Rating the Rich: An Ordinal Classification to Determine Which Rich Countries are Helping Poorer Ones the Most

• Published in: Social indicators research Vol. 116; no. 1; pp. 47 - 65
• Main Authors: Sianes, Antonio, Dorado-Moreno, Manuel, Hervás-Martínez, César
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01.03.2014; Springer Netherlands; Springer Nature B.V
• Subjects: Algorithms; Antipoverty Programs; Artificial Intelligence; Artificial neural networks; Civil Society; Classification; Cooperation; Countries; Developing Countries; Development sociology; Development Strategies; Energy; Environmental security; Experiments; Foreign aid; GDP; Gross Domestic Product; History, theory and methodology; Human Geography; International development; LDCs; Low Income Groups; Machine learning; Machinery; Methodology; Microeconomics; Modeling; Networks; Neural networks; Neurons; Peer Influence; Poverty; Public Health; Public policy; Quality of Life Research; Ratings & rankings; Research Design; Social research; Social Sciences; Sociologists; Sociology; Sociology of economy and development; Statistical Analysis; Studies; Policy coherence for development; Fight against poverty; Ordinal classification; Commitment to development index; Artificial neural networks; Congruence; Poverty; Development aid; Classification; Developing Countries; Fight; Commitment; Advanced Countries; Development policy
• ISSN: 0303-8300; 1573-0921
• DOI: 10.1007/s11205-013-0270-6

When talking about poverty, a lot of energy is expended by academics and sociologists in the identification and classification of the poor. Less attention is paid to classifying the rich. The Center for Global Development created the Commitment to Development Index in 2003, which ranks countries according to their contribution to the reduction of poverty in developing countries. Since its first report, "Ranking the rich, the Index has been quite successful. However, it has also been subject to multiple criticisms. This paper proposes the use of an ordinal classification to rate, not rank, the performance of rich countries. An ordinal classification, where an ordinal scale labels the examples, can help discovering the level of each country's commitment to development, automatically and independently from others' performances. It could stimulate both advocacy from civil society and the determination of more coherent public policies in rich countries for poorer ones. The methodology used is Artificial Neural Networks, a common machine learning tool for successfully solving classification problems. Experiments yield robust results, showing better outcomes than other alternative ordinal classifiers, opening the possibility of developing a classification technique which could overcome the limitations of the current ranking technique.