Consumer search with observational learning

Abstract

We discuss several extensions and show that the effect of emulation on prices is stronger when (i) the number of firms increases, (ii) consumers' first visits are more elastic with respect to market shares, and (iii) prices are adjusted more frequently. Science and Innovation (MINECO-ECO2014-59225-P) and AGAUR (2014 SGR 694). All errors are to be attributed to the authors only.1 These theoretical models have found extraordinary support in empirical work. For a recent discussion on the vast literature on social learning, see Möbius and Rosenblat (2014). 224C 2018, The RAND Corporation. 2 In Section 3, we review the empirical evidence on emulation and learning. 3 See the third subsection of Section 5 and the online web Appendix C for extensions to an infinite-horizon model with dynamic pricing. 4 A similar consideration arises in models of switching costs, further discussed in the literature review.C The RAND Corporation 2018. / THE RAND JOURNAL OF ECONOMICSconverges to zero as the search cost increases. A higher search cost exacerbates the impact of emulation because the share of consumers who never search increases. In the standard model, firms have monopoly power over this segment, so that as the proportion of these consumers increases, so does the price. In our model, however, these consumers follow their predecessors and as a result, their purchasing decisions depend on the decisions of those who actively search. The latter group becomes increasingly price-elastic when search costs increase, resulting in Bertrand-like competition and prices that can be as low as the marginal cost.Once a consumer has chosen where to start her search, learning has an opposite effect on prices. In equilibrium, a predecessor's purchase at a firm is taken as bad news by her successor for prospects elsewhere. This induces consumers to free-ride on each other and search less than in the standard model. Furthermore, a consumer is less responsive to price changes when she learns from a predecessor's purchase. Consider, for example, an unexpected price increase by a firm. This price increase triggers a direct demand reduction among those consumers who are able to compare prices. Consumers who are deciding whether or not to search become more pessimistic about utility draws at the other firm, reasoning that a firm with a higher-than-expected price is more likely to have attracted the predecessor when the other firm is offering a poorer product. Both lower search intensity and lower responsiveness to price increases induce firms to charge high prices.The total effect of learning on equilibrium prices is, therefore, not straightforward. On the one hand, emulation pushes prices down, but on the other hand, learning during searches pushes price up. We show that for intermediate search costs, emulation prevails; price is decreasing in search cost and is lower than in the standard model. The price can be as low as the marginal cost for high search costs, if learning is limited in scope and emulation dominates. For very low and very high searc...

Dynamic Pricing with Search Frictions

“…Finally, the model contributes to the literature on consumer search by introducing dynamic pricing and capacity constraints. To the best of our knowledge only Moraga-González and Watanabe (2016) and Garcia et al (2015) introduce capacity constraints in markets in which consumers search sequentially over different producers offering heterogeneous products but they abstract from dynamic considerations. Similarly, Coey et al (2016) study a random search model in which buyers search over time for better prices and sellers sort themselves into a faster auction market and a slower posted-price market, but do not face capacity constraints.…”

“…For instance, inArmstrong and Zhou (2015) oligopolistic sellers price discriminate over time between different buyers. Similarly, inGarcia and Shelegia (2015) firms compete over time in an environment in which consumers learn from each other.…”

Consumer Learning and Firm Dynamics

“…While many of these papers build on partial equilibrium consumer models, there has been a growing extension of observational learning to other environments. Garcia and Shelegia (2018) incorporate it into a search framework. Perhaps closer to the present paper, observational learning has been introduced into general equilibrium models with production, although primarily focusing on investment dynamics.…”

Social Information and Consumer Heterogeneity

“…In a sense, the display advertising in our setting helps firms achieve prominence. Mueller-Frank and Pai (2016) and Garcia and Shelegia (2018) analyze sequential search and observational learning by agents, which are also important elements of the model in this paper.…”