Boundary Conditions of Loss Aversion — When Past Performance Counteracts Evidence From a Field Experiment

Last registered on October 16, 2018

Pre-Trial

Trial Information

General Information

Title
Boundary Conditions of Loss Aversion — When Past Performance Counteracts Evidence From a Field Experiment
RCT ID
AEARCTR-0003411
Initial registration date
October 13, 2018

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
October 16, 2018, 12:36 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
BTU Cottbus

Other Primary Investigator(s)

PI Affiliation
Uni Paderborn

Additional Trial Information

Status
On going
Start date
2018-01-01
End date
2018-12-31
Secondary IDs
Abstract
We analyzed the effect of incentive-framing on performance in employment relations. If loss-aversion holds true, framing an incentive for employees as loss should lead to higher effort than framing the incentive as gain. However, current research sheds doubts on the universality of loss aversion. In most field settings, loss-framings do not increase performance as expected. Our results contribute to the understanding of the boundary conditions of loss-framings. Although loss-framings lead to increased performance among less-productive employees, they simultaneously lead to decreased performance among high-productive employees. Contrarily, gain-framings do not increase performance among less-productive employees but increase productivity among high-productive employees
External Link(s)

Registration Citation

Citation
Hoffmann, Christin and Kirsten Thommes. 2018. "Boundary Conditions of Loss Aversion — When Past Performance Counteracts Evidence From a Field Experiment ." AEA RCT Registry. October 16. https://doi.org/10.1257/rct.3411
Former Citation
Hoffmann, Christin and Kirsten Thommes. 2018. "Boundary Conditions of Loss Aversion — When Past Performance Counteracts Evidence From a Field Experiment ." AEA RCT Registry. October 16. https://www.socialscienceregistry.org/trials/3411/history/35775
Experimental Details

Interventions

Intervention(s)
In order to analyze whether loss- or game framings in repeated settings work better and whether there are any time effects, we use a truck fleet of a medium-sized company in Germany. Here fuel consumption is one of the main productivity measures. Truck drivers can effectively save or waste fuel by their driving behavior without generating any side effects on speed or punctuality. As fuel is one of the main cost components in transportation, many truck companies apply telematics systems in their trucks, which, among other features, measure the energy efficiency of the driving style and provide feedback how to improve driving behavior. We conducted our experiment in one business site with 34 trucks and 41 truck drivers as all of these trucks were equipped with the same telematics system. We changed the incentive system to target every driver and pay everybody for their performance. Moreover, we tested whether a loss-framing or a gain-framing would be more effective.
Intervention Start Date
2018-02-05
Intervention End Date
2018-05-05

Primary Outcomes

Primary Outcomes (end points)
dependent variable: drivers performance
explaining variables: incentive system, technological truck variables, variables measuring truck weight, topografie etc.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conducted our experiment at one company site of the large company with 34 trucks and 41 truck drivers, as all of these trucks were equipped with the same telematics system. The telematics system ranks driving behavior between 0 and 10 points with one decimal place, with 10 as the best result. We used this fuel-efficiency score as the dependent variable because it is largely unaffected by external circumstances. The telematics system is very sophisticated in terms of the data it provides. Using the data for travel distance, cargo weight, and driving behavior allows for fuel consumption to be estimated with a coefficient of determination of more than 95%. Any unobserved heterogeneity was purely due to air pressure and attrition of engine parts.
Experimental Design Details
Randomization Method
coin flip
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1
Sample size: planned number of observations
panel with 41 drivers, 1400 observations
Sample size (or number of clusters) by treatment arms
20 drivers in each treatment, control phase before and after treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials