Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Efficiency of Micro and Small Wood-Processing Enterprises in the EU – Evidence From DEA and Fractional Regression Analysis

Nikolay Neykov
* ORCID logo ,
Mariana Sedliačiková
ORCID logo ,
Petar Antov
* ORCID logo ,
Marek Potkány
,
Emil Kitchoukov
,
Aureliu-Florin Hălălișan
ORCID logo ,
Natália Poláková
Version 1 : Received: 27 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (07:49:48 CET)

A peer-reviewed article of this Preprint also exists.

Neykov, N.; Sedliacikova, M.; Antov, P.; Potkány, M.; Kitchoukov, E.; Halalisan, A.-F.; Poláková, N. Efficiency of Micro and Small Wood-Processing Enterprises in the EU—Evidence from DEA and Fractional Regression Analysis. Forests 2024, 15, 58. Neykov, N.; Sedliacikova, M.; Antov, P.; Potkány, M.; Kitchoukov, E.; Halalisan, A.-F.; Poláková, N. Efficiency of Micro and Small Wood-Processing Enterprises in the EU—Evidence from DEA and Fractional Regression Analysis. Forests 2024, 15, 58.

Abstract

Micro and small wood-processing enterprises represent the heart of the European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the pro-vision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is nonparametric, linear-programming-based approach, commonly used to analyse the efficiency of organizational units. This method allows estimating the economic efficiency of a certain eco-nomic system without assumptions about the functional form between resources and products. Furthermore, DEA determines the efficiency frontier and gives results of whether an enterprise, i.e., a Decision Making Unit (DMU) is efficient or not. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterpris-es in the EU countries and reveal the hidden inputs that facilitate efficiency generation. The eco-nomic efficiency evaluation was carried out on the basis of the official statistical data for the mi-cro and small wood-processing companies in the EU member states for the period 2015-2020 by performing a two-stage DEA analysis. The data used were standardized by value per employee. In addition to the first stage of DEA, fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically-efficient entities. The only contextual variable affecting the eco-nomic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase of the investments.

Keywords

DEA; wood processing enterprises; small enterprises; fractional regression

Subject

Business, Economics and Management, Economics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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