Appendix 3: Summary of Grants Awarded
Table of Contents
Introduction
The 10 Big Ideas
Digging Deeper
- Measure what matters to workers, capturing a full range of job quality indicators
- Center equity in measurement
- Increase mandatory human capital data disclosure
- Link public and private data to gain new insights into the quality of jobs
- Leverage business data to demonstrate the return on investment from good jobs
- Revise data systems to include and support the non-W2 workforce
- Strengthen workforce system metrics to deliver results for workers and businesses
- Use public and private spending to measure and strengthen equity and good jobs
- Strengthen state and local capacity for data-driven decision-making to advance good jobs
- Invest in strengthening job quality measurement
Understanding the Impact
Appendices
Acknowledgements
The cross-sector leaders involved in the Job Quality Measurement Initiative (JQMI) surfaced several exciting recommendations, prompting the initiative’s co-funders to invest in further exploration through a set of small grants. A diverse group of experts was selected to conduct additional research in the summer and fall of 2022 towards developing tactical and technical next steps, and laying the foundation for demonstration projects to test and scale promising recommendations.
Developing a Job Quality Metrics Scorecard for Federal Agencies
Jobs for the Future
Jobs for the Future (JFF) developed a standard set of key performance indicators and metrics for assessing and reporting job quality in a way that is feasible for and relevant to multiple stakeholders across the workforce system (including government, employers, and workers). JFF drew on its close partnerships with workforce boards across the country, and considered potential demonstration projects that could create a pathway to integrate these metrics into U.S. Department of Labor (DOL) and other federal agency programs.
Standardizing Job Quality Metrics for an Use in an Employer/Investor Scorecard
Kavya Vaghul, JUST Capital & Matt Walsh, Lightcast (formerly Emsi Burning Glass)
Kavya Vaghul and Matt Walsh, the co-chairs of the JQMI Commercial/Employer working group, took steps to to address the lack of publicly available data on companies by (1) developing a standard set of employer-level job quality metrics that can be leveraged by businesses and investors, and (2) identifying a methodology by which to aggregate those metrics into a composite score to easily measure and compare company performance on key job quality issues. They also proposed next steps to pilot the proposed employer scorecard with key users.
Crowdsourcing of Employer Data
Drucker Institute
Rick Wartzman of the Drucker Institute explored how crowdsourcing of employer data can strengthen and scale existing job quality data infrastructure in the absence of mandated disclosure, including identifying potential shortcomings in current crowdsourcing indicators, such as data bias, and strategies to address those issues. The project produced an inventory of existing crowdsourced and worker-provided job quality metrics (such as the data collected via Glassdoor, the Shift Project, and PayScale) and a set of recommendations for collecting and utilizing such data.
Building Job Quality Standards into Procurement
Center for American Progress
The Center for American Progress (CAP) developed a set of recommended implementation steps for government partners to collect job quality data as part of Infrastructure Investment and Jobs Act (IIJA) procurements in order to help agencies evaluate and monitor bidders for discretionary funds. This included exploring potential standard disclosures on job quality and equity for use in federal agency procurement, accompanying data collection approaches, and potential tools and technical assistance to support adherence to standards.
Capturing Worker Voice
The Worker Empowerment Research Network
A team of academic researchers from MIT and Cornell developed a set of validated survey questions to better capture worker voice in public and commercial data collection. They recommended a set of questions to assess workers’ ability to exercise voice and create change within their workplaces through both individual and collective action, and offered tactical pathways to implementation in both federal statistical and commercial surveys.
Strengthening Data Collection through Unemployment Insurance (UI) Wage Records
The Urban Institute
The Urban Institute conducted a landscape scan of UI wage records at the state level and provided recommendations to strengthen and standardize collection of job quality data across the United States, leveraging the unique benefits of employer-reported UI data. Bill Congdon, a co-chair of the JQMI Administrative working group, spearheaded this project, which mapped the current UI records data landscape, identified key constraints to developing standard and enhanced records, and identified promising directions for enhancements that better capture job quality and workforce equity.
Updating Measures of Work Schedules in National Survey Data
Susan Lambert, University of Chicago
Susan Lambert, a co-chair of the JQMI Federal Statistical working group, led a research team to develop a standard set of validated survey questions focused on worker schedules as a key aspect of job quality, in order to measure dimensions of scheduling quality that are not currently captured in federal statistical data. This included exploring questions focused on measuring work hour stability, predictability, and control in order to track changing configurations of scheduling practices across occupations and industries and to estimate disparities in the quality of work schedules by worker characteristics. The researchers also provided technical recommendations about pathways to implementation in federal statistical surveys.
Gathering Refugee and Immigrant Worker Voice to Strengthen Job Quality Measurement
International Rescue Committee
The International Rescue Committee (IRC) conducted focus groups and interviews with diverse refugee/immigrant workers (representing a range of regions, nationalities, languages, genders, ages, industries of employment, and lengths of time spent in the U.S.), capturing the voices of communities that are under-counted in government surveys. IRC gathered workers’ perspectives on job quality and job quality measurement, including human-centered research on convenient and comfortable ways to share data (e.g., via text message, phone interview, or online surveys). This project also established feedback loops with other JQMI research teams, to help ensure that the data collection processes and questions developed across the initiative are responsive to worker priorities and preferences.