Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and...
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Transcript of Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and...
![Page 1: Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and NBER 1.](https://reader036.fdocuments.in/reader036/viewer/2022083004/56649d9c5503460f94a85bbd/html5/thumbnails/1.jpg)
Comments onRetirement Patterns in China
by Lei, Wang, and Zhao
Andrew K. RoseUC Berkeley, CEPR and NBER
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![Page 2: Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and NBER 1.](https://reader036.fdocuments.in/reader036/viewer/2022083004/56649d9c5503460f94a85bbd/html5/thumbnails/2.jpg)
Summary
• Descriptive analysis of major new Chinese data set
• Focus: What drives incidence of retirement?• Key Finding: Big urban/rural differences– Urban retire sooner– Reasons both public (pension) and private (family
income)
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![Page 3: Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and NBER 1.](https://reader036.fdocuments.in/reader036/viewer/2022083004/56649d9c5503460f94a85bbd/html5/thumbnails/3.jpg)
Early Days
• Survey (and paper!) only recently completed– Some things unclear– Many aspects of data unexploited• Example 1: all health-, environment-related issues• Example 2: any use of longitudinal nature of data set
(eventually? currently a cross-section?)
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![Page 4: Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and NBER 1.](https://reader036.fdocuments.in/reader036/viewer/2022083004/56649d9c5503460f94a85bbd/html5/thumbnails/4.jpg)
Still, Applause for Data Collection
• CHARLS: an enormous new data set– Big Task (>10k households, >17k respondents,
spread over 150 counties, 28 provinces)– Great Potential Payoffs
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Urban/Rural Differences Striking
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Why? Potential Explanations1. Mandatory Retirement Policy– Strict in urban areas, especially for SOEs … but urban less likely
to retire formally/in practice (p9)2. Family Wealth– Hard to test with this data; consumption reflects many things
3. Support from Children– Small urban/rural differences in help– Size of transfers similar for urban/rural– More rural children (33%) give transfers than urban (18%)
4. Public Pensions– Higher urban (70%) than rural (41%) coverage– Urban (1350 RMB) much larger than rural (74 RMB)
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![Page 7: Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and NBER 1.](https://reader036.fdocuments.in/reader036/viewer/2022083004/56649d9c5503460f94a85bbd/html5/thumbnails/7.jpg)
Comment 1
• Reasonable Definition of Retirement?– Not engaging in farm/non-farm work and not
searching for job
• But labor force members must be able to work– Shouldn’t retirement be distinguished from chronic
illness? – Especially for elderly
• Doesn’t explain finding– Rural have harder work than urban, worse healthcare
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![Page 8: Comments on Retirement Patterns in China by Lei, Wang, and Zhao Andrew K. Rose UC Berkeley, CEPR and NBER 1.](https://reader036.fdocuments.in/reader036/viewer/2022083004/56649d9c5503460f94a85bbd/html5/thumbnails/8.jpg)
Comment 2
• Suspicious of urban Chinese retirement rates– ≈51% of Chinese are urban– Can they really be retiring so earlier• Absolute: >60% of urban Chinese 60-64 retired• Relative to OECD: <60% Koreans 65+ retired
– 60-64 retired: Germany, Canada, Japan have <60%
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Comment 3
• China faces serious demographic transition• Retirement ages are young compared to other
countries, longevity, expense … (males 60, blue-collar women 50, white-collar women 55)– Early retirement also common, medical reasons
• Hard to imagine these won’t change• Similarly, hard to imagine rural pensions will
change if government wants to urbanize
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