Testing Models of Consumer Search using Data on Web Browsing and Purchasing Behavior

Santos, Babur De los; Hortaçsu, Ali; Wildenbeest, Matthijs R.

doi:10.2139/ssrn.1483380

Cited by 102 publications

(130 citation statements)

References 33 publications

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“…Hortaçsu and Syverson (2004) analyze the mutual funds industry while assuming that consumers have identical tastes but different search costs. De los Santos et al (2012) utilize a large data set on Web-browsing and purchasing behavior to test how well various search models capture actual consumer search behavior. Dubois and Perrone (2015) extend the model of Hortaçsu and Syverson to allow for heterogeneous consumer preferences; thus, products are differentiated both vertically and horizontally.…”

Section: Empirical Literature On Price Dispersion and Search Costmentioning

confidence: 99%

Price dispersion, chain heterogeneity, and search in online grocery markets

González

Miles-Touya

2018

SERIEs

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This paper identifies patterns of cross-sectional and temporal price dispersion-in the Spanish online grocery retail market-and evaluates the extent to which search costs and chain heterogeneity explain such dispersion. We build a data set comprising 836,074 prices for the most popular grocery products sold online by Spanish national chains at different locations. Our results show that price dispersion is still present (albeit to a lesser extent) even after controlling for chain heterogeneity and that it persists over time. We structurally estimate search costs distributions for two different baskets of goods using price data while accounting for vertical product differentiation. The analysis suggests that the extent of search is low. According to our estimates more than two thirds of consumers do not compare prices among supermarkets. We also find that more frequently purchased products have lower search cost and also lower price-cost margins.

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Section: Empirical Literature On Price Dispersion and Search Costmentioning

confidence: 99%

Price dispersion, chain heterogeneity, and search in online grocery markets

González

Miles-Touya

2018

SERIEs

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“…and with the addition of Assumption A3, let f b k g K 1 k=1 be de…ned using equation (12) where w (z; q) is replaced by w(z; b ) and b is de…ned in (11), then p N b b converges in distribution to N (0; ) in probability.…”

Section: A3 As With A2(ii) Is Not Necessary For Nonrandom Measures mentioning

confidence: 99%

“…In particular our Tables 1 and 2 can be compared directly with their Tables 3(a) and 3(b) respectively. We also provide analogous statistics associated with the estimator for the cdf of the search cost evaluated at the cuto¤ points in Table 3 Tables 1 and 2 contain the true mean and standard deviation of various parameters for each estimator as reported in MGW, 11 in addition we include the mean square errors for the ease of comparison between our results and theirs (that include the empirical likelihood estimator). We provide the same statistics for the estimator of the cdf evaluated at the cuto¤ points in Table 3.…”

Section: Monte Carlomentioning

confidence: 99%

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Minimum Distance Estimation of Search Costs Using Price Distribution

Sanches

Silva

Srisuma

2017

Journal of Business & Economic Statistics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. (2006) show equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observed prices alone. These quantiles can be di¢ cult to estimate in practice. This paper uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least squares problem that can be straightforwardly programmed on softwares such as STATA. Permanent repository linkWe show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogeneous markets are available. We propose a two-step sieve estimator for that case. The …rst step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues'matches (as prices) and …nd evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely. JEL Classification Numbers: C13, C15, D43, D83, L13Keywords: Bootstrap, Generated Variables, M-Estimation, Search Cost, Sieve EstimationWe are very grateful to the authors of Moraga-González, Sándor and Wildenbeest (2013) for sharing their MATLAB code with us. We thank an anonymous referee for helpful comments and suggestions. We would also like to thank

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“…After each quotation, the consumer may decide whether he/she continues to make another price quotation. 7 Similarly, De los Santos et al (2012) reach the conclusion that fixed sample size search models have become more accurate than sequential search models.…”

Section: Introductionmentioning

confidence: 97%

Non-Sequential Search, Competition and Price Dispersion in Retail Electricity

2016

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We investigate the impact of consumer search and competition on pricing strategies in Germany s electricity retail. We utilize a unique panel dataset on spatially varying search requests at major online price comparison websites to construct a direct measure of search intensity and combine this information with zip code level data on electricity tariffs between 2011 and 2014. The paper stands out by explaining price dispersion by differing pricing strategies of former incumbents and entrant firms, which are distinct in their attributable shares in informed versus uninformed consumers. Our empirical results suggest causal evidence for an inverted U-shape effect of consumer search intensity on price dispersion in a clearinghouse environment as in Stahl (1989). The dispersion is caused by opposite pricing strategies of incumbents and entrants, with incumbents initially increasing and entrants initially decreasing tariffs as a reaction to more consumer search. We also find an inverted Ushape effect of competition on price dispersion, consistent with theoretical findings by Janssen and Moraga-González (2004). Again, the effect can be explained by opposing pricing strategies of incumbents and entrants.

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Testing Models of Consumer Search using Data on Web Browsing and Purchasing Behavior

Cited by 102 publications

References 33 publications

Price dispersion, chain heterogeneity, and search in online grocery markets

Price dispersion, chain heterogeneity, and search in online grocery markets

Minimum Distance Estimation of Search Costs Using Price Distribution

Non-Sequential Search, Competition and Price Dispersion in Retail Electricity

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