Firm accounting practices, accounting reform and corruption in Asia

10.1016/S1449-4035(05)70060-6Policy and Society24353-7

Public sector transparency and corporate accounting practices in Asia

10.4324/9780203884843Tranforming Asian Governance: Rethinking Assumptions, Challenging Practices74-9

Political institutions and corporate governance reforms in Southeast Asia

Reforming Corporate Governance in Southeast Asia: Economics, Politics, and Regulations16-3

Infrastructure coverage and the poor : the global perspective

The authors use the World Bank's Living Standards Measurement Study (LSMS) surveys from 15 countries (covering more than 55,500 households) to examine the relationship between infrastructure coverage and household income. The results show that throughout the world all income groups have much higher levels of coverage for electricity than for other formal infrastructure services (in-house piped water service, sewerage service, and private telephone service). In many countries most households in urban areas now have electricity service. As monthly household incomes increase from $100 to$250, coverage of all these infrastructure services rises, but at different rates. The findings confirm that the very poor rarely have these infrastructure services - with exceptions. The very poor often do have electricity if they live in urban areas. The very poor in Eastern Europe and Central Asia have much higher levels of coverage than those elsewhere in the world; they often have electricity, water, sewer, and telephone services. The results also suggest that if the poor gain access to services in their communities, many will decide to connect.Town Water Supply and Sanitation,Water Use,Housing&Human Habitats,VN-Acb Mis -- IFC-00535908,Health Economics&Finance

A Global Discretization Approach to Handle Numerical Attributes as Preprocessing

Discretization is a common technique to handle numerical attributes in data mining, and it divides continuous values into several intervals by defining multiple thresholds. Decision tree learning algorithms, such as C4.5 and random forests, are able to deal with numerical attributes by applying discretization technique and transforming them into nominal attributes based on one impurity-based criterion, such as information gain or Gini gain. However, there is no doubt that a considerable amount of distinct values are located in the same interval after discretization, through which digital information delivered by the original continuous values are lost. In this thesis, we proposed a global discretization method that can keep the information within the original numerical attributes by expanding them into multiple nominal ones based on each of the candidate cut-point values. The discretized data set, which includes only nominal attributes, evolves from the original data set. We analyzed the problem by applying two decision tree learning algorithms (C4.5 and random forests) respectively to each of the twelve pairs of data sets (original and discretized data sets) and evaluating the performances (prediction accuracy rate) of the obtained classification models in Weka Experimenter. This is followed by two separate Wilcoxon tests (each test for one learning algorithm) to decide whether there is a level of statistical significance among these paired data sets. Results of both tests indicate that there is no clear difference in terms of performances by using the discretized data sets compared to the original ones. But in some cases, the discretized models of both classifiers slightly outperform their paired original models