HIAS-E-32

The Value of Relational Adaptation in Outsourcing:
Evidence from the 2008 shock to the US Airline
Industry
Ricard Gil
Johns Hopkins University
Hitotsubashi Institute for Advanced Study, Hitotsubashi University

Myongjim Kim
University of Oklahoma

Giorgio Zanarone
CUNEF

September 2016

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8604
http://hias.ad.hit-u.ac.jp/
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The Value of Relational Adaptation in Outsourcing:
Evidence from the 2008 shock to the US Airline Industry *Ricard Gil

Myongjin Kim

Giorgio Zanarone

Johns Hopkins U

University of Oklahoma

CUNEF

Hitotsubashi Institutefor Advanced Study
Hitotsubashi University

September 2016
Abstract
In the airline industry, ex-post adaptation of flight schedules is necessary in the presenceof bad weather conditions. When major carriers contract with independent regionals,
conflicts over these adaptation decisions typically arise. Moreover, the celerity of neededadjustments requires that adaptation be informal, and hence enforced relationally. In thispaper, we theoretically analyze, and empirically test for, the importance of relational
adaptation in the airline industry. Our model shows that for relational contracts to be selfenforcing, the long-term value of the relationship between a major and a regional airline
must be at least as large as the regional’s cost of adapting flight schedules across jointroutes. Thus, when facing a shock that forces it to terminate some routes, the major ismore likely to preserve routes outsourced to regional airlines that have higher adaptation
costs, as the value of the major’s relationship with those regionals is larger. We analyze
the evolution of U.S. airline networks around the 2008 financial crisis, and we find thatconsistent with our theoretical predictions, regional routes belonging to networks withworse average weather, and hence higher adaptation costs, were more likely to survive
after the shock.
Keywords: Relational contracting, adaptation, natural experiment, airlines, outsourcing
JEL codes: L14, L22, L24, L93*

We thank Ricardo Alonso, Benito Arruñada, Mikhail Drugov, John de Figueiredo, William Fuchs, Bob
Gibbons, Bentley MacLeod, Joanne Oxley, Mike Powell, Nick Vikander, and audiences at Aarhus
University, Pompeu Fabra University, Tsinghua University, Johns Hopkins Carey, Hitotsubashi University,
the “Natural Experiments and Controlled Field Studies” Workshop in Ohlstadt (Germany), and the ALEA
and SIOE conferences, for useful comments. This study received financial support from the Spanish
Ministry of Science and Education through grant ECO2014-57131-R. The usual disclaimer applies.

1. Introduction
Adaptation to change is paramount to the success of organizations and markets. As
Hayek (1945: 523) states, “economic problems arise always and only in consequence ofchange”. Reinforcing Hayek’s point, Williamson (1991: 278) argues that “adaptability is
the central problem of economic organization,” and warns that coordinated responses tochange, which may require the cooperation of multiple parties with diverging interests
and an urgent implementation, can rarely be achieved in spot markets.
While agreeing on the importance of this fundamental adaptation problem and on the
difficulty to address it through spot market exchange, economic theory has emphasized atleast three different approaches to finding a solution. First, allocating decision authority
to a “boss”, as in firms (e.g., Simon 1951; Williamson 1991; Hart and Holmstrom 2010).
Second, agreeing ex ante on formal procedures that facilitate the renegotiation of key
decisions, as in procurement and construction contracts (Bajari and Tadelis 2001;
Chakravarty and MacLeod 2009). Third, using relational contracts—informal agreements
where coordinated adaptation is ensured by the parties’ desire to maintain a long-term
collaborative relationship (Baker, Gibbons and Murphy 2011).
This paper sheds light onto the incidence and relevance of the third solution,
relational adaptation, by empirically assessing the importance of relational contracts as asolution to the adaptation problem in outsourcing relationships. We exploit a setting thatis particularly well suited to study relational adaptation—namely, the networks of major
and regional airlines in the US. To begin with, as shown in previous empirical work by
Forbes and Lederman (2009), adaptation is key in this industry: when major carriers rely2

on outsourcing agreements with independent regional carriers to serve local routes,
important coordination challenges arise, as adverse weather conditions and otherunexpected contingencies require adapting flight schedules in ways that may collide withthe regionals’ profit-maximization objectives. In addition, outsourcing regionaltransportation offers significant labor cost advantages to major airlines, as regional
airlines are scarcely unionized. Thus, while allocating decision authority to major airlines
through integration is a potential solution to the adaptation problem (see Forbes andLederman, 2009, 2010, for convincing evidence on the incidence of adaptation inairlines), it is not always a feasible or desirable one. At the same time, formal contracts
between major and regional airlines are inherently incomplete, both ex ante and ex post,
when it comes to adaptation. On one hand, it is difficult to foresee ex ante under whatcircumstances flights on a specific route should be rescheduled, and on the other hand,rescheduling decisions must be implemented quickly, and full cooperation by the regional
airlines may be hard to verify. Finally, U.S. major airlines and independent regional
airlines are typically engaged in long-term business relationships, and hence are likely tosecure the more informal aspects of their collaboration, such as the adaptation of flightschedules, through relational contracts.
1.1. Overview of the results
To study relational adaptation in the U.S. airline industry, we begin by developing asimple model, adapted from Baker et al. (2011), where a major airline promises quasirents to an independent regional partner in exchange for compliance with the major’sschedule adaptation decisions. Following Levin (2002), we assume that deviations fromthis relational contract on one route trigger termination of the relationship between the3major and the regional on all routes, as that is the worst credible punishment. A key pointis that given the lack of court-enforcement, relational adaptation is achieved if, and only
if the long-term value of the relationship between the major and the regional is at least as
large as the regional’s present adaptation costs across all the routes in their joint network.
Thus, if the relational enforcement constraint is binding, the relationship’s value must belarger the larger the network-wide adaptation cost. A testable implication of this fact isthat facing a negative shock that induces the major to cut some routes, and given that
cutting routes may jeopardize the network’s viability, the major will choose to preserve
those routes that belong to networks with high aggregate adaptation costs, and hence highlong-term value.
We test our prediction on a comprehensive dataset of relationships between U.S.
major and regional airlines. As an exogenous industry-wide shock that induces major
airlines to cut routes, we exploit the economic crisis that unraveled after the collapse of
Lehman Brothers in 2008. Following Forbes and Lederman (2009), we proxy adaptation
costs within a major-regional network by the extent of bad weather conditions(precipitation, snow, and the lack of clear skies as inversely measured by the number offreezing months) across the routes in the network—the rationale being that in thepresence of adverse weather, major airlines are forced to reschedule more regional
flights, which in turn increases the regional airlines’ adaptation costs. Because a given
regional typically operates different sets of routes for different majors and a major may
use different regionals to operate the same route, we can include major airline, regional
airline, and route fixed effects in our regressions, thus controlling for the possibly
endogenous assignment of routes to major and regional carriers.
4Our empirical results support the hypothesis that the long-term relationships between
major and regional airlines are used to informally enforce the efficient adaptation offlight schedules. Controlling for a rich set of route and network characteristics, as well as
for major airline and regional airline fixed effects, we find that outsourcing routes in
networks characterized by high snow and precipitation are more likely to survive, andless likely to see their number of daily flights reduced, following the Lehman Brothers
shock in 2008. In contrast, routes in networks characterized by frequent freezing
weather—which Forbes and Lederman (2009) suggest to be a proxy for clear skies—areless likely to survive and more likely to experience flight reductions. Our results arerobust to the inclusion of route-level weather, route fixed effects, and route-major androute-regional fixed effects.
As a placebo test, we also study route survival in the 2003-2006 period, and we findthat absent a negative shock that pushes the major airlines to reduce route portfolios inthe less valuable networks, routes in bad weather major-regional networks are as likely to
be cut or downsized as routes in good weather major-regional networks. We also study
route survival around a different industry-wide shock—namely, the shock following the
9/11 terrorist attacks in 2001. The results there also imply overall no connection between
network-level weather variables and the likelihood of survival of outsourcing routes. This
is not surprising for two reasons. First, the impact of the 9/11 shock on the U.S. airline
industry, and on the U.S. economy as a whole, was shorter and more transitory than theimpact of the Lehman Brothers shock. In the same way, the shock itself did not
structurally affect the US airline industry in the same way as the 2008 financial crisis did.
Second, the use of regional airlines was sparse in 2001 relative to 2008. Therefore it is5not surprising to find that US major airlines did not significantly adjust their outsourced
regional networks in the 1999-2003 period.
We conclude our empirical investigation by further exploring the strategic decisionsof major airlines when they decide to sever an outsourcing relationship on a route. The
major airline may decide to stop flying that route completely or adjust to the shock
through alternative margins—namely, vertical integration and route reallocation to otherregionals. We find that routes were more likely to be integrated after the Lehman
Brothers shock if formerly flown by a regional with better network-wide weather, andhence lower continuation value. Similarly, when majors reallocated routes to otherpartners, they exclusively relied on existing partners—that is, regional airlines that wereoperating other routes for the same major airline before the shock. That means that
following the 2008 shock, major airlines cut some partnerships while intensifying theirrelationships with the stronger existing partners.
1.2. Contribution to the literature
1.2.1. Adaptation
Early empirical works have focused on how formal price adjustment provisions
facilitate adaptation and reduce its costs in procurement contracts. In particular, Masten
and Crocker (1985) show that gas supply contracts stipulate lower penalties against
breach when the supplier can more easily store the unsold gas or sell it to alternativeclients—that is, when breach by the buyer in the face of unforeseen market conditions ismore likely to be efficient. Crocker and Reynolds (1993) show that aircraft engine
procurement contracts contain more complete pricing provisions when intertemporal and6technological uncertainties are low (reduced need for ex post adaptation) and when the
supplier has a history of legal disputes with clients (high cost of negotiated adaptation).
More recent studies have focused on the allocation of authority as a means tofacilitate adaptation. Arruñada et al. (2001), and Zanarone (2009, 2013), show that
automobile distribution agreements assign to car manufacturers the right to adapt the
dealers’ performance and service standards ex post in networks where dealers are more
likely to freeride on the brand. Forbes and Lederman (2009, 2010) show that U.S. major
airlines vertically integrate into regional transportation in bad weather routes, where there
is more need for adapting flight schedules, and that vertical integration reduces the flightdelays and cancellations that would arise in the absence of coordinated adaptation.
Unlike our paper, none of the empirical studies discussed above focuses on relational
contracts—in the sense of informal, self-enforcing agreements—as mechanisms tofacilitate adaptation. The only other empirical paper we are aware of that explores thelink between relational contracts and adaptation is Barron et al. (2015). They study
contracts between a movie exhibitor and multiple distributors, and show that for any
given movie, the exhibitor’s revenue share is adjusted upwards if the exhibitor continues
showing the movie. They also show that price adjustments are more generous when the
exhibitor’s opportunity cost of showing the movie is high. Since price adjustments are notprescribed by the formal contracts signed by the parties at the beginning of theirrelationship, these findings suggest that the adjustments may serve as implicit bonuses toreward the exhibitor for adapting movie schedules.
Our empirical evidence on relational adaptation differs from, and complements that inBarron et al. (2015). While they look in great detail at how a given relational contract is7

adapted over time holding the set and depth of relationships constant, we provide
evidence on the economic significance of relational adaptation contracts by showing thatthose with higher long-term value are more likely to survive market shocks.
1.2.2. Relational contracts
Economic theory has extensively investigated relational contracts—that is, contracts
that are too rooted in the parties’ relationship to be verifiable by courts, and hence must
be self-enforcing (see MacLeod 2007, and Malcomson 2013, for up-to-date reviews of
the theoretical literature). The predictions of relational contracting theories have been
confirmed empirically by both case studies (e.g., Macauley 1963; Fast and Berg 1975;
Foss 2003; Helper and Henderson 2014) and econometric evidence (Gil and Zanarone,2015 and 2016, offer an up-to-date review and critical assessment of the empiricalliterature). As mentioned above, none of the existing empirical works, with the exceptionof Barron et al. (2015), study relational contracts as a means to achieve adaptation to
unforeseen contingencies.
Methodologically, the empirical paper on relational contracting that is perhaps mostclosely related to ours is Macchiavello and Morjaria (2015). In their study of flower
export agreements, they propose the idea, which we exploit in this paper, that the longterm value of a relational contract may be estimated by measuring the largest reneging
temptation, conditional on the parties being in a relationship. Macchiavello and Morjaria(2015) use this idea to show that informal contracts between Kenyan flower exporters
and their clients reduce the volume of stipulated flower deliveries following unexpected
increases in the spot market price, so that the exporters’ reneging temptation, given by the
product of spot price and relational quantity, remains constant, and hence equal to the8relationship’s long-term value. In contrast, our empirical exercise shows that positive
cross-sectional variations in the long-term value of relationships, as measured by thepresent reneging temptation, increase the parties’ willingness to preserve thoserelationships in the face of a shock. A second, important difference between our study
and Macchiavello and Morjaria (2015) is the type of relational contract under study.
While they focus on simple quantity-price agreements in an institutional environment
characterized by weak court enforcement, we focus on complex outsourcing agreements
in a strong institutional environment, and show that despite the presence of efficient
courts, because of the complexity of these agreements, major and regional airlines rely on
relational contracts to adapt them to unforeseen events.
The paper is organized as follows. While section 2 describes the US airline industry,
section 3 presents an illustrative model with testable implications that we take to data. In
section 4 we describe our data. Section 5 presents our empirical methodology and themain results of our empirical analysis. Section 6 presents our placebo test and ouranalysis of the 9/11 shock, and examines alternative margins of network adjustment—
vertical integration and route reallocation—employed by major airlines in response to the2008 shock. Section 7 concludes.

2. Adaptation in the U.S. airline industry
Major airlines fly routes using either their own fleets or those of regional airlines.
Regional airlines may be owned by the major airlines or independently owned. Major
airlines invest considerable planning efforts into designing schedules of flights andlanding times for their regional networks, so that passengers can reach their destinations
9

in a timely manner. While in regular and good weather adjustments to the landing
schedule are not necessary, they do become necessary under bad weather conditions.
Because landing in bad weather takes longer and requires more caution, the number oflanding slots available is reduced. Specifically, airport authorities unilaterally decrease
the number of slots through Ground Delay Programs (GDPs hereafter), and they do so
proportionally given each airline’s original schedule. Major airlines then rearrange theirschedule using the slots available to them and their integrated regionals, but they do nothave control (in the sense of real authority) over slots assigned to independently owned
regionals that they outsource to.
During GDPs, airlines are able to exchange slots with their independent regionals
through a formal mechanism called SCS (Slot Credit Substitution). SCS is a centralized
system where the major airline asks for an immediate time slot from any airline and often
from its independent regional, in exchange for a later time slot (Schummer and Vohray,2013; Vossen and Ball, 2006). If the regional accepts, it foregoes a landing slot and thus
it has to delay or even cancel one of its flights. Three important features of this process,which are captured by our theoretical model in the next section, should be stressed. First,
there is a conflict of interest between majors and regionals concerning the adaptation offlight schedules. While majors are residual claimants of flight revenues and pay a fixed
fee per flight to the regionals, delaying flights requires the regional to distort theiremployees’ schedules, which may result in higher labor and logistics costs (Forbes andLederman 2009).
Second, since independent regionals own, and hence control their slots, theseconflicts cannot be easily resolved via formal contracts. On the one hand, maximization10

of network-wide profits requires that flights be rescheduled quickly in the presence ofbad weather, so “haggling” between the major and the regional may be costly (e.g., Hart
and Moore 2008). On the other hand, allocating decision rights ex ante may also fail toelicit efficient adaptation. While contracts often assign to majors the formal right toreschedule their regionals’ flights, as owners of their slots the regionals have an option torefuse, perhaps in the hope to bargain harder with the major, or just to save on labor costs.
The major would then have to sue the regional for contract breach, and given theenvironmental changes due to bad weather conditions, a court may well “complete” thecontract in favor of the regional by applying flexible legal doctrines (e.g., Schwartz 1992).
Anticipating that, the major may have an incentive to breach. Based on these
considerations, and following the theoretical approach in Baker et al. (2011) and Barron
et al. (2015), we assume in our model that rescheduling decisions are non-contractible.
Finally, it appears that despite the conflict of interests and contractual frictions
discussed above, independent regionals do routinely accept the majors’ schedule
adjustment decisions, and major airlines compensate them for doing so. In particular,
while we do not have evidence of ex-post monetary payments being made by the majors
in exchange for the adjustments, conversations with industry practitioners suggest thatmajors informally count the flights cancelled by their regional partners as a consequenceof requested slot exchanges as valid for the yearly minimum number of flights that basedon the outsourcing agreements, the regionals have to reach in order to receive the fixed
operation fee. In other words, the major’s choice not to enforce the minimum clause in
the contract ex-post serves as an informal performance “bonus”.

11Considering this institutional background and the demand for relational adaptation
between major and regional airlines, we formally model in the next section relational
contracting as a solution to the adaptation problem in this industry.

3. A simple model of relational adaptation in the airline
industry
In this section we present a simple model of relational adaptation that captures the
key features of the U.S. airline industry as described above, and allows us to generate
empirically testable predictions.
3.1. Model setup
There are a major airline, M, and an independent regional airline, R, which may
operate up to ܰ routes on M’s behalf. Both M and R are risk-neutral, live forever, anddiscount next-period payoffs by the factor ߜ ‫ א‬ሾͲǡͳሿ. Time evolves in discrete periods.
At stage zero of any given period ‫ݐ‬, M and R observe a state, ‫ݖ‬௧ ‫ א‬ሼͲǡͳሽ, whose
realization is independent across periods. In “normal times”, ‫ݖ‬௧ ൌ Ͳ, and R’s cost ofoperating route ݅ in the current period is given by ݇௜ . In the presence of an unlikely
negative shock, ‫ݖ‬௧ ൌ ͳ, and R’s operating cost rises to ݇௜ ൅ ߝ௜௧ , where ߝ௜௧ is a random
adjustment, also independent across periods, distributed along the Թା line with pdf ݂ሺήሻ,and cdf ‫ܨ‬ሺήሻ. To capture the idea that the shock is unlikely, we assume ܲ‫ݎ‬ሺ‫ݖ‬௧ ൌ Ͳሻ ൎ ͳ,and ܲ‫ݎ‬ሺ‫ݖ‬௧ ൌ ͳሻ ൎ Ͳ, for all ‫ݐ‬.

12

At stage one, after observing the shock state, M decides which of the ܰ routes to
outsource to R. We write ݄௜௧ ൌ ͳ if route ݅ is outsourced to R, and ݄௜௧ ൌ Ͳ otherwise. If݄௜௧ ൌ ͳ, M offers to pay to R a fixed fee, ‫ݎ‬௜௧ ‫ א‬Թ, in exchange for operating the route. If
R accepts M’s offer, R incurs the operating cost, and the game moves to stage two. If݄௜௧ ൌ Ͳ, or if R rejects M’s offer, M receives payoff ݉௜଴ , R receives payoff zero, and thegame moves to period ‫ ݐ‬൅ ͳ. We may interpret ݉௜଴ as the maximum between M’s payofffrom not serving the route and M’s payoff from operating the route with its own planes orby using a vertically integrated regional company. If ݄௜௧ ൌ Ͳ and period ‫ ݐ‬has beennormal (‫ݖ‬௧ ൌ Ͳ), in period ‫ ݐ‬൅ ͳ the game repeats itself identically. In contrast, if ݄௜௧ ൌ Ͳ
and there has been a negative shock in period ‫ݖ( ݐ‬௧ ൌ ͳ), the game ends from period‫ ݐ‬൅ ͳ and thereafter with probability ‫ݍ‬௜ ‫ א‬ሾͲǡͳሿ, in which case M receives ݉௝଴ , and R zero,on every route ݆ ‫ܰ א‬, that is, including on those routes that had been outsourced to R attime ‫ݐ‬.
By assuming that lack of outsourcing on one route may trigger termination of thewhole regional network, we capture the idea that in the presence of a shock, restructuringof M’s portfolio of outsourced routes may cause negative network-wide externalities. For
instance, R may experience temporary financial constraints due to the shock, such that ifroute ݅ is not outsourced, and hence R does not receive the operation fee ‫ݎ‬௜ , R may gobankrupt and be forced to exit the market.
At stage two of period ‫ݐ‬, after the shock state ‫ݖ‬௧ has been realized and the outsourcing
decisions have been made, M and R observe the route state ‫ݓ‬௜௧ ‫ א‬ሼͲǡͳሽ, where ‫ݓ‬௜௧ ൌ ͳdenotes bad weather affecting flights on route ݅ , ‫ݓ‬௜௧ ൌ Ͳ denotes good weather, the13probability of bad weather is ‫݌‬௜ ‫ א‬ሾͲǡͳሿ, the probability of good weather is ͳ െ ‫݌‬௜ , and weassume for simplicity that weather states are independent across routes and time periods.
At stage three, after observing ‫ݓ‬௜௧ , R chooses the adaptation decision, ݀௜௧ ‫ א‬ሼͲǡͳሽ, atcost ݀௜௧ ܿ௜ . Consistent with the features of the US airline industry described in section 2,
we say that adaptation occurs, that is, ݀௜௧ ൌ ͳ, if R gives up one of its slots on route ݅ toM, thereby rescheduling and potentially delaying its own flights on that route. Conversely,݀௜௧ ൌ Ͳ if R does not give slots to M, and hence R does not need to reschedule its flights
on route ݅ . The adaptation cost, ܿ௜ ൐ Ͳ , may include the workers’ extra hours andadditional maintenance costs that R must incur if its flights on route ݅ are delayed as aconsequence of giving a slot to M. If ݀௜௧ ൌ ͳ , M may pay a bonus, ܾ௜௧ ‫ א‬Թ , tocompensate R’s adaptation cost.
Finally, at stage four, M receives profit ݉௜ ሺ݀௜௧ ǡ ‫ݓ‬௜௧ ሻ from each outsourced route ݅given the realized weather and R’s adaptation decision.
We make the following two assumptions:A1: ࢓࢏ ሺ૚ǡ ૚ሻ െ ࢉ࢏ ൐ ࢓࢏ ሺ૙ǡ ૚ሻ and ࢓࢏ ሺ૙ǡ ૙ሻ ൐ ࢓࢏ ሺ૚ǡ ૙ሻ െ ࢉ࢏ , for all ࢏.
A2: ࢖࢏ ࢓࢏ ሺ૙ǡ ૚ሻ ൅ ሺ૚ െ ࢖࢏ ሻ࢓࢏ ሺ૙ǡ ૙ሻ ൏ ࢓૙࢏ , for all ࢏.
Assumption A1 implies that it is efficient to reschedule flights only in the presence ofbad weather. This creates a potential conflict of interest between M and R, as adaptation
benefits M but is costly for R. Assumption A2 implies that in the absence of efficient
adaptation, it is optimal for M not to outsource a route. We interpret this assumption as
the joint result of the intense competition M may face from other airlines, and to the well14

documented fact that major airlines vertically integrate into poorly performing routes(Forbes and Lederman 2009).
It is useful to write M’s and R’s gross expected payoffs and the gross expectedsurplus in period ‫ݐ‬, conditional on the outsourcing decision ሺ݄ଵ௧ ǡ ǥ ݄௡௧ ሻ and on efficient
adaptation, respectively, as:ߨெ ሺ݄ଵ௧ ǡ ǥ ݄௡௧ ሻ ‫ ؠ‬σ௜ ݄௜௧ ሾ‫݌‬௜ ݉௜ ሺͳǡͳሻ ൅ ሺͳ െ ‫݌‬௜ ሻ݉௜ ሺͲǡͲሻሿ ൅ σ௜ሺͳ െ ݄௜௧ ሻ݉௜଴ ,(1)

ߨோ ሺ݄ଵ௧ ǡ ǥ ݄௡௧ ሻ ‫ ؠ‬െ σ௜ ݄௜௧ ൣ݇௜ ൅ ‫݌‬௜ ܿ௜ ൧, and(2)ܵሺ݄ଵ௧ ǡ ǥ ݄௡௧ ሻ ‫ߨ ؠ‬ெ ሺ݄ଵ௧ ǡ ǥ ݄௡௧ ሻ ൅ ߨோ ሺ݄ଵ௧ ǡ ǥ ݄௡௧ ሻ.

(3)

We make the following informational assumptions.
A3: ࢝࢏࢚ ǡ ࢓࢏ ሺࢊ࢏࢚ ǡ ࢝࢏࢚ ሻǡ ࢉ࢏ are observable but non-verifiable, for all ࢏ and ࢚.
A4: ࢊ࢏࢚ is observable but non-verifiable, for all ࢏ and ࢚.
Assumption A3 is standard in the incomplete contracting literature, and it implies
that efficient flight adaptation is ex ante non-contractible (e.g., Grossman and Hart 1986;
Hart and Moore 1988). Assumption A4 implies that efficient flight adaptation decisions
are formally non-contractible even ex post, after weather is observed (e.g., Baker et al.
2011). This is consistent with the institutional features of the airline industry discussed
above, according to which rescheduling decisions are too urgent, fast, and statecontingent to be formally contracted at a reasonable cost, either ex ante or ex post (Forbes
and Lederman 2009).

15

3.2. Spot market contracts
Suppose M and R rely on formal, spot market contracts to govern their outsourcing
agreement. Since adaptation decisions and contingent bonuses are non-contractible, atstage 3 of any given period ‫ݐ‬, M will pay no bonus to R: ܾ௜௧ ൌ Ͳ for all ݅. Anticipating
that, R will not adapt flight schedules, irrespective of the realized weather state: ݀௜௧ ൌ Ͳ
for all ݅. But then, our assumption A2 implies that at stage one, R will not outsource any
routes to R, irrespective of the realized shock state: ݄௜௧ ൌ Ͳ for all ݅. As a result, M’sexpected per period payoff will be ߨெ ሺͲǡ ǥ Ͳሻ, R’s payoff will be zero, and the total
surplus will be ܵሺͲǡ ǥ Ͳሻ ൌ ߨெ ሺͲǡ ǥ Ͳሻ.
3.3. Relational adaptation contracts
While rescheduling decisions are formally non-contractible, M and R may still
improve on the spot market by entering a relational adaptation contract, whereby R‫כ‬ሺ‫ݓ‬௜௧ ሻ ‫ݓ ؠ‬௜௧ , on
promises to execute the efficient state-contingent decision schedule, ݀௜௧

all the outsourced routes and in all periods, in exchange for the quasi-rents fromcontinuation of the relationship with M.
Formally, a relational adaptation contract is a complete plan for the relationshipbetween M and R, which specifies, for any realized history of play up to any given
period: M’s outsourcing decisions and upfront operation fees as a function of the state(normal times vs. shock), R’s adaptation decisions as a function of weather, and thediscretionary bonuses M has to pay R conditional on R’s adaptation decisions. We saythat a relational adaptation contract is self-enforcing if it describes a subgame perfectequilibrium of the repeated game between M and R. Following Levin (2002), we assume
16

that if M and R enter a relational adaptation contract, deviations on one route (that is, an
unexpected outsourcing decision or upfront payment, R’s failure to reschedule flights inthe presence of bad weather, or M’s failure to pay the bonus after R reschedules) arepunished through reversion to spot market contracting on all the outsourced routes, asthat is the worst credible punishment. Given perfect public monitoring and the absence ofliquidity constraints, the optimal relational contract is stationary, in the sense thatconditional on the realized states, ‫ݖ‬௧ and ‫ݓ‬௜௧ , outsourcing and adaptation decisions andpayments on any given route ݅ are the same in every period (MacLeod and Malcomson
1989; Levin 2003). Accordingly, we hereafter drop the time subscripts from all equations.
Notice that since the negative shock is unlikely, it does not affect M’s and R’s
expected payoffs from the relationship. Thus, we can separately analyze relational
adaptation in normal times (that is, conditional on ‫ ݖ‬ൌ Ͳ) and in the presence of a shock(‫ ݖ‬ൌ ͳ).
3.3.1. Normal times
Given assumption A2, M’s optimal relational contract in normal times can becharacterized as a vector of stationary outsourcing decisions, ሺ݄ଵ‫ כ‬ǡ ǥ ǡ ݄௡‫ כ‬ሻ , such that݄௜‫ כ‬ൌ ͳ if ݀௜ ‫ݓ ؠ‬௜ , and ݄௜‫ כ‬ൌ Ͳ otherwise, and a set of payments that guarantee M’s andR’s participation and efficient adaptation on the outsourced routes. Formally, M solves:݉ܽ‫ݔ‬௛೔ ǡ௥೔ ǡ௕೔ ሼߨெ ሺ݄ଵ ǡ ǥ ݄௡ ሻ െ σ௜ ݄௜ ሺ‫݌‬௜ ܾ௜ ൅ ‫ݎ‬௜ ሻሽ,
subject to the following constraints:ߨெ ሺ݄ଵ ǡ ǥ ݄௡ ሻ െ σ௜ ݄௜ ሺ‫݌‬௜ ܾ௜ ൅ ‫ݎ‬௜ ሻ ൒ ߨெ ሺͲǡ ǥ Ͳሻ,(4)ߨோ ሺ݄ଵ ǡ ǥ ݄௡ ሻ ൅ σ௜ ݄௜ ሺ‫ݎ‬௜ ൅ ‫݌‬௜ ܾ௜ ሻ ൒ Ͳ,(5)17σ௜ ݄௜ ሺܾ௜ െ ܿ௜ ሻ ൅െ σ௜ ݄௜ ܾ௜ ൅

ఋ
ଵିఋ

ఋ
ଵିఋ

ሾߨோ ሺ݄ଵ ǡ ǥ ݄௡ ሻ ൅ σ௜ ݄௜ ሺ‫ݎ‬௜ ൅ ‫݌‬௜ ܾ௜ ሻሿ ൒ Ͳ, andሾߨெ ሺ݄ଵ ǡ ǥ ݄௡ ሻ െ σ௜ ݄௜ ሺ‫݌‬௜ ܾ௜ ൅ ‫ݎ‬௜ ሻሿ ൒ఋଵିఋߨெ ሺͲǡ ǥ Ͳሻ.

(6)
(7)

Conditions (4) and (5) are M’s and R’s participation constraints, respectively.
Conditions (6) and (7) are R’s and M’s incentive constraints, which ensure, respectively,that R be willing to reschedule flights following bad weather (condition 6), and M be
willing to pay the promised contingent bonuses (condition 7), in the highest-temptation
state—that is, in case of bad weather on all the outsourced routes.1
Summing up (6) and (7), we obtain a necessary condition for the relational adaptation
contract to be self-enforcing:σ௜ ݄௜ ܿ௜ ൑ఋଵିఋሾܵሺ݄ଵ ǡ ǥ ݄௡ ሻ െ ܵሺͲǡ ǥ Ͳሻሿ.

(8)

In fact, it is easy to check that condition (8) is also sufficient, in the sense that there
are operation fees and bonuses such that if (8) holds, (4) through (7) hold as well, and M
extracts the whole surplus, ܵሺ݄ଵ ǡ ǥ ݄௡ ሻ.
If condition (8) is not satisfied—either because R’s cost of adapting schedules, on theleft-hand side, is too large, or because the present expected value of the relationship, on
the right-hand side, is too small—efficient adaptation on the agreed routes cannot occur,
so M will need to outsource fewer routes to R in order to keep the relational adaptation
contract within its self-enforcing range.
Let R’s total adaptation cost in the worst weather state, and the net continuation valueof the relationship between M and R, under the optimal outsourcing decision, be σ௜ ݄௜‫ܿ כ‬௜
1

We omit the constraint that R be willing to accept the contingent bonus if negative because this constraint
is looser than (6), and hence redundant.

18

and ܸ ‫ؠ כ‬ఋଵିఋሾܵሺ݄ଵ‫ כ‬ǡ ǥ ݄௡‫ כ‬ሻ െ ܵሺͲǡ ǥ Ͳሻሿ, respectively. Then, condition (8) implies that the

total adaptation cost, σ௜ ݄௜‫ܿ כ‬௜ , is a lower bound for the value of the relationship, ܸ ‫כ‬(Macchiavello and Morjaria 2015). In particular, when the discount rate is small enough,so that (8) is binding, the relationship’s value must equal the adaptation cost: ܸ ‫ כ‬ൌσ௜ ݄௜‫ܿ כ‬௜ .
3.3.2. A negative shock
In the unlikely event that a negative shock occurs (that is, if ‫ ݖ‬ൌ ͳ), its impact on theoutsourcing decision is twofold. On the one hand, R’s present cost of operating a givenroute ݅ is raised to ݇௜ ൅ ‫ߝݖ‬௜ , which disfavors outsourcing. On the other hand, if the routeis not outsourced to R, the network terminates with probability ‫ݍ‬௜ in the following period.
Thus, routes that are optimally outsourced in normal times may no longer be outsourced
following a shock, depending on the balance between the present costs and future
benefits of outsourcing those routes, as defined above.
Assume route ݅ is outsourced in normal times ( ݄௜‫ כ‬ൌ ͳ ), and let M’s per period
expected payoff from outsourcing route ݅ in normal times be:ܵ௜ ‫݌ ؠ‬௜ ሾ݉௜ ሺͳǡͳሻ െ ܿ௜ ሿ ൅ ሺͳ െ ‫݌‬௜ ሻ݉௜ ሺͲǡͲሻ െ ݇௜ .2
Given the above definitions and assumptions, M will continue to outsource route ݅ to
R following a shock if, and only if:ܵሺ݄ଵ‫ כ‬ǡ ǥ ǡ ݄௡‫ כ‬ሻ െ σ௡௝ୀଵ ݄௝‫ߝ כ‬௝ ൅ߝ௜ ൧ ൅ ‫ݍ‬௜

ఋ
ଵିఋ

ఋ
ଵିఋ

ܵሺͲǡ ǥ ǡͲሻ ൅ ሺͳ െ ‫ݍ‬௜ ሻܵሺ݄ଵ‫ כ‬ǡ ǥ ǡ ݄௡‫ כ‬ሻ ൐ ܵሺ݄ଵ‫ כ‬ǡ ǥ ǡ ݄௡‫ כ‬ሻ െ ܵ௜ െ ൣ൫σ௡௝ୀଵ ݄௝‫ߝ כ‬௝ ൯ െఋଵିఋܵሺ݄ଵ‫ כ‬ǡ ǥ ǡ ݄௡‫ כ‬ሻ, or2

Recall that under an optimal relational adaptation contract, M extracts all the expected surplus from theoutsourced routes.

19‫ݍ‬௜ ܸ ‫ כ‬൅ ܵ௜ ൐ ߝ௜ .
If self-enforcement condition (8) is binding, so that ܸ ‫ כ‬ൌ σ௜ ݄௜‫ܿ כ‬௜ , then the above
optimal outsourcing decision rule can be rewritten as:‫ݍ‬௜ σ௜ ݄௜‫ܿ כ‬௜ ൅ ܵ௜ ൐ ߝ௜ .

(9)

Thus, we can define the probability that a given route ݅ , which is optimallyoutsourced in normal times, is also outsourced following a shock—that is, the probability
that route ݅ survives the shock—as:ܲ‫ݎ‬ሺܵ‫݈ܽݒ݅ݒݎݑ‬௜ ሻ ‫ܨ ؠ‬ሺ‫ݍ‬௜ σ௜ ݄௜‫ܿ כ‬௜ ൅ ܵ௜ ሻ.

(10)

Condition (10) implies that the likelihood of survival of an outsourced route ݅ after
the shock, ܲ‫ݎ‬ሺܵ‫݈ܽݒ݅ݒݎݑ‬௜ ሻ, is higher the higher R’s aggregate adaptation cost across all
the routes that were optimally outsourced prior to the shock—that is, ܲ‫ݎ‬ሺܵ‫݈ܽݒ݅ݒݎݑ‬௜ ሻ
increases in σ௜ ݄௜‫ܿ כ‬௜ .3
Proposition: Assume the self-enforcement constraint in the relational adaptation
contract, (8), is binding. Then, the likelihood that R continues to outsource a givenroute ࢏ to R following a shock increases in σ࢏ ࢎ࢏‫ ࢏ࢉ כ‬, R’s aggregate adaptation costacross all the routes that M optimally outsourced to R before the shock.
The intuition behind our Proposition is simple. If M and R use a relational contract toefficiently adapt flight schedules to changes in weather, the binding relational3

Our assumption A2 matters here, because it rules out the possibility that M may outsource a route to R
without requiring efficient adaptation—that is, it rules out the possibility that ݄௜‫ כ‬ൌ ͳ and ݀௜ ሺͳሻ ൌ Ͳ forsome route ݅ . If M tolerated inefficient adaptation on some routes, σ௜ ݄௜‫ܿ כ‬௜ may overestimate therelationship’s long-term value. Again, we find our assumption consistent with the intense competition thatcharacterizes the U.S. airline industry, and on the existence of vertical integration as a way, albeit a costly
one, to overcome poor adaptation.

20

enforcement constraint implies that when R’s aggregate adaptation costs increase, thelong-term value of the relationship between M and R must also increase in order to keep
the relational contract within its self-enforcing range. In the next sections, we take thisprediction to the data.

4. Data Description
4.1. Data
The data we use in this paper results from the combination of several data sets. We
obtained airline ticket and flight information from the DB1B data, and ticket, market, andcoupon data from RITA, both data sets from the Bureau of Transportation Statistics.
These data contain not only information on the ticketing carriers, but also on theoperating carriers and reporting carriers of each flight.4 We complement these data withinformation on aircraft type, operators, flight frequency and other route and flightcharacteristics (seats, number of flights, group of aircraft, distance flown, number of
total passengers, and dummy of freighter flights), which we obtained from the T100-B41
and T100-B43 airline-aircraft data from the U.S. Department of Transportation. To merge
the T100 and DB1B databases, we checked the identity of the ticketing, operating andreporting carrier of each flight.
We drop the freighter flights and the flights that have zero passengers from our data.
We take the ticketing carrier identifier from DB1B market data because of the following
4

A ticketing carrier is the airline that sells airtickets to customers, whereas operating and reporting carriers
are those operating the flight and reporting flight characteristics to the BTS.

21

two reasons: first, to identify and match with the operator from other data sets like DB1B
ticket, coupon, and T100-B43, and second, in order to avoid overlooking code-sharing
between airlines. In order to correctly identify contracts between major and regional
airlines, we combine the merged DB1B and T100 datasets described above with the datafrom the Regional Airline Association (RAA), which provides the ownership type ofeach regional airline as well as the list of regional airlines, distinguished from charterairlines. We then merge this information with weather data on rainfall, snowfall and thenumber of freezing months per year-quarter (aggregated to the year-quarter level) fromthe National Oceanic & Atmospheric Administration.
By combining all these data sources, we obtain a rich data set that contains
information at the major/regional/quarter and at the major/regional/route/quarter level,
respectively. Following Forbes and Lederman (2009), we define a route as a set of one ormore nonstop flights connecting the same two airports, irrespective of the flights’
direction. We describe our two data sets below, and discuss our choice and use ofvariables in section 4.2.
The first data set, at the major/regional/quarter level, contains information on thenumber of joint routes, the number of flights, the number of seats, the highest value route
served within the major-regional network, and weather conditions (average precipitation,snow, and number of freezing months across routes served by the dyad). All variables arecomputed for each quarter and year, and for each major/regional airline dyad.
The second data set contains information at the major/regional/route/quarter level.
For each route, we create the following variables: a dummy for whether either airport in
the route is a hub for the major airline in the dyad, a dummy for weather either airport in22

the route is slot-controlled, the number of flights served in the route by the regional
airline, the average value of a flight on the route (number of seats times the average
price), the number of flights at the larger and smaller endpoints in the route, and weather
conditions in the route—namely, the 1971-95 average snowfall, rain precipitation andnumber of freezing months from the National Oceanic and Atmospheric Administration(NOAA) evaluated at the endpoint airport in the route where they are highest (Forbes andLederman 2009). We compute all of these variables for each quarter/year and for eachmajor/regional/route triad.
4.2. Measures
The purpose of our empirical analysis is to assess whether following a negative shock,
U.S. major airlines were more likely to drop or downsize routes outsourced to regional
airlines whose aggregate adaptation costs in the networks they operated for the major
before the shock were higher, as predicted by our relational adaptation model. As
explained in the introduction, we focus on the exogenous shock represented by thefinancial crisis following the collapse of Lehman Brothers in September 2008.
Accordingly, we define as our main dependent variable a dummy named “Survival 9/2008” that takes value 1 if a given route operated by a regional airline on behalf of amajor airline in 2006 (two years before the shock) is still operated by the same regional,
on behalf of the same major, in 2010 (two years after the shock), and it takes value zerootherwise. Note that if a regional did not operate a given route in 2006, our survival
variable excludes that route from the data.
Similarly, we create a second dummy variable, “Termination 9/2008,” that takes
value 1 if the number of flights operated by a regional airline on behalf of a major airline
23

in a route has decreased between 2006 and 2010; and zero if the number of flights in theroute has not decreased (that is, if it has stayed the same or increased). Again, routes thatthe regional airline did not fly in 2006 are left out of the sample. While the survival
variable measures an extensive margin of adjustment in the major-regional relationships,
the termination variable measures an intensive margin of adjustment.5
We provide graphical evidence on how the industry adjusted to the shock in Figure
1A and 1B below. Figure 1A shows that after the shock in 2008, the number of majorregional outsourcing relationships in the U.S. decreased, while the number of routes andflights ticketed by majors decreased slightly but far from the sudden and steep decrease
experienced by the number of relationships between major and regional airlines. Thisevidence seems to indicate that the 2008 shock forced majors to reevaluate the amount ofoutsourcing relationships with existing regionals and restructure their network of regional
flights accordingly.

<<Place Figure 1A here>><<Place Figure 1B here>>

Figure 1B focuses on the number of routes and flights outsourced to regionals andshows that, after the shock in 2008, even though the number of major-regional
relationships in the U.S. decreased, the total number of routes and flights outsourced bymajor airlines to regional airlines increased. Thus, the evidence in Figure 1B indicatesthat while the 2008 shock did not decrease the use of outsourced regionals by major
5

See the Data Appendix for details on our treatment of airline mergers and exits during the 2006-2010
period.

24

airlines, it did push the majors to concentrate outsourced routes and flights into fewer
regionals.

<<Place Table 1 here>>

Table 1 provides summary statistics for both the dependent and independent variables
used in our study. We first note that our definition of the dependent variables, survival
and termination, constrains the analysis to sample sizes of 6516 route-level observations.
The probability that a route that was outsourced in 2006 survives in 2010 is 59.3%,
whereas the probability that a route that was outsourced in 2006 has some of its flights
terminated in 2010 is 62.2%.
Regarding the independent variables, we follow Forbes and Lederman (2009) and use
adverse average weather conditions on a route — namely, snow (MAXsnowfall_r),
precipitation(MAXprecipitation_r),

and

low

numberoffreezing

months(NFreezingmonths_r) as a proxy for the lack of clear skies — as exogenous proxies forthe adaptation costs faced by the regional when bad weather hits that route. The
underlying idea is that in routes characterized by more severe weather conditions, themajor airline will more often require the regional to reschedule flights, thus inflating theregional’s personnel and maintenance costs.
To measure aggregate weather conditions across a major-regional network, we
computed the average of the key weather variables (maximum precipitation in inches,maximum snowfall in inches, and the number of freezing months in a route) across allroutes flown by a regional for a major airline (AVEweatherSnow_ij, AVEweatherRain_ij,
and AVEweatherFreez_ij, respectively).
25

As control variables, we use a number of route characteristics and major-regional
network characteristics. In particular, we include variables that may drive outsourcing
decisions regardless of network-level adaptation costs. Following Forbes and Lederman(2009), we include as route-level controls a dummy for whether either of the endpoints ina route is a hub for the major airline (Dhubinroute_ir), a dummy for whether either
airport is slot-controlled (slot_r), and the total number of flights at the largest andsmallest endpoints of the route (flight_largeendpoint_ijr and flight_smallendpoint_ijr).
These variables may capture the extent to which a given route is embedded in the major’s
network. In turn, a route’s embeddedness increases its strategic importance and the needfor adaptation on the route, both of which may affect outsourcing for any given level of
network-level adaptation costs. As additional route-level controls, we include the total
number of flights operated by regional j in route r for major i (NFlight_ijr), and theaverage value of those flights (AVEValue_ijr), and the total number of flights at thelargestandsmallest

endpoints

of

the

route

(flight_largeendpoint_ijr

andflight_smallendpoint_ijr).
<<Place Table 2 here>><<Place Table 3 here>>
While Table 1 shows summary statistics for all of our independent variables, it does
not provide any information on the characteristics of the different major-regional
networks. For this reason, we complement Table 1 by providing information on the
thickness and spread of the networks of major airlines in Table 2, and on differences inaverage network weather in Table 3. Table 2 tabulates the number of routes outsourced
by each of the major airlines to each of the regional airlines in our data set. Note that the26

number of regional partners, as well as the number of outsourced routes, varies acrossmajor airlines. The same pattern appears to be true from the regional perspective. Whilemost regionals work for all majors, some regionals tend to concentrate their operations on
one or two major airlines. Take for example the case of American Airlines (AA), which
outsources routes to 19 different regionals but uses most intensively Envoy Air (MQ).
Similarly, Envoy Air works mostly for American Airlines but also operates some routes
for the other five major airlines in our data. Similar patterns characterize United Airlines(UA) and SkyWest Airlines (OO). See Figure 2 for an illustration of the networks ofoutsourced routes recently operated by SkyWest for several major airlines.

<<Place Figure 2 here>>

Given the large variation in network size across major airlines, we present in Table 3
summary statistics for the average network weather variables in our base year, 2006, foreach major airline. Table 3 also reports the number of regional airlines with whom eachmajor airline had a relationship during that year. The number of relationships in 2006
ranged between 16 (US Airways) and 19 (AA, Delta and United Airlines). Table 3 showsthat there is a lot of variation in network weather variables (snow, rain, and number offreezing months) even within a major airline across its different regional networks. We
exploit this variation later in our empirical analysis.

27

5. Empirical Methodology and Main Results
5.1 Empirical Methodology
Given our theoretical model from section 3, our hypothesis is that if a major and aregional airlines have entered a relational adaptation contract, the long-term value of theirrelationship must be higher the worse the weather conditions in all the regional routes
they operate together, and therefore, the larger the cost of honoring the relational
adaptation contract. Hence, we would expect major airlines to be more reluctant to dropor downsize routes belonging to a regional network with worse average weather,following the 2008 shock.
Therefore, our main empirical specification is a traditional linear probability model,
estimated by OLS,6 such that:ܵ‫݈ܽݒ݅ݒݎݑ‬௜௝௥ ൌ ߙ ൅ ߚ‫ݐݏ݋ܥ݊݋݅ݐܽݐ݌ܽ݀ܣ‬௜௝ ൅ ߛܺ௜௝௥ ൅ ߜ௜ ൅ ߤ௝ ൅ ߠ௥ ൅ ߝ௜௝௥ ,‫ݐݏ݋ܥ݊݋݅ݐܽݐ݌ܽ݀ܣ‬௜௝ is the aggregate cost of adapting schedules across all routes
jointly operated by major i and regional j in 2006, before the shock, and ߝ௜௝௥ is a normally
distributed and iid error term. Xijr is a vector of observable characteristics of the ij
relationship in route r in 2006, which includes route-level adaptation cost. Finally, ߜ௜ ,ߤ௝
and ߠ௥ are major airline, regional airline, and route fixed effects, respectively, which are6

We choose to test our prediction with linear probability model and OLS because the number of fixed
effects increases rapidly and so we want to avoid changes in methodology throughout the empirical results.
Using probit for those specifications with no or few fixed effects does not qualitatively change our results.

28

included in the regression to control for unobservable components that are common
across routes and common across airlines within a route. These fixed effects are crucial to
our experimental design, because they allow us to compare the probability ofcontinuation of two outsourced routes belonging to regional networks with different
continuation values for the same major airline and vice versa..
Under the relational adaptation hypothesis, we expect ߚ ൐ Ͳ . Routes belonging to
networks with higher average adaptation costs are more likely to survive the Lehman
Brothers shock because prior to the shock, those routes were allocated by major airlines
to regionals whose relationship with the majors had a higher continuation value. Noticethat under a “spot adaptation” hypothesis, we would expect instead ߚ ൌ Ͳ , because
absent relational contracts, outsourcing of a route should only depend on adaptation costsat the route level, as in Forbes and Lederman (2009), and not on network-level adaptation
costs.
Because we do not directly observe‫ݐݏ݋ܥ݊݋݅ݐܽݐ݌ܽ݀ܣ‬௜௝ , we use our measures of bad
weather within the network ij as a proxy for ‫ݐݏ݋ܥ݊݋݅ݐܽݐ݌ܽ݀ܣ‬௜௝ in the above equation.
We rely on Forbes and Lederman (2009) to argue that there is a positive correlation
between adaptation costs on a route and the incidence of bad weather on that route, sothat bad weather aggregated at the network level is indeed a good proxy for network-level
adaptation costs.
In this setting, we are consistently estimating ߚ if the impact of the 2008 financial crisison the survival probability of different routes in our sample is uncorrelated to routecharacteristics that determined the formation of major-regional networks prior to the29shock, in 2006. Formally, under the relational adaptation hypothesis and our specification
above, our identification assumption is that ܿ‫ݒ݋‬൫ߝ௜௝௥ ǡ ܹ݁ܽ‫ݎ݄݁ݐ‬௜௝ ൯ ൌ Ͳ.7
While there is no apparent reason to believe that routes in regional networks with worse
weather were less likely to be affected by the financial crisis relative to routes in
networks with better weather, we can think of two potential reasons why it may be thatܿ‫ݒ݋‬൫ߝ௜௝௥ ǡ ܹ݁ܽ‫ݎ݄݁ݐ‬௜௝ ൯ ് Ͳ. The first reason is selection: a major may prefer to assign bad
weather networks to regionals that do not renege on adaptation decisions, and a regional
may prefer to operate a bad weather network for majors that do not renege on therelational adaptation bonus. The fact that networks are endogenously formed before theshock is not a problem per se—indeed, it is precisely this endogenous selection thatenables us to use adaptation costs, proxied by bad weather, as a measure of thecontinuation value of major-regional relationships. However, selection may be a problem
if it occurs in anticipation of the 2008 financial crisis shock. Our specification deals withthis potential problem in two ways. First, we observe and code outsourcing relationships
two years prior to the shock, so observed regional networks are unlikely to be formed in
the anticipation of the 2008 financial crisis. Second, we use major and regional airline
fixed effects, as well as combinations of major/route and regional/route fixed effects, in
our robustness checks specifications.
The second potential reason why it may be that ܿ‫ݒ݋‬൫ߝ௜௝௥ ǡ ܹ݁ܽ‫ݎ݄݁ݐ‬௜௝ ൯ ് Ͳ is that ourmeasures of network-level weather may be correlated with route-level weather (a routefrom a network with average bad weather is more likely to be itself a bad-weather route).
In turn, a route with worse weather may be more likely to be cut after the shock because7ܹ݁ܽ‫ݎ݄݁ݐ‬௜௝ is defined as average weather in the major-regional network i and j.

30

it has higher adaptation costs and hence it is less profitable. To control for this secondsource of endogeneity, we include route-level weather, as well as route fixed effects, inour baseline regressions.
Thus, our identification assumption is that conditional on route and network
characteristics, in the absence of a shock the profitability of routes should be rather
unrelated to differences in adaptation costs across networks. After the 2008 negative
shock, and if relational adaptation matters, we should find that major airlines are more
likely to preserve outsourced routes that belong to regional networks with higher average
adaptation costs, because those are the major-regional relationships with higher
continuation value, so the major does not want to jeopardize them by reducing their size.
In section 5.2, we present our main results, which provide evidence on how average
weather conditions across a major-regional network affected the survival and terminationof routes in that network following the Lehman Brothers shock.
5.2. Main Results: Route survival as a function of Relational Adaptation
Tables 4 and 5 below report the effect of network weather conditions on the survival
and termination of routes following the 2008 Lehman Brothers shock. Our independent
variables are all divided by their own standard deviation in order to provide easier-tointerpret coefficients. We provide results without fixed effects in columns 1 and 4, withmajor airline fixed effects in columns 2 and 5, and with regional airline fixed effects incolumns 3 and 6. In all specifications, standard errors are clustered at the major-regional
dyad.

<<Place Tables 4 and 5 here>>31

The results are consistent with our relational adaptation hypothesis. Routes in
networks characterized on average by higher precipitation and more abundant snow, andby a lower number of freezing months and hence less clear skies, are more likely tosurvive (Table 4), and less likely to see their flights reduced (Table 5), following theLehman Brothers shock. These results support our theoretical prediction that major
airlines are more reluctant to restructure routes when their overall relationship with theregional airline serving those routes is valuable. The relationship’s value is measured by
the aggregate temptation to renege on a relational adaptation contract, as proxied by
network-level adverse weather conditions, because for those contracts to be selfenforcing, higher-temptation outsourced networks must also have a higher long-termvalue.
Notice that the results are consistent regardless of the type of fixed effects included in
the specification, indicating that our hypothesis is supported across major and regional
airlines, within major airlines across their outsourced regional networks, and withinregional airlines across the major airlines that contract with them.
Because our independent variables are standardized by their individual standard
deviation, the interpretation of our empirical results is straightforward. Take, for example,
column 2 in Tables 4 and 5, which includes major airline fixed effects. Our results showthat a one standard deviation increase in the average precipitation across a major-regional
network increases the probability of survival of a route by 16 percentage points, anddecreases the probability of flight termination in a route by 8.5 percentage points.
When paying attention to the rest of independent variables included in the analysis tocontrol for potential confounders, we find interesting and yet intuitive results. On the one
32

hand, we find that the number of flights in a route, and whether an airport in the route isslot controlled, have a statistically significant positive effect on route survival. On theother hand, our results show that a route’s average value and the distance between its
endpoints decrease its likelihood of survival. On one hand, routes with higher averagevalue may be more likely to be vertically integrated after the shock (more below), and on
the other hand, airline passengers may dislike longer flights in regional airlines due to thesmaller aircrafts used. Finally, if anything, routes with a hub at an endpoint are less likely
to survive or more likely to experience a reduction in the number of flights. This can be
explained by the fact that, as shown by Forbes and Lederman (2009), routes with a hub at
an endpoint may be more important in order for major carriers to achieve coordination
with other flights and therefore, they may be more likely to be integrated after the 2008
shock.

<<Place Tables 6 and 7 here>>

Tables 6 and 7 reproduce the analysis in Tables 4 and 5, introducing route-level
weather variables as independent variables, in addition to the network-level weather
variables used previously. Our results show that route-level weather variables are
statistically insignificant and that if anything, they have a mildly negative effect onsurvival, as one would expect given that high route-level adaptation costs reduce route
profitability. In contrast, the network-level weather variables are still statisticallysignificant and their signs are fully consistent with our relational adaptation hypothesis.
Therefore we can conclude that our original results in Tables 4 and 5 are not due tocorrelations between route-level weather and network-level weather.
33

<<Place Tables 8 and 9 here>>

Tables 8 and 9 add route fixed effects to our specifications in Tables 4 and 5. Routefixed effects not only allow us to rule out differences in weather across routes as apotential explanation for our findings (as in Tables 6 and 7), but also allow us to control
for any cross-route differences that may affect their post-shock survival. The regressions
reported in Tables 8 and 9 hold the route constant, and exploit variation in survival andnetwork-level weather across pairs of major and regional airlines that jointly operate thatroute. The results are entirely consistent with those in Tables 4-7. Therefore, it seems fairto conclude that the error term in our regressions (that is, route-specific random effects ofthe financial crisis) is uncorrelated with our main explanatory variable, that is, networklevel average weather before the crisis.

<<Place Tables 10 and 11 here>>

We conclude this section by presenting Tables 10 and 11. These tables use the same
specifications in our original Tables 4 and 5, with the difference that we now introduce
major-route fixed effects (columns 1 and 3) and regional-route fixed effects (columns 2
and 4) for both our survival and termination dependent variables. The rationale behindthe use of these new sets of fixed effects is to control for the possibility that the 2008
shock may have differently affected different airlines in different routes. Therefore, thespecifications in Tables 10 and 11 only exploit variation within an airline-route dyad—
that is, variation coming from majors that use more than one regional in a given route or
regionals that operate the same route for more than one major.

34

The results in Tables 10 and 11 are again largely consistent with our findings inTables 4 to 9. Routes outsourced to regional airlines are more likely to survive after the2008 shock if they belong to networks that had worse weather (that is, higher
precipitation and snow, and fewer freezing months per year) prior to the shock. Ifanything, the precipitation and snow coefficients in columns 2 and 4 of Table 11 arestatistically less significant than those in previous table, although they remain large andnegative.

6. Robustness checks and Additional Margins of Adjustments
6.1. Placebo Test: Survival of Outsourced Routes between 2003 and 2006.
Since the Lehman Brothers shock does not seem to have differentially affecteddifferent groups of routes, we cannot use a differences-in-differences estimation approachin our study. To test for whether the effects of network-level adaptation costs on routesurvival and size documented in tables 4-11 are really driven by the Lehman Brothers
shock, we construct equivalent survival and termination variables taking as the initial andfinal years 2003 and 2006 — respectively, two years after the September 11 terrorist
attacks and two years prior to Lehman Brothers September 2008 shock. Because noshock occurred between 2003 and 2006, we would expect our network-level weather
variables, and hence the value of relational adaptation contracts, not to affect theprobability of routes’ survival and termination. Tables 12 and 13 below present ourplacebo test.

35

<<Place Tables 12 and 13 here>>

Table 12 provides evidence on route survival probability between 2003 and 2006. On
the one hand, our results show that a route is more likely to survive when one of the
airports in the route is a hub to the major airline, and when the number of flights operated
by the major and the regional in that route, and the average value of flights in the route,increases. On the other hand, we find no statistically significant relationship between the
average snow, precipitation and number of freezing months in the major-regional
network and the probability of route survival. If anything, we find a weak negative
relationship between precipitation levels and survival (columns 2 and 5), as well as apositive correlation between freezing months and route survival (columns 3 and 6).
Table 13 repeats the analysis in Table 12 with flight termination in the 2003 to 2006
period as our dependent variable. Our findings in Table 13 are consistent with those in
Table 12 in that overall, average network weather conditions do not have a statisticallysignificant effect on the likelihood that flights on a route are reduced.
Therefore, our placebo test suggests that absent an exogenous shock, there is no
statistical correlation between the average weather conditions of a major-regional
network and the survival and termination probabilities of a route within that same
network. This evidence corroborates our hypothesis that the Lehman Brothers events
unexpectedly forced major airlines to restructure their portfolios of regional routes, andare therefore appropriate “stress tests” for assessing the existence and significance ofrelational adaptation contracts.

36

6.2. The 9/11 Terrorist Attacks
As a robustness check, in this section we explore the relation between network-level
adaptation costs and the survival of outsourced routes in the aftermath of the 9/11
terrorist attacks in 2001. It is important to notice that the U.S. airline industry in 2001
differed much from the U.S. airline industry in 2008, and also the nature and effects of
the 9/11was different from that of the Lehman Brothers shock. In particular, we noticethat outsourcing to regional partners was less likely in 2001 than in 2008. Figure 1B
shows that not only there were fewer major-regional relationships (albeit with a higher
number of major airlines in operation) in 2001, but also, the number of outsourced routes
and flights per regional in 2001 was far smaller than in 2008. Given that outsourced
networks in 2001 were already thin, we do not expect major airlines to restructure themafter the 9/11 shock as much as they did after the Lehman Brothers shock.
Tables 14 and 15 below display our results on survival and termination of routes
before and after the 9/11 terrorist attacks. The dependent variables in these specifications
are constructed in the same way as those in Tables 4 and 5. For each route that was
outsourced to an independent regional in 1999 (two years prior to the 2001 shock), we
compute dummies for whether the route was still outsourced to that regional (survival)
and for whether its number of active flights had been reduced (termination) by 2003 (twoyears after the shock).

<<Place Tables 14 and 15 here>>

We find no robust statistical relationship between the average network weather
variables and the survival and termination probabilities in the regressions without fixed37effects (columns 1 and 4). When using major airlines fixed effects in columns 2 and 5, wefind that routes belonging to networks with more freezing months are more likely tosurvive and less likely to be terminated. When using regional airline fixed effects, wefind that routes with more precipitation are less likely to survive and more likely to beterminated or reduced in size. We interpret these contradictory results as suggesting thatthe 9/11 shock had a weaker impact on the US airline industry than the 2008 shock at thecore of our base analysis. This is consistent with our initial conjecture in that adjusting
the major’s networks is only possible through adjusting the number of outsourcing
relationship if the major-regional networks are dense enough.

6.3. Alternative Margins of Adjustment
Our empirical analysis so far sheds light on the relationship between network-level
adaptation costs and the termination or downsizing of outsourced routes in response to
the 2008 financial crisis. Yet, there are other margins of post-shock network adjustmentthat have been so far left out of our study. When the outsourcing relationship between amajor and a regional airline on a route or flight terminates, three things may happen. First,
the major airline may stop flying the route altogether. Second, the major airline may fly
the route with its own planes or through a vertically integrated regional company, as
documented by Forbes and Lederman (2009). Third and finally, the major airline may
continue to outsource the route but using a different regional airline, which may or may
not have been previously used on that route.

38

In this last section, we investigate the relevance of the two latter margins ofadjustment. Let us first study whether a route that was outsourced in 2006 is more likely
to become integrated in 2010 as a result of the Lehman Brothers. Our results here add tothe study of vertical integration by Forbes and Lederman (2009).
It is important to emphasize that the implications of our relational adaptation model
for vertical integration are not as clear-cut as those for route survival and termination,
because a major airline’s potential cost savings from integration are dubious. On the one
hand, labor costs tend to increase after integration because of the unionization of major
airlines (Forbes and Lederman, 2009). On the other hand, major airlines may be forced tointegrate some previously outsourced routes that were downsized after the shock, because
no other independent regional could profitably operate those routes below a minimum
number of flights. Thus, the net effect of the pre-shock expected value of major-regional
relationship, as proxied by our weather variables, on post-shock vertical integration,
appears to be an open empirical question.
To conduct this analysis, we use the same specifications used until now with two newdependent variables. First, we create a dummy variable, called Integration, that takes
value 1 if, conditional on a route being operated by a regional airline in 2006, at least aflight in the route that was previously operated by the regional airline is operated by the
major airline itself in 2010. We also create a second dependent variable, named
Integration2, which results from conditioning Integration to at least one flight in the route
being terminated after the shock (Termination =1). While the former variable checks
whether any flight has been integrated, the latter restricts the analysis to those routes thatexperienced restructuring—that is, that saw the number of outsourced flights go down.
39

Tables 16 and 17 below report the effect of the average weather conditions in the majorregional network on the probability of integration after the 2008 shock.

<<Place tables 16 and 17 here>>

The results indicate that routes in networks with worse weather conditions,conditional on being outsourced in 2006, are less likely to become integrated andoperated by the major airline after 2008. This is true within major airlines acrossoutsourced regional airlines (columns 2 and 5 in Table 16), and within regional airlines
across upstream major airlines (although less so due to conflicting signs between
snowfall and number of freezing months). A one-standard deviation increase in snow
decreases the probability of integration between 5% and 10.6%, and a one-standard
deviation increase in precipitation decreases the probability of integration by roughly 6%.
Results in Table 17 are very much consistent with those in Table 16 with Integration2 as
dependent variable.
<<Place Table 18 here>>
Finally, we explore whether terminated outsourcing relationships are likely to becontinued by another regional, and whether the continuing regional is an existingoutsourcing partner or a new partner. To undertake this analysis, we divide routes per
major airline and by the number of outsourcing regionals in 2006, and compute theprobability of survival—that is, continuation of the route with the same airline that was
operating it before the shock, as in our previous tables—, the probability of continuationof the route with any regional partner that was not operating the route before the shock,—conditional on survival = 0, and the probability of continuation of the route through a40

new partner (that is, a regional airline that was not operating any route for the major prior
to the 2008 shock), conditional again on survival = 0. We provide the results of thisexercise in Table 18.
We find that in routes where major airlines only used one regional airline before theshock, the likelihood of continuing operation of the route with another existing regional
partner, conditional on termination of the previous partner on that route, is high. Even
more importantly, the probability of using a new partner (that is, a partner not previously
used in other routes) is zero across all regionals in our sample. When we look at the
subsample of routes where major airlines used two regional airlines before the shock, wefind again that the probability of continuing operations on a route with an existingregional partner after severing an outsourcing relationship on that route, is high, and thatthe probability of using a completely new regional partner is zero in all cases except forContinental, where it is positive but small (2.22%). When we examine cases where major
airlines use more than two regionals before the shock, we find that the likelihood ofcontinued operation of a route is very high (close to 100% for all major airlines except for
Northwest8) but again, continuation under a new outsourcing partner (not used before on
any route) is zero for all major airlines except for US Airways.
In conclusion, the beginning of the financial crisis in 2008 marked by thedisappearance of Lehman Brothers forced US airlines to redesign their networks ofoutsourcing relationships and we found that they did so in the three following ways. First,
they terminated their existing outsourcing agreements on routes that were outsourced to8This is mainly driven by the merger of Delta and Northwest, and how Delta may have used their own
partner to continue operations. See in the Data Appendix how we treat in our data the Delta-Northwest
merger for clarification.

41

regionals with low continuation value, proxied in our analysis by the network-levelaverage weather. Second, they integrated routes that were previously outsourced toregionals with low continuation value. Third and last, the majors reallocated most of theterminated routes to pre-existing partners that had higher continuation value. All thesethree margins of adjustment confirm the importance of the value of relational contracting
for efficient adaptation in the US airline industry between major and regional airlines.

7. Conclusion
In this paper we study the value of relational adaptation in outsourcing relationships,
using data from the US airline industry. Our theoretical model shows that for relational
adaptation contracts between major and regional airlines to be self-enforcing, the longterm value of the relationship must be at least as large as the regional’s cost of adapting
flight schedules across joint routes. Thus, when facing a shock that forces it to terminate
some routes, the major is more likely to preserve routes outsourced to regional airlinesthat have higher adaptation costs, as the value of the major’s relationship with thoseregionals is larger.
In our empirical analysis, we analyze the evolution of major-regional airlines
networks in the U.S. around the 2008 financial crisis, and we find that consistent with ourtheoretical predictions, regional routes belonging to networks with worse average
weather, and hence higher adaptation costs, were more likely to survive after the shock.
This finding is robust to the inclusion of route-level weather variables as well as routeand airline fixed effects.

42While it is often argued that both adaptation to unforeseen contingencies and informal,
self-enforcing agreements are of fundamental importance to the success of inter-firm
collaborations, there is still little evidence supporting these claims. Our hope is that theevidence and methodology provided by this study will contribute to shed light on theseimportant phenomena. We also hope that our work will inspire future research that may
further expand our understanding of how relational contracts help solving adaptation
problems that spot and formal contracts fail to address.

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Data Appendix
We made a few assumptions in order to assemble our data when facing instances of
missing information and the various mergers and exits that occurred in the US airline
industry between 1999 and 2010.
To create our main dependent variables, Survival and Termination between 2006 and2010, as well as the dependent variables in our placebo test (Survival and Termination
between 2003 and 2006) and in the examination of the 2001 terrorist attacks (Survival
and Termination between 1999 and 2003), we utilize the information available in the
DB1B data set to the best of our ability. In particular, we code our major-regional airline
interaction based on the ticketing carrier code (major airline) and the operating
carrier/reporting carrier code (actual operator). Because some (about 20%) observationsin the DB1B data matched with T100 do not have an operating carrier number (code"99") but do have a reporting carrier number, we replace the operating carrier with thereporting carrier for the observations with a missing operating carrier. According to
BTS, 9 the reporting carrier is usually the operating carrier of the first segment of anitinerary. Because we only use nonstop flights and the segment for a nonstop flight isfrom the origin airport where you take off to the destination airport, this assumption
should not suppose a problem.
So far as our main analysis (2008 Lehman Brothers shock) is concerned, these are themergers and exits that we encountered:(1) Delta (DL) and Northwest (NW) merged in 2008 and were operating only under
DL in 2010. We assume that a route outsourced by NW to a given regional in2006 survived to 2010 if we observe DL outsourcing that route to the same
regional in 2010.
(2) Republic AL (RW) and Midwest AL (YX) merged. Even though Republic AL
survived, it changed its airline code to Midwest AL (YX). We apply same
assumption as for DL and NW merger.
(3) United (UA) and Continental (CO) announced their merger in 2010 but they were
not able to close it until 2012. Hence, this merger does not affect our data andempirical analysis.
(4) Pinnacle AL and Colgan AL merged in 2008 but operated separately through2010.
9https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/airline_information/accounting_and_r
eporting_directives/number_224.html

47

(5) Pinnacle AL and Mesaba AL merged in 2008 but operated separately through2010.
(6) Skywest, AS AL and ExpressJet AL merged in 2008 but operated separately
through 2010.
(7) A number of regional airlines declared bankruptcy but continued operating
afterwards. These are Sky Airlines in 2008, Mesa Airline in 2010, Skybus Airline
in 2008, Arrow Air in 2010, Sun country Air in 2008, AirMidwest in 2008, andBig sky in 2008.
(8) Two regional airlines ceased operations and exited in 2008: AirMidwest (used by
US Airways), and Big Sky (used by Delta). Their relationships with major US
Airways and Delta appeared as not surviving in our sample.
So far as our placebo test is concerned (period between 2003 and 2006), here are theoperations we have identified and the corresponding assumptions we have made inassembling our our data:(1) Two regionals named Republic AL (RW) and Shuttle America (S5) merged in2005 into Republic AL. S5 appeared only once in 2003. We classified a route thatwas outsourced by a given major to S5 in 2003 and to RW in 2006 as a route
outsourced to S5 in 2006.
(2) Skywest and Atlantic Southeast Airline merged in 2005. Despite that, both
operated separately in 2006.
(3) US Airways' acquired America West Airlines (HP) in 2005. HP ceased operationsin 2005, but HP still appears as HP (not US Airways) in 2006.
So far as our investigation of the terrorist attacks of 9/11, we only encountered the merger
between American Airlines (AA) and Trans World Airlines (TWA) in 2001. We assume
that a route outsourced by TWA to a given regional in 1999 survived to 2003 if we
observe AA outsourcing that route to the same regional in 2003.

48