Analyzing Regulatory Diffusion

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Scholars find that reuse of regulatory language has increased over the past 20 years.

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In 1980, the U.S. Department of Education published rules on sex discrimination. Over the next 20 years, numerous other federal agencies decided to copy most of these rules when enacting their own rules on sex discrimination. Is mimicking regulation in such a way a national trend? Moreover, if so, how should it impact the regulation reviewing process?

In a recent article, two law professors, Jennifer Nou and Julian Nyarko, explained that regulatory diffusion—a phenomenon where agencies copy “substantially similar” texts from the regulation of a different agency—had increased from 3 percent to 10 percent over the past two decades. They also found that the Trump Administration saw a significant spike in text reuse.

Nou and Nyarko used an algorithmic analysis all the relevant published rules in the Federal Register between January 1, 2000, and December 31, 2020. Under this approach, they divided each rule into shorter paragraphs so the algorithm could analyze the similarities and text reuse. The algorithm created a rating of a rule between 0 and 1, where 1 represented an exact copy of a different rule, and 0 represented no connection. They considered any rule with a score above 0.5 as diffused.

Their analysis indicated a steady seven percent increase in reused paragraphs, from 2000 to 2020. Their analysis also showed a spike in text reuse during 2017—the first year of the Trump Administration—even though the number of new rules declined. Nou and Nyarko attributed this to a slowdown in regulatory activity and short staffing in agencies at the beginning of the Trump Administration.

They discuss several reasons why diffusion might happen. First, Congress or the President sometimes gives a direct order to mimic the rules of a different agency. Second, interest groups and lobbyists use similar language when proposing rules to different agencies. Third, agencies learn from the experience of other agencies when they face a new issue. Finally, regulators reuse text to cope with time pressure or resource constraints.

Nou and Nyarko identify some agencies as leaders, referring to their influence on the Code of Federal Regulation. They measure this influence by the overall amount of reused text or the number of agencies that reuse their language. For example, they consider the U.S. Department of Treasury to a leading agency because it has the highest number of copied paragraphs. They also classify the Department of Justice as a leading agency because it has the highest number of agencies that copied its regulations.

Besides noting the trends in their data, Nou and Nyarko also address the normative implications of regulatory diffusion. They note that there are benefits to this diffusion—it can disseminate expertise and experience, advance regulatory accessibility to the general public, and reduce the costs of rulemaking. They also stress that diffusion has risks, such as excessive use of regulation templates for rules without tailoring them to the agency’s specific needs. Moreover, they contend that constant reuse of regulatory text stifles policy innovation because it encourages regulators to rely on former language and rules.

To address these concerns, Nou and Nyarko propose increased oversight of agencies’ use of regulatory diffusion. One method they suggest would be to include levels of text reuse as part of the arbitrary and capricious review under the Administrative Procedure Act – which examines if the agency’s judgment, reasoning and factual finding was reasonable. But they argue that a better way to mitigate text reuse would be within the executive branch. For example, they suggest requiring agencies to submit any proposed rules that reuse text for review by the Office of Information and Regulatory Affairs.

Furthermore, Nou and Nyarko discuss how their findings might influence judicial interpretation in the wake of Kisor v. Wilkie. In Kisor, the Supreme Court held that courts should have a more significant role in reviewing regulations by agencies. In this ruling, the Court departed from its previous practice of Auer deference, which guided courts not to intervene unless the agency’s interpretation was “plainly erroneous or inconsistent with the regulation.”

Nou and Nyarko describe how lower courts face questions about interpretation of textual similarities between rules when reviewing regulations. When it comes to statutory interpretation, some judges may wish to create similar interpretations for similar laws based on the in pari materia canon.

Similarly, Nou and Nyarko note that some judges could justify adopting similar interpretation for text reuse in regulatory rules based on the fact that agencies communicate and coordinate with each other more than legislators. Other judges, however, may argue that because regulatory diffusion is much more widespread than similarities in statutory legislation, courts should focus more on whether there is similarity on the merits.

Amid the ongoing trend of regulatory diffusion, Nou and Nyarko provide data about the actual scope of text reuse in the executive branch. In addition, they lay out how the judiciary and the executive branch can provide better oversight on regulatory diffusion. More broadly, their empirical analysis is an important platform for future scholarship about the proper policy to adopt on matters of regulatory diffusion.