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The value of environment across efficiency quantiles: A conditional regression quantiles analysis of rangelands beef production in north Eastern Australia

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  • Gregg, Daniel
  • Rolfe, John

Abstract

In agricultural systems the value of environmental inputs can be measured using the production function approach whereby the marginal contributions of factors are associated with a shadow value under perfect competition and rationality assumptions. However, empirical studies show that inefficiency in production is common indicating that the rationality assumption is not met. Furthermore, substantial evidence exists to suggest that the contributions of environmental inputs in particular may be differentiated across the efficiency distribution. This means that the frontier technology may not be an appropriate reflection of the technology in use by inefficient enterprises. This article presents the use of conditional regression quantiles to consider how the value of environmental inputs, measured by their contributions to production, vary across efficiency quantiles. We employed a case study of rangelands beef production in Australia to consider how environmental health differentially contributes to production values across efficiency quantiles. Our approach generates detailed insights into the nature of environmentally-sourced technical inefficiency and suggests that conditional quantile regression approaches are ideal for consideration of issues wherein substantial heterogeneity exists limiting the information content of conditional mean based analyses.

Suggested Citation

  • Gregg, Daniel & Rolfe, John, 2016. "The value of environment across efficiency quantiles: A conditional regression quantiles analysis of rangelands beef production in north Eastern Australia," Ecological Economics, Elsevier, vol. 128(C), pages 44-54.
  • Handle: RePEc:eee:ecolec:v:128:y:2016:i:c:p:44-54
    DOI: 10.1016/j.ecolecon.2016.02.009
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    3. Ramsey, Austin F. & Wang, Huaiyu, 2018. "Non-Farm Income and Technical Efficiency in the Uplands of Yunnan, China: A Bayesian Non-Crossing Quantile Regression Approach," 2018 Annual Meeting, August 5-7, Washington, D.C. 274375, Agricultural and Applied Economics Association.

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