Job Characteristics and Attraction to Public Sector Employment: The Moderating Effects of Public Service Motivation and Self-Efficacy

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

2021-05-15

2021-05-22

Job attraction (self-reported; based on responses to a survey item)

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

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 is carried out by simple randomization by computer (randomization based on a single sequence of random assignments).

The individual survey respondent

No

1,500 survey respondents (750 White respondents and 750 non-White respondents)

1,500 survey respondents (750 White respondents and 750 non-White respondents)

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).

Analysis Plan

Which job attributes attract prospective high-performing individuals to a job in the public sector? Research shows that the particular attributes of a job influence perceptions of job attractiveness. Moreover, public service motivation (PSM) and self-efficacy are valuable individual-level traits for public service performance. This article examines how variation within particular job attributes that are generic to many public service jobs may attract (or discourage) potential workers high in PSM and self-efficacy—i.e., prospective high-performers. We focus on eight job attributes encompassing total compensation, performance-based incentives, job performance assessment, diverse service recipient characteristics, overtime commitments, and key job tasks. Using data from a pre-registered conjoint survey experiment among 1501 US residents, we show how individuals higher in PSM and self-efficacy (relative to their counterparts with lower levels of these traits) exhibit distinct reactions to the job attributes of performance-based pay, service recipients’ resources and racial demographics, and key job tasks.

Favero, N., Pedersen, M. J., & Park, J. (2023). Which job attributes attract individuals high in public service motivation and self-efficacy to a public service job? Public Administration, 1–40. https:// doi.org/10.1111/padm.12975

https://onlinelibrary.wiley.com/doi/10.1111/padm.12975