Monday, January 09, 2017
Danny Ben-Shahar gave a really nice paper (co-authored with Roni Golan) at the ASSA meetings yesterday about a natural experiment in the impact of information provision on price dispersion. I want to talk about it, but first a little background.
Price dispersion is an ingredient in understanding whether markets are efficient. When prices for the same good vary (for reasons other than, say, transport costs or convenience), it means that consumers lack the information necessary to make optimal decisions, and the economy suffers from deadweight loss as a result.
Houses have lots of measured price dispersion, even after controlling for physical characteristics. Think about a regression for a housing market, where
HP = XB + h+e
where HP is a vector of house prices, and X is a matrix of house characteristics. The residual h+e has two components—unmeasured house characteristics, h, and an error term, e, which reflects “mistakes” consumers of houses make, perhaps because of an absence of information. The h might reflect something like the quality of view, or absence of noise, etc.
When we run this regression, we can compute a variance of the regression residuals. Because we can only observe h+e, we cannot know if this variance is the result of unobserved house characteristics, or of consumer errors. But if h remains fixed, and there is an information shock that reduces consumer errors, e will get smaller, and so will the regression variance.
Here is where Danny’s paper comes in. In April 2010, authorities in Israel began publishing on-line information about house transactions, and in October 2010, they launched a “user-friendly web site.” (Details may be found in the paper). The paper measures the change in measured price dispersion before and after the information was publicly available, and, at minimum, found reductions in dispersion of about 17 percent. The paper takes pains to make sure their result isn’t a function of some shock that happened simultaneously to the release of the information. For example, they show that price dispersion fell less in neighborhoods with well-educated people. This could either reflect that (1) well educated people were better informed about housing markets to begin with, and so got less benefit from the new information or (2) that a greater share of the residuals in well-educated neighborhoods comes from non-measured house characteristics.[i] In either event, the result is consistent with the idea that the information shock is what contributed to the decline in measured price dispersion.
So more information really does seem to produce a more efficient housing market. The policy implication may be that data, in general, should be a public good. Data meet half of Musgrave’s definition of a public good—they are non-rival (one person’s use of a data-set does not detract from another person’s use). And while data are excludable (services such as CoreLogic show this to be true), their creation produces a classical fixed-cost marginal-cost problem. The fixed cost of producing a good dataset is very large; once it is created, the marginal cost of providing the data to users is very low. This suggests that the efficient price of data should be very low.
Currently, data services have something like natural monopolies, with long downward sloping average cost curves. Theory says that this means they are setting prices such that marginal revenue equals marginal costs, instead of setting price equal to marginal cost. All this implies that data are underprovided. Danny and Roni’s work shows that this under-provision has meaningful consequences for the broader economy.