4.4 Article

Social Learning and Peer Effects in Consumption: Evidence from Movie Sales

期刊

REVIEW OF ECONOMIC STUDIES
卷 78, 期 1, 页码 356-393

出版社

OXFORD UNIV PRESS
DOI: 10.1093/restud/rdq014

关键词

Social interactions; Social multiplier; Peer effects

向作者/读者索取更多资源

Using box-office data for all movies released between 1982 and 2000, I quantify how much the consumption decisions of individuals depend on information they receive from their peers, when quality is ex ante uncertain. In the presence of social learning, we should see different box-office sales dynamics depending on whether opening weekend demand is higher or lower than expected. I use a unique feature of the movie industry to identify ex ante demand expectations: the number of screens dedicated to a movie in its opening weekend reflects the sales expectations held by profit-maximizing theatre owners. Several pieces of evidence are consistent with social learning. First, sales of movies with positive surprise and negative surprise in opening weekend demand diverge over time. If a movie has better than expected appeal and therefore experiences larger than expected sales in Week 1, consumers in Week 2 update upward their expectations of quality, further increasing Week 2 sales. Second, this divergence is small for movies for which consumers have strong priors and large for movies for which consumers have weak priors. Third, the effect of a surprise is stronger for audiences with large social networks. Finally, consumers do not respond to surprises in first-week sales that are orthogonal to movie quality, like weather shocks. Overall, social learning appears to be an important determinant of sales in the movie industry, accounting for 32% of sales for the typical movie with positive surprise. This implies the existence of a large social multiplier such that the elasticity of aggregate demand to movie quality is larger than the elasticity of individual demand.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据