Performance Measurement in Government Service Provision: The Case of Police Services in New South Wales

The NSW Government is implementing a financial framework which is designed to encourage government service providers to become more efficient and effective. NSW Treasury is using Data Envelopment Analysis (DEA) to measure the efficiency of major government service providers, such as police, courts and hospitals. This paper outlines the progress in implementing the new financial framework and illustrates the way NSW Treasury will use DEA to help improve the efficiency of government service providers by describing an analysis of the NSW Police Service. The results suggest that NSW police patrols (local police districts) could, on average, reduce input usage by 13.5 percent through better management, and by 6 percent if the patrols could be restructured to achieve the optimal scale. Results also indicate that differences in operating environments, such as location and socioeconomic factors, do not have a significant influence upon the efficiency of police patrols.     

Guest Editors' Introduction

Journal of Productivity Analysis -     

Efficiency and productivity in the Thai non-life insurance industry

This paper estimates a stochastic cost frontier for a sample of the non-life insurance industry in Thailand from 2000 to 2007. Our model explicitly considers the heteroscedasticity in the variances of the noise and inefficiency components that could affect the position of the cost frontier as well as the measurement of efficiency. Tests for double heteroscedasticity and appropriate specification for the cost frontier are performed. The chosen model is used to examine whether economies of scale and a change in technology exist. In addition, this paper calculates total factor productivity (TFP) change and decomposes it into scale effect, cost efficiency effect, technology effect, output effect, price effect, and environmental effect. Our results reveal that, on average, Thai non-life insurance firms are between 74 and 79 % efficient, that economies of scale exist, and that regress in technology shifts the cost frontier upward by 2.2 %. The regress in technology is the dominant contributor to a negative TFP growth of 1.15 %. The restructuring of the industry following the 1997 Asian financial crisis finally paid off when the industry experienced technology progress in 2005 and beyond. Between 2005 and 2007, technology progress and productivity growth occurred at a little over 2 % a year. In-depth analysis of the most and the least efficient firms reveals that most efficient firms tend to strategically select types of insurance services and underwrite average and small size sum insured per policy to diversify their risks. In addition, they tend to be savvy investors. The opposite holds for the least efficient firms which concentrate in providing labor-intensive, small sum automobile insurance policies or underwrite large coverage policies for fewer policyholders. Balancing the mix of insurance types, spreading risk across insurance types or across number of policyholders, investing in high yield assets, or developing a market niche may prove beneficial for improving cost efficiency.     

Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency

The ability of a production unit to transform inputs into outputs is influenced by its technical efficiency and external operating environment. This paper introduces a nonparametric, linear programming, frontier procedure for obtaining a measure of managerial efficiency that controls for exogenous features of the operating environment. The approach also provides statistical tests of the effects of external conditions on the efficient use of each individual input (for an input oriented model) or for each individual output (for an output oriented model). The procedure is illustrated for a sample of nursing homes.     

Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis

In this paper we propose a new technique for incorporating environmental effects and statistical noise into a producer performance evaluation based on data envelopment analysis (DEA). The technique involves a three-stage analysis. In the first stage, DEA is applied to outputs and inputs only, to obtain initial measures of producer performance. In the second stage, stochastic frontier analysis (SFA) is used to regress first stage performance measures against a set of environmental variables. This provides, for each input or output (depending on the orientation of the first stage DEA model), a three-way decomposition of the variation in performance into a part attributable to environmental effects, a part attributable to managerial inefficiency, and a part attributable to statistical noise. In the third stage, either inputs or outputs (again depending on the orientation of the first stage DEA model) are adjusted to account for the impact of the environmental effects and the statistical noise uncovered in the second stage, and DEA is used to re-evaluate producer performance. Throughout the analysis emphasis is placed on slacks, rather than on radial efficiency scores, as appropriate measures of producer performance. An application to nursing homes is provided to illustrate the power of the three-stage methodology.