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Measuring the economic impact of liberalization of international aviation on Hamburg airport
Journal of Air Transport Management 7 (2001) 25}34
Measuring the economic impact of liberalization of international aviation on Hamburg airport David Gillen *, Holger Hinsch Institute for Transportation Studies, University of California, Berkeley, CA 94720-1720, USA Science Applications International Corporation, Center for Economic and Financial Analysis, 1710 SAIC Drive, McLean, VA 22102, M/S T3-8-2, USA
Abstract The paper describes a four-step modeling approach to estimating the impact of changes to the international aviation bilaterals on airport revenues and the income, employment and tourism e!ects for the local economy. The model is estimated on and applied to Hamburg airport and the State of Hamburg using a set of 10 representative markets that would have access to the German market. We "nd that Hamburg airport would gain 149,000 new passengers of which 17,000 would be hubbing through the airport to other international destinations. The change in passenger and operations leads to a 6 percent increase in overall airport revenues, as well as increases in local output, investment and employment. Tourism impacts were estimated separately with 11,000 new tourists and 22 new jobs in the tourism industry. 2001 Elsevier Science Ltd. All rights reserved. Keywords: Economic impact; International aviation liberalization; airport impact models
1. Introduction This paper discusses the outcomes that a change in the bilateral agreements governing access to the German market as well as access by German and other European carriers to other markets might have on Hamburg airport and the region surrounding the Hamburg airport. Impacts examined include changes to the frequency of #ights, the gauge of aircraft #own and the number of passengers carried. The changes will be distributed among the various airports in Germany according to the competitive circumstances and the comparative advantage of each airport. The economic impacts resulting from changes to the Air Service Agreements can be represented in four separate components. First, the aggregate impact for Germany must be established. Second, the aggregate impact on passengers and #ight frequency must be distributed among the major and regional airports in Germany. The disaggregated values for individual airports are calculated using an airport choice model (see Mandel and Schnell, 2000). The airport choice model allows us to predict the changes at particular airports in both passen* Corresponding author. Tel.: #1-510-643-2310; fax: #1-510-6421246. E-mail address: [email protected] (D. Gillen).
gers and #ights and segmented by domestic, European and international markets. Changes in gauge of aircraft cam also are forecast. Third, once distributed the economic impacts can be divided between airport impacts and local economic impacts. The move to open sky, the general liberalization of the German aviation bilateral, will a!ect airport revenue while changes in access to external markets will a!ect the airport region economy. Finally, tourism impacts may be assessed using the most recent measures of tourist response to changes in access and applying the elasticity to the representative countries. A #ow model of the approach is shown in Fig. 1. 1.1. Moving to liberalization Policymakers debating the issue of whether to deregulate their aviation system always face the question of whether the domestic economy would gain or lose under a liberalized international bilateral regime. There is a plethora of experience with domestic deregulation but only a limited set of experiences with international deregulation or liberalization (Button, 1991). Regulation tends to raise the costs of air transport to the detriment of air transport users. Since the complete liberalization of intra-EU air transport, all EU countries compete with each other for international air tra$c #ows by means of the institutional design of their bilateral agreements with
0969-6997/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 9 - 6 9 9 7 ( 0 0 ) 0 0 0 2 5 - 9
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D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
Fig. 1. Study methodology.
non-EU countries. EU members that are successful in attracting these #ows have (or will) reap the greatest bene"ts from the liberalized EU air transport markets. Those that manage to attract international air tra$c to their own national air transport system by means of liberalizing still existing restrictive bilaterals, enable the exploitation of economies of scope and tra$c density by their own airlines and airports. Our conclusion from the assessment of the current state of liberalization of international air transport is that it provides an opportunity rather than an impediment to enhance social welfare of the German economy. Evaluation of the few cases in which international deregulation has been assessed support these conclusions. However, the investigations of international liberalization tend to be incomplete because the trade diversion e!ects and the identi"cation of winners and losers were ignored. In domestic deregulation, this identi"cation was considered a relatively minor issue, but it is not the case with international deregulation. The modeling in this investigation identi"es those stakeholders, particularly airports, that would gain and those that might lose. Section 2 of the paper provides an overview of the air liberalization model (ALM) and a description of the results for a simulation of liberalizing the German international aviation market. In Section 3 the airport choice model is described and the results of the airport allocation are reported. In Section 4 the airport economic impact models are described and the expected revenue impact reported. The economic impact modeling for the
local economy is developed in Section 5. Measures of the output, employment and investment results are also provided Section 6 contains the summary and conclusions.
2. Overview of the air liberalization model * ALM The Air liberalization model is best characterized as a competitive analysis set in a bilateral negotiating framework. The principal assumption of this case is that markets are competitive because it is assumed that there are no economies of "rm size or economies of tra$c density in air transportation. The implication of this assumption is that changes in bilateral regulatory conditions, which reduce airline costs, will translate into an equal reduction in fares paid by consumers. It supplies policy makers measures of the (welfare) impacts of bilateral air transport liberalization on domestic and foreign consumers, airlines and airports. It is adaptable to a full range of liberalization scenarios, regardless of speci"c national circumstances. The need for general, policy relevant results drives the model's structure. It strikes a balance between the This is not as limiting as it appears since the model does consider cost e$ciencies resulting from any scale and density economies but these are represented as a downward shift of a linear cost function (SAIC Report, 2000). In other words, all the cost savings are obtained by consumers in the form of lower fares.
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
Fig. 2. Sample market.
complexity of equilibrium modeling, the minute detail of a bilateral relationship, and the need to maintain a model structure that is applicable to a broad range of regulatory and market circumstances. Fig. 2 shows a representative market that the model can assess. In this market, there are direct and indirect passenger #ows between countries as well as airlines o!ering services with varying frequencies and fare levels. The model shows how #ows between two countries, e.g., Germany and Canada, can be redirected through other liberalized markets creating spillover e!ects that work in favor of those countries that liberalize, as they can divert tra$c #ows to their own advantage. In contrast, countries that do not liberalize will experience a loss of tra$c #ows. The threat of tra$c diversion must be regarded as a serious one for Germany because of its central location within the already liberalized EU. This model combines elements of many previous studies in international aviation to provide quantitative evidence of the magnitude and distribution of bene"ts and costs from liberalization. The model also takes account of the e!ects of non-competitive markets and third country e!ects. These model components then simulate liberalization scenarios to estimate the potential gains (losses) from
The demand model takes full advantage of the available demand data for international origin}destination (OD) routes by incorporating price, demand, and qualitative factors. The supply model utilizes airline data as the basis for constructing costs and adjusts these costs to re#ect international carrier circumstances. The modi"cations are made external to the model. That is, the competitive market structure remains while judgments with respect to the degree of competition and the e!ect of third country markets are made using secondary sources and available data to estimate the magnitude and distribution of e!ects.
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liberalization, for consumers and producers. Consumer bene"ts are measured in terms of the value travelers' place on reduced fares and changes in service quality as measured by #ight frequency and increase in carrier choice as a result of market entry. Producer e!ects are measured in terms of changes in #ight segment pro"ts. The model also distributes bene"ts and costs by stakeholder group. In a recent study for the State of Hamburg and Hamburg airport, the model was used to predict the consequences of moving to an open skies regime. This meant no restrictions on fares, entry or frequency. The markets included in the analysis (listed in Table 1) were a sample that currently do not have good access to or a presence in Germany; the US market was therefore excluded. From the base case market and regulatory characteristics in Germany and the foreign country of interest, the policy evaluation considers changes in the following areas: expansion of frequency on existing OD segments, removal of pricing restrictions, entry of a new carrier and, "nally, combinations of all three changes. The results of the analysis are presented in Table 1. As the table shows, the introduction of competition results in a lowering of fares to "ll the additional capacity and maintain market share. This generates additional air travel and raises consumer expenditures. The welfare impacts on travelers is also positive due to the lowering fares. Producers bene"t because of the increase in travel expenditures despite the fact that average fares fall. Producers can also still obtain positive pro"ts due to consumer preferences for their national carriers.
3. Airport choice modeling: distribution of bene5ts of liberalization among airports This modeling e!ort takes the outcome of the aggregate analysis provided by the air liberalization model, described above, and determines how the results, measured in terms of passengers and #ights of di!erent types or market segments, would be distributed among the "rst- and second-tier airports in Germany. In particular, how would Hamburg share in the bene"ts under the
The model explicitly allows for the calculation of changes in consumer welfare and demand as a consequence of adding or reducing route choices in a given OD market. The demand is structured to account for inter-route substitution in a network context. For example, liberalization of routes between Germany and Canada is likely to induce substitution from alternative routes connecting the two countries, but passing through a third country. The extent of inter-route substitution in response to liberalization and its welfare consequences for consumers will depend on key parameters such as the route substitution elasticity and the relevant quality characteristics of competing routes. A detailed description of the model can be found in, Gillen et al. (1999).
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Table 1 Air liberalization model results Canada Current Annual pas. (000) New annual pas. (000) Percentage change
Chile
China
Ghana
Japan
Russia
Thailand
UAE
Ukraine
512 574 12%
45 116 157%
382 423 11%
23 36 55%
430 496 15%
600 678 13%
390 438 12%
170 193 14%
80 109 36%
Current average fare New average fare Percentage change
$964 $922 !4%
$1500 $769 !49%
$2500 $2350 !6%
$1000 $827 !17%
$2058 $1927 !6%
$763 $739 !3%
$1910 $1819 !5%
$912 $888 !3%
$1000 $883 !12%
Current consumer surplus ($M) New consumer surplus ($M) Percentage change
$289 $337 17%
$17 $62 259%
$1029 $1183 15%
$13 $24 81%
$666 $807 21%
$362 $427 18%
$727 $852 17%
$134 $159 19%
$63 $95 52%
Current producer surplus ($M) New producer surplus ($M) Percentage change
$219 $250 14%
$16 $38 137%
$837 $886 6%
$16 $21 32%
$740 $793 7%
$361 $404 12%
$619 $667 8%
$118 $136 15%
$67 $81 21%
Table 2 Changes through open sky in 1997 1997 Considered market
Canada Chile China Ghana Hong Kong Japan Russia Thailand UAE Ukraine Germany EU Other Grand total
Germany Passengers (tsd)
#39.4 #125.0 #127.3 #31.9 #271.9 #208.9 #52.2 #187.9 #21.5 #161.6 !337.8 !259.9 !15.4 #614.5
Hamburg Passengers (tsd)
A/c movements strategy I
A/c movements strategy I
!4.2 0 #50.8 0 #70.5 #86.0 #15.9 #72.2 #0.2 #17.5 !5.6 !137.8 #0.5 #166.0
!18 #0 #259 0 #263 #352 !93 #266 0 #127 #79 !2704 0 !1470
!18 0 #191 0 #358 #430 !25 #361 #0 #204 #79 !1791 0 !212
current set of carriers and airports but in an open skies regulatory environment? The airport choice and demand distribution model results for Germany in total are presented in Table 2 below. The results, shown for passengers, illustrate the gains expected when open sky takes place. These results are somewhat downward biased as passengers on international routes (e.g. Russia}Canada) who presently change planes at hubs outside Germany would most likely switch to a German airport in the open sky scenario. These diverted passengers were not included in the
A detailed description of the modeling approach and interim outcomes are provided in the full copy of the report as well as in the paper by Mandel and Schnell (2000).
disaggregate analysis due to a lack of access to the required data. The same applies for the indicated change in the number of aircraft movements. The total e!ect for Germany from the introduction of an open sky agreement for the 10 markets considered results in 277,000 additional passengers on the non-stop #ights to, from and between the German airports. The number of passengers on domestic feeder #ights diminishes by 338,000. The 277,000 passengers are made up of If Airlines want to keep frequencies high at the feeder #ights and prefer to serve the new inaugurated routes by smaller aircraft (Airbus A340 instead of Boeing 747), the decrease of aircraft movements might be smaller. The results reported here are only for the 10 countries considered in the analysis listed in Table 1.
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
85,000 passengers from induced tra$c demand, and the remainder as transfer passengers at German airports. Although the German airports in the open sky scenario attract additional passengers, the number of aircraft movements diminishes by 12,000. This is caused by a shift from feeder #ights (reduced frequencies, no routes omitted) on principally domestic and European routes (using smaller aircraft) to the newly o!ered non-stop #ights (larger type of aircraft). This will be especially pronounced when intercontinental destinations are served. Hamburg airport was forecast to gain 149,000 passengers, of which 17,000 are transferring. Aircraft movements decrease by 1500 when airlines' supply is strictly in-line with passengers demand (scenario I). If airlines have the full scope in adjusting the size of aircraft (scenario II), the number of movements will decrease only by 200.
4. Economic impact models: assessing the changes for Hamburg The next step in the analysis is to take the change in #ights, aircraft gauge and passengers for Hamburg airport and determine how these changes would impact Hamburg airport and surrounding environs. The economic impact is direct for the airport, a!ecting both airside and non-aviation revenues as well as employment. The impact on the local economy can be expected to have an e!ect on employment, incomes and investment. Economic impacts are generally estimated using either input}output models or regional econometric models. Unless the multipliers from input}output models can be calculated for the local economy, this approach will overestimate the impacts. A set of simultaneous equations is estimated to develop measures of the economic impacts for the regional economy. Airport impacts are forecast from single equation models since the relationships are direct.
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for airport service. A change in aircraft operations a!ects aviation revenues and non-aviation revenues. We wish to distinguish a change in the frequency of #ights depending on the origin of the #ight; Germany, Europe or International. There is also a change in the numbers of passengers of di!erent origins and trip purpose that would impact the non-aviation revenues. 4.1.1. Aviation revenue The data distinguished three types of #ights: within Germany, intra-European and international #ights. We might also expect the aircraft gauge would change as well and this will have an impact on aviation revenues. Some airports, Hamburg included, levy a passenger handling charge and thus airside revenues will be in#uenced by the load factors and the origin of the passengers. The "rst model was estimated to investigate the impact of #ights, aircraft gauge and passengers on aviation revenue. The results of the estimation are contained in Table 3. The dependant variable is the amount of airside revenue in DM. At Hamburg, domestic/European #ights far outnumber international #ights so it is not unsurprising that the elasticity of airside revenue with respect to changes in European #ights is about three times that of international #ights. Similarly with respect to passengers, international passengers as a proportion of total passengers are relatively small, the elasticity of airside revenue with respect to international passengers is 0.06 while the corresponding value for European passengers is 0.46 yet one would expect on the margin that an international passenger would add more than a domestic (or European passenger). Revenue is also increasing over time. The time elasticity is 0.11 and re#ects change to rates and charges as well as the general growth in tra$c over time. For intra-German or European #ights, a 1 percent increase in these #ights will increase aviation revenue by 0.46 percent. If the #ights were international #ights, the 1 percent increase in #ights would lead to a 0.16 percent
4.1. Airport impacts Airport impacts from added #ights and passengers can include increases in employment, airside and terminal investment, aviation revenues and non-aviation revenues. The data only permitted us to examine the changes to airside and non-airside revenues. However, the methodology can be used on disaggregate data to forecast impacts on speci"c sources of revenue, such as autorentals or concessions. The sequence of events we are modeling is that a change in the bilateral leads to a change in the demand
In work undertaken in Canada recently we found the local and regional multipliers were less than 67% of the national multipliers.
We distinguish a change in #ights due to a given carrier increasing frequency and a change in #ights resulting from an increase in the number of carriers serving the airport. The reason is they would impact revenues (and expenses) quite di!erently. The terms airside revenue and aviation revenue are used synonymously in this document. The model was estimated using the a pooled time-series crosssection estimation method that takes account of both cross equation variation as well as serial correlation. The pooling technique employs a set of assumptions on the disturbance covariance matrix that gives a cross-sectionally heteroskedastic and timewise auto-regressive model. Estimates are obtained using generalized least squares. In our examination of the in#uences on airside revenue we also explore how changes in gauge or aircraft size would in#uence revenues. This variable is highly correlated with the &#ights' variable since most airports only have a distinction between classes in a gross way (i.e. only a few categories). If more detailed data were available, it would be useful to separate the impact of passengers, #ights and aircraft size.
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Table 3 Estimates of aviation revenue related to #ights and aircraft gauge Variable
Coe$cient
Std. error
t-Statistic
Elasticity
Constant term LOG (European #ights) LOG (International #ights) LOG (International pax) LOG (European Pax) Frankfurt dummy Hamburg dummy Hanover dummy LOG (time) Adjusted R-squared Log likelihood
6.53485 0.457201 0.163232 0.060806 0.469942 0.641598 0.013505 !0.02397 0.105029 0.9745 44.196
1.513581 0.288699 0.092433 0.030262 0.219671 0.135304 0.080592 0.059355 0.020804
4.317476 1.583685 1.766353 2.0093186 2.1392992 4.7418997 0.1675725 !0.403841 5.0485003
NA 0.46 0.16 0.06 0.47 NA NA NA 0.11
Table 4 Estimates of non-aviation revenue related to passenger information Variable
Coe$cient
Std. error
t-Statistic
Elasticity
Constant European pax International pax Time Frankfurt dummy Hamburg dummy Hanover dummy Proportion tourist
4.57E#08 33.82812 149.9273 4626011. 1.08E#08 78,442,724 17,810,241 25730013
1.25E#08 12.89160 15.86331 2443008. 88,443,662 27,976,468 15,732,710 7299623.
3.666542 2.624044 9.451197 1.893572 1.225558 2.803882 1.132052 3.524841
Na 0.29 0.59 0.08 Na Na Na 0.85
Adjusted R-squared Log likelihood
0.996419 !422.8523
S.D. dependent var F-statistic
increase in revenue. This is a signi"cant di!erence and implies airport o$cials should be careful in managing their tra$c. Focusing on International #ights because they are charged more per #ight and per passenger may lead to reduced revenue if the number of #ights decreases or passengers per #ight is less. 4.1.2. Non-aviation revenue The modeling of the linkages between #ights and passengers and non-aviation revenue parallel the investigation of aviation revenue. We separate these two sources of revenue since they are in#uenced by di!erent factors and represent quite di!erent management challenges and strategies. Non-aviation revenue is the revenue from non-airside activities. This would include revenue from concessions (food, clothing, other shopping items) parking and rental space to airlines, car rental agencies and other concessionaires. The primary driver of non-aviation revenue are passengers from di!erent destinations (domestic, other Europe and outside of Europe) and of di!erent types (business vs. tourist). Meeters, greeters, and airport personnel can also a!ect the magnitude of non-aviation revenues.
2.22E#08 915.2619
The estimates are contained in Table 4. The model is linear so the coe$cients can be interpreted directly. Each European passenger adds 33.82 DM while an International traveler adds 149.92 DM to non-aviation revenues. This result is consistent with the general literature showing the marginal contribution of international travelers to revenues tends to be higher than for domestic passengers. In this case, it is a di!erence factor of over four. Over time, non-aviation revenue has been increasing with 4.6 million DM being added annually. The dummy variables simply indicate the amount the constant term must be adjusted to re#ect a particular airport. Clearly, Frankfurt being such a dominant airport would be adjusted by a large amount while Hamburg would be adjusted upward as well by 78,442,724 DM. The elasticity of non-aviation revenue for German (domestic) and other European passengers is 0.29 while the elasticity of non-aviation revenue with respect to international passengers is 0.59. As the proportion of passengers who are tourists rises, revenue also increases. Given other things, if there is an increase by 10 percent of the proportion of passengers who are tourists, non-aviation revenue goes up by 0.85 percent, a signi"cant amount. Therefore, a change in the bilaterals leads to an
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
increase in passengers, the distinction between tourist and non-tourist is important in assessing the impact on non-aviation revenues. 4.2. Forecasting revenue impacts on airports In the modeling, we make a distinction between aviation and non-aviation revenue. Aviation or airside revenue is obtained from landing, parking and gate fees and passenger handling charges. They are tied directly to aircraft operations and aircraft size. Non-aviation revenue is generated from all other sources but mostly from parking, concessions (food, shops, car rental, etc.), leases and terminal rentals to airlines and associated aviation related activity. Aviation revenue (AR) is a!ected by both #ights and passengers. In developing the impact measures we focus on the combination of changes (operations by type, passenger by type and gauge of aircraft) rather than on a particular variable of interest and hold other variables at their mean values. This is intended to illustrate the net e!ect of the sum of changes of all the variables. It is certainly possible to simulate the outcome if a particular variable takes on a new value. In fact, this exercise may be useful for airport management for identifying targeted marketing and management strategies. As a result of the liberalization, open sky, there is a marginal decrease in European #ights. Under strategy I the decrease is 5.4 percent while this "gure is 3.6 percent under strategy II. Domestic #ights increase by approximately 0.2 percent under both strategies. International #ights increase under both strategies I and II by 5.7 and 7 percent, respectively. International passenger tra$c at Hamburg is up by 22 percent while domestic and EU passenger tra$c is down by 0.2 and 4 percent, respectively. Because of this mix of changes, airside revenue can expect to rise approximately 5.2 percent. The most signi"cant contributors to this revenue increase are increases in the number of international passengers and #ights. Non-aviation revenue can also be expected to rise. The impact of open sky on route passengers and the distribution between business and tourism across German, European and International enplaned passengers would result in a 6.2 percent increase in non-aviation revenue. The outcome is a result of an increase in EU passengers and a signi"cant increase in international passengers. The other important contributing factor is the major shift to a greater proportion of tourist tra$c. This calculation assumes a propensity to spend among new passengers from most international destinations to be the same as EU passengers. At Hamburg airport airside revenue has fallen from a high of 80 percent of total airport revenue in 1989 to 72.5 percent in 1997. Using this proportion as a weight to calculate the weighted average increase in airport revenue, the liberalization of bilaterals would result in an
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increase in total revenue of 5.7 percent. One might regard this as a conservative estimate since it takes existing strategies as "xed. One might expect airports would alter their marketing and operations strategies in response to changes to the bilateral and attract more revenues. The long-haul connecting hub is an example of such a strategic shift in behavior.
5. Employment, investment, income and tourism impacts Open markets and the reduction of barriers to trade provide opportunities for "rms to exploit their competitive advantage. As aviation is liberalized, access to and from the German market is enhanced and German "rms may also exploit their comparative advantage. Two related questions arise: how will increased access a!ect the local economy and how will aviation intensive industries such as tourism be in#uenced? The two questions are discussed in turn below. The "rst set of models examines the impact increased accessibility and mobility provided by the liberal bilateral regime might have on the regional economy surrounding an airport and particularly Hamburg airport. The two variables of particular interest are income (Y ) (measured by GDP), employment (L) and investment (K ). The three-equation model is illustrated below. The structural form of the model would be represented as >"f (¸, K), (1) ¸"f (>, K), (2) K"f (>, F, K ), (3) R\ where K is the amount of investment, F is the number of #ights and K is the investment in the previous period. R\ The "rst equation links aggregate output or sales to the two primary factors of production, labor and capital. Eq. (2) links the demand for labor to the level of economic activity and the amount of capital in place. Eq. (3) shows the demand for investment will depend on the level of economic activity, the amount of investment in the previous period and the availability of aviation services. It is in Eq. (3) that #ights have their role. They in#uence income and employment through their impact on investment. The three key elasticities from this model, for forecasting output, employment and investment are the elasticity of income (or GDP) with respect to #ights, m , the 7 $ elasticity of demand for labor with respect to #ights, m , * $ and , the elasticity of demand for investment with respect to #ights, m . These elasticities are measured as ) $ m "m m , 7 $ 7 ) ) $ m "m m m , * $ 7 ) ) $ * 7 m "dK/dF(F/K). ) $
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D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
Table 5 Results of reduced form estimation of structural economic impact model Log Y"5.715#0.6877 log K#0.10649 log K R\ #0.0621 log L Log L"!1.758#0.8976 log K#0.04742 log Y Log K"3.0534#0.1135 Log Y#0.41423 log F #0.34956 log K R\ System R"0.86
R"0.61 R"0.78 R"0.89
The reduced form of the model was estimated on the panel of airport and regional economic variables using seemingly unrelated regression estimation techniques. The basic results are reported below (Table 5). The values of the elasticities of interest are: m "0.27 elasticity of economic output or income with 7 $ respect to #ights, m "0.03 elasticity of employment with respect to * $ #ights, m "0.41 elasticity of investment with respect to #ights. ) $ Combining these measures with the expected changes in #ight activity resulting from the introduction of a more liberalized international aviation regime in Germany, we can estimate the impact on the Hamburg region. 5.1. Regional economic impact analysis There are a number of factors to consider in the analysis of aviation liberalization on the Hamburg economy. First, we consider the distribution between business and leisure passengers. The tourism model makes this an important distinction as well as the non-aviation revenue forecasting model. Second, we consider the distribution between domestic (within Germany), European and International #ights. Even if aggregate #ights decrease, the airport and local economy are not necessarily worse o! because the impacts of #ights are not homogeneous. Third, we make a distinction between passenger types in the same categories as types of #ights. The impact on the regional economy can be assessed using the models and reported in above. Using the elasticities calculated from the economic impact model the a!ects on the economy are presented in Table 6. Output (or income) increases by approximately $1.3 million, employment rises by 126 people and investment rises by over $30, 000. While these values appear to be small, they are the expected outcome of the changes only in the 10 markets described in Section 2. With a change in all markets these values would increase by a much larger margin.
The model is exactly identi"ed if investment in the previous year is treated as an exogenous variable.
5.2. Impacts on tourism We are also able to develop a tourism model allowing us to estimate the impact of changes in #ights on the numbers of tourist passengers. In turn we can also estimate the impact of changes in tourist passengers on the demand for labor in the tourism sector. The models are partial models and are intended only to provide an order of magnitude estimate of the impact of liberalization. These estimates are minimum values. The "nal actual outcome would be larger if because of the liberalization Germany became relatively less costly to visit than other countries. Two separate models are estimated. One related the number of tourist passengers to the level of income and the level of accessibility to the region measured by the number of #ights. The second model relates tourism employment to levels of income and levels of tourist passengers. The model results are reported below. The relevant elasticities that can be calculated from these models are the elasticity of tourism with respect to #ights, m , and the elasticity of employment with respect to 2 $ tourism, m : * 2 Log passengers 2 "9.4863#0.07973 Log F %># #0.06852 Log F #0.06493 Log >, R"0.93, 5 Log ¸"!0.3698#0.04927 Log > #0.02075 Log Passengers , R"0.56. 2 The values of the elasticities are m "0.08 the elasticity of tourism with respect to 2 $ #ights, m "0.02 the elasticity of employment with respect to * 2 tourism. Therefore, a 10 percent increase in #ights will lead to a 0.8 percent increase in tourist passengers and a 10 percent increase in tourism passengers leads to a 0.2 percent increase in employment, both rather small numbers. However, we have indicated these are quite conservative numbers since they do not account for tourist switching destinations. Impacts on the amount of tourism as well as the employment impacts for tourism can be calculated using the elasticity values. The results follow a two-stage approach. Increases in #ights and lower fares resulting from the liberalization may increase tourist tra$c to Germany. Because of the increase in tourism, employment in the tourism industry in the Hamburg area may rise.
Again the estimates reported are for changes only to the 10 markets described in Section 2.
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
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Table 6 Impact of bilateral change on regional output, employment and investment Output elasticity Employment Elasticity Investment elasticity New percent change in #ights
Output Employment Investment
Output Employment Investment
0.27 0.03 0.41 0.02 1999
Impact
Value in 1999#t
$234,325,358 210,433 $4,285,258
1,265,356.93 126 35,139.11
$235,590,715 210,559 $4,320,397
1994
Impact
Value in 1994#t
$223,167,008 200,412 $4,081,198
$783,316 78 $21,753
$223,950,324 200,490 $4,102,951
Note: Values translated to US$'s using 1994 exchange rate of 1.57 DM/US$.
In 1997, Hamburg airport handled 8,512,124 passengers (Mandel et al., 1999, Fig. 4). Of this total, approximately 30 percent were non-business, 2,553,637. In 1997, Hamburg airport handled 127,045 operations, excluding Germany and EU operations, 20,318 international #ights. Under the Open Sky liberalized-bilateral, the net increase in International #ights would be 5.69 percent (Mandel et al., 1999, Fig. 5). Using the elasticity of tourism with respect to #ights, 0.08, this would result in an increase in tourists by 11,624 and increase employment in the tourism industry by approximately 22 persons; the elasticity of tourism employment with respect to tourism is 0.02. Part of the increase can be explained by the shift from 30 to 60 percent of passengers travelling for non-business reasons.
6. Summary The open sky proposal has the potential to have a signi"cant impact on the Hamburg airport and Hamburg economy. The analysis begins with an assessment of what would be the outcome for the German economy with a change in the rules governing bilateral trade in aviation services between Germany and other countries. The outcome for changes in passengers and #ights is distributed across the major German airports because of both increased aggregate demands and induced tra$c #ows. Using a select group of 10 markets we estimate route passengers will increase by 165,970 with 17,000 as transfer passengers. There is a decrease in #ow from Canada, other EU countries and within the domestic economy but, there were gains on all other routes examined. The most signi"cant increases came in Japan, Thailand, Hong Kong and China with 85,957, 72,209, 70,459 and 50,796 passengers, respectively. Flight operations experience a net decrease. This is a result of a reduction in a large
number of smaller feeder aircraft moving away from Hamburg and a net increase in larger aircraft. Flights from Canada are down by 26 percent but average aircraft size is up. Similarly, Germany and the EU reduced #ights but all other countries increased #ights. International #ights (non-EU and non-domestic were up by between 1100 and 1500 #ights annually, depending on whether strategy I or strategy II is selected. The other signi"cant shift was from business travel to non-business travel; 63 percent of new passengers are on non-business trips. The impact of these changes is felt on airport revenue from airside and non-aviation sources as well as on the level of economic activity in the local and regional Hamburg economy. Airside revenue increases by about 5 percent because of an increase in the number international #ights and because of a signi"cant increase in the number of international passengers. Non-aviation revenue also increased by 6.7 percent. This revenue came from concessions, and the growing proportion of tourists in the passenger growth. Overall, airport revenue increases by approximately 6 percent. This is a weighted average of aviation and non-aviation revenue and the fact that non-aviation revenue is a growing proportion of aviation revenue. The output oft the Hamburg economy rises by a small amount, about 0.05 percent or $1.3 million US. Employment increases are small at only 126 people; a non-signi"cant amount in an employment population of over 200,000 workers. The increase in investment is also in the order of half a percent, about $35,000 US. However, with liberalization a!ecting all markets * not just the 10 analyzed here * the values would be much larger. Tourism impacts are somewhat more encouraging as tourism is labor intensive and the increasing proportion
Landing and parking fees increase with aircraft weight and carrying capacity.
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of new passengers are in the non-business type. Because of the increase in international #ights and passengers, tourism increases by approximately 11,000, about 0.4 percent. Due to the growth in tourism, tourism employment increases with 22 new jobs to accommodate the tourism increase. The numbers reported here might appear small, yet they represent perhaps the #oor of the impact of open sky. The analysis does not take account of what would occur should the airport take a more proactive role in marketing the airport, building connections with other countries and establishing a new retail plan.
References Button, K. (Ed.), 1991. Airline Deregulation: International Experiences. New York University Press, New York. Gillen, D., Harris, R., Oum, T., 1999. Evaluating air liberalization agreements: an integration of demand analysis and trade theory. In: Marc, G., Robert, M. (Eds.), Taking Stock of Air Liberalization. Kluwer Academic Publishers, Dordrecht, pp. 229}251. Mandel, B, Schnell, O., 2000. An &Open Sky' scenario for Hamburg airport and Germany. Paper Presented at Third Hamburg Aviation Conference, Hamburg February 10}11. SAIC Report, 2000. On the analysis of the Impact of Liberalization of International Air Bilaterals on the State of Hamburg and the Hamburg airport.
Measuring the economic impact of liberalization of international aviation on Hamburg airport David Gillen *, Holger Hinsch Institute for Transportation Studies, University of California, Berkeley, CA 94720-1720, USA Science Applications International Corporation, Center for Economic and Financial Analysis, 1710 SAIC Drive, McLean, VA 22102, M/S T3-8-2, USA
Abstract The paper describes a four-step modeling approach to estimating the impact of changes to the international aviation bilaterals on airport revenues and the income, employment and tourism e!ects for the local economy. The model is estimated on and applied to Hamburg airport and the State of Hamburg using a set of 10 representative markets that would have access to the German market. We "nd that Hamburg airport would gain 149,000 new passengers of which 17,000 would be hubbing through the airport to other international destinations. The change in passenger and operations leads to a 6 percent increase in overall airport revenues, as well as increases in local output, investment and employment. Tourism impacts were estimated separately with 11,000 new tourists and 22 new jobs in the tourism industry. 2001 Elsevier Science Ltd. All rights reserved. Keywords: Economic impact; International aviation liberalization; airport impact models
1. Introduction This paper discusses the outcomes that a change in the bilateral agreements governing access to the German market as well as access by German and other European carriers to other markets might have on Hamburg airport and the region surrounding the Hamburg airport. Impacts examined include changes to the frequency of #ights, the gauge of aircraft #own and the number of passengers carried. The changes will be distributed among the various airports in Germany according to the competitive circumstances and the comparative advantage of each airport. The economic impacts resulting from changes to the Air Service Agreements can be represented in four separate components. First, the aggregate impact for Germany must be established. Second, the aggregate impact on passengers and #ight frequency must be distributed among the major and regional airports in Germany. The disaggregated values for individual airports are calculated using an airport choice model (see Mandel and Schnell, 2000). The airport choice model allows us to predict the changes at particular airports in both passen* Corresponding author. Tel.: #1-510-643-2310; fax: #1-510-6421246. E-mail address: [email protected] (D. Gillen).
gers and #ights and segmented by domestic, European and international markets. Changes in gauge of aircraft cam also are forecast. Third, once distributed the economic impacts can be divided between airport impacts and local economic impacts. The move to open sky, the general liberalization of the German aviation bilateral, will a!ect airport revenue while changes in access to external markets will a!ect the airport region economy. Finally, tourism impacts may be assessed using the most recent measures of tourist response to changes in access and applying the elasticity to the representative countries. A #ow model of the approach is shown in Fig. 1. 1.1. Moving to liberalization Policymakers debating the issue of whether to deregulate their aviation system always face the question of whether the domestic economy would gain or lose under a liberalized international bilateral regime. There is a plethora of experience with domestic deregulation but only a limited set of experiences with international deregulation or liberalization (Button, 1991). Regulation tends to raise the costs of air transport to the detriment of air transport users. Since the complete liberalization of intra-EU air transport, all EU countries compete with each other for international air tra$c #ows by means of the institutional design of their bilateral agreements with
0969-6997/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 9 - 6 9 9 7 ( 0 0 ) 0 0 0 2 5 - 9
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Fig. 1. Study methodology.
non-EU countries. EU members that are successful in attracting these #ows have (or will) reap the greatest bene"ts from the liberalized EU air transport markets. Those that manage to attract international air tra$c to their own national air transport system by means of liberalizing still existing restrictive bilaterals, enable the exploitation of economies of scope and tra$c density by their own airlines and airports. Our conclusion from the assessment of the current state of liberalization of international air transport is that it provides an opportunity rather than an impediment to enhance social welfare of the German economy. Evaluation of the few cases in which international deregulation has been assessed support these conclusions. However, the investigations of international liberalization tend to be incomplete because the trade diversion e!ects and the identi"cation of winners and losers were ignored. In domestic deregulation, this identi"cation was considered a relatively minor issue, but it is not the case with international deregulation. The modeling in this investigation identi"es those stakeholders, particularly airports, that would gain and those that might lose. Section 2 of the paper provides an overview of the air liberalization model (ALM) and a description of the results for a simulation of liberalizing the German international aviation market. In Section 3 the airport choice model is described and the results of the airport allocation are reported. In Section 4 the airport economic impact models are described and the expected revenue impact reported. The economic impact modeling for the
local economy is developed in Section 5. Measures of the output, employment and investment results are also provided Section 6 contains the summary and conclusions.
2. Overview of the air liberalization model * ALM The Air liberalization model is best characterized as a competitive analysis set in a bilateral negotiating framework. The principal assumption of this case is that markets are competitive because it is assumed that there are no economies of "rm size or economies of tra$c density in air transportation. The implication of this assumption is that changes in bilateral regulatory conditions, which reduce airline costs, will translate into an equal reduction in fares paid by consumers. It supplies policy makers measures of the (welfare) impacts of bilateral air transport liberalization on domestic and foreign consumers, airlines and airports. It is adaptable to a full range of liberalization scenarios, regardless of speci"c national circumstances. The need for general, policy relevant results drives the model's structure. It strikes a balance between the This is not as limiting as it appears since the model does consider cost e$ciencies resulting from any scale and density economies but these are represented as a downward shift of a linear cost function (SAIC Report, 2000). In other words, all the cost savings are obtained by consumers in the form of lower fares.
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Fig. 2. Sample market.
complexity of equilibrium modeling, the minute detail of a bilateral relationship, and the need to maintain a model structure that is applicable to a broad range of regulatory and market circumstances. Fig. 2 shows a representative market that the model can assess. In this market, there are direct and indirect passenger #ows between countries as well as airlines o!ering services with varying frequencies and fare levels. The model shows how #ows between two countries, e.g., Germany and Canada, can be redirected through other liberalized markets creating spillover e!ects that work in favor of those countries that liberalize, as they can divert tra$c #ows to their own advantage. In contrast, countries that do not liberalize will experience a loss of tra$c #ows. The threat of tra$c diversion must be regarded as a serious one for Germany because of its central location within the already liberalized EU. This model combines elements of many previous studies in international aviation to provide quantitative evidence of the magnitude and distribution of bene"ts and costs from liberalization. The model also takes account of the e!ects of non-competitive markets and third country e!ects. These model components then simulate liberalization scenarios to estimate the potential gains (losses) from
The demand model takes full advantage of the available demand data for international origin}destination (OD) routes by incorporating price, demand, and qualitative factors. The supply model utilizes airline data as the basis for constructing costs and adjusts these costs to re#ect international carrier circumstances. The modi"cations are made external to the model. That is, the competitive market structure remains while judgments with respect to the degree of competition and the e!ect of third country markets are made using secondary sources and available data to estimate the magnitude and distribution of e!ects.
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liberalization, for consumers and producers. Consumer bene"ts are measured in terms of the value travelers' place on reduced fares and changes in service quality as measured by #ight frequency and increase in carrier choice as a result of market entry. Producer e!ects are measured in terms of changes in #ight segment pro"ts. The model also distributes bene"ts and costs by stakeholder group. In a recent study for the State of Hamburg and Hamburg airport, the model was used to predict the consequences of moving to an open skies regime. This meant no restrictions on fares, entry or frequency. The markets included in the analysis (listed in Table 1) were a sample that currently do not have good access to or a presence in Germany; the US market was therefore excluded. From the base case market and regulatory characteristics in Germany and the foreign country of interest, the policy evaluation considers changes in the following areas: expansion of frequency on existing OD segments, removal of pricing restrictions, entry of a new carrier and, "nally, combinations of all three changes. The results of the analysis are presented in Table 1. As the table shows, the introduction of competition results in a lowering of fares to "ll the additional capacity and maintain market share. This generates additional air travel and raises consumer expenditures. The welfare impacts on travelers is also positive due to the lowering fares. Producers bene"t because of the increase in travel expenditures despite the fact that average fares fall. Producers can also still obtain positive pro"ts due to consumer preferences for their national carriers.
3. Airport choice modeling: distribution of bene5ts of liberalization among airports This modeling e!ort takes the outcome of the aggregate analysis provided by the air liberalization model, described above, and determines how the results, measured in terms of passengers and #ights of di!erent types or market segments, would be distributed among the "rst- and second-tier airports in Germany. In particular, how would Hamburg share in the bene"ts under the
The model explicitly allows for the calculation of changes in consumer welfare and demand as a consequence of adding or reducing route choices in a given OD market. The demand is structured to account for inter-route substitution in a network context. For example, liberalization of routes between Germany and Canada is likely to induce substitution from alternative routes connecting the two countries, but passing through a third country. The extent of inter-route substitution in response to liberalization and its welfare consequences for consumers will depend on key parameters such as the route substitution elasticity and the relevant quality characteristics of competing routes. A detailed description of the model can be found in, Gillen et al. (1999).
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Table 1 Air liberalization model results Canada Current Annual pas. (000) New annual pas. (000) Percentage change
Chile
China
Ghana
Japan
Russia
Thailand
UAE
Ukraine
512 574 12%
45 116 157%
382 423 11%
23 36 55%
430 496 15%
600 678 13%
390 438 12%
170 193 14%
80 109 36%
Current average fare New average fare Percentage change
$964 $922 !4%
$1500 $769 !49%
$2500 $2350 !6%
$1000 $827 !17%
$2058 $1927 !6%
$763 $739 !3%
$1910 $1819 !5%
$912 $888 !3%
$1000 $883 !12%
Current consumer surplus ($M) New consumer surplus ($M) Percentage change
$289 $337 17%
$17 $62 259%
$1029 $1183 15%
$13 $24 81%
$666 $807 21%
$362 $427 18%
$727 $852 17%
$134 $159 19%
$63 $95 52%
Current producer surplus ($M) New producer surplus ($M) Percentage change
$219 $250 14%
$16 $38 137%
$837 $886 6%
$16 $21 32%
$740 $793 7%
$361 $404 12%
$619 $667 8%
$118 $136 15%
$67 $81 21%
Table 2 Changes through open sky in 1997 1997 Considered market
Canada Chile China Ghana Hong Kong Japan Russia Thailand UAE Ukraine Germany EU Other Grand total
Germany Passengers (tsd)
#39.4 #125.0 #127.3 #31.9 #271.9 #208.9 #52.2 #187.9 #21.5 #161.6 !337.8 !259.9 !15.4 #614.5
Hamburg Passengers (tsd)
A/c movements strategy I
A/c movements strategy I
!4.2 0 #50.8 0 #70.5 #86.0 #15.9 #72.2 #0.2 #17.5 !5.6 !137.8 #0.5 #166.0
!18 #0 #259 0 #263 #352 !93 #266 0 #127 #79 !2704 0 !1470
!18 0 #191 0 #358 #430 !25 #361 #0 #204 #79 !1791 0 !212
current set of carriers and airports but in an open skies regulatory environment? The airport choice and demand distribution model results for Germany in total are presented in Table 2 below. The results, shown for passengers, illustrate the gains expected when open sky takes place. These results are somewhat downward biased as passengers on international routes (e.g. Russia}Canada) who presently change planes at hubs outside Germany would most likely switch to a German airport in the open sky scenario. These diverted passengers were not included in the
A detailed description of the modeling approach and interim outcomes are provided in the full copy of the report as well as in the paper by Mandel and Schnell (2000).
disaggregate analysis due to a lack of access to the required data. The same applies for the indicated change in the number of aircraft movements. The total e!ect for Germany from the introduction of an open sky agreement for the 10 markets considered results in 277,000 additional passengers on the non-stop #ights to, from and between the German airports. The number of passengers on domestic feeder #ights diminishes by 338,000. The 277,000 passengers are made up of If Airlines want to keep frequencies high at the feeder #ights and prefer to serve the new inaugurated routes by smaller aircraft (Airbus A340 instead of Boeing 747), the decrease of aircraft movements might be smaller. The results reported here are only for the 10 countries considered in the analysis listed in Table 1.
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
85,000 passengers from induced tra$c demand, and the remainder as transfer passengers at German airports. Although the German airports in the open sky scenario attract additional passengers, the number of aircraft movements diminishes by 12,000. This is caused by a shift from feeder #ights (reduced frequencies, no routes omitted) on principally domestic and European routes (using smaller aircraft) to the newly o!ered non-stop #ights (larger type of aircraft). This will be especially pronounced when intercontinental destinations are served. Hamburg airport was forecast to gain 149,000 passengers, of which 17,000 are transferring. Aircraft movements decrease by 1500 when airlines' supply is strictly in-line with passengers demand (scenario I). If airlines have the full scope in adjusting the size of aircraft (scenario II), the number of movements will decrease only by 200.
4. Economic impact models: assessing the changes for Hamburg The next step in the analysis is to take the change in #ights, aircraft gauge and passengers for Hamburg airport and determine how these changes would impact Hamburg airport and surrounding environs. The economic impact is direct for the airport, a!ecting both airside and non-aviation revenues as well as employment. The impact on the local economy can be expected to have an e!ect on employment, incomes and investment. Economic impacts are generally estimated using either input}output models or regional econometric models. Unless the multipliers from input}output models can be calculated for the local economy, this approach will overestimate the impacts. A set of simultaneous equations is estimated to develop measures of the economic impacts for the regional economy. Airport impacts are forecast from single equation models since the relationships are direct.
29
for airport service. A change in aircraft operations a!ects aviation revenues and non-aviation revenues. We wish to distinguish a change in the frequency of #ights depending on the origin of the #ight; Germany, Europe or International. There is also a change in the numbers of passengers of di!erent origins and trip purpose that would impact the non-aviation revenues. 4.1.1. Aviation revenue The data distinguished three types of #ights: within Germany, intra-European and international #ights. We might also expect the aircraft gauge would change as well and this will have an impact on aviation revenues. Some airports, Hamburg included, levy a passenger handling charge and thus airside revenues will be in#uenced by the load factors and the origin of the passengers. The "rst model was estimated to investigate the impact of #ights, aircraft gauge and passengers on aviation revenue. The results of the estimation are contained in Table 3. The dependant variable is the amount of airside revenue in DM. At Hamburg, domestic/European #ights far outnumber international #ights so it is not unsurprising that the elasticity of airside revenue with respect to changes in European #ights is about three times that of international #ights. Similarly with respect to passengers, international passengers as a proportion of total passengers are relatively small, the elasticity of airside revenue with respect to international passengers is 0.06 while the corresponding value for European passengers is 0.46 yet one would expect on the margin that an international passenger would add more than a domestic (or European passenger). Revenue is also increasing over time. The time elasticity is 0.11 and re#ects change to rates and charges as well as the general growth in tra$c over time. For intra-German or European #ights, a 1 percent increase in these #ights will increase aviation revenue by 0.46 percent. If the #ights were international #ights, the 1 percent increase in #ights would lead to a 0.16 percent
4.1. Airport impacts Airport impacts from added #ights and passengers can include increases in employment, airside and terminal investment, aviation revenues and non-aviation revenues. The data only permitted us to examine the changes to airside and non-airside revenues. However, the methodology can be used on disaggregate data to forecast impacts on speci"c sources of revenue, such as autorentals or concessions. The sequence of events we are modeling is that a change in the bilateral leads to a change in the demand
In work undertaken in Canada recently we found the local and regional multipliers were less than 67% of the national multipliers.
We distinguish a change in #ights due to a given carrier increasing frequency and a change in #ights resulting from an increase in the number of carriers serving the airport. The reason is they would impact revenues (and expenses) quite di!erently. The terms airside revenue and aviation revenue are used synonymously in this document. The model was estimated using the a pooled time-series crosssection estimation method that takes account of both cross equation variation as well as serial correlation. The pooling technique employs a set of assumptions on the disturbance covariance matrix that gives a cross-sectionally heteroskedastic and timewise auto-regressive model. Estimates are obtained using generalized least squares. In our examination of the in#uences on airside revenue we also explore how changes in gauge or aircraft size would in#uence revenues. This variable is highly correlated with the &#ights' variable since most airports only have a distinction between classes in a gross way (i.e. only a few categories). If more detailed data were available, it would be useful to separate the impact of passengers, #ights and aircraft size.
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Table 3 Estimates of aviation revenue related to #ights and aircraft gauge Variable
Coe$cient
Std. error
t-Statistic
Elasticity
Constant term LOG (European #ights) LOG (International #ights) LOG (International pax) LOG (European Pax) Frankfurt dummy Hamburg dummy Hanover dummy LOG (time) Adjusted R-squared Log likelihood
6.53485 0.457201 0.163232 0.060806 0.469942 0.641598 0.013505 !0.02397 0.105029 0.9745 44.196
1.513581 0.288699 0.092433 0.030262 0.219671 0.135304 0.080592 0.059355 0.020804
4.317476 1.583685 1.766353 2.0093186 2.1392992 4.7418997 0.1675725 !0.403841 5.0485003
NA 0.46 0.16 0.06 0.47 NA NA NA 0.11
Table 4 Estimates of non-aviation revenue related to passenger information Variable
Coe$cient
Std. error
t-Statistic
Elasticity
Constant European pax International pax Time Frankfurt dummy Hamburg dummy Hanover dummy Proportion tourist
4.57E#08 33.82812 149.9273 4626011. 1.08E#08 78,442,724 17,810,241 25730013
1.25E#08 12.89160 15.86331 2443008. 88,443,662 27,976,468 15,732,710 7299623.
3.666542 2.624044 9.451197 1.893572 1.225558 2.803882 1.132052 3.524841
Na 0.29 0.59 0.08 Na Na Na 0.85
Adjusted R-squared Log likelihood
0.996419 !422.8523
S.D. dependent var F-statistic
increase in revenue. This is a signi"cant di!erence and implies airport o$cials should be careful in managing their tra$c. Focusing on International #ights because they are charged more per #ight and per passenger may lead to reduced revenue if the number of #ights decreases or passengers per #ight is less. 4.1.2. Non-aviation revenue The modeling of the linkages between #ights and passengers and non-aviation revenue parallel the investigation of aviation revenue. We separate these two sources of revenue since they are in#uenced by di!erent factors and represent quite di!erent management challenges and strategies. Non-aviation revenue is the revenue from non-airside activities. This would include revenue from concessions (food, clothing, other shopping items) parking and rental space to airlines, car rental agencies and other concessionaires. The primary driver of non-aviation revenue are passengers from di!erent destinations (domestic, other Europe and outside of Europe) and of di!erent types (business vs. tourist). Meeters, greeters, and airport personnel can also a!ect the magnitude of non-aviation revenues.
2.22E#08 915.2619
The estimates are contained in Table 4. The model is linear so the coe$cients can be interpreted directly. Each European passenger adds 33.82 DM while an International traveler adds 149.92 DM to non-aviation revenues. This result is consistent with the general literature showing the marginal contribution of international travelers to revenues tends to be higher than for domestic passengers. In this case, it is a di!erence factor of over four. Over time, non-aviation revenue has been increasing with 4.6 million DM being added annually. The dummy variables simply indicate the amount the constant term must be adjusted to re#ect a particular airport. Clearly, Frankfurt being such a dominant airport would be adjusted by a large amount while Hamburg would be adjusted upward as well by 78,442,724 DM. The elasticity of non-aviation revenue for German (domestic) and other European passengers is 0.29 while the elasticity of non-aviation revenue with respect to international passengers is 0.59. As the proportion of passengers who are tourists rises, revenue also increases. Given other things, if there is an increase by 10 percent of the proportion of passengers who are tourists, non-aviation revenue goes up by 0.85 percent, a signi"cant amount. Therefore, a change in the bilaterals leads to an
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
increase in passengers, the distinction between tourist and non-tourist is important in assessing the impact on non-aviation revenues. 4.2. Forecasting revenue impacts on airports In the modeling, we make a distinction between aviation and non-aviation revenue. Aviation or airside revenue is obtained from landing, parking and gate fees and passenger handling charges. They are tied directly to aircraft operations and aircraft size. Non-aviation revenue is generated from all other sources but mostly from parking, concessions (food, shops, car rental, etc.), leases and terminal rentals to airlines and associated aviation related activity. Aviation revenue (AR) is a!ected by both #ights and passengers. In developing the impact measures we focus on the combination of changes (operations by type, passenger by type and gauge of aircraft) rather than on a particular variable of interest and hold other variables at their mean values. This is intended to illustrate the net e!ect of the sum of changes of all the variables. It is certainly possible to simulate the outcome if a particular variable takes on a new value. In fact, this exercise may be useful for airport management for identifying targeted marketing and management strategies. As a result of the liberalization, open sky, there is a marginal decrease in European #ights. Under strategy I the decrease is 5.4 percent while this "gure is 3.6 percent under strategy II. Domestic #ights increase by approximately 0.2 percent under both strategies. International #ights increase under both strategies I and II by 5.7 and 7 percent, respectively. International passenger tra$c at Hamburg is up by 22 percent while domestic and EU passenger tra$c is down by 0.2 and 4 percent, respectively. Because of this mix of changes, airside revenue can expect to rise approximately 5.2 percent. The most signi"cant contributors to this revenue increase are increases in the number of international passengers and #ights. Non-aviation revenue can also be expected to rise. The impact of open sky on route passengers and the distribution between business and tourism across German, European and International enplaned passengers would result in a 6.2 percent increase in non-aviation revenue. The outcome is a result of an increase in EU passengers and a signi"cant increase in international passengers. The other important contributing factor is the major shift to a greater proportion of tourist tra$c. This calculation assumes a propensity to spend among new passengers from most international destinations to be the same as EU passengers. At Hamburg airport airside revenue has fallen from a high of 80 percent of total airport revenue in 1989 to 72.5 percent in 1997. Using this proportion as a weight to calculate the weighted average increase in airport revenue, the liberalization of bilaterals would result in an
31
increase in total revenue of 5.7 percent. One might regard this as a conservative estimate since it takes existing strategies as "xed. One might expect airports would alter their marketing and operations strategies in response to changes to the bilateral and attract more revenues. The long-haul connecting hub is an example of such a strategic shift in behavior.
5. Employment, investment, income and tourism impacts Open markets and the reduction of barriers to trade provide opportunities for "rms to exploit their competitive advantage. As aviation is liberalized, access to and from the German market is enhanced and German "rms may also exploit their comparative advantage. Two related questions arise: how will increased access a!ect the local economy and how will aviation intensive industries such as tourism be in#uenced? The two questions are discussed in turn below. The "rst set of models examines the impact increased accessibility and mobility provided by the liberal bilateral regime might have on the regional economy surrounding an airport and particularly Hamburg airport. The two variables of particular interest are income (Y ) (measured by GDP), employment (L) and investment (K ). The three-equation model is illustrated below. The structural form of the model would be represented as >"f (¸, K), (1) ¸"f (>, K), (2) K"f (>, F, K ), (3) R\ where K is the amount of investment, F is the number of #ights and K is the investment in the previous period. R\ The "rst equation links aggregate output or sales to the two primary factors of production, labor and capital. Eq. (2) links the demand for labor to the level of economic activity and the amount of capital in place. Eq. (3) shows the demand for investment will depend on the level of economic activity, the amount of investment in the previous period and the availability of aviation services. It is in Eq. (3) that #ights have their role. They in#uence income and employment through their impact on investment. The three key elasticities from this model, for forecasting output, employment and investment are the elasticity of income (or GDP) with respect to #ights, m , the 7 $ elasticity of demand for labor with respect to #ights, m , * $ and , the elasticity of demand for investment with respect to #ights, m . These elasticities are measured as ) $ m "m m , 7 $ 7 ) ) $ m "m m m , * $ 7 ) ) $ * 7 m "dK/dF(F/K). ) $
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D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
Table 5 Results of reduced form estimation of structural economic impact model Log Y"5.715#0.6877 log K#0.10649 log K R\ #0.0621 log L Log L"!1.758#0.8976 log K#0.04742 log Y Log K"3.0534#0.1135 Log Y#0.41423 log F #0.34956 log K R\ System R"0.86
R"0.61 R"0.78 R"0.89
The reduced form of the model was estimated on the panel of airport and regional economic variables using seemingly unrelated regression estimation techniques. The basic results are reported below (Table 5). The values of the elasticities of interest are: m "0.27 elasticity of economic output or income with 7 $ respect to #ights, m "0.03 elasticity of employment with respect to * $ #ights, m "0.41 elasticity of investment with respect to #ights. ) $ Combining these measures with the expected changes in #ight activity resulting from the introduction of a more liberalized international aviation regime in Germany, we can estimate the impact on the Hamburg region. 5.1. Regional economic impact analysis There are a number of factors to consider in the analysis of aviation liberalization on the Hamburg economy. First, we consider the distribution between business and leisure passengers. The tourism model makes this an important distinction as well as the non-aviation revenue forecasting model. Second, we consider the distribution between domestic (within Germany), European and International #ights. Even if aggregate #ights decrease, the airport and local economy are not necessarily worse o! because the impacts of #ights are not homogeneous. Third, we make a distinction between passenger types in the same categories as types of #ights. The impact on the regional economy can be assessed using the models and reported in above. Using the elasticities calculated from the economic impact model the a!ects on the economy are presented in Table 6. Output (or income) increases by approximately $1.3 million, employment rises by 126 people and investment rises by over $30, 000. While these values appear to be small, they are the expected outcome of the changes only in the 10 markets described in Section 2. With a change in all markets these values would increase by a much larger margin.
The model is exactly identi"ed if investment in the previous year is treated as an exogenous variable.
5.2. Impacts on tourism We are also able to develop a tourism model allowing us to estimate the impact of changes in #ights on the numbers of tourist passengers. In turn we can also estimate the impact of changes in tourist passengers on the demand for labor in the tourism sector. The models are partial models and are intended only to provide an order of magnitude estimate of the impact of liberalization. These estimates are minimum values. The "nal actual outcome would be larger if because of the liberalization Germany became relatively less costly to visit than other countries. Two separate models are estimated. One related the number of tourist passengers to the level of income and the level of accessibility to the region measured by the number of #ights. The second model relates tourism employment to levels of income and levels of tourist passengers. The model results are reported below. The relevant elasticities that can be calculated from these models are the elasticity of tourism with respect to #ights, m , and the elasticity of employment with respect to 2 $ tourism, m : * 2 Log passengers 2 "9.4863#0.07973 Log F %># #0.06852 Log F #0.06493 Log >, R"0.93, 5 Log ¸"!0.3698#0.04927 Log > #0.02075 Log Passengers , R"0.56. 2 The values of the elasticities are m "0.08 the elasticity of tourism with respect to 2 $ #ights, m "0.02 the elasticity of employment with respect to * 2 tourism. Therefore, a 10 percent increase in #ights will lead to a 0.8 percent increase in tourist passengers and a 10 percent increase in tourism passengers leads to a 0.2 percent increase in employment, both rather small numbers. However, we have indicated these are quite conservative numbers since they do not account for tourist switching destinations. Impacts on the amount of tourism as well as the employment impacts for tourism can be calculated using the elasticity values. The results follow a two-stage approach. Increases in #ights and lower fares resulting from the liberalization may increase tourist tra$c to Germany. Because of the increase in tourism, employment in the tourism industry in the Hamburg area may rise.
Again the estimates reported are for changes only to the 10 markets described in Section 2.
D. Gillen, H. Hinsch / Journal of Air Transport Management 7 (2001) 25}34
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Table 6 Impact of bilateral change on regional output, employment and investment Output elasticity Employment Elasticity Investment elasticity New percent change in #ights
Output Employment Investment
Output Employment Investment
0.27 0.03 0.41 0.02 1999
Impact
Value in 1999#t
$234,325,358 210,433 $4,285,258
1,265,356.93 126 35,139.11
$235,590,715 210,559 $4,320,397
1994
Impact
Value in 1994#t
$223,167,008 200,412 $4,081,198
$783,316 78 $21,753
$223,950,324 200,490 $4,102,951
Note: Values translated to US$'s using 1994 exchange rate of 1.57 DM/US$.
In 1997, Hamburg airport handled 8,512,124 passengers (Mandel et al., 1999, Fig. 4). Of this total, approximately 30 percent were non-business, 2,553,637. In 1997, Hamburg airport handled 127,045 operations, excluding Germany and EU operations, 20,318 international #ights. Under the Open Sky liberalized-bilateral, the net increase in International #ights would be 5.69 percent (Mandel et al., 1999, Fig. 5). Using the elasticity of tourism with respect to #ights, 0.08, this would result in an increase in tourists by 11,624 and increase employment in the tourism industry by approximately 22 persons; the elasticity of tourism employment with respect to tourism is 0.02. Part of the increase can be explained by the shift from 30 to 60 percent of passengers travelling for non-business reasons.
6. Summary The open sky proposal has the potential to have a signi"cant impact on the Hamburg airport and Hamburg economy. The analysis begins with an assessment of what would be the outcome for the German economy with a change in the rules governing bilateral trade in aviation services between Germany and other countries. The outcome for changes in passengers and #ights is distributed across the major German airports because of both increased aggregate demands and induced tra$c #ows. Using a select group of 10 markets we estimate route passengers will increase by 165,970 with 17,000 as transfer passengers. There is a decrease in #ow from Canada, other EU countries and within the domestic economy but, there were gains on all other routes examined. The most signi"cant increases came in Japan, Thailand, Hong Kong and China with 85,957, 72,209, 70,459 and 50,796 passengers, respectively. Flight operations experience a net decrease. This is a result of a reduction in a large
number of smaller feeder aircraft moving away from Hamburg and a net increase in larger aircraft. Flights from Canada are down by 26 percent but average aircraft size is up. Similarly, Germany and the EU reduced #ights but all other countries increased #ights. International #ights (non-EU and non-domestic were up by between 1100 and 1500 #ights annually, depending on whether strategy I or strategy II is selected. The other signi"cant shift was from business travel to non-business travel; 63 percent of new passengers are on non-business trips. The impact of these changes is felt on airport revenue from airside and non-aviation sources as well as on the level of economic activity in the local and regional Hamburg economy. Airside revenue increases by about 5 percent because of an increase in the number international #ights and because of a signi"cant increase in the number of international passengers. Non-aviation revenue also increased by 6.7 percent. This revenue came from concessions, and the growing proportion of tourists in the passenger growth. Overall, airport revenue increases by approximately 6 percent. This is a weighted average of aviation and non-aviation revenue and the fact that non-aviation revenue is a growing proportion of aviation revenue. The output oft the Hamburg economy rises by a small amount, about 0.05 percent or $1.3 million US. Employment increases are small at only 126 people; a non-signi"cant amount in an employment population of over 200,000 workers. The increase in investment is also in the order of half a percent, about $35,000 US. However, with liberalization a!ecting all markets * not just the 10 analyzed here * the values would be much larger. Tourism impacts are somewhat more encouraging as tourism is labor intensive and the increasing proportion
Landing and parking fees increase with aircraft weight and carrying capacity.
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of new passengers are in the non-business type. Because of the increase in international #ights and passengers, tourism increases by approximately 11,000, about 0.4 percent. Due to the growth in tourism, tourism employment increases with 22 new jobs to accommodate the tourism increase. The numbers reported here might appear small, yet they represent perhaps the #oor of the impact of open sky. The analysis does not take account of what would occur should the airport take a more proactive role in marketing the airport, building connections with other countries and establishing a new retail plan.
References Button, K. (Ed.), 1991. Airline Deregulation: International Experiences. New York University Press, New York. Gillen, D., Harris, R., Oum, T., 1999. Evaluating air liberalization agreements: an integration of demand analysis and trade theory. In: Marc, G., Robert, M. (Eds.), Taking Stock of Air Liberalization. Kluwer Academic Publishers, Dordrecht, pp. 229}251. Mandel, B, Schnell, O., 2000. An &Open Sky' scenario for Hamburg airport and Germany. Paper Presented at Third Hamburg Aviation Conference, Hamburg February 10}11. SAIC Report, 2000. On the analysis of the Impact of Liberalization of International Air Bilaterals on the State of Hamburg and the Hamburg airport.