Abstract
Efficiency analysis is very useful and important to measure the performance of the firms in com- petitive market of rapidly developing country like Bangladesh. The more efficient firms, and the decision making units (DMUs) are usually referred as benchmarking units for the development. In this study, efficiency scores are obtained using the non-parametric Data Envelopment Anal- ysis (DEA) technique for 1007 manufacturing firms in Bangladesh from the enterprise survey data. The DEA is used to calculate weights for inputs and outputs by assigning the maximum efficiency score for a DMU under evaluation. Total 29 firms are found efficient under variable returns to scale assumption. The significant determinants behind the inefficiency found in this analysis include mainly the firm size, manager’s experience in respective sector, annual losses due to power outage, number of production workers.
Highlights
Efficiency Analysis is a challenging issue for measuring the performance of various sectors.Per- formance measurement and benchmarking is very important for measuring efficiency.Frontier efficiency techniques can be used for finding the efficiency score which is related to the ‘best practices’
Output oriented Data Envelopment Anal- ysis (DEA) model focuses on the measurement of variation in output produced and an increases in efficiency will be achieved by increasing outputs proportionally holding the input quantities constant
Research on performance measurement was based on the traditional financial ratio anal- ysis
Summary
Efficiency Analysis is a challenging issue for measuring the performance of various sectors. DEA efficiency scores are sensitive to measurement error, specially with small samples It is a non-parametric technique used in the estimation of production functions and has been used extensively to estimate measures of technical efficiency in a range of industries [8], [16]. Syntax Error (24013): Invalid blend mode in ExtGState ity (TFP) growth rate in Malaysia analysed and decomposed the total factor productivity into technological change and technical efficiency change [15]. DEA is a linear programming model to measure efficiency under the assumption of constant returns to scale [6] and extended by Banker et al (1984) to allow variable returns to scale [3]. Grosskopf provided a good survey of statistical inference in non-parametric linear programming frontier models, analyzing the asymptotic properties of the estimators[12]. The determinants of efficiency score can be obtained by DEA approach
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