John Roberts is supposed to be a smart man.

But he makes a specious argument.  He says that because in the presence of Voting Rights Acts, there is no disparity in voter turn-out, there is no need for a Voting Rights Act.  Huh?

Are models that assume linear utility useful?

I just saw a paper on how the desire of households to match with particular houses could explain housing market dynamics--in particular why house prices are more volatile than incomes.

Performing such an exercise is very difficult, and requires simplifying assumptions.  One of the most important simplifying assumptions in the paper is that utility is linear--that people value their last unit of consumption just as much as their first.  This assumption is clearly wrong--we know that marginal utility diminishes in consumption.  Yet the assumption was necessary to make the model tractable.

So do we know more about the world because of the model or not?  I really don't know.

If in 1987 you bought the average house in the average place...

…you have about broken even relative to the consumer price index. The Case-Shiller National Index for March 1987 was 62.03; for March 2013, it was 136.70.  The Consumer Price Index in March 1987 was 112.7; in March 2013 it was 232.77.  So the Case-Shiller Index has risen by  120.4 percent in 26 years; the CPI has risen by 106.5 percent.  So in inflation adjusted terms, the average house in the average place has risen by 13 percent over the past 26 years, or a little less than half of one percent per year.

[At the suggestion of Austin Kelly, I looked to see what would happen if I used the unit-weighted FHFA index instead of the value-weighted Case-Shiller index.  I found that based on FHFA, real house prices rose by 11 percent since 1991 (the first year for which data are available), or a little less than .5 percent per year.  So even though the index is different, the result is the same.]

Reposted from Forbes.

Could someone explain the market failure that protecting car dealerships solves?

The Wall Street Journal has a good story today about how car dealerships are (successfully) lobbying legislatures to ban Tesla Motors from marketing their cars directly to consumers.  GOP legislators, who get the willies about regulation that actually solves real problems, are on board with supporting protectionist policies for auto dealerships.

Does anyone really think that the industrial organization of the automobile retail industry works well?  My family buys a car every five years or so, and our experience is that no one tries to exploit asymmetric information like auto dealers.  I have lots of reasons to believe that our experiences are not unique.

What amazes me is that even in the age of the internet, when one can use sites like Edmunds to figure out what to pay for a car, dealers start out by assuming that the consumer is stupid, hope they get an absurdly marked up price, and only get reasonable when they find out their customer actually knows something.

Elon Musk is a visionary in many ways.  With the Tesla, he might make two important contributions--he might free  from petroleum, and he might free us from car dealers.

A metaphor for why Goodness of Fit tests are, well, not very good.

I am proud to say I learned my econometric from Art Goldberger, who had little use for R-squared.

Anyway, a smart friend of mine (who works in industry and therefore might not want to be named) pointed out that he could probably fit the brushstrokes of a Jackson Pollack painting with a 17 degree polynomial and get an excellent R-squared.  But he still couldn't predict what a next brush stroke might look like.

A painting by Morgan Green

Reposting from my Forbes blog: the debate on Debt and GDP

My two cents:

Within the past day or so, economics conversations have been all about Rogoff and Reinhart and their critics, Herndon, Ash and Pollin.  The Rogoff and Reinhart (RR) paper purported to show that countries with more debt grow more slowly than countries with less; Herndon, Ash and Pollen (HAP) show that Rogoff and Reinhart’s data contains mistakes, and there is not much dispute about whether Herndon, Ash and Pollin’s corrections are right–they are.

HAP also do a pretty good job of showing that connections between debt to gdp ratio are not robust–they are sensitive to time period and country.  But they do not ask the question about direction of causality between debt and growth (page 3):

For the purposes of this discussion, we follow RR in assuming that causation runs from public debt to GDP growth. RR concludes, “At the very minimum, this would suggest that traditional debt management issues should be at the forefront of public policy concerns” (RR 2010a p. 578). In other work (see, for example, Reinhart and Rogo (2011)), Reinhart and Rogo acknowledge the potential for reverse causality, i.e., that weak economic growth may increase debt by reducing tax revenue and increasing public expenditures. RR 2010a and 2010b, however, make clear that the implied direction of causation runs from public debt to GDP growth.

But the question of direction matters a lot.  Consider a country whose GDP weakens–both tax revenues fall and social spending (on things like unemployment insurance) rises.  This means that in the absence of a policy change, weak GDP leads to higher debt.

There is a simple way to take a first cut at the question of direction of causation–by using a technique known as Granger Causality.  The set up is to try to explain something (such as GDP growth) by looking at its own lagged values and the lagged values of another variable (such as debt-to-GDP ratio).  I took the  data set in Herndon, Ash and Pollen and ran Granger tests using one lag explaining real GDP growth and debt-to-GDP ratios; I ran separate regressions for each country in the data set. I tested for significance at the 90 percent level of confidence.  I am happy to share my results with anyone who is interested (richarkg@usc.edu).

In the tests where I was exploring whether debt-to-GDP “caused” GDP growth, I found that debt’s impact was negative in five countries (Austria, Germany,Italy, Japan and Portugal); positive in four countries (Australia, Canada, New Zealand and Norway), and zero in 11 countries (Belgium, Denmark, Finland, France, Greece, Ireland, the Netherlands, Spain, Sweden, the UK and the US; although France was close to being statistically negative).

RR emphasize that there is a critical point at which debt becomes toxic, and that is at a debt-to-GDP ratio of more than 90 percent.  Doing Granger tests using this variable (on “on-off switch” for a country being at greater than 90 percent), we find that the impact of greater than 90 percent debt on GDP growth is positive in two cases (Australia and New Zealand), and is not statistically different from zero in eight cases (Belgium, Canada, Greece, Ireland, Japan, the UK and the US).  Ten countries have not had debt-to-GDP ratios above 90 percent.

When we look in the other direction, however, the impact of GDP growth on debt is negative 12 times (Australia, Austria, Belgium, Denmark, Finland, Germany, Greece, Ireland, Italy, Japan, Netherlands, and Sweden) and is not statistically different from zero in the eight other countries (Canada, France, New Zealand, Norway, Portugal, Spain, the UK and the US).  Reverse causality IS a big issue here, and until it is really sorted out, we can’t say what the true, structural relationship between GDP and debt really is.