The Best-Laid Plans. Randal O'Toole
Reagan administration revised planning rules in 1982; fires burned large areas of California forests in 1987; the spotted owl was listed as a threatened species in 1989; and President Clinton ordered the creation of a Northwest Forest Plan to address old-growth forest protection in 1993. Each of these events led many forests to scrap the plans they had written and start over. One result was that more forests took more than 10 years to write plans that were supposed to last for 10 years.
One way planners respond to change is to pretend it isn’t happening. Oregon’s Deschutes National Forest was nearly done with its plan when a new inventory revealed that the data planners had relied on were faulty. Planners decided it would be too much trouble to revise the plan so they simply used the faulty data. When planners do respond to change, it can significantly delay the plans.
Urban planners have had the same experiences. Portland’s plan called for higher-density housing on all available vacant land within the region’s urban-growth boundary. Then the northwest salmon was declared a threatened species and the National Marine Fisheries Service issued guidelines for protecting salmon habitat that specified that no more than 10 percent of any undeveloped area should be rendered impermeable by paving it or covering it with new buildings. Only low-density developments—one home per acre or less— could comply with these guidelines. Since they were only guidelines, planners ignored them because they did not fit preconceived preferences for high density.
7. Human Barriers
Any one of the technical barriers described in chapter 6 would be sufficient to render planning impossible. Yet planners simply ignore them and go on planning. If they cannot really do the jobs they say they are doing, how do they do them? First, since their work has no scientific basis, planners rely on fads—ideas that become popular, for a time, in the planning community even if they have no scientific bases. Zoning, urban renewal, public housing projects, and smart growth are all examples of such fads.
Second, to support these fads, many planners turn to pseudoscience, that is, the use of assumptions or claims that either are wrong or cannot be verified. Third, planners support many of their proposals with a public involvement process that is inherently undemocratic. Finally, no matter how good the process, the people who make the final decision for any plan have preconceived biases and inadequate information.
The Fad Problem
Any one of the technical problems—collecting data, predicting the future, building models, and dealing with the pace of change— would render planning infeasible. Rather than admit that planning is technically impossible, planners simply ignore these problems. But if they are not collecting data, accurately calculating the future, and building useful models, what are they doing?
Since planners can’t really do the rational planning that they advertise, they instead follow fads. In the 1920s, the fad was zoning. In the 1950s, it was urban renewal and public housing. Today, it is smart growth (which before 1996 was called New Urbanism), meaning policies that encourage high-density development and discourage driving. Given the technical barriers that prevent planning from working, it is not surprising that these fads end up doing more harm than good.
Planning is susceptible to fads for at least two reasons. First, because planning is so complex, planners must simplify, and fads are the ultimate simplification. Fads provide a substitute for real thinking. Rather than try to figure out the best transportation system or the best land-use plan, planners can simply apply the latest fad.
A second reason for planning fads relates to the nebulous nature of planning. While a private development is easily judged by whether it earned a profit, planning is supposed to produce all sorts of difficult-to-measure social benefits. The difficulty is greatly increased when a plan is written for a long period, such as 20 or more years; until the last year is reached, no one can know whether even a plan’s measurable goals will be attained, much less the nonmeasurable ones. By that time, most planners will have taken other jobs or retired.
Planners are therefore judged on other criteria, and most of the judges are other planners. The American Planning Association and other planning groups issue an endless series of awards to planners whose plans meet the approval of their peers. Because the awards are presented before the plans can be evaluated on the ground, these awards have nothing to do with whether the plans improve the livability of the cities for which they are written or otherwise accomplish their goals, and everything to do with whether the plans follow the latest fads.
Planners who win such awards may be more likely to get pay raises, promotions, or better-paying jobs in other cities. At the very least, they win the praise and admiration of their peers at conferences and other planning forums. Other planners respond by imitating the award-winning plans and few bother to ask whether the plans will really work. Except in the political sphere, where planners must do their wily best to sell their ideas, this means that actual innovation is extremely limited.
The Pseudoscience Problem
To provide a patina of support for the fads they follow, many planners turn to pseudoscience. As used here, pseudoscience means the use of data to give a patina of scientific validity to various claims that, when closely examined, are not really supported by the data. Urban planners and planning advocates using pseudoscience claim to have proven that
• it costs more to provide urban services to low-density developments than to high-density developments;
• expensive rail transit projects cost-effectively reduce traffic congestion;
• suburbs reduce people’s sense of community; and
• low-density suburbs cause obesity and other health problems.
Pseudoscientists start by finding a database. It doesn’t matter if the data were scientifically collected or even if they measure anything that is very closely related to what the pseudoscientists are trying to prove. For example, if they are trying to prove that cities are better than suburbs, they do not seem to need a database that actually compares cities and suburbs. If they cannot find a database, they will sometimes just fabricate data to make a database.
Once they have a database, they search the data to see if they can find some correlations between two sets of numbers. If they find any correlations, they presume that correlation proves causation. For example, if they find a correlation between suburbs and obesity, they assume that suburbs are causing the obesity and not that, perhaps, obese people prefer to live in the suburbs. They then declare that they have proven that suburbs are evil and high-density urban areas are good.
Planners use the term “the costs of sprawl” to describe the belief that providing urban services to low-density developments costs more than to higher densities. The original costs-of-sprawl study was based almost entirely on hypothetical—otherwise known as fabricated—numbers. Rather than actually measuring the costs of providing services to various low- and high-density communities, the authors of the study simply made up numbers. Not surprisingly, the numbers they made up “proved” their case.1 However, when a researcher at Duke University actually looked at the cost of urban services in hundreds of communities of various densities, she found that, at anything above very rural densities, higher densities were associated with higher urban-service costs.2
The most widely cited recent update to the original costs-of-sprawl study, The Costs of Sprawl 2000, is still partly based on hypothetical data. Yet its claims are extremely modest: the study estimates that low-density suburban development imposes about $11,000 more in urban-service costs on communities than more compact development.3
As modest as it is, this calculation is still questionable. When researchers at the Heritage Foundation looked at actual government expenditures in more than 700 cities, they found that local governments spend $1,180 per person per year in the highest-density cities and only $106 to $135 less in medium- to low-density cities. While they found costs of $1,265 in the very lowest-density cities, this is only $85 more than in the highest-density cities. They also found that other factors such as the age and growth rate of the city had as