Predatory Incentives and Predation Policy:The American Airlines Case

Connan Snider

UCLA

The American Case

I In May of 2000 the Department of Justice sued American forantitrust violations (predation)

I Case emphasized aggressive price and capacity response toentry of “low cost ”rivals

I The markets and firms in question.

1. Dallas to Long Beach (Sunjet)2. Dallas to Colorado Springs (Western Pacific)3. Dallas to Kansas City (Vanguard)4. Dallas to Wichita (Vanguard)

Dallas - Wichita Prices

Dallas-Wichita Capacities

Motivation

I American case was dismissed despite considerable qualitativeevidence

“If you are not going to get [the low cost rivals] out there is nopoint to diminish profit ”- American CEO Robert Crandall

I Ruling is typical of prevailing skepticism regarding predation

I Fear distortionary implications of preventing predation may becostlier than predation itself

I Difficult to assess this concern in real marketsI Theory: Predation is a dynamic and strategic ideaI Practice: Analyzed from a static and competitive perspective

This Paper

1. Analysis connecting a dynamic notion of predation to realmarket data

I Predation arises from market fundamentals (costs and demand)I Not reputation related

2. Why predation may be endemic to the industryI Low Cost v. Hub and Spoke cost structureI Aircraft decisions are (mostly) reversible

3. Estimates the model directly from market data

4. Uses the estimated model to quantitatively address 2 policyquestions

I Can static anti-predation rules improve welfare?i.e. is the medicine worse than the disease?

I Which static tests best proxy for dynamic incentives?I What is the “appropriate”measure of cost for a pricing below

cost test

Related Literature

1. Equilibrium Predation: Milgrom and Roberts (1982), Boltonand Scharfstein (1991), Saloner (1989), Fudenberg and Tirole(1984), Cabral and Riorden (1994)

2. Empirical Studies of PredationI Airline Industry: Bamberger and Carlton (2007), Ito and Lee

(2003)I Other: Scott-Morton (1998), Genesove and Mullin(2006)

3. Estimated Dynamic Industry Models: Gowrisankaran andTown (1997), Benkard (2003), Ryan (2007), Ellickson andBeresteanu (2005), Aguirregabiria and Ho (2009),Collard-Wexler (2007) Matzkin, McFadden, Jenkins, and Liu(2006)

Model Overview

I Dynamic industry model in the spirit Erickson and Pakes(1995)

I 4 essential components

1. Differentiated Products2. Capacity Constraints3. Costly Capacity Addition/Subtraction4. Fixed/Entry Costs

Demand

I Nested model: Outside option (no flight), all other products

I Utility of consumer i choosing carrier j in period t:

uijt = βxjt − αpjt + ξj + ∆ξjt + ν(λ) + εijt

I xjt - Observed carrier characteristics: Airport presence,Connecting/Nonstop

I ξj - Mean carrier unobserved product qualityI ∆ξjt - Deviation in product qualityI ν(λ), εijt - Unobserved consumer heterogeneityI ν(λ) + εijt- type 1 extreme value

Demand

I A carrier’s Demand state ξjt is defined as:

ξjt = βxjt + ξj + ∆ξjt

I qjt- number of passengersI For a market of size, M, a carrier’s residual demand is:

qjt(pjt , p−jt , ξt) = M

exp(ξjt−αpjt

λ )∑j′ exp(

ξj′t−αpj′t

λ )

(∑j′ exp(

ξj′t−αpj′t

λ ))λ

1 +(∑

j′ exp(ξj′t−αpj′t

λ ))λ

non-local demand

Variable Costs

I Constant marginal cost term plus an increasing ”soft”capacity constraint ala Besanko and Doraszelski (2004):

Cjt(qjt , qjt) = (ωj + ∆ωjt)qjt

+

(ωlf

1 + ν

)(qjt

qjt

)νqjt

I ωj - Mean carrier marginal cost

I ∆ωjt - i.i.d cost shock

I qjt - Capacity (Available Seats)

I ν - Determines hardness of constraint

I ωlf - Determines steepness of constraint

Full Cost

Capacity Costs

Capacity Adjustment Costs

C Cap.j (∆qjt , εjt) =

{(η+

1j + εjt)∆qjt + η+2j ∆q2

jt) if ∆qjt > 0

(η−1j + εjt)∆qjt + η−2j ∆q2jt) if ∆qjt < 0

I ∆qjt - Change in capacity

I η’s - Parameters determining cost of changing too much/tooquickly

I εjt ∼ Fj = N (0, σεj ) i.i.d

I Cost shock is private information

Entry Costs

Sunk Entry Costs

ψjt ∼ N (γEj + γE xjt , σ

E )

I ψjt-i.i.d private information

I May differ according to observed characteristics (xjt), e.g.airport presence

I E.g. Administrative and planning expenses

Fixed Costs

Per Period Fixed Costs

φj + γqqjt

I φj - Component independent of capacity may differ acrosscarriers

I γq - Fixed cost proportional to capacity

I E.g. Gate leases, allocated system/airport wide costs

Timing

Time is discrete (quarter) and infinite. Within a period:

1. Active firms pay fixed cost (φj + γq qjt) and compete in prices

2. Potential Entrants see entry cost (ψjt), become active bypaying cost or stay out

3. Active firms see investment cost shock (εjt) and choosecapacity adjustments (∆qjt) or exit and disappear

4. State variables (qt , ξt) update

Markov Perfect Equilibrium: Pricing Game

I Pricing decisions do not influence state variables

I Determined by static Bertrand competition with N firms:

qj (pt , ξt) +∂qj (pt , ξt)

∂pj

(pjt − ωj − ωlf

(qjt

qjt

)ν+ ∆ωjt

)= 0

j = 1, . . . ,N

I p∗(qt , ξt , ): an equilibrium, the reduced form profit functionis:

πj (q, ξt) = p∗j qjt(p∗, ξt)− Cj

(qj (p∗, ξt), qjt

)Full static game

Markov Perfect Equilibrium: Value Functions

Incumbent Value Function

Vj (S , εj ) = max∆qj∈[−qj ,∞],χ∈{0,1}

−(φj + γq qjt) + πj (S)− C Cap.j (∆qj , εj )

+ χ

(β

∫ ∫Vj (S ′, ε′j )Pr(dS ′|S ,∆qjt)F (dε′j )

)

Entrant Value Function

V Ej (S , ψj ) = max{0,−ψj +

∫Vj (S , ε)F (dε)}

I S = (qt , ξt)

Data

1. DB1B 10% Fare Sample : market level prices and quantitiesI Aggregate quarterly average round trip fare

2. T100 Traffic Data: Route Level Capacity (actual andscheduled seats)

3. Use a sample of 81 Dallas-Fort Worth markets 1993-2000I Exclude the bottom quartile in terms of populationI Only markets with non-stop service in each periodI Only consider carriers with over 1000 quarterly passengers

Table: Summary Statistics

variable mean s.d. min maxAmericanfare 211.56 86.1 52.3 585.3fare w/LCC 176.27 64.5 54.5 346.8share .52 .14 .06 1∆capacity≥ 0(per capita) .0023 .0035 0 .032∆capacity≤ 0 -.0025 .0033 -.027 0Low Costfare 130.02 53.66 50.6 287.3share .19 .16 0 .956∆capacity≥ 0 .0017 .0020 0 .013∆capacity≤ 0 -.0013 .0018 -.017 0Otherfare 166.0 89.5 35.3 462.5∆capacity≥ 0 .0011 .0014 0 .012∆capacity≤ 0 -.0013 .0019 -.02 0AA Market-periods 2554LCC Market-periods 188Obs 5231

Estimation Overview

I Estimate the parameters in 2 stages as proposed by Bajari,Benkard, and Levin (2007)

1. First stage uses standard techniques to estimate:

i. Demand, variable cost parameters → reduced form profitfunctions

ii. Capacity/entry/exit policy functions

2. Second stage estimates capacity/entry/exit costs,I Reverse engineer parameters that make observed behavior in

(ii) optimal

First Stage: Demand

I A market, m is a (non-directional) city pair

I Demand model gives

log(sjtm)− log(s0tm) = βxjt − αpjtm + λlog(sjtm|fly )

+ ξjm + ∆ξjtm

I sjtm - Overall share of carrier j in period t and market m

I s0tm - Share of outside good

I sjtm|fly - Carrier’s share of flying consumers

I BLP instruments for prices and shares: Functions ofcompetitors characteristics

Table: Utility Parameters

IV OLS

price($100) -0.6961 -.0488(0.2273) (.0040)

sj |fly 0.7281 .9581(0.1466) (.0025)

stop -0.3287 -.0919(0.1302) (.00913)

Dest. Pres.(millions) 0.0871 .0275(0.021) (.00375)

Origin Pres.(millions) 0.0727 .0092(0.0729) (.0006)

Obs. 12065

Implied Elasticities mean std. devAmerican -2.04 1.32Low Cost -2.01 .72Other -2.47 1.82

First Stage: Variable Costs

I Back out marginal costs:

cjtm = pjtm

(1− 1

ˆelas jmt

)I Estimate using form of variable cost function

cjtm = ωjm + ωlf

(qjtm

qjtm

)ν+ ∆ωjt

I Instruments: Number of connecting products, Competitorcharacteristics

I I set ν = 5

Table: Variable Cost Parameters ($100)

Coeff. Standard Errorωlf 1.34 .250

Other param. mean std.dev.American ωj .461 .307

ωlf

(qjt

qjt

)5.194 .153

Low Cost ωj .292 .212

ωlf

(qjt

qjt

)5.131 .114

Total ωj .561 .523

ωlf

(qjt

qjt

)5.135 .150

ωlf

(qjt

qjt

)5- Marginal capacity cost

First Stage: Policies

I Reduced form entry and exit policies are estimated via probitmodels on functions of the state variables

I Specify capacity policy function as a flexible polynomialfunction of state variables:

I Estimator formed using the moment conditions:

1

T

1

M

T∑t=1

M∑m=1

1

Nm

Nm∑j=1

f ∆q(qtm, ξtm, ωtm)(∆qjtm−f ∆q(qtm, ξtm, ωtm)θ′) = 0

I f ∆q(qtm, ξtm, ωtm) - Vector of 3rd order polynomial terms

Table: Fixed/Entry Costs

Coeff. S.E.Entry/Exit(Thousands of Dollars)ψ (Sunk Entry Cost) AA –

LC 481.87 84.19Oth. 338.90 137.94

Opres(millions) -8.67 5.68Dpres(millions) -11.42 1.12

φ (Fixed Cost) AA 132.91 43.17LC 236.06 51.23

Oth. 213.17 88.84γq 11.596 2.66

I origin or destination presence (+)→ Entry probability (+)

I High churn among LCCs v. Low churn for American

Table: Adjustment Costs

Coeff. S.E.

Fixed/Capacityη+

1 (Linear Positive) AA 19.05 2.28LC 26.60 2.96

Oth. 28.11 6.43η−1 (Linear Negative) AA 18.37 2.47

LC 24.44 3.63Oth. 21.47 5.55

η+2 (Quadratic Positive) AA 2.36e-4 .86e-4

LC 3.10e-4 1.33e-4Oth. 5.16e-4 3.88e-4

η−2 (Quadratic Negative) AA 1.51e-4 .77e-4LC 2.66e-4 1.09e-4

Oth. 1.01e-4 2.68e-4

I Adding 1 daily flight costs AA: 130K –2 costs: 300KI Removing 1 yields AA 120K – 2 yields –225KI Adding 1 for LC – 175K – 2 costs – 400KI Removing 1 for LC – 130K – 2 –215K

Anti-Predation Policy

I Use estimated model to look at predation v. anti-predationpolicy in Dallas-Wichita market

I Duopoly marketI American and Vanguard are potential competitors

I Assume firms in current world do not consider possiblepunishment for actions Model Cap. Model Prices

I How much observed behavior is driven by predatoryincentives?

I Use model to measure these incentives

I What are the equilibrium effects of anti-predation policy?I Simulate equilibrium implications of proposed Dept. of

Transportation policies.

Predatory Incentives

I How much of observed behavior is driven by predatoryincentives ?

I Ordover and Willig (1981) define predatory acts as those:

1. That are optimal when their effect on rival exit is taken intoaccount

2. Are suboptimal when this effect is ignored.

I Experiment with capacity FOC

β

∫Vj (S′

, ε)∂Pr(S′(-j exits)|S,∆qj )

∂∆qj

+ Vj (S′|S,∆qj )∂Pr(dS′(-j stays)|S,∆qj )

∂∆qj

F (dε) = 0

β

∫Vj (S′|S,∆qj )

∂Pr(dS′(-j stays)|S,∆qj )

∂∆qj

F (dε) = 0

Measuring Predatory Incentives

Antitrust Policy Experiments: Fair Competition Guidelines

I In the early 1990s growth of the low cost segment wasparticularly rapid.

I By the mid 90s growth has slowed

I Popular answer to why: Predation

I Led to draft and circulation of Fair Competition Guidelines(FCG)

I If low cost carrier enters and serves X passengers incumbentcan only serve X new passengers

Antitrust Policy Experiments: Fair Competition Guidelines

I In the context of the model, these restriction amount to capson capacity changes.

I I examine 3 of these caps

1. ∆qjt = 6500 (3 flights per week)2. ∆qjt = 13000 (6 flights per week)3. ∆qjt = q−jt

I Full equilibrium effects important:I Less monopolization by incumbent (+)I Dulled incentives for competition (”chilling effect”) (-)I Increased incentives for low cost entry (+)

Harsh FCG Equilibrium Capacity Series

Harsh FCG Equilibrium Price Series

Benchmark Capacity Distribution 2 Years Post Entry

Harsh FCG Capacity Distribution 2 Years Post Entry

Table: 2 year Actual and Expected Welfare Changes Under FCG typeRemedies(Millions of Dollars)

Actual Expected

Compensating Variation Cap = 6500 3.80 .6270Cap= 13000 4.03 1.99Cap = q−j 1.08 2.18

Vanguard Profit Cap = 6500 2.20 2.11Cap= 13000 1.35 1.15Cap = q−j .19 .96

American Profit Cap = 6500 -4.13 -2.46Cap= 13000 -2.44 -1.15Cap = q−j -.65 -1.04

I FCG type rules protect small entrant

I Enhances entry incentives but less distortionary

Conclusion and Extensions

I Predation may be worse than anti-predation policyI Prevailing skepticism may be detrimentalI Need for more appropriate tools

I Fundamental aspects of industry may make it prone topredation

I Differences in LCC v. Hub and SpokeI Nature of aircraft as a capital good

I ExtensionsI How big is the problem in the industry?I What market features make them prone to predation?I Implications for mergers

Benchmark Equilibrium Capacity Series

Return

Benchmark Equilibrium Price Series

Return

Non-Local Revenue

I Let qjt,NL be the total volume of non-local traffic on a route

I Revenue from these passengers is allocated to the non-stoproute by:

πNL(qjt,NL, ξNLjt ) = (αNL log qjt,NL + ξNL

jt )qjt,NL

I ξNLjt - Non-local demand state

Return

Variable Costs

I Normalize constant marginal cost of non-local passenger to 0

Cjt(qjt , qjt,NL, qjt) = (ωj + ∆ωjt)qjt

+

(ωlf

1 + ν

)(qjt + qjt,NL

qjt

)ν(qjt + qjt,NL)

Return

Markov Perfect Equilibrium: Local Prices, Non-LocalTraffic

I Non-stop prices and connecting traffic are static decisions

I Determined by the system of 2N first order conditions:

qj (pt , ξt) +∂qj (pt , ξt)

∂pj

(pjt − ωj − ωlf

(qjt + qjt,NL

qjt

)ν+ ∆ωjt

)= 0

αNL(1 + log(qjt,NL)) + ξNLjt − ωlf

(qjt + qjt,NL

qjt

)ν= 0

I Letting p∗(qt , ξt , ξNLt ) and q∗NL(qt , ξt , ξ

Qt ) be a solution,

define the reduced form profit function:

πj (q, ξt , ξNLt ) = (αNL log q∗j,NL + ξNL

jt )q∗j,NL

+p∗j qjt(p∗t , ξt)− C(qj (p∗, ξt), qjt

)Return

Cost Tests

Incremental Cost Test

π(qit , q−it , ξt)− π(qit−1, q−it , ξt) ≥ γq(qit − qit−1) +1

rC (∆qit , εit)

(R1)

Avoidable Cost Test

π(qit , q−it , ξt) ≥ γqqit +1

r(C (∆qit , εit) + C (−∆qit−1, εit) + φi

(R2)

I Incremental Cost Test: Compare the increase in profitassociated with an increase in capacity to the cost of addingthat increment of capacity

I Avoidable Cost Test: Compare the cost savings of exit withthe profit generated by adding capacity