The dynamics of efficiency and productivity growth in U.S. electric utilities

Rungsuriyawiboon, Supawat; Stefanou, Spiro E.

doi:10.1007/s11123-008-0107-5

Cited by 19 publications

(4 citation statements)

References 17 publications

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“…For example, Heshmati et al (2014) find productivity decline between 1995 and 2006 for Korean electricity generation, and Atkinson and Primont (2002) find only small productivity gains between 1961 and 1997 for United States electricity generation. Rungsuriyawiboon and Stefanou (2008) and Genius et al (2012) obtain similar results, i.e. partial productivity growth with respect to labor, but not with respect to fuel.…”

Section: Measuring Productivity Growth In Electricity Generationmentioning

confidence: 65%

Measuring Productivity When Technologies are Heterogeneous: A Semi-Parametric Approach for Electricity Generation

Seifert

2015

SSRN Journal

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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. Terms of use: Documents in AbstractWhile productivity growth in electricity generation is associated with multiple positive effects from an economic and environmental perspective, measuring it is challenging. This paper proposes a framework to estimate and decompose productivity growth for a sector characterized by multiple technologies. Using a metafrontier Malmquist decomposition and frontier estimation based on stochastic non-smooth envelopment of data (StoNED) allows for productivity estimation with few microeconomic assumptions. Additionally, evaluation of productivity at representative hypothetical units permits distribution-free analysis for the whole distribution of power plant sizes. The proposed framework is used to analyze a unique and rich dataset of coal, lignite, gas, and biomass-fired generators operating in Germany from 2003 to 2010. The results indicate stagnating productivity for the sector as a whole, technical progress for biomass plants, and very high productivity for gas-fired plants. JEL-Codes: D24,C14,O13,L94

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Section: Measuring Productivity Growth In Electricity Generationmentioning

confidence: 65%

Measuring Productivity When Technologies are Heterogeneous: A Semi-Parametric Approach for Electricity Generation

Seifert

2015

SSRN Journal

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“…We use the same data as in Lai and Kumbhakar (2019) which have been used before by Rungsuriyawiboon and Stefanou (2008). We have panel data on n = 82 US electric power generation plants during 1986-1997 (T = 12).…”

Section: Empirical Applicationmentioning

confidence: 99%

Constraints in models of production and cost via slack-based measures

Khan

Tsionas

2021

Empir Econ

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In this paper, we propose the use of stochastic frontier models to impose theoretical regularity constraints (like monotonicity and concavity) on flexible functional forms. These constraints take the form of inequalities involving the data and the parameters of the model. We address a major concern when statistically endogenous variables are present in these inequalities. We present results with and without endogeneity in the inequality constraints. In the system case (e.g., cost-share equations) or more generally, in production function-first-order conditions case, we detect an econometric problem which we solve successfully. We provide an empirical application to US electric power generation plants during 1986–1997, previously used by several authors.

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“…The price of capital is the yield of the firm's latest issue of long‐term debt adjusted for appreciation and depreciation of the capital good using the Christensen and Jorgenson (1970) cost of capital formula. The detailed description of the data sample, the assumptions underlying the data construction and a list of the electric utilities are presented in Rungsuriyawiboon and Stefanou (2007, 2008). The final dataset is an unbalanced panel of 82 electric utilities for the years 1986–1999.…”

Section: Datamentioning

confidence: 99%

Stochastic error specification in primal and dual production systems

Kumbhakar

Tsionas

2009

J. Appl. Econ.

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In this paper we derive both primal and dual-cost systems in which the stochastic specifications arise from the model (random environment or measurement errors and optimization errors)—not tacked on at the end after the deterministic system is worked out. Derivation of the error structures is based on cost‐minimizing behavior on the firms. The primal systems constitute the production function and the first‐order conditions of cost minimization. We consider two dual‐cost systems. The first dual system is based on the cost function and cost share equations. The second dual system is based on a multiplicative general error production model that is an alternative to McElroy's additive general error production model. Our multiplicative general error model gives a clear and intuitive economic meaning to the error components. The resulting cost system is easy to estimate compared to the alternative cost systems. The error components in the multiplicative general error model can capture heterogeneity in the technology parameters even in a cross‐sectional model. Panel data are not necessary to estimate either the primal or dual systems. The models are estimated using data on 72 fossil fuel‐fired steam electric power generation plants (observed for the period 1986–1999) in the USA. Copyright (C) 2009 John Wiley & Sons, Ltd.

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The dynamics of efficiency and productivity growth in U.S. electric utilities

Abstract: Productivity growth, Adjustment costs, Dynamic duality, Inefficiency, Decomposition, Deregulation, Electricity, D24, D92, L94,

Cited by 19 publications

References 17 publications

Measuring Productivity When Technologies are Heterogeneous: A Semi-Parametric Approach for Electricity Generation

Measuring Productivity When Technologies are Heterogeneous: A Semi-Parametric Approach for Electricity Generation

Constraints in models of production and cost via slack-based measures

Stochastic error specification in primal and dual production systems

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