Transcript Mergers Increase Output When Firms Compete by Managing Revenue

28 September, 2010
Canadian Competition Bureau
Ottawa Canada
MERGERS INCREASE OUTPUT WHEN FIRMS
COMPETE BY MANAGING REVENUE
ARTURS KALNINS
CORNELL SCHOOL OF HOTEL ADMINISTRATION
LUKE M. FROEB, STEVEN TSCHANTZ+
VANDERBILT UNIVERSITY
I. Antitrust in Industries where
Firms Manage Revenue
• 1999 Central Parking $585 Million acquisition
of Allright.
– Divestitures in 17 cities
• 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.
– Firms price to fill capacity, pre- and post-merger
Antitrust in Industries where
Firms Manage Revenue (I)
• 1999 Central Parking $585 Million acquisition
of Allright.
– Divestitures in 17 cities
• 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.
– Firms price to fill capacity, pre- and post-merger
Antitrust in Industries where
Firms Manage Revenue (II)
• 2003, the European Commission (EC) gave their
approval to Carnival's $5.5 billion takeover of rival
cruise operator P&O Princess
– Followed UK and US approvals
• Coleman et al. (2003) summarized the empirical
analysis done by the FTC,
– no correlation between prices and concentration
– no correlation between changes in capacity and
changes in price.
– firms were adding capacity, increasing amenities, and
competing on price
Antitrust in Industries where
Firms Manage Revenue (III)
• 2005, six luxury hotels in Paris exchanged
information about occupancy, average room
prices, and revenue
– French competition agency: "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)
– industry executives insisted that their information
sharing was to "to bring more people to the area and
to maximize hotel utilization"
Revenue Management: set price
before demand is realized
• Firm optimizes expected profit:
• Non linearity of min() function means that
capacity constrained firm “shades” price to
minimize expected error costs
– Over-pricing means unused capacity
– Under-pricing means foregone revenue
Figure 1: Deterministic unconstrained
profit function
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
Figure 2: Deterministic profit function
with non-binding capacity constraint
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
Figure 3: Deterministic profit function
w/tightly binding capacity constraint
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
Figure 4: Expected profit function
(solid) w/non-binding constraint
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
Figure 5: Expected profit function
(solid) w/tightly binding constraint
profit
3500
3000
2500
2000
1500
1000
500
price
60
80
100
120
140
Merger Theory
Demand
Uncertaint
y
Capacity
Constraint
Prediction for
occupancy
Prediction for price Comment
Not
binding
Down, unless
outweighed by
efficiencies
Up, unless
outweighed
by
efficiencies
P and Q
move in
opposite
directions
Low
Binding
No effect
No effect
Stochastic
High
economies of scale:
pricing to fill
capacity when
demand is uncertain
Binding
Up
Up, if tightly
binding
constraint
Price to fill
capacity, both
pre- and postmerger
Jointly
managed
capacity is
easier to fill
Demand
externalties:
merged firm is able
to bid for group
business
Varies
No effect if
capacity
constrained; Up
if not.
Up, if
Demand
capacity
increases for
constrained; merged hotel.
no prediction
if not.
Testable hypotheses
Unilateral effects:
price or quantity
competition
Pricing to fill
capacity: when
demand is known
Data
• Price and occupancy data from Smith Travel
Research (STR).
– 32,314 U.S. hotels reported to STR the average roomnight price actually received each day, as well as the
total number of rooms available and the number of
rooms sold.
– 97 monthly observations from 2001 –2009 for each
hotel for occupancy and price.
– These 32,314 hotels represent about 95% of chainaffiliated properties in the United States and about
20% of independent hotels and motels.
Table 2: Analysis of all 2628 mergers
Table 2: All tracts
1.Within-tract Merger
2. Out-of-tract merger
9,305 STR client hotels
2,628 hotels involved in 32,314 data-reporting
mergers
hotels
DV = Occ. DV =
price
.0041+
0.53
(.0023)
(.34)
DV = Occ. DV =
price
.0083*
.55
(.0033)
(.50)
DV = Occ. DV =
price
.0044**
1.51**
(.0017)
(.23)
.0010
(.0010)
.0023*
(.0011)
.0005
(.0003)
Observations
FX: Hotel*brand
FX: Tract*month
Ho: (1) – (2) = 0
(F-test)
.07
(.05)
369,627
9,607
8,975
1.93
2.11
.07
(.12)
93,368
2,285
1,868
3.80+
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
1.15
.48**
(.04)
1,826,487
36,139
42,579
5.6*
20.8**
Table 3: Market tracts split by capacity
constraints and then by uncertainty
Table 3: Split of Markets by likelihood of capacity constraints and by level of uncertainty
Likelihood of Capacity Constraints
Uncertainty
Lower Half
Upper Half
Lower Half
Upper Half
Occ.
ADR
Occ.
ADR
Occ.
ADR
Occ.
Within-tract Mgr
-.0002
0.81
.0070*
0.34
-.0003
-.273
.0074*
(.0032)
(.53)
(.0031)
(.43)
(.003)
(.356)
(.0032)
Out-of-tract Mgr
.0009
.15*
.0010+
-.0001
.0004
-.018
.0015**
.0005
(.06)
(.0006)
(.07)
(.0006)
(.058)
(.0006)
Observations
FX: Hotel*brand
FX: Tract*month
Within-tract mgr
Hotels in mergers
184,296
4,912
4,583
400
1,123
185,331
4,695
4,394
498
1,505
181,569
4,701
4,390
415
1,217
ADR
1.13*
(.51)
.14*
(.07)
188,058
4,906
4,587
483
1,411
Average of DV
0.60
$92.72
0.66
$102.98
0.62
$94.05
0.64
$101.48
Ho: (1) – (2) = 0
0.15
1.65
3.87*
0.69
.07
.55
3.57+
4.07*
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 4.1: High Capacity Constraints &
Low/High Uncertainty
Table 4 Part 1
Market tracts where
capacity constraints
are likely to bind
Low Uncertainty Markets
Occ.
ADR
High Uncertainty Markets
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
Out-of-tract merger
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 4.2: Low Capacity Constraints &
Low/High Uncertainty: No signif.
Results
Low Uncertainty
Market tracts where
capacity constraints
are unlikely to bind
In-tract Merger
Out-of-tract merger
High Uncertainty
Occupancy
ADR
Occupancy
ADR
-.0013
0.17
.0006
1.40
(.0045)
(.46)
(.0046)
(.94)
-.0008
.06
.0010
.24
(.0008)
(.08)
(.0008)
(.09)
Observations
96,663
87,633
FX: Hotels
2,581
2,331
FX: Tract*month
2,406
2,179
Within-tract mgr
203
197
Hotels in mergers
583
540
Average of DV
Ho: (1) – (2) = 0
0.595
.24
$89.79
0.615
$95.95
1.31
.01
1.70
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
Conclusions
• Mergers increases in occupancy , and lead to economically
significant gains of between $1700 and $3300 per month for a 100room hotel.
• Effects occur only in capacity-constrained and uncertain markets
– Mergers allow hotels to better forecast demand.
• No evidence that mergers decrease occupancy or raise price.
– Mergers in “revenue management industries,” should not be modeled
with “traditional” models of price or quantity competition.
– The same warning applies to the scrutiny of information sharing by
hotels in same market
• The Grand Dame hotels of Paris justification for information sharing might
have increased occupancy.