DYNAMIC NETWORK RANGE-ADJUSTED MEASURE VS. DYNAMIC NETWORK SLACKS-BASED MEASURE

We dedicate this paper to the memory of Professor William W. Cooper, 1914–2012, whose generous demeanor touched and inspired at least three generations of DEA researchers. It is up to the DEA community to make sure that his vision and legacy live on. Abstract We formulate weighted, dynamic network range-adjusted measure (DN-RAM) and dynamic network slacks-based measure (DN-SBM), run robustness tests and compare results. To the best of our knowledge, the current paper is the first to compare two weighted dynamic network DEA models and it also represents the first attempt at formulating DN-RAM. We illustrate our models using simulated data on residential aged care. Insight gained by running DN-RAM in parallel with DN-SBM includes (a) identical benchmark groups, (b) a substantially wider range of efficiency estimates under DN-RAM, and (c) evidence of inefficient size bias. DN-RAM is also shown to have the additional desirable technical efficiency properties of translation-invariance and acceptance of free data. Managerial implications are also briefly discussed

Dynamic network range-adjusted measure vs dynamic network slacks-based measure

We formulate weighted, dynamic network range-adjusted measure (DN-RAM) and dynamic network slacks-based measure (DN-SBM), run robustness tests and compare results. To the best of our knowledge, the current paper is the first to compare two weighted dynamic network DEA models and it also represents the first attempt at formulating DN-RAM. We illustrate our models using simulated data on residential aged care. Insight gained by running DN-RAM in parallel with DN-SBM includes (a) identical benchmark groups, (b) a substantially wider range of efficiency estimates under DN-RAM, and (c) evidence of inefficient size bias. DN-RAM is also shown to have the additional desirable technical efficiency properties of translation-invariance and acceptance of free data. Managerial implications are also briefly discussed

Sensitivity analysis of network DEA: NSBM versus NRAM

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 has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of efficiency estimates, further compounded by the recent attention network DEA has been receiving in literature. We compare network slacks-based measure (NSBM) with network range-adjusted measure (NRAM), where the latter is developed for the first time. 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 estimates, (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. Further insight gained by comparing NSBM with NRAM includes: (h) identical benchmark groups across both formulations, (i) a narrower range of efficiency estimates and a more stable mean across different sample sizes under NRAM, (j) distribution of NRAM efficiency estimates is negatively skewed whereas NSBM estimates are mostly positively skewed, and (k) there is no evidence of inefficient unit size bias among NRAM estimates, whereas larger inefficient units appear more inefficient under NSBM. Crow

Intertemporal analysis of organizational productivity in residential aged care networks: scenario analyses for setting policy targets

With an increasing ageing population, there is a growing concern about how the elderly would be looked after. The primary purpose of this paper is to develop scenario analysis using simulated data where various criteria are incorporated into modeling policy targets, and apply an intertemporal productivity analysis to observe inefficiencies as reform unfolds. The study demonstrates how dynamic network data envelopment analysis (DN-DEA) can be used to evaluate the changing productivity of residential aged care (RAC) networks over time. Results indicate that it takes 9 years for 90 % of the RAC networks to have 85 % or more of the total beds in high-level care, and an optimal bed capacity is reached by the end of year 7. Number of beds and registered nurses employed are the main sources of inefficiency. The common core inefficient cohort identified with the paper's method represents a sub-group of RAC networks more deserving of closer managerial attention because of their constantly inefficient operations over time