Left-digit Bias in Wage Perceptions and Labor Supply in an Online Labor Market

Last registered on March 01, 2017
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Pre-Trial

Trial Information

General Information

Title
Left-digit Bias in Wage Perceptions and Labor Supply in an Online Labor Market
RCT ID
AEARCTR-0001349
Initial registration date
July 06, 2016

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
July 06, 2016, 12:52 AM EDT

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

Last updated
March 01, 2017, 1:32 PM EST

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Suresh Naidu
Affiliation
Columbia University
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Other Primary Investigator(s)

PI Name
Alan Manning
PI Affiliation
LSE
Contact Investigator
PI Name
Arindrajit Dube
PI Affiliation
UMass Amherst
Contact Investigator

Additional Trial Information

Status
In development
Start date
2016-07-06
End date
2017-04-07
Keywords
Electoral, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Abstract
We randomize wages for census image classification task to estimate discontinuous labor supply elasticities at round numbers---in particular at 10 cents, to test for left-digit bias. We also aim to replicate the upward sloping labor supply functions to a given task estimated in Horton et al (2011).

External Link(s)

Registration Citation

Citation
Dube, Arindrajit, Alan Manning and Suresh Naidu. 2017. "Left-digit Bias in Wage Perceptions and Labor Supply in an Online Labor Market." AEA RCT Registry. March 01. https://doi.org/10.1257/rct.1349-2.0
Former Citation
Dube, Arindrajit, Alan Manning and Suresh Naidu. 2017. "Left-digit Bias in Wage Perceptions and Labor Supply in an Online Labor Market." AEA RCT Registry. March 01. https://www.socialscienceregistry.org/trials/1349/history/14601
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2016-07-06
Intervention End Date
2016-10-07

Primary Outcomes

Primary Outcomes (end points)
Indicator for accepting MTurk task. Time to complete task. Error rate in tagging.
Primary Outcomes (explanation)
Error rate will be calculated using 2 "known positives" that will be randomly assigned to workers.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will post a total of 5,500 unique HITS on MTurk tasks for $0.10 that includes a brief survey and a screening task, where respondents view a digital image of a historical slave census schedule from 1850 or 1860, and answer whether they see markings in the “fugitives” column. Some respondents are offered a choice of completing an additional set of classification tasks for a specific wage.

Extension: we have also run a version of this experiment with a recipe tagging task, at 90, 99, 100, 101, and 110 cents.
Experimental Design Details
We refer to the initial screening part as “stage-1.” Those who complete stage-1 and indicate that the primary reason for participation is "money" or "skills" (as opposed to "fun") are then offered an additional task of completing either 6 or 12 such image classifications (chosen randomly) for a specific (randomized) wage, w, which we refer to as "stage-2." If they accept the stage-2 offer, they are provided either 6 images (task type A) or 12 images (task type B) to classify, and are paid the wage w. These 5,500 HITs will remain posted until completed, or for 3 months, which ever is shorter. Any single individual on mTurk (identified by their MTurk ID) will be allowed to only do one of the HITs. We aim to assess the left-digit bias in wage perceptions experimentally by randomizing the offered wages for HITs on MTurk by randomizing a wage offer for a HIT to vary between $0.05 and $0.15, and assessing whether there is a jump in the acceptance probability between $0.09 and $0.10 as would be predicted by a left-digit bias.
Randomization Method
Randomization done by computer that automatically posts task on Amazon turk.
Randomization Unit
individual MTurk worker.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
5500

400 for extension
Sample size: planned number of observations
5500 400 for extension
Sample size (or number of clusters) by treatment arms
5500

400 for extension
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
see preanalysis plan.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Columbia IRB
IRB Approval Date
2016-06-07
IRB Approval Number
AAAQ9022
Analysis Plan

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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