Reducing fraud through pre- or post- control

Last registered on March 22, 2021


Trial Information

General Information

Reducing fraud through pre- or post- control
Initial registration date
January 13, 2021

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 14, 2021, 11:57 AM EST

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

Last updated
March 22, 2021, 10:13 AM EDT

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


There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information

Primary Investigator

The Hotel School, Cornell SC Johnson College of Business, Cornell University

Other Primary Investigator(s)

PI Affiliation
Arizona State University

Additional Trial Information

In development
Start date
End date
Secondary IDs
We are testing in the field the relative efficacy of controlling the costs of a job with an ex-ante third party assessment of the cost of the job versus an ex-post revision of estimates that seem inflated.
External Link(s)

Registration Citation

Casas-Arce, Pablo and Francisco de Asis Martinez Jerez. 2021. "Reducing fraud through pre- or post- control." AEA RCT Registry. March 22.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Repair estimate by the professional
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Time to perform the repair
Secondary Outcomes (explanation)
Time elapsed from the time the customer calls to notify the damage until the repair is finalized and accepted by the customer.

Experimental Design

Experimental Design
In collaboration with a leading French business process outsourcer of repairs for property and casualty insurance we engineered a field
experiment by randomizing whether the professional executing the repair received an ex-ante estimate of the cost of the repair or not.
Experimental Design Details
Not available
Randomization Method
Claims arrive randomly to the phone bank of the company.
We assigned them to treatments in two stages: first, by day of arrival and, second, by repair ID.
(1) Because of limitations in the operating system of the company we had to assign all the repairs of one day either to the control group or to the treatment groups. We flipped a coin to decide on the assignment for the first day. Then we switch daily between treatment and control so there was an even distribution among both groups in terms of day of the week, beginning and end of the month, and seasonality.
(2) All repairs were assigned an ID number. The days of treatment repairs were assigned to treatment a or b as a function of whether the repair ID number was even or odd.
Randomization Unit
The randomization unit was the repair
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Approximately 1,800 repairs
Sample size: planned number of observations
Approximately 1,800 repairs
Sample size (or number of clusters) by treatment arms
Approximately 900 control, 450 treatment a, and 450 treatment b
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
10% differences in estimates. Disguised numbers $400 average repair, $240 standard deviation, $40 minimum detectable effect at 5%

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number