Exact and superlative measurement of the Luenberger-Hicks-Moorsteen productivity indicator

This paper shows that the Bennet-Bowley profit indicator is an exact and superlative approximation of the additively complete Luenberger-Hicks-Moorsteen productivity indicator when the input and output directional distance functions can be represented up to the second order by a quadratic functional form. It also establishes the conditions under which the exact and superlative measures of the Luenberger productivity indicator and Luenberger-Hicks-Moorsteen productivity indicator coincide

Benchmarking with uncertain data:a simulation study comparing alternative methods

We consider efficiency measurement methods in the presence of uncertain input and output data, and without the (empirically problematic) assumption of convexity of the production technology. In particular, we perform a simulation study in order to contrast two well-established methods, IDEA and Fuzzy DEA, with a recently suggested extension of Fuzzy DEA in the literature (dubbed the HB method). We demonstrate that the HB method has important advantages over the conventional methods, resulting in more accurate efficiency estimates and narrower bounds for the efficiency scores of individual Decision Making Units (DMUs): thereby providing more informative results that may lead to more effective decisions. The price is computational complexity. Although we show how to significantly speed up computational time compared to the original suggestion, the HB method remains the most computationally heavy method among those considered. This may limit the use of the method in cases where efficiency estimates have to be computed on the fly, as in interactive decision support systems based on large data sets

Multi-Platform Next-Generation Sequencing of the Domestic Turkey (Meleagris gallopavo): Genome Assembly and Analysis

The combined application of next-generation sequencing platforms has provided an economical approach to unlocking the potential of the turkey genome

Beyond black box modeling in production: methodological advances in nonparametric methods.

Nonparametric production analysis supplements observed input-output combinations with a number of assumptions to model the production process. The doctoral thesis starts with a brief “crash course” in nonparametric production analysis in Chapter 1. Notwithstanding its simple basic premises, this black box analysis is quite powerful and has low data requirements. However, it can be made even more powerful by opening up this black box. The common thread in this doctoral thesis is to go beyond this black box modeling. “Beyond black box modeling” here refers to two interpretations. The first and conventional interpretation refers to more realistic models of production processes by, for example, explicitly modeling the different subprocesses and their links (cfr. Chapter 2) or by modeling intertemporal links between processes over time (cfr. Chapter 4). Apart from this conventional interpretation, it also refers to the idea of looking beyond mere efficiency scores or productivity measures produced by these black box models. An equally important analysis is tracing the underlying factors of these results (cfr. Chapter 2 and Chapter 3) and learning from (dominating) peers (cfr. Chapter 5).nrpages: 189status: publishe

A superlative indicator for the Luenberger-Hicks-Moorsteen productivity indicator : Theory and application

Consisting of the difference between an output indicator and an input indicator, the Luenberger-Hicks-Moorsteen (LHM) productivity indicator allows straightforward interpretation. However, its computation requires estimating distance functions that are inherently unknown. This paper shows that a computationally simple Bennet indicator is a superlative indicator for the LHM indicator when one can assume profit-maximizing behavior and the input and output directional distance functions can be represented up to the second order by a quadratic functional form. We also show that the Luenberger- and LHM-approximating Bennet indicators coincide for an appropriate choice of directional vectors. Focusing on a large sample of Italian food and beverages companies for the years 1995−2007, we empirically investigate the extent to which this theoretical equivalence translates into similar estimates. We find that the Bennet indicator is a close empirical alternative to the LHM indicator for the sample.</p

Robust nonparametric analysis of dynamic profits, prices and productivity: An application to French meat-processing firms

Appropriately considering adjustment costs, this paper develops a robust nonparametric framework to analyse profits, prices and productivity in a dynamic context. Dynamic profit change is decomposed into a dynamic Bennet price indicator and a dynamic Bennet quantity indicator. The latter is decomposed into explanatory factors. It is shown to be a superlative indicator for the dynamic Luenberger indicator. The application focuses on 1,638 observations of French meat-processing firms for the years 2012–2019. Using m-out-of-n bootstrapped data envelopment analysis, we obtain robust estimates and confidence intervals. The components of dynamic productivity growth fluctuate substantially. However, these fluctuations are often statistically insignificant

To mix or specialise? A coordination productivity indicator for English and Welsh farms

This paper introduces a nonparametric measure of coordination Luenberger 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 to assess how reallocation impacts the different sources of productivity growth. The empirical application focusses 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 Luenberger productivity growth ranges from -9.7 percent to 15.9 percent per year. It is driven by coordination technical change rather than coordination inefficiency change