It would be easy to read my previous two posts about the Compactness Index and get the impression that sprawl is an evil in-and-of itself, while compactness is similarly a virtue in-and-of itself. I would caution against such easy assumptions, however.
Sprawl is the tendency for people to move away from densely populated central areas into less dense areas on the edge of cities or just outside them. People have been doing it for a long time, as the word “suburban” was apparently invented by Cicero, the ancient Roman orator. The fact that people have been doing it for such a long time, and that the trend continues, might be taken by some as prima facie evidence that suburbs are “better” than city cores.
Sprawl became a topic of significant discussion for American urban planners when the automobile became ubiquitous after World War II, freeing millions of Americans to move to the suburbs. Jobs and commercial development went with them. The causes are complex, but many urban centers depopulated and suburban areas grew like wildfire. Urban centers were sometimes left with shrunken tax bases, reduced populations, empty and decaying buildings, and large infrastructure networks that were no longer fully used and difficult to maintain with decreased revenues. Often it was the impoverished dispossessed who remain behind. This obviously represented problems for those urban areas, and sprawl has been a contentious political issue ever since.
There has been a proliferation of research purporting to show certain advantages or problems associated with both high density and low density. The research is difficult because of inherent problems separating which factors are causes and which are effects, and because there are many, many complicating factors that are virtually impossible to control.
I am comfortable with the idea of developing the Compactness Index. As I noted in the previous post, there may be other factors that tell you more about the differences between regions, nevertheless having a measure of sprawl is bound to help study the phenomenon. Smart Growth America is a national organization that researches and advocates for what have come to be called “smart growth” policies. They are often anti-sprawl. They were one of the sponsors that paid for development of the Compactness Index discussed in my previous two posts. The development of the index and the index values for MSAs and counties was published in Measuring Urban Sprawl and Validating Sprawl Measures, a report by the index developers that is available on the website of the National Cancer Institute.
In addition, however, Smart Growth America published its own report describing the index: Measuring Sprawl 2014. In addition to describing the index and giving index values for MSAs and counties, this report provides illustrations of the way the index can be used in research, and makes some claims for what has been discovered about sprawl. One of these claims disturbed me: “Compactness has a strong direct relationship to upward economic mobility,” the report claims in bold, colored lettering. (p. 9)
I came away from reading this with the impression that the report was claiming that compact areas were economically more vital than sprawled areas. Living in the St. Louis region, where such a claim is obviously not true, I was appalled. Then I read more closely. I could see that it was easy to get that impression, and if you, dear reader, read the report, you might come away with it, too. But it is not exactly what the report claims.
Instead, the report specifically claims that if a child is born into the lowest 1/5 of the income distribution, it has a better chance of making the top 1/5 of the income distribution by age 30 in a compact region than in a sprawled region. Now, this is a bit of an arcane claim. I’m not sure why the extreme of moving from the bottom 1/5 to the top 1/5 should be of general interest. It might seem that the more likely scenario of moving up one quintile would be more important.
I’m also struck by the use of quintiles to begin with. The simple laws of mathematics require that if some people are moving to higher quintiles, they have to be balanced by other people moving to lower quintiles. So, it must be equally true that more people drift down toward the lowest income level in compact areas also. As you might suspect, Smart Growth America doesn’t mention it.
The report gives an example: “The probability of an individual in the Baton Rough, LA area (index score: 55.6) moving from the bottom income quintile to top quintile is 7.2 percent. In the Madison, WI area (index score: 136.6) that probability is 10.2 percent.” (p.9) Thus, by moving from the 6th most sprawled MSA to the 13th most compact MSA , a person’s chances of moving up have increased less than 3%. Not a very big difference, frankly.
It made me wonder, though, how does compactness relate to economic growth? And I decided to do my own “back-of-the-envelope” study. In fact, I did two.
First, I looked up the Forbes Magazine list of the most desirable and least desirable places to live in America. I used the Forbes list because the primary factors they consider in their rankings are economic growth and the unemployment rate. I took the 10 most desirable locations and computed the average Compactness Index for them.Then I took the 10 least desirable locations and computed the average Compactness Index for them. There were a couple of places for which there was missing data because Forbes and the Compactness Index defined their metropolitan areas differently. But I found exactly the inverse of what Smart Growth America seemed to be claiming. (See table at right.) The 10 best places to live, presumably the ones with the highest economic growth and lowest unemployment, had an average Compactness Index of 103.01. The 10 worst places to live, presumably the ones with the lowest economic growth and highest unemployment, had a Compactness Index of 109.20. The high economic growth areas were more sprawled, the low economic growth areas were more compact.
I wasn’t comfortable stopping there, however. Forbes is a mass media publication, and the articles I saw didn’t describe their data methods in sufficient detail. So I did my own study. I compared the economic growth of the most compact and least compact MSAs. Specifically, using data from the Bureau of Economic Analysis, I computed the average economic growth rate for 2010-2012 for the 10 most sprawled MSAs and for 10 of the 11 most compact MSAs (economic data for one MSA was not available). The second table at right shows the results. The most sprawled MSAs grew an average of 2.21% per year, while the most compact MSAs grew an average of 1.19% per year. That is more than a 1% per year difference, a very large difference!
Now, these are informal “back-of-the-envelope” studies, and there are some significant methodological problems. The fact that the various lists may not have defined metropolitan areas similarly may not matter, but it might matter a lot. My little studies don’t prove anything. But they do seem to align with common perception, and perhaps they are sufficient to cast doubt on the importance of the Smart Growth America claim.
They also illustrate the difficulty separating cause from effect. Economic growth seems associated with sprawl, but is that because sprawl creates economic growth, or because economic growth creates more sprawl? Interesting question, hard to answer!
The reason I’ve gone into these issues in such detail is because they illustrate two issues that are very important to this blog. I had no problem with the report issued by the developer of the Compactness Index. It was posted on a federal government website. It appeared to me to be a reasonable and competent bit of research. It was only in the report issued by the interest group that I found the problem. That is why I tend not to use reports by interest groups in this blog. There are wonderful interest groups out there, and I don’t mean in any way to impugn the overall work of Smart Growth America. But I can’t use reports from interest groups for this blog.
Second, it illustrates a problem with all statistics. The famous quote says that there are lies, damn lies, and statistics. To really understand something, you have to go beyond the raw statistics and understand what is really being said. This blog is all about statistics. I try to read the statistics I report in this blog with a thoughtful and skeptical eye, but I am not an environmental scientist. If you are aware of problems with statistics I reporrt, I hope you will comment and let me know.
For Cicero inventing the word “suburb”: “Suburb.” Wikipedia. Viewed 6/17/14 at http://en.wikipedia.org/wiki/Suburb.
Ewing, Reid, and Shima Hamidi. 2014. Measuring Urban Sprawl and Validating Sprawl Measures. Salt Lake City: Metropolitan Research Center, University of Utah. Downloaded 6/13/14 from http://gis.cancer.gov/tools/urban-sprawl/.
Smart Growth America. 2014. Measuring Sprawl 2014. Downloaded 6/13/2014 from http://www.smartgrowthamerica.org/measuring-sprawl.
Greenburg, Zack O. “America’s Most Livable Cities,” Forbes Magazine, 4/1/2009. Downloaded 6/13/14 from http://www.forbes.com/2009/04/01/cities-city-ten-lifestyle-real-estate-livable-cities.html.
“Gross Domestic Product by Metropolitan Region,” Regional Economic Accounts, Bureau of Economic Analysis. Data downloaded on 6/18/2014 from http://www.bea.gov/regional.