Ebook Prices and Price Dispersion in Online and Offline Markets for Contact Lenses
Due to the difficulty involved with creating standardized lenses, eye care professionals (ECPs) previously had to fit each pair of contact lenses a patient purchased, leading consumers almost invariably to purchase eye exams and contacts in a bundle from their ECP. Beginning in the late 1980s, however, technological improvements have eliminated the need for lenses to be fitted individually and subsequently have transformed contact lenses of the same brand and prescription into commodities. 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.
Contact lens consumers may have more sellers to choose from today than they did ten years ago, but a variety of factors likely have caused many consumers to remain unaware of their full range of options beyond their prescribing ECP. For example, it was not until 2004, when Congress passed the Fairness to Contact Lens Consumers Act (FCLCA) which prohibits ECPs from tying contact lens sales to eye examinations and requires ECPs to release their patients’ prescriptions that prescribing ECPs in all states were required to release contact lens prescriptions to their patients. Prior to 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. 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), and 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.
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. Given the relative youth of the replacement contact lens market, state regulatory impediments, lack of consumer knowledge of their right to their prescription, and reported historical 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. It should not be surprising that 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. Because all prescribing ECPs by necessity are associated only with offline sellers, it is likely that a large proportion of those who purchase offline never shop online for their lenses.
This paper shows, however, that if consumers shopped for lenses online, they could save substantially. It is reasonable to assume that the Internet allows consumers to compare competing online merchants’ prices more cheaply than they can those offered by offline stores. 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. I find evidence that the relative ease with which consumers can compare prices offered by competing online outlets has led online sellers to compete on price more intensely than their offline counterparts, leading to lower online prices.
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 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. Accordingly, in the case of homogeneous goods, all stores must meet the lowest price offered or make no sales. When consumers face positive search costs, they will visit fewer stores, which in turn increases the probability that a given store’s price will be the lowest that a consumer will observe during his search. Because high search costs reduce competitive pressures, it follows that margins and price dispersion are negatively related to consumers’ information about firms’ prices.
Due to the low cost of comparing online prices, by contrast with its offline counterparts, an online seller rationally may expect that almost all of its patrons have visited or will visit a large proportion of its online competitors. The online firm, then, must set its prices on the assumption that anyone visiting its Website has seen 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.
I examine a sample of the prices online and offline sellers charge for some of the most popular disposable lenses. Overall, the data indicate that search costs play an important role in stores’ pricing decisions. Consistent with competition among online firms being more intense, online prices are significantly less dispersed than offline prices. This result is robust to weighting for shares and controlling for interstore heterogeneity by examining the interstore variance of the residual from a regression of lens price on store and lens-fixed effects.
I also study differences in online and offline prices, but unlike previous studies, I take advantage of the variation in offline business models to control for factors that are likely to be associated with higher offline costs. I find that when these business model differences are taken into account, offline prices are on average 11 percent higher than online prices. When shipping & handling costs and offline travel costs including time costs are added to list prices, offline prices are between 7 and 19 percent higher than offline prices, depending on how offline travel costs are allocated. This is important, because some previous results have been sensitive to assumptions about weighting and costs associated with getting the good to the consumer (or the consumer to the good). Further, to my knowledge, this is the first study of online and offline prices explicitly to account for consumer time costs.
In contrast to the other lenses in the sample, competing sellers often advertise the prices they charge for Acuvue lenses. Because it is likely to be more costly for consumers of non-Acuvue lenses to determine the prices charged by competing offline sellers than for Acuvue wearers, online markets are likely to have less of an impact on the pricing for Acuvue brand lenses than other lenses (see, e.g., Kwoka 1983; Feldman & Begun 1978; Benham 1972). I take advantage of the variation in consumer knowledge of lens prices and use Acuvue brand lenses as a control group in a difference-in-difference approach to measuring the effect of Internet availability on online and offline price dispersion and levels. Consistent with search costs influencing pricing, I find that the ability to comparison shop more cheaply online has had a much smaller impact on the prices of advertised lenses.
Although the data are consistent with online markets functioning more efficiently than their offline counterparts, 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. Specifically, a consumer could save up to 37 percent off the average price of lenses online by finding the lowest online price. Consistent with earlier research, I also find evidence that differences among online firms related to consumers’ trust that a transaction will take place without a hitch is an important source of online price dispersion.
Not surprisingly, many economists have identified the online-offline dichotomy as fertile ground to test the predicted relationship between consumer search costs, price levels, and price dispersion. The empirical work in this area, which largely compares online and offline prices of books and CDs (see, e.g., Clay et al. 2002; Brynjolfsson and Smith 2000; Lee and Gosain 2002; Bailey 1998), has arrived at no consensus that online prices are lower or less dispersed than offline prices.10 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).
One assumption implicit in this literature is that offline stores set prices based on expectations of their patrons’ knowledge of other offline firms’ prices, not online prices. 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. 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.
Thus, 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 purchasing offline have not searched online as well; most of those who ultimately purchase offline may have no less knowledge of online prices than those who end up purchasing from an online store. 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 one reason why studies of these goods have arrived at no consensus that online and offline prices are statistically different.
Due to the reasons discussed at the outset of this paper, in contrast to goods previously studied, offline sellers of contact lenses are likely to face a large number of customers who are unaware of online pricing. Because the assumption that the markets are separable is more likely to hold in the case of contact lenses than in other goods, these data are likely to provide a cleaner test of the lower search cost hypothesis.
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.
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