Does risk aversion prevent new product adoption?

Last registered on November 15, 2024
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Pre-Trial

Trial Information

General Information

Title
Does risk aversion prevent new product adoption?
RCT ID
AEARCTR-0014812
Initial registration date
November 14, 2024

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
November 15, 2024, 1:55 PM EST

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

Locations

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Primary Investigator

Name
Grady Killeen
Affiliation
UC Berkeley
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2024-11-15
End date
2026-01-01
Keywords
Firms & Productivity
Additional Keywords
Risk aversion
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Does risk aversion prevent firms from adopting new products? This study offers firms new motorcycle helmets with or without an insurance contract and examines whether insurance increases uptake. Treated shops are offered the ability to choose between a guaranteed future cash payment or a larger payment that pays out only if the shops struggles to sell helmets, calibrated so that the value of the contracts is similar in expectation. The control group receives the guaranteed payment. I test for risk aversion by examine whether being offered the insurance contract increases adoption. In a secondary component, the markets entered at baseline are randomized to test for information externalities at a follow-up by examining whether shops in markets with an entrant are more willing to purchase helmet stock.
External Link(s)

Registration Citation

Citation
Killeen, Grady. 2024. "Does risk aversion prevent new product adoption? ." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.14812-1.0
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Experimental Details

Interventions

Intervention(s)
To test for risk aversion, we allow treated shops to choose between the following interventions:
The shop may opt to receive a guaranteed payment of p*X + e at the follow-up, regardless of whether they stock helmets, where p is the probability of failing to sell out a batch of helmets by midline if they opt to stock them. Or an insurance offer that pays X if the shops stocks helmets and fails to sell out, and 0 otherwise. The insurance offer is slightly less valuable in expectation, but hedges risk. Control shops receive the payment of p*X + e.

Second, I randomize which markets are entered at baseline, approaching 5 shops in each. This is designed to induce entrants to test for information spillovers at the follow-up. I plan to survey 5 new shops in treatment and control markets during the follow-up and offer them a batch of helmets to see if shops in treated markets are more willing to stock helmets.
Intervention Start Date
2024-11-15
Intervention End Date
2025-04-30

Primary Outcomes

Primary Outcomes (end points)
I test for risk aversion by examining whether baseline uptake of helmets is higher in the treatment group. I test for information externalities by examining whether take-up of helmets by shops not surveyed at baseline but surveyed at the follow-up is higher in treated versus control markets.
Primary Outcomes (explanation)
The control offer is essentially a mean preserving spread of the insurance offer, so this design offers a test of whether risk aversion prevents firm uptake of new products.

Information externalities are tested based on the fact that control markets are less likely to have entrants that new shops can learn from.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Markets will be assigned to treatment or control with equal probability, stratified by County. There will be about 7-8 treated markets per County, with 9 western Counties surveyed. 5 shops will be surveyed at baseline in each treated market. We aim to have 70 treatment markets.

The randomization of the insurance contract will be done at the firm level, with equal probability of being assigned to treatment or control stratified by market. I will randomize if markets have 2 or 3 treated firms, stratifying this design by county. I aim to sample 350 shops.
Experimental Design Details
Not available
Randomization Method
Computer
Randomization Unit
Firm for the risk aversion offer; market for the test of information externalities.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
140 markets (70 treated); 350 firms for test of risk aversion; 700 firms for test of information spillovers
Sample size: planned number of observations
140 markets (70 treated); 350 firms for test of risk aversion; 700 firms for test of information spillovers
Sample size (or number of clusters) by treatment arms
70 treated markets; 175 treated firms for risk aversion; 350 firms for spillovers in treated markets
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
UC Berkeley Committee for Protection of Human Subjects
IRB Approval Date
2024-10-30
IRB Approval Number
2023-03-16170
Analysis Plan