Reform Education’s General Administrative Regulations (EDGAR) and Grants Administration Processes
By strengthening state and local capacity to use data analytics, evaluation, and evidence in formula grant programs, the Department of Education (ED) could significantly increase the impact of its major investments in pre-K, K-12, and community college systems. Important changes could be made through coordinated regulatory and administrative actions that do not require congressional action, laying the groundwork for future congressional action to fill critical gaps.
Challenge and Opportunity
The Department of Education’s main initiatives to strengthen the use of data, evaluation, and evidence have focused on a small number of competitive grant programs (e.g., Education Innovation and Research, State Longitudinal Data Systems) with funding totaling less than $500 million annually. The vast majority of ED’s annual funding to state and local governments is allocated by formula to programs supporting pre-K, K-12, and community college systems (totaling over $39 billion). With the possible exception of a few recent ESSA provisions requiring states and localities to use evidence, ED lacks meaningful policies to strengthen state and local use of data, evidence and evaluation to improve the impact of formula grants. States and localities face multiple impediments to using data and evidence to make decisions, including impediments that stem from ED policies and practices:
- Lack of strong financial incentives for states and localities to use formula funding for data, analytics, evaluation, and adoption of evidence-based approaches.
- Lack of clarity about ways that existing program funds may be used to build data, analytics and evaluation capacity.
- Confusion about how to share and analyze data while meeting FERPA privacy requirements.
- An overemphasis on reporting compliance measures rather than outcomes and evaluation findings that would help programs improve. Many of these impediments could be addressed through ED regulatory and administrative reforms that do not require legislation. These reforms would help address systemic weaknesses and equity issues in state and local education systems that have been amplified by the COVID19 pandemic.
Plan of Action
The Secretary should designate a senior ED policy official and an attorney to lead a task force to devise regulatory and administrative reforms that can strengthen state and local data, analytics, and evaluation capacity. To be developed through extensive consultation with state and local officials, these reforms would include:
Regulatory reforms. ED should revise EDGAR provisions to:
- Require, as a condition of funding, that grantees use data and analytics to answer key questions that can improve program effectiveness. This common-sense change to both formula and competitive grants is long overdue, since no organization or program can function effectively without using data to guide decisions.
- Clarify that grantees with formula and competitive grants may use program funds (beyond those included in indirect costs) to strengthen their data, analytics, and evaluation capacity. This would include capacity to integrate and analyze education and non-education data to improve cross-program coordination that could lead to better student outcomes. This is consistent with GAO’s “necessary expense rule”, which clarifies that statutes need not specify all allowable uses of funds.
- Modify the “Strategy to Scale” selection criteria for competitive grants to award extra points to projects designed to scale up evidence-based interventions that will leverage formula grant funding (e.g., Title I, CTE), or state and local funding, above the minimum required by statute (e.g., 10 percent for EIR). This change would (1) increase adoption of effective interventions that cannot reach scale using competitive grant funds alone; and (2) increase the share of ED formula funding that supports evidence-based strategies. (There is precedent for this model, which was previously used in HHS’ 2008 home visiting initiative.)
Streamlining data collections. ED should continue to work with state and local grantees: (1) to eliminate unnecessary reporting that does not help grantees improve programs; and (2) to standardize data to improve its utility to users at all levels.
Technical assistance. ED, in collaboration with non-federal partners, should provide proactive technical assistance to help state and local governments make effective use of increased investments in data, analytics, and evaluation, including:
- additional guidance and examples on how individual level data can be shared and integrated with non-education data while meeting FERPA requirements;
- case studies on how to answer key questions with data analytics and evaluation;
- open-source technology solutions that can be deployed at the state and local level, including data-linkage platforms to link data across programs;
- ways to build staff data and technology skills;
- cost-allocation tools, vetted with auditors, for building shared data and analytics capacity with multiple funding streams; and
- strategies to reduce bias and promote equity in data analytics.
Innovative Personnel Exchanges and Public Private Partnerships. ED should employ the use IPAs, public-private partnerships, and other partnerships with relevant community organizations to engage state and local perspectives and non-government talent in implementing the action plan.
Assessment of state and local capacity. With state and local partners, ED should conduct a thorough assessment of state and local capacity gaps that cannot be adequately addressed through the regulatory and administrative actions above. This assessment would inform potential legislative and appropriations proposals to Congress.
While the focus of this initiative would be on federally funded programs, the potential benefits would extend to activities funded at the state and local level. This ED initiative could be part of a White House-led strategy to strengthen state and local data and analytics capacity across a broad range of federally funded programs, particularly those serving vulnerable populations.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.