Predatory Incentives and Predation Policy: The American Airlines Case

Transcription

1 Predatory Incentives and Predation Policy: The American Airlines Case Connan Snider UCLA

2 The American Case In May of 2000 the Department of Justice sued American for antitrust violations (predation) Case emphasized aggressive price and capacity response to entry of low cost rivals 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)

3 Dallas - Wichita Prices

4 Dallas-Wichita Capacities

5 Motivation American case was dismissed despite considerable qualitative evidence If you are not going to get [the low cost rivals] out there is no point to diminish profit - American CEO Robert Crandall Ruling is typical of prevailing skepticism regarding predation Fear distortionary implications of preventing predation may be costlier than predation itself Difficult to assess this concern in real markets Theory: Predation is a dynamic and strategic idea Practice: Analyzed from a static and competitive perspective

6 This Paper 1. Analysis connecting a dynamic notion of predation to real market data Predation arises from market fundamentals (costs and demand) Not reputation related 2. Why predation may be endemic to the industry Low Cost v. Hub and Spoke cost structure Aircraft decisions are (mostly) reversible 3. Estimates the model directly from market data 4. Uses the estimated model to quantitatively address 2 policy questions Can static anti-predation rules improve welfare? i.e. is the medicine worse than the disease? Which static tests best proxy for dynamic incentives? What is the appropriate measure of cost for a pricing below cost test

7 Related Literature 1. Equilibrium Predation: Milgrom and Roberts (1982), Bolton and Scharfstein (1991), Saloner (1989), Fudenberg and Tirole (1984), Cabral and Riorden (1994) 2. Empirical Studies of Predation Airline Industry: Bamberger and Carlton (2007), Ito and Lee (2003) Other: Scott-Morton (1998), Genesove and Mullin(2006) 3. Estimated Dynamic Industry Models: Gowrisankaran and Town (1997), Benkard (2003), Ryan (2007), Ellickson and Beresteanu (2005), Aguirregabiria and Ho (2009), Collard-Wexler (2007) Matzkin, McFadden, Jenkins, and Liu (2006)

8 Model Overview Dynamic industry model in the spirit Erickson and Pakes (1995) 4 essential components 1. Differentiated Products 2. Capacity Constraints 3. Costly Capacity Addition/Subtraction 4. Fixed/Entry Costs

9 Demand Nested model: Outside option (no flight), all other products Utility of consumer i choosing carrier j in period t: u ijt = βx jt αp jt + ξ j + ξ jt + ν(λ) + ɛ ijt x jt - Observed carrier characteristics: Airport presence, Connecting/Nonstop ξj - Mean carrier unobserved product quality ξjt - Deviation in product quality ν(λ), ɛijt - Unobserved consumer heterogeneity ν(λ) + ɛ ijt - type 1 extreme value

10 Demand A carrier s Demand state ξ jt is defined as: q jt - number of passengers ξ jt = βx jt + ξ j + ξ jt For a market of size, M, a carrier s residual demand is: ( q jt (p jt, p jt, ξ t ) = M exp( ξ jt αp jt λ ) j exp( ξ ) λ j t αp j t λ ) j exp( ξ j t αp j t ( λ ) 1 + j exp( ξ j t αp j t λ ) ) λ non-local demand

11 Variable Costs Constant marginal cost term plus an increasing soft capacity constraint ala Besanko and Doraszelski (2004): C jt (q jt, q jt ) = (ω j + ω jt )q jt ( ) ( ) ν ωlf qjt + q jt 1 + ν ω j - Mean carrier marginal cost ω jt - i.i.d cost shock q jt - Capacity (Available Seats) ν - Determines hardness of constraint ω lf - Determines steepness of constraint Full Cost q jt

12 Capacity Costs Capacity Adjustment Costs C Cap. j ( q jt, ε jt ) = { (η + 1j + ε jt) q jt + η + 2j q2 jt ) if q jt > 0 (η 1j + ε jt) q jt + η 2j q2 jt ) if q jt < 0 q jt - Change in capacity η s - Parameters determining cost of changing too much/too quickly ε jt F j = N (0, σ ε j ) i.i.d Cost shock is private information

13 Entry Costs Sunk Entry Costs ψ jt N (γ E j + γ E x jt, σ E ) ψ jt -i.i.d private information May differ according to observed characteristics (x jt ), e.g. airport presence E.g. Administrative and planning expenses

14 Fixed Costs Per Period Fixed Costs φ j + γ q q jt φ j - Component independent of capacity may differ across carriers γ q - Fixed cost proportional to capacity E.g. Gate leases, allocated system/airport wide costs

15 Timing Time is discrete (quarter) and infinite. Within a period: 1. Active firms pay fixed cost (φ j + γ q q jt ) and compete in prices 2. Potential Entrants see entry cost (ψ jt ), become active by paying cost or stay out 3. Active firms see investment cost shock (ε jt ) and choose capacity adjustments ( q jt ) or exit and disappear 4. State variables ( q t, ξ t ) update

16 Markov Perfect Equilibrium: Pricing Game Pricing decisions do not influence state variables Determined by static Bertrand competition with N firms: q j (p t, ξ t ) + q j(p t, ξ ( ( ) t ) ν qjt p jt ω j ω lf + ω jt) = 0 p j q jt j = 1,..., N p ( q t, ξ t, ): an equilibrium, the reduced form profit function is: π j ( q, ξ t ) = p j q jt (p, ξ t ) C j ( qj (p, ξ t ), q jt ) Full static game

17 Markov Perfect Equilibrium: Value Functions Incumbent Value Function V j (S, ε j ) = max (φ j + γ q q jt ) + π j (S) C Cap. j ( q j, ε j ) q j [ q j, ],χ {0,1} ( ) + χ β V j (S, ε j)pr(ds S, q jt )F (dε j) Entrant Value Function Vj E (S, ψ j ) = max{0, ψ j + V j (S, ε)f (dε)} S = ( q t, ξ t )

18 Data 1. DB1B 10% Fare Sample : market level prices and quantities Aggregate quarterly average round trip fare 2. T100 Traffic Data: Route Level Capacity (actual and scheduled seats) 3. Use a sample of 81 Dallas-Fort Worth markets Exclude the bottom quartile in terms of population Only markets with non-stop service in each period Only consider carriers with over 1000 quarterly passengers

19 Table: Summary Statistics variable mean s.d. min max American fare fare w/lcc share capacity 0(per capita) capacity Low Cost fare share capacity capacity Other fare capacity capacity AA Market-periods 2554 LCC Market-periods 188 Obs 5231

20 Estimation Overview 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 profit functions ii. Capacity/entry/exit policy functions 2. Second stage estimates capacity/entry/exit costs, Reverse engineer parameters that make observed behavior in (ii) optimal

21 First Stage: Demand A market, m is a (non-directional) city pair Demand model gives log(s jtm ) log(s 0tm ) = βx jt αp jtm + λlog(s jtm fly ) + ξ jm + ξ jtm s jtm - Overall share of carrier j in period t and market m s 0tm - Share of outside good s jtm fly - Carrier s share of flying consumers BLP instruments for prices and shares: Functions of competitors characteristics

22 Table: Utility Parameters IV OLS price($100) (0.2273) (.0040) s j fly (0.1466) (.0025) stop (0.1302) (.00913) Dest. Pres.(millions) (0.021) (.00375) Origin Pres.(millions) (0.0729) (.0006) Obs Implied Elasticities mean std. dev American Low Cost Other

23 First Stage: Variable Costs Back out marginal costs: ( ) ĉ jtm = p jtm 1 1 elas ˆ jmt Estimate using form of variable cost function ( ) ν qjtm ĉ jtm = ω jm + ω lf + ω jt q jtm Instruments: Number of connecting products, Competitor characteristics I set ν = 5

24 Table: Variable Cost Parameters ($100) Coeff. Standard Error ω lf Other param. mean std.dev. American ω j ( ) qjt 5 ω lf q jt Low Cost ω j ( ) qjt 5 ω lf q jt Total ω j ω lf ( qjt q jt ) ω lf ( qjt q jt ) 5 - Marginal capacity cost

25 First Stage: Policies Reduced form entry and exit policies are estimated via probit models on functions of the state variables Specify capacity policy function as a flexible polynomial function of state variables: Estimator formed using the moment conditions: 1 1 T M T M 1 N m N t=1 m=1 m j=1 f q ( q tm, ξ tm, ω tm )( q jtm f q ( q tm, ξ tm, ω tm )θ ) = 0 f q ( q tm, ξ tm, ω tm ) - Vector of 3rd order polynomial terms

26 Table: Fixed/Entry Costs Coeff. S.E. Entry/Exit (Thousands of Dollars) ψ (Sunk Entry Cost) AA LC Oth Opres(millions) Dpres(millions) φ (Fixed Cost) AA LC Oth γ q origin or destination presence (+) Entry probability (+) High churn among LCCs v. Low churn for American

27 Adding 1 daily flight costs AA: 130K 2 costs: 300K Removing 1 yields AA 120K 2 yields 225K Adding 1 for LC 175K 2 costs 400K Removing 1 for LC 130K 2 215K Table: Adjustment Costs Coeff. S.E. Fixed/Capacity η 1 + (Linear Positive) AA LC Oth η 1 (Linear Negative) AA LC Oth η 2 + (Quadratic Positive) AA 2.36e-4.86e-4 LC 3.10e e-4 Oth. 5.16e e-4 η 2 (Quadratic Negative) AA 1.51e-4.77e-4 LC 2.66e e-4 Oth. 1.01e e-4

28 Anti-Predation Policy Use estimated model to look at predation v. anti-predation policy in Dallas-Wichita market Duopoly market American and Vanguard are potential competitors Assume firms in current world do not consider possible punishment for actions Model Cap. Model Prices How much observed behavior is driven by predatory incentives? Use model to measure these incentives What are the equilibrium effects of anti-predation policy? Simulate equilibrium implications of proposed Dept. of Transportation policies.

29 Predatory Incentives How much of observed behavior is driven by predatory incentives? Ordover and Willig (1981) define predatory acts as those: 1. That are optimal when their effect on rival exit is taken into account 2. Are suboptimal when this effect is ignored. Experiment with capacity FOC β V j (S, ε) Pr(S (-j exits) S, q j ) + V j (S S, q j ) Pr(dS (-j stays) S, qj ) F (dε) q j q j = 0 β V j (S S, q j ) Pr(dS (-j stays) S, q j ) F (dε) q j = 0

30 Measuring Predatory Incentives

31 Antitrust Policy Experiments: Fair Competition Guidelines In the early 1990s growth of the low cost segment was particularly rapid. By the mid 90s growth has slowed Popular answer to why: Predation Led to draft and circulation of Fair Competition Guidelines (FCG) If low cost carrier enters and serves X passengers incumbent can only serve X new passengers

32 Antitrust Policy Experiments: Fair Competition Guidelines In the context of the model, these restriction amount to caps on capacity changes. I examine 3 of these caps 1. q jt = 6500 (3 flights per week) 2. q jt = (6 flights per week) 3. q jt = q jt Full equilibrium effects important: Less monopolization by incumbent (+) Dulled incentives for competition ( chilling effect ) (-) Increased incentives for low cost entry (+)

33 Harsh FCG Equilibrium Capacity Series

34 Harsh FCG Equilibrium Price Series

35 Benchmark Capacity Distribution 2 Years Post Entry

36 Harsh FCG Capacity Distribution 2 Years Post Entry

37 Table: 2 year Actual and Expected Welfare Changes Under FCG type Remedies(Millions of Dollars) Actual Expected Compensating Variation Cap = Cap= Cap = q j Vanguard Profit Cap = Cap= Cap = q j American Profit Cap = Cap= Cap = q j FCG type rules protect small entrant Enhances entry incentives but less distortionary

38 Conclusion and Extensions Predation may be worse than anti-predation policy Prevailing skepticism may be detrimental Need for more appropriate tools Fundamental aspects of industry may make it prone to predation Differences in LCC v. Hub and Spoke Nature of aircraft as a capital good Extensions How big is the problem in the industry? What market features make them prone to predation? Implications for mergers

39 Benchmark Equilibrium Capacity Series Return

40 Benchmark Equilibrium Price Series Return

41 Non-Local Revenue Let q jt,nl be the total volume of non-local traffic on a route Revenue from these passengers is allocated to the non-stop route by: π NL (q jt,nl, ξ NL jt ) = (α NL log q jt,nl + ξ NL jt )q jt,nl ξ NL jt - Non-local demand state Return

42 Variable Costs Normalize constant marginal cost of non-local passenger to 0 C jt (q jt, q jt,nl, q jt ) = (ω j + ω jt )q jt ( ) ( ) ωlf qjt + q ν jt,nl + (q jt + q jt,nl) 1 + ν q jt Return

43 Markov Perfect Equilibrium: Local Prices, Non-Local Traffic Non-stop prices and connecting traffic are static decisions Determined by the system of 2N first order conditions: q j (p t, ξ t ) + q j(p t, ξ t ) p j ( ( ) ν qjt + qjt, NL p jt ω j ω lf + ω jt) q jt = 0 α NL (1 + log(q jt,nl )) + ξ NL jt ω lf ( qjt + q jt,nl q jt Letting p ( q t, ξ t, ξt NL ) and qnl ( q t, ξ t, ξt Q ) be a solution, define the reduced form profit function: π j ( q, ξ t, ξt NL ) = (α NL log qj,nl + ξjt NL )qj,nl +pj q jt (pt, ξ t ) C ( q j (p, ξ ) t ), q jt ) ν = 0 Return

44 Incremental Cost Test: Compare the increase in profit associated with an increase in capacity to the cost of adding that increment of capacity Avoidable Cost Test: Compare the cost savings of exit with the profit generated by adding capacity Cost Tests Incremental Cost Test π( q it, q it, ξ t ) π( q it 1, q it, ξ t ) γ q ( q it q it 1 ) + 1 r C( q it, ɛ it ) Avoidable Cost Test (R1) π( q it, q it, ξ t ) γ q q it + 1 r (C( q it, ɛ it ) + C( q it 1, ɛ it ) + φ i (R2)