WORKING 

PAPERS 

Branding, Cannibalization, and Spatial Preemption: 

An Application to the Hotel Industry

Nathan E. Wilson 

WORKING PAPER NO. 309 

November 2011 

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acknowledgment by a writer that he has access to such unpublished materials) should be cleared with the 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
Branding, Cannibalization, and Spatial Preemption: An

Application to the Hotel Industry

Nathan E. Wilson∗

November 1, 2011

Abstract

In many settings where spatial preemption might be expected to produce tightly concentrated industry structures, firms share the market instead. Using a strategic investment

model, I show that this can be rationalized by heterogeneous brand preferences, which

cause new product introductions by incumbent firms to disproportionately cannibalize

sales from existing affiliated products. I then present an empirical example using data

on the branded segment of the lodging industry, which has many characteristics associated with spatial preemption, but is also characterized by strong brand-preferences.

Consistent with the theoretical model, I find large within-firm revenue cannibalization

effects from new hotel openings. These effects are attenuated – but not removed – by

brand-proliferation strategies. Moreover, I find evidence that the industry practice of

franchising through non-exclusive contracts softens inter-firm competition. Analyses of

growing hotel markets support the conclusion that intra-firm cannibalization inhibits

spatial preemption. Growth is far more likely to occur as a result of entry than expansion.

∗Federal Trade Commission, Bureau of Economics, 600 Pennsylvania Ave., Washington, DC, 20580. nwilson@ftc.gov. The opinions expressed here are those of the author and not necessarily those of the Federal Trade

Commission or any of its Commissioners. Versions of the paper – the third chapter of my dissertation at the University of Michigan – previously circulated with the title “Brand Effects and Entry Deterrence: An Examination of

Spatial Preemption.” I am particularly grateful for comments from my committee – Francine Lafontaine, Thomas

Lyon, Brian McCall, and Uday Rajan. In addition, I thank Anne Fleischer, Renata Kosova, Emek Basker, Dan

Hosken as well as other colleagues at the Federal Trade Commission, and audiences at the Econometric Society

World Congress in Shanghai, the American Economic Association 2011 Annual Meeting, and various departmental

seminars for their helpful comments. The usual caveat applies.

1
1 Introduction

Increasingly, many industries feature large firms targeting a national market with their advertising

and marketing programs. However, product competition in such industries often still appears to

occur at a local level, with firms’ relative positions differing significantly across geographic areas.

Such situations pose important questions to researchers and practitioners interested in competition.

For example, can assessments of local product market competition effectively ignore the national

character of firms? Should researchers focus only on nation-wide competition? Do the answers to

these questions vary across industries, and if so, under what conditions? To date, the industrial

organization literature does not have good or comprehensive answers to these questions.

The hotel industry provides an interesting opportunity to investigate these issues empirically. On

the one hand, the industry is dominated by a moderately small number of large firms, which cultivate

national reputations. On the other, local hotel markets bear a close resemblance to the setting

considered in Eaton and Lipsey (1979), who showed that incumbents could preserve market power

by “spatially preempting” through the early introduction of new products. In isolation, this would

suggest that local markets should be quite concentrated as a result of entry-deterrence by firstmovers. However, casual examination of local hotel markets suggests they are rarely concentrated.

In this paper, I argue that heterogeneity in consumers’ impressions of the national firms – such

as might be created by the endogenous sunk cost (Shaked and Sutton, 1987, Sutton, 2007) of nationwide marketing campaigns or loyalty programs – can explain the comparatively un-concentrated

nature of local hotel markets. I begin by presenting a game-theoretic model of strategic product

entry, which I have tailored to fit the stylized facts of the hotel industry. Prominent amongst these

facts are consumers’ brand preferences, which industry insiders emphasize for their economic significance. The model shows that a national firm becomes less able to credibly engage in spatial

preemption in a local market as consumers’ preferences about firms’ products grow more heterogeneous. This occurs because more varied preferences make it profit-maximizing for firms to concentrate on those consumers who especially like their products. However, such a focus exacerbates

2
within-firm cannibalization, limiting the appeal of spatial preemption.

The model also extends easily to accommodate several other important characteristics of the

hotel industry. First, most of the major hotel firms own multiple brands, even within a given

quality tier (e.g. Marriott owns both the Fairfield Inn and Courtyard brands in the mid-market

tier).1 The model shows that such brand-proliferation strategies soften cannibalization and intrafirm competition. As a result, they make spatial preemption more credible on the margin.

Second, like many other retail industries (e.g. fast food, gasoline sales), many hotel firms use

franchise arrangements that empower local managers in order to efficiently elicit hard-to-monitor

effort. Unlike most other such industries, however, the major hotel firms do not make their franchisees sign exclusive contracts. This allows an individual property owner to have, for example,

both a Marriott- and Hilton-affiliated hotel in the same market. The model shows that these nonexclusive arrangements soften inter-firm competition as they facilitate more concentrated – in terms

of local ownership – markets. Having more local hotels controlled by the same price-setting decisionmaker raises prices, benefiting franchisors insofar as franchise contracts typically allocate them a

share of their affiliated franchisees’ revenues (Blair and Lafontaine, 2005). However, it means that

firms are less likely to retain monopoly power in local markets via spatial preemption.

Using rich data on Texas lodging markets, I test the model’s predictions. Overall, the results

strongly support the model. First, examining the revenues of individual hotels, I find that new hotels

cannibalize a significant amount of their revenue from existing hotels affiliated with the same firm.

However, this effect is softened if the two hotels do not share a national brand. In other words,

the revenues of a Courtyard by Marriott are more negatively impacted by the presence of another

Courtyard than the presence of hotels affiliated with other Marriott brands (e.g. Fairfield Inn).

Hotels affiliated with other national firms also have negative impacts, but ones that are smaller in

magnitude. Second, I find evidence that the industry practice of franchising through non-exclusive

1For the rest of the paper, I use “firm” to refer to a central firm (e.g. Marriott), “brand” to refer to a product line

large enough to generate its own impression on consumers (e.g. Courtyard), and “hotel” to refer to a specific outlet

in a given local market. To be consistent, I discuss consumers’ preferences in terms of “firm preferences” that impact

all products affiliated with a single company and “brand preferences” that impact just a single brand.

3
contracts effectively softens inter-firm competition. These effects are not estimated with a high

degree of precision but are consistent in relative magnitudes with the predictions of the theoretical

model. Third, examining how existing markets grow, I find evidence that the lodging companies in

the branded segment do not engage in entry deterrence. Instead, my baseline estimates show that

conditional on growth taking place, a new hotel is almost 50 percent more likely to be associated

with a firm that did not previously operate a hotel in that market than an incumbent firm with

one hotel. Thus, in equilibrium, local market growth occurs on the extensive margin through entry

rather than on the intensive margin through incumbents’ expansion. Unfortunately, the data are

not rich enough to identify what impact – if any – franchising or brand proliferation plays in the

evolution of market structure.

The paper contributes to an emerging literature considering how brand preferences influence

local market structures in endogenous sunk cost industries. Like Bronnenberg et al. (2009), who

consider the long-run implications of being a first mover in local branded consumer goods markets,

I investigate how consumer opinion affects market structure. Whereas Bronnenberg et al. (2009)

find that first movers retain their dominance for long periods in local markets, my results show that

incumbency provides no long-run advantage to branded hotel firms. I believe our different findings

reflect the fact that whereas consumers in Bronnenberg et al. (2009) are highly local, the hotels

in my study cater to a customer base drawn from a wide area. Thus, the initial experience effects

documented in Bronnenberg et al. (2010) do not systematically affect the firms in my sample.

The paper also provides insight into why the empirical evidence for spatial preemption has

been equivocal. The model rationalizes why in industries where brand affiliations play a large

role in determining consumer interest, such as fast-food (Thomadsen, 2005) or personal computers

(Hui, 2004), cannibalization appears particularly significant, making spatial preemption less likely.2

By contrast, when it is likely that branding is opaque or consumers are driven more by product

2Toivanen and Waterson (2005) present evidence of network economies in fast food, which may lead to more aggressive

expansion strategies. Stavins (1995) finds evidence consistent with spatial preemption by incumbent computer makers;

however, the firms in her study separate their products widely, forcing entrants into tight clusters. This is consistent

with concerns about intra-firm cannibalization.

4
characteristics – as for movie theaters (Davis, 2006), radio markets (Berry and Waldfogel, 2001), or

consumer product marketing (Smiley, 1988) – revenue stealing and/or preemption strategies have

been documented.

Finally, the paper contributes to a small but growing literature examining the relationship

between vertical contracting and local market structure. Using data on retail gasoline markets,

Wilson (2011) shows how the use of vertically separated contracts (such as franchising) can lead

to moral hazard problems both in terms of pricing and the maintenance of reputational assets.

Similarly, Thomadsen (2005) shows that fast food outlets affiliated with the same franchisee engage

in softer price competition than those affiliated with different franchisees. Kalnins (2004) also

relies on data on the Texas lodging industry to assess differences in the extent of cannibalization

between brands that utilize franchising and those that rely on company-owned local outlets. He

finds that cannibalization is larger among franchisor brands. Thus, the prior literature also implies

that separating ownership of affiliated outlets in local markets can lead to moral hazard problems

for franchisors. However, the present paper is the first, to my knowledge, to consider how inter-firm

franchising can relax overall competition.

The paper proceeds as follows. Section 2 discusses the characteristics of the hotel industry.

Section 3 presents the theoretical model. Section 4 discusses the data used in the empirical analysis.

Section 5 analyzes the impact of market structure on individual hotel revenues, while Section 6

examines whether or not incumbent hotel firms engage in spatial preemption. Section 7 concludes.

2 The Lodging Industry

In many ways, the characteristics of the lodging industry are consistent with Sutton’s theory of

endogenous sunk costs (Shaked and Sutton, 1987, Sutton, 2007). The intuition is quickly sketched:

Most individual hotel traffic is non-repeat. This makes it difficult for any given hotel to credibly

signal quality, because customers enticed by false promises of a high quality experience cannot

“punish” the offending hotel by withholding future business. However, a firm affiliated with multiple

5
hotels can escape this trap. Since consumers are likely to travel somewhere again, they can credibly

threaten a national firm by withholding future business. Thus, the ability to be collectively punished

allows multi-hotel firms to credibly commit to higher levels of quality. If there are scale economies to

advertising and varying consumer tastes for quality, firms that choose to incur the endogenous sunk

cost of alerting consumers to their network of properties will constitute a concentrated high quality

segment and can earn higher price-cost margins. Meanwhile, a fragmented low-quality, independent

segment will cater to more price sensitive consumers.3

Consistent with this framework, the lodging industry is highly vertically differentiated by the

quality and amenities available to guests. Indeed, research indicates that the degree of differentiation across vertical segments is so large that competition across the different quality segments is

extremely weak (Freedman and Kosova, forthcoming); there may even be cross-segment agglomeration economies (Kalnins and Chung, 2004). Moreover, despite the fact that hotel construction

costs are modest, which might imply an atomistic industry structure, the industry is moderately

concentrated. For example, data compiled by Hotel and Motel Management (2004) show that 50%

of all hotels in the country are affiliated with 10 large firms. However, this understates the level

of concentration, for – consistent with Sutton’s theory – the bulk of the corporate hotels are of

relatively high quality (i.e. 2 or more stars according to AAA) while the bulk of independent hotels

tend to be low-quality. Finally, the large firms’ marketing efforts are large and sophisticated, featuring prominently in executives’ discussion of strategy. For example, Accor’s 2008 Annual Report

(p. 16) discusses how brand development and revitalization are a large part of its strategy in the

hotel sector.4

Importantly, the details of the lodging industry also depart from canonical endogenous sunk

cost models in important respects. First, individual local hotel markets have many of the character3With the rise of sites like TripAdvisor and Yelp, where consumers can leave public evaluations of hotels’ quality, it

is possible that the character of the lodging industry will change. One might imagine that branding would become

less important as a signal of quality. However, this possibility relies on the credibility of online reviews, and recent

reporting suggests there is ample evidence of gaming (Streitfeld, 2011).

4Further evidence of the size and sophistication of hotel firms’ marketing efforts can be found in the fact that many

hotel brands appear in Brandweek’s annual “Superbrands” issues listing the world’s most valuable brands.

6
istics of those considered in the canonical work of Eaton and Lipsey (1979) on spatial preemption.

They are often small and geographically distinct. This suggests that there is scope for dynamic interaction between incumbents and potential entrants, which the past literature has found evidence

of (Mazzeo, 2002, Conlin and Kadiyali, 2006). Moreover, it suggests that local markets should be

quite concentrated.

Second, and also seeming to point towards highly concentrated local markets, the national

lodging firms often own multiple brands within, as well as across, quality segments. For example,

Marriott Hotels controls the Fairfield Inn and Courtyard brands, both of which are in the midlevel segment. Nor is it the case that the existence of multiple brands within a given segment is

accidental, perhaps as a result of firms’ acquisition strategies. For example, Marriott created both

its Fairfield and Courtyard brands itself. Canonical oligopoly models of horizontal differentiation

suggest that firms can strategically differentiate their products in order to deter competitors and

ensure softer price competition (Schmalensee, 1978). Such brand proliferation capabilities might be

thought to further heighten firms’ abilities to spatially preempt in local hotel markets. However,

anecdotal evidence suggests that it is very rare for one firm to dominate a given local market.

Third, while little may objectively differentiate hotel brands within a segment, it is conventional

wisdom in the industry that individual consumers perceive different brands quite differently.5 Moreover, consumers’ heterogeneous, horizontally-differentiated preferences are believed to be of large

economic significance. An industry professional explained to me that while consumers may not

exactly know why they prefer certain firms’ hotels, their choices indicate they will pay a significant

premium to stay in a hotel affiliated with their preferred firm. Such behavior is consistent with

what researchers have found in other apparently homogeneous but brand-differentiated industries

(Allenby and Ginter, 1995, Dekimpe et al., 1997, Bronnenberg et al., 2010). Moreover, focusing on

loyal, high-value customers has become accepted strategy in the industry (Shoemaker and Lewis,

5Within a given quality segment, the extent of “objective” horizontal differentiation is limited. For example, Bowen

and Shoemaker (1998) state that in a survey of high-end hotel managers, many could not identify the brand of

pictured hotel rooms – even when the picture was of their own hotels.

7
1999, Bechard, 2011). The existence of brand-differentiated preferences also helps to explain the

decision to own multiple brands that compete within the same market segment. In order to exploit

consumer preferences over the long run, hotel firms are also focused on fostering and maintaining

them. For this reason, they pursue sophisticated marketing campaigns designed not only to separate their hotels from the unbranded competitive fringe (as emphasized by Sutton) but also to

influence consumers’ intra-segment preferences. For example, the industry has adopted marketing

innovations like loyalty programs and firm specific internet booking services for all of their affiliated

hotels (Shoemaker and Lewis, 1999). It is worth noting that such programs will amplify or create

preferences that span all of a firm’s brand offerings.

Fourth, similar to other retail industries where distance from headquarters makes the monitoring of local effort difficult, many large hotel firms use franchising contracts (Kalnins, 2004). The

contracts typically give residual rights to the revenues of a hotel affiliated with one of their national

brands to the local manager, who will have full control – including price-setting authority – subject

to their adherence to rules designed to preserve the brand’s reputation. In exchange, the franchisor

receives a set percentage of the hotel’s revenues and, perhaps, a fixed fee. By giving local managers

high-powered incentives, franchising can lead to higher franchisor profits, because the franchisee

has the incentive and ability to elicit greater effort.6 However, a franchisor’s use of multiple franchising contracts in the same market can lead to problems if the same franchisee is not used. This

is because neither of the local managers has an incentive to internalize the effect of their pricing

on their brand-affiliate’s profits.7Indeed, their private incentive is to cannibalize as much of their

affiliate’s sales as possible.

Because of this incentive conflict, there is widespread concern among hotel franchisees about

cannibalization from affiliated entry (Kalnins, 2004). Some franchise contracts contain exclusive

territory provisions to mitigate such fears. It is important to note, however, that there are strong

6See Blair and Lafontaine (2005) for more details on the economics of franchising.

7See Lafontaine and Slade (2007) for a survey of this literature or Wilson (2011) for more discussion and an example

of such behavior in gasoline stations.

8
reasons not to expect such exclusive territory clauses to bind. This is because franchisors can address

the encroachment problem directly either through the use of company-ownership of hotels or by

utilizing the same franchisee at all local outlets. As Krueger (1991) notes, while franchisees can

propose locations, the franchisor ultimately has veto power. Thus, ultimately, it is the hotel firms

who have the final say in whether or not multiple affiliated hotels are introduced in a single market.

Fifth, and finally, unlike many other industries where multi-outlet franchising is widespread

(Kalnins and Lafontaine, 2004), it is not the case in lodging that a franchisee is restricted to working

with just one firm.8 This may stem from the strong degree of intra-segment standardization, which

means that there are fewer trade secrets. Alternatively, it could indicate that hotel franchisees have

greater leverage because they own their hotels whereas fast-food franchises are typically owned by

the franchisor.9

3 A Strategic Model of Hotel Market Growth

3.1 Model Overview

In this section, I present a strategic growth model based on the stylized facts of the lodging industry,

which I use to understand how the various factors discussed above interact to influence local market

structure.

In the model, there are two rational hotel firms competing in the branded segment of a hotel

market: incumbent I and entrant E. I assume that a market where the incumbent has one hotel

has grown and can now support two hotels. Although the entrant does not yet have a hotel in

the market, it is a well-known company about which consumers already have (mixed) opinions. As

mentioned above, competition across segments has been shown to be extremely weak, so I abstract

8Gasoline retailing represents another such industry insofar as a jobber may have contracts with multiple refiners.

9For example, McDonald’s website states, “The site selection process is separate from our franchisee selection process.

We make the decision to develop a location because we believe it will be a success. McDonalds manages all the

site evaluation, acquires the property and constructs the building. After making the decision to develop a site,

McDonalds awards the franchise to the most qualified candidate.” See www.aboutmcdonalds.com/mcd/franchising/

us_franchising/franchising_faqs.html (accessed October 21, 2011).

9
from the presence of the low quality fringe segment.

A sequential game determines which firm introduces the new hotel. Figure 1 shows the timeline

of events. First, the incumbent decides whether to add a second hotel. Second, the entrant has the

option of opening a single hotel of its own. Third, and finally, the incumbent has the option of

closing its new hotel. The market structure is fixed once the incumbent has made its disinvestment

decision, and firms engage in Bertrand-Nash competition.

In order to assess preemption, I make certain simplifying assumptions about the costs involved

in changes to the market structure. In particular, I assume that the entrant’s fixed costs are too

high to offset in a three hotel market. Building in potentially random entry and/or exit costs would

be straightforward; however, I believe no additional insight would be gained from their inclusion.

Figure 1: Timing of events in the game

3.2 Consumer Demand and Firm Profits

To account for consumer heterogeneity, I assume that individual hotels’ payoffs stem from a random

utility model (RUM) of consumer demand, which has become the standard approach in empirical

demand modeling (Berry et al., 1995, Nevo, 2001). This approach implies that individual consumers

decide which (if any) of the existing hotels to stay at based on the utility it would provide.

10
I specify that the utility that consumer i gains from choosing a given hotel j affiliated with firm

B is:

Vi,j,B = δ − pj + μi,B + i,j . (1)

The utility from choosing the outside option o is:

Vi,o = μi,o + i,o. (2)

δ is the baseline benefit to all consumers of staying in a hotel and is common across hotels. pj is

the price charged at hotel j. For the sake of simplicity, I assume that consumers are homogeneous

in how they perceive the value of lodging and its price. Therefore, there are no individual specific

subscripts on δ or pj .

The μi,B capture consumer i’s heterogeneous taste for firm B, and are independent draws from

identical Normal distributions with mean zero and standard deviation σ. (While I assign a utility

shifter to the outside option, this could be normalized to 0 with no qualitative effect on the results.)

The draws will be independent across consumers and firms, so hotels associated with the same firm

share the same μ for any given person. The i,j are independent draws from a type 1 extreme value

distribution with variance normalized to 1.

Following the standard results for the mixed logit (Train, 2003), hotels’ market shares are

determined by integrating out the μ. Thus, the market share of each hotel j is:

Sj =

Z

P

exp Vi,j

j

exp Vi,j

f(μ)d(μ)

= Sj (pj , p−j ),

where f(μ) is the joint density function of the firm-specific utility shifters. This formulation shows

that hotel j’s share of the market as a function of its own price (pj ) and the prices of all other

11
hotels in the market (p−j ).

As noted in Kalnins (2006), marginal costs are very low in the hotel industry relative to fixed

costs. Therefore, I assume that marginal costs are 0, but being in the market requires payment of

a fixed cost f in each period. I abstract without loss of generality from entry or exit costs. Thus,

normalizing the size of the market to 1, the per period profit of hotel j affiliated with firm B can

be written as:

Πj = pj ∗ Sj (pj , p−j ) − f.

Firms simultaneously set prices to maximize overall profits. As is common in the applied literature

using these models of demand (Berry et al., 1995, Nevo, 2001), I assume the existence of a unique,

symmetric pure-strategy equilibrium in positive prices conditional on market structure. Because

the products are horizontally differentiated, I assume that an incumbent with two hotels charges

the same price in both.

3.3 Equilibria and Spatial Preemption

Firms play sub-game perfect investment strategies. Thus, the incumbent will not open a second

hotel if it knows that it would close it in the event that entry occurs. Similarly, because of the

aforementioned assumption about the magnitude of their fixed costs, the entrant will not challenge

the incumbent if it opens two hotels and would not remove the second in the event of entry. As

shown in Judd (1985), entry deterrence will not be sub-game perfect when it is more profitable for

the incumbent to remove one of its two hotels from the market once it sees that the entrant was

not deterred. This occurs when:

R

I

(2, 1) − 2f < RI(1, 1) − f

R

I

(2, 1) − f < RI(1, 1), (3)

12
where RB(·) represents the revenues earned by firm B conditional on the market’s structure. The

first term inside the parentheses in RB(·) indicates the number of hotels affiliated with the incumbent, and the second indicates the number affiliated with the entrant.

Equation (3) shows that entry deterrence will not occur when the profits of sharing the market

equally plus the scrap value are greater than the profits from operating two hotels in a three hotel

market net of the fixed cost of operating one hotel. The magnitude of f thus affects the viability

of entry-deterrence in a straightforward way. As the fixed cost of operating a hotel increases, the

incumbent is less able to credibly deter the entrant.

Due to the model of consumer demand employed here, σ also affects the viability of entry

deterrence. Unfortunately, the use of the RUM framework means there are no analytic solutions for

elements of interest (e.g. optimal prices, profits) with respect to σ

2

. Nevertheless, the consequences

of the demand system (explored through numerical simulations) are intuitive. Rewriting Equation

(3) as RI(2, 1) − RI(1, 1) < f shows that as the difference between the incumbent’s total revenues

when it has 2 hotels versus 1 hotel while competing with a hotel affiliated with the entrant falls,

preemption becomes less credible. In other words, the more the incumbent’s second hotel simply

cannibalizes sales from the first, the less credible spatial preemption becomes.

My numerical results demonstrate that as σ increases, the cannibalization effect increases in

magnitude. This occurs because as σ increases there are more consumers with very strong feelings

about each firm’s products. The presence of such partisan consumers allows firms to earn higher

profits by increasing prices and catering more to the segment of the population that really likes

them, even if this drives away consumers who were on the margin. However, as the firms focus more

on the segments of the population that have strongly favorable feelings about their firm, there is

significantly less inter-firm competition. This makes it less likely that spatial preemption will be

credible, because the incumbent gains few additional consumers when it adds a second hotel.

Figure 2 illustrates the relationship between the heterogeneity of firm preferences and the viability of spatial preemption. The X-axis shows the ratio of σ

2

to δ, representing the relative

13
Figure 2: The Variance of Consumer Preferences and the Credibility of Preemption: Base Case

importance of consumer heterogeneity, while the Y-axis is the ratio of the per period fixed cost

f to the per-hotel revenues received by the incumbent in a market with 2 incumbent hotels and

1 entrant.10 The changing frontier of Region I illustrates that as the relative importance of firm

preferences increases, the magnitude of the per period fixed costs (relative to per hotel variable

profits) needed to make preemption non-credible falls.

3.4 Extension: Brand Proliferation

The model presented above can straightforwardly extend to account for the common industry

practice of brand proliferation. If firms differentiate their hotels using different brands, Equation

(1) becomes:

Vi,j,B = δ − pj + μi,B + αi,j + i,j , (4)

where α indicates the effect on consumer i’s utility of brand j. I impose that the α are also

independent draws from identical Normal distributions with mean 0 and variance τ

2

.

10The model is parameterized with δ = 3.

14
Because the α differ for each consumer across products, firms can more easily expand their

product lines. This is because the relative impact of the firm preferences are diluted, reducing

cannibalization. Now, firms can extract surplus from a larger population, targeting a wider variety

of consumers with strong feelings about the firm and/or its products’ specific characteristics. As

noted elsewhere, this implies that in industries where firms are able to distinguish their products

from each other in the product space, larger product portfolios should be expected. Moreover, on

the margin, spatial preemption by the hotel-property-owner should be more likely since the extent

of intra-firm cannibalization has been reduced.11

The prediction that greater control over product location should be correlated with larger product portfolios appears upheld both anecdotally and in the empirical literature. For example, large

consumer products firms like Unilever offer a variety of closely related goods that are differentiated

at least as much by branding (e.g. the Dove and Axe brands of personal hygiene products) as by

product characteristics. Similarly, Berry and Waldfogel (2001) exploit a natural experiment to show

that the ability to position (and re-position) products in an industry where firm preferences are

small relative to preferences for product characteristics facilitates pre-emption.

3.5 Extension: Cross-firm Franchising

The model also extends straightforwardly to account for the possibility of non-exclusive franchising.

In this case, it is appropriate to think of the incentives of a local franchisee already working with

one brand in that market.

Recognizing that the market has grown, the franchisee assesses whether or not they should add

a second hotel to deter entry from hotels affiliated with other owners (be they firms themselves or

other franchisees). To add an additional hotel, the incumbent hotel-owning franchisee can partner

11These results connect straightforwardly to the earlier work of Gilbert and Matutes (1993), who consider competition

between two brand-differentiated competitors able to offer two quality-differentiated products. They model consumers

as being spread across a plane where one dimension reflects their taste for the two different firms and the second their

taste for quality. Though our models emphasize different elements and rely upon different formulations of demand,

both lead to the conclusion that stronger firm preferences relative to other product characteristics lead to smaller

product portfolios.

15
with the same firm again or open a hotel affiliated with a different company. If they partner with

the same firm, then the implications for cannibalization and spatial preemption are unchanged from

the baseline model results presented above.

However, if the franchisee partners with a new lodging firm, which will have a separate μ, then

the implications are quite different. The common manager has no incentive to reduce price at either

of the hotels it controls to try to poach sales from the other. They will set prices to extract as much

surplus as possible from the different consumer “types”, much as the monopolist studied in Mussa

and Rosen (1978) does for vertically-differentiated products. This has the opposite effect on the

franchisee’s propensity to engage in spatial preemption relative to the baseline model.

Figure 3: The Variance of Consumer Preferences and the Credibility of Preemption: Franchising

Case

I illustrate this relationship graphically in Figure 3 by varying σ

2

. All other parameters are set

at the same level as before. Intuitively, the Figure shows that as hotel owners are able to more

differentiate their products by affiliating with other firms, the viability of preemption increases.

This is consistent with the results found in Conlin and Kadiyali (2006), which suggested that within

individual markets hotel owners (as opposed to firms or brands) appear to engage in entry-deterring

16
capacity building.

It is worth emphasizing that if the franchisee rebuffs its original partner, that franchisor is

unlikely to wish to try to preempt by finding a new local franchisee to open a second hotel. (I

ignore the possible issue of exclusive territory clauses.) This is because of the resulting incentive

conflict between the two local franchisees. Each would be focused on their own hotel’s profits, and

would set price accordingly. In an effort to win consumers from each other, both of the highly

empowered local managers would cut prices significantly. Moreover, because of the importance of

branding, the lower prices at the franchisor’s affiliated hotels would not attract many new customers

that would not have gone with the original hotel at a higher price. Insofar as franchising contracts

give franchisors a cut of a local hotel’s revenues, an additional hotel might thus actually lead to

lower overall profits for franchisors.

Moreover, while the commonality of franchising and the lack of exclusive contracts means that

the hotel firms are unlikely to maintain monopoly power, they nevertheless benefit from the arrangement. This is because spatial preemption by local franchisees raises prices and profits within

the market. Insofar as franchisors receive a defined portion of the franchised hotel’s revenues, the

strategic behavior of multi-unit franchisees benefit their brand-owning franchisor partners as well

as themselves. In future work, I hope to formalize the factors affecting contract choice and the

potentially exclusionary usage of non-exclusive vertical relationships.

3.6 Motivating the Empirical Exercise

The simple framework presented above highlights the relationship between spatial preemption,

cannibalization, and heterogeneous consumer opinions about a finite number of known firms. It

shows that as consumers’ preferences about the different firms grow more varied, the magnitude

of intra-brand cannibalization increases. This, in turn, has clear implications for the feasibility of

spatial preemption by brand-owning firms.

One possible test of the model’s predictions would exploit cross-industry product portfolio data

17
in conjunction with information on the importance of branding in that industry to consumers.

Unfortunately, such data are difficult to find. Moreover, such a study would suffer from the need to

ensure that the nature of competition across the different industries was broadly similar. Because of

these problems, I pursue a narrower approach, exploiting data on one industry that seems to have

many of the hallmarks associated with spatial preemption, but which also has been characterized

as an industry where consumer tastes for firms display significant variance.

4 Data

Figure 4: Map of Market Locations in Texas

To test the implications of strong firm preferences on cannibalization and spatial preemption,

I use hotel revenues data from the Comptroller of Public Accounts (CPA) for Texas.12 These data

are based on each hotel’s sales tax receipts, and are collected at monthly and quarterly intervals

12The CPA data are available at: http://www.window.state.tx.us/taxinfo/taxfiles.html. Several other papers (Kalnins

and Chung, 2004, Kalnins, 2004, Conlin and Kadiyali, 2006, Suzuki, 2009) interested in studying differentiated

competition in the hotel industry have also used various periods of CPA data.

18
depending on the hotel’s characteristics. I aggregate the monthly submissions up to quarterly level.

Between 2000 and 2008, my sample of the CPA data identifies each hotel in the state by name, city

of location, and address. It also provides each hotel’s owner, capacity (in rooms), and revenues. In

order to focus on the hotel and motel market (as opposed to the boutique and bed and breakfast

segments), I exclude hotels with less than 30 rooms. To control for variation in corporate affiliation

and branding, I matched the hotel names to an author-constructed data set containing the names

of the national brands and their parent firms. To control for quality, I follow Kalnins and Chung

(2004) in assuming that hotels have the average quality rating of their brand, which I determine

using AAA Texas Tourbooks.

Like Mazzeo (2002), I use cities rather than zip-codes as the relevant market definition as even

cities of modest size often have more than one zip code. Moreover, focusing on cities follows the

convention adopted by the industry insofar as hotels advertise themselves based on their city of

location, and guidebooks organize their reviews around cities.

As in Bresnahan and Reiss (1987, 1991a,b), I focus on isolated markets to minimize concerns

about spillovers from nearby markets. I select isolated markets in the following manner. First, I

use Google Earth to determine the “centrum” of each city in the CPA data, where the centrum

is defined as the latitude-longitude point that the software converges to when the city is entered

into the search bar. Second, I calculate the distance between each of the centrums using the Great

Circle methodology. Third, I discard those cities whose centrums are less than 10 miles from that of

their nearest neighbor or less than 50 miles from the major economic hubs of San Antonio, Austin,

Dallas, El Paso, and Houston. Then, I drop the cities that never had a population of more than

1,000 people during the sample period according to U.S. Census data; I also drop the resort cities of

South Padre and Corpus Christi. These restrictions leave a total of 183 cities, none of which is large

enough to pose a concern that location within the city is likely to dramatically affect competition.

Figure 4 shows the locations in Texas of all markets in the sample.13

13The Census data can be downloaded at factfinder.census.gov. I exclude the coastal resort cities as they had vastly

more hotels than other markets of similar size. As in Bresnahan and Reiss (1987), I explored whether cities near the

19
The restriction of the sample to geographically isolated markets means that the very high end

of the quality spectrum is largely absent.14 Moreover, the data indicate both that the hotels in the

low quality tier are almost all independents with no national firm affiliation, and that almost all

independents are low-quality. In my empirical analysis, I focus on firms with substantial operations

in the mid- to high-level (i.e. at least two star) segment in Texas, and assume that the hotels

associated with smaller firms do not benefit from the same reputation effects as those affiliated

with the large firms. Instead, consumers view them as analogous to independents, though I do

allow for different competitive effects from high and low quality independents.15 Henceforth, I refer

to the mid- to high-quality segment as “branded.”

As seen in Table A-1 there is a natural break point in the number of high-quality branded

hotels that leaves six large firms: Choice Hotels, Continent Hotels, Hilton Hotels, La Quinta Inns

and Suites, Marriott International, and Wyndham, which together account for 9823 hotel-year

observations (over 91 percent of all branded hotels in the sample) in 94 different markets. Table

A-2 in Appendix A shows the brands affiliated with each firm in the sample.16

Consistent with the anecdote from Bowen and Shoemaker (1998) cited above, the hotels within

the branded segment are quite similar in terms of size. Overall, the coefficient of variation for the

number of rooms for all the branded hotels affiliated with the major firms is just 0.5. The degree

borders with Mexico or other states are outliers. I found that excluding these cities did not affect the results, so I

have left them in the sample.

14Quality averages are based off of ratings in the 2001, 2002, 2005, and 2006 AAA guides for the sample markets.

Individual hotels’ qualities range from 0 (in rare instances) to 5 (also rare) stars. Firms whose hotels do not appear

in the AAA data were set to 3 stars. Table A-1 in Appendix A shows the number of hotel-market-period observations

associated with each of the firms and their quality tiers, where the high quality tier is composed of all brands with

average ratings of between 2 and 3 stars.

15A further complication is the fact that several firms in the sample changed hands during the sample period. Baymont

Suites shifted from being a largely independent national firm to part of Wyndham’s brand portfolio in 2005; and

La Quinta was acquired by Blackstone in 2005, a private equity group. I assume that Baymont hotels were always

affiliated with Wyndham. I believe this assumption is innocuous as Baymont accounts for only 1% of all Wyndham

hotels. I also assume that Blackstone’s acquisition did not affect a change in La Quinta’s overall strategy. I also make

the assumption that the possibility of such transactions had no anticipatory effect on firm behavior.

16Careful observers will note that Best Western is not listed among the large brands in the data. This reflects the fact

that it is a membership association, imposing much less stringent rules on its members. Moreover, individual affiliated

hotels are allowed to keep their own name and/or incorporate Best Western into it. Such latitude is not afforded to

hotels affiliated with more traditional brands. Therefore, these hotels are different in kind from those affiliated with

the six major firms identified above.

20
of similarity is even higher within individual markets as the average within-market coefficient of

variation is 0.18.

Table 1 summarizes the level of concentration in terms of firm-affiliation for those marketperiods with at least two branded hotels. In some senses, it provides insight into the average

market structure. The Table examines the extent of concentration using Herfindahl-Hirschman

Indices (HHI) computed in three different ways: by firms’ shares of the total number of branded

hotels, by firms’ shares of the total number of rooms in branded hotels, and by firms’ shares of

branded revenues. It is useful to compare these figures to the reference value that assumes a perfectly

symmetric division among firms in all markets as well as to a score of 10000, which would indicate

that all hotels in all market-periods are associated with just one firm.

Overall, I believe that the Table is consistent with what my theoretical model predicts about

market structures in local hotel markets. While the HHI values always are greater than the reference

column, this necessarily occurs if market shares are ever other than perfectly symmetric; there is no

possibility of a score lower than the reference category that would serve to “balance” out the average.

Thus, I believe that the Table indicates that the equilibrium market configurations are inconsistent

with spatial preemption or other forms of entry deterrence by the large lodging companies in the

branded segment. Instead, they are in line with what my theoretical model predicts based on the

anecdotal evidence about the importance of consumer opinion. In addition, it is worth noting how

similar the different HHI measures are, supporting the inference that hotels within a market have

roughly the same size and perform similarly.

Table 1: HHI Summary Statistics by Market Size

# Branded Equal Share Obs HHI of Properties HHI of Revenues HHI of Rooms

2 5000 359 5836 6536 6014

3 3333 291 4166 4478 4282

4 2500 250 3735 4070 3922

5 2000 241 3291 3298 3430

6 1667 96 2581 2536 2641

Notes: The Table shows how concentration varies with the number of hotels affiliated with the large firms.

21
Further insight into the possible presence of spatial preemption can be gained through an

analysis of changes to market structure. After reducing the sample to just annual observations –

to account for construction delays – I consider whether growth in expanding markets came about

as a result of hotels affiliated with incumbent firms or not. Specifically, Table 2 looks at markets

whose total number of branded, high-quality hotels increased by one, and did not see any of the six

large hotel firms reduce their stock of hotels. The Table suggests that it is overwhelmingly likely

that market growth occurs on the intensive margin via entry by “new” firms, even when there are

many more incumbents capable of expanding than potential entrants.

While the descriptive results shown in Tables 1 and 2 support the model’s implications about

an industry where consumer heterogeneity in firm preferences is important, they do not account for

important factors that might vary across markets. Nor do they speak to the incumbents’ incentives

for expansion (or lack thereof). To obtain a more precise understanding of cannibalization and

spatial preemption, it is necessary to employ formal econometric frameworks, which I do in the

following sections.

Table 2: Origin of New Hotels in Growing Markets

Number of Incumbents Incumbent Entrant Total

1 3 12 15

20 80 100

2 4 13 17

23.53 76.47 100

3 5 9 14

35.71 64.29 100

4 5 7 12

41.67 58.33 100

5 3 4 7

42.86 57.14 100

Total 33 69 102

32.35 67.65 100

Notes: Entries in italics represent percentages of observations

within the row.

22
5 Cannibalization and Revenue Stealing

5.1 Econometric Approach and Identification

In this section, I assess the empirical relationship between branding and cannibalization, which

the theoretical model showed to be a critical element in understanding the feasibility of spatial

preemption.

My empirical approach in this section is fundamentally a static one, examining hotel revenues

as a function of contemporaneous market structure. This market structure, however, is the result of

firms’ entry decisions in the first stage of a dynamic game (Reiss and Wolak, 2007). Since Bresnahan

and Reiss (1987, 1991a,b), scholars have shown that information about the relationship between

market structure and outlet profits can be gained by considering the number of firms in the market,

assuming that entry continues up until the point that one additional outlet would lead to profits

that do not cover the cost of entry. When profit data are available, however, the parameters of

the second stage product market competition game can be directly estimated using a functional

representation of the local market structure as explanatory variables. Moreover, profit functions

can be estimated even in the absence of profit data by using revenue data so long as marginal

costs can reasonably be assumed to be 0. As noted above, this is a reasonable assumption in this

industry. Thus, I can exploit the CPA data’s information on hotel revenues.

As noted by Davis (2006), the function representing market structure can either be a reduced

form representation of an unspecified game or a derivation from a specific form of competition. As

in Davis’ (2006) study of movie theaters, it is difficult to assume a particular game structure in

the second stage for hotels. This is due to such issues as the commonality of temporal variation in

room pricing (Celen and Thomas, 2009), which makes the common assumption of a Bertrand-Nash

game impossible. Therefore, I estimate a reduced form model of hotel revenues as scholars can draw

causal inferences from both the reduced form and structural approaches.

I begin by taking an approach similar to that of Berry (1992) and assume that the variable

23
profits of an individual hotel h in market m at time t can be represented as:

vh,m,t = Xm,tβ − f(σ, N) + μh,m + h,m,t, (5)

where X are market-level characteristics affecting demand and supply, f(σ, N) is the function

mapping local market structure N to hotel revenues, μ is persistent unobserved hotel and/or market

level heterogeneity, and  is information unobserved to the econometrician.

I take a hybrid approach to identifying the impact of local market structure (i.e. f(σ, N)). Like

Davis (2006), I account for differentiation among market participants using linear parameterizations

of different types of market participants to allow for effects of different magnitudes. However,

whereas Davis (2006) addressed geospatial differentiation of homogeneous products, I exploit a focus

on small markets as in Mazzeo (2002) and Bresnahan and Reiss (1987) to abstract from geospatial

elements. Instead, I account for horizontal differentiation stemming from variation in corporate

ties, branding, and/or franchisee-affiliation. It is necessary to account for such heterogeneity insofar

as the theoretical model predicts that hotels closer together in the characteristic space or under

different management will have different impacts on each other’s revenues. In addition, I exploit

the longitudinal aspect of my data in order to deal with the possibility of significant unobserved

heterogeneity.

Thus, I model the variable profits of hotel h affiliated with category f in market m at time t

as:

vh,f,m,t = log(revenues)h,f,m,t = Xm,tβ − σf (Nf,m,t − 1) −

X

c6=f

σcNc,m,t + μf,h,m + h,f,m,t,(6)

where Nc represents the number of hotels affiliated with some category c. Given this specification, σf

captures the net effect of cannibalization and any market growing effect from a hotel in exactly the

same category as hotel h. Similarly, each of the other σc represents the net effect of business-stealing

24
and, again, any market-growing influence of hotels affiliated with the other possible categories.17

It is important to remember that the market structure variables represented by the Ns in

Equation (6) represent the outcome of choices made in the aforementioned first stage of the entry

game. As a result, there should be significant concern about the endogeneity of the market structure

variables. An obvious problem might be unobservable factors leading to large numbers of branded

hotels in specific markets. This would lead to upwardly biased estimates of the impact of the market

structure variables on revenues.

I can partially address this concern through the inclusion of observable information about the

different markets. I therefore supplement the CPA data with the aforementioned U.S. Census data

on the cities’ populations (in thousands) and data on average household income (in thousands)

taken from the Statistics of Income (SOI) collected by the Internal Revenue Service.18 In addition,

I include the number of high and low quality “independent” hotels as controls. These variables are

control rather than key independent variables because past research (Kalnins and Chung, 2004,

Freedman and Kosova, forthcoming) has shown that hotel segments are sufficiently differentiated

as to not compete.19

Of course, it is reasonable to fear that these additional controls will not solve the endogeneity

problem and that some markets will be disproportionately attractive for unobservable reasons. I

address these concerns in standard ways. First, I use market, firm, and/or hotel-level fixed effects

to address the possible presence of persistent, unobserved factors influencing the attractiveness of

different locations. Second, I estimate simple instrumental variables models using lagged values

of the different market structure variables as in Davis (2006). Previous periods’ market structure

should have no influence on current competition other than through its relationship with current

17This specification makes strong assumptions about the homogeneity of the hotels within each category. Given the

coefficient of variation evidence as well as industry anecdotes discussed above, I believe there is substantial evidence

for the objective similarity of hotels within quality segments, especially within individual hotel markets, making this

assumption reasonable.

18The SOI data are only available at the county level, so I use the value for the most common county for each

city. As the SOI data are only available through 2005, I linearly extrapolate the data for the remaining years. See:

http://www.irs.gov/taxstats/article/0,,id=120303,00.html.

19The results are qualitatively robust to simply aggregating all independent hotels.

25
market structure, and hence, in theory, make for appropriate instruments. However, one might

reasonably worry about serial correlation of errors. I believe that the market fixed effects included

in all models (implicitly in the hotel-fixed effects models) will control for this to a large extent;

nevertheless, I explore the implications of varying the lag order. Further details are provided below.

5.2 Results

Table 3 shows descriptive statistics for the variables used in the revenue analyses. Consistent with

the previously described choices made in defining the sample markets, the Table shows that the

majority of observations are in small cities, which have only a few branded hotels for travelers

to choose from. Moreover, the vast majority of hotels in any given market are affiliated with

different brands, which is not surprising given the market concentration results shown in Table 1.

Furthermore, in these small markets, the Table shows that it is rare that the same owner has more

than one hotel in a given market. This may reflect that franchisees are capital constrained, leaving

them unable to pursue profitable expansion. Alternatively, it may stem from the fact that the same

decision-maker may register legally distinct entities as the owner of the different properties, which

might be beneficial for tax or liability reasons.20

Table 4 shows the results of six different models of market structure impact revenues that exploit

between- and within-hotel variation to identify competitive effects. Models 1-3 pool observations

within markets, and address the possibility of firm or brand level differences using firm or brand

effects. In addition, time invariant market differences are addressed through the use of market-level

fixed effects. Column 1 focuses on the competitive effects of hotels affected with the same and

different firms. Column 2 considers the impact of brand-proliferation strategies, while Column 3

addresses the possible impact of franchising. Models 4-6 are analogous but identify effects based

solely upon within-hotel variation. In all cases, I include quarter-year effects and cluster the standard

20In addition, the Table suggests that in the few cases of an owner having two separate properties, it is more common for

owners to be affiliated with the same firm than multiple firms. However, these results are slightly misleading about the

role of inter-firm franchising insofar as several of the firms in the sample rely more heavily upon company-ownership

than others (this is particularly true of La Quinta, for example). This would increase the incidence of multiple hotels

in the same market affiliated with the same owner.

26
Table 3: Descriptive Statistics

Obs Mean Std. Dev. Min Max

Sales (’000) 9341 315.41 236.84 1.94 2547.46

Log (Sales (’000)) 9341 5.50 0.75 0.66 7.84

Same Firm 9341 1.07 1.47 0.00 8.00

- Same Brand 9341 0.10 0.35 0.00 2.00

- Diff. Brand, Same Firm 9341 0.97 1.31 0.00 8.00

Different Firms 9341 5.95 5.58 0.00 24.00

Premium Independent 9341 2.86 2.93 0.00 12.00

Budget Independent 9341 6.97 5.70 0.00 21.00

Own Same Firm 9341 0.04 0.21 0.00 1.00

Own Different Firm 9341 0.02 0.15 0.00 2.00

Own Independent 9341 0.02 0.15 0.00 2.00

Population (’000) 9341 68.59 70.42 1.68 223.18

Income (’000) 9341 44.24 8.99 22.05 93.61

errors. In the pooled models, I cluster at the market level, while in the fixed effects models I cluster

at the hotel level.

Overall, the different models’ results are strikingly consistent with each other and with the core

prediction of the theoretical model that in an industry where consumers value brands heterogeneously, there will be significant cannibalization following affiliated product entry. For example,

Columns 1 and 4 show that if a new hotel is opened that is affiliated with the same firm it reduces

revenues by 10-12 percent. By contrast, a hotel affiliated with a different national firm only reduces

revenues by 5 percent. (This difference is statistically significant at the 1 percent level.)21

Moreover, the models’ results are supportive of the other elements of the theoretical model. As

predicted, Columns 2 and 5 indicate that a given hotel firm can partly mitigate cannibalization

through the use of multiple different brands. However, in neither case is the difference between the

coefficient on the number of hotels sharing a brand as well as firm affiliation statistically significantly

different from the coefficient on the number of hotels sharing only a firm affiliation. The absence

of statistical significance is not very surprising given the small number of observations to identify

21These effects are broadly in line in magnitude with the competitive effects findings of Suzuki (2009), who uses a

different sample of the CPA data and a different econometric approach.

27
Table 4: Revenue Cannibalization and Market Structure

OLS OLS OLS FE FE FE

b/se b/se b/se b/se b/se b/se

Same Brand -0.132** -0.140**

0.06 0.06

Diff. Brand, Same Firm -0.120*** -0.093***

0.02 0.02

Same Firm -0.119*** -0.119*** -0.097*** -0.097***

0.01 0.01 0.02 0.02

Different Firms -0.048*** -0.049*** -0.048*** -0.049*** -0.049*** -0.049***

0.01 0.01 0.01 0.01 0.01 0.01

Own Same Firm 0.049 0.039

0.19 0.05

Own Different Firm 0.075 0.011

0.06 0.09

Premium Independent -0.034* -0.033** -0.033* -0.019 -0.018 -0.019

0.02 0.02 0.02 0.01 0.01 0.01

Budget Independent 0 -0.003 -0.001 -0.018* -0.017* -0.017*

0.01 0.01 0.01 0.01 0.01 0.01

Own Independent 0.146 -0.04

0.16 0.1

Population (’000) 0.003 0.002 0.004 0.005 0.005 0.004

0.01 0.01 0.01 0.01 0.01 0.01

Income (’000) 0.021*** 0.019** 0.021*** 0.022*** 0.022*** 0.022***

0.01 0.01 0.01 0.01 0.01 0.01

Firm Effects Yes No Yes No No No

Brand Effects No Yes No No No No

Market Effects Yes Yes Yes No No No

Hotel Effects No No No Yes Yes Yes

Quarter-Year Effects Yes Yes Yes Yes Yes Yes

Observations 9341 9341 9341 9341 9341 9341

* p<0.10, ** p<0.05, *** p<0.01 in two-sided tests. Standard errors clustered at market-level for OLS models

and hotel level for FE models.

28
these effects from. Nevertheless, the implication of only a minor effect from sub-brand proliferation

is interesting. It could reflect the increasing importance of firm loyalty programs relative to the

greater ease of expansion enabled by sub-brand proliferation. I hope to explore these issues further

in future research.

Turning to the possible impact of non-exclusive franchising, Columns 3 and 6 show that if an

incumbent is the owner of the of the new outlet, it substantially offsets its cannibalization effect.

This is particularly true if the hotel is affiliated with a branded competitor (or independent hotel),

which is in line with the prediction of the model. However, none of these effects are precisely

identified. Again, this is not terribly surprising given the low frequency of such events in the data.

The impacts of the control variables are broadly in line with intuition, theory, and previous

empirical work. Supporting the idea that they are further away in the product space than other

branded hotels, I consistently find that independent hotels have smaller revenue-stealing effects

than branded hotels, and that low-quality independents have smaller effects than high quality

independents. Intuitively, both population and household income are positively correlated with

revenue, although only income’s effect is statistically significant.

Despite the consistency of the revenue results, concern may remain about the endogeneity of

the market structure variables. If, for example, the market, firm, and hotel-level controls do not

sufficiently take account of unobservable factors favoring market development by certain firms,

then the coefficients would be biased.22 To address such concerns, I re-estimated the models shown

in Table 4 via instrumental variables using one period lags of the market structure variables as

instruments. The F-stats for these models were much in excess of the threshold of 10 identified

by Staiger and Stock (1997). As noted above, one might worry about serially correlated errors.

Therefore, I experimented with higher lag-orders; the results were qualitatively unchanged using

lag orders of up to two years (i.e. 8 periods). Details are available upon request. Because of the

consistency of the results across the choice of lags, I present models using just one period lags in

22It is worth remembering, however, that the most obvious type of endogeneity would lead to upwardly-biased coefficients, making the baseline results particularly conservative estimates of the importance of cannibalization.

29
order to leverage as much data as possible. Table 5 shows the results of these IV models. Columns

1-6 represent the analogues to Columns 1-6 of Table 4.

Table 5: Revenue Cannibalization and Endogenous Market Structure

IV-OLS IV-OLS IV-OLS IV-FE IV-FE IV-FE

b/se b/se b/se b/se b/se b/se

Same Brand -0.076 -0.110*

0.05 0.06

Diff. Brand, Same Firm -0.115*** -0.108***

0.02 0.03

Same Firm -0.105*** -0.104*** -0.108*** -0.108***

0.01 0.01 0.03 0.03

Different Firms -0.041*** -0.045*** -0.042*** -0.054*** -0.054*** -0.054***

0.01 0.01 0.01 0.01 0.01 0.01

Own Same Firm 0.046 0.028

0.2 0.06

Own Different Firm 0.04 -0.021

0.07 0.11

Premium Independent -0.028* -0.026* -0.025 -0.011 -0.011 -0.005

0.02 0.02 0.02 0.01 0.01 0.01

Budget Independent 0.003 -0.002 0.004 -0.015* -0.015* -0.013

0.01 0.01 0.01 0.01 0.01 0.01

Own Independent 0.178 0.022

0.18 0.1

Population (’000) 0 -0.001 0.001 0.005 0.005 0.005

0.01 0.01 0.01 0.01 0.01 0.01

Income (’000) 0.021*** 0.019** 0.020** 0.020*** 0.020*** 0.020***

0.01 0.01 0.01 0 0 0

Firm Effects Yes No Yes No No No

Brand Effects No Yes No No No No

Market Effects Yes Yes Yes No No No

Hotel Effects No No No Yes Yes Yes

Quarter-Year Effects Yes Yes Yes Yes Yes Yes

Observations 8887 8887 8887 8872 8872 8872

* p<0.10, ** p<0.05, *** p<0.01 in two-sided tests. Standard errors clustered at market-level for OLS models

and hotel level for FE models.

The models controlling for the possibility of endogenous market structure more directly through

instrumental variables methods remain consistent with the baseline results. Only in the instance

of Column 3 is there a marked divergence. In that case, the IV model shows a smaller effect of

30
hotels affiliated with the same brand than simply hotels sharing a firm affiliation. However, this

anomalous result disappears when used in conjunction with fixed effects in Column 6. Ultimately,

the consistency of the results between the two tables both in terms of signs and magnitudes supports

the idea that there is little concern about endogeneity after including market and hotel-level controls

for time-invariant heterogeneity.

Overall, I interpret the revenue model results as offering strong support for the idea that strong

brand preferences exacerbate intra-firm cannibalization. Firms can reduce these effects via strategies

such as brand-proliferation and/or the use of franchisees able to affiliate with multiple franchisors.

All of these findings are consistent with the simple theoretical framework laid out above in Section

3. Moreover, they are highly robust. For example, I explored the impact of strengthening the

assumption of independent markets by dropping all markets whose centrums were less than 20

miles apart as well as at least 50 miles from a major economic centers. As shown in Table A-3

in the Appendix, the results are qualitatively similar to those presented in Table 4. In addition,

although not shown here, I explored the robustness of the results to non-linear reparameterizations

of the market structure variables. In particular, including dummies for the number of affiliated and

non-affiliated outlets led to highly similar results. Details are available upon request.

While the cannibalization results are in line with the predictions of the model, they do not

speak directly to spatial preemption. In order to gain greater insight into this issue, I now turn to

estimating econometric models relating market structure to the manner of market growth.

6 Changes to Market Structure

6.1 Econometric Approach and Identification

Eaton and Lipsey (1979) showed that when spatial preemption is credible the break-even point

in time for introducing a new product for an incumbent occurs prior to when it occurs for a

potential entrant. By contrast, the theoretical model presented above suggests that in markets

31
where consumers’ preferences for different firms are important, growth is expected to occur via

entry. I exploit these insights, and test as close an analogue to the theoretical model as I can,

focusing on the nature of changes to market structure in growing markets.

I identify growing markets in the following manner. First, I reduce the sample to yearly observations on the grounds that it takes a year for a new hotel to be constructed and suitably staffed.

Then, I look at how the numbers of hotels affiliated with each of the six major hotel firms in the

data changed from year to year in each market in the data. (Thus, I include those firms who might

not yet have had a presence in a given market.) In order to focus only on those markets experiencing

growth, I then exclude all observations from markets in which any of the firms reduced their net

stock of affiliated hotels. In addition to considering behavior in these “non-shrinking” markets, I

create a smaller sample where I condition on growth having actually occurred.

As one would expect given the markets’ small sizes and the significant fixed costs involved,

the number of times that a firm chose to increase its stock by more than one was very rare in the

sample. This can be seen explicitly in Table 6. In non-shrinking markets, firms increased their stock

of hotels by more than one less than 0.1% of the time. In actively growing markets, it occurred

just 1% of the time. Therefore, for simplicity’s sake, I topcoded the size of a firm’s change in stock

to 1; however, the results were virtually unchanged when this assumption was relaxed. Details are

available upon request.

I take a similar approach to identifying market structure’s effect on the likelihood of accounting

for market growth as I did to identifying the magnitude of cannibalization. Letting ∆Nf,m,t+1

represent the increase in category f’s stock of hotels in market m from time t, I estimate models

of the following form:

∆Nf,m,t+1 = β01(Nf,m,t = 0) − β1Nf,m,t −

X

c6=f

βcNc,m,t + X0

m,tλ + μf,m + f,m,t. (7)

As before, I assume that market structure can largely be captured by using linear associations of

32
Table 6: Magnitude of Changes to Firms’ Stocks of Hotels

Frequency

Change in Stock of Hotels Non-Shrinking Markets Growing Markets

0 8,238 606

98.14 79.53

1 150 150

1.79 19.69

2 6 6

0.07 0.79

Total 8,394 762

100 100

Notes: Entries in italics represent percentages of observations within the column.

like and unlike hotels. To account for some possible non-linearity between incumbents – meaning

categories already present in the local market – and entrants – those that are not, I supplement

these variables with an indicator variable showing that f has no hotels in the market and is thus

a potential entrant. As before, Xm,t is a vector representing number of independent hotels, local population size and household income, and year fixed effects, while μ captures time-invariant

unobservable factors related to the category and/or market. Finally, j,m,t is the time-varying unobservable information.

The analytical approach to giving an observation to each market participant allows me to control for decision-maker heterogeneity, a factor that tests showed to be important in the revenue

regressions and which also proved significant in exploratory models of market growth.23 Unfortunately, it also means that the analytical approach can only be applied to examining the behavior of

the national firms. This is because the unit of observation is the choice of each possible market participant, regardless of whether or not the participant is contemporaneously present in the market.

This is feasible when looking at the behavior of the hotel firms insofar as there are only six of them.

By contrast, there are far too many brands – to say nothing of property-owners – to implement

this approach at that level. Thus, my results for hotel firms’ behavior should be considered reduced

23Details are available upon request.

33
form estimates of the net impact of all factors, including firms’ capacity for brand proliferation and

sharing of franchisees, on hotel firms’ expansion strategies.

The coefficients to focus upon in assessing whether spatial preemption is taking place are β0 and

β1. A positive coefficient on β0 would indicate that an entrant would be more likely to account for a

new hotel in a market than an incumbent. This would be inconsistent with spatial preemption, but

consistent with my model of market growth when consumers have strong preferences. Similarly, a

negatively signed β1 would indicate that the larger a firm’s contemporaneous stock of hotels, the

less likely it would be to account for any market expansion.

As in the analyses of cannibalization, I primarily address concerns about endogeneity through

the inclusion of market and firm or market-firm level fixed effects.24 However, as before, I also

explore the results’ robustness to instrumenting using lags of the potentially endogenous market

structure variables. I estimate Equation (7) linearly without weighting. As noted in Angrist and

Pischke (2009), weighting can lead to a more efficient estimator, but these benefits depend critically

upon an accurate model of the conditional variance. Therefore, I prefer simple linear estimation,

which will provide unbiased estimates designed to minimize the mean squared error. The marginal

effects recovered through linear estimation in this case are highly similar to those numerically

calculated after estimation via standard discrete choice methods. Details are available upon request.

6.2 Results

Table 7 shows descriptive statistics for the variables used in the analyses of firms’ behavior. Panel a)

shows results for the non-shrinking markets (i.e. no firm reduces its net stock of hotels, and growth

may or may not occur), while Panel b) presents results for markets where growth definitely took

place. Therefore, the mean value of a firm’s change in its stock is necessarily substantially larger

in the Panel b). While the samples are fairly similar, the Table shows that, on average, growing

24Insofar as I have 8 years of data for each market, I believe that the probability of incidental parameter bias due to

the inclusion of the market-firm fixed effects is small. This assumption is supported by Collard-Wexler (2009), who

finds evidence of only small bias in a pure panel with 12 observations per group.

34
markets were larger in both population and number of pre-existing hotels.

Table 7: Summary Statistics for Firm Behavior Models

a) Non-shrinking Markets

Variable Obs Mean Std. Dev. Min Max

Change in Stock 8394 0.02 0.14 0.00 1.00

1(Entrant) 8394 0.86 0.35 0.00 1.00

Stock of Hotels 8394 0.20 0.59 0.00 7.00

Other Branded 8394 1.01 2.25 0.00 21.00

Premium Independent 8394 0.60 1.10 0.00 10.00

Budget Independent 8394 2.10 2.79 0.00 21.00

Population (’000) 8394 12.64 27.28 0.97 217.51

Income (’000) 8394 40.30 8.95 18.57 89.08

b) Growing Markets

Variable Obs Mean Std. Dev. Min Max

Change in Stock 762 0.20 0.40 0.00 1.00

1(Entrant) 762 0.53 0.50 0.00 1.00

Stock of Hotels 762 0.82 1.16 0.00 7.00

Other Branded 762 4.11 4.54 0.00 21.00

Premium Independent 762 2.02 2.18 0.00 10.00

Budget Independent 762 5.64 5.57 0.00 21.00

Population (’000) 762 49.58 61.74 1.22 217.51

Income (’000) 762 44.05 9.02 24.85 71.70

Table 8 shows the results of models controlling for firm and market as well as firm-market

heterogeneity. The latter are fixed effect models insofar as they control for within-market decisionmaker specific heterogeneity. Columns 1 and 2 represent cross-sectional and fixed effect analyses of

non-shrinking market-periods where no incumbent firms reduced their stock of hotels. Columns 3

and 4 are analogous but condition on growth having taken place.

Overall, the results of my analyses support the theoretical model’s predictions about equilibrium strategic decision-making by firms in industries where consumers have well-developed, heterogeneous firm preferences. In all of the models, the coefficient on the entrant dummy is positive.

This implies that – on average – it is more likely that firms not currently present in the market

account for new hotels than any firm with an existing presence. Moreover, the coefficient on the

35
existing stock of hotels is negative, which means that the larger an incumbent’s stock of hotels,

the less likely they are to expand. Both results run counter to what would be observed if firms

engaged in spatial preemption. Furthermore, in most of the models, both effects are statistically

as well as economically significant. These results indicate that contrary to what initial intuition

might suggest about oligopolistic behavior in individual differentiated product markets, growth in

the branded hotel segment is more likely to occur on the extensive margin as new entrants build

new hotels than on the intensive margin via expansion by incumbent.

The economic significance of the key explanatory variables is larger in Columns 3 and 4. This

is intuitive as these regressions are conditional on growth having taken place. The fixed effects

models return coefficients of significantly larger magnitude, which is consistent with their better

accounting for unobserved market level factors favoring individual firms. Therefore, I believe those

results are preferable. They indicate that an entrant is 30 percent more likely to account for market

expansion in non-shrinking markets, and approximately 50 percent more likely to account for growth

in growing markets.

The results for Texas hotel firms provide an interesting contrast to those of Bronnenberg et

al. (2009), who document significant first-mover advantages in many consumer packaged goods

industries. Bronnenberg et al. (2010) present evidence suggesting that this effect stems in large part

from consumers’ initial experiences. My different results for Texas hotel markets may reflect that the

firms are mature with nation-wide presences. As a result, any given hotel’s potential customers are

likely randomly drawn from much of the country. Thus, while any individual consumers’ preferences

may be quite influenced by their initial hotel experiences, none of the major hotel firms in Texas

are likely to benefit systematically. I hope to explore these ideas further in future research.

The coefficients on the other explanatory variables in the market growth models are broadly

in line with intuition and past research. The coefficient on the stock of other branded firms is

usually more positive (and in one case actually positive) than the coefficient on the stock of a firms’

own hotels. This is consistent with the cannibalization results presented above. I find effects of

36
inconsistent sign and significance for the independent hotels variables. This could be due to the

high degree of collinearity among the explanatory market structure variables and the comparatively

small sample size. The findings for population are of small magnitude and inconsistently signed,

which is consistent with the fact that the sample markets were selected because they grew.

Table 8: Analyses of Market Structure and Expansion

OLS FE OLS FE

b/se b/se b/se b/se

1(Entrant) 0.035** 0.069 0.164*** 0.180*

0.02 0.06 0.06 0.11

Stock of Hotels -0.037* -0.238*** -0.047 -0.390***

0.02 0.05 0.04 0.06

Other Branded -0.043*** 0.001 -0.012 0.057*

0.01 0.01 0.03 0.03

Premium Independent 0.026** 0.026** 0.001 0.001

0.01 0.01 0.03 0.05

Budget Independent -0.006 -0.006 -0.021** -0.022

0 0.01 0.01 0.03

Population (’000) 0.013*** 0.012*** -0.006 -0.006

0 0 0.01 0.01

Income (’000) 0 0 0.006 0.006

0 0 0 0.02

Firm Effects Yes No Yes No

Market Effects Yes No Yes No

Firm-Market Effects No Yes No Yes

Year Effects Yes Yes Yes Yes

Observations 8394 8394 762 762

* p<0.10, ** p<0.05, *** p<0.01 in two-sided tests. Standard errors clustered at

market-level for OLS models and market-firm level for FE models.

Overall, I find that the results for market growth strongly support the theoretical model’s

predictions about the viability of spatial preemption in industries where consumers are highly

affected by branding. Consistent with the model’s predictions, I find that growth is much more

likely to take place on the extensive margin via entry by new (albeit nationally recognized firms)

rather than via expansion by incumbents. Moreover, like my findings for hotel revenues, these

results are highly robust. For example, Table A-4 in the Appendix shows the results of models

37
when I impose stricter limits on the proximity between markets. In addition, the Table also shows

the results of IV models of the larger non-shrinking markets sample. In both cases, the results are

quite similar to those of my baseline models.

My results also are qualitatively robust to reparameterizing the representation of market structure in more non-linear fashion. Furthermore, although not shown, the results are qualitatively

robust to different assumptions about the timing of market structure changes. In particular, I explored simply looking at one year changes in market structure for all quarters rather than simply

one quarter per year. Intuitively, this did not lead to substantially different results. Finally, controlling for whether or not an incumbent is a monopolist does not fundamentally change the results.

Details are available upon request.

7 Conclusion

In many settings where spatial preemption might be expected to produce tightly concentrated

industry structures, firms share the market instead. I explore these issues in the context of the hotel

industry, whose local markets resemble the stylized set-up of Eaton and Lipsey (1979), but also

are characterized by strong brand preferences. I develop a strategic investment model that suggests

that strongly varied consumer opinions about firms, such as might be created by the nationwide

marketing campaigns common in the hotel industry, inhibit spatial preemption by exacerbating

intra-firm cannibalization. Extensions to the model accommodate stylized facts in the hotel industry

such as brand proliferation and multi-firm franchising arrangements, which relax intra- and interfirm competition, respectively. I test the model’s predictions using rich longitudinal data on Texas

hotels. These data strongly support the model’s predictions insofar as I find very large intra-firm

revenue cannibalization effects, which can be mitigated via the use of different brands. Moreover,

the intensity of inter-firm competition appears to be relaxed when two hotel firms both use the

same local franchisee. Finally, I find that growth in the sample markets is much more likely to occur

on the extensive margin through entry rather than on the intensive margin through incumbents’

38
expansion.

Overall, the paper contributes to an emerging literature focusing on how factors relating to a

national firm’s identity may impact local competitive interaction. My results suggest that when

consumers are nationally drawn, being a local first-mover provides no long-term advantages. By

contrast, the results in Bronnenberg et al. (2009, 2010) indicate that when consumers are drawn

locally, advertising can help firms retain their dominant status. In future work, I hope to integrate

greater consideration of the determination and preservation of brand preferences into an analysis

of dynamic product market competition.

References

Allenby, Greg M. and James L. Ginter, “The effects of in-store displays and feature advertising

on consideration sets,” The International Journal of Research in Marketing, 1995, 12 (1), 67–80.

Angrist, J. D. and J. S. Pischke, Mostly harmless econometrics: an empiricist’s companion,

Princeton University Press, 2009.

Bechard, M., “Strategic Hotels Focuses on Creating Customer Loyalty,” REIT.com, June 16,

2011.

Berry, S., “Estimation of a Model of Entry in the Airline Industry,” Econometrica, 1992, 60,

889–917.

and J. Waldfogel, “Do Mergers Increase Product Variety? Evidence from Radio Broadcasting,”

Quarterly Journal of Economics, 2001, 116 (3), 1009–1025.

, J. Levinsohn, and A. Pakes, “Automobile prices in market equilibrium,” Econometrica,

1995, pp. 841–890.

Blair, R.D. and F. Lafontaine, The Economics of Franchising, Cambridge University Press,

2005.

Bowen, J.T. and S. Shoemaker, “Loyalty: A strategic commitment,” Cornell Hotel and Restaurant Administration Quarterly, 1998, 39 (1), 12.

Bresnahan, T. F. and P. C. Reiss, “Do Entry Conditions Vary across Markets,” Brookings

Papers on Economic Activity, 1987, (3), 833–881.

and , “Empirical-Models of Discrete Games,” Journal of Econometrics, 1991, 48 (1-2), 57–81.

and , “Entry and Competition in Concentrated Markets,” Journal of Political Economy, 1991,

99 (5), 977–1009.

39
Bronnenberg, Bart, Jean-Pierre Dube, and M. Gentzkow, “The Evolution of Brand Preferences: Evidence from Consumer Migration,” University of Chicago, mimeo, 2010.

, Sanjay K. Dhar, and Jean-Pierre Dube, “Brand History, Geography, and the Persistence

of Brand Shares,” Journal of Political Economy, 2009, 117 (1), 87 – 115.

Celen, B. and C. Thomas, “Organizational Form, Information Asymmetry, and Market Prices.,”

Columbia University, mimeo, 2009.

Collard-Wexler, A., “Mergers and Sunk Costs: An Application to the Ready-Mix Concrete Industry,” Working Paper, New York University, 2009.

Conlin, M. and V. Kadiyali, “Entry-deterring capacity in the Texas lodging industry,” Journal

of Economics & Management Strategy, 2006, 15 (1), 167–185.

Davis, Peter, “Measuring the Business Stealing, Cannibalization and Market Expansion Effects

of Entry in the U.S. Motion Picture Exhibition Market,” Journal of Industrial Economics, 2006,

54 (3), 293–321.

Dekimpe, M. G., J. E. M. Steenkamp, Martin Mellens, and Piet Vanden Abeele,

“Decline and Variability in Brand Loyalty,” International Journal of Research in Marketing,

1997, 14 (5), 405–420.

Eaton, B. C. and R. G. Lipsey, “The theory of market pre-emption: the persistence of excess

capacity and monopoly in growing spatial markets,” Economica, 1979, pp. 149–158.

Freedman, Matthew and R. Kosova, “Agglomeration, Product Heterogeneity, and Firm Entry,” Journal of Economic Geography, forthcoming.

Gilbert, R. J. and C. Matutes, “Product line rivalry with brand differentiation,” The Journal

of Industrial Economics, 1993, pp. 223–240.

Hotel and Motel Management, “Top Hotel Companies,” 2004, pp. 38–48.

Hui, Kai-Lung, “Product Variety Under Brand Influence: An Empirical Investigation of Personal

Computer Demand,” Management Science, 2004, 50 (5), 686–700.

Judd, K. L., “Credible spatial preemption,” The RAND Journal of Economics, 1985, pp. 153–166.

Kalnins, A., “An empirical analysis of territorial encroachment within franchised and companyowned branded chains,” Marketing Science, 2004, pp. 476–489.

, “Markets: The US Lodging Industry,” Journal of Economic Perspectives, 2006, 20 (4), 203–218.

and F. Lafontaine, “Multi-unit ownership in franchising: Evidence from the fast-food industry

in Texas,” RAND Journal of Economics, 2004, pp. 747–761.

and W. Chung, “Resource-seeking agglomeration: a study of market entry in the lodging

industry,” Strategic Management Journal, 2004, 25 (7), 689–699.

Krueger, A.B., “Ownership, Agency, and Wages: An Examination of Franchising in the Fast Food

Industry,” The Quarterly Journal of Economics, 1991, 106 (1), 75.

40
Lafontaine, F. and M. Slade, “Vertical Integration and Firm Boundaries: The Evidence,” Journal of Economic Literature, 2007, 45 (3), 629–685.

Mazzeo, M. J., “Product choice and oligopoly market structure,” The RAND Journal of Economics, 2002, 33 (2), 221–242.

Mussa, M. and S. Rosen, “Monopoly and product quality,” Journal of Economic Theory, 1978,

18 (2), 301–317.

Nevo, A., “Measuring market power in the ready-to-eat cereal industry,” Econometrica, 2001,

pp. 307–342.

Reiss, P. C. and F. Wolak, “Structural Econometric Modeling: Rationales and Examples from

Industrial Organization,” in “Handbook of Econometrics,” Vol. 6A, New York: Elsevier, 2007.

Schmalensee, Richard, “Entry Deterrence in the Ready-to-Eat Breakfast Cereal Industry,” The

Bell Journal of Economics, 1978, 9 (2), 305–327.

Shaked, A. and John Sutton, “Product Differentiation and Industrial Structure,” Journal of

Industrial Economics, 1987, 36, 131–146.

Shoemaker, Stowe and Robert C. Lewis, “Customer loyalty: the future of hospitality marketing,” Hospitality Management, 1999, 18 (4), 345–370.

Smiley, Robert, “Empirical Evidence on Strategic Entry Deterrence,” International Journal of

Industrial Organization, 1988, 6 (2), 167–180.

Staiger, D. and J.H. Stock, “Instrumental Variables Regression with Weak Instruments,” Econometrica, 1997, 65 (3), 557–586.

Stavins, J., “Model entry and exit in a differentiated-product industry: The personal computer

market,” The Review of Economics and Statistics, 1995, pp. 571–584.

Streitfeld, D., “In a Race to Out-Rave, 5-Star Web Reviews Go for $5,” New York Times, August

19, 2011.

Sutton, John, Sunk Costs and Market Structure: Price Competition, Advertising, and the Evolution of Concentration, Cambridge: The MIT Press, 2007.

Suzuki, Junichi, “Land Use Regulation as a Barrier to Entry: Evidence from the Texas Lodging

Industry,” University of Toronto, mimeo, 2009.

Thomadsen, Raphael, “The Effect of Ownership Structure on Prices in Geographically Differentiated Industries,” The RAND Journal of Economics, 2005, 36 (4), 908–929.

Toivanen, O. and M. Waterson, “Market Structure and Entry: Where’s the Beef?,” The RAND

Journal of Economics, 2005, 36 (3), 680 – 699.

Train, K., Discrete Choice Methods with Simulation, Cambridge: Cambridge University Press,

2003.

Wilson, N. E., “Local Market Structure and Strategic Organizational Form Choices: Evidence

from Gasoline Stations,” working paper, 2011.

41
Appendix A

Table A-1: Qualities of Nationally Branded and Unbranded Hotels

Quality Tier

Low High Total

Accor 931 106 1,037

AmericInn International 0 24 24

America’s Best Franchising 0 61 61

Budget Host 120 0 120

Candlewood Hotel Co. 0 3 3

Carlson Hotels Worldwide 0 104 104

Choice Hotels 0 2,340 2,340

Continent 0 1,685 1,685

Drury Hotels 0 9 9

Extended Stay Hotels 0 90 90

Hilton Hotels Corporation 0 827 827

Hyatt 0 125 125

La Quinta 0 828 828

Marriott International 0 638 638

Starwood 0 55 55

Vantage 0 343 343

Wyndham 0 3,505 3,505

Independent 13,730 3,488 17,218

Total 14,781 14,231 29,012

42
Table A-2: Brands Affiliated with each Firm

Firm Chain Observations

Clarion Inns & Suites 50

Comfort Inn 721

Comfort Suites 428

Choice Hotels Econo Lodge 591

Quality Inns & Suites 383

Rodeway Inn 83

Sleep Inn 84

Candlewood Suites 27

Crowne Plaza 2

Continent Hotels Holiday Inn 436

Holiday Inn Express 1,211

Staybridge Suites 9

Embassy Suites Hotels 39

Hampton Inn 671

Hilton Hotels Hilton 36

Hilton Garden Inn 5

Homewood Suites by Hilton 76

La Quinta La Quinta Inns 828

Courtyard 135

Fairfield Inn 320

Marriott International Residence Inn 123

Ritz-Carlton 15

Springhill Suites 11

Towneplace Suites 34

Baymont Inn & Suites 40

Days Inn Worldwide 1,488

Hawthorn Suites 7

Wyndham Howard Johnson International 243

Knights Inn 1

Microtel Inns & Suites 2

Ramada 594

Super 8 Motels 928

Travelodge Hotels 170

Wingate 32

43
Table A-3: Robustness Revenue Regressions: Markets at least 20 miles apart.

OLS FE IV-FE

b/se b/se b/se

Same Firm -0.076 -0.187*** -0.248***

0.06 0.06 0.06

Different Firms -0.140*** -0.099*** -0.126***

0.02 0.02 0.02

Premium Independent -0.073 -0.063 -0.051

0.05 0.04 0.04

Budget Independent 0.016 -0.009 -0.015

0.02 0.02 0.01

Population (’000) 0.008* 0.009 0.016**

0 0.01 0.01

Income (’000) 0.016 0.015 0.016*

0.01 0.01 0.01

Firm Effects Yes No No

Market Effects Yes No No

Hotel Effects No Yes Yes

Quarter-Year Effects Yes Yes Yes

Observations 3263 3263 3096

* p<0.10, ** p<0.05, *** p<0.01 in two-sided tests. Standard errors

clustered at market-level for OLS models and hotel level for FE models.

44
Table A-4: Robustness Entry Regressions

IV distance > 20 distance > 20

OLS FE OLS FE OLS FE

b/se b/se b/se b/se b/se b/se

1(Entrant) 0.038** 0.134 0.004 -0.061 0.116 0.017

0.02 0.12 0.04 0.08 0.12 0.19

Stock of Hotels -0.019 -0.260*** -0.062** -0.368*** -0.01 -0.458***

0.03 0.1 0.03 0.06 0.05 0.09

Other Branded -0.029 0.029 -0.051*** 0 0.02 0.084

0.02 0.02 0.01 0.01 0.03 0.05

Premium Independent 0.02 0.019 0.004 0.008 -0.090** -0.067

0.02 0.02 0.02 0.01 0.04 0.08

Budget Independent -0.007 -0.008 -0.011*** -0.011* -0.043** -0.058

0 0.01 0 0.01 0.02 0.04

Population (’000) 0.011 0.009 0.017*** 0.020*** -0.01 -0.004

0.01 0.01 0 0 0.01 0.02

Income (’000) 0.001 0.001 0 0 0.027 0.011

0 0 0 0 0.02 0.03

Firm Effects Yes No Yes No Yes No

Market Effects Yes No Yes No Yes No

Firm-Market Effects No Yes No Yes No Yes

Year Effects Yes Yes Yes Yes Yes Yes

Observations 7326 7326 7669 7669 529 529

* p<0.10, ** p<0.05, *** p<0.01 in two-sided tests. Standard errors clustered at market-level for OLS models

and hotel level for FE models.

45
Appendix B

In this section, I provide the results of numerical solutions to the theoretical model. In these simulations, I

assume that the μ are all drawn from normal distributions with mean 0 and standard deviation σ. I show

the relationship between heterogeneous brand preferences and cannibalization by varying the parameters

affecting the variance of consumers’ heterogeneous brand preferences (σ) and the baseline benefit to staying

in one of the hotels in the market (δ). Code for the simulations is written in Matlab 7.8 by the author and

is available upon request.

Table B-1 shows the prices, revenues, and market shares for one hotel under different market structures

for different parameter values. The first column of the Table indicates the behavior and payoffs for one hotel

in a 2-hotel market where both hotels are affiliated with a single brand. Column two indicates the results

for one hotel in a duopolistic market. Column 3 shows the results for one hotel affiliated with a 2-hotel

incumbent in 3-hotel markets. Column 4 indicates the results for the entrant in such markets.

As described in the text above, the results show that as the magnitude of σ grows relative to δ, the

returns to adding an additional hotel fall for brands. For example, when δ is fixed at 3, the tables show that

as σ increases from 0 to 4, the difference between RI(2, 1) and RI(1, 1) falls from 0.28 to 0.19.

Analogous results for franchising markets or those where firms can assign hotels to different brands are

available upon request.

46
Table B-1: Numerical Results of Theoretical Model for One Hotel Under Different Market Structures

2 Hotels 3 Hotels

Monopoly Competition Incumbent Entrant

Price 3.00 1.77 2.04 1.63

σ = 0 & δ = 3 Revenue 1.00 0.77 0.52 0.63

Mkt Share 0.33 0.44 0.26 0.39

Price 3.26 2.16 2.44 2.05

σ = 1 & δ = 3 Revenue 0.94 0.86 0.56 0.75

Mkt Share 0.29 0.40 0.23 0.37

Price 3.93 2.87 3.13 2.78

σ = 2 & δ = 3 Revenue 0.93 0.98 0.61 0.90

Mkt Share 0.24 0.34 0.19 0.33

Price 4.81 3.68 3.92 3.60

σ = 3 & δ = 3 Revenue 0.97 1.12 0.67 1.05

Mkt Share 0.20 0.30 0.17 0.29

Price 5.75 4.53 4.76 4.45

σ = 4 & δ = 3 Revenue 1.05 1.27 0.73 1.22

Mkt Share 0.18 0.28 0.15 0.27

Price 6.73 5.37 5.60 5.33

σ = 5 & δ = 3 Revenue 1.14 1.42 0.80 1.38

Mkt Share 0.17 0.26 0.14 0.26

Price 7.75 6.32 6.54 6.23

σ = 6 & δ = 3 Revenue 1.24 1.60 0.89 1.54

Mkt Share 0.16 0.25 0.14 0.25

Price 2.73 1.95 2.19 1.86

σ = 1 & δ = 2 Revenue 0.67 0.66 0.45 0.58

Mkt Share 0.25 0.34 0.21 0.31

47