Читать книгу The Best-Laid Plans - Randal O'Toole - Страница 28

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 much to do with urban-service costs as density.⁴

Even if urban services to homes in a low-density area cost $11,000 more than to houses in higher densities, homebuyers can more than make up the difference by getting access to the lower-priced land that is typically found in suburban areas. Most homebuyers would gladly add $11,000 to the cost of a $150,000 home to have a good-sized yard and not share an interior wall with next-door neighbors. On the other hand, chapter 14 will show that smart-growth policies designed to increase urban densities create artificial housing shortages that drive up housing prices by far more than $11,000 per home.

Another pioneer in the use of pseudoscientific databases in the cause of urban planning is Robert Putnam, the author of the 1995 bestseller Bowling Alone. Putnam heard that American participation in bowling leagues had declined even though bowling itself remained popular. It never occurred to him that people might be bowling with families and friends; he presumed that this meant people were bowling by themselves. He declared this was proof that Americans’ sense of community had declined. When people questioned the validity of this proof, Putnam gathered scores of data sets, none of which directly measured community, but which together proved, he claimed, that America’s sense of community was declining. For example, the data he found measured such things as “dwindling trust between adults and teenagers” and “the changing observance of stop signs.”⁵

Only two of Putnam’s data sets compared suburbs with cities. One measured the percentage of people who served as officers or committee members of a local group. The other measured the percentage of people who had attended a public meeting on town or school affairs. Both data sets showed higher participation in the suburbs than in the central cities.⁶ If these things measure a sense of community, Putnam’s conclusion should have been that people have a higher sense of community in low-density suburbs than in high-density cities. Instead, Putnam made the amazing claim that mobility and sprawl somehow “undermines civic engagement and community-based social capital.”⁷

Furthermore, Putnam somehow calculated that “suburbanization, commuting, and sprawl … account for perhaps 10 percent” of the decline in community participation.⁸ To “fix” this, he recommended New Urbanist planning: “It is surely plausible that design innovations like mixed-use zoning, pedestrian-friendly street grids, and more space for public use should enhance social capital.”⁹ In other words, Putnam proposed to apply to the suburbs the same features that are found in the cities that (according to his measures) have a lower sense of community than the suburbs.

Contrary to Putnam’s presumption, sociologists have worried more that dense cities, not low-density development, reduced people’s sense of community.¹⁰ A new study from University of California economist Jan Brueckner confirms Putnam’s data (but not his conclusions) by finding that suburban residents have more friends, more contact with neighbors, and greater involvement in community groups than residence of dense urban neighborhoods.¹¹

New Urbanists, however, are fond of claiming that their higher-density housing projects will provide a stronger sense of community than low-density suburbs. But they have a very restricted sense of community. To them, community is solely geographically based. Yet as University of California-Berkeley planning professor Melvin Webber pointed out more than 40 years ago, thanks to automobiles, telephones, and (more recently) the Internet, Americans no longer rely on their immediate neighbors for a sense of community.¹² Instead, they form communities with people all over the country and indeed all over the world.

I myself belong to communities of road cyclists; people who love trains and restore historic rail equipment; and owners of Belgian Tervuren dogs, among others. Very few members of any of these communities live in my town, yet I feel a strong sense of community with them all. If people were restricted to forming communities only with their geographic neighbors, their lives would be far shallower and narrower. Since they are no longer so restricted, they do not feel a need to form a strong sense of community with neighbors with whom they share few interests. Planners who mourn the loss of geographic community ignore the much larger gains in other forms of community.

More recent studies have claimed to prove that low-density suburbs cause obesity and other health problems. The databases used to support these allegations often do not actually compare suburbs with cities. One obesity study compares low-density counties with higher-density counties. A health study compares low-density urban areas with higher-density urban areas. Neither finds much statistical significance in the data, but that does not stop the pseudoscientists from making their claims.

The obesity study is based on the ominously named Behavioral Risk Factor Surveillance System, a telephone survey of 200,000 Americans conducted each year by state health departments. Among other things, surveyors ask people how much they exercise each day as well as their height and weight, which can be used to estimate the amount they are overweight or obese. It is likely that people responding to a telephone survey overestimate their height and underestimate their weight, but surveyors merely assumed that everyone lies equally. Because the database was so large, the Centers for Disease Control, which coordinated the survey, did not feel the need to do any statistical analyses testing the validity of the data.

The database indicates a very strong correlation between income and obesity. According to the data, among people with household incomes of less than $10,000, 27.5 percent are obese. As household incomes rise, this percentage steadily falls to as low as 15.1 percent in the $75,000 plus category. There is also a strong correlation between education and obesity: 28.3 percent of people with a grade-school education are obese, steadily decreasing with more education to 15.4 percent among college graduates.¹³

The surveillance system does not ask people whether they live in a city or suburb. So pseudoscientists at Smart Growth America and the Surface Transportation Policy Project compared obesity rates in counties with various amounts of “sprawl.”¹⁴ They adjusted for age, race, and education, but not for income, even though incomes vary widely by county and the data indicate that income has a huge effect on obesity. Their results show that sprawl is far less important to physical fitness than income or education.

For example, their results indicate that about 2 percent more people in Atlanta are obese than in San Francisco, which is about the same as the difference between people who ended their education in high school and people who went to, but did not finish, college. Or to use an example raised by planning critic Wendell Cox, Cook County, Illinois, is 70 times denser than Grundy County, Illinois, and the obesity formula indicates that people in Cook County exercise an average of 40 seconds per day longer and weigh 1 pound less than people in Grundy County.¹⁵ Despite these tiny differences, which could easily be accounted for by socioeconomic variations, flaws in the survey data, or other factors, the Smart Growth America study blames obesity on the suburbs.

To gain scientific credibility, the smart-growth pseudoscientists even submitted their report to a peer-reviewed journal. To get their report into the journal, however, they had to seriously weaken the claims. “Sprawling development has had a hand in the country’s obesity crisis” says the press release issued by Smart Growth America. This demonstrates “the urgent need to invest in making America’s neighborhoods appealing and safe places to walk and bicycle,” which to Smart Growth America means rebuilding the suburbs at higher densities.¹⁶

In contrast, the journal article says sprawl “had small but significant associations with minutes walked [and] obesity.”¹⁷ In popular use, significant means “having a major effect,” but in statistics, significant can refer to very tiny effects as long as they are “not mere chance.” So the article finds only small, nonrandom “associations” between sprawl and obesity. Unlike the press release, the article carefully does not assert that sprawl “had a hand” in causing obesity, merely that they are “associated,” which could mean that some other factor caused the obesity that was also associated with sprawl.

One such factor was revealed by a Canadian study that found “no evidence that urban sprawl causes obesity.” Instead, the study revealed, “Individuals who are more likely to be obese choose to live in more sprawling neighborhoods.” It appears that obesity contributes to sprawl, not the other way around. As a result, the researchers concluded, any effort to change “the built environment to counter the rise in obesity is misguided.”¹⁸ A study from Oregon State University confirms that “the association between sprawl and obesity reported in earlier studies is largely due to self-selection rather than to the impacts of the urban environment on physical activity and weight.”¹⁹

If sprawl does not cause obesity, there is no justification for Smart Growth America’s call to rebuild the suburbs. People who actually want to reduce obesity should work on increasing incomes, education, or other factors that have much larger effects on obesity and health and that would cost less and produce far more benefits than rebuilding neighborhoods to fit some planning utopia.

A similarly flawed study recently blamed chronic health problems on low-density suburbs. It was based on a telephone survey that asked people how many chronic diseases they had in their households. Like the obesity study, the survey did not distinguish between suburbs and cities, so the pseudoscientists who did this study compared answers for low-density urban areas versus high-density urban areas. The urban areas with the most diseases were in Florida, which was not surprising because the average age in those areas was much higher than most other areas. After adjusting for age, incomes, and education, they still found sprawl to be a factor, but the statistical reliability was low. For example, the sprawling Atlanta and Minneapolis-St. Paul regions both had lower incidences of chronic diseases than the much more compact San Francisco and New York regions.²⁰

Instead of relying on a crude telephone survey, researchers could have compared actual health and mortality records in cities and suburbs. One study that did found that mortality rates are significantly higher in cities than in rural areas, while suburban rates are only slightly higher.²¹

The application of databases to problems for which they were not designed, the assumption that correlation equals causation, and claims of strong results from weak correlations are all indicators of pseudoscience. Planners are especially ready to use and rely on pseudoscience because the scientific basis for their own work is so weak.

Many of the pseudoscientific studies were conducted by planning advocates and they are widely cited by planners who firmly believe that suburbs reduce people’s sense of community and increase obesity and other health problems. These planners also take for granted that improved urban designs will go far toward solving these problems.²²