Do consumers value price transparency?

Abstract

We examine the role of price transparency in consumer preferences and demand. We assemble a detailed dataset on the driving school industry in Portugal to quantify how firms present the price of the course of instruction, and its individual components, to potential students. Our unique data allows us to estimate a structural model of school choice and measure the impact of varying levels of price information on demand. The results show that consumers are willing to pay a significant amount for price transparency, on average 11% of the service price, and that consumer demographics drive heterogeneous preferences for transparency.

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Notes

  1. 1.

    Contexts range from financial product markets such as commercial banking to the travel sector including airline tickets and hotel accommodations and public utilities subject to various regulatory fees.

  2. 2.

    See Consumer Financial Protection Action (2013) “CARD Act Report,” available at http://www.consumerfinance.gov/f/201309_cfpb_card-act-report.pdf and accessed on October 30, 2016.

  3. 3.

    See, for instance, http://arstechnica.com/information-technology/2016/09/charter-fights-fccs-attempt-to-uncover-hidden-cable-modem-fees/, accessed on October 30, 2016.

  4. 4.

    See http://webarchive.nationalarchives.gov.uk/20140402142426/http://www.oft.gov.uk/OFTwork/markets-work/advertising-prices/ accessed on October 30, 2016.

  5. 5.

    See footnote 3 and https://apps.fcc.gov/edocs_public/attachmatch/FCC-16-59A1.pdf, accessed on October 30, 2016.

  6. 6.

    6Greenleaf et al. (2016) define partitioned pricing as “a strategy that divides a product’s price into a base price, charged for the product itself, and a mandatory surcharge(s) for products, services, fees, or taxes associated with purchasing or using the product” and all-inclusive or combined pricing as the pricing practice that “involves the use of a single, all-inclusive price that covers all costs.” The main focus of research on partitioned pricing is to study the psychological processes by which partitioned pricing influences consumers’ product evaluations and their choices through the use of experiments.

  7. 7.

    Students’ resulting underestimation of the total cost of the product, stirring demand to the firm using the partitioned pricing strategy, supports the regulatory concerns about highly detailed pricing being potentially misleading.

  8. 8.

    To the best of our knowledge, Xia & Monroe (2004) and Carlson & Weathers (2008) are the only papers in the experimental partitioned pricing literature that present subjects with a total price in the partitioned price condition. Xia & Monroe (2004) compare the likelihood of purchase and perceptions of fairness and seller trustworthiness under partitioned pricing and all-inclusive pricing when total price is presented. Carlson & Weathers (2008) study how purchase intentions change with the number of price components and presentation of total price under different levels of seller trustworthiness.

  9. 9.

    See, for example, Nelson (1974), Kihlstrom & Riordan (1984), Milgrom & Roberts (1986), Ackerberg (2003), and Honka et al. (2017) for models of the informative effects of advertising.

  10. 10.

    Licensing by the IMT requires, among other things, proof that the applicant has at least five years of experience in driving instruction, that the school is financially viable, and that the fleet and facilities satisfy certain minimum standards.

  11. 11.

    We analyze the information that schools provide about the base price of instruction. The price detail measure we employ thus does not capture whether the school provides information of any kind about contingent exam retake fees. We study the incidence and pricing of exam retakes in Seim et al. (2017).

  12. 12.

    In the data, we observe students switching schools rarely. Only 1.8 and 0.1% of all students transfer schools during the theory and on-road exam phases, respectively, while only 0.7% transfer in between the theory and on-road exam phases. The majority of these, or 64.5%, transfer to schools outside their original school’s municipality, suggesting that exogenous reasons such as moving explain a significant share of transfers (statistics not tabulated).

  13. 13.

    To construct the indicator “Secondary School” we first geocoded street addresses for all secondary schools in Portugal (obtained from the Gabinete de Estatística e Planeamento da Educação do Ministério da Educação on December 6, 2011), and then calculated straight-line distances from each secondary school to each of the driving schools in the sample.

  14. 14.

    The industry association Associação Nacional dos Industriais do Ensino da Condução Automóvel (ANIECA) provides estimates of annual operating costs to its members; the data is as of 2012.

  15. 15.

    As a result, there are no website aggregators that post the prices for different schools. Our focus, therefore, is not on the increased transparency that arises from publicly posting prices, as in Rossi & Chintagunta (2016), for example, but on the amount of price information provided by each school.

  16. 16.

    The mystery shoppers recorded prices from the price table for a subset of sampled schools; in 90% of cases, the prices listed on the price schedule differed from the prices the school official quoted the mystery shoppers during the same visit.

  17. 17.

    The school remits part, but typically not all, of the exam fees to the exam center to register the student for the theory and on-road exams.

  18. 18.

    Some schools, for example, reported prices that included a CD-ROM with the rules of the road. In such instances, the mystery shopper then asked to receive a discounted price quote that excluded the CD-ROM.

  19. 19.

    Note that we instructed the mystery shoppers not to let the amount of price information provided by each school influence their customer service rating.

  20. 20.

    Other work defines price transparency at the market-level. Examples include the theoretical work by Piccione & Spiegler (2012) and Chioveanu & Zhou (2013), who investigate the implications of firms choosing different – but not necessarily more or less transparent – price formats on the functioning of the market as a whole, and lab work by Lynch & Ariely (2000) who show that increasing the ease of cross-store price comparisons among two virtual wine retailers increases consumers’ price responsiveness.

  21. 21.

    The mystery shoppers reported that, in the cases where schools quoted a single price for the course of instruction, without a breakdown into price components, they typically ascertained the main price information very quickly. In contrast, when schools chose to (voluntarily) provide detailed information on their pricing, they spent considerably longer to explain pricing to the mystery shopper, without the shopper explicitly probing them for such information.

  22. 22.

    We use the mean age of all students who live within 10 km of each school, regardless of whether they ultimately attend the school, to calculate the mean age of the student population in the school’s catchment area.

  23. 23.

    Since the data only contain information on enrolled students, we are unable to model the choice of not obtaining a driver’s license. We, therefore, may not assess the extent to which providing students with detailed price information may affect the size of the market, rather than substitution between available options.

  24. 24.

    Student i’s choice set is defined as all the schools within r i kilometers of the student’s home address, where r i equals min {10 × [1 + (median (“PopDens”) − “PopDens i ”) /median (“PopDens”)], 20}. The radius r i takes on values between nine and 20 kilometers and equals 18 kilometers for the median student with respect to population density (not tabulated). The radius r i increases as population density decreases.

  25. 25.

    We also verified that, in the final sample, the students’ choice sets overlapped and effectively connected each school in the data with all remaining ones through student choice sets, thereby allowing us to identify all school fixed effects relative to a single reference school, as discussed in Section 3.1.

  26. 26.

    We interpret the estimated school fixed effect as the school’s contribution to passing, and, thus, as school quality. A second explanation for the within-school correlation in outcomes is sorting by students into schools based on ability. Swanson (2013) proposes an instrumental-variables based approach to correcting the estimated school quality for endogenous sorting in the hospital choice context. In our context we find that accounting for observable demographic determinants of sorting into schools results in a statistically significant improvement in model fit over a model that includes school fixed effects only (likelihood ratio test statistic of 1165.62; p-value of 0.0001). At the same time, adding student demographics does not significantly affect the estimated school fixed effects (i.e., the correlation between the school fixed effects based on models with and without demographics is 0.986). While this does not rule out a contamination of the estimated school quality by unobserved determinants of school sorting, it is suggestive that such effects might be limited in our setting.

  27. 27.

    The results of these robustness checks are available upon request from the authors.

  28. 28.

    The “need for cognition” refers to “the tendency of individuals to engage in and enjoy thinking” (Cacioppo & Petty 1982).

  29. 29.

    One could consider enriching our demand model to allow for unobserved heterogeneity in the valuation of transparency, akin to a latent class model. Our setting is not well suited to performing such an analysis, unfortunately. First, our data consist of a single cross-section, making it difficult to identify individual preference heterogeneity. Second, our current estimation strategy of isolating a fixed effect for each school in the first stage, which we then project on price detail and other school characteristics in the second stage, is not easily amenable to introducing unobserved heterogeneity in the mean valuation of transparency.

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Acknowledgements

We thank Susana Paulino at Instituto da Mobilidade e dos Transportes for access to the data and information about the industry. Ana Isabel Horta provided excellent research assistance, and Susana Belo assisted with local data collection. We thank Elisabeth Honka, JF Houde, and Yi-Lin Tsai for fruitful conversations throughout the writing of this paper and the co-editor Sanjog Misra and two anonymous reviewers for valuable comments. We also thank the seminar participants at the Alfred Lerner College of Business and Economics (The University of Delaware), Carlson School of Management (University of Minnesota), Foster School of Business (University of Washington), LeBow College of Business (Drexel University), Sauder School of Business (University of British Columbia), Stern School of Business (New York University), and Wharton School (University of Pennsylvania). We gratefully acknowledge support from the Office of the Vice President for Research at the University of Minnesota, and from the Global Initiatives Research Program at the University of Pennsylvania. All errors are our own.

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Correspondence to Maria Ana Vitorino.

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The first two authors are co-first authors in alphabetical order.

Appendices

Appendix A: Mystery shoppers query script

Fig. 9
figure9

Mystery shoppers’ price query script

Appendix B: Hedonic Regressions

Table 7 Hedonic price regressions
Table 8 Determinants of transparency

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Seim, K., Vitorino, M.A. & Muir, D. Do consumers value price transparency?. Quant Mark Econ 15, 305–339 (2017). https://doi.org/10.1007/s11129-017-9193-x

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Keywords

  • Pricing
  • Transparency
  • Information
  • Consumer valuation

JEL Classification

  • D43
  • D83
  • L13
  • L15
  • L84