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Purpose – This chapter investigates the role of infrastructure aid to developing countries for determining the effect on national income and consumer welfare. The chapter further demonstrates the conditions for the Dutch disease effect by decomposing the output effects of infrastructure aid into the initial factor-saving effect, factor-substitution effect and nontraded good effect.

Methodology/approach – This chapter extends the Heckscher−Ohlin model to a 3×2 case with two traded goods and a nontraded good, and derives comparative static results on factor prices, the price of nontraded goods, foreign exchange rate, sectoral outputs, and national income and consumer welfare.

Findings – It is shown that for a recipient country, infrastructure aid to either the export or import sector necessarily raises national income and consumer welfare, whereas the same aid to the nontraded good sector does not affect national income but raises consumer welfare. Infrastructure aid may lead to a Dutch disease effect via its three effects on industrial outputs: the initial factor-saving effect, factor-substitution effect and nontraded good effect.

Research limitations/implications – This chapter considers infrastructure capital as a public input, but it is devoid of analysis of inter-industrial spillover effects that the infrastructure capital generates to other sectors.

Practical implications – This chapter reveals several aspects of infrastructure aid that the practitioners of aids must consider.

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.

The Association of Southeast Asian Nations (ASEAN) members want to efficiently promote the flow of commodities and personnel within its service areas under given limited resources. Based on panel data from 2007 to 2014, this study applies the output-oriented data envelopment analysis method and focuses on the disaggregated output efficiencies of 42 ASEAN airports. Results show that the international airports of ASEAN members have significantly better output efficiency for passenger and movement output than regional airports. This work provides a relatively fair perspective in evaluating ASEAN’s airport operating efficiency. It helps policymakers measure the frontier forward or backward shift of an airport over the research period, in order to reveal the characteristics of airport efficiency and to present a new interpretation along with managerial implications.

Issues of performance measurement are ubiquitous in modern organizations and are often concerned with evaluations of outputs or efficiency (which encompasses both inputs and outputs) of an entity or process. Examples of output measures include revenue generated, defective units produced, on-time shipments, etc. Efficiency examples include standard cost variances, machine up-time rate, and efficiency scores from input–output models such as Data Envelopment Analysis (DEA).

Difficult-to-measure outputs are often included even though they cannot be measured with precision. When outputs of a production process are not easy to measure, serious dysfunctional decision-making can be expected and these problems may be particularly acute when efficiency measurements from input–output models are directly tied to rewards and incentives. Both for-profit firms and public sector organizations may share output measurability problems.

In this paper, we examine the possible problems of using input–output models (such as DEA) when outputs are difficult to quantify within an agency theory perspective and illustrate the potential problems using recent proposals in the UK for evaluating and rewarding police unit performance. We conclude that although input–output models, particularly those such as DEA may be useful as a diagnostic tool to assist decision-makers in altering future operating strategies and policies, it has serious limitations when rewards and incentives are attached to the DEA performance evaluations. In our view, overreliance on mechanical, formula-based approaches is potentially a serious threat to improving performance in these situations.