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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 02115 [email protected]

Carola Schenone

McIntire School of Commerce University of Virginia

Charlottesville, VA 22904 [email protected]

Draft: September 7, 2015

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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.

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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

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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, 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. 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

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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 2013. Data on executive contracts and bonuses were gathered manually using firm proxy

statements.

2.1 Data Sources

2.1.1 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.

2.1.2 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

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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-quarter-

month-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 quarter-

year. 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,

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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.

2. 2 Variable definitions

2.2.1 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

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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.

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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).

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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:

_ 1 0

3. 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.

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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 non-

negative. 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

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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.

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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 on-

time 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

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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.

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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 route-

carrier 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 1993-2013, 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

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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 -1.90 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 on-

time 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

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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.

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References

Aggarwal, Rajesh K., and Andrew A. Samwick (1999), ““Executive Compensation, Relative Performance Evaluation, and Strategic Competition: Theory and Evidence,” Journal of Finance 54: 1999-2043.

Ciliberto, Federico, and Carola Schenone (2012a), “Are the Bankrupt Skies the Friendliest?” Journal of Corporate Finance, 18: 1217-1231.

Ciliberto, Federico, and Carola Schenone (2012b), “Bankruptcy and Product-Market Competition: Evidence from the Airline Industry,” International Journal of Industrial Organization, 30: 564–577.

Holmstrom, Bengt and Paul Milgrom (1987), “Aggregation and Linearity in the Provision of Intertemporal Incentives,” Econometrica, 55: 303-328.

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 2011. 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 72,146 177,7521 13,244 15,652 28,896

118,850 87,798 206,648

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

0 151,443 112,598 264,0411 23,270 28,631 51,901

174,713 141,229 315,942

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

0 56 13 691 353 168 521

409 181 590

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 2011. 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

Arrival Delays (minutes) Departure Delays (minutes)

Mean delay

Mean non‐negative delay

Fraction Flights delayed 15’ or more

Mean delay

Mean non‐negative delay

Fraction Flights delayed 15’ or more

On Itself = 0 On Others = 0 N=105,606

8.15 (0.10)

11.27 (0.09)

0.22(0.002)

8.14(0.08)

8.70 (0.07)

0.16(0.001)


5.36 (0.22)

8.88 (0.19)

0.19(0.004)

5.50(0.15)

6.49 (0.14)

0.13(0.003)


5.01 (0.38)

8.21 (0.32)

0.17(0.0079)

6.31(0.30)

6.74 (0.29)

0.13(0.006)

On Both =1 N=15,652

8.80 (0.61)

12.05 (0.48)

0.24(0.01)

6.47(0.43)

7.93 (0.38)

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


133.042.20

133.05(2.20)

15.64(0.01)


143.537.51

143.39(7.50)

10.83(0.00)


131.269.82

129.96(9.80)

15.97(0.49)

On Both =1 N=15,652

109.1911.16

111.52(11.06)

14.48(0.86)

Table 2 (Continuation)

Panel C: Capacity

Departures Scheduled Departures Performed Seats Quarterly Load Factor

On Itself = 0 On Others = 0 Obs=151, 443

329.87 (244.60)

323.01(238.18)

42,384 (34008)

0.66(0.14)

On Itself = 1 On Others = 0 Obs=112,598

312.37 227.617

299.91221.39

33,308 (32349)

0.73(0.13)

On Itself = 0 On Others = 1 Obs=23,270

359.83 (266.50)

350.15(256.99)

44,116 (35,422)

0.71(0.13)

On Both =1 Obs=28,631

322.17 (234.09)

310.74(228.38)

39,879 (34,433)

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

0.20(0.05)

0.190.05

0.01(0.02)


0.25(0.03)

0.24(0.03)

0.02(0.01)


0.18(0.07)

0.17(0.07)

0.01(0.02)

Lag x2 On Both =1 168 Obs

0.16(0.05)

0.16(0.05)

‐0.004(0.02)

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 ‐0.111 (0.007)

‐0.068 (0.012)

0.069(0.013)

‐0.049(0.002)

‐0.042(0.004)

0.038(0.004)

‐0.072(0.003)

‐0.063(0.004)

0.05(0.005)

Observations 206,648 206,648 206,648

Groups 8,051 8,051 8,051

Min obs. per group 1 1 1

Avg. obs. per group 26 26 26

Max obs. per group 84 84 84

F‐Stat 253 455 357

R‐2 within 0.1079 0.1788 0.1458

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 ‐0.059 (0.004)

‐0.017(0.007)

0.043(0.008)

‐0.041(0.003)

‐0.026(0.004)

0.038(0.005)

‐0.052(0.003)

‐0.046(0.005)

0.061(0.006)

Observations 206,648 206,648 206,648

Groups 8,051 8,051 8,051




F‐Stat 324 545 485

R‐2 within 0.1341 0.2067 0.1885



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_Timerjt carrier j’s flight time for route r at time t as reported by j in the Computer Reservation System (CRS); Actual_Elapsed_Timerjt is

the actual flight time recorder for carrier j in route r at time t; and Pct_Routes_Servedrjt. 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.

CRS Time Actual Elapsed Time Log (Percent Routes Served)

Incentive

On Itself On Others Both On Itself On Others Both On Itself On Others Both

Percent Departure delay 0.005 (0.001)

0.009(0.002)

0.018(0.002)

0.004(0.001)

‐0.004 (0.002)

0.009(0.002)

0.04(0.002)

‐0.048(0.003)

‐0.01(0.003)

Observations 206,648 206,648 206,648

Groups 8,051 8,051 8,051




F‐Stat 166 151 177

R‐2 within 0.0737 0.0673 0.0780



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: Seatsjrt 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. Passengersjrt is the sum of all passengers carrier j transported in route r at time t. Pass_to_Seatsjrt is the sum of the daily ratios of passengers transported over seats available. In Panel B: is the total number of departures scheduled Departures_Scheduledjrt, and

performed Departures_Performedjrt, 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 0.03

(0.003) ‐0.04(0.005)

‐0.08(0.005)

0.05(0.003)

‐0.03 (0.005)

‐0.08(0.005)

0.01(0.00)

0.01(0.00)

‐0.000.00

Observations 315,924 315,924 315,924

Groups 10,655 10,655 10,655

Min. obs. per group 1 1 1



F‐Stat 627 458 1353

R‐2 within 0.1632 0.1247 0.2964



Panel B: Departures Scheduled and Performed

Log (Departures Scheduled) Log (Departures Performed)

Incentive Itself Others Both Itself Others Both

On Time Arrival Incentive 0.07

(0.003) 0.02

(0.004) ‐0.11 (0.005)

0.07(0.002)

‐0.00(0.004)

‐0.11(0.005)

Observations 315,924 315,924

Groups 10,655 10,655

Min obs. per group 1 1

Avg. obs. per group 30 30

Max obs. per group 84 84

F‐Stat 615 640

R‐2 within 0.1607 0.1663

Bankruptcy Controls Yes Yes

Carrier Active in Market Yes Yes

Table 6: The Impact of managerial incentives for on‐time arrivals on carrier profitabilityThe regression is of the form

_ _ _ _ _ _

_∈ , , , , , ,

is one of three measures of financial performance:

,

,

for carrier j in year‐

quarter 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

Lag x2 Both

Percent change 0.05 (0.03)

‐0.02 (0.03)

‐0.05(0.03)

0.05(0.03)

‐0.004 (0.03)

‐0.05(0.03)

0.003 (0.011)

‐0.010(0.011)

0.001 (0.011)

Observations 590 590 590

Groups 9 9 9




F‐Stat 4.68 2.17 10.84

R‐2 within 0.4097 0.2433 0.6529

Op. carrier FE Yes Yes Yes

Year Quarter FE Yes Yes Yes

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 1993‐2013, 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 _ .

CO DL AA UA US WN

_ 1.15***

(0.21)

1.82***

(0.16)

1.25***

(0.15)

0.85***

(0.19)

‐0.00

(0.26)

‐2.85***

(0.21)

_ _ 1.18***

(0.08)

‐0.50***

(0.11)

2.55***

(0.10)

1.94***

(0.12)

‐4.75***

(0.24)

‐0.25

(0.35)

_ _∗ _

‐1.90***

(0.27)

‐4.04***

(0.22)

‐0.20

(0.22)

2.05***

(0.28)

‐1.34***

(0.38)

0.55

(0.38)

F‐Stat 80 208 301 219 280 81

R‐Squared 0.01 0.03 0.06 0.05 0.14 0.05

Nu Obs. 20,874 17,545 13,500 11,678 4,695 6,426

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