Privatization and policy competition for FDI

In this paper, we provide an explanation of why privatization may attract foreign investors interested in entering a regional market. Privatization turns the formerly-public firm into a less aggressive competitor since profit- maximizing output is lower than the welfare-maximizing one. The drawback is that social welfare generally decreases. We also investigate tax/subsidy competition for FDI before and after privatization. We show that policy competition is irrelevant in the presence of a public firm serving just its domestic market. By contrast, following privatization, it endows the big country with an instrument which can be used either to reduce the negative impact on welfare of an FDI-attracting privatization or to protect the domestic industry from foreign competitors

Effective Classification using a small Training Set based on Discretization and Statistical Analysis

This work deals with the problem of producing a fast and accurate data classification, learning it from a possibly small set of records that are already classified. The proposed approach is based on the framework of the so-called Logical Analysis of Data (LAD), but enriched with information obtained from statistical considerations on the data. A number of discrete optimization problems are solved in the different steps of the procedure, but their computational demand can be controlled. The accuracy of the proposed approach is compared to that of the standard LAD algorithm, of Support Vector Machines and of Label Propagation algorithm on publicly available datasets of the UCI repository. Encouraging results are obtained and discusse

Leaf Classification

The purpose of this resource is to develop a classification system for a set of objects and learn about hierarchical classification systems. Any set of objects, such as insects or rocks, may be used as well. Educational levels: Primary elementary, Intermediate elementary, Middle school, High school