821 resultados para Financial liberalisation
Resumo:
Given their physical presence in India, banks are arguably well-placed to improve financial inclusion in rural areas. However, uncertain repayment capacities and high transaction costs mean formal financial institutions are often reluctant to lend to the rural poor. Conversely, high transaction costs in dealing with banks are also incurred by clients, through, for example, lengthy, cumbersome and potentially ignominious procedures. Negative attitudes towards poor clients can be an important component of such transaction costs. An applied research project funded by the Enterprise Development Innovation Fund (EDIF-DFID) developed an innovative training programme designed to encourage more positive attitudes of bank staff towards poor clients, and towards their own role in rural poverty alleviation and development. This paper examines the development of the training programme, its implementation, and the results of its evaluation. It is shown that training can bring about attitudinal change, which in turn is reflected in behaviour and social impact. Copyright © 2007 John Wiley & Sons, Ltd.
Resumo:
Discussions on banking reforms to reduce financial exclusion have referred little to possible attitudinal constraints, on the part of staff at both branch and institutional levels, inhibiting the provision of financial services to the poor. The research project, funded by the ESCOR (now Social Science Research) Small Grants Committee, has focused on this aspect of financial exclusion. The research commenced in May 2001 and was completed in April 2002. Profiles of the rural bank branch managers, including personal background, professional background and workplace, are presented. Attitudes of managers toward aspects of their work environment and the rural poor are examined, using results from both quantitative and qualitative analysis. Finally, the emerging policy implications are discussed. These include bank reforms to address human resource management, the work environment, intermediate bank management and organization, and the client interface.
Rural financial institutions and agents in India: a historical and contemporary comparative analysis
Resumo:
This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.
Resumo:
During the financial crisis, companies and lenders found themselves in distressed situations. Competition authorities across the globe had to deal with controversial issues such as the application of the failing firm defence in merger transactions as well as assessment of emergency aid granted by states. This article considers competition policy in periods of crisis, in particular the failing firm defence in merger control and its state aid policy.