scholarly journals A Two-Stage DEA to Analyze the Effect of Entrance Deregulation on Iranian Insurers: A Robust Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Seyed Gholamreza Jalali Naini ◽  
Hamid Reza Nouralizadeh
Keyword(s):  
Statistical Analysis ◽  
Relative Efficiency ◽  
Generalized Estimating Equations ◽  
Insurance Market ◽  
Data Envelopment ◽  
Two Stage ◽  
Robust Approach ◽  
Dea Model ◽  
Second Stage ◽  
First Results

We use two-stage data envelopment analysis (DEA) model to analyze the effects ofentrance deregulationon the efficiency in the Iranian insurance market. In the first stage, we propose arobust optimizationapproach in order to overcome the sensitivity of DEA results to any uncertainty in the output parameters. Hence, the efficiency of each ongoing insurer is estimated using our proposed robust DEA model. The insurers are then ranked based on their relative efficiency scores for an eight-year period from 2003 to 2010. In the second stage, a comprehensive statistical analysis usinggeneralized estimating equations(GEE) is conducted to analyze some other factors which could possibly affect the efficiency scores. The first results from DEA model indicate a decline in efficiency over the entrance deregulation period while further statistical analysis confirms that the solvency ignorance which is a widespread paradigm among state owned companies is one of the main drivers of efficiency in the Iranian insurance market.

scholarly journals Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chao Lu ◽  
Haifang Cheng
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Data Envelopment ◽  
Nonparametric Method ◽  
Two Stage ◽  
Dea Model ◽  
Second Stage ◽  
China Construction Bank ◽  
Overall Efficiency ◽  
Decision Making Units

Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. As an extension of the DEA, a multiplicative two-stage DEA model has been widely used to measure the efficiencies of two-stage systems, where the first stage uses inputs to produce the outputs, and the second stage then uses the first-stage outputs as inputs to generate its own outputs. The main deficiency of the multiplicative two-stage DEA model is that the decomposition of the overall efficiency may not be unique because of the presence of alternate optima. To remove the problem of the flexible decomposition, in this paper, we maximize the sum of the two-stage efficiencies and simultaneously maximize the two-stage efficiencies as secondary goals in the multiplicative two-stage DEA model to select the decomposition of the overall efficiency from the flexible decompositions, respectively. The proposed models are applied to evaluate the performance of 10 branches of China Construction Bank, and the results are compared with the results of the existing models.

Benchmarking the efficiencies of Indonesia’s municipal water utilities using Stackelberg data envelopment analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 588-609 ◽  
Author(s):  
Andreas Wibowo ◽  
Hans Wilhelm Alfen
Keyword(s):  
Data Envelopment Analysis ◽  
The Body ◽  
Water Utilities ◽  
Data Envelopment ◽  
Two Stage ◽  
Restrictive Assumption ◽  
Content Type ◽  
Second Stage ◽  
Municipal Water ◽  
The Impact

Purpose – The purpose of this paper is to present a yardstick efficiency comparison of 269 Indonesian municipal water utilities (MWUs) and measures the impact of exogenous environmental variables on efficiency scores. Design/methodology/approach – Two-stage Stackelberg leader-follower data envelopment analysis (DEA) and artificial neural networks (ANN) were employed. Findings – Given that serviceability was treated as the leader and profitability as the follower, the first and second stage DEA scores were 55 and 32 percent (0 percent = totally inefficient, 100 percent = perfectly efficient), respectively. This indicates sizeable opportunities for improvement, with 39 percent of the total sample facing serious problems in both first- and second-stage efficiencies. When profitability instead leads serviceability, this results in more decreased efficiency. The size of the population served was the most important exogenous environmental variable affecting DEA efficiency scores in both the first and second stages. Research limitations/implications – The present study was limited by the overly restrictive assumption that all MWUs operate at a constant-return-to-scale. Practical implications – These research findings will enable better management of the MWUs in question, allowing their current level of performance to be objectively compared with that of their peers, both in terms of scale and area of operation. These findings will also help the government prioritize assistance measures for MWUs that are suffering from acute performance gaps, and to devise a strategic national plan to revitalize Indonesia’s water sector. Originality/value – This paper enriches the body of knowledge by filling in knowledge gaps relating to benchmarking in Indonesia’s water industry, as well as in the application of ensemble two-stage DEA and ANN, which are still rare in the literature.

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

scholarly journals Two-stage DEA for Bank Efficiency Evaluation Considering Shared Input and Unexpected Output Factors

2020 ◽  
Vol 214 ◽  
pp. 01036
Author(s):  
Song Aifeng ◽  
Zhang XiaoYang ◽  
Huang Weilai ◽  
Yang xue ◽  
Yang Juan
Keyword(s):  
Market Competition ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Uncertain Information ◽  
Two Stage ◽  
Bank Efficiency ◽  
Dea Model ◽  
Stochastic Dea ◽  
Output Factors ◽  
Fierce Competition

With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method to effectively deal with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. In the process of bank efficiency evaluation based on DEA method, there will be a situation that banks have both dual role factors and unexpected output factors. The Two-stage DEA model provides an effective analysis method to solve the problem of bank efficiency evaluation of complex organizational structure. In order to evaluate the efficiency of unexpected output with uncertain information, a stochastic DEA model of unexpected output is established.

Evaluating Taiwanese Bank Efficiency Using the Two-Stage Range DEA Model

2016 ◽  
Vol 16 (04) ◽  
pp. 1043-1068 ◽  
Author(s):  
Wei-Hsin Kong ◽  
Tsu-Tan Fu ◽  
Ming-Miin Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Analysis Model ◽  
Negative Data ◽  
Two Stage ◽  
Data Set ◽  
Dea Model ◽  
Domestic Banks

This paper develops a range directional distance data envelopment analysis (DEA) model to simultaneously deal with the problems of negative data and undesirable outputs in the study of performance measurement with two-stage DEA. We report on the development of this model to handle both positive and negative data in a DEA framework and accommodate the problem of undesirable intermediate outputs in the first stage of operational processes. Unlike previous two-stage DEA models we allow for a nonuniform abatement factor imposing on stage 1’ production technology. Such a model is then applied to evaluate Taiwanese bank efficiencies both at the operational stage and profitability stage in banking activities based on a data set consisting of 35 domestic banks in Taiwan in the period 2007. The results indicate that, by the range directional two-stage data envelopment analysis model, the operational efficiency was smaller than the profitability efficiency. Many banks generated too many performing loans in which independent banks should reduce more performing loans than financial holding company subsidiary banks. Both the ratio of investments to loans and the ratio of nonperforming loans to performing loans did not have significant contributions to the efficiency. This paper is able to provide information for bank operators and researchers on the managerial and strategic implications of how negative data and undesirable outputs affect efficiency and how to measure efficiency appropriately.

Evaluating the efficiency of a two-stage network structure with the use of fractional programming

2017 ◽  
Vol 09 (03) ◽  
pp. 1750034 ◽  
Author(s):  
Reza Ahmadzadeh ◽  
Sohrab Kordrostami ◽  
Alireza Amirteimoori
Keyword(s):  
Linear Model ◽  
Fractional Programming ◽  
Nonlinear Models ◽  
Heuristic Method ◽  
Data Envelopment ◽  
Network Structures ◽  
Two Stage ◽  
Second Stage ◽  
Internal Structures ◽  
Stage Process

Recently, network data envelopment analysis (NDEA) models have been developed to evaluate the efficiency of decision making units (DMUs) with internal structures. The network structures range from a simple two-stage process to a complex system. Looking through the literature on two-stage network structures, we see that Li et al. (2012) extended a model by assuming that the inputs to the second stage include both the outputs from the first stage and additional inputs to the second stage. In the current study, a model is proposed to evaluate the performance of these types of general two-stage network structures. To this end, we provide a linear model using fractional programming. In fact, previous models were often nonlinear models which were solved with heuristic methods. But, since the model presented in this paper is a linear model, then it can be solved easily as a linear programming problem. In order to clarify the newly proposed approach of this study, it has been applied to a case of regional Research and Development (R&D) system related to 30 provincial level regions in China and results have been compared with the heuristic method of Li et al. (2012).

Efficiency-based recruitment plan for chained quick-service enterprise

2006 ◽  
Vol 25 (3) ◽  
pp. 197-209
Author(s):  
Chun-Hsiung Lan ◽  
Yu-Hua Lan ◽  
Chi-Chung Chang ◽  
Liang-Lun Chuang
Keyword(s):  
Production Efficiency ◽  
Quantitative Approach ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Two Stage ◽  
Stage Design ◽  
Second Stage ◽  
Two Stage Design ◽  
Using Data ◽  
Quick Service

This paper describes a research method called two-stage design consisting of the determination of the efficiency for each quick-service restaurant of chained enterprise at the first stage by using Data Envelopment Analysis (DEA), and then proposes an approach of Recruitment and Allocation (RA) plan for supporting the everlasting running of the enterprise in the second stage. The technical efficiency, the scale efficiency, the production efficiency, and the return to scale are conducted in the first stage of this two-stage research design. In addition, this study also proposes the potentially improved value to promote the relative efficiency of each chained restaurant through the improvement of inputs or outputs items. Besides, the RA plan is proposed in the second stage of the two-stage design. The RA plan is an efficiency-based quantitative approach to recruit employees as well as to determine the allocation of those recruited employees. This study indeed provides a constructive and quantitative approach of solving the dilemma issue “how to reasonably recruit and allocate employees” for decision makers with profound insight in the quick-service enterprise.

scholarly journals Weighted generalized estimating equations for response-adaptive treatment regimes in two-stage longitudinal studies

2017 ◽  
Vol 51 (1) ◽  
pp. 79-100
Author(s):  
JESSE YENCHIH HSU ◽  
ABDUS S. WAHED
Keyword(s):  
Longitudinal Studies ◽  
Generalized Estimating Equations ◽  
Rating Scale ◽  
Estimating Equations ◽  
Repeated Measurements ◽  
Two Stage ◽  
Data Set ◽  
Treatment Regimes ◽  
Second Stage ◽  
Generalized Estimating

Two-stage longitudinal studies are common in the treatment of mental diseases, such as chronic forms of major depressive disorders. Outcomes in such studies often consist of repeated measurements of scores, such as the 24-item Hamilton Rating Scale for Depression, throughout the duration of therapy. Two issues that make the analysis of data from such two-stage studies different from standard longitudinal data are: (1) the randomization in the second stage for patients who fail to respond in the first stage; and (2) the drop-out of patients which sometimes occurs before the second stage. In this article, we show how the weighted generalized estimating equations can be used to draw inference for treatment regimes from two-stage studies. Specifically, we show how to construct weights and use them in the generalized estimating equations to derive consistent estimators of regime effects, and compare them. Large-sample properties of the proposed estimators are derived analytically, and examined through simulations. We demonstrate our methods by applying them to a depression data set.

A Two Level DEA in Project Based Organizations

2012 ◽  
Vol 488-489 ◽  
pp. 1157-1162
Author(s):  
Mehrdad Hamidi Hedayat ◽  
Ehsan Saghehei ◽  
Yazdan Khoshjahan
Keyword(s):  
Life Cycle ◽  
Statistical Analysis ◽  
Systematic Approach ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Dea Model ◽  
Fuzzy Data Envelopment Analysis ◽  
Project Life Cycle ◽  
Project Life ◽  
Exact Amount

This paper presents a systematic approach for evaluating the performance of a project based organization. We applied a two level fuzzy Data Envelopment Analysis (DEA) technique in project based organizations. In order to determine the required inputs and outputs, important indicators have selected using both expert judgments and statistical analysis. Then the two-level DEA model is successfully adapted. In this model by considering the outputs through a hierarchical process, a large number of sub indicators have provided and then rolled up to the higher level. Since the exact amount cannot be attributed to the indicators and they includes interval of values during the project life cycle, the interval DEA model will be discussed as a model help to determine the most preferred solution. At the end, some of the projects have been successfully evaluated throughout the approach proposed in this paper.

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