This introduces temporal concerns, because ask prices, computer values, and offers may have been submitted at different times for the same computer.
For the sample as a whole, the coefficient on ln(Ask/Value) is negative and statistically significant, indicating that computers with higher ask prices are more likely to remain on the exchange between our observation dates.
We find that higher ask prices are associated with higher outstanding offers at the time of observation.
If sellers (irrationally) persisted in setting ask prices that were correlated with their reservation prices, then we would see a positive correlation between ask prices and the highest outstanding offer for a given computer.
Quan and Quigley (1991) develop a search model in which ask prices are completely binding.
They also yield an inverse relationship between ask prices and arrival rates in equilibrium.
In their model, ask prices are binding, but the intuition would still apply as long as setting a higher ask price deterred low-valuation buyers from making offers.
The search model would no longer apply, and ask prices would be irrelevant.
But it also implicitly tests whether computers with high ask prices ultimately receive higher transaction prices, under the assumption that final transaction prices are correlated with higher outstanding offers.
In fact, if high ask prices deter low-valuation buyers, we would expect that the distribution of received offers (not just the highest) would be higher for computers with high ask prices.
The general finding of empirical literature examining these other markets is that ask prices convey information.
As an empirical matter, it is not clear that ask prices should play a similar role in the online setting that we examine.
Our approach is to see whether the relationship between ask prices, offers, and time to sale on the exchange is similar to that in other markets characterized by costly search.