1. M ERGERS I NCREASE O UTPUT W HEN F IRMS C OMPETE BY M ANAGING R EVENUE A RTURS K ALNINS C ORNELL S CHOOL OF H OTEL A DMINISTRATION L UKE M. F ROEB, S TEVEN T SCHANTZ + V ANDERBILT U NIVERSITY 28 September, 2010 Canadian Competition Bureau Ottawa Canada

2. 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

3. 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

4. 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

5. 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"

6. 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

7. Figure 1: Deterministic unconstrained profit function

8. Figure 2: Deterministic profit function with non-binding capacity constraint

9. Figure 3: Deterministic profit function w/tightly binding capacity constraint

10. Figure 4: Expected profit function (solid) w/non-binding constraint

11. Figure 5: Expected profit function (solid) w/tightly binding constraint

12. Testable hypotheses Merger TheoryDemand Uncertaint y Capacity Constraint Prediction for occupancy Prediction for priceComment Unilateral effects: price or quantity competition Not binding Down, unless outweighed by efficiencies Up, unless outweighed by efficiencies P and Q move in opposite directions Pricing to fill capacity: when demand is known LowBindingNo effect Price to fill capacity, both pre- and post- merger Stochastic economies of scale: pricing to fill capacity when demand is uncertain HighBindingUpUp, if tightly binding constraint Jointly managed capacity is easier to fill Demand externalties: merged firm is able to bid for group business VariesNo effect if capacity constrained; Up if not. Up, if capacity constrained; no prediction if not. Demand increases for merged hotel.

13. Data Price and occupancy data from Smith Travel Research (STR). – 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. – 97 monthly observations from 2001 –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.

14. Table 2: Analysis of all 2628 mergers 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

15. 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 ConstraintsUncertainty Lower HalfUpper HalfLower HalfUpper Half Occ.ADROcc.ADROcc.ADROcc.ADR Within-tract Mgr-.00020.81.0070*0.34-.0003-.273.0074*1.13* (.0032)(.53)(.0031)(.43)(.003)(.356)(.0032)(.51) Out-of-tract Mgr.0009.15*.0010+-.0001.0004-.018.0015**.14*.0005(.06)(.0006)(.07)(.0006)(.058)(.0006)(.07) Observations184,296185,331181,569188,058 FX: Hotel*brand4,9124,6954,7014,906 FX: Tract*month4,5834,3944,3904,587 Within-tract mgr400498415483 Hotels in mergers1,1231,5051,2171,411 Average of DV0.60$92.720.66$102.980.62$94.050.64$101.48 Ho: (1) – (2) = 00.151.653.87*0.69.07.553.57+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

16. Table 4.1: High Capacity Constraints & Low/High Uncertainty Table 4 Part 1 Low Uncertainty MarketsHigh Uncertainty Markets Occ.ADROcc.ADR Market tracts where capacity constraints are likely to bind In-tract Merger.00018-0.65.0114**0.98+ (.0004)(.53)(.004)(.60) Out-of-tract merger-.00001-0.10.0018*0.07 (.0008)(.08)(.0008)(.09) Observations84,906100,425 FX: Hotels2,1202,575 FX: Tract*month1,9862,410 Within-tract mgr212286 Hotels in mergers684871 Average of DV0.65$98.910.67$106.30 Ho: (1) – (2) = 00.0011.235.20*2.45 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

17. Table 4.2: Low Capacity Constraints & Low/High Uncertainty: No signif. Results 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 Low UncertaintyHigh Uncertainty OccupancyADROccupancyADR Market tracts where capacity constraints are unlikely to bind In-tract Merger-.00130.17.00061.40 (.0045)(.46)(.0046)(.94) Out-of-tract merger-.0008.06.0010.24 (.0008)(.08)(.0008)(.09) Observations96,66387,633 FX: Hotels2,5812,331 FX: Tract*month2,4062,179 Within-tract mgr203197 Hotels in mergers583540 Average of DV0.595$89.790.615$95.95 Ho: (1) – (2) = 0.241.31.011.70

18. Conclusions Mergers increases in occupancy, and lead to economically significant gains of between $1700 and $3300 per month for a 100- room 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.