Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 199, 2018 DOI: 10.1016/j.ijpe.2018.02.006
Accepted author manuscript, 1.79 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 199, 2018 DOI: 10.1016/j.ijpe.2018.02.006
Accepted author manuscript, 968 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Measuring management practices
AU - Delis, Manthos D.
AU - Tsionas, Mike G.
N1 - This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 199, 2018 DOI: 10.1016/j.ijpe.2018.02.006
PY - 2018/5
Y1 - 2018/5
N2 - Good management practices are remarkably difficult to robustly measure, especially when unique data on firms and their managers are not available. We propose a new model estimated with Bayesian techniques that requires only the usual accounting data on inputs and outputs and thus can be applied to any firm. We show that our management practices estimates are more than 90% correlated with existing state-of-the-art measures from a very specialized data set by Bloom and Van Reenen (2007). We also obtain very high correlations when conducting an extensive Monte Carlo analysis. Finally, we show that frontier-based methods previously used to estimate management practices do not provide good approximations.
AB - Good management practices are remarkably difficult to robustly measure, especially when unique data on firms and their managers are not available. We propose a new model estimated with Bayesian techniques that requires only the usual accounting data on inputs and outputs and thus can be applied to any firm. We show that our management practices estimates are more than 90% correlated with existing state-of-the-art measures from a very specialized data set by Bloom and Van Reenen (2007). We also obtain very high correlations when conducting an extensive Monte Carlo analysis. Finally, we show that frontier-based methods previously used to estimate management practices do not provide good approximations.
KW - Management practices
KW - Productivity
KW - Cost efficiency
KW - Bayesian methods
U2 - 10.1016/j.ijpe.2018.02.006
DO - 10.1016/j.ijpe.2018.02.006
M3 - Journal article
VL - 199
SP - 65
EP - 77
JO - International Journal of Production Economics
JF - International Journal of Production Economics
SN - 0925-5273
ER -