Policy Impact Analysis - 117/HR/9208

Bill Overview

Title: Manufacturing Jobs for Veterans Act of 2022

Description: This bill requires the Department of Labor, as part of the Veteran's Workforce Investment Program, to implement the Veterans Manufacturing Employment Program to award competitive grants to three states for the establishment and administration of a State Manufacturing Employment Program. Such program must provide grants to manufacturing employers and joint-labor management organizations that provide eligible veterans with training, on-the-job training, apprenticeships, and training that leads to a recognized postsecondary credential. Eligible veterans are those who are employed by a manufacturing employer and enrolled or participating in a specified training, on-the-job training, apprenticeship, or certification class.

Sponsors: Rep. DelBene, Suzan K. [D-WA-1]

Target Audience

Population: Veterans employed in manufacturing and participating in training programs

Estimated Size: 200000

Reasoning

Simulated Interviews

Machinist (Ohio)

Age: 35 | Gender: male

Wellbeing Before Policy: 6

Duration of Impact: 10.0 years

Commonness: 8/20

Statement of Opinion:

  • I think this policy could really help vets like me get the training we need to advance in our careers.
  • Access to grants for apprenticeships would make it easier for me to gain more specialized skills.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 6
Year 2 8 6
Year 3 8 6
Year 5 9 6
Year 10 9 5
Year 20 8 5

Electrical Technician (North Carolina)

Age: 28 | Gender: female

Wellbeing Before Policy: 5

Duration of Impact: 5.0 years

Commonness: 7/20

Statement of Opinion:

  • This policy sounds like a good initiative but I'm concerned about how it will be managed and if it will actually reach those who need it.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 6 5
Year 2 6 5
Year 3 7 5
Year 5 7 5
Year 10 7 5
Year 20 6 4

Quality Control Specialist (Michigan)

Age: 42 | Gender: male

Wellbeing Before Policy: 6

Duration of Impact: 0.0 years

Commonness: 10/20

Statement of Opinion:

  • I don't expect this policy to affect me as I'm not in any training program right now.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 6 6
Year 2 6 6
Year 3 6 6
Year 5 6 6
Year 10 5 5
Year 20 5 5

Welder (California)

Age: 30 | Gender: male

Wellbeing Before Policy: 7

Duration of Impact: 0.0 years

Commonness: 9/20

Statement of Opinion:

  • Having just completed my training, I think this could have been beneficial knock on effects for others, but won't directly impact me now.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 7
Year 2 7 7
Year 3 6 6
Year 5 6 6
Year 10 6 6
Year 20 5 5

Production Manager (Texas)

Age: 50 | Gender: female

Wellbeing Before Policy: 7

Duration of Impact: 2.0 years

Commonness: 6/20

Statement of Opinion:

  • It's great that this policy focuses on providing skills to veterans, but it seems tailored more towards entry-level positions.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 7
Year 2 7 7
Year 3 7 7
Year 5 7 7
Year 10 6 6
Year 20 5 5

Forklift Operator (Illinois)

Age: 38 | Gender: male

Wellbeing Before Policy: 5

Duration of Impact: 6.0 years

Commonness: 8/20

Statement of Opinion:

  • I think the policy is timely. Grants could make these programs more accessible and beneficial for my career path.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 6 5
Year 2 7 5
Year 3 7 5
Year 5 8 5
Year 10 8 5
Year 20 7 4

Assembly Line Worker (Indiana)

Age: 45 | Gender: male

Wellbeing Before Policy: 6

Duration of Impact: 4.0 years

Commonness: 7/20

Statement of Opinion:

  • If this policy makes apprenticeships more available, I might finally find the opportunity to enroll. Sounds promising.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 6 6
Year 2 6 6
Year 3 7 6
Year 5 7 6
Year 10 7 5
Year 20 6 5

CNC Machine Operator (Florida)

Age: 33 | Gender: female

Wellbeing Before Policy: 6

Duration of Impact: 8.0 years

Commonness: 8/20

Statement of Opinion:

  • Programs like these could really open up more advancement opportunities for people like me in the manufacturing field.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 6
Year 2 8 6
Year 3 8 6
Year 5 9 6
Year 10 8 5
Year 20 7 4

Logistics Manager (Florida)

Age: 55 | Gender: male

Wellbeing Before Policy: 7

Duration of Impact: 0.0 years

Commonness: 10/20

Statement of Opinion:

  • As someone nearing retirement, I don't think this policy will impact me directly, but it could help younger vets.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 7 7
Year 2 7 7
Year 3 7 7
Year 5 7 7
Year 10 6 6
Year 20 5 5

Machine Operator (New York)

Age: 29 | Gender: other

Wellbeing Before Policy: 6

Duration of Impact: 5.0 years

Commonness: 7/20

Statement of Opinion:

  • If the grants cover tuition and living expenses, that would be a huge relief and incentive to enroll in more training.

Wellbeing Over Time (With vs Without Policy)

Year With Policy Without Policy
Year 1 6 6
Year 2 7 6
Year 3 8 5
Year 5 8 5
Year 10 7 5
Year 20 6 4

Cost Estimates

Year 1: $75000000 (Low: $60000000, High: $90000000)

Year 2: $75000000 (Low: $60000000, High: $90000000)

Year 3: $75000000 (Low: $60000000, High: $90000000)

Year 5: $75000000 (Low: $60000000, High: $90000000)

Year 10: $0 (Low: $0, High: $0)

Year 100: $0 (Low: $0, High: $0)

Key Considerations