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Previous studies of East Asian welfare regimes focus on similarities between social security schemes. In contrast, this paper explores cross-national variations in public–private pension mixes in six welfare states: China, Hong Kong, Japan, Singapore, South Korea and Taiwan. Our research echoes the pension policy analysis of international organisations but takes a step forward with emphasis on the historical and institutional characteristics of the respective pension systems. The analysis identifies three institutional patterns. First, the statist pension system (Taiwan and China) primarily relies on public pensions to provide old-age security, with private pensions playing a rather minor role. Second, in the dualist pension system (Japan and Korea) both public and private pensions work in parallel to ensure retirement income, though a clear security gap exists between workers in the formal and informal economies. Finally, the individualist pension system (Hong Kong and Singapore) is characterised by genuine fully funded individual accounts, emphasising citizens' own responsibilities for ensuring old-age security. These three types of pension systems demonstrate distinct institutional characteristics and policy outcomes, illustrated by the juxtaposition of their institutional structures as well as by the comparison of key indicators collected from government reports and Organisation for Economic Co-operation and Development statistics. The paper concludes with a theoretical reflection of East Asian pension policies and a diagnosis of the distinct challenges confronted by each of the various pension patterns. [ABSTRACT FROM AUTHOR]

Carrying out investigations into the relationships between the satisfaction in old-age security and its influence factors is of great significance for safeguarding social fairness and justice. As powerful statistical tools in machine learning, the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms are used to develop nonlinear estimation models for satisfaction in the old-age security in China. Five influence factors (educational background, educational satisfaction, satisfaction with family's financial situation, overall life satisfaction, society overall evaluation) were used as the input features. A SVM model obtained in this paper has prediction accuracies of 78.0% for the training set and 77.5% for the test, and a BPNN model possesses prediction accuracies of 77.8% and 77.0% for the two tests. Obviously, the SVM is superior to the BPNN in predicting satisfaction of old-age security in China. [ABSTRACT FROM AUTHOR]