Behavioral Biases and Annuity Choice

Last registered on April 27, 2023

Pre-Trial

Trial Information

General Information

Title
Behavioral Biases and Annuity Choice
RCT ID
AEARCTR-0006903
Initial registration date
December 16, 2020

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
December 17, 2020, 10:35 AM EST

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

Last updated
April 27, 2023, 12:06 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
UC Berkeley Economics

Other Primary Investigator(s)

PI Affiliation
Dartmouth College
PI Affiliation
UC Berkeley

Additional Trial Information

Status
Completed
Start date
2021-01-01
End date
2021-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The design investigates three mechanisms that can lead to lower-than-optimal take-up of annuities: status quo bias, failure to think through savings choices associated with annuity decisions, and a heuristic aversion to allocating income to states of the world in which there is both high marginal and high absolute utility from money. We study these through a controlled online experiment run on the AmeriSpeak panel.
External Link(s)

Registration Citation

Citation
de Oliveira , Priscila, Erzo Luttmer and Dmitry Taubinsky. 2023. "Behavioral Biases and Annuity Choice." AEA RCT Registry. April 27. https://doi.org/10.1257/rct.6903-1.4
Experimental Details

Interventions

Intervention(s)
To study status-quo bias, we introduce a treatment that removes the status quo of not owning an annuity. Unlike the control group, where participants have an endowment without an annuity and can choose to “buy” an annuity, in the no-status-quo group the annuity choice is presented using a neutral framing (people choose between taking an annuity or not, without either choice being the default).

Within the no-status-quo framing, we study four treatments that address people’s potential failures to think through how they would condition their savings choices on their choice of acquiring an annuity. In the first treatment, respondents first choose their level of savings in each of the two contingencies—having an annuity and not having annuity—and then they make the decision of whether to acquire an annuity or not. In other words, the first treatment encourages respondents to think through the dynamic decision using backwards induction. Because this treatment highlights how savings choices might optimally vary with the annuity decision, we call this the “savings salient” treatment.

The second treatment eliminates the need to solve the problem using backwards induction by showing the corresponding level of savings, as previously chosen by the respondent. We refer to this treatment as “explicit contingencies” because all consequences of choosing the annuity or not are fully explicit in this treatment. The third treatment is nearly identical, but presents the same choice without context—i.e., without discussing “savings”, “income,” or “annuities.” In other words, the choice is presented solely in terms of tokens corresponding to each of the two choices. We refer to this treatment as “explicit contingencies, no context.” The fourth treatment is like the third but ensures that the annuity stochastically dominates by adjusting the savings decision for the annuity such that the resulting tokens in stage 2 are identical with and without the annuity (and the resulting tokens in stage 1 are higher for the annuity).

Finally, we study a potential heuristic aversion to allocating income to states of the world in which there is both high marginal and high absolute utility from money. We do this by constructing a nearly identical setting where the impact of annuitization and feasible savings levels are identical, but where the state of the world in which the “annuity” pays off is actually the state with a lower absolute level of utility. We do this by reframing people’s decisions as being about insuring the loss of stage-2 income. Our hypothesis is that people find it more natural to insure states of the world with lower absolute utility than with high absolute utility. Because this treatment reverses the correlation between marginal utility and absolute utility relative to the standard positive relationship in the annuity context, we call this the “reverse correlation” treatment.
Intervention (Hidden)
Intervention Start Date
2021-01-01
Intervention End Date
2021-04-30

Primary Outcomes

Primary Outcomes (end points)
Take-up of the annuity option (or its equivalent when framed as insurance or Social Security)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Experimental savings decisions
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To summarize the interventions listed above, we have a total of 9 experimental groups.


• The control group (“G0”), which is constructed to best resemble the conditions of annuity choice that people typically face. These annuities are worse than actuarially fair, the status quo is not owning an annuity, respondents are not induced to think about savings nor are the reminded of their savings plans for each contingency, and the annuity choice uses natural wording such as “annuities”, “insurance” or “Social Security.”

Five treatments investigate the three mechanisms of primary interest:
• One treatment group which is like the control group but where the status quo of not owning an annuity is removed (“G1”)
• Four treatments that increase proper accounting for contingent savings decisions, leading to the following groups:
o Savings salient group (“G2”)
o Explicit contingencies group (“G3”)
o Explicit contingencies, No context, group (“G4”)
o Explicit contingencies, No context, Dominance, group (“G5”)
• The reverse correlation treatment group (“G10”)

Two treatment groups to help us gauge the magnitude of the response:
• The low-price group (“G20”)
• A combined treatment group (“G35”), which combines a low price, the treatment in G5, and reverses the correlation as in G10.

We randomize subjects such that the expected number of observations in each of these 9 groups is expected to be equal.

Each respondent makes 6 decisions, and the decisions are divided into three blocks. The order of the blocks varies across respondents.

Savings block (3 decisions):
• The desired amount of savings if the respondent does not have an annuity
• The desired amount of savings if the respondent has a better-than-fair annuity (low price)
• The desired amount of savings if the respondent has a worse-than-fair annuity (high price)

Regular annuity decisions (2 decisions):
• Annuity choice if the annuity has a low price (i.e., is better than fair)
• Annuity choice if the annuity has a high price (i.e., is worse than fair)

One of the three “Explicit contingencies” decisions:
• Annuity choice if the contingencies are made for the treatment (i) explicit contingencies, (ii) explicit contingencies, no context, or (iii) explicit contingencies, no context, dominance. Because contingencies can only be made explicit if the respondent has already made their savings choices, this block necessarily comes after the savings block.


The primary randomization allocates annuity decisions (not respondents, because respondents each make three annuity decisions) into one of the 9 experimental groups described above. Decisions are randomized into these 9 groups with equal probability.

All decisions of a given respondent are either randomized into “regular correlation” groups (in columns A or C of Figure 1 in the uploaded "Full summary of experimental design" ) or into “reverse correlation” groups (in columns B or D of Figure 1 of Figure 1 in the uploaded experimental design summary).

There are five secondary randomizations:
• Wording. In the explanation of the experiment and in the some of the annuity decisions (the ones with context), the annuity choice is described either in terms of “annuities,” “insurance,” or “Social Security.” One of these three wordings is selected with probability 1/3 for each of the respondents whose annuity decisions are randomized into “regular correlation” groups. Only the insurance wording is used for respondents whose annuity decisions are randomized into “reverse correlation” groups (the other two wordings would be unnatural).
• When there is no status quo, we randomize (with equal probability) which option is presented on the left or the right.
• If the savings block is asked first, the order of the two annuity blocks is randomized.
• The order of the 3 savings decisions in the savings block is randomized
• The order of the two annuity decisions in the regular annuity decision block is randomized
Experimental Design Details
Randomization Method
Pseudo-random number generator on office computer.
Randomization Unit
Our treatments are randomized both between and within participants.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
3000 participants
Sample size: planned number of observations
3,000 participants, at 3 annuity and 3 savings decisions per participant, meaning 9,000 annuity decisions and 9,000 savings decisions.
Sample size (or number of clusters) by treatment arms
We randomize subjects such that the expected number of observations in each of these 9 cells described above is expected to be equal.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Full summary of experimental design
Document Type
other
Document Description
This document contains the complete summary of the experimental design. The document overlaps with some of the specific experimental details questions, but given some of the complexity of our design this single document may present an easier-to-read summary.
File
Full summary of experimental design

MD5: 818b72d49cdf7522d425d543a35a0476

SHA1: 1d92138c012f1ee092c99c47f09c79b2b843ad3c

Uploaded At: December 16, 2020

IRB

Institutional Review Boards (IRBs)

IRB Name
UC Berkeley Committee for Protection of Human Subjects
IRB Approval Date
2020-01-14
IRB Approval Number
2019-12-12779
IRB Name
Dartmouth College Committee for the Protection of Human Subjects
IRB Approval Date
2019-12-06
IRB Approval Number
STUDY00031949
Analysis Plan

Analysis Plan Documents

Analysis+plan.pdf

MD5: 851a10598ffd8933b387c4299ad33df8

SHA1: 68f6d7b2bdd5d3add0e0eabb2817da67b8efd7c1

Uploaded At: December 16, 2020

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
March 04, 2021, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
March 04, 2021, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
3038
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials