|By:||Wanda Mimra (ETH Zurich, Switzerland)
Alexander Rasch (Universität zu Köln)
Christian Waibel (ETH Zurich, Switzerland)
In credence goods markets, experts have better information about the appropriate quality of treatment than their customers. As experts provide both diagnosis and treatment, this leaves scope for fraud. We experimentally investigate how intensity of price competition and the level of customer information about past expert behavior influence an expert’s incentive to defraud his customers when the expert can build up reputation. We show that the level of fraud is significantly higher under price competition than when prices are fixed. The price decline under competitive prices superimposes quality competition. More customer information does not necessarily decrease the level of fraud.
|Keywords:||Credence good; Expert; Fraud; Price competition; Reputation; Overcharging; Undertreatment.|
Mathias Drehmann, Jörg Oechssler, and Andreas Roider " Herding with and without payoff externalities — an internet experiment." International Journal of Industrial Organization, Volume 25, Issue 2, April 2007, Pages 391-415. DOI.
Most real world situations that are susceptible to herding are also characterized by direct payoff externalities. Yet, the bulk of the theoretical and experimental literature on herding has focused on pure informational externalities. In this paper, we experimentally investigate the effects of several different forms of payoff externalities (e.g., network effects, first-mover advantage, etc.) in a standard information-based herding model. Our results are based on an internet experiment with more than 6000 subjects, of which more than 2400 participated in the treatments reported here, including a subsample of 267 consultants from an international consulting firm. We also replicate and review earlier cascade experiments. Finally, we study reputation effects (i.e., the influence of success models) in the context of herding.
Keywords: Information cascades; Herding; Network effects; Reputation; Experiment; Internet
JEL classification codes: C92; D8