States Fiscal Condition

Via George Mason University’s Mercatus center:


  1. This is based on each state’s long run budget constraint, including long term liabilities such as pensions and healthcare benefits.
  2. Some of the richest U.S. states have the worst long run budget constraint.  There is no obvious correlation either with a state’s GDP per capita or GDP growth.
  3. Most states have negative unrestricted net assets. The exception is Alaska, and small mid-western states.

The link is here.



What’s Wrong with the Case-Shiller Price Index?

Last week, the think tank Bruegel hosted an event in Brussels on the use of micro data for the evaluation of the impact of investment at the macro level.

I gave a presentation at the panel. The main point is that macro stats on house prices such as the Case Shiller index explain very little of the overall variance of house prices. In practice a simple variance decomposition of house prices on metro area fixed effects, with transaction level data, will reveal that only about 35% of overall variance is captured by the Case Shiller. That means that 65% of the total variation of house prices is local.

Talking about prices in New York vs. prices in Montreal isn’t that informative compared to comparing prices in Brooklyn versus prices in Mile End.

Perhaps even more importantly, as prices rise overall in a metro, prices can fall significantly in neighborhoods. The most significant example is that of San Francisco, where my data shows that there was a compression of the price distribution during the house price boom of 2000-2006 (yes, there’s another one now).



Zipf law for City Size Distributions

A striking empirical regularity in urban economics is that there is a linear relationship between the log rank and the log population of metro areas. Take, for instance, the list of U.S. metro areas, ordered in decreasing population sizes. Assign rank 1 to the most populated metro area, rank 2 to the second most-populated area. Then Zipf’s “law” states that there is a linear relationship between the log rank and log population, with a slope of 1. With a simple scatter plot using 2015 data, and the 135 largest metro areas, we get this:

This is a fairly striking statistical regularity: the estimated coefficient is 1.01, but perhaps even more surprising the R squared of the regression is 98% ! I don’t particularly remember better fitting relationships in my career as an economist. Now this coefficient of 1, found in Gabaix (1999) and oft-repeated, turns out to be a non-robust estimate. This is what we get with the full sample of 380+ metro areas.

The coefficient is quite different from 1, it is actually 0.8 with a very small standard error. So here the impressive finding is the linearity of the relationship, not so much the slope of -1.  Replications of Zipf’s law for multiple countries also reach the same conclusion: for more than half of countries, the Zipf coefficient is statistically different from 1. The R squared remains strikingly close to 100% though. Zipf law should simply be about that fact, not the strong constraint on the slope.

So what does a deviation from Zipf’s law mean? Zipf’s law is the consequence of Gibrat’s law: all cities grow on average with the same proportionality coefficient, regardless of their size. If Gibrat’s law is not satisfied, then Zipf’s law typically won’t be. We typically find that smaller cities have higher proportional growth variance, suggesting that they are subject to more shocks — a lack of sectoral diversification in smaller metro areas makes them both more susceptible to booms and to busts.

For more, follow my Masters level urban economics course, at

Summer School in Urban Economics 2017 in Paris — Call for Papers

for Economics PhD Students

Co-sponsored by Ecole Polytechnique (l'X), CREST, Université Paris-Saclay and the Urban Economics Association (UEA)

June, 14-16, 2017
Fondation Hellénique, Cité Universitaire, Paris

This Summer School is designed for Economics PhD students with an interest in Urban Economics. In addition to those students specialized in Urban Economics, we encourage the participation from PhD students in Labour, Development, Public, Trade and other applied fields who want to improve their knowledge in the field. The objective of the school is twofold: to offer an intensive training program for interested PhD students, and to provide them with the opportunity to present and discuss their own ongoing research with leading researchers in the field in a relaxed and open atmosphere.

Faculty: Christian Hilber (London School of Economics), Henry Overman (London School of Economics), André de Palma (ENS Cachan), Diego Puga (CEMFI), Holger Sieg (University of Pennsylvania), Jacques-François Thisse (Université Catholique de Louvain).

Scientific Committee: Gilles Duranton (University of Pennsylvania), Matthew Kahn (University of Southern California), Isabelle Méjean (Ecole polytechnique), Amine Ouazad (Ecole polytechnique), Diego Puga (CEMFI), Jacques Thisse (Université Catholique de Louvain), Kurt Schmidheiny (Universität Basel),  Harris Selod (Development Research Group at the World Bank), Elisabet Viladecans-Marsal (University of Barcelona and IEB).

Organizing Committee: Amine Ouazad (Ecole polytechnique), Weronika Leduc (Ecole polytechnique).

The program and details about the application process can be found at:

Deadline for applications: March 5, 2017.