Chamber study claims to debunk EPA figures on job-creating regulations
The nation’s largest business lobby presented new research Wednesday rebutting claims from the Environmental Protection Agency that federal regulations create thousands of jobs.
The study commissioned by the U.S. Chamber of Commerce found fault with the modeling system the EPA uses to forecast the effect of regulations, saying the agency regularly fails to factor in all of the economic consequences of new rules.
In some cases, the study concluded, there was no evidence that the agency even attempted to analyze the effect of regulations on employment.
The study reveals a disconnect in a federal government “that pays lip service” to job creation without sufficiently tracking the effects of regulation on the private sector, said Bill Kovacs, the Chamber’s senior vice president for Environment, Technology & Regulatory Affairs.
“This report establishes that EPA has consistently failed to take into account the real-world impact of regulations on communities and industries,” he said.
{mosads}Requests for comment on the report, conducted by NERA Consulting, from several EPA officials were not answered on Wednesday afternoon.
But Josh Bivens, director of research and policy for the liberal-leaning Economic Policy Institute, said NERA’s job-forecasting model is flawed. Bivens accused the firm of trying “to play on totally-legitimate concerns about today’s weak labor market.”
The study looked at 16 years’ worth of EPA rules that carried an economic impact of $100 million or more. It concluded that the agency uses an outdated model that guarantees positive job-creation figures when it conducts analyses of regulatory impacts.
The agency, it found, has neglected to shift to a “whole economy” model that would take into account the costs of regulation compliance across interconnected industries. Misleading job creation totals could unfairly skew public opinion in favor of regulations, said Keith Hall, former commissioner of the Bureau of Labor Statistics and a senior research fellow at George Mason University.
“There’s a fairness issue — if they don’t do a good job of estimating costs and benefits, there’s a darkness and you’re not able to debate the impact,” Hall said. “Your not able to debate the true impact.”
In particular, the report points to a series of air quality rules that the EPA said would boost employment.
The agency estimated that the 2011 Utility Mercury and Air Toxics Standard would create some 46,000 temporary jobs and 8,000 permanent ones. NERA’s “whole economy” approach, in contrast, suggests the rule could cost the equivalent of 215,000 jobs by 2015.
The EPA’s Cross-State Air Pollution rule would create several hundred jobs, the agency estimated. But the Chamber report said it would lead to losses totaling the equivalent of 34,000 jobs.
Diana Furchtgott-Roth, a former Labor Department economist and senior fellow at the Manhattan Institute, said outside reports like the one from the Chamber are vital because federal officials are “predisposed to supporting regulations their agencies have proposed. ‘’
“There are many people there that have an ideology in favor of doing these regulations,” she said. “If we sit around waiting for EPA to do these, it’s never going to happen.”
But Bivens of the Economic Policy Institute said NERA’s methodology is misleading because it relies on a model that “assumes nobody who wants a job cannot find one, nor does anybody who separates from a job ever do it involuntarily.”
“This means that they have literally nothing at all to say about whether or not [regulations] will increase or decrease unemployment,” he said, adding that his research indicates the regulations in question would yield a modest boost in job creation over the coming years.
Michael Livermore, executive director of the Institute for Policy Integrity at New York University, lauded the report’s call for a set of best practices that economists could use to forecast the effects of regulation on the job market. But he agreed with Bivens that NERA’s model relies heavily on assumption.
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