Efficiency evaluation of the energy companies in CNX 500 Index of the NSE, India using data envelopment analysis

Discrepancy in efficiency scores due to sampling error in data envelopment analysis methodology: evidence from the banking sector

Background

Data Envelopment Analysis (DEA) methodology is considered the most suitable approach for relative performance efficiency calculation for banks as it is believed to be superior to traditional ratio-based analysis and other conventional performance evaluations. This study provides statistical evidence on the sampling error that can creep into performance evaluation studies using the DEA methodology. Inferences are drawn based on samples, and various preventive measures must be taken to eliminate or avoid sampling errors and misleading results. This study demonstrates the possibility of sampling error in DEA with the secondary data available in financial statements and reports from a sample set of banks.

Methods

The samples included 15 public sectors and five leading private sector banks in India based on their market share, and the data for calculating efficiencies were retrieved from the published audited reports. The sample data was collected from 2014 to 2017 because the banking sector in India witnessed a series of mergers of public sector banks post-2017, and the data after that would be skewed and not comparable due to the demonetization policy implementation and merger process-related consolidation implemented by the Government of India. The efficiency measures thus computed are further analyzed using non-parametric statistical tests.

Results

We found statistically significant discrepancies in the efficiency score calculations using DEA approach when specific outlier values. Evidence is provided on statistically significant differences in the efficiencies due to the inclusion and exclusion of particular samples in the DEA.

Conclusion

The study offers a novel contribution along with statistical evidence on the possible sampling error that can creep into the performance evaluation of organizations while applying the DEA methodology.

Decomposition of efficiency using DEA window analysis

Purpose

The purpose of this paper is to examine empirically the efficiency types of Islamic and conventional banks. It seeks to show whether the efficiency level of conventional and Islamic banks significantly differs from each other. In addition, it investigates the influential factors on each type of efficiency.

Design/methodology/approach

The paper utilises the data envelopment analysis in its windows version to estimate the efficiency scores reflecting the time variance and compares between banking models. The paper uses pure technical efficiency (TE) and scale efficiency to achieve the objective of the study. In addition, the panel data technique is adopted to assess the determinants of the efficiency of the banks econometrically.

Findings

The findings of panel regression initially indicate that the pure TE is higher for conventional banks compared to Islamic banks. However, the Islamic banks are more scale efficient than their conventional counterpart. Macro and micro indicators have different impacts on the both types of efficiency. However, the unique factors that show consistent influence on the efficiency types were loans/finance, non-interest income/finance/liquidity and GDP. Furthermore, the determinants are shaped differently for Islamic and conventional banks when the banking model is controlled for.

Originality/value

This paper examines the efficiency types using a unique window analysis approach to examine the types of efficiency with a longitudinal set of data from 1996 to 2011.

Productivity changes in Indian steel plants: DEA approach

Purpose

The purpose of this paper is to evaluate the technical efficiency and productivity changes in the integrated steel plants in India over a period of five years.

Design/methodology/approach

Since this evaluation of integrated steel plants needs consideration of multiple input and output factors, data envelopment analysis (DEA) has been employed including bootstrapping (to account for statistical noise) to evaluate the relative efficiency of the steel manufacturing units. The efficiency and Malmquist productivity indices of a sample of ten integrated steel plants producing around 55 percent of the industry’s output were determined for the period 2008-2013. The results of these changes were further categorized according to the management control, route followed to produce crude steel, size and age of these steel plants, for gaining insights.

Findings

The study finds that private sector steel plants with larger capacity and which have adopted the latest and most modern technologies are more efficient and productive over the study period.

Practical implications

Public sector steel plants should therefore be provided with more autonomy and delegation of power and should be agiler in responding to market requirements as well as increasing their installed capacities to be competitive in technical efficiency and productivity as well as profitability in the long term to ensure sustainable achievements.

Originality/value

Productivity changes over time, both with respect to technological and efficiency changes, for the Indian integrated steel plants producing comparable products using DEA.