Australia’s mining productivity decline: implications for MFP measurement

Zheng, Simon; Bloch, Harry

doi:10.1007/s11123-012-0329-4

Cited by 22 publications

(21 citation statements)

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“…Additionally, the frontier function represents a best-practice technology against which the efficiency of firms within the industry can be measured. 2 Also called multi-factors productivity (MFP) in some lectures, like [25]. The present study utilized the logarithmic form of translog production function, thus Equation (1) above can be written as:…”

Section: Methodsmentioning

confidence: 99%

Technical Efficiency and Its Determinants of Rice Production in Cambodia

Kea¹,

Li²,

Pich³

2016

Preprint

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: The present study aims to measure the technical efficiency and establish core factors affecting rice production in Cambodia. A four-year dataset generated from the central government document "Profile on Economics and Social" of 25 entire provinces between 2012 and 2015 and the stochastic production frontier model (SFA) was applied. The results indicated that the level of output (quantity) of Cambodian rice production varied according to the different level of capital investment in agricultural machineries, total rice actual harvested area, and technical fertilizer application within provinces. Furthermore, evidence revealed that the overall mean efficiency of rice production is 78.4%, which implies that there is still room to further improve technical efficiency given the same level of inputs and technology. More importantly, the findings revealed that irrigation, production techniques and amount of agricultural supporting staff are the most important influencing factors of rice production's technical efficiency in Cambodia. In conclusion, the present study strongly recommends the development of irrigation systems and good water management practices to be considered and bring about more effective actions by the central government as well as related agencies for improving rice production in Cambodia in addition to capital investment and improving technical skills of supporting staff and rural farmers. Terms of use: Documents in

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Section: Methodsmentioning

confidence: 99%

Technical Efficiency and Its Determinants of Rice Production in Cambodia

Kea¹,

Li²,

Pich³

2016

Preprint

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“…In the tables released in December 2015, this raises the estimated growth rate of mining MFP over 2000-01 to 2014-15 from -4 percent per year to -1 percent per year. Similarly, Zheng and Bloch (2014) In the post-2004 period the average share of investment in oil and gas structures in value added of business is about 0.9 percent. But, plausibly about half of that, or about 0.5 percent of business output, is the structure itself (which is improving faster than measured); the remainder is actually the subsoil asset (where the quality is getting worse).…”

Section: Technological Innovation In Oil and Gas: The Fracking Revolumentioning

confidence: 99%

Does the United States have a Productivity Slowdown or a Measurement Problem?

Byrne¹,

Fernald²,

Reinsdorf³

2016

ERWP

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After 2004, measured growth in labor productivity and total factor productivity (TFP) slowed. We find little evidence that the slowdown arises from growing mismeasurement of the gains from innovation in information-technology (IT)-related goods and services. First, mismeasurement of IT hardware is significant prior to the slowdown and because the domestic production of these products has fallen, the quantitative effect on productivity is larger in the 1995-2004 period than since, despite mismeasurement worsening for some types of IT. Hence, our adjustments make the slowdown in labor productivity worse. The effect on TFP is more muted. Second, many of the tremendous consumer benefits from smartphones, Google searches, and Facebook are, conceptually, non-market: Consumers are more productive in using their nonmarket time to produce services they value. These benefits raise consumer well-being but do not imply that market-sector production functions are shifting out more rapidly than measured. Moreover, estimated gains in non-market production are too small to compensate for the loss in overall well-being from slower market-sector productivity growth. In addition to IT, other measurement issues we can quantify (such as increasing globalization and fracking) are also quantitatively small relative to the slowdown. * Byrne, Fernald and Reinsdorf are at the Federal Reserve Board (david.m.byrne@frb.gov), the Federal Reserve Bank of San Francisco (fernaldjg@gmail.com), and the International Monetary Fund (mreinsdorf@imf.org), respectively. We thank Martin Baily, Erik Brynjolfsson, Erwin Diewert, Carol Corrado, Jean Flemming, Bob Gordon, Pete Klenow, Jim Stock, and Hal Varian for helpful comments and conversations. We thank Travis Adams, Genevieve Denoeux, and Arthi Rabbane for excellent research assistance. The title refers to a phrase in a Wall Street Journal article by Aeppel (2015). The views expressed in this paper are those of the authors and should not be attributed to the IMF or its managers or Executive Directors, the Federal Reserve Bank of San Francisco, the Board of Governors, or the Federal Reserve System. "The things at which Google and its peers excel, from Internet search to mobile software, are changing how we work, play and communicate, yet have had little discernible macroeconomic impact.…Transformative innovation really is happening on the Internet. It's just not happening elsewhere."Greg Ip, Wall Street Journal, August 12, 2015 U.S. productivity data highlight the paradox at the heart of the quotation above. The fast pace of innovation related to information technology (IT) seems intuitive and obvious. Yet productivity growth has been modest, at best, since the early 2000s. We examine the hypothesis that the U.S. economy has a growing measurement problem rather than a productivity slowdown (e.g., Aeppel, 2015, Feldstein, 2015, and Hatzius and Dawsey, 2015. Some components of real output, including the services provided by information technology, are indeed poorly measured.Yet for mismeasure...

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“…The mining industry is a very important part of the global economy and provides essential raw materials such as coal, metals, minerals, sand and gravel to the factories and building industries, utilities and other businesses [8,9]. In other words, mining is and will remain a vital part of the global economy for many years.…”

Section: Aims and Objectivesmentioning

confidence: 99%

“…Because of some factors like low energy prices and lower rates of capital investment in the manufacturing sector, Australia's industrial sector had not improved its energy intensity as much as that of other countries [9,10]. The international energy agency estimates that by 2030, Australia energy need will be 60% higher than today.…”

Section: Aims and Objectivesmentioning

confidence: 99%

“…Neural networks typically have an input layer, one or more hidden layers and an output layer. Each input is multiplied by its connected weight and in the simplest state, these quantities and biases are combined; they then pass through the activation functions to create the output (see [6][7][8][9][6][7][8][9][10]. Figure 6-11 shows the data treatment in a node (it should be noted that the hidden layer nodes may use any differentiable activation function to generate their output).…”

Section: Neural Network Structure Training and Developmentmentioning

confidence: 99%

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Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines

Soofastaei¹

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Truck haulage is responsible for a majority of cost in a surface mining operation. Diesel fuel, which is costly and has a significant environmental footprint, is used as a source of energy for haul trucks in surface mines. Reducing diesel fuel consumption would lead to a reduction in haulage cost and greenhouse gas emissions. The determination of fuel consumption is complex and requires multiple parameters including the mine, fleet, truck, fuel, climate and road conditions as input. Data analytics is used to simulate the complex relationships between the input parameters affecting the truck fuel consumption. This technique is also used to optimise the input parameters to minimise the fuel consumption without losing productivity or further capital expenditure for a specific surface mining operation.The aim of this research thesis is to develop an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines. The most important controllable parameters affecting fuel consumption are first identified, namely payload, truck speed and total resistance. These parameters are selected based on the results of an online survey. A comprehensive analytical framework is developed to determine the opportunities for minimising the truck fuel consumption. The first stage of the analytical framework includes the development of an artificial neural network model to determine the relationship between truck fuel consumption and payload, truck speed and total resistance. This model is trained and tested using real data collected from some large surface mines in USA and Australia. A fitness function for the haul truck fuel consumption is successfully generated. This fitness function is then used in the second stage of the analytical framework to develop a computerised learning algorithm based on a novel multi-objective genetic algorithm. The aim of this algorithm is to estimate the optimum values of the three effective parameters to reduce the diesel fuel consumption.The following studies are also conducted to enhance the analysis of haul truck fuel consumption.First, a comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation is carried out. Then, a discrete-event model to simulate the effect of payload variance on truck bunching, cycle time and hauled mine materials is developed. The influence of rolling resistance on haul truck fuel consumption in surface mines is investigated.

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Australia’s mining productivity decline: implications for MFP measurement

Cited by 22 publications

References 11 publications

Technical Efficiency and Its Determinants of Rice Production in Cambodia

Technical Efficiency and Its Determinants of Rice Production in Cambodia

Does the United States have a Productivity Slowdown or a Measurement Problem?

Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines

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