How cost efficient are Australia's mining industries?

This paper uses a stochastic translog cost frontier model and a panel data of five key mining industries in Australia over 1968-1969 to 1994-1995 to investigate the sources of output growth and the effects of cost inefficiency on total factor productivity (TFP) growth. The results indicate that mining output growth was largely input-driven rather than productivity-driven. Although there were some gains from technological progress and economics of scale in production, cost inefficiency which barely exceeded 1.1% since the mid-1970s in the mining industries was the main factor causing low TFP growth. (C) 2002 Elsevier Science B.V. All rights reserved.

The productivity-inflation nexus: The case of the Australian mining sector

This paper examines the causal links between productivity growth and two price series given by domestic inflation and the price of mineral products in Australia's mining sector for the period 1968/1969 to 1997/1998. The study also uses a stochastic translog cost frontier to generate improved estimates of total factor productivity (TFP) growth. The results indicate negative unidirectional causality running from both price series to mining productivity growth. Regression analysis further shows that domestic inflation has a small but adverse effect on mining productivity growth, thus providing some empirical support for Australia's 'inflation first' monetary policy, at least with respect to the mining sector. Inflation in mineral price, on the other hand, has a greater negative effect on mining productivity growth via mineral export growth.

Data mining and simulation: a grey relationship demonstration

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Cost-benefit analysis of alternative forms of hedgerow intercropping in the Philippine uplands

Considerable resources have been expended promoting hedgerow intercropping with shrub legumes to farmers in the Philippine uplands. Despite the resources committed to research and extension, persistent adoption by farmers has been limited to low cost versions of the technology including natural vegetation and grass strips. In this paper, cost-benefit analysis is used to compare the economic returns from traditional open-field maize farming with returns from intercropping maize between leguminous shrub hedgerows, natural vegetation strips and grass strips. An erosion/productivity model, Soil Changes Under Agroforestry, was used to predict the effect of erosion on maize yields. Key informant surveys with experienced maize farmers were used to derive production budgets for the alternative farming methods. The economic incentives revealed by the cost-benefit analysis help to explain the adoption of maize farming methods in the Philippine uplands. Open-field farming without hedgerows has been by far the most popular method of maize production, often with two or more fields cropped in rotation. There is little persistent adoption of hedgerow intercropping with shrub legumes because sustained maize yields are not realised rapidly enough to compensate farmers for establishment and maintenance costs. Natural vegetation and grass strips are more attractive to farmers because of lower establishment costs, and provide intermediate steps to adoption. Rural finance, commodity pricing and agrarian reform policies influence the incentives for maize farmers in the Philippine uplands to adopt and maintain hedgerow intercropping.

Cost functions and duality for stochastic technologies

Cost functions dual to stochastic production technologies are derived and their properties are discussed. These cost functions are shown to be consistent with expected-utility maximization without placing serious structural restrictions on the underlying technology.

The cost of life expectancy and the implicit social valuation of life

A new method of estimating the economic value of life is proposed. Using cross-country data, an equation is estimated to explain life expectancy as a function of real consumption of goods and services. The associated cost function for life expectancy in terms of the prices of specific goods and services is used to estimate the cost of a reduction in age-specific mortality rates sufficient to save the life of one person. The cost of saving a life in OECD countries is as much as 1000 times that in the poorest countries. Ethical implications are discussed.

Cost-effectiveness analysis of humanitarian relief interventions: visceral leishmaniasis treatment in the Sudan

Spending by aid agencies on emergencies has quadrupled over the last decade, to over US$ 6 billion. To date, cost-effectiveness has seldom been considered in the prioritization and evaluation of emergency interventions. The sheer volume of resources spent on humanitarian aid and the chronicity of many humanitarian interventions call for more attention to be paid to the issue of 'value for money'. In this paper we present data from a major humanitarian crisis, an epidemic of visceral leishmaniasis (VL) in war-torn Sudan. The special circumstances provided us, in retrospect, with unusually accurate data on excess mortality, costs of the intervention and its effects, thus allowing us to express cost-effectiveness as the cost per Disability Adjusted Life Year (DALY) averted. The cost-effectiveness ratio, of US$ 18.40 per DALY (uncertainty range between US$ 13.53 and US$ 27.63), places the treatment of VL in Sudan among health interventions considered 'very flood value for money' (interventions of less than US$ 25 per DALY). We discuss the usefulness of this analysis to the internal management of the VL programme, the procurement of funds for the programme, and more generally, to priority setting in humanitarian relief interventions. We feel that in evaluations of emergency interventions attempts could be made more often to perform cost-effectiveness analyses, including the use of DALYs, provided that the outcomes of these analyses are seen in the broad context of the emergency situation and its consequences on the affected population. This paper provides a first contribution to what is hoped to become an international database of cost-effectiveness studies of health outcome such as the DALY.

Life-cycle preferences over consumption and health: when is cost-effectiveness analysis equivalent to cost-benefit analysis?

This paper studies life-cycle preferences over consumption and health status. We show that cost-effectiveness analysis is consistent with cost-benefit analysis if the Lifetime utility function is additive over time, multiplicative in the utility of consumption and the utility of health status, and if the utility of consumption is constant over time. We derive the conditions under which the lifetime utility function takes this form, both under expected utility theory and under rank-dependent utility theory, which is currently the most important nonexpected utility theory. If cost-effectiveness analysis is consistent with cost-benefit analysis, it is possible to derive tractable expressions for the willingness to pay for quality-adjusted life-years (QALYs). The willingness to pay for QALYs depends on wealth, remaining life expectancy, health status, and the possibilities for intertemporal substitution of consumption. (C) 1999 Elsevier Science B.V. All rights reserved.

Knowledge discovery and data mining in biological databases

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.