Evaluating the Super-Efficiency of Working Capital Management Using Data Envelopment Analysis: Does COVID-19 Matter?

This study evaluates the working capital management (WCM) super-efficiency in UAE firms and determines how the COVID-19 crisis may affect WCM super-efficiency from a statistical perspective. Data envelopment analysis (DEA), difference tests, and Tobit regression techniques are used. Integration of DEA with Tobit regression is required to recognize the potential statistical consequence of COVID-19 on the super-efficiency of WCM. The results show that WCM super-efficiency is moderate and requires decision-makers' corrective measures to attain best practices. The study results show no significant difference in WCM super-efficiency, and COVID-19 does not significantly influence WCM super-efficiency. The results would rouse decision-makers' interests, who would look into the firm's efficiency and weak spots. They would then take major steps to improve efficiency and achieve the best practices. Stakeholders such as managers, financiers, and investors may need these results to monitor how firms manage their assets and liabilities. We contribute to the literature by investigating and evaluating firms' WCM super-efficiency pre and post the COVID-19 pandemic employing a unique technique based on DEA super-efficiency and examining whether COVID-19 has affected WCM super-efficiency to extend the scope of knowledge and stressing the importance of sound WCM, especially in periods of economic distress.

On Asymptotic Efficiency of the M2M4 Signal-to-Noise Estimator for Deterministic Complex Sinusoids

The moment-based M2M4 signal-to-noise (SNR) estimator was proposed for a complex sinusoidal signal with a deterministic but unknown phase corrupted by additive Gaussian noise by Sekhar and Sreenivas. The authors studied its performances only through numerical examples and concluded that the proposed estimator is asymptotically efficient and exhibits finite sample super-efficiency for some combinations of signal and noise power. In this paper, we derive the analytical asymptotic performances of the proposed M2M4 SNR estimator, and we show that, contrary to what it has been concluded by Sekhar and Sreenivas, the proposed estimator is neither (asymptotically) efficient nor super-efficient. We also show that when dealing with deterministic signals, the covariance matrix needed to derive asymptotic performances must be explicitly derived as its known general form for random signals cannot be extended to deterministic signals. Numerical examples are provided whose results confirm the analytical findings.

Impacts of Dynamic Agglomeration Externalities on Eco-Efficiency: Empirical Evidence from China

Ecological efficiency (eco-efficiency) reflects the synergetic degree of the development of resource, economic, and environmental systems. This paper measures urban eco-efficiency based on a nonconvex metafrontier data envelopment analysis (DEA) approach using data from 191 cities in China during the years of 2003 to 2013. In particular, the impacts of dynamic agglomeration externalities on urban eco-efficiency are investigated. Our empirical results show that eco-efficiency decreased from 2003 to 2013, and its spatial distribution demonstrates significant regional heterogeneity. Additionally, there exists an inverted U-shape relationship between dynamic externalities, including Marshall-Arrow-Romer (MAR), Jacobs and Porter externalities, and eco-efficiency. We also find that eco-efficiency can be enhanced by strengthening environmental regulations, optimizing industrial structures, and improving technological capacity. These findings are robust to alternative eco-efficiency measures, model specifications, and estimation approaches. Furthermore, we discuss related policy implications of our research results.