Declining Family Size, Public Pension, and Old-Age Support ...Declining Family Size, Public Pension,...

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Declining Family Size, Public Pension, and Old-Age Support in Rural China

Yiqun Chen, M.A.

Stanford University

[email protected]

Frank A. Sloan, Ph.D. (Corresponding Author)

Duke University

213 Social Sciences Building

Box 90097

Durham, NC 27708

[email protected]

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:


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:


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




≤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:


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




≤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:


Table 7. Robustness Checks

(1) (2) (3) (4) (5) (6)

Variables Financial transfers

NRPI enrollment

Keep working




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.

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