One of the important factors for economic development is the existence of an effective tax system. The paper, which is the second part of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), deals with the concept and empirical estimation of countries' taxable capacity and tax effort to develop further country tax effort typologies. It employs a cross-country study from a sample of 110 developing and developed countries during 1994-2009. Taxable capacity refers to the predicted tax to gross domestic product ratio that can be estimated with the regression, taking into account a country's specific macroeconomic, demographic, and institutional features. Tax effort is defined as an index of the ratio between the share of the actual tax collection in gross domestic product and the predicted taxable capacity. The use of tax effort and actual tax collection benchmarks allows us to rank countries into four different groups: low tax collection, low tax effort; high tax collection, high tax effort; low tax collection, high tax effort; and high tax collection, low tax effort. This classification is based on the benchmark of the global average of tax collection and a tax effort index. The analysis provides guidance for tax reforms in countries with various levels of tax collection and tax effort.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO -MLP and 97% for APGWO -MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

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