Remote work and team productivity

Last registered on December 06, 2023


Trial Information

General Information

Remote work and team productivity
Initial registration date
November 21, 2023

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
December 06, 2023, 7:42 AM EST

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


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Primary Investigator

Ohio University

Other Primary Investigator(s)

PI Affiliation
University of North Carolina Charlotte

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Remote work policies remain controversial mainly because of productivity concerns. The existing literature highlights how the remote setting affects individual productivity yet little is known about how the remote setting affects work in teams - where productivity losses are potentially higher given the additional role of beliefs over partner productivity. Our study closes this gap by examining the effort of individuals randomly assigned to work in either a remote or office setting with partners who are remote and office based.
External Link(s)

Registration Citation

Dutcher, Glenn and Krista Saral. 2023. "Remote work and team productivity." AEA RCT Registry. December 06.
Experimental Details


Participants are randomly assigned a location (in the lab or working remotely) and teammates. They always engage in the same real effort production tasks.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We are interested in individual and team productivity based on the location of the individual and their teammates as well as the team incentive scheme. One primary outcome metric will be the difference in team contributions (output) when a participant is paired with office-based vs. remote-based teammates. We will test for differences using t-tests and standard (random effects) panel regression analysis. We will also examine overall productivity (individual output + team output) by location of the worker, using t-tests and regressional analysis to determine differences in overall productivity. Last, we will also examine the proportion shared (constructed variable explained below) using t-tests and regression analysis.
Primary Outcomes (explanation)
Proportion shared is constructed by taking the ratio of the contribution made to the team to total output (individual + team). This is a percentage measure for how much is shared with the team out of all output.

Secondary Outcomes

Secondary Outcomes (end points)
The minimum effort pay scheme game has a prediction that the amount given to the team directly measures beliefs of partner productivity. We also measure beliefs using a survey question at the end of the experiment as a comparison to this explicit measure. The revenue-sharing game does not have a belief measurement directly taken from the game, so we only have the survey question at the end to measure. In all cases, we ask whether the remote or office worker partners contributed more to the team (a direct binary measure of productivity). In the minimum effort treatment, we also ask an incentivized belief question about the minimum amount contributed to the group account by both types of partners. We ask the same question in the revenue sharing treatment but shift from the minimum to a question about the average contribution of teammates. We use all of these questions to examine stated beliefs and to use these beliefs to explain work behavior in the team. We use these beliefs to isolate different types of subjects: those who believe office workers are more productive, those that believe remote workers are more productive and those who believe there are no differences in productivity by work location. We also measure ability in a single round at the start of the experiment, where subjects are paid a piece rate for completed tasks. This is used as an explanatory variable in regression analysis.

Earlier version of this experiment were not registered and they were run prior to Covid. A secondary outcome of interest is whether or not there is a change due to covid. Only the minimum effort treatments were conducted before Covid. Revenue sharing treatments and additional minimum effort treatments will be conducted post-pandemic.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants are asked to complete a real effort typing task. The work is performed on an online platform which guides all participants through the experiment. The experiment is self-guided for both office workers and remote workers to ensure that the work platform and information given to all participants is identical. After a set of instructions and an unpaid practice work exercise, the paid portion of the experiment begins with an individual work round, followed by two team work rounds. The experiment ends with a basic survey.
Experimental Design Details
Not available
Randomization Method
We will use for determining the initial sessions to conduct. We aim for a balanced sample and as such the final sessions may be run on an as-needed basis. Subjects do not know which treatment they are in when they sign up to take part in the study.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
165 in each team payment scheme.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Ohio University IRB
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Additional details PAP

MD5: 92274b64ab49d8d1eccfe39edbc68219

SHA1: 342af35444ed7a0f2f11a8abcbad48948e1c79fe

Uploaded At: November 21, 2023