Paternalistic interventions by Certified Financial Planners

Last registered on April 18, 2023


Trial Information

General Information

Paternalistic interventions by Certified Financial Planners
Initial registration date
January 05, 2023

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
January 22, 2023, 10:48 AM EST

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

Last updated
April 18, 2023, 12:32 PM EDT

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



Primary Investigator

Stanford University

Other Primary Investigator(s)

PI Affiliation
Stanford University
PI Affiliation
UBS Center for Economics in Society, University of Zurich
PI Affiliation
University of Zurich

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
What do financial advisors consider good for a client? To what extent do advisors merely provide information and advice, and to what extent do they push the client towards the course of action they recommend, if given the opportunity? Do different advisors give similar advice to the same client, or does advice differ widely? How do these proclivities vary depending on whether the client is black or white, male or female, and financially more or less sophisticated? We answer these questions using an artefactual fi eld experiment in which professional financial advisors give advice and place restrictions on how a real individual may invest a principal of $20,000. We also explore clients' demand for financial advisors by studying to what extent they delegate the investment decision. Finally, how does advisors' supply and clients' demand for advice match?
External Link(s)

Registration Citation

Ambuehl, Sandro et al. 2023. "Paternalistic interventions by Certified Financial Planners." AEA RCT Registry. April 18.
Sponsors & Partners

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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The main outcome variables are:
- Financial Planner's beliefs about how the client will allocate the funds (with and without the planner's advice)
- The level and content of the advice given to the client by the Financial Planner's about how to allocate the funds.
- The surrogate choice the Financial Planner makes for the client when nothing is left for the client's discretion
- The amount left for client's discretion by the Financial Planner
- Level of upper and lower bounds on investments imposed by the Financial Planner's in the decision in which they can leave funds for the client's discretion
- The surrogate choice the Financial Planner makes for the client over the amount that the client delegated to the Financial Planner
- The client's decision about how much to delegate to the Financial Planner.

All of these are for both decisions about risk and decisions about time.
Primary Outcomes (explanation)
Decisions about risk are about the fraction of $20,000 invested in shares of a stock market index fund, with the alternative investment being shares of a bond market index fund.
Decisions about time are about the fraction of $20,000 invested in a systematic withdrawal plan, with the alternative being disbursed in cash.
Advice comes in the form of a point recommendation, lower and upper bound recommendations on the amount allocated to the assets, and an open-ended message.

Secondary Outcomes

Secondary Outcomes (end points)
Risk exposure that the Financial Planners pick for Systematic Withdrawal Plan (yearly installments).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Financial Planners are matched with a client of whom they observe a profile with information. Known to them, only one advisor faces the real client.
Across advisors we vary the following main client characteristics: age, gender, education and race. We also vary the client's job.
The financial planner's decisions will partly determine the form in which the client receives a $20,000 gift. The money may be paid in one of two
forms, determined at random at the end of the study: (a) The Risk-Form: part allocated to a broad Stock Market Index Fund (such as the
Vanguard VTI Total Stock Market Index Fund) , and a part allocated to a diversified bonds fund (such as Vanguard BND Total Bond Market Index Fund); (b) The Timing-Form: a part allocated to an artificial asset akin a Systematic Withdrawal Plan with no early withdrawal (subjects get annual payments for 10 years with no possibility to immediately access the funds), and a part in cash.
Ultimately, the "client" will decide on the fraction of the money invested in stocks vs. bonds (for the Risk Form) and the fraction that is put into the systematic withdrawal plan (for the Timing Form). The planner will have the ability to constrain the client's available options.
Advisors go through 8 scenarios across two environments, risk and time. These is what each of the 4 scenarios in each environment is about:
1) Beliefs about how the client will allocate the funds absent intervention
2) Advice client how to allocate funds & Beliefs about how the client will allocate the funds after observing advice
3) Set lower and upper bounds on how much the client can invest in each fund.
4) invest on behalf of the client (surrogate choice)

We further randomize the order in which the funds appear on the page when advisors make decisions, which also determines whether they give beliefs for how one or the other fund is allocated (with the compleemnt going to the other one).
Experimental Design Details
Randomization Method
Computers do all randomizations. In particular, most randomizations are done in Qualtrics using their embedded "randomization" feature or randomly drawing numbers using Javascript.
To randomly select the participant in the role of client who receives the $20k prize from the sweepstakes we use the second digit after the comma of the end-of-day prices of the Dow Jones Industrial Average index on three consecutive days to create a 3-digit number. We call that number "the chosen number." Each digit of the chosen number corresponds to the second digit after the comma of the end-of-day price of the index for dates 04/03/2023, 04/04/2023, and 04/05/2023, respectively. For example, if the end-of-day prices for the three consecutive dates are $29,634.83, $29,678.30, and $29,655.11, then the chosen number is 301. We assign each participant with a 3-digit nubmer at the beginning of the experiment (which is before the dates above). The winner is the participant whose assigned number is closest to the chosen number. If more than one participant is the closest, we create another chosen number using the price for the 3 business days that precede (e.g., 03/29/2023, 03/30/2023, and 03/31/2023 ), and the participant closest to this number is the one who receives the $20,000 gift. We repeat this procedure until there is only one participant who is the closest to the chosen number.
Randomization Unit
We randomize which Financial Planner gets matched with which participant in the role of client.
We further randomize, for advisors, whether the elicitations are carried out for the Equity fund (e.g., VTI), or for the Bonds fund (e.g., BND). For example, some advisors actively decide how much to allocate to the Equity fund and the remainder is allocated to the Bonds fund, and other advisors actively decide for the Bonds fund and the remainder is allocated to the Equity fund. This also determines the order in which the funds are presented when elicitations require an active decision for both. We carry out the same randomization described for Equity and Bonds fund when eliciting preferences for the time domain, between Cash and the Yearly Installment (Systematic Withdrawal Plan).
We also randomize whether the point recommendation about how to invest the $20k is elicited from Financial Planners before or after eliciting the lower and upper bounds for that allocation.
We randomly select the participant in the role of client who receives the $20k prize from the sweepstakes.
All these randomization are carried out at the individual level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Same as obs
Sample size: planned number of observations
We will email around 34,000 financial advisors in 5 email waves (only following up on those who have not yet answered the survey or who requested no more emails) within a period of 2 months. Any respondents who complete the survey after this period will still be included in the analysis. Our main analysis includes participants who self-report as Certified Financial Planners (CFP) in our survey. If it turns out more than 10% of our sample are participants who are not CFPs, we expect to replicate the analysis, including them as well, as a robustness check. We include in our analysis observations by participants who completed an entire part of the survey; the survey has a part with the decisions about risk and another with decisions about time. If a respondent did not complete a part entirely, we drop that respondent for that part, but if the same respondent did complete the other part in its entirety, we include that respondent's answers for that part they entirely completed. We do not exclude respondents based on missing data regarding their background information (e.g., gender, race, years working as a financial planner). We recruit participants in the role of the client until we get 250 completed surveys. For analysis, we only use those who entered the sweepstakes, for which elicitations are monetarily incentivized. Moreover, the survey they complete has two repeated questions about the participant's characteristics to test their consistency and attention. We disregard participants in the role of client who give different responses in the two elicitations; in particular, we don't match them to a Financial Advisor. We also discard participants who give nonsensical responses (e.g., having worked in their current job for 100 years).
Sample size (or number of clusters) by treatment arms
Randomizations are uniform, so that advisors have same chance of observing each client profile. We may force the randomization to be balanced to get enough power if some treatments (i.e. some client profiles) are relatively too small.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Stanford University IRB
IRB Approval Date
IRB Approval Number
IRB Name
OEC Human Subjects, University of Zurich
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials