Applying integrated DEA/AHP to evaluate the economic performance of local governments in China

Lin, Ming-Ian; Lee, Yuan-Duen; Ho, Tsai-Neng

doi:10.1016/j.ejor.2010.08.006

Cited by 89 publications

(44 citation statements)

References 25 publications

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“…The first one focuses on the provision of services by municipalities using frontier models (usually data envelopment analysis) to assess LGs' technical and cost efficiency (Afonso & Fernandes, 2006;Lin, Lee, & Ho, 2011;Rogge & De Jaeger, 2013), evaluate specific services provided by LGs (Chang, Liu, & Yeh, 2013), and develop budget plans using optimization models and multi-criteria techniques (Doumpos & Cohen, 2014;Gomez, Rios Insua, Lavin, & Alfaro, 2013). The second research strand is about predicting instances of financial distress of LGs (Garcia-Sanchez, Cuadrado-Ballesteros, Frias-Aceituno, & Mordan, 2012; Kloha, Weissert, & Kleine, 2005;Zafra-Gómez, Lopez-Hernández, & Hernández-Bastida, 2009b).…”

Section: Introductionmentioning

confidence: 99%

A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France

Galariotis

Guyot

Doumpos

et al. 2016

European Journal of Operational Research

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Section: Introductionmentioning

confidence: 99%

A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France

Galariotis

Guyot

Doumpos

et al. 2016

European Journal of Operational Research

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“…Such as converting the qualitative data in DEA to the quantitative data using AHP [27][28][29][30][31][32][33][34], ranking the efficient/inefficient units in DEA models using AHP in a two stage process [35][36][37], weighting the efficiency scores obtained from DEA using AHP [38], weighting the inputs and outputs in the DEA structure [39][40][41][42], constructing a convex combination of weights using AHP and DEA [43] and estimating missing data in DEA using AHP [44].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Aggregation Approach for Indicators Based on Data Envelopment Analysis and Analytic Hierarchy Process

Pakkar

2016

Systems

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This research proposes a hierarchical aggregation approach using Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) for indicators. The core logic of the proposed approach is to reflect the hierarchical structures of indicators and their relative priorities in constructing composite indicators (CIs), simultaneously. Under hierarchical structures, the indicators of similar characteristics can be grouped into sub-categories and further into categories. According to this approach, we define a domain of composite losses, i.e., a reduction in CI values, based on two sets of weights. The first set represents the weights of indicators for each Decision Making Unit (DMU) with the minimal composite loss, and the second set represents the weights of indicators bounded by AHP with the maximal composite loss. Using a parametric distance model, we explore various ranking positions for DMUs while the indicator weights obtained from a three-level DEA-based CI model shift towards the corresponding weights bounded by AHP. An illustrative example of road safety performance indicators (SPIs) for a set of European countries highlights the usefulness of the proposed approach.

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“…Such as converting the qualitative data in DEA to the quantitative data using AHP (Azadeh et al, 2008;Ertay et al, 2006;Jyoti et al, 2008;Korpela et al, 2007;Lin et al, 2011;Ramanathan, 2007;Yang and Kuo, 2003;Raut, 2011), ranking the efficient/inefficient units in DEA models using AHP in a two stage process (Ho and Oh , 2010;Jablonsky, 2007;Sinuany-Stern et al, 2000), weighting the efficiency scores obtained from DEA using AHP (Chen, 2002), weighting the inputs and outputs in the DEA structure (Pakkar, 2014b;Cai and Wu, 2001;Feng et al, 2004;Kim, 2000), constructing a convex combination of weights using AHP and DEA (Liu and Chen, 2004) and estimating missing data in DEA using AHP (Saen et al, 2005). Nevertheless, the above-mentioned literature is limited to the applications with onelevel DEA models, which may not entirely satisfy the need for increasingly complex assessment problems.…”

Section: Introductionmentioning

confidence: 99%

Using DEA and AHP for Hierarchical Structures of Data

Pakkar¹

2016

Industrial Engineering and Management Systems

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In this paper, we propose an integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) methodology in which the information about the hierarchical structures of input-output data can be reflected in the performance assessment of decision making units (DMUs). Firstly, this can be implemented by extending a traditional DEA model to a three-level DEA model. Secondly, weight bounds, using AHP, can be incorporated in the three-level DEA model. Finally, the effects of incorporating weight bounds can be analyzed by developing a parametric distance model. Increasing the value of a parameter in a domain of efficiency loss, we explore the various systems of weights. This may lead to various ranking positions for each DMU in comparison to the other DMUs. An illustrative example of road safety performance for a set of 19 European countries highlights the usefulness of the proposed approach.

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Applying integrated DEA/AHP to evaluate the economic performance of local governments in China

Cited by 89 publications

References 25 publications

A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France

A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France

A Hierarchical Aggregation Approach for Indicators Based on Data Envelopment Analysis and Analytic Hierarchy Process

Using DEA and AHP for Hierarchical Structures of Data

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