Primal and dual dynamic Luenberger productivity indicators

Lansink, A.G.J.M. Oude; Stefanou, Spiro E.; Serra, Teresa

doi:10.1016/j.ejor.2014.09.027

Cited by 47 publications

(41 citation statements)

References 22 publications

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“…The decomposition of input‐ and investment‐specific dynamic productivity growth indicators in (a and b) show that these indicators provide more information on the sources of productivity growth than dynamic productivity growth measures accounting for all inputs simultaneously that dominate the existing literature (see for example Kapelko et al ., ,c, ; Oude Lansink et al ., ).…”

Section: Input‐specific Productivity Change In a Dynamic Settingmentioning

confidence: 97%

“…Silva and Stefanou () advance a non‐parametric approach to dynamic production analysis that distinguishes between the variable and the dynamic factors, and serves as a foundation for the measurement of dynamic productivity and efficiency measurement . Luenberger‐based approaches that have followed can decompose productivity change to identify the contributions of technical inefficiency change, scale inefficiency change and technical change (Kapelko et al ., ,c, ; Oude Lansink et al ., ). However, the existing dynamic approaches do not consider input‐specific contributions to dynamic productivity growth, which is demonstrated in this paper.…”

Section: Introductionmentioning

confidence: 97%

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Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Kapelko¹,

Stefanou²

2016

J Agricultural Economics

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We propose a new method for measuring and decomposing input‐specific productivity change in a dynamic context. The resulting input‐specific dynamic Luenberger productivity change indicator is decomposed to identify the contributions of input‐specific dynamic technical, technical inefficiency and scale inefficiency changes. The empirical application of the paper focuses on panel data of large firms in the European dairy processing industry over the period 2005–2012. The results show similar patterns for dynamic input‐specific productivity change and its components for labour across European regions (Eastern, Western and Southern), while differences between regions are found regarding materials and investments.

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Section: Input‐specific Productivity Change In a Dynamic Settingmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Kapelko¹,

Stefanou²

2016

J Agricultural Economics

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“…Building on the static Luenberger indicator of productivity growth defined by Chambers et al (1996) to the dynamic setting by using the dynamic directional distance function (assuming constant returns to scale (CRS)) leads to the dynamic Luenberger productivity growth indicator (L) (Oude Lansink et al, 2015):…”

Section: Dynamic Luenberger Indicator Of Productivity Growthmentioning

confidence: 99%

“…This study takes on a dynamic adjustment perspective and facilitates a more complete decomposition of the sources of productivity changes that are associated with investment spikes with the dynamic Luenberger productivity growth indicator being used to control for adjustment costs in the year of investment (Oude Lansink et al, 2015;Kapelko et al, 2014b).…”

Section: Introductionmentioning

confidence: 99%

Analyzing the impact of investment spikes on dynamic productivity growth

Kapelko

Stefanou

2015

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“…However, it requires a specification of a functional form and technical changes cannot be determined at the firm level. We opt for the nonparametric approach which does not require such a specification and allows for determination of firm-specific technical changes (Oude Lansink et al, 2015).…”

Section: Practical Implementationmentioning

confidence: 99%

To Mix or Specialise? A Coordination Productivity Indicator for English and Welsh farms

Ang¹,

Kerstens²

2016

J Agricultural Economics

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This paper introduces a nonparametric measure of coordination productivity growth where the subprocesses are explicitly modelled in the production technology. The coordination productivity indicator is decomposed into a coordination technical inefficiency change component and a coordination technical change component. This decomposition allows assessment of reallocation impacts on the different sources of productivity growth. The empirical application focuses on a large panel of English and Welsh farms over the period 2007–2013. The results show that coordination inefficiency significantly increases with the proportion of resources allocated to livestock production in economic and statistical terms. Coordination inefficient farms should generally allocate more land to crop production. Depending on the region, the average coordination productivity growth ranges from −9.7% to 15.9% per year. It is driven by coordination technical change rather than coordination inefficiency change.

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Primal and dual dynamic Luenberger productivity indicators

Cited by 47 publications

References 22 publications

Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Input‐Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms

Analyzing the impact of investment spikes on dynamic productivity growth

To Mix or Specialise? A Coordination Productivity Indicator for English and Welsh farms

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