The Malmquist Productivity measure for UK-listed firms in the aftermath of the global financial crisis
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The Malmquist Productivity measure for UK‑listed firms in the aftermath of the global financial crisis Apostolos Christopoulos1 · Ioannis Dokas2 · Sofia Katsimardou2 · Eleftherios Spyromitros2 Received: 3 June 2020 / Revised: 5 August 2020 / Accepted: 18 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Using a Bootstrap Malmquist Productivity Index approach, this paper investigates productivity changes of 24 high capitalization firms, listed in the London Stock Exchange over the period from 2009 to 2016. In the aftermath of the global financial crisis, we find evidence of technological and technical efficiency variations for our sample of industrial firms. Specifically, we show that, on average, only 26.2% of the examined firms managed to perform a positive increase in their Total Factor Productivity for the investigated period. There is an apparent deterioration in the technological efficiency for all firms, enhancing the view that these companies avoided investing in new technologies. However, in general, an improvement of technical efficiency is observed, meaning that firms improve the allocation of their available inputs in the production process. Keywords Firm efficiency · DEA · Bootstrap · Malmquist Productivity Index · Accounting data · Financial analysis JEL Classification C44 · D24
* Ioannis Dokas [email protected] Apostolos Christopoulos [email protected] Sofia Katsimardou [email protected] Eleftherios Spyromitros [email protected] 1
Department of Business Administration, University of Aegean, Chios, Greece
2
Department of Economics, Democritus University of Thrace, Komotini, Greece
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1 Introduction Data Envelopment Analysis (DEA) is widely used in the investigation of the firms’ efficiency status. It is a non-parametric linear programming technique based on the Farrell (1957) approach to estimate the efficiency status of DecisionMaking Units (DMUs), creating a classification order of these DMUs. Following the seminal work by Charnes et al. (1978), numerous studies focused on the ability of DEA to classify economic entities according to a relative efficiency score, which is mainly based on individual financial characteristics. According to the literature, the implementation of DEA is expanded in various sectors such as the banking industry (Bergendahl 1998; Casu et al. 2004; Beccalli et al. 2006; Pasiouras et al. 2008; Tortosa-Ausina et al. 2008; Avkiran 2011; Tan and Floros 2013; Christopoulos et al. 2020), transportations (Tongzon 2001; Martin and Roman 2001; Cullinane et al. 2006; Cui and Li 2014; Liu et al. 2017; Chang et al. 2018; Tian et al. 2020), agriculture and farming (Dhungana et al. 2004; ReigMartı́nez and Picazo-Tadeo 2004; Hansson 2007; Amores and Contreras 2009; Bolandnazar et al. 2014; Nandy et al. 2019; Ullah et al. 2019), food and beverage (Kumar and Basu 2008; Dokas et al. 2014; Giokas et al. 2015; Engida et al. 2018), hotels industry (Hwang and Chang 2003; Barros and Dieke 2008
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