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In his 2005 paper in this journal, Kuosmanen argues that Shephard’s specification of weak disposability in activity analysis (DEA) models is not correct. We show that Shephard’s specification does satisfy weak disposability and is the “smallest” technology to do so.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

In 1957 Farrell demonstrated how cost inefficiency could be decomposed into two mutually exclusive and exhaustive components: technical and allocative inefficiency. This result is a consequence of the fact that—as shown by Shephard—the cost function and the input distance function (the reciprocal of Farrell's technical efficiency measure) are 'dual' to each other. Similarly, the revenue function and the output distance function are dual providing the basis for the decomposition of revenue inefficiency into technical and allocative components (see for example, Färe, Grosskopf and Lovell (1994)). Here we extend those results to include the directional distance function and its dual, the profit function. This provides the basis for defining and decomposing profit efficiency. As we show, the output and input distance functions (reciprocals of Farrell efficiency measures) are special cases of the directional distance function. We also show how to use the directional distance function as a tool for measuring capacity utilization using DEA type techniques.

In 2007 Nicholas Stern's Review (in Science 317:201-202, 2007) estimated that global GDP would shrink by 5-20% due to climate change which brought forth calls to reduce emissions by 30-70% in the next 20 years. Stern's results were contested by Weitzman (in J Econ Lit XLV(3):703-724, 2007) who argued for more modest reductions in the near term, and Nordhaus (in Science 317:201-202, 2007) who questioned the low discount rate and coefficient of relative risk aversion employed in the Stern Review, which caused him to argue that 'the central question about global-warming policy—how much how, how fast, and how costly—remain open.' We present a simulation model developed by Fare et al. (in Time substitution with application to data envelopment analysis, 2009) on intertemporal resource allocation that allows us to shine some light on these questions. The empirical specification here constrains the amount of undesirable output a country can produce over a given period by choosing the magnitude and timing of those reductions. We examine the production technology of 28 OECD countries over 1992-2006, in which countries produce real GDP and CO₂ using capital and labor and simulate the magnitude and timing necessary to be in compliance with the Kyoto Protocol. This tells us 'how fast' and 'how much'. Comparison of observed GDP and simulated GDP with the emissions constraints tells us 'how costly'. We find these costs to be relatively low if countries are allowed reallocate production decision across time, and that emissions should be cut gradually at the beginning of the period, with larger cuts starting in 2000.

This paper analyses trends in labour productivity and its underlying determinants in a panel of OECD countries from 1979 to 2002. Data Envelopment Analysis (DEA) is used to estimate a Malmquist measure of multifactor productivity (MFP) change. We decompose the growth in labour productivity into (i) net technological change (ii) input biased technical change (IBTC) (iii) efficiency change and (iv) capital accumulation. We analyse the effect of each of these factors in the transition towards the equilibrium growth paths of both labour productivity and per capita GDP for the OECD countries, controlling for the effects of different policies and institutions. The results indicate that on average gaps in productivity or income levels are narrowing down although there is no evidence to suggest that the entire OECD area comprises a single convergence \"club\". Using kernel estimation methods we find that that labour productivity and per capita GDP are settling toward a twin peak (bimodal) distribution. Panel unit root tests over an extended (1960-2001) period provide general support for the convergence hypothesis. Analysis of the contributions of productivity growth within industries and sectoral composition changes show that aggregate productivity change is predominantly driven by 'net' within sector effects with very little contribution emerging from sectoral shifts (the 'in-between' static or dynamic effects resulting from higher or above average productivity industries gaining employment shares or low productivity industries losing shares).

The passage of environmental legislation was accompanied by concerns about its potential detrimental effect on productivity. We assume inputs can be assigned to either abatement activities or good output production. This allows us to specify regulated and unregulated production frontiers to determine the association between pollution abatement and productivity growth. We then employ our “assigned input” model to determine the association between productivity and abatement activities for manufacturing industries in Germany, Japan, the Netherlands and the United States.