WORKING 

 PAPERS

 Prices and Price Dispersion in 

Online and Offline Markets for Contact Lenses

James C. Cooper

WORKING PAPER NO. 283

Original Version: April 2006

Revised: November 2006

___________________________________________________________________________

FTC Bureau of Economics working papers are preliminary materials circulated to stimulate discussion

and critical comment. The analyses and conclusions set forth are those of the authors and do not

necessarily reflect the views of other members of the Bureau of Economics, other Commission staff, or

the Commission itself. Upon request, single copies of the paper will be provided. References in

publications to FTC Bureau of Economics working papers by FTC economists (other than

acknowledgment by a writer that he has access to such unpublished materials) should be cleared with the

author to protect the tentative character of these papers.

______________________________________________________________________________

BUREAU OF ECONOMICS

FEDERAL TRADE COMMISSION

WASHINGTON, DC 20580
Price Levels and Dispersion in Online and Offline 

Markets for Contact Lenses 

James C. Cooper*

 

Federal Trade Commission 

600 Pennsylvania Ave., N.W. 

Washington, D.C. 20580 

202-326-3367 (phone) 

202-326-3548 (fax) 

jcooper1@ftc.gov

 

November 29, 2006 

Abstract: I examine online and offline prices for popular disposable contact lenses. Idiosyncratic features 

of this market make it likely that offline firms set prices on the assumption that most of their customers are 

unaware of online prices. Consistent with lower online search costs, offline prices are more dispersed and 

approximately 11 percent higher than online prices when controlling for differentiated retail services. I 

also find that the Internet has had a smaller effect on the prices of widely-advertised lenses. Overall, the 

results suggest that contact lens consumers still are relatively uninformed about their options. 

JEL Classification: K20, L81, L10, D83 

Key Words: Contact Lenses, Price Dispersion, Search Costs, Internet, E-commerce 

 

*

 Deputy Director, Office of Policy Planning, Federal Trade Commission. The views expressed herein are 

the author’s own and do not purport to represent the views of the Federal Trade Commission or any 

Commissioner. I would like to thank Rick Geddes, Dan Hosken, John Yun, and two anonymous referees 

from the FTC Working Papers series for valuable comments. I also thank Zach Mabel for research 

assistance. 
1

1. Introduction 

 Theory predicts that price dispersion and margins should be positively related to 

consumer search costs.1 In most models of consumer search, given search costs and 

knowledge of the price distribution, a consumer determines how many stores to visit and 

purchases from the lowest price firm observed; he or she will visit an additional store 

only if the expected gain (from a price lower than the lowest one observed to date) is 

greater than the cost of search. When consumers face no costs to obtain an additional 

price quote, stores must set their prices on the assumption that anyone visiting their store 

already knows – or will soon discover – the lowest price offered. Conversely, when 

search is costly, consumers visit fewer stores. Because consumers with positive search 

costs do not learn the entire price distribution, they are more likely to buy from a store 

that does not offer the lowest price. 

 It is reasonable to assume that consumers can shop among competing online 

merchants more cheaply than offline merchants.2 Recently, several economists have 

taken advantage of this “natural experiment” to test the predictions of search theory by 

comparing online and offline price distributions of commodity goods.3 This line of 

 

1 See Stahl (1989); Burdett & Judd (1983); Carlson & McAfee (1983); Salop & Stiglitz (1976). 

2

 Visiting an online merchant’s Web site to find a price almost certainly takes less time than visiting or 

even calling an offline merchant for the same information. Additionally, “shopbot” Web sites like 

Shopping.com or BizRate.com allow consumers to compare large numbers of online competitors’ prices 

with the click of a mouse. The online firm, then, must set its prices on the assumption that anyone visiting 

its Website has seen – or will see – the lowest online price offered. Accordingly, we would expect to see 

online prices for homogeneous goods to be lower and less dispersed than those offline; indeed, in the 

limiting case where all online consumers are perfectly informed about competitors’ prices and view all 

online vendors as perfect substitutes, a zero-profit Bertrand equilibrium obtains. See Bakos (1997) who 

catalogues several claims by commentators has to how the internet would bring about “frictionless” 

markets, where prices are driven to marginal cost. 

3 See, e.g., Clay et al. (2002); Clemons, Hann, and Hitt (2002); Lee and Gosain (2002); Clay, Krishnan, 

and Wolff (CKW) (2001); Brynjolfsson and Smith (2000); Bailey (1998). Several economists have found 
2

research, however, has arrived at no consensus that online prices are lower or less 

dispersed than offline prices. Further, and perhaps not unexpectedly, no studies have 

found levels of online price dispersion at levels low enough to suggest online firms 

selling homogenous goods are Bertrand competitors (see, e.g., Brynjolfsson and Smith 

2000; Clay, Krishnan, and Wolff (CKW) 2001; Clay et al. 2002; Clemons, Hann, and 

Hitt 2002).4 

In this paper, I examine the online and offline prices of disposable contact lenses. 

Overall, the data indicate that search costs play an important role in stores’ pricing 

decisions and that online markets are more efficient that their offline counterparts. 

Specifically, I find that using various measures and controlling for differentiation among 

outlets, online prices are significantly less dispersed that offline prices. I also find that 

controlling for business model differences that are likely to be associated with differences 

in the provision of costly retail services, online prices are on average 11 percent lower 

than offline prices. Although the data are consistent with online markets functioning 

more efficiently than their offline counterparts, however, like other studies of online 

pricing, I find that the online market for contact lenses still exhibits levels of dispersion 

greater than what we would expect to see in Bertrand competition. I also take advantage 

of variation in advertising, using widely-advertised lenses as a control group in a 

difference-in-difference approach to measure the effect of Internet availability on online 

and offline price dispersion and level. I find that the difference between most measures 

 empirical support for this prediction by identifying other proxies for consumers’ knowledge of price 

distributions. See Dahlby & West (1986), Van Hoomiseen (1988), Sorensen (2000). 

4 Indeed, recent research by Baye et al. (2004) presents compelling evidence that persistent dispersion in 

prices listed at a price comparison site is an equilibrium phenomenon. 
3

of online and offline price dispersion for widely-advertised lenses is only about half that 

for the remainder of the lenses in the sample. Further, average online and offline prices 

of widely-advertised lenses are statistically equivalent in contrast to non-advertised 

lenses. These results suggest that online markets have had less of an impact on the 

pricing for widely-advertised lenses than other lenses. 

This study improves on previous work comparing online and offline prices to test 

search theory in two ways. First, one assumption implicit in previous studies of online 

and offline pricing is that offline stores set prices based on expectations of their patrons’ 

knowledge of other offline firms’ prices, not online prices.5

 It is more expensive to 

compare among offline than online firms. But, for those consumers with Internet access, 

comparing an offline price to an online price should be no more expensive than 

comparing among offline firms. Once online, moreover, it is extraordinarily cheap to 

gain additional price quotes from online merchants. For instance, if a consumer with 

Internet access already knows the price that Borders charges for a particular CD, it 

appears that it would be equally costly to phone Barnes & Noble for a price quote or to 

go online and search several merchant’s prices. 

Although it is reasonable to assume that those who shop at one online outlet are 

likely to obtain many additional online quotes, there is no a priori reason to believe that – 

apart from those consumers who either do not have Internet access or who are unwilling 

to purchase goods online due to idiosyncratic reasons – a large proportion of those who 

shop offline obtain only offline quotes. If this is the case, offline sellers of books and 

CDs are likely to take into account online pricing when setting their prices. This may be 

 

5

 That is, most of those who shop at Barnes & Noble base their purchase decision on their knowledge of 

Borders’, Crown’s, and Wal-Mart’s prices for the same book, not on Amazon.com’s price. 
4

one reason why studies of these goods have arrived at no consensus that online and 

offline prices are statistically different. 

 A variety of factors likely have caused many consumers to remain unaware of 

their full range of options beyond their prescribing eye care professional (ECP). For 

example, prescribing ECPs in all states were not required to release contact lens 

prescriptions to their patients until 2004.6 Prior to the Fairness to Contact Lens 

Consumers Act (FCLCA), several states’ laws made it difficult for consumers to receive 

a copy of their contact lens prescription, which is necessary to purchase lenses from 

someone other than a prescribing ECP.7

 Further, there is anecdotal evidence that 

prescribing ECPs are hesitant to let their patients know that their prescriptions are 

portable (See 1-800 Contacts 2005b, pp. 18-30).8

 

 When consumers do not know the distribution of prices and have difficulty 

determining what individual merchants charge, they are more likely to purchase from the 

first store they visit, which, in the case of contact lenses, always will be their prescribing 

ECP.9 Given the relative youth of the replacement contact lens market, state regulatory 

 

6

 15 U.S.C. § 7601; 16 C.F.R. § 315.3. FCLCA prohibits ECPs from tying contact lens sales to eye 

examinations and requires ECPs to release their patients’ prescriptions. 

7 See, e.g., Hardy v. City Optical, Inc., 39 F.3d 765 (7th Cir. 1994), a case in which an ECP claimed that 

Indiana law prevented him from releasing contact lens prescriptions to patients who wanted to purchase 

lenses at cheaper outlets. Additionally, FTC (2004, p.23-25) discusses anecdotal evidence that even in 

states that explicitly allowed prescription release before FCLCA, some prescribers refused to release 

contact lens prescriptions to their patients. 

8

 The FTC, which is in charge of enforcing the prescription release requirements of FCLCA, recently 

reported violations involving prescribing ECPs not releasing prescriptions to their patients. See FYI on The 

Contact Lens Rule and the Eyeglass Rule (Oct. 24, 2004) at

http://www.ftc.gov/opa/2004/10/contactlens.htm. 

9

 This is because the decision to engage in additional search is a positive function of the probability of 

finding a lower price. If costs of search are high, the expected benefit from additional search 

(savings*probability a lower price is found) must be sufficiently high to justify additional search. If a 

consumer over-estimates the lower bound of the price distribution, she necessarily will under-estimate the 

probability of find a lower price with additional search. 
5

impediments, lack of consumer knowledge of their right to their prescription, and 

reported reluctance on the part of some prescribers to release prescriptions, it is probably 

reasonable to assume that many contact lens consumers do not routinely search for prices 

lower than the one their prescribing ECP offers.10 Because prescribing ECPs can be 

affiliated only with offline sellers, in contrast with goods previously studied, there are 

strong prior reasons to believe that a large proportion of offline contact lens sellers’ 

customers are unaware of online pricing. 

 A second improvement on previous work is that unlike previous studies in this 

area, I take advantage of the variation in offline business models to control for the 

provision of costly retail services that consumers may value. Search theory relates 

margins, not prices, to consumer search costs; unless it is reasonable to assume that 

online and offline merchants have similar costs, a comparison of prices alone is not likely 

to provide much information about search costs. Although I find that offline prices are 

on average higher than online prices, I also find that the magnitude and significance of 

this result depends on the composition of the offline sample. When controls are added 

for features specific to certain offline vendors, the average difference between online and 

offline prices falls dramatically. These results suggest that absent data on costs, the 

choice of offline comparison group will influence measured price differences and thus 

inferences regarding search costs. 

 

10 Indeed, data indicate substantial inertia toward purchasing from prescribing ECPs; independent ECPs 

perform over 60 percent of all adult eye examinations and, despite charging the highest prices of any retail 

channel, sell a similar proportion of all contact lenses. 
6

 The remainder of this paper is organized as follows. Section 2 provides a brief 

overview of the contact lens industry. Section 3 describes the data and Section 4 presents 

the main results. Section 5 discusses some implications of these results and concludes. 

 

2. Overview of the Contact Lens Industry 

The FDA approved soft contact lenses in 1971, but in the early stages of 

development, they were manufactured in a way that did not always accurately reproduce 

the original prescription. Because each lens required a great deal of ECP effort to fit, 

consumers generally purchased lens from their ECP after an exam and replaced them 

infrequently. The evolution in contact lens manufacturing technology now allows the 

sale of lenses to be unbundled from the fitting exam. Technological improvements have 

solved standardization problems; the replacement lens a consumer purchases pursuant to 

a prescription that specifies a brand will be identical, regardless of where it is purchased. 

These advances have transformed contact lenses of the same brand and prescription into 

commodities.11 Now, a consumer with a valid prescription can purchase contact lenses 

from an array of merchants, including optical chains, independent ECPs, warehouse 

clubs, mass merchandisers, and online vendors. 

 A consumer needs a prescription from an ECP to purchase contact lenses, which 

will specify a brand name typically will last between one and two years.12 Data indicate 

 

11 Contact lenses – like books and CDs – are differentiated products and specific brands compete against 

one another. Once a consumer has been prescribed a certain brand of lens, however, that lens can be 

treated as a commodity because it is the same regardless of where it is purchased. For example, a Focus 

Toric lens of a certain prescription is identical at every location it is sold; a consumer will treat the lens as a 

commodity, and if retailers are undifferentiated as well, she will be purchase the lens from the seller with 

the lowest price. 

12 Under FCLCA, unless there are special health-related circumstances, a contact lens prescription must last 

at least one year. 15 U.S.C. § 7604(a). Under some state laws, a prescription can last for as long as two 

years. 
7

that 70-80% of contact lens wears purchase less than a year’s supply at a time, so most 

will purchase lenses at least twice during the length of their prescription.13 Under 

FCLCA, contact lens prescriptions are portable; despite some prior contradictory state 

law and industry practice, ECPs must provide patients with a copy of their contact lens 

prescription to allow them to purchase their lenses from whomever they wish. According 

to public data, independent ECPs (both optometrists and ophthalmologists) account for 

approximately 68 percent of sales, with the remaining offline channels, such as optical 

chains, mass merchandisers, warehouse clubs, accounting for between 18 and 25 percent 

of sales.14 The same data has online and mail order outlets accounting for between 8 and 

13 percent of sales. 

 Contact lenses are classified in two major categories—spherical and specialty. 

Spherical lenses contain a single refractive power and are by far the most commonly 

prescribed lens. Varieties of specialty lenses include toric (to correct astigmatism), 

multifocal (to correct near and far-sightedness simultaneously), cosmetic tint, and 

extended wear. According to industry data, spherical lenses accounted for 70 percent of 

dispensing visits and 57 percent of total soft lens sales in 2003.15 Within the specialty 

 

13 According to this data, in 2004, surveyed ECPs reported that after the exam 64% of patients purchased a 

six-month supply, 20% purchased a year’s supply, and 6% purchased a three-month supply. Additional 

data provided to the FTC also suggests that consumers purchase less than a year’s supply of contact lenses, 

showing that only 12% of consumers from a national survey purchased a year’s supply at once, whereas 

31% purchased lenses two times a year, and 43% purchased 3 - 4 times a year. See FTC (2005, pp. 5-6). 

14 See FTC (2005, p. 11). 

15 Optistock (2003) reports that clear spherical accounted for approximately 70% of patient visits where a 

lens was dispensed for the first three quarters of 2003). Similarly, CooperVision (2003, p.21) notes that 

specialty lenses account for 43 percent of U.S. soft lens market sales. The disparity in data for sales and 

lenses dispensed may reflect the fact that specialty lenses typically are more expensive than spherical 

lenses. 
8

segment in 2003, toric, cosmetic tint, and multifocal lenses accounted respectively for 16 

percent, 9 percent, and 5 percent of patient visits when contacts lenses were dispensed.16

Most consumers wear lenses that are taken out every night and disposed of according to a 

replacement schedule. Lenses requiring replacement every two weeks are the most 

popular option, followed by lenses that are replaced on a monthly basis.17 

There are four major contact lens manufacturers (Bausch & Lomb, 

CooperVision/Ocular Sciences,18 Ciba Vision, and Vistakon). According to Census 

Bureau (2004) data U.S. shipments of all contact lenses were valued at $1.9 billion in 

2002,19 and estimates place annual U.S. soft contact lens sales somewhere between $1.4 

and $1.8 billion.20 Recent data indicate that 36 million Americans—almost 13 percent of 

the population—wear contact lenses (FTC 2005). 

3. Data 

 During the week of November 29 – December 5, 2004, price information was 

collected for a six-month supply of ten of the most widely-worn contact lenses from 20 

online and 14 offline retailers. A six-month supply was chosen based on public data that 

suggest this to be the most commonly purchased quantity of lenses.21 Six spherical 

 

16 Id.

17 Optistock (2003) reports that for first three quarters of 2003, two-week and monthly replacement lenses 

account for 64% and 20% of new contact lens fits, respectively. FTN Midwest Research (2004, p.7) 

reports that 62% and 22% of ECPs surveyed responded that two-week and monthly disposables were the 

most common lenses prescribed, respectively). 

18 CooperVision acquired Ocular Sciences in 2005. 

19 This estimate is consistent with private research, which projected U.S. sales of contact lenses in 2003 to 

reach $1.92 billion, or 11.8% of total U.S. retail optical sales. See The State of The Optical Market, 20/20

MAGAZINE (2003), at http://www.2020mag.com/index.asp?page=3_190.htm; Jobson Optical Research, The 

State of the Optical Market, 2nd Quarter, 2003, at http://optistock.com/jobson-som-2003-06.pdf. 

20 CooperVision (2003) reports that total U.S. sales of soft lenses are $1.4 billion. The 2002 Census data 

lists U.S. soft contact lens sales at $1.8 billion. 

21 See note 15, supra.
9

lenses (Acuvue, Acuvue2, Acuvue Advance, Frequency55, Biomedics55, Proclear 

Compatable), three toric lenses (Frequency55 Toric, Softlens66 Toric, Focus Toric), and 

one multifocal lens (Softlens Multifocal) were selected for the study. The mixture of 

spherical and specialty lenses is roughly consistent with consumer purchasing patterns. 

 No publicly available data exists on market shares of individual lenses, but the 

lenses sampled were chosen to be among the most frequently purchased and are thus 

likely to capture a large proportion of actual consumer purchasing patterns. For example, 

Vistakon is the leading contact lens manufacturer and its Acuvue brand contact lenses are 

the world’s leading selling brand of spherical lens.22 Additionally, Proclear Compatibles, 

Biomedics55, and Frequency55 are the leading brands of CooperVision, which due to its 

recent acquisition of Ocular Sciences is among the top four contact lens sellers in terms 

of sales.23 Trade press and company reports suggest that CooperVision, Bausch & Lomb, 

and Ciba Vision account for the most of toric lens prescriptions, thus the inclusion of 

CooperVision’s Frequency55 Toric, CibaVision’s Focus Toric, and Bausch & Lomb’s 

Softlens66 Toric are likely to capture a large proportion of actual consumer purchases of 

toric lenses.24 Finally, Bausch & Lomb’s Softlens Multifocal is the leading multifocal 

lens.25 

 Of the online retailers (listed in the first column of Table 1), 16 are pure online 

sellers—those with no offline presence—and 4 are hybrids, meaning that they have both 

online and offline sales. Pure online sellers were selected based on the results of a search 

 

22 See Vistakon’s website statement at http://www.jnjvision.com/about_vistakon.htm; OSI (2004, p. 6). 

23 See CooperVision (2003). OSI (2004, p. 29) refers to its Biomedics brand as its “flagship product.” 

24 Bausch & Lomb (2003, p. 2); FTN Midwest Research (2004, p.10); Optistock (2003, p.3). 

25 See Bausch & Lomb Annual Report; FTN Midwest Research (2004). 
10

for “contact lenses” at shopping.com, a price comparison search engine.26 Hybrid sellers 

were selected by determining whether well-known offline outlets also had a Web site. 

 The offline retailers sampled (listed in the second column of Table 1) were all 

located in the Northern Virginia Area (primarily Alexandria and Arlington) and fell into 

one of four channels: wholesale clubs, mass merchandisers, optical chains, and 

independent ECPs. Sam’s and BJ’s were sampled to represent wholesale clubs and 

Target and Wal-Mart were sampled to represent mass merchandisers. With the exception 

of Costco, which would not give price quotations for contact lenses over the phone, the 

sample of mass merchandisers and wholesale clubs is likely to comprise almost the entire 

population for the geographic area.27 LensCrafters, Pearle Vision, Hour Eyes, and Sears 

Optical were sampled to represent optical chains. 

 The independent ECPs in the sample were chosen by first searching for 

“optometrists” in the Yahoo yellow pages for the zip code 22301 (Alexandria, Virginia), 

which produced a list of 21 independent ECPs. To assure reliability, this list was crossreferenced with another list of independent ECPs from the area to arrive at 13 ECPs who 

appeared on both lists. From this list, six were chosen at random. Although resource 

constraints prevented sampling a greater number of optical chains and independent ECPs, 

the sample is likely to be representative of the population. For example, each of the 

optical chains sampled belong to one of the two largest grossing optical retailing groups 

 

26 This methodology for determining a sample of online merchants has been employed by Clay et al. 

(2002), Zoonky and Gosain (2002), Clay et al. (2001); Brynjolfsson and Smith (2000). 

27 Although K-Mart would be another potential mass merchandiser competitor to Wal-Mart and Target, 

according to K-Mart’s Web site it does not offer optical services at its local stores. 
11

(Luxottica and Eye Care Centers of America).28 Further, the sample is likely to include a 

substantial proportion of the largest independent ECP practices in the market area. 

 For online merchants, researchers visited each Web site and gathered the price of 

each lens and the standard shipping option. Researchers posing as potential customers 

collected prices quotes from offline merchants over the phone.29 For every outlet 

sampled, researchers collected information on manufacturer and retailer rebates. No 

online site offered rebates on a six-month supply of lenses (most rebate offers only cover 

a year’s supply of lenses), and very few offline merchants offered rebates.30 Because it is 

unclear whether the clerks surveyed at these stores were providing accurate information 

regarding rebates on six-month supplies or how frequently consumers follow-through 

with mail-in rebates, rebate information is not included in the data. 

 As seen in Table 1, almost all outlets carried at least 80 percent of the lenses in 

the sample. A store reported a lens as unavailable in only 8.5 percent of the potential 340 

observations, leaving a total of 311 observations. Online and offline stores carried on 

average 88.5 percent and 95.7 percent of the lenses studied, respectively. Taken as a 

whole, online and offline availability are approximately equal. A majority of online and 

offline stores carried all lenses (55 and 57 percent, respectively). The lower online 

average is reflective of very low carriage rates from two sellers; when these two sellers 

are dropped from the sample, the average percentage of lenses carried by online retailers 

rises to 93.9 percent. Further, if hybrid Web sites are eliminated from the sample to 

 

28 See FTC (2005, p. 10). 

29 Virginia does not tax contact lens sales to consumers. 

30 Specifically, Wal-Mart offered manufacturer rebates on five spherical lenses ranging from $12-15, 

Target offered a retailer rebate ranging from $15.99 to $19.96 on three different lenses, and LensCrafters 

offered an $8.80 rebate on one lens. 
12

focus on only pure online merchants, the average percentage of lenses carried rises to 

97.9 percent. More of the missing observations are for CooperVision’s Proclear 

Compatible lens than any other lens, presumably because the manufacturer has a policy 

to limit this lens’ distribution. 

 Table 2 presents summary statistics of price information collected. For online 

observations, prices that include shipping and handling are presented in parentheses. 

Online prices for all lens types are less than offline prices taken together, but warehouse 

clubs offer the lowest average prices of any channel. Further, hybrid pricing is 

substantially higher than that for pure online merchants. In fact, a closer examination of 

the data reveals that with the exception of Wal-Mart online, hybrid sites’ pricing reflects 

the pricing of their offline counterparts. Accordingly, comparisons of online and offline 

channels for the remainder of the paper focus only on pure online merchants. 

4. Empirical Results

 4.1 Price Dispersion 

 As discussed above, one hypothesis that follows from search theory is that prices 

should be less dispersed online than offline. Table 3 presents various measures of online 

and offline price dispersion (standard deviation, coefficient of variation, range, and range 

as a percentage of the average price).31 All measures of dispersion are calculated at the 

individual lens level and then averaged across lenses.32 Overall, offline prices are about 

twice as dispersed as online prices. For example, the average range of offline prices is 

$67.32 compared to $33.33 online and the average offline standard deviation is $19.77 

 

31 Because online firms can compete on shipping costs but offline firms can compete with regard to 

proximity to consumers only in the long run, I include shipping and handling costs in online merchants’ 

prices, but do not include transportation costs for offline stores. 

32 See Appendix for details of how dispersion measures are calculated. 
13

compared with $8.53 online. A Wilcoxon rank-sign test shows all differences are 

significant at standard levels.33 

 Because consumer purchases are not uniformly distributed across outlets, giving 

each price observation equal weight may distort comparisons of online and offline prices. 

For example, if consumers tend to purchase more from relatively higher (lower) priced 

outlets, equal weighting will tend to bias average prices downward (upward). Further, if 

outlets that charge outlier prices receive little business, equal weighting will overstate 

price dispersion. To remedy this potential problem, I construct weights for online and 

offline observations that proxy for shares of sales.34 

 As seen in Table 4, weighting observations by a proxy for share does not 

significantly alter either online or offline price dispersion, and differences remain 

statistically significant. It also is important to note that the relatively larger level of price 

dispersion offline is not driven by the inclusion of warehouse clubs in the sample. The 

third columns of Tables 3 and 4 show that although excluding warehouse clubs from the 

offline sample does reduce offline standard deviation and range, online dispersion is still 

less than offline dispersion, and that these differences remain mostly significant. 

 

33 Given the relatively small sample for these comparisons (N=20) and doubt that the small-sample 

distribution of these variables justifies an assumption of normality, I used the non-parametric Wilcoxon 

rank-sign test. Additionally, an F-test for equality of variances for each lens (not reported) shows that 

online prices for all but two lenses (Proclear and Softlens 66 Toric) are statistically significantly more 

dispersed than offline prices for the same lenses. 

34 There is no publicly available information on online shares, but data indicate that online contact lens 

sales are around $200 million. Using this as a denominator, I calculated online shares for 1-800 Contacts, 

Vision Direct, and Coastal Contact from publicly available revenue information ($93.5 million, $40 

million, and $28 million, respectively). I divided the remaining 19 percent equally among the remaining 13 

online firms for which no revenue information is available. With regard to offline weighting, again there is 

no publicly available share information for specific outlets, but public data provided to the FTC indicate the 

following shares of total offline sales for each offline channel: 74.4 percent for ECPs; 10.4 percent for 

optical chains; and 15.2 percent for mass merchandisers and warehouse clubs combined. I construct 

weights for each outlet sampled by dividing channel shares equally among all firms within a channel. As a 

result of this process, each independent ECP is assigned a 12.4 percent share of offline purchases, each 

optical chain outlet is assigned a 2.6 percent share of offline purchases, and each warehouse club and retail 

outlet is assigned a 3.8 percent share of offline purchases. 
14

 Because online and offline channels differ significantly in a variety of important 

ways, one cannot ignore the role that factors beyond search costs may be playing in the 

data. Most significantly, offline stores clearly are more differentiated than their offline 

counterparts. Although disposable contacts of the same brand and prescription are 

themselves identical regardless of where a consumer purchases them, there are likely to 

be differences in service and convenience among offline outlets. For example, if the wait 

is longer and the staff less knowledgeable at a warehouse club than an independent ECP’s 

office, some consumers may be willing to pay more for the same contacts at the latter 

outlet. Further, bricks-and-mortar merchants are geographically dispersed and utilize a 

wide array of business models; independent ECPs and optical chains operate in 

professional offices and malls and specialize in selling optical goods, while mass 

merchandisers and warehouse clubs operate in large free-standing stores in which optical 

goods comprise only a tiny proportion of all sales. Online sellers’ business models, by 

contrast, are relatively homogeneous and all share the same “location” from consumers’ 

perspectives.35 

 Table 5 indicates that offline stores are more easily categorized into high or lowpriced outlets than online outlets. For example, the warehouse clubs sampled offered one 

of the four lowest prices for 90 percent and 70 percent of the lenses, respectively, and 

independent ECPs and optical chains appear consistently to charge among the highest 

 

35 Several studies comparing online and offline pricing of books and CDs have found online price 

dispersion and price levels to be at least as large as that offline. It is likely to be the case, however, that 

offline book and CD sellers sampled in those studies are less-differentiated than offline contact lens sellers. 

For instance Clay et al. (2002) collect offline book prices from stores with similar business models (Barnes 

& Noble and Borders) and Lee & Gosain (2002) collect offline CD prices exclusively from stores where 

record sales comprise a major portion of their business (Barnes & Noble, Borders, Sam Goody, Tower, and 

Virgin). Bynjolffson and Smith (2001) have a slightly more diverse sample of offline merchants, which 

Clay et al. (2002) point out may, in part, be driving their price level results because the smaller stores that 

they sampled tend to have higher prices than larger chains. Further, the more diverse sample also may play 

a role in the finding of a significant difference between online and offline price dispersion. 
15

prices for the lenses sampled. Although three online firms appear consistently to offer 

among the highest online prices (1-800 Contacts, LensesforLess, and 1-Save-on-Lens) 

and two appear to offer among the lowest online prices on most lenses (Coastal Contacts 

and Contact Lens Discount), the overall pattern of pricing from Table 5 suggests that 

online firms are not as easily classified into high and low-price outlets as their offline 

counterparts. Thus, it seems plausible that at least some proportion of offline price 

dispersion is a result of relatively more differentiated stores rather than higher search 

costs. 

 To control for heterogeneity across outlets that may be driving price dispersion, I 

regressed lens price on store and lens-fixed effects:36 

 ij i j ij p =+ + C e α +δ , (1) 

where pij is the price of lens i at outlet j, αi is a lens fixed effect, and δj is a store fixed 

effect. The goal is for δj to sweep away the effects of firm-specific pricing strategies that 

may be related to idiosyncratic cost or demand parameters. Table 6 shows measures of 

price dispersion for the residuals from this regression (eij), which proxy for prices that 

have been adjusted for interstore differentiation.37 Consistent with offline firms being 

more differentiated than their online counterparts, these results suggest that interstore 

heterogeneity is responsible for almost twice as much of the observed offline dispersion 

compared to online dispersion. The online standard deviation and range do not differ 

 

36 As noted earlier, the most important component of online differentiation is likely to be consumers’ 

willingness to trust that a transaction will be completed. Ideally, I would have a direct measure for trust, 

but store fixed-effects are likely to serve as a good proxy. Pan et al. (2002) and Clay et al. (2002) find that 

in price regressions proxies for online store attributes have little explanatory power, and Clay et al. 

specifically find that such proxies have no explanatory power when store fixed-effects are included. 

37 The R2

 from this regression is 0.90, compared to 0.74 for a regression with only lens-specific effects and 

0.81– 0.89 for the price regressions reported in Tables 8-10. This suggests that store-specific effects are an 

important source of price variance. 
16

significantly from those reported in Table 4: $8.40 versus $7.09 and $33.33 versus 

$30.44, respectively (a decrease of only 17% and 9%). By contrast, controlling for 

interstore heterogeneity reduces offline standard deviation and range by 30% and 31%, 

respectively. Even so, after controlling for interfirm differentiation online prices still are 

statistically significantly less dispersed than their offline counterparts, which suggests 

that consumers who shop online for contact lenses enjoy lower search costs.38

 Although online price dispersion appears to be lower than that offline, it is still at 

higher levels than would be expected in a model of Bertrand competition. Clearly the 

law of one price has not obtained: the coefficient of variation is 0.10 for all lenses and the 

range of online prices as a percentage of the average online price is 0.37 for all lenses.39

These data are consistent with consumers viewing the package of services that online 

sellers bundle with the lenses as a differentiated product. A review of Web sites does not 

reveal important differences in the shopping experience among online vendors,40 but one 

important dimension of differentiation is likely to be trustworthiness, which may be 

especially important with regard to a medical product like contact lenses. As B&S (2000, 

p.578) note: 

While the importance of factors such as search costs may be reduced on the 

Internet, factors such as trust may play an enhanced role because of the spatial 

and temporal separation between buyer, seller, and the product on the Internet. 

Most consumers have little history or physical contact with Internet retailers and 

 

38 To check for whether lens availability affected the results, all dispersion tests also were run on a subset of 

lenses that were the most widely available (Acuvue2, Biomedics55, Frequency55, Softlens66 Toric, and 

Frequency55 Toric), defined missing from no more that two merchants (only Softlens66 Toric was 

available at all outlets). Offline dispersion rises, but results are qualitatively nearly identical. 

39 These levels of dispersion, in general, are slightly lower, but within the range of those that Clay et al. 

(2002) and CKW (2002) report for online book prices. The coefficient of variation is very similar to that 

reported by Baye et al. (2005), while they report a lower range as a percentage of average price. 

40 In addition, a review of ratings at BizRate.com reveals that of those online sellers rated, all have roughly 

similar customer ratings. 
17

they must be wary of falling prey to a site that posts low prices but is proficient 

only in charging credit cards, not delivering the goods.41

Thus, consumers may enjoy lower search costs online, but choose to forego lower prices 

in return for greater assurances that their transaction will be completed without a hitch. 

This may explain why 1-800 Contacts—the best-known online seller—is able to 

command higher prices than less-well known sellers.42 These results also are consistent 

with those of Smith and Brynjolfsson (2001), who find that the most heavily branded 

online retailers charge higher prices than less well-known online retailers. 

 Another contributing factor to online dispersion is that although price 

comparisons for contact lenses are easier online than offline, they are not costless. It is 

likely that consumers are aware of only one or two online contact lens vendors from 

advertising and may not feel it worth their while to search for others. Indeed, finding 

sizable online dispersion even after controlling for firm-specific effects suggests that 

online consumers—although better informed than their offline counterparts—still are not 

perfectly informed with respect to the distribution of prices. This finding is consistent 

with Sorensen (2000) and Pan et al. (2002b), who use similar techniques to conclude that 

interfirm heterogeneity is not the key driver of price dispersion. 

 4.2 Price Levels 

 Another prediction of search theory is that average margins should fall with 

search costs. In support of this hypothesis, Table 2 shows that average online prices are 

lower than average offline prices. I also estimate the following equation: 

 

41 Similarly, Pan et al. (2002b, p.58) conclude with respect to their finding that hybrid sellers charge more 

than pure online sellers, “improving trust and entering online markets early might result in greater traffic 

and possibly higher prices.” 

42 This is also consistent with hybrid sites charging higher prices. Having an offline presence may offer an 

assurance of trustworthiness to consumers, and hybrids may be able to charge for this. 
18

1 log p C ij = +αi + β OFFLINEj ij +e , (2) 

where pij is the price of lens i at outlet j, OFFLINE is a dummy variable equal to 1 if 

outlet j is an offline outlet and αi is a lens-specific effect to capture unobserved cost and 

demand factors specific to each lens that may affect prices. I estimate (2) in the semi-log 

form so that coefficients may be more readily interpreted as percentage differences in 

prices.43 The first column of Table 7 reports results of this baseline regression and, as 

expected the estimated coefficient of OFFLINE is positive and significant, showing that 

offline outlets sampled set prices that are on average 25 percent higher than those 

online.44 

 Although finding that higher average offline prices is consistent with lower online 

search costs, it also may reflect offline firms’ provision of costly services for which 

consumers are willing to pay.45 If offline outlets charge more because consumers value 

the additional services they provide, then 1 β

ˆ would be biased upward because it would 

include the premium that consumers are willing to pay for these services. That is, online 

and offline firms may charge different prices for different price/quality packages, but 

 

43 Results of a linear specification are qualitatively and statistically unchanged. 

44 To the extent that offline prices in Northern Virginia are higher than those in other localities, the online 

and offline differences would be biased upward. A review of locality adjustments in pay for government 

employees shows that Washington D.C. metropolitan area upward adjustment is similar to that in other 

major urban area. For example, the locality pay adjustment for D.C. is 17.5%, compared with 15.1% for 

Atlanta, 19.9% for Boston; 21.15% for Chicago, 23.1% for Los Angeles; 17.8% for Miami, 18.4% for 

Philadelphia, 22.9% for New York; 15.5% for Raleigh-Durham; 17.9% for Seattle, and 28.6% for San 

Francisco. Thus, it would be reasonable to assume that these results would be likely to hold for major 

segments of the population. Further, the ranking of prices charged by channels is similar to those found in 

other public data submitted to the FTC (available from author upon request). Future research, however, 

would include offline samples from both urban and rural areas. 

45 As part of a settlement to an antitrust suit, Vistakon, Cibavision, and Bausch & Lomb each agreed to sell 

to online and offline channels on non-discriminatory terms. See In re Disposable Contact Lens Antitrust 

Litigation, MDL 1030 (M.D. Fla. 2001). Thus, it may be reasonable to assume that all channels enjoy 

similar wholesale prices, although large national offline chains may still be able to negotiate volume 

discounts that small online sellers cannot. 
19

have similar margins. Because models of price dispersion relate search costs to margins, 

it is impossible to know if lower online prices reflect more intense competition without 

detailed cost data, which is unavailable. 

 To control for retail service heterogeneity that may be driving the observed 

online-offline price difference, I include EYESONLY and INDEP as independent 

variables in (2). EYESONLY is equal to 1 if offline outlet j carries only ophthalmic 

goods. These outlets (optical chains and independent ECPs) typically provide attentive 

sales assistance and operate in small settings, located primarily in professional offices 

and malls, which are likely to be substantially more expensive locations than most online 

operations. INDEP is equal to 1 if offline outlet j is not affiliated with a national chain. 

Past empirical research into the effect of state-imposed restrictions on the commercial 

practice of optometry has suggested that chains may enjoy lower costs of operation than 

independent ECPs.46 

 The second column of Table 7 reports regressions including EYESONLY and 

INDEP, and shows that when these business model differences are taken into account the 

price differential remains significant but falls to 11.4%. The estimated coefficient on 

EYESONLY is significant and shows that offline stores selling only ophthalmic goods 

charge an average of 18 percent more for their lenses than other offline sellers. This 

result is consistent both with multiproduct retailers enjoying economies of scope and 

scale and with consumers placing additional value on the retail package that stores 

specializing in ophthalmic goods offer. The estimated coefficient on INDEP is positive, 

but insignificant, suggesting that (at least in Northern Virginia) independent and 

 

46 See Haas-Wilson (1986, 1987); FTC (1980, 1983). 
20

commercial sellers of ophthalmic goods enjoy similar costs. 47 If the EYESONLY price 

premium primarily reflects a cost disadvantage associated with a business model that 

consumers do not value, then the fact that optical chains and independent ECPs can pass 

this extra expense and still retain at least 70 percent of sales suggests that many 

consumers in this market remain unaware of many prices beyond those quoted by their 

prescribing ECP. 

 Columns 4 and 5 of Table 7 report the same regressions as columns 1 and 2, but 

weight observations by share using the method reported in the previous section. When 

observations are weighted, both online and offline average prices increase; online prices 

increase because 1-800 Contacts’ prices – which are on average the highest of all the 

online merchants sampled – are much more important in the data, and average offline 

prices increase because ECPs and optical chains—the most expensive channels—take on 

greater importance. The results indicate that the increase in online prices due to greater 

weight being put on more expensive sellers overwhelms the similar effect in the offline 

data. Although the baseline regression (reported in column 4) shows that offline prices 

are 23 percent higher than online prices, when business model differences are controlled 

for, the difference falls to only 6 percent, and is statistically insignificant. 

 With the inclusion of EYESONLY and INDEP, the offline control group with 

which online prices are compared is an average of warehouse club and mass 

merchandiser pricing. However, this average masks the fact that mass merchandisers in 

the sample charge significantly higher prices than warehouse clubs and that warehouse 

 

47 That independent ECPs and optical chains offer statistically equivalent pricing may be an artifact of 

Virginia’s regulation prohibiting commercial optical goods sellers from employing an ECP directly. 

According to discussions with some industry representatives, in instances where the ECP is not an 

employee of the optical chain, the optical chain may allow the ECP to sell all replacement lenses as part of 

the compensation scheme. 
21

club and online prices are statistically equivalent. The results in columns 3 and 6 of 

Table 7 show that mass merchandiser pricing is much closer to that of optical chain and 

independent ECP pricing is driving the sign and significance 1 β

ˆ ; when a dummy variable 

equal to 1 if outlet j is a mass merchandiser is added (which causes 1 β

ˆ to represent the 

average difference between online and warehouse club prices), 1 β

ˆ becomes much smaller 

(negative in three of the four specifications) and highly insignificant. At the same time, 

mass merchandisers charge prices 28% higher than average prices offered by warehouse 

clubs and online sellers, and the coefficient on EYESONLY almost doubles and is 

statistically indistinguishable from that on MASS MERCHANDISER because the 

comparison group’s average prices are lower. 

 A key question is whether mass merchandisers charge higher prices than 

warehouse clubs because their customers are aware of online and warehouse club pricing, 

but prefer the mass merchandiser price/quality combination, or because their consumers 

are less informed about prices than those who shop online or at warehouse clubs. That is, 

is there some uncontrolled-for variable that distinguishes warehouse clubs from mass 

merchandisers in consumers’ eyes and that explains the price differential between these 

two outlets. For example, it may be that consumers are willing to pay a premium to mass 

merchandisers because they provide more service, have shorter lines, and have more 

numerous and convenient locations than warehouse clubs. This would imply that, despite 

major business model differences, consumers place a similar value on the mass 

merchandiser retail package and those offered by optical chains and independent ECPs. 

If the difference between warehouse club and mass merchandiser pricing is wholly based 

on differentiation, then observed price differences across retail channels merely reflect 
22

different price/quality combinations; consumers may willingly pay around 30 percent 

more to purchase lenses from optical chains, mass merchandisers, and independent ECPs 

because these outlets offer a higher quality retail experience or have some other 

locational or reputational advantage over online sellers and warehouse clubs.48 

 An alternative explanation consistent with lower online search costs is that mass 

merchandisers charge a premium over warehouse clubs because they primarily serve 

consumers who are unlikely to have knowledge of online or warehouse club pricing. 

Consumers who shop at warehouse clubs – perhaps due to lower opportunity costs of 

time – may be expected to have greater knowledge of both online and offline prices. 

Rather than there being separate online and offline markets for contact lenses, it may be 

more precise to view online and offline merchants of part of one market in which 

warehouse clubs and online vendors compete for informed customers, and the remaining 

offline sellers concentrate on making sales to their share of uninformed customers. Thus, 

warehouse clubs – like online outlets – set prices on the assumption that most of their 

patrons know what other online and offline stores charge. High-price offline outlets, on 

the other hand, set their prices on the assumption that their customers have very little 

knowledge of actual prices. 

 These results demonstrate the sensitivity of online and offline price comparisons 

to the offline control group. Previous studies have either sampled only one type of 

offline retailer [e.g., Clay et al. 2002] or sampled different types of offline retailers, but 

failed to control for business model differences [e.g., Brynjolfsson & Smith 2000]. As 

 

48 The relative price for independent ECPs, mass merchandisers, and optical chains may be biased upwards 

to the extent that consumers purchasing from these channels tend to receive discounts due to managed 

vision plans in greater proportions than do consumers purchasing from other channels. 
23

seen above, unless there is a sound reason to assume that online and offline retailers face 

similar costs and provide consumers with similar retail packages, this approach is likely 

to overstate the differences between online and offline prices. Because search theory 

links margins – not prices – to search costs, caution must be taken when inferring 

anything about relative search costs from comparing online and offline prices. 

4.3 Variation in Advertising and Pricing 

 Another method to test whether consumers face lower search costs online is to use 

the variation in consumer knowledge of prices across lenses. Acuvue brand lenses are 

the most widely worn, and casual empiricism suggests that they are the most heavily 

advertised of those lenses sampled.49 Because price advertising and word of mouth on 

what various outlets charge for Acuvue lenses (which is likely to be a positive function of 

the number of people who purchase Acuvue lenses) reduce consumers’ search costs, we 

would expect consumers to be relatively more informed about the price distribution of 

Acuvue brand lenses than other brands.50 If this is the case, the marginal impact of the 

Internet on consumer search costs should be greater for non-Acuvue than for Acuvue 

lenses. 

 The last two panels of Tables 3, 4, and 6 show measures of dispersion for Acuvue 

and non-Acuvue lenses. First, it is important to note that even for Acuvue lenses online 

price dispersion is less than that offline. Indeed, that after controlling for interfirm 

differentiation, measures of dispersion for the most heavily advertised lenses still are 

 

49 For example, an informal sampling of advertisements the Sunday Washington Post during 2005 finds that 

the price of Acuvue lenses often accompanies circulars from optical stores and other outlets, whereas 

advertisements for other lenses in the sample are never seen. Further, OSI (2004, p.30) notes that it rarely 

advertises directly to consumers. 

50 Kwoka (1983), Feldman & Begun (1978), and Benham (1972), show a negative relationship between 

price levels and advertising in optical goods markets. 
24

larger offline than online, is strong evidence that consumers in online markets for contact 

lenses enjoy lower search costs. Consistent with the hypothesis that consumers are 

relatively more informed about the distribution of Acuvue prices, however, intrachannel 

comparisons reveal that all measures of price dispersion for Acuvue brand lenses are less 

than non-Acuvue lenses. Because the average price of non-Acuvue lenses is higher than 

Acuvue lenses, we would expect the range and standard deviation to be larger for the 

former category. However, even when normalized by average price, these measures are 

largest for non-Acuvue lenses in both online and offline channels. 

 A comparison of residual dispersion measures in Table 6 with those in Tables 3 

and 4 indicates that differentiation is an important source of dispersion only for nonAcuvue lenses sold offline. Controlling for store-specific attributes has a very slight 

negative effect on measures of dispersion for all online prices and offline prices for 

Acuvue lenses, but has a much larger negative effect on offline prices for non-Acuvue 

lenses. This is consistent with purchasers of Acuvue lenses viewing retailers as more 

fungible than non-Acuvue customers. 

 This variation in consumer knowledge across lenses can be used to further test the 

hypothesis that search costs are lower online. Specifically, I use a difference-indifference approach, with Acuvue prices serving as a control group and non-Acuvue 

lenses serving as the treatment group to compare online and offline measures of 

dispersion. I use the data from Tables 3, 4, and 6 to calculate:

1 1

1 1 () ()

NAV AV

NAV AV

Off On Off On

N N

ii ii

i i

dd dd

N N = =

⎛ ⎞ ⎛ ⎞ Δ = ⎜ ⎟ − − ⎜ ⎟ −

⎝ ⎠ ⎝ ⎠ ∑ ∑ (3) 

where di is the relevant measure of dispersion (standard deviation, coefficient of 

variation, range, and normalized range) for a specific lens and channel, subscripts NAV
25

and AV denote non-Acuvue and Acuvue brand lenses, respectively, and superscripts On

and Off denote on and offline, respectively. If online and offline consumers are similarly 

informed about the price distribution in each respective channel for Acuvue lenses, and 

consumers shopping online for non-Acuvue contact lenses enjoy lower costs for 

comparing lens prices than those shopping offline, then Δshould be positive. 

 The results shown in Table 8 are supportive of the lower search cost hypothesis. 

All measures ofΔ are positive and almost half are statistically significant at standard 

levels using a Wilcoxon rank-sum test.51 Further, these results generally are robust to 

weighting and the exclusion of warehouse stores from the offline sample, although 

differences-in-differences for residual measures are positive, but not significant. Taken 

as a whole, the results are consistent with search theory: The marginal impact of lower 

search costs online does appear to be largest for consumers of non-Acuvue lenses. 

 The price dispersion results suggest that due to advertising online and offline 

consumers may face similar search costs for Acuvue brand lenses. If this is true, it 

should be the case that the difference between online and offline prices for Acuvue lenses 

is smaller than those for other lenses in the sample. Table 9 reports estimations of 

equation (2) including an interaction dummy equal to 1 if lens i is an Acuvue brand lens 

and outlet j is an offline seller, allowing Acuvue lenses to act as the control group and 

other lenses as the treatment group. I expect the estimated coefficient on the Acuvue 

interaction dummy to be negative. 

 

51 Given the small number of observations the lack of knowledge of small sample distributions of the 

measures of dispersion, I used the Wilcoxon rank-sum test, which does not rely on distributional 

assumptions. 
26

 Consistent with search theory, the data suggest that the Internet has had the largest 

impact on the prices of lenses for which offline consumers have the least information. 

The difference between online and offline prices for Acuvue brand lenses is about 14 

percentage points less than for other lenses. When business model differences are 

controlled for, online and offline prices for Acuvue lenses are roughly equivalent, and the 

coefficient on OFFLINE rises by about 4 percentage points. As shown in columns 3 and 

4 of Table 9, these results are robust to weighting for intrachannel share. 52 It appears that 

advertising has informed consumers about offline prices for Acuvue brand prices, causing 

offline firms to set prices for Acuvue brand lenses on the assumption that a relatively 

large proportion of consumers with prescriptions for Acuvue lenses know the 

competitors’ prices. 

4.4 Transportation Costs

So far, the estimates of differences in online and offline prices have not taken into 

account consumers’ costs of obtaining the good, and thus may not provide a realistic 

picture of the actual trade-offs that consumers face. To purchase contacts offline, a 

consumer must incur the cost of physically traveling to the outlet, and although online 

shopping eliminates the need for consumers to travel to a store, the consumer pays the 

online outlet for delivery.53 Table 10 shows the results of a regression of total price – list 

price with shipping and handling fees for the standard delivery option added to online 

 

52 As an additional robustness check, I ran the regressions reported in Tables 8 and 9 on a subset of lenses 

that were the most widely available, defined as missing from no more that two merchants (only one lens 

was available at all outlets). The lenses in the limited sample were Acuvue2, Biomedics55, Frequency55, 

Softlens66 Toric, and Frequency55 Toric. The results (not reported) are nearly identical in magnitude and 

statistical significance to those from the full sample. 

53 This price may be explicit in the form of a shipping and handling fee, or may be built in to the price of 

the lens in cases where online outlets offer free shipping. 
27

prices – on OFFLINE and business model controls.54 The estimated coefficient of 

OFFLINE shows that when travel costs associated with purchasing a lens offline are not 

included, lenses purchased offline are on average $6.20, or 7 percent more expensive than 

those purchased online. When we control for Acuvue lenses in column 2, the results 

show that with shipping and handling costs included, non-Acuvue lenses are $10.89, or 

12 percent more expensive online and that Acuvue lenses are less expensive offline, 

although this difference is not statistically significant. 

Although many trips may be undertaken for the sole purpose of purchasing 

contact lenses, in some circumstances it is appropriate to assume that consumers can 

spread the fixed cost of travel over other shopping activities (e.g., grocery shopping while 

at Wal-Mart, clothes shopping while at the mall, running errands adjacent to an eye 

doctor’s office or receiving an eye examination). Assuming a ten-mile round trip to an 

offline store takes one hour, and using the government reimbursement rate of $0.38/mile 

to proxy for direct costs (e.g., gas, depreciation) and $6.75 as the hourly opportunity cost 

of time,55 Table 11 shows the offline premium under various assumptions of how the 

costs of travel should be allocated to the purchase of lenses.56 The results show that 

regardless of how offline travel costs are allocated, lenses are less expensive online. For 

cases where a trip is solely for purchasing lenses – as may be more likely when 

purchasing from an independent ECP –lenses offline are on average 19 percent more 

 

54 The dependent variable is in dollar, rather than log, form for ease in calculating total offline costs. 

55 Henscher (1997) has estimated that the value of transit time for leisure trips (including shopping) is 

between 26 and 42 percent of the average wage. Small (1992) concludes that weekend time in transit is 

more highly valued than transit to work, and has offered 50 percent of the average wage as an 

approximation for the value of time in a journey to work. Using the Bureau of Labor Statistics (2003) of 

$17.75 average hourly wage and taking the mid-point of Small’s and Henscher’s estimates of the value of 

travel time yields an opportunity cost of time for travel to purchase contact lenses of $6.75 per hour. 

56 I implicitly assume that the time taken actually to complete the online and offline purchases is identical. 
28

expensive than lenses online. In instances when a consumer purchases so many items in 

addition to contact lenses that the allocation of travel costs to the contact lenses 

approaches zero (perhaps most relevant for warehouse clubs and mass merchandisers), 

higher search costs still cause offline lenses to be 7 percent more expensive. 

5. Conclusion 

 Like other studies, this paper exploits the Internet’s reduced information costs to 

test two implications of search theory: because it is more costly to compare the prices of 

offline merchants than those of online merchants, all else equal, price dispersion and 

margins should be lower online than offline. I find that for disposable contact lenses 

online prices are significantly less dispersed than offline prices and average online prices 

are 25 percent lower than average offline prices. The online-offline price level 

difference, however, is sensitive to controls for offline business model features that are 

likely to be associated with the provision of services that consumers value. When I 

control for unique characteristics of independent ECPs and optical chains, the difference 

between online and offline price levels is more than halved, and when all business 

models save warehouse clubs are taken into account, there is no statistical difference 

between online and offline prices. Thus, it is unclear how much of the measured price 

difference is due to consumers’ lack of price information versus merely the premium 

consumers are willing to pay for more valuable retail packages. These results indicate 

that without information on costs, caution must be used in interpreting comparisons of 

online and offline prices because the outcomes are likely to be sensitive to the 

composition of offline retailers. 
29

 With these caveats in mind, however, taken as a whole the empirical results seem 

to indicate that online contact lens markets are more efficient than their offline 

counterparts so that at least some portion of measured lower online prices is likely 

attributable to lower online search costs. For example, the price dispersion results are 

robust to the exclusion of warehouse clubs and to controls for interstore differentiation. 

Further, the data indicate that the online-offline price differential for Acuvue lenses is 

only half of what it is for non-Acuvue lenses; because it is unlikely that offline vendors 

have lower costs of selling Acuvue lenses than non-Acuvue lenses, this difference-indifference suggests that the Internet has lowered consumer search costs non-Acuvue 

lenses. Finally, because selling replacement contact lenses consists only of transferring a 

pre-packaged product to the consumers – almost always without the involvement of an 

ECP – one has to wonder whether fully-informed consumers are wiling to pay an 

additional $60 a year to purchase lenses from a mass merchandiser, optical chain, or 

independent ECP. 

 The empirical results seem to beg the following questions: if consumers can buy 

the same good more cheaply online or at a warehouse club, why don’t they, and why 

haven’t all offline merchants lowered their prices to compete with their online 

counterparts? One obvious answer is that the Internet has not lowered all consumers’ 

search costs; not every contact lens wearer has access to the Internet. According to the 

Department of Commerce (2004) as of October 2003, approximately 28 percent of 
30

Americans from 14 - 49 (the demographic representing 75 percent of contact lens wearers 

(see 1-800 Contacts 2005b, at p.9)) are not “Internet users.”57 

 Another factor to consider is the relative youth of the market for replacement 

lenses and its regulatory history. Many consumers still are likely to be unaware that 

someone other than their prescribing ECP can fill their contact lens prescription or, even 

if they are aware that they can shop their prescription around, they may not know that 

replacement lenses are sold by outlets other than independent ECPs and optical chains. 

Consumers will get an additional price quote only if the expected benefit is greater than 

or equal to the cost of obtaining the quote. General lack of market information – both the 

lower bound of the price distribution and specific prices in the distribution – coupled with 

the fact that a consumers’ first draw from the price distribution will be from an offline 

seller would tend to create inertia toward purchasing from the prescribing ECP. This is 

likely to help explain why independent ECPs and optical chains charge the highest prices 

and together account for over 70 percent of contact lens sales. 

 As discussed earlier, FCLCA was intended to intensify competition among 

contact lens sellers by allowing consumers to shop their prescriptions around. The data 

suggest that many consumers still are unaware of their full range of options. As 

relatively nascent market for replacement lenses develops, however, consumers are likely 

to become more mindful of, and comfortable with, their options. More intense 

competition among contact lens sellers should accompany this increased consumer 

knowledge, allowing the goal of FCLCA to be realized more fully. Increased consumer 

 

57 Internet usage is likely positively correlated with income, however, so to the extent that contact lens 

usage also is positively correlated with income, the true proportion of contact lens wearers that do not use 

the Internet is probably to be lower. 
31

knowledge in this market could have important welfare implications. Further, contact 

lens wearers in states that have and enforce regulations in a manner that impede online 

sellers’ ability to operate would gain if these laws were eliminated. For example, that 

North Carolina, Tennessee, Mississippi and Washington have laws or regulations that 

seem to require anyone selling contact lenses to hold a valid ECP license issued by the 

state, and that Alaska and Georgia have considered similar laws.58 Additionally, current 

Georgia law requires that contact lens sales take place in a “face-to-face” transaction,59

and Arizona and New Hampshire require that nonresident sellers of contact lenses 

register with the state optometry board and hold a valid optometry or pharmacy license 

from their home state.60 Although the extent to which states enforce these restrictions 

against online sellers of contact lenses is unknown, these laws have the potential to raise 

online sellers’ costs of serving consumers in these states, causing them to raise their 

prices. 

 Future research would examine a sample of online and offline contact lens prices 

over time to see if prices converge or if offline sellers lose marketshare. In a fullinformation equilibrium, offline firms can charge higher prices than their online 

counterparts for the same lenses only if they are providing consumers something 

additional of value. Thus, unless independent ECPs, optical chains, and mass 

merchandisers enjoy persistent quality or locational advantages over online sellers, we 

should expect to see online and offline prices converge as more consumers become 

informed about their marketplace options. If observed price differences are solely a 

 

58 See 1-800 Contacts (2004b, p. 31) 

59 GA. CODE ANN. § 31-12-12(h). 

60 See, e.g., ARIZ. REV. STAT. ANN. § 32-1773; N.H. RSA § 327:31. 
32

function of higher offline costs, however, increased consumer information will cause 

shares of higher-priced offline outlets to fall as their business models become 

unprofitable. 

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36

Appendix 

 The average standard deviation of online price lenses prices (Son) is calculated 

as

10

1

1

10

On

i

i

s

=

∑ , where On

i s is the standard deviation of lens i, as measured by deviation from 

the mean online price of lens i over all online stores. 

Specifically, ( ) 1 2

1

On On

i ij i

j

s pp k = − − ∑ , pij is the price of lens i at store j, k is the number 

of online stores sampled that carried lens i, and On

i p is the average price of lens i over all 

k online stores. The average online coefficient of variation (SOn/ On P ) is calculated as 

10

1

1

10

On

On

i

i i

s

= p ∑ . The same methodology is used to calculate SOff and SOff/ Off P . 

 The average range of online prices (ROn), is calculated as 

10

1

1

10

On

i

i

R

=

∑ , where 

( )

Max Min

On On On

i ii R = − p p . The average online normalized range (ROn/ On P ) is calculated as 

10

1

1

10

On

i

On

i i

R

= P ∑ . The same methodology is used to calculate ROff and ROff/ Off P . 

 
37

TABLE 1 

AVAILABILITY

Online Outlet Percent of 

Lenses 

Carried 

Offline Outlet Percent of 

Lenses 

Carried 

1-800 Contacts 100 BJ’s Wholesale Club 100 

1-Save-on-Lens 100 Clear Vision Express 100 

Aalens.com 30 Hoang & Bradley 90 

Aclens 100 Hour Eyes 100 

America’s Best Online 80 Lenscrafters 100 

BJ’s Online 90 MacDonald Eye Care 90 

CLE Contact Lenses 90 May & Hettler 100 

Coastal Contacts 100 Northern Virginia Doctors of 

Optometry 

90 

Contact Lens King 100 Pearle Vision 90 

Contact Lenses Discount 90 Rosslyn Eye Associates 100 

Contactsland.com 100 Sam’s Club 90 

Discount Contact Lenses 100 Sears 100 

First Choice Contacts 90 Target 100 

Lens Discounters 100 Wal-Mart 90 

Lenses for Less 100 

Lensmart.com 50 

Sears Online 70 

The Contact Lens Store 100 

Vision Direct 100 

Wal-Mart Online 80 

Average 88.5% 95.7% 

Notes: Availability computed as percentage of lenses sampled available at outlet j. 
38

TABLE 2 

SUMMARY STATISTICS

All Lenses Spherical Speci

Channel Mean SD Min Max Mean SD Min Max Mean SD All Online 87.92 

(91.70) 

38.90 

(38.31) 

29.90 

(33.85) 

259.96 

(259.96) 

65.51 

(69.57) 

16.37 

(15.95) 

29.90 

(33.85) 

103.96 

(103.96) 

119.85 

(123.23) 

39.45 

(38.84) 

Pure 84.40 

(88.47) 

35.72 

(35.25) 

29.90 

(33.85) 

191.96 

(201.96) 

63.57 

(68.00) 

15.51 

(15.51) 

29.90 

(33.85) 

95.80 

(101.75) 

113.93 

(117.45) 

35.51 

(35.09) 

Hybrid 103.85 

(106.38) 

48.42 

(47.85) 

39.98 

(39.98) 

259.96 

(259.96) 

74.19 

(76.62) 

17.73 

(16.43) 

39.98 

(39.98) 

103.96 

(103.96) 

147.20 

(149.86) 

46.44 

(45.39) 

All Offline 107.95 45.82 31.86 280 81.89 16.56 31.86 112 146.36 48.41 Warehouse 83.18 36.51 31.86 180 60.11 13.83 31.86 80 114.91 34.16 Mass Merchandiser 

108.38 43.01 69.92 216 79.39 10.49 69.92 99.80 148.23 38.28 

Optical Chain 

109.02 40.84 40.00 239.80 86.61 15.77 40.00 112 143.37 43.91 

Independent ECP 

115.63 50.86 44.00 280 86.56 13.89 44.00 112 158.61 55.29 

All Channels Combined 

96.55 43.11 29.90 280 72.69 18.32 29.90 112 131.13 45.26 

Notes: Unit of observation is price of lens i at outlet j. Observations are not weighted for intrachannel shares. Statistics including shipare in parentheses. Statistics for All Channels Combined computed without including shipping and handling costs. 
39

TABLE 3 

ONLINE AND OFFLINE PRICE DISPERSION

 All Acuvue Non Acuvue

Online Offline Offline 

(Warehouse 

Excluded) 

Online Offline Offline 

(Warehouse 

Excluded) 

Online Offline 

Std. Dev. 8.53 19.77*** (0.01) 16.80** (0.02) 6.27 11.65** (0.05) 7.02 (0.14) 9.49 23.26*** (0.01) Std. Dev./ Ave. Price 0.10 0.19*** (0.01) 0.15* (0.10) 0.08 0.14 (0.14) 0.08 (0.42) 0.10 0.20** (0.02) Range 33.33 67.62*** (0.01) 53.30** (0.05) 23.56 37.75** (0.05) 23.99 (0.99) 37.52 79.99** (0.02) Range /Ave. Price 0.37 0.62** (0.02) 0.47 (0.24) 0.31 0.45** (0.05) 0.27 (0.30) 0.39 0.69** (0.04) 

Notes: Standard deviation, coefficient of variation, range, and standardized range are measured for a specific lens across either the online or offline channel, and then averaged over all lenses sampled. See Appendix for details of how dispersio

were calculated. P-values from a Wilcoxon rank-sign test for difference between relevant offline and online dispersion mparentheses. ***significant at 1% level, one-tailed test; **significant at 5% level, one-tailed test; *significant at 10% levetest. 
40

TABLE 4 

WEIGHTED ONLINE AND OFFLINE PRICE DISPERSION

 All Acuvue Non Acuvu

Online Offline Offline 

(Warehouse 

Excluded) 

Online Offline Offline 

(Warehouse 

Excluded) 

Online Offline 

Std. Dev. 8.40 20.08*** (0.01) 18.18*** (0.01) 6.60 9.55** (0.05) 6.32 (0.99) 9.17 24.59*** (0.01) Std. Dev./ Ave. Price 0.09 0.18*** (0.01) 0.16* (0.09) 0.06 0.11* (0.09) 0.07 (0.30) 0.09 0.21*** (0.01) Range /Ave. Price 0.35 0.60** (0.02) 0.47 (0.11) 0.23 0.44** (0.05) 0.27 (0.30) 0.37 0.66** (0.04) 

Notes: Standard deviation, coefficient of variation, range, and standardized range are measured for a specific lens across all either the online or offline channel, and then averaged over all lenses sampled. See Appendix for details of how dispersion 

were calculated. Range is omitted because it does not vary with weighting. P-values from a Wilcoxon rank-sign test for difbetween relevant offline and online dispersion measure are in parentheses. ***significant at 1% level, one-tailed test; **sig5% level, one-tailed test; *significant at 10% level, one-tailed test. 
41

TABLE 5 

PRICING DISTRIBUTIONS FOR ONLINE AND OFFLINE SELLERS

Online Offline 

Outlet Low 

Price 

Middle High 

Price 

Outlet Low 

Price 

Middle High 

Price 

1-800 Contacts 1 2 7 BJ’s Wholesale 

Club 

7 3 0 

1-Save-on-Lens 0 3 7 Clear Vision 

Express 

4 6 0 

Aalens.com 1 0 2 Hoang & Bradley 0 0 8 

Aclens 0 7 3 Hour Eyes 0 7 3 

CLE Contact Lenses 3 3 3 Lenscrafters 4 1 5 

Coastal Contacts 8 2 0 MacDonald Eye 

Care 

3 5 2 

Contact Lens King 5 4 1 May & Hettler 0 4 6 

Contact Lenses 

Discount 

9 0 0 Northern Virginia 

Doctors of 

Optometry 

0 3 6 

Contactsland.com 2 8 0 Pearle Vision 0 6 3 

Discount Contact 

Lenses 

0 10 0 Rosslyn Eye 

Associates 

4 6 0 

First Choice 

Contacts 

0 7 2 Sam’s Club 9 0 0 

Lens Discounters 5 4 1 Sears 2 4 4 

Lenses for Less 0 4 6 Target 4 5 1 

Lensmart.com 0 1 4 Wal-Mart 4 3 2 

The Contact Lens 

Store 

4 5 1 

Vision Direct 0 9 1 

Notes: “Low Price” is one of the four lowest prices offered for lens i; “High-Price” is one of the four 

highest prices offered for lens i. 
42

TABLE 6 

ONLINE AND OFFLINE PRICE DISPERSION 

MEASURED BY RESIDUALS FROM FIXED-EFFECTS REGRESSION

 All Acuvue Non-Acuvue 

Online Offline Online Offline Online Offline 

Std. Dev. 7.10 13.90*** 

(0.01) 5.24 10.03** (0.05) 7.88 

15.56**

(0.03) 

Std. Dev./ 

Ave. Price 0.08 0.13**(0.03) 0.07 0.12**(0.05) 0.09 0.14*(0.06) 

Range 30.44 46.39**

(0.03) 21.46 32.48**(0.05) 34.29 52.36*(0.06) 

Range 

/Ave. Price 0.37 0.45 (0.11) 0.35 0.38 (0.14) 0.37 0.48 (0.20) 

Notes: Notes: Standard deviation, coefficient of variation, range, and standardized 

range are measured for a specific lens across all store in either the online or offline 

channel, and then averaged over all lenses sampled. See Appendix for details of 

how dispersion measures were calculated. P-values from a Wilcoxon rank-sign test 

for difference between relevant offline and online dispersion measure are in 

parentheses. ***significant at 1% level, one-tailed test; **significant at 5% level, 

one-tailed test; *significant at 10% level, one-tailed test. 

. 
43

TABLE 7 

REGRESSION RESULTS FOR LOG LENS PRICE 

Unweighted Weighted 

(1) (2) (3) (4) (5) (6) 

OFFLINE 0.251***

(0.021) 

0.114***

(0.037) 

-0.027 

(0.039) 

0.227***

(0.033) 

0.058 

(0.044) 

-0.083 

(0.046) 

EYESONLY 0.179***

(0.043) 

0.320***

(0.044) 

 0.179***

(0.043) 

0.320***

(0.045) 

INDEP 0.020 

(0.036) 

0.020 

(0.036) 

 0.022 

(0.036) 

0.022 

(0.053) 

MASS MERCHANDISER 

 0.282***

(0.054) 

 0.282***

(0.053) 

CONSTANT 4.15***

(0.30) 

4.16***

(0.026) 

4.16***

(0.024) 

4.20***

(0.058) 

4.21***

(0.056) 

4.21***

(0.056) 

R2 0.83 0.85 0.87 0.81 0.82 0.83 F 162.72*** 174.24*** 183.31*** 84.63*** 78.32*** 76.93***

Notes: N = 279. Unit of observation is the log of price of lens i at outlet j. Robust standard errors in parentheses. ***significant at 1% level; **significant at 5% level. Acuvue lenses is the omitted category for lens fixed-effects. 
44

TABLE 8 

ACUVUE AND NON-ACUVUE LENSES: 

DIFFERENCE-IN-DIFFERENCE FOR MEASURES OF PRICE DISPERSION

Δ

 Std. Dev. Range Std. Dev./Ave. Price Range/Av

Unweighted Price 8.43 (0.15) 30.28 (0.11) 0.07 (0.11) 0.1

(0.2

Unweighted Price (without warehouse) 

10.74**

(0.04) 

27.92**

(0.04) 

0.10*

(0.07) 

0.2

(0.1

Weighted Price 15.17*** (0.01) - 0.09* (0.09) 0.1

(0.2

Weighted Price (without warehouse) 

14.37***

(0.01) - 

0.12**

(0.02) 

0.21

(0.0

Residuals 2.90 (0.21) 7.91 (0.21) 0.01 (0.34) 0.0

(0.2

Notes: Notes: Standard deviation, coefficient of variation, range, and standardized range are measured for a specific lens 

store in either the online or offline channel, and then averaged over all lenses sampled. See Appendix for details. Rang

for weighted statistics because it does not vary with weighting. P-values from a Wilcoxon ranksum test are in parenthese***significant at 1% level, one-tailed test;**significant at 5% level, one-tailed test; *significant at 10% level, one-tailed te
45

TABLE 9 

REGRESSION RESULTS FOR LOG LENS PRICE WITH ACUVUE INTERACTION

Unweighted Weighted 

(1) (2) (3) (4) 

OFFLINE 0.290***

(0.027) 

0.154***

(0.340) 

0.269***

(0.043) 

0.101**

(0.50) 

OFFLINE*ACUVUE -0.137***

(0.038) 

-0.132***

(0.033) 

-0.144***

(0.057) 

-0.139***

(0.056) 

EYESONLY 0.178***

(0.043) 

 0.178***

(0.043) 

INDEP 0.018 

(0.036) 

 0.020 

(0.037) 

CONSTANT 4.19***

(0.027) 

4.20***

(0.024) 

4.25***

(0.053) 

4.25***

(0.052) 

R2 0.84 0.86 0.81 0.83 F 149.03*** 158.83*** 90.03*** 85.45***

Notes: N = 279. Unit of observation is the log of price of lens i at outlet j. 

Robust standard errors in parentheses. ***significant at 1% level; **significant at 5% level. Acuvue lenses is the omitted category for lens fixed-effects. 
46

TABLE 10 

REGRESSION RESULTS FOR TOTAL LENS PRICE 

 

(1) (2) 

OFFLINE 6.20**

(3.24) 

10.89***

(3.61) 

OFFLINE*ACUVUE -15.66*** 

(3.14) 

EYESONLY 16.30*** 

(3.88) 

16.23***

(3.87) 

INDEP 4.00 

(4.00) 

3.77 

(3.96) 

CONSTANT 65.99***

(2.15) 

71.11***

(1.64) 

R2 0.85 0.86 

F 77.89*** 79.17***

Notes: N = 279. Unit of observation is total price of lens i at 

outlet j. Robust standard errors in parentheses. ***significant 

at 1% level; **significant at 5% level. Acuvue lenses is the 

omitted category for lens fixed-effects. 

TABLE 11 

ESTIMATED OFFLINE PREMIUM WITH TRANSPORTATION COSTS

Premium (%) 

Trip Cost Allocation 

(%) 

All Lenses Non-Acuvue 

0 7.0 11.7 

50 13.0 17.3 

100 18.9 23.0 

Notes: The relevant offline premium is calculated by dividing 

coefficient on OFFLINE from Table 10 plus relevant trip costs 

by the average online price for all lenses ($88.47) and nonAcuvue lenses ($93.26).