Statewide Job Quality Measurement for Climate and Infrastructure Jobs

At a glance

Type of work: Workforce analytics and job quality measurement
Client type: State workforce council and partners
Geography: Colorado
Lead economist: Guanyi Yang, PhD
Deliverables: Measurement brief, datasets, sector profiles, skills insights, workflow documentation, slide deck

The question we were asked to answer

Colorado’s climate and infrastructure workforce strategy required more than wage data. The client needed a way to measure non wage job quality so they could answer practical questions like:

• Which parts of the climate and infrastructure workforce offer stable schedules, safe conditions, and advancement pathways?
• Where do job quality gaps create barriers to recruiting and retaining a diverse workforce?
• How can the findings connect to skills first hiring and advancement practices?

The catch is that many job quality dimensions are not captured in one clean dataset. A good solution had to be transparent about proxies and limitations and still be usable for reporting.

What Focalize Solutions did

We translated the client’s job quality framework into measurable indicators using free, publicly available datasets. We then built a structured occupation dataset and produced sector-level profiles, plus example occupation profiles to make the framework easy to understand.

We also built the work so staff can update it annually without restarting from scratch.

Data sources and indicator strategy

We used public datasets that can support repeatable measurement, including:

• O NET measures for working conditions, autonomy, training, and advancement related attributes

• American Community Survey microdata proxies for benefits coverage, full time status, and multiple jobholding at broader occupation categories

• BLS Survey of Occupational Injuries and Illnesses indicators at the state by industry level

• Colorado employment projections for demand context

Because public datasets come with different levels of detail, we documented where we used proxies and what that means for interpretation.

How we structured the work

We broke the project into clear tasks so each step produced something usable:

1. Confirm the final dimensions and occupation universe, then publish a short measurement brief so everyone agrees on definitions.

2. Clean and standardize SOC and O NET mappings and build a master occupation and industry dataset.

3. Extract indicators and produce a raw indicator dataset.

4. Normalize and scale indicators within dimensions, then construct sector and industry profiles and example occupation profiles.

5. Pull skills and knowledge signals from O NET, identify skills linked to higher scoring roles, and translate that into skills first guidance.

6. Document a reusable workflow with clear instructions for updates, including optional code support.

7. Deliver a presentation ready deck and walk the team through the findings.

What we delivered

The project produced:

• A short measurement brief that defines indicators, proxies, and sources

• A master occupation and industry dataset built for repeatable reporting

• Sector profiles and example occupation profiles that make the framework concrete

• A skills first insights memo and slide content tied to the job quality results

• Workflow documentation that staff can update annually

• A final slide deck and virtual briefing

Why this mattered

Job quality work often fails when it becomes either too academic or too vague. This project balanced both needs:

• It stayed defensible and transparent about what we can and cannot measure from public data.

• It produced outputs that staff can use with partners, employers, and community stakeholders without a long technical report.

Confidentiality and work samples

Because the workflow is designed for public data, we can share a redacted sample of the sector profile format and the update workflow outline upon request.

Related service: Workforce and job quality measurement