The Negative Consequences of Loss-Framed Performance Incentives

Last registered on January 29, 2020
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Pre-Trial

Trial Information

General Information

Title
The Negative Consequences of Loss-Framed Performance Incentives
RCT ID
AEARCTR-0005362
Initial registration date
January 28, 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
January 29, 2020, 1:47 PM EST

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Alex Rees-Jones
Affiliation
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Other Primary Investigator(s)

PI Name
Lamar Pierce
PI Affiliation
Olin Business School
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2017-01-01
End date
2018-01-01
Keywords
Firms & Productivity, Labor
Additional Keywords
loss aversion, field experiments, worker incentives, franchise contracts
JEL code(s)
D03, D81, J22, J31
Secondary IDs
Abstract
Behavioral economists have proposed that loss-averse employees increase productivity when bonuses are "loss framed"---prepaid then clawed back if targets are unmet. We theoretically document that loss framing raises incentives for costly risk mitigation and for inefficient multitasking, potentially leading to large negative performance effects. We empirically document evidence of these concerns in a nationwide field experiment among 294 car dealers. Dealers randomized into loss-framed (but financially identical) contracts sold 5% fewer vehicles than control dealers, generating a revenue loss of $45 million over 4 months. We discuss implications regarding the use of behavioral economics to motivate both employees and firms.
External Link(s)

Registration Citation

Citation
Pierce, Lamar and Alex Rees-Jones. 2020. "The Negative Consequences of Loss-Framed Performance Incentives." AEA RCT Registry. January 29. https://doi.org/10.1257/rct.5362-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Car manufacturers commonly provide bonus payments to car dealers based on the number of cars sold in a month.

The intervention we consider modifies the timing of payment in a manner intended to make participants feel a sense of loss if the bonus is not earned.

Prior to the intervention, as well as in the control arm during the experiment, the number of cars sold was assessed at the end of the month. Bonus payments were made shortly thereafter.

In the intervention, it was assumed that the dealer would sell enough cars to reach a particular point that grants a large, fixed bonus, and this amount was paid at the beginning of the month. If, at the end of the month, sales were insufficient to earn this bonus, the difference was paid back. If excess bonus was due, it was paid at that time.
Intervention Start Date
2017-05-01
Intervention End Date
2018-01-01

Primary Outcomes

Primary Outcomes (end points)
Number of cars sold per month.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
As described above, the key intervention was the random assignment of the timing of bonus payments: they either stayed in the status-quo arrangement of being paid after sales occur, or the were changed to prepayment with the threat of clawback.

This study was conducted throughout 2017. The first four months of 2017 were effectively used as a baseline period (held back as the pre-period in a difference in differences design). The second four months were the primary treatment period, in which approximately half of participating dealers were randomly assigned to treatment. The last four months served as a secondary treatment period, in which treatment and control status is flipped for all participants.
Experimental Design Details
Randomization Method
Randomization was conducted at the DMA level, due to concerns that dealers in the same area would be affected by the treatment assignment of their competitors.

To maximize the efficacy of a difference in differences design, a bipartite matching procedure was used to create pairs of DMAs that minimized the distance between pairs pre-period sales trends. Within each pair, assignment to treatment and control was then randomized by a computer.
Randomization Unit
DMAs.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
116 DMAs
Sample size: planned number of observations
294 dealers.
Sample size (or number of clusters) by treatment arms
140 dealers in the initial treatment group, 154 dealers in the initial control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
January 01, 2018, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
January 01, 2018, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
116 DMAs
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
294 dealers.
Final Sample Size (or Number of Clusters) by Treatment Arms
140 dealers in the initial treatment group, 154 dealers in the initial control group.
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials