Will Trump’s 2-for-1 executive order lead to ‘dynamic scoring’ for regulations?

Among the actions taken in the first 100 days of the Trump administration, Executive Order 13771 stands out for altering the way executive branch agencies create regulations. Known as the “2-for-1” rule, it requires agencies to offset the costs of a new rule by modifying or eliminating at least two existing rules.

Harnessing agencies’ inclination to make new rules, and turning some of that energy towards cutting red tape, is an idea with merit. After all, over one million regulatory restrictions have accumulated in federal code, many of which are obsolete or even counterproductive. Yet the effectiveness of the 2-for-1 Executive Order — like so many things in Washington — will depend on the details.

{mosads}The key questions center on the quality of agencies’ cost assessments for both new and old rules. The Office of Management and Budget (OMB) issued related guidance for agency use, providing some information. The new guidelines stick to the methodology formalized by President Clinton in 1993: notably, that regulations should be measured by their opportunity costs to society.

For those who slept through Econ 101, opportunity cost is the value of the best alternative use of a resource. So if reading this column takes you four minutes, the opportunity cost is the value of what you could have otherwise done with that time.

But what exactly should we consider as an opportunity cost to society? In reality — and in spite of OMB and agency analysts’ best intentions — agencies typically adhere to a “static analysis” framework, failing to consider some dynamic costs of regulation.

To put that in more familiar terms, static analysis of a regulation is similar to static scoring of a federal budget bill. That practice — since put to rest — ignores changes to people’s behavior that a bill might induce.

If a bill, for example, affected income tax rates and would induce some people to work more or fewer hours, static scoring would not account for this. But people do indeed act differently under different incentives, and dynamic scoring considers such changes to behavior.

For a regulatory example, consider the many Transportation Security Administration (TSA) regulations that followed the 9/11 terrorist attacks. A static analysis might consider the cost of security personnel, X-ray and other scanning machinery, and even the time that airline employees devote to ensuring TSA compliance.

Dynamic analysis, meanwhile, would have considered a critical behavioral change. A 2003 study by Harumi Ito and Darin Lee, and a 2007 study in the Journal of Law & Economics, found that new, time-consuming TSA regulations led many travelers to substitute driving for flying. But because flying is significantly safer, this likely induced over 100 road fatalities that would not have occurred otherwise. Like many changes in behavior, this could have been anticipated and incorporated into the analysis.

The same kind of dynamic analysis can be used with the 2-for-1 order, to estimate the cost of a new rule or savings of modifying an existing rule. But that would require many agencies to adjust the way they analyze regulatory impacts.

The typical regulatory impact analysis — the document agencies produce to assess a prospective rule’s anticipated benefits and costs — focuses on the implementation costs of a new regulation for businesses or individuals. These might include one-time “sunk” costs like newly required machinery, ongoing or repeated costs like physical materials or software, and the associated labor needed to comply with the rule.

This approach treats affected industries as if they are set in stone, considering only the costs borne by companies that already exist. But the economy is dynamic, with new businesses forming and existing businesses falling to the wayside every day. When a new regulation adds to the start-up cost for new businesses, fewer may get off the ground — a detail lost in a static analyses.

Dynamic analyses overcome some of these limitations by accounting for how rules can alter businesses’ decisions, including the decisions of possible start-ups, as well as how existing firms invest in different sectors and technologies. Although they may require a bit more work than static models, they are well-established, broadly accepted, and part of virtually all modern economy-wide models. In fact, CBO’s implementation of dynamic scoring for potential legislation shows its feasibility. 

Consider one last hypothetical: a regulation requiring all firms in an industry to create and follow a workplace safety training program. Like a static model, a dynamic model would estimate the benefits and costs for existing businesses. But it would also assess whether the rule would put some out of business, or prevent others from entering it.

For how many nascent or even unformed businesses would this rule’s initial costs represent “the straw that broke the camel’s back?” Agencies need to look at more than the marginal effects of a rule alone, and at how it works in concert with other rules already in existence.

Time will tell whether agencies attempt more dynamic modeling under the 2-for-1 executive order. But if the focus is indeed on the opportunity cost to society, dynamic modeling should be used whenever possible. The reason is simple: The potential value that nonforming start-ups represent to our economy — but never actually produce — represents one of the biggest opportunity costs of all.

The Trump administration, numerous U.S. states, and other countries are attempting to regain some of the economic growth lost to regulatory accumulation — which previous research pegs at nearly one percentage point taken away from the economy’s growth rate each year. If growth is the goal, then the dynamics of the economy need to be in the driver’s seat.

Patrick McLaughlin is a senior research fellow at the Mercatus Center at George Mason University. Stephen Strosko is a research assistant at Mercatus.

The views expressed by contributors are their own and are not the views of The Hill.