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

Abstract

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.

Analysing determinate components of an approximated Luenberger–Hicks–Moorsteen productivity indicator: An application to German dairy‐processing firms

“…One can alleviate the problem of sampling bias with a bootstrapping procedure (Simar & Wilson, 1998, 2020). However, as observed in the study of Ang and Kerstens (2023), such a procedure may be computationally intensive. Another route is the use of stochastic frontier analysis (Aigner et al, 1977; Meeusen & van Den Broeck, 1977), which may overcome such computational hurdles.…”

“…Here, $$BLHM$$ is shown as the sum of output growth, $$OC\equiv \frac{1}{2}({\hat{{\bf{p}}}}_{t}+{\hat{{\bf{p}}}}_{t+1})\cdot ({{\bf{y}}}_{t+1}-{{\bf{y}}}_{t})$$, and input decline, $$IC\equiv -\frac{1}{2}({\hat{{\bf{w}}}}_{t}+{\hat{{\bf{w}}}}_{t+1})\cdot ({{\bf{x}}}_{t+1}-{{\bf{x}}}_{t})$$, in which we decompose $$OC$$ and $$IC$$ separately. We closely follow the exposition of Ang and Kerstens (2023, pp. 26–29) in taking Laspeyres and Paasche perspectives for further decomposition.…”

How limiting is finance for Dutch dairy farms? A dynamic profit analysis

“…Using a linear programming procedure in line with Aigner and Chu (1968), we approximate a dynamic directional distance function by a quadratic functional form. Following Ang and Kerstens (2023), we adapt the approximation of the static directional distance function (see, for example, Färe et al, 2005 and Ang & Kerstens, 2020) to the dynamic context. The shadow prices are determined by exploiting the dual relationship between the dynamic directional distance function and the dynamic profit function.…”