Airline liberalization effects on fare: The case of the Philippines

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Abstract

This article explores the impact of liberalization on airfare using a framework that builds on previous studies but adapted to the peculiarities of the Philippine airline industry. The data consist of ten routes with varying market characteristics for the period 1981–2003, while the model consists of three equations estimated simultaneously using the generalized method of moments based on the Newey-West covariance estimator. The findings indicate that airfare per kilometer is 10% lower, on average, on route with at least two airlines after liberalization. Twenty-three routes, representing more than 90% of domestic airline passengers, have at least two airlines by 2003, indicating that most passengers benefit from lower fares due to the prevalence of discounts and promos as a result of competition.

Introduction

The Philippine government liberalized the country's domestic airline industry in 1995 under Executive Order (EO) 219, reducing regulations on tariffs and fares, as well as regulations on the entry into and exit from the airline industry. Prior to liberalization, only one airline—the Philippine Airlines (PAL)—operated scheduled domestic flights due to the government's one-airline policy. Under the provisions of EO 219, the Philippine government privatized PAL, removed restrictions on number of routes served and number of departures on each route, and reduced controls over fare in markets served by at least two airlines.

The liberalized domestic airline industry attracted five new entrants at one time but the number of airlines has dwindled to four following the failures in 1998 of both Grand Air and Corporate Air's Mindanao Express. The demise of new entrants in a relatively short period is comparable to the experience of the domestic airline industry in the United States (US) in the early 1980s (see Kahn, 1988, Borenstein, 1992). With the entry of South East Asian Airlines (SEAir) to the scheduled airline sector in 2003, three airlines (PAL, Cebu Pacific, and Air Philippines) now compete in major routes while two airlines (Asian Spirit and SEAir) serve minor and short-distance routes. By 2003 passengers in 23 airline markets have at least two airlines to choose from, giving them more choices on fare, departure time, and service quality.

A number of studies document the impact of deregulation and liberalization on the airline industry in the US and Europe. Previous studies on the Philippine airline industry, however, tend to be descriptive and do not use econometric analysis of available data. To estimate the impact of liberalization on airfare, this article builds on the empirical framework employed by Dresner and Tretheway (1992), Maillebiau and Hansen (1995), Marin (1995), Jorge-Calderon (1997), and Rietveld et al. (2002).

Section snippets

The impact of deregulation on the airline industry

Competition under regulation usually revolves around service quality, encouraging overcapacity that tends to inflate price (fare). Under deregulation, competition shifts toward price (Douglas and Miller, 1974, Graham et al., 1983). In the US domestic airline industry, deregulation has resulted in lower fares and higher load factors.

To determine who has benefited from regulation, Olson and Trapani (1981) examine the policies of the US Civil Aeronautics Board (CAB) by developing a method that

The impact of deregulation and liberalization on fare

Graham et al. (1983) test two hypotheses that are central to the arguments for deregulation by analyzing the US domestic airline industry before and after deregulation. The first hypothesis argues that fare regulation promotes service competition among airlines by employing excess capacity (Douglas and Miller, 1974). The second hypothesis contends that “potential competition will keep fares at competitive levels, even in highly concentrated markets,” which rests on the idea that capital is

Empirical framework

The econometric model consists of three equations since a system of equations is a more realistic depiction of the underlying theory on airline demand and is usually more appropriate when modeling the data generation process (Judge et al., 1988) on the number of passengers, the number of flights, and the level of fares in one or more airline markets. Furthermore, supply and demand simultaneously determine the values of fare, the number of passengers, and the number of departures whether under

Method

Maillebiau and Hansen (1995) consider the fare variable as exogenous and estimate both the demand and fare equations using OLS, while Dresner and Tretheway (1992) treat the passenger variable as endogenous in the fare equation and estimate the fare equation using two-stage indirect least squares. Marin (1995) estimates the demand and fare equations separately using instrumental variables for the endogenous variable in both equations, whereas Jorge-Calderon (1997) considers fare as exogenous and

Estimation results and analysis

The measurement of the fare variable is quite problematic because liberalization has intensified the practice of third-degree price discrimination among airlines. One solution proposed by Rietveld et al. (2002) is the use of the full economy fare since the average of business class and discount fares tends to approximate the full economy fare. Maillebiau and Hansen (1995), on the other hand, use discount fares since most passengers fly using discount fares under liberalization. This study uses

Conclusion and policy implication

This paper has empirically explored the impact of airline liberalization on fare using a sample of ten routes with varying market characteristics and state of competition for the period 1981–2003. The results of the estimated fare equation indicate that the average fare per kilometer on routes served by at least two airlines is, on average, 10% lower. Twenty-three routes, representing more than 90% of total domestic passengers in 2003, have at least two airlines, implying that most passengers

References (30)

  • J.D. Jorge-Calderon

    A demand model for scheduled airline services on international European routes

    J Air Transp Manag

    (1997)
  • ABN-AMRO

    Costs — key to a runway success

    (2002)
  • E.E. Bailey

    Contestability and the design of regulatory and antitrust policy

    AEA Pap Proc

    (1981)
  • E.E. Bailey

    Airline deregulation: confronting the paradoxes

    Regul Mag

    (1992)
  • B.H. Baltagi

    Econometric analysis of panel data.

    (2001)
  • D. Besanko et al.

    Economics of strategy

    (2004)
  • S. Borenstein

    The evolution of US airline competition

    J Econ Perspect

    (1992)
  • J.A. Brander et al.

    Market conduct in the airline industry: an empirical investigation

    Rand J Econ

    (1990)
  • Civil Aeronautics Board for airline-related...
  • G.W. Douglas et al.

    Quality competition, industry equilibrium, and efficiency in the price-constrained airline market

    Am Econ Rev

    (1974)
  • M. Dresner et al.

    Modeling and testing the effect of market structure on price: the case of international air transport

    J Transp Econ Policy

    (1992)
  • D.R. Graham et al.

    Efficiency and competition in the airline industry

    Bell J Econ Manage Sci

    (1983)
  • W.H. Greene

    Econometric analysis

    (1997)
  • M.D. Intriligator

    Econometric models, techniques, and applications

    (1978)
  • J. Johnston et al.

    Econometric methods

    (1997)
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