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The purpose of this paper is to consider a new application of stochastic frontier analysis, in which the method is applied to demand data for a food product category, in an attempt to benchmark category consumption and segment food consumers.

In a unified, two‐stage approach, a stochastic frontier model is first estimated and subsequently deviations from the demand frontier are regressed on customer characteristics. The method is illustrated in scanner panel data.

A frontier demand function estimated in scanner data of a frequently‐bought food category has significant and consistent parameters. Specific descriptor variables can explain excessive category demand and profile customers with considerable sales potential.

More work is needed to generalise the usefulness of the proposed model in different food categories. Future research may employ alternative functional specifications and explanatory variables.

The empirical identification of salient characteristics improves consumer understanding and can assist in the design of data‐driven marketing action. Applied researchers can use marketing and demographic variables that are found in standard consumer panels to estimate frontier models.

The paper introduces stochastic frontier analysis as a means to determine consumer differences in food demand. This is an important area for retailers, producers and researchers.

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.

Using a panel of 1,122 UK firms listed on the London Stock Exchange over the period of 1981-2009, corporate efficiencies are predicted in this paper as inverse proxies of agency cost and the agency cost hypotheses are tested. The paper aims to discuss this issue.

Stochastic frontier analysis is used to estimate corporate efficiency of firms, but from two different perspectives. The long-run and short-run corporate efficiencies are predicted focussing on modern approach of value maximization and traditional approach of profit maximization, respectively.

The estimation results reveal that, an average firm in the sample achieves 74.5 percent of its best performing peer's market value and 86.6 percent of its best performing peer's profit and both of them are highly significant in the analysis. The long-run market value efficiency supports the agency cost of outside equity and the short-run profit efficiency supports the agency cost of outside debt hypothesis. Also there is a positive rank correlation between these two efficiencies which confirms that an average firm in the UK suffers from inefficiency or agency conflicts to a certain extent, no matter whether the firm is driven by short-run or long-run growth perspectives.

The predicted broad measures of agency costs in the paper have wider implications in enhancing the understanding of the UK firms’ corporate performance especially when they operate under a relatively free and market based governance and financial system.

The work is distinguished by the large panel of UK firms and a long period of time that is considered. Emphasizing on the empirical implications of the distinctions between short-run and long-run efficiency is also novel.

The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error.

The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived.

This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simulations, this paper shows that SDEA is a useful model for outlier detection.

The model developed in this paper is original; the authors add distributional assumptions to derive the optimal quantile with SDEA to remove outliers. The authors believe that the value of the paper will lead to many citations because real-world data are often subject to outliers.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This paper analyses the cost efficiency of Italian football clubs using a stochastic frontier model. The frontier estimation confirmed that the model fits the data well with all coefficients correctly signed and in line with the theoretical requirements. Marketing and Sponsorship is taken into account as an explanatory variable and the factors which contributed to these findings, as well as other policy implications, are provided.

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.

The aim of this paper is to use stochastic frontier regression to measure the effect of the introduction and expansion of prevailing wage requirements on the cost efficiency of public school construction in British Columbia. The results provide evidence from a specific sector and location that contributes to the accumulated evidence on the topic.

Stochastic frontier regression is used to measure the effect of the introduction and expansion of prevailing wage requirements on the cost efficiency of public school construction in British Columbia. The results of a likelihood ratio test indicate that stochastic frontier regression is preferred to OLS cost estimation because of the presence of statistically different cost inefficiencies across a sample of covered and uncovered projects.

Specifically, projects covered by the introductory stage of the wage requirements are significantly less cost efficient compared to other public school projects. However, by the time of the expansion of the wage policy 17 months later, covered projects were no more cost inefficient than other projects. The results indicate that if prevailing wage laws are associated with changes in the cost efficiency of construction, stochastic frontier regression is the preferred estimation technique.

This is the first study of its kind with results specific to school construction in British Columbia. Further research based on data from other projects may/may not confirm the existence of differences in construction cost inefficiencies for projects covered by construction wage policies.

By implication, the results suggest that if the cost inefficiency of prevailing wage laws is short‐lived, the impact on the total cost of construction may similarly be ephemeral. The results imply that prevailing wage regulations do not contribute to long‐run construction cost inefficiency.

This is the first study to use stochastic frontier regression to examine the cost inefficiencies associated with prevailing wage laws. Previous studies that examine the effect of the wage policy on construction costs are based on OLS estimation.

The chapter examines the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries as well as total manufacturing industry of Gujarat during the period from 1981–82 to 2010–11, using a stochastic frontier approach. The empirical finding clearly states that although factor accumulations as well as resource allocations in most of the 2-digit manufacturing industries of the state have been improved during the postreform period, technological progress (TP) and technical efficiency change (TEC) of the same have deteriorated in most industries of the state during that period. As a result TFPG in the major manufacturing industries as well as total manufacturing industry of the state have declined because the combined effect of their improvement in scale effect (SC) and allocation efficiency effect (AEC) could not offset the declining effect of both the TP and TEC of the same during that period. In this context, the government should take some policy initiatives to improve productive efficiency of the organized manufacturing industries in Gujarat. Once efficiency increases, it enhances competitiveness, thereby increasing productivity growth and its different sources of organized manufacturing industries of the state.

Restaurants in South Africa have a notoriously high failure rate. This study aims to identify drivers of restaurant efficiency in South Africa.

A stochastic cost frontier function with three inputs (i.e. labour, food and beverage and materials) and one output as the total revenue is specified and used to estimate restaurant efficiency. An extensive data collection using primary and secondary sources enabled the researcher to gather data from 42 restaurants, for the year 2016, on a variety of parameters.

The findings show that on average restaurants were operating at 77%, with the most and least efficient restaurants operating at a 97 and a 43% efficiency level, respectively. From the study, it is clear that two structural drivers, namely, “location” and “operation type”, and two executional drivers, namely, “restaurant type” and “revenue per available seat hour”, significantly impacted (p < 0.05) on restaurant efficiency in South Africa.

Despite the importance of this study, it is not free of limitations. First, the research was based on efficiency drivers for restaurants situated in a specific South African province. Caution is therefore required when generalising the findings of this study to restaurants in other geographic areas, as a replication of this study in other geographic areas might reveal varying levels of efficiency. Second, the measurement of restaurant efficiency was limited to five efficiency drivers. Even though these efficiency drivers were included in other studies as well, there could be other relevant efficiency drivers that are likely to influence restaurant efficiency.

To improve efficiency, restaurateurs should first concentrate on the drivers that can be changed in the short term (executional drivers) and then later focus on the drivers that require long-term planning (structural drivers). Restaurateurs should understand the use of RevPASH strategies to manipulate demand during peak and off-peak periods. Furthermore, restaurants should be able to change the table mix to optimise table configuration. Changing a restaurant’s table configuration during peak times increases efficiency.

This paper is a first attempt to identify drivers of operational efficiency using a stochastic approach in the restaurant industry in South Africa. As restaurants in South Africa have a high failure rate, the results could assist restaurateurs in managing more successful entities.