Work Meaning and Labor Supply

Last registered on January 01, 2018

Pre-Trial

Trial Information

General Information

Title
Work Meaning and Labor Supply
RCT ID
AEARCTR-0002651
Initial registration date
December 31, 2017
Last updated
January 01, 2018, 4:45 PM EST

Locations

Region

Primary Investigator

Affiliation
KU Leuven

Other Primary Investigator(s)

PI Affiliation
Tilburg University
PI Affiliation
KU Leuven

Additional Trial Information

Status
Completed
Start date
2017-01-01
End date
2017-08-31
Secondary IDs
Abstract
Long-term unemployment imposes large costs on both individuals and society. Reducing long-term unemployment therefore is of first-order importance for public policy. In this project, we analyze to what extent work meaning – the significance of a job for others or for society – increases the willingness to accept a job and job performance of employed and unemployed individuals. To this end, we conduct a large-scale online experiment with a representative sample of the population. All of our subjects participate in the “Panel Study of Labour Market and Social Security” (PASS) of the German Labour Agency. In the experiment, we offer a job that can be completed from home. We elicit subjects’ reservation wage for this job through the Becker-DeGroot-Marschak mechanism. The treatment variation is the description of the job as having either “high” or “low” meaning. To study heterogeneity in subjects’ reservation wages, we link their behaviour in the experiment to rich survey and administrative data provided by PASS.
External Link(s)

Registration Citation

Citation
Kesternich, Iris, Heiner Schumacher and Bettina Siflinger. 2018. "Work Meaning and Labor Supply." AEA RCT Registry. January 01. https://doi.org/10.1257/rct.2651-1.0
Former Citation
Kesternich, Iris et al. 2018. "Work Meaning and Labor Supply." AEA RCT Registry. January 01. https://www.socialscienceregistry.org/trials/2651/history/24613
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2017-01-01
Intervention End Date
2017-08-31

Primary Outcomes

Primary Outcomes (end points)
Participation in the experiment, reservation wages, job completion, quality of work​.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We o er subjects a job that takes an hour to complete. Their task in the job is to digitize scanned PDF documents from the medical faculty of the University of Munich). Subjects can work from home using their own computer. No particular skills are equipment are needed to perform
the job. Subjects receive their salary after working on the job for one hour. In the experiment, we elicit subject’s reservation wage for the job. To this end, we apply the Becker-DeGroot-Marschak mechanism, which is a standard tool in experimental economics to elicit reservation values. After describing the job, subjects are asked at which wage between 9 and 35 Euros they are willing to work for one hour. The computer then randomly draws a number x between 9 and 35. If this number x is (weakly) above the respondent’s reservation wage, the respondent is admitted to the job and is paid a wage of x. Otherwise, the experiment ends. This procedure ensures that each subject has an incentive to indicate the true reservation wage. We also included the option to state that a subjects does not want to accept the job even if the wage is 35 Euros. Our goal is to check how the reservation wage and productivity during the job changes with the meaning of work. We therefore assign subjects to two treatments that vary in the description of the job, i.e., the high-meaning treatment or the low-meaning treatment. In the high-meaning treatment, we informed subjects that the documents they enter would be relevant for future research at the medical faculty. In contrast, in the low-meaning treatment, we told subjects that the documents would be stored, but most likely would not be used in the future.
Experimental Design Details
Randomization Method
Randomization is done by a computer.
Randomization Unit
The unit of randomisation is the individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering
Sample size: planned number of observations
Maximum 1000 subjects
Sample size (or number of clusters) by treatment arms
Maximum 500 subjects in high and low meaning treatment.
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