the factors determining the profitability of international airlines: some econometric results

MANAGERIAL AND DECISION ECONOMICS, VOL. 13, 503-514 (1992)

The Factors Determining the Profitability of International Airlines: Some Econometric

Results Andreas Antoniou

University of the Witwatersrand, South Africa

The deregulation of the US domestic airline market in 1978 and its apparent relative success continues to attract a great deal of interest among US and other economists. This uniquely US experience has clearly shown that factors such as market contestability, the multiple output nature of costs, the structure of networks and airport presence play a crucial role in the survival and, ultimately, profitability of relatively free and unregulated airlines. However, most international airlines still operate in a heavily regulated environment. While the tendency is clearly toward liberalization, the question remains: What can these airlines do to improve their profitability within this framework? Our results indicate that profitable airlines have high passenger load factors, a relatively low proportion of capacity related costs, younger and more efficient fleets and supplement their passenger loads with freight.

INTRODUCTION

The worldwide trend towards the liberalization of the airline industry, domestically and interna- tionally, has placed financial survival at the top of the economic agenda for most airlines. Profitability is but one of the many factors contributing to this survival, but it is a most crucial element. While, however, most of the other key factors have been analysed at various levels, the profitability of international airlines and the factors influencing it remain largely unexplored.

Although many studies have addressed the profitability issue with reference to the 1978 (US) domestic industry deregulation, there is, to date, no systematic treatment of its determinants for international airlines. In this study we therefore first assemble and classify a number of potentially pertinent explanatory variables. Then, in the third section the definitions and the data used in this study are discussed. The fourth section contains an assessment of the estimated relationships and the main conclusion of this study is drawn in the final section.

THE PROFITABILITY OF INTERNATIONAL AIRLINES

The 1978 deregulation of the US airline industry has had profound and lasting effects on the (US)

domestic as well as the international airlines in- dustries. Both these broad sets of subjects have attracted a great deal of interest, in both the USA and the rest of the world, and have generated an extensive literature which is impossible to detail here.' Some of the most salient points on US domestic airlines are discussed in Bailey et al. (1985), Morrison and Winston (1986, 1990), Meyer and Oster (1987), Bruning and Hu (1988), Van Scyoc (1989), Berry (1 990) and Borenstein (1990). The effects on the international scene have also been the subject of several recent studies, including Taneja (1981, 1988), Doganis (1985), Pryke (1987), EIU (1987, 1988), Kasper (1988) and IATA (1986, 1987).

This literature has analysed most of the relevant questions regarding the US domestic industry, including its pre- and post-deregulation performance and profitability. This, however, is not true for the international side of the industry, for which many questions remain unanswered.

One such question relates to the factors determining the profitability of international airlines. Most of the studies mentioned above, along with earlier works by Straszheim (1 969), Pearson (1 976) and Laprade (198 11, posit several possible relationships between profitability and a number of potentially pertinent variables. However, there is. to date, no study where these variables are collected, classi- fied and measured with a view to testing their effect

0 1 4 3-6 5 70/92/060503 - 1 2 $1 1.00 0 1992 by John Wiley & Sons, Ltd.

504 A. ANTONIOU

on profitability. Drawing from both sets of studies, we propose to do just that.

To begin with, the dependent variable selected is the operating profit margin (OPRM). Indeed, most studies in this area (James, 1982; Koran, 1983; Bailey et al., 1985; Morrison and Winston, 1976; IATA, 1986, 1987; Taneja, 1988) consider OPRM as the less flawed measure of profitabilitye2 Focussing on operating profits (rather than net profits or net revenue) allows for cross-sectional comparisons between airlines from different countries, following different amortization policies, which are subject to different tax and subsidy re- gimes, with different capital gains and losses and different foreign exchange operations. While, as Doganis (1985) points out, interest payment and other non-operating items may represent a significant portion of operating profits for many international airlines (sometimes up to more than loo%), there is, nevertheless, no evidence to suggest that operating and net profits are not subject to the same operating factors. The choice of the denomin- ator (i.e. total revenue rather than capital) is a direct result of the first choice, bypassing at the same time many of the difficulties associated with devising a meaningful measure of capital. Moreov’er, OPRM is usually under the more direct control of the airlines and can be interpreted as the marginal profitability of revenue (Taneja, 1981). Unfortu- nately, being an accounting measure of profitability, it cannot be used directly as a measure of economic profits. Even so, Morrison and Winston (1986) and Martin (1989) find that, with suitable adjustments, such a measure could give a useful approximation.

Turning to the choice of independent variables, a preliminary comment is in order. Clearly, in a similar study of any other unregulated industry, such as that undertaken by Morrison and Winston (1986) or Van Scyoc (1989), economic theory dic- tates that the following variables be included: price(s) of output(s), prices of inputs and those related to market structure. However, this is not such an industry. Although a definite liberalization trend can clearly be identified (EIU, 1987,1988), the great majority of airlines, including US carriers, still determined their international fares in the context of IATA ‘regionals’ and/or the basis of very restrictive bilateral agreements (‘pools’). Therefore, this variable is not expected to play a key role within the context of a cross-sectional study similar to this. Rather, what is important on the revenue

side is the volume and the composition of output. As for costs, the price of its most important non- labour component (i.e. fuel) is largely outside the immediate control of airline management and is external to the industry. Regarding labour costs, it is generally agreed that their effects on profitability could be better captured indirectly through such variables as labour productivity, G N P per capita and type of ownership. In addition, the conditions of employment and pay are usually stipulated in the collective agreement, the terms of which cannot be changed during the course of a given year. Finally, variables related to market structures, such as market share and contestability, are not likely to play a central role, given the proliferation of ‘pooling’ and capacity limitations agreements de- scribed by Doganis (1985), Pryke (1987), EIU. (1987, 1988) and Kasper (1988).

The selected explanatory variables are grouped into four broad ~ategories ,~ i.e. Service, Network, Fleet and General Economic. The first Service variable (Table 1) to be included and tested is the passenger load factor. Its inclusion needs no special explanation here, other than to point out that it was chosen over the possible alternative to ton-km load factor. Given the predominance of passenger transport over freight, this choice seems most ap- propriate. Clearly, ceteris paribus, this variable should have a positive effect on operational profitability, unless the average yield is lower than the unit operating cost per passenger. Next, we have included a number of variables describing the composition of airline output in view of determining which component is more likely to have an effect on profitability and in which direction. Thus, the second service variable is the share of international traffic in total output. There is no a priori reason to expect that the international component of traffic is per se more or less profitable than its comple- mentary (i.e. domestic) traffic. However, to the extent that domestic traffic is sheltered from foreign competition in many countries and given the great aversion of many airlines to the idea of granting cabotage rights to foreign operators, it is not un- reasonable to expect a negative influence of this variable on operating profitability.

The other possible decomposition of output is the scheduled versus non-scheduled or charter passenger service. Pryke (1987) is one of many ob- servers, such as EIU (1987, 1988) or IATA (1984), who show a positive effect of an increased scheduled component on profitability. Indeed, Pryke

THE PROFITABILITY OF INTERNATIONAL AIRLINES 505

Table 1. Definition, Measurement and Expected Sign of Variable Type

Dependent

System

System

System

System

Network

Network

Network

Network

Fleet

Fleet

Fleet

General Economic variable General Economic variable General Economic variable General Economic variable

General Economic variable

Definition

O P R M : Operating Profit Margin

PLOFA: Passenger Load Factor

S H I N T Share of International Traffic

SHSCH: Share of Scheduled Traffic

SHPAS: Share of Passenger Traffic

L A P R O : Labour Productivity

COSTS?? Cost Structure

A L P D P : Average Length per Departure

TTKA V: Total Ton-km Available

Y F L U T Weighted Fleet Utilization

FLESZ: Fleet Size

AGE: Average Fleet Age

POP: Population A R E A : Area G N P P C : GNP per capita 0 W N E R D : Ownership Dummy

REGSH: Regional Share of Traffic

Measurement

Total Operating Profit Total Operating Revenue

Passenger-km Performed Seat-km Available

International T-km Performed Total T-km Performed

Scheduled T-km Performed loo Total T-km Performed

Passenger T-km Performed Total T-km Performed

Total T-km Available Number of Employees

Capacity Related Costs Total Operating Costs

Total T-km Flown Number of Departures

Z Number of planes i i Total number of planes x Util. of i

Total number of planes

Z Number of planes i Average age i Total number of planes

= 1 if + 50% state ownership

Total T-km available (company) Total T-km available (region)

Sign

+

-?

+

-

+

-

?

+?

+

+?

-

+ + + -

+

argues that it would seem that most airlines tend to ‘overcharge’ their full-fare passengers in order to make up for the loss on their charter-competitive fares (pp. 10 f .).

Next, the share of passenger (as opposed to freight) in total traffic is also introduced. It is well documented (see James, 1982; Doganis, 1985) that the yields per passenger ton-km are, on average, more than double those generated by freight, thus implying a positive effect of this variable. However, a more careful reasoning would lead to the opposite conclusion. Indeed, in studying the factors influencing profitability one must also take into account

the costs side of these two types of traffic. Most airlines see cargo in the hold of a passenger aircraft as a byproduct arising from the supply of passenger service (IATA, 1984) and therefore, provided freight covers its direct operating cost (i.e. ground hand- ling, sales and marketing, extra fuel), any excess revenue is considered a contribution towards passenger cost service. In addition, strong competitive pressures push airlines to price their cargo holds just above their direct operating cost, thus ignoring indirect costs. Therefore, and for given passenger load factors, we can expect a negative effect of this variable on profitability.

506 A. ANTONIOU

Turning to the next group of network variables, we have canvassed and included four such variables. Labour productivity is clearly expected to have a positive effect on profitability, ceteris paribus. A more complete set of data would have al- lowed us to separate between flight and ground employees, as suggested by Meyer and Oster (1 987), as the performance of the former is more passenger traffic-related while that of the latter is more aircraft departure-related.

This distinction is partially captured by the next cost-related variable, i.e. the ratio of capacity (or flight-aircraft) related costs over total cost. The remaining are passenger traffic-related costs, i.e. passenger services, traffic servicing, reservation and sales, advertising and promotion, and other admin- istrative costs. Taneja (1981) and Koran (1983) discuss this ratio, and posit that, for given load factors, it would be negatively related to profitability and negatively correlated among themselves.

Flight stage length is a network variable often discussed, but the assessment of its effect on profitability requires some care. Taneja (1981), Laprade (1981) and, to a certain extent, Pryke (1987) suggest a positive effect on profitability. However, Mor- rison and Winston (1986) and Meyer and Oster (1987) found it to be negative in the case of the US carriers. Clearly, if short-haul services are used as ‘feeders’ for long-haul segments, thus increasing the latter’s load factors, what we are really dealing with is cross-subsidization between the two, so that a positive relation would be implied. Alternatively, if shorter routes are mainly domestic, assumed more profitable and/or if the load factor remains constant, a negative relationship should be expected.

The next variable, i.e. Total Ton-km available, iB introduced in order to capture possible ‘size’ effects on profitability. It is well known that there are (albeit limited) potential economies of scale and some economies of scope to be reaped.4 However, beyond the MES, the effect of size is a priori un- certain, depending on whether one is dealing with a U-shaped or a T-shaped LAC curve.

The third category of varjables are fleet- or aircraft-related. Fleet size is another proxy for size, and as such its effects on profitability should be the same as its network counterpart. However, if the fleet is ‘too big’, ‘too old’ or not adjusted to the network of the airline, then its effect will definitely be negative.

Thus, the next variable to be tested is the ‘age’ of the fleet. All authors are unanimous: older, less

fuel-efficient, aircraft should have a negative effect on operating profitability. However, the reverse causation is also true: less profitable airlines also tend to operate older and less efficient aircraft.

Finally, a proxy for aircraft productivity is also introduced, measuring the extent of utilization of each aircraft. As such, this should have a positive effect. However, and as suggested by Meyer and Oster (1987), a negative sign would imply an ‘aged’ and thus ‘under-utilized’ fleet. This would also imply that the last two fleet variables are negatively correlated.

The airline industry is traditionally considered as a largely cyclical one, as argued by Doganis (1985) and Meyer and Oster (1987), among others. Thus a number of variables are needed to capture the effect of the general economic conditions on airline operations. Therefore we included the Population, the Area and the G N P per capita of the country as possible variables. They all have, ceteris paribus, a stimulating effect on demand so they are expected to have a positive effect.

We have also introduced two industry-specific general economic variables. In particular, Pryke (1987) is one of the many authors who have argued that state ownership or control has a negative effect on profitability. Thus an ownership dummy is also included. Finally, in order to test the effect of relative size (thus market power) on profitability, we have also included the relative, regional share of traffic as a possible determinant.

THE VARIABLES AND THE DATA

Definitions and Measurement

The dependent variable OPRM (i.e. Operating Profit Margin) is defined as the Total Operating Profit over Total Operating Revenue (in per- centage). The system-explanatory variables are: Average Passenger Load Factor (PLOFA), i.e. Pas- senger-km Performed over Seat-km Available (%); Share of International Traffic ( S H I N T ) , i.e. Inter- national Ton-km Performed over Total Ton-km Performed (YO); Share of Scheduled Traffic ( S H S C H ) , i.e. Scheduled Ton-km Performed over Total Ton-km Performed; and Share of Passenger Traffic ( S H P A S ) , i.e. Passenger Ton-km Performed over Total Ton-km Performed (Yo).

The Network variables are: Labour Productivity (LAPRO), i.e. Total Ton-km Performed over Total

THE PROFITABILITY OF INTERNATIONAL AlRLINES 507

Number of Employees (YO); Cost-structure (COSTST) , i.e. Capacity Related Costs over Total Operating Costs (%); Average Lengths per Depar- ture (ALPDP) , i.e. Total Ton-km Flown over Total Number of Departures; and Total Ton-km Avail- able ( T T K A V ) .

The Fleet variables are: Weighted Fleet Util- ization ( W F L U T ) , i.e. the relative number of type i aircraft in the total fleet times the average utilization of type i planes; Fleet Size ( F L E S Z ) , i.e. total number of planes; and Average Fleet Age (AGE), i.e. the relative number of type i planes in the total times the average age of that type of plane in the fleet.

The General Economic variables are defined as follows: Population ( P O P ) ; Area ( A R E A ) ; and GNP per Capita (GNPPC) . Also included are an Ownership Dummy (0 W N E R D ) which is equal to one if more than 50% is state owned; and the Share of Regional Traffic (REGSH), measured as the relative share in terms of T T K A V of a given company relative to the corresponding IATA-subregio- nal total traffic (Ye).

Data and Sample Description

The sample of 58 airlines was chosen out of a total of 114 active IATA members, 1985. Observations on the dependent variable were chosen from the ICAO publication for the same year while observa- tion on the regressors had to be drawn from both sources. The final number of airlines was dictated by the availability of data on the dependent variable for that year.5

Sampled airlines are taken from all continents and include major as well as smaller operators (see Table 2). In Table 3 are reported the basic statistics of the series used for reference purposes. In particular, we can observe that the mean OPRM of the sample for 1985 was 1.38%, not a particularly good year, with a standard deviation (SD) of 9% and a range of 13.77 to 40.00%. This indicates wide dis- persion of the dependent variable. Furthermore, the sample companies had an average PLOFA of 61 Yo and the mean share of international traffic was 76.1%, that of passenger 72.4% and scheduled operations 96%. The average worker produced

Table 2. Aer Lingus (Republic of Ireland) Aeromexico (Mexico) Ghana Airlines (Ghana) Aero Peru (Peru) Iberia (Spain) Avianca (Colombia) Icelandair (Iceland) Air Afrique (Yaoundt Treaty States) Air Canada (Canada) Air France (France) KLM (Netherlands) Air India (India) Air Malawi (Malawi) Air Mauritius (Mauritius) Ladeco (Chile) Air New Zealand (New Zealand) Air U K (United Kingdom) Air Zimbabwe (Zimbabwe) Alitalia (Italy) Philippine Airlines (Philippines) American Airlines (USA) Pluna (Uruguay) Austrian Airlines (Austria) Quantas (Australia) British Caledonian Airways (UK) Sabena (Belgium) British Midlands (UK) Saudi Arabian Airline (Saudi Arabia) Mexican (Mexico) Scandinavian Airline System (Denmark, Norway, Sweden) Continental Airlines (USA) South African Airways (South Africa) C P Air (Canada) Swissair (Switzerland) Cyprus Airways (Cyprus) TAP (Portugal) Deutsche Lufthansa (Federal Republic of Germany) Eastern Airlines (USA) Egyptair (Egypt) UTA (France) Ecuatoriana (Ecuador) United Airlines (USA) Ethiopian Airlines (Ethopia) Varig (Brazil) Finnair (Finland) Yemenia (Yemen) Flying Tiger (USA)

The Sampled Companies and their Countries of Origin Garuda Indonesian (Indonesia)

Indian Airlines (India) Japan Airlines (Japan)

Kuwait Airways (Kuwait) Lacsa (Costa Rica)

Lan (Chile) Pakistan International Airlines (Pakistan) Pan American (USA)

TWA (USA) Tunis Air (Tunisia)

Zambia Airways (Zambia)

508 A. ANTONIOU

Table 3. Description of Sampled Series and Sources Used Series Mean S.D. Max.

OPRM (Yo) 1.38 9.00 13.77 PLOFA (Yo) 61.01 10.33 76.30 S H I N T (Yo) 76.13 27.85 100.00 SHSCH (Yo) 95.97 7.85 100.00 SHPAS (Yo) 72.42 16.50 98.63 LAPRO (OOO) 194.38 159.57 902.26 COSTST (Yo) 43.67 7.56 67.08 TTK A V (OOO) 2688964.00 3355290.00 13 286310.00 ALPDP (OOO) 1.29 0.69 4.13 FLESZ 53.98 69.20 325.00 AGE (years) 9.94 2.84 16.23 WFLUT (minutes) 453.64 100.36 677.00 POP (millions) 83.25 149.63 765.10 GNPPC (US$) 6 471.53 5 959.94 16 690.00 AREA (OOO km2) 2 376.19 3 496.42 9 976.00 REGSH (Yo) 23.34 26.96 96.95 0 W N E R D 0.74 0.44 1 .00

Min. sources

-40.00 ICAO 0.00 ICAO 4.34 ICAO

63.69 ICAO 0.00 IATA 4.50 IATA and ICAO

22.71 ICAO 18732.00 IATA

0.29 IATA 4.00 IATA 4.61 IATA

161.80 IATA 0.24 IMF

110.00 IMF 2.00 IMF 0.00 IATA 0.00

ICAO: Civil Aoiation Statistics o f t h e World, 1986, Table 3.1. IATA: World Air Transport Statistics, 1986. WD: World Deoelopment Report, Table 1. 'Author's sources.

194 000 ton-km and the capacity-related cost re- presented, on average, 43.7% of total operating costs. The sampled companies had, on average, a fleet of 54 planes, of mean age 9.9 years which flew 453.6 hours.

RESULTS Overall Results

Entries in Table 4(a) are the estimated parameters, using OLS, for various linear combinations of the

Table 4(a). Regression Results: All Carriers (N=58) Service variables

Equation PLOFA SHINT SHSCH SHPAS

(1) 0.32b -0.13d 0.22' 0.31" (2.45) (-1.99) (1.58) (-3.18)

(2) 0.3Ib -0.17' 0.20' - 0.29" (2.45) (~ 2.95) (1 SO) ( - 3.14)

(3) 0.28' -0.14' 0.17' -0.27" (2.32) (-2.29) (1.30) (-299)

(4) 0.30" - 0.08 0.19' - 0.29' (2.43) ( - 1.06) (1.44) ( - 3.22)

(5) 0.29' -0.09' 0.18' -0.27' (2.41) (- 1.34) (1.39) (-3.12)

(6) 0.29b -0.08 0.18' -0.25 (2.47) ( - 1.26) (I .40) (-2.91)

(7) 0.29b -0.09' 0.19' - O.2@ (2.49) ( - 1.39) (1.48) ( - 2.95)

(8) 0.27' -0.07 0.17' -0.26' (2.23) (-1.09) (1.36) ( - 2.91)

(9) 0.30" - 0.07 0.20c - 0.27" (2.51) (- 1.08) (1.50) ( - 3.04)

(10) 0 2 - 0.33 -0.21/0.21 1.26/ - 1.08 -0.20/-0.11 (l.l5)/( -0.67) (- 1.32)/( 1.13) (0.73)/( -0.62) ( -0.96)/( - 0.46)

LAPRO

0.01 1

0.006 (0.63) 0.007

(0.70) 0.015'

( 1.45) 0.0 12

(1.17) 0.0 1 s

(1.54) 0.014" ( 1.44) 0.0 13

(1.27) 0.019'

(1.73)

(1.00)

- 0.002/0.01 ( - 0.06)/(0.3 1)

Network variables COSTST T T K A V ALPDP

-0.55' - 1.55D6 - 1.53 ( - 3.62) (- 1.46) ( - 0.71) -0.56. - 9.15D7' -

-0.66' -9.33D7' -

-0.67' - 1.90D6d - 2.52

-0.64' - 2.14D6' -

- 0.6 1 -2.47D6 -

-0.60' - 2.47D@ -

( - 2.54) -0.61' -2.48D6b -

-0.63' - 2.23D6' - 2.58

(- 3.86) ( - 2.00-

( - 4.42) (-1.78)

(-3.38) (-1.86) (-1.11)

(-4.48) (-2.18)

(-4.34) ( - 2.56)

( - 4.27)

( - 4.27) ( - 2.54)

( - 4.60) (-2.21) (-1.51) -0.60/-0.04 -8.67D7/-2.4D7 -

(-0.61)/(-0.04) (-0.43)/(-0.93)


explanatory variables discussed in the previous section.6 Hence, the variables are grouped in the four categories discussed therein and for each regression we reported the t-statistics (in parentheses), the values of R 2 , R2, F the standard error of the regression ( S ) , and the sum of squared errors (SSE) .

First observe that Service and Network variables are, as groups, the most significant variables, with an R2 = 37% and F =4.22 (Eqn (2) in Table 4(a)). Among the Fleet and General Economic variables only one is consistently significant (AGE and POP, respectively).

Turning to individual variables, PLOFA, SHPAS, COSTST, T T K A V , AGE and POP are consistently the most statistically significant. To- gether, these six regressors alone can change the value of OPRM by up to 2%. Given that the mean for OPRM is only 1.38% (Table 3), it is clear that, together, these variables play a key role in airline profitability. All, except perhaps T T K A V , have the expected signs. The negative signs for T T K A V will seem to indicate that, given a U-shaped long-run average cost, most airlines have surpassed their minimum efficiency scale. One should note, however, that the absolute value of the parameter is fairly small, thus indicating that 'absolute size' has a limited influence on profitability.'

SHINT, SHSCH, LAPRO and FLESZ, while not statistically significant at the usual levels (1 YO

and 5%), have, nevertheless, non-negligible effects on O P R M and should not be excluded as possible regressors. It is worth noting that LAPRO is found in this second set and to observe that all variables have the expected signs. More surprisingly, however, some variables are not significant, i.e. ALPDP, W F L U T , GNPPC, AREA, REGSH and 0 W- NERD. This in turn seems to imply that a small, publicly owned airline operating in a relatively small and poor country need not, a priori, be un- profitable, ceteris paribus.

Referring more specifically to Eqns (6) and (7) in Table 4(a), the largest effect comes from (and airlines should rather concentrate on) the following factors: the average age of the fleet (AGE) , their cost structure (COSTST), the passenger load factor (PLOFA ), the passenger/totai traffic ratio ( S H P A S ) and the size of the 'captured' market (POP). Other important factors are the development of the local (protected) scheduled network (SHSCH and S H I N T ) , labour productivity ( L A P R O ) and perhaps the size of their fleet (FLESZ) .

Ownership and Profitability

The on-going debate in many countries on the need for privatizing many state-owned airlines has

Fleet variables General economic variables FLESZ AGE WFLUT POP GNPPC A R E A REGSH O W N E R D Constant R 2 F S SSE

0.03 - 0.002 - - - - - 17.94 0.39 0.26 2.96' 7.75 2.828 (0.68) (0.16) (0.82) - - - - - - - 22.33 0.37 0.28 4.22' 7.62 2.901

(1.16) - - - O.OZb 2.74D5 -3.2D4 - - 26.13 0.45 0.33 3.85" 7.35 2.538

(2.54) (0.10) (-0.77) (1.37) 0.05 - -5.43D4 0.02' 2.21D5 -1.57D4 - - 21.63 0.49 0.33 3.2W 7.35 2.374

- - 19.65 0.46 0.36 4.55' 7.20 2.490 0.06 (1.25) (2.67) (1.05)

- 23.18 0.50 0.40 4.72' 7.00 2.303 0.07' -0.6gd - 0.02b ( 1.46) (-1.95) (2.55) (1.27) 0.06' - 0.74' - 0.02b - - -0.03 - 24.14 0.51 0.39 4.32' 7.02 2.269 (1.37) ( - 2.07) (2.66) (-0.83) (1.32)

- - - 1.20 25.52 0.50 0.38 4.23' 7.06 2.295 0.07' -0.71' - 0.02 -

(1.44) (- 1.97) (2.55) (-0.41) (1.32) 0.05 -0.76' -8.2D5 0.02' 7.29D6 - 1.46D4 -0.03 25.90 0.53 0.37 3.2P 7.16 2.154

(1.13) (-2.06) (-2.06) (2.85) (0.02) (0.30) (-0.52) (1.27)

(-0.26)/(1.30) (0.18)/(0.30) (0.79) (-0.66)

-

(0.99) (-0.04) (2.86) (0.08) (-0.34) (1.04) __ - 0.02' - -

- - -

-0.02/0.18 - - 0.009/0.016 - __ - 113.2 -94.18 0.51 0.26 2.04' 7.75 2.313

510 A. ANTONIOU

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prompted the author to investigate further the effect of the type of ownership on profitability.* It has been pointed out above that 0 W N E R D was statistically insignificant, but the question merits further analysis. In particular, we re-estimated Eqn (7) explicitly, introducing intercept and slope ownership dummy variables. The results (Eqn (lo), Table 4(a)) confirm the earlier finding that no significant difference exists between state and privately owned airlines as far as factors influencing profitability are concerned.

A related but different question is also addressed here, namely, whether the inclusion of US carriers in the sample has any effect on the results reported in Table4(a). Indeed, it can be argued that US carriers, except for being privately owned, operate domestically in an almost completely unregulated environment, have large and lucrative local mar- kets and are thus larger than the average IATA carrier with the additional bonus of not having the 'national flag carrier' burden. All these factors make US carriers 'non-typical' IATA airlines and a valid case could be made to exclude them from the original sample.

Therefore, in Table 5 we have renamed the variables, mutatis mutandis, by adding an A to the predetermined variables. Compared to Table 3, it can be seen that, as expected, the exclusion of US carriers lowers the mean of O P R M A , LAPROA, T T K A V A , ALPDPA and FLESZA, but results in higher mean PLOFAA, SHINTA, AGEA and WF LU TA.

Comparing the regression results in Table 6(a) with those in Table 4(a), it is clear that Eqns (6) and (7) still give the best fit (R2 =41%) and variables SHPASA, COSTSTA, T T K A V A , AGEA and POPA have still the most statistically significant effect on O P R M A . However, there is a noticeable difference in that PLOFAA is no longer statistically significant at the usual levels but it cannot, nevertheless, be totally excluded. The signs and magnitudes of the parameters are, however, similar in all cases.

Comparing the partial correlation coefficients in Tables4(b) and 6(b), we can observe some sub- stantial and significant differences in magnitudes and signs. In particular, looking at the partial correlation coefficients for PLOFAA we find eight changes in sign and increases in magnitude for the coefficient of the following variables: SHINTA, ALDPDA, FLESZA and AGEAA. This last obser- vation explains why the standard deviation of 1

THE PROFITABILITY O F INTERNATIONAL AIRLINES 51 1

Table 5. Description of Sampled Series: Seven US Carriers Excluded"

Series Mean

O P R M A 1.32 PLOFAA 62.13 SHIN T A 8 1.96 SHSCHA 95.77 SHPASA 72.28 LAPROA 164.03 COS TS TA 43.61 TTK A V A 1 772 850.00 ALPDPA 1.26 FLESZA 35.06 AGEA 10.13 W F L U T A 437.02 P O P A 61.83 G N P A 5 069.00 A R E A A 1 41 7.22 REGSHA 24.57 0 W N E R D A 0.82

S.D.

9.35 6.62

22.05 8.08

13.50 123.28

8.03 2 137581.00

0.70 31.17 2.91

92.75 147.13

4 885.58 2 482.80

28.52 0.39

Max.

13.77 76.30

100.00 100.00 98.63

777.59 67.08

9708 151.00 4.13

121.00 16.23

600.00 765.10

16 370.00 9 976.00

96.95 1 .00

Min

-40.00 49.00 4.95

63.69 46.63

4.50 22.71

18 732.00 0.29 4.00 4.61

161.80 0.24

110.00 2.00 0.00 0.00

a For measuring units and sources, see Table 3.

PLOTAA increased in Table 6(a). We should also note, however, that the degree of collinearity between some other key variables has decreased. More specifically, while SHPAS,9 T T K A V, ALPDP, W F L U T and GNPPC are all highly correlated with LAPRO in Table4(b) (near or more than 50%), only T T K A V A remains high in Table 6(b). This explains why the standard deviation of LA- PROA decreases in the second series of regressions. This decrease in the value of the partial correlation coefficients is repeated in the following cases: T T K A V and ALPDP, WFLUT, GNPPC, AREA; between ALPDP and FLESZ, AGE, WFLUT, POP, GNPPC and REGSH; between FLESZ and GNPPC, AREA; and between W F L U T and GNPPC, AREA and 0 WNERD. This general low- ering of the degree of collinearity between regressors explains why Eqns (6a) and (7a), in Table 6(a) have a lower S S E and a higher R 2 than their counterparts in Table 4(a).

In interpreting these correlation coefficients, we can, on the one hand, explain some of the more surprising results reported earlier, and, on the other, verify some of the a priori posited relations between regressors. In particular, as expected, COSTST and PLOFA are negatively correlated, while ALPDP and SHINT are positively correlated. Also, as expected, ALPDP and W F L U T are highly (positively) collinear, which may explain why their coefficients are not individually signific-

ant. The same applies to GNPPC, which is collinear with LAPRO, T T K A V , FLESZ and WFLUT. As also could have been expected, REGSH is collinear with SHINT, POP and AREA. Finally, AGE seem to have a non-negligible relation with both ADPDP and FLESZ. Indeed, the negative correlation coefficients with the former confirms the expectation that older aircraft tend to be used less. In addition, in the case of FLESZ, the change of sign, from positive to negative, following the exclusion of the US carriers, clearly reflects the influence of US carriers which have a larger but also a younger fleet.

CONCLUSIONS

The deregulation of the US domestic airline market and its apparent relative success continues to attract a great deal of interest among US and other economists. This uniquely US experience has clearly shown that factors such as market contestability, the multiple output nature of costs, the structure of networks and airport presence play a crucial role in the survival and ultimately the profitability of relatively free and unregulated airlines.

However, most of the international airlines still operate in a heavily regulated environment. While the tendency is clearly toward liberalization, the

512 A. ANTONIOU

Table 6(a). Regression Results: US Carriers Excluded (N=51) Service variables Network variables

Equation PLOFAA SHINTA SHSCHA SHPASA LAPROA COSTSTA T T K A V A ALPDPA

0.29" (1.45) 0.29' (1.46) 0.20 (1.03) 0.19 (0.95) 0.19 (0.98) 0.19

0.17 (0.91) 0.16 (0.83) 0.19 (0.97)

(1.02)

-0.23d (-1.81) -0.16'

(-2.35) -0.12d

( - 1.70) - 0.06

(-0.83) -0.10'

( - 1.57) - 0.07

( - 0.99) - 0.08

(-1.16) -0.05

(-0.67) - 0.07

(-0.88)

0.21' (1.37) 0.20' (1.35) 0.15 (1.05) 0.16 (1.11) 0.15 (1.06) 0.15 (1.09) 0.16 (1.17) 0.15 ( 1.06) 0.16

(1.15)

-0.35" (- 2.72) -0.32"

( - 2.70) -0.32"

( - 2.77) -0.36"

(-3.01) -0.31"

(-2.74) - 0.27"

( - 2.49) - 0.27"

( - 2.49) - 0.27"

( - 2.46) 0.32b

(-2.69)

0.01 (0.94) 0.007 (0.57) 0.01 (0.93) 0.02' (1.58) 0.01

(0.94) 0.02'

(1.67) 0.02d

(1.73) 0.02'

(1.35) - 0.03'

(1.89)

- 0.56"

-0.57"

- 0.68" ( - 4.30)

(-3.37)

( - 3.66)

-0.71" (-4.23) -0.68

(-4.47) - 0.60"

(- 3.99) -0.58"

(- 3.82) - 0.59"

(-3.81) - 0.65"

(- 3.86)

- 1.88D6 - 1.51 (-1.26) (-0.52) - 1.26D6' ~

( - 1.60)

(-1.73) -1.38D6d -

- 1.95D6' -3.26 (-1.36) (-1.13) -1.41D6d -

(-1.89) -3.OD6' ~

( - 2.37) -3.1D6b ~

( - 2.47) -2.98D6' -

(- 2.34) - 2.53D6d - 2.68

(-1.78) (-0.95) ~~

See note to Table 3 for levels of significance.

Table 6(b). Partial Correlation Matrix: US Carriers Excluded OPRM

0.20 - 0.02

0.30 - 0.26

0.2 1 -0.38

0.18 0.10 0.22

-0.33 0.13 0.23 0.14 0.05 0.03

-0.13

PLOFAA S H I N T A SHSCHA SHPASA LAPROA COSTSTA T T K A V A ALPDPA

0.34 -0.01 - 0.29

0.35 -0.12

0.35 0.32 0.2 1

-0.03 0.23 0.05 0.28 0.09

-0.15 -0.17

-0.10 -0.43

0.13 -0.20

0.05 0.42

-0.18 - 0.03

0.05 -0.35

0.12 -0.25 -0.41

0.23

-0.23 0.22

-0.07 0.24 0.18 0.20

-0.09 0.11 0.15 0.06 0.13 0.16

-0.08

-0.36 - 0.08 - 0.5 1 -0.48 - 0.30

0.21 -0.29 - 0.02 - 0.22 -0.10

0.03 -0.11

-0.14 0.62 0.47 0.34

- 0.02 0.44

- 0.05 0.42 0.14

-0.03 -0.36

-0.17 -0.13 -0.20

0.08 -0.16

0.33 -0.35

0.03 0.20 0.11

0.30 0.83 -0.07

-0.19 -0.10 0.46 0.46 0.07 0.08 0.51 0.26 0.23 0.30 0.10 0.002

-0.06 -0.06

question remains: What can these airlines do to improve their profitability within this framework?

Many commonly held views on the factors influencing the profitability of international airlines have never been tested. In this study, we have examined the effects of several variables on the operating profit margins of 58 IATA airlines for 1985. Variables such as passenger load factors, share of capacity related costs, fleet age, population

share of passenger traffic and total ton-km available were found to be the most significant. The share of international and scheduled traffic, labour productivity, fleet size and average length per departure, while not statistically significant at the usual levels, cannot be totally ignored. A last group of variables, including weighted fleet utilization, GNP per capita, share of regional traffic and (more surprising) type of ownership, were found to have


Fleet variables F L E S Z A A G E A W F L U T A P O P A

-9.2D4 - - 0.04 (0.43) (0.06)

0.02 (0.25)

0.10' (1.39) 0.1te

( I .56) o.l le (1.45) 0.06

(0.61)

- 0.77' ( - 2.07) -0.87'

(- 2.29) -0.81'

(-2.13)

(- 2.08) - 0.82'

- 0.005 (-0.32) -

- 0.005 (-0.34)

0.02" (2.53) 0.03"

(2.87) 0.02'

(2.56) 0.02b

(2.52) 0.02"

(2.72) 0.02'

(2.56) 0.03a

(2.79)

General economic variables G N P P C A A R E A A R E G S H A

- _ - -

2.14D5 -3.8D4 -

(0.08) (-0.81) 7.81D5 -6.18D5 -

(0.26) (-0.11)

- 0.05 -

(-1.11)

7.49D5 -1.02 -0.021 (0.23) (-0.17) (-0.36)

0 W N E R D A ~

-

2.04

C

25.25 (0.86) 26.06 (0.96) 36.65" (1.39) 40.12' (1.42) 35.17' (1.37) 31.91 (1.27) 33.80' (1.35) 34.08' (1.34) 41.5' (1.51)

R' R' F S SSE

0.39 0.24 2.55' 8.16 2.666

0.37 0.27 3.65" 7.98 2.738

0.47 0.33 3.50" 7.63 2.330

0.51 0.33 2.90" 7.64 2.160

0.46 0.35 4.43" 7.51 2.368

0.52 0.41 4.41" 7.21 2.077

0.54 0.41 4.14a 7.18 2.013

0.53 0.40 3.98" 7.26 2.058

0.56 0.37 2.97 7.41 1.922

F L E S Z A A G E A W F L U T A P O P A C N P P C A A R E A A R E G S H A O W N E R D A

-0.12 0.29 -0.17 0.10 -0.11 0.12 0.45 -0.003 0.42 -0.22 0.28 -0.13 0.32 0.21 0.15 0.21 -0.24 0.29 0.40 -0.28 0.51 0.004 -0.20 -0.14 0.06 -0.41 -0.01 0.26

no significant effect on operating profits. The exclusion of US carriers from the sample, as a major effect, increased the level of significance of passenger load factors and lowered the SSE of the regressions.

Thus the message to airline managers (whether

younger and more efficient fleets and supplement, as much as possible, their passenger traffic with cargo loads.

Acknowledgements

The author would like to thank Dr R. S. Thompson and privately or state owned) is that, on average, profit-

have high passenger load factors, a relatively low proportion of capacity related costs,

anonymous referees for their helpful and constructive comments of earlier versions of this paper, and Sylvia Bohling for her assistance with the data manipulations. The remaining errors are the author's responsibility.

514 A. ANTONIOU

NOTES

1. See, for example, the somewhat dated references in Stanbury and Tretheway (1986).

2. An alternative but closely related measure is the Oper- ating Ratio ( O R ) or the Total Operating Cost over Total Revenue; but, of course, O P R M = 1 - O R .

3. A similar classification was first proposed by Sarndal and Statton (1975) and the follow-up study by Sarndal et ul. (1978).

4. See Antoniou (1991) for a review of this extensive and rich literature.

5. When observations were missing on the independent variables, the series mean was used instead as a proxy.

6. For a similar estimation, Morrison and Winston (1986) rejected the log-specification as OPRM take negative values. They also considered the quadratic specification estimated jointly with the underlying factors demand equations, but the results were not satisfactory. See also Bruning and Hu (1988) and Van Scyoc (1989) for alternative specifications.

7. The reader is referred to Antoniou (1991) for a full discussion on the problems associated with measuring economies of scale in the airline industry.

8. Pryke (1987) characteristically writes in this regard: ‘Most IATA airlines were in state ownership. They had no incentive to maximize profits and few were under pressure to d o much more than break even’

9. The sign and magnitude of the correlation coefficients between LAPRO and SHPAS is rather puzzling. Venturing one possible (conjectural) explanation we would say that this is a refection of the relatively high proportion of ground versus flight personnel.

(P. 10).

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the factors determining the profitability of international airlines: some econometric results

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