Canadian Transportation Research Forum

i.e Groupe de Recherches sur les ransports u Canada

GOING THE DISTANCE Franchir le fil d'arrivee

PROCEEDINGS of the 29th vo- Annual Meeting

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.110 ACTES de la 29ieme Conference annuelle 50

Seasonality and Economic Efficiency in the Canadian Aldine Industry: Performance in Air Farts Since Deregulation.

Bradley Snider, Aviation Statistics Centre, Transportation Division, Statistics Canada

Abstract

Seasonal price swings represent a real cost to the economy, because ofthe. inefficiency it represents. In the airline industry, it represents excess demand in the third quarter and unused capacity in the first and fourth quarters of each year.

In an ideal economy,price changes provide incentives to shift capacity and demand,thus eliminating seasonal pricing. One ofthe goals ofderegulation was to give the airlines the ability to be more competitive by changing capacity on a route, or by using price incentives to affect demand. This paper will look at one aspect of economic efficiency, seasonal price factors, to see if deregulation has succeeded in improving this aspect of economic efficiency and, therefore, the performance and competitiveness of .

The conclusion reached is that deregulation has produced precisely the results that theory predicts. Seasonal variation in prices has been decreasing since 1988, and is substantially less now than when deregulation was introduced.

The efficiency of Canada's airline industry has long been studied in terms of technical and allocative efficiency, using annual data for inputs and factors of production.' Less common has been an examination ofthe seasonal efficiency of the industry, using the published sub-annual data.

The concept ofseasonal efficiency is most commonly encountered in the literature concerning public utilities, where problems of peak demand and rate-setting must be addressed by regulatory authorities. Simply put, the existence of seasonality in any industry is an undesirable inefficiency that results in real costs to the economy, because ofexcess demand at one point in time and under-used capacity

1 B. Snider 51 at some other point in tirne.2 It has always been maintained in the literature dealing with seasonal efficiency that a free market with allocative pricing would act to level out seasonal excesses of demand and capacity over the course ofthe Year. Since prices are the signals used in a free market to shift supply and demand from one time or place to another, one would expect to eventually see no seasonality in prices in a free market.3

Before the beginning of domestic deregulation in 1984, airlines were restricted in their ability to change capacity on a route, or to use price incentives to shift demand. One ofthe goals of deregulation was to give airlines this flexibility, and to increase efficiency of the industry.' The goal of this paper, therefore, is to examine the published quarterly air fare data' for evidence of evolving seasonal efficiency since deregulation.

This paper will analyze average domestic and international discount air fares, and their respective indexes, using the seasonal adjustment tools developed at Statistics Canada. As well as studying seasonal efficiency, some of the many of using seasonally adjusted data for analysis will be discussed. advantages unlike Discount air fares are used because they respond to seasonal market forces, full fares,(see Figure 1). They also account for no less than two thirds economy 6 of domestic scheduled passengers and three quarters ofinternational passengers. Domestic Air Fares 1 Fares Figute Average Economy and Discount Air 300

280 —

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S S 220 —

Fare, Fare, 200 — Air Air 180 —

160 — Average Average

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100

11.1 FII 80 11i FIT ill Fli 1989 1990 1991 1992 1983 1984 1985 1986 1987 1988 Source: Aviation Statistics Centre, sic Year and Quarter The Analysis Unit, Aviation Statistics Centre, Statistics Canada, produces Fare Basis quarterly data for average air fares and air fare indexes from the Fare Survey. The average air fares are simply the air carriers' total scheduled revenue

2 B. Snider 52 divided by the total number of scheduled passengers, as measured by coupon origin and destination in the survey. These are tabulated by sector and fare type. A change in an average fare could be for two reasons: either the price ofthe same ticket has changed, or passengers have shifted their buying patterns towards either cheaper or more expensive flights.

In order to decide which event has occurred, the Fare Analysis Unit developed and publishes air fare indexes. The index weights the air fares according to a "basket" offares bought in the base year, in this case 1983.(All indexes currently published are converted to a 1986=100 time base). The index thus produces a measurement of true change in the price of air travel, which is not confounded with changes in passenger travel patterns.

To analyze the seasonal datn, they were seasonally adjusted using Statistics Canada's X-11-ARIMA software'.In essence,the X-11-ARIIvIA programme takes the original time series and breaks it down into three components9: the trend- cycle, the seasonal component, and the irregular component. The final seasonally adjusted series is the sum ofthe trend-cycle and the irregular component. Any of the three components may be analyzed individually for their information content. Fig= 2 Domestic Discount Air Fares Average and Seasonally Adjusted 160

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1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 Source: Aviation Statistics Centre, STC Year and Quarter The main purpose of seasonal adjustment is to allow comparisons to be made between any consecutive data points. For example, consider Figure 2. This shows the average domestic discount air fares alongside the seasonally adjusted data. The strong seasonal peak in the third quarter is obvious. This means that the average air fares cannot be meaningfully compared from one quarter to the nextw. The

3 B. Snider 53 normal procedure, then, is to make comparisons only to the same quarter in a Previous year. Seasonally adjusted data, however, can be legitimately compared between any two quarters. This can yield a different analytical result than the more usual year-over-year analysis.

For example, a year-over-year analysis of the raw data in Figure 2 would note (correctly) that the average discount air fare in the third quarter of 1992 had declined by 1.0% from the same period in 1991. However, this analysis does not make full use of all of the information which the time series- contains. The seasonally adjusted series shows that the third quarter of 1992 increased by 3.3% over the second quarter, indicating the start ofa recovery in domestic discount air fares, which continued into the fourth quarter. Domestic Discount Air Fares Hglne 3 Seasonally Adjusted and Trend-Cycle

tit It'll- Fill 90 ririiiTliiTtriltrirtilt 1989 1990 1991 1992 1983 1984 1985 1986 1987 1988 Source: Aviation Statistics Centre, src Year and Quarter The trend-cycle reveals the most fundamental trends in the data, with all irregular effects removed.(Since the trend-cycle is generated by a moving-average process, the final two data points are normally suppressed to avoid signalling false turning points). The irregular component (which contains all random effects, such as strikes, wars, et cetera), can be studied by a comparison ofthe seasonally adjusted data with the trend-cycle. In Figure 3, the trend-cycle for domestic discount air fares is superimposed on the seasonally adjusted series. It is thus possible to show that the Gulf War in the first quarter of 1991 coincided with a drop in seasonally adjusted domestic discount air fares of 6.8% below the trend level. This was followed in the second quarter by a rebound of 7.2% above the trend.

To see whether this effect is due to price changes or changes in consumer travel

4 B. Snider 54

patterns during this period, the domestic discount fare index can also be seasonally adjusted and plotted in a similar manner in Figure 4. The decline in the price index in the first quarter is less marked, but the rebound above trend in the second quarter is just as noticeable. Thus, one may conclude that a drop in travel by consumers caused the sharp drop in the fust quarter average fares, while the rebound in the second quarter was the result ofrising prices, due to a combination of demand postponed by the war, and attempts by the industry to recoup some of their first quarter losses and fuel costs. figure 4 Domestic Discount Air Fare Index Seasonally Adjusted and Trend-Cycle 150

140 —

130 — 0 0 0 Seasonally Adjusted

100 o o 0 Trend-Cycle

1986:

Value, 110 —

Index 100 —

tit tilmitit tiiiitriltillt t titillt 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 Source: Aviation Statistics Centre, Sit Year and Quarter The international discount air fares (which include both United States and other international destinations) are shown in Figure 5. The extreme seasonality, and the lack of any noticeable effect from the Gulf War is striking. A similar result was obtained for the international discount index (Figure 6). The lack of response to outside forces in the still largely regulated international sector leads us back to the original question posed at the beginning ofthis paper: has deregulation made the domestic market more efficient? More interestingly, how does this compare with the efficiency of the regulated international market?

To analyze this, we must turn to the third component of seasonally adjusted model, which is the seasonal component itself This is the part ofthe time series removed by the X-11-ARIMA programme. When examined on its own, it shovvs in percentage terms how much,on average, the air fares in each quarter differ from the annual average air fare.

5 B. Snider 55

5

MgiIle International Discount Air Fares

S S

Fare. Fare. Average Average

Ili it' rir r 1 lir iii 1 tr it' iitiiar 1989 1990 /991 1992 1983 1984 1985 1986 1987 1988 Quarter Source: Aviation Statistics Centre, SW Year and

Figure 6 International Discount Air Fare Index

Seasonally Acriusted, and Trend-Cycle

100 100

= =

1986 1986

Value. Value. Index Index

1992 1983 1984 1985 /986 1987 1988 1989 1990 1991 Source: Aviation Statistics Centre, SIC Year and Quarter

Snider 6 B. 56

The seasonal component ofthe domestic discount air fares is plotted in Figure 7. The pattern is striking. From 1983 to 1985, third quarter fares commanded an average 11.6% premium over the annual average. Since 1986, this premium has declined every year. By 1992, the third quarter premium was only 5.9%, about half the value before deregulation. Figute 7 Domestic Discount Air Fares Seasons/ Component m 00 109 108 i 107 IR 106 .5 105 1 104 E. 103 102 1! 101 S : loci A 9§ 98 97 96 95 94

II 11111111 111111111I111111111 1983 1984 1985 1988 1989 1990 1991 1992 Source: Aviation Statistics Centre, STC Year and Quarter This trend is mirrored by the fourth quarter. In 1983,fourth quarter discount fares had an average markdown of5.9% below the annual average discount fare. This has since narrowed to the point of vanishing, the seasonal factor in 1992 being less than half of a percent below the annual average discount fare.

To test whether this was caused purely by changing travel habits, the seasonal factors of the domestic discount index are plotted in Figure 8. The trend here is even more pronounced than for the domestic discount fares. By 1992,the seasonal factors for the price index were less than 2% above or below the annual average. Indeed, for the domestic discount price index seasonality has all but disappeared.

It could be argued that overcapacity and fare wars were the cause of the decline of peak fares, but that would not explain the relative rise of off-peak fares.

It could also be argued that the introduction of better consumer information through computer reservation systems has caused the rationalization of capacity and demand, and that this effect could have taken place independently of deregulation.

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Domestic Discount Air Fare Index Seasonal Component 108

107 —

106 —

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101 —

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TIlTT- I 11111111 95 MITIII /992 1988 1989 1990 1991 1983 1984 1985 1986 1987 Source: Aviation Statistics Centre, sir Year and Quarter air can be addressed by an examination of international discount This argument market and The international market includes both the United States fares. information overseas markets, which are well served with computerized terms ofaccess, technology. However, these markets are still largely regulated in capacity, and frequency restraints. Figure factors for the international discount air fares are shown in The seasonal factors are The contrast with the domestic market is striking. The seasonal 9. passenger larger and show little trend to diminished size. To see whether changing international travel patterns were obscuring a trend, the seasonal factors for the results were discount index were plotted in Figure 10. However, the same obtained: large seasonal factors, with no significant diminishing trend. Survey data, In conclusion, we have seen how the seasonally adjusted Fare Basis the Aviation Statistics Centre, can yield extra information available from a by more conventional analysis. In particular, this paper has noted unobtainable market, tend toward a less seasonal pattern of prices in the domestic scheduled demand over the which indicates that there is a better match between capacity and economic theory course of the year. This is precisely the result predicted by the real economic of allocative pricing in a free market', and is a genuine gain in shown very little efficiency. In contrast, the regulated international market has real cost on the tendancy toward greater seasonal efficiency, thus imposing a industry and a loss on the economy in general.

Snider 8 B. 58

Iigule 9 International Discount Air Fares Seasonal Component 120 118 116 114 112 110 108 106 104 102 100 , 98 96 94 92 90

r 1 II I III II- 1111111 1983 1984 1985 1986 1987 1988 1989 1990 1991 Source: Aviation Statistics Centre, src Year and Quarter

Fig-um 10 International Discount Air Fare Index Seasonal Component

.1^

106 -,

105 - 104 - \ 103 -

102 -

101 -

100

99 -

98 -

97 -

96 i I I VIII I I , IIIIIIIT 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 Source: Aviation Statistics Centre, STC . Year and Quarter These results should also be of interest to other modes of transportation besides aviation. Allocative pricing has been advocated in many transport modes and public utilities as a means ofreducing peak demands and rationalizing the use of expensive infrastructure, but empirical evidence of successful cases is often lacking. This study provides evidence that for domestic scheduled air transport,

9 B. Snider 59 at least, free market pricing has succeeded in rationalizing capacity and demand throughout the year.

1Kobia, Kobina, "The Cost of Inefficient Allocation of Inputs in the Canadian Airline Industry", Proceedings of the 28th Uwadian Transportation Research Forum, University of Saskatchewan Printing Services, 1993, pp. 269-281. 2 3arfield, P.J., and Lovejoy W.F., Public Utility Economics, Prentice-Hall Inc., Englewood Cliff NJ, 1964, pp. 19-22. 3I3onbright, J.C., Principles of Public Utility Rates, Columbia University Press, 1969, pp. 357-358. "Graham, D.R, and Kaplan, D.P., "Developments in the IX-regulated Airline Industry", Office of Economic Analysis, U.S. Civil Aeronautics Board, 1981, p 39. 'Statistics Canada, Air Canier Operations in Canada, April-June 199Z Catalogue No. 51-002, Special Articles. All data used in this paper has been published by the Aviation Statistics Centre, Statistics Canada, or is available on request.

6Statistics Canada, Canadian Civil Aviation, 1992, Catalogue No. 51-206, pp. 29- 37. 'The survey samples the scheduled operations of all Level I Canadian air carriers. Since 1983 this has included , and at various times CP Air, Eastern Provincial,Pacific Western,, , ,Canadian Airlines International Ltd, AirBC, and . Beginning in 1993, two Level II carriers were added to the survey: Inter-Canadien and . Changes in the coverage of the survey can affect average air fares. In the X-11-ARIMA programme, however, dummy variables compensate for coverage changes, and the seasonal adjustment is unaffected. 8Dagum,E.B., The X-11-ARINIA Seasonal Adjustment Method, Statistics Canada, Catalogue No. 12-546E, 1980. 9A.fourth component, the trading-day component, is not significant for quarterly data and is ignored here. W.L., Macroeconomics, Richard D.Irwin Inc., Homewood IL, 1971, pp. 60-61. The author wishes to acknowledge RolfHakim, who helped do the X-11-ARIMA modelling, Gabrielle Lorrain and Sylvie Moreau, who produced the quality survey data; Gord Baldwin and Lisa Di Pietro, who gave me the time and it; encouragement to do this paper, Jerry Ciasnocha,(NTA) who reviewed and David Dodds, the Director of the Transportation Division, STC.

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