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Lock-in Effects in Online Labor Markets
Last registered on April 29, 2021

Pre-Trial

Trial Information
General Information
Title
Lock-in Effects in Online Labor Markets
RCT ID
AEARCTR-0006136
Initial registration date
February 01, 2021
Last updated
April 29, 2021 6:31 AM EDT
Location(s)

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Primary Investigator
Affiliation
University of Bremen
Other Primary Investigator(s)
PI Affiliation
UCLouvain
PI Affiliation
University of Bremen
Additional Trial Information
Status
In development
Start date
2021-02-08
End date
2021-06-01
Secondary IDs
DFG HO 5296/3-1
Abstract
Lock-in effects arise when individuals are dependent on a particular product or service and cannot switch to another product or service without incurring significant switching costs. In this paper, we theoretically and experimentally study switching behavior of digital workers, which results from the exploitation of locked-in digital workers by the respective labor market platform. We investigate the extent and frequency of the exploitation as second-order effects. Lock-in effects frequently result from the absence of reputation portability, thereby generating high switching costs for platform users. We therefore analyze whether reputation portability increases switching behavior in an online labor market context. Our experimental design explains switching based on (1) monetary motives and (2) other-regarding behavior, i.e., we examine whether digital workers base their decisions on the economic outcome of their earnings, or whether they react to what they believe to be the intentions of the platform.
External Link(s)
Registration Citation
Citation
Ciotti, Fabrizio, Lars Hornuf and Eliza Stenzhorn. 2021. "Lock-in Effects in Online Labor Markets." AEA RCT Registry. April 29. https://doi.org/10.1257/rct.6136-1.2000000000000002.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
There are three interventions:
(1) the strength of lock-in (reputation portability vs. no reputation portability)
(2) the exploitation of lock-in (platform fee vs. no platform fee)
(3) the equality of exploitation of lock-in (platforms that simultaneously charge identical fees vs. only the focal platform charges a fee).
Intervention Start Date
2021-02-08
Intervention End Date
2021-06-01
Primary Outcomes
Primary Outcomes (end points)
We distinguish switching based on monetary motives and other-regarding behavior. Our first outcome of interest is whether a worker, due to monetary motives, switches platforms if the focal platform charges a fee.

Our second outcome of interest is whether a worker, due to other-regarding behavior, switches platforms if the focal platform charges a fee.

Our third outcome of interest is whether reputation portability increases workers’ switching behavior.
Primary Outcomes (explanation)
If a worker switches the platform after a fee increase and the task is compensated at a higher rate on the other platform, we define this as a combination of monetary motives and other-regarding behavior. Pure monetary motives are calculated as the difference between the combined monetary motives and other-regarding behavior minus switching due to pure other-regarding behavior.

If a worker switches the platform after a fee increase and the task is equally or less compensated on the other platform, we define this as switching behavior due to other-regarding behavior.

Reputation portability is considered to increase switching between platforms if workers switch more often when reputation portability is enforced.
Secondary Outcomes
Secondary Outcomes (end points)
We investigate the following second-order effects: the extent of lock-in exploitation (fee of USD 0.00, USD 0.01, USD 0.05) and the frequency of lock-in exploitation (no, one, two fee increases), risk attitude, and the experience as a worker in crowdsourcing markets.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We have designed a randomized control trial, where all subjects will be randomly assigned to treatment and control groups. The study relies on a 2 × 2 × 2 design. We use a combination of controlled manipulations in a field setting and observe switching behavior of workers in online labor markets. The experiment is followed by a questionnaire to identify mediating factors, to control for confounding variables and individual heterogeneity.
Experimental Design Details
Not available
Randomization Method
A designated function by the software package Unipark will randomly assign participants to the treatments. Before we start with our analysis, we will check whether randomization by Unipark resulted in a balanced sample. In case socio-economic and other factors of the subjects are not balanced between control and treatment conditions, we will apply appropriate matching techniques.
Randomization Unit
Randomization will be done on an individual level.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Since the experiment is not clustered, the number of clusters is the same as the sample size (see below).
Sample size: planned number of observations
2,112 individual workers from the crowdsourcing platform Amazon Mechanical Turk.
Sample size (or number of clusters) by treatment arms
- 132 individuals in the no portability, no platform fee, and identical platforms treatment conditions
- 132 individuals in the no portability, no platform fee, and differentiating platforms treatment conditions
- 396 individuals in the no portability, platform fee, and identical platforms treatment conditions
- 396 individuals in the no portability, platform fee, and differentiating platforms treatment conditions
- 132 individuals in the portability, no platform fee, and identical platforms treatment conditions
- 132 individuals in the portability, no platform fee, and differentiating platforms treatment conditions
- 396 individuals in the portability, platform fee, and identical platforms treatment conditions
- 396 individuals in the portability, platform fee, and differentiating platforms treatment conditions
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our targeted sample size is based on an a-priori power calculation using logistic regression as statistical test at the 0.05 significance level with a power of 0.80.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Ethikkommission Universität Bremen
IRB Approval Date
2020-07-16
IRB Approval Number
2020-16