A paper that we wrote on "Consumer surplus in online auctions" was recently accepted to the leading journal Information Systems Research. Reuters interviewed us about the paper (Study shows eBay buyers save billions of dollars), which is of special interest these days due to the change in CEO at eBay. Although the economic implications of the paper are interesting and important, the neat methodology is a highlight in itself. So here's what we did:
Consumer surplus is the difference between what a consumer pays and what s/he was willing to pay for an item. eBay can measure the consumer surplus generated in their auction, because they run a second-price auction. This means that the highest bidder wins, but pays only the second highest bid. [I'm always surprised to find out that many people, including eBay users, do not know this!]
So generally speaking, eBay has the info both on what a winner paid and what s/he was willing to bid (if we assume that the highest bid reflects their true willingness-to-pay value). Adding up all the differences between the highest and second highest bids would, say over a certain year, would then (under some assumptions) give the total consumer surplus generated in eBay in that year. The catch is that eBay makes public all bids in an auction besides the highest bid! This is where we came in: we used a website that allows eBay bidders to bid on their behalf during the last seconds of the auction (called a sniping agent). At the time, this website belonged to our co-author Ravi Bapna, who was the originator of this cool idea. For those users who won an eBay auction we then had the highest bid!
In short, the beauty of this paper is in its novel use of technology for quantifying an economic value. [Not to mention the intricate statistical modeling to measure and adjust for different biases]. See our paper for details.