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This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.

This chapter examines the impact of recent airline consolidations in the United States on the technical efficiencies of the airlines involved. Data envelopment analysis (DEA) is used to assess the efficiencies, and the consolidations examined are those that occurred among major network carriers between 2005 and 2013. The airline production process is conceptualized as the transformation of labor, fuel, and fleet-wide seating capacity into available seat-miles, or, under an alternative model specification, into user value, as measured by the airline’s operating revenue. Efficiency is conceptualized in terms of minimizing the airline’s usage of the three inputs, given its output level. The analysis seeks to determine whether the airlines that consolidated were more efficient, post-consolidation, than they were prior to consolidation, compared to airlines that did not enter into consolidations. Although there are limitations owing to the small number of airlines in the dataset, the chapter finds no evidence that the consolidations enhanced the efficiencies of the airlines involved, relative to the efficiencies of the airlines that did not enter into consolidations.

This paper aims to identify drivers of efficiency and their influence on airline performances in South Africa. Unfortunately, the methods currently used to measure airline efficiency fail to address the heterogeneity problem, which blurs inefficiency.

To remedy the heterogeneity problem, this paper adopts the meta-frontier framework to identify drivers of efficiency. The interesting feature of the model is that it ensures that heterogeneous airlines are compared based on one homogeneous technology. The model is tested using a panel data sample of nine South African airlines, which operated from 2015 to 2018.

The paper demonstrates that structural drivers, namely, “aircraft size”, and “airline ownership” and one executional driver, namely, “the cost structure” significantly influence (p < 0.05) airline efficiency thereby corroborating evidence from some prior studies.

First, because of the small size of the industry, fewer airlines and a lack of detailed data, the study could not consider other important factors such as optimal routing and network structure. Second, a more rigorous analysis over a period of time would yield better understanding about the growth of the industry in South Africa and recognise the variation in the influence of drivers of efficiency on airline performances over time.

The results have potential policy implications. First, as the market in South Africa is too small to operate with a smaller aircraft probably, for airlines that operate with smaller aircraft to operate efficiently they should first identify niche markets where they can have a route monopoly. Second, while all state-owned airlines are perfect statehood symbols that define and represent countries, most state carriers in South Africa are highly inefficient. The researcher recommends policymakers to privatise state airlines or seek equity partners. Many nationalised airlines have turned losses to profits in the run-up to privatisation. British Airways, once a large burden on the British taxpayer, is now one of the world’s most efficient airlines. After the privatisation of Air France and Iberia, all two turned from loss-making concerns into profitable airlines. It, therefore, makes no sense for the South African government to expect state carriers to pursue a commercial mandate with such political interference. The very notion of efficiency itself is at risk.

This paper is a first attempt to identify drivers of operational efficiency using a bootstrapped meta-frontier approach in the airline industry in South Africa. By applying the meta-frontier approach the paper ensures that all heterogeneous airlines are assessed based on their distance from a common and identical frontier.

The airline industry is structurally challenged by its very nature, because of high overhead and capital costs. This is further exacerbated by macro-predictability and micro-uncertainty, thereby making it difficult for airlines in South Africa to attain operational efficiency. The purpose of this study is to identify drivers of operational efficiency and their impacts on airline performances in South Africa.

An extensive data collection using primary and secondary sources enabled the researchers to gather data on all the airlines operating in South Africa, for the period of 2012-2016, on a variety of parameters. A two-stage empirical analysis was carried out, which involved estimation of operational efficiencies during the first stage by using data envelopment analysis (DEA) and determination of performance drivers during the second stage by using a two-way random-effects generalised least squares regression and also a Tobit model.

From the study, it is clear that two structural drivers, namely, “aircraft size” and “seat load factor”, and two executional drivers, namely, “low cost business model” and “revenue hours per aircraft”, significantly impacted (p < 0.05) positively on airline efficiencies in South Africa. To improve efficiency, management should first concentrate on the drivers that can be changed in the short-term (executional drivers) and later focus on the drivers that require long-term planning (structural drivers). However, among the structural drivers, only “aircraft families” had a negative impact on airline efficiencies, whilst among executional drivers, only “block hours” negatively impacted on airline efficiencies.

Despite the importance of this study, it is not free of limitations. Firstly, because of the small size of the industry, fewer airlines and lack of detailed data, the study could not consider other important factors such as optimal routing and network structure. Secondly, although non-aeronautical revenues have become increasingly important in airline management, they were not included in this study. Further studies may investigate the impact of these factors on airline efficiency.

The results have potential policy implications. Firstly, as the domestic airline market in South Africa is too small to operate with a smaller aircraft efficiently, airlines that intend to make use of smaller aircraft should first identify niche markets where they can have a route monopoly, such as SA Airlink. Secondly, as block time negatively affected airline efficiency, airlines can undertake schedule adjustments to reduce block time and thus improve technical efficiency.

This paper is a first attempt to identify drivers of operational efficiency in the airline industry in South Africa. The results indicate that DEA is a useful tool to identify factors impacting airline efficiency and could improve airline performances in South Africa.

For strategic and competitive insights, the purpose of this paper is to measure and benchmark the comparative operating efficiencies of Association of Southeast Asian Nations’ (ASEAN) major airlines and present a new interpretation along with managerial implications.

This research statistically tests returns to scale and the equality of mean efficiencies for 15 ASEAN airlines covering the period 2010-2014. The disaggregate input efficiency of ASEAN airlines is computed by comparing the target and actual inputs.

The disaggregate input efficiency of ASEAN airlines shows that aircraft efficiency is the lowest, operating cost efficiency is better, and available seat efficiency is the best.

This paper applies data envelopment analysis models, disaggregated input efficiency measures, and bootstrapping approaches to compute the operational efficiency of ASEAN airlines. Strategic suggestions are made to improve the operational efficiency of ASEAN airlines.

Air carriers and aircraft manufacturers are investing in technologies and strategies to reduce fuel consumption and associated emissions. This chapter reviews related issues to assess airline fuel efficiency and offers various empirical evidences from our recent work that focuses on the U.S. domestic passenger air transportation system. We begin with a general presentation of four methods (ratio-based, deterministic frontier, stochastic frontier, and data envelopment analysis) and three perspectives for assessing airline fuel efficiencies, the latter covering consideration of only mainline carrier operations, mainline–subsidiary relations, and airline routing circuity. Airline fuel efficiency results in the short run, in particular the correlations of the results from using different methods and considering different perspectives, are discussed. For the long-term efficiency, we present the development of a stochastic frontier model to investigate individual airline fuel efficiency and system overall evolution between 1990 and 2012. Insight about the association of fuel efficiency with market entry, exit, and airline mergers is also obtained.

This chapter considers the productivity of 77 airlines between 1980 and 2013. We do so by estimating a stochastic frontier and decomposing the total factor productivity growth into efficiency, technical and scale efficiency change. Our results show that, on average, airlines increased productivity over the period but that, while efficiency and technical change improved, scale efficiency results indicate that the average airline moved away from the most productive scale size. This was especially so in the two decades after 1980. Comparisons between geographical areas, business models, networks and alliances are also made.

In recent years, the airline industry has been growing and transforming rapidly in the Asia-Pacific area. This study analyzes and benchmarks the comparative operational efficiencies of the major Asian air carriers. Data envelopment analysis model and disaggregate output efficiency measures are used to evaluate the operational efficiencies of 31 Asian airlines from 2015 to 2019. The findings suggest that nonflag carriers, low-cost carriers, and high-income regions' carriers have significantly higher levels of efficiency than flag carriers, full-service carriers, and low-income regions' carriers in overall, revenue, and passenger traffic efficiencies. The efficiencies between alliance carriers and nonalliance carriers along with those of ASEAN and non-ASEAN carriers are not significantly different.

This chapter examines the influence of outsourcing on airlines’ performance from countries of the Asia Pacific region. Performance in the context of this study is drawn from productivity growth and technical efficiency scores that are calculated using the standard data envelopment analysis (DEA) approach. We utilize data from airlines over the period 2003–2011 and estimate the impact of outsourcing on productivity and technical efficiency using generalized method of moments (GMM) estimators. The findings from DEA reveal an improvement in the technical efficiency score of airlines from Asia Pacific. Nonetheless, productivity estimates indicate fluctuations in the productivity growth trend of airlines, attributable to global economic recession in 2007/2008. GMM estimation results, however, suggest negative impacts of outsourcing on technical efficiency and productivity of the airlines from Asia Pacific countries. We offer several explanations for these outsourcing findings. Heavy outsourcing of airlines activities particularly maintenance of aircraft may negatively affect aircraft utilization and ultimately erode the service level of airlines. The erosion of the service level of airlines would affect the demand for air travel in a downward manner, thereby lowering the technical efficiency and productivity of airlines. Also, relatively low labor costs enjoyed by airlines in the Asia Pacific region would suggest that having many airline activities in-house would save operating expenses attributable to labor costs.