Digital transformation in labor and education systems:

Improving the government response to the next unemployment crisis

Editor’s note: In case you missed it, watch a webcast held on July 28 on Improving labor and education data systems after the COVID-19 unemployment crisis.

July 26 2021

In spring 2020, as the COVID-19 public health lockdowns unfolded, an unprecedented wave of displaced workers applied for unemployment insurance (UI). But in many cases, getting UI to these millions of workers was a fraught process.

States were hard pressed to process claims accurately and quickly, because UI application processes rely heavily on a staff member making decisions about claims, even for applications filed online. Hiring new staff and contractors was a necessary first step, but it often added friction since it can take years for staff to fully onboard. There are also racial disparities in who is covered and who receives UI benefits. In one survey, Black workers represented 16% of the unemployed, but only 9% received UI; Latino or Hispanic workers represented 23% of the unemployed, but only 10% received UI. This suggests that state UI programs reproduce historical patterns of racial and ethnic inequality either by design, through program implementation, or both.

And even though Congress passed unusually generous expansions to UI in March 2020’s CARES Act, that relief didn’t get out quickly enough to end users. These systems failures imposed deeper costs by further eroding trust in government.

These high-profile UI breakdowns were rooted in more than 30 years of declining funding in broader labor market programs, including cuts to the technology, staff capacity, and data infrastructure that support them. Decentralization gave states and local areas more control, but with fewer resources; it also produced a landscape in which the resulting 53 systems across U.S. states and territories became more divergent from each other over time, with different benefit levels, processes, and more customized data systems.

As policymakers debate how to fix our UI systems, it is important to situate UI benefits in the larger ecosystem of labor and education data systems, which suffer from many of the same root problems as UI benefit delivery. Simply throwing billions of dollars at 53 separate systems over a short time frame will not address the root problems, as we learned from the previous effort to modernize UI after the Great Recession—only one out of five of those projects was completed on time, on budget, and with the required functionality.

This report takes a closer look at what it will take to succeed. Although the framework offered here applies broadly to labor and education data systems, we focus on labor market information, employment, and training systems, such as those administered through the Workforce Innovation and Opportunity Act (WIOA). The COVID-19 crisis offers a unique opportunity to hit the reset button on these systems and embark on a more holistic redesign guided by basic principles of continuous improvement grounded in user experience and improving equity in access.

To inform this report, we convened three roundtables with 19 local workforce board leaders, state data systems experts, national civic technology experts, and other subject matter experts on transforming labor and education data systems in the U.S.

Key problems affecting labor and education data systems

The report outlines four key problems affecting labor and education data systems:

1) A misaligned culture undermines the accessibility of programs and services for the end user.

2) There are significant procurement and data ownership challenges, including the high costs of maintaining separate custom systems in each state and territory, inadequate attention to the end user experience and data ownership rights, and the wholesale outsourcing of systems development to third-party vendors that states can become locked into.

3) Outdated policy and legal frameworks, as well as low capacity, signify that serious efforts are required to improve data quality and security and implement a national framework to rebuild and update these systems.

4) There are serious gaps in coverage in the labor and education data collected through UI wage records, with major implications for equity.

Lessons from the civic technology movement

Additionally, the report assesses what the civic technology movement can offer policy leaders in labor and education who are trying to transform their systems. The civic technology movement aims to make government technology and processes for receiving benefits and services easier for end users to navigate, drawing on agile methods of iterative software development that focus on continuous improvement of small elements at a time rather than large, one-off projects with complex requirements. For example, civic technology leaders played a vital role in rebuilding health care exchanges when they crashed during Affordable Care Act implementation, and have similarly provided vital support to state UI programs throughout the COVID-19 crisis. Though there has been a surge of activity and pilots at all levels of government, civic tech has faced barriers to becoming more deeply institutionalized and influential in government.

Case studies of digital transformation

As the federal government reinvigorates its role in digital transformation in the wake of the COVID-19 crisis, there are existing efforts and case studies that federal and state leaders can build on and learn from.

A vision for digital transformation in labor and education systems

The report concludes by laying out a vision for a better way to approach digital transformation in labor and education systems, including a set of policy recommendations for how Congress and states could approach digital transformation efforts with a higher likelihood of success and cost-effectiveness.

Vision to guide digital transformation: The report’s vision to guide digital transformation efforts is rooted in the following overarching goals:

  • Reducing the administrative burden on workers, employers, program staff, and other end users.
  • Improving access to more frequent, more secure, and higher-quality data to better serve the public.
  • Increasing equity in access to labor and education programs and services by prioritizing the needs of individuals with multiple barriers to employment and populations that have historically been excluded from the safety net.
  • Building a more up-to-date and consistent national governance framework for data privacy and security.
  • Making it easier to scale software solutions and data infrastructure across states and programs, rather than relying on 53 custom systems and duplicative data entry across programs, which is costly and inefficient. 

The report’s policy recommendations are focused in four areas: national data standards, continuous improvement, coverage, and security.

  1. National data standards and data-sharing: Federal policymakers should create an institutional “home” for data governance, and establish a shared data dictionary and data management blueprint for collecting, sharing, accessing, reporting, and protecting program participant and employment data across states, federal agencies, and programs.
  2. Continuous improvement: Build the capacity of states and the federal government to pursue continuous improvement of labor and education data systems over time, rather than making one-time large investments in technology that are disconnected from process redesign efforts and typically have limited input from end users.
  3. Coverage: Develop more comprehensive and representative data sources of employment data that are more timely and higher-quality; capture information from individuals who have historically been excluded from UI wage records; and include data elements that UI wage records do not capture (such as occupation of employment) that would improve transparency, policy responsiveness, and service delivery.
  4. Security: Update the national legal and regulatory framework to clarify data ownership rights, security standards, and privacy and data ethics assurance processes, including provisions to govern the ethical use of artificial intelligence and platform data.


The historic surge in demand for unemployment benefits during the COVID-19 pandemic laid bare many of the underlying problems that have been festering for decades in labor and education data systems. The growing chorus of outrage at how challenging it was for states to get relief to the right people quickly and the widespread instances of fraudulent claims are creating a window of opportunity to reassess data, technology, and processes in this ecosystem. In a fast-changing economy with large-scale disruptions like the pandemic and the rise of new technologies that pose new opportunities and privacy risks, it is time to start on a long-term journey of improvement.

The members of our roundtables articulated a vision for a labor market and education data ecosystem that is “a living resource” and can evolve as technology continues to change. Critically, this ecosystem should embed user experience more fundamentally into the process and, in doing so, better serve the workers who have the most barriers to employment and those we have historically excluded from our safety net. It is time to put this vision of ongoing digital transformation into action to restore basic trust in government.

About the Authors

Annelies Goger

Annelies Goger

David M. Rubenstein Fellow – Metropolitan Policy Program
Janie McDermott

Janie McDermott

Research Intern – Metropolitan Policy Program