The Patterns of Technological Capabilities of Countries: A Dual Approach using Composite Indicators and Data Envelopment Analysis

2011 ◽  
Vol 39 (7) ◽  
pp. 1108-1121 ◽  
Author(s):  
Andrea Filippetti ◽  
Antonio Peyrache
Keyword(s):  
Data Envelopment Analysis ◽  
Composite Indicators ◽  
Data Envelopment ◽  
Technological Capabilities ◽  
Dual Approach

scholarly journals Financial Performance Assessment of Construction Firms by Means of RAM-Based Composite Indicators

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1347
Author(s):  
Ioannis E. Tsolas
Keyword(s):  
Data Envelopment Analysis ◽  
Financial Performance ◽  
Composite Indicators ◽  
Upper Class ◽  
Practical Implementation ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Construction Firms ◽  
Relational Analysis ◽  
Grey Relational

This paper aims to provide a novel construct that is based on data envelopment analysis (DEA) range adjusted measure (RAM) of efficiency and demonstrate its practical implementation by evaluating the financial performance of a sample of three upper-class contracting license (Classes 5–7) Greek construction firms. In a two-step framework, firm efficiency (i.e., composite indicators (CIs)) is produced firstly by means of RAM using single financial ratios, which are selected by grey relational analysis (GRA), and then Tobit regression is employed to model the CIs. In light of the results, only 4% of the sampled firms are efficient, and the firm ranking is consistent with the ranking of Grey Relational Grande (GRG) values produced by GRA. Moreover, the firms with a contracting license of the highest level (Class 7) appear not to be superior in efficiency to their counterparts that belong to Classes 5–6.

scholarly journals A digitális fejlődés rangsorolása a DEA-típusú összetett indikátorok és a TOPSIS módszerével

2021 ◽  
Vol 99 (3) ◽  
pp. 253-265
Author(s):  
Zoltán Bánhidi ◽  
Imre Dobos
Keyword(s):  
Data Envelopment Analysis ◽  
Digital Economy ◽  
Composite Indicators ◽  
Data Envelopment ◽  
Ideal Solution

A szerzők a digitális gazdaság és társadalom index (digital economy and society index, DESI) öt alapdimenziója segítségével, de az önkényes, szubjektív súlyozáson (scoring modellen) alapuló kompozit index helyett objektívebb, az adatsorok statisztikai tulajdonságait felhasználó rangsorolási módszerekkel kívánnak választ adni arra a kérdésre, hogy Magyarország hol helyezkedik el a digitális fejlettséget tekintve az EU (Európai Unió) országai között. A rangsorolást az összetett indikátorok burkológörbe-elemzése (data envelopment analysis/composite indicators, DEA/CI) és az ideális megoldásokhoz hasonló preferenciarendszer technikája (technique for order of preference by similarity to ideal solution, TOPSIS) alkalmazásával végzik el, majd összehasonlítják az EU-s országok két döntéselméleti eljárással kapott rangsorát. A TOPSIS- és a DEA/CI-módszer előnye a hagyományos DEA-val szemben, hogy abban az esetben is adattranszformáció nélkül alkalmazhatók, ha az adatok között csak (maximalizálandó) outputkritériumok vannak, ezáltal elkerülhetők a transzformációval járó torzítások. Az eredményül kapott rangsorok alapján Magyarország az uniós országok második harmadában helyezkedik el, így közepes digitális fejlettségűnek tekinthető.

Modeling qualitative data in data envelopment analysis for composite indicators

2011 ◽  
Vol 2 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Yongjun Shen ◽  
Da Ruan ◽  
Elke Hermans ◽  
Tom Brijs ◽  
Geert Wets ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Qualitative Data ◽  
Composite Indicators ◽  
Data Envelopment

La costruzione di un indicatore sintetico ponderato. Un'applicazione della procedura Benefit of the Doubt al caso della dotazione infrastrutturale in Italia

2009 ◽  
pp. 35-69
Author(s):  
Claudia Mazziotta ◽  
Francesco Vidoli
Keyword(s):  
Data Envelopment Analysis ◽  
Maximum Level ◽  
Jel Classification ◽  
Composite Indicator ◽  
Composite Indicators ◽  
Data Envelopment ◽  
Synthesis Procedure ◽  
Italian Province ◽  
Simple Indicator ◽  
Keywords Composite

- (Paper first received, October 2007; in final form, febbraio 2008) This paper provides a review of a methodology for constructing composite indicators who constitute the synthesis of a series of simple indicators and, above all, characterize a system of weights to use in the synthesis procedure. We apply the Benefit of the Doubt (BoD) approach, an application of the technique of linear programming Data Envelopment Analysis (DEA). In our formulation, however, weights constraints are endogenously determined, differently to BoD usual applications, analyzing variability of every simple indicator. Our methodology allows to find a matrix of the weights characterizing not only the simple indicators, but also the territorial units involved, obtaining, for every unit, the system of weights more favourable and, consequently, a composite indicator with maximum level. BoD approach has been applied to construction of a composite indicator of the Italian infrastructural endowment, whose elementary indicators were available to level of Italian province and infrastructural categories. Keywords: Composite Indicators; Data Envelopment Analysis; Infrastructure Endowment JEL classification: H54. C43. C61

Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model

2012 ◽  
Vol 114 (2) ◽  
pp. 739-756 ◽  
Author(s):  
Yongjun Shen ◽  
Elke Hermans ◽  
Tom Brijs ◽  
Geert Wets
Keyword(s):  
Data Envelopment Analysis ◽  
Composite Indicators ◽  
Data Envelopment ◽  
Layer Model ◽  
Multiple Layer

Composite Indicators Construction by Data Envelopment Analysis

2017 ◽  
pp. 98-126
Author(s):  
Gordana Savić ◽  
Milan Martić
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Indicators ◽  
A Priori ◽  
Real Data ◽  
Individual Performance ◽  
Composite Indicators ◽  
Data Availability ◽  
Data Envelopment ◽  
Expert Opinions

Composite indicators (CIs) are seen as an aggregation of a set of sub-indicators for measuring multi-dimensional concepts that cannot be captured by a single indicator (OECD, 2008). The indicators of development in different areas are also constructed by aggregating several sub-indicators. Consequently, the construction of CIs includes weighting and aggregation of individual performance indicators. These steps in CI construction are challenging issues as the final results are significantly affected by the method used in aggregation. The main question is whether and how to weigh individual performance indicators. Verifiable information regarding the true weights is typically unavailable. In practice, subjective expert opinions are usually used to derive weights, which can lead to disagreements (Hatefi & Torabi, 2010). The disagreement can appear when the experts from different areas are included in a poll since they can value criteria differently in accordance with their expertise. Therefore, a proper methodology of the derivation of weights and construction of composite indicators should be employed. From the operations research standpoint, the data envelopment analysis (DEA) and the multiple criteria decision analysis (MCDA) are proper methods for the construction of composite indicators (Zhou & Ang, 2009; Zhou, Ang, & Zhou, 2010). All methods combine the sub-indicators according to their weights, except that the MCDA methods usually require a priori determination of weights, while the DEA determines the weights a posteriori, as a result of model solving. This chapter addresses the DEA as a non-parametric technique, introduced by Charnes, Cooper, and Rhodes (1978), for efficiency measurement of different non-profitable and profitable units. It is lately adopted as an appropriate method for the CI construction due to its several features (Shen, Ruan, Hermans, Brijs, Wets, & Vanhoof, 2011). Firstly, individual performance indicators are combined without a priori determination of weights, and secondly, each unit under observation is assessed taking into consideration the performance of all other units, which is known as the ‘benefit of the doubt' (BOD) approach (Cherchye, Moesen, Rogge, & van Puyenbroeck, 2007). The methodological and theoretical aspects and the flaws of the DEA application for the construction of CIs will be discussed in this chapter, starting with the issues related to the application procedure, followed by the issues of real data availability, introducing value judgments, qualitative data, and non-desirable performance indicators. The procedure of a DEA-based CI construction will be illustrated by the case of ranking of different regions of Serbia based on their socio-economic development.

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