Willingness-to-Pay for Job Attributes in Germany

Last registered on June 15, 2022

Pre-Trial

Trial Information

General Information

Title
Willingness-to-Pay for Job Attributes in Germany
RCT ID
AEARCTR-0009559
Initial registration date
June 08, 2022

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
June 15, 2022, 10:07 AM EDT

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

Locations

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

Request Information

Primary Investigator

Affiliation
University of Erlangen-Nuremberg

Other Primary Investigator(s)

PI Affiliation
University of Erlangen-Nuremberg
PI Affiliation
University of Erlangen-Nuremberg

Additional Trial Information

Status
In development
Start date
2022-06-15
End date
2022-09-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this pre-analysis plan, we describe a choice experiment that induces exogenous variation in job attributes. The key features of the experimental design follow Maestas et al. (2018). We aim at identifying workers' willingness to pay for certain job attributes. First, we plan to analyze the willingness to pay for job attributes indicating high work pressure (i.e., the extent to which a job is characterized by frequent deadlines and multitasking). Second, we plan to investigate willingness to pay for other job attributes, in particular of commuting time and the option to telecommute, and how these dimensions interact in shaping the subjects' willingness to pay for each job attribute.
External Link(s)

Registration Citation

Citation
Nagler, Markus, Johannes Rincke and Erwin Winkler. 2022. "Willingness-to-Pay for Job Attributes in Germany." AEA RCT Registry. June 15. https://doi.org/10.1257/rct.9559
Sponsors & Partners

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

Request Information
Experimental Details

Interventions

Intervention(s)
This pre-analysis plan refers to an online choice experiment that allows us to elicit workers’ preferences over jobs under exogenous variation in job attributes. The experimental design aims at identifying the workers’ willingness to pay for two types of job attributes. First, we consider attributes that are associated with high work pressure. We consider two high-pressure job attributes: the extent to which a job is characterized by frequent deadlines, and the extent of multitasking. Second, we plan to analyze workers' willingness to pay for job attributes other than pressure. These attributes are, on the one hand, motivated by recent changes in labor markets that happened in the past two years of the COVID pandemic. Here, we specifically focus on workers' willingness to pay to decrease commuting time and the option to telecommute, and how both dimensions interact in shaping the subjects' willingness to pay (WTP) for changes in both job attributes. On the other hand, we include job characteristics such as the number of paid days off that differ substantially between the United States (where there is evidence on workers' willingness to pay for such job attributes, and Germany or Europe more generally. We will likely report the experimental evidence in two different papers: one focusing on work pressure, and a separate one on the other job attributes, in particular on commuting/telecommuting.

We follow the stated-preference method. The idea is to randomize job characteristics and observe the choices individuals make when facing the tradeoff between hypothetical jobs with different characteristics that also differ in pay. The resulting data allow us to identify the workers' average willingness to pay for the presence of certain job characteristic in those hypothetical choices.

We plan to produce two separate papers. The first one will focus on the WTP to avoid work pressure, whereas the second one will focus on other job characteristics, especially commuting/telecommuting.
Intervention Start Date
2022-06-15
Intervention End Date
2022-09-30

Primary Outcomes

Primary Outcomes (end points)
Binary variable indicating the subjects' choice between two hypothetical jobs
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design aims at identifying the workers’ willingness to pay for two types of job attributes. First, we consider attributes that are associated with high work pressure, measured by the extent to which a job is characterized by frequent deadlines, and the extent of multitasking. Second, we plan to analyze workers' willingness to pay for job attributes other than pressure. Here, we focus on workers' willingness to pay to decrease commuting time and the option to telecommute.

The job attributes associated with work pressure are defined by statements whether the respective attribute would apply "frequently" or just "occasionally." Commuting time to the workplace is presented in minutes and varies between 15, 30, 45, and 60 minutes. Options to telecommute in a given job are given as "none", "2 days per week", or 5 days per week". We complement the job profiles by three further non-wage attributes: control over schedule, number of paid days off, and hours.

To each survey respondent, we administer a series of ten stated-preference experiments. In each of these experiments, survey respondents are asked to select between two jobs, each defined by a partially varying set of non-wage job characteristics, hours, and monetary compensation. For each respondent, we use a description of the respondent's current job as a baseline profile. To facilitate the derivation of the baseline profile, before participating in the experiments, each respondent answers a short survey about current job characteristics. Each survey item corresponds to one of the non-wage job attributes in the experiment. Based on the respondents' baseline job, we create hypothetical Job A and Job B by randomly selecting two non-wage attributes (including hours) to vary across the two jobs. Within each of the two randomly selected attributes, we choose corresponding attribute values at random sequentially for both jobs without replacement. This makes sure that Job A and Job B actually vary in the selected attributes. The variation in the variable referring to control over schedule is binary (yes or no).

In addition to the two randomly selected non-wage attributes to vary in a given experiment, the wage always varies between Job A and Job B. We anchor the randomly determined wage using the respondent's actual hourly wage w. The anchoring is achieved by setting the wages of Job A and Job B as c_A*w and c_B*w, respectively, where c_A and c_B follow a N(1, 0.01) distribution. We truncate both weights to lie between 0.75 and 1.25. The wage offer is converted back to the units in which the respondent originally reported their earnings (hourly, monthly, or yearly) in the choice experiment.

To elicit the subjects' self-reported stress-related health, we let the subjects (after completing the stated-choice experiment) answer a series of four health questions. These questions read as follows:

"In your current job, do you feel that you are mostly up to the task in terms of quantity of work, or do you feel under- or overwhelmed?" Response options: Mostly up to the task; mostly overwhelmed; mostly underwhelmed; don't know.

"During the past 12 months, did you frequently experience any of the following on work days?" Response options: Sleep problems; general feelings of tiredness, fatigue or weariness; feeling nervous or irritable; feeling physically exhausted; feeling mentally exhausted; none of these; don't know.

"Does it frequently happen at work that ..." Response options: Work is emotionally taxing; reaching personal limit often; hard to relax after work; none of these; don't know.

"During the past two years, or since you started on your current job: Have stress and work pressure increased, did they stay constant, or have they decreased?" Response options: Increased; stayed constant; decreased; don't know.

From these items, we construct indicators for subjects who suffer from self-reported health problems associated with work pressure. For details, see the pre-analysis plan.
Experimental Design Details
Not available
Randomization Method
Randomization by a computer
Randomization Unit
Individual choice between two hypothetical jobs
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
33,000 individual choices over hypothetical jobs
Sample size: planned number of observations
3,300 subjects (individuals), each making 10 choices over hypothetical jobs, e.g. 33,000 observations in total
Sample size (or number of clusters) by treatment arms
There are no treatment arms in our design, but the job characteristics vary randomly in each individual choice experiment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We do not have any baseline data and thus cannot provide minimum detectable effect sizes. However, assuming that our key variables are distributed similarly as the key variables in Maestas et al. (2018), we are confident that we will be able to detect relatively small effects. For instance, Maestas et al. (2018) estimate that a switch from a fast-paced to a relaxed job (holding all other job characteristics constant) is equivalent to a 4.4 percent wage increase. Similarly, they estimate that the option to telecommute (without a differentiation on how intensely this option may be used by the worker) is equivalent to a 4.1 percent wage increase. These estimates are based on a sample of 1,815 survey respondents and significant at the one percent level. Given our planned sample size of 3,300 respondents, we expect to be able to identify wage premia for our high-pressure variables and the commuting-related variables of less than 4 percent.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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

Request Information