Corresponding author: Michael Anderson, Department of Agricultural and Resource Economics, University of California, Berkeley, 207 Giannini Hall, MC 3310, Berkeley, CA 94720-3310, USA. Email: mlanderson@berkeley.edu.
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Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database†
Article first published online: 9 MAR 2012
DOI: 10.1111/j.1468-0297.2012.02512.x
© 2012 The Author(s). The Economic Journal © 2012 Royal Economic Society
Additional Information
How to Cite
Anderson, M. and Magruder, J. (2012), Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database. The Economic Journal, 122: 957–989. doi: 10.1111/j.1468-0297.2012.02512.x
- †
We gratefully acknowledge support from the Giannini Foundation of Agricultural Economics. We thank seminar participants at U.C. Berkeley and U.C. Davis for valuable comments. The authors are responsible for all errors in the article.
Publication History
- Issue published online: 3 SEP 2012
- Article first published online: 9 MAR 2012
- Accepted manuscript online: 16 JAN 2012 10:13AM EST
- Submitted: 5 September 2011 Accepted: 23 September 2011
- Abstract
- Article
- References
- Cited By
Internet review forums increasingly supplement expert opinion and social networks in informing consumers about product quality. However, limited empirical evidence links digital word-of-mouth to purchasing decisions. We implement a regression discontinuity design to estimate the effect of positive Yelp.com ratings on restaurant reservation availability. An extra half-star rating causes restaurants to sell out 19 percentage points (49%) more frequently, with larger impacts when alternate information is more scarce. These returns suggest that restaurateurs face incentives to leave fake reviews but a rich set of robustness checks confirm that restaurants do not manipulate ratings in a confounding, discontinuous manner.