DRAFT: 3 JUNE, 2010
INCOMPLETE DRAFT--COMMENTS SOLICITED—DO NOT CITE
MERGERS INCREASE OUTPUT:
THE CASE OF A REVENUE-MANAGEMENT INDUSTRY
ARTURS KALNINS#
CORNELL SCHOOL OF HOTEL ADMINISTRATION
LUKE M. FROEB*, STEVEN TSCHANTZ+
VANDERBILT UNIVERSITY
ABSTRACT
From a sample of 898 hotel mergers, we find that mergers increase occupancy without reducing
capacity. In some regressions, price also increases. These effects are small, but statistically and
economically significant. And they only occur only in markets with the highest capacity
utilization and highest uncertainty. These findings lead us to reject simple models of price or
quantity competition in favor of models of “revenue management,” where firms price to fill
available capacity in the face of uncertain demand.
JEL classification: D21; L41; L83.
Keywords: merger; hotel; antitrust; revenue management.

School of Hotel Administration, Cornell University, atk23@cornell.edu

William Oehmig Associate Professor of Management, Owen Graduate School of Management,
Vanderbilt University, luke.froeb@owen.vanderbilt.edu

Department of Mathematics, Vanderbilt University, tschantz@math.vanderbilt.edu
I.
Introduction
For mergers, the predictions of theory—anticompetitive mergers increase price and
decrease output, while pro-competitive mergers do the opposite—are so well accepted that they
are part of both law and policy.1 But the evidence on mergers remains thin, which has made
calls for more and better empirical work a frequent mantra, especially from those who work at
the enforcement agencies, e.g., Froeb et al. (2005), Carlton (2007), and Farrell et al. (2009).
Ideally, we would like enough data on mergers, and enough variation across observed merger
effects to help us determine which theoretical model should be used to predict whether a
particular merger is going to have an effect on competition (FTC, 2005, p.174).
But estimating the competitive effects of mergers presents two problems. The first is
censoring. Most mergers must obtain regulatory clearance from competition agencies, which
means that we observe only mergers that were thought not to be anticompetitive,2 or those
which were allowed to proceed after court or regulatory challenges failed.3 The second problem
is that mergers may have a variety of different effects, many of them unrelated to their effect on
competition. For example, an acquisition may induce higher levels of effort among employees at
an acquired firm as they try to impress the new boss. This could lead to an observed merger
effect that has nothing to do with competition.
To address these issues, we examine a large sample of mergers in the same industry—the
U.S. lodging industry—but in different local markets. Our sample of 898 local mergers
involving 2628 hotels nationwide is the largest in any study of merger effects that we are aware
1
See, for example, the US Dept. of Justice and Federal Trade Commission, Horizontal Merger Guidelines,
See PautlerXX (2003) and ZonaXX (2009) for summaries of empirical work in the banking, and
petroleum industries.
3
Werden et al. (199X) estimate the effects of the Northwest/Republic airline merger; and Farrell et al.
(2009) report on hospital mergers.
2
of.4 The large size of the sample allows us to perform separate analyses in markets of different
types. In particular, we separate markets by the level of uncertainty about future demand and by
the level of capacity utilization, two dimensions that theory suggests are relevant for merger
effects when firms compete my managing revenue.
Most of these mergers are too small to raise anticompetitive concerns, so we do not
expect to find big anticompetitive merger effects. However, due to our large sample, we believe
we will find small effects, if they exist. In other words, our empirical strategy relies on being
able to precisely estimate even small merger effects. To isolate the competitive effects of
mergers, we measure the effects of mergers among firms in the same market relative to out-ofmarket mergers. If there are merger effects unrelated to competition, this differencing should
remove them.
We find that hotel mergers raise and occupancy, and sometimes price, without reducing
capacity. These effects are small, but statistically and economically significant, and occur
primarily in markets with the highest capacity utilization and the most uncertainty. These
findings lead us to reject simple models of price or quantity competition in favor of models of
“revenue management,” so named because firms maximize profit by maximizing revenue, i.e.,
by pricing to match demand to capacity (e.g., Anderson and Xie, 2010). Here we take the
capacity of the hotel as fixed, at least in the short run, and define revenue management in this
application as the setting of price to maximize expected profit subject to a capacity constraint.
In this framework, we identify two theoretical mechanisms that could explain the
empirical results. “Stochastic economies of scale” imply that the merged firm is better able to
manage demand uncertainty, just as a large insurance company can be better manage risk than
4
The largest published study we are aware of is that by Dranove and Lindrooth (2003) who studied the
cost effects of 122 hospital mergers.
two smaller ones. In particular, a merged hotel faces less uncertainty (per room), and can set
price so that demand is closer to available capacity. A second mechanism—that the merged
hotel is better able to compete for group and convention business—could also account for price
and/or quantity increases. If the merged hotels are large enough to host groups, but the premerger hotels are not, then the merger could increase convention demand for the merged hotels.
However, the fact that the biggest price and quantity increases occur in areas with higher levels
of capacity utilization lead us to prefer the former explanation.
In what follows, we motivate the topic by reviewing three antitrust actions against firms
in industries that manage revenue. We then review merger theories to develop testable
hypotheses, and then present the data, estimation, and results. We conclude by discussing the
implications of our results for oligopoly merger models.
II.
Motivation: Antitrust actions in industries where firms manage revenue
In this section, we review three recent antitrust decisions in industries where firms
manage revenue, paying particular attention to the role that uncertainty and capacity constraints
play in the antitrust analyses.
First, in March 1999, the Antitrust Division of the U.S. Department of Justice approved
Central Parking's $585 million acquisition of Allright after the companies agreed to divest
seventy-four off-street parking facilities in eighteen cities (United States v. Central Parking
Corp., 99-0652, D.D.C. filed Mar. 23, 1999). The Division's press release stated ``Without these
divestitures, Central would have been given a dominant market share of off-street parking
facilities in certain areas of each of the cities, and would have had the ability to control the prices
and the type of services offered to motorists'' (U.S. Dept of Justice, 1999).
Froeb et al. (2002) criticize the Justice Department's enforcement action by arguing that
the merger would not have raised price because there is very little uncertainty about parking
demand. Each day, each parking facility gets to “see” a realization of demand, so individual lots
can price to fill capacity with very little error. A simple algorithm achieves this result: if the lot
is full before 9:00 am, the parking lot increases price; otherwise, it reduces price. An optimal
price is one that just fills the lot at 9:00 am. If the merging lots are still capacity constrained
after the merger, then pricing has not changed, and there is no merger effect.
This is an old result (Dowell, 1984), and the intuition behind it is the same as that behind
a standard microeconomics exam question, “what is the difference between monopoly and
competition for a vertical supply curve?” The answer is “none” if the monopolist is capacity
constrained.
Second, in February, 2003, the European Commission (EC) gave their approval to
Carnival's $5.5 billion takeover of rival cruise operator P&O Princess. Carnival, the largest firm,
and Royal Caribbean, the second-largest firm, had been bidding for P&O Princess Cruises, the
industry's third-largest firm. The EC approval followed U.S. Federal Trade Commission (FTC)
and British Competition Commission approvals. In the published decisions there were lengthy
discussions of the “revenue management” problem, that prices for cruises had to be set long
before demand for the cruise was realized.
Coleman et al. (2003) summarized the empirical analysis done by the FTC, noting that
there was no correlation between pre-merger prices and concentration, and no correlation
between changes in capacity and changes in price. They also noted that behavior in the industry
appeared inconsistent with collusion as all firms were adding capacity, increasing amenities, and
competing on price.
Third, in November, 2005, six luxury hotels in Paris, all ranked by the Michelin travel
guides at the highest level of luxury (five red stars), were fined for running an illegal price-fixing
cartel. According to a report by the French competition agency, the hotels shared many features:
“a prestigious site in the center of Paris; a high proportion of suites, some of which are
exceptional; a gastronomic restaurant; exceptional amenities such as swimming pools or gyms;
and a large number of personnel at the disposal of guests.” According to the report, the six
hotels exchanged information about occupancy, average room prices, and revenue (Crampton,
2005). An Associated Press newswire reported the Council's specific charge: "Although the six
hotels did not explicitly fix prices, …, they operated as a cartel that exchanged confidential
information which had the result of keeping prices artificially high" (Gecker, 2005).
An obvious pro-competitive justification for this kind of behavior is that by sharing
information, the hotels can better forecast demand. Better forecasts mean fewer pricing errors—
and higher expected occupancy—as the hotels are better able to match demand to available
capacity. Indeed, in response to the charges, industry executives insisted that their information
sharing was to "to bring more people to the area and to maximize hotel utilization" (Hotels
Magazine, 2006). Apparently, they were unable to convince the court of this merits of this
practice.
III.
Merger theories and testable hypotheses
In this section, we review models of competition, and their associated merger predictions.
We call these “merger models” or “merger theories” but recognize that they are built on models
of firm behavior, and how merger changes firm behavior. The purpose of this section is to
develop testable hypotheses about the effects of mergers when firms compete by managing
revenue.
A. A simple model of revenue management
Any model of the hotel industry must capture three significant features of competition:
high fixed cost, low marginal cost, and uncertain demand. When demand is high, we postulate
that the firms want to fill all available capacity because MR>>MC at capacity; but when demand
is low, the firms price as if they are unconstrained.
In addition, firms must set price before demand is realized. We work with a one-period
model, where each hotel sets price, and then demand is realized. While this is a simplification of
the way that hotels actually price, it captures the essential tradeoff faced by hotels: a price too
high means unused capacity, and a price too low means that the hotel could have priced higher,
without sacrificing occupancy. The hotel chooses an optimal price that maximizes expected
profit, and which balances the expected costs of over- and under-pricing.
To make this concrete, suppose that demand for hotels is characterized by a Poisson
arrival process on top of a logit random utility choice model. We imagine that market demand is
governed by the random arrival process, and then each customer chooses among the hotels
within a given area or market according to a logit probability function. A Poisson process with
mean 𝜃 has distribution function
𝐹(𝑘) =
𝑒 −𝜃 (𝜃)𝑘
𝑘!
, for k=0, 1, … ,
and an n-choice logit choice model has a probability choice function,
𝑠𝑗 =
𝛼 −𝛽𝑝𝑗
𝑒 𝑗
𝛼𝑖 −𝛽𝑝𝑖
1+∑𝑛
𝑖=1 𝑒
,
where each choice represents that probability 𝑠𝑗 that a customer will choose a given hotel,
including a “no purchase” or “outside” option. Here the indirect utility of choosing hotel j is
𝛼𝑗 − 𝛽𝑝𝑗 where 𝛼𝑗 represents the quality of hotel j and 𝑝𝑗 represents the price charged by hotel j.
The parameter 𝛽 measures the sensitivity of choice to price. The demand curve 𝑞𝑖 (𝑝𝑖 , 𝑝~𝑖 ) is
the choice probability times the number of arrivals, and is downward sloping in own price. A
higher quality results in a less elastic demand which allows a higher quality hotel to charge a
higher price and capture a bigger market share, e.g., Werden and Froeb, 1994.
Putting a Poisson process on top of a logit choice model (including an outside option)
results in n independent Poisson processes {𝑞𝑖 } with means {𝜃𝑠𝑖 }. The expected profit of each
hotel is thus
π𝑖 (𝑝𝑖 , 𝑝~𝑖 ) = 𝐸[min(𝑞𝑖 (𝑝𝑖 , 𝑝~𝑖 ), 𝜅𝑖 ) ∗ (𝑝𝑖 −𝑚𝑐𝑖 )]
where 𝜅𝑖 is the capacity of each hotel and the expectation is taken over the arrival process facing
each hotel. Note that the demand for each hotel depends on its own price, as well as on rival
prices 𝑝~𝑖 .
This demand model ignores the consumers who choose a hotel but are turned away
because the hotel reaches capacity, i.e., the consumers 𝑞𝑖 in excess of capacity 𝜅𝑖 are not allowed
to move to their second choice. If hotels are pricing accurately, this excess should not be large,
and so any added demand one hotel might expect from second-choice consumers when a rival’s
demand exceeds its capacity should be small. It is possible to keep track of these extra terms, but
aside from the added complexity, it is not clear that doing so would change the basic intuition of
the results that follow. For ease of exposition, we are going to ignore the effect of rival capacity
constraints on the demand for each hotel, and implicitly assume that a customer who does not
receive her first choice drops out of the model.
In what follows we are going to characterize competition using Nash equilibrium in
prices under a variety of scenarios.
B. No uncertainty, not constrained: unilateral merger effects
Under this scenario, we assume that each hotel knows its demand with certainty. This
assumption turns out not to matter when the firms are not capacity constrained because of a
certainty equivalence result that we will establish below.
When one firm i acquires a substitute product j, its concern changes from maximizing
profit on a single product to maximizing profit on both products. Formally, we go from a premerger Nash equilibrium defined by a set of first-order conditions:
Pre-merger:
𝜕𝜋𝑖
𝜕𝑝𝑖
= 0,
𝜕𝜋𝑗
𝜕𝑝𝑗
= 0,
𝜕𝜋𝑘
𝜕𝑝𝑘
, 𝑘 ≠ 𝑖, 𝑗
to a post-merger Nash equilibrium defined by a different set of first-order conditions, in which
the merged firm (i+j) internalizes the effects of price on its commonly owned products,
Post-merger:
𝜕𝜋𝑖
𝜕𝑝𝑖
𝜕𝜋
+ 𝜕𝑝𝑗 = 0,
𝑖
𝜕𝜋𝑖
𝜕𝑝𝑗
𝜕𝜋
+ 𝜕𝑝𝑗 = 0,
𝑗
𝜕𝜋𝑘
𝜕𝑝𝑘
, 𝑘 ≠ 𝑖, 𝑗.
Here we implicitly assume that the non-merging hotels are independently owned, but this is not
essential. Assuming the hotels are not capacity constrained, these derivatives exist and are
continuous, and the conditions can be solved for the unique pre- and post-merger Nash
equilibria, e.g., Werden and Froeb (1994). Post merger prices are higher, and quantity lower, for
the merged firms. Non-merging firms respond by raising price, provided that their demand
curves become less elastic with increases in rival prices.
The effect of the merger can be easily understood through its effect on marginal revenue.
After the acquisition, an increase in sales on one product will “steal” some sales from the other.
In other words, common ownership of two substitute products reduces the post-acquisition
marginal revenue of each, and the size of the decline is determined by how “close” the two
products are. If the acquisition does not also reduce marginal cost, the merger will increase price
and reduce output (on both products) because post-merger marginal revenue falls below marginal
cost. This is the “unilateral” anti-competitive merger effect, so named because the merged firm
does not need the cooperation of non-merging firms in order to raise price (e.g., Werden and
Froeb, 2007). Unilateral effects arise in models of price, quantity, auction, or bargaining
competition (Werden and Froeb, 2008). If, however, merger synergies push marginal cost below
post-merger marginal revenue, the merged firm decreases price and increases output (Werden,
1996; Froeb and Werden, 1998; and Goppelsroeder et al., 2006). In this case we say the merger
is “pro-competitive.”
In the pro-competitive scenario, price decreases and quantity increases; in the anticompetitive case, they do the opposite. This merger characterization can be tested by testing
whether mergers move price and quantity in opposite directions. This result does not depend on
certainty. To see this, note that the expected profit of each hotel in the unconstrained case can be
expressed as a linear function of expected profit, provided marginal cost is constant.
π𝑖 (𝑝𝑖 , 𝑝~𝑖 ) = 𝐸[𝑞𝑖 (𝑝𝑖 , 𝑝~𝑖 ) ∗ (𝑝𝑖 −𝑚𝑐𝑖 )] = 𝐸[𝑞𝑖 (𝑝𝑖 , 𝑝~𝑖 )] ∗ (𝑝𝑖 −𝑚𝑐𝑖 )
This implies a “certainty equivalence” between the optimal price in the face of uncertainty, and
optimal price in the case of no uncertainty.
C. No uncertainty, constrained: pricing to fill capacity
If hotel demand is as predictable as parking demand and the merged hotel is capacity
constrained because MR>>MC at capacity, then we would expect no price or quantity changes
following hotel mergers. Hotels will price to fill available capacity,
𝐸[𝑞𝑖 (𝑝𝑖 , 𝑝~𝑖 )] = 𝜅𝑖 , 𝑖 = 1, 2 , … , 𝑛 ,
and this profit calculus will not change post merger. Here we are careful to limit our conclusion
to the short run, the period of time during which the structure of the industry is fixed.
This “pricing to fill capacity” characterization of mergers can be tested relative to the
“unilateral” characterization of mergers above by comparing merger effects in environments
where there is less uncertainty about demand and where firms are more likely to be capacity
constrained, to mergers where firms are less likely to be capacity constrained. Together, the
theories predict bigger merger effects in the unconstrained environment.
D. Uncertainty, constrained: stochastic economies of scale
In this section, we consider the effect of a merger that reduces uncertainty. This could
occur in one of two ways. First, a merged firm may be better able to forecast demand than the
pre-merger individual firms. For example, if the parameters of the arrival process are not known,
then the merged firm might have a more informative prior than the individual pre-merger firms
because it gets to “see” order flow at two hotels instead of one, i.e., a “coarser” partition of the
information set, as in Mares and Shor (2008). The other way that this might occur is through the
law of large numbers (Rothschild and Werden, 1979). If the arrival process facing the merged
firm has lower variance than the processes facing the individual firm, and the merged firm can
refer customers who “arrive” at a full hotel to commonly owned “sister” hotel, then the law of
large numbers implies that it is easier to fill the capacity of merged hotels (less variance per
room), than it is to fill separate individual hotels.
When demand is uncertain, and the firm is operating near capacity, the non-linearity of
the min() function (recall that the firm sells the minimum of demand and its capacity) implies
that the price which maximizes expected profit is different than the price which sets expected
demand to capacity. In other words, a firm may “shade” price higher or lower than the prices
that matches expected demand to capacity depending on the relative costs of over-pricing or
under-pricing.
We illustrate this with a simple two-choice logit model:
𝑞(𝑝) = 100 ∗
𝑒 −0.1(𝑝−100)
1 + 𝑒 −0.1(𝑝−100)
Marginal cost is a constant, mc=40. We compare a non-binding capacity constraint at 𝜅 = 85, to
a tightly binding capacity constraint at 𝜅 = 50. Instead of a Poisson arrival process (to clearly
illustrate this effect, we need a process with more variance than a Poisson), we take random
demand to be log-normally distributed with mean 𝑞(𝑝) and a standard deviation of 40%. In
Figures 1-5, we plot the logit profit function (no uncertainty) and the expected profit function for
the two different capacities.
In Figure 1, we plot the deterministic profit function with a deterministic (non-random)
logit demand. We see that the profit function is smooth, and that small errors in pricing have
very small effects on profit. This is our benchmark case, to which the other cases will be
compared.
In Figure 2, we plot the deterministic profit function with the capacity constraint that
does not bind. When demand is known, the non-binding capacity constraint does not affect the
optimal price. However, when demand is unknown (Figure 4), the steep fall off in the
deterministic profit function to the left implies that expected profit reaches a maximum at a
optimal price above the unconstrained price. Intuitively, the hotel prices high to avoid the high
cost of under-pricing errors.
The difference between Figures 4 and 2 can be thought of as a metaphor for the effect of
a merger that reduces uncertainty for the case of non-binding capacity constraints. The
difference between the optimal price when demand is known (dotted line) and the difference
between the optimal price under uncertainty (solid line) is plotted in Figure 4. The difference
between the two is the effect of reducing uncertainty, i.e. a lower price. And with less
uncertainty, expected occupancy increases due to fewer over-pricing errors. In this case, a
merger that reduces uncertainty decreases price and increases output.
In Figure 3, we plot the deterministic profit function with the capacity constraint that
does bind. In this case, the firm prices at the point where demand equals capacity. In Figure 5,
we plot the expected profit function (solid line) for the binding capacity constraint when demand
is uncertain. We see that the steep fall off in the deterministic profit function to the right implies
that the expected profit reaches a maximum at a price below the constrained (deterministic)
price. Intuitively, the hotel prices low to avoid over-pricing errors.
The difference between Figures 5 and 3 can be thought of as a metaphor for the effect of
a merger that reduces uncertainty for the case of tightly binding capacity constraints. Pre-merger
(Figure 5) the firm sets a low price to reduce the high cost of over-pricing. The difference
between the optimal price when demand is known (dotted line) and the optimal price under
uncertainty (solid line) is plotted in Figure 5. The difference between the two is the effect of
reducing uncertainty, i.e. a higher price. And with less uncertainty, expected occupancy
increases due to fewer over-pricing errors. In this case, a merger that reduces uncertainty
increases price and output.
This “stochastic economies of scale” characterization of mergers can be tested relative to
the “pricing to fill capacity” characterization for firms that are likely to be capacity constrained,
by comparing merger effects in uncertain environments to mergers where firms are facing less
uncertainty. The two theories imply a bigger quantity effect in the more uncertain environment.
For a tightly binding constraint, we would also expect price to increase (Figure 5).
E. Demand externalities among the merging firms.
Approximately 50% of U.S. hotel business is group and convention business. Large
conventions are often booked years in advance. To be able to compete for this demand, a hotel
must have both meeting space, and enough capacity to handle the size of the group or
convention. If the merged hotels are large enough to host groups, but the pre-merger hotels are
not, then the merger could increase demand for the merged hotels. Of course, it is possible that
two hotels located near each other could “share” a group booking, or coordinate to host a
convention, but this kind of joint effort is often subject to incentive conflicts. If merger allows
the hotels to manage this incentive conflict, and to bid for previously unattainable business, then
the merger could increase demand.
How such an increase in demand would affect price or quantity depends on whether the
hotels are currently operating at capacity or not. If they are, then it is likely that the increase in
demand would be reflected by an increase in price, rather than occupancy. However, if the hotel
is selling group space that would be otherwise unoccupied due to low or “off-peak” demand,
then the increase in demand would likely be reflected by an increase in occupancy. Whether
price would increase as well depends on how price elastic the demand is for group business. If
group demand is more elastic than non-group demand, e.g., due to the bargaining power of
groups (Kalnins, 2006), then price could go down.
This “demand externalities” characterization of mergers can be tested relative to the
“pricing to fill capacity” theory by comparing merger effects in environments where firms are
more likely to be capacity constrained to mergers where firms are less likely to be constrained.
The theories predict bigger quantity effects in the unconstrained environment.
.
Table 1: Testable hypotheses
Merger Theory
Demand
Uncertainty
Capacity
Constraint
Prediction for Comment
price
Not binding
Prediction
for
occupancy
Down, unless
outweighed
by
efficiencies
Unilateral
effects: price or
quantity
competition
Low
Pricing to fill
capacity: when
demand is
known
Stochastic
economies of
scale: pricing to
fill capacity
when demand is
uncertain
Demand
externalties:
merged firm is
able to bid for
group business
Low
Binding
No effect
No effect
High
Binding
Up
Up, if tightly
binding
constraint
Varies
No effect if
capacity
constrained;
Up if not.
Up, if capacity
constrained;
no prediction
if not.
Up, unless
P and Q
outweighed by move in
efficiencies
opposite
directions
Price to fill
capacity,
both pre- and
post-merger
Jointly
managed
capacity is
easier to fill
Demand
increases for
merged
hotel.
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
Figure 1: Deterministic unconstrained profit function
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
Figure 2: Deterministic profit function with a non-binding capacity constraint
120
140
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
Figure 3: Deterministic profit function with a tightly binding capacity constraint.
120
140
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
100
120
140
Figure 4: Expected profit function with a non-binding capacity constraint
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
Figure 5: Expected profit function with a tightly binding capacity constraint
III.
Data and Estimation
A. Data
The price and occupancy data was provided by Smith Travel Research (STR). Between
2001 and 2009, 32,314 U.S. hotels reported to STR the average room-night price actually
received each day, as well as the total number of rooms available and the number of rooms sold.
Hotels that provide data receive, for a fee, aggregated data from competing hotels in their
vicinity. We have up to 97 monthly observations from December 2001 – December 2009 for
each hotel for occupancy and price. These 32,314 hotels represent about 95% of chain-affiliated
properties in the United States and about 20% of independent hotels and motels.
Smith Travel Research has split the United States into 618 tracts within which they
believe the hotels are to some degree substitutes. Large cities are typically split into five tracts or
more. Atlanta is split into the maximum of twelve tracts; Houston, Washington, Chicago and Los
Angeles are split into nine, Dallas into eight, Orlando into seven, Philadelphia and several other
cities into six, and Detroit and Manhattan and many others into five. We define a “market” as a
tract, and postulate that competitive effects occur within a tract.
We have obtained from STR the management company/franchisee identity for 9,503 out
of these 32,314 U.S. hotels from January 2004 through October 2009.5 STR was able to provide
us the identity of these management companies because the companies are STR’s clients, that is,
they purchase data and analysis. We have complete "portfolio" information for 752 management
companies that operate and maintain these 9,503 properties. In July 2009, for example, the
largest of these management companies operated 298 properties, and the next largest two
5
Management companies typically do not own the physical property while franchisees do. For the
purpose of this study there is no need to distinguish between the two because these are the firms that
set prices and make related strategic decisions. We refer to both simply as “management companies”
from here on.
operated 96 and 80 properties. The median size of these management companies in terms of
properties managed was 5, while the 75th percentile was 10. The largest three management
companies operated properties of 16, 5 and 11 brands respectively. The median management
company operated hotels affiliated with 3 brands.
We defined a local, or within-tract, merger as the case where a management company
that has been operating one or more hotels in a tract takes over operations at an additional
property in that same tract, which was previously operated by someone else. This definition
yields 898 within-tract mergers involving 2,628 hotels that took place between January 2004 and
October 2009 among the 9,503 STR-client hotels. At least one within-tract merger took place
within 398 of the 618 market tracts.
Because we have information about mergers only from January 2004 through October
2009, all our analyses below use panels that begin in December 2003, one month before our
management company information begins, and that end in November 2009, one month after the
management company information ends.
B. Estimation and Variable definition
In a policy application and with a panel data structure very similar to ours (many panels
with a long time series, before and after a policy change) Bertrand et al. (2006) found that
standard errors for DID estimators were biased downward, i.e., they rejected the null hypothesis
of no effect too frequently. Because our estimation strategy depends on us being able to estimate
small merger effects, we use a heteroskedasticity-consistant covariance estimator (White, 1980),
clustering on each panel (hotel*brand combination) in addition to fixed effects, that allows for a
flexible pattern of within-tract correlation. When the number of panels is large as is the case
here, Bertrand et al. (2006) find that this estimator performs well.
We defined a local, or within-tract, merger as the case where a management company
that has been operating one or more hotels in a tract takes over operations at an additional
property in the same tract, which was previously operated by someone else. The binary “withintract merger” is set to zero for all properties for the earliest observations from December 2003.
The value of one is added to this variable for any hotel involved in a merger, for all its monthly
observations from the date of the merger onwards. In a few cases the merger is undone, at which
point the value of one is subtracted for all subsequent monthly observations for the involved
hotels. A total of 331 hotels are involved in more than one merger so the “within-tract merger”
variable can take on values greater than one. Five hotels are involved in nine mergers, the
maximum. We note that our results are robust to the case where we count only the very first
merger in which a hotel is involved, and to the case where we simply exclude all multiplemerger hotels.
We created a variable for non-local mergers. The variable “Out-of-tract merger” is
included to distinguish effects of local merger from more general effects associated with the
types of management companies that are likely to be observed merging. By adding this variable,
we can determine that any occupancy effects of merger hold only if the merger is a within-tract
one; it is not just the result of better management companies doing the merging, for example. In
the latter case, we should observe similar effects for both local within-tract and non-local out-oftract mergers.
We create two sets of fixed effects. First, create a fixed effect for each of 11,462
hotel*brand combinations of our 9,503 hotels in the data set. Thus, almost all our analyses are
“within-brand-at-hotel" in nature. That is, we are comparing pre- and post-merger occupancies
and prices while holding the location AND brand constant. Hotels often change brand names as
they get older and revenues may vary substantially as a result of these brand changes.
Occasionally the management company changes when the brand changes so we do not want to
confound effects from these two issues. We note that all our results remain similar when we use
9,503 hotel fixed effects and ignore brand changes. However, we feel that the estimates are more
accurate using separate fixed effects for every hotel*brand combination.
Second, we wish to include a precise set of fixed effects that isolates temporal effects by
market. Thus we create fixed effects, one for each month for each of the 618 tracts in the data, as
long as there are at least two STR-client hotels operating in that tract in that month. This yields
9,607 tract*month fixed effects. These are included because local macroeconomic conditions
may substantially influence price and occupancy at hotels throughout each period. Including
these fixed effects allows us to distinguish merger effects from macroeconomic effects that apply
to all hotels in a given tract in a given month.
Estimating large data sets with two large dimensions of fixed effects is quite difficult due
to memory and computer processing limitations. We cannot simply add 9,607 fixed effects as
dummy variables to our half million monthly observations after already incorporating the
hotel*brand fixed effects using the standard mean-based approach. Therefore, we have used the
memory-saving algorithm provided by Abowd, Creecy, and Kramarz (2002) as implemented by
Cornelissen (2008), to estimate all our regressions.
C. Measuring capacity constraints and uncertainty
The theories that we wish to test make distinctions between markets where capacity
constraints are likely to be binding and those where they are not, and between markets where
uncertainty about future demand is high and those where it is low. To create these categories, we
used the price and occupancy data from the period from December 2001 through November
2003. These data are not included in our main analyses because we do not have management
company information from this time period. To estimate the likelihood that capacity constraints
are binding we calculate total occupancy at the level of the tract for the year 2002. We then split
tracts into upper and lower halves based on the 2002 occupancies. While even the upper half of
2002 occupancy has a median (50th percentile) occupancy of 0.68, seemingly far from binding
capacity constraints, hotels often begin to consider themselves constrained at occupancies 75%
or 80%. While hotels still have empty rooms at 80% they often do not have the workforce in
place to service these rooms.
To assess the level of uncertainty in a lodging market we calculated the correlation
between daily occupancies from December 2001 through November 2002 with those exactly 364
days later, (not 365, so that the day of the week remains the same). A high correlation indicates
that hotels can base their predictions about occupancy in a given year at least to some extent to
predict occupancy for the next year. Hotel managers have confirmed to us that the previous
year’s occupancy is an important baseline that they use to predict occupancies for a given day.
D. Appropriate Comparison Groups
An important question in any longitudinal analysis of merger effects is the group of nonmerging hotels to be included as a control group in the analysis. We have three obvious
possibilities. First, given that the 898 mergers take place at different times, we could plausibly
identify merger effects by including only the panels of the 2,628 hotels that have been involved
in mergers. This approach eliminates the concern that merged hotels are somehow different than
non-merging properties, and this unobservable difference, not the actual merger, is driving what
appear to be merger effects. The disadvantage is that market tracts with only one merger must be
eliminated because there will be no variation in the “within-tract merger” variable for all
tract*months. Second, at the other extreme, we could include all 32,314 hotels for which price
and occupancy data exist, even though we have no information about any merger activity from
the 22,811 non-client hotels. The advantage to this approach is that the tract*month effects can
be better identified without discarding information from tracts with only one merger. A middle
ground would be to include only those 9,503 hotels that are managed by STR-client management
companies. We calculated some descriptive statistics to help us make the decision. In particular,
the average number of rooms offered per night in December 2003, before any of these mergers
took place, was 135 for the merging hotels and 134 for the non-merging STR client hotels. But
the non-client hotels were far smaller, offering only 87 rooms per night in December 2003.
Similarly, prices and occupancies were $75.89 and 0.488, respectively, for the STR-client
properties but only $63.87 and 0.454 for the non-client properties.
In our most general analysis, we use all three possibilities of comparison group. For our
more fine-grained analyses that separate market tracts by likelihood of capacity constraints and
level of uncertainty, we restrict our analysis to the STR-client properties. We note, however, that
including only merged hotels or including all properties in these analyses largely leads to the
same conclusions.
IV.
Results
A. Effects on price and occupancy
In Table 2, we analyze the effects of merger throughout our entire sample, regardless of
level of uncertainty or the likelihood of binding capacity constraints, using the three possible
comparison groups discussed above: (1) all STR-client properties, (2) merged properties only,
and (3) all data-reporting properties. When we analyze occupancy, we see quite similar results
across all three possible comparison groups, namely, mergers appear to raise occupancy. This
result is most statistically significant, and biggest in magnitude, when the comparison group is
the set of merged hotels only (in column 3). The statistically significant coefficient of the withintract merger variable in the third column of 0.008 suggests that hotels gain just over four-fifths of
a percentage point of occupancy when they merge under one management company. We
conclude that the increases in occupancy are not simply an effect of economic conditions in the
tracts where mergers took place because the tract*month fixed effects are included. Significant
price effects appear only when all 32 thousand data-reporting hotels are included in the analysis.
This suggests that an unobservable difference between the STR-client hotels and the non-clients
may be the cause of this effect.
Table 2: All tracts
9,305 STR client hotels
1.Within-tract Merger
2. Out-of-tract merger
Observations
FX: Hotel*brand
FX: Tract*month
DV = Occ.
.0041+
(.0023)
DV = price
0.53
(.34)
2,628 hotels involved in
mergers
DV = Occ. DV = price
.0083*
.55
(.0033)
(.50)
.0010
(.0010)
.07
(.05)
.0023*
(.0011)
369,627
9,607
8,975
.07
(.12)
32,314 data-reporting
hotels
DV = Occ. DV = price
.0044**
1.51**
(.0017)
(.23)
.0005
(.0003)
93,368
2,285
1,868
Ho: (1) – (2) = 0
1.93
2.11
3.80+
1.15
5.6*
(F-test)
Huber-White standard errors in parentheses, clustered by hotel*brand combination.
** p < 0.01; * p < 0.05; + p < 0.10 as per two-tailed tests
.48**
(.04)
1,826,487
36,139
42,579
20.8**
B. Effects in presence of uncertainty and capacity constraints
These finding—that occupancy goes up post-merger—appears to reject the first two
merger theories in Table 1, and are consistent with the latter two theories, stochastic economies
of scale and demand externalities. To explicitly determine the role that capacity and uncertainty
play in the estimation, we split the sample based on the 2002 occupancy levels and the 20032003 same-day correlations, as described above. For all regressions below we use only the STR
client hotels as the comparison group, however all results below are very similar if we use only
the actual merged hotels. We still use the tract*month fixed effects in all regressions because
they isolate the merger effects from those of any tract-based unobservables.
As we discussed above, we calculated total occupancy at the level of the market tract for
the year 2002. We then split hotels into two halves based on the 2002 tract-level occupancies.
Separate results from the upper and lower half are included in the first four columns of Table 3.
Here we see that occupancy increases only for mergers in the capacity-constrained tracts. Hotels
increase their occupancy by seven tenths of a percentage point post-merger in the capacityconstrained markets and insignificantly so in the lower half. Price, on the other hand, does not
appear to increase regardless of the level of capacity constraints. Note that all STR-client hotels
appear twice in the analyses of Table 3: once in the split by capacity constraints and once in the
split by level of uncertainty.
Table 3: Split by likelihood of capacity constraints and by level of uncertainty
Likelihood of Capacity Constraints
Lower Half
Upper Half
Occ.
ADR
Occ.
ADR
-.0002
0.81
.0070*
0.34
(.0032) (.53)
(.0031)
(.43)
.0009
.15*
.0010+
-.0001
.0005
(.06)
(.0006)
(.07)
Uncertainty
Lower Half
Upper Half
Occ.
ADR
Occ.
ADR
-.0003
-.273
.0074*
1.13*
(.003)
(.356)
(.0032)
(.51)
.0004
-.018
.0015** .14*
(.0006)
(.058)
(.0006)
(.07)
Observations
FX: Hotel*brand
FX: Tract*month
Within-tract mgr
Hotels in mergers
Average of DV
184,296
4,912
4,583
400
1,123
0.60
$92.72
185,331
4,695
4,394
498
1,505
0.66
$102.98
181,569
4,701
4,390
415
1,217
0.62
$94.05
188,058
4,906
4,587
483
1,411
0.64
$101.48
Ho: (1) – (2) = 0
0.15
3.87*
.07
3.57+
Within-tract Mgr
Out-of-tract Mgr
1.65
0.69
.55
Huber-White standard errors in parentheses, clustered by hotel*brand combination.
** p < 0.01; * p < 0.05; + p < 0.10 as per two-tailed tests
4.07*
The last four columns of Table 3 split the population into a lower half and upper half of
market-level uncertainty. Occupancy and price increase significantly due to merger only in the
tracts in the upper half of uncertainty. Given the interesting results that mergers in high
uncertainty tracts appear to cause significant price increases but that high capacity constraint
market tracts do not, we intersect these two criteria. Below, in Table 4, we split market tracts into
quadrants based on both likelihood of binding capacity constraints and level of uncertainty.
Table 4
Market tracts
where
capacity
constraints
are likely to
bind
Market tracts
where
capacity
constraints
are unlikely
to bind
Low Uncertainty Markets
High Uncertainty Markets
Occ.
ADR
Occ.
ADR
.00018
(.0004)
-.00001
(.0008)
-0.65
(.53)
-0.10
(.08)
.0114**
(.004)
.0018*
(.0008)
0.98+
(.60)
0.07
(.09)
Observations
FX: Hotels
FX: Tract*month
Within-tract mgr
Hotels in mergers
Average of DV
0.65
84,906
2,120
1,986
212
684
$98.91
0.67
100,425
2,575
2,410
286
871
$106.30
Ho: (1) – (2) = 0
0.001
1.23
5.20*
2.45
In-tract Merger
-.0013
(.0045)
-.0008
(.0008)
0.17
(.46)
.06
(.08)
.0006
(.0046)
.0010
(.0008)
1.40
(.94)
.24
(.09)
96,663
2,581
2,406
203
583
$89.79
0.615
87,633
2,331
2,179
197
540
$95.95
1.31
.01
1.70
In-tract Merger
Out-of-tract merger
Out-of-tract merger
Observations
FX: Hotels
FX: Tract*month
Within-tract mgr
Hotels in mergers
Average of DV
Ho: (1) – (2) = 0
0.595
.24
Huber-White standard errors in parentheses, clustered by hotel*brand combination.
** p < 0.01; * p < 0.05; + p < 0.10 as per two-tailed tests
Table 4 splits all market tracts by both the likelihood of capacity constraints and by
market-level uncertainty. Each hotel appears only in one quadrant. In the upper right quadrant,
which includes hotels in tracts that have high uncertainty on average as well as a high likelihood
of being capacity constrained, we observe a strong positive effect of merger on hotel occupancy,
raising the occupancy more than one full point. And we observe an estimate of a room-night
price increase due to within-tract merger of $0.98, which is marginally significant relative to
zero, but significantly different from the coefficient for out-of-tract mergers. This increase in
both occupancy and price due to a merger is consistent with the “stochastic economies of scale”
theory for the case of capacity constrained firms with uncertain demand.
In the upper left quadrant, which includes hotels in tracts that have the lowest uncertainty
on average but are in tracts with the highest occupancies, we observe no statistically significant
effects of either occupancy or price. This lack of any significant effect due to a merger is
consistent with “pricing to fill capacity” for the case of capacity constrained firms with fairly
certain demand.
The lower half of the table displays results from hotels from tracts with a low likelihood
of capacity constraints that are also in the lower half of tract-level uncertainty (lower left) and in
the upper half in terms of uncertainty (lower right). In neither group do we observe any
statistically significant occupancy or price effects. These results are not fully consistent with
those predicted by the theory described above. The “stochastic economies of scale” model
suggests that occupancies should increase in the tracts with high uncertainty and where capacity
constraints do not likely bind for example. Yet we see no hint of that. For those tracts where
occupancies are typically low and where there is little uncertainty, the theory does not make any
clear predictions save the possibility of the basic pro- or anti-competitive models of merger.
Perhaps the hotels merging in these “unconstrained but certain” tracts are simply too small to
exert any market power post-merger. Alternatively the pro- and anti-competitive effects of
merger may be cancelling each other out.
V.
Conclusions
We have found evidence most consistent with the presence of “stochastic economies of
scale”. Merged multi-hotel management companies appear to be able to improve occupancies
because they have information about future demand coming from multiple hotels within a market
tract. The fact that this improvement in occupancy occurs only in capacity constrained and
uncertain markets leads us to conclude that models of revenue management can best capture
competition in this industry.
For industries in which firms compete by managing revenue, our results call into question
the policy presumption against mergers with large market shares. It also calls into question the
policy presumption against information sharing by proximate hotels. Our results suggest that the
sharing of information among the hotels in Paris might well have raised occupancies.
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