This paper attempts to examine and reflect on my experiences of being a beginning social worker and beginning teacher. I describe the similarities between the two processes. In many respects they were painful experiences, fraught with disappointments, frustrations, and, at times, loss, sadness, and anger. Yet, out of these painful experiences, there are many humorous events and many different, diverse, and interesting opportunities. All these experiences enriched my learning, and produce a worker and teacher who is able to draw on all these experiences for the benefit of his clients and students.

"What a long, strange trip it's been".

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.

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

Efficiency and productivity in research and development must not be confused as being the same as efficiency in production or ordinary service activities. The greatest output and its highest quality in scientific terms comes from the time and energy of the scientifically productive research workers, and the organiser of a laboratory who has efficiency in mind would have constantly under review ways in which this time and energy can be conserved and the research quality enhanced.

Efficiency and productivity in R&D

An Interactive Approach to Improve Estimates of Value Effiency in Data Envelopment Analysis

In the United States credit unions are not-for-profit cooperative financial institutions, owned by and operated for the benefit of their members. Being part of a cooperative movement enhances the likelihood that credit unions will share performance-enhancing ideas, for the benefit of weak credit unions and the movement as a whole. In this paper we show how a conventional performance evaluation methodology has evolved into a novel performance enhancement methodology. The evolution of the methodology is the result of cooperations among individual credit union managements, movement officials and the authors. The evolution is illustrated with 1990 data on nearly 9,000 credit unions.

Enhancing The Performance of Credit Unions : The Evolution of a Methodology

Data Envelopment Analysis (DEA) is an increasingly popular management tool. DEA is commonly used to evaluate the efficiency of a number of producers. A typical statistical approach is characterized as a central tendency approach and it evaluates producers relative to an average producer. In contrast, DEA compares each producer with only the "best" producers. By the way, in the DEA literature, a producer is usually referred to as a decision making unit or DMU. DEA is not always the right tool for a problem but is appropriate in certain cases.

Dieter Gstach

Papers

with Data Envelopment Analysis