- Consulting with Canadians-

Air Travel Demand Elasticities: Concepts, Issues and Measurement: 3
- Table of Contents - Previous - 

5. Discussion

In a number of cases, studies that are focused on the impact of price changes or fees on demand use a single elasticity measure to compute the quantity, revenue and profit change for a route, market, airline or entire economy (see for example, PODM (Transport Canada) which uses one elasticity of business and one for leisure; economic impact studies for airports often use this approach as well). Using a single value implicitly assumes that the elasticity measure is transferable across markets and time. There is a rich and extensive literature that explains the conditions under which such estimates are transferable.^[47] The properties or characteristics of the data in different markets should meet statistical tests in order to have statistical validity in having a common elasticity.

We have shown that elasticity values can and do differ significantly between travel distance, type of traveller and even domestic and international routes. This is illustrated in Table 5.1 in which we report elasticities for three different route types and two passenger types. We have argued that the usefulness of estimates should be based upon other criteria such as the inclusion of income coefficients and distinctions between types of passengers and airports. We have also argued that route-specific data is especially valuable in capturing competitive, geographic and market differences.

In the report we show that for the entire set of studies as well as for categories of studies the distribution of elasticity estimates is highly skewed. Such a distribution makes the use of the mean or average tenuous at best. The mean may turn out to be a value that was yielded by none of the studies. The variance is also large which makes the level of confidence we can place in a 'mean' value particularly low. Therefore, we have used the 'median' value of the elasticity estimates as an indicator of what elasticity value might be used in forecasting changes in revenue, passengers and profit in markets where the elasticity is appropriate – short or long-haul, business or leisure etc. as a result of a policy change.^[48] In addition to the reported median values in the various categories, we have also reported quartile information from the distributions of elasticity values; dividing the observations into quartiles simply means we have divided it into quarters, so the 1^st quartile would be the first 25 percent of observations. In particular, table 5.1 draws attention to the first and third quartiles (twenty-five percent of the values in a distribution fall below the first quartile and seventy-five percent fall below the third quartile).^[49] The first and third quartiles form a useful range around the median that widens (narrows) as the tails of the distribution grow thicker (thinner). This is illustrated in figures 5.1a and 5.1b.

Figure 5.1a: Inter-quartile range with wide tails in the distribution

 

Figure 5.1b: Inter-quartile range with narrow tails in the distribution

Table 5.1 below, shows median absolute values (meaning we have dropped the negative sign in front of the elasticity value) of estimated demand elasticities along with the first and third quartiles of the distribution for all studies and for 'passing grade' studies in six categories.^[50]

In the long-haul international market, there is no apparent difference between the elasticity values from all studies and the group regarded as having a 'passing grade', based on our scoring system.^[51] The median values are low (0.265) for business travel and close to unity for leisure travel. This seems reasonable, since long-haul international business travel demand has relatively few close substitutes, making demand insensitive to fare changes. On the other hand, international leisure travellers are more likely to postpone trips to specific locations in response to higher fares, or shop around for those

Table 5.1

Summary Table of Absolute Elasticity Values

Category	Median		Median
	(1st quartile)	(3rd quartile)	(1st quartile)	(3rd quartile)
	Elasticity Values All Studies		Elasticity Values Studies Scoring ³ 12 points
Own-price: Long-haul international business	0.265		0.265
	0.475	0.198	0.475	0.198
Own-price: Long-haul international leisure	0.993		1.040
	1.65	0.535	1.700	0.560
Own-price: Long-haul domestic business	1.150		1.150
	1.428	0.836	1.428	0.836
Own-price: Long-haul domestic leisure	1.120		1.104
	1.472	0.887	1.228	0.787
Own-price: Short/medium-haul leisure	1.520		1.520
	1.745	0.885	1.743	1.288
Own-price: Short/medium-haul business	0.730		0.700
	0.798	0.608	0.783	0.595
Income Elasticity	1.390		1.140
	0.840	2.169	0.807	2.0489

locations offering more affordable fares. In the vacation market, international travel competes more directly with domestic travel for vacation destinations.

The long-haul domestic business segment elasticities are the same whether looking at all studies or the sub-set of 'passing grade' studies. The value of 1.15, being close to unity indicates that domestic business travellers will have higher elasticities (in this case about four times the value) than international business travellers. In domestic markets, alternatives such as telecommunications are more substitutable than in international markets due to common culture, laws, contracts etc. International trips are typically planned well in advance, with the travel spread over more time we would expect the airfare to be a lower proportion of overall trip costs.

The median long-haul domestic leisure elasticity values do not differ significantly between all studies and those rated superior, however the range of elasticity values in the passing grade studies (as defined by the first and third quartiles of the distribution) is narrower and slightly lower. The value of 1.104 does not seem unreasonable in comparison to the domestic business travel elasticities.

The median elasticity value from all studies versus 'passing grade' studies for short/medium-haul leisure are identical and characteristically elastic at 1.52. Notice however that the range of values around the median is narrower in the passing grade studies, excluding the possibility of inelastic demand at the lower bound of the range.

The estimated median fare elasticity for short/medium-haul business travel is moderately inelastic at 0.73 with a very tight range around the median. Generally, the price or fare elasticity will decline with distance other things held constant. However, we can see in Table 5.1 that the short/medium haul business elasticity is smaller in value than the long haul domestic business elasticity. The explanation is quite straightforward, people using aviation as a business tool, will have an especially high value of time. They will therefore willingly pay high fares to save time on short haul trips. Secondly, there are numerous 'must-go' short haul trips that arise in the course of business dealings, trips that are not easily planned and can be completed in a morning or afternoon without requiring scheduling meetings or packing or making family arrangements. These short haul business trips will have low elasticities; for example, in flying to Montreal from Toronto to close a multi-million dollar deal and sign the contact, the high air fare is low when factored into the overall value of the trip.

Table 5.1 provides ample evidence that using a single elasticity for all market segments is inappropriate just as a single elasticity will not reflect impacts on the aggregate market. Furthermore, simply segmenting markets by business and leisure is insufficient to provide any degree of accuracy to forecast changes in passengers with changes in fares. For example, given the differences between short and long-haul market elasticities, using long-haul values to evaluate impacts on short-haul markets would generally provide an underestimate.

It may be the case that elasticity values reported in the empirical literature are underestimated for both leisure and business travel, particularly in short to medium-haul markets. The reasoning is based on research, which shows that the entry of low cost carriers into markets leads to a reduction in the average fares on those routes (Windle and Dresner, 1999).^[52] For example, if a low cost carrier like Southwest enters a market, the effect has been a reduction in fares of almost 50 percent. Table 5.2 compares very recent revenue yields in various US markets that are delineated based on length of flight but also on whether there is competition from Southwest Airlines, and the form of that competition. The table shows, quite dramatically, that the more direct the competition from Southwest, the lower the yields (which translates into lower average fares).

Table 5.2

Revenue yields of other airlines (OA) and Southwest (SW)

Market type	Yields (cents per passenger mile)
	500 Miles	1000 Miles
OA-no SW presence	51	26
OA-SW connecting competition	31	20
OA- SW direct competition	26	19
SW-connect	21	14
SW-non-stop	18	12
Sources: US Department of Transportation (2002); D. Gillen "Frills, no frills or Wal-Mart: The future of Canada's Aviation industry" (Forthcoming, Van Horne Institute, University of Calgary)

There are few studies that have included, as a time-series, the growth in markets where low cost carriers have concentrated their activity. In empirical studies, routes are usually aggregated so an average elasticity is estimated across short, medium and long-haul routes. Thus, in studies using detailed US data markets served by Southwest Airlines and more recently by other low cost carriers such as JetBlue and Air Tran are aggregated with those served by other full service carriers even though the growth in traffic in these markets is quite different.

In order to look at the potential underestimation of demand response in markets where low cost carriers participate we used US data from 1999-2000 2nd Quarter. The year-over -year changes in passengers and fares are used to calculate arc-elasticities for routes of different length and for fare increases and fare decreases. We found the calculated arc elasticities did not differ in any significant way from the values we have found from our survey of the literature. This applies to values for long-haul as well as short to medium-haul markets; short/medium-haul markets are more price sensitive than long-haul markets. We therefore feel the values reported in Table 5.1 fairly reflect the sensitivity of markets including those served by low cost and low fare carriers.^[53]

As we note in the discussion of Table 5.1, there is no single elasticity value that is representative of air travel demand. There are several distinct markets and several different elasticities should be used when exploring the impact on markets from changes to the aviation environment. Furthermore, even given the elasticity for a market segment, there is a range around this elasticity that should be considered in using the elasticity to forecast the impact of fare changes. The aggregate elasticities for the market segment reflect the combined effect of demand relationships in each component market. Each market will typically exhibit different elasticities than that considered for the aggregate market level.

Bibliography

Abrahams, M., "A Service Quality Model of Air Travel Demand: An Empirical Study", Transportation Research, 17A(5), 385-93, 1983.

Agarwal, V. and W. K. Talley, "The Demand for International Air Passenger Service Provided by U.S. Air Carriers", Interntaional Journal of Transport Economics, 12 (1), 63-70, 1985.

Anderson, James E., and Marvin Kraus, "Quality of Service and the Demand for Air Travel", The Review of Economics and Statistics, Volume 63, Issue 4, 533-540, 1981.

Andrikopoulos A.A., and T. Terovitis, "An Abstract Mode Model: A Cross-section and Time-series Investigation", International Journal of Transport Economics, 10(3), 563-76, 1983

Battersby, B. and E. Oczkowski, "An Econometric Analysis of the Demand for Domestic Air Travel in Australia", International Journal of Transport Economics, Vol. 28(2),193-204, 2001.

Bhadra, Dipasis, "Demand for Air Travel in the United States: Bottom-Up Econometric Estimation and Implications for Forecasts by O&D Pairs", Center for Advanced Aviation System Development – The Mitre Corporation, 2002.

Brons Martijn, Eric Pels, Peter Nijkamp and Piet Rietvela, "Price Elasticities of Demand for Passenger Air Travel: A Meta-Analysis", Tinbergen Institute, Amsterdam, 2001.

Bureau of Industry Economics, "Expenditures by overseas visitors and Australians travelling abroad", Tourist Expenditure in Australia, BIE Research Report 16. Canberra, AGPS, 1984.

Bureau of Transport and Communications Economics, International Aviation, Report 86, 19-22.

Bureau of Transport Communications and Economics, "Demand Elasticities for Air Travel to and from Australia", Working Paper 20, Department of Transport and Communications, 1995.

Bureau of Transport Economics, "Demand for Australian Domestic Aviation Services Forecasts by Market Segment", AGOS, Canberra. Occasional Paper 79., 1986.

Fridstrom, L. and H. Thune-Larsen, "An Econometric Air Travel Demand Model for the Entire Conventional Domestic Network: The Case of Norway", Transportation Research, 23B(3), 213-24, 1989.

Gillen D. W. and Morrison W. G., "Airport Financing, Costing, Pricing and Performance", Report to the Canadian Transportation Act review Committee, April 2001.

Hamal, K., "Australian Outbound Holiday Travel Demand: Long-haul Versus Short-haul", Bureau of Tourism Research, Canberra, BTR Conference Paper 98.2, 1998.

Hollander, G., "Determinants of Demand for Travel to and from Australia", BIE Working Paper no. 26, Bureau of Industry Economics, Canberra., 1982.

Ippolito, R.A., "Estimating Airline Demand With Quality of Service Variables", Journal of Transport Economics and Policy, 15(1), 457-64, 1981.

Jung, J.M. and E.T. Fujii, "The Price Elasticity of Demand for Air Travel", Journal of Transport Economics and Policy, Volume 10, 257-262, 1976.

Lubulwa, A.S.G., "Brandow Demand Functions For Australian Long Distance Travel", Forum Papers, 11th Australian Transport Research Forum, volume (2), 200-218, 1986.

May, T.E., E.W.A. Butcher and G. Mills, "Consumer Responsiveness to Changes in Air Fares, Independent Review of Economic Regulation of Domestic Aviation", vol. 2, Appendix L, 1986.

Morrison, Steven A., and Clifford Winston, "An Econometric Analysis of the Demand for Intercity Passenger Transportation", Research in Transportation Economics, Volume 2, 213-237, 1985.

Nairn, R.J. and P. Hooper, "Tourism Related Movement Study Final Report", Roads and Traffic Authority, NSW, Sydney., 1992.

Oum T.H. and D. W. Gillen , "The Structure of Intercity Travel Demands in Canada: Theory Tests and Empirical Results", Transportation Research, 17B(3), 175-91, 1982.

Oum T.H., D. W. Gillen and S.E. Noble, "Demand for Fareclasses and Pricing in Airline Markets, Logistics and Transportation Review", 22(3), 195-222, 1986.

Oum, T.H., Waters, W.G. and Yong , J.S., "A Survey of Recent Estimates of Price Elasticities of Demand for Transport", World Bank Working Paper, WPS 359, 1990.

Oum T.H., Waters, W.G. and Yong , J.S., "Concepts of Price Elasticities of Transport Demand and Recent Empirical Estimates", Journal of Transport Economics and Policy, vol.26(2), 139-154, 1992.

Oum T.H., A. Zhang and Y. Zhang, "Inter-Firm Rivalry and Firm-Specific Price Elasticities in Deregulated Airline Markets", Journal of Transport Economics and Policy, vol. 27(2), 171-92, 1993.

Talley, W.K. and A. Schwarz-Miller, "The Demand for Air Services Provided by Air Passenger-Cargo Carriers in a Deregulated Environment", International Journal of Transport Economics, 15(2), 159-68, 1988.

Taplin, J.H.E., "A Coherence Approach to Estimates of Price Elasticities in the Vacation Travel Market", Journal of Transport Economics and Policy, 14(1), 19-35, 1980.

Taplin, J.H.E., "A Generalised Decomposition of Travel-Related Demand Elasticities into Choice and Generation Components", Journal of Transport Economics and Policy, Volume 31 (2), 183-191, 1997.

Appendix A: Survey of Demand Elasticity Studies

We have adopted a standard reporting sheet to summarize existing studies in the literature. Each sheet denotes (when possible), the publication or completion date of each study; the country or countries studied; the modes of travel studied and the type and sources of data used. In addition, we note which market segments are identified (business/leisure; long-haul/short-haul) and the types of elasticities estimated (own-price, cross-price or income). Finally, the summary sheet specifies where possible, the type and functional form of the model employed along with any relevant statistical properties.

Many studies in the literature were completed and/or published prior to 1990 – more than twelve years ago. Consequently, actual estimate values in these studies provide less relevance for forecasting air travel demand in Canada. For this reason, we have grouped the summary sheets into older studies (completed prior to 1990), and more recent studies.

A.1 Older Studies (prior to 1990)

A.1.1	Title of study	A Coherence Approach to Estimates of Price Elasticities in the Vacation Travel Market
	Authors	John H. E. Taplin
	Date completed/published	1980

General Summary information

Country/countries studied	Various
Modes of travel studied	Air (Passenger)
Data Sources	A summary of results of 8 previous studies.

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes
Long-haul between major centres	Yes		Only country-pairs are provided.
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes*
Income elasticity values	Yes

Technical report

Type of model employed	Unknown
Modes of travel included in study
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates

Other Comments

* Taplin calculates cross-elasticity values, which include estimates with respect to car operating costs, price of domestic accommodation, price of overseas accommodation, and the price of other consumer goods and services.

A.1.2	Title of study	Estimating Airline Demand With Quality Of Service Variables
	Authors	Richard A. Ippolito
	Date completed/published	1981

General Summary information

Country/countries studied	U.S.
Modes of travel studied	Single mode: Air (Passenger)
Data Sources	Data sources unknown.

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		Use dummy variables for segments that served Florida, California, or Las Vegas
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No*
Income elasticity values		No

Technical report

Type of model employed	Log-linear demand function, allow for inter-modal competition by dummy; Simultaneous equation model
Modes of travel included in study
Functional form of the model
Policy or price change relevant to estimate(s) in the study	Pre-deregulation in the U.S.
Statistical properties of estimates	Only t-stat values

Other Comments

* Ippolito: To account for mode choice, distance (in logs) is added to the demand specification. Moreover, since the preference for the auto mode may be particularly strong for very short trips, zero-one dummy variables were included in the model, which equalled unity when trip distance was 100 miles or less.

A.1.3	Title of study	The Structure of Inter-city Travel Demands in Canada: Theory Tests and Empirical Results
	Authors	Tae H. Oum and David W. Gillen
	Date completed/published	July 1982

General Summary information

Country/countries studied	Canada
Modes of travel studied	Three travel modes (Air, Bus, Rail)
Data Sources	Statistics Canada (CANSIM data base); 1961-76.

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand		No
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		(Includes: bus, rail, goods, and services)
Income elasticity values	Yes

Technical report

Type of model employed	Structural analysis – demand equations derived from utility maximization. Estimated by Non-linear least squares.
Modes of travel included in study	Air, Bus, Rail
Functional form of the model
Policy or price change relevant to estimate(s) in the study
Statistical properties of estimates	Air: D.W. Statistics = 1.645; R-square = 0.9048

 

A.1.4	Title of study	Determinants of Demand for Travel to and From Australia
	Authors	G. Hollander
	Date completed/published	1982

General Summary information

Country/countries studied	Australia, New Zealand, UK, US, Japan, Canada, Germany, Italy
Modes of travel studied	Air (Passenger)
Data Sources	Bureau of Industry Economics

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		Country of origin to Australia.
Long-haul between major centres	Yes		Country-pairs are provided.
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No	Unknown if model includes intermodal effects.
Income elasticity values	Yes

Technical report

Type of model employed	Double-log; pooled time-series, cross-section 1975-1981
Modes of travel included in study	Air (Passenger)
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates	Unknown

Other Comments

All data was retrieved from the Bureau of Transport and Regional Economics website at: http://dynamic.dotars.gov.au/btre/tedb/tablist_detail.cfm?ID=153

A.1.5	Title of study	A Service Quality Model of Air Travel Demand: An Empirical Study
	Authors	Michael Abrahams
	Date completed/published	April, 1983

General Summary information

Country/countries studied	United States
Modes of travel studied	Air passenger travel (demand equation includes automobile operating costs)
Data Sources	C.A.B. Service Segment Data Base (100 most heavily traveled domestic origin-destination pairs in the U.S.); Measure of price employed is the lowest unrestricted coach fare deflated by the Consumer Price Index (1973-77); Value of schedule delay time are estimates obtained from a procedure developed by Ericson (1977) and Swan (1978)

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		Northeastern U.S. major centres to Florida; West Coast to Hawaii.[54]
Long-haul between major centres	Yes		Transcontinental U.S. (> 1500 miles)
Long-haul between small communities		No
Short-haul between major centres	Yes		Eastern U.S. (< 500 miles)
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No	Effect of fare on demand through changes in the air: auto model split
Income elasticity values		No

Technical report

Type of model employed	2 SLS estimation with Cochrane-Orcutt Transformation (used to correct for first order serial correlation); Time-series (20 quarters)
Models of travel included in study
Functional form of the model	Demand and Elasticity equation: See 'Other Comments'.
Policy or price change relevant to estimate(s) in the study	The study was conducted pre-1978 Airline Deregulation Act in the U.S.
Statistical properties of estimates	Durbin-Watson values indicate that the hypothesis that there exists no first order serial correlation cannot be rejected; R-square for all pools were > 0.96 for dummy variable, and > 0.91 for non-dummy variable;

Other Comments

City-pairs were pooled on the basis of common characteristics.

Demand equation:

Where,

Elasticity equation:

Where,

A.1.6	Title of study	An Abstract Mode Model: A Cross-section and Time-series Investigation
	Authors	Andreas A. Andrikopoulos and Theophilos Terovitis
	Date completed/published	1983

General Summary information

Country/countries studied	Greece
Modes of travel studied	Air-ship, Air-Bus, Air-bus-rail
Data Sources	Civil Aviation Service, Olympic Airline Time and Fare Tables

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand		No
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes
Income elasticity values	Yes

Technical report

Type of model employed	Linear demand; Cross-section (1970-80) and Time-series data (1969-80); Estimated by OLS
Models of travel included in study
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates

Other Comments

Number of air passengers per unit of time is taken as the dependent variable. Two sets of explanatory variables: 1) the mode's attributes relative to the close substitute, 2) Socio-economic variables including population and tourism.

A.1.7	Title of study	Expenditure by Overseas Visitors and Australians Travelling Abroad
	Authors	Bureau of Industry Economics (Australia)
	Date completed/published	1984

General Summary information

Country/countries studied	Australia, New Zealand, UK, US, Japan, Canada, Germany Italy
Modes of travel studied	Air (Passenger)
Data Sources	Bureau of Industry Economics (Australia)

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes
Long-haul between major centres	Yes		Between countries of origin and Australia.
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		Intermodal, prices in alternative destinations.
Income elasticity values	Yes

Technical report

Type of model employed	Unknown; Pooled time-series and cross-section; 1970-1980
Modes of travel included in study	Air (Passenger)
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates	Unknown

Other Comments

All data was retrieved from the Bureau of Transport and Regional Economics website at: http://dynamic.dotars.gov.au/btre/tedb/list_detail.cfm?Ref_ID=21

A.1.8	Title of study	The Demand for International Air Passenger Service Provided by U.S. Air Carriers
	Authors	Vinod Agarwal and Wayne K. Talley
	Date completed/published	1985

General Summary information

Country/countries studied	U.S. and undisclosed foreign-country destinations. (city-pairs)
Modes of travel studied	Single mode: Air (Passenger)
Data Sources	International Civil Aviation Organization (ICAO), Official Airline Guide (World-wide Edition)

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		All elasticities are assumed to be 'excursion' travel.
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No
Income elasticity values		No

Technical report

Type of model employed	Log linear demand estimated by OLS. Cross-section data (Dec. 1981), 63 flight segments.
Models of travel included in study
Functional form of the model	See 'Other Comments'.
Policy or price change relevant to estimate(s) in the study	Post-deregulation data after 1978 in the U.S.
Statistical properties of estimates	Only variables P, FF, LF are significant at the .05 level; Adjusted R-square = .8766

Other Comments

Demand equation:

Where,

A.1.9	Title of study	An Econometric Analysis of the Demand for Intercity Passenger Transportation
	Authors	Steven A. Morrison and Clifford Winston
	Date completed/published	1985

General Summary information

Country/countries studied	U.S.
Modes of travel studied	Rail, Air, Bus (Vacation and Business trips)
Data Sources	1977 Census of Transportation National Travel Survey; June 1977 Official Airline Guide

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand	Yes
Leisure travel demand	Yes
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		Intermodal coefficients are included for rail, car, and bus.
Income elasticity values		No

Technical report

Type of model employed	Nested logit model.
Modes of travel included in study	Rail, Bus, Air.
Functional form of the model
Policy or price change relevant to estimate(s) in the study	Data taken from 1977 (Pre-deregulation in the U.S.)
Statistical properties of estimates

Other Comments

Morrison and Winston note that the elasticity results for vacation travellers (Air) are not too large (less than 1.0). They assert that given the large share of the market that this mode possesses, the results are not too surprising.

Morrison and Winston: More generally, the air destination elasticities indicate that mode choice price elasticities for air may understate the total traveller responsiveness to changes in airfare. Consequently, such elasticities should be viewed with some caution.

A.1.10	Title of study	Demands for Fareclasses and Pricing in Airline Markets
	Authors	Tae H. Oum, David W. Gillen and S.E. Noble
	Date completed/published	1986

General Summary information

Country/countries studied	U.S.
Modes of travel studied	Domestic air passenger travel
Data Sources	Official Airline Guides, North American Edition; Domestic Origin-Destination Survey of Airline Passenger Traffic, Air Transport Association of America; Two hundred intra-U.S. routes were selected from 1978 cross-sectional data.

Specific focus

Identified elasticities	Yes	No	Comment
Business travel demand	Yes		Aggregate data includes business and non-business travel.
Leisure travel demand	Yes		Washington D.C. – Miami, Pittsburgh – Miami, N.Y. - Miami
Long-haul between major centres	Yes
Long-haul between small communities		No
Short-haul between major centres	Yes
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes[55]		Between Fare classes: first class, economy, and discount.
Income elasticity values	Yes		Vacation routes

Technical report

Type of model employed	First stage: Partial Elasticities, translog demand system. Second stage: Total Elasticities, Log-linear aggregate demand model; cross-sectional data (1978), intra-U.S. routes
Models of travel included in study	Air (Passenger)
Functional form of the model	Demand equation: see other comments.
Statistical properties of estimates	Total Price Elasticities, R-square = 0.6793 Vacation routes (price-elasticity) t-ratio = 2.08 Vacation routes (income-elasticity) t-ratio = 1.43 Non-Vacation routes (price elasticity) t-ratio = 2.52

Other Comments

Demand equation:

Where,

A.1.11	Title of study	Demand for Australian Domestic Aviation Services by Market Segment
	Authors	Bureau of Transport Economics
	Date completed/published	1986

General Summary information

Country/countries studied	Australia
Modes of travel studied	Air (Passenger)
Data Sources	Bureau of Transport Economics

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		Tasmania and Queensland.
Long-haul between major centres	Yes		O-D points not given.
Long-haul between small communities		No
Short-haul between major centres	Yes		O-D points not given.
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		Transport mode not given.
Income elasticity values	Yes

Technical report

Type of model employed	Double-log; time-series data; 1977-1983
Modes of travel included in study	Air (Passenger); Alternative mode for cross-elasticity not given.
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates	Unknown

Other Comments

All data was retrieved from the Bureau of Transport and Regional Economics website at: http://dynamic.dotars.gov.au/btre/tedb/list_detail.cfm?Ref_ID=27

A.1.12	Title of study	Brandow demand functions for Australian long distance travel
	Authors	A.S.G. Lubulwa
	Date completed/published	1986

General Summary information

Country/countries studied	Australia
Modes of travel studied	Air (Passenger)
Data Sources	Compilation of 7 studies.

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand	Yes
Leisure travel demand	Yes
Long-haul between major centres	Yes[56]		O-D points not specified for all elasticities.
Long-haul between small communities		No
Short-haul between major centres	Yes[57]		O-D points not specified for all elasticities.
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		Cost of car.
Income elasticity values	Yes

Technical report

Type of model employed	Unknown; Compilation of seven studies
Modes of travel included in study	Air; (Cost of travel by car is provided as cross-elasticity)
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates	Unknown

Other Comments

All data retrieved from the Bureau of Transport & Regional Economics website at: http://dynamic.dotars.gov.au/btre/tedb/tablist_detail.cfm?ID=42

A.1.13	Title of study	Consumer responsiveness to changes in air fares
	Authors	T.E. May, E.W.A. Butcher, and G. Mills
	Date completed/published	1986

General Summary information

Country/countries studied	Australia
Modes of travel studied	Air (Passenger)
Data Sources	Unknown

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes
Long-haul between major centres	Yes		O-D points not given.
Long-haul between small communities		No	Unknown.
Short-haul between major centres	Yes		O-D points not given.
Short-haul between small communities		No	Unknown.
Own-price elasticity values	Yes
Cross-price elasticity values		No
Income elasticity values		No

Technical report

Type of model employed	Unknown; data set from 1977-1984
Modes of travel included in study	Air
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates	Unknown

Other Comments

All data retrieved from the Bureau of Transport & Regional Economics web site at: http://dynamic.dotars.gov.au/btre/tedb/tablist_detail.cfm?ID=43

A.1.14	Title of study	The Demand for Air Services Provided By Air Passenger-Cargo Carriers In A Deregulated Environment
	Authors	Wayne K. Talley and Ann Schwarz-Miller
	Date completed/published	1981

General Summary information

Country/countries studied	U.S.
Modes of travel studied	Single Mode: Air (Passenger and Cargo)
Data Sources	22 U.S. air-passenger-cargo carriers for the year 1983: Moody's Transportation Manual and the Air Carrier Traffic Statistics

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand		No
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No
Income elasticity values		No

Technical report

Type of model employed	Log-linear two stage least squares demand function; Cross-section data
Models of travel included in study
Functional form of the model	See 'Other Comments'.
Policy or price change relevant to estimate(s) in the study	Post-deregulation in the U.S.
Statistical properties of estimates	R-square: 0.9662, Adjusted R-square: 0.9696; t-stat: -3.453

Other Comments

General form of the demand function:

Where,

A.1.15	Title of study	An Econometric Air Travel Demand Model For the Entire Conventional Domestic Network: The Case of Norway
	Authors	Lasse Fridstrom and Harald Thune-Larsen
	Date completed/published	July 1988

General Summary information

Country/countries studied	Norway
Modes of travel studied	Air (Passenger)
Data Sources	Origin-Destination data set (source unknown).

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand		No
Long-haul between major centres		No	Travel time including checking, checkout, access and egress times between city center and airport (in minutes) is a variable in the equation.
Long-haul between small communities		No	""
Short-haul between major centres		No	""
Short-haul between small communities		No	""
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		Cross demand elasticity of air travel with respect to surface fares.
Income elasticity values	Yes

Technical report

Type of model employed	Gravity Model: the model was estimated applying ordinary least squares regression to a logarithmic transformation of the equation. Norway Time-series (1972-83 annual) and Cross-section (95 intercity links) data.
Models of travel included in stud
Functional form of the model	See 'Other Comments'.
Policy or price change relevant to estimate(s) in the study
Statistical properties of estimates	Sample size: 1140 annual observations. R-square: 0.6923 < R < 0.7520

Other Comments

The model includes a short-term/medium-term and a long-term variable.

A.2 More Recent Studies (1991 - )

A.2.1	Title of study	Tourism Related Movement Study Final Report
	Authors	Nairn, R.J. and Hooper, P.
	Date completed/published	1992

General Summary information

Country/countries studied	USA, Australia
Modes of travel studied	Air (Passenger)
Data Sources	Pickrell (1984), BTE (1983)

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand	Yes		U.S.
Leisure travel demand	Yes		Australia
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres	Yes		U.S. (Centres unknown)
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No	Unknown if intermodal coefficients are used.
Income elasticity values		No

Technical report

Type of model employed	Unknown
Modes of travel included in study	Air (Passenger)
Functional form of the model	Unknown
Policy or price change relevant to estimate(s) in the study	Unknown
Statistical properties of estimates	Unknown

Other Comments

All data was retrieved from the Bureau of Transport and Regional Economics website at: http://dynamic.dotars.gov.au/btre/tedb/list_detail.cfm?Ref_ID=111

A.2.2	Title of study	Demand Elasticities for Air Travel to and from Australia
	Authors	Bureau of Transport Communications and Economics
	Date completed/published	1995

General Summary information

Country/countries studied	Australia and numerous destination countries around the world.
Modes of travel studied	Air (Passenger)
Data Sources	ABS, Overseas Arrivals and Departures Australia, Cat. 3402.0; Book1 Worldwide Fares, Air Tariff Publications

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand	Yes
Leisure travel demand	Yes
Long-haul between major centres	Yes
Long-haul between small communities		No
Short-haul between major centres		No
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values		No[58]
Income elasticity values	Yes

Technical report

Type of model employed	Double log and linear models are employed; Time-series data
Modes of travel included in study	Air (Passenger)
Functional form of the model	See 'Other Comments'.
Policy or price change relevant to estimate(s) in the study	Unknown.
Statistical properties of estimates

Other Comments

Leisure travel

for i = 1, 2, ... , 24 and t = 1, 2, ... , 32

where i is the leisure travel market analysed for foreign visitors and Australian travellers between Australia and each of the 12 countries, t is the quarterly time period and j is the number of quarterly lags.

The variable LD is leisure demand for air travel, LF is the real leisure airfare, REX is the real exchange rate, and Y is real income.

The coefficients and represent the short–run effect on demand of a change in the airfare, the real exchange rate and income. The sum of the coefficients and represents the long–run effect on demand of a change in the airfare, the real exchange rate and income. These coefficients can be used to derive long-run elasticities of demand.

Business travel

for i = 1, 2, ... , 24 and t = 1, 2, ... , 32

where i is the business market analysed for foreign visitors and Australian travellers between Australia and each of the 12 countries, t is the quarterly time period and j is the number of quarterly lags.

The variable BD is business demand for air travel, BF is the real business airfare, REX is the real exchange rate, AGDP is real Australian gross domestic product and FGDP is real foreign gross domestic product.

A.2.3	Title of study	A Generalised Decomposition of Travel-Related Demand Elasticities into Choice and Generation Components
	Authors	John H. E. Taplin
	Date completed/published	May 1997

General Summary information

Country/countries studied	Australia and unknown overseas O-D pairs.
Modes of travel studied	Air (Passenger)
Data Sources	Uses elasticities sourced from other studies cited in Taplin (1980) to calculate one own-price and cross price elasticity for domestic travel within Australia, and one for overseas travel to/and from Australia.

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		All elasticities are considered 'Vacation Air Trips'.
Long-haul between major centres	Unknown		O-D pair unknown. Only 'Overseas' si cited in the paper.
Long-haul between small communities	Unknown		""
Short-haul between major centres	Unknown		O-D pair for domestic travel unknown.
Short-haul between small communities	Unknown
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		"Car Operating Costs" is a variable used to infer cross-price. Other cross-price elasticities were also calculated for various goods and services (such as hotel price).
Income elasticity values	Yes

Technical report

Type of model employed	Unknown. The paper is not explicit about the model employed.
Modes of travel included in study	Air.
Functional form of the model	Unknown.
Policy or price change relevant to estimate(s) in the study	Taplin uses elasticities calculated by other papers published prior to 1978.
Statistical properties of estimates	Not provided in the paper.

Other Comments

A.2.4	Title of study	Australian Outbound Holiday Travel Demand: Long-haul Versus Short-haul
	Authors	Hamal, K.
	Date completed/published	1998

General Summary information

Country/countries studied	Australia and Foreign Destinations
Modes of travel studied	Air (Passenger)
Data Sources	Bureau of Tourism Research, Canberra

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand	Yes		Australian outbound holiday travel.
Long-haul between major centres	Yes
Long-haul between small communities		No
Short-haul between major centres		No[60]
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes
Income elasticity values	Yes

Technical report

Type of model employed	Double-log linear, time-series data (1974-1996)
Modes of travel included in study	Air
Functional form of the model	Unknown.
Policy or price change relevant to estimate(s) in the study	N/A
Statistical properties of estimates	Unknown

 

A.2.5	Title of study	An Econometric Analysis of the Demand for Domestic Air Travel in Australia
	Authors	B. Battersby and E. Oczkowski
	Date completed/published	2001

General Summary information

Country/countries studied	Australia
Modes of travel studied	Air (Passenger): Three distinct segments – discount, full economy, and business.
Data Sources	Quarterly data (1992 – 1998); Bureau of Transport Economics;

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand	Yes
Leisure travel demand		No
Long-haul between major centres		No
Long-haul between small communities		No
Short-haul between major centres	Yes[61]
Short-haul between small communities		No
Own-price elasticity values	Yes
Cross-price elasticity values	Yes		No specific transport mode is highlighted, just an index.
Income elasticity values	Yes

Technical report

Type of model employed	Linear
Modes of travel included in study	Air (Passenger)
Functional form of the model	Not provided in detail. Demand determinants included: price, income, substitute prices and seasonality.
Policy or price change relevant to estimate(s) in the study	N/A
Statistical properties of estimates

Other Comments

The authors note that their own-price elasticity estimates are generally at the lower end of the estimates reported by previous studies. The estimates reported for the Melbourne-Brisbane route are particularly low. This general divergence from previous estimates may in part be due to the explicit modelling of individual market segments, which contrasts to the aggregate route analysis conducted by most other studies.

A.2.6	Title of study	Demand for Air Travel in the United States: Bottom-Up Econometric Estimation and Implications for Forecasts by O&D Pairs
	Authors	Dipasis Bhadra
	Date completed/published	2nd Draft (2002)

General Summary information

Country/countries studied	United States
Modes of travel studied	Air (Passenger)
Data Sources	Aviation statistics from Bureau of Transportation Statistics (10% Survey) (50,000 records for eight quarters, 1999 and 2000); and local area data from Bureau of Economic Analysis

Specific focus

Identified Elasticities	Yes	No	Comment
Business travel demand		No
Leisure travel demand		No
Long-haul between major centres	Yes		Distance (in miles) is used to identify long or short haul.
Long-haul between small communities	Yes		""
Short-haul between major centres	Yes		""
Short-haul between small communities	Yes		""
Own-price elasticity values	Yes		""
Cross-price elasticity values		No
Income elasticity values		No

Technical report

Type of model employed	Semi-logarithmic linear model with Limited Information Maximum-Likelihood (LIML) estimation.
Modes of travel included in study	Air (Passenger)
Functional form of the model	See 'Other Comments'.
Policy or price change relevant to estimate(s) in the study	N/A
Statistical properties of estimates

Other Comments

Where,

Density = population density per sq. miles

Interactions = intensity of economic activities are represented by interactions between population and income

Distance = distance traveled between O&D markets

Market Power = share of passenger demand by airlines in total O&D market

Southwest = presence of Southwest Airlines in O&D market

Season = adverse spring and summer weather

Take log for those independent variables for which interpretations are meaningful. Leave out hub status, Southwest presence and season as dummy variables.

---

Footnotes

1. The distribution of elasticity estimates is highly skewed in each distinct market.  Since the estimates are not distributed normally around their average value, the median is used as a measure of central tendency.  (The median is the middle observation: half of the estimates are bigger and half are smaller.) The variance of the estimates was also found to be large, which further reduces the level of confidence in using the average for this purpose.   [Return]

2.The elasticity estimates for each distinct market are divided into four equal parts, or quartiles.  The reported range of values comprises those observations lying within the second and third quartiles.   [Return]

3. In some cases separate equations are estimated for these markets; PODM (The Transport Canada air travel forecasting model) for example uses different equations and variables for leisure and business markets.   [Return]

4. Previous surveys (e.g. Oum et al., 1992) provide a listing of the elasticities and their ranges but no basis for choosing from among the values within the range.   [Return]

5. Theoretically an alternative to the ordinary demand function is the compensated demand function, obtained from a resource expense minimization subject to a given level of utility. Elasticity values from the compensated demand function incorporate only substitution effects, however in practice we can estimate only the ordinary demand function. Nevertheless the distinction is important since large price changes may yield significant income effects.   [Return]

6. The exception would obviously be the constant elasticity demand function.   [Return]

7. A panel is a data set that contains both time-series and cross-sectional information.   [Return]

8. This will be true for all factors other than own-price.   [Return]

9. The exception here is durable goods, for the opposite relationship is expected between long and short run elasticities.   [Return]

10. The difference between this point and the previous one is that hub airports will have different service levels and will generally have a hub premium.   [Return]

11. Fortunately, several techniques have been developed for the estimation of the structural parameters of an a priori specified system of simultaneous stochastic equations.  These include indirect least squares, two stage least squares, instrumental variables, three stage least squares, full information maximum likelihood, and limited information maximum likelihood.   [Return]

12.  The term 'best' means researchers observe this data source to be the most geographically comprehensive, detailed and temporally available.   [Return]

13.  The growth of the Internet in booking tickets is being integrated into the DB1A database, as is the growing use of electronic tickets.   [Return]

14. IATA is the International Air Transport Association.   [Return]

15. OAG is the Official Airline Guide.   [Return]

16. These data are sold and can be expensive.   [Return]

17.  Estimates were that in excess of $1 Million was spent on data collection alone.   [Return]

18.  This figure is adapted from Figure 1 in Oum et al. (1992)   [Return]

19. In computing numerically descriptive measures of data we are generally interested in two indicators: the measure of average or central value of the data and the measure of the degree to which the data are spread out about this average value. The most popular measure of central tendency is the arithmetic mean (known generally as simply the mean) but this can be unduly influenced by extreme observations; which is the case here. We have therefore used the median as the measure of central location. The median is the value that splits the data set exactly in half. The measure of spread is called the variance; how widely the data are dispersed around the measure of central location. Two added descriptors of the distribution of the data are skewness and kurtosis. The first, skewness, describes whether the data are distributed symmetrically around the measure of central location. If they are not, they are said to be 'skewed' or have a short tail on one side and a longer tail on the other rather than having equally sized tails in each side. Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. That is, data sets with high kurtosis tend to have a distinct peak near the measure of central location, decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend to have a flat top near the mean rather than a sharp peak. Since in many cases, the distribution of estimated values is skewed, the mean is not a useful measure of central tendency and the standard deviation is not useful in providing a range around the mean.   [Return]

20. The data-set is comprised of all twenty-one studies located in the appendix.   [Return]

21. The study by Anderson and Kraus (1981) is not included in this histogram since they do not calculate elasticities directly. Doubt over the quality of estimated positive elasticities in Jung and Fuji (1976) lead us to exclude their estimates also.   [Return]

22. It is conventional to present own-price elasticities with a negative sign indicating the general negative relationship between price and quantity demanded. Larger values of the elasticity imply greater price sensitivity while lower values imply less price sensitivity.   [Return]

23. Source: Bhadra (2002), Hamal (1998), Taplin (1997), BTCE (1995), Oum et al. (1986), BTE (1986), Lubulwa (1986), May and Butcher (1986), BIE (1984), Abrahams (1983), Hollander (1982), Taplin (1980).   [Return]

24. Source: Battersby and Oczkowski (2001), Bhadra (2002), Nairn (1992), Oum et al. (1986), BTE (1986), Lubulwa (1986), May and Butcher (1986), Abrahams (1983).   [Return]

25. Source: Hamal (1998), Taplin (1997), BTCE (1995), Lubulwa (1986), BIE (1984), Hollander (1982), Taplin (1980).   [Return]

26. Source: Oum et al. (1986), May and Butcher (1986), Lubulwa (1986), BTE (1986), Bhadra (2002), Abrahams (1983).   [Return]

27. Source: BTCE (1995), Lubulwa (1986).   [Return]

28. Source: Hamal (1998), Taplin (1997), BTCE (1995), Lubulwa (1986), BIE (1984), Hollander (1982), Taplin (1980).   [Return]

29. Source: Oum et al. (1986), Lubulwa (1986).   [Return]

30. Source: Lubulwa (1986), Abrahams (1983).   [Return]

31. Source: Battersby and Oczkowski (2001), Nairn (1992), Lubulwa (1986), Oum et al. (1986).   [Return]

32. Source: Nairn (1992), Oum et al. (1986), BTE (1986), May and Butcher (1986), Abrahams (1983).   [Return]

33. Source: Oum et al. (1986), Talley (1988), Agarwal and Talley (1985), Morrison and Winston (1985), Ippolito (1981).   [Return]

34. Source: Bhadra (2002), Battersby and Oczkowski (2001), Hamal (1998), BTCE (1995), Fridstrom (1989), BTE (1986), May and Butcher (1986), BIE (1984), Abrahams (1983), Andrikopoulos (1983), Oum and Gillen (1982), Hollander (1982), Taplin (1980).   [Return]

35. Source: BTCE (1995), Nairn (1992).    [Return]

36. Bhadra (2002), Battersby and Oczkowski (2001), Hamal (1998), Taplin (1997).   [Return]

37. Source: Battersby and Oczkowski (2001), Hamal (1998), Taplin (1997), BTCE (1995), Fridstrom (1989), Oum et al. (1986), BTE (1986), Lubulwa (1986), BIE (1984), Abrahams (1983), Oum and Gillen (1982), Hollander (1982), Ippolito (1981), Taplin (1980).   [Return]

38. Source: Battersby and Oczkowski (2001), Oum et al (1986), Lubulwa (1986).    [Return]

39. Source: Oum et al. (1986), BTE (1986), Abrahams (1983).   [Return]

40. Source: BTCE (1995), Lubulwa (1986).    [Return]

41. Source: Hamal (1998), Taplin (1997), BTCE (1995), Lubulwa (1986), BIE (1984), Taplin (1980).    [Return]

42. Source: Oum et al. (1986), Lubulwa (1986).   [Return]

43. Source: Lubulwa (1986), Abrahams (1983).   [Return]

44. Source: Battersby and Oczkowski (2001), Hamal (1998), Taplin (1997), BTCE (1995), Oum et al. (1986), BTE (1986), Lubulwa (1986), BIE (1984), Abrahamas (1983), Taplin (1980).   [Return]

45. Source: Battersby and Oczkowski (2001), Hamal (1998), Taplin (1997), Fridstrom (1989), BTE (1986), Lubulwa (1986), Morrison and Winston (1985), BIE (1984), Abrahams (1983), Andrikopoulos (1983), Oum and Gillen (1982), Ippolito (1981), Taplin (1980).   [Return]

46. Source: Battersby and Oczkowoski (2001), BTE (1986), Lubulwa (1986), Abrahams (1983).   [Return]

47. This literature grew out of the early demand modeling efforts. See for example, Watson, Peter L. and Richard Westin. Transferability of Disaggregate Mode Choice Models, Amsterdam: North-Holland, 1975 and Frank S. Koppelman and Eric I. Pas, Multidimensional Choice Model Transferability,. Transportation Research. Part B, Methodological. Vol. 20B, no. 4 (Aug. 1986) p. 321-330   [Return]

48. We remind the reader the median is the value that divides the sample in half so 50 percent of observations will lie above the median value and 50 percent will lie below it.   [Return]

49. When using mean values as a measure of central tendency, the standard deviation of the distribution can be used to create confidence intervals of plus and minus one standard deviation around the mean. Since we are using the median as a measure of central tendency, we cannot use standard deviations (which assume a normal distribution which by definition is not skewed).   [Return]

50. Absolute values mean we have dropped the negative sign before the elasticity value. We had included the sign in section 4 because price elasticities were negative while income elasticities were positive. In a few cases studies reported positive price elasticities, a clear error. Here we are interested in only the degree of difference in price sensitivity as reported by the magnitude of the elasticity value.   [Return]

51. These are not the same set of studies but the superior group is a subset of the total.   [Return]

52. Robert Windle, Martin Dresner (1999) Competitive Responses to Low Cost Carrier Entry, Transportation Research. Part E, Logistics and Transportation Review. Vol. 35E, no. 1 (Mar. 1999) p. 59-75   [Return]

53. We have not carried out any analysis on Canadian data since is there is no comparable data set to that available in the US.   [Return]

54. We infer that the leisure routes from the Northeastern U.S. to Florida are short/medium haul routes.   [Return]

55. No intermodal coefficients are used in the model.   [Return]

56. We calculate distances for city-pair routes provided and distribute the estimates accordingly based on short/medium or long-haul.   [Return]

57. Ibid.   [Return]

58. BTCE: For Australia, there is effectively no intermodal competition to international air travel. In 1993, only 0.3% of arrivals and departures were by ship. P.28-29   [Return]

60. Hamal differentiates between short-haul and long-haul international travel. Australia-U.K. is determined to be long-haul, Australia-Singapore is determined to be short-haul. We calculate distances between all country-pairs and determined that these country-pairs all satisfied the long-haul condition. We group all estimates into long-haul elasticities, as alternative modes of transportation are improbable.   [Return]

61. We calculate city-pair distances for the Australian domestic O-D pairs used. Based on these distances all four (4) of the routes studies are short/medium-haul domestic routes.   [Return]

- Table of Contents - Previous -