The Impacts of Worker Monitoring

Last registered on May 16, 2023

Pre-Trial

Trial Information

General Information

Title
The Impacts of Worker Monitoring
RCT ID
AEARCTR-0011365
Initial registration date
May 08, 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
May 16, 2023, 2:29 PM EDT

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

Locations

Region
Region

Primary Investigator

Affiliation
MIT Sloan School of Management

Other Primary Investigator(s)

PI Affiliation
UCSD

Additional Trial Information

Status
In development
Start date
2023-05-10
End date
2024-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Remote hiring and work have the potential to expand the set of labor market opportunities across geographies. We will use an online platform to study how worker input monitoring affects worker productivity and morale, and how this relationship varies by the reasons cited for monitoring.
External Link(s)

Registration Citation

Citation
Kala, Namrata and Elizabeth Lyons. 2023. "The Impacts of Worker Monitoring." AEA RCT Registry. May 16. https://doi.org/10.1257/rct.11365-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Remote hiring and work have the potential to expand the set of labor market opportunities across geographies, and disproportionately benefit workers traditionally underemployed in their local labor markets (such as those in remote areas, or those preferring flexible work arrangements). However, which practices optimize worker productivity and well-being in remote work is less studied. In this paper, we focus on one management practice frequently and increasingly employed by remote work employers; digital monitoring of workers. Software that monitors digital activities can help managers better identify shirking and thus may make riskier hiring more feasible, but may also reduce worker motivation and well-being. Our study will test the impacts of digital monitoring on worker performance and job satisfaction among remote workers on an online platform.
Intervention (Hidden)
Intervention Start Date
2023-05-10
Intervention End Date
2024-06-30

Primary Outcomes

Primary Outcomes (end points)
a) Productivity, which we will measure as the number of accurate cells of data entered during the hours that they are under the different treatment conditions.
b) Subjective assessment of working conditions, and job satisfaction, as measured by a short survey administered at the end of the contract. We will form an index from the following survey questions, each of which will be standardized:
1. On a scale of 1-7, how much did you enjoy working on this text as compared to similar tasks you have worked on?
2. I feel like I was treated with respect on this job (1=not at all, 7=completely)
3. On a scale of 1-7, how likely would you be to accept another data entry task from us?

We will also test whether workers reported feeling whether they were paid fairly for this job, which should not vary across treatments.

We will test for heterogeneity by the following variables:
a) Baseline productivity (whether above or median productivity)
b) Internal and external motivation (by high or low of each of these measures, split at the median). These will be measured by the following survey questions at the end of the task:
• Internal Motivation: How much do you agree with the following statement in response to the question of why do you work? “For the satisfaction I experience when I am successful at doing difficult tasks”
• External Motivation: How much do you agree with the following statement in response to the question of why do you work? “For the income it provides me”
c) Hofstede’s Power Distance Index, where each worker is assigned the power distance measure of their country
d) Gender
e) Worker wages (above vs. below median)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Number of Mistakes Made in Data Entry
Total number of hours worked (if some work less than max allowed)
Whether or not work is done in monitoring condition (some allowed not to may opt to)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will hire workers in an online labor market for a data entry job. All workers will work for 2 hours in the same conditions, which will be in the "Team Room", which is an online room where their presence is monitored, and screenshot and activity summary is sent every 10 minutes that a freelancer tracks time. This is what we call "surveilled conditions" or “monitored conditions”, which in particular focus on input monitoring such as screenshots.

After two hours, we will randomly assign workers to one the following arms:
a) A random subset of high (above) median productivity workers (N=130), as measured by the number of accurate cells of data entered, will be told that they can work the other 6 hours out of the monitored conditions because they have performed well so far. We call this the "justified lack of monitoring" arm, since workers get the ability to work unmonitored as a reward for higher productivity.
b) A random subset of high (above) median productivity workers (N=130) will be informed about their productivity, and have the requirement to work in the monitored conditions digitally removed,. We call this the "lack of monitoring" arm, since workers get the ability to work unmonitored but without directly linking it to their productivity.
c) A random subset of high (above) median productivity workers (N=130) will be informed about their productivity, but there will be no change in their working condition.
d) A random subset of low (below median) productivity workers (N=130) will be told that they will work the other 6 hours under monitored conditions because they have made a high number of errors in the data entry. We call this the "justified monitoring" arm.
e) A random subset of low (below median) productivity workers (N=130) will be informed about their productivity, but there will be no change in their working condition. We call this the "monitoring" arm.
f) A random subset of low (below median) productivity workers (N=130) will be informed about their productivity, and have the requirement to work in the monitored conditions digitally removed. We call this the "lack of monitoring" arm.
Experimental Design Details
Randomization Method
Randomization will be done in the office with a computer
Randomization Unit
Worker-level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
800 workers
Sample size (or number of clusters) by treatment arms
800 workers split equally into the above treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
UCSD
IRB Approval Date
2022-09-19
IRB Approval Number
804867

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