Economic Modeling and the Modernization of the National Airspace System

Economists Ink: A Brief Analysis of Policy and Litigation


View Bono’s Profile
James W. Bono is a Senior Economist with EI. He specializes in Industrial Organization and has worked on a range of projects, including aviation, cyber security, and antitrust.

Demand for air travel is projected to increase dramatically over the next several decades. The Federal Aviation Administration (FAA) estimates that revenue passenger miles among US commercial carriers will increase from 826 billion in 2013 to 1,462 billion in 2033, an increase of 77%. Boeing forecasts an even greater growth rate for air passenger traffic and also estimates that the number of airplanes in service will reach 41,240 by 2032, more than double the 20,310 in service in 2012. To accommodate the increased demand while maintaining safety and improving efficiency, the FAA has developed NextGen, a plan to modernize the National Airspace System (NAS).

While NextGen focuses on developing a variety of technologies and policies for introduction to the NAS, the backbone of NextGen is a migration from ground-based radar navigation to satellite-based navigation. To achieve this migration, aircraft must be equipped with automatic dependence surveillance-broadcast (ADS-B). ADS-B has two parts; ADS-B Out transmits aircraft number, location, altitude, and velocity relative to the earth, and ADS-B In receives this information. In an airspace with ADS-B Out equipped aircraft, an ADS-B In equipped aircraft will see a complete picture of its surroundings.

The FAA predicts a number of tangible safety and efficiency benefits from the adoption of ADS-B. Because ADS-B allows aircraft to see other aircraft and even ground vehicles that are also ADS-B equipped, it will improve safety by reducing the risk of runway incursions and violations of separation assurance. It also extends coverage to remote areas where radar is unavailable, improving navigation and flight safety in those areas. ADS-B improves NAS efficiency by allowing pilots to fly more directly between origin and destination, saving time and fuel. In addition, controllers will eventually be able to reduce the minimum separation between aircraft, increasing NAS capacity.

To achieve these predicted benefits, the FAA has developed a plan for the adoption of ADS-B. This plan is well underway. The FAA has already prepared much of the ground infrastructure for ADS-B and will complete that infrastructure in 2014. In addition, the FAA has issued a mandate that aircraft must be equipped with ADS-B Out to fly in certain airspaces by January 1, 2020. The mandate airspaces include those through which commercial aircraft regularly fly. Thus, the mandate will effectively require domestic commercial airlines to equip their entire fleets.

The adoption of ADS-B is not without issue. Equipping an entire commercial fleet is a major capital investment. Factoring in the cost of equipment and the cost of having the aircraft idle while the equipment is installed, a single commercial aircraft can cost as much as $900,000 to retrofit. This investment is especially costly when considering active fleet sizes of major US carriers: American (633), Delta (740), United (700), Southwest (577), US Airways (355), and JetBlue (191). Individual airlines could pay more than $500 million to retrofit their fleets, not including the cost of training pilots to fly with the new technology.

To help with costs, the FAA has proposed ideas such as loan guarantees to support equipage financing. Industry leaders, however, say that financing is already inexpensive and what they really need is for the FAA to deliver infrastructure and policy that guarantees significant efficiency gains. In this respect, airlines, like most businesses, are averse to the risk that accompanies major changes in their business environment.

To better understand the uncertainty about the benefits of ADS-B equipage, it has become increasingly important to build and analyze sophisticated economic models of the airline equipage decision process. Unlike previous models, the new models are both probabilistic and strategic. As such, they yield insights about a number of issues. For example, the predicted efficiency gains depend on the details of the policies that the FAA enacts to give preferential treatment to equipped aircraft. Even if the FAA adopts the policy that equipped aircraft are able to leapfrog non-equipped aircraft in merging scenarios (preferential merge), the resulting time savings do not guarantee efficiency gains due to the extra fuel burn required to take advantage of the preferential treatment and the fact that on-time flights are not generally rewarded for early arrivals. Another problem is that without the right policies in place, ADS-B would confer a free-rider benefit to non-equipped aircraft because the NAS becomes more efficient as a whole.

In general, optimal policies depend on airline decisions about which aircraft to equip, their routes, and their schedules. Moreover, airlines will make those decisions strategically based on the FAA’s policies and the decisions of other airlines. Additional uncertainty arises in this decision process from asymmetries in airline networks and costs. There is even uncertainty about what, if any, penalties the FAA will impose for not equipping. All told, it is very difficult for airlines to predict how the ADS-B equipage mandate will play out.

In light of such uncertainty, airlines could choose to delay adopting ADS-B to keep their options open. That is, they may delay equipping until some of their uncertainty is resolved, either because they will be able to observe how ADS-B equipage affects their competitors or because the FAA changes its policies. The FAA does not want airlines to delay the adoption of ADS-B because of the central role the technology plays in the NextGen plan. Developing economic models to better understand the decisions airlines face is an important step in creating NextGen policies that engage airlines and encourage the appropriate adoption of new technology.