In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problema...In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problematic in several situations,for example(a)when input proportions change in the long run,(b)when inputs are heterogeneous,and(c)when firms face ex-ante price uncertainty in making their production decisions.To address these situations,a scale elasticity evaluation was performed using a value-based cost efficiency model.However,this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data.Therefore,in this study,we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty.An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years(1998–2005)was made to compare inferences about their efficiency and scale properties.The key findings are as follows:First,both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints.However,both models yield the same results at a tolerance level of 0.5,implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks.Second,the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart.Third,public banks exhibit higher efficiency than private and foreign banks.Finally,public and old private banks mostly exhibit either decreasing or constant returns to scale,whereas foreign and new private banks experience either increasing or decreasing returns to scale.Although the application of our proposed stochastic model is illustrative,it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs,which have ample potential for reaping scale and scope benefits.展开更多
In performance analysis with tools such as data envelopment analysis,calculations of scale properties of the frontier points are studied using both qualitative and quantitative approaches.When the production process i...In performance analysis with tools such as data envelopment analysis,calculations of scale properties of the frontier points are studied using both qualitative and quantitative approaches.When the production process is a bit complicated,the calculation needs to be modified.Most existing studies are focused on a single-stage production process under the constant or variable returns to scale specification.However,some processes have two-stage structures,and,in such processes,the concepts of scale elasticity and returns to scale are inextricably related to the conditions of the stages of production.Thus,an evaluation of efficiency,scale elasticity,and returns to scale is sensitive to stages.In this study,we introduced a procedure to calculate technical efficiency and scale elasticity in a two-stage parallel-series production system.Then,our proposed technical efficiency and scale elasticity programs are applied to real data on 20 insurance companies in Iran.After applying our estimations to a real-world insurance industry,we found that,(i)overall,the total inputs of insurers in the life insurance sector should be reduced by 9%.Moreover,the inputs of nonlife insurers should be reduced by 50%.The final output in the investment sector must be increased by 48%.(ii)There are inefficiencies among all insurers in the investment sector,and to improve technical efficiency,the income from investments should be increased significantly.(iii)Finally,the efficiency and elasticity characterizations of insurers are directly subject to stages.展开更多
In current cluster computing, several distributed frameworks are designed to support elasticity for business services adapting to environment fluctuation. However, most existing works support elasticity mainly at the ...In current cluster computing, several distributed frameworks are designed to support elasticity for business services adapting to environment fluctuation. However, most existing works support elasticity mainly at the resource level, leaving application level elasticity support problem to domain-specific frameworks and applications. This paper proposes an actor-based general approach to support application-level elasticity for multiple cluster computing frameworks. The actor model offers scalability and decouples language-level concurrency from the runtime environment. By extending actors, a new middle layer called Unisupervisor is designed to ＂sit＂ between the resource management layer and application framework layer. Actors in Unisupervisor can automatically distribute and execute tasks over clusters and dynamically scale in/out. Based on Unisupervisor, high-level profiles （MasterSlave, MapReduce, Streaming, Graph, and Pipeline） for diverse cluster computing requirements can be supported. The entire approach is implemented in a prototype system called UniAS. In the evaluation, both benchmarks and real applications are tested and analyzed in a small scale cluster. Results show that UniAS is expressive and efficiently elastic.展开更多
文摘In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problematic in several situations,for example(a)when input proportions change in the long run,(b)when inputs are heterogeneous,and(c)when firms face ex-ante price uncertainty in making their production decisions.To address these situations,a scale elasticity evaluation was performed using a value-based cost efficiency model.However,this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data.Therefore,in this study,we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty.An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years(1998–2005)was made to compare inferences about their efficiency and scale properties.The key findings are as follows:First,both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints.However,both models yield the same results at a tolerance level of 0.5,implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks.Second,the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart.Third,public banks exhibit higher efficiency than private and foreign banks.Finally,public and old private banks mostly exhibit either decreasing or constant returns to scale,whereas foreign and new private banks experience either increasing or decreasing returns to scale.Although the application of our proposed stochastic model is illustrative,it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs,which have ample potential for reaping scale and scope benefits.
文摘In performance analysis with tools such as data envelopment analysis,calculations of scale properties of the frontier points are studied using both qualitative and quantitative approaches.When the production process is a bit complicated,the calculation needs to be modified.Most existing studies are focused on a single-stage production process under the constant or variable returns to scale specification.However,some processes have two-stage structures,and,in such processes,the concepts of scale elasticity and returns to scale are inextricably related to the conditions of the stages of production.Thus,an evaluation of efficiency,scale elasticity,and returns to scale is sensitive to stages.In this study,we introduced a procedure to calculate technical efficiency and scale elasticity in a two-stage parallel-series production system.Then,our proposed technical efficiency and scale elasticity programs are applied to real data on 20 insurance companies in Iran.After applying our estimations to a real-world insurance industry,we found that,(i)overall,the total inputs of insurers in the life insurance sector should be reduced by 9%.Moreover,the inputs of nonlife insurers should be reduced by 50%.The final output in the investment sector must be increased by 48%.(ii)There are inefficiencies among all insurers in the investment sector,and to improve technical efficiency,the income from investments should be increased significantly.(iii)Finally,the efficiency and elasticity characterizations of insurers are directly subject to stages.
基金Acknowledgements This work was supported by the National High-Tech Research and Development Plan of China （2015AA01A202）, National Basic Research Program of China （973） （2011CB302604）, and the National Natural Science Foundation of China （Grant Nos. 61272154 and 61421091）.
文摘In current cluster computing, several distributed frameworks are designed to support elasticity for business services adapting to environment fluctuation. However, most existing works support elasticity mainly at the resource level, leaving application level elasticity support problem to domain-specific frameworks and applications. This paper proposes an actor-based general approach to support application-level elasticity for multiple cluster computing frameworks. The actor model offers scalability and decouples language-level concurrency from the runtime environment. By extending actors, a new middle layer called Unisupervisor is designed to ＂sit＂ between the resource management layer and application framework layer. Actors in Unisupervisor can automatically distribute and execute tasks over clusters and dynamically scale in/out. Based on Unisupervisor, high-level profiles （MasterSlave, MapReduce, Streaming, Graph, and Pipeline） for diverse cluster computing requirements can be supported. The entire approach is implemented in a prototype system called UniAS. In the evaluation, both benchmarks and real applications are tested and analyzed in a small scale cluster. Results show that UniAS is expressive and efficiently elastic.
