Mergers and Product Quality: The Impact of De-Hubbing in the U.S. Airline Industry

Transcription

1 Mergers and Product Quality: The Impact of De-Hubbing in the U.S. Airline Industry Nicholas G. Rupp Kerry M. Tan April 2016 Abstract This paper studies how de-hubbing, which occurs when an airline ceases hub operations at an airport, impacts product quality. Using an event study of five cases of de-hubbing following a U.S. airline merger between 1998 and 2015, we analyze how three measures of product quality change following de-hubbing: on-time performance, travel time, and flight cancellations. While state attorney generals have opposed recent airline mergers due to concerns about potential job losses and a reduction in airport service should an airport be de-hubbed, this study finds that mergers improve product quality as travelers at de-hubbed airports experience more reliable flight schedules and shorter travel times. JEL classifications: L93, L13 Keywords: De-Hubbing, Product Quality, Legacy Carriers, Hub-and-Spoke Network, Airline Competition We would like to thank Gary Fournier and participants at the Southern Economic Association Conference. This research was supported by a Summer Research Grant from the Sellinger School of Business and Management at Loyola University Maryland. Department of Economics, East Carolina University, Greenville, NC, 27858; ruppn@ecu.edu. Department of Economics, Loyola University Maryland, Baltimore, MD, 21204; kmtan@loyola.edu. 1

2 1 Introduction Our hub in Cleveland hasn t been profitable for over a decade, and has generated tens of millions of dollars of annual losses in recent years. We simply cannot continue to bear these losses. (former United CEO Jeff Smisek s letter to United s Cleveland employees, 1 February 2014). A series of bankruptcies and mergers in the airline industry since 2004 has trimmed the number of major U.S. airlines from ten down to just four large carriers which dominate the domestic market. These mergers have caused carriers to re-evaluate their flight network structure in search of efficiency gains. A frequent consequence of an airline merger is the decision to reduce the number of hub airports, a phenomenon called de-hubbing. Since 1998, there have been five cases in the U.S. where an airport has been de-hubbed after an airline merger. This paper examines what impact these de-hubbing events have had on product quality. This research also has public policy implications given the testimony by Gerald L. Dillingham, the Director of Civil Aviation for the Government Accounting Office (GAO), to the Subcommittee on Aviation Operations regarding the U.S. Airways - American Airlines merger indicated that the GAO s evaluation of the merger is to determine if the potential benefits for consumers outweigh the potential negative effects (GAO T). Hence, this paper sheds some light on whether airline mergers and subsequent de-hubbing of an airport improves (or worsens) product quality for consumers. Airlines seek to merge for both revenue and cost reasons. From the revenue viewpoint, the merged entity now has a more valuable network of flight offerings and the potential for having greater market power from having one less competitor in overlapping markets. For example, for the recent American and US Airways merger completed in 2015, US Airways estimates that the financial benefits to shareholders following the consummation of the American and US Airways merger provide $1.4 billion in annual benefits with the majority of these benefits coming from additional revenue due to improvements in network connectivity, a more valuable frequent flier network, and optimization in the use of aircraft (GAO T). From a cost standpoint, a merged 2

3 company can also provide potential cost savings. Once again, examining the American and US Airways merger, US Airways executives expect to generate $640 million in annual cost savings by reducing or eliminating duplicative operating costs, including inefficient (or redundant) hubs or routes (GAO T). Due to the frequency in which de-hubbing of airports has occurred following an airline merger, the state Attorney Generals of Arizona, Florida, Michigan, Tennessee, Pennsylvania, Virginia, and District of Columbia filed lawsuits in opposition to the US Airways - American Airlines merger out of fear that their state could potentially suffer substantial job losses at existing hub airports. As a condition for allowing American and US Airways to merge, the AMR Corporation reached a settlement agreement with the Department of Justice and state Attorney Generals to maintain its hubs in Charlotte, New York (Kennedy), Los Angeles, Miami, Chicago (O Hare), Philadelphia, and Phoenix consistent with historical operations for a period of three years (AMR Corporation press release, 12 November 2013). Hence, the possibility still exists that after three years (beginning in 2016:Q4) that American may close one of its existing hub airports. So this research on the product quality impact following a de-hubbing event is of particular interest for travelers at current American hub airports. There has been some recent research exploring the link between airline mergers and product quality. Prince and Simon (2015) find that airline mergers have minimal impact on quality (ontime performance) immediately following the merger, while finding some evidence of long-run improvements in service quality (between three and five years) after the merger. Our work differs from Prince and Simon since they examine all flight operations throughout the U.S. following a merger, while this paper focuses solely on the product quality at the de-hubbed airport after a merger. Chen and Gayle (2013) investigate the directness of the itinerary routing as their quality measure following the Delta/Northwest and Continental/United mergers. They find a decrease (increase) in product quality post-merger if the merging firms were competitors (not competitors) in the market. Mjyongjin, Liu, and Rupp (2016) find airlines offer higher quality flight amenities (Wi-Fi, larger seats, entertainment, and seat power) on more competitive routes. 3

4 Researchers have also investigated the impact of an airport de-hubbing on fares and flight operations. Examining de-hubbing at U.S. airports, Tan and Samuel (2016) find lower fares following the de-hubbing at airports that have a low cost carrier presence, while higher fares occur after de-hubbing at airports without a low cost carrier presence. Examining thirty-seven instances of de-hubbing events worldwide, Redondi et al. (2012) find that the typical de-hubbed airport does not recover their original traffic level after five years. Beyond the airline industry, others have examined the impact on product quality following mergers and acquisitions. The empirical results show considerable variation. For example, using a structural model of convenience store expansion in Japan, Nishida and Yang (2015) report that mergers have a detrimental effect on the underlying unobserved performance dynamics of the merged entity, whereas others have found a neutral effect on quality following a merger. Examining data from Consumer Reports across a variety of brands (e.g., washing machines or vacuum cleaners), Sheen (2014) finds that when two manufacturers of a given product merge, the product quality of their products converge after a two to three year period. There have also been numerous studies which document improvements in product quality and firm performance following a merger. Reviewing 10-K product descriptions following mergers, Hoberg and Phillips (2010) find that the merged entity has improved operational performance as evidenced by an increase in the creation of new products which offer greater product differentiation compared to its rivals. In the Japanese cotton spinning industry, Braguinsky et al. (2015) find that there is a noticeable improvement in the acquired plants productivity and profitability once new management/ownership took control. Maksimovic and Phillips (2001) show that transfer of corporate assets (by mergers, acquisitions, or plant sales) typically improve the allocation of resources and hence result in an increase in productive efficiency. McGuckin and Nguyen (1995) examine more than 28,000 plant ownership changes in the U.S. food manufacturing industry over an eleven year period (in 1970s-80s) and find that a plant ownership change is associated with an improvement in the acquired plants productivity. More recently, Gugler and Siebert (2007) find that mergers in the semi-conductor industry are associated with net efficiency gains. 4

5 In sum, this paper explores the impact on product quality and mergers by investigating the ontime performance before and after the merged airline de-hubs an airport. We study five cases where U.S. airports have been de-hubbed since While the number of airports served typically falls following airport de-hubbing, we find evidence that mergers improve product quality as travelers at de-hubbed airports experience more reliable flight schedules and shorter travel times. Therefore, policymakers and state Attorney Generals should consider the efficiency gains from more reliable flight schedules when calculating the costs and benefits from a proposed airline merger. 2 Data We construct a dataset using three databases from the Bureau of Transportation Statistics in order to test the effect of de-hubbing on product quality. The main dataset used in this paper is the Airline On-Time Performance Data, which provides monthly data on the number of delayed flights, flight times, and the number of cancelled flights. We also obtain monthly data on the number of scheduled flights and available seats for travel within the United States by domestic airlines from the T-100 database. The data from these two sources is aggregated at the carrier-route level for each year-quarter in order to be matched with the quarterly data in the third dataset, the Airline Origin and Destination Survey (DB1B). An observation in the raw DB1B dataset provides information on the number of passengers who paid a certain price to fly with a particular airline on a given route, as well as the distance between the two endpoints of the route. Data from 1998:Q1 to 2015:Q2 are collected from each of the three data sources. Although we only keep observations for airports in the contiguous United States, we place no restrictions on the number of airports or the number of routes. We focus our attention, however, on the top ten airlines during our sample time period (in alphabetical order): AirTran Airways, Alaska Airlines, American Airlines, Continental Airlines, Delta Air Lines, JetBlue Airways, Northwest Airlines, Southwest Airlines, United Airlines, and US Airways. Although this ranking is based on the number of passengers serviced by ticketing carrier, using either the number of flights or total 5

6 revenue as our metric yields the same ten airlines. De-hubbing is defined using two criteria and is loosely based on Tan and Samuel (2016) and Redondi et al. (2012). First, de-hubbing is said to occur in a particular year-quarter whenever an airline decreases its total number of flights to and from that airport by at least 35% in the time period before de-hubbing, defined to be the one to four quarters prior to that year-quarter, versus the time period after de-hubbing, defined to be the one to four quarters following that year-quarter. This allows for the quarter in which the de-hubbing event occurred to be excluded in order to account for the transitional time period in which de-hubbing takes effect, which we believe is necessary given that Jeff Smisek, the former CEO of United Airlines, mentioned that we have made the difficult decision to substantially reduce our flying from Cleveland. We will make this reduction in stages... in the same 2014 letter to United s Cleveland employees as quoted in the introduction of this paper. Moreover, there are some cases in which an airline will dramatically decrease capacity at smaller airports that are not used for typical hub operations. Therefore, the second criterion for de-hubbing is that the de-hubbed airport must be one of the top 50 airports in terms of airport operations (the number of take-offs and landings). 1 This effectively focuses de-hubbing cases to major airports located in the United States. Table 1 presents the five major mergers in the U.S. airline industry in chronological order of when the merger was publicly announced, as well as the airport that was de-hubbed as a result of each merger: Norman Y. Mineta San Jose International Airport (SJC), Lambert-St. Louis International Airport (STL), McCarran International Airport (LAS), Memphis International Airport (MEM), and Cleveland Hopkins International Airport (CLE). 2 Although the de-hub date is based on our 35% reduction in airport operations criterion, the accuracy and timing of these de-hubbing instances were then verified using public sources. 1 The 50th ranked airport in 1998 and 2014 were Bradley International Airport (BDL) and Grand Canyon National Park Airport (GCN), respectively. This data was obtained from the Federal Aviation Administration s website: 2 Southwest Airlines announced its acquisition of AirTran Airways in 2010:Q3. There have been some reports in the media that this merger would lead to a de-hubbing at General Mitchell International Airport (MKE). However, any de-hubbing efforts by Southwest Airlines has taken place too recently to be observed in the available data at the time of this writing. 6

7 Table 1: List of Mergers and De-Hubbed Airports Merger Airport Ranking Acquirer Acquired Merger Announced Airport De-Hub Date Before After American Reno Air 1999:Q1 SJC 2001:Q American TWA 2001:Q2 STL 2003:Q America West US Airways 2005:Q2 LAS 2009:Q1 7 8 Delta Northwest 2008:Q2 MEM 2011:Q United Continental 2010:Q2 CLE 2013:Q Figure 1: Number of Flights by De-Hubbing Airline (a) SJC (American Airlines) (b) STL (American Airlines) (c) LAS (US Airways) (d) MEM (Delta Air Lines) (e) CLE (United Airlines) The total number of flights is the sum of the scheduled departure and arrival flights at the particular airport. The gray dashed line indicates the year-quarter that the airport became de-hubbed by the airline identified in parentheses. The shaded rectangles illustrate the before de-hubbing and after de-hubbing time periods used in the regression analysis. 7

8 Figure 1 graphs the total number of flights scheduled to and from each of these five airports for the de-hubbing airline between 1998:Q1 to 2015:Q2. The gray dashed line indicates the time period when the airport is considered to become de-hubbed, while the shaded rectangles illustrate the before de-hubbing and after de-hubbing time periods used in the regression analysis. Given the criteria for de-hubbing, Figure 1 unsurprisingly shows a sharp decline in the number of flights following de-hubbing at each of the five airports, which typically results in a reduction in the airport s ranking (see Table 1). 3 Empirical Analysis In order to test the effect of de-hubbing on product quality, we conduct an event study analysis of the de-hubbed airports as a result of mergers between 1998 and By using a differencein-differences (DID) approach, we are able to infer a before and after effect of de-hubbing on product quality measures for hub markets. In order to determine whether the de-hubbing airline experiences a similar effect on product quality as its rival airlines, we conduct a difference-indifference-in-differences (DDD) estimation. The regression results generally imply that de-hubbed airports following mergers typically experience an increase in product quality. This improvement in providing more reliable flight schedules for passengers should be considered by policymakers when analyzing the effects of a proposed airline merger. We use a two-way fixed effects model in order to yield a DID estimate (Tables 3, 5, and 6) and a DDD estimate (Tables 7, 8, and 9) on the effect of de-hubbing on three measures of product quality relevant to the airline industry. The dependent variable is either the proportion of flights with delayed arrivals (pdelay), the average travel time in minutes (traveltime), 3 and the proportion of cancelled flights (pcancel). We control for the natural log of airport operations at both the origin airport (lnorigin f lights) and destination airport (lndest f lights), the number of carriers operating on the route (ncom), the carrier s route-level market share (marketshare), and the carrier s yield 3 The traveltime variable is defined as the actual arrival time - scheduled departure time in order to take time zone conversions and AM/PM time changes into account. 8

9 (yield), which is defined as the average one-way airfare divided by the flight s one-way distance. We also include a dummy variable (airport) that indicates whether the de-hubbed airport is one of the route s endpoint airports, 4 another dummy variable (dehub) that indicates whether the time period is pre- or post-de-hubbing, and the interaction term of the two dummy variables (airport dehub). The airport variable is constructed to limit the data sample to a before and after period, where the before period starts four quarters prior to the de-hubbing time period and ends one quarter before de-hubbing, whereas the after period starts one quarter following the de-hubbing time period and ends four quarters after de-hubbing. This allows for the quarter when de-hubbing occurs to be excluded from the analysis. The resulting dataset consists of 254,542 observations on 9,355 routes. Summary statistics appear in Table 2. Table 2: Summary Statistics Variable Definition Mean (Std. Dev.) pdelay i jt Proportion of flights with delayed arrivals for carrier i on route j in time period t (0.111) traveltime i jt Average number of minutes (actual arrival time - scheduled departure time) for carrier i to fly route j in time period t (84.77) origin f lights jt Number of flights at origin airport of route j in time period t 17, Note: lnorigin f lights = ln(origin f lights) (22,973.08) dest f lights jt Number of flights at destination airport of route j in time period t 17, Note: lndest f lights = ln(dest f lights) ( ) ncom jt Number of carriers operating on route j in time period t 4.74 (2.37) marketshare i jt Market share for carrier i on route j in time period t Note: marketshare for monopolist = 1.0 (0.270) yield i jt Yield for carrier i on route j in time period t price distance (0.286) Note: yield = price i jt Average one-way fare for carrier i on route j in time period t (73.89) distance j One-way distance (in miles) between the endpoints of route j (601.46) Routes Number of routes in the sample 9,355 N Number of observations 254,542 4 Since flight cancellations are more vulnerable to adverse conditions at the origin airport, the airport variable indicates whether the de-hubbed airport is the origin airport of the route only when pcancel is used as the dependent variable. Regardless, removing this constraint produces qualitatively similar results. 9

10 The general specification for the difference-in-differences (DID) regressions is as follows: y i jt = α + δ i j + τ t + β 1 X i jt + β 2 airport j + β 3 dehub t + β 4 (airport j dehub t ) + ε i jt, (1) where y i jt is either the proportion of flights with delayed arrivals (pdelay i jt ), the average travel time in minutes (traveltime i jt ), or the proportion of cancelled flights (pcancel i jt ) for airline i on route j in time t, δ i j is the carrier-route fixed effects, τ t is the year-quarter fixed effects, airport j is the airport dummy variable, dehub t is the de-hub time dummy variable, and X i jt are the other control variables explained above. By construction, the airport variable becomes absorbed by the carrierroute fixed effects, while the year-quarter fixed effects absorb the dehub variable. We cluster the standard errors by carrier-route in order to account for intragroup correlation over time. Since the airport and dehub variables serve as the treatment variable and time variable in the standard DID approach, respectively, our variable of interest is the interaction term (airport dehub). A negative and statistically significant coefficient for the interaction term implies that product quality increases (due to either a reduction in the proportion of delayed arrivals, a shorter travel time, or a reduction in the proportion of cancelled departures), on average, after the airport has been dehubbed. Since a separate regression was run for each de-hubbed airport case the airport and dehub dummies are specific to each of the five instances of de-hubbing. Table 3 reports the proportion of delayed arrivals (pdelay) from the DID regression for each of the five de-hubbed airports. Two regressions are run for each de-hubbed airport with the column on the left including just the fixed effects and airport dehub interaction term, whereas the column on the right additionally includes competition variables as in Rupp et al. (2006). The coefficient for airport dehub, the control variable of interest, is negative and statistically significant in nearly all of our DID regression specifications, which suggests that product quality improvements following an airport de-hubbing. The results for the standard DID specifications for SJC (Column (1)), STL (Column (3)), LAS (Column (5)), and MEM (Column (7)) suggest that de-hubbing contributed to a 4.4%, 3.1%, 2.0%, and 2.8% reduction in the proportion of delayed arrivals, respectively, for all 10

11 airlines servicing the de-hubbed airport, whereas the estimated coefficient for airport dehub is statistically insignificant for CLE (Column (9)). 5 Table 3: Difference-in-Differences Estimation Results (Dependent Variable: pdelay) SJC STL LAS MEM CLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) airport dehub (0.007) (0.008) (0.004) (0.004) (0.004) (0.004) (0.010) (0.010) (0.016) (0.017) lnorigin f lights (0.003) (0.004) (0.005) (0.004) (0.007) lndest f lights (0.003) (0.004) (0.005) (0.004) (0.006) ncom (0.001) (0.001) (0.001) (0.001) (0.001) marketshare (0.013) (0.012) (0.016) (0.012) (0.013) yield (0.008) (0.011) (0.013) (0.011) (0.017) N 28,423 25,685 27,513 24,792 29,489 26,583 28,896 25,885 29,456 26,141 Note: This table reports the results of the difference-in-differences regressions (Equation 1) using pdelay as the dependent variable. Observations are at the carrier-route-year-quarter level. By construction, the airport dummy variable becomes absorbed by the carrier-route fixed effects, while the dehub dummy variable gets absorbed by the year-quarter fixed effects. Carrier-route and year-quarter fixed effects suppressed. Standard errors, which are presented in parentheses, are clustered by carrier-route to account for correlation between a route-carrier combination over time. indicates significance at 10% level. * indicates significance at 5% level. ** indicates significance at 1% level. Although some of the competition variables are statistically significant, their inclusion in the regressions did not have a qualitative impact on the estimated coefficient for the airport dehub interaction term. The results in Table 3 for the DID specifications that include competition variables for SJC (Column (2)), STL (Column (4)), LAS (Column (6)), and MEM (Column (8)) suggest that de-hubbing contributed to a 4.5%, 3.4%, 2.0%, and 2.3% reduction in the proportion of delayed arrivals, respectively. The estimated coefficient for airport dehub remains statistically insignificant for CLE (Column (10)) even after including the competition variables. Regardless, the results generally suggests that one of the positive ramifications for mergers is an increase in product quality through more reliable flight schedules. 5 We focus our attention on delayed arrivals instead of delayed departures since pilots can make up time following a delayed departure in the air. Airlines can also pad their scheduled departure and arrival times in order to reduce the likelihood of delays and avoid potential fines from the FAA associated with prolonged delays. In other words, it can be the case that flights depart behind schedule, but still arrive at the destination on time. 11

12 Table 4: The Number of Spoke Airports Serviced by the De-Hubbed Airline Airport (Airline) SJC (AA) STL (AA) LAS (US) MEM (DL) CLE (UA) Before De-Hubbing After De-Hubbing Note: This table reports the number of spoke airports that the de-hubbed airline serviced. The before de-hubbing time period is the year preceding de-hubbing, whereas the after de-hubbing time period is the year following de-hubbing. It is also worth noting that a de-hubbed airport also significantly reduces the number of nonstop flight offerings by the de-hubbed airline, as Table 4 shows that the magnitude of this reduction averages 45% and ranges considerably from 26% (MEM) to 69% (STL). In sum, less congested airports improve schedule reliability and reduce travel time; however, they significantly reduce the number of non-stop offerings by the de-hubbing airline. Table 5: Difference-in-Differences Estimation Results (Dependent Variable: traveltime) SJC STL LAS MEM CLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) airport dehub (0.624) (0.638) (0.348) (0.353) (0.360) (0.373) (0.742) (0.754) (1.685) (1.754) lnorigin f lights (0.274) (0.404) (0.671) (0.413) (0.829) lndest f lights (0.246) (0.255) (0.432) (0.348) (0.744) ncom (0.082) (0.080) (0.109) (0.094) (0.131) marketshare (1.078) (1.194) (1.597) (1.101) (1.573) yield (0.712) (0.907) (1.273) (1.175) (1.877) N 28,406 25,680 27,501 24,787 29,472 26,572 28,887 25,880 29,451 26,138 Note: This table reports the results of the difference-in-differences regressions (Equation 1) using traveltime as the dependent variable. Observations are at the carrier-route-year-quarter level. By construction, the airport dummy variable becomes absorbed by the carrier-route fixed effects, while the dehub dummy variable gets absorbed by the year-quarter fixed effects. Carrier-route and year-quarter fixed effects suppressed. Standard errors, which are presented in parentheses, are clustered by carrier-route to account for correlation between a route-carrier combination over time. indicates significance at 10% level. * indicates significance at 5% level. ** indicates significance at 1% level. Table 5 reports the results of the DID regression for each of the five de-hubbed airports using traveltime as the dependent variable. As with Table 3, two regressions are run for each de-hubbed airport with the column on the left including just the fixed effects and airport dehub interaction term, whereas the column on the right additionally includes competition variables as in Rupp et 12

13 al. (2006). The coefficient for airport dehub, the control variable of interest, is negative and statistically significant in nearly all of our regression specifications, meaning that the DID method implies that product quality increases due to a reduction in travel time after an airport has been de-hubbed. The results for the standard DID specifications for SJC (Column (1)), STL (Column (3)), LAS (Column (5)), and MEM (Column (7)) suggest that de-hubbing contributed to a 1.6, 2.9, 1.8, and 2.2 minute reduction in travel time (calculated as actual arrival time - scheduled departure time), respectively, for all airlines servicing the de-hubbed airport, whereas the estimated coefficient for airport dehub is statistically insignificant for CLE (Column (9)). Although some of the competition variables are statistically significant, their inclusion in the regressions did not have a qualitative impact on the estimated coefficient for the airport dehub interaction term. The results for the DID specifications that include competition variables for SJC (Column (2)), STL (Column (4)), LAS (Column (6)), and MEM (Column (8)) suggest that de-hubbing contributed to a 1.4, 3.4, 1.9, 1.5 minute reduction in travel time, respectively. As in Table 3, the estimated coefficient for airport dehub remains statistically insignificant for CLE (Column (10)) after including the competition variables. Nonetheless, the regression results reported in both Tables 3 and 5 provide evidence that mergers improve product quality as travelers at de-hubbed airports experience more reliable flight schedules and shorter travel times. Table 6 provides the results of the DID estimation results when we track the proportion of cancellations at the five de-hubbed airports. Once again, we include two regression specifications, one with just the fixed effects and airport dehub interaction term and a second specification that includes controls for competition. The coefficient of interest is the control variable airport dehub which registers significantly lower cancellation proportions at both LAS and MEM following dehubbing. The estimation results suggest 0.3% and 0.4% reductions in the proportion of flight cancellations at LAS (Column 5) and MEM (Column 7), respectively. While such a change on the surface may appear minor, recall that prior to de-hubbing the cancellation rate for the hub airline at these airports is already very low: 1.21% (LAS) and 1.38% (MEM), hence these proportion changes correspond to a 25% and 29% reduction in flight cancellations at LAS and MEM, respec- 13

14 tively. For the three other de-hubbed airports (SJC, STL, and CLE) we find no significant changes in the proportion of flight cancellations following the de-hubbing event. Table 6: Difference-in-Differences Estimation Results (Dependent Variable: pcancel) SJC STL LAS MEM CLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) airport dehub (0.003) (0.003) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002) (0.003) (0.003) lnorigin f lights (0.001) (0.001) (0.002) (0.001) (0.002) lndest f lights (0.002) (0.000) (0.002) (0.001) (0.002) ncom (0.000) (0.000) (0.000) (0.000) (0.000) marketshare (0.005) (0.002) (0.005) (0.002) (0.005) yield (0.004) (0.002) (0.006) (0.003) (0.004) N 28,423 25,685 27,513 24,792 29,489 26,583 28,896 25,885 29,456 26,141 Note: This table reports the results of the difference-in-differences regressions (Equation 1) using pcancel as the dependent variable. Observations are at the carrier-route-year-quarter level. By construction, the airport dummy variable becomes absorbed by the carrier-route fixed effects, while the dehub dummy variable gets absorbed by the year-quarter fixed effects. Carrier-route and year-quarter fixed effects suppressed. Standard errors, which are presented in parentheses, are clustered by carrier-route to account for correlation between a route-carrier combination over time. indicates significance at 10% level. * indicates significance at 5% level. ** indicates significance at 1% level. With the inclusion of controls for the number of airport operations and route competition, Table 6 shows that only one de-hubbed airport (LAS) registers significantly lower cancellation rates (Column 6). All other de-hubbing airports have no significant changes in cancellation rates when the estimation includes controls for airport operations and route competition measures. The DID approach is used to get a pooled effect of the impact of de-hubbing on product quality; however, a difference-in-difference-in-differences (DDD) approach can tease out whether the dehubbing airline experiences a different effect on product quality as its rivals at the airport that it de-hubs. The general specification for the DDD regressions is as follows: y i jt = α + δ i j + τ t + β 1 X i jt + β 2 airport j + β 3 dehub t + β 4 (airport j dehub t ) + β 5 carrier i + β 6 (carrier i dehub t ) + β 7 (carrier i airport j ) + β 8 (carrier i airport j dehub t ) + ε i jt, (2) 14

15 where y i jt is either the proportion of flights with delayed arrivals (pdelay i jt ), the average travel time in minutes (traveltime i jt ), or the proportion of cancelled flights (pcancel i jt ) for airline i on route j in time t, δ i j is the carrier-route fixed effects, τ t is the year-quarter fixed effects, airport j is the airport dummy variable, dehub t is the de-hub time dummy variable, and X i jt are the other control variables explained above. By construction, the airport, carrier, and carrier airport variables becomes absorbed by the carrier-route fixed effects, while the year-quarter fixed effects absorb the dehub variable. We cluster the standard errors by carrier-route in order to account for intragroup correlation over time. As with the DID approach, our variable of interest is the interaction term (airport dehub); however, we are now additionally interested in the difference-in-difference-in-differences estimator (carrier airport dehub). Given the inclusion of the carrier variable in our DDD analysis, the interpretation of our original variable of interest differs slightly. Now, a negative and statistically significant coefficient for the airport dehub interaction term implies that rival firms product quality increases (due to either a reduction in the proportion of delayed arrivals, a shorter travel time, or a reduction in the proportion of cancelled departures), on average, after the airport has been de-hubbed, whereas this variable represented a pooled effect of the de-hubbing airline and its rival airlines in the DID analysis. Moreover, the carrier airport dehub interaction term is interpreted as the difference between the difference-in-differences estimators for the de-hubbing airline and its rival airlines. As such, a negative and statistically significant coefficient for the carrier airport dehub interaction term implies that product quality for the de-hubbing airline improves relatively more than product quality for rival airlines. A separate regression was run for each de-hubbing case such that the airport and dehub dummies are specific to each of the five instances of de-hubbing. Table 7 presents the DDD estimation results using pdelay as our dependent variable. In this estimation there are two coefficients of interest: airport dehub interaction term and the carrier airport dehub estimator. First, the airport dehub term shows how rival firms delay rates change at the de-hubbed airport compared to non-de-hubbed airports. The odd number columns 15

16 (1-9) represent specifications for just the fixed effects and interaction terms, while the even number columns (2-10) also include controls for the number of flight operations and route competition measures. Table 7: Difference-in-Difference-in-Differences Estimation Results (Dependent Variable: pdelay) SJC STL LAS MEM CLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) airport dehub (0.009) (0.010) (0.008) (0.008) (0.004) (0.004) (0.021) (0.022) (0.042) (0.043) carrier dehub (0.003) (0.003) (0.003) (0.003) (0.004) (0.004) (0.003) (0.003) (0.004) (0.004) carrier airport dehub (0.014) (0.015) (0.010) (0.010) (0.008) (0.009) (0.024) (0.025) (0.044) (0.044) lnorigin f lights (0.003) (0.004) (0.005) (0.004) (0.007) lndest f lights (0.004) (0.004) (0.005) (0.004) (0.006) ncom (0.001) (0.001) (0.001) (0.001) (0.001) marketshare (0.013) (0.012) (0.016) (0.012) (0.013) yield (0.008) (0.011) (0.013) (0.011) (0.017) N 28,423 25,685 27,513 24,792 29,489 26,583 28,896 25,885 29,456 26,141 Note: This table reports the results of the difference-in-differences-in-differences regressions (Equation 2) using pdelay as the dependent variable. Observations are at the carrier-route-year-quarter level. By construction, the airport, carrier, and carrier airport dummy variables becomes absorbed by the carrier-route fixed effects, while the dehub dummy variable gets absorbed by the year-quarter fixed effects. Carrier-route and year-quarter fixed effects suppressed. Standard errors, which are presented in parentheses, are clustered by carrier-route to account for correlation between a route-carrier combination over time. indicates significance at 10% level. * indicates significance at 5% level. ** indicates significance at 1% level. Table 7 shows that the DDD estimations are robust to whether we include or exclude the additional controls for airport operations and route competition. First, examining the performance of rival carriers at the de-hubbed airport, we find that rival carriers experience significant improvements in the proportion of flight delays in three of the five de-hubbed cases (SJC, STL, and LAS) compared to other airports that they operate in the U.S. which were not de-hubbed. Columns (1), (3), and (5) show a reduction in the proportion of flight delays by rival carriers of 3.4%, 2.9%, and 1.6% at SJC, STL, and LAS, respectively. While at two de-hubbed airports (MEM and CLE), there is no noticeable change in the performance of rival carriers in comparison to airports which were not de-hubbed. One possible explanation for these positive spillovers to rival carriers from a de-hubbed airport is that airport congestion is reduced when the hub carrier cuts the number 16

17 of flights. Similar results are recorded when we include airport operations and route competition (see the even numbered estimations for the airport dehub interaction term in Table 7). Second, the carrier airport dehub term provides insight into the performance of the de-hubbed airline following the de-hubbing event and these hub airline results are compared to the rival carriers to determine the relative change in performance. As discussed previously, we know that in three of five de-hubbing events, rival carriers experienced a reduction in delays. We find that the de-hubbed airline at three de-hubbing airports (STL, LAS, and CLE) registers significantly better on-time performance compared to rival carriers at the de-hubbed airport. Columns (3), (5), and (9) show the magnitude of these changes are 4.2%, 3.3%, and 8.8% reduction in the proportion of flight delays at STL, LAS, and CLE, respectively. These findings combined with the previously discussed results for the rival carriers suggest that in a majority of de-hubbing events both rival and hub carriers experience improved service quality. With that said, at de-hubbed airports it is the hub airline that benefits the most in terms of improved service quality. We should also note that at two de-hubbed airports (SJC and MEM) we find no improvement in service quality by the de-hubbed airline. Given the similar quantitative and qualitative results occur for the even column specifications with include additional controls for flight operations and route competition, we now turn to the triple difference results for travel time. Table 8 presents the travel time estimation results for the difference-in-difference-in-differences. The airport dehub interaction term is negative and significant at both STL and LAS, which suggests that rival carriers experience shorter travel times following the de-hubbing event compared to the rivals performance at other non-de-hubbed U.S. airports. We follow the same specification as in the previous DDD table where the odd number columns include fixed effects and the interaction terms, while the even number columns also include additional controls for the number of flight operations at origination and destination along with route competition measures. Given that the results are comparable among the two specifications, we will focus our attention on the odd number column estimation results. Columns (3) and (5) indicate a reduction in travel time of 2.9 and 1.0 minutes by rival carriers following de-hubbing at STL and LAS, respectively. These re- 17

18 sults are consistent with the previous DDD specification for probability of delays as rival carriers at STL and LAS were also significantly less likely to be delayed following de-hubbing. Hence rivals appear to benefit from a reduction in airport congestion following de-hubbing. The result that rivals benefit from shorter travel time does not hold for every de-hubbing event. While SJC and MEM also have a negative airport dehub interaction term, it fails to achieve statistical significance. Also, rival airlines at CLE experience longer travel times of 7.5 minutes (Column (9)) after de-hubbing occurs. This result, however, is only marginally significant (at 10%). Table 8: Difference-in-Difference-in-Differences Estimation Results (Dependent Variable: traveltime) SJC STL LAS MEM CLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) airport dehub (0.646) (0.687) (0.540) (0.543) (0.375) (0.389) (1.061) (1.192) (4.261) (4.276) dehub carrier (0.365) (0.307) (0.290) (0.305) (0.334) (0.334) (0.297) (0.302) (0.437) (0.470) carrier airport dehub (1.301) (1.288) (0.737) (0.752) (0.905) (0.926) (1.372) (1.486) (4.438) (4.479) lnorigin f lights (0.284) (0.406) (0.672) (0.404) (0.830) lndest f lights (0.247) (0.255) (0.433) (0.337) (0.743) ncom (0.082) (0.079) (0.109) (0.092) (0.131) marketshare (1.087) (1.194) (1.597) (1.093) (1.576) yield (0.722) (0.913) (1.270) (1.115) (1.883) N 28,406 25,680 27,501 24,787 29,472 26,572 28,887 25,880 29,451 26,138 Note: This table reports the results of the difference-in-differences-in-differences regressions (Equation 2) using pdelay as the dependent variable. Observations are at the carrier-route-year-quarter level. By construction, the airport, carrier, and carrier airport variables becomes absorbed by the carrier-route fixed effects, while the dehub variable gets absorbed by the year-quarter fixed effects. Carrier-route and year-quarter fixed effects suppressed. Standard errors, which are presented in parentheses, are clustered by carrier-route to account for correlation between a route-carrier combination over time. indicates significance at 10% level. * indicates significance at 5% level. ** indicates significance at 1% level. Turning our attention to the de-hubbed airline performance, the carrier airport dehub estimates in Table 8 reveal that the de-hubbed airline at three de-hubbed airports (STL, LAS, and CLE) experienced significantly shorter travel times compared to rival airlines. The magnitude of these effects range from 1.8 minutes in STL (Column (3)) to 4.9 minutes in LAS (Column (5)) to a high of 10.4 minutes in CLE (Column (9)). The de-hubbed airline, in most cases, experiences the largest benefit from reduced airport congestion by de-hubbing the airport. The remaining two 18

19 airports (SJC and MEM) have no significant change in travel times for the de-hubbed airline. Once again, similar results were obtained with the inclusion of the airport flight operations and route competition controls. Table 9: Difference-in-Difference-in-Differences Estimation Results (Dependent Variable: pcancel) SJC STL LAS MEM CLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) airport dehub (0.003) (0.003) (0.001) (0.001) (0.001) (0.001) (0.003) (0.003) (0.002) (0.002) carrier dehub (0.001) (0.001) (0.000) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) carrier airport dehub (0.006) (0.005) (0.001) (0.002) (0.002) (0.002) (0.004) ( 0.004) (0.005) (0.005) lnorigin f lights (0.001) (0.001) (0.002) (0.001) (0.002) lndest f lights (0.002) (0.000) (0.002) (0.001) (0.002) ncom (0.000) (0.000) (0.000) (0.000) (0.000) marketshare (0.005) (0.002) (0.005) (0.002) (0.005) yield (0.004) (0.002) (0.006) (0.003) (0.004) N 28,423 25,685 27,513 24,792 29,489 26,583 28,896 25,885 29,456 26,141 Note: This table reports the results of the difference-in-differences-in-differences regressions (Equation 2) using pdelay as the dependent variable. Observations are at the carrier-route-year-quarter level. By construction, the airport, carrier, and carrier airport variables becomes absorbed by the carrier-route fixed effects, while the dehub variable gets absorbed by the year-quarter fixed effects. Carrier-route and year-quarter fixed effects suppressed. Standard errors, which are presented in parentheses, are clustered by carrier-route to account for correlation between a route-carrier combination over time. indicates significance at 10% level. * indicates significance at 5% level. ** indicates significance at 1% level. Our final DDD specification tracks the proportion of flight cancellations at de-hubbed airports with the results appearing on Table 9. The airport dehub interaction term is negative and significant at both STL (-0.3% in Column (3)) and LAS (-0.2% in Column (5)), which indicates that rival airlines have fewer cancellations at these two de-hubbed airports compared to other airports that the rival airline operates in the U.S. This result indicates that rivals provide better service quality following the de-hubbing, perhaps benefiting from reduced airport congestion which enables more flights to depart as scheduled. The remaining three de-hubbed airports (SJC, MEM, and CLE) show no significant change in the cancellation rates of rival airlines. Similar results are recorded for odd and even column specifications which again differ only by the inclusion or exclusion of airport operations and route competition controls. Examining the performance of the de-hubbed airline 19

20 the carrier airport dehub estimator shows a reduction in cancellations for the de-hubbed airline only occurs following de-hubbing at LAS where the hub carrier has a 0.5% reduction in the proportion of flight cancellations (Column (5)). At MEM we find the opposite result as the hub airline has a significant 0.8% increase in the proportion of flight cancellations compared to rival airlines. Perhaps this adverse result for the hub carrier in MEM reflects a reduction in resources at the de-hubbed airlines disposal, such as replacement aircraft, available maintenance staff, and/or on-call flight crews. At the three other de-hubbed airports (SJC, STL, and CLE), the de-hubbed airline experienced no significant difference in flight cancellations compared to the rival airlines. In sum, the DDD estimations present mixed evidence regarding the link between product quality (proxied by flight cancellations) and de-hubbed aiports as we find all three situations exist: de-hubbed airlines in one case performed better than rivals registering fewer cancellations (LAS), de-hubbed airlines performed worse than rivals as they have more cancellations (MEM), and dehubbed airlines performance is no different from rivals at three de-hubbed airports: SJC, STL, and CLE. 4 Conclusion The $2.6 billion merger between Virgin America and Alaska Airlines in 2016 continues a two decades long trend of consolidation in the airline industry. Until recently, each merger in the U.S. airline industry has resulted in a hub airport becoming de-hubbed. In fact, one of the conditions required before regulators would approve the US Airways and American Airlines merger was requiring that American maintain the level of flight operations at their hub airports for at least three years following the completion of the merger. Although previous papers have studied the effect of competition on product quality (Rupp et al. (2006)) and more recently, the impact of mergers on on-time performance (Prince and Simon (2015)), this paper contributes to the literature by examining how product quality changes at the subset of airports that were de-hubbed as a result of these mergers. Our difference-in-difference-in-differences estimations reveal improved on-time 20

21 performance and shorter travel times by the de-hubbed airline compared to their rival airlines operating at the same airport in three of five de-hubbing events. In the remaining two de-hubbing situations, we find no significant differences in on-time performance and travel times between the de-hubbed airline and rivals at the de-hubbed airport. We find no clear link between flight cancellations and the performance of the de-hubbed airline operations. While policymakers and state attorney generals worry about a reduction in employment and flight offerings following an airport de-hubbing, we find that airline passengers benefit from airport de-hubbing due to higher product quality in the form of more reliable flight schedules and reduced travel times. References Braguinsky, Serguey, Atsushi Ohyama, Tetsuji Okazaki, and Chad Syverson (2015). Acquisitions, Productivity, and Profitability: Evidence from the Japanese Cotton Spinning Industry, American Economic Review, 105(7), Hoberg, Gerard and Gordon Phillips (2010). Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis, The Review of Financial Studies 23, Maksimovic, Vojislav and Gordon Phillips (2001). The Market for Corporate Assets: Who Engages in Mergers and Asset Sales and Are There Efficiency Gains?, Journal of Finance 56(6), McGuckin, Robert, and Sang Nguyen (1995). On Productivity and Plant Ownership Change: New Evidence from the Longitudinal Research Database, Rand Journal of Economics 26 (2), Myongjin, Kim, Qihong Liu, and Nicholas G. Rupp (2016). When Do Firms Offer Higher Product Quality? Evidence from the Allocation of Inflight Amenities, working paper, University of Oklahoma. Nishida, Mitsukuni and Nathan Yang (2015). Better Together? Retail Chain Performance Dynamics in Store Expansion Before and After Mergers, NET Institute Working Paper No