Work Meaning and Labor Supply

Last registered on January 01, 2018


Trial Information

General Information

Work Meaning and Labor Supply
Initial registration date
December 31, 2017

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
January 01, 2018, 4:45 PM EST

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



Primary Investigator

KU Leuven

Other Primary Investigator(s)

PI Affiliation
Tilburg University
PI Affiliation
KU Leuven

Additional Trial Information

Start date
End date
Secondary IDs
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

Kesternich, Iris, Heiner Schumacher and Bettina Siflinger. 2018. "Work Meaning and Labor Supply." AEA RCT Registry. January 01.
Former Citation
Kesternich, Iris, Heiner Schumacher and Bettina Siflinger. 2018. "Work Meaning and Labor Supply." AEA RCT Registry. January 01.
Experimental Details


Intervention Start Date
Intervention End Date

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?

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)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information


Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials