The Law of Demand in Action

On Monday, Virgina began imposing flexible tolls on the I-66 stretch between the Beltway and Washington, DC.  I-66 is one of the most congested roads in the nation during rush hour and the goal of these tolls was to have drivers look for alternative routes so that the interstate remained relatively free-flowing for those who needed to get into the city quicker.

 

Lo and behold, it worked:

Traffic moved smoothly throughout the morning, and WTOP’s traffic center reported that the number of drivers on I-66 declined compared to typical Monday morning volume.

“There were no delays inside the Beltway; that’s the point of congestion pricing — to keep the carpools and paying solo drivers moving. As demand goes up, the price does too,” said WTOP’s traffic reporter Dave Dildine.

VDOT reported that the average speed on I-66 during the morning rush hour was 57 mph, up from 37 mph at the same time a year ago.

The George Washington Parkway absorbed the brunt of the traffic, with Virginia Route 123 and U.S. 50 picking up extra drivers as well.

As price rises, quantity demanded falls as people seek substitutes.  Those who are willing to pay the higher price are those who value the resource most highly.

There has been a backlash, of course.  No one wants to pay a $40 toll one-way.  There have already been calls to cap the tolls.  How does the state respond?

“If we don’t get the tolling right, all we’re going to do is clog up those lanes again, and so that’s why the algorithm is multifaceted. It may change, we’ll study it. But in terms of moving traffic, it looks like it’s doing its job,” [Virginia Transportation Secretary] Aubrey Layne said.

“I know all the publicity is ‘Oh, $40,’ but the whole idea is for the person to make a rational decision. ‘Is it worth [it for] me to pay this to use it or is another method better?’ If you start limiting that, you impact the entire network,” Layne said of requests to cap tolls or make other dramatic changes.

Price goes up, quantity demanded falls.  You put a price ceiling on the market, you “impact the entire network.”

Good to see some Econ 101 knowledge on the part of the Transportation Secretary.

Taking Models Too Literally

At Cafe Hayek, Don Boudreaux points us to a wise quote from Milton Friedman.  Below is a comment I left on that post, expanded:

 

In the highly stylized world of models, where information is perfect, markets are costless, where all preferences are known, where government is costless, and things never change, it is trivially easy to come up with exceptions to free trade and free enterprise. Shift a curve here, refuse to count costs there, and boom! a theoretical reason why tariffs or export subsidies can be beneficial.

However, when those stylized assumptions are relaxed, in other words in a more realistic world where information is imperfect, markets have transaction costs, where preferences are revealed, where governments have administration and operation costs, and where things change, these theoretical reasons disappear like a shadow in the sun. Conversely, the case for unilateral free trade becomes stronger, since it is not dependent upon those assumptions the way the other theoretical cases are; free trade is formulated under those assumptions, yes, but it is robust to movements away. Things like optimal tariffs are formulated under those assumptions but are not robust to movements away from those assumptions.

The true test of any theory is not how well it holds up in perfect conditions, or how well does it perform in the circumstances in which it was conceived, but how robust it is to movements away from those idealized conditions.  Economists from Adam Smith to Harold Demsetz and beyond have warned us against these nirvana fallacies.  True knowledge is gained when we stress-test our models and see how robust they are.  Testing this robustness gave us such fields as Public Choice, Law & Economics, Political Economy, Money and Banking, and the like.

Economic models serve a purpose: they are ways of thinking, methods of analyzing phenomena. However, they are not descriptive of reality. They were never meant to be. When basing policy off of those models, the policy-proponents are making a grave mistake: they are moving their models away from the abstract and into the descriptive. In other words, they are taking their models too literally. This literal interpretation of models can be extremely dangerous.