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Declining Family Size, Public Pension, and Old-Age Support in Rural China
Yiqun Chen, M.A.
Stanford University
Frank A. Sloan, Ph.D. (Corresponding Author)
Duke University
213 Social Sciences Building
Box 90097
Durham, NC 27708
Phone: 919-613-9358
Fax: 919-681-7984
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Abstract
This study examines how decreasing family size affects rural Chinese arrangements for old-age
financial support using China’s one-child policy to instrument for endogeneity of fertility. With
data from the China Health and Retirement Longitudinal Study, we find that having fewer
children reduces transfers from children to elders. Rural residents with fewer children are more
likely to enroll in the New Rural Pension Insurance (NRPI) program and expect to work as long
as they can. Individuals with fewer children do not rely more on pensions or savings as primary
sources of old-age support or less on support from children. These results suggest that in
response to fewer children and thus declines in financial transfers from children, rural residents
use pension programs and own labor as supplements to financial transfers from children rather
than as substitutes. This finding could reflect NRPI’s shallow benefits and low returns to work to
rural older adults.
Keywords: old-age support; China; public pension; one-child policy; CHARLS
JEL Classification H2, I3, J2, O5
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I. Introduction
China has a large and increasing aging population. The fraction of population aged 65+
has increased from 3.9 to 8.4 percent from 1970 to 2010 and is expected to increase to 23.9
percent by 2050 (United Nations, 2013). At the same time, the proportion of children (aged 0-14)
in China’s population is falling (National Bureau of Statistics of China, 2014). The old-age
dependency ratio (population aged 65+ divided by population aged 15-64) is projected to
increase from 11.4:100 in 2010 to 39.0:100 by 2050 (United Nations, 2013). The changing age
structure is creating much concern about old-age support in China (e.g., Chou, 2011; Zhang and
Goza, 2006; Zimmer and Kwong, 2003).
Financial support for older adults mainly comes from three sources: social security
programs; personal resources; and family. In contrast to most high-income countries where
public programs are well developed so that elders rely mostly on social security programs for
financial support, such programs have historically been underdeveloped in China, especially in
the rural areas. Adult children have traditionally been the primary providers of financial support
to elders in China’s rural areas (Cai et al., 2012; Coeurdacier et al., 2014; Feng et al., 2015).
However, this traditional mechanism of financial support for rural elders in China has
been substantially undermined by the one-child policy that dramatically reduced fertility rates.
The total fertility (children per woman) fell from as high as 4.8 in the early 1970s, just before the
implementation of the one-child policy in 1979, to 2.7 in 1980-1985, right after the one-child
policy was launched, and to below replacement level in recent years (United Nations, 2013).
There is much concern that declines in family size resulting from fewer children would lead to
increased proportion of elders with inability to finance their old-age needs, especially for rural
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elders who are substantially poorer than their urban counterparts (Cai et al., 2012) but constitute
70 percent of the elderly population in China (Pei and Tang, 2012).
This study examines how reductions in family size have affected rural individuals’
arrangements for old-age financial support. Specifically, in response to the potential declines in
size and probability of financial assistance from children, whether rural parents are more likely
to seek assistance from their own labor and social security programs, i.e., whether they are more
likely to work longer and enroll in the public pension program--New Rural Pension Insurance
(NRPI). Rural individuals refer to those with a rural household registration (hukou). The NRPI is
the major pension program in rural China first introduced in 2009. Recipients aged 60+ obtain a
monthly income consisting of a basic part funded by the central and local governments and an
individual account payment largely determined by the beneficiaries’ contributions.
While this study focuses on the causal effect of reductions in the number of children on
old-age support arrangements, the number of children may be endogenous. One possibility is the
reverse effect: individuals with lower expected pension benefits and higher preferences for early
retirement may tend to have more children if they believe they can depend on transfers from
children to sustain consumption in old age. There could also be a simultaneous equation bias: for
example, higher wage rates may raise the cost of children as wealthier families demand “higher
quality” children that requires more investment per child, which could in turn reduce demand for
children--a quantity-quality tradeoff (Becker, 1960; Becker and Lewis, 1973). But higher wages
may also extend working life if the substitution effect of higher wages outweighs the income
effect. The exogenous change in family size under China’s one-child policy helps us address this
problem. We use a timing variable related to the year the one-child policy was launched and the
birth year of respondents’ first child as an instrumental variable (IV).
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Elders’ plans for old-age support may not be affected by family size if the availability of
adult children has no effect on financial transfers from children. Therefore, before gauging the
effects of declines in fertility rates on individuals’ old age financial support arrangements, we
first examine whether reductions in the number of children reduce the amount of inter-
generational upstream transfers. Such intergenerational support may not be affected in that first,
empirical evidence supports that altruistic model, i.e., family members are altruistic in that they
care about the well-being of other members (Barro, 1974; Becker, 1974), best explains the
intergenerational financial transfers in China.1 Second, filial piety is deeply rooted in Chinese
tradition. Elder care and care of parents are seen as a moral as well as a legal obligation of adult
children (Feng et al., 2012; Lang, 1946). Third, literature supports a quantity-quality tradeoff in
China’s context: reductions in quantity of children increase human capital investment per child
so that average children quality improves (e.g., Li et al., 2008; Rosenzweig and Zhang, 2009;
Zhu et al., 2014). Increased educational attainment and returns to education may lead to more
generous transfers from children to compensate for the reduced family size. However, elders
may still receive lower total transfers if reductions in family size cannot be fully compensated by
increases in transfers per child. Cai et al. (2006) and Wu and Li (2014) provide empirical
evidence that income reductions of the elderly parents cannot be fully compensated by increases
in transfers from children. Also, Zimmer and Kwong (2003) found that elders’ probability of
receiving financial transfers from children declines with the number of children.
Using data from China Health and Retirement Longitudinal Study (CHARLS), the
Chinese version of the Health and Retirement Study (HRS), we find that having fewer children
1 For example, Lee and Xiao (1998) found that more needy elderly parents received higher financial transfer from children. Wu and Li (2014) found a negative effect of elderly parents’ income on the amount of transfer they receive from the children. Cai et al (2006) found that intergenerational upstream transfer responded to low income of parents when the income is at or below the poverty line.
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reduces the amount of financial assistance rural elders receive from children. Rural residents
with fewer children are more likely to enroll in the NRPI and to report that they will keep
working as long as they are able than their counterparts with more children do. These results
suggest that as family size declines, rural elderly individuals depend more on social security
programs and their own labor. However, individuals with fewer children are not more likely to
report will rely on pension or saving as the main source of old-age financial support or less likely
to report that they will mainly rely on children. One potential explanation for this pattern of
findings is the low pension benefits and low returns to work to rural elders. Our findings are
robust to a series of robustness checks.
This study, to the best of our knowledge, is the first study examining how family size
affects old-age support arrangements in rural China by exploiting the unique aspect of China’s
one-child policy to deal with the endogeneity of fertility decisions. Previous studies on China
have investigated impacts of family size on probability of receiving financial assistance from
children (Zimmer and Kwong, 2003) and motives of financial transfers from children (altruism
or exchange) (Cai et al., 2006; Lee and Xiao, 1998; Wu and Li, 2014). But little attention has
been paid to how availability of adult children affects old-age support arrangements. Some
studies (Chou, 2011; Li, 2014; Rohland, 2012; Saunders et al., 2003) provide descriptions of old-
age support and the primary role of children in old-age support in China, but without empirical
analysis.
The rest of this paper is structured as follows. Section II briefly describes the one-child
policy, old-age support in rural China, and the NRPI. Section III discusses the survey data.
Section IV presents our empirical strategy. Results are reported and discussed in Section V.
Section VI concludes the study.
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II. Background
a. The one-child policy
China launched its one-child policy in 1979 to limit its rapid population growth. Under
this policy, each urban couple was allowed to have only one child, but in most provinces, rural
couples were allowed to have a second child if the first child was a girl (1.5-child policy). A few
families in remote areas were allowed a second or third child, irrespective of the first child’s
gender. Both financial and social sanctions were imposed on families if they failed to adhere to
the family size limits. In particular, above-quota births were subject to fines ranging from 20 to
200 percent of a household’s annual income; the fines imposed on rural households were
substantial, even at the lower end of the range given that many of them had incomes below the
poverty line (Li and Zhang, 2009). Some families had to sell family belongings or had personal
belongings confiscated to fund the part of the fines they were unable to pay (Doherty et al.,
2001). Families have to bear the full cost of obstetric services for pregnancies exceeding the
approved family size. Above-quota children are discriminated against access to education and
health care. Strong incentives were also imposed on local governments to limit above-quota
births: local governments received bonuses for fulfilling the birth targets but heavy penalties for
falling short; officials could be demoted for allowing too many above-quota births in their
localities (Li and Zhang, 2009). With incentives facing both individuals and local governments,
the population growth was successfully controlled and the fertility rate was significantly lowered
in China during the 1980s.2 The implementation of the one-child policy was unforeseeable by
households.
2 Changing one’s hukou status from urban to rural for the purpose of having an additional child is almost impossible in China. First, household registration is strictly regulated in China, which prevents people from moving for having above-quota children. Second, there are substantial gaps in welfare benefits in terms of health care and education opportunities for children between urban and rural residents.
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b. Old-age support in rural China
Among the three main sources of old-age support, social security programs, family and
self, social security support has historically been meager in rural China; few rural elderly
received financial assistance from the state. Using data from 2002, 2005 and 2008 waves of the
Chinese Longitudinal Healthy Longevity Survey (CLHLS), Feng et al. (2015) documented that
only 4.9% and 5.6% of elders in rural China received financial support from public transfers and
pension programs, respectively; only 3.8% received public medical care. In 2006, fewer than 5%
of rural elders relied on pension income as the primary source of support (Cai et al., 2012).
Although older adults with income below the local minimum living standard are eligible to
receive dibao, a major public subsidy program for low-income persons in China, the transfer
amount is low. For example, in Beijing, dibao accounts for under 2% of mean disposable income
of urban residents (Wu and Li, 2014).
Adult children have traditionally been the primary source of old-age financial support in
rural China. Family values and filial piety remain strong in rural areas and care for parents is
seen as a moral obligation to adult children (Feng et al., 2012). The obligation to support parents
has also been formalized into laws and refusing to fulfill this obligation could result in
imprisonment. For decades, public policies on pensions and safety nets neglected the rural
elderly as families were assumed to be a main source of old-age support (Cai et al., 2012). Based
on data from 2002, 2005 and 2008 CLHLS, Feng et al. (2015) reported that 78.8% of the rural
elderly relied on financial assistance from family members, primarily children.
Own labor is another source of financial support. However, due to their low educational
attainment, the rural elderly usually engage in hard-labor jobs, mostly farming household land
(Cai et al., 2012; Pang et al., 2004); returns to labor are low and become even lower at older ages
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when marginal product declines due to decreased physical stamina. In addition, there is no
retirement benefit for farm jobs.
Declines in fertility should have greater negative effects on old-age support for rural
elders than for their urban counterparts due to the wide rural-urban disparities in social welfare
benefits in China. Elders with an urban hukou (household registration) received a national mean
pension benefit of 1,511 yuan per month in 2011 in addition to payments from an individual
pension account contributed by both individuals and their employers; by contrast, for rural elders,
the pension payment is a combination of a basic part which is only 55 yuan per month and an
individual account which is extremely small due to the low incomes among rural elderly persons
(Wu and Li, 2014). For health care, although Chinese governments have sought to narrow the
substantial gaps in health care benefits between rural and urban areas, the rural population still
receives a lower inpatient reimbursement rate than urban population does (43.7% vs. 54.6% in
2011) and self-discharge from inpatient facilities for financial reasons remains common among
rural residents (28.3%) (Meng et al., 2012). The rural elderly depend far more on their children
than do the urban elderly for whom pension income is the most important source of support (Cai
et al., 2012). It was, and remains, very difficult to change one’s hukou status from rural to urban
in China due to the strictly regulated household registration system.
c. New rural pension insurance (NRPI)
To supplement the traditional family-based old-age support system, China initiated the
Rural Old-Age Pension Program in 1991. However, the pension benefits were shallow, and few
rural residents enrolled (Luo, 2012; Pei and Tang, 2012; Wang, 2006). A new pension scheme
for the rural population—New Rural Pension Insurance (NRPI) was introduced by the central
government in 2009, which is now the main pension program in rural China. The NRPI provides
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recipients of age 60+ a monthly income if the person made contributions to the NRPI for at least
15 years. Those who became 60 at the time the NRPI was introduced or earlier were eligible for
the basic pension benefit if their children were contributing to the NRPI. Those with fewer than
15 years left before age 60 can make a lump-sum contribution to cover the short-fall in the
vesting period. The enrollment is voluntary; all rural residents aged 16+ (except students) that
are not covered by the urban employee pension scheme are eligible to enroll. The financing of
the pension program comes from central and local governments, individuals, and local villages.3
The benefit consists of a basic part--55 yuan (about $8.90) per month in 2009, and an individual
account payment largely determined by enrollee’s contribution. The rate of return for individual
account is the one-year deposit interest rate of the People’s Bank of China, the low returns
strongly weaken the incentives for additional contributions.
III. Data
The data for this study are from the 2013 China Health and Retirement Longitudinal
Study (CHARLS), the Chinese version of the Health and Retirement Study (HRS). The survey
covers persons aged 45+ and their spouses who can be of any age in 28 of the total 34 province-
level administrative divisions in China. CHARLS first selected 150 county-level units by
probability proportional to size (PPS) method, stratified by region and urban/rural status. It then
randomly selected three primary sampling units (PSUs) within each county-level unit. In each
sampled PSU, the survey randomly picked 80 households from a complete list of dwelling units
generated from a map for each PSU, with a targeted sample size of 24 households per PSU,
taking into account non-response and non-age-eligible households. Within each household, the
3 The central government subsidizes 50 percent of the basic pension benefit for eastern provinces and 100 percent for central and western provinces; individual contributions range from 100 to 500 yuan annually based on enrollees’ choices; local governments are required to subsidize at least 30 yuan per person per year; local villages can decide their subsidies at the discretion of their own revenues.
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survey randomly interviewed one of the age-eligible members and his/her spouse if there was a
spouse.4 In addition to its large sample size and wide coverage of Chinese provinces, CHARLS
includes detailed information on older adults’ old-age support arrangements, intra-family
transfers and information on both the size (number and gender of children) and quality
(educational attainment) of children, which are all useful for our analysis.
The baseline survey was conducted in 2011; the response rate for age-eligible households
for the baseline survey was 80.5%. We used the 2013 rather than the 2011 wave because the later
wave contains information on NRPI enrollment, and the sample is nearly the same as 2011’s.
The 2013 CHARLS contains 18,605 respondents. We limit our sample to respondents
with a rural hukou, han ethnicity, and ages 45-70 in 2013. Individuals from ethnic minorities are
excluded because they are generally exempt from the one-child policy. Only starting in the late
1980s did minorities with a population over 10 million (only the Zhuang and Manchu minorities)
became subject to the same one-child policy as persons of han ethnicity. We limit our sample to
persons ages 45-70, relatively younger older adults who are presumably more cognitively able
than the older elderly to alter their old-age financial support plans following the introduction of
NRPI in 2009. We exclude respondents from areas where NRPI was not introduced. All these
restrictions reduce our sample size to 9,786. To the extent possible, when time-invariant
variables are missing from the 2013 wave, we use values from the 2011 wave. After eliminating
observations still with missing values in the variables used, our final sample consists of 9,135
respondents.5
4 See Zhao et al. (2013) for additional information on CHARLS. 5 Observations with missing values in financial transfers from non-coresident children are not dropped, because financial transfers are not used in estimating impacts of family size on old-age support arrangements, but eliminating observations with missing financial transfer information would result in an additional loss of 1,628 observations.
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The community level data used for our robustness checks comes from the 2011 CHARLS
community survey. The community survey was not conducted in 2013. It contains detailed
information on local populations’ education and income.
Table 1 reports sample means and standard deviations for the whole sample and
subsamples with more than two children and two children or fewer. The mean age for the whole
sample is 56.9. Slightly over half (52.9%) are female. Mean years of schooling is 4.9; mean
household income is 26,420 yuan. 93.0 percent of respondents have a spouse. 5.8 percent have
physical disabilities, and 22.9 percent report self-rated health status to be good, very good or
excellent (henceforth referred to good). About half (54.9%) of the sample have two or fewer
children and 87.6% have a living son(s). The mean birth year of first child is nearly 1980.
The first dependent variable is the total value of direct money assistance and in-kind
support from non-coresident children last year reported by the parents. Direct money assistance
includes both regular monetary allowance and non-regular transfers received as gifts on holidays
and special events. A few respondents reported not enrolling in the NRPI because they were
covered by other pension programs; we consider these respondents to be enrolled in the NRPI.
For retirement decisions, the CHARLS asks “At what age do you plan to stop working?” Stop
working in the questionnaire refers to ceasing all income-related activities. Most rural persons in
China engage in hard-labor jobs that provide no retirement benefits; they have to keep working if
they want to retain their incomes. Therefore, one response option to the retirement decision
question is to keep working as long as physically able. We create a binary variable for rural
individuals’ retirement decisions, 1 if the respondent reported intended to keep working as long
as able and 0 otherwise. For the expected main source of old-age financial support, the CHARLS
asks “whom do you think you can rely on for old-age support?” Possible responses are children,
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savings, pension and retirement salary, commercial insurance, and other. As rural persons
generally receive no explicit retirement benefits, the response of pension and retirement salary
can be viewed as equivalent to an affirmative response of pension. We create three binary
variables for whether the respondent stated s/he will mainly rely on children, a pension, and
savings for old-age financial support, respectively. Only a few respondents stated they would
mainly rely on commercial pension insurance (0.4%) and other sources (3.8%).
The mean financial transfer from non-co-resident children is 3,718 yuan. More than three
quarters (78.7%) of the sample enroll in the New Rural Pension Insurance program; 63.7%
expect to keep working as long as they are able. The majority (76.4%) reports that they will rely
on children as the main source of old-age financial support; fewer respondents report that they
will rely on a pension (13.9%) or savings (5.5%).
Compared to respondents with more than two children, those with two or fewer than two
children are younger, slightly less likely to be female, have at least one male child, and are more
likely to be enrolled in pension programs, slightly more likely to have a spouse, report self-rated
health status to be good, have any physical disability, and are more likely to report that they will
keep working as long as able. They also tend to have had more years of schooling and higher
household income, have had their first child later, receive less financial assistance from all
children, and are less likely to report they will rely on children as main source of old-age
financial support and more likely to report they will mainly reply on a pension and savings.
IV. Empirical Method
Our empirical work focuses on estimating the following equation:
𝑦! = 𝛽! + 𝛽!𝑓𝑒𝑤𝑒𝑟_𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛! + 𝛽!𝑋! + 𝜀! (1)
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where 𝑖 stands for an individual. 𝑦 is an outcome—logarithm value of amount of last year’s
financial transfer from children, whether enrolled in NRPI, whether planning to keep working as
long as able, and expected main source of old-age financial support (children/pension/saving).
𝑓𝑒𝑤𝑒𝑟_𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 is a binary variable set to 0 if the individual has more than two children and 1
for two or fewer than two children. Only 0.26% respondents in our sample are childless. As
robustness checks, we estimate impacts of family size on old-age support arrangement by (1)
using one child as the cutoff in defining 𝑓𝑒𝑤𝑒𝑟_𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 and (2) using the continuous
respondents’ total number of children rather than a dummy for fewer children. 𝑋 is a vector of
control variables for respondents’ age, gender, ethnicity (whether han or not), educational
attainment, marital status, household income, self-reported health status (1 if good, 0 otherwise),
and whether there are living son(s). As children’s earning ability may affect their ability to
support parents, we use children’s educational attainment as a proxy for their earning ability.
Specifically, we control for the respondent’s proportion of children with at least high school
educational attainment for those children who have completed their schooling. For the small
proportion of respondents (2.4%) whose children are all in school, we set their proportion of
children with at least a high school educational attainment to 1 and create an additional dummy
for these individuals in the regression. We also control for province fixed effects. 𝜀 is the random
error term, clustered at the household level in the estimation. The main parameter of interest is
𝛽!, representing the impact of family size on rural residents’ old age support arrangements.
As discussed in the introduction, estimates of effects of the number of children may be
biased as fertility decisions could be endogenous. To deal with this problem, we use two-stage
least squares (2SLS) for the continuous dependent variables and IV probit when the dependent
variable is a binary. Our IV exploits the one-child policy launched in China in 1979 and
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measures the degree to which individuals’ fertility decisions were affected by the one-child
policy. Specifically, for respondents who had a first child in or after 1979, we assume that they
were fully impacted by the one child policy. We set the value of the IV to 1 for these respondents.
In contrast, for those who had already had their ideal number of children before 1979, their
fertility was almost surely completed and thus they were likely to have been unaffected by the
policy. The IV value for these respondents is therefore set to 0. The mean number of children per
woman in China in the early 1970s was 4.77. Mean inter-birth spacing in 1970 was about 2.5
years (Scharping, 2013). The infant mortality rate had dropped to below 50 per one thousand
births in the early 1970s in China (United Nations, 2013). Therefore, we treat respondents who
had had their first child 12 years before the launch of the one-child policy, i.e., 1967, as
unaffected by the one-child policy and set the IV for such persons to 0. For respondents who had
their first-born child between 1967 and 1979, the IV equals (12-(1979-birth_year_first_child))/12.
As a robustness check, we extend the number of years for individuals to obtain their desired
numbers of children from 12 to 16, i.e., rather than using 1967, we use 1963 as the threshold. We
discuss the validity of our IV below.
V. Results and Discussions
a. Impacts of family size on financial transfers from children
Table 2 shows effects of family size on financial transfers parents receive from children
using 2SLS. We take the logarithm of financial transfers to deal with the skewed distribution of
such transfers. There are no negative values and only one zero value in the logarithm variable;
we therefore set the logarithm of zero transfers to 0 rather than treat them as missing. As
relatively younger persons may not have adult children who can financially assist them at the
time of the survey, we drop observations for respondents under age 55 from the financial
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transfers analysis sample, but retain them in the analysis of NRPI enrollment, retirement
decisions, and the expected main source of old-age support. Results in Table 2 show that rural
elders with two or fewer children experience a sharp decline in financial assistance from children:
about 200%6 less than their counterparts with more than two children.
Two concerns remain in estimating effects of number of children on financial transfers
from children. First, there could be systematic differences in the probability of living with
children for elders with low versus high numbers of children (Weil, 1997), and co-residence can
be a means of financial assistance. This concern could partly be mitigated by the inclusion of
pecuniary values of in-kind gifts from children in the measure of financial transfers. More
important, as parents with more children have a higher probability of living with children (Cai et
al., 2006) and co-residence tends to reduce intergenerational transfers, our estimate is a lower
bound on the negative effect of number of children on financial transfers, i.e., the actual negative
effect could be larger. Second, financial transfers from children last year could reflect parents’
health shocks and whether parents took care of the grandchildren. Therefore, in Table 2, column
2, we include binary variables for whether parents live with financially-independent children,
take care of grandchildren, and have had any hospital stays in the last year as additional
covariates. Since a few respondents did not report whether they took care of grandchildren, we
treat them as not having done this and add an additional binary variable for these individuals.
Statistically significant declines in financial assistance from children are still observed.
The coefficient on the binary variable for whether the respondent has two or fewer children
becomes larger (in absolute value) after controlling for whether the respondent lives with
children (col. 2), supporting the explanation that our estimate on impact of family size on amount
of financial transfer is a lower bound. 6 This needs to be interpreted with caution as we set the logarithm value of zero transfers to zero.
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Table 2 also shows that respondents with a higher percentage of children with at least a
high school education receive more transfers. This finding is plausible since higher educational
attainment improves children’s earning ability and thus ability to support parents. Respondents
with living sons receive lower financial transfers from children than those without sons; this
result may reflect an increasing role of daughters in supporting their parents in China and may
support other evidence suggesting that daughters are more altruistic than sons (e.g., Wu and Li,
2014). Living with financially independent children reduces transfers. Taking care of
grandchildren and having hospital stays increase transfers, indicating both exchange and
altruistic motives exist in inter-generational upstream transfers in China.
b. Impacts of family size on old-age support arrangements
We next examine how rural residents respond to the decline in fertility and thus the
decline in financial transfers from adult children. Columns 1 and 2 of Table 3 show that rural
individuals with two or fewer children are 50% and 52% more likely to enroll in the New Rural
Pension Insurance program and intend to keep working as long as they are able, respectively,
than their counterparts with more than two children are. These results suggest that in response to
potential declines in financial assistance from children, the traditional mechanism for old-age
financial support in rural China, rural residents are more likely to enroll in social security
programs and work longer.
Columns 3-5 of Table 3 show that rural respondents with fewer children are not more
likely to report that they will rely on pensions or savings or are less likely to report they will rely
on children as the main source of old-age financial support, although they are more likely to
enroll in the pension program and keep working as long as they are able. The majority (73.3%)
still expect children to be the main source of old-age financial assistance. These results suggest
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that in response to declines in the availability of adult children and thus declines in financial
transfers from children, rural residents use pension insurance and own work as supplements
rather than substitutes to children for old-age support.
One explanation for this phenomenon could be the shallow benefits of pension programs
for the rural older population. The pension benefits consist of a basic part which is only 55 yuan
per month and an individual account payment which is also low due to rural elders’ low income
and the disincentive from low returns of additional contributions to pension programs. The mean
received or expected NRPI benefit is only 94.1 yuan ($15.2) per month among respondents,
much below the poverty line. Further evidence supporting this explanation is seen in columns 1
and 2 of Table 4: a 10% increase in expected or received pension benefits is associated with a
0.0125 unit, i.e., 1.25%, increase in respondents’ likelihood of relying on a pension as the main
source of old age financial support and a 1.22% decrease in the probability of mainly relying on
children. The sample size is lower than for the whole sample because questions on expected or
pension benefits or benefits actually received were only asked of respondents enrolled in NRPI.
We also lose some observations because not all NRPI enrollees reported expected or actually
received pension benefits. Therefore, we employ a Heckman selection correction model in
columns 3 and 4 to deal with possible sample selection bias. Results are similar.
A second explanation could be low returns to labor for rural elders. Due to their low
educational attainment, most rural elders in China engage in physical-demanding and low-return
jobs, mostly farming. Pang et al. (2004) documents that over 85 percent of working rural adults
aged 50+ work on the land. The mean annual net income7 per household from crops and forestry
7 Pecuniary values of all crops and forestry products produced net of production costs and values of products consumed by families. A few responses with estimated ranges rather than exact amounts of product values/production costs/family consumptions are not included. Similar methods apply to calculating annual net income from livestock and fisheries.
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products reported by the CHARLS respondents who engage in cropping and forestry is only
1,987 yuan ($319.90) in 2013; the mean annual net income per household from livestock and
fisheries is 2,589 yuan ($417.30). It is hard to accumulate sufficient financial assets for old-age
security from these jobs: of our total analysis sample, 62.5% respondents have zero savings; only
2.3% have any other financial assets including government bonds stocks, and funds.
Other evidence suggesting that low pension benefits and low returns to work explain the
insignificant effects of family size on expected main sources of old-age support is shown in
Table 7, row 2. As a robustness check, we use one child rather than two children as the cutoff in
defining respondents with fewer children. The coefficients on the new binary variable for having
fewer children are substantially larger (in absolute value) on financial transfers (-11.11 vs.-1.99),
NRPI enrollment (1.76 vs. 0.50), and willingness to keep working as long as able (1.82 vs. 0.52)
than those reported in Tables 2 and 3 that use two children as the cutoff. However, as before, no
significant effects are observed on the expected main source of old-age support. Therefore,
although rural elders with one child or no children receive less than one tenth of the financial
transfers from children and are about two times more likely to enroll in the pension program and
to work as long as able than their counterparts with more than one child, they still are not less
likely to expect children to be the main source of old-age support. Indeed, 64.4% of elders with
only one child report will mainly rely on children for old-age support.
We do not examine effects of the number of children on wealth in the form of financial
assets or real non-housing property parents accumulated for old-age because, as stated above,
few respondents own such assets. Also, very few respondents (5.5%) report that they expect to
rely on savings as the main source of old-age support; moreover, we observe no statistically
significant effects of decreased numbers of adult children on individuals’ expectations that they
20
will mainly reply on savings for old-age needs. Land is the main property for rural population,
but government’s strict restrictions on individuals’ ability to rent the land allocated to them limit
elders’ ability to earn property income from land in China. The mean annual per capita property
income for rural households is only 293 yuan ($47.20) in 2013 (National Bureau of Statistics of
China, 2015).
c. Instrument validity
An appropriate IV should be highly correlated with the endogenous variable, i.e.,
respondents’ fertility decisions. Intuitively, our IV should affect the number of children
individuals had as the closer the year individuals began to have children is to 1979, the year the
one-child policy was implemented, the fewer children they can have due to the one-child
policy’s strict limits on higher-parity births. Empirically, the first-stage regressions show that our
IV strongly predicts the endogenous variable (significant at 1% level).
A second condition for the IV to be valid is to satisfy the exclusion restriction. In our case,
except through an impact on fertility, the IV should not be correlated with individuals’ pension
and retirement decisions, anticipated sources of old-age financial support, and financial transfers
from children. Intuitively, the timing of individual’s first birth relative to 1979, the year one-
child policy was launched, could not directly influence their old-age support plans. However, one
possible concern is that older respondents are likely to have had their first child earlier and thus
have been less likely to have been affected by the one-child policy, and there may be systematic
differences in preferences for pension enrollments, retirement plans, saving decisions and
financial assistance from children between the relatively younger and older cohorts. We include
the respondent’s age as a covariate to partially address this concern. In addition, we test whether
our results are sensitive to the inclusion of binary variables for age cohort. Respondents are
21
grouped into three cohorts: ages 45-50, 51-60 and 61-70. Table 5 shows that the parameter
estimates of the augmented models are similar to those reported in Tables 2 and 3. Moreover, the
coefficients on the binary variables for age cohorts are not significant (except one: the covariate
for cohort 51-60 in the NRPI enrollment regression, significant at 10% level). These results
suggest that omitted cohort effects could not have biased our IV-based parameter estimates.
Another potential concern is that individuals from less affluent villages tend to marry
earlier and have children earlier; also individuals’ preferences for old-age support arrangements
may be systematically different in less versus more affluent areas. Traditional Confucian filial
piety tends to be more deeply rooted in poorer areas and thus children are more likely to be
expected to be the main source of old-age support. We cannot completely eliminate this
possibility, but we address this concern partially with these sensitivity tests. First, we test
whether our results are sensitive to the inclusion of community’s development level proxied by
mean educational attainment8 and per-capita disposable income. Table 6 shows that the results
are robust to the inclusion of these covariates. The numbers of observations are slightly lower
than those in Tables 2 and 3 due to missing values on educational attainment and income for a
few communities.
Second, we examine whether the age at which respondents had their first child is
correlated with the mean educational level and income of the local community. All of the
exogenous variables in eq. (1) are included as covariates in this regression. The results show that
age at which individuals had their first child is not associated with local community’s
8 Mean educational attainment= 0*fraction of local population illiterate+6* fraction of local population with primary school as the highest educational attainment+ 9*fraction of local population with middle school as the highest educational attainment+ 12*fraction of local population with high school as the highest educational attainment+16* fraction of local population with college as the highest educational attainment+19* fraction of local population with more than college education
22
educational level and income. These results further support the use of timing of first child as an
IV.
d. Robustness checks
We perform three robustness checks. First, we use an alternative IV. Rather than using
1967 as the cutoff to construct our IV, we use 1963, which allows a longer period for individuals
to obtain their desired numbers of children prior to the year the one-child policy was
implemented. Specifically, our new IV is set to 1 for respondents who had no child in 1979, 0 if
they had already had their first child in 1963, and equals to (16-(1979-birth_year_first_child))/16
if respondents had their first child between 1963 and 1979. Table 7, row 1 shows that our results
are robust to use of this alternative IV.
Second, as mentioned above, we use a different way of defining respondents with fewer
children: instead of using two children as the cutoff, we use one child. We obtain larger
coefficients (in absolute value) on financial transfers, NRPI enrollment, and individuals’
willingness to keep working as long as able (Table 7, Row 2). These results are consistent with
our conclusion that having fewer children reduces inter-generational upstream transfers and
increases parents’ propensity to enroll in pension programs and to work longer. The coefficients
on whether elders intend to rely on children, pension, or savings as the main source of old-age
financial support are still not significant.
Third, rather than using a binary variable for whether respondents have fewer children as
the key covariate, we use a continuous variable--respondents’ total number of children as a
robustness check. Table 7, row 3 shows that rural residents with more children receive more
transfers from children and are less likely to enroll in NRPI and less likely to intend to keep
23
working as long as able. We still observe no statistically significant effects of number of children
on the expected main source of old-age support.
Conclusion
In rural China, where social security safety nets are under-developed, adult children have
traditionally been the primary providers of financial assistance to older adults. However, the
decline in fertility caused by the one-child policy has elicited much concern about relying on this
traditional mechanism of old-age support. With 70 percent of its elderly population living in
rural areas and with many such persons vulnerable to poverty (Pei and Tang, 2012; Cai et al.,
2012), how to deal with the problems of financial support for rural elders is one of the greatest
challenges facing China.
In this paper, we test the impacts of family size on rural residents’ old-age financial
support arrangements, using a timing variable related to one-child-policy as the instrumental
variable to deal with the endogeneity of fertility decisions. Based on data from CHARLS, we
find that declines in the number of children sizably reduce financial transfers elderly persons in
rural China receive from their children. This in turn increases rural elders’ propensity to enroll in
the public pension insurance program and their intentions to keep working as long as they are
able. However, rural individuals with fewer children are not less likely to report they will rely on
children or more likely to report they will rely on pensions or savings as the main source of old-
age support than their counterparts with more children. One plausible explanation to this pattern
of finding could be the shallow benefits of pensions and low returns to work for the rural elderly
population.
The current shallow pension benefits and low labor incomes for elders in rural China may
not be sufficient to compensate for the decline in financial transfers resulting from fewer children.
24
There is an empirical basis for concern that an increasing proportion of rural elderly persons may
not have sufficient income to sustain their old-age consumptions in the future. Such a scenario
suggests that interventions from the state may be warranted. One possible intervention would be
tax concessions for children who support parents to encourage familial assistance. Another
possibility is for the public sector to increase the generosity of pension benefits. One potential
concern about expanding pension benefits is its crowding out effects on private transfers, as
occurred in some developing countries (Cox et al., 2004; Jensen, 2004; Juarez, 2009). However,
empirical evidence in China suggests public transfer expansions are unlikely to completely
crowd out private transfers for the elderly, at least from their current levels (Cai et al., 2006; Cai
et al., 2012; Gibson et al., 2011). Also, improving pension generosity could reduce burdens of
support for children with lower incomes, and could allow the older adults to provide fewer
services to children and enjoy more leisure to the extent that inter-generational upstream
transfers may be payments in exchange for services such as child care.
25
Acknowledgments
The research was supported in part by a grant from the National Institute on Aging (NIA)
grant R01-AG017473. NIA had no role in any aspect of this research. The views expressed here
are entirely ours.
Yiqun Chen participated in conceptualizing the research questions, performing literature
review, performing the computational work, and writing a first draft of the paper. Frank Sloan
participated in conceptualizing the paper, assisting with the literature review, and revising drafts
of the paper.
Conflict of Interest Statement
Neither author has any financial or intellectual conflict of interest to report.
26
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Table 1. Summary Statistics Whole sample >2 children ≤2 children Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Age 56.932 6.912 59.779 6.704 54.594 6.164 Female 0.529 0.499 0.557 0.497 0.507 0.500 Educational attainment (years) 4.909 3.834 4.268 3.723 5.435 3.843 Have a spouse 0.930 0.255 0.913 0.282 0.944 0.230 Disabled 0.058 0.235 0.053 0.225 0.063 0.242 Self-reported health: good 0.229 0.420 0.200 0.400 0.253 0.435 Household income (10,000 yuan) 2.642 4.558 2.248 5.121 2.966 4.008 Had living son(s) 0.876 0.329 0.946 0.225 0.819 0.385 % children ≥high school education 0.300 0.393 0.204 0.299 0.380 0.440 All children in school 0.027 0.161 0.004 0.066 0.045 0.207 Have ≤2 children 0.549 0.498 -- -- -- -- Birth year of first child 1979.644 7.945 1975.832 7.895 1982.774 6.485 Transfer last year (1000 yuan)a 3.718 9.395 4.210 9.729 3.187 8.993 NRPI enrollment 0.787 0.410 0.793 0.405 0.781 0.414 Keep working as long as able 0.637 0.481 0.617 0.486 0.654 0.476 Expected main source of old-age financial support Children 0.764 0.424 0.802 0.398 0.733 0.442 Pension 0.139 0.346 0.120 0.325 0.155 0.362 Savings 0.055 0.227 0.041 0.199 0.066 0.248 Commercial pension insurance 0.004 0.064 0.002 0.047 0.006 0.075 Other 0.038 0.191 0.034 0.182 0.040 0.197 Observations 9,135 4,119 5,016 Note: a number of observations: 7,507 for whole sample, 3,897 for subsample with >2 children, 3,610 for subsample with ≤2 children. We lose some observations in the amount of financial transfer due to missing values.
Table 2. Impacts of Family Size on Financial Transfers from Children
Variables (1) (2) ≤2 children -1.99*** -2.02***
[0.47] [0.46]
Age 0.01 0.02
[0.02] [0.02]
Female 0.09 0.10
[0.07] [0.07]
Educational attainment (years) 0.02 0.02
[0.01] [0.01]
Have a spouse -0.01 -0.06
[0.14] [0.14]
Disabled 0.13 0.17
[0.16] [0.16]
Self-reported health: good 0.06 0.08
[0.11] [0.11]
Household income -0.03* -0.02
[0.02] [0.02]
Have living son(s) -0.43** -0.46**
[0.22] [0.22]
% children ≥high school 1.27*** 1.24***
[0.19] [0.19]
All children in school -6.36*** -6.50***
[0.40] [0.42]
Take care grandchildren 0.45*** [0.11] Take care grandchildren: missing 0.24 [0.22] Live with children -0.56*** [0.13] Hospital stay last year 0.20* [0.12] Province fixed effects Yes Yes Observations 5,133 5,133 Notes:
1) Standard errors in brackets; 2) *, **, *** indicate significant at 10%, 5% and 1%, respectively.
Table 3. Impacts of Family Size on Old-Age Support Arrangements
(1) (2) (3) (4) (5)
Variables NRPI
enrollment Keep
working
Old-age support: pension
Old-age support: children
Old-age support: savings
≤2 children 0.50*** 0.52*** -0.05 0.12 -0.29
[0.17] [0.14] [0.19] [0.17] [0.25]
Age 0.03*** -0.01* 0.02*** -0.00 -0.04***
[0.00] [0.00] [0.01] [0.00] [0.01]
Female 0.05* -0.08*** 0.01 0.09*** -0.23***
[0.03] [0.03] [0.03] [0.03] [0.04]
Educational attainment (years) 0.01* -0.01*** 0.02*** -0.02*** 0.01
[0.00] [0.00] [0.01] [0.00] [0.01]
Have a spouse 0.09 0.21*** 0.03 -0.07 0.18
[0.06] [0.05] [0.07] [0.06] [0.11]
Disabled -0.00 -0.46*** 0.04 -0.02 -0.10
[0.07] [0.06] [0.07] [0.07] [0.11]
Self-reported health: 0.05 -0.01 0.02 -0.04 0.15*** Good [0.04] [0.03] [0.04] [0.04] [0.05] Household income -0.00 -0.02*** 0.01 -0.01*** 0.01*
[0.00] [0.00] [0.00] [0.00] [0.00]
Have living son(s) 0.00 0.14*** -0.28*** 0.29*** -0.07
[0.06] [0.05] [0.06] [0.06] [0.08]
% children ≥high 0.13** -0.20*** 0.24*** -0.19*** 0.06 school [0.06] [0.05] [0.06] [0.05] [0.08] All children in -0.30*** -0.07 -0.02 -0.06 -0.03 school [0.11] [0.10] [0.11] [0.10] [0.14] Province fixed Yes Yes Yes Yes Yes effects Observations 9,135 9,135 9,135 9,135 9,135 Notes:
1) Standard errors in brackets; 2) *, **, *** indicate significant at 10%, 5% and 1%, respectively.
Table 4. Impacts of Expected/Received Pension Benefits on Old-Age Support Arrangement (1) (2) (3) (4) OLS Heckman selection Variables Pension Children Pension Children Log pension benefits 0.125*** -0.122*** 0.125*** -0.122***
[0.013] [0.014] [0.009] [0.011]
Age 0.004*** -0.003*** 0.005 -0.007
[0.001] [0.001] [0.004] [0.005]
Female -0.006 0.027** -0.005 0.020
[0.009] [0.011] [0.011] [0.014]
Educational attainment (years) -0.002 0.002 -0.002 0.002
[0.001] [0.002] [0.001] [0.002]
Have a spouse 0.013 -0.029 0.013 -0.026
[0.018] [0.020] [0.017] [0.021]
Disabled 0.012 -0.001 0.013 -0.006
[0.020] [0.023] [0.019] [0.024]
Self-reported health: good -0.005 -0.012 -0.004 -0.018
[0.011] [0.014] [0.012] [0.015]
Household income -0.003*** 0.001 -0.003** 0.002
[0.001] [0.002] [0.001] [0.002]
Have living son(s) -0.051*** 0.067*** -0.050*** 0.059***
[0.018] [0.022] [0.016] [0.020]
% children ≥high school 0.004 -0.027 0.004 -0.031*
[0.015] [0.019] [0.014] [0.017]
All children in school 0.020 -0.089 0.018 -0.078*
[0.039] [0.056] [0.036] [0.045]
Province fixed effects Yes Yes Yes Yes
Observations 5,420 5,420 9,135 9,135 Notes:
1) Standard errors in brackets; 2) *, **, *** indicate significant at 10%, 5% and 1%, respectively.
Table 5. Impacts of Family Size on Old-Age Support: Adding Cohort Dummies
(1) (2) (3) (4) (5) (6)
VARIABLES Financial transfer
NRPI enrollment
Keep working
Old-age support: pension
Old-age support: children
Old-age support: savings
≤2 children -2.00*** 0.36** 0.46*** 0.05 -0.02 -0.31
[0.48] [0.18] [0.15] [0.20] [0.18] [0.27]
Age 0.02 0.02*** -0.01 0.02** -0.01 -0.04***
[0.02] [0.01] [0.01] [0.01] [0.01] [0.01]
Female 0.09 0.04 -0.08*** 0.01 0.08*** -0.23***
[0.07] [0.03] [0.03] [0.03] [0.03] [0.04]
Educational attainment (years) 0.02 0.01* -0.01*** 0.02*** -0.02*** 0.01
[0.01] [0.00] [0.00] [0.01] [0.00] [0.01]
Have a spouse -0.01 0.09 0.20*** 0.03 -0.08 0.17
[0.14] [0.06] [0.05] [0.07] [0.06] [0.11]
Disabled 0.13 0.01 -0.46*** 0.03 -0.00 -0.09
[0.16] [0.07] [0.06] [0.08] [0.07] [0.11]
Self-reported 0.06 0.06 -0.01 0.02 -0.04 0.15*** health: good [0.11] [0.04] [0.03] [0.04] [0.04] [0.05] Household -0.03* -0.00 -0.02*** 0.01 -0.01*** 0.01* Income [0.02] [0.00] [0.00] [0.00] [0.00] [0.00] Have living son(s) -0.43** -0.01 0.13*** -0.27*** 0.27*** -0.07
[0.22] [0.06] [0.05] [0.06] [0.06] [0.08]
% children ≥high 1.27*** 0.15** -0.19*** 0.23*** -0.18*** 0.06 school [0.19] [0.06] [0.05] [0.06] [0.05] [0.08] All children in -6.36*** -0.27** -0.05 -0.04 -0.03 -0.03 school [0.39] [0.11] [0.10] [0.11] [0.10] [0.14] Age 51-60 0.12* 0.03 -0.04 0.09 0.04 [0.06] [0.06] [0.07] [0.06] [0.08] Age 61-70 -0.12 0.11 0.00 0.03 0.04 0.06 [0.16] [0.11] [0.09] [0.12] [0.11] [0.16] Province fixed Yes Yes Yes Yes Yes Yes effects Observations 5,133 9,135 9,135 9,135 9,135 9,135 Notes:
1) Standard errors in brackets; 2) *, **, *** indicate significant at 10%, 5% and 1%, respectively.
Table 6. Impacts of Family Size on Old-Age Support: Adding Community Controls
(1) (2) (3) (4) (5) (6)
Variables Financial transfer
NRPI enrollment
Keep working
Old-age support: pension
Old-age support: children
Old-age support: savings
≤2 children -2.06*** 0.53*** 0.58*** -0.00 0.11 -0.33
[0.50] [0.18] [0.14] [0.20] [0.18] [0.26]
Age 0.01 0.03*** -0.01 0.02*** -0.00 -0.03***
[0.02] [0.00] [0.00] [0.01] [0.01] [0.01]
Female 0.07 0.05 -0.07** 0.03 0.08*** -0.24***
[0.08] [0.03] [0.03] [0.04] [0.03] [0.05]
Educational attainment (years) 0.02 0.01* -0.01*** 0.02*** -0.01*** 0.01
[0.01] [0.01] [0.00] [0.01] [0.00] [0.01]
Have a spouse -0.02 0.09 0.19*** 0.06 -0.09 0.16
[0.14] [0.06] [0.06] [0.07] [0.06] [0.11]
Disabled 0.19 -0.01 -0.45*** 0.02 -0.01 -0.08
[0.16] [0.07] [0.06] [0.08] [0.07] [0.11]
Self-reported 0.05 0.06 -0.01 0.02 -0.04 0.15*** health: good [0.11] [0.04] [0.04] [0.04] [0.04] [0.05] Household -0.02 -0.00 -0.02*** 0.00 -0.01** 0.01 Income [0.01] [0.00] [0.00] [0.00] [0.00] [0.00] Have living son(s) -0.38* -0.03 0.13** -0.27*** 0.29*** -0.08
[0.23] [0.07] [0.05] [0.06] [0.06] [0.08]
% children ≥high 1.28*** 0.13** -0.18*** 0.24*** -0.19*** 0.08 school [0.20] [0.06] [0.05] [0.06] [0.05] [0.08] All children in -6.34*** -0.28** -0.08 -0.03 -0.05 -0.06 school [0.40] [0.11] [0.10] [0.11] [0.10] [0.14] Community 0.02 -0.02* -0.02* -0.02* 0.01 0.00 mean education [0.03] [0.01] [0.01] [0.01] [0.01] [0.02] Community -0.00 -0.01*** -0.01*** 0.01*** -0.02*** 0.02*** mean income [0.01] [0.00] [0.00] [0.00] [0.00] [0.01] Province fixed Yes Yes Yes Yes Yes Yes effects Observations 4,793 8,539 8,539 8,539 8,539 8,539 Notes:
1) Standard errors in brackets; 2) *, **, *** indicate significant at 10%, 5% and 1%, respectively.
Table 7. Robustness Checks
(1) (2) (3) (4) (5) (6)
Variables Financial transfers
NRPI enrollment
Keep working
Old-age support: pension
Old-age support: children
Old-age support: savings
Alternative IV ≤2 children -2.02*** 0.60*** 0.53*** -0.07 0.11 -0.22
[0.48] [0.17] [0.13] [0.19] [0.17] [0.26]
Alternative definition for respondents of fewer children ≤1 children -11.11*** 1.76*** 1.82*** -0.27 0.52 -1.14 [3.18] [0.47] [0.40] [0.75] [0.66] [0.91] Number of children No. of children 0.73*** -0.18*** -0.18*** 0.01 -0.04 0.10 [0.17] [0.06] [0.05] [0.07] [0.06] [0.09] Observations 5,133 9,135 9,135 9,135 9,135 9,135
Notes:
1) Other covariates used are the same to those used in Table 2 and Table 3; 2) Standard errors in brackets; 3) *, **, *** indicate significant at 10%, 5% and 1%, respectively.