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This thesis uses a computable Overlapping Generation (OLG) model to examine the Chinese pension reform. The research involves calibrating an OLG model for the Chinese economy and undertaking policy evaluations. Model calibration involves choosing a set of household and firm parameters to capture the key features of the Chinese economy and pension system. Policy evaluation involves using the calibrated model to compare economic variables under alterative pension policies. The major pension policy focus is a comparison of Pay-As-You-Go (PAYG) and full funded social security designs. The thesis…mehr

Produktbeschreibung
This thesis uses a computable Overlapping Generation (OLG) model to examine the Chinese pension reform. The research involves calibrating an OLG model for the Chinese economy and undertaking policy evaluations. Model calibration involves choosing a set of household and firm parameters to capture the key features of the Chinese economy and pension system. Policy evaluation involves using the calibrated model to compare economic variables under alterative pension policies. The major pension policy focus is a comparison of Pay-As-You-Go (PAYG) and full funded social security designs. The thesis examines the effect of pension reform on the economy from both economy-wide and inter-generational dimensions. Based on the simulation results the thesis argues for two conclusions. Firstly, China s economic output would increase if the pension reform occurred, as the capital stock under the full funded scheme is higher than under the PAYG scheme. Secondly, the thesis finds that the losers are the older generation at the time of the pension reform but that all other generations would benefit from the reform.
Autorenporträt
Dr Xin YI obtained his PhD degree in Economics at the University of New South Wales (UNSW), Australia. His research interests includes using Computable Overlapping Generation (OLG) Model to investigate the pension reform in China. Dr YI has taught Econometrics and Statistics at University of Newcastle, University of New England and UNSW since 2003.