Efficiency measurement with multiple outputs and multiple inputs

This paper discusses modeling technical and allocative inefficiencies in both cost minimizing and profit maximizing frameworks with special emphasis on multiple inputs and multiple outputs. Both primal and dual models are considered for this purpose. In the primal approach we use a separable output and input function (the constant elasticity of transformation output function and Cobb-Douglas input function). The dual models assume translog cost or profit functions. Technical inefficiency is assumed to be random in the cross-sectional models, and fixed firm-specific parameter in the panel data models. Allocative inefficiencies are always treated as input-specific parameters. We derive exact relations linking technical inefficiency and allocative inefficiencies to cost and profit when the underlying technology is represented by a flexible functional form such as the translog. It is shown that appending a one-sided homoscedastic error term to model technical inefficiency, or neglecting technical inefficiency altogether in a translog profit tunciton results in model misspecification and inconsistent parameter estimates.     

The role of capital investment project teams in organisational learning

The management and organisation of capital projects in the British National Health Service (NHS) is dependent upon project teams. An analysis of four case studies shows how these teams also act as agents of learning for individuals and the organisation. This article considers the process by which learning came about. In particular it identifies the ability of the project teams to develop a parallel organisation within a wider organisational context. These teams develop specific rules, roles and relationships which help individual project team members to more effectively share their knowledge with others and their organisation.     

Restricted maximum likelihood estimation under Eisenhart model Ill

For a balanced two-way mixed model, the maximum likelihood (ML) and restricted ML (REML) estimators of the variance components were obtained and compared under the non-negativity requirements of the variance components by L ee and K apadia (1984). In this note, for a mixed (random blocks) incomplete block model, explicit forms for the REML estimators of variance components are obtained. They are always non-negative and have smaller mean squared error (MSE) than the analysis of variance (AOV) estimators. The asymptotic sampling variances of the maximum likelihood (ML) estimators and the REML estimators are compared and the balanced incomplete block design (BIBD) is considered as a special case. The ML estimators are shown to have smaller asymptotic variances than the REML estimators, but a numerical result in the randomized complete block design (RCBD) demonstrated that the performances of the REML and ML estimators are not much different in the MSE sense.     

Air pollution-mortality models: A demonstration of the effects of random measurement error

Errors of measurement have long been recognized as a chronic problem in statistical analysis. Although there is a vast statistical literature of multiple regression models estimating the air pollution-mortality relationship, this problem has been largely ignored. It is well known that pollution measures contain error, but the consequences of this error for regression estimates is not known. We use Lave and Seskin's air pollution model to demonstrate the consequences of random measurement error. We assume a range of 0% to 50% of the variance of the pollution measures is due to error. We find large differences in the estimated effects on mortality of the pollution variables as well as the other explanatory variables once this measurement error is taken into account. These results cast doubt on the usual regression estimates of the mortality effects of air pollution. More generally our results demonstrate the consequences of random measurement error in the explanatory variable of a multiple regression analysis and the misleading conclusions that may result in policy research if this error is ignored.     

Relationship between generalizedh-Statistics and polykays

Summary In this paper, relationships between generalizedh-statistics which estimate powers and products of central moments unbiasedly and the polykays by using ordered partitions are established. A table expressing generalizedh-statistics of weight 12 in terms of polykays and vice versa is presented. Expressions of weight less than 12 are obtained from this table.

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Zusammenfassung In der vorliegenden Arbeit wird mittels geordneter Zerlegungen eine Beziehung zwischen verallgemeinerterh-Statistik, welche Potenzen und Produkte von zentralen Momenten erwartungstreu abschätzt, und polykays hergestellt. Eime Tabele mit verallgemeinerterh-Statistik vom Gewicht 12 in polykay-Termen, und umgekehrt, wird gegeben. Gewichtsausdrücke kleiner als 12 werden aus dieser Tabelle gewonnen.

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This research has been supported by a grant from FINEP/Ministério do Planejamento e Coordenacão Geral to the IMUFRJ.     

关于备件物流研究现状的综述

论述了备件物流研究的基本过程,并对国了内外备件物流的研究状况进行了综述,最后对未来备件物流研究做了展望。     

Labor force migration, non-labor force migration, and non-employment reasons for migration

This paper documents the extent and nature of non-employment factors in migration. The labor force status of over 18 million recent interstate migrants in the United States and stated reasons for moving reported in several surveys in the U.S. are examined. Labor force migrants are heterogeneous in terms of the relationships between acquisition of employment and the migration decision, and in terms of the influence of decision makers outside of the migrant household including firms and the government. Numerically important categories of migrants not traditionally captured in migration models exist, including the elderly, the military, and movers from abroad. Whereas non-employment reasons are secondary to employment reasons for a majority of labor force migrants, non-employment factors are singularly important or operate in combination with employment factors for the majority of all migrants. The heterogeneity of migrant types and migration reasons needs to be better captured in migration models.     

Variables sampling plans for the Poisson and the binomial

Variables sampling plans based upon continuous distributions are well known. The usual assumption is that a measurable characteristic associated with a product has a normal distribution, a case which has been treated extensively in the literature. Other continuous distributions, particularly the exponential, have also been used as models. In this paper we discuss variables sampling plans for situations in which the measurable characteristic has either a Poisson or a binomial distribution.