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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

#4: Link public and private data to gain new insights into the quality of jobs.

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These actions are intended for…

Pairing data from diverse government and commercial sources, standardizing collection processes, and creating shared data goals that prioritize privacy, consent, and equity1 will not only improve government effectiveness but also provide fresh insights into who workers are, their experiences in the labor market, and the support they need to advance.

Elements of job quality data sit in a diverse set of administrative, programmatic, statistical and business databases but the current lack of critical linkages between and among these public and private datasets limits stakeholders’ ability to understand the full dimensions of jobs in the United States and how these impact worker stability and mobility outcomes.

This recommendation advances several federal priorities. The Foundations for Evidence-based Policymaking Act of 20182 requires federal agencies to build capacity to share data for research purposes to answer important policy questions, and authorized the Advisory Committee on Data for Evidence Building that includes experts from federal and state governments, academia, and private research organizations. This Committee is charged with issuing recommendations for implementing the National Secure Data Service (NSDS)3, which was authorized as a new National Science Foundation program in the CHIPS-Plus Act. NSDS will be a secure, privacy-protecting data-linkage platform that enables data from federal, state, and local governments and the private sector to be merged to produce research findings. The U.S. Department of Labor’s (DOL) Enterprise Data Strategy,4 which calls for making data findable, accessible, interoperable, and reusable, also supports these efforts.

To put this into practice, the federal government, state and local governments, and commercial platforms can:

1. Centralize, standardize, and expand wage records across all states.

Unemployment wage records are a central source of administrative data on worker and firm-level wages and hours, which provide crucial inputs into the success of programs like Workforce Innovation Opportunity Act (WIOA). However, each state manages the data collected from employers separately and according to its own standards, creating much variation in the level of detail available within the records.

To standardize and expand records, federal and state agencies can take the following steps:

  • DOL could collaborate with a coalition of willing states to develop a standardized, enhanced wage record to include a richer set of fields capturing job quality elements beyond quarterly wage to support statistical, programmatic and research purposes. Fields could include hours, occupation codes or job titles, employment status, start dates and location5, building on some of the work already in place in states like Washington, Indiana and Nebraska. Occupational coding could be done at the state or federal level, based on job titles provided by employers, to minimize burden to businesses. DOL and state partners could also explore pathways and pilots to expand wage records to include information on non-W2 workers including schedules, location and occupation. This work will build on multiple Bureau of Labor Statistics (BLS) efforts, including pilots related to wage and claims records and the efforts of the Labor Market Information network, as well as the Workforce Information Advisory Council’s6 work to ensure that individual students and workers have the information they need to make good decisions regarding their education and employment.
  • Government agencies could consider requiring or incentivizing states to use a small portion of the funding they receive from DOL for Unemployment Insurance (UI) wage record program administration and modernization to implement the new wage reporting standard. This would enable the federal government to build consensus across states for creating a centralized database of UI wage records, such as through a public-private partnership under the Federally Funded Research and Development Center (FFRDC) model, or a federally held database at DOL—much like the National Directory of New Hires (NDNH) at the U.S. Department of Health and Human Services (HHS). This consolidated database would lower the costs and administrative burden on states, mitigate the challenges faced by programs and researchers in obtaining direct agreements with individual states, and provide access to valuable microdata from UI wage and claim records to better understand job quality. Because UI wage record data elements are determined largely by state statutes, aligning data elements could require state legislative action in some cases.

2. Expand access to existing federal job quality data

NDNH is an under-utilized federal repository of data housed at HHS that includes unemployment insurance quarterly wage records, unemployment compensation records, and wage data for federal workers and the military as well as individuals working in a state that differs from their residence. This set of comprehensive data goes beyond what is currently available on job quality from any single source at DOL, and increased access would directly support both the implementation and evaluation of a variety of workforce programs including WIOA Title I. Unencumbered access to NDNH would also avoid lengthy individual negotiations with states and avoid burdening state UI agencies who are already overtasked with UI administration. This proposed change has appeared in prior administrations’ budgets to Congress including under the Obama Administration in the 2016 budget request but to date has not been implemented.

Federal agencies can expand access to the NDNH in the following ways:

  • DOL should explore the viability, in collaboration with HHS, of using the existing provision (Subsection (j)(5) of 42 U.S.C 658) that authorizes the HHS Secretary to provide access to NDNH data for “research purposes found by the Secretary to be likely to contribute to achieving the purposes of” the Temporary Assistance for Needy Families (TANF) program. A statutory purpose of TANF is “promoting job preparation” and “work,” which is highly aligned with ETA’s mission of supporting effective workforce preparation for low-income individuals who are parents or may become parents who may need TANF assistance in the future. Utilizing HHS’ existing statutory authority to broaden NDNH access for research will require addressing specific privacy and confidentiality components including requirements for data minimization, narrow and specific scoping of data use, security processes and penalties for unauthorized access, use, or re-disclosure. Currently data is available to DOL’s Chief Evaluation Office on a limited basis but requires manual transmission of lists of individuals involved in current studies through WIOA programs and project-specific MOUs; establishing a mechanism for unencumbered access would reduce overhead for both agencies and improve collaboration to advance job quality.

3. Link administrative and statistical data to better understand the labor market.

While statistical, administrative and programmatic datasets each provide important insights into the labor market, the data are currently federated, inhibiting the ability of program administrators or researchers to fully understand non-wage components of job quality or equity. Lack of access to comprehensive datasets prevents agencies and their grantees from accurately measuring the results of federal programs intended to improve economic mobility. It also impairs researchers’ capacity to build a deeper understanding of population needs and effective interventions. And it prevents adoption of outcomes-based payment models that can incentivize innovation and improvement in workforce and social service programs. To address these issues and to systematically uproot inequity, creating mechanisms to connect demographic data with accurate information on workers’ earnings, benefits, schedules, and employment arrangements is vital. Efforts to link data would also advance the goals of the Commission on Evidence-Based Policymaking launched by the Obama Administration, as well as the Foundations for Evidence-Based Policymaking Act7 and inter-agency Advisory Committee on Data for Evidence Building.

To address this, federal agencies, in partnership with state and local government, can:

  • Provide coordinated messaging to states and localities on permissible methods to link and analyze their data in order to maximize data use while simultaneously protecting individual privacy, consent, ethics and transparency. This includes incentivizing intra-state collaboration in areas such as UI records, WIOA programs, or education data so that states can structure collaborative programming to meet the needs of businesses operating across state lines or workers living in one area and working in another.
  • Explore possibilities for linking administrative data on non-compensation aspects of job quality to existing population-level research datasets at the Census Bureau, the Internal Revenue Service (IRS), and HHS. This could include injury rates, visa access, and union coverage available in sources such as the Occupational Safety and Health Administration (OSHA), workers compensation systems, the National Labor Relations Board (NLRB) and the National Vital Statistics System (NVSS). This would require exploring pathways to consistently identify business establishments, as described in greater detail in the discussion of a single firm identifier in Idea #5. Research should specifically address understanding legal restrictions, privacy and equity considerations, as well as data availability and storage format.

4. Aggregate job quality to family level measures so that earnings, benefits, schedules and working conditions of multiple workers are considered.

There is currently no formal federal monitoring of the total earnings and benefits that households receive from employers, the public benefits that may be needed to fill in gaps, or the job conditions across families. While the BLS does produce “A Profile of the Working Poor”8 and the Employment Characteristics of Families,9 neither include a household earnings measure. The Census Bureau also issues an Income and Poverty publication10 but family earnings are not compared to family poverty thresholds and it does not include data on employer-provided health insurance for working families. While the existing resources provide important insights, lack of family-level earnings data limits data-driven policy discussions.

There are three federally-sponsored household-based economic and program benefit surveys: the Current Population Survey (CPS)/Annual Social and Economic Supplement (ASEC), American Community Survey (ACS), and Survey of Income and Program Participation (SIPP). These surveys have household unit-based weights to create family-level measures, but have incomplete measures of job quality.

To better understand the realities and impacts of job quality for families and not just individual workers, federal agencies, such as DOL and HHS, should:

  • Explore using existing data sources to estimate family-sustaining earnings (CPS, ACS, SIPP), access to health insurance (CPS, ACS, SIPP), pensions (CPS), and nonstandard work schedules (SIPP) at the family level. For example, using CPS data, earnings can be calculated for working families and included in existing publications. Household surveys such as the CPS use proxy respondents such that one person answers all employment questions on behalf of all other household members. However, it may be that the designated respondent does not know the details of all other family members’ jobs. While rigorous testing and validation of proxy measures should be undertaken for the CPS, in the meantime a comprehensive set of job benefit and working conditions questions could be added to the Survey of Income and Program Participation (SIPP), which already interviews all working household members. For more details on this recommendation, see Appendix 2.

Endnotes

  1. Actionable Intelligence for Social Policy, University of Pennsylvania. A Toolkit for Centering Racial Equity Throughout Data Integration.
  2. Foundations for Evidence-Based Policymaking Act of 2018. P.L.:115-435 (January 14, 2019).
  3. Data Foundation. Congress Authorizes Establishment of National Secure Data Service to Improve Data Analytics. (July 28, 2022).
  4. United States Department of Labor. Enterprise Data Strategy (2022).
  5. Batia Katz, William J. Congdon, and Jessica Shakesprere, Urban Institute. Measuring Job Quality: Current Measures, Gaps, and New Approaches (April 2022).
  6. Workforce Information Advisory Council. Supporting a Full Recovery – Recommendation of the Workforce Information Advisory Council to the Secretary of Labor (May 11, 2022).
  7. Foundations for Evidence-Based Policymaking Act of 2018. P.L.:115-435 (January 14, 2019).
  8. BLS Reports, U.S. Bureau of Labor Statistics. A profile of the working poor, 2019 (May 2021).
  9. U.S. Bureau of Labor Statistics. Employment Characteristics of Families – 2021 (April 20, 2022).
  10. United States Census Bureau. Poverty Publications.

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