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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.

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.

Purpose – This chapter explores the basic characteristics of stochastic frontier estimation, discusses advantages of the method that make it conducive to research in international marketing, and provides an application to demonstrate its use. Potential applications in international marketing research are also discussed.

Methodology – Stochastic Frontier Estimation.

Findings – Stochastic frontier estimation models, prevalent in other fields, are very limited in the international marketing literature. Many potential opportunities exist for its use in the context of international marketing.

Originality/value of paper – The intent of this chapter is to show that stochastic frontier estimation is a potentially valuable tool for international marketing research. We show this by demonstrating the use of the tool and by providing examples of potential research studies.

Historically standard regression has been used to assess performance in marketing, especially of salespeople and retail outlets. A model of performance is estimated using ordinary least squares, the residuals are computed, and the decision-making units, say store managers, ranked in the order of the residuals. The problem is that the regression line approach characterizes average performance. The focus should be on best performance. Frontier analysis, especially stochastic frontier analysis (SEA), is a way to benchmark such best performance. Deterministic frontier analysis is also discussed in passing. The distinction between conventional ordinary least squares analysis and frontier analysis is especially marked when heteroscedasticity is present. Most of the focus of benchmarking has been on identifying the best performing units. The real insight, though, is from explaining the benchmark gap. Stochastic frontier analysis can, and should, model both phenomena simultaneously.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

There are two main dimensions in which the performance of a production unit can be assessed. The first is the dimension of time. The basic question here is: how is this or that production unit doing over time? Assessing a unit's performance over time is called monitoring. The second dimension is characterized by the question: how is this or that production unit doing relative to other, similar units? To answer this question one needs to specify the reference set of units and one needs sufficient information on each of the members of this set. This activity is usually called benchmarking. A combination of the two dimensions in the setting of a panel is also possible.

The goal of this contribution is to shed light on the benefits for research in health care coming from the use of administrative data, especially in terms of measuring hospitals’ outcomes. The main approaches to health outcome evaluation are reviewed and the possible improvements deriving from the use of administrative data are highlighted. Administrative data may be an essential element in the process of gathering to the public true rankings of health care organizations, reducing the degree of asymmetric information that typically arises in health care. Patients will be more aware of the best institutions, which will induce most of them to demand to be admitted in them, taking into account the costs associated with distance and with the severity of the illness. This in turn may ask for a reorganization of the sector, closing some organizations and expanding others, having as final goal to improve the health status of the population, without income barriers. This is one of the first attempts to provide an overview of the advantages that administrative data may gather in health care.

We review and synthesize the burgeoning literature on institutions and agents engaged in the commercialization of university-based intellectual property. These studies indicate that institutional incentives and organizational practices play an important role in enhancing the effectiveness of technology transfer. We conclude that university technology transfer should be considered from a strategic perspective. Institutions that choose to stress the entrepreneurial dimension of technology transfer need to address skill deficiencies in technology transfer offices, reward systems that are inconsistent with enhanced entrepreneurial activity, and education/training for faculty members, post-docs, and graduate students relating to interactions with entrepreneurs. Business schools at these universities can play a major role in addressing these skill and educational deficiencies through the delivery of targeted programs to technology licensing officers and members of the campus community wishing to launch startup firms.