Labour efficiency in construction industry in Europe based on frontier methods: data envelopment analysis and stochastic frontier analysis

    Joanicjusz Nazarko Info
    Ewa Chodakowska Info

Abstract

The primary problems pertaining to productivity or – more precisely – efficiency are: how to define it and how to measure it. This article studies technical efficiency in Stochastic Frontier Analysis (SFA) – the input-oriented frontier model – in the construction industry and compares it with Data Envelopment Analysis (DEA) results. The models ex­plored in this paper were constructed on the basis of two outputs and personnel cost as an input. The research sample consisted of European countries. The aim was to determine whether there are substantial differences in estimation of ef­ficiency derived from those two alternative frontier approaches. The comparison of results according to the models may translate into higher reliability of the undertaken labour efficiency analysis in construction and its conclusions. Although the results are not characterized by high compatibility, the conducted analysis indicated the most attractive countries taking into account labour cost to profit and turnover ratios of enterprises. One of the determinants which should not be ignored when analysing the labour efficiency is the level of development of a country; however, it is not the sole factor affecting the efficiency of the sector.

Keywords:

Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), Tobit regression, labour efficiency, construction industry

How to Cite

Labour efficiency in construction industry in Europe based on frontier methods: data envelopment analysis and stochastic frontier analysis. (2017). Journal of Civil Engineering and Management, 23(6), 787-795. https://doi.org/10.3846/13923730.2017.1321577

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June 22, 2017
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2017-06-22

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How to Cite

Labour efficiency in construction industry in Europe based on frontier methods: data envelopment analysis and stochastic frontier analysis. (2017). Journal of Civil Engineering and Management, 23(6), 787-795. https://doi.org/10.3846/13923730.2017.1321577

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