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29 CHAPTER 5 Areas for Future Research This project undertook an analysis of how large changes in into the overall macro-economy. The demand for travel fuel prices may affect future projections of airport activity. A and, therefore, the demand for aviation services, is prima- statistical model tying these and other economic elements rily a derived demand--most people consume scheduled together was developed and embedded inside a user-friendly aviation services not because they like to fly per se, but software program in order to allow airport planners and because it enables them to engage in desirable or necessary sponsors to accurately assess how fuel, economic, and other activities such as vacations and business meetings at remote uncertainties might affect their own airport forecasts. locations. So it makes sense to assess how energy price shocks Great care was taken to develop a statistically sound and may affect overall consumer demand, and then try to ascer- defensible model of how airport activity may be affected by tain how that translates into changes in the demand for air fuel price changes and other factors. By design, the model was travel. then embedded in a software program to assist airport plan- A common theme in some recent academic studies is ners with anticipating changes to existing forecasts of air ser- that the effects of rises in energy prices are felt mainly as vices. It accomplishes this by calculating percentage changes reductions in consumer purchasing power. Because many in seat departures based on a defined set of explanatory vari- of the primary demand uses for energy are relatively price- ables and then applying those percentages to the chosen exist- inelastic (for example, commuter travel to work and home ing forecast. This approach is less than perfect because these heating and electricity use), rising energy prices result in existing forecasts have their own embedded statistical rela- consumers spending more on energy consumption, thereby tionships and uncertainties which the model developed here leaving less discretionary income for purchases of other cannot fully account for. At best, it is hoped that the projected goods and services. This scenario is primarily how oil price percentage changes from the model are reasonably similar to shocks would be expected to affect aviation demand, with what would be obtained if the existing forecasts themselves the impacts on discretionary leisure travel likely to be greater were to be re-estimated with the same user-specified changes than the impacts on business travel. This and related issues in explanatory variables that appear in the software. are discussed further in the literature review contained in the With this limitation in mind, additional research could appendix. involve a so-called "meta-analysis" of airport forecasts. Such Another feature of the current analysis is that it was designed an approach would focus on combining the results of differ- to be relevant for hundreds of different-sized airports. While ent forecasts in the hopes of finding more accurate measures this feature means that the findings and potential usefulness of the impacts ("effect sizes") of specific factors such as oil of the software may be fairly widespread, it also means that prices on airport activities. If carried out properly, a meta- the analysis was quite restrictive in terms of how variations in analysis may be able to assess the reasons behind variations local conditions and factors could be accounted for. Perhaps between forecasts and expose any biases or weaknesses that future analyses could focus on one specific type of airport may exist in specific forecasts. (e.g., large reliever airports) in order to gain more insight into Another area for fruitful research may be in focusing on how oil prices and other economic shocks are likely to affect a more direct assessment of how airport aviation activity fits facilities with similar roles and uses.