Air deregulation revisited: Choice of aircraft, load factors, and marginal-cost fares for domestic air travel

Tnvupn. Rex.4 Vol. ZOA.No. 5. pp. 361-371. 19% Ptitttedin Gtcat Btitaia.

0191-m/86 s3.m+ .oo 0 1986Pcrgamm JoutttalsLtd.

AIR DEREGULATION REVISITED: CHOICE OF AIRCRAm, LOAD FACTORS, AND MARGINAL-COST FARES FOR DOMESTIC AIR TRAVEL PHILIP A. VmN Department of City and Regional Planning, The Ohio State University, Columbus, OH 43210, U.S.A.

(Received 25 May 1984; in rev&cd form 21 January 1986) Abstmet-If airline deregulation is working as predicted, then fares should fall to marginal costs. This paper computes such optimal fares, allowing for a wide variation in point-to-point distance, traffic, and available a&raft type. It is shown thaf marginal-cost fares are exceeded by average carrier costs. This implies an unpleasant dilemma: either the industry is naturally destructive (if the returns are internal to firms) or else the sustainable industry configuration is less than welfare-optimal. In the latter case, the possibility of a Pareto-improvement with a judicious form of re-regulation cannot be ruled out. In lighht of this, a certain amount of caution about the results of deregulation is suggested.

other words, in these industries it is insufficient to consider only carrier costs. It can be argued that the contribution of Douglas and Miller (1974) was to incorporate this into the analysis of air transport. But for somewhat different reasons, the Douglas/Miller results cannot, of themselves, support a claim for the viability of competition. There are two reasons for this. The first is that, although their model allowed them to do so. Douglas and Miller apparently failed to calculate marginalcost fares: rather, their fare calculations are average costs.t Thus the important question in the deregulation context, whether marginal-cost fares are viable, is not yet answered. The second reason is more important, for it throws doubt on the numerical results themselves. In computing optimal service quality, the cost and technological characteristics of aircraft are of crucial importance. Douglas and Miller sidestepped the question of aircraft substitutability by assigning a particular aircraft to each market segment, and then, conditional on that assigned aircraft, proceeding to optimize with respect to service quality. Thus, to the extent that their initial assignment was wrong, then their derived load-factors and fares may be seriously biased. Theoretical support for this possibility may be adduced from the fact that the CAB consistently allowed the regulated airline industry to set fares in order to earn a rate of return substantially above the market cost of capital (Keeler, 1978).t With this type of regulation, it is known since the work of Averch and Johnson (1%2) that the firm has an incentive to over-invest in capital. To the extent that the carriers anticipated a higher-than-

1. INTRODUCTION The performance

of the domestic passenger airline industry has been closely monitored since passage of the Airline Deregulation Act of 1978. In large part this interest is attributable to the fact that this was the first of a series of planned relaxations of govemment interventions in the marketplace. Thus, the question of whether air deregulation has “sueceeded” is of some significance. And on this question opinions are divided. N. K. Taneja, in his study of the regulation of international air transportation, said of domestic developments that “the jury is still out (1980, Ch. 2).” In an unpublished paper, Daniel P. Kaplan, then director of the Civil Aeronautics Board’s (CAB) Bureau of Economic Analysis, asks “Is the Airline Industry Behaving Competitively?” and concludes that it appears to be. Conversely, there was a tendency among the major carriers to blame deregulation for an unprofitable 1980. However, the important fact is that the principal published research has not adequately addressed the crucial question of post-deregulation market stmcture. Keeler’s important 1972 paper, analyzing the situation of the early 1970’s, focused on the average carrier costs of point-to-point operations. ‘He was able to show that on the major city-pair routes, fares considerably exceeded average (and, by his assumption of constant returns to city-pair operations, marginal) carrier costs. Keeler’s results may be interpretedasanalyzingtheefficacyoftheCAB’sostensibly cost-based fare setting. This, however, has little relevance for a post-deregulation world, for the workability of competition is not addressed. For, as has long been recognized, the distinguishing characteristic of the transportation industries is that production of trips requires the commitment of resources from the user as well as the carrier. Thus, there arises another potential source of decreasing costs in the transportation industries, namely the shape and magnitude of the average user-incurred costs. In

?I owe this observation to T. E. Kecler. tThe Douglas and Miller fares may be considered as Ramsey-prices for a single-product (i.e. no route cross-elasticities of demand) carrier; this, of course, is just another way of saying that viability is not considered. For what is relevant in a market-structure analysis is whether the Ramsey constraint is binding. 361

P. A. 362 market rate of return, they may have invested too soon in over-large aircraft. On the other hand, the fact that the airlines seldom earned the regulated rate, coupled with the substitution of service quality for price-competition suggests pressures on aircraft selection from the opposite direction. In other words, the aircraft actually flown on particular routes-generally the basis for the selection [see also Keeler (1972)j-may have been (for whatever reason) far from optimal.* This paper attempts to correct these problems by re-estimating optimal service quality and the associated marginal-cost fares using a wide range of available aircraft. In addition, to provide some insight as to how large factor-price increases should affect op timal market characteristics, the influence of recent fuel price increases is investigated. This is also relevant to an evaluation of deregulation, as large fuel price increases are often cited as explaining presumably “temporary” loss equilibria in some markets. 2.

THE MODEL

Extending the basic framework of Douglas and Miller, in each time period t Pareto-optimal flight frequencies f, and aircraft types a, are determined by trading off user-incurred and carrier-incurred resource costs. Suppose that in period t there are Q, (one-way) travellers between a particular origin and destination. We represent average user-incurred resource costs by C,(Q,, a,, fi) and average (per-flight) carrier costs by C,‘(a,, Q,, f,). Define the (inverse) demand functions P,( Q,). Then the planner’s problem is to choose P,, Q,, and f, to Max NB = T r

p&t) 4s -

QC,(Q,, a,, fi) - frc,)(~,, Qr,fr>

Clearly, Pareto-optimal frequencies and aircraft types are determined by the sub-problem hf4 C’ =

Q,C,(Q,, a,, f,) + fiC:@,, Qt. fi)

(2.1)

The first order conditions for an interior solution, X2' =

afi

VITON

ter” types of just balance the marginal carrier-incurred cost of so doing. From the first-order conditions of the planner’s problem,

= P,. Thus, we have the conventional result that the op timal short-run marginal-cost price of travel is composed of two terms. One of these, C,, is borne directly by the consumer; the other, Q,(aC,laQ,) + fi(ac,‘laQ,), represents the,optimal fare which is to be charged in order to ensure that the traveller bear the full marginal cost of his actions. We turn now to a more detailed specification of the consumer cost C,. Following Douglas and Miller, we conceive of average consumer costs as composed of two parts. The first is termed frequency delay, denoted FD,cf,), and represents the elapsed time between an individual traveller’s preferred departure time and the time of the next scheduled flight. Observe that it does not depend on the travel volume Q, since it is strictly related to individual preferences only. The second component, called stochastic delay, and denoted SD,(Q,, u,, fi), represents the additional time that a traveller must spend in trip-making because earlier (preferred) flights may be fully booked. Thus, stochastic delay is the expected time elapsed between the first flight scheduled after the preferred departure time, and the first flight with available seats. In the literature, the sum of frequency delay and stochastic delay is known as “schedule delay”. Finally, recognizing that different aircrafts fly at different speeds we modify the Douglas and Miller framework by incorporating the point-to-point travel times. If the point-to-point distance between the origin and destination in question is L miles, and if the average airborne speed of aircraft type a, is r,(a,, L), then the travel time, TT(a,, L), is TT(a,, L) = Llr,(a,, L). If we now define V, to be_the value of consumer travel (in-vehicle) time, and V, to be the value of schedule delay time then we may write the individual consumer-cost component of the full-cost minimization problem (2.1) as:

0: Q,$ I

and

ac*

-=O:Q,$+r;$+

aa,

,

,

require that the aggregate consumer benefits from additional frequency and from using aircraft of “bet*Dorman (1976) estimates a model which allows aircraft selection over a short (4 type) menu, but, eliminates most of them on particular routes for unstated “technological and/or economic constraints”.

Thus we are trading off the total delay-plus-travel time, that is to say the expected elapsed time between the preferred departure and final arrival at destination, against the carrier cost of providing the service. This leads to an immediate intuitive conceptualization of the optimal fare. As far as consumers are concerned, the additional traveller affects only the elapsed time until a flight with available seats departs: that is to say, affects stochastic delay and not frequency delay. The marginal traveller increases this

363

Air deregulation revisited time by (XZI,/aQ,) for each to the Q, period-r travellers. The aggregate economic cost of this delay is V,Q,(t3SD,laQ,). In addition, he imposes on carriers the marginal resource cost f@C,‘/aQ,); the sum of these is just the optimal fare. Note that we weigh both components of schedule delay, namely stochastic delay and frequency delay, by the same value of time V,. This goes against a substantial tradition in the urban transportation literature [see, e.g. Viton (1983)] but the present formulation is readily justified. Because of the prevalence of reservations in air transportation, we may interpret the stochastic delay term as the time between preferred departure time and the departure time of the flight on which one is first able to get a reservation. Then it is plausible that reductions in this delay and in frequency both give rise to additional time to be used for the same set of nontransportation activities. Hence a single value of time for this component is appropriate. Douglas and Miller have also estimated empirical stochastic and frequency delay functions; and we shall use their estimates here. For frequency delay they estimate

sufficient to sidestep this aspect of the problem; and additional justification, based on the empirical results, will appear infra. [For a discussion of the simple analytics of hub-and-spoke operations, see Morrison and Winston (1986)].

3. CARRIER COSLS

The problem formulation of eqn (2.1) implicitly assumes a carrier cost function C, continuous over aircraft types. However, with only a small set of actual aircraft types available it is doubtful that a continuous approximative envelope would adequately reveal the true technological possibilities open to the carriers. For this reason an alternative procedure is adopted here. We define a set A, of available aircraft in period t. For each a, E A, we then derive the relevant cost and technological information. For any trip characteristics there will be a set A,* of feasible aircraft (the feasibility criterion might be determined, for example, by the aircraft’s being able to cover the trip distance nonstop). Then for each a,* E A,* we define B*(a,*) as the value of the solution of

FD,(fi) = !32f,-“.ub where FD, is average frequency delay per traveller in minutes. Accepting their assumptions underlying the queuing model of stochastic delay, assuming that all movements take place over a lrlhour “operating day” and that flights are equally spaced over this period, then,?

where SD, is average stochastic delay per traveller in minutes, and &(a,) is the seating capacitiy of aircraft type 4. It is appropriate to observe at this point, first, that the Douglas and Miller formulation of delay, and perhaps even more, their empirical estimates, have not met with total acceptance. See, for example, the alternative formulation due to Swan in Abrahams (1983). However, partly because of the immense impact that the Douglas and Miller formulation has had on policy discussions, their framework is retained in this paper. Second, the model here set out is of point-to-point travel only. No attention is given to the economics of hub-and-spoke operations. While the influence of these will certainly be important in smaller markets, it may plausibly be argued that in the larger trunk markets, the volume of origin-destination travel is

tTlu derivation of this equation from eqn (2) on p. 83 of Douglas and Miller (1974) proceeds by noting that their term of isgiven by (p. 105) 4.12 fl$ In the present context h’, = (Q,ljJ and 1, the interval between flights, is l/f. Finally, since Q, andfi are in daily units, we assume a 1Chour day and convert the constant term to minutes by multiplying itby840.

The aircraft of choice for the given set of trip characteristics will be the minimum of the set of the B*(aF). In other words, we simply solve (2.1) for each of the feasible aircraft, and pick that aircraft which minimizes traveller-plus-carrier costs. We turn next to the generation of the set of available aircraft. We restrict the analysis to the provision of nonstop service, in order to avoid difficult questions connected with repeated landings and take-offs. The set of available aircraft is composed of those domestically-used long-haul aircraft which were purchased new by carriers in 1979. The first four columns of Table 1 show the set of available aircraft, together with technological data on the maximum attainable distance, average ton capacity, and coefficients used in the calculation of air and block times. These coefficients have been obtained by the CAB for broad categories of aircraft types and sizes of airports from which operations take place. In keeping with the focus of this paper, namely on those markets in which the bulk of air travel takes place, we shall assume that in all cases operations are between the largest category (“large hubs”) of airports. Block time is the sum of time on the ground during which the aircraft is in motion, shown in Table 1 as the “A-value”, and flying time, the product of air distance and the “Bvalue” shown in the table. We assume further that for travellers that schedule delay is delay until the aircraft takes off. (Thus the question of delays at the airport which cause elapsed time to exceed the sum. of schedule delay and flying time is ignored; however if this is, as it appears to be, an airport-specific constant independent of either aircraft size or load factor, then the effect is to add a constant to the optimization, and to leave unchanged the results). The

P. A.

364

Vrrou

Table 1. Aircraft characteristic-1979 Time Com&mtation.7 Aircraft1

Xaximum Nonstop Flight Distance

A - Value

B - Value

Average Ton Capacity3

Seating Capacity

DC-g-30

1500 mi.

.4101

.002162

11.7 tons

115

DC-g-50

1500

.4101

.002162

14.6

139

B-737-200

1800

.4101

.002162

12.0

130

A-3008

2400

.6042

.001915

32.8

340

L-1011

3400

.6059

.001873

37.7

400

E-727-200

3400

.5019

.001967

17.4

165

DC-10-10

3900

.6059

.001973

36.8

380

B-747

5200

.7208

.001827

52.7

550

1. DC: WzDonn~ll-Douglas Aircraft Corp.: 8: Boeing Aircraft fndustrie; L: Lockheed Aircraft Corp. 2. Based on unpublished

CAS data;

sea taxt

3. source: CAB ~QR8rAUmQmAll4

sixth column of the table shows the assumed seating capacity, based in all cases on an all-coach high density configurati0n.t We turn now to the costs of operation. These may be conveniently divided into costs that vary directly with the type of equipment used, and those which vary with carrier traffic volume. We assume, as regards the first type, that for a given aircraft type, constant returns prevail with respect to the number of trips provided. Thus, in this model the only factor which can give rise to carrier cost economies is the utilization of larger aircraft. This appears roughly consistent with the findings of Caves, Christensen and Tretheway (1984) in an econometric study using somewhat different measures of outputs to those employed by Keeler and updated here. They find substantial economies of density (holding points served, average stage length, average load factor and prices constant: approximately 1.2 for all periods and subsamples) and constant returns to scale (average stage length, average load factor and prices constant). The second column of Table 2 shows in 1979 dollars the sum of the cost per block-hour of flying operations (crew, fuel and oil, and insurance) and of airframe and engine maintenance, from CAB Form-41 data. Capital costs present a special problem. Because the analysis is restricted to those aircraft which were actually purchased new, we know, from CAB data, the (average) purchase price. Assuming some useful lifetime for the capital equipment, and an opportunity cost of funds, annual capital costs are readily derived. The problem is to formulate them on a per-

tBoth the average airborne speed and maximum flying distance will depend to some extent on each other and on the loading of the aircraft. Thus the figures in Table 1 must be considered approximations.

Corp.:

A: Airbus

for explanation. aae

m,

1890.

block-hour basis. Since the carriers used different aircraft on different average stage lengths, merely to allocate those costs on the basis of miles actually flown might well give rise to substantial biases. The following procedure is therefore adopted. We first estimate an average utilization of the aircraft, as a function of average stage length. This utilization is used as the common aircraft utilization for all those aircraft over which the optimization is taken. The effect is that, for a given route characterized by a given linehaul length (which, on our assumption of nonstop flight is equivalent to stage length) all the potential choices of aircraft have the same utilization; but that this utilization will differ for different routes. Based on the carrier-by-carrier utilization of the aircraft listed in Table 1, a sample of 27 observations was formed. This sample excluded use of the DC10-10 by all carriers, since the aircraft was grounded for a time during 1979. In addition, observations involving carriers on strike during that year were also omitted. Data on average stage length and blockhours per aircraft were obtained from the CAB’s Aircraft Operating Cost and Performance Report (1980a). The following equation was estimated (t-

statistics in parentheses): Block Hours = 3413.4115 + 4495 Stage Length (27.17) (3-M) R2 = 0.28 (3.1) This equation was used to determine the common utilization rate for aircraft available for a given route. To compute the actual capital cost per block hour, the purchase price shown in column 6 of Table 2 was converted to an annual cost assuming an l&year useful life and a pre-tax interest cost of 20%; and the

Air deregulation revisited

36.5

Table 2. Aircraft costs-1979 Cost par Block-Hour At ‘1975’ Fuel Pricas2*5 DC-g-30 DC-9-50

$

$ 989

9.50 m

$ 742

11.30

717

B-737-200

1210

9.17

972

A-3008

1867

26.60

1346

L-1011

2613

29.596

1939

B-727-200

1338

11.87

1003

DC-lo-10

2513

32.80

1882

S-747

3673

44.74

2714

995

1. For abbrwiations,

sea Table 1, note 1.

2. In dollars per block-hour. 3. Sourcm: CAB G!mxuUmwnnQ 4. Source: CAB Kcl; N

B m,

a,

1980.

dated July 30, 1980.

5. Soo text. Flying Operations + Uaintenanca only. 6. Based on average for

all

L-1011 models.

resulting figure was divided by the predicted block hours of utilization. t (The last column of Table 2 will be discussed later.) Costs varying with carrier traffic have been investigated by Keeler (1972) for 1968data, and his results are updated here. Based on econometric studies of the trunk carriers, Keeler estimated that indirect capital costs (the costs of overhead capital) amounted to $1.94 per available ton-hour; that costs fixed with trip _length were $176 per available seat departure at full load factor, and that indirect operating costs (the costs of sales, reservations, aircraft cleaning and fueling, cabin service, administration and airport rentals) amounted to $0.0136per passenger-mile, and SO.0023per seat-mile at a 100% load factor. To update these estimates we assume that they increased over the period EM-1979 at the same rate as the GNP deflator for nonresidential fixed investment. Since this index increased by a factor of 2.074, we estimate indirect capital costs at $5.62 per available ton-hour; fixed costs at $0.3650 per available seat departure and indirect operating costs at $0.0050 per seat-mile, and $0.0282 per passenger-mi1e.S

tA certain amount of caution is necessary in selecting a rutum on capital during a presumably temporary period of high inflation. What matters is the return in the future, which may bear no particular relation to historical data. Nvrtwithstandiig this, the 20% used here is close to the 1970-MO average of the pretax return on equityfor US manufacturing (21%). and somewhat below the 1975-1980 average of 23%. .$Keeler’s indiit capital cost depends of course on the assumed return on capital, 12% in his case. If his original figure had been based on a 20% return, the cost per available ton-hour would have been $2.71.

Based on these costs we may now calculate, for particular travel volumes, trip distances and values of time, the data which will enable us to anstier the questions raised in the introduction: optimal aircraft types, the associated load factors and marginal-cost fares.0 4. RJSULls

AND

POLICY

IMPLICATIONS

Table 3 shows, for a wide range of daily one-way travel volumes and longhaul distances, the Paretooptimal nonstop aircraft types, load factors and marginal-cost fares. The range of daily travel volumes covers approximately the top 180 domestic city-pair markets in 1979, accounting for 39% of total domestic traffic in that year.1 The two values of time chosen reflect the fact that most indices of consumer income increased over the period 1972-1979 by a factor of two: hence the values of time used here are an updating of the $5 and $10 range used by Douglas OFora particularaircraftchoice,carrierper-tripcostswill be the sumof (1): (cost per block-hour Fable 21 + (average ton capacity [Table l] x cost per available ton-hour $X62] x (distaDce/bltxkspccd); 2):aircmftpurcha~price ITable l] x capital recovery factor (0.2056]lestimated uti-

lization [equation 3.11; (3): cost per seat-mile (SO.OOSO] x seating capacity [Table l] x diitancc; (4): cost per available seat-departure [$0.3650] x seating capacity and (5): cost per passenger-mile [SO.ozs2] x distance x passengers on board; which last is seating capacity x optimal load factor. ‘In 1979 the first-ranked city pair by volume was New York-Washington, with average daily unidirectional traffic of 3086; the MO& was Miiwaukee-Ncw York with average daily traffic of 245. Source: Civil Aeronautica Board Origin Dmination Survey, (1979).

f’. A.

366

VITON

Table 3. Optimal market characteristics-1979 One-Way Daily

Value

500

10

& 20

1000

10

20

2000

10

20

3000

10

20

Distance a!Q

(mf.) ’

zQ!2!2zx!Qlppp

Au!!2 .76

.80

40 1.31

1.::

117 1.17

.82 146 1.16

.03 173 1.14

.61 38 1.49

1.43

.76 116 1.25

.78 145 1.22

.79 171 1.20

Ld9

.81 62 1.19

.84 115 1.13

.a5 145 1.12

.86 171 1.11

.73 38 1.38

.76 61 1.28

.80 115 1.20

-82 144 1.17

.82 171 1.16

.73

.78

.71

.84

.82 39

1.19

1.1":

.79 38 1.29

1.::

.81

.84

.86

.87 114 1.10

.88 143 1.09

.89 170 1.09

-84 114 1.15

.85 143 1.14

.86 169 1.12

1.::

1.1":

.89 114 1.09

.90 143 1.08

.90 169 1.07

.80 38 1.25

.83 60 1.19

.86 113 1.13

.87 143 1.12

.88 169 1.11

1. Entries in each cell are: optimal marginal-cost fare; ratio of average marginal cost. The optimal aircraft for the three shorter stage lengths, the two longest hauls.

and Miller. (An alternative approach to values of time, due to Morrison and Winston (1985a) will be discussed later). Turning first to the optimal aircraft selection, note that in all cases the optimal aircraft is a wide-body aircraft, with the A-300-B used on the three shorthaul distances, and the L-1011 used on the two longest hops. This confirms the suggestion, made in the introduction, that previous research may have misjudged the optimal aircraft types. Since most previous research assigned narrow-bodied aircraft to shorter hops, the results here indicate that biases may exist. In addition, there is an important implication for post-deregulation analyses: to the extent that narrow-bodied aircraft are used on routes falling within the descriptive parameters of Table 3, then it is likely that, even after deregulation, excess service competition prevails.8 Next, optimal fares. Two features of the Table are of interest here, both of which have important implications for policy recommendations. It is apparent llStrictly, this compares the long-run optimal structure derived here with that obtained under the “regulatory bequeathed capital structure”. However, with relatively wellfunctioning aircraft markets, and predominately easy exit and entry into point-to-point markets (though exceptions exist), the comparison does not seem unwarranted.

average load factor; carrier cost to is always the A-300-B and the L-1011 for

that the optimal marginal-cost fares are virtually independent, first, of value of time and, second, of travel volume as well. This holds particularly for nonstop distances of over 608 miles. Independent of time values, the maximum variations in marginal-cost fares are 4.8% for a 600-mile trip and 3.5% for a 3000mile journey. This implies that relatively accurate indications of price-cost differences in actual air markets can be made without detailed research into demand conditions. In addition, these results imply that the static optimization framework adopted here is a relatively good predictor of optimal fares even when demand is responsive to price changes (except possibly for markets whose observed demand is low and overpriced). And finally they provide a justification for ignoring the feeder-route contribution to huband-spoke operations, at least as far as the long-haul section is concerned. We turn next to the viability of marginal-cost pricing. Here the answer is simple. Marginal-cost pricing never suffices to recover costs, and it is more difficult to do so in the case of short-haul routes than for longhaul travel.* Specifically, the ratio of average carrier

tThis result holds with values of time as low as $5 per hour and a pre-tax return on investment of 12%-that is, it encompasses the original results of Douglas and Miller.

Air deregulation revisited cost to marginal-cost fares at optimal load factors and aircraft types ranges from a maximum of 1.49 (500 daily passengers, value of time $20 per hour, 600 mile trip) to a minimum of 1.07 (3,000 daily passengers, value of time $10 per hour, 3,000 mile trip). The important consequence of this is that some degree of market power will be necessary for carrier survival in trunk markets: competitive pricing will not work. If this is so, then what accounts for the seeming health of the new regional entrants following deregulation? For the year ending in December 1979that is, for the first full year of deregulation-operating profits for the eleven trunk airlines decreased by 98.7% over the previous year, to $14.6 million (Civil Aeronautics Board, Quarterly Financial Review--Trunks for third quarter 1980, p. 5.) By contrast the four large new entrants made operating profits of $68.1 million, the eleven reporting small new entrants earned $2.6 million (Civil Aeronautics Board, Quarterly Financial Review-New Entrants for third quarter 1980, pp. 5, 15.) and the seven local service carriers earned $128.6 million (Civil Aeronautics Board, Quarterly Financial Review-Local Service Carriers for third quarter 1980, p. 5.) It would thus appear that the state of the non-trunk carriers is a good deal more healthy than that of the trunks: and yet the results presented here would suggest precisely the opposite. Leaving aside the question of whether market power is being exercised in these non-trunk markets-a difgcult question since, on the one hand they do tend to be characterized by fewer carriers, but on the other the exercise of market power is limited by the attractiveness of alternative modes-an important Teason may be found in the demographic characterization of the markets. It is plausible to regard travellers in the non-trunk markets as having lower values of time than their counterparts in dense markets. This follows from equilibrium models of land use [see, e.g. Fujita and Ogawa (1982)], where wage profiles follow land rent profiles; rents in denser markets tend to be higher than in thinner markets; and values of time are implicitly based on wages. The effect of this can be to increase substantially the ratio of marginal to average cost. For example, as already noted, in the case of a market characterized by 500 daily passengers making 600-mile trips, when the value of time is $20 per hour the ratio of average carrier cost to the marginal-cost fare is 1.49. If the same market were characterized by a value of time of $5, that ratio would be 1.21. The lower the marginal valuation of time, the more closely marginal-cost pricing comes to being viable. For a long-haul market characterized by a large number of travellers with low ($lO/hour) values of time, efficient pricing can recover 94% of operating cost. These considerations may explain the success of the new entrants in markets previously neglected by trunk carriers. The Douglas and Miller values of time, which are here updated, were originally chosen with very little behavioral motivation, except that, in the opinion of

367

the authors, they represented “plausible” bounds for the values. A recent study, Morrison and Winston (1985a), affords us the opportunity of validating the range of time values. Morrison and Winston studied intercity travel in 1977, and their behavioral model of choice distinguished between vacation and business travel, and additionally incorporated a mode choice model allowing for travel modes to vary over autos, airplanes, trains and buses. They did not, however, incorporate stochastic delay fully into their model, although frequency delay is included. For our purposes this is no handicap, since, as pointed out in Section 2, we can take the two time components to have equal values, at least for the air mode. Based on their econometric estimates, Morrison and Winston found widely varying values of air time for their sample population. For example, the implied values of time for business travellers were $12.20 for “travel time” (i.e. time spent in the air, plus an assumed 300 minutes in access and egress time) and $20.67 for the value of frequency delay. For pleasure travellers, the values were $15.37 for travel time and $2.32 for frequency delay. (All values in 1977 dollars). It would be possible to incorporate these values directly, by making an additional assumption about the distribution of the two types of travellers on a given flight. We shall assume, however, that the proportions of the two travellers are fixed at their-averages for travel in the United States, and use a single weighted average of their estimated values. In 1977, approximately 49% of air travel was made for business or convention purposes; the remaining 51% was for various nonbusiness reasons.t With these weights, and updating the estimates to 1979 units using the deflator for personal consumption expenditures, which increased by 17% over the period, we estimate that travel time (i.e. in-vehicle time) is valued at a composite $16.16 per hour, and other time (schedule delay) at $13.23. Table 4 compares, for the four extreme cases of distance and passenger traffic, the results of optimizing with values of time uniformly at S10.00 per hour, and with the Morrison and Winston estimates. Not surprisingly, there is virtually no difference in the two sets, and this holds true for all the other cases of Table 3 as well. Fares, for example, differ by less than $0.50 in a fare of some $173; and in no case is the optimal aircraft type different. Although it would be possible for these results to be altered if the distribution of the types of travellers changed substantially, it is to be observed, given the similarity of the results in the face of a doubling of the values of time (from $10 to $20) that any distributional change would have to be extreme. Had deregulation forced fares down to costs by mid

tU.S. Census Bureau, l!V7 Census of Transportation, National Travel Survey: Travel During 1977, Table 2.1. Fig-

ures quoted in the text are for person-trips.

P. A. Vrro~ Table 4. Comparison with other values of time’ One-Way Dail Traff x c -500

Distance

600

All Time Valued at SlO/hour A-300-B

.73 1% 500

3000

3000

3000

600

3000

$16.16 In-vehicle/ SM.23 Other* A-300-B .71 1.:;

L-1011 .83 173 1.14

L-1011 .81 173 1.16

A-3.00-B .84 39 1.16

A-300-B .63 39

L-1011 .90 169 1.07

L-1011 .89 169 1.09

1.19

1. Entries in each cell are: optimal aircraft type: optimal average load factor; marginal-cost fare; ratio of average carrier coat 'to marginal cost. 2. Souroe: Horrison and Winston (1985a). The values of time are averages over business and pleasure travellers, with weights given by 1977 air traveller proportions. See text.

1979? As we have seen, marginal cost fares are not financially viable for the firm, though they are still the social-welfare-optimal prices. Thus, there are two cost concepts of interest: the marginal and the average cost. Note that the average-cost price has a possible welfare property of its own, since it is the Ramsey price when cross-subsidies between markets are infeasible, and when firms produce a single product in each market. We have already seen that fares are roughly invariant to demand and to value of time. Table 5 presents, for four selected city-pair markets, details of the marginal and average carrier costs at optimal load factors and equipment. These are then compared to actual carrier experience. The short-haul Washington DC-Indianapolis market is the only market in which there occurs a change in aircraft type over those previously revealed as op timal. Because of the thinness and short distance characterizing this particular market, the optimal aircraft is now the narrow-bodied DC-9-50. It is to be observed that this is the only market in which the participating carriers used only narrow-bodied equip ment. In the other three markets the optimal aircraft type is wide-bodied, and examination of the operations of the actual carriers reveals that at least one type of wide-bodied aircraft was used, although narrow-bodied equipment was also in use. Note too, that some carriers used aircraft no longer in current production, assumed inadmissable in our model. Load factors (the only measure of service quality) are difficult to compare, since we have assumed an all-coach configuration, and the carriers generally included a first-class section on each aircraft. Nonetheless, some insight is possible. In three of the four

cases, the optimal load factor is above the average 1979 coach load factor actually experienced; and the exceptional case is the market which, because of its size and distance, fits least well into the assumed “trunk” nature of the problem here examined. All in all, it seems fair to say that load factors remained too low in these markets even after a year of deregulation-induced competition. Finally, fares. The.standard, no-restrictions coach fare in all four markets was well above average (hence, marginal) costs. To the extent that these fares were predicted to fall to cost, then it does not appear that deregulation had yet brought it about. However, the post-deregulation era has seen a proliferation of fares incorporating various restrictions on length of stay or advance purchase requirements. Table 5 also shows one such fare in each market. For the shortest-haul market, the 2-day excursion fare falls almost precisely at the calculated marginal cost; note, however that this is the only market in which no wide-bodied service was provided. In the Minneapolis-New York market, the 2-day excursion fare is actually (five dollars) below the calculated marginal cost. This result may be explained by a combination of market-specific factors such as value of time or travel volume: as previously noted, the influence of these factors is small, but perceptible. Equally, it may have to do with the utilization of sub-optimal aircraft on those flights to which the excursion fare applies. In the Detroit-Los Angeles market, the night-coach fare exceeds the calculated average cost of wide-bodied service in that market. Finally, New York-San Francisco. The low fare, $108, is so far below the calculated marginal cost of $147 as to be unbeliev-

Air deregulation revisited Table 5. 1979 market comparison Actual Average Dirtancm

Load

Harginal

Carrier

Coach

yama

Washington, DC' -Indianapolis

.I6

$ 39

$ 65

B727-1OOC 8727-200

.59 .65 .67

Hlnnoapol~s -NW York

.71

62

64

0727-1OOC B727-200 8707-3006 DC-lo-40 0-747

.s7 .lS .63

.76

115

144

1)727-1OOC

.51 .95 .69

Detroit -Los Ang*lo*

A-300-B

1974

8711-100

n707-1000 B707-3008 0707-3ooc

s

‘I

S 266

a

114

576

4

169

151'

3

DC-10-10

DC-I-61 n-747 New York -San yranoisco

2974

L-1011

.65

147

167

A-300-8 DC-6-30 DC-I-50 DC-6-61 DC-6-62

.I6 .75 .6!l

2J6

106'

6

t-1011 B-747 De-10-10

1. Ba66d on actual dlstanco,

1979 average dally trawlora,

3. 2 Care is unr9mtriotod coach farm. 4. IOUrC*I e

u

5. All mtropolltan

airports.

6. Ex-97 tare: Z-day 7.

night

coach

fare.

6.

World Alnays.

Bourcar m

20/s-c

u

Sourc*: CAB SarVicm

f&i&,

Iqmnt

(tR

S66)

Juna 1979.

wI1pI. Juna 1979.

lxcurxion, 6OUrEW

valid Saturday, m

u

Sunday

only.

u,

Juna

SoUrc~r

e

u

WlipI,

Juna 1979.

1979.

smm tax+.

able. Three factors may lessen the incredulity. Fit, the fare was an initial offering by World Airways, and was widely perceived in the industry as promotional and money-losing. Second, World offered a single flight per day (2 flights daily between Los Angeles and New York at the same fare). And third, although the fare was listed in the June 1979 Oj&ial Airline Guide it appears that no flights actually took place. World’s DC-10s were grounded during June and July by FAA orders; and the carrier was struck from August 3 to December 11, 1979. When transcontinental service resumed, the fare was S108 for one month, after which it climbed, in the April 1, 1980 OjJkial Airline Guidr, to S194. In the years since deregulation increases in the price of jet fuel have been used by the industry to justify regular fare increases and by commentators to explain the industry’s lackluster financial performance. What is the influence of changes in fuel prices on the optimal characteristics derived here? To investigate this, we perform the following conceptual experiment. We suppose that all prices except that of fuel remain constant, and that fuel prices fall to 1975 levels. Since 1975 the price of jet fuel has ap

TR

for all time componmta.

820 valm

2. Entries am: minimum. maximum, average coach load Lactors, 1979. data, June 1979.

proximately doub1ed.t The last column of Table 2 shows aircraft-related costs obtained by substituting for 1979 fuel-and-oil costs per block-hour one half that value.* Table 6 compares, at a S20 value of time for all time components, the four extreme traffic and distance cases of Table 3 under the two cost assumptions.5 tSource: Civil Aeronautic Board, Long Tern Fuel lib pensr dated February 27,1981. Between 1975 and 1979 the price per gallon of fuel increased by a factorof 1.99 (system trunks) to 2.02 (local servicecarriers).Of course, to the extent that additional operations required fuel purchases on the spot market, this average increase will underestimate the marginal fuel cost increase. *Note that by retaining all other cost assumptions we are implicitly ignoring general equilibrium adjustments, which, depending on the story told regarding the impact of fuel price increases elsewhere in the economy, could be substantial. ONote too that speeds remain unchanged under the two sets of assumptions. This ignores the very real possibility thatonereactiontoknverfbelpricesistoinazaseairspceds, leading to reductions in consumer travel time. However, in light of the results presented in the body of the text, it is unlikely that this would materially alter the results.

P. A.

370

VlTON

Table6. 1979and "1975"cost com0arison Coet Charaotarietice"' one-way Daily

Air Distance

Traffic

(miles)

500

600

8-727-200 .67 1.44:

3000

3000

"1975" Fuel Prices

600

3000

A-300-8 .67 1.49 39

L-1011 .78 161 1.21

L-1011 .79 173 1.20

A-300-B .60 36 1.26

A-300-B .60 38 1.25

L-1011

L-ioii .ss

.a0 156 1.11

1. All comparisons

1979 Fuel Pricas'

169 1.11

assume a 520 value for all time

oomponante. 2. Entries in each cell ara: optimal aircraft type; optimal average load factor; marginal-cost fare; ratio of average carrier cost to marginal cost. 3. Prom Table 3.

Note first that the use of wide-bodied aircraft as optimal persists at the lower fuel prices except for the shortest-hop, lowest travel volume case, where a narrow-bodied B-727-200 is now optimal. This is the only case of the 60 in Table 3 where this reversal occurs. Except for this case, the impact of fuel price changes on the optimal structure of air transport provision is negligible. Optimal load factors and the cost-revenue ratio change hardly at all. Marginal-cost fares do change, of course: usually by between 5 and 8%, in the face of changes in block-hour costs of approximately 20%.

5. CONCLUDING REMARKS The first years of deregulation have not been especially good to the airline industry. One of the former trunks (Braniff) is no more; Air Florida, an early and aggressive entrant into medium-haul markets has filed for bankruptcy; and even Provincetown-Boston Airlines is no longer flying high. In a recent article, the Wall Street Journal estimates that since the Deregulation Act of 1978, 120 carriers have closed or filed for protection from creditors (Jan. l&1985, p. 31, col. 3). These facts in’themselves provide no reason to be skeptical about’ air deregulation-as many have noticed in hindsight, Braniff s post-deregulation strategy seems to have been particularly in-

opportune, Air Florida appears to have been the victim of the intense competition on its route structure, and Provincetown-Boston’s problems were chemical. Moreover, in any specific instance, the possibility of financial collapse due to the unwillingness of the market to support firm inefficiency cannot be ruled out, and may indeed be counted a long-run welfare gain. The results of this paper, however, suggest that we should be cautious for another reason about too readily applauding deregulation. It appears that, taking account of both carrier and user costs of airline trip production, the nonstop domestic airline industry may actually experience increasing returns over an extremely wide set of trip and market characteristics. Whether this observation implies that the deregulated industry is not viable depends crucially, as Keeler (1978) points out clearly, on whether these economies are internal or external to the firm. Only in the latter case will there be the possibility of destructive competition. The present paper does not attempt to answer this question. Rather, it is important to note that one faces an unpleasant dilemma no matter which is the case. If the economies are internal to the firm, then, so long as they persist, (and the results of this paper suggest that whether internal or external, they are indeed persistent) the industry is naturally destructive. Conversely, of course, if the economies are external to the firm, then the deregulated industry is financially viable. However, there will re-

Air deregulation revisited main welfare losses, since the Pareto-optimal characteristics derived here will not obtain. t This remains

true whether or not the industry is “contestable” in the sense of Baumol, Panzar and Willig (1982). For the focus of the contestable markets approach is to use as a basis of comparison the sustainable industry configuration-that is, an industry in which revenues cover costs. In this connection, the influence of potential entry serves to guarantee that the industry configuration will result in Ramsey prices. However, as is well known, the Ramsey solution is dominated in a welfare sense by marginal-cost pricing with nondistortionary taxation to make up the required subsidies, at least when taxation is costless to administer. Moreover, some recent research, particularly Graham, Kaplan and Sibley (1983), suggests that airline markets may not be perfectly contestable. In this sense, the possibility of a Pareto-improvement accompanying some form of (re)regulation cannot be ruled out. It is important to realize what this does not say. It is not a claim that deregulation has led to a worsening in the welfare situation. Indeed, in a recently published book, Morrison and Winston (1986) provide persuasive evidence that there has been a general welfare gain to consumers.$ Rather, the question here raised is whether one could do better than with deregulation. The relevant questions then become whether a form of regulation is likely in practice to lead to such an improvement (and the history of the administration of the Civil Aeronautics Act of 1938 should render us skeptical); and whether the costs of regulatory administration outweigh any benefits. These are important questions for future research, and, until that research is performed, a certain amount of caution about the results of deregulation is suggested. Acknowledgements-l acknowledge with gratitude discussions with Theodore E. Keeler and with Clifford Winston;

tNote that we have not taken account of externalities, principally air pollution and noise. Very little is known of the economic impact of the former. As regards the latter, it can be shown that the impact is minimal. Small (1977) has estimated the air-pollution costs per landing and. take: off cycle for a B-747. Focusing on the costs in Californiathe region of greatest impact-, and updating his 1974 costs to 1979 by the GNP deflator (1.65). then, at the lowest load factor of Table 3 (0.67) the cost per cycle per passenger is So.30. $Morrison and Winston’s analysis is actually directed towards the question of whether airline markets are contestable or not. In the course of their analysis they attempt to compare the welfare change that has occurred since deregulation with the optimal welfare measure. However, since their analysis takes only partial account of stochastic delay (as mentioned in the text, the demand model on which their welfare calculations are based explicitly includes frequency delay only, even though frequency is an important component of stochastic delay), and since it appears from the discussion of Section 2 that it is precisely this source of delay that is an important determinant of the fare structure, it may be doubted that their analysis is the final word on the welfare implications of deregulation.

371

and the valuable comments of two anonymous reviewers of this Journal; none of whom, of course, is responsible for my errors.

REFERENCES

Abrahams, M. (1983) A service quality model of air travel demand: an empirical study. TransportationResearch A, 17A(S), 385-35’4. Averch H. and Johnson L. (1962) Behavior of the firm under regulatory constraint. American Economic Review 52, 1052-1069. Baumol W. J., Panxar J. C. and Willig R. D. (1982) Contestable Markets and the Theory of Industry Structure. Harcourt Brace Jovanovich. San Diego. California. Caves D. W.. Christensen L. R. and Trethewav M. W. (1980) Economies of density versus economieiof scale: why trunk and local service airline costs differ. Rand Journal of Economics l&4,471-489. Dorman G. J. (1976) Airline competition: a theoretical and empirical analysis. Working paper SL-7603. University of California, Department of Economics, Berkeley. Douglas G. W. and Miller 1. C. Ill (1974) Economic Regulation of Domestic Air Transport. The Brooking Institution, Washington, D.C. Fujita M. and Ogawa H. (1982) Multiple equilibria and structural transition of non-monocentric urban con&urations. Regional Science and Urban Economics l&161GAtrn D. R Kaplan D P and Sibley D S (1983) Efficiencv and zomnetitionin the airline indust;. Bell JourMI of -Economics l4( 1). 118-138. Kaplan D. P. (1980) Is the airline industry performing competitively? Civil Aeronautics Board, Bureau of Economic Analysis, processed, Washington, D.C. Keeler T. E. (1972) Airline regulation and market performance. Bell Journal of Economics 3(2), 399-424. Keeler T. E. (1978) Domestic trunk airiine regulation: an economic evaluation. U.S. Senate. Committee on Governmental Affairs. Studyon Federal RegulationAppendix to Vol. 6. Morrison S. A. and Winston C. (198Sa) An econometric analysis of the demand for intercity passenger transportation, in Research in TransportationEconomics II (Edited by Keeler T. E.). JAI Press, Greenwich, ConnectiMrhn S. A. and Winston C. (1986) The economic effects of airline deregulation. The Brookings Institution, Washington. D.C. Oficti Airline Guide (1979) R. H. Donnelly Inc., Oak Brook, Illinois. Small K. A. (1977) Estimating the air pollution costs of tamsoortation modes. Journal of Tmnsport Economics and Policy 11(2), 109-132. _ _ Taneja N. K. (1980) VS. InternationalAviation Policy. Lexinaton Books. Lexington. Massachusetts. United states Bureau of thg Census (19n) Census of transwrtation: National Travel Survev: Travel Durina 1977. &ted States Civil Aeronautics Board (1979) Or&/Destinadon Survey. United States Civil Aeronautics Board (198Oa) Aircraft Operating Cost and Performance Report. United States Civil Aeronautics Board (l!@Ob) New aircraft purchases. Dated July 30. United States Civil Aeronautics Board (1981a) Long term fuel expense. Dated February 27. United States Civil Aeronautics Board (1981b) Quarterly fimancial review, various issues, third quarter. The WallStreet Journal (1985) January 16. Viton P. A. (1984) Pareto-optimal urban transportation equilibria. In Research in TransportationEconomics, I (Edited by Keeler T. E.). JAI Press, Greenwich, Connecticut.