institutions insured with the pension fund and at the firm level for the remaining firms / institutions. The design allows to analyse potential information spill-over effects across individuals within the same firm / institution. Individuals working in the same firm receive the invitation letter at the same time. We randomize the timing of the intervention across firms / institutions. The empirical analysis will be carried out using registratoin data from the digital application and administrative pension fund data.
Experimental Design Details
Not available
Randomization Method
Randomization done by a computer with the program STATA
Randomization Unit
Individual level randomization: Individuals working for one of the 6 largest firms in the sample
Group level randomization: Remaining 152 firms in the sample
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Clusters: 152 distinct firms / institutions
Sample size: planned number of observations
Individual assignment: 32'803 individuals plus 2'724 individuals in last wave Clustered assignment: 26'264 individuals in 152 clusters (firms) plus 1'664 individuals in last wave
Sample size (or number of clusters) by treatment arms
10 treatment arms with randomization on the individual level: treatment 1: 3210, treatment 2: 3336, treatment 3: 3335, treatment 4: 3372, treatment 5: 3214, treatment 6: 3315, treatment 7: 3388, treatment 8: 3330, treatment 9: 3438, treatment 10 (only women): 2865

4 treatment arms with clustered randomization on firm level: each treatment arm includes 38 firms / institutions.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number