- Consulting with Canadians-

Air Travel Demand Elasticities: Concepts, Issues and Measurement: 2
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4. Evaluation of Elasticity Studies

Overall we have collected some 254 demand elasticity estimates from 21 studies. Each of these studies is described, using a standardized summary sheet as illustrated in Appendix A. To aid our understanding of how existing elasticity estimates might inform policy makers in forecasting air travel demand, we provide a descriptive meta-analysis of various distributions of estimated values in section 4.1. We next develop a weighted scoring table with respect to generally desirable data, design and output characteristics of the studies. This allows us to generate a rank ordering of the studies, from which to generate a sub-sample of estimates from studies with a 'passing grade' score. A passing grade is simply defined as 50 percent of the maximum score attainable. From these studies we provide suggested ranges of elasticity values in six key market segments:

1. Short-haul business travel
2. Short-haul leisure travel
3. Long-haul, domestic business travel
4. Long-haul, domestic leisure travel
5. Long-haul, international business travel
6. Long-haul, international leisure travel

Before we discuss our scoring system for the studies, we first present some more general descriptive information on the distribution of estimated elasticity values in various categories.

4.1 Descriptive Distributions of elasticity estimates

Here, we present for the aggregate and for several important sub-categories, histograms of the estimates in the studies we have researched. We begin with the most general distribution: the set of all the studies containing some 254 estimates of own-price elasticity. We next present sub-categories in increasing detail defined in terms of market characteristics. We also present sub-samples of the estimates based on data type (cross-section versus time-series) and the age of the study (less than five years old, versus between five and ten years old). In each case we report the median value as a measure of central tendency, along with the kurtosis and skewness of the distributions.^[19]

4.1.1. All studies^[20]

We generate a histogram for all own-price elasticities with 254 estimates taken from 21 studies.^[21] The minimum estimated elasticity value is –3.20.^[22] The histogram demonstrates a crowd of estimates between zero and –2.5. The median, or midpoint, of all estimates is -1.122. We use the median as the measure of central tendency, as opposed to the mean, in order to remove the effects of outliers in our data set. The skewness of the histogram is (-0.37). This indicates that our data is not normally distributed.

All Studies Own-Price Elasticities
5th percentile	-1.967
First quartile	-1.418
Median	-1.122
Third quartile	-0.633
95th percentile	-0.190
Interquartile range	0.785
Number of estimates	254
Minimum	-3.200
Maximum	0.040
Variance	0.312
Skewness	-0.370
Kurtosis	0.177

The histogram below provides a more detailed depiction of the crowd of elasticity estimates with each column representing a one-tenth (0.1) segment.

4.1.2. All long-haul studies^[23]

We subdivide the aggregate data into a subset of long-haul own-price elasticity estimates. The data set includes estimates for distances greater than 1500 miles, or estimates that are reported as 'long-haul' or 'international' in their respective study. The subset is comprised of 100 estimates with a median elasticity of –0.857. A majority of the values are bunched up between zero and –2 as indicated by the skewness of the histogram at –0.275.

All Long-haul Own-price Elasticity Estimates
5th percentile	-1.851
First quartile	-1.365
Median	-0.857
Third quartile	-0.495
95th percentile	-0.190
Interquartile range	0.870
Number of estimates	100
Minimum	-2.234
Maximum	-0.010
Variance	0.298
Skewness	-0.275
Kurtosis	-0.874

4.1.3. All Short/medium-haul studies^[24]

The data set for short/medium-haul own-price elasticity estimates includes estimates for distances less than 1500 miles, or estimates that are reported as 'short-haul', 'medium-haul', or 'regional' in their respective study. The subset is comprised of 109 estimates. Note that the sum of long-haul and short/medium-haul estimates (100+109) does not equal the number of estimates in the aggregate data set. This is a result of the exclusion of elasticity estimates that are not defined by their distance in their respective reports. The median elasticity in this subset is –1.15. A crowd of estimates is located between zero and –1.5. The minimum value (–3.20) represents a Sydney-Brisbane route taken from Milloy et al. (1985). The skewness of the histogram is –0.434.

All Short/Medium-haul Own-price Elasticity Estimates
5th percentile	-1.992
First quartile	-1.520
Median	-1.150
Third quartile	-0.712
95th percentile	-0.112
Interquartile range	0.808
Number of estimates	109.000
Minimum	-3.200
Maximum	0.040
Variance	0.329
Skewness	-0.434
Kurtosis	0.710

The histogram below provides a more detailed depiction of the crowd of elasticity estimates with each column representing a one-tenth (0.1) segment.

4.1.4. All long-haul international travel estimates^[25]

This sub-category of long-haul international travel is comprised of 69 estimates extracted from the aggregate data set. The data set represents estimates for country-to-country international travel taken from seven studies. The estimates are distributed between zero and –2.7, with some crowding below –0.5. The median elasticity is –0.79 and the distribution is somewhat skewed.

Long-haul International Travel Own-price Elasticities
5th percentile	-1.960
First quartile	-1.400
Median	-0.790
Third quartile	-0.349
95th percentile	-0.172
Interquartile range	1.051
Number of estimates	69.000
Minimum	-2.700
Maximum	-0.010
Variance	0.407
Skewness	-0.672
Kurtosis	-0.456

4.1.5. All long-haul domestic studies^[26]

This subset is comprised of 36 estimates extracted from six studies. The majority of the estimates are bunched between the maximum value (-0.44) and –2.3. The skewness is 0.168.

All Long-haul Domestic Travel Own-price Elasticities
5th percentile	-1.685
First quartile	-1.528
Median	-1.150
Third quartile	-0.828
95th percentile	-0.553
Interquartile range	0.700
Number of estimates	36.000
Minimum	-1.810
Maximum	-0.440
Variance	0.149
Skewness	0.168
Kurtosis	-1.078

4.1.6. All long-haul, international business travel estimates^[27]

The international business travel subset contains 16 estimates from two studies. A majority of the estimates (15) are calculated by the Bureau of Transport Communications and Economics (1995) for business travellers to and from Australia. The lowest estimate (-2.0) represents Australian business travellers to the U.K. The majority of the estimates are bunched between the maximum value (-0.01) and –0.6. The median elasticity estimate is –0.265. The histogram is negatively skewed (-2.405), which indicates a non-normal distribution.

All long-haul international business travel Own-price Elasticities
5th percentile	-1.423
First quartile	-0.475
Median	-0.265
Third quartile	-0.198
95th percentile	-0.093
Interquartile range	0.278
Number of estimates	16.000
Minimum	-2.000
Maximum	-0.010
Variance	0.251
Skewness	-2.405
Kurtosis	6.095

 

All long-haul international business travel Own-price Elasticities
5th percentile	-1.423
First quartile	-0.475
Median	-0.265
Third quartile	-0.198
95th percentile	-0.093
Interquartile range	0.278
Number of estimates	16.000
Minimum	-2.000
Maximum	-0.010
Variance	0.251
Skewness	-2.405
Kurtosis	6.095

4.1.7. All long-haul international leisure travel estimates^[28]

The long-haul leisure travel segment contains a total of 55 estimates, representing seven studies. Nearly 50 percent of the estimates (24) are taken from the Bureau of Transport Communications and Economics (1995) study. The median of the estimates is –0.993 with estimates distributed between -0.14 and –2.7. The minimum values (-2.7) are taken from Taplin (1980) and represent elasticity estimates calculated by Jud and Joseph (1974) (for travel from the U.S. to Latin America), and from Straszheim (1978) (for high discount travel). The skewness of the histogram is –0.555.

All Long-Haul International Leisure Own-price Elasticities
5th percentile	-2.070
First quartile	-1.650
Median	-0.993
Third quartile	-0.535
95th percentile	-0.220
Interquartile range	1.115
Number of estimates	55.000
Minimum	-2.700
Maximum	-0.140
Variance	0.423
Skewness	-0.555
Kurtosis	-0.393

4.1.8 All Long-haul Domestic Business Estimates^[29]

The long-haul domestic business travel subset is comprised of 26 estimates from two studies. The estimates are bunched up between the –0.5 and –1.6. The median of the histogram is –1.15. The skewness (0.270) indicates a non-normal distribution.

All Long-haul Domestic Business Own-price Elasticities
5th percentile	-1.670
First quartile	-1.428
Median	-1.150
Third quartile	-0.836
95th percentile	-0.780
Interquartile range	0.591
Number of estimates	26.000
Minimum	-1.700
Maximum	-0.543
Variance	0.113
Skewness	0.207
Kurtosis	-1.119

4.1.9 Long-haul Domestic Leisure Histogram^[30]

The long-haul domestic leisure travel subset is comprised of seven estimates from two studies. The estimates are distributed between –0.44 and –3.20. The median elasticity is –1.120.

All Long-Haul Domestic Leisure Own-price Elasticities
5th percentile	-2.744
First quartile	-1.472
Median	-1.120
Third quartile	-0.887
95th percentile	-0.514
Interquartile range	0.585
Number of estimates	7.000
Minimum	-3.200
Maximum	-0.440
Variance	0.821
Skewness	-1.640
Kurtosis	3.265

4.1.10. All short-haul business travel estimates^[31]

The short-haul business travel subset is comprised of 18 estimates taken from four studies. The median elasticity is –0.73. The histogram demonstrates some crowding of values between –0.5 and –0.8.

All Short-haul Business Travel Own-price Elasticities
5th percentile	-1.169
First quartile	-0.798
Median	-0.730
Third quartile	-0.608
95th percentile	-0.126
Interquartile range	0.190
Number of estimates	18.000
Minimum	-1.500
Maximum	-0.100
Variance	0.106
Skewness	-0.151
Kurtosis	1.509

4.1.11. All short-haul leisure travel estimates^[32]

This subset is comprised of 19 estimates from five studies. The median elasticity is –1.52 with estimates distributed across the range of values with little crowding. The histogram is positively skewed (0.158), which indicates that the number of estimates decrease as we approach zero.

All Short-haul Leisure Travel Own-price Elasticities
5th percentile	-2.307
First quartile	-1.745
Median	-1.520
Third quartile	-0.885
95th percentile	-0.688
Interquartile range	0.860
Number of estimates	19.000
Minimum	-2.370
Maximum	-0.400
Variance	0.307
Skewness	0.158
Kurtosis	-0.704

4.1.12 All Cross section study estimates^[33]

The subset of all cross-sectional studies is comprised of 85 estimates, of which 80 estimates are taken from Oum et al. (1986) and represent U.S. city-pair routes. All of the estimates are taken from studies between 1981 and 1986. The median elasticity is –1.33. The histogram is positively skewed (0.314).

All Cross-section Study Own-price Elasticities
5th percentile	-1.766
First quartile	-1.520
Median	-1.330
Third quartile	-0.810
95th percentile	-0.606
Interquartile range	0.710
Number of estimates	85.000
Minimum	-2.010
Maximum	-0.181
Variance	0.158
Skewness	0.314
Kurtosis	-0.563

4.1.13 All Time-series study estimates^[34]

This subset is comprised of 136 estimates, twenty-eight of which are taken from studies published within the last five years. The histogram is negatively skewed with a crowd of estimates between zero and –2. The median elasticity is –0.847.

All Time-series Study Estimates
5th percentile	-1.870
First quartile	-1.196
Median	-0.847
Third quartile	-0.470
95th percentile	-0.138
Interquartile range	0.726
Number of estimates	136.000
Minimum	-2.540
Maximum	0.040
Variance	0.313
Skewness	-0.542
Kurtosis	-0.227

4.1.14 Studies 5-10 years old^[35]

Two subsets have been created based on the age of the studies. The first subset is comprised of estimates calculated in studies published between 1992 and 1997. This subset contains 45 estimates from three studies.

Estimates for all studies 1992-1997
5th percentile	-1.972
First quartile	-1.160
Median	-0.560
Third quartile	-0.290
95th percentile	-0.124
Interquartile range	0.870
Number of estimates	45.000
Minimum	-2.300
Maximum	-0.010
Variance	0.369
Skewness	-0.907
Kurtosis	-0.152

The median elasticity is –0.56 with the majority of estimates residing between –0.1 and –1.1. The histogram is negatively skewed with a skewness of –0.907, which indicates a non-normal distribution. The second subset of estimates based on the age of the study is comprised of estimates calculated in studies published between 1997 and 2002.^[36] Four studies qualify for this subset resulting in 30 estimates. The histogram demonstrates no crowding around a small range of values. Instead, there is a wide distribution of values between zero and –2.3. The median elasticity is –0.847.

In comparing the median elasticity value for 1997-02 studies (-0.847) with the median elasticity for studies produced between 1992-97 (-0.56), it would appear that own-price elasticity of demand has become more price sensitive (elastic) over time. However, interpreting this change is not straightforward. The completion date of a study does not map directly to the age of the data employed. For example, the Nairn (1992) study utilizes data from 1983 and 1984, while the Hamal study (1998) uses time series data from 1974-1996. Furthermore, the comparison becomes less informative when we examine the range between first and third quartiles for each distribution. The range for 1997-02 studies is -0.5 to –1.4 while the range for 1992-97 studies is –0.3 to –1.2.

Own-price Elasticity Estimates for all studies 1997-2002
5th percentile	-1.978
First quartile	-1.368
Median	-0.847
Third quartile	-0.484
95th percentile	-0.084
Interquartile range	0.883
Number of estimates	30.000
Minimum	-2.234
Maximum	0.040
Variance	0.407
Skewness	-0.426
Kurtosis	-0.731

4.1.15 All income elasticities^[37]

The subset of all income elasticities contains 132 estimates from 14 studies. The minimum estimated elasticity value is –1.21, which represents inbound pleasure travel to Australia from the United States, as calculated by Hollander (1982). The maximum value is 11.58, which is calculated in the Bureau of Transport Communications and Economics (1995) report for leisure travel by Australian residents to Taiwan. The median estimate is 1.39. There is a crowd of estimates bunched up between 0.5 and 2.5.

All studies Income Elasticities
5th percentile	0.249
First quartile	0.840
Median	1.390
Third quartile	2.169
95th percentile	4.640
Interquartile range	1.329
Number of estimates	132.000
Minimum	-1.210
Maximum	11.580
Variance	2.506
Skewness	2.671

4.1.16 Summary

Table 4.1.16 summarizes the median values of estimated own-price elasticities by market segment and study characteristics (data type and age).

Table 4.1.16

Summary of median elasticity values by type
Category	Median Own-price Elasticity Value
All estimates	-1.122
All long haul estimates	-0.857
All long-haul international estimates	-0.790
All long-haul international business estimates	-0.265
All long-haul international leisure estimates	-0.993
All long-haul domestic estimates	-1.150
All long-haul domestic business estimates	-1.150
All long-haul domestic leisure estimates	-1.120
All short/medium haul estimates	-1.150
All short/medium haul business estimates	-0.730
All short/medium haul leisure estimates	-1.520
All cross-section study estimates	-1.330
All time-series study estimates	-0.847
All estimates from studies 1992-1997	-0.560
All estimates from studies 1997-2002	-0.847

The table indicates that there are significant differences between some market segment elasticities (long-haul international business and short-haul leisure in particular) and the median value for all estimates (-1.122). Time-series estimates indicate relatively less price sensitivity than those derived from cross-section studies. Moreover, recent studies have returned relatively elastic values compared with older studies.

4.2 Scoring the studies

To improve the level of confidence regarding the practical use of elasticity values in forecasting air travel demand, we developed a scoring system based on desirable input and output characteristics of empirical demand studies. Following on from our earlier discussion of theoretical and measurement issues, we have identified eleven characteristics that contribute to the quality of elasticity estimates. In each case, the point scores represent our assessment of the relative importance of either the inclusion or exclusion of the characteristic in question. We readily acknowledge that the subjective assignment of point scores cannot provide definitive scientific results. Nevertheless we feel that in the absence of time or resources for more sophisticated analysis (meta regression analysis, risk analysis and bootstrapping techniques for example), the scoring rule provides a useful rule of thumb for comparing the reliability of estimates we feel should be of higher quality with the overall set of estimates from all the studies surveyed.

Specifically, we have rated the studies based on the following characteristics:

i. Separation of business and leisure travel
ii. Separation of long-haul vs. short-haul travel
iii. Inclusion of an income coefficient
iv. Inclusion of intermodal substitution
v. Data type: panel vs. time-series vs. cross-section
vi. Country focus
vii. Route-specific estimates
viii. Hub vs. non-hub airports
ix. Connecting vs. O-D passengers
x. Age of the study
xi. Adjusted R-squared values

i. Separation of business and leisure travel

We expect business travel to be more price insensitive than leisure travel. Consequently studies that do not distinguish between these market segments are likely to provide elasticity estimates that would be biased if applied in any detailed analysis whether applied to specific business or leisure market segments, or to routes, which are predominantly business or leisure oriented.

Scoring rule: Estimates for both business and leisure = 3 points Estimates for either business or leisure = 2 points No separation of business and leisure = 0 points

ii. Separation of Long-haul vs. short-haul travel

We expect less price sensitivity for long-haul flights than for short-haul flights (where more inter-modal substitution is possible). In a similar fashion to the business/leisure distinction, studies that do not distinguish market segments by flight length will provide elasticities that underestimate price sensitivity for short-haul flights and over-estimate it for long-haul flights.

Scoring rule: Estimates for both long and short-haul = 3 points Estimates for either long or short-haul = 2 points No separation of long and short-haul = 0 points

iii. Inclusion of an income coefficient

Without an income coefficient, demand studies will confuse a shift of the demand curve with movements along the demand curve. With a positive income elasticity for air travel, and increasing per-capita real income, demand studies with no income coefficient will overestimate the absolute price elasticity of demand for price decreases and underestimate it for price increases.

Scoring rule: Income coefficient = 2 points No income coefficient = 0 points

iv. Inclusion of intermodal substitution (for short-haul flights)

The shorter the distance comprising a trip, the more road and trail transportation become effective substitutes for air travel. Therefore we would expect the price and other characteristics of alternative modes to have a more significant (shift) impact on the demand for short-haul air travel, ceteris paribus. Studies of short-haul flights that do not include intermodal effects are likely to provide bias estimates if the shadow prices of alternative modes change. The scoring rule in this case attempts to award short-haul studies that incorporate intermodal effects, without penalizing studies of longer-haul air travel.

Scoring rule: Intermodal substitution in short-haul study = 2 points Not a short-haul study = 1 point No intermodal substitution in short-haul study = 0 points

v. Data: panel vs. time-series vs. cross-section

Policy analysis should not be guided by immediate or short-term reactions to prices that result from policy changes. Consequently, policies that impact air travel demand should rely more on long-term elasticity measures. While panel studies are ideal as they capture cross-section and time-series effects, studies from time-series data that are sufficiently long in duration will also capture longer-term elasticities.

Scoring rule: Use of panel data or time-series = 2 points Use of cross–section data = 0 points

vi. Country focus

There are likely to be many structural details of price sensitivity that relate to the specific national context of the airline industry, including the degree of competition, the size of the market and the regulatory environment. The impact of policies on air travel prices in Canada can be more readily related to some countries more than others. The close geographical proximity of international hubs and agreements within the EU make European studies somewhat less relevant to the Canadian context. US studies are more relevant given the geographic proximity of the US to Canada and the number of US cities to which Canadians travel. Australia on the other hand, provides reasonably comparable demographic, urban, geographical, governmental and regulatory structures.

Scoring rule: Study relates directly to Canada = 2 points Study relates to similar foreign country (US or Australia) = 1 point Study relates to non-similar foreign country = 0 points

vii. Route-specific estimates

Studies that aggregate the effects of price changes on multiple routes will not capture the effects of market competition in which certain airlines enjoy significant market power on some routes but not others. A well-known example of this in the US is the effects of low-cost competition by Southwest Airlines on routes flown by full-service carriers. A related issue is that studies, which focus on multiple short-haul routes run the danger of aggregating effects of routes that are predominantly used by business travellers with routes that are more leisure-oriented. This latter category often constitutes a significant portion of business for low-cost carriers, who offer cheap short-haul flights in competition with alternative leisure activities and entertainment. An example of this is the market for special event parties in Dublin (wedding stags for example) that was created by flights offered by RyanAir from various locations in the UK.

Scoring rule: Study provides route or airline-specific estimates = 1 point Study does not provide route or airline-specific estimates = 0 points

viii. Hub vs. non-hub airports

Studies that do not separate out hub from connecting airports will not be able to distinguish "hub premium" effects. Passengers with an itinerary that utilizes a hub airport may be willing to pay a "hub premium" for the integrated service that hubs provide, including sequenced flight segments that minimize waiting time, and baggage that is checked through to the final destination. The existence of a hub premium effect is supported by research in the US.

Scoring rule: Study identifies hub airports = 1 point Study does not identify hub airports = 0 points

ix. Connecting vs. O-D passengers

There is a difference between an itinerary and the measurement of traffic volumes between city pairs. If a passenger is travelling from Moncton to Vancouver via Toronto, then their willingness-to-pay and their price sensitivity relates to the trip from Moncton to Vancouver. However, such a passenger could be included in the data that is measuring price sensitivity on the city pair Toronto-Vancouver.

Scoring rule: Study identifies connecting vs. O-D passengers = 1 point Study does not distinguish connecting vs. O-D passengers = 0 points

x. Age of the study

The airline industry is a dynamic and changing industry, in the evolution of business models (full-service versus low cost carriers for example), infrastructure (airport business practices) and government regulation. Studies conducted in the US prior to 1978 would not incorporate the effects of deregulation. A similar argument applies to studies that predate 1984 in Canada. Further the National Airport Policy in Canada has led to a gradual devolution of airports from Transport Canada to independent local airport authorities throughout the 1990's. This devolution has led to important infrastructure and pricing decisions. Only the most recent studies would capture system-wide effects of this evolution as some local airport authorities have only come into being in the last year or two.

Scoring rule: Studies completed during 1997-2002 = 3 points Studies completed during 1990-1997 = 2 points Studies completed prior to 1990 = 1 point

xi. Adjusted R-squared coefficient values

This last item addresses the quality of output in the studies rather than the quality of inputs. In regression results, a low R-squared value indicates that only a small portion of variation in the dependent variable (O-D passengers), is explained by the independent variables. The adjusted R-squared value is a weighted measure that penalizes the addition of a large number of independent variables with low explanatory power.

Scoring rule: Adjusted R-squared value over 0.8 = 3 points Adjusted R-squared value between 0.6 and 0.8 = 1 point Adjusted R-squared value lower than 0.6 = 0 points

The highest possible score under the criteria we have developed is 23 points. Table 4.2.1 below summarizes the scores of each study, from which we have generated histograms in six sub-categories using only those studies with a 'passing grade' of 12 points or higher. The categories provide separation of long and short-haul, international and domestic travel and business and leisure travel. Note that the column headings in the table refer to the numbered characteristics discussed above.

Table 4.2.1 Summary of Study Scores (cont'd)

4.2.1 Short-haul business travel^[38]

This subset is comprised of 16 estimates taken from three studies, the most recent of which is Battersby-Oczkowski (2001). The median elasticity for the data set is –0.70.

Short-haul Business Travel Own-price Elasticities:  Studies Scoring ³ 12 Points
5th percentile	-1.103
First quartile	-0.783
Median	-0.700
Third quartile	-0.595
95th percentile	-0.123
Interquartile range	0.188
Number of estimates	16.000
Minimum	-1.110
Maximum	-0.100
Variance	0.072
Skewness	0.697
Kurtosis	1.396

4.2.2 Short-haul leisure travel^[39]

Three studies scoring 12 or more points in our scoring system generate 16 estimates of short-haul leisure travel. The estimates are distributed between a range of –0.4 and –2.37. The minimum value is taken from the Bureau of Transport Economics (1986) and represents winter vacation travel in Australia. The median estimate for all values is –1.520.

Short-haul Leisure Travel Own-price Elasticities: Studies Scoring ³ 12 Points
5th percentile	-2.100
First quartile	-1.743
Median	-1.520
Third quartile	-1.288
95th percentile	-0.640
Interquartile range	0.455
Number of estimates	16.000
Minimum	-2.370
Maximum	-0.400
Variance	0.278
Skewness	0.485
Kurtosis	-0.116

4.2.3 Long-haul international business travel^[40]

The subset of international business travel estimates provides 16 estimates taken from two studies. The median elasticity is –0.265, which is the same value derived prior to applying the scoring model to the aggregate data set. This occurred because both data sets are comprised of the same estimates.

Long-haul International Business Travel Own-price elasticities Studies scoring ³ 12 points
5th percentile	-1.423
First quartile	-0.475
Median	-0.265
Third quartile	-0.198
95th percentile	-0.093
Interquartile range	0.278
Number of estimates	16.000
Minimum	-2.000
Maximum	-0.010
Variance	0.251
Skewness	-2.405
Kurtosis	6.095

4.2.4 Long-haul international leisure travel^[41]

There are 49 international leisure travel price elasticity estimates from six studies with at least 12 points in our scoring system. A majority of the estimates (31) are taken from studies published after 1995. The median elasticity is –1.040 with a large proportion of the estimates bunched up between –0.14 and –1.

Long-haul International Leisure Travel Own-price elasticities Studies scoring ³ 12 points
5th percentile	-2.140
First quartile	-1.700
Median	-1.040
Third quartile	-0.560
95th percentile	-0.254
Interquartile range	1.140
Number of estimates	49.000
Minimum	-2.700
Maximum	-0.140
Variance	0.420
Skewness	-0.465
Kurtosis	-0.474

4.2.5 Long-haul domestic business travel^[42]

The domestic long-haul business travel subset consists of 26 estimates from two studies. The median elasticity is –1.15. The estimates are taken from Lubulwa (1986) and Oum et al. (1986).

Long-Haul Domestic Business Travel Own-price Elasticities Studies scoring ³ 12 points
5th percentile	-1.670
First quartile	-1.428
Median	-1.150
Third quartile	-0.836
95th percentile	-0.780
Interquartile range	0.591
Number of estimates	26.000
Minimum	-1.700
Maximum	-0.543
Variance	0.113
Skewness	0.207
Kurtosis	-1.119

4.2.6 Long-haul domestic leisure travel^[43]

There are six long-haul domestic leisure travel price elasticity estimates taken from two studies. The median elasticity is –1.104. The histogram demonstrates no crowding around a range of values.

Long-haul Domestic Leisure Travel Own-price Elasticities Studies scoring ³ 12 points
5th percentile	-1.576
First quartile	-1.228
Median	-1.104
Third quartile	-0.787
95th percentile	-0.502
Interquartile range	0.441
Number of estimates	6.000
Minimum	-1.680
Maximum	-0.440
Variance	0.191
Skewness	0.015
Kurtosis	-0.171

4.2.7 Income elasticities^[44]

A subset of 103 income elasticity estimates is generated from the 'passing grade' studies. In similar fashion to the histogram for all studies, a crowding of estimates around the values of 0.5 to 2.5. The median value of the subset is 1.14.

Income Elasticities for all Studies Scoring ³ 12 points
5th percentile	0.242
First quartile	0.807
Median	1.140
Third quartile	2.089
95th percentile	4.636
Interquartile range	1.282
Number of estimates	103.000
Minimum	-1.039
Maximum	11.580
Variance	2.642
Skewness	3.051
Kurtosis	14.139

4.2.8 Studies that account for Intermodal effects^[45]

The data set for studies that include the effects of intermodal competition (e.g. auto, rail, bus, ship) is comprised of 104 own-price elasticity estimates taken from thirteen studies (including short, medium and long haul routes). The histogram does not demonstrate any bunching around a set of values as supported by the skewness (-0.641). The median elasticity value is –1.113.

Own-price Elasticities: Studies with Intermodal Effects
5th percentile	-2.085
First quartile	-1.290
Median	-1.113
Third quartile	-0.588
95th percentile	-0.138
Interquartile range	0.703
Number of estimates	104.000
Minimum	-3.200
Maximum	0.040
Variance	0.389
Skewness	-0.641
Kurtosis	0.614

A subset of estimates is extracted that includes estimates for short/medium-haul elasticities.^[46] The data set is comprised of 34 estimates taken from four studies including those elasticities calculated by Battersby-Ozckowski (2001). The median estimate

(–0.720) is lower than the median elasticity calculated for all intermodal studies (-1.113). However, some details of these studies make the interpretation of this result difficult.

First, the subset includes discount, economy, and business fare-class estimates. The elasticities reflect both the nature of travel on the routes (business or leisure) and the fare class. For example, the Sydney-Melbourne route is a significant business route in Australia with relatively low elasticity estimates (Discount= –0.07, Economy= -0.81, Business = -0.1). The dataset contains several estimates from routes that are historically business travel city-pairs.

Secondly, thirteen of the estimates are from city-pair routes with distances of approximately 870 to 1000 km (the high end of the short-haul distance condition). Only five out of 34 estimates are explicitly defined as short-haul routes of less than 750 km. These elasticity estimates are: Melbourne-Adelaide (-0.46); Australia short-haul <500 km (-0.728); New South Wales, Australia, routes <200km (-2.54); Short-haul Western and Mid-western, U.S., routes <500 miles (-0.08); Short-haul Eastern city-pairs, U.S. <500 miles (-0.36). Two of the estimates (Short-haul Eastern U.S., and Western and Mid-Western U.S.) are likely capturing business travel.

Lastly, 28 of the 34 estimates are taken from studies comprised of Australian city-pair routes. These studies do not provide sufficient information about the city-pair characteristics, such as whether or not a specific route has one or more (or possibly no) competing transportation modes. If the use of a competing mode is infeasible or highly unlikely then the elasticity estimate is not capturing intermodal effects.

Short/Medium-haul Own-price Elasticities:  Studies with Intermodal Effects
5th percentile	-2.176
First quartile	-1.108
Median	-0.720
Third quartile	-0.415
95th percentile	-0.077
Interquartile range	0.693
Number of estimates	34.000
Minimum	-3.200
Maximum	0.040
Variance	0.508
Skewness	-1.524
Kurtosis	2.925

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