A spatial efficiency index proposal: an empirical application to SMEs productivity

Maté-Sánchez-Val, Mari Luz; Guijarro, Antonia Madrid

doi:10.1007/s00168-010-0382-8

Cited by 7 publications

(2 citation statements)

References 31 publications

(27 reference statements)

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“…To support the robustness of our results, we applied alternative proxies to measure the input variables. We proposed the total number of employees as alternative of labour input (Arcas et al, 2011; Maté-Sánchez-Val and Madrid-Guijarro, 2011). Summary descriptive statistics of the data used to compute the MPI are given in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Spatial effects on the productive structure of Spanish agri-food cooperatives

2016

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This article analyses the role of spatial effects on the productive growth of Spanish agri-food cooperatives. The study was based on a sample data from 172 Spanish cooperatives for the period 2010–2012. We applied the Malmquist productivity index method to compute the productivity growth and its components. We then developed a multi-equational productivity growth model with spatial effects. Our results show that the productivity growth in agri-food cooperatives depends on the geographical location of the company and its interaction with its neighbours. This analysis supports the importance of taking into account the spatial dimension when regional agri-food policies are designed, particularly, for cooperatives characterized by the inter-cooperation principle.

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Section: Methodsmentioning

confidence: 99%

Spatial effects on the productive structure of Spanish agri-food cooperatives

2016

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“…Both Mokaddem [27] and Schaffer, Simar and Rauland [28] incorporated a spatial weight matrix W to describe the spatial interactions in the post-DEA analysis. Maté-Sánchez-Val and Madrid-Guijarro [29] used this approach in reverse: they introduced a spatial weight matrix in the pre-DEA stage of their study concerning the productivity of small and medium enterprises. Their modification of the DEA methodology assumed that each DMU should be compared only with the neighbouring units, as recognised by the W matrix.…”

Section: Introductionmentioning

confidence: 99%

Spatial Solution to Measure Regional Efficiency — Introducing Spatial Data Envelopment Analysis

Olejnik¹,

Żółtaszek²,

Olejnik³

2021

EoR

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When investigating healthcare efficiency at the regional level, the problem of interactions between neighbouring locations arises. The health of the population in a given region is related to the healthcare in other areas through a medical tourism, a limited number of highly specialised institutions, competition between institutions, etc. Ignoring these inter-regional links may result in a systematic bias in the efficiency analysis. Similar issues may hinder any regional studies. Hence, the main purpose of this paper is to introduce a new approach to measuring efficiency in regional studies through spatial data envelopment analysis (SDEA). The paper offers a proper mathematical formulation of the new methodology and highlights differences between classic data envelopment analysis (DEA) and the newly developed method. The motivation for seeking a new solution to the problem of spatially adequate assessment of regional efficiency is derived from the literature review and a discussion of the presented theoretical examples. The classic DEA allows for multidimensional analysis of the performance of homogenous independent decision-making units. However, in regional studies, an area where DEA has gained popularity, the assumption of the isolation of decision-making units seems to be unfounded. In the SDEA approach, the region-specific spatial context is incorporated into the analysis via the W matrix and spatial interactions are reflected in the model through spatially weighted inputs and outputs. Therefore, in our paper, we verify the hypothesis that spatial interactions are an indispensable factor of regional efficiency analysis. A study of healthcare efficiency in European regions is presented as an illustration of the utility of the new methodology. Furthermore, we compare the results of the classic DEA approach with those of the SDEA, which is augmented with the spatial equivalents of inputs and outputs. Our results suggest that classic DEA undervalues regional healthcare efficiency by underestimating the region-specific spatial context.2 Researchers may find the introduced SDEA method useful in all space related fields when investigated phenomenon exhibits spatial autocorrelation. In particular, the new approach may deepen the regional efficiency analysis of innovation, development, logistics, tourism, etc.

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Spatial dependence in production frontier models

Ayouba

2023

J Prod Anal

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A spatial efficiency index proposal: an empirical application to SMEs productivity

Abstract: C43, C61, D24, O18,

Cited by 7 publications

References 31 publications

Spatial effects on the productive structure of Spanish agri-food cooperatives

Spatial effects on the productive structure of Spanish agri-food cooperatives

Spatial Solution to Measure Regional Efficiency — Introducing Spatial Data Envelopment Analysis

Spatial dependence in production frontier models

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