Are Ratings Informative Signals? The Analysis of The Netflix Data

Ivan Maryanchyk ()
Additional contact information
Ivan Maryanchyk: Department of Economics, The University of Arizona, http://econ.arizona.edu/

No 08-22, Working Papers from NET Institute

Abstract: The aim of this research is to analyze whether and when ratings are informative signals about the quality of movies. The ratings data of Netflix is used to fit a structural Bayesian learning model. This model links revealed experience utilities of raters, previous consumers, to the product choice of the future consumers of the same good. I postulate that movies are chosen based on the prior beliefs' and signals' precisions. The extent of signals' use depends on their informativeness, that is on how many consumers revealed their preferences before. The results demonstrate that consumers learn about the quality using ratings as signals. The signal produced by one rating is very noisy and might not be taken into account. The more people rate, the better are signals' quality. Consumers are not considerably dispersed in how they value quality.

Keywords: rating; quality; learning; motion pictures (search for similar items in EconPapers)
JEL-codes: L15 L81 L82 L86 D83 C35 (search for similar items in EconPapers)
Date: 2008-09, Revised 2008-10
View list of references

Downloads: (external link)
http://www.netinst.org/Maryanchyk_08-22.pdf (application/pdf)
no

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:net:wpaper:0822

Access Statistics for this paper

More papers in Working Papers from NET Institute
Series data maintained by Nicholas Economides ().