Assessing the impact of economic liberalization across countries: a comparison of dairy industry efficiency in Canada and the USA

Abstract

This paper examines and compares the technical efficiency measures of Ontario and New York dairy producers for the period 1992 to 1998. A nonparametric stochastic frontier model is introduced to estimate technical efficiency. The backfitting algorithm of Breiman and Friedman is used to estimate the frontier. Empirical results indicate that during the period of study, New York dairy farmers produced milk more efficiently than Ontario dairy producers, but the magnitude of the difference was small. The estimated mean technical efficiency for the former group is 0.602 as compared to 0.532 for the latter. The results also indicated that over time, dairy farms in both regions improved their level of technical efficiency. Furthermore, no correlation was found between farm size and estimated technical efficiency.

Analysing the technical efficiency of the Spanish Football League First Division with a random frontier model

“…Homogenous stochastic frontier models are common in many research fields, for example, Haghiri et al (2004) apply this type of model to the dairy industry; in Cullinane and Song (2003), it is applied to seaports; Mahadevan (2000) applies it to manaufacturing in Singapore; Lothgreen (2000) applies it to the health services sector;…”

Technical Efficiency in Dairy Farms: Research Synthesis and an Application in the South of Brazil

“…On the other hand, there are many studies that evaluate the effect of different methodologies and study-specific characteristics on TE Haghiri, et al, 2004;Areal, et al, 2012;Van Der Voort, et al, 2014). Some authors defend the two-stage procedure (Banker & Natarajan, 2008;McDonald, 2009) and other authors Battese & Coelli, 1995) criticise this approach by arguing that "the factors should be incorporated directly in the estimation of the production frontier because such factors may have a direct impact on efficiency" (Sharma, et al, 1999, p. 26).…”

“…This software is easy to use and the results that are generated are easy to understand" (Fraser & Cordina, 1999, p. 268). Finally, DEA does not require a parametric specification of a functional form to form the frontier (Coelli, 1995;Haghiri, et al, 2004). "This means that unnecessary restrictions about functional form that can impinge upon the analysis and distort efficiency measures are avoided."…”

“…Non-parametric models are subdivided in deterministic and stochastic. Contrary to non-parametric frontiers models, parametric frontiers requires predetermined functional forms (e.g., Cobb-Douglas, Translog, Transcendental) and error distributions (e.g., half-normal, truncated normal, exponential, gamma) (Coelli, 1995;Haghiri et al, 2004). The Cobb-Douglas functional form has been most commonly used in the empirical estimation of frontier models and its most attractive feature is its simplicity.…”

“…There are two basic approaches used for the measurement of TE: parametric and non-parametric models. Parametric frontiers must use predetermined functional forms (e.g., CobbDouglas, Translog, Transcendental) and they rely on pre-specified types of error distributions (e.g., half-normal, truncated normal, exponential, gamma), while non-parametric models do not have limitations of estimators (Coelli, 1995;Haghiri, et al, 2004). In addition, estimation procedures can be deterministic or stochastic.…”