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Job Characteristics and Attraction to Public Sector Employment: The Moderating Effects of Public Service Motivation and Self-Efficacy
Last registered on May 23, 2021

Pre-Trial

Trial Information
General Information
Title
Job Characteristics and Attraction to Public Sector Employment: The Moderating Effects of Public Service Motivation and Self-Efficacy
RCT ID
AEARCTR-0007666
Initial registration date
May 12, 2021
Last updated
May 23, 2021 6:49 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Copenhagen
Other Primary Investigator(s)
PI Affiliation
American University
PI Affiliation
American University
Additional Trial Information
Status
Completed
Start date
2021-05-15
End date
2021-05-22
Secondary IDs
Abstract
How can public sector organizations ensure and improve the performance of their services? One important factor is job design. We know that job characteristics have a sorting effect: Some individuals are more attracted to jobs involving particular job arrangements than other individuals are. Moreover, research shows that individuals’ public service motivation (PSM) and self-efficacy are significant predictors of work performance. We ask: Which job arrangements are most important for attracting these “high-performers”—people high in PSM and self-efficacy—to public sector employment? Using a within-subjects conjoint survey experimental design among a non-probability sample of US residents (n = 1,500), this study examines how the effects of eight particular job characteristics on job attraction are moderated both by PSM and self-efficacy.
External Link(s)
Registration Citation
Citation
Favero, Nathan, Joohyung Park and Mogens Jin Pedersen. 2021. "Job Characteristics and Attraction to Public Sector Employment: The Moderating Effects of Public Service Motivation and Self-Efficacy." AEA RCT Registry. May 23. https://doi.org/10.1257/rct.7666-2.0.
Experimental Details
Interventions
Intervention(s)
The interventions consist of a within-subject conjoint experiment embedded in an electronic survey.
The design is a paired profiles conjoint in which job arrangement profiles for two jobs—A and B—are presented next to each other in a conjoint table.

The first column of the conjoint table lists a total of eight job attributes. The second and third columns list the job attribute values for jobs A and B, respectively. All job attribute values are assigned at random.

The exact text for the eight job attributes and their respective attribute values appear below. Unless noted otherwise, attribute values are assigned with equal probability for each attribute.

Attribute 1: “Total pay: Expected pay (including bonuses), compared to similar jobs elsewhere”
Attribute values (3):
• Slightly above average
• About average
• Slightly below average

Attribute 2: “Performance bonuses: How much of the expected pay is bonuses that depend on performance”
Attribute values (4; “fixed salary” assigned with 50% probability, the others with 16,7% probability each):
• A large part of your potential pay (20%)
• A moderate part of your potential pay (10%)
• A small part of your potential pay (5%)
• No performance bonuses; fixed salary

Attribute 3: “Job performance evaluation: How your performance is measured”
Attribute values (4):
• Attendance numbers for Project HOPE events
• Satisfaction surveys of Project HOPE event participants
• Changes in community crime, poverty, and blight
• A supervisor evaluation of your work

Attribute 4: “Current community involvement: Current participation levels for the program”
Attribute values (3):
• Frequent participation
• Moderate participation
• Rare participation

Attribute 5: “Community income: Average income in target community”
Attribute values (3):
• High income
• Average income
• Low income

Attribute 6: “Community demographics: Racial/ethnic makeup of neighborhoods”
Attribute values (4):
• Mostly white
• Mostly African American
• Mostly Hispanic
• Multiracial

Attribute 7: “Overtime work: How often you will work extra evening hours”
Attribute values (3):
• Frequently required
• Occasionally required
• Never required

Attribute 8: “Key job task: Most important job qualification”
Attribute values (4):
• Analysis identifying community needs
• Teamwork with peers and supervisors
• Coordination with community groups and organizations
• Direct interaction with community residents
Intervention Start Date
2021-05-15
Intervention End Date
2021-05-22
Primary Outcomes
Primary Outcomes (end points)
Job attraction (self-reported; based on responses to a survey item)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The conjoint survey experiment is carried out among a non-probability sample of US residents. Participants are recruited via Prolific, with enrollment limited to individuals with current residence in the US (using Prolific’s prescreening feature). In order to ensure a racially diverse sample of respondents, we will recruit 750 White respondents and 750 non-White respondents. This quota sampling approach is accomplished using Prolific’s prescreening feature. Specifically, two identical studies are created in Prolific, except that one study limits enrollment to only White participants (using Prolific’s demographic category “Ethnicity (Simplified)”) and the other study limits enrollment to non-White participants.

All survey respondents are presented with a job choice task. First, respondents are exposed to an introductory text describing the task and providing basic information about a city government job (as Community Active Worker on a community empowerment program called Project Hope). Next, respondents are exposed to a paired profiles conjoint in which two specific job arrangement profiles—A and B—for the Community Active Worker job are presented next to each other in a conjoint table.

The first column of the conjoint table lists eight job attributes. The second and third column list the job attribute values (for those eight job attributes) for jobs A and B, respectively. All job attribute values are assigned at random.

The exact job attributes and job attribute values appear under ‘INTERVENTIONS.’

As our outcome measure, all respondents are asked to indicate their choice between the two job arrangement profiles (“Which of the two jobs would you personally prefer?”). Response options are “Job A” and “Job B.”

Respondents are presented with similar paired profiles conjoints (i.e., involving the same job attributes and random assignment of job attribute values for the Community Active Worker position) two more times. Thus, each respondent will see a total of three pairs of job profiles.

We measure respondents’ PSM using a five-item continuous scale measure (developed by Wright, Christensen, and Pandey 2013). We measure respondents’ self-efficacy using a eight-item continuous scale measure (developed by Chen, Gully and Eden 2001).

Based on existing theory and research, we derive and test the following hypotheses on how PSM and self-efficacy moderate the effects of particular job characteristics on job attraction:

For individuals high (vs. low) in PSM…:
H1: Total pay matters less for job attraction (i.e., higher pay matters more when PSM is low)
H2: Target community characteristics (income level, racial composition, past involvement) matters more for job attraction
- H2a) Lower income level (vs. moderate or high) matters more when PSM is high
- H2b): Mostly non-white racial composition (vs. mostly white) matters more when PSM is high
- H2c): Low past involvement (vs. moderate or high) matters more when PSM is high
H3: Workload (overtime) requirements matter less for job attraction (i.e., frequent overtime matters more when PSM is low)
H4: Main job task matters more for job attraction (i.e., job tasks involving collaboration with community groups/organizations or residents (vs. analytical and teamwork tasks) matters more when PSM is high)

For individuals high (vs. low) in self-efficacy…:
H5: Performance pay (vs. fixed pay) is more positively associated with job attraction
H6: Job performance evaluated by achievement of program goals (vs. supervisor assessment) is more positively associated with job attraction
Experimental Design Details
The conjoint survey experiment is carried out among a non-probability sample of US residents. Participants are recruited via Prolific, with enrollment limited to individuals with current residence in the US (using Prolific’s prescreening feature). In order to ensure a racially diverse sample of respondents, we will recruit 750 White respondents and 750 non-White respondents. This quota sampling approach is accomplished using Prolific’s prescreening feature. Specifically, two identical studies are created in Prolific, except that one study limits enrollment to only White participants (using Prolific’s demographic category “Ethnicity (Simplified)”) and the other study limits enrollment to non-White participants.

All survey respondents are presented with a job choice task. First, respondents are exposed to an introductory text describing the task and providing basic information about a city government job (as Community Active Worker on a community empowerment program called Project Hope). Next, respondents are exposed to a paired profiles conjoint in which two specific job arrangement profiles—A and B—for the Community Active Worker job are presented next to each other in a conjoint table.

The first column of the conjoint table lists eight job attributes. The second and third column list the job attribute values (for those eight job attributes) for jobs A and B, respectively. All job attribute values are assigned at random.

The exact job attributes and job attribute values appear under ‘INTERVENTIONS.’

As our outcome measure, all respondents are asked to indicate their choice between the two job arrangement profiles (“Which of the two jobs would you personally prefer?”). Response options are “Job A” and “Job B.”

Respondents are presented with similar paired profiles conjoints (i.e., involving the same job attributes and random assignment of job attribute values for the Community Active Worker position) two more times. Thus, each respondent will see a total of three pairs of job profiles.

We measure respondents’ PSM using a five-item continuous scale measure (developed by Wright, Christensen, and Pandey 2013). We measure respondents’ self-efficacy using a eight-item continuous scale measure (developed by Chen, Gully and Eden 2001).

Based on existing theory and research, we derive and test the following hypotheses on how PSM and self-efficacy moderate the effects of particular job characteristics on job attraction:

For individuals high (vs. low) in PSM…:
H1: Total pay matters less for job attraction (i.e., higher pay matters more when PSM is low)
H2: Target community characteristics (income level, racial composition, past involvement) matters more for job attraction
- H2a) Lower income level (vs. moderate or high) matters more when PSM is high
- H2b): Mostly non-white racial composition (vs. mostly white) matters more when PSM is high
- H2c): Low past involvement (vs. moderate or high) matters more when PSM is high
H3: Workload (overtime) requirements matter less for job attraction (i.e., frequent overtime matters more when PSM is low)
H4: Main job task matters more for job attraction (i.e., job tasks involving collaboration with community groups/organizations or residents (vs. analytical and teamwork tasks) matters more when PSM is high)

For individuals high (vs. low) in self-efficacy…:
H5: Performance pay (vs. fixed pay) is more positively associated with job attraction
H6: Job performance evaluated by achievement of program goals (vs. supervisor assessment) is more positively associated with job attraction
Randomization Method
Randomization is carried out by simple randomization by computer (randomization based on a single sequence of random assignments).
Randomization Unit
The individual survey respondent
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1,500 survey respondents (750 White respondents and 750 non-White respondents)
Sample size: planned number of observations
1,500 survey respondents (750 White respondents and 750 non-White respondents)
Sample size (or number of clusters) by treatment arms
Providing an exact sample size estimate for all potential constellations of job attribute values across job attributes is not meaningful given our conjoint design and research focus.

Below is our expected sample size by treatment arms for each of our eight job attributes:

1): “Total pay”
Attribute values: 3 with n = 3,000 in each.
Note: Our main analyses use a binary variable capturing “low” pay (n = 3,000) vs. “average” and “high” pay (n = 6,000).

2): “Performance bonuses”
Attribute values: 4 with n = 4,500 for “fixed salary” and n = 1,500 for each of the other three attribute values.
Note: Our main analyses use a binary variable capturing “Fixed salary” (n = 4,500) vs. “PfP” (n = 4,500; all three PfP attribute values).

3: “Job performance evaluation”
Attribute values: 4 with n = 2,250 in each.
Note: Our main analyses use a binary variable capturing “Supervisor” (n = 2,250) vs. “Performance goals” (n = 6,750; all three program goal attribute values).

4: “Current community involvement”
Attribute values: 3 with n = 3,000 in each.
Note: Our main analyses use a binary variable capturing “low” participation (n = 3,000) vs. “moderate” and “high” participation (n = 6,000).

5: “Community income”
Attribute values: 3 with n = 3,000 in each.
Note: Our main analyses use a binary variable capturing “low” income (n = 3,000) vs. “moderate” and “high” income (n = 6,000).

6: “Community demographics”
Attribute values: 4 with n = 2,250 in each.
Note: Our main analyses use a binary variable capturing “Mostly white” (n = 2,250) vs. “Mostly non-white” (n = 6,750; all three other attribute values).

7: “Overtime work”
Attribute values: 3 with n = 3,000 in each.
Note: Our main analyses use a binary variable capturing “Frequent” (n = 3,000) vs. “Occasional” and “Never” (n = 6,000).

8: “Key job task”
Attribute values: 4 with n = 2,250 in each.
Note: Our main analyses use a binary variable capturing “Collaboration with community groups/organizations” and “Direct interaction with community residents (n = 4,500) vs. “Analyses” and “Teamwork with peers” (n = 4,500).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Institutional Review Board for Protection of Human Subjects in Research (IRB) at American University
IRB Approval Date
2021-05-11
IRB Approval Number
IRB-2021-351
Analysis Plan
Analysis Plan Documents
Analysis Plan

MD5: 0a8f861f460f768810918a4276f6764c

SHA1: dcc027f07e7c2cbef9492a979193078cc48f61d6

Uploaded At: May 12, 2021

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
May 15, 2021, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
May 15, 2021, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS