376 research outputs found
Study of Economic Efficiency of Utah Dairy Farmers : A System Approach by Subal C. Kumbhakar, Basudeb Biswas, and DeeVon Bailey
No full textA Study of Economic Efficiency of Utah Dairy Farmers : A System Approach by Subal C. Kumbhakar, Basudeb Biswas, and DeeVon Bailey. Reprinted from The Review of Economics and Statistics, published for Harvard University by Elsevier Science Publishers, Copyright 1989, by the President and Fellows of Harvard College, Vol. LXXI, No. 4, November 1989
Pitfalls in the Estimation of Cost Function Ignoring Allocative Inefficiency: A Monte Carlo Analysis
No full textLe ralentissement de la productivité des entreprises d'électricité au Texas : le rôle des marges, des rendements d'échelle et du progrès technique
No full textAn Anatomy of the Productivity Slowdown: Markups, Returns to Scale, and Technical Change in Electric
Utilities in Texas By Subal C. Kumbhakar
This paper considers a profit maximizing model to measure total factor productivity (TFP) growth and decompose it into components attributed to technical change, returns to scale and markups. Technical change is further decomposed into pure, nonneutral, and scale augmenting components. The role of these factors is analyzed in accounting productivity growth of electric utilities in Texas. An interesting feature of the study is the use of panel data on individual electric utilities in Texas during 1966-1985, which covers periods before and after the introduction of statewide regulation. The availability of panel data allows us to model markup behavior over time without imposing any a priori behavior on them. The model also introduces and controls for heterogeneity in the cost structure of the utilities. Empirically we find that markups and TFP growth declined substantially during the year of statewide regulation. In spite of this declining tendancy it is found that markups were the major contributing factor in TFP growth during the entire sample period.Le ralentissement de la productivité des entreprises d'électricité au Texas :
le rôle des marges, des rendements d'échelle et du progrès technique
par Subal C. Kumbhakar
Cet article présente un modèle de maximisation du profit de la firme permettant de mesurer la croissance de la productivité totale des facteurs (PTF) et de la décomposer en trois éléments reliés au progrès technique, aux rendements d'échelle et à l'existence de marges sur les coûts. Le progrès technique est ensuite décomposé en un progrès technique pur, un progrès technique non neutre et un progrès technique augmentant l'échelle de production. Nous examinons le rôle de ces facteurs dans l'explication de la croissance de la PTF d'entreprises de production d'électricité dans l'État du Texas. Une caractéristique intéressante de cette étude réside dans l'utilisation de données de panel relatives à des entreprises d'électricité de l'État du Texas, observées entre 1966 et 1985, période qui inclut la période de réglementation des tarifs par les autorités publiques. L'utilisation de données de panel permet de modéliser le comportement de fixation des marges dans le temps sans avoir besoin d'imposer des contraintes a priori sur ces évolutions. Ces données permettent également de prendre en compte l'hétérogénéité des structures de coût des entreprises. Les résultats des estimations montrent que les marges et la croissance de la PTF ont sensiblement baissé durant la période de réglementation. Malgré cela, sur l'ensemble de la période étudiée, les comportements de fixation des marges par les entreprises ont largement contribué aux hausses de productivité observées.Kumbhakar Subal C. Le ralentissement de la productivité des entreprises d'électricité au Texas : le rôle des marges, des rendements d'échelle et du progrès technique. In: Économie & prévision, n°126, 1996-5. Analyse des comportements économiques à partir de données de panel, sous la direction de Pierre Blanchard et Patrick Sevestre. pp. 77-89
Estimation of Technical and Allocative Inefficiencies in a Cost System: An Exact Maximum Likelihood Approach
Get PDFEstimation and decomposition of overall (economic) efficiency into technical and allocative components goes back to Farrell (1957). However, in a cross-sectional framework joint econometric estimation of efficiency components has been mostly confined to restrictive production function models (such as the Cobb-Douglas). In this paper we implement a maximum likelihood (ML) procedure to estimate technical and allocative inefficiency using the dual cost system (cost function and the derivative conditions) in the presence of cross-sectional data. Specifically, the ML procedure is used to estimate simultaneously the translog cost system and cost increase due to both technical and allocative inefficiency. This solves the so-called ‘Greene problem’ in the efficiency literature. The proposed technique is applied to the Christensen and Greene (1976) data on U.S. electric utilities, and a cross-section of the Brynjolfsson and Hitt (2003) data on large U.S. firms.Technical inefficiency, allocative inefficiency, the Greene problem, translog cost function
RISK PREFERENCES AND TECHNOLOGY: A JOINT ANALYSIS
Get PDFThis paper deals with derivation and estimation of the risk preference function in the presence of output price uncertainty. The derivation depends neither on a specific parametric form of the utility function nor on any distribution of output price. The risk preference function is flexible enough to test different types of risk behavior (e.g., increasing, constant, and decreasing absolute risk aversion). We also test for asymmetry in the distribution of output price, which appears in the risk preference function. Moreover, we allow heterogeneity in production technology. Parameters of production technology and risk preference function are jointly estimated using the system of equations derived from the first-order conditions of expected utility of profit maximization and the production function. The estimated parameters of the risk preference function are used to calculate absolute, relative, and downside risks for each producer. A panel data on salmon farming from Norway is used as an application.Resource /Energy Economics and Policy,
A General Model of Technical Change with an Application to the OECD Countries
Get PDFIn the neoclassical production functions model technical change (TC) is assumed to be exogenous and it is specified as a function of time. However, some exogenous external factors other than time can also affect the rate of TC. In this paper we model TC via a combination of time trend (purely non-economic) and other observable exogenous factors, which we call technology shifters (economic factors). We use several composite technology indices based on appropriate combinations of the external economic factors which are indicators of different aspects of technology. These technology indices are embedded into the production function in such a way that they can complement to different inputs. By estimating the generalized production function, we get estimates of TC which is decomposed TC into a pure time component as well as several producer specific external economic factors. Furthermore, the technology shifters allow for non-neutral and biased shifts in TC. We also consider a simple model in which the technology shifters are aggregated into one single index. The empirical model uses panel data on OECD, accession and enhanced engagement countries observed during 1980-2006.technical change, total factor productivity growth, technology indicator, technology shifter, OECD countries
MODELLING FARMS' PRODUCTION DECISIONS UNDER EXPENDITURE CONSTRAINTS
Get PDFLimited budget for the purchase of variable inputs might adversely affect producer's input use decisions and might result in a non-optimal input usage. If expenditure constrains are present and binding, unconstrained profit-maximization is not valid for modelling producers' input use decisions. In this paper we apply the indirect production function approach which describes output maximization subject to a given technology, a set of quasi-fixed inputs and a given budget for the purchase of variable inputs. By employing the indirect production function in the stochastic frontier framework we can estimate producer's output loss due to both expenditure constraints and technical inefficiency. Our estimation results show that most of the study farms were expenditure constrained during the considered period. Expenditure constraints have caused on average a potential output loss of 11 percent. Output loss due to technical inefficiency is quite moderate and averages 18 percent.Indirect production function, SFA, expenditure constraints, technical efficiency, Russian agriculture, Farm Management, Research Methods/ Statistical Methods,
Measuring productivity differentials – An application to milk production in Nordic countries
Get PDFThe aim of this paper is to analyse the regional productivity differentials on dairy farms in Denmark, Finland and Sweden. Several methods have been suggested for analysing productivity differentials in agriculture between groups of farms or countries. Hayami [5] and Hayami and Ruttan [7] suggested the meta-production function approach. This idea has been further developed by Lau and Yotopoulos [9] and Fulginity and Perrin [13]. Battese and Rao [2] suggested the meta-frontier analysis for these comparisons. One of the advantages of meta-frontiers with respect to metaproduction functions is that they are able to separate technological differences from the differences in technical efficiency. Battese et al. [5] and O’Donnell et al. [16] have extended this idea and developed both parametric and nonparametric approaches. In this paper, we extend the metafrontier analysis to the concave nonparametric least squares estimation of the production function suggested by Kuosmanen [18,19]. In addition, we compare the results with the approach where the estimation of meta-frontier can be avoided. The reference can also be the maximum output providing technology that is the one that yields the maximum estimated output, given inputs [21]. In this case the estimation can be based either on average or frontier production functions. The farm level data is obtained from the EU’s Farm Accountancy Data Network data set for Denmark, Finland and Sweden. They cover 954 dairy farms in 2003. The results suggest that different method provide slightly different results but in all approaches productivity differentials are considerable in favour of Danish farms. In addition, the Danish technology is not only dominating at the mean but also at most of the data points.productivity, technical efficiency, meta-frontier, Productivity Analysis,
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