A novel model of costly technical efficiency

Tsionas, Efthymios G.; Izzeldin, Marwan

doi:10.1016/j.ejor.2018.01.016

Cited by 6 publications

(6 citation statements)

References 36 publications

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“…In this respect the term 'rational' is fully consistent with the classification of Bogetoft and Hougaard (2003) who argue that that inefficiency in some cases may be the outcome of a rational choice (see also Tsionas and Izzeldin, 2018). The decomposition of cost inefficiency into its constituent parts allows us to examine their evolution over the sample period, and the speed of decline of this respective inefficiency.…”

Section: Introductionsupporting

confidence: 69%

Rational cost inefficiency and convergence in Chinese banks

Matthews

Xiao

2020

Economic Modelling

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We argue in this paper that the widely held view that the high level of cost inefficiency in Chinese banks is indicative of management deficiency is questionable. Using a non-parametric bootstrapping method, we decompose cost-inefficiency into technical-inefficiency and allocative-inefficiency and argue that allocative inefficiency should be viewed as rational in the sense that they are the outcomes of political and administrative constraints. Published studies of Chinese banks tend to place cost-inefficiency in the region of 50%. Such estimates would suggest that the management of Chinese banks was grossly inefficient. Over-utilization of labour in Chinese banks meets the social goals of the semi-planned economy. This paper finds evidence of over-utilization of labour and suggests that allowing for rational allocative inefficiency, Chinese banks have converged on a low level of technical inefficiency indicating a high level of management efficiency

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Section: Introductionsupporting

confidence: 69%

Rational cost inefficiency and convergence in Chinese banks

Matthews

Xiao

2020

Economic Modelling

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“…However, mechanization of industry has propagated the need to include other inputs in the form of capital, material and energy as the important factors of production (Blackburn, 1991;Jacobs and Chase, 2011). The existing literature (Hilmola, 2007;Wu, 2009;Grieco and McDevitt, 2012;Jola-Sanchez et al, 2016;Tang, 2017;Tsionas and Izzeldin, 2018) defines this as total factor productivity (TFP) or multifactor productivity (MFP) or total productivity. The factors which influence TFP are numerous that includes useable knowledge (Kuznets, 1966;Lovell, 2003;Xue et al, 2008;Wang et al, 2013;Kapelko et al, 2014), difference in technological advances, resistance to the use of better technologies (Mokyr, 1990;Pinto and Prescott, 1990), differences in working environment and practices, firm size and age (Little et al, 1987;Lundvall and Battese, 2000;Cheng and Lo, 2004;Dubey et al, 2017;Duman and Kasman, 2018).…”

Section: Introductionmentioning

confidence: 99%

An empirical study on productivity analysis of Indian leather industry

Gupta

Dhamija

2019

BIJ

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Purpose It is essential to track the development of resource and pollution intensive industries such as textile, leather, pharmaceutical, etc., under burgeoning pressure of environmental compliance. Therefore, the purpose of this paper is to analyze the progress of Indian leather industry in terms of individual factors and total factor productivity. Design/methodology/approach This study applies and examines the various concepts of productivity such as labor productivity, capital productivity, material productivity and energy productivity. Further, it assesses and compares the performance of Indian leather industry in Tamil Nadu (TN), West Bengal (WB) and Uttar Pradesh (UP) based on productivity analysis, spatial variations determinants in productivity and technology closeness ratio. Findings The findings suggest that as per the productivity analysis, WB leather clusters have performed remarkably better in terms of partial factor productivity and technical efficiency (TE), followed by TN and UP. This can be attributed to shifting of leather cluster of WB to a state-of art leather complex with many avenues for resource conservation. Further, the findings reveal that the firm size and partial factor productivities have significant positive correlation with TE which supports technological theory of the firm. Practical implications The results of this study can be useful for the policy makers associated with the Indian leather industry especially to design interventions to support capacity building at individual firm level as well as cluster level to enhance the efficiency and productivity of overall industry. Social implications The findings also support the resource dependence theory of firm according to which the larger size firms should reflect on resource conservation practices, for instance the concept of prevention is better than cure based upon 3R (reduce, recycle and reuse) principles. Originality/value The paper gives an explanation of the productivity in the leather industry in terms of its factor productivity and TE.

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“…In both cases the R 2 's drop dramatically indicating that these variables should be present in the reduced form. Similarly to other empirical studies (see for example Tsionas and Izzeldin, 2018), we compute autocorrelation functions (acf) for each parameter to examine autocorrelation of MCMC draws. In Figure 9, we report the maximal values of autocorrelation coefficients estimated at the median (50 th percentile) and at the 10 th and 90 th percentile of w (the particular point at which we consider the local likelihood / posterior) for each lag from 1 to 50 (in absolute values but retaining the sign for plotting).…”

Section: Diagnostic Procedures For Bayesian Analysismentioning

confidence: 99%

“…However, there are at least three crucial shortcomings with this approach. First, the inputs are endogenous (also known as "simultaneity bias problem") as is well known from previous studies (Tsionas and Izzeldin, 2018;De Loecker, 2007) because under a variety of behavioral assumptions, they are correlated with the productivity component.…”

Section: Introductionmentioning

confidence: 99%

On the estimation of total factor productivity: A novel Bayesian non-parametric approach

Tsionas

Polemis

2019

European Journal of Operational Research

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This paper provides an alternative general empirical method for the estimation of Total Factor Productivity (TFP). We use a decomposition which allows non-parametric estimation and at the same time addresses the issue of endogeneity of inputs. In this way, we also deal with the unavailability of input prices which is common in the TFP literature. We apply the new techniques to U.S four-digit manufacturing data using a novel Bayesian nonparametric model based on local likelihood. We use Markov Chain Monte Carlo (MCMC) techniques organized around the method of Girolami and Calderhead (2011). We compare and contrast the estimates from the proposed new method with standard parametric methods such as the translog, the Generalized Leontief and the Normalized Quadratic and we also propose novel diagnostic tests for correct specification and validity of instruments. We show that parametric methods lead to biased estimation of TFP growth. Our empirical findings show that the new model passes successfully a battery of robustness checks including diagnostic testing and tests for weak identification as well as weak instruments. Finally policy implications relating to the nature of TFP growth are also provided.

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A novel model of costly technical efficiency

Cited by 6 publications

References 36 publications

Rational cost inefficiency and convergence in Chinese banks

Rational cost inefficiency and convergence in Chinese banks

An empirical study on productivity analysis of Indian leather industry

On the estimation of total factor productivity: A novel Bayesian non-parametric approach

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