Targeting Conservation Investments in Heterogeneous Landscapes: A distance function approach and application to watershed management

To achieve a given level of an environmental amenity at least cost, decision-makers must integrate information about spatially variable biophysical and economic conditions. Although the biophysical attributes that contribute to supplying an environmental amenity are often known, the way in which these attributes interact to produce the amenity is often unknown. Given the difficulty in converting multiple attributes into a unidimensional physical measure of an environmental amenity (e.g., habitat quality), analyses in the academic literature tend to use a single biophysical attribute as a proxy for the environmental amenity (e.g., species richness). A narrow focus on a single attribute, however, fails to consider the full range of biophysical attributes that are critical to the supply of an environmental amenity. Drawing on the production efficiency literature, we introduce an alternative conservation targeting approach that relies on distance functions to cost-efficiently allocate conservation funds across a spatially heterogeneous landscape. An approach based on distance functions has the advantage of not requiring a parametric specification of the amenity function (or cost function), but rather only requiring that the decision-maker identify important biophysical and economic attributes. We apply the distance-function approach empirically to an increasingly common, but little studied, conservation initiative: conservation contracting for water quality objectives. The contract portfolios derived from the distance-function application have many desirable properties, including intuitive appeal, robust performance across plausible parametric amenity measures, and the generation of ranking measures that can be easily used by field practitioners in complex decision-making environments that cannot be completely modeled. Working Paper # 2002-01

The change in productivity of Chinese state enterprises, 1983–1987

This study estimates the change in productivity of Chinese state enterprises during 1983–1987 using a panel data set of 403 firms. A new approach to productivity measurement is used. Under this approach, the production functions can differ arbitrarily across firms, important given the heterogeneity of the sample. The resulting coefficients estimate the marginal products of each factor as well as overall productivity growth. The results suggest Chinese productivity increased by 4.6% per year, with about half of this growth due to the rapidly improving education of the labor force.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47564/1/11123_2005_Article_BF01073492.pd

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Environmental efficiency with multiple environmentally detrimental variables : estimated with SFA and DEA

The objective of this paper is to estimate comprehensive environmental efficiency measures for Dutch dairy farms. The environmental efficiency scores are based on the nitrogen surplus, phosphate surplus and the total (direct and indirect) energy use of an unbalanced panel of dairy farms. We define environmental efficiency as the ratio of minimum feasible to observed use of multiple environmentally detrimental inputs, conditional on observed levels of output and the conventional inputs. We compare two methods for the calculation of efficiency; namely Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). This paper reveals the strengths and weaknesses for estimating environmental efficiency of the methods applied. Both SFA and DEA can estimate environmental efficiency scores. The mean technical efficiency scores (output-oriented, SFA 89°DEA 78€and the mean comprehensive environmental efficiency scores (SFA 80°DEA 52€differ between the two methods. SFA allows hypothesis testing, and the monotonicity hypothesis is rejected for the specification including phosphate surplus. DEA can calculate environmental efficiency scores for all specifications, because regularity is imposed in this method

Stochastic frontiers incorporating exogenous influences on efficiency

Until very recently, the incorporation of exogenous variables in the analysis of productive efficiency relative to a stochastic frontier was an arbitrary process. The practitioner had to assume that these variables were influencing either the structure of production possibilities or the degree of productive inefficiency. This paper proposes an alternative model which allows exogenous variables to influence both the structure of production possibilities and the degree of productive inefficiency. This new approach to the incorporation of exogenous influences on productive efficiency overcomes some of the economic and statistical problems that plague previous approaches

Biased Technical Change and the Malmquist Productivity Index

The Malmquist productivity index has many attractive features. One is that it decomposes into a technical efficiency change index and a technical change index. Under constant returns to scale, its technical efficiency change index has been decomposed into a "pure" technical efficiency change index, a scale efficiency change index, and a congestion change index. Here we maintain the same assumption, and we decompose its technical change index into a magnitude index and a bias index. We then decompose the bias index into an output bias index and an input bias index, and we state conditions under which either bias index makes no contribution to productivity change.productivity, technical change, Malmquist index