Thesis advisor: Julie H. Mortimer / Three self-contained essays explore government regulation in the airline industry, and how such policies affect competition. The first essay explores the proposed merger between US Airways and American Airlines in 2013, approved by the US Department of Justice (DOJ) under the condition that 104 airport slots (“landing rights”) at Ronald Reagan Washington National Airport, DC, be divested to low cost carriers. To investigate the efficacy of the slot divestment, I estimate demand and cost parameters along with bounds on the shadow price of an airline slot, and simulate counterfactual post-merger prices and quantities with and without the regulatory divestment. I find that the merger and associated divestment together increased consumer surplus for markets involving Reagan Airport by roughly 25%. This increase in consumer welfare happened because the median price fell and the quantity of passengers increased. I show that the marginal value of a slot to an airline is decreasing in total slots, validating the DOJ’s decision to divest slots from the largest incumbent (US Airways, whose marginal value was $153 per flight) to new entrants with high valuation (like Southwest, $852). Beyond providing a key input to merger analyses, my approach can also aid in analyzing voluntary exchanges of airline slots, which are subject to DOJ approval due to their perceived anti-competitive effects. The second essay investigates the impact of airport slots on competition in general. Congestion is managed in high-density airports by capping the number of flights permitted in any given hour and allocating the rights (or slots) to a takeoff or landing among airlines. Airlines must use their slots at least 80% of the time to keep them for the next season. This rule creates a perverse incentive for airlines to hold on to underutilized slots by operating unprofitable flights instead of forfeiting these slots to a rival. Using exogenous removal of slot control at the Newark Airport in 2016, we investigate the lengths at which airlines go to meet the minimum requirements that let them keep the slots while violating what a neutral observer might call the “spirit” of the regulation. In my third essay, I assess the effectiveness of the gross upward pricing pressure index (GUPPI) in predicting price changes of the 2013 merger between US Airways and American Airlines. I compute GUPPI using only publicly available data, and find that it is close to the observed average increase in price. However, unlike most markets, flights to/from Reagan Airport experience a price drop, likely due to mandated structural remedies; the GUPPI predicts a price increase at Reagan Airport, whereas a full merger simulation correctly predicts a price reduction. I argue that the divergence between GUPPI and, if appropriate, the more accurate predictions of the merger simulation is due to the weaker assumptions made under the simulation. This underscores the fact that while GUPPI, with its restrictive assumptions and low computational burden, can be a good primary screening tool, it does not negate the necessity of employing a more rigorous secondary tool (such as a merger simulation) when assessing mergers. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.