Incentives and Competition in the Airline Industry

Transcription

1 Preliminary and Incomplete Comments Welcome Incentives and Competition in the Airline Industry Rajesh K. Aggarwal D Amore-McKim School of Business Northeastern University Hayden Hall 413 Boston, MA r.aggarwal@neu.edu Carola Schenone McIntire School of Commerce University of Virginia Charlottesville, VA schenone@virginia.edu Draft: September 7, 2015

2 Incentives and Competition in the Airline Industry Abstract: We examine how performance changes at airlines in response to a change in incentives for their executives. When airline executives are given bonuses based on on-time arrival performance, on-time arrival does indeed improve. Interestingly, competitors on the same routes also improve their on-time performance, even when the executives of the competitors did not receive bonuses based on their own on-time performance. As a result, there is no improvement in airline financial performance. Our results suggest that incentives simply heighten competition in on-time performance, which then erodes any possible financial gains from improved service. 1

3 1. Introduction While incentives for executives have been the subject of an enormous amount of academic research, very little is known about what managers do in response to incentives. For example, much of the managerial incentives literature focuses on grants of stock and stock options, and then examines performance measures such as stock or accounting returns. What is unexplored is what actions managers take in response to incentives to affect stock or accounting returns. Do managers reduce labor costs, or build new factories, or enter new markets, or try to increase customer satisfaction, or restructure their span of control, etc.? In short, there are an enormous number of actions managers could take in response to changes in incentives, and even isolating which actions are feasible for managers is a daunting challenge. As a result, research has focused on end outputs (stock returns or accounting measures) and ignored managerial inputs (actions). However, in principal-agent models (e.g., Holmstrom and Milgrom (1987)), it is managerial actions that are directly influenced by incentives, while outputs are measured with noise. Thus, understanding how managerial actions respond to changes in incentives is a crucial step in understanding how incentives influence firm performance. Further, relatively little is known about how competitors respond to incentives. Specifically, when a firm changes managerial incentives, this presumably induces managers to take certain actions, and may induce or provoke a strategic response from industry competitors. Indeed, firms may alter incentives strategically for precisely this reason (see Aggarwal and Samwick (1999)). The empirical challenge is again in isolating the actions taken by managers in response to incentives, and the actions taken by competitors in response to another firm s changes in incentives. We address these issues by looking at a specific industry, the airline industry, and at specifically delineated incentives that have measurable outcomes very specifically tied to actions. Notice that we do not have the actual actions themselves. Instead, we argue that the outcomes we measure are a relatively noise free transformation of actions, and thus represent a valid measure of actions. We focus 2

4 on bonuses provided by airlines to executives for on-time arrival performance. On-time arrival is a key metric for customer satisfaction in the airline industry. Over the past 20 years, virtually every surviving airline has adopted on-time arrival as a measure used in the provision of executive bonuses. Importantly for us, the introduction of bonuses tied to on-time arrival has been staggered in time across airlines. We are able to identify when each airline adopted on-time arrival bonuses. Second, the Bureau of Transportation Statistics provides data for on-time performance for all sufficiently large airlines in the United States. Thus, we can directly see how an airlines on-time performance has varied in response to changes in incentives based on on-time arrival. Moreover, we are able to disaggregate on-time performance into departure times, scheduled flight times, and arrival times, thus more precisely seeing how the airline responds to the introduction of the incentive for executives. Further, since on-time performance is a key metric of customer satisfaction, it is also an area in which airlines can and do compete with each other. We are able to see how competitors respond to the introduction of an on-time arrival incentive by rivals. For example, competition in on-time performance may be either competition in strategic complements or strategic substitutes. If competition is in strategic complements, then instituting an on-time arrival incentive may cause both the initiating firm and its rivals to toughen competition. This would increase consumer surplus but not result in better financial performance for the firm that initiates an on-time arrival incentive. We find that when airline executives are given bonuses based on on-time arrival performance, ontime arrival does indeed improve. Interestingly, competitors on the same routes also improve their ontime performance, even when the executives of the competitors did not receive bonuses based on their own on-time performance. When both executives of the own and rival airline receive bonuses based on on-time performance, both airlines improve on-time performance on the routes on which they compete, but not as much as if only one had instituted an incentive. As for financial performance, there is no improvement in airline profitability, as both revenues and expenses increase after the incentive is 3

5 initiated. Our results suggest that incentives simply heighten competition in on-time performance, which then erodes any possible financial gains from improved service. This paper is organized as follows. We describe our data and variable construction in Section 2. Section 3 presents our econometric specifications and details our identification strategy. Section 4 presents our results and Section 5 concludes. 2. Data The airline data in our work is a compilation of different databases from the Office of Airline Information at the Bureau of transportations Statistics (BTS). The sample ranges from January 1993 to December Data on executive contracts and bonuses were gathered manually using firm proxy statements. 2.1 Data Sources Data on Airline Executives and Performance Based Incentives Data on executive bonuses were collected from firm proxy statements (Form DEF 14a). All proxy statements were read to see if bonuses depended on on-time arrival or performance. Firms generally disclose key measures for firm bonuses. Firms are not required to disclose how much of the bonus depends upon each measure, and they generally do not do so Airline Performance Data Ontime performance: Arrival and departures delays, CRS and elapsed times The Bureau of Transportation Statistics (BTS) collects on-time performance data. It records daily data reported by US certified air carriers that account for at least one percent of domestic scheduled passenger revenues. The unit of observation in this data is a route. A route is part of a market. This distinction is important. A market is created by a trip break. Trip Breaks are points in the itinerary at which a passenger is assumed to have stopped for a reason other than changing planes. For example: an 4

6 itinerary BOS-LAS-BOS would have two markets BOS-LAS and LAS-BOS. The trip break occurred at LAS. Thus, for the on-time data, the unit of observation is a route, r, carrier, c, and year-quartermonth-day. This database provides detailed information on daily departure and arrival statistics (scheduled departure and arrival times, actual departure and arrival times, scheduled and actual flight times, departure delays, wheels-on and -off times, and taxi times) as well as reports of cancellation and diversion. We collapse this daily data to quarterly data taking the average and median of the relevant variables for each carrier and market, across days and months in a year quarter. The numbers of observations for these data are given in Table 1, Panel A. T-100 Domestic Segment of Form 41-Traffic: Passengers enplaned, seats available, load factors, number of departures performed and schedules The BTS reports capacity data such as number of enplaned passengers, and number of available seats, departures scheduled and performed, load factor and frequency of flights in the T100 Domestic Segment of Form 41-Traffic. This database is not a sample of flights, but a record of all monthly flight segments between an origin and destination airport located within the US boundaries or its territories for US certified air carriers that account for at least one percent of domestic scheduled passenger revenue. For thess data, the unit of observation is a route, carrier, year-month. We collapse the data to a quarterly basis adding the relevant variables for each carrier and market across months in a quarteryear. The numbers of observations for these data are given in Table 1, Panel B. Financial Data Data on a carrier s financial performance is from the BTS under Schedules B1 and P1.2 of the Domestic Segment Form 41-Financial Data. For large certified U.S. carriers with annual operating revenues of $20M or more, Schedule B1 contains quarterly operating balance sheet statements, and includes items such as current and total assets, cash, accounts receivables, short and long term debt, 5

7 etc.; and Schedule P1.2 provides quarterly profit and loss statements, and includes operating revenues, expenses and profits, depreciation and amortization, etc. The unit of observation for this dataset is a carrier c, year-quarter t. The numbers of observations for these data are given in Table 1, Panel C Variable definitions Operations Performance Measures Arrival delays are calculated as the difference between scheduled and actual arrival times. The BTS defines Actual Arrival (Act Arr) time as The time the aircraft touches down upon arrival, and Scheduled Arrival (Sch Arr) time as The scheduled time that an aircraft should cross a certain point (landing or metering fix). For each reporting carrier j, on date (day, month, year) dmy, flying route r, the categorical variable _ _15,, takes the value of one if carrier j, on day dmy, serving route r is delayed 15 minutes or more. We collapse this data to a quarterly level as follows. For carrier j serving route r during a year-quarter t, we calculate the total number (fraction) of flights delayed 15 minutes or more as the sum (average) of _ _15,, across days and months in a quarter. We also calculate the minutes of arrival delays. Using the actual arrival time,, and the scheduled arrival time,, we calculate the minutes of arrival delay, _ _,,, as the difference between actual and scheduled arrival times. If a flight arrives ahead of time, _ _,,, is negative. Since we are interested in actual delays we define _ _ _ _,, as _ _,,, if this is non-negative, and zero if negative. We similarly aggregate the data to a year quarter observation: _ _ _,,, _ _,, _,, Table 2, Panel A, reports summary statistics on arrival delays for the entire sample of firms used in the estimation. The BTS dataset includes data that carriers provide to the Computer Reservation System (CRS). The CRS provides information on airline schedules, fares, and seat availability to travel agencies and 6

8 allow agents to book seats and issue tickets. The variable CRS scheduled elapsed time measures the difference between the scheduled arrival and departure time as reported by the airlines to the CRS. If carriers wanted to pad the flight time as a response to on time arrival incentives, then we should see a significant increase in CRS scheduled elapsed time. Again this data is on a day, month and year basis, and we aggregate to a year quarter observation as with the previous variables. Table 2, Panel B, reports summary statistics for scheduled and actual elapsed times. Executives receiving an on-time performance bonus might strategically change the number of scheduled and performed flights in an attempt to turn around the planes faster and meet departure and arrival times. Executives could also introduce changes in the number of passengers and seats available as a way to expedite the boarding and deplaning of passengers, making it easier for the carrier to depart on time. We therefore use the capacity data reported in the T100 Domestic Segment and define for,, carrier j in route r on year quarter t:,,, where,, is the monthly record of seats, passengers, departures scheduled, and departures performed. We define the quarterly load factor as the ratio of revenue passenger per mile to the available seats per mile. Table 2, Panel C, reports summary statistics for capacity and capacity utilization. If the on-time bonus incentive works to reduce delays and improve a carrier s efficiency, then the carrier s financial performance should improve. Lower delays can increase demand for the carrier s flights and allow carriers to increase revenues. However, if a carrier s on-time incentive translates into more competition across airlines serving the routes that the incentive-initiating carrier serves, the financial performance effect of the incentive might be eroded away. To consider this we use the financial data in Schedules B1 and P1.2 of the Domestic Segment Form 41-Financial Data, and use a carrier s quarterly operating revenue and expenses as a fraction of total assets, as well as operating profits, to test this. Table 2, Panel D, reports summary statistics for financial performance. 7

9 Own Incentive effect To capture a carrier s own incentive effect, define for all routes r served by carrier c at time t: _ 1 0 Therefore, the categorical variable _ takes the value of one for flights (observations) operated by carrier c, at the time carrier c has an incentive. If we are interested in the effect any carrier s incentive has on that carrier, we use: Effect on competitors _ We are also interested in identifying if a carrier s performance based incentive has an impact on the performance of its product market competitors. To capture the effect of carrier c s incentive at time t on competitor j serving the same route r as carrier c at the time of c s incentive, define: _ 1, 0 Thus this categorical variable turn on for flights operated by carrier j in routes where it competes with carrier c, and at the time c has an incentive. If we are interested in any carrier c s incentive on all competitors flying the same route as c at time t, define: _ When any n-competitors serving route r have an incentive at time t: Tables 1 and 2 provide breakdowns of the numbers of observations and summary statistics for the operating and financial performance variables for the subsamples where 1, 1, 1 (and both 0 and 0). 8

10 Market presence In the econometric specification we need to control for whether a carrier s presence in a market generates a particular competitive dynamic for the carriers serving that market, regardless of the existence of a performance based incentive. To control for carrier c s presence in route r at time t let: _ Econometric Specification Let be the specific performance measure for carrier j, in market m, at time t (e.g., on-time arrival (departure); CRS elapsed time; quarterly load factor, etc.):,,,,,, _ _ Where is a route-carrier fixed effect; is a year-quarter fixed effect; and is an idiosyncratic unobservable. For financial performance data, the unit of observation is a carrier j, on a year-quarter t. The econometric specification is,,,,,,, _ where is a carrier fixed effect, is a year-quarter fixed effect, and an idiosyncratic unobservable. 9

11 4. Results Table 3, Panel A, provides results from our baseline specification. The left three columns examine the reduction in the minutes of delay. Airlines that institute an on-time arrival incentive significantly decrease their arrival delays. A typical flight is approximately 135 minutes long from Table 2, Panel B. Instituting an on-time arrival incentive reduces arrival delays by 11.1% of 135 minutes, or 15 minutes. For competitors, if a rival airline in the same market (routes or city-pairs) initiates an on-time arrival incentive, then the competitor airline that did not introduce the incentive still significantly reduces its arrival delays by 6.8% of 135 minutes, or 9.2 minutes. If both airline competitors have an on-time arrival incentive, then there is no incremental reduction in delays. In this case, both having an incentive offsets the reduction in delays from the rival having an incentive by an incremental and statistically significant 6.9%. The net effect for both airlines is to reduce their delays by 11% (11.1% + 6.8% - 6.9%) or 14.9 minutes. The middle three columns restrict attention to situations in which the arrival delay is nonnegative. Here we exclude cases in which flights arrive early (prior to their scheduled arrivals). Flights arriving early were included in our previous results, partially explaining the magnitude of the reduction of the delays. When we exclude early arrivals, we find that instituting an on-time arrival incentive reduces delays by 4.9%, or 6.6 minutes on a 135 minute flight. The effect on rivals is a reduction in delays of 4.2%, or 5.7 minutes. If both competitors have on-time arrival incentives, then the total effect is 4.9% + 4.2% - 3.8% = 5.3%, or a reduction in delays of 7.2 minutes. All of these effects are statistically significant. The right three columns consider the fraction of flights delayed more than 15 minutes, an industry standard definition for a flight to not be on-time. Instituting an on-time arrival incentive reduces the fraction of flights delayed more than 15 minutes by a significant 7.2%. For rivals, the fraction of flights delayed more than 15 minutes is reduced by a significant 6.3%. If both competitors 10

12 on a route have on-time arrival incentives, then the effect of the reduction for both is attenuated by a significant 5.0%, so that the total fraction of flights delayed more than 15 minutes is 7.2% + 6.3% - 5.0% = 8.5% for both airlines. 1 We next dig deeper into the sources of the reduction in the arrival delays. In Table 3, Panel B, we consider departure delays. In the left three columns, for airlines instituting an on-time arrival incentive, their departure delays decrease by a significant 5.9% or 8.0 minutes. For rival airlines on the same route without an incentive, their departure delays decrease by a significant 1.7% or 2.3 minutes. When both have an incentive, these effects are significantly attenuated by 4.3%, so that the total departure delay decrease for both competitors is 5.9% + 1.7% - 4.3% = 3.3%, or 4.5 minutes. We see similar effects for the non-negative departure delays (eliminating those flights that depart the gate early) and for the fraction whose departures are delayed more than 15 minutes. For non-negative departure delays when both have an incentive, the total decrease in departure delays is 4.1% + 2.6% - 3.8% = 2.9%, or 3.9 minutes. This 3.9 minutes reduction in departure delays accounts for 54% of the 7.2 minutes reduction in arrival delays. An interesting effect here is that both airlines instituting an incentive actually results in less of a reduction in departure delays than does only one airline instituting an incentive. In principle, if one airline introduces an incentive, the rival airline would be better off not introducing an incentive, while still getting the benefit of a reduction in departure delays. Of course, reducing departure delays is not a goal in and of itself, since the incentives are for on-time arrivals. The key point here is that airlines seem to respond to an on-time arrival incentive by attempting to get flights to depart the gate closer to on-time. 1 As a robustness check, we consider whether the reduction in arrival delays that we document in Table 3, Panel A, is concentrated in problem markets. We define problem markets as routes in which flights are delayed more than 15 minutes over 20% of the time prior to the initiation of the on-time arrival incentive. In unreported results, we find that essentially all of the reductions in delays happen in the problem markets. 11

13 Another possibility is that airlines might try to game the on-time arrival incentive. When faced with a new incentive, executives might strategically increase the scheduled amount of time for a flight. To examine this, we look at both the scheduled amount of time for flights and the actual duration of the flights in Table 4. The left panel shows that an airline instituting an on-time arrival incentive increases its scheduled flight times by 0.5%, or about 41 seconds. While statistically significant, this effect is not economically meaningful. A rival airline will increase its scheduled flight times by 0.9%, or about 1.2 minutes. If both have an on-time arrival incentive, then both will incrementally increase their scheduled flight times by 1.8% or 2.4 minutes, so that the total effect would be 0.5% + 0.9% + 1.8% = 3.2%, or 4.3 minutes, suggesting some degree of collusive behavior. The middle panel considers actual elapsed times. An airline instituting an on-time arrival incentive sees its actual elapsed times increase 0.4% or about 32 seconds. A rival airline decreases its elapsed flight times by 0.4%, or about 32 seconds. While statistically significant, these effects are not economically meaningful. If both have an on-time arrival incentive, then both will incrementally increase their elapsed flight times by 0.9% or 1.2 minutes, which is the same as the total effect (0.4% - 0.4% + 0.9%). This is again a relatively small magnitude, but suggests that airlines increase their scheduled times by much more than the actual times needed to fly these routes. In situations in which both airlines on a route have an incentive, scheduled times increase by 4.3 minutes while actual elapsed times only increase by 1.2 minutes. This incremental 3.1 minutes of scheduled time can account for 43% of the reduction of 7.2 minutes in non-negative arrival delays when both competitors have an ontime arrival incentive. The right panel considers markets served by the airlines in response to the on-time arrival incentive. An airline that institutes an on-time arrival incentive expands the number of routes that it serves by a significant 4.0%. This suggests that the reduction in delays is not due to the creation of more slack by eliminating routes. The rival airline by contrast reduces the number of routes served by 12

14 a significant 4.8%, suggesting some competition in strategic substitutes. If both have an incentive, the routes served decrease by 1.8% (4.0% - 4.8% - 1.0%). Table 5 digs deeper into capacity. Panel A considers seats, passengers, and quarterly load factors. Instituting an on-time arrival incentive is associated with an increase in seats (3%) and passengers (5%) on each route. For the rival airline on the same route, seats shrink by 4% and passengers shrink by 3%, suggesting a significant increase in competition on these routes that the rival then accommodates. If both have an incentive, seats and passengers both shrink by an incremental 8%. Not surprisingly, the load factor increases for the incentive-initiating airline. It also increases for the rival airline, as the number of seats shrinks more than do passengers. Load factor does not change if both have an incentive, as seats and passengers shrink by similar amounts. 2 Panel B considers departures scheduled and departures performed. An incentive-initiating airline increases both its departures scheduled and performed by 7%. The rival airline on the same routes will increase its departures scheduled by 2%, but with no significant difference in departures performed. If both have an incentive, then the incremental effect on departures scheduled and performed is a decrease of 11%, so that the number of departures scheduled and performed shrinks by 2% and 4%, respectively, if both have an incentive. Table 6 considers the effect of on-time arrival incentives on financial performance. Here our sample consists of airline-year observations rather than route-airline-quarter observations. Given that actions are taken to improve on-time arrivals once an incentive is initiated, we ask whether this then translates into improved financial performance. For an incentive-initiating airline, we find that revenues and expenses as a fraction of assets both increase (with marginal significance), but there is no significant change in profits and the magnitude of the coefficient on profits is economically very small. For the rival airline, all of the coefficients (on revenues, expenses, and profits) are insignificant. If both have 2 Load factors take into account passenger seat miles, so it is possible that airlines may optimize their routes to focus on longer haul routes in response to an on-time arrival incentive. This may be optimal if it is easier to have longer haul flights arrive on-time as departure delays can be made up while flying if the distance is greater. 13

15 an incentive, there is a marginally significant incremental reduction in revenues and expenses, but again no significant effect on profits. Thus, we do not see significant effects on financial performance from the introduction of on-time arrival incentives. Our previous results use pooled data and specifications that are saturated with fixed effects to absorb variation by quarters, markets, and carriers. As an alternative, we can use the richness of our data to examine changes in on-time performance around the introduction of an incentive. We consider a difference-in-difference specification around the introduction of an on-time arrival incentive. Each carrier introduces an on-time arrival incentive at a different point in time (one carrier in our sample, JetBlue, does not merge with another carrier and does not introduce an incentive over our sample period). We use this fact to estimate the reduction in arrival delays for a carrier around the introduction of the incentive relative to carriers that do not have an incentive at that time. The advantage of this approach is that it allows us to estimate reduction in arrival delays by carrier at the time of the introduction of the incentive. The disadvantage of this approach is that we do not control for routecarrier fixed effects. For each of the six carriers that instituted an incentive, we select a time window of one year before and after the carrier introduced an incentive. We then select as control carrier those carriers that have not had an incentive introduced at any time before the selected time window. For example, Delta introduced an incentive in 1997, the only carrier that had an incentive in place before 1997 is Continental, so we include as control groups for Delta all carriers, excluding Continental. For carriers that instituted an incentive in the later part of our full sample period, such as Southwest, the set of control carriers is limited to those carriers that have not introduced an incentive at any point in time within our sample years of , and they include America West, Northwest, and Jet Blue. We also deal with carriers that introduced an incentive in overlapping time windows as follows: US Airways introduced an incentive in 2008 and Southwest in 2009, therefore in the one year time window we 14

16 exclude Southwest as a control for US Airways treatment. We run a difference-in-difference specification of the type: _ _ _ _ _ _ _ _ _ where _ is a categorical variable, equal to 1 if the operating carrier is carrier i; _ _ is a categorical variable equal to 1 if the observation corresponds to the time window for the year(s) after the carrier implemented an incentive. Table 7 contains the results. The coefficient of interest is _, the coefficient on the introduction of the incentive interacted with the carrier. Continental, Delta, and US Airways, show significant reductions in delays after the introduction of an incentive. For example, the coefficient for Continental of implies that Continental reduces its arrival delays by 1.9 minutes in from the year after the introduction of the incentive relative to the year prior to the incentive and relative to rival carriers without an incentive (in this case, all of them, as Continental was the first to introduce an incentive). Interestingly, United Airlines shows an increase in delays after the introduction of an ontime arrival incentive relative to rival carriers. We note that United s introduction of an on-time arrival incentive coincided with its emergence from bankruptcy. For the other two carriers, American and Southwest, there is no significant effect on delays. 5. Conclusion We find that airlines reduce the length and frequency of arrival delays in response to the introduction of on-time arrival bonuses for their executives. Because these bonuses were staggered in their introduction across airlines, we are able to identify the effect of the bonus on arrival delays. Because on-time arrival is an important quality metric for passengers and airlines, we can directly 15

17 observe the impact of an incentive on the outcome variable it is meant to influence. Interestingly, we find that competitors increase their on-time arrival performance in response to a rival introducing an incentive, even if the competitor does not have an on-time arrival incentive in place. This suggests that competition in quality is competition in strategic complements. Consistent with this, we find that there is no improvement in airline profitability after the introduction of incentives. Instead, all of the improvement in on-time performance seems to go to consumer surplus. We also find some evidence that part of the improvement in on-time performance comes from strategic gaming by airlines. Especially when two airlines on a route have on-time performance incentives, airlines will increase the scheduled time of flights. This effect can explain 43% of the improvement in on-time performance when two competitors each have an incentive. Another 54% of the improvement in on-time performance when two competitors each have an incentive can be attributed to a reduction in departure delays (getting airplanes out of the gate on time), and this represents most of the positive effect of incentives. When only one airline introduces an on-time arrival incentive, then the effect of strategic gaming is negligible, and most of the improvement comes from the reduction in departure delays. These results suggest that the introduction of on-time arrival incentives by multiple carriers in a market may in fact foster tacit collusion to the benefit of managers. 16

18 References Aggarwal, Rajesh K., and Andrew A. Samwick (1999), Executive Compensation, Relative Performance Evaluation, and Strategic Competition: Theory and Evidence, Journal of Finance 54: Ciliberto, Federico, and Carola Schenone (2012a), Are the Bankrupt Skies the Friendliest? Journal of Corporate Finance, 18: Ciliberto, Federico, and Carola Schenone (2012b), Bankruptcy and Product-Market Competition: Evidence from the Airline Industry, International Journal of Industrial Organization, 30: Holmstrom, Bengt and Paul Milgrom (1987), Aggregation and Linearity in the Provision of Intertemporal Incentives, Econometrica, 55:

19 Table 1: Summary Statistics for On Itself, On Others, and On Both The data for incentives is collected from firm proxy statements. The sample of carriers and flights is from the On Time Performance Database in Panel A, and from the T100 Dataset in Panel B; Schedules B1 and P1.2 of the Domestic Segment Form 41 Financial Data. The Bureau of Transportation Statistics (BTS) provides all three datasets. The sample ranges from January 1993 to December For panels A and B, the entries in this table correspond to the market carrier year quarter count for the categorical variables On Itself and On Others. On Itself is defined as _ where _ equals 1 if carrier c has an incentive at time t and operates in route m and the observation is for carrier c. On Others is defined as _ where _ equals 1 if carrier c has an incentive at time t, and carrier c and j operate route r at time t, and the observation corresponds to carrier j. On Both equals 1 when 1. Thus On Itself captures the effect of the on time arrival incentive for the carrier receiving the incentive; On Itself captures the effect of the on time arrival incentive for competitors of the carrier receiving the incentive; and On Both captures the effect of multiple carriers having an on time incentive at the (overlapping) same time and operating in the same route. Panel C reports financial data and uses the 2 times lagged variables On Itself and On Others. Panel A: On Itself, On Others and On Both On Time performance Note that there are 15,652 observations for which more than one carrier has an incentive at (partially) overlapping times and operate in the same market at that time. Thus, in our estimation sample, On Both equals one for 15,652 year quarter market carrier observations. On Itself On Others 0 1 Totals 0 105,606 72, , ,244 15,652 28, ,850 87, ,648 Panel B: On Itself, On Others and On Both Capacity T100 Dataset The entries in this table correspond to the market carrier year quarter count for the categorical variables On Itself and On Others for the capacity sample built using from the T100 Domestic Segment of Form 41: Air Carrier Statistics US Carriers, reported by the BTS. Stats on data for estimation sample. On Itself On Others 0 1 Totals 0 151, , , ,270 28,631 51, , , ,942 Panel C: Lag x2 On Itself, Lag x2 On Others and Lag x2 On Both Financial Data The entries in this table correspond to the market carrier year quarter count for the categorical variables Lag x2 On Itself and Lag x2 On Others for the financial data sample sourced from the BTS under Schedules B1 and P1.2 of the Domestic Segment Form 41 Financial Data. Note that there are 174 observations for which more than one carrier has an incentive at (partially) overlapping times and operate in the same market at that time. Lag x2 On Itself Lag x2 On Others 0 1 Totals

20 Table 2: Summary statistics. Arrival and departure delays (Panel A), and computer reservation system (CRS) scheduled flight times and actual flight times, as well as percent of markets served (Panel B). Panel C reports summary statistics for carrier capacity: Scheduled and Performed flights, seats and passengers per seats. Finally, Panel D reports statistics for financial data: The ratio of operating revenues expenses and profits to total assets. Data source: On Time Performance Database provided by the BTS, for Panels A and B; the T100 Domestic Segment of Form 41: Air carrier Statistics US Carriers, reported by the BTS, Panel C; and from the BTS under Schedules B1 and P1.2 of the Domestic Segment Form 41 Financial Data, Panel D. The sample ranges from January 1993 to December On Itself is defined as _ where _ equals 1 if carrier c has an incentive at time t and operates in route m and the observation is for carrier c. On Others is defined as _ where _ equals 1 if carrier c has an incentive at time t, and carrier c and j operate route r at time t, and the observation corresponds to carrier j. On Both equals 1 when 1. Thus On Itself captures the effect of the on time arrival incentive for the carrier receiving the incentive; On Others captures the effect of the on time arrival incentive for competitors of the carrier receiving the incentive; and On Both captures the effect of multiple carriers having an on time incentive at the (overlapping) same time and operating in the same route. Panel A: Arrival and Departure delays On Itself = 0 On Others = 0 N=105,606 On Itself = 1 On Others = 0 N=72,146 On Itself = 0 On Others = 1 N=13,244 On Both =1 N=15,652 Mean delay 8.15 (0.10) 5.36 (0.22) 5.01 (0.38) 8.80 (0.61) Arrival Delays (minutes) Mean nonnegative Fraction Flights delay delayed 15 or more (0.09) (0.002) 8.88 (0.19) 8.21 (0.32) (0.48) 0.19 (0.004) 0.17 (0.0079) 0.24 (0.01) Mean delay 8.14 (0.08) 5.50 (0.15) 6.31 (0.30) 6.47 (0.43) Departure Delays (minutes) Mean nonnegative Fraction Flights delay delayed 15 or more (0.07) (0.001) 6.49 (0.14) 6.74 (0.29) 7.93 (0.38) 0.13 (0.003) 0.13 (0.006) 0.15 (0.008) Panel B: CRS and actual elapsed time and percent of markets served CRS Elapsed Time Actual Elapsed time Pct Markets Served On Itself = 0 On Others = 0 N=105,606 On Itself = 1 On Others = 0 N=72,146 On Itself = 0 On Others = 1 N=13,244 On Both =1 N=15, (2.20) (7.50) (9.80) (11.06) (0.01) (0.00) (0.49) (0.86)

21 Table 2 (Continuation) Panel C: Capacity On Itself = 0 On Others = 0 Obs=151, 443 On Itself = 1 On Others = 0 Obs=112,598 On Itself = 0 On Others = 1 Obs=23,270 On Both =1 Obs=28,631 Departures Scheduled Departures Performed Seats Quarterly Load Factor , (244.60) (238.18) (34008) (0.14) (266.50) (234.09) (256.99) (228.38) 33,308 (32349) 44,116 (35,422) 39,879 (34,433) 0.73 (0.13) 0.71 (0.13) 0.77 (0.11) Panel D: Financial Performance Operating Revenues to Total Assets Operating Expenses to Total Assets Operating Profits Lag x2 On Itself = 0 Lag x2 On Others = 0 56 Obs Lag x2 On Itself = 1 Lag x2 On Others = 0 13 Obs Lag x2 On Itself = 0 Lag x2 On Others = Obs Lag x2 On Both =1 168 Obs 0.20 (0.05) 0.25 (0.03) 0.18 (0.07) 0.16 (0.05) (0.03) 0.17 (0.07) 0.16 (0.05) 0.01 (0.02) 0.02 (0.01) 0.01 (0.02) (0.02)

22 Table 3: The Impact of managerial incentives for on time arrivals on delays at arrival (Panel A) and departures (Panel B). The regression is of the form _ _,,,,,, is one of three measures of arrival or departure delays (minutes delayed, non negative minutes delays, fraction of flights delayed more than 15 minutes), for carrier j in route r at year quarter t. Minutes of delay for j in route r at t, is the average, across the daily delays in that route and time. equals 1 when carrier j at time t has an incentive and the observation corresponds to carrier j at t; is 1 when carrier j at time t serving route r, has an incentive and the observation corresponds to a competitor of j in route r at t. equals 1 when equal 1. _ controls for periods in which carrier c serving route r operates under bankruptcy protection; _ controls for the presence of carrier c in route r, at time t. is a route carrier fixed effect; is a year quarter fixed effect; and is an idiosyncratic unobservable. Data source: On Time Performance Database provided by the BTS. Panel A: Arrival Delays Log (Arrival Delay) Log (Non negative Arrival Delay) Log (Fraction Delayed>15 ) Incentive On Itself On Others On Both On Itself On Others On Both On Itself On Others On Both Percent Arrival delay (0.007) (0.012) (0.013) (0.002) (0.004) (0.004) (0.003) (0.004) (0.005) Observations 206, , ,648 Groups 8,051 8,051 8,051 Min obs. per group Avg. obs. per group Max obs. per group F Stat R 2 within Bankruptcy Controls Yes Yes Yes Carrier Active in Market Yes Yes Yes Panel B: Departure Delays Log (Departure Delay) Log (Non negative Departure Delay) Log (Fraction Delayed>15 ) Incentive On Itself On Others On Both On Itself On Others On Both On Itself On Others On Both Percent Departure delay (0.004) (0.007) (0.008) (0.003) (0.004) (0.005) (0.003) (0.005) Observations 206, , ,648 Groups 8,051 8,051 8,051 Min obs. per group Avg. obs. per group Max obs. per group F Stat R 2 within Bankruptcy Controls Yes Yes Yes Carrier Active in Market Yes Yes Yes (0.006)

23 Table 4: The Impact of managerial incentives for on time arrivals on actual flight times and on the airline s scheduled flight time as reported to the Computer Reservation System (CRS) The regression is of the form _ _,,,,,, is one of three variables: CRS_Time rjt carrier j s flight time for route r at time t as reported by j in the Computer Reservation System (CRS); Actual_Elapsed_Time rjt is the actual flight time recorder for carrier j in route r at time t; and Pct_Routes_Served rjt. is the ratio of the number of routes carrier j served at time t to the number of routes all carriers served at time t. equals 1 when carrier j at time t has an incentive and the observation corresponds to carrier j at t; is 1 when carrier j at time t serving route r, has an incentive and the observation corresponds to a competitor of j in route r at t. equals 1 when equal 1. _ controls for periods in which carrier c serving route r operates under bankruptcy protection; _ controls for the presence of carrier c in route r, at time t. is a route carrier fixed effect; is a year quarter fixed effect; and is an idiosyncratic unobservable. Data source: On Time Performance Database provided by the BTS. Incentive CRS Time Actual Elapsed Time Log (Percent Routes Served) On Itself On Others Both On Itself On Others Both On Itself On Others Both Percent Departure delay (0.001) (0.002) (0.002) (0.001) (0.002) (0.002) (0.002) (0.003) Observations 206, , ,648 Groups 8,051 8,051 8,051 Min obs. per group Avg. obs. per group Max obs. per group F Stat R 2 within Bankruptcy Controls Yes Yes Yes Carrier Active in Market Yes Yes Yes 0.01 (0.003)

24 Table 5: The Impact of managerial incentives for on time arrivals on a carrier s capacity and utilization The regression is of the form,,,,,, _ _ is one of three variables in Panel A: Seats jrt is the number of available seats carrier j offers in route r at time t, and is calculated as the sum of the daily number of seats available in the airplanes used by j in r during year quarter t. Passengers jrt is the sum of all passengers carrier j transported in route r at time t. Pass_to_Seats jrt is the sum of the daily ratios of passengers transported over seats available. In Panel B: is the total number of departures scheduled Departures_Scheduled jrt, and performed Departures_Performed jrt, by carrier j in route r at time t. equals 1 when carrier j at time t has an incentive and the observation corresponds to carrier j at t; is 1 when carrier j at time t serving route r, has an incentive and the observation corresponds to a competitor of j in route r at t. equals 1 when equal 1. _ controls for periods in which carrier c serving route r operates under bankruptcy protection; _ controls for the presence of carrier c in route r, at time t. is a route carrier fixed effect; is a year quarter fixed effect; and is an idiosyncratic unobservable. Data Source: T100 Domestic Segment of Form 41: Air carrier Statistics US Carriers, reported by the BTS Panel A: Capacity and Utilization Log (Seats) Log (Passengers) Log (Quarter_Load factor) Incentive Itself Others Both Itself Others Both Itself Others Both On Time Arrival Incentive (0.003) (0.005) (0.005) (0.003) (0.005) (0.005) (0.00) (0.00) 0.00 Observations 315, , ,924 Groups 10,655 10,655 10,655 Min. obs. per group Avg. obs. per group Max obs. per group F Stat R 2 within Bankruptcy Controls Yes Yes Yes Carrier Active in Market Yes Yes Yes Panel B: Departures Scheduled and Performed Log (Departures Scheduled) Log (Departures Performed) Incentive Itself Others Both Itself Others Both On Time Arrival Incentive (0.003) (0.004) (0.005) (0.002) (0.004) (0.005) Observations 315, ,924 Groups 10,655 10,655 Min obs. per group 1 1 Avg. obs. per group Max obs. per group F Stat R 2 within Bankruptcy Controls Yes Yes Carrier Active in Market Yes Yes

25 Table 6: The Impact of managerial incentives for on time arrivals on carrier profitability The regression is of the form _ _ _ _ _ _ _,,,,,, is one of three measures of financial performance:,, for carrier j in yearquarter t. _ controls for periods in which carrier c operates under bankruptcy protection; is a year quarter fixed effect; is a carrier fixed effect, and is an idiosyncratic unobservable. Data Source: BTS under Schedules B1 and P1.2 of the Domestic Segment Form 41 Financial Data. Incentive Lag x2 On Itself Lag x2 On Others Lag x2 Both Lag x2 On Itself Lag x2 On Others Lag x2 Both Lag x2 On Itself Lag x2 On Others Percent change (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.011) (0.011) Observations Groups Max obs. per group Avg. obs. per group Max obs. per group F Stat R 2 within Op. carrier FE Yes Yes Yes Year Quarter FE Yes Yes Yes Lag x2 Both (0.011)

26 Table 7: Difference in Difference For each of the six carriers that received an incentive, we select a time window of one year before and after the carrier introduced an incentive. We then select as control carrier those carriers that have not had an incentive introduced at any time before the selecteded time window. For example, Delta introduced an incentive in 1997, the only carrier that had an incentive in place before 1997 is Continental, so we include as control groups for Delta all carriers, excluding Continental. For carriers that instituted an incentive in the later part of our full sample period, such as Southwest, the set of control carriers is limited to those carriers that have not introduced an incentive at any point in time within our sample years of , and they include America West, Northwest, and Jet Blue. We run a diff in diff specification of the type: _ _ _ _ _ _ _ _ _ where _ is a categorical variable, equal to 1 if the operating carrier is carrier i; _ _ is a categorical variable equal to 1 if the observation corresponds to the time window for the year after the carrier implemented an incentive. The coefficient of interest is _. _ 1.15 *** (0.21) _ _ 1.18 *** _ _ _ CO DL AA UA US WN (0.08) 1.90 *** (0.27) 1.82 *** (0.16) 0.50 *** (0.11) 4.04 *** (0.22) F Stat R Squared Nu Obs. 20,874 17,545 13,500 11,678 4,695 6, *** (0.15) 2.55 *** (0.10) 0.20 (0.22) 0.85 *** (0.19) 1.94 *** (0.12) 2.05 *** (0.28) 0.00 (0.26) 4.75 *** (0.24) 1.34 *** (0.38) 2.85 *** (0.21) 0.25 (0.35) 0.55 (0.38)