Individual versus team-based pay - a field experiment

Last registered on December 21, 2020

Pre-Trial

Trial Information

General Information

Title
Individual versus team-based pay - a field experiment
RCT ID
AEARCTR-0005345
Initial registration date
February 05, 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
February 07, 2020, 1:54 PM EST

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

Last updated
December 21, 2020, 3:36 AM EST

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

Locations

Region
Region
Region

Primary Investigator

Affiliation
University of Cologne

Other Primary Investigator(s)

PI Affiliation
University of Cologne
PI Affiliation
University of Cologne
PI Affiliation
University of Cologne

Additional Trial Information

Status
In development
Start date
2020-02-04
End date
2020-12-31
Secondary IDs
Abstract
We collaborate with a retail company and investigate changes in the salesworkers' compensation system. In an RCT, we study the effect of different combinations of team and individual incentives on performance. To that end, we implement two new compensation systems. The first system is solely based on workers' individual performance, whereas the second is based on both individual and team performance. We study how the different compensation systems affect revenues, turnover, conversion rates (i.e., the rate at which customers being served by salesworkers buy the firm's products), and granted discounts.
External Link(s)

Registration Citation

Citation
Gürtler, Oliver et al. 2020. "Individual versus team-based pay - a field experiment." AEA RCT Registry. December 21. https://doi.org/10.1257/rct.5345
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2020-02-04
Intervention End Date
2020-05-31

Primary Outcomes

Primary Outcomes (end points)
Revenue
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Conversion rate, discounts, turnover
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We collaborate with a retailer operating 68 stores in Austria, Germany and Switzerland. The retailer employs salesworkers whose main task is to sell the firm's products in the stores. In an RCT, two (new) compensation systems are implemented in half of the firm's stores, respectively, and the company has agreed to keep the treatments in the field for at least four months (i.e., until the end of May). The first system is solely based on workers' individual performance, whereas the second is based on both individual and team performance. The commission systems are designed such that the average commission is about the same across systems (as calibrated with the 2019 revenue distribution).

Treatment A: Salesworkers' monthly compensation depends only on their own revenue in that month. The corresponding compensation scheme is piecewise linear; it has kinks at two pre-specified targets and it becomes steeper once a target is reached.

Treatment B: Salesworkers' monthly compensation depends both on their own revenue in that month and on the total revenue of all workers in their store (with a ratio of about 3 to 1). For each type of revenue (i.e., individual and store revenue), the compensation scheme is piecewise linear; it has kinks at two pre-specified targets and it becomes steeper once a target is reached.

We study how the different compensation systems affect revenues, turnover, conversion rates (i.e., the rate at which customers being served by salesworkers buy the firm's products), and granted discounts. We also study heterogeneous treatment effects with respect to workers’ pre-treatment performance (high- vs. low-performers) and tenure. We further study differences in gaming between treatments. Finally, we conduct employee surveys to measure employee attitudes. In particular, we explore heterogeneous treatment effects with respect to social preferences (as elicited by a survey).

Due to the Covid-19 pandemic the travel agency unfortunately had to close their stores in spring 2020 and the RCT had to be stopped.
Experimental Design Details
Randomization Method
Pairwise matching by revenues in 2019
Randomization Unit
The randomization was implemented on the store level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
68 stores (7 stores in Austria, 49 stores in Germany and 12 stores in Switzerland)
Sample size: planned number of observations
Actual headcount on the 4th of February (we do not yet have the precise data)
Sample size (or number of clusters) by treatment arms
Half of the stores (34) in each of the two treatments, respectively
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