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ARS Home » Pacific West Area » Corvallis, Oregon » Forage Seed and Cereal Research Unit » Research » Publications at this Location » Publication #273059

Title: Directional output distance functions: endogenous directions based on exogenous normalization constraints

item FARE, ROLF - Oregon State University
item GROSSKOPF, SHAWNA - Oregon State University
item Whittaker, Gerald

Submitted to: Journal of Productivity Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/31/2012
Publication Date: 12/31/2013
Citation: Fare, R., Grosskopf, S., Whittaker, G.W. 2013. Directional output distance functions: endogenous directions based on exogenous normalization constraints. Journal of Productivity Analysis. 40(3):267-269.

Interpretive Summary: The relative efficiency of farms in combining multiple inputs to produce multiple desirable and undesirable socioeconomic and environmental outputs provides one of the best ways to quantify the effects of agriculture on the environment. Directional distance functions are often applied to mathematically estimate the efficiency levels in environmental studies of production. These functions require fewer assumptions than alternative approaches, and allow simulation alternative policies. In all previous applications of directional distance functions, the direction of the function had to be supplied by the analyst. The direction was always assumed to be 1, essentially eliminating it as factor in the analysis. This approach gave reasonable results, because there was no information on the value of the direction parameter. This paper introduces a method that chooses a direction based on the characteristics of the data, and requires no input from the analyst. This method opens a new area for research in environmental economics.

Technical Abstract: In this paper we develop a model for computing directional output distance functions with endogenously determined direction vectors. We show how this model is related to the slacks-based directional distance function introduced by Fare and Grosskopf and show how to use the slacks-based function to estimate the optimal directions.