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Purpose: This paper seeks to model and compute productivity, including a measure of quality, of a service which does not have marketable outputs--namely public education at the micro level. This application is a case study for Sweden public schools. Design/methodology/approach: A Malmquist productivity index is employed which allows for multiple outputs or outcomes such as test results and promotions without requiring price data with which to aggregate these outputs. It also allows one to account for inputs such as teachers and facilities as well as proxies for quality of the inputs (e.g. experience of teachers) and outputs. This model generalizes the basic data envelopment analysis (DEA) models--used successfully to measure performance in many educational applications--to the intertemporal case. A way of computing quality and quantity components of overall productivity is employed. Findings: The case study is an application to the Swedish primary and secondary school system over the 1992 to 1995 period. It was found that quality "matters", i.e. productivity growth changes when one accounts for quantity. Research limitations/implications: The data available implied that the specification is restricted to an intermediate production model, i.e. the output data only account for the intermediate outcomes of education like grades and promotions, but not the longer term outcomes related to success in the job market or higher education, which one proposed as a task for future research. Originality/value: The indices which are computed at the micro level are of value for policy purposes (does investment in quality matter?) and in an evaluation context. (Contains 1 figure and 10 tables.)

A study on environmental performance indicators is presented. A formal index number of environmental performance which can be computed using data envelopment analysis (DEA) techniques is discussed.

This paper compares teaching and non-teaching hospitals in terms of their provision of patient services. We proceed by comparing the frontiers of the teaching and non-teaching hospitals using a data envelopment (DEA) type approach, which we apply to a sample of 236 teaching hospitals and 556 non-teaching hospitals operating in the US in 1994. Our results suggest that only about 10% of the teaching hospitals can effectively "compete" with non-teaching hospitals based on the provision of patient services. [ABSTRACT FROM AUTHOR]

The passage of environmental legislation in the United States immediately raised the issue of its impact on the growth rate of productivity. Expenditures to abate pollution typically draw resources away from the production of other goods and services, affecting productivity of 'goods' production. This has been of particular concern in the electric utility sector where the annual growth rate of 7.6% for 1948-66 declined to 3.3% for 1966-78 (Kendrick, 1983).
These 'productivity' effects reflect the opportunity cost of environmental policy. Evidence as to the absolute and relative costs are of course important (in conjunction with estimates of benefits) in evaluating that policy. This paper investigates one aspect of this issue by estimating the change in the relative efficiency of electric utilities between 1969 and 1975. All of the 23 plants included in this study installed precipitators between the end of 1969 and the end of 1975. If there is a measurable decline in the relative efficiency of these plants, it might then be argued that it is at least in part due to the installation of pollution control equipment to abate particulate matter. In addition, the method employed here to measure efficiency can be used to calculate the opportunity cost due to loss of efficiency in terms of lost potential output.
This paper supplements these earlier studies by directly calculating the relative technical efficiency of a sample of electric utilities before and after precipitators were installed. In addition, the method we use (based on that developed by Fre, Grosskopf and Lovell (1985)) allows us to identify the magnitude and the sources of inefficiency. We find that overall technical efficiency declined after the regulations, although not significantly. The major cause of that efficiency loss was due to congestion and scale inefficiency. Section II outlines the model that is used in this paper. (For details see Byrnes and Fre (1987) or Fre, G... [ABSTRACT FROM AUTHOR]

In the next section, we will introduce the efficiency measures applied in this paper. In particular, we will present a decomposition of overall technical efficiency into scale efficiency, congestion and purely technical efficiency. The scale measure will measure lost output resulting from producing at a nonoptimal scale of production, i.e. not where constant returns to scale prevail. The congestion measure will measure lost output caused by producing on the backward-bending part of the isoquant and the pure technical efficiency measure will measure lost output caused by producing in the interior of the upper level set, i.e. not on the isoquant. The product of these three efficiency measures will equal the overall technical efficiency measure. [ABSTRACT FROM AUTHOR]

This article presents a unified discussion of efficiency, productivity and quantity indexes. Intuitively, these notions are clearly related, but, in practice economists generally do not recognize these interrelationships. En route to their goal, the authors appeal frequently to duality. Although distance functions are defined in terms of input and output quantities, i.e., "primal" data, their dual relationship to various value functions allows them to establish links among indexes defined in terms of both "dual" and "primal" data. In facts the interested reader is invited to extend the work here to the consideration of price indexes. The paper is laid out as follows. The authors begin with distance functions in Section 2, proceed to Farrell efficiency measures in Section 3, input quantity indexes in Section 4, and Malmquist productivity indexes in Section 5. They also include a discussion of computation of these various indexes using simple linear programming techniques in Section 6. Section 7 concludes.