Using data envelopment analysis to screen the possibility of a fair globalization

The International Labor Office, an arm of the UN based in Geneva, has as its goal to promote opportunities for women and men to obtain decent and productive work, in conditions of freedom, equity, security and human dignity. Since 1999, the ILO has conducted a series of studies of the effects of globalization.In 2004, the organization posed the challenge of tempering the perceived effects of globalization, aiming for A Fair Globalization. Fair rules on trade and finance need to be put in place benefiting men and women in rich and poor countries alike. Using standard economic terms, A Fair Globalization may be seen as the output of a generalized input-output function, dependent upon variables of both economic performance and economic and social policy. Using data envelopment analysis, we fit a piece-wise linear frontier to observations for 72 countries from all continents. Inefficient countries reveal conditions of lacking fairness

Chance-Constrained Efficiency Analysis

Data envelopment analysis (DEA) is extended to the case of stochastic inputs and outputs through the use of chance-constrained programming. The chance-constrained envelope envelops a given set of observations "most of the time." We show that the chance-constrained enveloping process leads to the definition of a conventional (certainty-equivalent) efficiency ratio (a ratio between weighted outputs and weighted inputs). Furthermore, extending the concept of Pareto and Koopmans efficiency to the case of chance-constrained dominance (to be defined), we establish the identity of the following two chance-constrained efficiency concepts: (i) the chance constrained DEA efficiency measure of a particular output-input point is unity, and all chance-constraints are binding; (ii) the point is efficient in the sense Pareto and Koopmans. Finally we discuss the implications of our approach for econometric frontier analysis.IC2 Institut

Comprehensive economic and spatial bio-energy modelling . Chania : CIHEAM / INRA

To cite th is article / Pou r citer cet article -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Abstract: Life Cycle Activity Analysis (LCAA) -a mathematical programming decision support model for the optimization of the entire life cycle of products -is presented. LCAA is a new tool for the mapping of hierarchical production and recovery chains, their impact on the environment, and for a holistic evaluation of new technologies, environmental strategies or policies. LCAA involves three successive stages of analysis: i) a description of all participating activities (processing, transport, use, recovery, …) as a good travels from its "cradle" to its "grave", including the inventory of ancillary materials and energy supplied to each activity, economic costs and environmental burdens; ii) the formulation and numerical solution of a linear or nonlinear mathematical programming model and iii) the evaluation of a set of environmental scenarios of interest to policy-decision-makers or stakeholders. It is shown how LCAA contributes to the conceptualization of Industrial Ecology, which can be seen as a new paradigm for the integration of environmental and economic performance. The antecedents of LCAA (classical Activity Analysis adjoined to the environmental Life Cycle Assessment framework) are surveyed. Illustrative conceptual mathematical programming formats are discussed and the potential of LCAA, the type of problems to be addressed and its relevance to environmental policy are further explored

Estimation of the Time Path of the Supply Price of an Exhaustible Resource: The Case of Oil and Natural Gas

Explores the interplay of geopolitical, economic, geological and operational factors that may disrupt the gradual upwards shift of the supply price of non-replenishable resources, specifically oil and natural gas. Describes the econometric identification of the supply price curve for oil and gas in the U.S. using two alternative independent variables: total footage drilled and number of wells completed. Develops a new constrained least squares method, the Thore regression map, of studying shifts of the supply price function over time and uses the techniques to estimate supply price drift. Provides, for the first time, statistical evidence of the time path of the drift upwards of these schedules. Shows that the shifts accelerate during times of upheaval in the oil markets, reflecting the need for a higher risk premium, but may be delayed during times of industry consolidation. A by-product of the analysis is a format for forecasting the supply price of an exhaustible resource.IC2 Institut