中国加入 WTO后房地产市场与国际市场接轨的答案

中国加入WTO后房地产市场与国际接轨的答案即应构建并发展房地产的二级市场和租赁市场 ,精心培育房地产的中介服务行业 ,同时改变粗放型的经营运作方式为精耕细作 ,以形成与一级市场联动完整的房地产大市场。     

South African Capital Structure Decisions: A Survey of Listed Companies

ABSTRACT

This paper explores the factors that influence capital structure decisions in South Africa from the perspective of the Chief Financial Officer (CFO). The results of a survey of 33 CFOs of JSE listed companies find that South African CFOs are equally likely to follow the Pecking Order and Static Trade-Off theories. However, small companies are more likely to follow the Pecking Order theory while large companies are more likely to follow the Static Trade-Off theory. In addition, the results show that South African companies are more likely to follow the Static Trade-Off theory than companies in other emerging countries.     

Has the Internet Eliminated Regional Price Differences? Evidence from the Used Car Market

We study the effects of the Internet on regional price differences. Comparing two Dutch regions, we find that before the rise of the Internet, price differences of used cars between those regions amounted to some 11–15%, controlling for mileage, age, fuel type and engine volume. These price differences have completely disappeared after the rise of the Internet, in particular a website that allows consumers to make detailed comparisons between almost all used cars for sale in the Netherlands.     

How social and human capital influence opportunity recognition and resource mobilization in India's handloom industry

Small-scale firms in rural areas play an extremely important role in the development of any country, and especially in developing countries. To understand entrepreneurs who operate in a low-technology industry, we rely on the network perspective on entrepreneurship. In this paper, we investigate how the social and human capital of entrepreneurs (in this case master weavers in the handloom industry) influence their ability to recognize opportunities and mobilize resources. In addition to examining the direct effects, we also explore the possibilities of social capital mediating between human capital, on the one hand, and opportunity recognition and resource mobilization on the other. This paper adds to existing literature in two ways: firstly, we expand the social capital paradigm by including different cultural settings and links to existing studies regarding small enterprises. Secondly, we provide additional evidence to the ongoing debate as to what constitutes a ‘good network’.     

Economic consequences of intifada

In 2003 the World Bank (WB) and the International Monetary Fund (IMF) published estimates of macro-economic indicators for 2002 of the economy of Palestine. The WB used a micro-founded recursive dynamic computable general equilibrium (CGE) model, calibrated on the 1998 Social Accounting Matrix (SAM) of Palestine, to which shocks were applied, whereas the IMF based its estimates on a macro-founded income-expenditure model relying on more recent data. It turned out that there were substantial differences: the estimate by the WB of the real gross national income (at 1998 prices) was 25% less than the corresponding figure calculated by the IMF. This huge difference is not only relevant for a full understanding of the economic consequences of the intifada, but also for the size of the international community intervention. In this paper we propose our own evaluation with the help of a static CGE model, based on the 1998 SAM and the so-called intifada shock derived from data of the WB that we constructed for the analysis of some forms of emergency assistance in a previous article. It turns out that our estimates, based on an entirely different methodology, are remarkably close to those of the IMF.     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.