2.3 Economic Characteristics
2.3.2 Microeconomic Matters
Cost Categories Airline managements have considerable leeway in the supply of services, whether passenger or cargo, that they offer. And fleet selection and flight scheduling are principal determinants of costs. Fleet selection, for example, is the main component of capital costs as well as fuel burn rates and many maintenance items, while scheduling is a significant determinant of labor-staffing cost structure.
Airlines have considerably less control over demand. They can advertise and promote and cut fares and offer new frequent-flyer programs. But in view of the industry’s consolidation into just a few mega-carriers, so can their competitors.
Fig. 2.4 Flights per capita versus GDP per capita, 2013.Source: IATA/Tourism Economics’Air Passenger Forecasts, http://www.iata.org/whatwedo/Documents/economics/20yearsForecast- GAD2014-Athens-Nov2014-BP.pdf
2.3 Economic Characteristics 75
Because of this, airline managements can have the greatest effects on prospective profitability through their attempts to control costs, about half of which are variable (and dependent on definition).
It is thus imperative in deregulated markets to operate with average costs per passenger-mile or ton-kilometer as low as possible. Otherwise, long-run survival becomes an open question. In fact, studies such as those by Straszheim (1969) and White (1979) suggest that—except in marketing and perhaps in terms of the mix of fleet equipment—there is a point beyond which the industry does not tend widely toward significant further cost economies of scale.
Airlines nevertheless tend to benefit from what are known as economies of density(i.e., economies of scale along a given route) that appear when passenger traffic through hub airports is aggregated. By establishing hub-and-spoke traffic networks that combine passengers from different origination points, airlines derive important advantages: In filling larger aircraft average cost per seat-mile is lowered, load factors are raised, and profitability is enhanced. The economies of density occur because, as traffic increases, not all input factors (e.g., vehicles or fixed facilities) need be scaled proportionally upward.
However, such networks require that more miles be flown. And hub costs are higher than otherwise because passengers making connections increase their use of hub-airport facilities, with arrivals and departures unevenly scheduled in waves of activity (i.e., within narrow windows of time calledbanks).36In addition, crews and
-10 0 10 20
80 90 00 10
Constant $ GDP
RPK
%
Fig. 2.5 Percent changes in world real GDP versus percent changes in ICAO revenue passenger- kilometers, 1980–2013
36Hubs have traditionally involved flying 50–60 airplanes at a time for arrival within 20 min of each other. As arrivals become more dispersed and connecting passengers must wait longer, the hub, in industry jargon, is then called a “rolling hub.” Clusters of arrivals and departures are also calledcomplexes. In a large hub such as American’s base in Dallas, there might be between six and eight such complexes per day. Mouawad (2011b, 2012c) writes that the ability to fly more direct routes and land along smoother glide paths, thus saving time and fuel and minimizing congestion, is the goal of the FAA’s new NextGen air traffic control system, which replaces ground radar with satellite positioning technology at an estimated cost of up to $42 billion. See also Antoniou (1991), Fujii et al. (1992), Trottman and McCartney (2002), McCartney (2005a, 2010a, 2014c), Mouawad (2012d), Carey (2013b), Carey and Pasztor (2014), Pasztor (2014), and Carey (2015a).
other personnel remain idle while awaiting arrivals. Also, in economic down- turns, removal of even a few flights feeding into hubs may be greatly disrup- tive, depriving the remaining flights of the marginal revenue generated by the boarding of perhaps only a couple of extra connecting passengers on whom profits hinge.
Hubs, therefore, may not always necessarily lower total operating costs or boost profitability, although they do tend to keep ticket prices high.37 With sufficiently high traffic, direct service between two cities will be less expensive than would be a hub-and-spoke arrangement.
From a larger systemic point of view, though, it is also important to recognize that hub-and-spoke systems exhibit behavior consistent with theLaw of Connec- tivity, the basis of the Internet’s exponential growth.38This law states that the utility (value) of a network rises by at least the number of users (or nodes) squared. More formally, the relationship can be stated as:
V¼aN2þbNþc;
whereVis the value,Nis the number of nodes, and the other terms are constants.
37Bamberger and Carlton (2002) found that average local fares increase as airport hub activity increases (more passengers use an airport as a connecting point) but that the number of flights available to local passengers (i.e., service quality) increases as well. Maynard (2004) suggests that the whole industry is undergoing a major shift in structure, moving toward fewer hubs and dividing into three layers of competition. The top tier would include premium-fare service, primarily served by legacy airlines on long-distance international routes where discount-carriers cannot easily compete.
At the opposite end of pricing and service would be markets served by discount-fare carriers providing minimal services and earning minimal profits. In the vast middle market, the battle would be waged in terms of degree of predominance at hubs and provision of more service amenities than available on pure discount-priced flights. Olson (2009) and Millman and Esterl (2009) report on how smaller-city airports are trying to retain service and hub status by reducing fees (waiving landing fees and sharing of marketing expenses, etc.) and even paying cash to airlines. Stellin (2010b) discusses the impact of rising airfare taxes. See also Borenstein (1989), Ramsey (2011), Levere (2012), Carey and Nicas (2013a, b) and Nicas (2015).
This evolving business model differs greatly from the post-deregulation model that had been supported by customers willing to pay a premium for convenience and wide availability of flights.
McCartney (2005a) cites a 2001 U.S. Department of Transportation study that found that ticket prices for hub-market travelers such as in Charlotte, Cincinnati, Minneapolis and Pittsburgh were 41 % higher than in competitive markets.
38TheLaw of Connectivityis also called Metcalfe’s Law, named after Robert Metcalfe, one of the Internet and Ethernet engineering pioneers. The law is usually applied to electronic networks such as the Internet. It becomes operative once the number of nodes surpasses a critical mass.
Otherwise, the network fails. See also Mayer and Sinai (2003).
2.3 Economic Characteristics 77
As Shy (2001, p. 5) notes, in network industries, including those of software development, banking, broadcasting, cable, and airlines, the huge upfront sunk cost (i.e., cost that cannot be recovered) of developing the first unit of a product or service “together with almost negligible marginal cost implies that the average cost function declines sharply” as the number of product or service units sold increases.
“This means that a competitive equilibrium does not exist and that markets of this type will often be characterized by dominant leaders that capture most of the market.”39Once an airline is able to fly twice the number of departures out of a hub as compared to its next largest competitor, it is usually able to win the majority of the highest-paying business travelers.
There is, nevertheless, is a wide variation of unit costs among airlines, especially those of the international foreign-flag carriers, which are also often established to serve social and political purposes.40In the early 1990s, for example, Lufthansa and Swiss Air had unit costs (as shown in Fig.2.6) twice that of Singapore Airlines which still flies predominantly what are known as “long, thin” routes.41 When Fig. 2.6 Unit operating costs as a function of stage length.Source: Comite´ des Sages (1994).
Expanding Horizons, Civil Aviation in Europe: An Action Programme for the Future. Brussels:
European Commission. (See also, Hanlon, 1996, p. 20, 1999 ed., p. 23)http://aei.pitt.edu/8690/1/
8690.pdf
39Using game theory concepts, Shy (2001, pp. 218–229) further explains how and why airlines find it beneficial to establish hub-spoke and code-sharing systems. Button (2002) discusses airline network economics.
40See Doganis (1991, p. 129).
41Ng (2012) writes that as of 2013, long, thin routes such as those flown by Singapore Airlines and Cathay Pacific are gradually being eliminated as a result of slower global economic growth and the sheer weight of the increasingly expensive fuel needed to fly for more than 15 h, during which aircraft burn more fuel per mile than on shorter routes without a commensurate increase in ticket
defined by the degree to which these costs can be affected by management deci- sions, costs can be categorized into three groups.
In the first category are costs such as those for fuel, prevailing wages, landing, navigation, and other user fees and taxes. In the second category are costs over which an airline has somewhat greater, but limited, control. Costs of this type are to a degree determined by the geographic location and predominant conditions (moun- tainous, flat, foggy, sunny, snowy, etc.) at the carrier’s home base; by the average length of routes (stages or sectors, i.e., the distance between two airports) flown; by bilateral agreements reached with other lines; and by decisions concerning the class of aircraft to be used and the frequency of schedules on which the equipment is to be operated. In the third category, however, are the costs over which management has potentially the greatest amount of control. Such costs might include those for marketing, financial leverage, aircraft ownership, and acquisitions and expansions.
As is apparent from the long history of airline-business failures, cumulative errors in judgment on short run escapable variable costs can be just as debilitating as errors in judgment on fixed costs, which do not vary over the short run if a particular flight or series of flights were to be canceled.
The most important and largely inescapable influence on variable cost, however, is the price of fuel, which traces a long run uptrend that is often laced by unpredictably extreme short run fluctuations. Such fluctuations heighten the poten- tial for tactical and strategic errors to be made and greatly disrupt the industry’s presumed cost allocation structure. The immediate reduction of profitability that occurs when fuel prices rise rapidly cannot be readily mitigated through hedging and surcharges on fares: Hedging is expensive (and sometimes ineffective and/or incorrectly placed) and extensive application of surcharges is counterproductive because it reduces demand.
More specifically, for international scheduled services, Table2.4shows that as of 2012 spending for fuel and oil (see also Fig.1.24) accounted for around 30 % of total costs and amounted to around $200 billion a year.42 With fuel, passenger
prices. Rising competition from budget airlines (carrying one-fourth of the traffic) in the Asia- Pacific region has also been a factor. On this, see Raghuvanshi (2014).
42Each $1 per barrel price change is estimated to be equivalent to around a $425 million a year change in total expenses for US-based carriers. Each penny-per-gallon increase in jet fuel prices is estimated to cost the industry $180 million. Michaels (2008a) describes how airlines are reducing their long nonstops as the cost of flying “18 hours in one hop could double the cost of flying the same route with three stops. To fly far, a plane needs lots of fuel onboard, and to carry all that fuel, it needs even more fuel.” Still, airplanes are relatively efficient: A Boeing 747, for example, burns about one gallon of fuel every second, or 36,000 gallons on a 10-h flight. With 500 passengers on board, it is transporting 500 people one mile using 5 gallons of fuel, or 0.01 gallons per person per mile, or 100 miles per gallon per person, as compared to a car carrying one person at perhaps 30 miles per gallon. See “How Higher Fuel Prices Affect Aviation” available at wwwlabacuspub.
com and McCartney (2012d). Fuel burned per passenger for US airlines was 28.6 gallons in 2000 and 22.5 gallons in 2011.
2.3 Economic Characteristics 79
service, en route, and landing costs being primarily variable or semi-variable, perhaps as much as 55 % of all costs (exclusive of network and/or opportunity costs) may be considered as being of a variable nature.43Figure2.7illustrates the trends of major costs (fuel and labor) as a percentage of total operating expenses for Airlines for America (A4A)—formerly known as Air Transport Association of America (ATA)—member airlines.
Table 2.4 Operating cost per average ton-kilometer (ATK) by item, 1998, 2003, and 2011 estimated for IATAInternational Scheduled Servicesa
US cents
% of total
% of total
% of total
1998 1998 2003 2011
Direct operating costs (DOC)
Cockpit crew 2.8 7.2 % 6.2 % 4.9 %
Fuel and oil 4.9 12.5 16.5 30.0
Flight equipment, insurance, depreciation, rentals
5.0 12.7 14.0 15.0
Maintenance and overhaul 3.9 10.0 10.4 10.5
Airport (landing) and En route charges 3.9 10.0 9.3 9.2
Total DOC 20.5 52.4 56.4 69.6
Indirect Operating Costs (IOC)
Station and ground costs 4.7 12.0 9.8 8.5
Cabin crew and passenger service 5.3 13.6 12.8 7.3
Ticketing, sales, and promotion 6.4 16.4 14.5 9.5
General and administrative 2.2 5.6 6.5 5.1
Total IOC 18.6 47.6 43.6 30.4
Total DOC and IOC 39.1 100.0 % 100.0 % 100.0 %
Sources: IATA Annual Report, 1999, IATA Airline Economic Results and Prospects 2004. See alsohttp://www.iata.org/whatwedo/Documents/economics/IATA-Economic-Performance-of-the- Industry-mid-year-2015-report.pdf
aEstimated from industry sources and company reports
43Although network costs and opportunities are relatively difficult to estimate, they ought not to be ignored when looking at an overall profitability profile, which is explored in Borenstein (2011).
For instance, Significant capital may be tied to routes that are locally unprofitable but that contribute strongly to network profitability because they feed the network with a high percentage of connecting short-haul passengers. See Baldanza (2002) and also Belobaba (2002), in which the latest embellishments to network revenue maximization models are discussed. In part, the com- plexity of such network optimization models arises from the fact that two relatively low-fare local itinerary flight legs might contribute more revenue than one high-fare connecting passenger traveling on both legs. Earlier versions of yield management models simply analyzed pricing strategies on single origin to destination (O & D) legs without regard for network profitability effects. Modern O & D systems are designed to counter the number of inexpensive and unprof- itable fares that can be found on the Internet. A carrier would rather book a short-haul low fare to a passenger who continues on to a more expensive long-haul segment than to a passenger just on the short-haul. See Perez and Trotman (2006) and Sengupta and Wiggins (2014).
Productivity Factors In analyzing the determinants of airline profitability there are, in addition, a few that are not quite as obvious as the price of fuel and wages.
With about half of transportation workers unionized and covered by collective bargaining agreements (CBAs), unions play an important role in determining productivity and profitability. In periods when industry profits are relatively high demands for wage premiums normally rise, whereas in periods of losses and bankruptcies, contract concessions will likely be made. And because workers such as mechanics, pilots, and flight attendants cannot be replaced quickly or easily, unions gain considerable bargaining power via their potential threats of strikes or
“slowdowns.” Although different representative unions will have dissimilar and divergent agendas, demands, and contract durations, work-rule and wage conces- sions granted to one group will be often used to obtain similar benefits when the next round of contract negotiations come due.
The size of aircraft and their cruising speed and range also significantly affect the airline’s average hourly productivity—with payload times average speed deter- mining the average output per hour.44Generally, the larger the aircraft, the less it will cost to operate per unit of output, whether it be a passenger-mile or a ton-kilometer. However, the higher trip costs of flying large aircraft can potentially offset any lower average cost per passenger-mile or ton-kilometer produced.
Any analysis of productivity would also not be complete without considering the effect of aircraft speed, which is also a measure of output per hour. A faster plane
0 10 20 30 40 50
71 81 91 01 11
Fuel Labor Fig. 2.7 Costs as a percent %
total operating expenses, selected major cost categories for major airlines, 1971–2014.
Sources: ATA, A4A, DOT
44Holloway (1997, p. 91) notes, “[B]lock speed (i.e., average speed chock to chock) is more important than average cruising speed on short-haul routes, but the two correspond more closely as stage length increases because a greater portion of longer journeys is spent at cruise rather than maneuvering. . .on the ground.” As an example of average hourly productivity, a plane flying at an average speed of 600 km per hour and carrying a 20-ton payload produces 12,000 ton-km per hour.
Passenger weights, including free and excess baggage, are conventionally assumed at either 90 kg or sometimes 95 kg (209 lbs) each. Wei and Hansen (2003) found that “for any given stage length there is an optimal size, which increases with stage length.” See also Hirsch and Macpherson (2000) on labor market premiums.
2.3 Economic Characteristics 81
will by definition be able to transport more passengers or tons per hour than a slower one even though landing fees, flight crew, and other costs might be almost the same for each aircraft. In this regard, additional cost considerations such as engine performance (fuel burn rates) at different average flight speeds,stage length—the typical length of route between airports over which a particular aircraft is flown—as well as frequency of service and airspace overflight fees will come into play.45
Of these factors, stage length has the greatest impact on relative productivity.
Indeed, it was the matching of stage-length to airport and airplane size that led to development of hub-and-spoke networks (such as American’s Dallas-Ft. Worth hub). Airlines feed shorter stage-length flights from smaller cities using smaller planes into their hubs and then fly the longer stage-length (and more filled) flights from such hubs.46 An idealized representation of potential hourly productivity in terms of available tonne-kilometers (ATKs/h as a function of payload capacity and block speed as related to stage length) is shown in Fig.2.8. ATK is a measure of an airline’s total capacity for lifting both passengers and cargo and is derived by multiplying capacity in tonnes by kilometers flown.
For an airline thinking of buying new equipment or changing that already being used on a particular route, potential market demand growth is always a key consid- eration. However, the range capability of the aircraft being used (or to be used) on that route (and also the aircraft ownership hourly lease rate ascribed to the route) will ultimately affect the buy or change decision. Obviously, the less time spent on the ground loading and unloading relative to distance covered, the greater the productivity
declining payload
rising average speed ATKs/hour
Stage length
Fig. 2.8 Hourly productivity versus stage length.Source: Adapted from Holloway (1997, p. 90)
45Fee schedules for airspace overflight rights vary by country and may be computed using maximum takeoff weight, distance, use of traffic control services, and other factors. As described in Carey (2007), overflight fees are balanced against additional fuel costs, weather and runway conditions, average speed, and other variables as computed with sophisticated software.
46At overcrowded hubs, the ratio of block-hours to air-hours rises. As Pearson and Strahler (1995, p. 426) note, every 0.01-point increase in the annual ratio collectively costs the nine largest US airlines $150 million in additional operating expenses. This datum is found in U.S. DOT Form 41.
Despite such costs, however, even overcrowded hubs help protect traditional regional strongholds even while making it difficult for lines to naturally expand into new geographic markets. Mergers then appear to be the best and sometimes only way to expand and strengthen a service network. See also Murphy (2001), and McCartney (2002, McCartney 2002, 2013b).