Illustration by Sean Kelly
Dollars and jobs gained or lost—everyone’s got numbers to back up their argument when it comes to topics of public debate. But where do the numbers come from? And can you believe them?
February 1, 2008
In the late 1970s and 1980s, the United States Department of Agriculture’s Forest Service found itself wrestling to answer questions about the economic impact of its work. Legislation required the forest service to report annually on the impact of its activities. So the forest service had developed a mainframe computing tool called Implan, short for “impact analysis for planning.” Then it developed a DOS version of the software that could run on PCs. Now, in 1989, it needed help constructing better economic data sets—production, value-added, employment, wage, and supplier data—that it could run through the software to better demonstrate how forest service activities were affecting local economies.
For help, the agency turned to Wilbur Maki, a University of Minnesota economics professor who’d worked on forestry issues in the past. Maki, in turn, called on his research assistants, Scott Lindall and Doug Olson.
If they had needed a lesson in the value of wielding sound economic data and methodology, they soon got it. Environmentalists in the Pacific Northwest had petitioned the federal government to classify the spotted owl as an endangered species in the late ’80s. The timber industry balked at the idea of protection for the bird. Under the National Forest Management Act, endangered status would block logging in forests where the spotted owl lived—putting loggers on unemployment rolls and ruining companies and communities, the argument went. The environmentalists, the logging companies, and the forest service all built economic models to calculate the effects of protecting the owl—and all got different results. They were using the same basic formulas, but their inputs were different, Lindall recalls.
Maki, Lindall, and Olson hadn’t been hired to save the spotted owl, but the data sets they constructed did help resolve the question, and the owl was eventually designated an endangered species. They wrapped up their contracted work for the forest service.
Soon after, in 1993, Lindall and Olson negotiated with the University of Minnesota and the forest service to spin off the economic databases they’d helped to develop and the technology they’d used to do it. They overhauled their earlier work and established a private company to sell it, the Minnesota Implan Group, based in Stillwater.
Since then, their mission has been to provide affordable, user-friendly software and economic data that their clients can use to study regional economies—not just the effects of lost logging business, but what tourism or manufacturing contributes to the economy, what the results of increased or decreased product demand or employment in a particular industry will be, or how effective government spending is in bringing about economic development.
Minnesota Implan Group doesn’t stake out a position on any of these questions, and it doesn’t try to generate answers. It’s neutral on the issues its clients are studying. Ditto the company’s Implan software and the economic data that the company aggregates from government sources and sells to clients for their economic calculations—those numbers are agnostic on the questions they’re used to answer.
But the numbers that Implan yields for clients—the number of jobs that ethanol production is projected to bring to a state, the dollars of spending that a casino is expected to generate in the local economy—those numbers are hardly ever neutral. Minnesota Implan Group isn’t in the advocacy business, but its clients are: government agencies, nonprofits, industry associations, independent consultants. They can use Implan software and data to generate numbers that support any side of an argument—and get wildly varying results depending on who’s clicking the mouse.
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