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OCR for page 207
9
Patterns and Correlates of
Intergenerational Nontime Transfers:
Evidence from CHARLS1
Xiaoyan Lei, John Giles, Yuqing Hu, Albert Park,
John Strauss, and Yaohui Zhao
C
hina is now facing an unprecedented aging process, which is rapid,
but occurring at a low level of economic development and with
few social safety nets. With the introduction of family planning
policies in the 1970s that caused a plummet in the birth rate during the
past few decades, and with the earlier “baby boom” generation who
will soon pass their 60th birthday in the next 15–20 years, it is projected
that the old-age dependency ratio will climb from the current 10–40% by
2050.2 However, unlike the advanced industrialized countries such as
the United States and in Europe, which have long experienced aging and
whose social safety nets cover the majority of the elder population, China
is aging at a relatively low level of development with many times lower
per capita income and underdeveloped political and financial institutions.
In comparison with the old-age support system that is operating with
fragmented infrastructure and noncomprehensive coverage, informal
familial support has long been the most important source of help in low-
income countries. As a country with a long-standing history and culture,
1 The research was supported by the National Institute on Aging (Grant Number
R21AG031372), Natural Science Foundation of China (Grant Numbers 70773002 and
70910107022), the World Bank (Contract 7145915), and the Fogarty International Center
(Grant Number R03TW008358), as well as the Knowledge for Change Trust Fund at the
World Bank. The content is solely the responsibility of the authors and does not necessarily
represent the official views of any of the funders.
2 Authors’ calculations based on the numbers provided by the United Nations. Available:
http://esa.un.org/wpp/unpp/panel_population.htm.
207
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208 AGING IN ASIA
China has unique traditional family values, especially strong in the rural
areas. The deep-rooted Confucian “filial piety,” characterized by money
and time transfers from children to their parents and the co-residence of
multiple generations, has effectively helped sustain an informal old-age
security.
The traditional foundation of old-age support is changing today for
several reasons. First, economic shifts involving smaller household sizes,
greater mobility of the population, and perhaps weakening of ties of kin
outside the household are potentially undermining this tradition, making
it increasingly difficult for older Chinese to receive support from their
adult children. Thus, it is important at this stage to understand the pat -
terns of intergenerational transfers among Chinese families and evaluate
to what extent intergenerational transfers still function as a part of elderly
support. Second, despite the existence of “filial piety,” other Chinese
traditional norms may also linger and influence family transfer behaviors.
For instance, there is a shared ideal of family continuity through the male
line in which the females are considered inferior to the males, so parents
tend to favor sons, reflected in the inequitable distribution of transfers
(Lee et al., 1994). Therefore, we also try to investigate the correlates of
intergenerational transfers, with a hope to better understand the driving
forces behind transfer behaviors between elderly parents and their adult
children.
In this chapter, we examine incidence and net amount of transfers
and their correlations with parental demographics, socioeconomic status
(SES), and health status, as well as children’s demographics and SES. The
main findings include (1) contrary to the situations in most developed
countries, transfers are predominantly from children to elderly parents,
and are large in magnitude compared with parental pre-transfer income;
(2) older people with a larger number of offspring tend to receive more
transfers; those residing with other children are less likely to receive
transfers from their nonco-resident children; (3) the relationship between
parental pre-transfer income and transfers is mixed, depending on the
income level of the parents; (4) married children are more able to provide
transfers; (5) educated children transfer more frequently and a larger net
amount; and (6) oldest sons are less likely to provide transfers.
Our findings reveal that given the insufficient pension system and
social safety nets in today’s China, children remain the major source of
elderly support, implying the traditional social norms still play an impor-
tant role. The incidence and amount of transfers are responsive to parents’
income levels, and affected by the socioeconomic status of children. As
China is growing quickly and approaching a graying society, we expect
adult children will continue to shoulder the most responsibility for elder
support.
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209
LEI, GILES, HU, PARK, STRAUSS, and ZHAO
LITERATURE REVIEW
Who will pay for the rising army of retirees? Does the government
have the resources to meet the challenges? The situation is far from satis -
factory. Under the Chinese traditional “pay-as-you-go” (PAYG) pension
system, governments collect pension contributions and other taxes to
pay current pensions, and each employee gets a promise that he/she will
receive a pension paid for by other workers in the future. As China ages
rapidly, the number of new workers entering the workforce will decline,
and rising longevity will increase the size of the pension-age population.
The PAYG system will start to run deficits as the dependency ratio rises,
and the value of future net liabilities will start to increase sharply as well.
Although the Chinese government has recently introduced a series
of social insurance and new pension programs, it faces great difficulty in
implementation due to the rapid demographic transition and urbaniza-
tion (Cai, Giles, and Meng, 2006). Although the current social insurance
system contains three pillars in name (PAYG, funded individual accounts,
and voluntary complementary insurance), this system does not differ
much from the PAYG system: The money in the individual account is
often used to pay for the pensions of existing retirees and is, in large
part, an empty account, and the third pillar is very small. It has also been
shown that even with the extremely high current payroll tax rate (28%),
the pension system, due to its low return rate, would never be able to
achieve the promised replacement rate if taking the demographic transi-
tion into consideration (Lei, Zhang, and Zhao, 2011a). With this insuffi-
cient social insurance, private transfers will continue to be important, at
least in the foreseeable future.
A large literature has been devoted to theorizing about the patterns
and determinants of private transfers (Altonji, Hayashi, and Kotlikoff,
1997; Becker, 1974; Cox, 1987; Cox and Fafchamps, 2008; Cox and Soldo,
2004; Kotlikoff, 1998; McGarry and Schoeni, 1995). Following the theo -
retical models, relevant empirical studies have also been conducted.
Regarding the patterns of intergenerational transfers, more than one-
third of parents give money to children in the United States (Hurd,
Smith, and Zissimopoulos, 2007) and parental assistance is important in
supporting young men (Rosenzweig and Wolpin, 1993). In Poland, high-
income parents transfer to low-income young couples (Cox, Jimenez, and
Okrasa, 1997).
In contrast, two or three out of five households provide financial
transfers for their aged parents in Korea (Kim, 2010), which is similar
to most areas of Asia where children transfer to parents to insure them
against low retirement incomes (Cai, Giles, and Meng, 2006; Nugent,
1985). However, multiple transfer patterns between adult children and
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210 AGING IN ASIA
their parents exist in Malaysian and Indonesian families (Frankenberg,
Lillard, and Willis, 2002; Lillard and Willis, 1997). In particular, children
are an important source of old-age security, which in part is children’s
repayment for parental investments in their education; in the meantime,
parents and children engage in the exchange of time help for money.
Regarding the determinants of intergenerational transfers, most
studies focus on the relationship between transfers and recipients’ income
with the purpose of exploring the underlying motives. The evidence is
mixed, varying across regions. A strong negative correlation has been
found in the United States (McGarry and Schoeni, 1995), but a positive
one is detected in Peru (Cox, Eser, and Jimenez, 1998).
Studies regarding intergenerational transfers in China are few, pos-
sibly because available data are in short supply and not well suited to
study this set of questions. In recent years, with China’s rapid develop -
ment and aging, more studies have been done (Cai, Giles, and Meng,
2006; Chou, 2010; Goh, 2009; Lee and Xiao, 1998; Secondi, 1997). For
example, Secondi (1997) used data from a large 1988 household survey to
test the hypotheses of altruism and exchange and to study the size and
direction of transfers in rural China. He found that most of the money
flows appeared to be transfers from adult children to elderly parents and
remittances from migrants. Cai, Giles, and Meng (2006) addressed how
households with elderly members coped when enterprise-based or local
public pension systems failed to provide sufficient income. They found
evidence that the transfer flow was from children to parents and that
private transfers responded to low household income of retired workers
when income fell below the poverty line. However, these studies have the
same weaknesses in that transfers are defined as a household aggregate
for which the donors are unspecified, rendering these studies unable to
differentiate between intergenerational and intragenerational transfers.
DATA
We draw on the released 2008 pilot of the China Health and Retire-
ment Longitudinal Study (CHARLS), a survey conducted from July to
September in 2008 by the National School of Development at Peking Uni-
versity (Zhao et al., 2009). As one of the sisters of the Health and Retire-
ment Study (HRS)-serial surveys, CHARLS, with its rich information, is
ideal for research on transfers. In the interview, the respondents were
asked whether they had received transfers from and/or given transfers
to each of their children, and if so the corresponding amount.3 Transfers
3 Amounts are asked if financial transfers occur, and frequencies are asked if time transfers
occur. In this chapter, we only focus on financial transfers.
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LEI, GILES, HU, PARK, STRAUSS, and ZHAO
are specified in two categories: financial transfers4 and in-kind transfers
(mostly in the form of goods); both are nontime transfers.
The survey was conducted in Zhejiang and Gansu, representing two
very different development levels in China (see the two provinces loca -
tion on the map in Figure 9-1). Zhejiang, a southeastern costal province,
has been enjoying rapid economic growth since the implementation of the
reform policies, and now it is one of the richest areas in China. Gansu,
located in the hinterland of northwest China, is one of the poorest prov -
inces. Its development has been constrained by its inclement natural
environment and insufficient commercial opportunities. The two different
economic and natural conditions contribute to different living status of
the residents and potentially influence intergenerational transfers. Both
provinces had major declines in fertility and mortality, with the fertility
decline most rapid starting in the 1970s, when stronger family planning
policies began (National Bureau of Statistics of China, 2009).
CHARLS main respondents are a random sample of aged 45 and
older. Both the main respondents and their spouses are interviewed in
the survey. They are asked for detailed information on themselves and
on their families. In this chapter, we are particularly interested in the
transfers between the respondents and their adult children.5 CHARLS
has information on all living children of each respondent and spouse, no
matter where they live. In order to fully employ the rich information on
each of their children, the basic sample of interest are the children of the
CHARLS respondents. Specifically we first choose the 789 households
with either the main respondent or his/her spouse older than 60.6 The
2,667 adult children (aged 25 and older7) of the 789 respondents are then
treated as our study sample.
Several sample restrictions are further applied according to differ-
ent purposes of the study. For the estimation on transfers, we restrict the
analyses to nonco-resident children (2,202 observations) because transfers
within the household are not clearly specified conceptually, and CHARLS,
like other aging surveys, does not attempt to measure them. For the
4 Financial transfers are further classified into two types: regular financial transfers and
nonregular financial transfers. Nonregular transfers are those made at special times of the
year, such as Spring Festival or a parent’s birthday.
5 The incidence of transfers between the respondents and their elderly parents are quite
small (only 13.4%), so we did not take them into consideration in our analysis. Family
transfers can entail interactions among members of three or even four generations, but it is
beyond the scope of this chapter to give a comprehensive treatment of this issue.
6 Only 16 main respondents do not have any children in the sample; they are dropped for
the purpose of studying parent-child transfers.
7 We choose age 25 because many adult Chinese people younger than 25 are full-time col -
lege students who are incapable of supporting their parents.
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212 AGING IN ASIA
Gansu
Zhejiang
FIGURE 9-1 Location of Zhejiang and Gansu provinces.
SOURCE: China: Outline of Provinces [map]. Daniel Dalet/d-maps.com. Avail -
able: http://d-maps.com/carte.php?&num_car=1749&lang=en.
regressions regarding family fixed-effects, only those families with at
least two nonco-resident adult children are included (2,068 observations).
In order to use individual information on both parent and child, we
need to match individual child and parental characteristics. We choose
the information of the main respondent parent because every child has a
main respondent parent and, as stated earlier, the main respondents are
chosen randomly by the survey.
MEASURES AND SUMMARY STATISTICS
Parent-Level Characteristics
In our analysis, characteristics of parents are mainly concerned with
three aspects: demographics, SES, and health.
Demographics include age, age squared, gender, marital status (mar-
ried and living with his/her spouse/partner, married but not living with
spouse, separated, divorced, widowed, or never married), location (urban
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LEI, GILES, HU, PARK, STRAUSS, and ZHAO
or rural, Zhejiang or Gansu), the number of children, and living arrange -
ment vis-à-vis their children.
SES has three dimensions: house ownership, education level, and
pre-transfer income.8 Education is classified into five discrete educational
groups: (1) illiterate, (2) less than primary education, (3) finished primary,
(4) junior high, and (5) senior high and above. In particular, the second
category—less than primary education—includes those who did not fin -
ish primary school but are capable of reading or writing, or those who
reported to have been in “Sishu.”9
Health-related variables include the Center for Epidemiological Studies
Depression Scale (CES-D), a score of cognition using questions from the
Telephone Interview of Cognition Status (TICS) used by the HRS and other
surveys of the elderly, and dummies indicating whether one has poor
general health, has any difficulties performing ADLs (Activities of Daily
Living) or IADLs (Instrumental Activities of Daily Living), and has a major
diagnosed chronic disease. Following the HRS example, the CHARLS ques-
tionnaire asked respondents to assess their general health using a scale of:
excellent, very good, good, fair, or poor. Here, we look at whether a respon-
dent reports poor health. ADL or IADL disability is defined as having diffi-
culty in any of the ADL (including physical limitations) and IADL activities.
The cognition of the respondents comprises three questions about
time orientation,10 one question about serial-7 subtraction from 100,11 and
one question concerning picture drawing. These are standard cognition
questions from TICS (Smith, McArdle, and Willis, 2010). We differentiate
people as those with full marks (11 points), those without full marks but
with a score above 8, and those with a score below 8. We choose 8 as the
cutoff point because about one-third of the sample have scores below 8.
Respondents are also asked whether they have particular diagnosed
diseases. They are coded as having major illness if they have one of the
following: (1) cancer or malignant tumor (excluding minor skin cancers);
(2) heart attack, coronary heart disease, angina, congestive heart failure,
or other heart problems; (3) stroke (including transient ischemic attack or
TIA); or (4) chronic lung diseases, such as chronic bronchitis or emphy-
sema (except for asthma, excluding tumors, or cancer). If they have any
8 In our analysis, pre-transfer income with and without public transfers are both con -
ducted, and the results are similar. We only report the results without public transfers
included in the pre-transfer income, because public transfers are arguably endogenous in a
reasonable economic model.
9 Sishu is a kind of old-style private Chinese education that mainly taught young children
Chinese classics before the 20th century.
10 Respondents are asked about today’s date (year, month, and day), week, and season.
11 The question is to subtract 7 from 100, then another 7 from that, and so on until the
fifth 7.
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214 AGING IN ASIA
of the other canvassed diseases,12 they are considered as having a minor
illness. Otherwise, they are defined as having no diseases.
Table 9-1 summarizes parents’ characteristics by living arrangement.13
Among all the main respondent parents of the children studied, 51.8% are
co-residing with adult children, 42.6% are fathers, and each has 3.4 chil-
dren on average, with a mean age of 68.4 years. Overall, our parent sample
has low education levels: as many as 54.9% are illiterates, and 21.8% have
not graduated from primary school. The annual per capita pre-transfer
income is 5.3 thousand RMB on average, with those co-residing with chil-
dren having slightly lower income than their nonco-residing counterparts
(5.1 vs. 5.6 thousand RMB).
Intergenerational transfers seem to play an important role for the
Chinese elderly. The sample parents on average receive 2.5 thousand RMB
of net transfers from all of their children, amounting to 15.15% of their
household pre-transfer income. The amount is much larger for those who
are not living with their children (2.9 vs. 2.1 thousand) and the discrep -
ancy in the ratio is especially larger (26.7% vs. 9.7%). This implies that
transfers and co-residence are possible substitutes. In addition, more than
one-quarter of these Chinese elderly report poor general health, 24% of
them have a cognition score lower than 8, nearly 30.4% are diagnosed to
have a major chronic illness, and as many as 51.1% have some difficulties
in performing ADLs.
The p-values reported in the last column show significant differences
between co-resident and nonco-resident parents in their marital status,
place of residence, house ownership, and cognition. Specifically, parents
living with their children are more likely to be widowed, have more chil-
dren, be from Gansu, and live in rural areas.
Child-Level Characteristics
Child characteristics are grouped into demographics and SES, the
former of which consists of age, the number of their own children (grand-
children of the parents), and five dummies representing whether the child
12 These diseases include hypertension, high cholesterol, diabetes or high blood sugar, liver
disease, such as Hepatitis B, or other liver disease (except fatty liver, tumors, and cancer),
kidney disease (except for tumor or cancer), stomach or other digestive disease (except for
tumor or cancer), emotional, nervous, or psychiatric problems, memory-related disease, and
arthritis or rheumatism.
13 For more detailed information on living arrangement of CHARLS elderly, please see
Lei et al. (2011b).
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LEI, GILES, HU, PARK, STRAUSS, and ZHAO
TABLE 9-1 Summary Statistics of Parent Characteristics
Co- Nonco-
Parent Characteristics All resident resident P-values
Age 68.44 68.34 68.54 0.726
Father (%) 42.59 43.52 41.58 0.582
Marital Status (%)
Married 60.84 54.28 67.89 0.029
Separated 1.14 0.98 1.32 0.657
Divorced 0.51 0.00 1.05 0.045
Widowed 37.01 43.77 29.74 < 0.001
Never married 0.51 0.98 0.00 0.045
# of children 3.40 3.54 3.26 0.016
Zhejiang 52.85 44.01 62.37 < 0.001
Urban 41.95 34.23 50.26 < 0.001
Living with adult children 51.84 100.00 0.00
House owner 89.48 94.38 84.21 < 0.001
Education (%)
Illiterate 54.88 57.21 52.37 0.172
Less than primary education 21.80 22.00 21.58 0.885
Primary school 13.43 12.71 14.21 0.539
Middle school 5.83 4.65 7.11 0.144
High school and above 4.06 3.42 4.74 0.353
Pre-transfer income per capita (PTI, 000s) 5.32 5.08 5.58 0.501
Household Pre-transfer income (HPTI, 000s) 16.52 21.68 10.98 < 0.001
Total net amount of transfer (TT, 000s) 2.50 2.10 2.93 0.111
Transfer-income ratio (HPTI/TT, %) 15.15 9.68 26.71
Self-Reported Health (%)
Excellent 1.39 1.47 1.32 0.856
Very good 8.75 7.82 9.74 0.344
Good 15.34 15.16 15.53 0.886
Fair 35.23 32.03 38.68 0.051
Poor 26.24 27.87 24.47 0.278
Cognition (%)
Score = 11 10.27 10.02 10.53 0.817
Score in [8, 11) 32.32 25.18 40.00 < 0.001
Score in [0, 8) 23.95 22.98 25.00 0.508
Disease (%)
Minor illness 46.51 47.68 45.26 0.498
Major illness 30.42 31.30 29.47 0.579
CES-D score 8.52 8.81 8.21 0.090
ADL or IADL disability 51.08 58.92 42.63 0.090
Observations 789 409 380
NOTES: (1) Sample are main respondent parents with children no younger than age 25, and
older than age 60 or spouse older than age 60. (2) P-values are from t-test of the co-resident
and nonco-resident groups.
SOURCE: Data from CHARLS 2008 pilot.
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216 AGING IN ASIA
is the oldest son, youngest son,14 daughter, whether he/she is married,
and the highest level of education he/she has attained.15
Table 9-2 summarizes child demographic characteristics and SES by
living arrangement using the child sample, i.e., those who are aged 25
and older and with at least one parent over 60. Among the 2,667 children,
465 (17.4%) are living with their parents, and this co-residence is highly
related to many child characteristics. The average age of those who co-
reside is 39.1, significantly less than the mean of 42.5 for those who are
nonco-resident. Daughters are less likely to live with parents, and old -
est sons and especially youngest sons are more likely to live with their
parents. Furthermore, education is also associated with co-residence, but
the pattern varies by different level: adult children with low education
(those who are illiterate and those with primary school education) and
high education (college and above) are not likely to live with parents,
but those with intermediate levels (middle school) are significantly more
likely to live with parents.
PATTERNS OF TRANSFERS
Transfers are also measured in the family module in the survey.
Respondents are asked about the amount and frequency of non-time
transfers, and these transfers include financial transfers and in-kind
transfers (in the form of goods) received from and given to each child.
Financial transfers involve giving money, helping pay bills such as medi -
cal care or insurance, schooling, and down payment for a home or rent.
These transfers are further divided into regular and irregular financial
transfers. Regular transfers were paid on a regular basis, such as monthly
payments. Irregular transfers occurred irregularly, such as around a festi -
val, marriage, large medical expenses, and the like. In-kind transfers are
nonmonetary gifts provided or given in the past year.
We first separately analyze prevalence of transfers from a child to
parents and then examine the amount of net transfers to parents, defined
by subtracting the amount given to a particular child from the amount
received from the same child.
Table 9-3 summarizes the patterns of transfers. Overall, familial inter-
generational transfers are pervasive, with about 60% of the children hav -
ing provided transfers to their parents. The prevalence of transfers from
parents to their adult children is smaller, only 3.3%. The net amounts in
terms of financial and in-kind transfers are all positive, toward parents.
14 A
son is classified as both the youngest son and the oldest son if he is an only child.
15Children’s education is classified into five categories: illiterate, primary education, middle
school, high school, college and above.
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LEI, GILES, HU, PARK, STRAUSS, and ZHAO
TABLE 9-2 Summary Statistics of Child Characteristics
All Co-resident Nonco-resident P-values
Child Characteristics
Age 41.95 39.12 42.54 < 0.001
Oldest son (%) 15.90 22.37 14.53 < 0.001
Youngest son (%) 27.30 61.29 20.12 < 0.001
Daughter (%) 46.04 12.04 53.22 < 0.001
Married (%) 91.38 78.49 94.10 < 0.001
# of children (age < 16) 0.34 0.47 0.31 0.001
Education (%)
Illiterate 16.91 12.26 17.89 0.001
Primary 34.91 33.12 35.29 0.369
Middle school 28.65 38.06 26.66 < 0.001
High school 13.65 13.55 13.67 0.945
College and above 5.89 3.01 6.49 < 0.001
Co-resident 17.44 100.00 0.00
Observations 2,667 465 2,202
NOTES: (1) The sample includes adult children no younger than age 25 with at least one
parent who is older than age 60. (2) P-values are from t-test of the co-resident and nonco-
resident groups.
SOURCE: Data from CHARLS 2008 pilot.
About 38% of children give financial transfers to their parents, roughly
commensurate with in-kind transfers, which have a 42% prevalence rate.
Irregular transfers account for the largest part of financial transfers, with
prevalence rates roughly three times that of regular financial transfers.
The average net amount of total transfers is about 741 RMB per child,
in which financial transfers take up 548 RMB and in-kind transfers take
up 192 RMB. The net amount of regular transfers is much smaller than
irregular financial transfers (190 RMB compared with 358 RMB).
There exist large disparities between regions: Zhejiang/urban chil-
dren are more likely to provide transfers to their parents: about 64/68% in
general, compared with 55/53% in Gansu/rural. Zhejiang/urban children
give 1,140/1,192 RMB per year, while those in Gansu/rural only give
325/421 RMB.
CORRELATES OF TRANSFERS
A series of descriptive results from multivariate analyses of the inci -
dence and magnitude of transfers are discussed in this section, first using
ordinary least squares (OLS) models, and then family fixed-effect (FE)
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218 AGING IN ASIA
TABLE 9-3 Transfer Patterns
All Zhejiang Gansu Urban Rural
Incidence (%) Children to Parents
Financial transfer 38.07 47.56 28.19 47.18 31.61
Regular 8.47 13.70 3.01 11.66 6.21
Irregular 30.09 34.74 25.25 36.34 25.66
In-kind transfer 42.23 40.52 44.02 46.27 39.37
Total 59.51 64.15 54.67 68.03 53.46
Incidence (%) Parents to Children
Financial transfer 1.93 2.00 1.85 3.55 0.78
Regular 0.42 0.44 0.39 1.00 0.00
Irregular 1.55 1.63 1.47 2.64 0.78
In-kind transfer 1.70 1.11 2.32 1.82 1.62
Total 3.33 2.89 3.78 5.01 2.13
Amount (RMB/year) Net Transfer
Financial transfer 548.46 864.78 218.70 894.03 303.18
Regular 190.34 360.96 12.47 339.25 84.65
Irregular 358.12 503.81 206.23 554.78 218.53
In-kind transfer 192.32 275.09 106.04 297.92 117.37
Total 740.78 1139.87 324.75 1191.96 420.55
NOTE: The sample includes nonco-resident adult children aged 25 and older with at
least one parent older than age 60.
SOURCE: Data from CHARLS 2008 pilot.
models. Transfers are investigated in two dimensions: the incidence of
transfers provided by the child, and the net amount of transfers provided
by the child.16
Associations with Parent Characteristics
Tables 9-4 and 9-5 report the results from the OLS estimations. Spe-
cifically, Table 9-4 reports incidence of gross transfers from children to
parents and Table 9-5 examines the net amount of these transfers. We have
two specifications, with and without the parental health measures, which
can arguably be considered as endogenous.
As is shown in Table 9-4, pre-transfer parental income, number of
parents’ children, province, and living arrangements are all correlated with
the incidence of children giving transfers, while the coefficients of age,
16 An earlier version of this chapter included analyses of gross transfers from parents to
children, which as noted is far less common than from children to parents. Results are avail -
able upon request.
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LEI, GILES, HU, PARK, STRAUSS, and ZHAO
age squared and gender are not significant.17 We create a linear spline for
pre-transfer income with three linearly connected segments based on two
percentile points (1/3 and 2/3) of pre-transfer income. Coefficients for one
segment show the slope over that segment. Higher pre-transfer income is
correlated with a higher likelihood of a child giving, perhaps because of
strategic motives having to do with potential bequests, but, too, perhaps
because higher income parents invested more in the child earlier in life and
this is an implicit exchange repayment. This relationship is very nonlinear,
and at higher levels of income the association becomes flat.
Having more offspring is related to a higher probability of trans-
fers given by children, which is surprising. However, if the parents live
with another child, the likelihood of transfers from nonco-resident chil-
dren declines. Children thus share the burden of support. Interestingly,
parental health is not generally associated with transfer incidence, except
for CES-D scores, for them having a higher score (so more likely to be
depressed) is associated with a lower chance of receiving transfers.
Table 9-5 shows that for the net transfer amounts, pre-transfer parental
income has a weakly positive relationship for the bottom one-third income
group, but it becomes significantly negative for the top one-third group. We
do not have a good explanation for this change in slope. Parental educa-
tion and health status do not have significant relationships with transfer
amount.
Associations with Child Characteristics
Correlates of transfers from the perspective of children are examined
by both OLS and family FE models. The OLS models are able to estimate
the coefficients of parent characteristics, while the family FE models cor-
rect for unobserved family heterogeneity and compare transfer behaviors
among different children within the same family. FE results are displayed
in Tables 9-6 and 9-7, where the sample is further restricted to those hav-
ing at least one eligible (i.e., nonco-resident and adult) sibling. Net trans -
fers are classified into three categories: financial transfers, in-kind trans -
fers, and the total of both. In the following, we will discuss the estimation
results of child characteristics from both models (Tables 9-4 through 9-7)
but will focus mainly on the FE results (Tables 9-6 and 9-7).
Among children’s demographic variables, age has a positive, con -
cave relationship with transfer incidence in both the OLS and family FE
models. Married children are more likely to transfer to parents, with the
effect in the FE model being larger and more significant. The oldest son
is less likely to provide transfers, if he lives apart, in the FE models, espe -
17 We have tried interacting age with gender, but none of the coefficients are significant.
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TABLE 9-4 OLS Analysis of Gross Transfer Incidence (from children to parents)
220
(1) (2) (3) (4)
Parent Characteristics
Demographics Age 0.006 (0.034) 0.009 (0.034)
Age squared/100 –0.011 (0.024) –0.012 (0.024)
Father 0.020 (0.035) 0.017 (0.035)
Widowed –0.036 (0.033) –0.029 (0.033)
Number of children 0.030** (0.013) 0.029** (0.013)
Zhejiang –0.024 (0.106) –0.077 (0.111)
Urban 0.059 (0.038) 0.051 (0.039)
Living with other adult children –0.180** (0.075) –0.177** (0.074)
SES House owner –0.060 (0.043) –0.061 (0.043)
Education (illiterates omitted)
Less than primary education 0.009 (0.039) –0.002 (0.041)
Primary school 0.002 (0.049) –0.006 (0.049)
Middle school 0.068 (0.063) 0.043 (0.066)
High school and above 0.050 (0.081) 0.020 (0.080)
P-value for education 0.828 0.958
Pre-transfer income (000s)
For the lowest 1/3 income group 0.016*** (0.005) 0.017*** (0.005)
For the middle 1/3 income group –0.010 (0.011) –0.008 (0.011)
For the highest 1/3 income group –0.002 (0.002) –0.002 (0.002)
P-value for pre-transfer income 0.017 0.008
Health Health poor 0.054 (0.037)
CES-D –0.009*** (0.003)
ADL or IADL disability –0.045 (0.038)
Cognition score in [8, 11) –0.029 (0.045)
Cognition score in [0, 8) 0.001 (0.056)
Major illness 0.010 (0.033)
P-value for health 0.089
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Children Characteristics
Demographics Age 0.043*** (0.011) 0.045*** (0.011)
Age squared/100 –0.037*** (0.011) –0.039*** (0.011)
Oldest son 0.008 (0.035) 0.009 (0.035)
Youngest son 0.030 (0.032) 0.032 (0.032)
Daughter 0.016 (0.030) 0.016 (0.030)
Married 0.086* (0.050) 0.077 (0.049)
# of children (age < 16) 0.013 (0.011) 0.012 (0.011)
SES Education (illiterates omitted)
Primary 0.118*** (0.035) 0.119*** (0.035)
Middle school 0.108** (0.043) 0.111*** (0.043)
High school and above 0.229*** (0.045) 0.225*** (0.044)
P-value for SES <0.001 <0.001
County Dummies Yes Yes
Observations 2,202 2,202
R-squared 0.133 0.142
NOTES: (1) The sample includes those who are no younger than age 25 and with at least one parent no younger than age 60.
(2) Parent characteristics are from main respondents. (3) Clustered standard errors at family level are in parentheses. (4) * denotes p < 0.1;
** p < 0.05; *** p < 0.01.
SOURCE: Data from CHARLS 2008 pilot.
221
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TABLE 9-5 OLS Analysis of Net Transfer Amount
222
(1) (2) (3) (4)
Parent Characteristics
Demographics Age –4.397 (103.567) 12.873 (107.494)
Age squared/100 –14.772 (73.821) –26.042 (76.321)
Father 334.428 (206.879) 319.738 (199.056)
Widowed –197.960* (117.107) –171.012 (115.615)
Number of children 22.342 (45.193) 16.288 (43.332)
Zhejiang 707.374** (336.641) 557.533* (323.821)
Urban 248.471 (233.937) 245.186 (239.288)
Living with other adult children 37.868 (185.181) 68.194 (176.818)
SES House owner –563.412 (486.384) –625.092 (504.109)
Education (illiterates omitted)
Less than primary education 54.473 (188.363) 0.550 (234.450)
Primary school 82.972 (281.356) 46.336 (281.129)
Middle school –83.550 (242.299) –174.869 (295.173)
High school and above –173.389 (427.434) –312.513 (452.015)
P-value for education 0.953 0.912
Pre-transfer income (000s)
For the lowest 1/3 income group 24.746* (14.701) 22.020 (14.157)
For the middle 1/3 income group 14.018 (53.407) 9.771 (53.486)
For the highest 1/3 income group –17.654** (8.209) –17.544** (7.973)
P-value for pre-transfer income 0.104 0.098
Health Health poor 229.529 (159.597)
CES-D –3.524 (13.107)
ADL or IADL disability –232.873 (158.287)
Cognition score in [8, 11) –89.988 (420.400)
Cognition score in [0, 8) –235.406 (379.792)
Major illness 49.144 (148.420)
P-value for pre-transfer income 0.551
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Children Characteristics
Demographics Age 45.188 (33.500) 44.621 (33.803)
Age squared/100 –48.652 (34.362) –47.573 (34.734)
Oldest son 269.309 (358.486) 260.596 (351.774)
Youngest son 13.740 (211.908) 20.508 (224.390)
Daughter –207.249 (126.633) –226.639* (133.484)
Married –26.839 (339.553) –32.229 (323.768)
# of children (age < 16) 98.688 (78.154) 102.906 (78.549)
SES Education (illiterates omitted)
Primary –145.349 (170.049) –154.430 (172.322)
Middle school –5.779 (187.153) –5.304 (186.350)
High school and above 859.219*** (264.286) 839.743*** (277.622)
NOTE: Robust standard errors in parentheses. * denotes p < 0.1; ** p < 0.05; *** p < 0.01.
SOURCE: Data from CHARLS 2008 pilot.
223
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TABLE 9-6 Family Fixed Effect of Gross Transfer Probability (from children to parents)
224
(1) (2) (3) (4) (5) (6)
Children’s Characteristics Transfer Financial Transfer In-kind Transfer
Age 0.060*** (0.011) 0.026*** (0.009) 0.055*** (0.011)
Age squared/100 –0.050*** (0.012) –0.020** (0.009) –0.047*** (0.011)
Oldest son –0.076** (0.034) –0.008 (0.031) –0.074** (0.032)
Youngest son –0.002 (0.030) 0.008 (0.029) –0.007 (0.028)
Daughter –0.043 (0.029) –0.035 (0.028) 0.031 (0.028)
Married 0.143*** (0.055) 0.127** (0.054) 0.120** (0.053)
# of children (age < 16) 0.002 (0.016) 0.010 (0.013) –0.016 (0.010)
Education (illiterate omitted)
Primary school 0.104*** (0.037) 0.082** (0.032) 0.075** (0.037)
Middle school 0.069 (0.047) 0.076* (0.043) 0.068 (0.044)
High school and above 0.122** (0.049) 0.140*** (0.047) 0.072 (0.048)
P-value for education 0.011 0.015 0.243
Observations 2,068 2,068 2,068
R-squared 0.067 0.033 0.058
NOTES: (1) The sample includes those who are no younger than age 25, with at least one parent no younger than age 60, and at least one adult
sibling who is not living with parents. (2) Clustered standard errors at family level are in parentheses. (3) * denotes p < 0.1; ** p < 0.05; *** p < 0.01.
SOURCE: Data from CHARLS 2008 pilot.
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TABLE 9-7 Family Fixed Effect of Net Transfer Amount
(1) (2) (3) (4) (5) (6)
Children’s Characteristics Transfer Financial Transfer In-kind Transfer
Age –31.192 (54.853) –46.214 (53.380) 15.022* (8.036)
Age squared/100 19.621 (55.265) 36.560 (53.616) –16.938* (8.961)
Oldest son 539.203 (453.147) 559.623 (444.135) –20.421 (35.361)
Youngest son –16.693 (237.860) –30.752 (226.217) 14.059 (48.846)
Daughter –231.014 (141.305) –225.758* (124.928) –5.256 (49.848)
Married 140.903 (228.044) 46.342 (208.387) 94.562 (62.126)
# of children (age < 16) 82.852 (91.813) 81.733 (89.796) 1.119 (9.196)
Education (illiterate omitted)
Primary school –143.953 (167.942) –175.741 (160.958) 31.787 (34.413)
Middle school –336.365 (336.321) –362.308 (322.653) 25.943 (55.805)
High school and above 735.808 (449.759) 675.833 (435.832) 59.975 (45.856)
P-value for education 0.251 0.202 0.557
Observations 2,059 2,059 2,059
R-squared 0.017 0.018 0.004
NOTES: (1) The sample includes those who are no younger than age 25, with at least one parent no younger than age 60, and at least one adult
sibling who is not living with parents. (2) Clustered standard errors at family level are in parentheses. (3) * denotes p < 0.1.
SOURCE: Data from CHARLS 2008 pilot.
225
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226 AGING IN ASIA
cially for in-kind and total transfers. There is no relationship with being
the youngest son or daughter.
Regarding the SES of the children, children’s educational attainment
is significantly associated with the incidence of transfers, even in the more
demanding family FE specification, although the magnitude of the coeffi-
cients drops substantially in the FE specification.
On the amount of net transfers, oldest sons are more likely to give
more financial and total transfers, though the coefficients, while large,
are not significant. Daughters, on the other hand, provide less, weakly
significant for financial transfers.
Child schooling at the high school or above level is strongly related to
the amount of net transfers given in the OLS regressions, but the educa-
tion dummies as a group become insignificant, and the coefficient magni -
tudes decline once we take into account fixed family effects.
CONCLUSIONS
The economic literature has studied intergenerational transfers exten-
sively. Most of the research is conducted in developed countries where
the direction of transfers mainly goes from parents to children. In China,
intergenerational transfers have long been an important source of elderly
support. With rapid population aging, shrinking family size, and greater
mobility of children, it is possible that the family may be losing its impor-
tance in the role of elderly support. In recent years the Chinese govern -
ment has taken various efforts to develop its old-age support system,
which may have further crowded out family support. As yet, we cannot
say this with any degree of scientific validity. It is thus necessary to evalu-
ate the current situation of intergenerational transfers first.
With detailed and high-quality data on intergenerational transfers, as
well as rich information on both parents and their children, the CHARLS
2008 pilot provides a fine opportunity to achieve this goal. This chapter
develops empirical models to explore the patterns and correlates of inter-
generational transfers between the elderly parents and adult children in
Zhejiang and Gansu provinces.
Contrary to the situations in most developed countries, we find that
transfers are predominantly from children to elderly parents and still
play important roles in the elderly support of current China. Our results
reveal that older people with a larger number of offspring are more likely
to receive transfers, a result indicating the potential challenge faced with
dwindling number of children. Parental income has a mixed predica-
tion depending on how large is the pre-transfer income of the parent.
For those among the bottom income group, the relationship is positive
but becomes negative for the top income group. Within family, there is
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227
LEI, GILES, HU, PARK, STRAUSS, and ZHAO
responsibility-sharing among children, possibly based on children’s capa-
bilities. For example, highly educated children transfer more frequently,
as do married children. Although there is no significant difference in
amount of transfers, oldest sons appear less likely to provide any transfer,
which seems to contradict the conventional impression. Daughters are
just as likely to give as other children but are likely to give less on net.
The one caveat about these results is that the older cohorts we studied
still had an average of 3.4 children each, so the bite of the more stringent
family planning programs that began in the 1970s has not been reached as
yet. How transfers will evolve in later cohorts, who have fewer children
but with more human capital and higher lifecycle incomes due to China’s
rapid development, will need to be studied.
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