Performance of the Norwegian dairy farms: A dynamic stochastic approach

Habtamu Alem

doi:10.1016/j.rie.2020.07.006

Abstract

From a theoretical perspective, it is well stated that the farm's decision on the use of inputs depends on the farmer's ability to make an efficient decision over time. The existing literature in performance analysis of the dairy farms based on static modeling and thus ignores the inter-temporal nature of production decisions. This paper aims to construct a dynamic stochastic production frontier incorporating the sluggish adjustment of inputs, to measure the performance of dairy farms in Norway. The empirical application focused on the farm-level analysis of the Norwegian dairy sector for 2000- 2018. The dynamic frontier estimated using the system Generalized Method of Moments estimator. The analysis shows that the static model in the previous studies underestimates the performance of the dairy farms.

Highlights

The standard neoclassical frontier function applied in empirical efficiency models entails an assumption that all farms are fully efficient (Alem, 2018)
As the dynamic efficiency scores are higher, this suggests that, in our sample, the static model underestimate the performance of the dairy farms
Considering the dynamic technical efficiency (TE) score which implies that these dairy farms producing only 97% of the maximum possible output, given the input used

Summary

Introduction

The standard neoclassical frontier function applied in empirical efficiency models entails an assumption that all farms are fully efficient (Alem, 2018). Following the pioneering contributions by Aigner et al (1977) and Meeusen and van Den Broeck (1977), who independently proposed the stochastic production frontier framework using cross-sectional data, the literature diverges from the standard neoclassical production function model by including two distinct error components. These two studies have suggested that given the input, there are two main causes for the deviation of the actual output of a given farm from the maximum possible or the potential output. In Stochastic Frontier Analysis (SF) the gap between observed output and the potential output is explained in terms of both inefficiency and random errors

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