Retirement Consumption Puzzle in Malaysia: Evidence from Bayesian Quantile Regression Model
Ros Idayuwati Alaudin,
Noriszura Ismail and
Zaidi Isa
Journal of Probability and Statistics, 2019, vol. 2019, 1-8
Abstract:
The objective of this study is to use the Bayesian quantile regression for studying the retirement consumption puzzle, which is defined as the drop in consumption upon retirement, using the cross-sectional data of the Malaysian Household Expenditure Survey (HES) 2009/2010. Three different measures of consumption, namely, total expenditure, work-related expenditure, and nonwork-related expenditure, are suggested for studying the retirement consumption puzzle. The results show that the drop in consumption upon retirement is significant and has a regressive distributional effect as indicated by larger drops at lower percentiles and smaller drops at higher percentiles. The smaller drops among higher consumption retirees (or higher income retirees) may imply that they have more savings and/or retirement benefits than the smaller consumption retirees (or lower income retirees). Comparison between the three types of consumption shows that the work-related expenditure has a uniform drop across the distribution. The drop under the nonwork-related expenditure varies across the distribution, implying that it is the source behind the variation of the consumption drop.
Date: 2019
References: Add references at CitEc
Citations:
Downloads: (external link)
http://downloads.hindawi.com/journals/JPS/2019/2723069.pdf (application/pdf)
http://downloads.hindawi.com/journals/JPS/2019/2723069.xml (text/xml)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:2723069
DOI: 10.1155/2019/2723069
Access Statistics for this article
More articles in Journal of Probability and Statistics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().