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DIGGING DEEPER INTO THE 10 BIG IDEAS

Table of Contents

Introduction
The 10 Big Ideas
Digging Deeper
  1. Measure what matters to workers, capturing a full range of job quality indicators
  2. Center equity in measurement
  3. Increase mandatory human capital data disclosure
  4. Link public and private data to gain new insights into the quality of jobs
  5. Leverage business data to demonstrate the return on investment from good jobs
  6. Revise data systems to include and support the non-W2 workforce
  7. Strengthen workforce system metrics to deliver results for workers and businesses
  8. Use public and private spending to measure and strengthen equity and good jobs
  9. Strengthen state and local capacity for data-driven decision-making to advance good jobs
  10. Invest in strengthening job quality measurement
Understanding the Impact
Appendices
Acknowledgements

#2: Center equity in measurement.

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These actions are intended for…

We measure equity, writes Deputy Secretary of Labor Julie Su in the U.S. Department of Labor’s (DOL) 2022 Enterprise Data Strategy, 1 “not simply because it is the right thing to do but because building an inclusive economy measured by the experiences of these workers is the way to make sure no one is left behind.”

Reimagining our data systems so that racial and gender equity sits at their core is a shared priority for data system leaders inside and outside of government, and the Biden Administration has already made tremendous progress.2

Federal, state, and local agencies can build on these efforts by collecting disaggregated data, examining program performance metrics and the narratives shaping them, and conducting equity assessments, leading to more responsive, evidence-based program design. Measurement systems should be purposefully structured to include input from workers who have historically lacked access to good jobs at every step of the process, from design through evaluation.

To put this into practice, federal agencies should work with state and local government, nonprofit, and business partners to:

1. Implement requirements for data disaggregation in workforce and social service programs, administrative data, and federal surveys.

In order to address equity gaps it is critical to understand how existing programs are serving distinct communities, especially those historically locked out of opportunity such as people of color, women, and immigrants. Unfortunately, existing data systems do not always require demographic data and present many limitations in breaking down information by sub-groups and intersectionalities (such as by both race and gender) or connecting basic demographics to life circumstances such as parenting, immigration status and country of origin, or other barriers to employment. This restricts visibility into disparate impacts that populations may be experiencing.

To understand and address inequities, federal and state agencies can:

  • Adjust federal statistical sources, including Bureau of Labor Statistics (BLS) and the American Community Survey (ACS) data, to allow for deeper disaggregation to better understand the needs of communities. This requires ensuring that surveys have sufficient sample sizes to both protect privacy of individuals and break down data by race, gender, geography, and other characteristics, including going beyond high-level ethnicity categorizations to understand identity by historic nation of origin. These changes would require either collecting larger samples from surveys, changing data collection, or merging existing data sets, which would likely necessitate investments in human or technical resources, rule changes, and information collection reviews. This effort would build on significant progress already underway, such as the BLS’ important move3 to begin publishing monthly jobs data on Native American and Native Alaskan workers, efforts to disaggregate demographic data for the nation’s Asian American, Native Hawaiian and Pacific Islander communities, and the recommendation from President Biden’s Equitable Data Working Group4 to generate “disaggregated statistical estimates to characterize experiences of historically underserved groups using survey data.” The White House Office of Management and Budget (OMB) has also taken steps to support agencies in disaggregation, including publishing plain language guidance5 on flexibilities that agencies can use to improve disaggregation, presentation, and analysis of race and ethnicity standards, as well as launching a formal revision process6 to revisit race and ethnicity standards.
  • Encourage states to collect and report demographic data for all workforce development, training and social services programs as well as in Unemployment Insurance (UI) claims data. 7 For example, using OMB and DOL guidance,8 states could require consistent reporting and incentivize analysis of program-level performance data by race, to uncover and address disparate outcomes.9 DOL and the U.S. Department of Health and Human Services (HHS) can use funding and reporting processes, including more flexible timelines, access to performance-based funds, and high performing workforce board recognition as practical incentives to reward expanded reporting. DOL and HHS can also provide technical assistance to state and local areas to increase understanding of how to protect individual privacy and use disaggregated data to surface important learning for ongoing program improvements as well as post-program evaluation. This could include drawing on OMB guidance, existing DOL Training and Employment Guidance Letters, and new analysis to provide guidance and templates for disaggregated reporting, including specific job quality questions and metrics, and recommendations about potential sources of data that state and local agencies can use as part of program evaluation. A technical assistance strategy targeted to states and localities could also leverage the resources that federal agencies have already developed to accelerate cross-agency sharing of federal data through the Federal Data Strategy and the Advisory Committee on Data for Evidence-Building. Federal agencies could also partner with data-focused nonprofits such as Actionable Intelligence for Social Policy, which helps state and local governments, academics, and community-based organizations navigate the confusing array of federal privacy laws and regulations to build integrated data systems.
  • Incentivize employers participating in the public workforce system or public procurements to track and share hiring and separation data at a disaggregated level, including enabling multiple data elements to be analyzed simultaneously (such as percent of Black women). Workforce agencies can infuse requirements for disaggregated data in employer wage or loan subsidy programs and procurement and purchasing processes, as well as offer prioritized services to businesses willing to share disaggregated data.

2. Uproot harmful assumptions and metrics embedded in workforce and social services systems.

The default performance metrics baked into many publicly-funded social programs are not evidence-based, but instead based on age-old assumptions about what causes poverty — that it is due to individual character flaws versus social, political, or economic systems that have repeatedly failed10 people of color, immigrants, and other marginalized communities. What we choose to measure or not measure can either perpetuate these harmful narratives and disparities, or can help to uproot assumptions and address inequities.

In social services and workforce programs, data collected primarily focuses on participant compliance, through metrics such as employment status or the completion of programmatic requirements, and the measure of success is typically reduction in “dependency” on public benefits rather than evidence that a participant has secured a good job and is on a pathway to economic mobility. The Temporary Assistance for Needy Families (TANF) program, for instance, gives states the authority to impose strict work requirements that may perpetuate racial stereotypes 11, and rewards providers who connect participants to any available job, however unstable. As a result, the program fails to deliver optimal12 results for participants.

Such deficit-based approaches often mask the poor quality of jobs as data collection centers primarily on “improving” the individual and negates the responsibility of governments and employers to tackle systemic inequities. The Workforce Innovation Opportunity Act (WIOA) program, for example, relies on assumptions of individual responsibility that can obscure the role that occupational segregation plays in shaping labor markets, and inadvertently reproduce disparities. Workforce agencies are rewarded for job placements, but metrics fail to track, let alone disaggregate, job quality data. As a result, many participants are placed into high-turnover jobs that lead to program churn.13 Black workers, who are overrepresented in the program, have the lowest post-placement earnings among all participants.14

To uproot discrimination from federally-funded programs and instead prioritize evidence and outcomes, JQMI working groups proposed that federal agencies such as DOL and HHS:

  • Conduct a definitional reset15 of what data are collected and why. For example, shift programs like TANF, Supplemental Nutrition Assistance Program Education and Training (SNAP E&T), and WIOA away from a punitive focus on compliance requirements and reducing government expenditures to an evidence-based approach that rewards effective programs that deliver results for all workers, such as by providing participants with equitable opportunities to secure quality jobs. Overhaul data collection systems to focus on understanding and harnessing worker potential rather than documenting deficiencies, including shifting reporting from emphasizing negative statistics (e.g. barriers, demonstration of eligibility) to documenting assets, and reducing burden to the worker by eliminating duplicative data collection requirements that can re-traumatize participants, in alignment with the Biden Administration’s focus on reduction of administrative burden.16
  • In accordance with the Biden-⁠Harris Administration’s Recommendations for Advancing Use of Equitable Data,17 conduct equity assessments of federal programs18 to uncover programs delivering disparate impacts. Federal, state and local agencies adapting programs to advance equity should employ human-centered design approaches to engage those the program intends to serve in envisioning not only how to meet their needs but how to capture and communicate results. Refining and streamlining what, when and how data are collected and communicated—in collaboration with program participants—can help to direct measurement and resources toward the job quality and equity outcomes prioritized by workers themselves and enable local communities to hold government and employers accountable. This shift will require collaboration between agencies that collect data, particularly DOL and HHS, and with communities to address bias. However, this investment can also serve as an important step forward in rebuilding trust with diverse communities as services become more responsive and inclusive.

3. Include worker voice in data collection.

Workers are experts in their own needs and experiences at work, and should be centered in data collection. It is especially important to hear from workers who have been most locked out of opportunity, such as contract workers with limited protections and enforcement recourse. This includes both defining what measures are important and determining how collection is carried out. Existing federal and commercial surveys collect a wealth of data but stop short of asking questions that surface key job quality considerations like whether workers feel safe, experience discrimination and bias, are free to unionize, or have the benefits needed to care for themselves and their families.

In addition, we must measure whether workers have access to voice mechanisms at work, including whether workers feel that they have the power to change things about their workplaces through individual or collective action. Across JQMI working groups, participants noted that while there is strong consensus that workers want and need more voice19 to improve their jobs and support innovation and productivity within their companies, there is no shared, cross-sector definition of “worker voice”, and the ability to exercise voice to improve working conditions and contribute to company operations is rarely measured in public or private data collection.

To elevate worker voice, federal, state, and local agencies, with support from philanthropy and investors, can take the following steps:

  • DOL and HHS can engage workers in the design of data collection and evaluation processes, in accordance with HHS’ commitment 20 to incorporating individuals with lived experience into program design and administration. This includes exploring how workers prefer to be engaged, what topics are most meaningful, and what compensation or incentives are appropriate for time spent sharing their perspectives. Insights gathered should be used to monitor and improve operational standards in programmatic service delivery.
  • The U.S. Census Bureau should explore including additional worker voice 21 questions that capture whether workers have individual or collective power to improve their workplaces, and whether they are free to represent their interests through collective bargaining or other structures. This includes exploring workers’ preferred means for taking actions to improve their economic, psychological, and social outcomes and experiences at work. A first step will be to draw on prior research22 to document existing, well-validated, and broadly-accepted concepts and measurements, including the kinds of objective measures typically preferred by federal statistical agencies, along with select attitudinal or perception-based items with a proven link to worker economic and behavioral outcomes. Questions should be standardized across government surveys and collected regularly through tools such as National Longitudinal Surveys (NLS). Responses should be analyzed for trends such as how women and people of color feel regarding their current workplace and their future labor prospects; the correlation between worker voice and other elements of job quality; and what voice channels (e.g., participation in a union, access to employee ownership, or participation in an employer-provided survey) result in workplace changes. This work directly aligns with the Executive Order23 signed in April 2021 creating a task force dedicated to mobilizing the federal government’s policies, programs and practices to empower workers. See Appendix 3 for sample worker voice questions.
  • Philanthropy and investors should support expanded and standardized collection of crowdsourced worker data through platforms such as Glassdoor, PayScale, Coworker.org and The Shift Project to capture worker-provided, firm-level data on job quality. While many firms currently crowdsource data, the data are not standardized nor are there enough observations at the individual company level to provide statistically reliable results, Philanthropy and investors can use market-based incentives to scale and systematize data collection. Data collected can be made publicly available in a clear, easy to consume manner such as an aggregated job quality index to inform individual employment decision-making and investments. The index should provide transparent information on data sourcing, collection, and aggregation methodologies to address questions about bias and support clear interpretation. Such standardized scoring mechanisms—including the questions, metrics, and benchmarks that guide the analysis of the crowdsourced data—would also serve as a guide for workers navigating employment opportunities and employers looking to better understand how to attract and retain workers. Making this information publicly available can help to highlight high-road employers, spotlight and spread promising practices, encourage dialogue with businesses, and shed light on the industries and occupations experiencing poor job quality, including higher rates of discrimination. See Appendix 1 for a landscape analysis of data platforms and Appendix 3 for a proposed aggregation approach.

Endnotes

  1. United States Department of Labor. Enterprise Data Strategy (2022).
  2. Briefing Room, The White House. Executive Order On Advancing Racial Equity and Support for Underserved Communities Through the Federal Government (January 20, 2021).
  3. United States Bureau of Labor Statistics Commissioner’s Corner. BLS Now Publishing Monthly Data for American Indians and Alaska Natives (February 14, 2022).
  4. Briefing Room, The White House. Fact Sheet: Biden-⁠Harris Administration Releases Recommendations for Advancing Use of Equitable Data (April 22, 2022).
  5. The White House. Flexibilities and Best Practices for Implementing the Office of Management and Budget’s 1997 Standards for Maintaining, Collecting, And Presenting Federal Data on Race and Ethnicity (Statistical Policy Directive No. 15) (July 2022).
  6. Karin Orvis, Briefing Room, The White House. Reviewing and Revising Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity (June 15, 2022).
  7. Chad Stone and William Chen, Center on Budget and Policy Priorities. Introduction to Unemployment Insurance (July 30, 2014).
  8. Rosemary Lahasky, United States Department of Labor. Eligible Training Provider (ETP) Reporting Guidance under the Workforce Innovation and Opportunity Act (WIOA) (August 31, 2018).
  9. Alex Camardelle, Joint Center for Political and Economic Studies. Principles to Support Black Workers in the Workforce Innovation and Opportunity Act (October 2021).
  10. Clair Minson, Federal Reserve Bank of Atlanta. The Workforce Development Field or a Conduit for Maintaining Systemic Racism (February 19, 2021).
  11. Ife Floyd et al., Center on Budget and Policy Priorities. TANF Policies Reflect Racist Legacy of Cash Assistance (August 4, 2021).
  12. LaDonna Pavetti and Ali Zane, Center on Budget and Policy Priorities. TANF Cash Assistance Helps Families, But Program Is Not the Success Some Claim (August 2, 2021).
  13. The Center for Law and Social Policy (CLASP). WIOA and Job Quality Memo.
  14. Alex Camardelle, Joint Center for Political and Economic Studies. Principles to Support Black Workers in the Workforce Innovation and Opportunity Act (October 2021).
  15. FrameWorks. Framing 101.
  16. Shalanda D. Young and Dominic J. Mancini, Executive Office of the President Office of Management and Budget. Memorandum for Heads of Executive Departments and Agencies: Improving Access to Public Benefits Programs Through the Paperwork Reduction Act (April 13, 2022).
  17. Briefing Room, The White House. Fact Sheet: Biden-⁠Harris Administration Releases Recommendations for Advancing Use of Equitable Data (April 22, 2022).
  18. PolicyLink. For Love of Country: A Path for the Federal Government to Advance Racial Equity (July 2021).
  19. MIT Institute for Work and Employment Research. Worker Voice in America’s Working Future.
  20. Syreeta Skelton-Wilson et al., Office of the Assistant Secretary for Planning and Evaluation and United States Department of Health and Human Services. Methods and Emerging Strategies to Engage People with Lived Experience – Improving Federal Research, Policy, and Practice (December 20, 2021).
  21. Thomas Kochan et al., Good Companies, Good Jobs Initiative. Worker Voice in America.
  22. William T. Kimball, Erin L. Kelly, Thomas A. Kochan, and Duanyi Yang. Worker Voice in America: Is There a Gap between What Workers Expect and What They Experience? (October 11, 2018).
  23. Briefing Room, The White House. Fact Sheet: Executive Order Establishing the White House Task Force on Worker Organizing and Empowerment (April 26, 2021).

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