Democratizing Labor Market Information



Frauke Kreuter, Ph.D., Professor, University of Maryland Joint Program in Survey Methodology and Co-director, Social Data Science Center 

Julia Lane. Ph.D., Professor, New York University Wagner Graduate School of Public Service

It has never been more important to have timely, local, and actionable data on jobs and earnings – labor market data. Just in the past three years, we have seen the need in many areas – ranging from local responses to COVID19, to  the training of workers for the jobs  resulting from such investments as the $280 billion CHIPS and Science Act, and requested for AI .  And in past decades, the mismatch between workers and jobs has been blamed for the “deaths of despair”. It’s time to expand our infrastructure to include state and local data in a way that can democratize our access to and use of labor market information. The new Evidence Act(1) and the recommendations of the Advisory Committee on Data for Evidence Building(2) provide an opportunity to the public data user community to help support such an effort.

In a new piece published by the American Enterprise Institute, one of us argues for just such a decentralized, local driven approach.  It is inspired by two important and very successful American institutions: the US weather service and the US agricultural extension program.  Each provides a successful framework that combines local knowledge and data to meet local needs, generate timely information, and provide evidence that is immediately actionable.  In the case of weather data, the National Oceanic and Atmospheric Administration provides national coordination and much national funding, but local institutions provide weather information that is useful to people living in Austin, Texas and that is very different from that produced in Green Bay, Wisconsin or Sacramento, California.   Similarly, the National Institute of Food and Agriculture provides funding to land grant universities and agricultural programs that support not only the customized development and dissemination of local information about cattle, apple, or grape farming but also trains local farmers in how to apply those techniques – and how to ask new questions to inspire research(3).

New technology has transformed the way in which we do our work in so many ways, and not least in making such a decentralized approach possible. Work that was initiated to inform the deliberations of the Commission on Evidence-based Policy-making (2, 4) led to the establishment of both a secure remote access facility and a robust training program(5, 6). That work has now matured into a thriving collaboration between state labor market and education agencies.  Just like the Farmers’ Institutes in the 19th Century that led to the land grant system and the ag extension system, state agencies have joined forces with their professional associations (NASWA and SHEEO) and established a formal governance structure to produce new projects and products.  They have identified low cost ways to produce extremely granular information to serve diverse populations in a way that protects privacy but does not distort the information. Simply put, they are democratizing local labor market and education information just as weather and agricultural information was democratized many decades ago(7).

I’m particularly excited that the training programs now have acquired the distinction of academic certification.  The University of Maryland’s Social Data Science Center and the New York University’s Wagner School of Public Service have created a joint Executive Certificate in Data Literacy and Evidence Building joint with Accenture, KYStats and the Coleridge initiative.  The syllabus, code, and training materials will be open source and available for any schools  – from public policy to data science – to adopt and reuse.  Indeed, the framework means that a data infrastructure could support the creation of data and evidence far beyond education and workforce information.  The need is just as great to inform decision making in criminal justice, welfare programs, and child nutrition programs, which are all administered at the state and local levels.   As the American Enterprise Institute piece concludes, a new national collaborative should be established that is grounded in the success of a set of regional collaboratives inspired by the Foundations of Evidence-Based Policymaking Act.   It should be designed to (1) empower state and local stakeholders—such as government agencies, governors’ offices, education and training providers, and chambers of commerce— to identify and solve local problems; (2) foster innovation and build capacity through innovation sandbox training programs with local universities; and (3) produce high-value products for timely decision-making.

What can the APDU community do to help effect such a change?  If you’re affiliated with an academic institution, or with a state agency that is interested in developing a similar training program, you can reach out to Julia Lane , to Frauke Kreuter, or to the multi-state data collaboratives to get started.   If you have interesting ideas about how to expand the approach to other domains, or to work with programmatic agencies or foundations, they could be presented at the next APDU conference or annual collaborative convening.   If you’re a federal or state decision maker, the approach is fully consistent with the White House push for evidence and evaluation as well as the 12 grand challenges identified by the National Academy of Public Administration

It’s an exciting time to be thinking about how data and evidence can be used to transform the public service, and we’re thrilled at the potential to connect with the APDU community.



  1. US Congress, editor Foundations for Evidence-Based Policy Making Act of 2018. 115 th Congress HR; 2018.
  2. Advisory Committee on Data for Evidence Building. Advisory Committee on Data for Evidence Building: Year 2 Report Washington DC2022.
  3. Lane J. Democratizing Our Data: A Manifesto: MIT Press; 2020.
  4. Advisory Committee on Data for Evidence Building. Year 2 Report, Supplementary Materials. In: Office of Management and Budget, editor. 2022.
  5. Foster I, Ghani R, Jarmin RS, Kreuter F, Lane J. Big data and social science: data science methods and tools for research and practice: CRC Press; 2020.
  6. Kreuter F, Ghani R, Lane J. Change through data: A data analytics training program for government employees. Harvard Data Science Review. 2019;1(2):1-26.
  7. Cunningham J, Hui A, Lane J, Putnam G. A Value-Driven Approach to Building Data Infrastructures: The example of the MidWest Collaborative. Harvard Data Science Review. 2021.