Measuring farm productivity under production uncertainty - Ait Sidhoum, Amer (Wiley, 2023)
This research introduces a novel empirical application to the assessment of farm productivity growth. While the existing research on productivity change has primarily focussed on ex post output observations, it has been shown that ignoring production uncertainty can lead to unreliable results. Using a state-contingent framework to represent the stochastic production environment, we extend the recent line of research that merged the state-contingent approach and efficiency measurement to productivity change using the Malmquist and Luenberger productivity indices. Using a balanced panel of 117 arable crop farms surveyed in 2011 and 2015, we show through the study results that productivity decreased, with technological regress being the major source of productivity change. Differences in productivity change between nonstochastic and stochastic modelling show the relevance to consider the state-contingent framework when assessing farms' productivity.
Keywords: agricultural productivity; data envelopment analysis; state-contingent framework; uncertainty modelling