Nonparametric Frontier Models Research Articles

Has DRG payment influenced the technical efficiency and productivity of diagnostic technologies in Portuguese public hospitals? An empirical analysis using parametric and non-parametric methods.

The use of Diagnosis Related Groups (DRG) as a mechanism for hospital financing is a currently debated topic in Portugal. The DRG system was scheduled to be initiated by the Health Ministry of Portugal on January 1, 1990 as an instrument for the allocation of public hospital budgets funded by the National Health Service (NHS), and as a method of payment for other third party payers (e.g., Public Employees (ADSE), private insurers, etc.). Based on experience from other countries such as the United States, it was expected that implementation of this system would result in more efficient hospital resource utilisation and a more equitable distribution of hospital budgets. However, in order to minimise the potentially adverse financial impact on hospitals, the Portuguese Health Ministry decided to gradually phase in the use of the DRG system for budget allocation by using blended hospital-specific and national DRG case-mix rates. Since implementation in 1990, the percentage of each hospital's budget based on hospital specific costs was to decrease, while the percentage based on DRG case-mix was to increase. This was scheduled to continue until 1995 when the plan called for allocating yearly budgets on a 50% national and 50% hospital-specific cost basis. While all other non-NHS third party payers are currently paying based on DRGs, the adoption of DRG case-mix as a National Health Service budget setting tool has been slower than anticipated. There is now some argument in both the political and academic communities as to the appropriateness of DRGs as a budget setting criterion as well as to their impact on hospital efficiency in Portugal. This paper uses a two-stage procedure to assess the impact of actual DRG payment on the productivity (through its components, i.e., technological change and technical efficiency change) of diagnostic technology in Portuguese hospitals during the years 1992-1994, using both parametric and non-parametric frontier models. We find evidence that the DRG payment system does appear to have had a positive impact on productivity and technical efficiency of some commonly employed diagnostic technologies in Portugal during this time span.

Nonparametric Frontier Models for Assessing the Market and Cost Efficiency of Large-Scale Bank Branch Networks

In this paper the author proposes models for assessing the efficiency in large networks of bank branches. He distinguishes bank branch efficiency into market and cost components suitably modified to capture different tiers of bank-management. The paper proposes a methodology which includes the use of multivariate analysis in order to ensure the homogeneity of the branches assessed and then data envelopment analysis for assessing efficiency. The methodology is applied on a sample of 580 branches of a commercial bank in the United Kingdom. The results obtained reinforced previous claims regarding the presence of high technical inefficiencies and economies/diseconomies of scale at the branch level from a production and cost point of view. Furthermore, the decision to precluster the network of branches into homogenous groups has had profound implications on the magnitude of the assessed efficiencies.

Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models

Efficiency scores of production units are generally measured relative to an estimated production frontier. Nonparametric estimators (DEA, FDH, ⋯) are based on a finite sample of observed production units. The bootstrap is one easy way to analyze the sensitivity of efficiency scores relative to the sampling variations of the estimated frontier. The main point in order to validate the bootstrap is to define a reasonable data-generating process in this complex framework and to propose a reasonable estimator of it. This paper provides a general methodology of bootstrapping in nonparametric frontier models. Some adapted methods are illustrated in analyzing the bootstrap sampling variations of input efficiency measures of electricity plants.

Detecting Outliers in Deterministic Nonparametric Frontier Models With Multiple Outputs

This article provides a statistical methodology for identifying outliers in production data with multiple inputs and outputs used in deterministic nonparametric frontier models. The methodology is useful in identifying observations that may contain some form of measurement error and thus merit closer scrutiny. When data checking is costly, the methodology may be used to rank observations in terms of their dissimilarity to other observations in the data, suggesting a priority for further inspection of the data.