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Prepaid Auto Insurance Contracts and Willingness-to-Pay for Lower Liquidity Requirements
Last registered on July 22, 2019


Trial Information
General Information
Prepaid Auto Insurance Contracts and Willingness-to-Pay for Lower Liquidity Requirements
Initial registration date
December 26, 2017
Last updated
July 22, 2019 3:18 PM EDT

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Primary Investigator
Other Primary Investigator(s)
Additional Trial Information
On going
Start date
End date
Secondary IDs
Despite a universal insurance mandate, 30 million drivers in the United States do not carry the minimum automobile insurance required by law. Traditional contracts pool high and low frequency drivers and require large upfront payments to enroll. High upfront premiums may make these contracts unappealing to low income drivers, who also drive fewer miles on average. We introduce a flexible "prepaid" auto insurance contract designed to increase take-up among uninsured drivers by lowering liquidity requirements and charging drivers an incremental premium per day of driving. We randomize auto insurance contract offers to uninsured drivers in California (where 15% of drivers lack insurance), varying the flexibility of the contract (traditional versus prepaid), the price of coverage, and quantity discounts for longer coverage terms. The design tests the potential of flexible prepaid contracts to increase insurance take-up among uninsured drivers, estimates willingness-to-pay for lower liquidity requirements, and explores potential barriers to insurance take-up.
External Link(s)
Registration Citation
Kluender, Raymond. 2019. "Prepaid Auto Insurance Contracts and Willingness-to-Pay for Lower Liquidity Requirements." AEA RCT Registry. July 22. https://doi.org/10.1257/rct.2537-4.0.
Former Citation
Kluender, Raymond. 2019. "Prepaid Auto Insurance Contracts and Willingness-to-Pay for Lower Liquidity Requirements." AEA RCT Registry. July 22. http://www.socialscienceregistry.org/trials/2537/history/50470.
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Experimental Details
We introduce a novel auto insurance contract into the minimum liability auto insurance market in California. In traditional contracts, drivers pay one lump sum premium which covers all of their driving for the period of coverage (often three or six months). We will introduce a new daily insurance contract with no up front monthly premium which will allow drivers to pay for coverage only on days they are driving. The contract offered, price of coverage, and quantity discounts will be experimentally varied as described in the Experimental Design section.

Insurance coverage can be paused and reactivated using SMS messages. We will additionally passively monitor the driving behavior (driving time and safety) for subsamples of each treatment group using a phone-based safe driving application and on-board diagnostic devices.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
(1) Insurance Take-Up (Binary): Whether an individual accepted the quoted insurance offer and enrolled in the plan (defined as signing the contract and making the initial payment/deposit).

(2) Insurance Enrollment (Positive Integer): The number of days the individual was enrolled in their insurance plan.

(3) Total Amount Spend on Insurance (Continuous): Total spent on insurance during the three month study period.

(4) Activations (Positive Integer): The number of days insurance coverage was active.

Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
(5) Observed Driving Time (Continuous): For individuals in subsamples who install the phone-based application or on-board diagnostic device to passively track their total driving time, we will be able to measure their observed driving time on both days their coverage is active and days coverage is inactive.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will randomly offer drivers applying for minimum liability auto insurance either a traditional contract offer (control) or a daily auto insurance plan. Those assigned to the daily auto insurance plan will be randomly assigned to one of three price tiers (low, medium, high) and a quantity discount (either no discount or discount).

The medium price will be the actuarially fair price. The low and high prices will be 20% deviations from that price (down and up, respectively). The discounts will be 2 "free days" (14% off) if they purchase 14 days and 6 "free days" (20%) off if they purchase 30 days of coverage.
Experimental Design Details
Not available
Randomization Method
We will pre-randomize the initial offer in blocks of 49 applicants. We will additionally pre-randomize within each treatment group of enrolled drivers.
Randomization Unit
We will randomize each individual to a treatment group, blocking the randomization every 49 applicants.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
We will not have separate clusters.
Sample size: planned number of observations
The study sample will be uninsured drivers in California shopping for minimum liability auto insurance coverage. We are targeting 3,000-5,000 applications, which will result in several hundred drivers (depending on take-up rates from the first stage of the study). We will have a pool of funding which we will apply to subject acquisition or providing insurance for individuals who enroll. The budget constraint will bind and we will stop soliciting applications when forecasted outlays exhaust the pool of funding. The number of individuals we can include in the study thus depends on the realizations of the following cost parameters: - Price per lead (this is a market rate, per-click price that will fluctuate) - Conversion rates (the share of individuals directed to our website who apply) - Take-up rates (the share of drivers offered a quote who choose to enroll) - Insurance costs (traditional premium less spending on daily insurance) for those who enroll insurance
Sample size (or number of clusters) by treatment arms
We will allocate drivers uniformly across the treatment groups for the take-up experiment. The number of drivers for the driving outcomes will depend on the take-up rates and the number of drivers assigned to each treatment group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subject
IRB Approval Date
IRB Approval Number