Premium
Event‐specific data envelopment models and efficiency analysis *
Author(s) -
Chambers Robert G.,
Hailu Atakelty,
Quiggin John
Publication year - 2011
Publication title -
australian journal of agricultural and resource economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.683
H-Index - 49
eISSN - 1467-8489
pISSN - 1364-985X
DOI - 10.1111/j.1467-8489.2010.00517.x
Subject(s) - data envelopment analysis , computer science , partition (number theory) , event (particle physics) , representation (politics) , production (economics) , event data , set (abstract data type) , data set , stochastic process , state space , data mining , operations research , mathematical optimization , mathematics , statistics , artificial intelligence , economics , physics , quantum mechanics , combinatorics , analytics , politics , political science , law , programming language , macroeconomics
Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state‐contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event‐specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event‐specific DEA representation, we apply it to a data set for Western Australian barley production data. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.