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Direct air transport and demand interaction: A vector error-correction model approach
Journal of Air Transport Management 28 (2013) 14e19
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Direct air transport and demand interaction: A vector error-correction model approach Tay T.R. Koo a, *, David T. Tan b, David Timothy Duval c a
School of Aviation, University of New South Wales, Sydney, New South Wales 2052, Australia Research School of Finance, Actuarial Studies, and Applied Statistics, Australian National University, School of Aviation, University of New South Wales, Sydney, New South Wales 2052, Australia c Faculty of Business and Economics, University of Winnipeg, School of Business, University of Otago, School of Aviation, University of New South Wales, Sydney, New South Wales 2052, Australia b
a b s t r a c t Keywords: Market equilibrium Vector error-correction Air transport capacity
The goal of this paper is to impose a causeeeffect structure into the relation between tourism demand and air transport capacity. Specifically, we apply a vector error-correction model to assess if, and to what extent, capacity or passenger demand are first-movers that return to long-run equilibrium following short-run deviations. Using data on international aviation between Australia and our test cases of China and Japan, we find that demand on the JapaneAustralia market corrects for short-run deviations from the long-run equilibrium quicker than the ChinaeAustralia market. Reasons for such variation in adjustment speeds are discussed and we show that the results are robust to the phenomenon of airlines pre-empting demand when setting capacity. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Air transport dynamics are carefully monitored by governments, destination marketing organizations and the local tourism industry for strategic planning purposes. Information about how passenger demand interacts with available capacity can help destinations target advertising and promotional efforts. The same information can assist airlines with forecasting capacity needs across a network. Air transport and tourism are inter-related - the strong association between the levels of tourism demand and air travel demand are especially noticeable in medium to long-haul international travel. On direct routes, incoming passenger numbers can be a useful proxy for inbound tourists when the balance of bilateral flows is skewed towards one direction (as is the case in our test cases). Tourists are commonly defined as overnight visitors travelling for any main purpose other than to be employed by a resident entity in the country or place visited (United Nations World Tourism Organization, 2008); thus, for an end-point region such as Australia, most non-resident persons entering Australia are tourists. Although we are using incoming passenger data, this paper is also relevant to the study of tourists and tourism. Determinants of tourism demand have been studied extensively (see Crouch, 1995; Sinclair, 1998; Lim and McAleer, 2001). Empirical
* Corresponding author. E-mail address: [email protected] (T.T.R. Koo). 0969-6997/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jairtraman.2012.12.005
analyses focus commonly on price and income, but other variables, including relative prices of destinations and transport costs (usually airfares) and the availability of substitute modes of travel (Brons et al., 2002), as well as the temporary or permanent nature of exogenous shocks (Njegovan, 2006; Narayan, 2008), have been considered. Clearly, air transport can influence tourism demand through a number of channels, with price being one of these factors. In the context of international air transport liberalization, it has been shown that, in addition to safety and airline efficiency, the adequacy and consistency of air transport capacity were important air transport attributes from a tourism viewpoint (Shaw, 1982). Air transport capacity is considered as one of the key factors in tourism forecasting, along with other key determinants of tourism demand such as income and significant events (Tourism Forecasting Committee, 2010). Despite this, there exists a gap in understanding the relation between air transport capacity and demand such that the causal nature of these variables is rarely examined. The goal of this paper is to impose a causeeeffect structure into the relation between air transport capacity and demand with emphasis on tourism.
2. Background and objectives In mapping the causality between capacity and demand, two points need to be considered. First, the relation between air transport capacity and demand can be viewed as an interaction
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where the projected demand influences capacity, and capacity influences actual demand. With respect to the former, airlines adjust capacity based on demand forecasts using computer algorithms to process large volumes of itinerary choice information (Garrow, 2010), as well as smoothing techniques and simple growth rate projections (Doganis, 2006). Their forecast accuracy can be reasonable given forward booking data, past observations and knowledge of seasonal fluctuations in the demand for tourism e including tourism infrastructure capacity of destinations (e.g. accommodation capacity) e and scheduled fixtures and events: as for the latter airlines use revenue management to manage seat factors (Belobaba and Wilson, 1997). If it is in the interest of an airline’s profitability, given excess capacity, sales can be influenced directly through pricing on airline websites or indirectly through channels such as on-line and off-line travel agents. Second, and in the short-run (i.e., less than a year), capacity is usually fixed on a given route for a variety of reasons, including contractual obligations for a scheduling season(s), although there may be incidences of spot additions/cancellations. However, as the time horizon increases, we can expect capacity to adjust to meet changing market conditions. In this situation both passenger numbers and capacity will adjust towards a postulated long-run equilibrium. In other words, the tourism demand e aviation capacity interaction can be dissected along a temporal dimension that is characterized through short-run and long-run horizons. With these points in mind, we apply a vector error-correction model (VECM) to help assess whether, and to what extent, capacity or passenger demand are first-movers that return to long-run equilibrium following an event that punctuates the established equilibrium. In doing so, the paper shows the air transport capacity e tourism demand interaction within the VECM framework. We also highlight the differences in the speed at which short-run deviation is returned to long-run equilibrium in different markets, and discuss why differences in adjustment speed may occur. The article concludes by discussing the relevance of the findings with particular reference to international aviation between Australia and our test cases of China and Japan. 3. The Australian inbound tourism market Nearly all inbound visitors to Australia arrive by air. In 2009, there were over 16 million direct (and connect-direct) seats into Australia of which more than 20 percent were from NZ (Australia’s closest trading partner). New Zealand and Singapore combined account for nearly 35 percent of capacity (Table 1). Japan and China are significant tourism-generating markets for Australia. While each rank high in terms of visitor arrival numbers (Tourism Research Australia, 2010), they do not have the corresponding rankings for inbound direct capacity. This is a function of airlines originating from each country and exercising Fifth and Sixth Freedom rights to Australia by utilizing major regional hubs in Malaysia, Singapore, Hong Kong, Thailand, UAE and Qatar. Nonetheless, the three countries account for 7% of total inbound direct capacity and this is expected to grow as China becomes one of Australia’s most significant markets for inbound tourism. The aviation environments in ChinaeAustralia and JapaneAustralia routes are comparable in a number of ways, including market size, similar medium-haul routings with similar equipment needs, and the presence of capacity restrictions within bilateral air service agreements. Travel agents assume significant roles for outbound tourism in both countries compared to direct internet (and independent) sales, which are more popular in Western counterparts. The key difference, however, is the development stage of each market and the rate of growth. China is a rapidly emerging market, whereas Japan is a relatively stagnating
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Table 1 Direct capacity to Australia. Country New Zealand Singapore UAE and Qatar USA Hong Kong Malaysia Indonesia UK Thailand Japan Pacific Islands China Korea South Africa Taiwan Latin America Vietnam Philippines Germany Canada India France Total
2009 Direct and connect capacity 3 2 1 1 1
576 151 295 278 161 966 816 815 769 621 611 456 315 195 180 180 172 151 144 93 42 24 16 021
105 926 675 196 518 746 435 581 067 833 632 895 357 088 585 578 504 342 377 066 238 388 132
Source: Department of Transport and Infrastructure (2010).
market. This provides a unique test environment for modelling because of the contrasting tourism growth trajectories and different position in which Australia is located along the destination lifecycle (Table 2). Fig. 1 displays the passenger demand (PAX) and seat capacity (SEATS) series for the Australia-China routes between January 1999 and June 2010. Note the general upwards trend in both series over the sample. Over the last 10 years, travel between China and Australia has increased by over 250 percent. The passenger demand (PAX) and seat capacity (SEATS) for the Australia-Japan routes are presented in Fig. 2. Note that PAX exhibits significant seasonality across time; this characteristic is less present in the SEATS series. We can observe that the overall demand and seat capacity for travel between Australia and Japan has steadily declined over the recent 5 years. 4. Empirical methodology and data Air transport capacity (commonly measured in seat numbers) and demand (measured in paid passenger numbers) often exhibits a linearepositive relation. The co-movements of passenger numbers and seat capacity are commonly observed across many air Table 2 Characteristics of China and Japan markets in relation to Australia. China First air service agreement (ASA) in 1984; most recent ASA in 2004, amended last in March 2011 (Bureau of Infrastructure and Transport, 2009); Strong growth over the last decade; Australia received Approved Destination Status (ADS) in April 1999 e first Western country to be approved, allowing self-funded group leisure travel visas. The ADS coverage was broadened to other regions in China in 2003; Allowable capacity of 16,118 seats each way per week to/from Sydney, Melbourne, Brisbane and Perth, and unlimited capacity to all other points in Australia (February 2012). Japan Flat and stagnating capacity levels, especially since 2009 with JAL withdrawing from main routes; Unlimited capacity between any points in Australia and Japan except some restrictions to Narita and Haneda airports; Substantial reduction by JAL in recent years; a net reduction by Qantas but net increase by Jetstar; Currently, substantial Jetstar (a long-haul low-cost) presence.
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Fig. 1. Passenger demand and seat capacity on AustraliaeChina routes. Source: Department of Transport and Infrastructure.
transport routes, as well as many other time series such as inventories and sales. If these series are non-stationary and exhibit patterns of co-movement, the series may be co-integrated in that there may be an equilibrium and interdependence between the two such that the linear combination of the two series (or more) are stationary (Hamilton, 1994; Engle and Granger, 1987). Co-integrated series are common in business, economics and finance; for instance, inventories to sales ratio often appear I(0), which is a result of firms adjusting inventories to sales (Diebold, 2004). Air transport data often have these features; demand and supply information often move together, while preserving the ratio of the two at a constant mean, i.e., roughly constant seat factor, which is I(0). In air transport context, however, services are perishable and the ability of airlines to adjust capacity in the short-run is marred by predetermined schedules and fixed costs. In other words, seats cannot adjust to sales in the short-run as inventories do when there are fluctuations in sales. While capacity can adjust in
the long-run towards a long-run equilibrium, in the short-run we can expect demand to adjust to capacity. As the series in our studies appear to be co-integrated, we thus postulate the following application of a vector error-correction model:
Ptþ1 þ a ¼ Fttþ1 þ εtþ1
(1)
where Ptþ1 is the passenger demand in t þ 1, a is a constant to account for the fact that seat factor is always less than one, Fttþ1 is airline’s forecast capacity (based on demand forecasts) for t þ 1 in time t. εtþ1 is a shock term realized in t þ 1, however, at time t, the best information we have about εtþ1 is that it is a random variable with a mean of zero. In an event where the equilibrium is disturbed (which we term punctuated equilibrium), the following inequality will occur because εtþ1 is non-zero.
Fig. 2. Passenger demand and seat capacity on AustraliaeJapan routes. Source: Department of Transport and Infrastructure.
T.T.R. Koo et al. / Journal of Air Transport Management 28 (2013) 14e19
Ptþ1 þ asFttþ1
(2)
In the above case, the following simultaneous equations can provide us with information on which variable/s (passenger demand or airline capacity) move to restore the equilibrium in the subsequent period and to what extent.
i h tþ2 Ftþ1 ¼ ðPtþ1 þ aÞ Fttþ1 bf þ εf 1;tþ2
(3)
h i Ptþ2 þ a ¼ ðPtþ1 þ aÞ Fttþ1 bp þ εp1;tþ2
(4)
If bf (bp) is found to be statistically equal to zero then none of the short-run deviation will be closed by the airline (passenger demand) in t þ 1. From our discussion above, we expect bf to be close to zero at least for a given scheduling season (approximately six to seven months). Since airlines have a degree of control over the short-run seat-factor through revenue management, we expect bp to be positive and responsive to short-run punctuated events. Data have been obtained from Department of Transport and Infrastructure. We use passenger data (PAX series) representing all inbound travel to Australia, which we take as a measure of effective demand embodying both the willingness and the ability to purchase. While this data captures returning Australian residents, which is outside our interest, we see it nonetheless as a reasonable measure of incoming tourist flow because: (1) Most international tourists to Australia are air arrivals; (2) UNWTO (2008) defines visitor as a traveller “taking a trip to a main destination outside his/her usual environment, for less than a year, for any main purpose (business, leisure or other personal purpose) other than to be employed by a resident entity in the country or place visited. These trips taken by visitors qualify as tourism trips” (UNWTO, 2008, p.10). UNWTO (2008) defines an overnight visitor as a tourist. Thus, with the exception of a very small number of passengers passing through (transit passengers e according to data from Australian government (Dept. Resources, Energy and Tourism (DRET)) transit passengers are approximately 0.25% and 0.2% of total inbound traffic from Japan and China, respectively), all non-resident arrivals into Australia are tourists; (3) Based on DRET data, averaged between the best data available at the time of writing (2004e2009 data), for every Australian outbound (to Japan and China) there is five Japanese inbound (to Australia) and three Chinese inbound (to Australia). Thus, tourist influxes are the dominant flows. Indeed, the pattern of PAX and SEATS follow the pattern governed by the dominant flows, which are the rapidly declining (increasing) Japan (China)-Australia SEATS and PAX as shown by Figs. 1 and 2. Airlines will be most concerned about the dominant visitor segment and their behaviour. (4) In addition to the dominance of inbound flows, inbound capacityedemand dynamics explain the majority of variation in our proxy: foreign visitors are able to exercise greater discretion over their decision to travel to Australia than Australian travellers returning home when there is a shock1 to the longrun air transport capacity and demand relation.
1 A short-run shock to the airline capacity and demand relation, as is shown in Section 5.3, is almost completely corrected for by the end of the subsequent month. Unless a significant number of potential Australian passengers are able to alter their demand for outbound travel (due to a shock in the capacityedemand relation) and return to Australia within the month, any significant changes in inbound traffic within the subsequent month of a shock is likely to be that of foreign travellers.
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With the absence of readily accessible data on exact Japanese and Chinese arrival shares on a given aviation route, we believe the above reasons warrant the adequacy of the chosen proxy. Capacity between two nations is measured by the number of seats available. This is based on the monthly time series between Jan 1999 and Dec 2009. Data prior to 1999 were not suitable to be included in the analyses due to shocks such as the Financial Crisis in 1997 and significant changes in policy (e.g., the Chinese government’s policy on outbound travel to Australia). 5. Results 5.1. Diagnostics We begin with tests for stationary characteristics in the capacity (SEATS) and passenger demand (PAX) series on the premise that violation of the assumption of stationarity may lead to spurious results. Indeed, previous studies have documented that tourism demand exhibits characteristics of a unit-root (Lim and McAleer, 2001). Similar evidence exists in the air travel demand context (Marazzo et al., 2010). Applying the PhillipsePerron test for a unitroot confirms its presence in both the SEATS and PAX series given MacKinnon p values of 0.383 and 0.431, respectively, for China. Similarly, there is evidence supporting a unit-root on Japane Australia PAX and SEATS.2 An alternative specification of the PhillipsePerron test including a time trend yields similar conclusions. Moreover, tests conducted on the first-differenced SEATS and PAX series are stationary at the 1 percent level for China and Japan. This suggests that SEATS and PAX are integrated of order 1, i.e. I(0). Given the two time series are integrated of order 1, the results of the Johansen test for co-integration on the natural logarithm of SEATS and PAX indicate that there exists one co-integrating relation between SEATS and PAX, thus implying that SEATS and PAX comove and that there exists a long-term equilibrium between these series. A co-integrated relation suggests that not only does a long-term relation between SEATS and PAX exist, but short-term deviations from this equilibrium are subsequently corrected for, confirming the potential suitability for a vector error-correction modelling framework. 5.2. Vector error correction estimation The vector correction model (VECM) is estimated for the SEATS and PAX time series. Such a model does not apply any economic theory on the underlying relation as both the supply and demand for airline seats are known to be endogenously determined (e.g., Jorge-Calderón, 1997). The VEC specification is rather a vector autoregressive (VAR) specification augmented with an error-correction term highlighting the nature of convergence in short-run deviations in the co-integrated relation. Table 3 contains the results for the VECM of the SEATS and PAX relation using a lag structure of up to 13 months in the underlying VAR specification. The lag length is selected by opting for the lag structure that minimises the SBIC and HQIC3 (Lutkepohl, 2005). This suggests the SEATS and PAX series exhibit some degree of partial correlation up to a period of 13 months. Alternate lag
2 Due to the significant seasonality displayed by the AustraliaeJapan PAX series, it has been de-seasonalized using monthly dummy variables. Other series in the analysis were also similarly de-seasonalized as a test for robustness, and the results remain consistent. 3 An alternative lag specification of up to 6 months resulted in similar conclusions.
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Table 3 Vector error correction models for SEATS & PAX. China Y ¼ seats
a L1.Seats L2.Seats L3.Seats L4.Seats L5.Seats L6.Seats L7.Seats L8.Seats L9.Seats L10.Seats L11.Seats L12.Seats L1.Pax L2.Pax L3.Pax L4.Pax L5.Pax L6.Pax L7.Pax L8.Pax L9.Pax L10.Pax L11.Pax L12.Pax Trend Const Observations AIC HQIC SBIC
0.0755 0.1859 0.0553 0.2091 0.2753 0.1976 0.2204 0.1815 0.1304 0.1774 0.1236 0.1726 0.2913 0.1745 0.058 0.1693 0.125 0.1737 0.0963 0.1638 0.2772 0.1588 0.1557 0.1395 0.0812 0.1333 0.1264 0.1537 0.1271 0.1438 0.0965 0.1335 0.0154 0.131 0.0233 0.1296 0.0899 0.1296 0.1811 0.127 0.0096 0.1282 0.1037 0.1185 0.1916 0.1155 0.1372 0.0962 0.1403 0.0871 0.0001 0.0002 0.0248 0.0191 125 3.6463 3.6243 2.4018
Japan Y ¼ Pax 0.8273*** 0.2564 0.3509 0.2883 0.2496 0.2725 0.3757 0.2503 0.3176 0.2445 0.5116** 0.238 0.6112** 0.2406 0.6617*** 0.2335 0.4838** 0.2395 0.5915*** 0.2258 0.3648 0.2189 0.182 0.1923 0.5516*** 0.1838 0.1958 0.212 0.0302 0.1982 0.1068 0.184 0.1393 0.1807 0.0047 0.1786 0.2079 0.1787 0.1507 0.1752 0.1718 0.1768 0.1486 0.1634 0.1718 0.1593 0.1166 0.1326 0.5368*** 0.1201 0.0000 0.0003 0.0024 0.0263
Y ¼ seats 0.0705 0.2082 0.122 0.1988 0.1642 0.1855 0.1739 0.1734 0.0201 0.1628 0.2439 0.1526 0.2801** 0.1328 0.0172 0.1212 0.01072 0.1102 0.1464 0.1032 0.0954 0.1015 0.4045*** 0.1008 0.4616*** 0.1079 0.1585 0.2726 0.1358 0.2554 0.0406 0.2383 0.1803 0.2266 0.1458 0.2112 0.1662 0.185 0.1684 0.162 0.0645 0.1391 0.1329 0.125 0.0725 0.1102 0.074 0.0936 0.0039 0.0845 0.0002 0.0001 0.0091 0.009 125 6.2188 5.7132 4.9743
Y ¼ Pax 1.1924*** 0.2886 0.457 0.2755 0.5995** 0.2571 0.6223*** 0.2403 0.5023** 0.2256 0.4492** 0.2115 0.22 0.1841 0.1081 0.168 0.2235 0.1527 0.1949 0.143 0.0413 0.1407 0.0065 0.1397 0.2684 0.1495 1.0957*** 0.3778 0.9461*** 0.354 0.9112*** 0.3303 0.9308*** 0.3141 0.6769** 0.2928 0.5489** 0.2564 0.3471 0.2245 0.4141** 0.1928 0.3827** 0.1733 0.2203 0.1528 0.0011 0.1297 0.1265 0.1171 0.0000 0.0002 0.0015 0.0125
Seats is the natural logarithm of the number of inbound seats made available across all airlines travelling from China/Japan to Australia in a given month. Pax is the natural logarithm of the number of incoming revenue passengers travelling from China/Japan to Australia in a given month. a is the coefficient of the co-integrating equation, otherwise referred to as the error-correction term. Trend is the quadratic time trend and const is the intercept. L is the one period lag operator. The Johansen normalisation procedure is imposed such that the coefficient of Seats in the cointegrating equation equal to 1. AIC is the Akaike information criterion, HQIC is the HannaeQuinn information criterion and SBIC is the Schwarz-Bayesian information criterion. ** and *** denote significance at the 5 and 1 percent level, respectively.
specifications yield similar results. The VECM specification incorporates a quadratic time trend in the natural logarithm of the SEATS and PAX relation.4 As expected, the error-correction term for SEATS is statistically insignificant, suggesting that airline seat capacity does not adjust to short-term deviations in the SEATS-PAX relation in both markets. However, over longer periods of one year or more, both SEATS and PAX granger cause (Granger, 1969) one another in the Chinae Australia market. That is, SEATS is no longer a fixed-scheduled variable and adjusts to longer-term trends in passenger demand. In the JapaneAustralia market, SEATS granger causes PAX and not vice versa, suggesting that SEATS is less responsive to PAX even in longer time horizons. It can be observed that the error-correction term for the PAX series is statistically significant at the one percent level for China (0.7616) and Japan (1.1924). In the ChinaeAustralia aerotourism market, passenger numbers will close 76 percent of the deviation in the co-integrating relation in the subsequent month. Passenger numbers will close 119 percent of the deviation in Japane Australia.5 In other words, the speed at which PAX corrects for short-run deviation from long-run equilibrium is faster in the JapaneAustralia than the ChinaeAustralia market. Japanese demand to Australia may be more responsive to route capacity compared to Chinese demand because Japan is more affluent with more outbound choices. Australia was the first country to attain Approved Destination Status (ADS) by the Chinese authority. Even so, at the onset of the ADS, outbound travel was limited to certain regions of China, although this was eventually relaxed over time. Greater destination choice alternatives suggest greater substitution possibilities, increasing the elasticity with respect to a particular destination. This effect could be compounded by the fact that the JapaneAustralia market has strong low-cost airline presence. Jetstar International has been gradually increasing capacity on the route from a daily service to begin with (March/April 2007) to a thrice daily service (April 2009). During the same period, Qantas decreased frequencies from four to two, while Japan-based carriers’ frequencies remained relatively unchanged. Although there has been some equipment switching, the overall impact was increased share of seats by a low-cost carrier, selling more through direct internet sales, bypassing Japanese travel agents. There is evidence of a link between low-cost carrier services and changing consumer behaviour (see Mason, 2005). In particular, low-cost carriers tend to attract leisure travellers seeking flexibility who are more responsive to capacity changes and prices in the short-run. We believe these differences between the two markets can contribute to different responses of tourism demand to air transport capacity.
5.3. Robustness tests In this section, we consider alternative model specifications to assess the robustness of the findings so far. The income of the Chinese and Japanese economies and their currency exchange rates with the Australian dollar are factors that are expected to have an impact on the demand for travel and airlines’ forecast of the demand for seat capacity. The natural logarithm of GDP per capita and the natural logarithm of the exchange rate are both non-stationary
4 Regression analysis and the examination of the overall fit of the models indicate that the natural logarithm of the SEATS and PAX series follow a quadratic time trend. Alternative specifications using different time trend assumptions yielded similar conclusions. 5 Due to excess capacity between PAX and SEATS, it is possible for demand to “overshoot” when moving to restore equilibrium.
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processes, and are incorporated into the following analysis. The Johansen test for the co-integrating rank of SEATS and PAX whilst including both income and exchange rates indicate the existence of a co-integrating relation in both markets. However, estimation of the co-integrating equation suggests that income and exchange rates are insignificant in the PAX and SEATS relation. This indicates that income and exchange rates are not present in the equilibrium between SEATS and PAX. However, it is possible that SEATS and PAX are functions of an omitted factor that may play an important role in the timing of adjustments in both variables. For example, if SEATS responds to the omitted variable at time t and PAX at time t 1, then studying the dynamics of the co-movement between SEATS and PAX alone may incorrectly lead to the conclusion that PAX adjusts to deviations in the co-integrating relation, and not vice versa. One possible scenario is income levels. It may be possible that GDP per capita for China/Japan is one such omitted variable in the VECM of Table 3; as it can be argued that airlines must forecast future income levels in order to forecast demand whilst PAX responds to changes in income levels. The contemporaneous and lag values of changes in the natural logarithm of GDP per capita are included as exogenous variables in the VECM, and the results remain consistent. Similarly, the above-mentioned analysis is also conducted using changes in the natural logarithm of the exchange rate as an exogenous variable in the VECM. The results again remain consistent. Finally, a VECM is estimated with both income and exchange rates as exogenous variables. The conclusions remain unchanged. That is, in both the China and Japan routes, PAX adjusts to innovations in the co-integrating relation whilst the variable SEATS does not and the coefficient of the error-correction term is more economically significant in the JapaneAustralia market. 6. Conclusions In the short-run, airline seats are generally static due to schedule commitments. Passenger demand, however, is elastic to economic conditions. As one might expect, this demand response differs by market. Our results suggest that the speed at which the equilibrium between supply and demand is reached is different for Japan and China. It was found that PAX in the JapaneAustralia market correct for short-run deviations from the long-run equilibrium quicker than in the ChinaeAustralia market. Airlines with different business models may help explain some of these results. For Japan, established carriers such as JAL face competitive pressure from new arrivals such as Jetstar. Capacity is being replaced by a different (and perhaps a more affordable) business model (including pricing, distribution, branding and positioning, innovative product delivery, etc.), thus facilitating a faster response by passengers. Related to the above, tourism markets in mature and affluent nations such as Japan may be more flexible and better equipped to adapt to changes in aviation environment (e.g., a punctuated event). Although this study used data on inbound passenger demand and capacity to Australia, the findings can be relevant to other tourism destinations interested in gathering market intelligence. Destination marketing organisations in air transport-reliant destinations are aware of the stimulative and limitative nature of aviation capacity for tourism demand. However, there exists limited
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research showing how the two interact. Mature markets may be more responsive to capacity changes, while emerging markets may be characterised by a greater lag in response. Thus, before tending to the question of whether or not demand follows the provision of capacity (it may be the case that demand appears to follow because airlines may attempt to pre-empt demand in setting their capacity), an equally fruitful question examines how fast demand will adjust when airlines fail to pre-empt demand. This paper has made some initial progress in formalising and empirically testing these ideas. Acknowledgements Tay Koo would like to thank Tourism Australia and colleagues in Japan for market intelligence and anecdotal evidence. Duval’s participation was made possible through funding from the Ministry of Science and Innovation (New Zealand). David Tan would like to thank Dr Kathleen Walsh and participants at the 2011 Air Transport Research Society Conference for their valuable comments and suggestions. References Belobaba, P.P., Wilson, J.L., 1997. Impacts of yield management in competitive airline markets. Journal of Air Transport Management 3, 3e9. Brons, M., Pels, E., Nijkamp, P., Rietveld, P., 2002. Price elasticities of demand for passenger air travel: a meta-analysis. Journal of Air Transport Management 8, 165e175. Crouch, G.I., 1995. A meta-analysis of tourism demand. Annals of Tourism Research 22, 103e118. Department of Resources, Energy and Tourism, 2012. Tourism Research Online Database. www.ret.gov.au (accessed 14.04.12.). Diebold, F., 2004. Elements of Forecasting. South-Western Publishing, Cincinnati. Doganis, R., 2006. The Airline Business. Routledge, London. Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica 55 (2), 251e276. Garrow, L., 2010. Discrete Choice Modeling and Air Travel Demand: Theory and Application. Ashgate, Aldershot. Granger, C.W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424e438. Hamilton, J., 1994. Time Series Analysis. Princeton University Press, Princeton. Jorge-Calderón, J.D., 1997. A demand model for scheduled airline services on international European routes. Journal of Air Transport Management 3, 23e35. Lim, C., McAleer, M., 2001. Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia. Applied Economics 33, 1599e 1619. Lutkepohl, H., 2005. New Introduction to Multiple Time Series Analysis. Springer, New York. Marazzo, M., Scherre, R., Fernandes, E., 2010. Air transport demand and economic growth in Brazil: a time series analysis. Transportation Research Part E: Logistics and Transportation Review 46, 261e269. Mason, K., 2005. Observations of fundamental changes in the demand for aviation services. Journal of Air Transport Management 11 (1), 19e25. Narayan, P.K., 2008. Examining the behaviour of visitor arrivals to Australia from 28 different countries. Transportation Research Part A: Policy and Practice 42, 751e761. Njegovan, N., 2006. Are shocks to air passenger traffic permanent or transitory? Implications for long-term air passenger forecasts for the UK. Journal of Transport Economics and Policy 40 (2), 315e328. Sinclair, T., 1998. Tourism and economic development: a survey. Journal of Development Studies 34, 1e51. Shaw, S., 1982. Airline deregulation and the tourist industry. Tourism Management 3, 40e51. Tourism Research Australia, 2010. International Visitors to Australia December Quarter 2010. Department of Resources, Energy and Tourism. is.gd/B4uyDv (accessed 02.05.11.). Tourism Forecasting Committee, 2010. Forecast 2010 Issue 2. Tourism Research Australia, Canberra. United Nations World Tourism Organisation, 2008. International Recommendations for Tourism Statistics 2008. United Nations Publications. unstats.un.org/unsd/ publication/Seriesm/SeriesM_83rev1e.pdf (accessed 17 April).
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Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Direct air transport and demand interaction: A vector error-correction model approach Tay T.R. Koo a, *, David T. Tan b, David Timothy Duval c a
School of Aviation, University of New South Wales, Sydney, New South Wales 2052, Australia Research School of Finance, Actuarial Studies, and Applied Statistics, Australian National University, School of Aviation, University of New South Wales, Sydney, New South Wales 2052, Australia c Faculty of Business and Economics, University of Winnipeg, School of Business, University of Otago, School of Aviation, University of New South Wales, Sydney, New South Wales 2052, Australia b
a b s t r a c t Keywords: Market equilibrium Vector error-correction Air transport capacity
The goal of this paper is to impose a causeeeffect structure into the relation between tourism demand and air transport capacity. Specifically, we apply a vector error-correction model to assess if, and to what extent, capacity or passenger demand are first-movers that return to long-run equilibrium following short-run deviations. Using data on international aviation between Australia and our test cases of China and Japan, we find that demand on the JapaneAustralia market corrects for short-run deviations from the long-run equilibrium quicker than the ChinaeAustralia market. Reasons for such variation in adjustment speeds are discussed and we show that the results are robust to the phenomenon of airlines pre-empting demand when setting capacity. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Air transport dynamics are carefully monitored by governments, destination marketing organizations and the local tourism industry for strategic planning purposes. Information about how passenger demand interacts with available capacity can help destinations target advertising and promotional efforts. The same information can assist airlines with forecasting capacity needs across a network. Air transport and tourism are inter-related - the strong association between the levels of tourism demand and air travel demand are especially noticeable in medium to long-haul international travel. On direct routes, incoming passenger numbers can be a useful proxy for inbound tourists when the balance of bilateral flows is skewed towards one direction (as is the case in our test cases). Tourists are commonly defined as overnight visitors travelling for any main purpose other than to be employed by a resident entity in the country or place visited (United Nations World Tourism Organization, 2008); thus, for an end-point region such as Australia, most non-resident persons entering Australia are tourists. Although we are using incoming passenger data, this paper is also relevant to the study of tourists and tourism. Determinants of tourism demand have been studied extensively (see Crouch, 1995; Sinclair, 1998; Lim and McAleer, 2001). Empirical
* Corresponding author. E-mail address: [email protected] (T.T.R. Koo). 0969-6997/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jairtraman.2012.12.005
analyses focus commonly on price and income, but other variables, including relative prices of destinations and transport costs (usually airfares) and the availability of substitute modes of travel (Brons et al., 2002), as well as the temporary or permanent nature of exogenous shocks (Njegovan, 2006; Narayan, 2008), have been considered. Clearly, air transport can influence tourism demand through a number of channels, with price being one of these factors. In the context of international air transport liberalization, it has been shown that, in addition to safety and airline efficiency, the adequacy and consistency of air transport capacity were important air transport attributes from a tourism viewpoint (Shaw, 1982). Air transport capacity is considered as one of the key factors in tourism forecasting, along with other key determinants of tourism demand such as income and significant events (Tourism Forecasting Committee, 2010). Despite this, there exists a gap in understanding the relation between air transport capacity and demand such that the causal nature of these variables is rarely examined. The goal of this paper is to impose a causeeeffect structure into the relation between air transport capacity and demand with emphasis on tourism.
2. Background and objectives In mapping the causality between capacity and demand, two points need to be considered. First, the relation between air transport capacity and demand can be viewed as an interaction
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where the projected demand influences capacity, and capacity influences actual demand. With respect to the former, airlines adjust capacity based on demand forecasts using computer algorithms to process large volumes of itinerary choice information (Garrow, 2010), as well as smoothing techniques and simple growth rate projections (Doganis, 2006). Their forecast accuracy can be reasonable given forward booking data, past observations and knowledge of seasonal fluctuations in the demand for tourism e including tourism infrastructure capacity of destinations (e.g. accommodation capacity) e and scheduled fixtures and events: as for the latter airlines use revenue management to manage seat factors (Belobaba and Wilson, 1997). If it is in the interest of an airline’s profitability, given excess capacity, sales can be influenced directly through pricing on airline websites or indirectly through channels such as on-line and off-line travel agents. Second, and in the short-run (i.e., less than a year), capacity is usually fixed on a given route for a variety of reasons, including contractual obligations for a scheduling season(s), although there may be incidences of spot additions/cancellations. However, as the time horizon increases, we can expect capacity to adjust to meet changing market conditions. In this situation both passenger numbers and capacity will adjust towards a postulated long-run equilibrium. In other words, the tourism demand e aviation capacity interaction can be dissected along a temporal dimension that is characterized through short-run and long-run horizons. With these points in mind, we apply a vector error-correction model (VECM) to help assess whether, and to what extent, capacity or passenger demand are first-movers that return to long-run equilibrium following an event that punctuates the established equilibrium. In doing so, the paper shows the air transport capacity e tourism demand interaction within the VECM framework. We also highlight the differences in the speed at which short-run deviation is returned to long-run equilibrium in different markets, and discuss why differences in adjustment speed may occur. The article concludes by discussing the relevance of the findings with particular reference to international aviation between Australia and our test cases of China and Japan. 3. The Australian inbound tourism market Nearly all inbound visitors to Australia arrive by air. In 2009, there were over 16 million direct (and connect-direct) seats into Australia of which more than 20 percent were from NZ (Australia’s closest trading partner). New Zealand and Singapore combined account for nearly 35 percent of capacity (Table 1). Japan and China are significant tourism-generating markets for Australia. While each rank high in terms of visitor arrival numbers (Tourism Research Australia, 2010), they do not have the corresponding rankings for inbound direct capacity. This is a function of airlines originating from each country and exercising Fifth and Sixth Freedom rights to Australia by utilizing major regional hubs in Malaysia, Singapore, Hong Kong, Thailand, UAE and Qatar. Nonetheless, the three countries account for 7% of total inbound direct capacity and this is expected to grow as China becomes one of Australia’s most significant markets for inbound tourism. The aviation environments in ChinaeAustralia and JapaneAustralia routes are comparable in a number of ways, including market size, similar medium-haul routings with similar equipment needs, and the presence of capacity restrictions within bilateral air service agreements. Travel agents assume significant roles for outbound tourism in both countries compared to direct internet (and independent) sales, which are more popular in Western counterparts. The key difference, however, is the development stage of each market and the rate of growth. China is a rapidly emerging market, whereas Japan is a relatively stagnating
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Table 1 Direct capacity to Australia. Country New Zealand Singapore UAE and Qatar USA Hong Kong Malaysia Indonesia UK Thailand Japan Pacific Islands China Korea South Africa Taiwan Latin America Vietnam Philippines Germany Canada India France Total
2009 Direct and connect capacity 3 2 1 1 1
576 151 295 278 161 966 816 815 769 621 611 456 315 195 180 180 172 151 144 93 42 24 16 021
105 926 675 196 518 746 435 581 067 833 632 895 357 088 585 578 504 342 377 066 238 388 132
Source: Department of Transport and Infrastructure (2010).
market. This provides a unique test environment for modelling because of the contrasting tourism growth trajectories and different position in which Australia is located along the destination lifecycle (Table 2). Fig. 1 displays the passenger demand (PAX) and seat capacity (SEATS) series for the Australia-China routes between January 1999 and June 2010. Note the general upwards trend in both series over the sample. Over the last 10 years, travel between China and Australia has increased by over 250 percent. The passenger demand (PAX) and seat capacity (SEATS) for the Australia-Japan routes are presented in Fig. 2. Note that PAX exhibits significant seasonality across time; this characteristic is less present in the SEATS series. We can observe that the overall demand and seat capacity for travel between Australia and Japan has steadily declined over the recent 5 years. 4. Empirical methodology and data Air transport capacity (commonly measured in seat numbers) and demand (measured in paid passenger numbers) often exhibits a linearepositive relation. The co-movements of passenger numbers and seat capacity are commonly observed across many air Table 2 Characteristics of China and Japan markets in relation to Australia. China First air service agreement (ASA) in 1984; most recent ASA in 2004, amended last in March 2011 (Bureau of Infrastructure and Transport, 2009); Strong growth over the last decade; Australia received Approved Destination Status (ADS) in April 1999 e first Western country to be approved, allowing self-funded group leisure travel visas. The ADS coverage was broadened to other regions in China in 2003; Allowable capacity of 16,118 seats each way per week to/from Sydney, Melbourne, Brisbane and Perth, and unlimited capacity to all other points in Australia (February 2012). Japan Flat and stagnating capacity levels, especially since 2009 with JAL withdrawing from main routes; Unlimited capacity between any points in Australia and Japan except some restrictions to Narita and Haneda airports; Substantial reduction by JAL in recent years; a net reduction by Qantas but net increase by Jetstar; Currently, substantial Jetstar (a long-haul low-cost) presence.
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Fig. 1. Passenger demand and seat capacity on AustraliaeChina routes. Source: Department of Transport and Infrastructure.
transport routes, as well as many other time series such as inventories and sales. If these series are non-stationary and exhibit patterns of co-movement, the series may be co-integrated in that there may be an equilibrium and interdependence between the two such that the linear combination of the two series (or more) are stationary (Hamilton, 1994; Engle and Granger, 1987). Co-integrated series are common in business, economics and finance; for instance, inventories to sales ratio often appear I(0), which is a result of firms adjusting inventories to sales (Diebold, 2004). Air transport data often have these features; demand and supply information often move together, while preserving the ratio of the two at a constant mean, i.e., roughly constant seat factor, which is I(0). In air transport context, however, services are perishable and the ability of airlines to adjust capacity in the short-run is marred by predetermined schedules and fixed costs. In other words, seats cannot adjust to sales in the short-run as inventories do when there are fluctuations in sales. While capacity can adjust in
the long-run towards a long-run equilibrium, in the short-run we can expect demand to adjust to capacity. As the series in our studies appear to be co-integrated, we thus postulate the following application of a vector error-correction model:
Ptþ1 þ a ¼ Fttþ1 þ εtþ1
(1)
where Ptþ1 is the passenger demand in t þ 1, a is a constant to account for the fact that seat factor is always less than one, Fttþ1 is airline’s forecast capacity (based on demand forecasts) for t þ 1 in time t. εtþ1 is a shock term realized in t þ 1, however, at time t, the best information we have about εtþ1 is that it is a random variable with a mean of zero. In an event where the equilibrium is disturbed (which we term punctuated equilibrium), the following inequality will occur because εtþ1 is non-zero.
Fig. 2. Passenger demand and seat capacity on AustraliaeJapan routes. Source: Department of Transport and Infrastructure.
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Ptþ1 þ asFttþ1
(2)
In the above case, the following simultaneous equations can provide us with information on which variable/s (passenger demand or airline capacity) move to restore the equilibrium in the subsequent period and to what extent.
i h tþ2 Ftþ1 ¼ ðPtþ1 þ aÞ Fttþ1 bf þ εf 1;tþ2
(3)
h i Ptþ2 þ a ¼ ðPtþ1 þ aÞ Fttþ1 bp þ εp1;tþ2
(4)
If bf (bp) is found to be statistically equal to zero then none of the short-run deviation will be closed by the airline (passenger demand) in t þ 1. From our discussion above, we expect bf to be close to zero at least for a given scheduling season (approximately six to seven months). Since airlines have a degree of control over the short-run seat-factor through revenue management, we expect bp to be positive and responsive to short-run punctuated events. Data have been obtained from Department of Transport and Infrastructure. We use passenger data (PAX series) representing all inbound travel to Australia, which we take as a measure of effective demand embodying both the willingness and the ability to purchase. While this data captures returning Australian residents, which is outside our interest, we see it nonetheless as a reasonable measure of incoming tourist flow because: (1) Most international tourists to Australia are air arrivals; (2) UNWTO (2008) defines visitor as a traveller “taking a trip to a main destination outside his/her usual environment, for less than a year, for any main purpose (business, leisure or other personal purpose) other than to be employed by a resident entity in the country or place visited. These trips taken by visitors qualify as tourism trips” (UNWTO, 2008, p.10). UNWTO (2008) defines an overnight visitor as a tourist. Thus, with the exception of a very small number of passengers passing through (transit passengers e according to data from Australian government (Dept. Resources, Energy and Tourism (DRET)) transit passengers are approximately 0.25% and 0.2% of total inbound traffic from Japan and China, respectively), all non-resident arrivals into Australia are tourists; (3) Based on DRET data, averaged between the best data available at the time of writing (2004e2009 data), for every Australian outbound (to Japan and China) there is five Japanese inbound (to Australia) and three Chinese inbound (to Australia). Thus, tourist influxes are the dominant flows. Indeed, the pattern of PAX and SEATS follow the pattern governed by the dominant flows, which are the rapidly declining (increasing) Japan (China)-Australia SEATS and PAX as shown by Figs. 1 and 2. Airlines will be most concerned about the dominant visitor segment and their behaviour. (4) In addition to the dominance of inbound flows, inbound capacityedemand dynamics explain the majority of variation in our proxy: foreign visitors are able to exercise greater discretion over their decision to travel to Australia than Australian travellers returning home when there is a shock1 to the longrun air transport capacity and demand relation.
1 A short-run shock to the airline capacity and demand relation, as is shown in Section 5.3, is almost completely corrected for by the end of the subsequent month. Unless a significant number of potential Australian passengers are able to alter their demand for outbound travel (due to a shock in the capacityedemand relation) and return to Australia within the month, any significant changes in inbound traffic within the subsequent month of a shock is likely to be that of foreign travellers.
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With the absence of readily accessible data on exact Japanese and Chinese arrival shares on a given aviation route, we believe the above reasons warrant the adequacy of the chosen proxy. Capacity between two nations is measured by the number of seats available. This is based on the monthly time series between Jan 1999 and Dec 2009. Data prior to 1999 were not suitable to be included in the analyses due to shocks such as the Financial Crisis in 1997 and significant changes in policy (e.g., the Chinese government’s policy on outbound travel to Australia). 5. Results 5.1. Diagnostics We begin with tests for stationary characteristics in the capacity (SEATS) and passenger demand (PAX) series on the premise that violation of the assumption of stationarity may lead to spurious results. Indeed, previous studies have documented that tourism demand exhibits characteristics of a unit-root (Lim and McAleer, 2001). Similar evidence exists in the air travel demand context (Marazzo et al., 2010). Applying the PhillipsePerron test for a unitroot confirms its presence in both the SEATS and PAX series given MacKinnon p values of 0.383 and 0.431, respectively, for China. Similarly, there is evidence supporting a unit-root on Japane Australia PAX and SEATS.2 An alternative specification of the PhillipsePerron test including a time trend yields similar conclusions. Moreover, tests conducted on the first-differenced SEATS and PAX series are stationary at the 1 percent level for China and Japan. This suggests that SEATS and PAX are integrated of order 1, i.e. I(0). Given the two time series are integrated of order 1, the results of the Johansen test for co-integration on the natural logarithm of SEATS and PAX indicate that there exists one co-integrating relation between SEATS and PAX, thus implying that SEATS and PAX comove and that there exists a long-term equilibrium between these series. A co-integrated relation suggests that not only does a long-term relation between SEATS and PAX exist, but short-term deviations from this equilibrium are subsequently corrected for, confirming the potential suitability for a vector error-correction modelling framework. 5.2. Vector error correction estimation The vector correction model (VECM) is estimated for the SEATS and PAX time series. Such a model does not apply any economic theory on the underlying relation as both the supply and demand for airline seats are known to be endogenously determined (e.g., Jorge-Calderón, 1997). The VEC specification is rather a vector autoregressive (VAR) specification augmented with an error-correction term highlighting the nature of convergence in short-run deviations in the co-integrated relation. Table 3 contains the results for the VECM of the SEATS and PAX relation using a lag structure of up to 13 months in the underlying VAR specification. The lag length is selected by opting for the lag structure that minimises the SBIC and HQIC3 (Lutkepohl, 2005). This suggests the SEATS and PAX series exhibit some degree of partial correlation up to a period of 13 months. Alternate lag
2 Due to the significant seasonality displayed by the AustraliaeJapan PAX series, it has been de-seasonalized using monthly dummy variables. Other series in the analysis were also similarly de-seasonalized as a test for robustness, and the results remain consistent. 3 An alternative lag specification of up to 6 months resulted in similar conclusions.
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Table 3 Vector error correction models for SEATS & PAX. China Y ¼ seats
a L1.Seats L2.Seats L3.Seats L4.Seats L5.Seats L6.Seats L7.Seats L8.Seats L9.Seats L10.Seats L11.Seats L12.Seats L1.Pax L2.Pax L3.Pax L4.Pax L5.Pax L6.Pax L7.Pax L8.Pax L9.Pax L10.Pax L11.Pax L12.Pax Trend Const Observations AIC HQIC SBIC
0.0755 0.1859 0.0553 0.2091 0.2753 0.1976 0.2204 0.1815 0.1304 0.1774 0.1236 0.1726 0.2913 0.1745 0.058 0.1693 0.125 0.1737 0.0963 0.1638 0.2772 0.1588 0.1557 0.1395 0.0812 0.1333 0.1264 0.1537 0.1271 0.1438 0.0965 0.1335 0.0154 0.131 0.0233 0.1296 0.0899 0.1296 0.1811 0.127 0.0096 0.1282 0.1037 0.1185 0.1916 0.1155 0.1372 0.0962 0.1403 0.0871 0.0001 0.0002 0.0248 0.0191 125 3.6463 3.6243 2.4018
Japan Y ¼ Pax 0.8273*** 0.2564 0.3509 0.2883 0.2496 0.2725 0.3757 0.2503 0.3176 0.2445 0.5116** 0.238 0.6112** 0.2406 0.6617*** 0.2335 0.4838** 0.2395 0.5915*** 0.2258 0.3648 0.2189 0.182 0.1923 0.5516*** 0.1838 0.1958 0.212 0.0302 0.1982 0.1068 0.184 0.1393 0.1807 0.0047 0.1786 0.2079 0.1787 0.1507 0.1752 0.1718 0.1768 0.1486 0.1634 0.1718 0.1593 0.1166 0.1326 0.5368*** 0.1201 0.0000 0.0003 0.0024 0.0263
Y ¼ seats 0.0705 0.2082 0.122 0.1988 0.1642 0.1855 0.1739 0.1734 0.0201 0.1628 0.2439 0.1526 0.2801** 0.1328 0.0172 0.1212 0.01072 0.1102 0.1464 0.1032 0.0954 0.1015 0.4045*** 0.1008 0.4616*** 0.1079 0.1585 0.2726 0.1358 0.2554 0.0406 0.2383 0.1803 0.2266 0.1458 0.2112 0.1662 0.185 0.1684 0.162 0.0645 0.1391 0.1329 0.125 0.0725 0.1102 0.074 0.0936 0.0039 0.0845 0.0002 0.0001 0.0091 0.009 125 6.2188 5.7132 4.9743
Y ¼ Pax 1.1924*** 0.2886 0.457 0.2755 0.5995** 0.2571 0.6223*** 0.2403 0.5023** 0.2256 0.4492** 0.2115 0.22 0.1841 0.1081 0.168 0.2235 0.1527 0.1949 0.143 0.0413 0.1407 0.0065 0.1397 0.2684 0.1495 1.0957*** 0.3778 0.9461*** 0.354 0.9112*** 0.3303 0.9308*** 0.3141 0.6769** 0.2928 0.5489** 0.2564 0.3471 0.2245 0.4141** 0.1928 0.3827** 0.1733 0.2203 0.1528 0.0011 0.1297 0.1265 0.1171 0.0000 0.0002 0.0015 0.0125
Seats is the natural logarithm of the number of inbound seats made available across all airlines travelling from China/Japan to Australia in a given month. Pax is the natural logarithm of the number of incoming revenue passengers travelling from China/Japan to Australia in a given month. a is the coefficient of the co-integrating equation, otherwise referred to as the error-correction term. Trend is the quadratic time trend and const is the intercept. L is the one period lag operator. The Johansen normalisation procedure is imposed such that the coefficient of Seats in the cointegrating equation equal to 1. AIC is the Akaike information criterion, HQIC is the HannaeQuinn information criterion and SBIC is the Schwarz-Bayesian information criterion. ** and *** denote significance at the 5 and 1 percent level, respectively.
specifications yield similar results. The VECM specification incorporates a quadratic time trend in the natural logarithm of the SEATS and PAX relation.4 As expected, the error-correction term for SEATS is statistically insignificant, suggesting that airline seat capacity does not adjust to short-term deviations in the SEATS-PAX relation in both markets. However, over longer periods of one year or more, both SEATS and PAX granger cause (Granger, 1969) one another in the Chinae Australia market. That is, SEATS is no longer a fixed-scheduled variable and adjusts to longer-term trends in passenger demand. In the JapaneAustralia market, SEATS granger causes PAX and not vice versa, suggesting that SEATS is less responsive to PAX even in longer time horizons. It can be observed that the error-correction term for the PAX series is statistically significant at the one percent level for China (0.7616) and Japan (1.1924). In the ChinaeAustralia aerotourism market, passenger numbers will close 76 percent of the deviation in the co-integrating relation in the subsequent month. Passenger numbers will close 119 percent of the deviation in Japane Australia.5 In other words, the speed at which PAX corrects for short-run deviation from long-run equilibrium is faster in the JapaneAustralia than the ChinaeAustralia market. Japanese demand to Australia may be more responsive to route capacity compared to Chinese demand because Japan is more affluent with more outbound choices. Australia was the first country to attain Approved Destination Status (ADS) by the Chinese authority. Even so, at the onset of the ADS, outbound travel was limited to certain regions of China, although this was eventually relaxed over time. Greater destination choice alternatives suggest greater substitution possibilities, increasing the elasticity with respect to a particular destination. This effect could be compounded by the fact that the JapaneAustralia market has strong low-cost airline presence. Jetstar International has been gradually increasing capacity on the route from a daily service to begin with (March/April 2007) to a thrice daily service (April 2009). During the same period, Qantas decreased frequencies from four to two, while Japan-based carriers’ frequencies remained relatively unchanged. Although there has been some equipment switching, the overall impact was increased share of seats by a low-cost carrier, selling more through direct internet sales, bypassing Japanese travel agents. There is evidence of a link between low-cost carrier services and changing consumer behaviour (see Mason, 2005). In particular, low-cost carriers tend to attract leisure travellers seeking flexibility who are more responsive to capacity changes and prices in the short-run. We believe these differences between the two markets can contribute to different responses of tourism demand to air transport capacity.
5.3. Robustness tests In this section, we consider alternative model specifications to assess the robustness of the findings so far. The income of the Chinese and Japanese economies and their currency exchange rates with the Australian dollar are factors that are expected to have an impact on the demand for travel and airlines’ forecast of the demand for seat capacity. The natural logarithm of GDP per capita and the natural logarithm of the exchange rate are both non-stationary
4 Regression analysis and the examination of the overall fit of the models indicate that the natural logarithm of the SEATS and PAX series follow a quadratic time trend. Alternative specifications using different time trend assumptions yielded similar conclusions. 5 Due to excess capacity between PAX and SEATS, it is possible for demand to “overshoot” when moving to restore equilibrium.
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processes, and are incorporated into the following analysis. The Johansen test for the co-integrating rank of SEATS and PAX whilst including both income and exchange rates indicate the existence of a co-integrating relation in both markets. However, estimation of the co-integrating equation suggests that income and exchange rates are insignificant in the PAX and SEATS relation. This indicates that income and exchange rates are not present in the equilibrium between SEATS and PAX. However, it is possible that SEATS and PAX are functions of an omitted factor that may play an important role in the timing of adjustments in both variables. For example, if SEATS responds to the omitted variable at time t and PAX at time t 1, then studying the dynamics of the co-movement between SEATS and PAX alone may incorrectly lead to the conclusion that PAX adjusts to deviations in the co-integrating relation, and not vice versa. One possible scenario is income levels. It may be possible that GDP per capita for China/Japan is one such omitted variable in the VECM of Table 3; as it can be argued that airlines must forecast future income levels in order to forecast demand whilst PAX responds to changes in income levels. The contemporaneous and lag values of changes in the natural logarithm of GDP per capita are included as exogenous variables in the VECM, and the results remain consistent. Similarly, the above-mentioned analysis is also conducted using changes in the natural logarithm of the exchange rate as an exogenous variable in the VECM. The results again remain consistent. Finally, a VECM is estimated with both income and exchange rates as exogenous variables. The conclusions remain unchanged. That is, in both the China and Japan routes, PAX adjusts to innovations in the co-integrating relation whilst the variable SEATS does not and the coefficient of the error-correction term is more economically significant in the JapaneAustralia market. 6. Conclusions In the short-run, airline seats are generally static due to schedule commitments. Passenger demand, however, is elastic to economic conditions. As one might expect, this demand response differs by market. Our results suggest that the speed at which the equilibrium between supply and demand is reached is different for Japan and China. It was found that PAX in the JapaneAustralia market correct for short-run deviations from the long-run equilibrium quicker than in the ChinaeAustralia market. Airlines with different business models may help explain some of these results. For Japan, established carriers such as JAL face competitive pressure from new arrivals such as Jetstar. Capacity is being replaced by a different (and perhaps a more affordable) business model (including pricing, distribution, branding and positioning, innovative product delivery, etc.), thus facilitating a faster response by passengers. Related to the above, tourism markets in mature and affluent nations such as Japan may be more flexible and better equipped to adapt to changes in aviation environment (e.g., a punctuated event). Although this study used data on inbound passenger demand and capacity to Australia, the findings can be relevant to other tourism destinations interested in gathering market intelligence. Destination marketing organisations in air transport-reliant destinations are aware of the stimulative and limitative nature of aviation capacity for tourism demand. However, there exists limited
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research showing how the two interact. Mature markets may be more responsive to capacity changes, while emerging markets may be characterised by a greater lag in response. Thus, before tending to the question of whether or not demand follows the provision of capacity (it may be the case that demand appears to follow because airlines may attempt to pre-empt demand in setting their capacity), an equally fruitful question examines how fast demand will adjust when airlines fail to pre-empt demand. This paper has made some initial progress in formalising and empirically testing these ideas. Acknowledgements Tay Koo would like to thank Tourism Australia and colleagues in Japan for market intelligence and anecdotal evidence. Duval’s participation was made possible through funding from the Ministry of Science and Innovation (New Zealand). David Tan would like to thank Dr Kathleen Walsh and participants at the 2011 Air Transport Research Society Conference for their valuable comments and suggestions. References Belobaba, P.P., Wilson, J.L., 1997. Impacts of yield management in competitive airline markets. Journal of Air Transport Management 3, 3e9. Brons, M., Pels, E., Nijkamp, P., Rietveld, P., 2002. Price elasticities of demand for passenger air travel: a meta-analysis. Journal of Air Transport Management 8, 165e175. Crouch, G.I., 1995. A meta-analysis of tourism demand. Annals of Tourism Research 22, 103e118. Department of Resources, Energy and Tourism, 2012. Tourism Research Online Database. www.ret.gov.au (accessed 14.04.12.). Diebold, F., 2004. Elements of Forecasting. South-Western Publishing, Cincinnati. Doganis, R., 2006. The Airline Business. Routledge, London. Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica 55 (2), 251e276. Garrow, L., 2010. Discrete Choice Modeling and Air Travel Demand: Theory and Application. Ashgate, Aldershot. Granger, C.W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424e438. Hamilton, J., 1994. Time Series Analysis. Princeton University Press, Princeton. Jorge-Calderón, J.D., 1997. A demand model for scheduled airline services on international European routes. Journal of Air Transport Management 3, 23e35. Lim, C., McAleer, M., 2001. Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia. Applied Economics 33, 1599e 1619. Lutkepohl, H., 2005. New Introduction to Multiple Time Series Analysis. Springer, New York. Marazzo, M., Scherre, R., Fernandes, E., 2010. Air transport demand and economic growth in Brazil: a time series analysis. Transportation Research Part E: Logistics and Transportation Review 46, 261e269. Mason, K., 2005. Observations of fundamental changes in the demand for aviation services. Journal of Air Transport Management 11 (1), 19e25. Narayan, P.K., 2008. Examining the behaviour of visitor arrivals to Australia from 28 different countries. Transportation Research Part A: Policy and Practice 42, 751e761. Njegovan, N., 2006. Are shocks to air passenger traffic permanent or transitory? Implications for long-term air passenger forecasts for the UK. Journal of Transport Economics and Policy 40 (2), 315e328. Sinclair, T., 1998. Tourism and economic development: a survey. Journal of Development Studies 34, 1e51. Shaw, S., 1982. Airline deregulation and the tourist industry. Tourism Management 3, 40e51. Tourism Research Australia, 2010. International Visitors to Australia December Quarter 2010. Department of Resources, Energy and Tourism. is.gd/B4uyDv (accessed 02.05.11.). Tourism Forecasting Committee, 2010. Forecast 2010 Issue 2. Tourism Research Australia, Canberra. United Nations World Tourism Organisation, 2008. International Recommendations for Tourism Statistics 2008. United Nations Publications. unstats.un.org/unsd/ publication/Seriesm/SeriesM_83rev1e.pdf (accessed 17 April).