Summary Aggregation of environmental pressures into a single environmental damage index is a major challenge of eco‐efficiency measurement. This article examines how the data envelopment analysis (DEA) method can be adapted for this purpose. DEA accounts for substitution possibilities between different natural resources and emissions and does not require subjective judgment about the weights. Although DEA does not require subjective or normative judgment, soft weight restrictions can be incorporated into the framework. The proposed approach is illustrated by an application to assessing ecoefficiency of road transportation in the three largest towns of eastern Finland.

The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stochastic homoskedastic composite error term. To estimate this model, a new twostage method is proposed, referred to as Stochastic Nonsmooth Envelopment of Data (StoNED). The first stage of the StoNED method applies convex nonparametric least squares (CNLS) to estimate the shape of the frontier without any assumptions about its functional form or smoothness. In the second stage, the conditional expectations of inefficiency are estimated based on the CNLS residuals, using the method of moments or pseudolikelihood techniques. Although in a cross-sectional setting distinguishing inefficiency from noise in general requires distributional assumptions, we also show how these can be relaxed in our approach if panel data are available. Performance of the StoNED method is examined using Monte Carlo simulations.

As student numbers in the UK's higher education sector have expanded substantially during the last 15 years, it has become increasingly important for government to understand the structure of costs in higher education, thus allowing it to evaluate the potential for expansion and associated cost implications. This study applies Data Envelopment Analysis (DEA) to higher education institutions (HEIs) in England in the period 2000/01-2002/03 to assess the cost structure and the performance of various HEI groups. The paper continues and complements an earlier study by Johnes, Johnes and Thanassoulis (2008), who used parametric regression methods to analyse the same panel data. Interestingly, the DEA analysis provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. We then extend the previous analysis by examining potential cost savings and output augmentations in different HEI groups using several different DEA models. The findings include a suggestion that substantial gains of the order of 20-27% are feasible if all potential savings are directed at raising student numbers so that each HEI exploits to the full not only operating and scale efficiency gains but also adjusts its student mix to maximise student numbers. Finally we use a Malmquist index approach to assess productivity change in UK HEIs. The results reveal that for a majority of HEIs productivity has actually decreased during the study period.

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The Flexible System of Global Models (FSGM) is a group of models developed by the Economic Modeling Division of the IMF for policy analysis. A typical module of FSGM is a multi-region, forward-looking semi-structural global model consisting of 24 regions. Using the three core modules focused on the G-20, the euro area, and emerging market economies, this paper outlines the theory underpinning the model, and illustrates its macroeconomic properties by presenting its responses under a wide range of experiments, including monetary, financial, demand, supply, fiscal and international shocks.

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The value of statistical life is an essential parameter used in ascribing monetary values to the mortality costs of air pollution in health risk analyses. However, this willingness to pay estimate is virtually non-existent for most developing countries. In the absence of local estimates, two major benefit transfer approaches lend themselves to the estimation of the value of statistical life: the value transfer method and the meta-regression analysis. Using Nigeria as a sample country, we find that the latter method is better tailored than the former for incorporating many characteristics that vary between study sites and policy sites into its benefit transfer application. It is therefore likely to provide more accurate value of statistical life predictions for very low-income countries. Employing the meta-regression method, we find Nigeria's value of statistical life estimate to be $489,000. Combining this estimate with dose response functions from the epidemiological literature, it follows that if Nigeria had mitigated its 2006 particulate air pollution to the World Health Organisation standards, it could have avoided at least 58,000 premature deaths and recorded an avoided mortality related welfare loss of about $28 billion or 19 % of the nation's GDP for that year. © 2012 Springer Science+Business Media B.V