The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Subal C Kumbhakar - One of the best experts on this subject based on the ideXlab platform.
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A Zero Inefficiency Stochastic Frontier Estimator
Journal of Econometrics, 2020Co-Authors: Subal C Kumbhakar, Christopher F Parmeter, Efthymios G TsionasAbstract:Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce the zero Inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function, conditional mean of Inefficiency to estimate observation-specific Inefficiency and discuss testing for the presence of fully efficient firms. We provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method. JEL Classification No.: C13, C23, C33
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technical and allocative efficiency in a panel stochastic production frontier system model
European Journal of Operational Research, 2019Co-Authors: Subal C KumbhakarAbstract:Abstract In this paper, we consider a state-of-the-art panel stochastic production frontier model and estimate it using a system that includes both technical and allocative Inefficiency. The system consists of the optimal input choice rule (the first-order conditions (FOCs)) under cost minimization together with the production function. The FOCs are used to take care of endogeneity of inputs. Allocative Inefficiency is modeled as non-fulfillment of the FOCs. We use distributional assumptions on the noise and Inefficiency components and estimate the model parameters using the maximum likelihood method. In estimating technical and allocative Inefficiency components and costs therefrom we control for fixed firm-effects in each equation of the system. We also correct for the incidental parameter bias by using the half-panel jackknife estimator.
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firm heterogeneity persistent and transient technical Inefficiency a generalized true random effects model
Journal of Applied Econometrics, 2014Co-Authors: Efthymios G Tsionas, Subal C KumbhakarAbstract:SUMMARY This paper considers a panel data stochastic frontier model that disentangles unobserved firm effects (firm heterogeneity) from persistent (time‐invariant/long‐term) and transient (time‐varying/short‐term) technical Inefficiency. The model gives us a four‐way error component model, viz., persistent and time‐varying Inefficiency, random firm effects and noise. We use Bayesian methods of inference to provide robust and efficient methods of estimating Inefficiency components in this four‐way error component model. Monte Carlo results are provided to validate its performance. We also present results from an empirical application that uses a large panel of US commercial banks. Copyright © 2012 John Wiley & Sons, Ltd.
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a zero Inefficiency stochastic frontier model
Journal of Econometrics, 2013Co-Authors: Subal C Kumbhakar, Christopher F Parmeter, Efthymios G TsionasAbstract:Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce the zero Inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function, conditional mean of Inefficiency, to estimate observation-specific Inefficiency and discuss testing for the presence of fully efficient firms. We provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method.
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a generalized production frontier approach for estimating determinants of Inefficiency in u s dairy farms
Journal of Business & Economic Statistics, 1991Co-Authors: Subal C Kumbhakar, Soumendra N Ghosh, Thomas J McguckinAbstract:This article investigates farm-level efficiency of U.S. dairy farmers by estimating their technical and allocative efficiency. Technical Inefficiency is assumed to be composed of a deterministic component that is a function of some farm-specific characteristics and a random component. By doing this we extend the stochastic frontier methodology in which determinants of technicial Inefficiency are explicitly introduced in the model. Given the inputs, variations in efficiency of farms are then explained by both deterministic and random components of technical Inefficiency. The empirical results indicate that (a) levels of education of the farmer are important factors determining technical Inefficiency and (b) large farms are more efficient (technically) than small and medium-sized farms. Both technical and allocative Inefficiency are found to decrease with increase in the level of education of the farmer.
Spiro E Stefanou - One of the best experts on this subject based on the ideXlab platform.
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Dynamic productivity change differences between global and non-global firms: a firm-level application to the U.S. food and beverage industries
Operational Research, 2019Co-Authors: Pinar Celikkol Geylani, Magdalena Kapelko, Spiro E StefanouAbstract:This study employs the dynamic Luenberger productivity change indicator and its components (i.e., technical change, technical Inefficiency change, and scale Inefficiency change) to analyze the productivity differences between global and non-global firms in U.S. food and beverage manufacturing industries during the period 2004–2009. Overall, an average dynamic productivity change for both global and non-global firms is negative, with − 0.4%, although there is heterogeneity in the magnitudes of the growth rates across both groups of firms. The productivity change differences come from the technological regress for non-global firms in spite of the technological progress experienced by global firms. The study finds that while the global firms experience moderate dynamic technical efficiency loss, the contribution of dynamic technical Inefficiency to productivity change for non-global firms is positive. Further, the negative contribution of dynamic scale Inefficiency change to dynamic productivity change is apparent for both global and non-global firms over the course of this study. These results emphasize the importance of productivity change components for firm managers in designing strategies aimed at improving the firm’s productivity and for policy makers in designing clever trade policies to be competitive in both domestic and international markets.
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effect of food regulation on the spanish food processing industry a dynamic productivity analysis
PLOS ONE, 2015Co-Authors: Magdalena Kapelko, Alfons Oude Lansink, Spiro E StefanouAbstract:This article develops the decomposition of the dynamic Luenberger productivity growth indicator into dynamic technical change, dynamic technical Inefficiency change and dynamic scale Inefficiency change in the dynamic directional distance function context using Data Envelopment Analysis. These results are used to investigate for the Spanish food processing industry the extent to which dynamic productivity growth and its components are affected by the introduction of the General Food Law in 2002 (Regulation (EC) No 178/2002). The empirical application uses panel data of Spanish meat, dairy, and oils and fats industries over the period 1996-2011. The results suggest that in the oils and fats industry the impact of food regulation on dynamic productivity growth is negative initially and then positive over the long run. In contrast, the opposite pattern is observed for the meat and dairy processing industries. The results further imply that firms in the meat processing and oils and fats industries face similar impacts of food safety regulation on dynamic technical change, dynamic Inefficiency change and dynamic scale Inefficiency change.
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assessing dynamic Inefficiency of the spanish construction sector pre and post financial crisis
European Journal of Operational Research, 2014Co-Authors: Magdalena Kapelko, Alfons Oude Lansink, Spiro E StefanouAbstract:This paper undertakes the full decomposition of dynamic cost Inefficiency into technical, scale and allocative Inefficiency based on the dynamic directional distance function. The empirical application estimates dynamic Inefficiency in the Spanish construction industry before and during the current financial crisis over the period 2001–2009. Static Inefficiency measures are biased in a context of a significant economic crisis with large investments and disinvestments as they do not account for costs in the adjustment of quasi-fixed factors. Allocative Inefficiency is smaller, while technical Inefficiency is larger when using the dynamic compared to the static framework. Results further indicate that overall dynamic cost Inefficiency is very high with technical Inefficiency being the largest component, followed by allocative and scale Inefficiency. Moreover, overall dynamic cost Inefficiency is significantly larger before the beginning of the financial crisis than during the financial crisis. Larger firms are less technically and scale inefficient than smaller firms on average, but have more problems in choosing the mix of inputs that minimizes their long-term costs. Firms that went bankrupt, on average, have a higher overall dynamic cost Inefficiency and scale Inefficiency than continuing firms.
Efthymios G Tsionas - One of the best experts on this subject based on the ideXlab platform.
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A Zero Inefficiency Stochastic Frontier Estimator
Journal of Econometrics, 2020Co-Authors: Subal C Kumbhakar, Christopher F Parmeter, Efthymios G TsionasAbstract:Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce the zero Inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function, conditional mean of Inefficiency to estimate observation-specific Inefficiency and discuss testing for the presence of fully efficient firms. We provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method. JEL Classification No.: C13, C23, C33
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understanding relative efficiency among airports a general dynamic model for distinguishing technical and allocative efficiency
Transportation Research Part B-methodological, 2014Co-Authors: George A Assaf, David Gillen, Efthymios G TsionasAbstract:The paper introduces a new dynamic frontier model that is used to analyze the impact of both ownership and regulation on airport technical and allocative efficiencies. We differentiate between the short and long-term effects. Based on a large sample of international airports, we find in the short-run the majority of the improvements are from reducing technical Inefficiency, which come for the most part from adjusting output, something that can be accomplished in the short-term. There are relatively small changes, in the short run, resulting from improving allocative efficiency. We find that adding economic regulation leads to a decrease in technical efficiency in the short-run. Quite different conclusions hold for the long-term; there are improvements available from reducing allocative Inefficiency and comparable benefits are available from cutting technical Inefficiency. In the long-run we find that technical and allocative Inefficiency decreases by moving away from government owned to fully privatized airports and moving away from rigid regulation.
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firm heterogeneity persistent and transient technical Inefficiency a generalized true random effects model
Journal of Applied Econometrics, 2014Co-Authors: Efthymios G Tsionas, Subal C KumbhakarAbstract:SUMMARY This paper considers a panel data stochastic frontier model that disentangles unobserved firm effects (firm heterogeneity) from persistent (time‐invariant/long‐term) and transient (time‐varying/short‐term) technical Inefficiency. The model gives us a four‐way error component model, viz., persistent and time‐varying Inefficiency, random firm effects and noise. We use Bayesian methods of inference to provide robust and efficient methods of estimating Inefficiency components in this four‐way error component model. Monte Carlo results are provided to validate its performance. We also present results from an empirical application that uses a large panel of US commercial banks. Copyright © 2012 John Wiley & Sons, Ltd.
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a zero Inefficiency stochastic frontier model
Journal of Econometrics, 2013Co-Authors: Subal C Kumbhakar, Christopher F Parmeter, Efthymios G TsionasAbstract:Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce the zero Inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function, conditional mean of Inefficiency, to estimate observation-specific Inefficiency and discuss testing for the presence of fully efficient firms. We provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method.
W W Cooper - One of the best experts on this subject based on the ideXlab platform.
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decomposing profit Inefficiency in dea through the weighted additive model
European Journal of Operational Research, 2011Co-Authors: W W Cooper, Juan Aparicio, Jesus T Pastor, Fernando BorrasAbstract:An issue that has received little attention in the Data Envelopment Analysis literature is the decomposition of profit Inefficiency by means of measures that account all sources of technical Inefficiency. In this paper we introduce a new way to measure and decompose profit Inefficiency through weighted additive models. All our results are derived from a new Fenchel-Mahler inequality using duality theory.
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measures of Inefficiency in data envelopment analysis and stochastic frontier estimation
European Journal of Operational Research, 1997Co-Authors: W W Cooper, Kaoru ToneAbstract:This paper discusses recent work in developing scalar measures of Inefficiency which (a) comprehend all inefficiencies, including non-zero slacks, and (b) are readily interpretable and easily used in a wide variety of contexts. The opening section of the paper discusses some of the varied contexts in which uses of DEA are now being reported. This provides background for some of these measures. The closing section turns to simulation studies of DEA-regression combinations and possible Inefficiency measures. Serious problems of bias in SF (Stochastic Frontier) regression approaches are identified. Extensions and modifications are suggested which can make a development of other Inefficiency measures worthwhile for SF extensions to input-specific and multiple output evaluations.
Walter Briec - One of the best experts on this subject based on the ideXlab platform.
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a dea estimation of a lower bound for firms allocative efficiency without information on price data
International Journal of Production Economics, 2009Co-Authors: Herve Leleu, Walter BriecAbstract:In this paper, we estimate a lower bound for the sum of firms' allocative efficiencies in the absence of information on prices. For this purpose, we only estimate technical efficiency at both the firm and the industry level using a directional distance function and choosing a relevant direction. Our result relies on the decomposition of overall Inefficiency into technical and allocative Inefficiency at both the firm and the industry level. The convexity of a technology induces a transfer from both total technical Inefficiency and part of allocative Inefficiency at the firm level to technical Inefficiency solely at the industry level. The remaining firms' allocative Inefficiency could be counted at the industry level. Hence, the difference between technical inefficiencies at both levels can be interpreted as a lower bound for the sum of allocative Inefficiency in the industry. We show how to implement this bound in a DEA framework.
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a dea estimation of a lower bound for firms allocative efficiency without information on price data
Post-Print, 2009Co-Authors: Herve Leleu, Walter BriecAbstract:In this paper, we estimate a lower bound for the sum of firms' allocative efficiencies in the absence of information on prices. For this purpose, we only estimate technical efficiency at both the firm and the industry level using a directional distance function and choosing a relevant direction. Our result relies on the decomposition of overall Inefficiency into technical and allocative Inefficiency at both the firm and the industry level. The convexity of a technology induces a transfer from both total technical Inefficiency and part of allocative Inefficiency at the firm level to technical Inefficiency solely at the industry level. The remaining firms' allocative Inefficiency could be counted at the industry level. Hence, the difference between technical inefficiencies at both levels can be interpreted as a lower bound for the sum of allocative Inefficiency in the industry. We show how to implement this bound in a DEA framework. (This abstract was borrowed from another version of this item.)
