Testing DEA Models of Efficiency in Norwegian Psychiatric Outpatient Clinics

While measures of output in mental health care are even harder to find than in other health care activities, some indicators are available. In modelling productive efficiency the problem is to select the output variables that best reflect the use of resources, in the sense that these variables have a significant impact on measures of efficiency. The paper analyses cross-sectional data on the psychiatric outpatient clinics of Norway using the Data Envelopment Analysis (DEA) non-parametric efficiency measurement method, and tests the variable specification using statistical tools recently introduced in the literature. In addition to outputs, the importance of different profession or educational groups on efficiency is examined, and results are compared for separate samples of clinics for children and youths (BUP) with clinics for adults (VP).Mental health care; efficiency; psychiatric outpatient clinics; DEA; Norway

Sensitivity analysis of network DEA illustrated in branch banking

Users of data envelopment analysis (DEA) often presume efficiency estimates to be robust. While traditional DEA has been exposed to various sensitivity studies, network DEA (NDEA) has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of NDEA, further compounded by the recent attention it has been receiving in literature. NDEA captures the underlying performance information found in a firm?s interacting divisions or sub-processes that would otherwise remain unknown. Furthermore, network efficiency estimates that account for divisional interactions are more representative of a dynamic business. Following various data perturbations overall findings indicate positive and significant rank correlations when new results are compared against baseline results - suggesting resilience. Key findings show that, (a) as in traditional DEA, greater sample size brings greater discrimination, (b) removing a relevant input improves discrimination, (c) introducing an extraneous input leads to a moderate loss of discrimination, (d) simultaneously adjusting data in opposite directions for inefficient versus efficient branches shows a mostly stable NDEA, (e) swapping divisional weights produces a substantial drop in discrimination, (f) stacking perturbations has the greatest impact on efficiency estimates with substantial loss of discrimination, and (g) layering suggests that the core inefficient cohort is resilient against omission of benchmark branches. Various managerial implications that follow from empirical findings are discussed in conclusions.

New tools for dealing with errors-in-variables in DEA.

Errors in variables; Tool;

New Tools for Dealing with Errors-in-Variables in DEA.

We develop a series of novel conceptual tools to systematically account for errors-in-variables in Data Envelopment Analysis (DEA). These tools allow for statistical inference while requiring minimal statistical distribution assumptions, and therefore constitute a valuable addition to the tools currently available for dealing with errors-in-variables. An empirical application for large European Union financial institutions illustrates the proposed approach.Data Envelopment Analysis (DEA), errors-in-variables, efficiency depth, robust reference sets, financial institutions

Assessing the Relative Performance of U.K. University Technology Transfer Offices: Parametric and Non-Parametric Evidence

We present evidence on the relative efficiency of U.K. university technology transfer offices (TTOs) using data envelopment analysis (DEA) and stochastic frontier estimation (SFE). We find that U.K. TTOs exhibit low levels of absolute efficiency. Universities located in regions with higher levels of R&D and GDP appear to be more efficient in technology transfer, implying that there may be regional spillovers in technology transfer. Our results suggest that TTOs may need to be reconfigured into smaller units, since there may be scope for the development of regionally-based, sector focused TTOs. Consistent with qualitative evidence from U.S. TTOs (e.g., Siegel et al. (2003a, b, c)), we find that there may be a need to enhance the skills and capabilities of U.K. TTO managers and licensing professionals.

Robust DEA efficiency scores: A probabilistic/combinatorial approach

In this paper we propose robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the DEA model is modelled with a probability distribution. This proba- bilistic approach allows us to obtain three different robust efficiency scores: the Conditional Expected Score, the Unconditional Expected Score and the Expected score under the assumption of Maximum Entropy principle. The calculation of the three efficiency scores involves the resolution of an exponential number of linear problems. The algorithm presented in this paper allows to solve over 200 millions of linear problems in an affordable time when considering up 20 inputs/outputs and 200 DMUs. The approach proposed is illustrated with an application to the assessment of professional tennis players

The Cost of Corporate Social Responsibility: The Case of the Community Reinvestment Act

A Data Envelopment Analysis (DEA) cost minimization model is employed to estimate the cost to thrift institutions of achieving a rating of 'Outstanding' under the anti-redlining Community Reinvestment Act, which is viewed as an act of voluntary Corporate Social Responsibility (CSR). There is no difference in overall cost efficiency between 'Outstanding' and minimally compliant 'Satisfactory' thrifts. However, the sources of cost inefficiency do differ, and an 'Outstanding' rating involves annual extra cost of $7.4 million or, 1.3% of total costs. This added cost is the shadow price of CSR since it is not an explicit output or input in the DEA cost model. Before and After-tax rates of return are the same for the 'Outstanding' and 'Satisfactory' thrifts, which implies a recoupment of the extra cost. The findings are consistent with CSR as a management choice based on balancing marginal cost and marginal revenue. An incidental finding is that larger thrifts are less efficient