The following includes a variety of data elements that can be collected and measured to better understand job quality for each of the components from the framework. This information is intended to give users of the Job Quality Playbook a starting point for collecting and measuring job quality data. This is a compilation of metrics and no one organization is implementing all of the metrics listed.


Earnings includes base pay, bonus and profit sharing.


Benefits includes health, well-being, education, wealth building and safety net support.


Schedules include flexible, stable and fair scheduling.

Learning and Development

Learning and Development includes career path support, training and skill development, recognition, and advancement.

Safety and Security

Safety and Security includes physical, mental, emotional, and structural security.

Voice and Representation

Voice and Representation includes formal representation, participatory management, and employee engagement.

Environment and Culture

Environment and Culture includes use of skills, sense of connection, stability, and autonomy.

Purpose and Meaning

Purpose and meaning includes mattering and personal alignment.

While each program, policy or investment may require different metrics; users should view this tool as a menu from which they select a subset of metrics and then customize what, how, and when information is collected to fit their needs. Don’t be afraid to start small and add additional metrics over time.

Collecting and analyzing the data is just one part of the process. Consider what questions you want the data to answer, what goals you hope to achieve, what portion of the programs/employers/positions currently meet them based on the data, and then devise a pathway to close the gaps.

Keep in mind that job quality measurement is still a relatively nascent space. Users of this tool are encouraged to experiment and adapt this information as needed. As new metrics, data sources or benchmarks become available over time, they will be incorporated into the tool.

Information may be collected about a geographic area, an industry, an employer or a work-based learning program. Metric information can be used to

  1. Structure a new job training program with employers
  2. Target sector strategies or rapid response/layoff aversion activities for incumbent workers
  3. Collect data from employers about a job opening or future need 
  4. Help employers explore options to invest in additional benefits for employed individuals
  5. Target expansion of business services offerings
  6. Identify focus areas for local research to highlight trends or needs in your community. Users may select one or more metrics to collect for any given activity.

Note: The metrics included here go beyond those listed in the detailed interventions. The intent is that this will serve as a resource when exploring what could be measured, even if a specific intervention has not yet been designed.

The Metrics DB includes three elements:

  1. Job Quality Components which names the component (e.g. earnings) and lists each of its corresponding elements from Results for America's Job Quality Framework.

  2. Key Job Quality Questions are questions that should be applied either to a specific intervention (e.g. economic or workforce development program or activity) or to interactions with an employer (either as you establish a partnership, specialized program or in providing business services).

  3. Metrics are different types of data that may be captured to better understand and measure the component. Each Metrics section includes a set of overall metrics which are relevant to workforce development and economic mobility interventions in addition to specific items that can be measured.

General guidelines for using this information:

  1. Data should be disaggregated by race, ethnicity, gender, immigration status and other available identifiers whenever possible

  2. Take variances by level, role, education and industry into consideration when reviewing trends

  3. If comparing data, collect it for the same period of time (e.g. program year, fiscal quarter) whenever possible

  4. Analyze trends over time to see if specific policy, procedure or related implementations are driving shifts

  5. Make sure to distinguish between self reported data and system collected/automated information as it may influence reliability

  6. Determine what impact you seek to measure and then select the data elements/metrics that you believe will best provide insights into your desired impact

  7. Take into consideration the burden associated with data collection for the worker, the program personnel, and the employer. Fewer, more relevant metrics that are reliably collected are better than a large set of incomplete data

  8. Couple quantitative data with qualitative insights, such as survey or focus group input, wherever possible to gain a more robust picture

  9. Keep in mind that job quality includes a variety of components; no single metric is by itself a good indicator of whether a job is high quality