Review of Network Economics - New York...

Volume 11, Issue 1 2012 Article 4

Review of Network Economics

Comparing Price Dispersion on and off theInternet Using Airline Transaction Data

Anirban Sengupta, Analysis Research PlanningCorporation

Steven N. Wiggins, Texas A&M University

Recommended Citation:

Sengupta, Anirban and Wiggins, Steven N. (2012) "Comparing Price Dispersion on and off theInternet Using Airline Transaction Data," Review of Network Economics: Vol. 11: Iss. 1, Article4.DOI: 10.1515/1446-9022.1244

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Comparing Price Dispersion on and off theInternet Using Airline Transaction Data

Anirban Sengupta and Steven N. Wiggins

AbstractThe internet presumably reduces search costs and creates an “efficient” market. Prior research

quantifying the dispersion in the electronic market, however, has yielded mixed results. Somerecent research has documented very low levels of dispersion in internet attributing it to the use oftransaction prices as opposed to posted prices. We revisit this issue using contemporaneous onlineand offline transaction data for airline tickets including ticket characteristics and medium of salepermitting comparison of online versus offline price dispersion. We find evidence of significantlylower price dispersion on the internet compared to offline market, though some positive dispersionstill persists.

KEYWORDS: internet, electronic markets, traditional markets, price dispersion, online, offline,airlines

Author Notes: I thank the NET Institute www.NETinst.org for financial support. Steven N.Wiggins: Department of Economics, Texas A&M University.

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1. Introduction

The internet presumably reduces search cost and in the limit could at least in principle collapse the distribution of prices of homogenous products to the “law of one price”, as often argued in classical undergraduate textbooks. Despite arguments that sufficient competition will eliminate price dispersion, empirical research has failed to corroborate the existence of the “law of one price” in empirical settings. In contrast, ample empirical research provides a robust support for pervasive and persistent dispersion in both online and offline markets.

Price dispersion is one important indicator of market efficiency. The internet has been argued to lower search costs, enhancing market efficiency, which could lead in the limit to a collapse of the price distribution around the competitive price. Recent research, however, tends to support the continued presence of significant price dispersion.1 A key weakness of this research, however, is that much of it relies on posted prices rather than transaction prices. Posted prices, however, differ from transaction prices as transactions may not have actually occurred at particular posted prices, and erroneously overestimating price dispersion. Further, results using transaction prices have yielded different results. For example, Ghose and Yao (2009) study price dispersion for consumer durable goods like paint, brushes and hardware. They present evidence of “near-zero” dispersion in the electronic market place. The authors attribute their findings to the use of transaction data in measuring dispersion contrary to posted prices used more widely in the existing literature. Accordingly, it is important to investigate online and offline price dispersion in other settings to determine the generality of the Ghose and Yao findings and to assess price dispersion using transaction prices. This paper provides a comparative analysis of online and offline price dispersion. We exploit a unique dataset to conduct this analysis. This data set consists of a census of online and offline transaction data for airline tickets for travel during the final quarter of 2004. This novel data includes individual ticket transactions and ticket characteristics associated with these transactions, as well as detailed information including the flight level load factor, date of purchase and departure, carrier and most importantly the channel of sale - online travel agent or a traditional agent.2 The ticket characteristics information includes refundability, advance purchase requirement, travel and stay restrictions and other factors affecting the price of an airline seat. The paper provides some of the first direct comparisons of online versus offline price dispersion for one of the major

1 Baye et al. (2006), Handbook on Economics and Information. 2 Travelocity, Expedia, Orbitz and Priceline are few examples of online travel agents. Traditional travel agents are the local (or national) travel agent who has a physical presence.

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Sengupta and Wiggins: Comparing Price Dispersion Using Airline Transaction Data

Published by De Gruyter, 2012



industries in the US, where the seller of the online and offline products is the same ultimate supplier (carrier).3 Using this transaction data, we find robust evidence of significantly lower price dispersion on the internet. This finding is consistent with prior findings. Our results, however, also show significant, positive dispersion for internet transactions. The use of transaction data, however, does show significantly lower online price dispersion than earlier studies using posted prices. 2. Review of Theoretical and Empirical Models to Explain Persistent

Price Dispersion

Earlier models explaining price dispersion rely on search costs as a primary drive of price dispersion. The internet substantially reduces search costs and, arguably, may drive such costs to zero. Maturing internet markets complemented by the formation of price comparison sites, and shopbots provide a platform where consumers may obtain extensive price comparison data at little cost. Yet, even in these environments where search costs appear to be low, empirical investigation has continued to reveal significant price dispersion. The first empirical evidence of reduced price dispersion in internet markets was provided by Bakos (1997), who examined electronics products. Subsequently, Bailey (1998) compared prices of books, CDs, and software titles sold through internet and conventional stores, and found similar dispersion online and offline. Brynjolfsson and Smith (2000) compared the prices of approximately twenty titles of CDs and books, and found lower prices in online markets but similar dispersion in both online and offline markets.4 In contrast, Erevelles et al. (2001) found higher average prices and dispersion for vitamins, as did Scholten and Smith (2002) for a variety of consumer products. Empirical evidence comparing price dispersion among online and traditional travel agents for airline tickets is also extremely limited. Clemons et al. (2002) studied the dispersion in airline ticket prices across different online travel agents (OTA) using data from 1997. After controlling for different ticket attributes like Saturday night stay-over, time of arrival and departure, and number of connections, they find a price variation of about 18 percent across the different OTAs. This large dispersion can be attributed to the lack of adequate controls for product characteristics namely refundability, advance purchase requirement, travel restrictions, and meal offerings among others. These characteristics have

3 Persistence price dispersion in the internet market has been attributed to a violation of one of the three Bertrand assumptions: homogenous sellers and products, zero search costs and perfectly informed consumers (Salop and Stiglitz, 1977; Varian, 1980). 4 Dispersion in online market was marginally lower when market shares of online retailers were accounted for. Lee and Gosain (2002) supported this finding in the market for music CDs.

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Review of Network Economics, Vol. 11 [2012], Iss. 1, Art. 4



been found to explain a large share of the variation in airline ticket prices.5 Chen (2006) using 2002 data from OTAs, however, finds evidence of little fare differential among OTAs. He attributes this departure from earlier findings to the gradual maturation of OTAs over time. All the above analyses share a common limitation in that the timing of these analyses corresponds to less mature internet market, which might contribute to observed price dispersion. The effects of the internet on prices and dispersion can be better understood by analyzing internet markets after they have matured.6 For example, Pan et al. (2003) compares prices of books, CDs, DVDs, computers and other consumer electronic goods between November 2000 and February 2003 and find a 10 percent decline in average prices from 38.5 to 28 percent. These findings are consistent with the findings of Baye et al. (2004) who studied online monthly prices for thirty-six consumer electronic products for eighteen months and find a significant decline in the percentage price differential. The existing work suffers from significant limitations. One key limitation is that much of the existing literature relies on posted prices on the internet as their source for “internet prices” to estimate price dispersion. Posted price data can lead to an overestimation of price dispersion because few if any sales may occur at high posted prices.7 Another criticism is that certain studies do not control properly for product attributes leading to an increase in measured price dispersion that merely reflects product heterogeneities.8 Another key limitation of each of these studies is that online and offline markets are not directly comparable. While internet “markets” are often national in scope, offline markets generally have much more limited geography. In particular, it is not clear that the group of purchasers of a product, for example a CD, is the same for a national internet market as it is for any particular physical market in a particular geographic location. This limitation means that the two types of markets have different numbers of consumers and firms, undermining the comparability of their prices and measured price levels and dispersion. More recently Ghose and Yao (2009) provide an intriguing study comparing price dispersion in the online and offline markets for paints, brushes and other hardware merchandise. Their results, in contrast to those above, indicate “near-zero” dispersion in online markets. They attribute their finding to

5 See Sengupta and Wiggins (NET Institute Working Paper #06-07, 2006). 6 A variety of other factors, namely, brand loyalty (Lal and Sarvary, 1999), product bundling (Varian, 1980) and difference in service quality (Pan et al., 2002), can all plausibly explain persistent price dispersion in the two markets. Unfortunately, synthesis of these factors effecting price dispersion lies beyond the scope of this paper. 7 This line of criticism warrants the prices to be weighed by the market share of the sellers which may yield lower dispersion in contrast to assigning equal weights. 8 The higher dispersion is incorrectly attributed to lower search costs when actually it is being driven by heterogeneity in product characteristics.

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the use of transaction level data to measure the dispersion in contrast to frequently used posted prices for online prices. Since posted prices may differ from transactions prices for reasons discussed above, use of transaction data may provide more reliable estimates of price dispersion in online markets. This evidence suggests lower or perhaps ‘near-zero’ dispersion in at least some online markets. Our paper is designed to address these issues using a unique data set that includes actual transactions prices in an important U.S. industry. Our data contain contemporaneous online and offline transaction data set for airline tickets. Our data permit us (a) to provide the first direct comparison of transaction-based price dispersion in airline fares between online and offline distribution channels, (b) permit control for the group of buyers and sellers, and (c) to investigate if ‘near-zero’ dispersion persists in the online market for airline tickets thereby affirming Ghose and Yao’s claim that use of transaction data may support a finding in favor of the “law of one price”. A key advantage of our analysis is that we can control for heterogeneities in ticket characteristics and restrictions as well as time of purchase using actual transactions data. In addition, the sellers are the same since we control at the carrier - route level and the group of buyers is generically the group of buyers that want to travel between two cities on a particular date. While this approach still has limitations because there can never be complete control for different buyer groups online and offline, our method offers a substantial improvement over previous work. The rest of the paper is structured as follows. Section III presents some stylized facts regarding airline price dispersion. Section IV provides a description of the data while Section V discusses the econometric framework and issues associated with the estimation. Section VI provides a detailed discussion of the results from the econometric models. Section VII concludes the study and discusses its limitations. 3. Price Dispersion in Airlines Market

The online and offline markets for airline tickets differs significantly from other markets used to analyze these differences such as books, CDs, and consumer durable goods. One of the key differences is that the airlines offer a wide array of fares for travel on the same flight on the same day. The available evidence indicates that airlines offer tickets for sale in a conceptual series of “bins” or “buckets,” where a bucket is defined by a series of ticket characteristics or restrictions, such as class of travel, refundability, advance purchase requirements, and travel and stay restrictions, including minimum and maximum stays and/or

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Saturday stay-over.9 The received wisdom is that airlines limit the quantity of low price tickets by limiting the number of tickets in low price buckets. For example, certain combinations of characteristics may only be used during certain days of the week (e.g. TWF), and certain tickets may only be available for round trips. Certain fares may not be available on certain flights for a period of time and then later once again becomes available. High priced tickets are sometimes sold far in advance of departure, and deeply discounted tickets in certain bins may be available on the day of departure. The existing literature has identified two key sources of price dispersion in the airline industry – constrained capacity with uncertain demand (Dana, 1999) and price discrimination (Borenstein and Rose, 1994). Constrained capacity and uncertain demand can be measured through load factor. Ideally, one would want to measure load factor at the time of purchase. On full flights, cheaper seats may have sold out or airlines may restrict their availability. Hence it is important to measure load factor. Price variation may also be tied to ticket characteristics. It is widely argued that ticket restrictions are used by the airlines’ as fencing mechanisms. In general ticket characteristics like refundability, advance purchase requirements, Saturday-night stay over, minimum or maximum stay restrictions and day of travel restrictions are widely used by the airlines’ to segment demand between high and low value customers in order to increase revenues. Such pricing strategies, as is evident, would yield price dispersion. Though, earlier studies have identified these drivers of dispersion in the airline industry, data limitations have meant that previous investigators could not control for these factors. As a result existing studies have been unable to isolate the effects of capacity constraints, ticket characteristics, and price discrimination on price dispersion and also to establish a causal relationship between these factors. More recent works by Sengupta and Wiggins (2006) and Puller et al. (2009) have filled this void in the literature by demonstrating how ticket characteristics and flight level load factors contribute to airline prices and dispersion. We use these two above studies as a benchmark for controlling for potential sources of price dispersion in the present study.

3.1 Search Theory, Pricing, and the Internet

Following the literature, we assume that the internet lowers search costs. The motivation for our analysis is built on Stahl’s search model (1989). Stahl assumes that a certain exogenous share of customers is fully informed regarding all prices in the market, and that another group of customers must pay a search cost for each

9 See Smith et al. (2001).

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price quote received. Because customers search sequentially in the Nash equilibrium, stores choose prices from a price distribution. Searchers with positive search costs stop searching endogenously whenever they observe a price is at or below their endogenous reservation price. Fully informed customers have no search costs and search exhaustively, buying from the lowest price seller. We assume that online customers are better informed than offline customers, but we ultimately test this assumption by determining if dispersion in online markets is lower than in the offline market. We use transactions rather than posted prices, which means that we measure the actual price distribution rather than the distribution of posted prices, some of which might net few if any sales. Our methodology also ensures that the data used only includes fares available both online and offline. The data we use incorporates observations where transaction fares were matched to those found offline in an offline CRS.10 This matching procedure means that the fares considered were available both online and offline. The matching procedure is discussed in more detail in the next section.

4. Data

The data consists of contemporaneous online and offline transaction data provided by a leading Computer Reservation System (CRS) for the last quarter of 2004. The data includes all transactions conducted through the CRS during that quarter, including transactions at airline sites, travel agents, and numerous online sites.11 As noted above, the data from the CRS includes the airline and flight number, origin and destination, fare, booking class, a fare code, and dates of purchase, departure and return. Overall, these data represent roughly thirty percent of U.S. domestic tickets sold. These data do not include data regarding refundability, advance purchase requirements, or travel and stay restrictions. To obtain these missing ticket characteristics, we electronically matched the data with a data set from another CRS containing both fares offered and purchased for travel in particular routes organized by departure date, airline, and route.12 This second data set includes ticket characteristics not available in the

10 Note that the available information indicates that in general the same prices and fare combinations are available online and offline. Note that in the early days of Orbitz the prices listed there included pricing specials offered ‘directly’ by the airlines, falling outside the travel agent contracts. It may also be possible that some offered prices on particular airline sites that are lower than fares offered elsewhere. However, this paper does not include web special fares by virtue of the construction of the data set. See the Appendix. 11 The data however, does not permit us to identify the transactions through airline sites. 12 A more detailed description of the matching procedure is included in Appendix A.

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first data set.13 The data set from this second CRS was, however, incomplete in that certain fares had been deleted from the archive, and so we were only able to match the fares imperfectly.14 The criterion used was to keep transactions if we could match the fares within two percent; for multiple matches within two percent we kept the closest.15 The matching procedure also ensured that tickets matched with respect to carrier and booking class, and that travel and stay restrictions were met by the observed purchase, departure, and return data from the original data set. The resulting matched data set contains individual ticket transactions that include ticket characteristics and restrictions, together with carrier, flight information, and dates of purchase, departure, and return. This procedure matched roughly thirty-five percent of the observations from the first data set.16 This procedure matched roughly thirty-five percent of the observations from the first data set. For both the online and offline transactions, our match rate is somewhat lower for the lowest priced tickets. Kernel densities for matched and unmatched data for different routes are illustrated in Figures 1-3 in the appendix. For example, Figure 1 shows the matches for Chicago to Newark; Panel A shows matches for all airlines, and Panel B for Continental, the market leader. Figures 2-3 show similar kernel densities for two other of the largest routes. The kernel densities show an under-representation of the very lowest fares for both all airlines and for the largest airlines on a route. Our analysis of online and offline fares, however, does not appear to be affected because we only consider matches for online and offline fares, and the under-representation of matches is comparable in both data sets. More specifically, Figure 4 compares the kernel densities for matched and unmatched transactions broken down by online versus offline transactions. Both online and offline transactions have fewer matches for very low fares, but there do not appear to be significant differences in the match rate for online versus offline fares. The small difference in online versus offline matches is also illustrated by examining the match rate in the left hand tail of the distributions in Figure 4 in the

13 We have been informed that fares offered on the various CRSs are normally the same, but that at times a fare will only be offered on some CRSs. This permits the use of departure dates to match the route, carrier, fares, and fare classes in the first data set with the detailed ticket characteristics found in the second data set. 14 The data in the second archive are kept for unknown intervals of time. Individual fares are then deleted in an unknown pattern over time. 15The primary transaction data reported the base prices only, which do not include the FAA administered air transportation excise tax of 7.5 percent or any other taxes or surcharges. The second data source containing the ticket characteristics reported the prices inclusive of the FAA administered 7.5 percent air transportation excise tax. To make the prices in the two data sets comparable and facilitates the matching process between the two data sets, we added 7.5 percent to the reported base price in the primary transaction data. 16 For a more detailed discussion on the matching of the two CRS data sets please refer to Sengupta and Wiggins (2006) and Puller et al. (2009).

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appendix. This tail consists of price observations below $221, the price at which the matched versus unmatched kernel densities cross. Below $221, the match rate for offline tickets is about 22.6 percent while the match rate for online tickets is about 19.1 percent. These match rates are quite comparable, and the differences point toward a small over-sampling of offline fares. These effects point toward a comparative under-representation of low fares on the internet, although these effects will be minimized in the regressions because we control for ticket characteristics. This study uses data for 150 largest U.S. domestic routes, according to the original data set. The routes include a mix of both business and tourist routes, and routes with varying groups of customers. A complete list of routes is contained in the Appendix.17 Following the literature, we define a route as a city-pair regardless of direction (see, e.g. Borenstein and Rose, 1994). We include itineraries with at most one stop-over in either direction. The prices used are for roundtrip fares, doubling the fares for one-way tickets to obtain comparability. We exclude itineraries with open-jaws and circular trip tickets. This study includes tickets for flights operated by American Airlines, Continental, Delta, Northwest, US Airways, United Airlines, Frontier, Air Tran, Spirit, Alaska, America West, Sun Country, Hawaiian Airlines and American Trans Air.18 We also include variables indicating the presence of discount carriers on routes, and a separate variable for Southwest. The final data included 513,628 unique transactions.

4.1 Measuring Dispersion Airline tickets are sold for a particular flight, operated by a specific carrier departing on a particular date.19 Previous studies examining price dispersion in airline markets have included all observations within a calendar quarter using the U.S. Department of Transportation’s DB1B, which aggregates tickets at that level. Almost all existing studies in airlines fail to adequately control for ticket characteristics that explain almost 80 percent of the variation in ticket prices (Sengupta and Wiggins, 2006). To overcome the void in the present literature on airline price dispersion, we exploit the richness of our data to study airline price dispersion while accounting for both ticket and market characteristics, and flight characteristics.

17 The largest 150 routes based on the total number of transactions provided by the CRS were included in the analysis. 18 We can identify routes served by Southwest, but we do not have data regarding Southwest’s ticket characteristics because they are not included in one of the data bases. 19 In other words, one buys a ticket on American Airlines’ flight number 202 leaving from Houston (IAH) for Boston (BOS) on December 25, 2004.

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Individual ticket transactions can be categorized into a large number of product categories based on their product and market characteristics. More specifically, in this analysis, the product categories are constructed based on route, carrier, departure date and times, refundability, advance purchase requirement, minimum or maximum stay restriction, travel restriction, Saturday night stay, length of the trip, travel during weekday or weekend, and channel of sale - online or offline. Using the individual product categories, we construct our two measures of price dispersion – the coefficient of variation (CV) and price difference (PD). For each product category we calculate the mean price and its corresponding standard deviation. CV is then defined as the ratio of standard deviation to mean price within the product category.20 PD is defined as the difference between the maximum and minimum transaction price divided by the mean price within the product category. The measures of dispersion are computed separately for the electronic market (EM) and offline sales through traditional travel agents. For certain route-carrier-departure date-product category level, we observed only a single transaction. In such cases, CV was calculated as missing while PD would be equal to zero. In these situations, we excluded these observations from the analysis to ensure that the same number of observations was used in both empirical estimations. Table 1 provides the average dispersion in airline fares across all product categories, without controlling for other external factors that may affect dispersion. Consistent with the existing literature, dispersion in the electronic market is significantly lower than for the traditional market, but remains strictly positive.

20 Transaction prices are represented by the roundtrip fares. For one-way itineraries, the fares were multiplied by two to get the roundtrip fare. This method of using roundtrip fares is commonplace in the airline literature.

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Table 1: Average Price Dispersion

Cells defined by Route, Carrier, Refundability, Departure Date, Advance Purchase Restriction, Travel Restriction, Saturday-night stayover, Length of Trip, Online

and Weekend (in Percentage)

Market CV PD

Electronic Market 3.07 5.42

Traditional Market 8.55 17.32

t statistics (H0: difference=0) 68.12*** 78.88***

Wilcoxon rank-sum (H0: difference=0) 64.63*** 66.01***

Source: Author's calculations.

Note: There are 93,352 observations.

Using, CV as the measure of dispersion, the online market exhibits about 5.5 percent lower dispersion than the traditional markets, while using PD the online market exhibits about 12 percent lower dispersion. We use both Student’s t-test and non-parametric Wilcoxon rank-sum tests, to test for the equality of dispersion between the electronic and the traditional market. Both tests reject the null hypothesis, implying that price dispersion (both CV and PD) in the electronic market is significantly lower than in the traditional market. 5. Econometric Model

We now turn our attention to a detailed econometric model, which will permit a more thorough investigation of potential differences in airline price dispersion in electronic versus traditional markets. To investigate these differences, we begin with measured price variation as described above. We then estimate the following econometric model:

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)()()( 3210 StayAPbleNonrefunda

Dispersion

ijkmtijkmtijkmt

ijkmt

βββα +++=

In equation [1], `i’ denotes route, `j’ represents carrier, `k’ represents the product category, `m’ denotes the channel of distribution (online or offline) and `t’ represents the departure date. CV and PD are standard measures of price dispersion and represent the dependent variable. The variables “Nonrefundable” is a dummy variable that takes a value of 1 if the product category includes non-refundable tickets and zero otherwise. Similarly, the variables ‘AP’, ‘Stay’, ‘Travel’, and ‘Saturday stay’ are dummy variables that are assigned a value equal to 1 respectively, if the product category involves advance purchase requirement, minimum or maximum stay restriction, travel restriction, or the itinerary involves a Saturday stay. The dummy variable ‘weekend’ has a value of 1 if the product category includes tickets with travel during weekend and zero if travel occurs during the week.

EM denotes the channel of sale, taking a value of one for online sales and zero for traditional travel agent sales. Trip lengths are dummy variables that represent the number of days between departure and return. Load factor and Days represent the average load factor and the average number of days prior to departure the tickets were purchased within a product category. The share of roundtrip and non-stop itineraries within a product category are represented by the variables ‘Roundtrip’ and ‘Direct’. Tourist is a dummy variable if either end-point of a route includes Las Vegas, Reno, Orlando, Memphis or New Orleans. The remaining variables control for the route, carrier and departure dates fixed effects.

6. Results

Table 2 presents the descriptive statistics for the variables used for the electronic and traditional markets and overall. The dependent variables CV and PD are either zero or positive. On average, 94 percent of tickets bought in the electronic market are non-refundable as compared to 80 percent in the traditional market. Typically, tickets purchased over the internet are purchased almost a month prior

)()()()( 7654 EMWeekendstaySaturdayTravelmijkmtijkmtijkmt

ββββ ++++

)()()( 1098 DaysfactorLoadlengthTripijkmtijkmtijkmt

βββ +++

)()( 1211 RoundtripdummiestravelofTimeijkmtijkmt

ββ ++

)()()()( 16151413 RouteCarrierTouristDirectijiijkmt ββββ ++++

)()( 1817 εββ ijkmttDateDeparture ++ (Equation 1)

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to scheduled departure date, while offline customers on average buy their tickets about two weeks in advance. Online customers are more likely to purchase tickets with travel and advance purchase restrictions, travel on weekends and stay over a Saturday. The data also indicates that travelers on tourist routes are more likely to buy their tickets online.

Table 2: Summary Statistics

Electronic

Market

N = 13, 718

Traditional

Market

N = 79,634

Combined

N= 93,352

Variables Mean [SD] Mean [SD] Mean [SD]

Coefficient of Variation (CV) 3.07 [7.60] 8.55 [13.40] 7.74 [12.86]

Price Difference 5.42 [13.25] 17.32 [28.16] 15.57 [26.83]

Electronic Market 1 [0.00] 0 [0.00] 0.15 [0.35]

Product Characteristics

Refundability 0.94 [0.23] 0.80 [0.40] 0.82 [0.38]

Advance purchase restriction 0.76 [0.42] 0.59 [0.49] 0.62 [0.48]

Minimum or maximum stay restriction

0.35 [0.48] 0.29 [0.45] 0.30 [0.46]

Travel restriction 0.64 [0.48] 0.40 [0.49] 0.31 [0.49]

Saturday stay-over 0.52 [0.50] 0.14 [0.35] 0.20 [0.40]

Weekend 0.22 [0.41] 0.18 [0.38] 0.18 [0.39]

Tourist route 0.16 [0.37] 0.08 [0.28] 0.10 [0.30]

Others

Load factor at time of departure 0.07 [0.05] 0.11 [0.07] 0.11 [0.07]

Number of days in advance tickets purchased prior to departure date

31.28 [29.96] 14.87 [17.74] 17.28 [20.84]

Share of roundtrip tickets 0.88 [0.32] 0.75 [0.41] 0.77 [0.40]

Share of tickets with departure and return at peak times

0.03 [0.15] 0.06 [0.17] 0.05 [0.16]

Share of tickets with departure at peak and return at off-peak times

0.21 [0.31] 0.24 [0.29] 0.23 [0.30]

Share of tickets with departure at off-peak and return at peak times

0.11 [0.25] 0.17 [0.26] 0.16 [0.26]

Share of tickets with departure and return at off-peak times

0.65 [0.40] 0.53 [0.37] 0.55 [0.38]

Share of non-stop itineraries 0.97 [0.16] 0.99 [0.07] 0.99 [0.09]

Trip length 3.75 [3.38] 2.02 [2.70] 2.27 [2.87]

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Table 3 shows that almost 46% of dispersion measures (PD or CV) are equal to zero. Simple ordinary least squares (OLS) models may predict negative values of dispersion which intuitively is not sound. Also, zero mass may respond differently to covariates and this problem becomes worse as the mass at zero increases.21 In such situations, estimates from OLS are inconsistent (Greene, 1999). The Tobit model accounts for such censored distribution yielding consistent estimates (Amemiya, 1973; Greene, 1999).22 For the Tobit model setup the regression of interest is specified as an unobserved latent variable y*,

Nixy iii K,2,1,'* =+= εβ (Equation 2.1)

where ~N(0,σ2), y* here represents the measure of dispersion and xi denotes the vector of exogenous and fully observed regressors explaining variations in dispersion. If y* were observed then (2.1) can be estimated by OLS in the usual way. However, the observed variable yi is related to the latent variable y* through the observation rule

≤

>=

0*,0

0**,

yif

yifyy (Equation 2.2)

An important limitation of the standard Tobit model is that a single mechanism determines the choice between y*=0 versus y*>0 and the amount of y given y*>0. Alternatives to the Tobit model have been suggested to allow the initial decision of y*>0 versus y*=0 to be separate from the decision of how much y given that y>0. These are often called hurdle models or two-tiered models.23 The hurdle or two-part models take advantage of the basic rule of probability, that is:

),0*|*(*)0*Pr()|( >>= yyEyxyE (Equation 2.3)

The hurdle of the first tier is whether or not to y* is positive y while the second tier is to estimate the amount of y given that y*>0. We estimate the first

21 For more detailed discussion please refer to http://harrisschool.uchicago.edu/faculty/articles/ASHE_Minicourse_2006.pdf , Greene (1999) and Wooldridge(2002). 22 We report both OLS and Tobit estimates, though we rely on Tobit estimates for our discussion. 23 These are also sometime referred to as two-part models. For further details please refer to Wooldridge (2002), Cameron and Trivedi (2007).

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stage model using a Logit model while the second stage (positive dispersion) is measured using simple OLS.24 The prediction in the two-part model depends on both parts or tiers of the model. We limit our discussion to the results from the Tobit and the hurdle models.25

Table 3: Number of Zero and Non-Zero Price Dispersion

Zero Non-Zero Total

Electronic Market 10,041 (73.2%)

3,677 (26.8%)

13,718 (100%)

Traditional Market 33,306

(41.8%) 46,328 (58.2%)

79,634 (100%)

Total 43,347

(46.4%)

50,005

(53.6%)

93,352

(100%)

Source: Author’s calculations

Table 3 counts the number of zero and non-zero observations in individual markets and in the overall market. Approximately, 50.2 percent observations in the overall data are zeros, while 73.2 percent in the electronic market are zeros, and 41.8 percent in the traditional market are zeros. The existence of such a substantial zero mass reinforces the reliance on Tobit and two-part model estimates as compared to OLS estimates. Table 4 presents the results from estimating [1].26 The coefficients for the electronic market are negative and statistically significant (β=-11.22, p<0.001 for CV; β=--23.63, p<0.001 for PD) indicating that price dispersion in the online market is significantly lower than in the traditional market, using the Tobit model. Coefficient estimates from the two-part model, using the restricted sample consisting of positive dispersion only, qualitatively similar, though significantly lower in magnitude (β=-1.67, p<0.001 for CV; β=-5.10, p<0.001 for PD). Ticket restrictions including refundability and restricted travel during certain days of the week decrease dispersion for both CV and PD. In contrast, advance purchase requirements and the number of days in advance a ticket was purchased and stay restrictions are associated with increased price dispersion.

24 See http://harrisschool.uchicago.edu/faculty/articles/ASHE_Minicourse_2006.pdf. 25 For results from the OLS estimation, please refer to tables A2 and A3 provided in the appendix. 26 We also used a partial model which excludes the ‘other controls’ reported in this section. These results are provided in Table A2 of the appendix. Further, the estimates of the first part of the two-part model are included in Table A3 of the appendix.

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Higher load factors are associated with higher dispersion, consistent with prior airline literature using both estimation techniques (Tobit: β=17.7, p<0.001 for CV; β=42.07, p<0.001 for PD, Two-part: β=2.24, p<0.001 for CV; β=15.20, p<0.001 for PD). More generally, a higher proportion of tickets with departure, return, or both at off-peak times of the day are not statistically significant in influencing dispersion as compared to the share of tickets with both departure and return during peak hour.27 Travel on weekends and on predominantly tourist routes exhibits lower dispersion (β=-18.25, p<0.001 for CV; β=-37.11, p<0.001 for PD.

Table 4: Regression of Price Dispersion

on Ticket and Other Characteristics

CV CV | CV>0 PD PD | PD>0 Tobit Two-part Tobit Two-part

Refundability -0.5088** -3.9806*** -2.0215*** -9.7324***

(0.258) (0.246) (0.531) (0.514)

Advance purchase restriction 1.7484*** 0.1068 3.5457*** 0.4200

(0.199) (0.190) (0.410) (0.397)

Minimum or maximum stay restriction 2.0412*** 1.7694*** 3.8296*** 3.0609***

(0.207) (0.193) (0.426) (0.404)

Travel restriction -2.5595*** -2.2266*** -5.1350*** -4.4940***

(0.168) (0.152) (0.345) (0.318)

Saturday stay-over -0.9286*** -0.1591 -1.9814*** -0.4022

(0.294) (0.277) (0.607) (0.580)

Weekend -9.8340*** 0.8556 -20.7910*** 0.2596

(2.097) (1.554) (4.354) (3.256)

Electronic market -11.2195*** -1.6692*** -23.6330*** -5.1070***

(0.254) (0.277) (0.525) (0.581)

Tourist route -18.2475*** 3.2873 -37.1113*** 2.5700

(4.557) (5.781) (9.429) (12.111)

Other controls

Average load factor 17.7063*** 2.2430 42.0750*** 15.2049*** (1.515) (1.450) (3.117) (3.038)

27 The share of tickets with departure during peak times but return during off-peak hours increases dispersion when measured in terms of CV. However, these estimates are significant at the 5% and 10% significance level and not at the 1% significance level.

15





Table 4: Regression of Price Dispersion


CV CV | CV>0 PD PD | PD>0 Tobit Two-part Tobit Two-part

Average number of days in advance tickets purchased prior to departure date 0.0074 -0.0207*** 0.0570*** 0.0442***

(0.005) (0.006) (0.011) (0.013)

Share of roundtrip tickets 0.2776 -1.1662*** 0.8680 -2.0347**

(0.461) (0.439) (0.947) (0.921)

Share of tickets with departure at peak and return at off-peak times 1.0510** 0.8157* 1.3872 0.5070

(0.504) (0.479) (1.039) (1.004)

Share of tickets with departure at off-peak and return at peak times 0.6401 0.0442 1.0637 -0.1775

(0.528) (0.501) (1.089) (1.050)

Share of tickets with departure and return at off-peak times 0.0674 0.5576 -0.4434 0.2230

(0.478) (0.456) (0.984) (0.954)

Share of non-stop itineraries -3.0632*** -0.8366 -5.3099*** 0.3803

(0.879) (0.983) (1.828) (2.059)

Trip length dummies Yes Yes Yes Yes

Departure date dummies Yes Yes Yes Yes

Route dummies Yes Yes Yes Yes

Carrier dummies Yes Yes Yes Yes

Constant 8.5745*** 25.6064*** 16.4674*** 49.4688***

(1.784) (1.755) (3.686) (3.676)

Sigma 19.0259*** 39.1323***

(0.065) (0.133)

Observations 93,352 50,005 93,352 50,005

R2 . 0.095 . 0.116

Note: Measures of Price Dispersion calculated in terms of Route, Carrier, Refundability, Departure Date, Advance Purchase Restrictions, Stay Restriction, Travel Restriction, Saturday-night stayover, Length of Trip, Online and Weekend. The estimates from the OLS specification and the results from the first part of the two-part model specification is included in Tables A1 and A3 respectively.

16




Table 5 presents estimates of the predicted values of price dispersion at sample means from the Tobit and the two-part models for both the electronic and traditional markets for airline tickets. Price dispersion, when measured using CV, is nearly 6 percent less in the online market than in traditional markets while the difference increases to 13 percent when PD is the measure of dispersion, depending on the method of estimation. These differences in dispersion between online and traditional market are statistically significant at 1% level.

Table 5: Predicted Percentage Price Dispersion

Cells defined by Route, Carrier, Refundability, Departure Date, Advance Purchase Restritctions, Stay Restriction, Travel Restriction, Saturday-night

stayover, Length of Trip, Online and Weekend (in Percentage)

CV PD

Tobit

Model

Two-part

Model

Tobit

Model

Two-part

Model

Electronic Market 3.29 3.81 6.43 6.96

Traditional market 9.12 9.35 18.65 19.06

t statistic (H0: difference=0) 277.26*** 147.46*** 276.72*** 152.22***

Wilcoxon rank-sum (H0: difference=0)

155.56*** 147.15*** 154.77*** 146.32***

Price dispersion appears to be significantly lower in the market for airline

tickets. The dispersion in online markets averages between 3 and 7 percent of the mean price, depending on the measure of dispersion and the econometric specification. Though significantly lower than previous estimates of 18 percent (Clemons et al., 2001) which controlled for some ticket characteristics, the estimated dispersion is significantly higher than the near-zero estimates of Ghose and Yao. However, Borenstein and Rose (1994) estimate of 36 percent dispersion in the offline market, appears to be overly excessive when ticket restrictions are adequately controlled for in addition to carrier and route characteristics.28

7. Discussion and Concluding Remarks

The study of price dispersion in the economy and in particular in electronic markets has been of much interest to the economics profession. This interest is rooted in classical textbook models that competition and access to low cost

28 Borenstein and Rose (1994) estimated the dispersion in airline fare using data from Q2, 1986. During this time the online market for travel was non-existent and hence comparable to the offline market only.

17





information eliminate price dispersion resulting in the ‘law of one price’. Most empirical literature has, however, found robust evidence of persistent dispersion in internet markets. This persistent dispersion has been quite often attributed to less matured market, lack of market maturity, differences in service quality, differences in costs, and other factors. Tests of these models for electronic markets have often relied on posted prices compared to transaction prices, which may have contributed to higher dispersion. Ghose and Yao (2009) using online and offline transaction data on hardware, paints and brushes presented a finding “near-zero” dispersion in the electronic market. Ghose and Yao attribute this finding primarily to the use of transaction data, unlike earlier works using posted prices. Using contemporaneous online and offline transaction data for airline tickets, this paper finds robust evidence of significantly lower dispersion in online markets as compared to traditional market outlets. Though we fail to corroborate the ‘near-zero’ dispersion in the airline market, our estimated average dispersion ranging between 4 and 9 percent is significantly lower than that documented by earlier studies. It is highly feasible that with more exhaustive data, a finer synthesis of the transactions may yield near-zero dispersion, which unfortunately cannot be achieved with the present data. One source of the higher price dispersion may stem from the construction of the product categories. One may argue that the product categories used in this analysis, may not be sufficient and the product categories requires to be more narrowly defined. While this is a legitimate concern, the underlying combinations of the different ticket characteristics actually used by the airlines to segment customers is expansive, and isolating each such possible combination and measuring dispersion within that particular ‘bin’, is a formidable challenge. More importantly, even if product categories were finely defined, this would result in very few tickets within each product category such that the skewness of zero mass observations would attenuate the estimations. As electronic markets continue to mature, the use of contemporaneous online and offline transaction data for airline tickets provides some evidence of shrinking price distributions. The paper finds strong support for lower but positive dispersion in the electronic market for airline tickets, with dispersion ranging between 4 to 9 percent, depending on the measure of dispersion and econometric specification. Further studies using transaction data are warranted.

18




8. Appendix

8.1 Appendix A

Data Description and Matching Procedure

We sketch below a detailed description of the variables used and how they were constructed. The final data set used for the analysis has been comprised from three different data sets. The first data set includes contemporaneous online and offline transaction data from the fourth quarter of 2004. However, our period includes some of the peak travel period, particularly Thanksgiving, Christmas, and New Years. To sidestep the problems of pricing during these peak travel periods, we dropped transactions for travel during the Thanksgiving week. We also kept transactions that included departure and return within the 22nd of December, 2004. Thus we do not include itineraries involving travel during the last week of the year, since pricing can be different for these periods. This transaction data comes from one of the major computer reservation systems. Unfortunately, due to confidentiality reasons, they did not provide us with the ticket restrictions. To overcome this limitation, we collected computer reservation system data by gathering the same from one of the local travel agents. The travel agents systems can access historical data for a year. However, due to the time difference between the actual period for which we had data and the data that we could collect, we could obtain a subset of the prices and their characteristics that were offered for the last quarter of 2004, since much of the data was taken out from the reservation systems in a random manner. We matched our transaction data to the travel agents data to obtain the restrictions on the individual tickets. To overcome, the data limitation problem arising from the sub-set of the data that we could collect, we adopted a matching rule. If the two prices from the data sets matched within a two percent range, we assigned it as a match. We are thereby assuming, that for a ticket priced at $150 will be qualitative similar to one priced at $147 or $153. However, note that some price adjustments were made in order to facilitate the matching process. The primary sales data reported the base prices which does not include the mandatory 7.5 percent excise tax levied by the Federal Aviation Administration (FAA). The price reported in the second data set containing the ticket characteristics however, was inclusive of the 7.5 percent FAA excise tax, such that it was required to add 7.5 percent to the base price in the primary sales data to ensure comparability. Furthermore, no other taxes or surcharges like the airport taxes were included in either data sets and hence no further adjustments to the reported prices were required. We however, took full precaution that the other matching criteria like carrier, booking class and coach class, the day of the week of travel (in case we matched it with a

19





ticket that has a travel day restriction), and the advance purchase requirement were matched in both the data sets. Following Borenstein (1989) and Borenstein and Rose (1994), we include itineraries which has at most of one break (stop-over) in either direction. The prices are for roundtrip fares. For the one-way itineraries, the fares are multiplies by two. We exclude all itineraries which are open-jaw and circular trip tickets. This study includes tickets which are operated by American Airlines, Continental, Delta, Northwest, US Airways, United Airlines, Frontier, Air Tran, Spirit, Alaska, America West, Sun Country, Frontier Airlines, and American Trans Air.

Figure 1

Comparing the Kernel Densities of Matched and Unmatched Transactions

for flights between Chicago O’Hare – Newark Liberty

0

.00

.002

.003

.00

0 50 100 150 200Roundtrip Fare

Unmatched Matched

Comparison of Matched versus Unmatched Observations for ORD-EWR on CO

Ker

nel

Den

sity

Market Leader: Continental

0

.00

.00

.00

.00

Ker

nel

Den

sity

0 50 100 150 2000 Roundtrip Fare

Unmatched Matched

Comparison of Matched versus Unmatched Observations for ORD-

All Airlines

20




0

.001

.002

0 500 1000 1500 2000Roundtrip Fare

Unmatched Transactions Matched Transactions

Comparison of Matched versus Unmatched Observations for JFK-LAX on AA

Market Leader: American Airlines

.003

0 500 1000 1500 2000Roundtrip Fare


Comparison of Matched versus Unmatched Observations for JFK-LAX

Ker

nel

Den

sity

.004

.003

.002

.001

All Airlines

0

Ker

nel

Den

sity

Figure 2 Comparing the Kernel Densities of Matched and Unmatched Transactions

for flights between Kennedy, New York – Los Angeles, California

21





0

.0005

.001

.0015

.002

.0025

0 500 1000 1500 2000 Roundtrip Fare


All Airlines

Ker

nel

Den

sity

0

.001

.002

.003

Ker

nel

Den

sity

0 50 1000 1500 2000 Roundtrip Fare


Comparison of Matched versus Unmatched Observations for LGA-ORD on AA

Market Leader: American Airlines

Comparison of Matched versus Unmatched Observations for LGA-ORD

Figure 3


for flights between LaGuardia, New York – Chicago O’Hare

22




0

.001

.002

.003

.004

Ker

nel

Den

sity

0 500 1000 1500 2000Roundtrip Fare


Comparing Matched versus Unmatched Observations: All Offline Transactions

0

.002

.004

.006

.008

0 500 1000 1500 2000Roundtrip Fare


Ker

nel

Den

sity

All Airlines Online

Comparing Matched versus Unmatched Observations: All Online Transactions

Figure 4


for Overall Online and Offline Transactions

23





Table A1: Regression of Price Dispersion


[Including OLS Estimates]

CV CV CV | CV>0 PD PD PD | PD>0 OLS Tobit Two-part OLS Tobit Two-part

Refundability -1.1306*** -0.5088** -3.9806*** -3.1127*** -2.0215*** -9.7324***

(0.148) (0.258) (0.246) (0.307) (0.531) (0.514)

Advance purchase restriction 0.5060*** 1.7484*** 0.1068 0.9718*** 3.5457*** 0.4200

(0.114) (0.199) (0.190) (0.236) (0.410) (0.397)

Minimum or maximum stay restriction 1.1888*** 2.0412*** 1.7694*** 2.1402*** 3.8296*** 3.0609***

(0.118) (0.207) (0.193) (0.244) (0.426) (0.404)

Travel restriction -1.5918*** -2.5595*** -2.2266*** -3.1226*** -5.1350*** -4.4940***

(0.097) (0.168) (0.152) (0.200) (0.345) (0.318)

Saturday stay-over -0.6574*** -0.9286*** -0.1591 -1.4419*** -1.9814*** -0.4022

(0.165) (0.294) (0.277) (0.342) (0.607) (0.580)

Weekend -0.3641 -9.8340*** 0.8556 -1.3999 -20.7910*** 0.2596

(0.831) (2.097) (1.554) (1.722) (4.354) (3.256)

Electronic market -3.7536*** -11.2195*** -1.6692*** -7.8327*** -23.6330*** -5.1070***

(0.129) (0.254) (0.277) (0.268) (0.525) (0.581)

Tourist route -4.8804** -18.2475*** 3.2873 -10.6390** -37.1113*** 2.5700

(2.416) (4.557) (5.781) (5.007) (9.429) (12.111)

Other controls

Average load factor 9.2738*** 17.7063*** 2.2430 23.4860*** 42.0750*** 15.2049***

(0.866) (1.515) (1.450) (1.795) (3.117) (3.038)

Average number of days in advance tickets purchased prior to departure date 0.0047* 0.0074 -0.0207*** 0.0373*** 0.0570*** 0.0442***

(0.003) (0.005) (0.006) (0.006) (0.011) (0.013)

Share of roundtrip tickets -0.1007 0.2776 -1.1662*** 0.0809 0.8680 -2.0347**

(0.264) (0.461) (0.439) (0.547) (0.947) (0.921)

Share of tickets with departure at peak and return at off-peak times 0.5740** 1.0510** 0.8157* 0.5664 1.3872 0.5070

(0.286) (0.504) (0.479) (0.593) (1.039) (1.004)

Share of tickets with departure at off-peak and return at peak times 0.3422 0.6401 0.0442 0.4537 1.0637 -0.1775

(0.300) (0.528) (0.501) (0.621) (1.089) (1.050)

24




Table A1: Regression of Price Dispersion


[Including OLS Estimates]


Share of tickets with departure and return at off-peak times 0.1834 0.0674 0.5576 -0.0771 -0.4434 0.2230

(0.271) (0.478) (0.456) (0.561) (0.984) (0.954)

Share of non-stop itineraries -0.8604* -3.0632*** -0.8366 -1.0396 -5.3099*** 0.3803

(0.460) (0.879) (0.983) (0.953) (1.828) (2.059)

Trip length dummies Yes Yes Yes Yes Yes Yes

Departure date dummies Yes Yes Yes Yes Yes Yes

Route dummies Yes Yes Yes Yes Yes Yes

Carrier dummies Yes Yes Yes Yes Yes Yes

Constant 13.3917*** 8.5745*** 25.6064*** 26.7156*** 16.4674*** 49.4688***

(0.997) (1.784) (1.755) (2.065) (3.686) (3.676)

Sigma 19.0259*** 39.1323***

(0.065) (0.133)

Observations 93,352 93,352 50,005 93,352 93,352 50,005

R2 0.091 . 0.095 0.103 . 0.116

Note: Measures of Price Dispersion calculated in terms of Route, Carrier, Refundability, Departure Date, Advance Purchase Restritctions, Stay Restriction, Travel Restriction, Saturday-night stayover, Length of Trip, Online and Weekend. This table includes OLS estimates of the model specification reported in Table 4 of the main text.

25





Table A2: Regression of Dispersion on Ticket Characteristics


Refundability -1.0480*** -0.2927 -3.9678*** -2.9598*** -1.6079*** -9.7916***

(0.148) (0.257) (0.244) (0.306) (0.529) (0.510)

Advance purchase restriction -0.0010 0.7266*** -0.3022* -0.0004 1.5734*** -0.1663

(0.102) (0.176) (0.164) (0.211) (0.363) (0.344)

Minimum or maximum stay restriction 1.1222*** 1.8724*** 1.7465*** 2.0028*** 3.5012*** 3.0615***

(0.118) (0.206) (0.192) (0.244) (0.424) (0.402)

Travel restriction -1.6906*** -2.7488*** -2.3135*** -3.3315*** -5.5177*** -4.6228***

(0.096) (0.167) (0.151) (0.199) (0.343) (0.316)

Saturday stay-over -0.8645*** -1.4034*** -0.2876 -1.8380*** -2.8747*** -0.4341

(0.163) (0.289) (0.270) (0.337) (0.597) (0.565)

Weekend -0.7745 -10.9175*** 0.7446 -2.2378 -23.1307*** -0.1646

(0.829) (2.097) (1.549) (1.719) (4.356) (3.246)

Electronic market -3.7981*** -11.2877*** -1.7256*** -7.8638*** -23.6850*** -4.9749***

(0.128) (0.252) (0.275) (0.266) (0.522) (0.576)

Trip length dummies Yes Yes Yes Yes Yes Yes

Departure date dummies Yes Yes Yes Yes Yes Yes

Route dummies Yes Yes Yes Yes Yes Yes

Carrier dummies Yes Yes Yes Yes Yes Yes

Constant 14.8312*** 9.8902*** 25.5772*** 30.9400*** 20.8928*** 53.1213***

(0.814) (1.423) (1.320) (1.688) (2.935) (2.765)

Sigma

19.0441*** 39.1801*** (0.065) (0.133)

Observation 93,352 93,352 50,005 93,352 93,352 50,005

R2 0.089 . 0.0940 0.101 . 0.115

Note: Measures of Price Dispersion calculated in terms of route, carrier, refundability, departure date, advance purchase restrictions, stay restriction, travel restriction, Saturday-night stayover, length of trip, online, and weekend. In contrast to Table 4 in the main text, this specification excludes other control variables including average load factor, average number of days in advance tickets are purchased prior to departure, share of roundtrip tickets, share of tickets with departure at peak and return at off-peak times, share of tickets with departure at off-peak and return during peak hours, and share of tickets with both departure and return during peak hours.

26




Table A3: Part 1 Regression for Two-part Model

(Partial and Full Model)

Positive CV/PD

Dummy1,2

Positive CV/PD

Dummy1,3

Logit Logit

Refundability 0.2748*** 0.2507***

(0.026) (0.026)

Advance purchase restriction 0.1384*** 0.2281***

(0.018) (0.020)

Minimum or maximum stay restriction 0.0865*** 0.1071***

(0.021) (0.021)

Travel restriction -0.1555*** -0.1404***

(0.017) (0.017)

Saturday stay-over -0.1797*** -0.1285***

(0.029) (0.030)

Weekend -1.1948*** -0.3393**

(0.206) (0.152)

Electronic market -1.1677*** -1.1601***

(0.024) (0.024)

Tourist route -0.6404

(0.482)

Other controls

Average load factor 1.9642***

(0.156)

Average number of days in advance tickets purchased prior to departure date 0.0020***

(0.001)

Share of roundtrip tickets 0.1537***

(0.047)


0.0538

(0.051)


0.0735

(0.054)

27





Table A3: Part 1 Regression for Two-part Model

(Partial and Full Model)

Positive CV/PD

Dummy1,2

Positive CV/PD

Dummy1,3

Logit Logit

Share of tickets with departure and return at off-peak times

-0.0348

(0.049)

Share of non-stop itineraries -0.2876***

(0.085)

Trip length dummies Yes Yes

Departure date dummies Yes Yes

Route dummies Yes Yes

Carrier dummies Yes Yes

Constant 0.3529** 0.1795

(0.141) (0.177)

Observations 93,101 93,101

.

Notes: Measures of Price Dispersion calculated in terms of Route, Carrier, Refundability, Departure Date, Advance Purchase Restritctions, Stay Restriction, Travel Restriction, Saturday-night stayover, Length of Trip, Online, and Weekend.

1. Dependent variable is a dummy variable that takes a value of 1 if CV (hence, PD) is positive and 0 otherwise.

2. The estimates from the two-part model is presented in Table A2 of Appendix A.

3. The estimates from the two-part model are presented in Table 4 of the main text.

28




8.2 Appendix B

Instrument Variable approach

It has been argued that load factor is endogenous to prices. In our case, our dependent variable is a measure of price dispersion and hence by similar argument, load factor can be endogenous to price dispersion as well. Previous literature, however, has argued otherwise on this issue (Borenstein and Rose, 1994). Typically, ticket prices are set exogenously, and each ticket is placed in a bin or bucket (please refer to the discussion in section II). Common wisdom suggests, depending on current demand and expectations of demand, the airlines restrict or release tickets in specific bins, so as to maximize revenue, thereby making the case for exogenous prices. To address any potential issue of endogenous load factor, in our case, we use an instrument variable approach to this concern. Since the load factor, typically varies by the time of day, departure and return time can be used as an instrument for load factor. In our earlier specifications, we used these departure and return time variables (share of transaction within a product category with departure and return at peak time, departure during peak hours and return during off-peak hours, departure during off-peak but return during peak hours, and both departure and return during off-peak hours) as an exogenous control, but to address the endogenity issue, we use them as instrument variables (IV) for load factor. The results from the IV regression and the average predicted dispersion thereof are presented below in tables A4 and A5. The results from the IV regressions are qualitatively similar to those presented earlier in the paper, where we treated load factor as an exogenous variable. The average predicted price dispersion in the IV specifications also appears to be consistent with that reported in the main discussion. Based on the results, it appears that endogeneity of the load factor does not seem to be a concern in our context. Table B1: Regression of Dispersion Measures on Ticket Characteristics and

Other Controls

CV CV PD PD OLS [IV] Tobit [IV] OLS [IV] Tobit [IV]

Refundability -1.2113*** -0.7601*** -3.3006*** -2.5719***

(0.158) (0.276) (0.327) (0.569)

Advance purchase restriction 1.0871*** 3.5701*** 2.3319*** 7.5435***

(0.392) (0.702) (0.812) (1.446)

Minimum or maximum stay restriction 1.2677*** 2.2830*** 2.3186*** 4.3530***

(0.128) (0.226) (0.265) (0.465)

29





Table B1: Regression of Dispersion Measures on Ticket Characteristics and

Other Controls

CV CV PD PD OLS [IV] Tobit [IV] OLS [IV] Tobit [IV]

Travel restriction -1.4734*** -2.1914*** -2.8494*** -4.3327***

(0.124) (0.218) (0.257) (0.450)

Saturday stay-over -0.4477** -0.2930 -0.9810** -0.6336

(0.215) (0.386) (0.446) (0.796)

Weekend -2.0275* 0.8605 -3.9477* 1.1660

(1.041) (1.477) (2.158) (3.052)

Electronic market -3.7193*** -11.1159*** -7.7584*** -23.4146***

(0.132) (0.258) (0.273) (0.533)

Tourist route -4.7390* -17.7657*** -10.3442** -36.0278***

(2.420) (2.188) (5.016) (4.532)

Other controls

Average load factor1

21.5568*** 56.2758*** 52.2987*** 126.7898***

(8.025) (14.387) (16.637) (29.635)

Average number of days in advance tickets purchased prior to departure date 0.0188** 0.0515*** 0.0702*** 0.1537***

(0.010) (0.017) (0.020) (0.036)

Share of roundtrip tickets -0.0639 0.3950 0.1949 1.1811

(0.258) (0.451) (0.535) (0.927)

Share of non-stop itineraries -0.9743** -3.4190*** -1.3016 -6.0831***

(0.465) (0.890) (0.964) (1.851)

Trip length dummies Yes Yes Yes Yes

Departure date dummies Yes Yes Yes Yes

Route dummies Yes Yes Yes Yes

Carrier dummies Yes Yes Yes Yes

Constant 11.1788*** 1.0286 20.9040*** -0.8433

(1.907) (3.421) (3.954) (7.054)

Observations 93,352 93,352 93,352 93,352

R2

0.089 . 0.1 .

Notes: Measures of Price Dispersion calculated in terms of Route, Carrier, Refundability, Departure Date, Advance Purchase Restritctions, Stay Restriction, Travel Restriction, Saturday-night stayover, Length of Trip, Online, and Weekend .

1. Average load factor has been instrumented by (i) share of itineraries with departure and return at peak time, (ii) share of itineraries with departure at peak time but return at off-peak times, (iii) share of itineraries with departure at off-peak times but return during peak times.

30




Table B2: Predicted Average Price Dispersion (from IV Regressions) by

Route, Carrier, Refundability, Departure Date, Advance Purchase

Restrictions, Stay Restriction, Travel Restriction, Saturday-night stayover,

Length of Trip, Online

and Weekend (in Percentage) CV PD

OLS Model Tobit Model OLS Model Tobit Model

Electronic Market 3.07 3.32 5.42 6.5

Traditional market 8.55 9.22 17.32 18.89

t statistic (H0: difference=0)

173.36*** 265.70*** 170.07*** 263.32***

Wilcoxon rank-sum (H0: difference=0)

138.81*** 152.89*** 136.49*** 151.94***

Source: Author's calculations.

Table B3: Measuring Online and Offline Dispersion

at the Route-Carrier-Departure Date Level

CV OLS

CV Tobit

CV|CV>0 Two-part

PD OLS

PD Tobit

PD|PD>0 Two-part

Electronic Market -26.2706*** -29.8639*** -23.6284*** -101.4560*** -115.6684*** -99.6447***

(0.298) (0.332) (0.323) (1.016) (1.127) (1.136)

Departure date Fixed Effects Yes Yes Yes Yes Yes Yes

Route Fixed Effects Yes Yes Yes Yes Yes Yes

Carrier Fixed Effects Yes Yes Yes Yes Yes Yes

Constant 53.1628*** 52.0909*** 59.8106*** 142.6714*** 137.0921*** 154.7231***

(2.610) (2.872) (2.787) (8.901) (9.767) (9.805)

Sigma 25.9102*** 87.5109***

(0.114) (0.384)

Observations 29,934 29,934 26,633 29,934 29,934 26,633

R2 0.334 0.311 0.379 0.357

Note:

Price dispersion measures are computed in terms of route, carrier and departure date.

31





Table B4. List of Routes Included in the Analysis

Routes Routes

Atlanta (ATL)-Boston (BOS) Chicago O’Hare (ORD)-Orange County (SNA)

Atlanta (ATL)-Cincinnati (CVG) Chicago (MDW)-Detroit (DTW)

Atlanta (ATL)- Fort Lauderdale (FLL) Cleveland (CLE)-Chicago (MDW)

Atlanta (ATL)-Dulles, DC (IAD) Cleveland (CLE)-Chicago O’Hare (ORD)

Atlanta (ATL)-Houston (IAH) Cincinnati (CVG)-Chicago O’Hare (ORD)

Atlanta (ATL)-Los Angeles (LAX) Columbus (CMH)-LaGuardia (LGA)

Atlanta (ATL)-LaGuardia (LGA) Dallas (DFW)-Atlanta (ATL)

Atlanta (ATL)-Orlando (MCO) Dallas (DFW)-Denver (DEN)

Atlanta (ATL)-Memphis (MEM) Dallas (DFW)-Washington (IAD)

Atlanta (ATL)-Miami (MIA) Dallas (DFW)-Houston (IAH)

Atlanta (ATL)-New Orleans (MSY) Dallas (DFW)-Los Angeles (LAX)

Atlanta (ATL)-Chicago O’Hare (ORD) Dallas (DFW)-Long Beach (LGB)

Atlanta (ATL)-Philadelphia (PHL) Dallas (DFW)-Kansas City (MCI)

Atlanta (ATL)-Tampa (TPA) Dallas (DFW)-Chicago O’Hare (ORD)

Baltimore (BWI)-Atlanta (ATL) Dallas (DFW)-Phoenix (PHX)

Baltimore (BWI)-Cleveland (CLE) Denver (DEN)-Atlanta (ATL)

Baltimore (BWI)-Dallas (DFW) Denver (DEN)-Boston (BOS)

Baltimore (BWI)-Fort Lauderdale (FLL) Denver (DEN)-Washington (DCA)

Baltimore (BWI)-Los Angeles (LAX) Denver (DEN)-Newark (EWR)

Baltimore (BWI)-Orlando (MCO) Denver (DEN)-Houston (IAH)

Boston (BOS)-Baltimore (BWI) Denver (DEN)-New York (LGA)

Boston (BOS)-Charlotte (CLT) Denver (DEN)-Kansas City (MCI)

Boston (BOS)-Washington (DCA) Denver (DEN)-Orlando (MCO)

Boston (BOS)-Dallas (DFW) Denver (DEN)-Portland (PDX)

Boston (BOS)-Detroit (DTW) Denver (DEN)-Philadelphia (PHL)

Boston (BOS)-Los Angeles (LAX) Denver (DEN)-Phoenix (PHX)

Boston (BOS)-Philadelphia (PHL) Denver (DEN)-St. Louis (STL)

Boston (BOS)-Pittsburgh (PIT) Denver (DEN)-Tampa (TPA)

Boston (BOS)-Fort Myers (RSW) Detroit (DTW)-Atlanta (ATL)

Boston (BOS)-Tampa (TPA) Detroit (DTW)-Baltimore (BWI)

Charlotte (CLT)-Orlando (MCO) Detroit (DTW)-Dallas (DFW)

Chicago O’Hare (ORD)-Boston (BOS) Detroit (DTW)-Newark (EWR)

Chicago O’Hare (ORD)-Baltimore (BWI) Detroit (DTW)-Fort Lauderdale (FLL)

Chicago O’Hare (ORD)-Charlotte (CLT) Detroit (DTW)-Las Vegas (LAS)

Chicago O’Hare (ORD)-Denver (DEN) Detroit (DTW)-Orlando (MCO)

Chicago O’Hare (ORD)-Washington (IAD) Detroit (DTW)-Chicago O’Hare (ORD)

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Routes Routes

Chicago O’Hare (ORD)-New York (LGA) Fort Lauderdale (FLL)-Boston (BOS)

Chicago O’Hare (ORD)-Miami (MIA) Fort Lauderdale (FLL)-Chicago O’Hare (ORD)

Chicago O’Hare (ORD)-Minneapolis (MSP) Hartford (BDL)-Washington (DCA)

Chicago O’Hare (ORD)-New Orleans (MSY) Hartford (BDL)-Chicago O’Hare (ORD)

Chicago O’Hare (ORD)-Omaha (OMA) Honolulu (HNL)-Los Angeles (LAX)

Chicago O’Hare (ORD)-Ft. Myers (RSW) Houston (IAH)-New Orleans (MSY)

Chicago O’Hare (ORD)-San Diego (SAN) Houston (IAH)-Chicago O’Hare (ORD)

Las Vegas (LAS)-Burbank (BUR) New York (LGA)-Cincinnati (CVG)

Las Vegas (LAS)-Los Angeles (LAX) New York (LGA)-Dallas (DFW)

Las Vegas (LAS)-Chicago O’Hare (ORD) New York (LGA)-Detroit (DTW)

Long Beach (LGB)-Dallas (DFW) New York (LGA)-Houston (IAH)

Los Angeles (LAX)-Denver (DEN) New York (LGA)-Palm Beach, FL (PBI)

Los Angeles (LAX)-Detroit (DTW) Oakland (OAK)-Denver (DEN)

Los Angeles (LAX)-Houston (IAH) Oakland (OAK)-Seattle (SEA)

Los Angeles (LAX)-Miami (MIA) Ontario (ONT)-Denver (DEN)

Los Angeles (LAX)-Chicago O’Hare (ORD) Orlando (MCO)-Washington (DCA)

Los Angeles (LAX)-Philadelphia (PHL) Orlando (MCO)-Dallas (DFW)

Los Angeles (LAX)-Reno (RNO) Orlando (MCO)-New York (LGA)

Los Angeles (LAX)-Tampa (TPA) Palm Beach (PBI)-Boston (BOS)

Miami (MIA)-New York (LGA) Philadelphia (PHL)-Chicago O’Hare (ORD)

Miami (MIA)-Boston (BOS) Philadelphia (PHL)- Palm Beach (PBI)

Miami (MIA)-Newark (EWR) Phoenix (PHX)-Minneapolis (MSP)

Milwaukee (MKE)-Minneapolis (MSP) Phoenix (PHX)-Ontario (ONT)

Minneapolis (MSP)-Denver (DEN) Pittsburgh (PIT)-New York (LGA)

Minneapolis (MSP)- Dallas (DFW) Pittsburgh (PIT)-Chicago O’Hare (ORD)

Minneapolis (MSP)-Detroit (DTW) Portland (PDX)-Las Vegas (LAX)

Minneapolis (MSP)-Los Angeles (LAX) Portland (PDX)-Los Angeles (LAX)

Minneapolis (MSP)-New York (LGA) Portland (PDX)-Oakland (OAK)

Minneapolis (MSP)-Chicago (MDW) St. Louis (STL)-Los Angeles (LAX)

Newark (EWR)-Minneapolis (MSP) Sacramento (SMF)-Los Angeles (LAX)

Newark (EWR)-Chicago O’Hare (ORD) Salt Lake City (SLC)-Denver (DEN)

Newark (EWR)-Atlanta (ATL) San Francisco (SFO)-Boston (BOS)

Newark (EWR)-Boston (BOS) San Francisco (SFO)-Dallas (DFW)

Newark (EWR)-Los Angeles (LAX) San Jose (SJC)-Denver (DEN)

New Orleans (MSY)-New York (LGA) Tampa (TPA)-New York (LGA)

New York (JFK)-Los Angeles (LAX) Washington (DCA)-Atlanta (ATL)

33






Routes Routes

New York (LGA)-Boston (BOS) Washington (DCA)-Dallas (DFW)

New York (LGA)-Cleveland (CLE) Washington (DCA)-LaGuardia (LGA)

New York (LGA)-Charlotte (CLT) Washington (DCA)-Chicago O’Hare (ORD)

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