Quality disclosure when firms set their own quality targets

Accepted Manuscript

Quality Disclosure When Firms Set Their Own Quality Targets Silke J. Forbes, Mara Lederman, Michael J. Wither PII: DOI: Reference:

S0167-7187(18)30016-X 10.1016/j.ijindorg.2018.04.001 INDOR 2446

To appear in:

International Journal of Industrial Organization

Received date: Revised date: Accepted date:

15 August 2017 5 April 2018 9 April 2018

Please cite this article as: Silke J. Forbes, Mara Lederman, Michael J. Wither, Quality Disclosure When Firms Set Their Own Quality Targets, International Journal of Industrial Organization (2018), doi: 10.1016/j.ijindorg.2018.04.001

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Highlights • We study a setting in which firms are subject to quality disclosure, and they are able to choose the target quality levels. • We investigate whether firms and their competitors adjust their target quality levels

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when they become subject to mandatory quality disclosure, and we find that they do.

• The empirical setting for our study is the U.S. airline industry.

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• We find that airlines lengthen their schedule times when they become subject to disclosure of on-time performance, where on-time performance is measured by actual arrival times, relative to scheduled arrival times.

their schedule times.

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• Other airlines on routes which are served by these new reporters also lengthen

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• For these other airlines, we can also observe on-time performance before and after the change in reporting. We find that an additional minute of schedule time

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translates almost exactly into a one-minute reduction in arrival delays. • Actual flight time increases by a small amount, i.e. this dimension of quality wors-

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ens.

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• The airlines actions lead to an improvement in reported quality, even though underlying metrics of actual performance do not improve.

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Quality Disclosure When Firms Set Their Own Quality Targets

Abstract

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Silke J. Forbes, Mara Lederman, and Michael J. Wither∗

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We investigate how firms adjust their target quality levels when they - or their competitors - become subject to an information disclosure requirement. Our setting is the U.S. airline industry, where all large domestic carriers are required to report their on-time performance (OTP). OTP is measured by comparing a flight’s actual arrival time to its scheduled arrival time, which is chosen by the airline. Therefore, airlines can improve their OTP by simply increasing their scheduled flight times. We study three airlines which become subject to the disclosure requirement and find that they lengthen their schedule times by 1.4 minutes on average. Moreover, other airlines also increase their schedule times on routes where they compete with newly reporting airlines, by about 2.3 minutes, while actual flight times remain unchanged. While these numbers are small, the longer schedule times translate into a 15 percent improvement in OTP for previously reporting airlines. We conclude that newly reporting airlines and their direct competitors adjust their quality targets when they become subject to quality disclosure, which improves their reported quality without improving the actual time that it takes to travel from gate to gate.

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Keywords: Information Disclosure, Gaming, Airlines

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JEL codes: L1, L9, D8

∗ Forbes:

Tufts University ([email protected]). Lederman: University of Toronto, Rotman School of Management ([email protected]). Wither: Powerlytics, Inc. Forbes gratefully acknowledges funding from NSF grant SES-1124154. Wither gratefully acknowledges financial support from UC San Diego, Department of Economics.

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1 Introduction Many products are experience goods whose quality is not observable to consumers prior to purchase. In such settings, quality disclosure programs can be valuable as they provide consumers with systematic information about the quality of the products avail-

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able in the market.1 It is common for disclosure programs to report simplified and summary quality metrics, often based on discrete thresholds - for example, letter grades or progress against a specified quality target.2 A growing literature has shown that the choice of metric is important because organizations often find ways to ‘game’ the

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programs, improving the reported quality metric without necessarily improving underlying quality. For example, when programs are based on discrete thresholds, firms focus their quality improvement efforts on transactions near the threshold, since these can be brought over to the ‘right’ side of the threshold at the lowest cost.3 In this paper, we consider an alternative form of ‘gaming’. We study a disclosure

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program which measures firms’ quality against a target that the firms themselves set

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and we investigate whether firms strategically lower the target in order to improve their measured quality. Our setting is the disclosure of airline on-time performance (OTP). In the U.S., the Department of Transportation (DOT) requires all large domestic airlines to

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submit data on the on-time performance of their flights. The DOT uses this data to issue

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a monthly ranking of airlines based on the fraction of their flights which arrive less than 15 minutes delayed. The DOT defines ‘delay’ as the difference between a flight’s actual

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arrival time at the gate and its scheduled arrival time, which is set by the airline. The disclosure rule imposes no restrictions on how airlines set their schedules. Thus, under 1 Dranove

and Jin (2010) provide an extensive review of the literature on quality disclosure programs.

2 This

approach is used, among others, in the assessment of public schools under the No Child Left Behind Act, for hospital report cards, and for restaurant hygiene scores. 3 See, for example, Dobbie, Rockoff, Dee and Jacob (2016), Forbes, Lederman and Tombe (2015), Jacob and Levitt (2003), and Neal and Schanzenbach (2010).

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this regulation, airlines can improve their reported OTP by simply scheduling more time for each flight. While lengthening schedules would appear to be an easy way for airlines to improve their on-time performance, there are costs associated with longer schedule times. All else

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equal, consumers prefer shorter flight times and may avoid flights with long schedule times. Moreover, scheduling more time for one flight means that the aircraft cannot be scheduled for the next flight until later. As a result, aircraft utilization may fall, leading to higher costs and/or lower revenue for the airline. In addition, labor contracts in this industry typically specify that flight crews are compensated based on the maximum

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of the actual and the scheduled flight times. Finally, computer reservations systems traditionally sorted flights by schedule length and would show the shortest flights on a route before other flights. For all these reasons, airlines contemplating lengthening their schedules would need to trade-off these costs against the benefits of fewer actual and

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reported delays.4 Furthermore, even if airlines have lengthened flight schedules over time, this may not necessarily be an attempt to ‘game’ the disclosure program.5 Longer

or higher fuel prices.

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schedules may be a rational response to increased congestion, changes in aircraft types

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We develop a novel empirical approach to estimate whether the DOT disclosure program, and the particular quality metric used, induces airlines to lengthen their schedules.

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Airlines are only required to submit their data to the DOT, and only appear in the DOT rankings, if they account for at least one percent of domestic passenger revenues. We

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exploit the fact that, in 2003, three full-service airlines cross this size threshold and begin reporting their OTP to the government. The new reporters cross this threshold both because they are growing and because other airlines are shrinking in the aftermath of 4 If

passengers care about delays relative to schedule rather than total flight time, and the change leads to fewer delays, then there could be a positive demand response to the schedule change. 5 See

Forbes, Lederman and Yuan (2017) for evidence on this.

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the September 11, 2001 attacks.6 The fact that we observe some airlines before and after they are required to report their OTP allows us to estimate whether airline schedules strategically respond to the disclosure requirement.7 Furthermore, because a large set of other airlines had been reporting their data through-

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out this period, we can also estimate whether these airlines respond when the new reporters become subject to the disclosure requirement. This allows us to investigate whether there is strategic interaction in scheduling and efforts to improve an observable dimension of product quality. Airlines offer differentiated products and compete with each other in price and quality. If the airlines’ choices of quality levels are strategic com-

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plements, then an incentive for one firm to improve its (reported) quality would induce the firm’s competitors to increase their (reported) quality as well. We limit our estimation to one year before and after the new reporters qualify and only look at routes that were either already served by new reporters at the beginning of

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2002 or were never served by new reporters.8 Thus, we are not considering any routes that are entered by the new reporters at the same time as they begin reporting their

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OTP. We estimate how the expansion in the set of qualifying airlines impacts relative schedule length on three distinct sets of flights. The first set are flights by new reporters.

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The second set are flights by existing reporters on routes where they compete with new reporters. The last group of flights are those operated by existing reporters on

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routes where they do not directly compete with new reporters. Our empirical approach

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resembles a differences-in-differences strategy where the first two sets of flights could be 6 Thus, it is important in our estimation to carefully control for changes in the size of the airlines’ networks, the levels of congestion, and the number of possible connections. 7 Shumsky

(1993) obtained data on the schedules of 219 flights for March 1987 - prior to the introduction of the disclosure program - and March 1988-1991. He finds that, on average, scheduled flight times increased between March 1987 and March 1988, and scheduled as well as actual flight times increased between March 1988 and March 1989. Shumsky does not have information on other covariates that might be able to explain increases in flight times, such as the level of airport congestion. 8 Throughout

the analysis, we define a route as a directional airport pair.

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considered ‘treatment groups’ while flights by existing reporters on routes in which they do not compete with a new reporter could be considered a ‘control group’. However, we recognize that even these latter flights might be affected by the expansion in the set of reporting carriers as there is an indirect incentive for airlines to also improve

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OTP on these routes because the main OTP metric is reported as the percentage of all of an airline’s flights that arrive fewer than fifteen minutes delayed. Therefore, OTP improvements anywhere in an airline’s network can affect its overall OTP.9

For the airlines that newly qualify for the disclosure requirement, we find that their scheduled flight times increase in the year after they begin reporting their data.10 We

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also find increases in scheduled flight time by competitors of the newly reporting airlines on the routes on which they directly compete with one of the newly reporting airlines. Specifically, newly reporting airlines increase their scheduled flight times by about 1.4 minutes, or 1.2 percent, on average after they begin reporting their data to the DOT.

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Existing reporters increase their scheduled flight times on routes where they compete with a new reporter by about 2.3 minutes, or 2.2 percent, on average. Both of these

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changes are relative to any change in flight schedules by existing reporters on routes where they do not compete with a new reporter. Thus, we find evidence of airlines

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responding to the DOT program by strategically lengthening their schedules. Since our comparison group - flights on routes without new reporters - also has some incentive to

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lengthen schedules, we believe that our results are a lower bound estimate of the effect of the disclosure program on flight schedules.

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After establishing that airlines lengthen flight schedules on routes with new re-

porters, we then investigate whether this behavior impacts flight delays. We are unable to examine this for the new reporters as data on their OTP is not available until they 9 Today,

online reservation systems commonly show the on-time percentage for each individual flight. This information was not readily available to travelers during the time period we study in this paper. 10 Scheduled

flight time is the time from scheduled departure to scheduled arrival.

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qualify for the program. However, for existing reporters, we have data on actual flight times and flight delays before and after the new reporters begin reporting. We compare their on-time performance on routes on which they compete with a new reporter and routes on which they do not, before and after the new reporters qualify. We find that

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their increase in schedule times after the new reporters begin reporting directly translates into shorter arrival delays. In particular, on routes on which an airline competes with a new reporter, they experience a two minute reduction in average arrival delays relative to routes on which they do not compete with a new reporter. Given that we find that they lengthen their schedules by about two minutes, this suggests that a minute of

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extra schedule time translates almost one-for-one into a minute of reduced arrival delay. Furthermore, this two minute reduction in average arrival delays translates into a 15 percent reduction in the airlines’ fraction of flights that arrive 15 or more minutes late, the key metric reported by the DOT. At the same time, we find no statistically significant

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change in departure delays and only a small (about 0.5 minute) increase in actual flight times. Thus, the actions airlines take with respect to their schedules improve their re-

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ported performance; however, other, unreported, measures of on-time performance are not improved.

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This paper makes several important contributions. First, we show that in a setting where firms are subject to a ‘pass-fail’ type disclosure regulation, and the firms them-

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selves can influence the threshold for passing, firms do in fact lower the passing threshold to their advantage. At the same time, the effect is not very large, which suggests

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that, at least in our setting, firms may be constrained by the potential costs of lowering the threshold, in this case, lower capital utilization, higher labor costs, and potentially lower demand. We also show that the reporting requirement elicits a strategic response in competitors, who lower their own threshold for passing the disclosure requirement. Moreover, for these competitors we can show that lowering the threshold directly trans-

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lates into better reported performance without a significant improvement in the actual underlying performance. To our knowledge, this is the first study to demonstrate such a strategic response from competitors to a disclosure program. Most disclosure programs are introduced industry-wide, but our setting is unique in that there is a size threshold

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for reporting and firms pass these thresholds at different points in time. The remainder of the paper is organized as follows. Section 2 provides relevant institutional background, Section 3 describes our data, Section 4 outlines our empirical approach, Section 5 presents our results, and a final section concludes.

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2 Institutional Background

The U.S. airline industry has been dominated by a small number of large airlines since its deregulation in the 1970’s. Out of concern that a lack of competition could

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lead airlines to offer low-quality service, Congress began to require, in 1987, that large airlines report on four dimensions of service quality: on-time performance, baggage

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handling, oversales and customer complaints. This requirement applies to all airlines that account for at least one percent of domestic passenger revenues. The DOT publishes

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monthly reports which rank airlines based on the percentage of their flights that arrive ‘on-time’, which is defined as arriving at the gate fifteen or more minutes after the flight’s

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scheduled arrival time. These rankings are frequently reported in national and local media. Travelers observe ticket prices and scheduled flight times at the time of booking.

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In recent years, airline websites and online travel agents have also been reporting average delays for individual flights.11 Because all delays are reported as the difference between actual and scheduled arrival

11 Today,

it is fairly easy for travelers to retrieve detailed OTP information online. During the time period we study, the vast majority of travelers would only have had access to anecdotal or summary information on OTP, if any.

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times, airlines can improve their on-time performance by reducing departure delays, reducing actual flight times or by scheduling more time for flights. All of these approaches are costly. Reducing departure delays or actual flight times requires costly effort from employees, the deployment of additional resources, such as gates or ground crew or

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flying faster and utilizing more fuel.12 Lengthening scheduled flight times can lead to a reduction in aircraft utilization and to an increase in labor costs. These costs of improving on-time performance have to be traded off against the benefits; Forbes (2008) presents evidence that better on-time performance increases passengers’ willingness-topay for air travel. In addition, fewer delays also mean fewer costly accommodations for

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disrupted crew, passengers, and aircraft. Figure 1 shows a histogram of actual arrival delays for our sample. It illustrates how shifting arrival delays from above to below the threshold for being late (i.e., below fifteen minutes) can improve reported on-time performance.

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All of the largest U.S. airlines have been required to report their service quality since this program began in 1987. The number of airlines that were subject to the reporting re-

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quirement remained stable at around ten until 2002. At this time, the growth of low-cost carriers and the decline in the market share of traditional airlines led several carriers to

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cross the market share threshold of one percent which required them to begin reporting their on-time statistics.13 In January 2003, eight new carriers began reporting. Of these

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eight, three were standalone airlines and five were regional airlines.14 Regional airlines do not sell tickets directly to consumers. Instead, they operate flights on behalf of larger

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carriers which are responsible for ticketing and marketing. In our analysis, we do not 12 Some

airlines report their actual flight times manually and may be able to manipulate their on-time performance through misreporting. See Forbes, Lederman and Tombe (2015) for more details. 13 Appendix

Tables A.1 and A.2 show the total domestic passenger revenues and the share of domestic passenger revenues by major airlines for select years from 1995-2005. 14 We focus on this period because it is the only year in which multiple standalone airlines begin reporting at the same time.

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include regional carriers because their flight schedules can be dependent on decisions made by the major carriers for which they operate.15 Instead we focus on the three newly reporting standalone airlines. These carriers are AirTran Airways, American Trans Air (ATA), and JetBlue Airways.16

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In our analysis, we focus on the time period 2002-2003. This allows us to compare airlines’ flight schedules and their actual flight times during the year before and the year after new reporting began. In extensions, we also include data from 2004 to investigate whether the response to new reporting changes over time. We do not include 2001

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because of the disruptions to the airline industry during that year.

3 Data

Our data come from two sources. The first is the Offical Airlines Guide (OAG), a

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flight schedule database from which we have the complete flight schedule for all airlines for one representative week per quarter, from the first quarter of 2002 to the second

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quarter of 2003. This data set includes information on the departure airport, arrival airport, carrier, scheduled departure time and scheduled arrival time for each flight.

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Our second data source is the on-time performance data base from the U.S. Bureau of Transportation Statistics (BTS). These are the data that airlines report under the dis-

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closure program. We use this data base to obtain flight schedules for the the remainder of 2003 and for 2004. We also use the BTS data to get information on actual flight times,

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delays, and taxi times for the years 2002-2004 for the airlines that were already reporting in 2002. For these carriers, we estimate how both schedule times and delays are affected

15 See,

for example, Forbes and Lederman (2010).

16 JetBlue

voluntarily started reporting its on-time performance in 2003, even though it did not formally cross the threshold for being required to report until January 2004. We find that our results are robust when we exclude JetBlue from our analysis.

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when their competitors start reporting flight delays. Our sample includes twelve carriers: The three new reporters, and nine other large airlines which had been reporting their OTP for many years before 2003.17 Section A.1 in the appendix details our sample construction. Our sample includes domestic flights

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within the continental U.S. We only keep routes that were served by at least two carriers for at least four consecutive quarters since we are specifically interested in the effect of new reporting on the new reporters and their competitors on the same route. We drop routes that were entered by a newly reporting carrier during our sample period, i.e. we keep only routes that were already served by a new reporter at the beginning of 2002

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and routes that were never served by a new reporter during our sample period. We drop routes that are served by newly reporting regional carriers so that we can focus on responses to new reporting by standalone airlines. We also drop routes that are longer than 1,000 miles because they are rarely served by new reporters.

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Our schedule sample includes over 170,000 flights, and the OTP sample includes over 1.5 million flights. The OTP dataset is much larger because it includes every day of the

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year while the schedule data only include one week per quarter. Note that schedules

to flight.

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only change occasionally, while actual flight times and flight delays change from flight

Table 1 presents baseline summary statistics for the schedule sample. We report

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means and standard deviations for existing reporters and new reporters separately. For existing reporters, the mean scheduled flight time is 108 minutes and the average route

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distance is 559 miles. For new reporters, the average flight has a distance of 621 miles and a scheduled flight time of 117 minutes. The scheduled flight time is defined as the time from scheduled departure from the gate to scheduled arrival at the gate.

17 These carriers are Alaska, America West, American, Continental, Delta, Northwest, Southwest, United and US Airways.

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Most of our empirical estimations include route and carrier fixed effects. In order to capture time-varying incentives to adjust schedule times, we include controls for congestion and for the number of potential connecting flights. To proxy for the amount of congestion at the airport at the time a flight is scheduled to depart, we calculate the total number of flights scheduled to depart or arrive during the same hour as the focal flight,

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divided by the number of parallel runways at the airport.18 We construct two separate variables for the departure airport and for the arrival airport. As a proxy for potential flight connections, we calculate the daily number of flights the carrier operates at the departure airport and the arrival airport, respectively.

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Table 2 shows the summary statistics for the OTP sample. Since this sample includes every day of the year, instead of only one representative week per quarter, the means of the scheduled flight time and the control variables deviate slightly from the schedule sample. More importantly, Table 2 shows the means and standard deviations of the

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additional dependent variables that we have in this sample. Figure 2 illustrates the definitions of these variables. Actual Flight Time is defined as the time from actual departure

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from the gate to actual arrival at the gate. It has a mean of 104 minutes in our sample. Air Time is the time from take-off at the departure runway to touchdown at the arrival

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runway, and its mean is 82 minutes.

Departure Delay is the difference between actual departure time and scheduled de-

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parture time from the gate, and it can be negative if a flight leaves the gate earlier than scheduled. Arrival Delay is the difference between actual arrival time and scheduled ar-

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rival time and, again, it can be negative. On average, flights are delayed by 5.3 minutes

at departure and by 3.3 minutes at arrival. The Percent Delayed ≥ 15 or 30 min. refer to flights which arrive 15 minutes or more late and 30 minutes or more late, respectively. 18 Our

results are not sensitive to whether or not we divide flights by the number of runways because we include route fixed effects and the number of runways on these routes does not change during our sample period.

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On average, 16.1 percent of flights are delayed 15 minutes or more, and 8.6 percent of flights are delayed 30 minutes or more.

4 Empirical Approach and Identification

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We are interested in estimating how the disclosure requirement affects the schedule times of both new reporters and existing reporters as well as the actual flight times and on-time performance of existing reporters. Our sample includes two types of routes: routes that are served by both new reporters and existing reporters before and after the

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new reporters qualify for disclosure, and routes that are never served by new reporters. Our first set of regressions estimates how schedules change, between 2002 and 2003, on flights operated by new reporters and on flights operated by existing reporters on routes on which they compete with a new reporter. Both of these changes are estimated relative

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to any change in schedule times on routes that are never served by the new reporters. In estimating the impact of the disclosure program, it is important to keep in mind

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that the most visible disclosure metric that is published is the percentage of all of an airline’s flights that are delayed less than fifteen minutes. Thus, if new reporters make

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efforts to improve their reported OTP, then existing reporters may choose to respond to this behavior by lengthening schedule times on all of their routes. As a result of strategic

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interaction between firms, there is no set of flights which is entirely unaffected by the new reporting requirement and could be used as a pure control group. Hence, we focus

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on the relative changes between routes with and without new reporters. While our specification has the set-up of a differences-in-differences approach, it should be viewed as estimating the differential response across these groups of flights rather than estimating the difference between a treatment and control group. As a result, our estimates provide a lower bound to the full schedule response.

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Our main regression equation for estimating the schedule response of new and existing reporters is as follows: Flight Timeirct = β 0 + β 1 Airline Is New Reporteri × Postt

+ XΓ + αr + γc + δt + ε ict

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+ β 2 Airline Competes With New Reporteri × Postt (1)

The dependent variable is the scheduled flight time for flight i in time period t, operated by carrier c, along route r. We include fixed effects for each route (αr ), carrier

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(γc ), and year-quarter (δt ) in our estimation. X contains our other control variables, including departure hour fixed effects. We cluster the standard errors at the route level. Airline Is New Reporter is a dummy for whether or not the flight is operated by a new reporter. Airline Competes With New Reporter is a dummy for flights operated by

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existing reporters on routes where they compete with new reporters. These dummies are not identified separately from the route fixed effects and the carrier fixed effects,

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respectively; therefore they only appear as interactions in the estimation equation. β 1 and β 2 represent our primary coefficients of interest. β 1 estimates whether, on

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average, new reporters change their schedule times during the Post period. The Post period is the year 2003, and the Pre period is the year 2002. β 2 estimates whether existing

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reporters change their schedule times for flights on routes where they compete with new reporters in the Post period. Both of these changes are relative to any changes in schedule

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times on routes that are not served by new reporters, captured by the year-quarter fixed effects.

In a second set of estimations, we investigate how actual flight times and flight delays

of existing reporters respond in the Post period. For these airlines, we can observe actual flight times and flight delays before and after reporting begins for the new reporters.

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As in equation (1) above, we compare these airlines’ change in flight times and on-time performance after the new reporters begin reporting on routes where they compete with new reporters relative to routes where they do not. For these estimations, our regression equation is as follows:

+ XΓ + αr + γc + δt + ε ict

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Dependent Variableirct = α0 + α1 Airline Competes With New Reporteri × Postt (2)

Table 3 shows a simple comparison of means for schedule times, actual flight times

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and arrival delays. Panel A shows mean schedule times in 2002 and 2003 for new reporters, existing reporters on routes with new reporters, and existing reporters on other routes. The data in this table are from the schedule sample described above. The table indicates that schedule times increased for new and existing reporters on routes with new

on other routes by 1.1 minutes.

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reporters by 1.6 and 2.7 minutes, respectively, and they increased for existing reporters

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Panels B and C show two of the outcome measures that are only available for existing reporters, specifically actual flight times (from gate to gate) and arrival delays (difference

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between actual and scheduled arrival at gate). This table uses the larger on-time performance sample described above. Average actual flight times increased by 1.3 minutes on

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routes with new reporters and by 2.3 minutes on routes without new reporters. Average arrival delays decreased for existing reporters on routes with new reporters (by 1.4

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minutes), while these delays increased on other routes (by 0.9 minutes).

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5 Estimation Results 5.1 Effects of New Reporting on Schedule Time for New and Existing Reporters

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We begin our discussion with Table 4 which presents the results of estimating Equation (1). For this estimation, we use the schedule sample described above, which includes the new reporters. Our dependent variable is scheduled flight time. In order to show the effect of including our various sets of fixed effects, we build these up from the first to the last column. We begin in the first column by regressing scheduled flight time on

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Airline Is New Reporter, Airline Competes With New Reporter, Post, their interactions, route distance, endpoint airport fixed effects, the time-varying controls for congestion and for potential connections, and departure hour fixed effects. In Column 2, we add route fixed effects and drop endpoint fixed effects and distance. Column 3 adds carrier fixed effects,

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and Column 4 adds year-quarter fixed effects. Column 5 adds the squares of departure congestion and arrival congestion to show robustness to nonlinear measures of conges-

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tion. The specification in Column 4 is our preferred specification because it includes the full set of fixed effects and it measures congestion in a parsimonious way.

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Our main variables of interest are the interactions of Airline Is New Reporter and Airline Competes With New Reporter with Post. Across specifications, we find that, in the post

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period, new reporters add between 1.3 and 1.5 minutes to their scheduled flight times and existing reporters, on routes where they compete with new reporters, add between

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2.1 and 2.4 minutes to their scheduled flight times. In our preferred specification, the effects are 1.4 minutes and 2.3 minutes, respectively. These results demonstrate that the beginning of OTP disclosure for three new re-

porters leads these three airlines as well as their competitors to add a small amount of additional time to their flight schedules, a move that would allow them to improve their 14

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reported OTP potentially without any changes in actual flight times. Thus, the fact that in this pass-fail type disclosure program the firms themselves can choose the threshold for passing leads these firms to ”lower the bar” for passing once their OTP becomes publicly reported, though the magnitude of the effect is small.

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The coefficients on the controls show, not surprisingly, that flights which depart or arrive during busier (and potentially more congested) times of day have longer schedule times. In Column 4, for example, the coefficient of 0.071 on Departure Congestion implies that carriers add about one minute of schedule time to a flight for each additional 14 flights that are departing or arriving at the departure airport during the flight’s depar-

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ture hour. At the arrival airport, the coefficient of 0.061 implies that carriers add one minute to the schedule time for each additional 16 flights arriving or departing at the arrival airport during the flight’s scheduled arrival hour. In Column 5, when we add squared terms of the congestion variables, we find that the coefficient estimates on these

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variables are negative and statistically significant but very small in magnitude. The addition of the squared terms has a small effect on the estimates on our variables of interest.

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Interestingly, the coefficient on the squared term indicates that the effect of congestion is concave, i.e. when congestion is already high, then a further increase leads to a smaller

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effect on schedule than it would if congestion were low. Our proxy for potential connections, the carrier’s total number of flights at the de-

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parture airport and at the arrival airport have a very small negative effect on schedule time. In our preferred specification, we estimate that an increase of 250 flights at the

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departure airport would lead to a one minute reduction in schedule time and a change in the number of flights at the arrival airport would have no statistically significant effect on schedule time. Recall that these effects are only identified by variations within routes since we include route fixed effects in our model. In Table 5, we estimate a series of interactions of the difference-in-difference vari-

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ables with route and flight characteristics. The purpose of this exercise is to investigate whether there is heterogeneity in the magnitude of the effect we estimate based on flight and route characteristics that could be associated with different benefits or costs to airlines of lengthening their schedule. We build on our preferred specification from Table

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4, Column 4 with the full set of fixed effects. This specification is repeated in Column 1 of Table 5. In the second column of Table 5, we interact Airline Is New Reporter and Airline Competes With New Reporter with Post and Distance. The coefficient on the interaction of Airline Is New Reporter with Post and Distance is not statistically different from zero. The interaction of Airline Competes With New Reporter with Post and Distance is

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negative, which implies that, for these carriers, there is more schedule lengthening on shorter routes. A possible explanation for this pattern is that passengers care more about flight delays on shorter routes, and thus airlines have a stronger incentive to engage in schedule lengthening on these routes.

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The third column of Table 5 interacts Airline Is New Reporter and Airline Competes With New Reporter with Post and a dummy for whether a flight is the airline’s last flight of

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the day on that route. We add this interaction because the costs of scheduling additional time for the last flight of the day are lower than for other flights, since there is no

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flight immediately following that flight. As a result, adding schedule time will not have an impact of subsequent flights or aircraft utilization throughtout the rest of the

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day. The coefficients estimates on these interaction terms are not statistically significant, suggesting that airlines do not systematically adjust schedules differently on the last

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flight of the day, relative to other flights on the same route. In the final column of Table 5, we interact Airline Is New Reporter and Airline Competes

With New Reporter with Post and a dummy for whether there are three or more carriers that offer direct service on the route. 21 percent of our observations are on routes with three or more carriers, while the remaining observations come from routes with two

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carriers. We find that these interactions are also statistically insignificant, i.e. there is no evidence that schedule lengthening is different on routes with more competitors.19

5.2 Effects of New Reporting on Actual Flight Times and Delays for

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Existing Reporters The remainder of this section investigates how other outcome variables, besides schedule time, respond after the three new reporters begin reporting. For these estimations, we use the OTP sample described above. Recall that this sample has daily

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observations on each individual flight for the existing reporters only. We estimate Equation (2) for a number of different outcomes, and we include the same set of fixed effects and control variables as in our preferred specification in Table 4, Column 4. Our results for this set of regressions are presented in Table 6. Our primary variable of interest is the interaction of Airline Competes With New Reporter with Post. The first

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column uses schedule time as a dependent variable and shows that our results for this

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sample are consistent with the schedule sample.20 In Column 2, we use actual flight time from gate to gate as the dependent variable. We find that actual flight time increases slightly in the Post period, by 0.5 minutes, while schedule time increases by 2.2 minutes.

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In other words, despite scheduling more than two additional minutes on average, airlines

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only take about an additional half minute to get from origin to destination. This implies that airlines are overscheduling additional time in the post period, relative to what they

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would need to schedule in order to maintain the same level of on-time performance. Columns 3 to 5 decompose the difference between the schedule time response and

the actual flight time response. Column 3 shows that arrival delays, measured as the 19 Several

authors, including Mayer and Sinai (2003), Mazzeo (2003) and Rupp, Owens and Plumly (2006) have found a relationship between market concentration and on-time performance. 20 The remaining outcome measures in this table are not available for cancelled flights. For this reason, the number of observations varies between Column 1 and the other columns in the table.

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difference between actual arrival and scheduled arrival at the gate, improve by 2.2 minutes on average. Thus, the longer schedule times in the post period translate almost entirely into shorter arrival delays. The effect is large relative to the mean arrival delay of 3.2 minutes. Column 4 shows that there is no significant change in departure delays,

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measured as the time between actual departure and scheduled departure from the gate. In Column 5, we see that in the post period there is no statistically significant change in air time, measured as the time between takeoff from the departure runway to landing at the arrival runway.

The last two columns of Table 6 use two additional measures of arrival delay as the

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dependent variables, namely the percentage of flights that have arrival delays of fifteen minutes or more in Column 6, and the percentage with arrival delays of 30 minutes or more in Column 7. We use these alternative measures for two reasons. First, the percentage of arrival delays over fifteen minutes is the main metric reported under the

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disclosure program, and, second, travelers may particularly care about avoiding flights with very long delays. We find that both of these percentages fall in the post period.

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Specifically, we find that the share of flights that are 15 or more minutes late falls, on average, by 2.4 percentage points. Given the mean of this variable in our data, this effect

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is economically meaningful. On average, in our sample, 16.1 percent of an airline’s flights are 15 or more minutes late. The coefficient estimate from Column 6 of −2.4 therefore

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represents a 15 percent reduction in this important measure of on-time performance. In Table 7, we add an interaction of Airline Competes With New Reporter with Post

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and Distance to the specifications from Table 6. As in Table 5, we find that there is

less schedule lenghtening on longer routes than on shorter routes. For example, a 400mile route with a new reporter has an estimated schedule increase of 3.1 minutes in the post period, while an 800-mile route has an estimated increase of 1.4 minutes. We see in Column 3 that these results on schedule times are mirrored very closely in the coefficient

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estimates for arrival delays, but with the opposite sign. Specifically, arrival delays are reduced, but more so on shorter routes than on longer routes, and the reduction in arrival delays is very close to the increase in schedule times. Consistent with our earlier finding, we again find no significant differences in changes

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in actual flight times, departure delays or air times on routes with new reporters and on routes without new reporters in the post period and the distance interactions are insignificant. Consistent with the reductions in average arrival delays, we also see a reduction in the percentages of flights that are 15 or more minutes delayed and 30 or more minutes delayed and there are larger reductions in these variables on shorter routes than

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on longer routes (though the differential effect by distance is not statistically significant for the percentage of delays over 30 minutes).

In our final set of results, we add the year 2004 as a second year in the post period. We keep 2002 as the pre-period, but we now have two separate post period dummies,

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one for observations from 2003 (Post1) and one for observations from 2004 (Post2). We interact each post period dummy with the Airline Competes With New Reporter dummy.

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These results are presented in Table 8. We find that most of the coefficients on the Post1 and the Post2 interactions are quite similar. In particular, the results in Column

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1 imply that the schedule changes persist for at least two years. Interestingly, we find that average arrival delays are still lower in 2004 than in 2002, but there is no statistically

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significant difference in the percentage of flights delayed 15 minutes or more and 30 minutes or more. This implies that, although airlines maintain the additional schedule

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time in 2004, this does not translate into improved reported on-time performance (flights delayed fewer than 15 minutes) during that year.

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6 Conclusion We have empirically investigated how firms respond to a pass-fail type disclosure program in which the firms themselves choose the target for what passes as high quality. Such a program design allows direct input from firms into what should be considered

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the appropriate quality level and, at least in our setting, consumers are able to observe the quality target prior to purchase. However, this type of program design also begs the question of whether firms will lower the target quality in order to make it easier for themselves to achieve this target.

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Our empirical findings suggest that they do. We find that, when a new set of airlines begin publicly reporting their on-time performance, they lengthen their scheduled flight times which makes it easier for them to arrive ‘on time’. The magnitude of the effect we estimate is not very large - these airlines on average increase their schedules after reporting by about 1.4 minutes - which may reflect the fact that there are costs to airlines

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potentially lower demand.

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of lengthening their schedules, notably lower aircraft utilization, higher labor costs and

Perhaps more interesting, when we examine whether established airlines react when their competitors begin disclosing their on-time performance, we find that there is a

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strategic response by competitors on the same route, even though these airlines’ report-

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ing status does not change. These competitors also increase their scheduled flight time, by more than the new reporters. Furthermore, we find that, for these airlines, the in-

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creases in schedule time directly translate into shorter arrival delays and the magnitude of these effects are significant. For example, we find that average arrival delays for these airlines decrease by about two minutes and the fraction of flights that arrive 15 or more

minutes late - the key metric reported by the DOT - decreases by 2.4 percentage points or 15 percent. The fact that airlines can achieve these improvements in reporting on-time performance without any significant reductions in departure delays or actual travel time 20

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is striking. As described in the Introduction, there is a growing literature that investigates whether and how firms subject to disclosure requirements ’game’ these programs in various ways. Here, we consider a novel type of gaming that results from the fact that, in this setting,

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firms’ quality is measured relative to a target that they themselves set. These findings suggest that regulators should take into account all types of potential manipulation from firms when designing disclosure programs. In our setting, the costs of manipulating reported quality appear to constrain the behavior of firms, but in settings where these

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costs are lower firms may engage in more substantial manipulation.

References

Dobbie, W., J. Rockoff, T.S. Dee and B. Jacob (2016). The Causes and Consequences of

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Test Score Manipulation: Evidence from the New York Regents Examinations. National Bureau of Economic Research Working Paper 22165.

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Dranove, D. and G. Jin (2010). Quality Disclosure and Certification: Theory and Practice. Journal of Economic Literature 48(4), 935-963.

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Forbes, S.J. (2008). The effect of air traffic delays on airline prices. International journal of industrial organization 26(5), 1218-1232.

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Forbes, S.J. and M. Lederman (2010). Does Vertical Integration Affect Firm Performance? Evidence from the Airline Industry. RAND Journal of Economics 41(4), 765-790.

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Forbes, S.J., M. Lederman and T.Tombe (2015). Quality Disclosure Programs and In-

ternal Organizational Practices: Evidence from Airline Flight Delays. American Economic Journal: Microeconomics 7(2), 1-26. Forbes, S.J., M. Lederman and Z. Yuan (2017). Do Airlines Pad Their Schedules? Mimeo.

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Jacob, B. and S. Levitt (2003). Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating. Quarterly Journal of Economics 118(3), 843-877. Mayer, C. and T. Sinai (2003). Network Effects, Congestion Externalities, and Air Traffic Delays: Or Why All Delays Are Not Evil. American Economic Review 93(4), 1194-

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1215. Mazzeo, M.J. (2003). Competition and Service Quality in the U.S. Airline Industry. Review of Industrial Organization 22(4), 275-296.

Neal, D. and D. Schanzenbach (2010). Left Behind by Design: Proficiency Counts and Test-Based Accountability. Review of Economics and Statistics 92(2), 263-283.

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Rupp, N.G., D.H. Owens, and L.W. Plumly (2006). Does Competition Influence Airline On-Time Performance?. In D. Lee (ed.), Advances in Airline Economics, Vol. I, Elsevier. Shumsky, R. (1993). Response of US air carriers to on-time disclosure rule. Trans-

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portation Research Record 1379, 9-16.

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Tables and Figures

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Figure 1: Histogram of Arrival Delays in 2002 Vertical Bar is at 15 minutes. Flights delayed 15 minutes or more are counted as late under the Department of Transportation’s definition.

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Figure 2: Flight Schedule and Flight Delay Measures

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New Reporter on Route (Dummy)

Distance in 00s of miles

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Arrival Congestion

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Departure Congestion

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Carrier’s Daily Flights at Dep. Airport

Carrier’s Daily Flights at Arr. Airport

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(1) Existing Reporters 107.7 (33.44)

(2) New Reporters 116.6 (29.14)

0.167 (0.373)

1 (0)

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Scheduled Flight Time

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Table 1: Summary Statistics Schedule Sample

5.586 (2.473)

6.210 (2.196)

23.80 (19.01)

19.23 (10.89)

23.25 (18.83)

17.34 (10.69)

174.1 (223.6)

68.84 (68.63)

169.6 (220.7) 158,440

62.76 (66.28) 13,367

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Source: Official Airlines Guide and U.S. Bureau of Transportation Statistics; authors’ calculations.

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Table 2: Summary Statistics OTP Sample (1) Existing Reporters 105.8 (33.49)

Scheduled Flight Time

103.8 (35.71)

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Actual Flight Time

81.98 (31.63)

Air Time

5.250 (22.98)

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Departure Delay

3.289 (26.94)

Arrival Delay

16.14 (36.79)

Percent Delayed ≥ 30 min.

8.609 (28.05)

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Percent Delayed ≥ 15 min.

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New Reporter on Route (Dummy)

23.42 (18.26)

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Departure Congestion

Arrival Congestion

22.79 (17.90)

Carrier’s Daily Flights at Dep. Airport

169.5 (219.1)

Carrier’s Daily Flights at Arr. Airport

163.9 (215.1)

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0.176 (0.381)

Distance in 00s of miles

5.433 (2.473) 1,539,756

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Source: U.S. Bureau of Transportation Statistics; authors’ calculations.

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Panel B: Mean Actual Flight Time Existing Reporters l With New Rep. Other 115.3 100.1 116.6 102.4 1.3 2.3

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Routes: 2002 2003 2003-2002

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Panel A: Mean Scheduled Time New Reporters Existing Reporters With New Rep. With New Rep. Other 115.8 114.9 105.4 117.4 117.6 106.5 1.6 2.7 1.1

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Routes: 2002 2003 2003-2002

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Table 3: Comparison of Means

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Routes: 2002 2003 2003-2002

Panel C: Mean Arrival Delay Existing Reporters With New Rep. Other 7.0 2.2 5.6 3.1 -1.4 0.9

Panel A shows data from the schedule sample. Panels B and C show data from the OTP sample.

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28 -0.004*** (0.001) -0.003*** (0.001) No No No Yes 171,807 0.979

1.296*** (0.394) 2.228*** (0.339)

(5) b/se

0.163*** (0.017) -0.001*** (0.0001) 0.061*** 0.089*** (0.010) (0.017) -0.0003* (0.0002) -0.002*** -0.002*** (0.001) (0.001) -0.0004 -0.0001 (0.001) (0.001) No No Yes Yes Yes Yes Yes Yes 171,807 171,807 0.980 0.981

0.071*** (0.008)

1.386*** (0.391) 2.288*** (0.335)

(4) b/se

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0.063*** (0.010)

0.073*** (0.008)

-0.620*** (0.109) 1.337*** (0.390) 2.363*** (0.343)

(3) b/se

Note: *, **, and *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels respectively. The table reports estimates of the effects of the disclosure requirement on a flight’s scheduled flight duration, measured in minutes. The standard errors are clustered at the route level.

EndpointFE CarrierFE YearQuarterFE RouteFE N R-squared

Carrier’s Daily Flights at Arr. Airport

-0.004*** (0.0001) -0.002*** (0.0001) Yes No No No 171,807 0.975

0.072*** (0.009)

0.082*** (0.008)

-0.684*** (0.108) 1.402*** (0.386) 2.096*** (0.328)

(2) b/se -0.745 (0.513)

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0.079*** (0.002)

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Carrier’s Daily Flights at Dep. Airport

Arr Congestion Squared

Arrival Congestion

Dep Congestion Squared

Departure Congestion

Route Distance

Compete w/ New Reporter x Post

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Airline is New Reporter x Post

Post

Compete w/ New Reporter

Airline is New Reporter

(1) b/se -1.845*** (0.132) -1.101*** (0.113) -0.711*** (0.028) 1.528*** (0.104) 2.120*** (0.073) 12.712*** (0.009) 0.102*** (0.002)

Table 4: Results on Schedule Time of New and Existing Reporters

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0.071*** (0.008) 0.062*** (0.010) -0.002*** (0.001) -0.000 (0.001) 171,807 0.980

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0.111 (0.229) -0.018 (0.515) -0.027 (0.402) -0.015 (0.402) 1.607 (1.204) 0.191 (0.592) 0.071*** (0.008) 0.062*** (0.010) -0.002*** (0.001) -0.000 (0.001) 171,807 0.980

(3) (4) Coeff./SE Coeff./SE 1.392*** 1.148*** (0.417) (0.406) 2.295*** 2.237*** (0.342) (0.405)

Note: *, **, and *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels respectively. The table reports estimates of the effects of the disclosure requirement on a flight’s scheduled flight duration, measured in minutes. The standard errors are clustered at the route level.

N R-squared

Carrier’s Daily Flights at Arr. Airport

Carrier’s Daily Flights at Dep. Airport

Arrival Congestion

Departure Congestion

0.071*** (0.008) 0.061*** (0.010) -0.002*** (0.001) -0.000 (0.001) 171,807 0.980

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Compete w/ New Reporter x Post x Three or More

Airline is New Reporter x Post x Three or More

Three or more carriers

Compete w/ New Reporter x Post x Last Flight

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Airline is New Reporter x Post x Last Flight

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(1) (2) Coeff./SE Coeff./SE Airline is New Reporter x Post 1.386*** 2.172** (0.391) (1.006) Compete w/ New Reporter x Post 2.288*** 4.782*** (0.335) (0.856) Airline is New Reporter x Post x Distance (in 00s) -0.127 (0.180) Compete w/ New Reporter x Post x Distance (in 00s) -0.405*** (0.139) Last Flight

Table 5: Regressions with Additional Interactions

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Delay ≥ 15 Coeff./SE -2.427*** (0.752) 0.101*** (0.014) 0.145*** (0.020) 0.002 (0.001) -0.003** (0.001) 1,514,270 0.038

Delay ≥ 30 Coeff./SE -1.413*** (0.424) 0.069*** (0.011) 0.096*** (0.012) -0.0004 (0.001) -0.003*** (0.001) 1,514,270 0.031

Note: *, **, and *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels respectively. The table reports estimates of the effects of the disclosure requirement on different dependent variables. The standard errors are clustered at the route level. Column (1) includes all flights. The remaining columns do not include cancelled flights because the outcome measures are not available for those flights.

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Dep Delay Air Time Coeff./SE Coeff./SE -0.498 0.130 (0.312) (0.146) 0.052*** -0.002 (0.008) (0.006) 0.033*** 0.074*** (0.009) (0.009) 0.001** -0.002*** (0.001) (0.001) -0.001 0.0004 (0.001) (0.001) 1,516,081 1,514,270 0.034 0.949

Table 6: Existing Reporters: Other Dependent Variables

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Sched Time Actual Time Arr Delay Coeff./SE Coeff./SE Coeff./SE Compete w/ New Reporter x Post 2.190*** 0.496** -2.182*** (0.290) (0.251) (0.593) Departure Congestion 0.078*** 0.114*** 0.088*** (0.009) (0.012) (0.013) Arrival Congestion 0.062*** 0.155*** 0.126*** (0.010) (0.014) (0.016) Carrier’s Daily Flights at Dep. -0.003*** -0.004*** 0.0004 (0.001) (0.001) (0.001) Carrier’s Daily Flights at Arr. -0.001 -0.002** -0.002** (0.001) (0.001) (0.001) N 1,539,756 1,514,270 1,514,270 R-squared 0.981 0.872 0.035

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Delay ≥ 15 Coeff./SE -5.747*** (1.723) 0.540* (0.298) 0.101*** (0.014) 0.145*** (0.020) 0.002 (0.001) -0.003** (0.001) 1,514,270 0.038

Delay ≥ 30 Coeff./SE -3.044*** (0.908) 0.265 (0.164) 0.070*** (0.011) 0.096*** (0.012) -0.000 (0.001) -0.003*** (0.001) 1,514,270 0.031

Note: *, **, and *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels respectively. The table reports estimates of the effects of the disclosure requirement on different dependent variables. The standard errors are clustered at the route level. Column (1) includes all flights. The remaining columns do not include cancelled flights because the outcome measures are not available for those flights.

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Dep Delay Air Time Coeff./SE Coeff./SE -0.894 0.100 (0.620) (0.352) 0.064 0.005 (0.105) (0.050) 0.052*** -0.002 (0.008) (0.006) 0.033*** 0.074*** (0.009) (0.009) 0.001** -0.002*** (0.001) (0.001) -0.001 0.0004 (0.001) (0.001) 1,516,081 1,514,270 0.034 0.949

Table 7: Existing Reporters: Distance Interactions

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Sched Time Actual Time Arr Delay Coeff./SE Coeff./SE Coeff./SE Compete w/ New Reporter x Post 4.706*** 0.356 -5.240*** (0.631) (0.535) (1.219) Compete w/ New x Post x Distance -0.409*** 0.023 0.497** (0.109) (0.092) (0.224) Departure Congestion 0.077*** 0.114*** 0.088*** (0.009) (0.012) (0.013) Arrival Congestion 0.062*** 0.155*** 0.126*** (0.010) (0.014) (0.016) Carrier’s Daily Flights at Dep. -0.003*** -0.004*** 0.0003 (0.001) (0.001) (0.001) Carrier’s Daily Flights at Arr. -0.001 -0.002** -0.003** (0.001) (0.001) (0.001) N 1,539,756 1,514,270 1,514,270 R-squared 0.981 0.872 0.035

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Delay ≥ 15 Coeff./SE -2.207*** (0.737) -0.788 (0.746) 0.133*** (0.017) 0.171*** (0.022) 0.004*** (0.001) -0.001 (0.001) 2,237,824 0.045

Delay ≥ 30 Coeff./SE -1.292*** (0.418) -0.348 (0.486) 0.092*** (0.014) 0.120*** (0.013) 0.0003 (0.001) -0.003*** (0.001) 2,237,824 0.038

Note: *, **, and *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels respectively. The table reports estimates of the effects of the disclosure requirement on different dependent variables. The standard errors are clustered at the route level. Column (1) includes all flights. The remaining columns do not include cancelled flights because the outcome measures are not available for those flights.

N R-squared

Carrier’s Daily Flights at Arr.

Carrier’s Daily Flights at Dep.

Arrival Congestion

Departure Congestion

Compete w/ New Reporter x Post2

Sched Time Actual Time Arr Delay Coeff./SE Coeff./SE Coeff./SE 2.018*** 0.420* -1.991*** (0.286) (0.250) (0.583) 1.940*** 0.532 -1.430** (0.390) (0.363) (0.642) 0.095*** 0.143*** 0.113*** (0.009) (0.013) (0.015) 0.068*** 0.174*** 0.156*** (0.010) (0.015) (0.017) -0.005*** -0.005*** 0.002** (0.001) (0.001) (0.001) -0.003*** -0.004*** -0.001 (0.001) (0.001) (0.001) 2,278,647 2,237,824 2,237,824 0.980 0.867 0.041

Dep Delay Air Time Coeff./SE Coeff./SE -0.403 0.084 (0.308) (0.143) -0.014 0.293 (0.455) (0.227) 0.066*** -0.001 (0.010) (0.006) 0.050*** 0.079*** (0.011) (0.009) 0.002** -0.003*** (0.001) (0.0005) -0.001 -0.001 (0.001) (0.001) 2,240,687 2,237,824 0.040 0.949

Table 8: Existing Reporters: Two Years in Post Period

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A

Appendix

A.1 Sample Construction We construct two data sets. The first, which we call the schedule sample, has data on

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airline schedules, while the second, which we call the OTP sample has data on on-time performance.

The construction of the schedule sample is as follows: We obtained flight schedule data from the Official Airlines Guide (OAG) for the first quarter of 2002 to the second quarter of 2003. These data include all flights between U.S. airports for the third week

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of every quarter. We obtain schedule data for the the third quarter of 2003 to the last quarter of 2005 from the Bureau of Transportation Statistics (BTS). The BTS data are available for every day, but we only keep the third week of every quarter from this data source in order to create a sample that is consistent with the data from the OAG. During

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this time period, the BTS data includes all carriers in our sample, whereas not all of these carriers are included in the BTS data in 2002.

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We drop flights by foreign carriers and flights with either endpoint in Alaska, Hawaii, or a U.S. territory. We identify routes that are served by a newly reporting regional car-

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rier (Atlantic Coast Airlines, Atlantic Southeast Airlines, SkyWest Airlines, and ExpressJet Airlines), and we drop all flights (by major airlines and by regional airlines) on these

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routes. We do this because there is a possibility that airlines on these routes respond to the new reporting by a regional carrier, and we do not want to confound these reponses

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with the effects we are trying to estimate. It is unclear whether regional carriers or their competitors face the same incentives for schedule lengthening as other carriers because regionals report delays under their own name, but they sell tickets to passengers under the name and brand of the major they are operating for. On the remaining routes, we drop all flights that are operated by regional carriers on 33

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behalf of another carrier. We keep flights by the following carriers: American Airlines, Alaska Airlines, Continental, Delta, America West, Northwest, Southwest, United Airlines, US Airways, JetBlue, AirTran, and ATA. We keep airports with at least ten flights per day by these carriers. We then restrict to routes that are served by at least two car-

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riers for at least four consecutive quarters. We define a route as a directional airport pair. We restrict to routes with at least two carriers because we are interested in markets with direct competition between at least two airlines. We restrict to service for at least four consecutive quarter in order to rule out seasonal markets or test markets. We drop routes that were entered by JetBlue, AirTran, and ATA during the sample period because

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we do not want to confound the effects of low-cost carrier entry with the effects of new reporting. Therefore, all routes with new reporters were already served by these carriers at the beginning of 2002. Finally, we drop routes with a distance of greater than 1000 miles because these routes are rarely served by the newly reporting carriers.

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The OTP sample is constructed as follows: We begin with the full BTS data set on on-time performance for 2002 and 2003. These data include all domestic flights by the

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carriers that are subject to the reporting requirement. We again drop all flights (by major airlines and by regional airlines) on routes with newly reporting regionals and all other

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flights that are operated by regional carriers on behalf of another carrier. This keeps flights by the following carriers: American Airlines, Alaska Airlines, Continental, Delta,

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America West, Northwest, Southwest, United Airlines, US Airways, JetBlue, AirTran, and ATA. We drop the last three carriers in this list because they only appear in the data

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in our post period.

We keep the same airports and routes as we did for the construction of the schedule

sample. We then drop observations which we believe may have erroneous or miscoded information. Specifically, we drop flights that are recorded as departing more than 90 minutes before their scheduled departure. We drop flights with a negative air time or a

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negative actual flight time. We drop flights with a departure time or arrival time greater than 24 hours. We also drop flights that are delayed past midnight of the following day because the delays appear to be miscoded. Finally, we drop flights with departure delays or arrival delays of greater than twelve hours since they typically will have to be

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rescheduled for the following day.

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A.2 Appendix Tables Table A.1: Total Passenger Revenues by Airline ($ millions) 2005 1,447 2,118 375 16,572 626 7,943 11,346 750 1,623 372 8,838 7,092 12,460 7,555 14,332 93,449

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2000 2001 2002 2003 2004 624 665 733 918 1,041 1,577 1,565 1,624 1,788 1,961 242 259 278 340 384 2,176 1,933 1,920 2,108 2,192 16,371 16,463 14,418 14,236 14,983 750 778 820 1,006 1,019 8,062 7,156 6,585 6,556 7,071 14,138 11,876 10,768 10,272 10,823 475 488 615 627 700 310 529 965 1,221 380 347 306 260 279 9,523 8,219 7,753 7,617 8,432 5,397 5,290 5,237 5,612 6,116 3,243 16,603 13,466 11,519 10,619 11,954 7,556 3,580 5,224 4,925 5,051 6,505 8,552 5,248 9,530 12,419 93,622 80,947 73,577 77,379 85,646

AN US

1998 439 1,371 193 1,853 14,688 510 6,388 13,179 354 312 7,513 3,964 2,895 15,202 7,021 5,170 81,052

M

1995 958 179 1,443 13,326 355 4,354 11,386 298 205 7,762 2,761 2,836 13,027 6,268 4,677 69,835

ED

Airline AirTran∗ Alaska Aloha America West∗∗ American ATA Continental Delta Hawaiian JetBlue Midwest Express Northwest Southwest TWA∗∗∗ United US Airways Others Total

AC

CE

PT

This table presents the total passenger revenues of each ‘large’ airline in millions of U.S. dollars from select years from 1995-2005, collected from the U.S. Bureau of Transportation Statistics (BTS). ∗ AirTran Airways was officially formed on November 17, 1997, when ValuJet Airlines acquired Airways Corporation, the holding company for AirTran, and then adopted the name AirTran Airways. ∗∗ America West merged with US Airways in 2005. ∗∗∗ TWA merged with American in 2001.

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2001 2002 0.8 1.0 1.9 2.2 0.3 0.4 2.4 2.6 20.3 19.6 1.0 1.1 8.8 8.9 14.7 14.6 0.6 0.8 0.4 0.7 0.4 0.4 10.2 10.5 6.5 7.1 16.6 15.7 4.4 7.1 10.6 7.1

2003 1.2 2.3 0.4 2.7 18.4 1.3 8.5 13.3 0.8 1.2 0.3 9.8 7.3 13.7 6.4 12.3

AN US

2000 0.7 1.7 0.3 2.3 17.5 0.8 8.6 15.1 0.5 0.4 10.2 5.8 3.5 17.7 8.1 6.9

M

1998 0.5 1.7 0.2 2.3 18.1 0.6 7.9 16.3 0.4 0.4 9.3 4.9 3.6 18.8 8.7 6.4

ED

Airline 1995 ∗ AirTran Alaska 1.4 Aloha 0.3 ∗∗ America West 2.1 American 19.1 ATA 0.5 Continental 6.2 Delta 16.3 Hawaiian 0.4 JetBlue Midwest Express 0.3 Northwest 11.1 Southwest 4.0 ∗∗∗ TWA 4.1 United 18.7 US Airways 9.0 Others 6.7

CR IP T

Table A.2: Percent of Passenger Revenues by Airline 2004 2005 1.2 1.5 2.3 2.3 0.4 0.4 2.6 17.5 17.7 1.2 0.7 8.3 8.5 12.6 12.1 0.8 0.8 1.4 1.7 0.3 0.4 9.8 9.5 7.1 7.6 14.0 13.3 5.9 8.1 14.5 15.3

AC

CE

PT

This table presents the percent of passenger revenues by airline from select years from 1995-2005, collected from the U.S. Bureau of Transportation Statistics (BTS). ∗ AirTran Airways was officially formed on November 17, 1997, when ValuJet Airlines acquired Airways Corporation, the holding company for AirTran, and then adopted the name AirTran Airways. ∗∗ America West merged with US Airways in 2005. ∗∗∗ TWA merged with American in 2001.

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