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Paul W. Wilson - One of the best experts on this subject based on the ideXlab platform.
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do large banks have lower costs new estimates of Returns to Scale for u s banks
Social Science Research Network, 2011Co-Authors: David C. Wheelock, Paul W. WilsonAbstract:The number of commercial banks in the United States has fallen by more than 50 percent since 1984. This consolidation of the U.S. banking industry and the accompanying large increase in average (and median) bank size have prompted concerns about the effects of consolidation and increasing bank size on market competition and on the number of banks that regulators deem “too–big–to–fail.” Agency problems and perverse incentives created by government policies are often cited as reasons why many banks have pursued acquisitions and growth, though bankers often point to economies of Scale. This paper presents new estimates of ray-Scale and expansion-path Scale economies for U.S. banks based on non-parametric local-linear estimation of a model of bank costs. Unlike prior studies that use models with restrictive parametric assumptions or limited samples, our methodology is fully non-parametric and we estimate Returns to Scale for all U.S. banks over the period 1984–2006. Our estimates indicate that as recently as 2006, most U.S. banks faced increasing Returns to Scale, suggesting that Scale economies are a plausible (but not necessarily only) reason for the growth in average bank size and that the tendency toward increasing Scale is likely to continue unless checked by government intervention.
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non parametric tests of Returns to Scale
European Journal of Operational Research, 2002Co-Authors: Leopold Simar, Paul W. WilsonAbstract:This paper discusses various statistics for testing hypotheses regarding Returns to Scale in the context of non-parametric models of technical efficiency. In addition, the paper presents bootstrap estimation procedures which yield appropriate critical values for the test statistics. Evidence on the true sizes and power of the various proposed tests is obtained from Monte-Carlo experiments. This paper is an extension of earlier work in [Manage. Sci. 44 (1998) 49 J. Appl. Statist. 27 (2000b) 779]. (C) 2002 Elsevier Science B.V. All rights reserved.
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new evidence on Returns to Scale and product mix among u s commercial banks
Journal of Monetary Economics, 2001Co-Authors: David C. Wheelock, Paul W. WilsonAbstract:Abstract This paper presents new estimates of Scale and product mix economies for U.S. commercial banks. We compare estimates derived from fitting a translog function to bank costs with estimates derived from nonparametric methods. We refine measures of Scale and product mix economies introduced by Berger et al. (J. Monet. Econ. 20 (1987) 501) to accommodate nonparametric estimation, and estimate confidence intervals to assess the statistical significance of Returns to Scale. Broadly, we find evidence that potential economies have increased since 1985, with Scale economies not exhausted until banks have $300–$500 million of assets. We generally fail to reject constant Returns for larger banks.
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nonparametric tests of Returns to Scale
1998Co-Authors: Leopold Simar, Paul W. WilsonAbstract:This paper discusses various statistics for testing hypotheses regarding Returns to Scale in the context of nonparametric models of technical eciency. In addition, the paper presents bootstrap estimation procedures which yield appropriate critical values for the test statistics. Evidence on the true sizes and power of the various proposed tests is obtained from Monte Carlo experiments. This paper is an extension of earlier work in Simar and Wilson (1998a).
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New evidence on Returns to Scale and product mix among U.S. commercial banks
1997Co-Authors: David C. Wheelock, Paul W. WilsonAbstract:Numerous studies have found that banks exhaust Scale economies at low levels of output, but most are based on the estimation of parametric cost functions which misrepresent bank cost. Here we avoid specification error by using nonparametric kernal regression techniques. We modify measures of Scale and product mix economies introduced by Berger et al. (1987) to accommodate the nonparametric estimation approach, and estimate robust confidence intervals to assess the statistical significance of Returns to Scale. We find that banks experience increasing Returns to Scale up to approximately $500 million of assets, and essentially constant Returns thereafter. We also find that minimum efficient Scale has increased since 1985.
Emmanuel Farhi - One of the best experts on this subject based on the ideXlab platform.
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darwinian Returns to Scale
Social Science Research Network, 2021Co-Authors: David Baqaee, Emmanuel FarhiAbstract:How does an increase in the size of the market, say due to fertility, immigration, or globalization, affect welfare? We study this question using a model with heterogeneous firms, Kimball preferences, fixed costs, and monopolistic competition. We decompose changes in welfare from increased Scale into changes in technical efficiency and changes in allocative efficiency due to reallocation. We non-parametrically identify residual demand curves with firm-level data from Belgian manufacturing firms and, using these estimates, quantify our theoretical results. We find that around 80% of the aggregate Returns to Scale are due to changes in allocative efficiency. As markets get bigger, competition intensifies and triggers Darwinian reallocations: socially-valuable firms expand, small firms shrink and exit, and new firms enter. However, important as they are, improvements in allocative efficiency are not driven by reductions in markups or deaths of unproductive firms. Instead, they are caused by a composition effect that reallocates resources from low- to high-markup firms.
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the darwinian Returns to Scale
National Bureau of Economic Research, 2020Co-Authors: David Baqaee, Emmanuel FarhiAbstract:How does an increase in the size of the market, say due to fertility, immigration, or globalization, affect welfare? We study this question using a model with heterogeneous firms, Kimball preferences, fixed costs, and monopolistic competition. We decompose changes in welfare from increased Scale into changes in technical efficiency and changes in allocative efficiency due to reallocation. We non-parametrically identify residual demand curves with firm-level data from Belgian manufacturing firms and, using these estimates, quantify our theoretical results. We find that around 80% of the aggregate Returns to Scale are due to changes in allocative efficiency. As markets get bigger, competition intensifies and triggers Darwinian reallocations: socially-valuable firms expand, small firms shrink and exit, and new firms enter. However, important as they are, improvements in allocative efficiency are not driven by reductions in markups or deaths of unproductive firms. Instead, they are caused by a composition effect that reallocates resources from low- to high-markup firms.
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the darwinian Returns to Scale
Social Science Research Network, 2020Co-Authors: David Baqaee, Emmanuel FarhiAbstract:How does an increase in the size of the market, say due to fertility, immigration, or globalization, affect welfare? We study this question using a model with heterogeneous firms, Kimball preferences, fixed costs, and monopolistic competition. We decompose changes in welfare from increased Scale into changes in technical efficiency and changes in allocative efficiency due to reallocation. We non-parametrically identify residual demand curves with firm-level data from Belgian manufacturing firms and, using these estimates, quantify our theoretical results. We find that around 80% of the aggregate Returns to Scale are due to changes in allocative efficiency. As markets get bigger, competition intensifies and triggers Darwinian reallocations: socially-valuable firms expand, small firms shrink and exit, and new firms enter. However, important as they are, improvements in allocative efficiency are not driven by reductions in markups or deaths of unproductive firms. Instead, they are caused by a composition effect that reallocates resources from low- to high-markup firms. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Rajiv D Banker - One of the best experts on this subject based on the ideXlab platform.
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Returns to Scale in dea
2011Co-Authors: Rajiv D Banker, W W Cooper, Lawrence M Seiford, Joe ZhuAbstract:This chapter discusses Returns to Scale (RTS) in data envelopment analysis (DEA). The BCC and CCR models described in Chap. 1 of this handbook are treated in input-oriented forms, while the multiplicative model is treated in output-oriented form. (This distinction is not pertinent for the additive model, which simultaneously maximizes outputs and minimizes inputs in the sense of a vector optimization.) Quantitative estimates in the form of Scale elasticities are treated in the context of multiplicative models, but the bulk of the discussion is confined to qualitative characterizations such as whether RTS is identified as increasing, decreasing, or constant. This is discussed for each type of model, and relations between the results for the different models are established. The opening section describes and delimits approaches to be examined. The concluding section outlines further opportunities for research and an Appendix discusses other approaches in DEA treatment of RTS.
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Returns to Scale in different dea models
European Journal of Operational Research, 2004Co-Authors: Rajiv D Banker, W W Cooper, Lawrence M Seiford, Robert M Thrall, Joe ZhuAbstract:Abstract This paper discusses Returns to Scale (RTS) in data envelopment analysis (DEA) for each of the presently available types of models. The BCC and CCR models are treated in input oriented forms while the multiplicative model is treated in output oriented form. (This distinction is not pertinent for the additive model which simultaneously maximizes outputs and minimizes inputs in the sense of a vector optimization.) Quantitative estimates in the form of Scale elasticities are treated in the context of multiplicative models, but the bulk of the discussion is confined to qualitative characterizations such as whether RTS is identified as increasing, decreasing or constant. This is discussed for each type of model and relations between the results for the different models are established. The opening section describes and delimits approaches to be examined. The concluding section outlines further opportunities for research.
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equivalence and implementation of alternative methods for determining Returns to Scale in data envelopment analysis
European Journal of Operational Research, 1996Co-Authors: Rajiv D Banker, Hsihui Chang, W W CooperAbstract:Abstract This paper discusses alternative methods for determining Returns to Scale in DEA. The methods for estimating Returns to Scale in DEA, as developed by Banker (1984), Banker, Charnes and Cooper (1984) and Banker and Thrall (1992), are proved to be conceptually equivalent to the two-stage methods of Fare, Grosskopf and Lovell (1985) when their assumptions apply. Here the emphasis is on the CCR model of DEA and very simple methods are introduced for determining Returns to Scale locally with this model by reference to Banker's concept of Most Productive Scale Size.
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a note on Returns to Scale in dea
European Journal of Operational Research, 1996Co-Authors: Rajiv D Banker, Indranil R Bardhan, W W CooperAbstract:Abstract This brief note adds computational convenience and efficiency to the article by Banker and Thrall on Returns to Scale in DEA by modifying one of their suggestions to avoid the need for examining all alternate optima in order to reach a decision.
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estimation of Returns to Scale using data envelopment analysis
European Journal of Operational Research, 1992Co-Authors: Rajiv D Banker, Robert M ThrallAbstract:Abstract Generalization of the measure of Returns-to-Scale from a single number to an interval permits extension of the concept to DEA data domains with multiple inputs and multiple outputs. The key new approach is a partition of the optimal frontier into three parts corresponding, respectively to increasing, constant, and decreasing Returns to Scale. These parts are characterized in terms of optimal primal solutions, and optimal dual solutions for both the original Charnes, Cooper, Rhodes model (1978) and the later Banker, Charnes, Cooper model (1984) and relying on concepts developed by R.D. Banker (1984) and R.M. Thrall (1988).
Victor V. Podinovski - One of the best experts on this subject based on the ideXlab platform.
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marginal values and Returns to Scale for nonparametric production frontiers
Operations Research, 2016Co-Authors: Victor V. Podinovski, Robert Chambers, Kazim Baris Atici, Iryna D DeinekoAbstract:We present a unifying linear programming approach to the calculation of various directional derivatives for a very large class of production frontiers of data envelopment analysis (DEA). Special cases of this include different marginal rates, the Scale elasticity, and a spectrum of partial and mixed elasticity measures. Our development applies to any polyhedral production technology including, to name a few, the conventional variable and constant Returns-to-Scale DEA technologies, their extensions with weight restrictions, technologies with weakly disposable undesirable outputs, and network DEA models. Furthermore, our development provides a general method for characterization of Returns to Scale (RTS) in any polyhedral technology. The new approach effectively removes the need to develop bespoke models for the RTS characterization and calculation of marginal rates and elasticity measures for each particular technology.
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combining the assumptions of variable and constant Returns to Scale in the efficiency evaluation of secondary schools
European Journal of Operational Research, 2014Co-Authors: Victor V. Podinovski, Ihsan Ismail, Tatiana Bouzdinechameeva, Wenjuan ZhangAbstract:Our paper reports on the use of data envelopment analysis (DEA) for the assessment of performance of secondary schools in Malaysia during the implementation of the policy of teaching and learning mathematics and science subjects in the English language (PPSMI). The novelty of our application is that it makes use of the hybrid Returns-to-Scale (HRS) DEA model. This combines the assumption of constant Returns to Scale with respect to quantity inputs and outputs (teaching provision and students) and variable Returns to Scale (VRS) with respect to quality factors (attainment levels on entry and exit) and socio-economic status of student families. We argue that the HRS model is a better-informed model than the conventional VRS model in the described application. Because the HRS technology is larger than the VRS technology, the new model provides a tangibly better discrimination on efficiency than could be obtained by the VRS model. to assess the productivity change of secondary schools over the years surrounding the introduction of the PPSMI policy, we adapt the Malmquist productivity index and its decomposition to the case of HRS model.
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bridging the gap between the constant and variable Returns to Scale models selective proportionality in data envelopment analysis
Journal of the Operational Research Society, 2004Co-Authors: Victor V. PodinovskiAbstract:In data envelopment analysis (DEA), the use of constant Returns-to-Scale (CRS) models requires the assumption of full proportionality between all inputs and outputs. Often such proportionality cannot be assumed, although there may be a subset of outputs proportional to a subset of inputs. By using the variable Returns-to-Scale (VRS) model, this information is effectively ignored and the efficiency of units is overestimated. This paper develops a hybrid approach that combines the assumption of CRS with respect to the selected sets of inputs and outputs, while preserving the VRS assumption with respect to the remaining indicators. The resulting hybrid Returns-to-Scale models exhibit better discrimination than the VRS model. In certain cases, their discrimination surpasses that of the CRS model, an example of which is given.
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On the linearisation of reference technologies for testing Returns to Scale in FDH models
European Journal of Operational Research, 2004Co-Authors: Victor V. PodinovskiAbstract:Abstract One of the methods of testing Returns to Scale (RTS) in data envelopment analysis is based on the use of specially constructed reference technologies. Recently Kerstens and Vanden Eeckaut constructed such technologies for testing RTS in the free disposal hull (FDH) model. The use of these technologies requires solving mixed integer non-linear programming models. In this note we construct equivalent linear models, which have an obvious computational advantage.
Joe Zhu - One of the best experts on this subject based on the ideXlab platform.
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multivariate Returns to Scale production frontiers
Journal of the Operational Research Society, 2021Co-Authors: Dariush Khezrimotlagh, Joe ZhuAbstract:In this study, we develop multivariate Returns to Scale (MRTS) and illustrate the advantages of MRTS over the existing standard Returns to Scale (RTS) such as: constant RTS (CRS), variable RTS (VRS...
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dea cross efficiency evaluation under variable Returns to Scale
Journal of the Operational Research Society, 2015Co-Authors: Sungmook Lim, Joe ZhuAbstract:Cross-efficiency evaluation in data envelopment analysis (DEA) has been developed under the assumption of constant Returns to Scale (CRS), and no valid attempts have been made to apply the cross-efficiency concept to the variable Returns to Scale (VRS) condition. This is due to the fact that negative VRS cross-efficiency arises for some decision-making units (DMUs). Since there exist many instances that require the use of the VRS DEA model, it is imperative to develop cross-efficiency measures under VRS. We show that negative VRS cross-efficiency is related to free production of outputs. We offer a geometric interpretation of the relationship between the CRS and VRS DEA models. We show that each DMU, via solving the VRS model, seeks an optimal bundle of weights with which its CRS-efficiency score, measured under a translated Cartesian coordinate system, is maximized. We propose that VRS cross-efficiency evaluation should be done via a series of CRS models under translated Cartesian coordinate systems. The current study offers a valid cross-efficiency approach under the assumption of VRS—one of the most common assumptions in performance evaluation done by DEA.
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Returns to Scale in dea
2011Co-Authors: Rajiv D Banker, W W Cooper, Lawrence M Seiford, Joe ZhuAbstract:This chapter discusses Returns to Scale (RTS) in data envelopment analysis (DEA). The BCC and CCR models described in Chap. 1 of this handbook are treated in input-oriented forms, while the multiplicative model is treated in output-oriented form. (This distinction is not pertinent for the additive model, which simultaneously maximizes outputs and minimizes inputs in the sense of a vector optimization.) Quantitative estimates in the form of Scale elasticities are treated in the context of multiplicative models, but the bulk of the discussion is confined to qualitative characterizations such as whether RTS is identified as increasing, decreasing, or constant. This is discussed for each type of model, and relations between the results for the different models are established. The opening section describes and delimits approaches to be examined. The concluding section outlines further opportunities for research and an Appendix discusses other approaches in DEA treatment of RTS.
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Returns to Scale in different dea models
European Journal of Operational Research, 2004Co-Authors: Rajiv D Banker, W W Cooper, Lawrence M Seiford, Robert M Thrall, Joe ZhuAbstract:Abstract This paper discusses Returns to Scale (RTS) in data envelopment analysis (DEA) for each of the presently available types of models. The BCC and CCR models are treated in input oriented forms while the multiplicative model is treated in output oriented form. (This distinction is not pertinent for the additive model which simultaneously maximizes outputs and minimizes inputs in the sense of a vector optimization.) Quantitative estimates in the form of Scale elasticities are treated in the context of multiplicative models, but the bulk of the discussion is confined to qualitative characterizations such as whether RTS is identified as increasing, decreasing or constant. This is discussed for each type of model and relations between the results for the different models are established. The opening section describes and delimits approaches to be examined. The concluding section outlines further opportunities for research.
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sensitivity and stability of the classifications of Returns to Scale in data envelopment analysis
Journal of Productivity Analysis, 1999Co-Authors: Lawrence M Seiford, Joe ZhuAbstract:Sensitivity of the Returns to Scale (RTS) classifications in data envelopment analysis is studied by means of linear programming problems. The stability region for an observation preserving its current RTS classification (constant, increasing or decreasing Returns to Scale) can be easily investigated by the optimal values to a set of particular DEA-type formulations. Necessary and sufficient conditions are determined for preserving the RTS classifications when input or output data perturbations are non-proportional. It is shown that the sensitivity analysis method under proportional data perturbations can also be used to estimate the RTS classifications and discover the identical RTS regions yielded by the input-based and the output-based DEA methods. Thus, our approach provides information on both the RTS classifications and the stability of the classifications. This sensitivity analysis method can easily be applied via existing DEA codes.
