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

Workshop 2013 on Dynamic and Network DEA (January 29-30, 2013)We formulate weighted, dynamic network range-adjusted measure (D-NRAM) and dynamic network slacksbased measure (D-NSBM), 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 D-NRAM. We illustrate our models using simulated data on residential aged care. Insight gained by running D-NRAM in parallel with D-NSBM includes (a) identical benchmark groups, (b) a substantially wider range of efficiency estimates under D-NRAM, and (c) evidence of inefficient DMU size bias. D-NRAM is also shown to have the additional desirable technical efficiency properties of translation-invariance and acceptance of data. Managerial implications are also briefly discussed.This workshop is supported by JSPS KAKENHI Grant Number 22310095 under the title “Theory and Applications of Dynamic DEA with Network Structure.

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

Tax and leverage: evidence from China

This study exploits two institutional features of China to test the causal link between tax and capital structure. First, the central government exclusively determines the corporate tax rate in China, which results in changes in corporate income tax rates across different Chinese public firms over the period of 2000–2011. Such mandatory tax shifts provide a quasi-natural experimental setting for our difference-in-differences analysis investigating the impact of tax on leverage. We find evidence supporting the dynamic trade-off theory, namely that firms are unresponsive to tax cuts but increase long-term leverage when taxes rise (particularly those in low statutory tax regimes). Second, governmental intervention in capital allocation is common in China such that political connections are usually regarded as an asset for firms in accessing bank loans. Using anti-corruption events as shocks to the value of political connections over the sample period, our research is the first study to show that political connections become a liability that enables banks to recall loans from affected firms during the anti-corruption campaign periods. This change overturns the typical tax-leverage relationship observed, as we find anti-corruption affected firms reduce long-term leverage when taxes are cut and they become insensitive to tax increases. Our results reveal the importance of political ties in explaining how firms adjust their capital structure to tax changes, which is extremely relevant to policy makers and regulators when monitoring bank loan markets

Bias, stability, and predictive ability in the measurement of systematic risk

Purpose – Estimates of systematic risk or beta are an important determinant of the cost of capital. The standard technique used to compile beta estimates is an ordinary least squares regression of stock returns on market returns using four to five years of monthly data. This convention assumes that a longer time series of data will not adequately capture risks associated with existing assets. This paper seeks to address this issue. Design/methodology/approach – Each year from 1980 to 2004, equity betas are estimated for 1,717 Australian firms over periods of four to 45 years, and form equal value portfolios of high, medium and low beta stocks. The paper compares expected returns – derived from the capital asset pricing model (CAPM) and subsequent realised market returns – and actual returns over subsequent annual and four-year periods. Findings – The paper shows that the ability of beta estimates to predict future stock returns systematically increases with the length of the estimation window and when the Vasicek bias correction is applied. However, estimation error is insignificantly different from that associated with a naïve assumption that beta equals one for all stocks. Research limitations/implications – The implication is that using all available returns data in beta estimation, along with the Vasicek bias correction, reduces the imprecision of expected returns estimates derived from the CAPM. A limitation of the method is the use of conditional realised returns as a proxy for expected returns, given that it is not possible directly to observe expected returns incorporated into share prices. Originality/value – The paper contributes to the understanding of corporate finance practitioners and academics, who routinely use beta estimates derived from ordinary least squares regression.Australia, Beta factor, Capital asset pricing model, United States of America

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