Aging in Asia Findings from New and Emerging Data Initiatives (2012) / Chapter Skim
Currently Skimming:
7 Relying on Whom? Poverty and Consumption Financing of China's Elderly--Albert Park, Yan Shen, John Strauss, and Yaohui Zhao
7 Relying on Whom? Poverty and Consumption Financing of China's Elderly--Albert Park, Yan Shen, John Strauss, and Yaohui Zhao
Pages 148-172
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 148... ...
Because of strict family planning policies, tomorrow's elderly will have many fewer children than today's elderly, and large-scale migration as well as modernizing values also could undermine traditional family support systems. Public pension programs remain immature, and most elderly lack 1 This research was supported by the National Institute on Aging (Grant Number R21AG031372)
|
From page 149... ...
We calculate elderly consumption poverty rates and analyze the extent to which the elderly rely upon their own income (including from pensions) , income from other household members, public transfers, private trans fers, and savings to finance their consumption.
|
From page 150... ...
Section 3 estimates income and consumption poverty rates and describes how different financing sources help to reduce consumption poverty, and ana lyzes which characteristics of the elderly are most closely associated with poverty status. Section 4 describes financing sources in greater detail, analyzes the determinants of reliance on different financing sources, and assesses the extent to which saving behavior contributes to poverty.
|
From page 151... ...
5 The resulting sample of main respondents thus is representative of the populations of Gansu and Zhejiang provinces. The sample's demographic structure is similar to that found in the 2005 population mini-census in the two provinces.6 In this study, we focus attention on the subsample of main respondents aged 60 and older, which we refer to as the elderly sample.
|
From page 152... ...
Income was measured at both the individual and household levels. Main respondents and their spouses were asked about all sources of income and public transfers that went to them individually, and a financial respondent -- the person most familiar with the household's finances -- answered questions about the individual income of other household members, income from household activities such as agriculture, household expenditures, and household-level transfers, including private transfers from nonhousehold members.
|
From page 153... ...
Household consumption expenditure items are measured by recall questions covering the past week, month, or year depending on the expected frequency of different types of expenditures. The survey asks about food expenditure during the past week, including expenditures on dining out, food bought from the market, and the value of home-produced food.9 Monthly expenditures include fees for utilities, communications, nannies, and other costs.
|
From page 154... ...
Median expenditure per capita is 4,418 yuan, which, in 12 Public transfers include medical expenditure subsidies, workers' compensation, ru ral and urban minimum living standards subsidies, subsidies for those unable to work (wubaohu) , compensation for land seizure, agricultural subsidies, family planning subsidies, elderly pension subsidies, reforestation subsidy, unemployment benefits, rural poverty sub sidies, disaster relief subsidies, and social donations.
|
From page 155... ...
TABLE 7-1 Household per Capita Income and Expenditure by Age and by Urban Versus Rural (in RMB) HH R/S Income Pre-Transfer Income Post-Transfer Income Expenditure Number Mean Median Mean Median Mean Median Mean Median Age 45−59 794 17,649 8,400 15,886 7,200 15,958 7,624 8,401 6,416 737 5,114 650 5,897 1,600 7,365 3,712 6,192 4,418 Age ≥ 60 Urban 136 14,502 12,000 12,843 11,400 14,654 10,633 9,876 8,000 Rural 601 2,955 400 4,299 1,000 5,689 2,783 5,345 3,920 Urban/rural 4.91 30.00 2.99 11.40 2.58 3.82 1.85 2.04 SOURCE: Authors' calculations using sample of main respondents in CHARLS pilot data.
|
From page 156... ...
. Interest ingly, for urban residents, median values decline monitonically in this progression while rural residents' median values increase monitonically, suggesting that the rural elderly are made increasingly better off by living with others, receiving transfers, and dissaving while urban residents are made increasingly worse off because they subsidize the consumption of people they live with, give money to relatives, and save funds rather than spend them.
|
From page 157... ...
ELDERLY POVERTY The most direct way to study the adequacy of consumption financing is to calculate poverty rates for the elderly. In this section, we use our detailed measurements of income and consumption expenditure to calculate the extent of poverty among the elderly, and the contributions made by different sources of finance in altering the extent of poverty among China's elderly.
|
From page 158... ...
accounts for a significant share of consumption for the income poor. This consumption poverty rate is not far from the 12.9% poverty headcount ratio estimated for the elderly in all of China in 2003 using National Bureau of Statistics national household survey data and a lower poverty line (World Bank, 2009)
|
From page 159... ...
In both cases, private transfers are more important than public transfers. However, in rural areas, co-residence and dissaving also play key roles, accounting for decreases in poverty rates of 11 and 23 percentage points, respectively, while for urban residents these sources of finance are relatively unimportant.
|
From page 160... ...
This suggests that sup port mechanisms for urban residents are less well targeted to the poorest of the poor than for rural residents. To illustrate more clearly the importance of distinguishing between income poverty and consumption poverty, we note the relatively low correlation between income poverty and consumption poverty in the data.
|
From page 161... ...
161 ALBERT PARK, YAN SHEN, JOHN STRAUSS, and YAOHUI ZHAO TABLE 7-4 Income and Consumption Poverty Headcount Ratios for the Elderly Poverty Headcount Ratio Pre-transfer Post-transfer Income Income Expenditure Gender Women 0.512 0.328 0.167 Men 0.490 0.333 0.144 Rural or Urban Urban 0.236 0.058 0.078 Rural 0.562 0.393 0.174 Pension Status Without pension 0.626 0.418 0.190 With pension 0.076 0.033 0.040 Number of Children Children = 0 0.890 0.201 0.139 Children = 1 0.543 0.481 0.236 Children = 2 0.360 0.234 0.100 Children > 2 0.521 0.348 0.165 Education Background Illiterate 0.606 0.404 0.192 Informally educated 0.428 0.237 0.152 Primary 0.452 0.331 0.111 Junior or above 0.217 0.170 0.049 Living Arrangements Men living alone 0.617 0.233 0.187 Women living alone 0.738 0.358 0.228 Live w/spouse only 0.353 0.242 0.109 Live w/adult children 0.445 0.352 0.154 Live w/others 0.647 0.493 0.121 Health Status Poor health 0.654 0.489 0.234 Fair health 0.432 0.260 0.103 Good health or above 0.408 0.266 0.134 Residence Urban Zhejiang 0.225 0.062 0.076 Rural Zhejiang 0.476 0.260 0.117 Urban Gansu 0.252 0.053 0.081 Rural Gansu 0.746 0.675 0.297 With or Without Agricultural Income Without agri income 0.728 0.705 0.343 With agri income 0.495 0.320 0.151 Total 0.501 0.330 0.156 SOURCE: Authors' calculations using main respondents sample of CHARLS pilot data.
|
From page 162... ...
p < 0.01. SOURCE: Authors' estimates using elderly sample of CHARLS pilot data.
|
From page 163... ...
163 ALBERT PARK, YAN SHEN, JOHN STRAUSS, and YAOHUI ZHAO Pre-Transfer Income Post-Transfer Income Expenditure Poor (1 = yes)
|
From page 164... ...
. Not living with children increases poverty rates substantially for rural households when measured by pre-transfer income, but all of the living arrangement variables are much smaller in magnitude and statisti cally insignificant in the corresponding urban regression.
|
From page 165... ...
SOURCES OF EXPENDITURE FINANCE The differences in income poverty and consumption poverty point to the fact that a significant portion of the elderly are consuming more than their income. While life cycle theory implies that the elderly may support themselves by depleting savings that they have accumulated during their prime working years, it is unclear how important dissaving is for Chinese elderly.
|
From page 166... ...
and dissaving (7%) , suggesting that poor urban residents have much greater ability to obtain private assistance than rural residents.
|
From page 167... ...
Urban residents living alone or in poor health rely significantly more on private transfers; interestingly, these relation ships are not statistically significant for rural households, although the estimated magnitude of private transfer response to poor health is similar. Having three or more children increases reliance on private transfers and reduces reliance on public transfers.
|
From page 168... ...
p < 0.01. SOURCE: Authors' estimates using elderly sample of CHARLS pilot data.
|
From page 169... ...
169 ALBERT PARK, YAN SHEN, JOHN STRAUSS, and YAOHUI ZHAO Private Transfer Share Public Transfer Share Dissaving Share Urban Rural Urban Rural Urban Rural –0.55 2.10 0.29 0.13 1.53 0.75 (0.68)
|
From page 170... ...
. To meet these challenges, China is aggressively increasing public support for the elderly in the form of expanded pension coverage, as well as social assistance programs, such as subsidies to maintain minimum living standards and family planning subsidies for those elderly who followed family planning guidelines throughout their lives and so have fewer chil
|
From page 171... ...
As noted, public transfers may reduce elderly labor supply and private support for the elderly. Studies that address behavioral responses to public policies and impact evaluations of current policies may help shed important light on how different policies will ultimately affect the welfare of China's elderly.
|
From page 172... ...
. Income and family support among rural elderly in Zhejiang Province, China.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.