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Price Salience and Product Choice
Tom Blake , Sarah Moshary, Kane Sweeney, Steve Tadelis

To cite this article:
Tom Blake , Sarah Moshary, Kane Sweeney, Steve Tadelis (2021) Price Salience and Product Choice. Marketing Science
40(4):619-636. https://doi.org/10.1287/mksc.2020.1261
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MARKETING SCIENCE
Vol. 40, No. 4, July-August 2021, pp. 619–636
ISSN 0732-2399 (print), ISSN 1526-548X (online)

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Price Salience and Product Choice
Tom Blake,a Sarah Moshary,b Kane Sweeney,c Steve Tadelisd,e,faeBay Research, San Jose, California 95125; b University of Chicago Booth School of Business, Chicago, Illinois 60637; c Instacart, San
Francisco, California 94105; d University of California Berkeley, Berkeley, California 94720; e National Bureau of Economic Research,
Cambridge, Massachusetts 02138; f Center for Economic and Policy Research, Washington, District of Columbia 20009
Contact: tomblake@gmail.com (TB); sarah.moshary@chicagobooth.edu,
https://orcid.org/0000-0002-4624-6941 (SM);kane.sweeney@instacart.com (KS); stadelis@berkeley.edu (ST)

Received: December 23, 2019
Revised: April 5, 2020; June 3, 2020
Accepted: June 16, 2020
Published Online in Articles in Advance:
May 19, 2021
https://doi.org/10.1287/mksc.2020.1261
Copyright: © 2021 INFORMS

Abstract. Online vendors often employ drip-pricing strategies, where mandatory fees are

displayed at a later stage in the purchase process than base prices. We analyze a large-scaleﬁeld experiment on StubHub.com and show that disclosing fees upfront reduces both thequantity and quality of purchases. The effect of salience on quality accounts for at least 28%of the overall revenue decline. Detailed click-stream data show that price shrouding makes
price comparisons difﬁcult and results in consumers spending more than they would
otherwise. We also ﬁnd that sellers respond to increased price obfuscation by listinghigher-quality tickets.
History: Avi Goldfarb served as the senior editor and Catherine Tucker served as associate editor forthis article.

Supplemental Material: Data/replication ﬁles are available at https://doi.org/10.1287/mksc.2020.1261.

Keywords: price salience • e-commerce • ﬁeld experiment

what small-scale studies have shown, and we use ourdetailed data to expose behaviors along the purchasefunnel. Second, and more novel, we show that pricesalience affects not only whether a consumer chooses
to purchase any product, but also affects their choiceof which product to purchase. Our setting is a secondary marketplace for event tickets where more
expensive tickets are associated with better (higherquality) seats. We show that when fees are less salient,consumers are more likely to select and purchasemore expensive tickets. Intuitively, reducing the salience of a percent-based purchasing fee makes all
goods appear less expensive, enticing more consumers to select and then purchase a ticket. Because apercentage fee levies a larger fee level for more expensive goods, salience also changes the perceived
marginal cost of quality. As a result, reducing salience
encourages consumers to substitute to high-quality
tickets. We therefore offer a more complete analysisof the effect of price salience on consumer choice,ﬁrst, by demonstrating effects on the intensive margin, and second, by quantifying the relative importance of both the extensive and intensive margins in
our setting.1
We begin our analysis by presenting two hypotheses that follow from the existing theoretical literature: ﬁrst, that consumers are more likely to purchasegoods if fees are obfuscated, and second, that consumers are more likely to purchase expensive, highquality goods if fees are obfuscated. The former effecthas been documented by many studies, but the latter

1. Introduction
The past two decades have witnessed a steady shift inpurchasing from brick-and-mortar stores to online
retailers and marketplaces. A common pricing strategy used by online vendors—most notably for event
ticket sales—is “drip pricing,” where mandatory fees
are disclosed at a later stage in the consumer’s purchasing process than the base price of a good. Textbook models of consumer choice assume that economic agents are rational and sophisticated in theirability to discern a product’s true price, implying thatpurchase decisions fully account for any fees, taxes,
or add-on features. However, a growing literaturedemonstrates that consumers often struggle to determine ﬁnal prices. For example, Chetty et al. (2009)
document that tax salience affects consumers’ decisions to purchase personal care goods in grocery
stores, implying that consumers have trouble inferring ﬁnal prices when taxes are not displayed on the
shelf. Morwitz et al. (1998) ﬁnd that students in alaboratory react less to surcharges presented as percentages rather than dollars, suggesting a cognitive
difﬁculty in calculating prices. Hossain and Morgan(2006) and Brown et al. (2010) present evidence
that eBay buyers respond more to list price than toshipping cost.
Studies have therefore demonstrated that consumers are more likely to purchase goods when fees
are obfuscated. Our paper contributes in two ways.
First, we employ a large-scale ﬁeld experiment involving millions of online consumers to conﬁrm
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620

has not been explored because of data limitations inearlier work.
We take these predictions to data generated from alarge-scale ﬁeld experiment conducted by StubHub, aleading online secondary-ticket marketplace. Before
the experiment was launched in August 2015, theplatform used an upfront-fee (UF) strategy, where thesite showed consumers the ﬁnal price, including fees
and taxes, from their very ﬁrst viewing of ticket inventory. The platform then experimented with aback-end-fee (BF) strategy, where mandatory fees wereshown only after consumers had selected a particularticket and proceeded to the checkout page.
StubHub randomly selected 50% of U.S. users for
the BF experience, whereas the remaining 50% wereassigned to the UF experience. The experiment provides exogenous variation in fee salience in a settingwith rich data on consumer choices, including choice
sets, signals of purchase intent (e.g., product selection
and clicks toward checkout), and ﬁnal purchases.
These rich data allow us to infer the effect of salienceon both the extensive and intensive margins of productchoice. Our empirical results support our hypotheses:
price obfuscation distorts both quality and quantity
decisions. A simple lower-bound estimate shows thatthe intensive margin—how expensive a ticket tobuy—accounts for at least 28% of the increase inrevenue raised from back-end fees.
Further analysis of detailed individual-level clickstream data suggests that back-end fees play on consumer misinformation. UF users are more likely to exit
before exploring any ticket, whereas BF users differentially exit at checkout, when they ﬁrst see the fee.
Furthermore, BF users go back to examine other listings more often than their UF counterparts. They are
more likely to go back multiple times, which suggeststhat back-end fees make price comparisons difﬁcult.
Finally, back-end fees affect even experienced users,
although on a smaller scale, which is consistent with
consumers facing optimization costs even when they
anticipate a fee, as in Morwitz et al. (1998).
We also investigate how sellers who list on StubHub respond to the change in fee salience on theplatform following the experiment’s conclusion, when
StubHub shifted the whole site to back-end fees.
Because back-end fees cause buyers to purchase more
Figure 1. Timeline of Fee Presentation at StubHub

Blake et al.: Price Salience and Product ChoiceMarketing Science, 2021, vol. 40, no. 4, pp. 619–636, © 2021 INFORMS

tickets, and, in particular, more expensive tickets, thetwo-sided nature of the platform should incentivize
sellers to list relatively more expensive, high-quality
tickets. Using row numbers as a proxy for quality, ouranalysis shows that sellers indeed choose to list
higher-quality tickets after the transition to back-end
fees. We also ﬁnd that sellers respond in how they set
prices; in particular, they are more likely to set list
prices at round numbers. Hence, consistent withEllison and Ellison (2009), we ﬁnd that sellers respondto the change in buyer experience.
As a robustness check, we present evidence on
price salience from an earlier experiment at StubHub
performed in 2012. One advantage of this earlier
experiment is that StubHub’s default user experienceduring the experiment was BF, as shown in Figure 1.
Thus, comparing the results from the 2012 and 2015
experiments can shed light on whether the effect ofsalience depends on the initial environment. Ourﬁndings indicate that the effect of salience is remarkably similar across the two experiments. A second
feature of the 2012 experiment is that it randomized
fee presentation across events, rather than acrossusers. This experiment design circumvents interference from device-switching, when a user is randomized into different conditions on their mobile/
laptop/desktop computers. Reassuringly, the results
are broadly consistent with our ﬁndings from the2015 experiment, indicating that this concern is notﬁrst-order in our setting.
Our paper also contributes to studies of alternative methods of obfuscation, such as add-on pricing
and partitioned pricing. Ellison (2005) and Gabaix
and Laibson (2006) explore models where some consumers ignore the price of complimentary goods(e.g., parking at a hotel) when making purchasedecisions. Predictions from these models have been
examined in recent empirical work, such as Ellison
and Ellison (2009) and Seim et al. (2017) (see Heidhues
and Kőszegi 2018 for an overview). In the language of
Gabaix and Laibson (2006), StubHub fees constitutesurcharges rather than add-ons because they are
unavoidable. We might interpret the StubHub fee as aform of partitioned pricing because it is broken out
from the base price of the ticket (see Greenleaf et al.
2016 for a review of the partitioned pricing literature).

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Marketing Science, 2021, vol. 40, no. 4, pp. 619–636, © 2021 INFORMS

One interpretation of our ﬁndings is that salience
ampliﬁes the effect of partitioned pricing. Salience
may therefore help explain the persistence of markups
and price dispersion in online markets, as documented
by Brynjolfsson and Smith (2001), among others.
Closest to our paper is a recent study by DertwinkelKalt et al. (2019), who examine the online purchasebehavior of over 34,000 consumers of a large German cinema that obfuscated a surcharge for threedimensional movies until checkout. They ﬁnd that
consumers initiate a purchase process more often
when surcharges are obfuscated, but they also dropout more often when the overall price is revealed at
checkout. In their setting, these two effects counteract
each other, so that the demand distribution is independent of the price presentation. Hence, our ﬁndings differ from theirs in three important ways. First,
as in previous studies, we ﬁnd that obfuscation increases demand, meaning that the increased rate ofpurchase initiation outweighs the increased dropout
rate caused by obfuscation. Second, our richer setting
allows us to document how salience affects the intensive margin. Third, and most importantly, ourﬁndings contravene the argument in Dertwinkel-Kalt
et al. (2019) that the salience effects documented inprevious studies, such as Chetty et al. (2009), Taubinsky and Rees-Jones (2018), or Feldman and Rufﬂe(2015), do not generalize to online settings because
e-commerce transactions often involve a single, focal
product. Dertwinkel-Kalt et al. (2019) argue further
that low cancellation costs, such as clicking back on apage, limit the effectiveness of practices like drippricing. Our results suggest otherwise, as we ﬁnd alarge effect of price salience in a large online marketplace with very low cancellation costs.
The next section presents a standard framework for
consumer choice with price obfuscation and describes
its empirical implications. Section 3 discusses theexperiment run at StubHub, as well as the data used in
the analysis. Section 4 describes robustness checks on
the randomization, and Section 5 presents our main
results. Section 6 contains evidence on mechanisms,
and Section 7 explores two-sided market responses.
Section 8 concludes.

2. Consumer Choice with Fee
Obfuscation: Hypotheses
As a starting point, we build on the insights of Bordalo
et al. (2013) and DellaVigna (2009), who each present
simple models of consumer choice that explore theimpact of price salience on purchase decisions. In
Appendix A, we present a simple model based onthese studies that formalizes our two main hypotheses: that obfuscating checkout fees causes moreconsumers to purchase goods, and that the goods they621

purchase will be more expensive and of higher quality
compared with an environment with upfront fees.
In our setting, consumers visit the StubHub
website—a platform for secondary-market ticket
sales—in order to purchase tickets for events. As we
describe in more detail in Section 3, ﬁnal prices oftickets are made up of two components: a list price set
by sellers and fees set by StubHub. We consider twosalience conditions under which consumers makepurchase decisions: the ﬁrst is the “upfront-fee” (UF)condition, where the ﬁnal purchase price including all
fees is shown to consumers upfront, when they search
for available tickets; and the second is the “back-end
fee” (BF) condition, where consumers observe only
list prices set by sellers when searching for tickets andthe fees imposed by StubHub are revealed only after
the consumer proceeds to the checkout stage with aparticular ticket. Section 3 offers more details about
the experiment’s design and execution.
Consider the UF case. If all ticket prices exceed aconsumer’s willingness to pay, then she will not buy
any ticket. If some are priced below her willingness topay, then she will buy the ticket that maximizes her
net surplus. Naturally, the higher her value for a given
event, the more likely she is to purchase a ticket.
Conditional on purchasing, the more she values theevent, the more likely she is to buy an expensive, highquality ticket. Finally, because fees are included
upfront, the purchase price that the consumer faces atcheckout is identical to the price that she saw on thelisting page.
Now consider the BF case, where fees are revealed
for the ﬁrst time at checkout. Because fees amount toabout 15% of the list price, if a consumer considers
only the list price, then all tickets appear to be 15%
cheaper during the consumer’s search phase. The
consumer therefore makes a choice from a seemingly
cheaper set of tickets. This is akin to reducing thesalience of prices relative to quality, as in Bordalo
et al. (2013), and is also similar to the way salience ismodeled in Finkelstein (2009). As a consequence,
consumers who would not have chosen any ticket
under UF may believe that they have found a cheapenough ticket under BF to warrant purchase, andproceed to the checkout page with that ticket in hand.
Upon reaching the checkout and purchase page, theticket’s actual price—including all fees—is revealed.
Absent behavioral biases, the consumer ought toexit without buying the ticket, but we assume thatsome consumers will complete their purchase due
to loss aversion or other behavioral biases.2 This results in the following well-established and previously
tested hypothesis:
1. Quantity Effect: A consumer is more likely to purchase under BF than under UF.

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622

One of our main innovations compared with theprevious literature is going beyond this quantity
effect to explore how the composition of products
purchased changes across the two conditions. To see
this, consider a consumer who would have chosen aticket listed at $100 under UF. Under BF, she instead
selects a $100 ticket to which a $15 fee will be added at
checkout, so that her purchase under BF is equivalent
to a $115 ticket in the UF condition. With no behavioral biases and no search costs, this BF consumer
would go back to the listing page and select a ticketthat maximizes her utility (an $87 ticket, which will
cost just about $100 after the fee is included at
checkout). We again assume that some consumers
will not reoptimize and instead will purchase theirinitial choice due to loss aversion or search costs,
resulting in the following hypothesis that has notbeen analyzed previously in the literature:
2. Quality Upgrade Effect: Consumers who buy tickets
under both UF and BF conditions will purchase higherquality and more expensive tickets under BF.
The earlier salience literature overlooks this effect,
perhaps because previously studied settings offeredlittle to no vertical product differentiation (e.g., shipping fees as in Brown et al. 2010, electronic toll collection systems as in Finkelstein 2009, or supermarket
Figure 2. (Color online) Event Page (UF Users)

Blake et al.: Price Salience and Product ChoiceMarketing Science, 2021, vol. 40, no. 4, pp. 619–636, © 2021 INFORMS

beauty aids as in Chetty et al. 2009). Indeed, the loglog demand speciﬁcation favored by earlier work
leaves no scope for quality upgrades.
The Quality Upgrade Effect emphasizes how identiﬁcation strategies must respect the impact of salienceon quality choice. Consider the alcohol sales analysis
of Chetty et al. (2009). They compare an excise (lumpsum) tax to a sales (percentage) tax. The excise tax
should arguably have no effect on the quality of beerchosen (conditional on purchase), since it makes eachcan of beer “in the choice set” more expensive by thesame amount. The sales tax, however, may affect boththe quantity and quality margins, since it is a percentage of the price. Simple comparisons of the revenue effects of excise and sales tax salience may
therefore lead to inconclusive results.
The next section describes the experiment in detailand elaborates our empirical strategy for separately
estimating the quantity effect, bounds on the Quality
Upgrade Effect, revenue effects, and the change in the
average purchase price.

3. Experimental Design
We exploit an experiment in price salience performedon StubHub, a platform for secondary-market ticket
sales. Between January 2014 and August 2015, theBlake et al.: Price Salience and Product Choice

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platform showed all fees upfront, so the initial pricesthat a consumer saw when browsing ticket inventory was the ﬁnal checkout price. Figure 2 shows anevent page, which is what consumers see when they
select an event that they are interested in attending.
Ticket inventory is listed on the right, and prices
including all fees are presented for each ticket.
Between August 19 and August 31 of 2015, the ﬁrm
ran an experiment where treated consumers were
initially shown ticket prices without fees (Smith 2015).
For treated customers, fees were added at the checkout
page, much like sales taxes at the register of a store. We
refer to this user experience as back-end fees.3 StubHub’s
fee structure is nonlinear: the buyer fee is 15% of theticket price plus shipping and handling, if applicable.
StubHub also charges seller fees, which peak at 15%.
The experimental condition was assigned at thecookie-level, which identiﬁes a browser on a computer.
Half of U.S. site visitors were assigned to the treatment (BF)
Figure 3. (Color online) Treatment vs. Control Experiences623

group at their ﬁrst touch of an event page. On the event
page, users are shown a list of tickets. Consumers assigned
to the pre-experimental UF experience (the control
group) were shown conspicuous onsite announcements
conﬁrming that the prices they saw upfront included all
charges and fees. On the other hand, treated users in the
BF group were shown only the base price when they
perused available listings. Once a user in the BF group
selected a ticket, they were taken to a ticket details
page, where they could log in to purchase the ticket
and then review the purchase. It is at this point that theBF group was shown the total price (ticket cost plus feesand shipping charges). Users could then checkout or
abandon the purchase. Figure 3 shows the different
prices on the event page that result in the same priceon the checkout page for treatment and control.
First, we exploit the randomization to estimate thequantity effect described in Section 2 as the differencein purchase probabilities between UF and BF users.4

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Because sellers on StubHub cannot price-discriminate
between BF and UF users, we need not worry thatthe two groups face different prices because of thetreatment (nor do we include other control variables).
In practice, we estimate the following equation via an
ordinary least-squares regression, where Qi is anindicator that consumer i purchases a ticket and Ti is a
BF treatment indicator:
Qi  α + βTi + i .

(1)

The parameter β represents the difference in the levelsof purchasing (Qi ) for BF compared to UF users. To
protect business-sensitive information, however, weβreport estimates of α, which is the percent change inthe likelihood of purchase for BF users.
Measuring the Quality Upgrade Effect is challenging because the random assignment of the BF
experience changes the identity of the marginal consumer. Our intuition, developed more fully in Appendix A, suggests that the marginal consumer whopurchases under BF has a lower valuation for the
event and chooses lower-quality tickets.5 Measuring
the Quality Upgrade Effect requires adjusting for thisselection. Namely, conditional on i making a purchase, let Pi be the purchase price of the ticket that iselects. Let Qi0 be an indicator for whether consumer
i purchases a ticket when he observes fees upfront(Ti  0) and Qi1 for when he observes fees at the backend (Ti  1). We formulate the Quality Upgrade
Effect (QUE) using the potential outcomes notation as
QUE  E[Pi |Qi0  1, Ti  1] − E[Pi |Qi0  1, Ti  0]. (2)The second term is observed by the econometrician
and is the average price of tickets purchased by UF
users. The challenge is that the econometrician cannot
observe the ﬁrst term, which is the average price oftickets that UF users would buy if they were exposed to
the BF treatment. Instead, we observe the change in
the average price, conditional on purchasing:⎪⎧ΔP  E[Pi |Qi1  1, Ti  1] − E[Pi |Qi0  1, Ti  0]⎪⎨
 QUE + E[Pi |Qi1  1, Ti  1] − E[Pi |Qi0  1, Ti  1]⎪⎪
⎩ ≤ QUE.
(3)

Equation (3) shows that the change in the averagepurchase price (ΔP) combines two separate effects:
ﬁrst, the Quality Upgrade Effect, where BF encourages consumers to purchase more expensive tickets
than they would otherwise, and, second, a change inthe marginal consumer, as BF induces more consumers to purchase tickets.6 The former increases
the average purchase price, whereas the latter depresses it (because marginal consumers buy cheapertickets). We therefore use ΔP as a lower bound for the
Quality Upgrade Effect; we estimate (3) using regression speciﬁcation (1) with price as the left-handside variable.
We note that the change in average purchase priceis inherently interesting in this setting, as it maps toa change in platform revenue. We decompose thechange in revenue from treatment as7
ΔE[Ri ]  ΔE[Pi |Qi  1] ·E[Qi ] + ΔE[Qi ]⏟̅⏞⏞̅⏟⏟̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅⏟ΔP

ΔQ· E[Pi |Qi  1].

(4)

We also use conditional probability to derive an upperbound for the Quantity Upgrade Effect. The boundattributes the observed change in revenue entirely tothe quality upgrade effect by setting the price paid bymarginal consumers to zero. The formal derivation of
the bound is presented in Appendix B.

4. Randomization Check
The experiment included several million users who
visited the site over 10 days. To check randomization,
we test whether we can reject a 50% treatment assignment probability. Results are shown in Table 1.
Although the odds of assignment to the treatmentgroup are 50.11% in the full sample, the large scale ofthe experiment allows us to reject the null hypothesisof a 50% assignment probability at the 5% level. Uponcloser scrutiny, we discovered two glitches in the
randomization: ﬁrst, all users who logged in during
the ﬁrst 30 minutes of the experiment were assigned
to the treatment group. Second, users on a particularbrowser–operating system combination were also
skewed to the treatment group. After eliminatingthese two groups, we can no longer reject a 50%

Table 1. Treatment Assignment
SampleFullTime restriction
Time and browser restriction% Unidentiﬁed% Site in sample% Back-end fees

T-statistic

0.78
0.78
0.82

100
99.82
66.12

50.11
50.09
50.06

4.28
3.41
1.99

Notes. This table reports the assignment of StubHub users (cookies) to different treatment cells. Eachrow corresponds to a different sample restriction. The T-statistics are from a two-sided test with a nullof a 50% assignment probability.

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Marketing Science, 2021, vol. 40, no. 4, pp. 619–636, © 2021 INFORMS

Table 2. Covariate Balance
User characteristic

5. Results% Difference

T-statistic

0.01
−0.08
0.16

0.02
−1.6
0.01

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ExperienceHour
Mac user

Note. This table presents summary statistics for differences between
the BF (treatment) and UF (control) groups in our experiment from
August 19 to August 31, 2015.

assignment at the 1% level.8 We therefore exclude
these users in our main analysis.9 Although theprobability of treatment remains slightly above 50%,
the difference is economically insigniﬁcant.
As a robustness check on randomization, we test
whether UF and BF users share similar observablecharacteristics. Unfortunately, as treatment was assigned before users are required to log in, the set ofobservables is limited. For example, we observe auser’s purchase history only if they log onto the site
during the experiment or if they have not cleared theircookies after a recent visit. However, we do see site
visits since the last cookie reset, which we use tomeasure experience. We use this proxy as a left-handside variable in speciﬁcation (1). Row 1 of Table 2
shows that the two groups have almost identicalexperience levels. BF and UF users also visit the site at
similar hours of the day and are equally likely to use
Mac computers (rows 2 and 3). These results give us
conﬁdence that the randomization was successful.

Our framework indicates that obfuscation should
encourage consumers with a low willingness to pay
for quality to switch from the outside option topurchasing a ticket on StubHub, and also encourage consumers to switch from purchasing lower- tohigher-quality tickets. Column 1 of Table 3 shows thenet effect on revenue of the price-salience treatment.
Consumers identiﬁed with cookies in the BF group,
where fees are obfuscated, spend almost 21% morethan those assigned to the UF group. We show revenue effects for the session (same day) and over theentire experiment (10 days), and point estimates arelarge and statistically signiﬁcant at the 1% levelfor both.
Unfortunately, quantifying salience is difﬁcult, so it
is hard to benchmark our estimate to Chetty et al.
(2009). (Although the change in user experience in the
StubHub experiment is similar in spirit to their experiment of adding taxes to supermarket shelf prices,
it is not clear how closely they align.) They ﬁnd that
obfuscating a 7.35% tax leads to an 8% revenue increase. On StubHub, obfuscating a 15% fee leads to a
21% revenue boost.10 Our ﬁndings, detailed below,
suggest that upgrades augment the salience effect inour setting.
5.1. Quantity EffectWe ﬁrst examine the effect of salience on quantity. The
third row of Table 3 shows that price obfuscation

Table 3. Effect of Salience on Purchasing
Back-end vs. upfront fees: % difference
Baseline

Conditional on purchasing

20.64(1.38)

5.42(1.37)

—

5.73
(1.5)

Propensity to purchase at least once

14.1
(0.09)—

Number of transactions within 10 days

13.24
(0.88)−0.9(0.58)

Number of seats within 10 days

11.37
(1.17)−2.32(0.84)—−3.29(0.66)

18.96(1.27)
12.43
(0.6)

5.61
(1.27)—

Cookie 10-day
Revenue
Average seat price

12-month churn
Cookie session
Revenue
Cookie session
Propensity to purchaseNotes. This table presents estimates of how fee salience affects purchasing. Effects are presented aspercent differences between treatment (BF) and control (UF) users, as per Equation (1). Heteroskedasticityrobust standard errors are reported in parentheses. The sample in column 1 is all visitors to StubHub
between August 19 and August 31, 2015. Column 2 restricts to users who made at least one purchaseduring the same period.

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increased the transaction rate over the full course of
the experiment by 14.1%. The second-from-last row
shows that, within a cookie session, consumers in the
BF group are 12.43% more likely to purchase a ticket
during a visit (the estimate is signiﬁcant at the 1%
level). Fees average roughly 15% of ticket prices,suggesting a per-session salience elasticity of 0.1243/
0.15  0.87, which is a similar order of magnitude to
the elasticity of 1.1 found in Chetty et al. (2009). The
10-day elasticity is larger than the session elasticity(0.141/0.15  0.94), suggesting that the long-run effects of salience may be even greater.
Table 3 also provides estimates of how salience
impacts the number of tickets purchased. Our framework ignores the consumer’s decision of how manyseats to buy and describes a world where consumers
need a ﬁxed number of seats and either buy that exact
number or buy none at all. In reality, of course,
consumers might enlarge their parties if they perceive
prices to be lower. To the contrary, we ﬁnd that BF
users buy 2.4% fewer seats, conditional on making atleast one purchase at StubHub. Admittedly, this effectis swamped by the increased probability of buying atleast one ticket on StubHub, but hints at the nuance insalience responses. The lower number of seats suggests that the marginal consumers lured by the BF
treatment buy slightly fewer tickets.11
5.2. Quality Upgrade Effect
The second column of Table 3 compares differences in
the BF and UF groups’ behavior conditional on apurchase. This comparison allows us to assess how
salience affects average purchase prices: BF users
spend 5.42% more than their UF counterparts. Fromthe platform’s perspective, the combination of the
Quantity Effect and the Quality Upgrade Effect implies that the effect of salience on their bottom line
is substantially larger than suggested in the earlier literature, which did not consider product quality upgrades.
Using Equation (4), we can calculate the increased
revenues that are due separately to the Quantity Effect and the Quality Upgrade Effect. From Table 3, we
observe that ΔP  5.42P and ΔQ  14.1Q, and hence,
rewriting Equation (4) without the expectations operator and subscripts for brevity,

ΔR  ΔP · Q + ΔQ · P  5.42 · QP + 14.1 · QP.

declines slightly, so that the change in the averagepurchase price per seat is even greater (5.73%).
We interpret the change in purchase price as evidence of an upgrade effect, where obfuscating fees
leads consumers to buy more expensive, higherquality tickets. This ﬁnding is consistent with Lynch
and Ariely (2000), who ﬁnd that subjects in a laboratory experiment bought higher-quality wine when
prices were not displayed alongside product descriptions (and were only shown at checkout). Our
framework indicates that the change in the averagepurchase price constitutes a lower bound for the
upgrade effect—and although smaller than the quantity effect, even this lower bound is economicallymeaningful. Our upper bound calculation in (8) is20.28%, suggesting that the Quality Upgrade Effectmay even exceed the Quantity Effect.
We provide auxiliary evidence on the upgrade
effect using data on seat locations. In particular, we
examine whether BF users bought seats closer to thestage. Rows are often labeled using letters, whereletters earlier in the alphabet correspond to a betterview.12 Conditional on purchasing a ticket, we separately calculate the probability that a BF and UF userpurchases a seat in each row. Figure 4 graphs therelative probability (the ratio of the two probability
mass functions), along with 95% conﬁdence intervals,
which are calculated pointwise. BF users are relatively more likely to purchase seats in rows A throughD, which are the very ﬁrst rows, and the likelihood
declines for rows later in the alphabet. These patterns provide further evidence of the Quality Upgrade Effect.
Figure 4. (Color online) Difference in Likelihood of
Purchase by Row (BF vs. UF Users)(5)

Dividing both the left- and right-hand sides of (5) byrevenues, R  QP, we calculate the percent change inrevenues (ΔR/R) to be 19.52%, of which 5.42% (about
28% of increased revenues) are from the Quality
Upgrade Effect. Note that the number of seats

Notes. This ﬁgure plots the relative purchase likelihood by ticket row
letter for users in the treatment (BF) and control (UF) groups. Lettersearlier in the alphabet generally correspond to seats that are nearer tothe event stage.

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5.3. A Second Experiment: EventLevel Randomization
The 2015 experiment randomized salience acrossusers so that BF and UF users had the same StubHub
experience except for fee presentation—fees were
included in the search results only for UF users. In an
earlier experiment performed in 2012 at StubHub, feesalience was randomized at the event level, which
presents distinct challenges but offers a nice robustness check for the 2015 experiment.
First, StubHub’s unique inventory threatens theindependence assumption for the 2015 experiment,
but not for its 2012 counterpart. Suppose that priceobfuscation merely accelerates, but does not actually
alter, the consumer’s purchase decision. In this case,
BF users will tend to buy early in the 2015 experiment,
which may reduce inventory for UF users. Comparing
purchase probabilities without taking this censorship
into account would mistakenly indicate a positive
treatment effect. In other words, treating user A affects user B (see Blake and Coey 2014 for a discussionof this challenge on eBay). Fortunately, the 2012 experiment does not suffer from the same contamination concern, because all tickets for a particular event
share the same treatment status.
A second challenge that the 2012 experiment addresses is multidevice use. In the 2015 experiment, wesort users into BF or UF the ﬁrst time that they touchan event page on StubHub during the experimentperiod. StubHub employs cookies to track users,
so that the user remains in the appropriate groupthroughout the trial. However, cookies differ acrossdevices, and a user would be rerandomized into
the BF or UF group if she used a different device.
Switching devices is particularly problematic if its
incidence depends on initial treatment assignment.
As an example, if UF users—upon seeing higher initial
prices—delay their purchases and revisit StubHub
on a second device, then the BF treatment would bepositively correlated with purchasing. In the 2012
experiment, tickets to each event retain their treatment status, regardless of the device that consumers
use. Finally, randomization at the event level provides insight into general equilibrium effects examined in Section 7. We have shown that when StubHub
alters the consumer’s experience, it alters sellers’
behavior. Salience might also affect price levels,
which is hard to gauge given the unique inventory on
StubHub. For example, if price obfuscation attracts
more elastic buyers, then sellers might lower theirprices. If these effects are large, then the 2015 experiment does not provide the true counterfactual ofinterest: What happens when all users face BF? Instead, the econometrician only observes what happens on StubHub when fees are shrouded for 50% ofusers. The 2012 experiment answers this question,

because a ticket seller for a particular match faces anentirely BF or UF audience, but not a mix of both.
In the 2012 experiment, 33 out of 99 Major League
Soccer games were randomly selected for UF. Prices
for tickets to these games included fees, even from theinitial event page. The remaining 66 matches had the
BF experience, which, at the time, was the site-wideuser experience. The results from the 2012 experiment, displayed in Table 4, conﬁrm our 2015 ﬁndings: fee salience reduces revenue substantially.
Consumers are 13% less likely to buy tickets to an
upfront-fee match.13 The difference has a p-value of
0.076, with standard errors clustered at the event level.
We also examine whether users upgrade to more
expensive tickets for BF games. Unfortunately, tests
based on purchase prices are underpowered becauseof the high sampling variance across matches. To
control for the unobserved popularity of matches, wetest whether users purchase from the same quantile ofprice in BF versus UF matches. For each transaction,
we calculate where the purchase ranks in a user’s
choice set (StubHub’s entire inventory for the matchat the time of purchase). On average, consumers buy
from a 12% lower quantile for UF compared with BF
games. Figure 5 shows the full distribution of purchase quantiles for BF and UF matches.
Although these results are heartening, we prefer
the 2015 experiment for its larger sample size. Further, experimentation at the event level suffers from adifferent kind of contamination bias: consumers may
substitute away from UF matches (which appear
more expensive) to BF matches. The 2015 experimentis not vulnerable to this type of contamination. Another complementarity between the two experiments
is that they differ in initial conditions: in early 2012,
StubHub used a BF policy, whereas, in 2015, the siteused a UF policy. Our results suggest that the effect ofprice salience at StubHub is similar despite the difference in the status quo. The ability to execute two
experimental designs is one advantage of the StubHub setting.

Table 4. Experiment Results: Back-End vs.
Upfront Fees, 2012
% Difference
Purchase probability−12.38(6.63)

Percentile of choice set selected−11.97(5.62)

Notes. This table presents estimates of how fee salience affects
customer purchasing based on data from the 2012 StubHub
experiment, where salience is randomized at the event level.
Effects are presented as percent differences between BF and UF
users. Standard errors are clustered at the event level and reported
in parentheses.

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Figure 5. (Color online) Percentile of Choice Set Purchasedin the 2012 ExperimentNotes. This ﬁgure plots the probability density function of purchases
by price percentile separately for treatment (BF) and control (UF)users on StubHub.com. To calculate price percentiles, we reconstruct
the set of available tickets on StubHub.com during each user’ssite visit.

6. Mechanisms
6.1. Misinformation
In this section, we leverage StubHub’s detailed data tobetter understand why fee salience affects consumers
so greatly. First, we examine consumer misinformation
using web-browsing behavior. If consumers do not
anticipate fees, then they will receive a negative
surprise at checkout and should be more likely to exit
when the fee ﬁrst appears. For consumers who arenearly indifferent between purchasing at the baseticket price, the fee makes the outside option theirutility-maximizing choice. Importantly, a misinformation theory offers implications about where (inthe purchase funnel) BF and UF users will differentially exit.
To buy a ticket, a user follows StubHub’s “purchasefunnel” on the website as follows: (1) the consumer
ﬁrst sees the event page, which contains a seat map

and a sidebar with top ticket results, sorted by price inascending order; (2) once a consumer clicks on aticket, the ticket details page appears; (3) the consumer proceeds to the checkout page, where a ﬁnal
purchase decision is made; (4) the purchase conﬁrmation page completes the process.14 BF users areshown lower prices than their UF peers until stage (3),when they are shown the ﬁnal price, inclusive of fees.
If consumers are ignorant of fees, then there should
be a larger drop off between stages (1) and (2) for the
UF group, since they see higher prices initially. But
there should be a larger drop-off between stages (3)and (4) for the BF group. If the former is larger than thelatter, then back-end fees increase the quantity sold.
The left panel of Table 5 shows the absolute andrelative rate of UF and BF user arrivals between these
key steps in the purchase process. Consistent withmisinformation, BF users are almost 19% more likely
to select tickets (transition from stage 1 to 2) than UF
users. The difference is statistically signiﬁcant at the
1% level and economically large. In contrast, thedrop-off rate at the ﬁnal stage (purchase) is muchlarger for BF users, as they are almost 45% less likely
to purchase at checkout.
The right panel of Table 5 presents the average
selected ticket price at each step in the purchasefunnel for a subset of events. The average price oftickets under consideration declines at each step,
suggesting that quality also drops. As the theory
predicts, UF users always select cheaper tickets than
BF users, but the difference narrows as users movecloser to purchase. When fees are revealed, the gap isjust under 7%, compared to an initial difference ofalmost 19%. In sum, BF users are more likely tocontemplate buying expensive tickets, but when fees
are revealed, more of the (potentially surprised) BF
users exit than the UF users who see no change in their
expected outcome.
One important question, from both the ﬁrm’s and apolicy maker’s perspective, is whether consumers
learn about the fees over time. As an example,

Table 5. Purchase Funnel Behavior by Fee Salience
Percentage click through fromprior page

Average ticket price

BF

UF% Difference

BF

UF% Difference

Event page
Ticket details—
27.96(0.01)—
23.56(0.01)—
18.67(0.00)

$1.00
$0.86

$0.84
$0.78

18.73
10.16

Review and submit
Purchase—
18.52(0.06)

—
33.41
(0.1)

—
−44.58(0.00)

$0.56
$0.42

$0.52
$0.39

7.44
6.57

Notes. This table reports means and standard errors (in parentheses) of user behavior in the StubHubpurchase funnel. Average ticket prices are normalized by the average price of tickets selected by BF users
on the event page.

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markets where consumers purchase infrequently (forexample, real estate or automobile markets). However, effects are still large for the most experienced
group (the top 6% of users), which indicates only
limited consumer learning. Because experience is not
randomly assigned in the population, we interpret
this evidence as suggestive, rather than causal.
We examine user churn to understand the long-run
effects of salience. If obfuscation preys on misinformation, then marginal BF consumers, who would
not purchase if shown fees upfront, may be more
likely to abandon StubHub after seeing fees for theﬁrst time. Unfortunately, we cannot identify marginal
consumers among the pool of BF consumers. We also
cannot compare the return rates of all BF and UF
users, as there is no way to track future purchases ofusers who do not log into the site. Instead, we compare the return rates of BF and UF users who purchaseduring the experiment. As Table 3 shows, BF users
are 3.3% less likely to churn, which is inconsistent
with the simple misinformation story. We emphasize
caution in interpreting churn, however, as it potentially confounds multiple treatments: BF users maylearn about the platform fees when they make apurchase, but they may also learn about StubHub’s
reliability, speed, quality, and so on. This additional
learning may increase a consumer’s likelihood ofpurchase, even if obfuscation effects are short-lived.
As a robustness check, we compare the likelihoodof return for consumers who were logged into StubHub
before the experiment. We can track these users’ purchases after the experiment’s conclusion, regardlessof whether they made a purchase during the experiment window. The difference between BF and UF
return rates drops to 0.65% and loses statistical signiﬁcance. Although this sample contains consumers
with high attachment to StubHub, this comparison
also indicates that salience effects persist beyond
initial misinformation.

consumers could act as if they do not anticipate fees intheir ticket selection each time they visit the site. In
this case, websites stand to gain substantially byshrouding fees. This implication contrasts with amodel where consumers anticipate a fee but do notknow the exact level. In a model with learning, once a
consumer makes a purchase, she updates her priors
on future StubHub fees and does not make the same
“mistake” twice.
To examine learning, we repeat our principal
analysis (Table 3) separately by level of user experience. If consumers learn, then experience ought tolessen the response to obfuscation. Of course, experience is endogenous, so experienced users may react
differently to salience for other reasons (as an example, they may have higher incomes). Nonetheless,examining responses across experience groups hints
at how learning might work in this setting.
To measure experience, we calculate the number ofvisits that each cookie has made to StubHub prior to
the experiment. A 2006 ComScore study found that
31% of users clear their cookies within 30 days, so weinterpret this as a short-term measure of experience.15
Unfortunately, we cannot exploit information about
logged-in users (like number of past transactions)because logging in is a potential response to ourtreatment; users who see lower prices initially may be
more likely to log into the website in order to purchase. Our measure does capture the most recent
interactions with StubHub, which are likely to be themost relevant for a user’s knowledge of the site.
We hypothesize that frequent StubHub users ought tobe aware of fees and therefore less sensitive to salience.
We split users into three groups: new users (no recorded
visits), low experience (1–9 visits), and high experience(10 or more visits). Table 6 shows that the treatment
effect is smaller for cookies with at least 10 site visits:
the revenue effect is 15% compared with 21%. These
results suggest that salience may be most important inTable 6. Salience by User Experience% Difference
New user

Low experience

High experience

User 10-day revenue

21.52(1.92)

21.80(2.29)

15.09
(4.4)

Propensity to purchase at least once

15.33
(0.653)13.68(1.15)

10.19(2.42)

Number of transactions within 10 days

14.33
(1.17)

13.53(1.23)

8.81(2.94)

67

27

6% Sample

Notes. This table reports coefﬁcient estimates of how fee salience affects purchasing (Equation (1)) for
users of different experience levels. Estimates are presented as percent differences between treatment(BF) and control (UF) users. Heteroskedasticity-robust standard errors are in parentheses. See Table 3 forpooled estimates.

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Finally, to shed light on the persistence of salience,
we construct a panel data set that tracks the purchasesof BF and UF users over a six-month period centeredaround the experiment window (May 18 throughDecember 1, 2015). We have already established that
BF users spend more, conditional on purchasing, during the experiment. On September 1, the entire site
switched to BF, so that the only difference between
users who had been assigned to BF versus UF is theirexperience with the back-end fees. If salience effects
are short-lived, then we would expect UF users, who
now experience back-end fees for the ﬁrst time, tooutspend their BF counterparts, who have 10 days ofexperience. On the other hand, if salience effects
persist, then the UF-BF difference should dissipate
after the experiment, as both groups spend more thanthey would have in an UF environment. If i denotes
the user and t the purchase date, then we model
purchase price using the following speciﬁcation:W∑ln pit  α0 +αw · 1{weekt  w}
+

w1
W
∑

βw · 1{weekt  w} × Ti + it , (6)the purchase process, users log into the site, allowing
us to identify their prior and subsequent purchases.
Standard errors are clustered at the user level to account for serial correlation in individual purchasing decisions.
Figure 6 displays the estimates of the interactions
between the BF treatment indicator and each time
period. BF and UF users spend similar amounts
before the experiment, when both groups experience
UF. As in Table 3, we ﬁnd that, during the experiment,
BF users spend almost 6% more than UF users, conditional on purchasing at least one ticket. However, inthe three-month period following the experiment,
when all users experience BF, there is no differencein spending between the two groups. The results
are robust to the inclusion of both buyer and day
ﬁxed effects.
These event study ﬁndings, taken together with our
results on experienced users and churn, indicate thatsalience effects are persistent. They suggest that users
do not learn to anticipate the correct fee level after
going through the purchase funnel with back-end fees
at least once.

w1

where 1{weekt  w} is an indicator that the purchaseoccurred during week t in our sample and Ti is atreatment indicator. For ease of interpretation, theweek-14 indicator is labeled experiment and comprises
10 rather than 7 days. Purchases the ﬁrst day of andafter the experiment are omitted to account for any
engineering lags in the user interface switch. We
estimate Equation (6) using the sample of users whopurchase during the experiment window, because
these are the only users we can reliably track. During
Figure 6. (Color online) Spending Before, During, and Afterthe Platform Switch to Back-End Fees

Notes. This table includes data on purchases between May 18 andDecember 1, 2015, excluding August 9 (the ﬁrst day of the experiment) and September 1 (the ﬁrst day after the experiment). The data
include only those customers who purchased at least once during theexperiment window.

6.2. Consideration Sets and Search Frictions
In this section, we present evidence on forces beyond
misinformation that might contribute to the importance of salience: consideration sets and search frictions. First, we consider whether fee obfuscation
widens users’ consideration sets. A growing body ofliterature (e.g., Goeree 2008) suggests that potential
consumers often ignore a large fraction of inventory,
and instead focus on choosing between a few products. StubHub presents inventory to consumers inascending price order, so that expensive tickets are
not visible to the consumer unless she actively scrolls
down or ﬁlters the results (e.g., by section). It is
possible that obfuscating fees might draw user attentions to a wider array of products, leading BF users
to make different purchase decisions than their UF
counterparts. We ﬁnd that BF users scroll 10% more
often, a difference that is statistically signiﬁcant at the
1% level.
When fees are revealed, BF consumers are alreadyat checkout with their tickets, but they may go back to
the event page to reoptimize and purchase cheaperseats. We ﬁnd that less than a quarter of BF users
exercise this option, which is consistent with a search
friction beyond misinformation. Figure 7 shows theaverage number of tickets viewed by BF and UF users.
BF cookies are 56% more likely to view multiple ticket
listing compared with their UF counterparts. Table 7
shows that BF users view cheaper tickets upon theirreturn to the listings page from the checkout page (sixpercentage points cheaper). In contrast, UF users,

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Figure 7. Number of Listings Viewed by Fee Salience

Notes. This histogram plots the number of listings viewed acrossusers. The distribution is plotted separately for treatment (BF) andcontrol (UF).

who are less likely to return overall, view more expensive tickets if they do.
Figure 7 shows that BF users are twice as likely toview three or more listings than their UF counterparts. Viewing more than two tickets suggests that theeffects of price obfuscation extend beyond an initialconfusion about fees. BF consumers who return to theevent page have seen fees for their initial selection, butthey must calculate the StubHub fee for each newticket that they consider. If calculation costs are high,as hypothesized by Morwitz et al. (1998) or Ellison
and Ellison (2009), then consumers might choose to go
down the funnel multiple times rather than computethe fees themselves. Obfuscation as a search friction isconsistent with our ﬁndings on experienced customers, who ought to anticipate fees but might stillbear a higher search cost when fees are hidden. Thisevidence is in line with Ellison and Ellison (2009), who
ﬁnd that ﬁrms endogenously create such frictions tosoften price competition.

7. Two-Sided Responses
In this section, we provide evidence on the effect of fee
salience beyond changes in consumer behavior. Noteﬁrst that, in two-sided markets like ticket resale,changes to the buyer experience may spill over onto

sellers. As an example, if obfuscation lifts seller proﬁts(by increasing buyer spending), then more sellers
may enter the marketplace. In turn, increased seller
participation may bolster competition and help buyers.
These sorts of externalities complicate welfare analyses
in two-sided markets.
7.1. Ticket Quality
As a ﬁrst step, we examine whether inventory responds to the use of BF pricing, with a focus on ticket
quality. Section 5.2 shows that buyers upgrade tohigher-quality seats when fees are less salient, making
StubHub a more attractive platform to sellers of highquality tickets. Figure 8 shows the evolution of inventory on StubHub over time by row letter. Visualinspection suggests that the relative number of seatsin front rows (A–E) compared with back rows (U–Z)
increases after the switch to BF. Consistent withEllison and Ellison (2009), we ﬁnd that sellers respondto the change in the buyer experience.
To further investigate seller responses, we test for abreak in listing quality during and after the experiment, when the whole site switched to BF. To measurequality, we construct a row-number variable, Position,
which counts the number of rows between the seatand row A plus one (taking a value of one for seats in
row A). We then construct an event study, where thelog number of listings is the dependent variable. We
are interested in the coefﬁcient on the interaction
between ln(Position) and an indicator for the post
period as follows:

ln Listingsit  β0 + β1 · ln(Positionit )+ β2 · Postt ln(Positionit ) + Γt + it . (7)Our preferred speciﬁcation includes date ﬁxed effects, Γt , which control for any site-wide ﬂuctuations
that affect all types of tickets simultaneously. Columns 1 and 2 in Table 8 present the coefﬁcient estimates on the interaction term, which is negative andstatistically signiﬁcant at conventional levels. The
point estimates imply that a ticket listed on StubHub
is 3.7% more likely to be in row A than row B following the experiment (under BF) compared to before(under UF). The increase in high-quality listings
underscores the complexity of platform design, as

Table 7. Average Price of Tickets Viewed Relative to UF Initial Selections
Back-end fees
% Initial checkout
8.3
(1.9)

Upfront fees

% Follow-up actions% Initial checkout

Follow-up actions

0.8
(1.2)

0.0
(—)

1.8
(0.6)

Notes. This table reports means and standard errors for the relative price of tickets viewed across thetreatment and control groups. Estimates are normalized by the price of tickets initially brought tocheckout by UF users.

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Figure 8. (Color online) Fraction of Listings by Row Letter

Notes. This ﬁgure plots the number of listings by row letter relative tobase rows U–Z. The two vertical red lines denote the start and end of
the 2015 fee salience experiment.

changes to one side of the market inﬂuence entry
decisions on the other.
7.2. Ticket Prices
Second, we consider whether prices respond to backend fees. Ideally, we could test whether back-end
fees induce sellers to increase or decrease prices
by comparing price levels before and after the site
switches from UF to BF in September 2015. However,this time-series variation is confounded by changes insite inventory over time. The challenge is that the
tickets listed and sold in August differ from thoselisted and sold in September because different events
are held in the two months. As an example, the 2015
NFL season kicked off on September 10th. Instead of
examining price levels, we focus on another aspect ofpricing: the use of round numbers.
An extensive literature in marketing (e.g., Monroe
1973 or, more recently, Backus et al. 2019) documents

the appeal of round-number pricing (amounts thatend in zeros or nines). If sellers aim to employ roundnumber pricing, then they ought to adjust prices in
response to the site’s switch from UF to BF. That is,under UF, a seller should set its list (or “base”) price sothat the fee-inclusive price (list price + buyer fee) thatis shown to the consumer is round. In contrast, under
BF, the seller should set a round list price. Thus, we
examine whether sellers are more likely to set baseprices at round numbers after the switch to backend fees.
As shown in Figure 9, the share of listed tickets withround base prices increases by approximately ﬁve
percentage points following the switch to back-end
fees. To be transparent, we examine only the prices oflistings that were added or modiﬁed on each date,
and we categorize prices that end in “.00” or “.99” asround. Columns 3 and 4 in Table 8 present results of
the regression analogue of Figure 9, where we adopt
speciﬁcation 7 so that the independent variable ofinterest is an indicator for a round listing price. The
results indicate an economically and statistically significant increase in the use of round listing prices
following the switch to BF. This trend shows thatsellers adjust their pricing policies in response thebuyer’s experience, which is consistent with Ellison
and Ellison (2009).

8. Discussion
As the online share of transactions continues togrow, so too does the scope for regulations thatguarantee the efﬁcient functioning of markets. Chiefamong proposed regulations has been increasing the
Figure 9. (Color online) Percent of Listings withRound Prices

Table 8. Changes in Listings Following Back-End Fees
Log number of listings

Log Position × Post

(1)

(2)

−0.123(0.020)−0.123(0.017)

Round × Post
Date ﬁxed effectObservations

No
4,680

Yes
4,680

(3)

(4)

0.315
(0.064)0.315(0.018)

No
360

Yes
360

Notes. Heteroskedasticity-robust standard errors in parentheses.
Data are from June 1, 2015, to December 1, 2015, at the daily level.
Controls include log Position (the letter’s position in the alphabet,where A occupies the ﬁrst position) in columns 1 and 2 and an indicator for a round base price in columns 3 and 4. Columns 1 and 3
also include an indicator for the Post period.

Notes. This ﬁgure plots the fraction of StubHub listings with roundbase prices for a six-month window around the 2015 fee-salience
experiment. The two vertical red lines denote the start and end date of
the experiment. The sample comprises listings that were created or
modiﬁed each day.

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Marketing Science, 2021, vol. 40, no. 4, pp. 619–636, © 2021 INFORMS

transparency of mandatory fees. Using data from arandomized control trial on StubHub, we ﬁnd thatshrouding buyer fees increases total revenue by about
20%. In the experiment, the control group was shown
fee-inclusive prices from the initial search page,
whereas the treatment group was shown base prices
until the checkout page. We decompose the impact ofobfuscation into a quantity effect and a quality effect.
The latter accounts for at least 28% of the revenue
bump, because consumers upgrade to higher-quality
products when they observe lower prices initially.
We ﬁnd that consumers who are shown fees upfront drop off early in the purchase funnel, whereas
those shown fees later are more likely to exit after
the site displays total prices, consistent with consumer misinformation.
We ﬁnd that salience persists beyond initial misinformation. Experienced users, who arguably should
anticipate the fee, spend 15% more on StubHub
when the fee is shrouded. More strikingly, after theplatform switched to back-end fees, the users exposed to the BF treatment during the experiment
spend similar amounts to those newly exposed to
back-end fees. This behavior suggests that short-term
experience with back-end fees does not give users anadvantage in anticipating true ﬁnal prices. These
patterns indicate that salience is not a one-off phenomenon, which becomes irrelevant as consumers
learn about the sales environment. It is perhaps
unsurprising, if not reassuring, that we ﬁnd thatsellers respond to changes in the salience of the buyer
experience. Sellers are more likely to list high-quality
tickets and to use round-number prices when fees arepresented at the back end, highlighting the nuance ofsalience effects on a platform.
Our results also demonstrate that price saliencelooms large in markets where consumers purchase
only intermittently. The existing literature focuseson contexts where consumers purchase frequently,
such as grocery stores in Chetty et al. (2009). In thesesettings, consumers plausibly hold strong beliefs aboutboth the amount and presentation of fees and taxes,16
and so we might interpret their response to an abruptchange in salience as a reaction to off-equilibrium
path play. In contrast, most users who visited StubHub during our experiment were new to the site.
Their reactions to salience may more closely parallel
reactions in markets like real estate, higher education, or automobiles, where policy makers may wish
to mandate fee disclosure.17
Acknowledgments
The authors are grateful to the executives and employees
at StubHub for sharing the data for this study. They thanknumerous seminar participants for helpful comments.

Appendix A. A Model of Consumer Choice with
Limited Fee Salience
Consider a consumer who makes purchase decisions under
two regimes. In the ﬁrst, which we call upfront fees (UF), theﬁnal purchase price including all fees is shown to consumers when they browse the set of available tickets. In thesecond, which we call back-end Fees (BF), consumers observe only list prices when they browse available products,
and fees are revealed only after a particular ticket is selected
for purchase.
First, we consider a consumer’s choice when she observes
fees upfront. She is presented with a convex and compact
set of available tickets J, where her utility vj from ticket j ∈ Jdepends on its price pj and quality qj (e.g., section and row,delivery method, etc.) as follows:
vj  θqj − pj .
The consumer’s willingness to trade off quality formoney is captured by her type θ ∈ [0, θ̄]. Let 0 denote theoutside option, with q0  p0  0. Figure A.1(a) illustrates her
optimization problem: the set J of available tickets lies on
and above the curved line, and the dashed line v0  0 marks
the consumer’s indifference curve from not purchasing.
The consumer chooses the ticket j∗ ∈ J on her highest indifference curve, yielding a utility of v∗ > 0. A higher θ
consumer will purchase a higher-quality ticket at a higher
price. For consumers with low-enough values of θ [lesssteep indifference curves in Figure A.1(a)], their indifference curve v0  0 lies fully below the set J, and they will notpurchase any ticket. It therefore follows that, given a set oftickets J, there exists a threshold type θ > 0 such that a
consumer of type θ will purchase a ticket if and only if θ > θ.
We model consumer optimization with back-end fees
as a shift in the boundary of J. Namely, her choice now
depends on the perceived price p̃j of ticket j rather than its
true ﬁnal price. This is akin to reducing the salience of prices
relative to quality as in Bordalo et al. (2013) and is alsosimilar to the way salience is modeled in Finkelstein (2009).
The consumer then selects j ∈ J to solve her optimization problem:
max ṽj  max θqj − p̃j ,
j∈J

j∈J

where the perceived price of not purchasing a ticket is also
zero, p̃0  p0  0. The established view on price salience isthat p̃j < pj . That is, when fees are obfuscated, prices appear
lower to consumers than they actually are, as illustrated
in Figure A.1(a). The true price-quantity frontier is still J;
however, when the consumer chooses a ticket for purchase,she perceives the frontier to be J̃, choosing the ticket j̃ ∗which has quality q̃∗ and perceived price p̃∗ .
Upon reaching the checkout and purchase phase, theticket’s actual price—including all fees—is revealed to bep̃ > p̃∗ . We assume, however, that the consumer will continue with the purchase at this ﬁnal stage of the purchasefunnel rather than go back to the selection stage with a
newfound understanding that the true choice set is J.18
Recall that the set of consumers with θ < θ will prefer not
to purchase if they perceive the set of tickets to be J. Some ofthese consumers, however, will select a ticket for purchase634

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Figure A.1. (Color online) Optimal Ticket Choice

if they perceive the set of tickets to be J̃. It follows immediately that there exists a threshold type θ̃ ∈ [0, θ] suchthat a consumer of type θ will purchase a ticket if and only if
θ > θ̃. Hence, the analysis above implies that fee obfuscation has two effects on consumer choice:
1. Quantity Effect: Under the BF treatment, a consumer ismore likely to purchase.
This prediction is consistent with the existing literature: more salient fees reduce the likelihood of purchase.
However, it precludes at least two alternative effects of salience: ﬁrst, if consumers anticipate fees (or hold unbiased
beliefs), then perceived prices may not be lower than actual
prices. Second, it is also possible that price obfuscation
generates a “disgust” factor, wherein last-minute fees upset
consumers. In that case, the quantity effect could be negative, contravening the standard price-salience model.
When true ﬁnal prices are higher than perceived prices
and the difference is increasing in the listing price, themodel generates a second prediction: customers buy higherquality items than they would under the UF regime. Thiscondition would be satisﬁed, for example, if consumers simply
ignored or underestimated a proportional fee or tax. Moreformally, for any ticket j, let p̃j be the perceived BF priceexcluding fees, and let pj be the true ﬁnal price observed
at checkout. We have the following:
2. Quality Upgrade Effect: If pj − p̃j > 0 and pj − p̃j isincreasing in qj , then consumers buy higher-quality tickets
under BF.
Conditional on purchasing, consumers upgrade tohigher-quality tickets under back-end fees and therefore
spend more on the site. The earlier salience literatureoverlooks this effect, perhaps because previously studied
settings offered little vertical product differentiation (e.g.
electronic toll collection systems as in Finkelstein 2009 orsupermarket beauty aids as in Chetty et al. 2009). Indeed,
the log-log demand speciﬁcation favored by earlier work
leaves no scope for quality upgrades.

The Quality Upgrade Effect emphasizes how identiﬁcation strategies must respect the impact of salience on
quality choice. Consider the alcohol sales analysis of Chetty
et al. (2009). They compare an excise (lump sum) tax to asales (percentage) tax. The excise tax should arguably noteffect the quality of beer chosen (conditional on purchase),since it makes each can of beer “in the choice set” more
expensive by the same amount. The sales tax, however, may
affect both the quantity and quality margins, since it is apercentage of the price. Simple comparisons of the revenue
effects of excise and sales tax salience may therefore lead toinconclusive results.

Appendix B. An Upper Bound for the Quality
Upgrade EffectWe derive an upper bound for the Quality Upgrade Effectby setting the purchase price among marginal consumers to
zero. That is, we assume that users who buy under BF but
abstain under UF get tickets for free under the BF treatment.
Formally, consider the following expression for the expected purchase price under back-end fees:
E[Pi |Qi1  1, Ti  1]
P{Qi0  1}
P{Qi1  1}()
P{Qi0  1}
+ E[Pi |Qi0  0, Qi1  1, Ti  1] · 1 −P{Qi1  1}
P{Qi0  1}
 (QUE + E[Pi |Qi0  1, Ti  0]) ·P{Qi1  1}()
P{Qi0  1}
+ E[Pi |Qi0  0, Qi1  1, Ti  1] · 1 −.
P{Qi1  1}⏟̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅⏞⏞̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅⏟ E[Pi |Qi0  1, Qi1  1, Ti  1] ·>0

The ﬁrst equality follows from a conditional probability decomposition of E[Pi |Qi1  1, Ti  1]. Note that it also

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Marketing Science, 2021, vol. 40, no. 4, pp. 619–636, © 2021 INFORMS

relies on choice monotonicity, which implies that Pr{Qi0 
Pr{Qi0 1}
1|Qi1  1}  Pr{Q
. In the second equality, we add andi1 1}
subtract an additional term to create a term including QUE.
This last equality contains two expressions, the second ofwhich includes the expected price of tickets bought by themarginal users who buy under BF but abstain under UF,19
which we cannot observe but is greater than zero. If weassume that these consumers buy at a price of zero, thereby
setting this last term to zero, then we obtain the following
upper bound for QUE:
Pr{Qi1  1}
QUE ≤ E[P|Qi1  1, Ti  1] ·Pr{Qi0  1}
− E[Pi |Qi0  1, Ti  0].

(B.1)

Importantly, all of the terms on the right-hand side inequation (B.1) can be estimated directly from the data.

Table C.1. Changes in Google Searches Following Back-

End Fees
Google queries index
(1)

(2)

(3)

Ticketmaster × Post

0.019
(2.691)0.019(2.408)−1.092(4.877)

SeatGeek × Post

15.827(2.691)15.827(2.155)−8.765(3.643)

Date ﬁxed effect
Site × time trend
Observations

No
No
312

Yes
No
312

Yes
Yes
312

Notes. Heteroskedasticity-robust standard errors in parentheses.
Observations from September 1, 2014, to September 1, 2016, at the
weekly level. All columns include main effects for Ticketmasterand SeatGeek. Column 1 includes an indicator for after the experiment, Post.

Appendix C. Competition with Other Platforms
An additional consideration is how fee presentation at StubHub
affects the broader competitive environment, including prices
and inventory on rival sites. We focus on Ticketmaster and
SeatGeek, two alternative secondary markets for tickets, with
Ticketmaster serving as the primary market for certain sportingand music events. At the time of the 2015 experiment, both sites
employed back-end fees. It is possible that, in comparison,
StubHub appeared more expensive to consumers (because its
listing prices included fees) and therefore less attractive tosellers. Thus, when StubHub itself switched to back-end fees in
September 2015, it may have drawn sellers and buyers who
would otherwise have frequented a rival platform. Unfortunately, we do not have access to listing or sales data fromTicketmaster or SeatGeek, so we investigate the effect of
StubHub’s switch to back-end fees using data from GoogleTrends
on queries.
Figure C.1 shows the evolution of queries over three
years from September 2014 to September 2016. (To be clear,
Google normalizes weekly query volume separately for
Figure C.1. (Color online) Google Queries for Competing
Ticket Resale Platforms

each platform by dividing by the site’s peak from 2012 to2017, so that the index ranges from 0 to 100 for each site.
Queries for Ticketmaster are virtually ﬂat, indicating that
there is no effect of StubHub’s switch to BF. During theentire period, SeatGeek seems to be gaining popularity, but,
again, there is no evidence of a trend break in September
2015 when StubHub makes the change. We formally test
for a change in Ticketmaster and SeatGeek queries byadapting speciﬁcation 7 so that the right-hand side interactions
are with indicators for Ticketmaster and SeatGeek (rather than
Position) and the left-hand side variable is the Google queryindex. The omitted category is queries for StubHub itself. Table C.1 presents results that show an economically and statistically insigniﬁcant change in searches for Ticketmaster. In
contrast, the coefﬁcient on the interaction between SeatGeek andthe post indicator is positive and statistically signiﬁcant in columns 1 and 2, where the latter includes date ﬁxed effects. To
accommodate the gradual increase in SeatGeek queries during
this period visible in Figure C.1, we add a site-speciﬁc timetrend in column 3; the coefﬁcient on the interaction term for
SeatGeek and the post indicator reduces by half in magnitude
and reverses sign. Our interpretation of these results is thatthey provide little evidence that other ticket resale platforms
were affected by StubHub’s switch to back-end fees. Morework with data that speak to rivals’ sales and not simply
queries is needed, however, to give a deﬁnitive answer.

Endnotes1

In their working-paper version, Chetty et al. (2009) note that therevenue effect is bigger than the quantity effect, which is potentially
due to consumers switching to lower-priced items. Their data are
insufﬁcient to investigate that possibility further.

2

An alternative explanation is that by entering payment informationen route to the checkout page, BF users face lower barriers to purchase than UF users. We ﬁnd this explanation unlikely because hassle
costs must be very large to explain the salience effects.
3

Ticketmaster and other platforms also employ a similar back-endfee pricing scheme.

Notes. This ﬁgure plots the Google trend index for StubHub, SeatGeek, and Ticketmaster for a two-year window around the feesalience experiment. The index is normalized separately for eachsite based on its peak from 2012 to 2017.

4

Using the potential outcomes notation, we can write the quantity
effect as ΔQ  E[Qi |Ti  1] − E[Qi |Ti  0].

5

In the language of the model that appears in the appendices, themarginal consumer has a lower θ.

636
6

The derivation employs the standard monotonicity of choice for agiven consumer (i.e., Pr{Qi1  1|Qi0  1}  1).
Expected revenue using conditional probability is E[Ri ]  E[Pi |Qi  1] ·Pr{Qi  1}  E[Pi |Qi  1] · E[Qi ].

7

8

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And we cannot reject at the 5% level in a one-sided test against thenull that the treatment assignment is greater than 50%.

9However, our main results are robust to their inclusion in
the sample.

10

Fee documented in Osborn (2015).

11

A second possibility is that the revelation of fees at checkout induces BF users to reduce the number of seats that they intend topurchase once they observe the fee-inclusive price.
12
As numbering schemes vary across venues, letter position only
proxies for quality.
13

Note that fees were approximately 10% in 2012.

14

Before reaching the checkout page, a log-in page appears unless theconsumer was already logged into their account. Many searches are
nonlinear, where consumers examine multiple event pages (see Blake
et al. 2016). BF users might even return to stage (1) once they see theadditional fees leveed at stage (4).
15
See https://www.comscore.com/Insights/Blog/When-the-Cookie-Crumbles.
16

Chetty et al. (2009) provide survey evidence that the modal consumer in their setting identiﬁes the correct tax level.

17
For example, starting in 2012, the Department of Transportation
required airlines to advertise fee-inclusive prices.
18

Several frictions could prevent consumers who reach checkout
from going back to purchase a different ticket, such as loss aversion or
the anticipation of reoptimization costs (e.g., having to calculate thefee for each set of tickets). We remain agnostic as to which of thesebest explain why consumers do not reoptimize, which is what we ﬁnd
in the data.
19

The types θ ∈ [θ̃, θ] in the model we present in Appendix A.

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