A consumer’s decision to engage in search depends on the beliefs the consumer has about an unknown product characteristic (e.g., price). Because beliefs are rarely observed, researchers typically assume that consumers have rational expectations or update beliefs consistent with Bayesian updating. These assumptions are restrictive and do not enable the researcher or the retailer to price discriminate among consumers on the basis of heterogeneity in beliefs. The authors use Monte Carlo experiments to show how these assumptions affect estimates of search cost. Next, they design an incentive-aligned online study in which participants search over the price of a homogeneous good, and the authors elicit distributions of price beliefs before and after each search. Drawing on data collected from a nationally representative panel, they find substantial heterogeneity in prior price beliefs, such that participants update their beliefs in response to search outcomes but deviate from Bayesian updating in that they underreact to new information. Importantly, the authors show that (1) assuming Bayesian updating does not significantly bias search cost estimates at the aggregate level if the researcher accounts for heterogeneous prior beliefs, (2) eliciting heterogeneity in prior expected prices is much more important than eliciting heterogeneity in prior price uncertainty, and (3) a retailer can increase profits through third-degree price discrimination by recognizing the heterogeneity in prior beliefs.
BikhchandaniSushilSharmaSunil (1996), “Optimal Search with Learning,” Journal of Economic Dynamics and Control, 20 (1), 333–59.
4.
BlakeThomasNoskoChrisTadelisSteven (2015), “Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment,” Econometrica, 83 (1), 155–74.
5.
BronnenbergBart J.KimJun B.MelaCarl F. (2016), “Zooming In on Choice: How Do Consumers Search for Cameras Online?” Marketing Science, 35 (5), 693–712.
6.
CamererColin (1995), “Individual Decision Making,” in Handbook of Experimental Economics, KagelJohn H.RothAlvin E., eds. Princeton, NJ: Princeton University Press, 587–703.
7.
ChickStephen E.FrazierPeter (2012), “Sequential Sampling with Economics of Selection Procedures,” Management Science, 58 (3), 550–69.
8.
ChingAndrew T.HossainTanjimTehraniShervin S.ZhaoClarice (2020), “How Do People Update Beliefs? Evidence from the Laboratory,” working paper.
9.
Costa-GomesMiguel AWeizsäckerGeorg (2008), “Stated Beliefs and Play in Normal-Form Games,” Review of Economic Studies, 75 (3), 729–62.
De los SantosBaburHortacsuAliWildenbeestMatthijs R. (2012), “Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior,” American Economic Review, 102 (6), 2955–80.
12.
De los SantosBaburHortacsuAliWildenbeestMatthijs R. (2017), “Search with Learning for Differentiated Products: Evidence from E-Commerce,” Journal of Business & Economic Statistics, 35 (4), 626–41.
13.
DelavandeAdeline (2008), “Pill, Patch or Shot? Subjective Expectations and Birth Control Choice,” International Economic Review, 49 (3), 999–1042.
14.
DellaertBenedict G.C.HäublGerald (2012), “Searching in Choice Mode: Consumer Decision Processes in Product Search with Recommendations,” Journal of Marketing Research, 49 (2), 277–88.
15.
DominitzJeffManskiCharles F. (1996), “Eliciting Student Expectations of the Returns to Schooling,” Journal of Human Resources, 31 (1), 1–26.
16.
DominitzJeffManskiCharles F. (1997), “Using Expectations Data to Study Subjective Income Expectations,” Journal of the American Statistical Association, 92 (439), 855–67.
17.
DominitzJeffManskiCharles F. (2004), “How Should We Measure Consumer Confidence?” Journal of Economic Perspectives, 18 (2), 51–66.
18.
DominitzJeffManskiCharles F. (2005), “Measuring and Interpreting Expectations of Equity Returns,” Journal of Applied Econometrics, 26 (3), 352–70.
19.
EpsteinLarry G. (2006), “An Axiomatic Model of Non-Bayesian Updating,” Review of Economic Studies, 73 (2), 413–36.
20.
EpsteinLarry G.NoorJawwadSandroniAlvaro (2010), “Non-Bayesian Learning,” BE Journal of Theoretical Economics, 10 (1), 3.
21.
ErdemTülinKeaneMichael P. (1996), “Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets,” Marketing Science, 15 (1), 1–20.
22.
ErdemTülinKeaneMichael P. T.ÖncüSabriStrebelJudi (2005), “Learning About Computers: An Analysis of Information Search and Technology Choice,” Quantitative Marketing and Economics, 3 (3), 207–47.
23.
FergusonThomas S. (1973), “A Bayesian Analysis of Some Nonparametric Problems,” The Annals of Statistics, 1 (2), 209–30.
24.
FerrellWilliam R.McGoeyPaul J. (1980), “A Model of Calibration for Subjective Probabilities,” Organizational Behavior and Human Performance, 26 (1), 32–53.
25.
GarthwaitePaul H.KadaneJoseph B.O’HaganAnthony (2005), “Statistical Methods for Eliciting Probability Distributions,” Journal of the American Statistical Association, 100 (470), 680–700.
HäublGeraldDellaertBenedict G.C.DonkersBas (2010), “Tunnel Vision: Local Behavioral Influences on Consumer Decisions in Product Search,” Marketing Science, 29 (3), 438–55.
28.
HongHanShumMatthew (2006), “Using Price Distributions to Estimate Search Costs,” RAND Journal of Economics, 37 (2), 257–75.
29.
HonkaElisabeth (2014), “Quantifying Search and Switching Costs in the U.S. Auto Insurance Industry,” RAND Journal of Economics, 45 (4), 847–84.
30.
HonkaElisabethChintaguntaPradeep (2017), “Simultaneous or Sequential? Search Strategies in the U.S. Auto Insurance Industry,” Marketing Science, 36 (1), 21–42.
31.
HossainTanjimOkuiRyo (2013), “The Binarized Scoring Rule,” Review of Economic Studies, 80 (3), 984–1001.
32.
HuMantianDangChuChintaguntaPradeep (2019), “Search and Learning at a Daily Deals Website,” Marketing Science, 38 (4), 609–42.
33.
JusterF. Thomas (1966), “Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design,” Journal of the American Statistical Association, 61 (315), 658–96.
34.
KahnemanDanielTverskyAmos (1979), “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, 47 (2), 263–91.
35.
KogutCarl A. (1990), “Consumer Search Behavior and Sunk Costs,” Journal of Economic Behavior & Organization, 14 (3), 381–92.
36.
KoriatAsherLichtensteinSarahFischhoffBaruch (1980), “Reasons for Confidence,” Journal of Experimental Psychology: Human Learning and Memory, 6 (2), 107–18.
37.
KoulayevSergei (2013), “Search with Dirichlet Priors: Estimation and Implications for Consumer Demand,” Journal of Business Economic Statistics, 31 (2), 226–39.
38.
LandsbergerMichaelPeledDan (1977), “Duration of Offers, Price Structure, and the Gain From Search,” Journal of Economic Theory, 16 (1), 17–37.
39.
LippmanSteven A.McCallJohn J. (1976), “The Economics of Job Search: A Survey,” Economic Inquiry, 14 (2), 155–89.
40.
LiuDavid (2015), A Model of Optimal Consumer Search and Price Discrimination in the Airline Industry. Cambridge, MA: MIT Economics.
41.
ManskiCharles F. (2004), “Measuring Expectations,” Econometrica, 72 (5), 1329–76.
42.
MatsumotoBrettSpenceForrest (2016), “Price Beliefs and Experience: Do Consumers’ Beliefs Converge to Empirical Distributions with Repeated Purchases?” Journal of Economic Behavior & Organization, 126, 243–54.
43.
McCallJohn Joseph (1970), “Economics of Information and Job Search,” Quarterly Journal of Economics, 84 (1), 113–26.
44.
MehtaNitinRajivSurendraSrinivasanKannan (2003), “Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation,” Marketing Science, 22 (1), 58–84.
MortensenDale T. (1970), “Job Search, the Duration of Unemployment, and the Phillips Curve,” American Economic Review, 60 (5), 847–62.
47.
NyarkoYawSchotterAndrew (2002), “An Experimental Study of Belief Learning Using Elicited Beliefs,” Econometrica, 370 (3), 971–1005.
48.
OakleyJeremy E.O’HaganAnthony (2007), “Uncertainty in Prior Elicitations: A Nonparametric Approach,” Biometrika, 94 (2), 427–41.
49.
RosenfieldDonald B.ShapiroRoy D. (1981), “Optimal Adaptive Price Search,” Journal of Economic Theory, 25 (1), 1–20.
50.
RothschildMichael (1974), “Searching for the Lowest Price When the Distribution of Prices Is Unknown,” Journal of Political Economy, 82 (4), 689–711.
51.
RustJohn (1987), “Optimal replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher,” Econometrica, 55 (5), 999–1033.
52.
SchotterAndrewTrevinoIsabel (2014), “Belief Elicitation in the Laboratory,” Annual Review of Economics, 6, 103–28.
53.
ShinSangwooMisraSanjogHorskyDan (2012), “Disentangling Preferences And Learning in Brand Choice Models,” Marketing Science, 31 (1), 115–37.
54.
SonnemansJoep (1998), “Strategies of Search,” Journal of Economic Behavior & Organization, 35 (3), 309–32.
55.
StiglerGeorge J. (1961), “The Economics of Information,” Journal of Political Economy, 69 (3), 213–25.
56.
ThalerRichard H.JohnsonEric J. (1990), “Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice,” Management Science, 36 (6), 643–60.
WallstenThomas S.BudescuDavid V.RapoportAmnonZwickRamiForsythBarbara (1986), “Measuring the Vague Meanings of Probability Terms,” Journal of Experimental Psychology: General, 115 (4), 348–65.
59.
WeitzmanMartin L. (1979), “Optimal Search for the Best Alternative,” Econometrica, 47 (3), 641–54.
60.
WuMengxi (2017), “The Mechanisms of Consumer Learning and Price Search in a Homogeneous Goods Market,” working paper, http://economics.mit.edu/files/12499.
ZwickRamiRapoportAmnonLoAlison King ChungMuthukrishnanA.V. (2003), “Consumer Sequential Search: Not Enough or Too Much?” Marketing Science, 22 (4), 503–19.
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