Working Papers

An unified framework for measuring pollution-adjusted productivity change

Abstract: This paper aims to define an unified framework to analyse pollution-adjusted productivity change. Equivalence conditions for the additive and the multiplicative pollution-adjusted productivity measures (Abad and Ravelojaona, 2022, 2021) are established. 

An exponential analysis of total factor productivity (with P. Ravelojaona and Z. Shen)  

Abstract: As an important economic measure, the total factor productivity indices can be employed to evaluate the performance of decision-making units over time. These indices are usually based on multiplicative or additive distance functions estimated by either parametric or non parametric approaches. Under a non parametric analytic framework, this paper introduces a multiplicative directional productivity measure: the Directional Hicks-Moorsteen (DHM) indicator. A dynamical combination of multiplicative directional distance functions is introduced and non convex production technologies are assumed to estimate the distance functions. In an empirical illustration, the proposed model is applied to display productivity changes among Chinese provincial public hospitals over the period 2014-2018. The results indicate that a consistent outcome is obtained under the multiplicative technology and the production technology of free disposal hull while more productivity gains are observed under free disposal hull technology. 

BDisposal.jl - A non parametric efficiency and productivity analysis through the B-disposal scheme software implemented in the Julia programming language (with A. Lobianco) 

Abstract: The BDisposal package proposes a series of environmental efficiency and productivity algorithms for non-parametric modelling when we relax the disposability assumption of some of the outputs and/or inputs (e.g. pollution). These efficiency and productivity measures are implemented through convex and non-convex Data Envelopment Analysis (DEA) (aka Frontier Efficiency Analysis) models.