Using a large data set on consumers' web browsing and purchasing behavior we contrast various classical search models. We find that the benchmark model of sequential search with a known distributions of prices can be rejected based on the recall patterns we observe in the data. Moreover, we show that even if consumers are initially unaware of the price distribution and have to learn the price distribution, observed search behavior for given consumers over time is more consistent with non-sequential search than sequential search with learning. Our findings suggest non-sequential search provides a more accurate description of observed consumer search behavior. We then utilize the non-sequential search model to estimate the price elasticities and markups of online book retailers.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by NET Institute in its series Working Papers with number
09-23.