Policy reform and productivity change in Chinese agriculture: A distance function approach

Agricultural policy reform has been an important source of change in the Chinese agricultural sector. The reforms led to productivity growth and helped China to pursue its self-sufficiency goal especially in the grain sector. In order to analyze whether observable productivity growth stems from technologically induced components or from the market induced parts, a multi-input–multi-output model is derived by using an econometric distance function framework. A decomposition allows to distinguish allocative effects, scale effects, technological change, and technical efficiency change. Data on farms in Zhejiang from 1986 to 2000 are used to analyze the impact of policy reform.     

E-banking comes to town: Exploring how traditional UK high street banks are meeting the challenge of technology and virtual relationships

UK high street banks are continuing to extend the choice of channel through which customers can manage their moneycolon; an obvious example is e-banking. They have been keen to exploit technological advances and changing customer attitudes to alternative channels. Additionally, competition from leaner new market entrants has provided an added incentive to adopt new approaches. In the light of such developments, it is worth reflecting on the changing nature of the bank–customer relationship. This paper suggests that banks are finding it difficult to manage relationships effectively due, in large measure, to the sheer volume of customer data generated by new interactive, technology-based channels. Paradoxically, it seems that the more data banks have about customers the less likely they are to know them on a personal level. It is further suggested that channels such as e-banking potentially reduce the level of personal contact between bank and customer to the extent that a ‘virtual’ relationship develops. This paper concludes that, given the tendency towards ‘virtualisation’, it is inconceivable that bank–customer relationships will become any more intimate in the future. Indeed, a greater degree of personalisation in customer communication may be the very best that banks are able to offer.     

Consistent estimation in the bilinear multivariate errors-in-variables model

A bilinear multivariate errors-in-variables model is considered. It corresponds to an overdetermined set of linear equations AXB=C, A∈ℝ^m×n, B∈ℝ^p×q, in which the data A, B, C are perturbed by errors. The total least squares estimator is inconsistent in this case.  An adjusted least squares estimator is constructed, which converges to the true value X, as m →∞, q →∞. A small sample modification of the estimator is presented, which is more stable for small m and q and is asymptotically equivalent to the adjusted least squares estimator. The theoretical results are confirmed by a simulation study. Acknowledgements. We thank two anonymous reviewers for their suggestions and corrections.? A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe.? S. Van Huffel is a full professor with the Katholieke Universiteit Leuven.? I. Markovsky is a research assistant with the Katholieke Universiteit Leuven.? This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP V-22), initiated by the Belgian State, Prime Minister's Office – Federal Office for Scientific, Technical and Cultural Affairs of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) “Predictive computer models for medical classification problems using patient data and expert knowledge”, of the FWO projects G.0078.01, G.0200.00, and G0.0270.02.? The scientific responsibility is assumed by its authors.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.