Now the question is: why?

What if the extra VMT are a dead weight economic loss? Money that could have been used for investment, consumption, etc just gets literally burned out the tail pipe?

Thank you for including the information for T statistics this time around.  I had meant to e-mail Stuart to ask him to include that in the future.

As to the why, have you considered breaking down the GSP by a couple of gross categories?  Say, the fraction in agriculture and financial services as two indicators?  I'm willing to advance the hypothesis that states with more emphasis on agriculture (and other activities dependent on natural resources) will require more vehicle miles to produce a dollar's output than states that depend more on people sitting at desks.

From Paula's weblog

I think Stuart has the causation backwards. It isn't driving that makes you poor, its being poor that makes you drive.

And now, PG, you're giving us the "multivariate regression on the data". Sigh. And Stuart's stuff which this follows re: "So, what's the takeaway? Stuart's right: states with higher vmts have lower gsp/cap, even after controlling for education and population density".

What about people? I mean, human beings--they are not statistics or variables like at the University of Chicago (Milton Friedman school of economics)--my alma mater, by the way.

Let's be honest here. Paula's statement (above) is simply correct. Common sense tells us this, not sophisticated modelling and multivariate regressions. This has to do with the real experiences of actual people trying to live their lives. Being poor doesn't mean shorter commutes because they live outside the highly centralized, expensive world in which the rich people live. It means longer commutes because the people providing these services to the well-off can't afford to live where they (the rich) do and so therefore must live in cheaper, more affordable outlying areas. This is a great tragedy and a large (perhaps the biggest) reason why exurban and suburban sprawl has taken place.

Modelling, statistical analysis, differential equations et.al. are just fine but this is the real world where people live--as best they can in a ever-more marginalized and impoverished social situation. Less math & modelling and more paying attention to how and where people actually have to live, please?

Do you know WHY this kind of modelling is fucked up? Because economists took individual people out the equation and starting dealing with their notion of aggregates. I suppose that would be OK if there was any reality in their results but there isn't. It's all self-serving and serves the political powers representing the rich & well-off. Do you remember the Laffer curve (President George the 1st called it voodoo ecomonics). Now, tax cuts for the rich and benefits cut offs for the poor are par for course.Come on, let's get real.

Please stop this madness! "The states of the U.S." are not a collection that it makes sense to study in this fashion. They vary by over two orders of magnitude in land area, and nearly two orders of magnitude in population. There are an infinite number of demographic variables that you could attempt to "control for" (and with only 50 data points, added variables make the whole process suspect).

And what is the point, anyway? How does this help us learn how to live with 5% less energy, or with 50%?

In Stuart's post you can see that there is a relationship between GSP/Cap and VMT/Cap of 42% using R2.

To check if the reason for this is the distinction between rural states or urban states he uses population density. So a state with low density ought to be rural and a state with high density should be urban. I claimed in his post that using population density would be too simplistic to differentiate between rural and urban.

One alternative would be to take a compensated population density. I will take an example to explain this:
Take Hawaii with a population density of 196 hab/mi2. It has 5 counties:
Hawaii County     40 hab/mi2   with 13% of the pop.
Honoluu County  1500 hab/mi2   with 72% of the pop.
Kalawao County    10 hab/mi2   with  0% of the pop.  
Kauai County     100 hab/mi2   with  5% of the pop.
Maui County      120 hab/mi2   with 10% of the pop.

So it makes sense to do a compensated average:
.13*40 + .72*1500 + 0*10 + .05*100 + .1*120= 1102 hab/mi2

As Stuart noted: "So your idea is that this would allow us to compute the average density that the average resident experiencies, rather than the average density of the average square mile of the state"
Exactly!

I did this for all states with the corresponding counties using the following census bureau data.

I took out Delaware and Wyoming because they were not included in the relationship between GSP/Cap and VMT/Cap.

Apart from them I excluded New York, Alaska and Nevada for the following reasons. The compensated population density of New York is huge (15671 hab per squere mile) and this is because the most populated counties are tiny, for example New York County has 1.5 millon hab and is only 23 square miles so 68000 pop density. Alaska and Nevada have the opposite problem, Anchorage Municipality has 272000 hab but is very big 1697 square miles and Clark County has 1.7million hab with big cities (Las Vegas and Henderson) but is huge 7877 square miles. So the urbanness of New York is exagerated by tiny counties and the ruralness of Alaska and Nevada is exagerated beacause the counties are huge in area. The population density of Alaska and Nevada is  1 and 21 and the compensated population density is 70 and 169, but it should be much bigger.

Finally excluding these five states (Delaware, Wyoming, New York , Alaska and Nevada) we obtain fairly good R2. Taking the compensated density instead of the normal density (like the one Stuart used) has the effect of moving the poinsts to the right, but some points are moved more than others (note that the heights are not changed). And  we see an improvement. By excluding other states I saw the same pattern: it is almost always better to take compensated densities (specially if they are bad in the sense of New York, Alaska and Nevada).

First the VMT/Cap versus Density and below VMT/Cap versus compensated density.




Second the GSP/Cap versus Density and below the GSP/Cap versus compensated density

The state sticking out with 53000$ per capita is Connecticut probably its compensated density is too low.

Interesting analysis.  Here are few thoughts.

1.  I have access to individual US county data.
All 3288 counties.  It would give more data
points.  However, I don't have the GDP
statistics.

2.  I think all that you are really
seeing in your regression analysis is
that City folks earn more money than
rural folks!!  This is no surprize.
Agriculture has been under tremendous
pressure for decades!  Corn sells for
$2.00/bushel!  in 1927 it sold for $0.50
per bushel!!   Not much change in revenues
for farmers.  Lots of change in cost of
living, though.

Peak oil will improve the lot of farmers.
Eventually food prices will rise faster
than most other goods due to production
declines.  Basically, it will be a catch
up for decades of low farm prices.

3.  If you had good county by county data
you could regress all of the rural
counties and see what you get.
VMT (vehicle miles travelled) might not
even be significant.  If it is, then it
is a more significant finding.

I think Roberto's used of 'compensated density' is a nice refinement to the general analysis.

However, I'm having some difficulty getting a grasp on what we are really trying to accomplish here. Is it is to prove that low-income people drive more than high-income people? Is it to prove that people tend to drive less in high-density metropolitan areas relative to low-density rural and exurb area? I think we already know this, at least intuitively. Or it to suggest some sort of causal relationship between driving and income?

While I think this is a very interesting effort, I am skeptical that you are ever going to firmly pin anything down as 'proof' that A causes B and B causes C. There are just too many variables, many that we are probably not even aware of.

Furthermore, I would pay some closer attention to the 'outlyers' that have been dismissed as 'anomalous'. They might tell you something.

 Both Connecticut and Delaware are each physically small states with a moderate to high population density, but without any really large cities actually within the state itself.  However, northern Delaware can be considered to be continguous with the southern suburbs of Philadelphia, and southern Connecticut can be considerd to be contiguous to the norhern suburbs of New York City.

 Both DE and CT have  a large white-collar highly suburbaned population with generally good income. However, what's important to realize is that they might appear to be anomalous outlyers only because someone in Colonial times drew a line on a map and called this circumscribed area 'Delaware' and that one 'Connecticut'. In reality neither is much different from almost any area in the US comprised of the well-developed suburbs surrounding a major metropolitan area.. I think the same thing is true for say Wyoming or Alaska but in the opposite (rural) end of the scale.

Can I legitimately conclude from all these analyses that if I drive less I will move into a higher socioeconomic class?  :-)  

Here are some reasons why rural states are poorer. First is a lack of a truly competitive market for the most common agricultural commodities i.e. corn, wheat, and soybeans. The number of buyers can be counted on one hand, mainly ADM and Cargill. Secondly most manufactured goods are come from out of state. They export low value goods and import high value goods which is a sure path to poverty. Thirdly is opposition to organized labor is overwhelming in the retail (Walmart) and health care (nursing homes and hospitals) which are the major non farm employers. Add to that the long average distances to jobs and health care services and we see why there is more VMT. The production of biofuels may not improve the situation since ADM and Cargill are the major players in distilling and would probably come to corner the market for biodiesel feedstocks.