The Guy at the Controls: Labor Quality and Power Plant Efficiency

Bushnell, James; Wolfram, Catherine

doi:10.3386/w13215

Cited by 22 publications

(26 citation statements)

References 18 publications

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“…Existing papers in this context typically focused on single-industry or single-firm data, which is not surprising because these inquiries require very detailed information. 5 A nice example of this work can be found in Bloom et al (2016) or Bushnell and Wolfram (2009). Also, the quality of management could affect productivity of a firm over time, leading to persistence in the effect of an initial good draw.…”

Section: Economic Issuesmentioning

confidence: 99%

Measuring Productivity Dispersion

Bartelsman

Wolf

2017

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 AbstractMeasuring the dispersion of productivity or efficiency across firms in a market or industry is rife with methodological issues. Nevertheless, the existence of considerable dispersion now is well documented and widely accepted. Less well understood are the economic features and mechanisms underlying the magnitude of dispersion and how dispersion varies over time or across markets. On the one hand, selection mechanisms in both output and input markets should favor the most productive units through resource reallocation, thereby reducing dispersion. On the other hand, innovation and technological uncertainty tend to increase dispersion. This chapter presents a guide to measurement of dispersion and provides empirical evidence from a selection of countries and industries using a variety of methodologies.JEL Codes: D2, O3

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Section: Economic Issuesmentioning

confidence: 99%

Measuring Productivity Dispersion

Bartelsman

Wolf

2017

SSRN Journal

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“…Bushnell and Catherine Wolfram (2009), who find that power plant operators have nontrivial impacts on the thermal efficiency of power plants. The best can boost their plant's fuel efficiency by over three percent, saving millions of dollars of fuel costs per year.…”

Section: Managerial Practice/talentmentioning

confidence: 99%

What Determines Productivity?

Syverson

2011

Journal of Economic Literature

2,256

1,086

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Economists have shown that large and persistent differences in productivity levels across businesses are ubiquitous. This finding has shaped research agendas in a number of fields, including (but not limited to) macroeconomics, industrial organization, labor, and trade. This paper surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels. The causes are manifold, and differ depending on the particular setting. They include elements sourced in production practices -- and therefore over which producers have some direct control, at least in theory -- as well as from producers' external operating environments. After evaluating the current state of knowledge, I lay out what I see are the major questions that research in the area should address going forward. (JEL D24, G31, L11, M10, O30, O47)

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“…Likewise, Chan et al (2014) point out that fuel quality is a key determinant for efficiency of combustion plants, and that operating and maintenance practices can lead to considerable efficiency improvements. Labor quality is also identified as a major determinant of power plant operational efficiency (Bushnell and Wolfram, 2007). Technical progress and the introduction of new technologies also drive productivity growth (Aghion and Howitt, 1992).…”

Section: Measuring Productivity Growth In Electricity Generationmentioning

confidence: 99%

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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The Guy at the Controls: Labor Quality and Power Plant Efficiency

Cited by 22 publications

References 18 publications

Measuring Productivity Dispersion

Measuring Productivity Dispersion

What Determines Productivity?

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

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