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HOUSEHOLD STRUCTURE, LEFT-BEHIND ELDERLY, AND RURAL MIGRATION IN
CHINA
CHEN FENGBO, HENRY LUCAS, GERRY BLOOM and DING SHIJUN
Journal of Agricultural and Applied Economics / FirstView Article / August 2016, pp 1 - 19
DOI: 10.1017/aae.2016.16, Published online: 05 August 2016
Link to this article: http://journals.cambridge.org/abstract_S107407081600016X
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CHEN FENGBO, HENRY LUCAS, GERRY BLOOM and DING SHIJUN HOUSEHOLD
STRUCTURE, LEFT-BEHIND ELDERLY, AND RURAL MIGRATION IN CHINA. Journal of
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doi:10.1017/aae.2016.16
HOUSEHOLD STRUCTURE, LEFT-BEHIND
ELDERLY, AND RURAL MIGRATION IN CHINA
CHEN FENGBO
School of Economics and Management, South China Agricultural University, Guangzhou, Guangdong, People’s
Republic of China
HENRY LUCAS∗
Institute of Development Studies, Brighton, East Sussex, United Kingdom
GERRY BLOOM
Institute of Development Studies, Brighton, East Sussex, United Kingdom
DING SHIJUN
School of Public Administration, Zhongnan University of Economics and Law, Wuhan, People’s Republic of China
Abstract. The influence of household demographic composition on rural
migration in China has received limited attention. With data from a household
survey in China’s Sichuan and Hubei Provinces, this paper uses Probit models to
explore the influence of household structure on migration decisions. It suggests
that the three-generation household encourages out-migration, with the elderly
playing an important role in supporting the migration of younger members by
caring for their children. In contrast with earlier findings, the serious illness of an
elderly member did not encourage the return of young migrants or discourage
migration decisions unless the household included young children.
Keywords. China, household structure, left-behind elderly, Probit model, rural
migration
JEL Classifications: J11, R23
1. Introduction
Rural–urban migration has been one of the key drivers of demographic change
over the past two decades in China. Such population movements can be
viewed from two perspectives (Wood, 1981): microeconomic and historical
structural. The microeconomic model addresses the personal cost-benefit calculus
The authors acknowledge financial support from the European Union Sixth Framework Programme (grant
agreement no. INCO-CT-2005-517657). The National Natural Science Foundation of China (grant no.
71173239) and the Social Science Foundation of China (grant no. 14BGL094) provided financial support
in analyzing the data. This research was also supported by the Institute of Development Studies (United
Kingdom), Zhongnan University of Economics and Law, and Huazhong Agricultural University. The
authors thank two anonymous reviewers and the editor of Journal of Agricultural and Applied Economics
for helpful comments.
∗Corresponding author: e-mail: h.lucas@emeritus.ids.ac.uk
1
2CHEN FENGBO ET AL.
executed by potential migrants as they confront rural–urban wage, employment,
and amenity differentials (Todaro, 1969). The historical-structural perspective
explores the factors that lead to changes in the organization of production
and seeks to make explicit the mechanisms by which social, economic, and
political forces directly and indirectly affect the demand for labor (Wood,
1981). Focus on household behavior can provide a bridge between these social
and individual levels of analysis (Schmink, 1984). In most developing counties,
labor is typically the most important asset of poor households (Scoones, 1998),
and, depending on both household characteristics and the socioeconomic and
institutional environment, labor migration may play a central role in an effective
livelihood strategy.
Rapid economic development in China has provided rural labor with
enormous opportunities for nonfarm employment if they are willing to migrate.
This has resulted in substantial increases in income and diversification of income
sources for rural households (De Brauw et al., 2002; Yang and Zhishui, 2003;
Zhao, 1999). The literature on rural migration has largely focused on the
individual characteristics of migrants. It has shown that younger males (Fan,
2003; Mu and van de Walle, 2011; Zhong and Jing, 2012), particularly those
with higher levels of education (Dewen, Fang, and Guoqing, 2008; Zhao,
1997), have been most successful in gaining the benefits of migration, primarily
through urban employment. In this article, we consider factors that operate
at both the individual and household levels. In particular, we suggest that
household demographic structure may exert a major influence on migration
decisions.
There has been limited research on the relationship between household
structure and migration. There is evidence that migration decisions are influenced
by the collective goals of household members. From studies on seasonal
migration in India, Haberfeld et al. (1999) find that migration-related decisions
should be evaluated not only on the basis of utility maximization by individual
migrants, but also in terms of risk reduction at the household level. In particular,
the illness of some members will influence the division of labor within a
household and hence migration decisions. For example, at least one study in
China (Giles and Mu, 2007) suggests that younger adults are less likely to migrate
in search of employment when one or both parents are ill.
Although international evidence on migration may be relevant, China does
exhibit a number of specific contextual factors. The transition to a market
economy is relatively recent, which has resulted in extremely rapid growth, first
in agricultural output and then increasingly in manufacturing. The associated
demand for labor resulted in remarkable levels of internal migration, initially
to work in village and township enterprises and then to seek the higher wages
offered in urban areas. These workers were often young men who left behind
spouses, children, and elderly parents—the liushou, or those left holding the fort
(Tan, 2009). The one-child policy, introduced in 1979, reduced the burdens on
Household Structure and Rural Migration in China 3
parents in terms of child care but also limited the aggregate supply of young
adults entering the labor force; led to an aging population profile; and, given
the traditional emphasis on the obligations of children to their parents and the
associated limited provision for institutional care of the elderly, increased the
potential burden of care to be assumed by the rising generation (Zhang and
Goza, 2006). It is far from evident as to how the resulting tensions between
long-standing cultural expectations and economic incentives will be resolved.
Finally, the traditional Chinese hukou system, which limits the provision of
social services, including education and health mainly to the official place of
residence, seriously limits the options of migrants, who are seen as strictly
temporary workers and often live in communal accommodation, in terms of
obtaining education or health care for themselves or those who might wish to
accompany them (Tan, 2009).
The article proceeds as follows. Section 2 develops a conceptual framework for
the analysis of the links between serious illness and labor use. We then consider
the traditional model of the division of labor in Chinese rural households and,
based on a review of the literature, question the extent to which this model
still applies. Section 3 describes a large-scale survey of rural households in
two Chinese provinces. In Section 4, data from this study are then used in
Probit models to explore the influence of household demographic composition,
including the number of children and number of elderly members, and the serious
illness of an elderly member. Section 5 describes the model results. Finally,
the conceptual framework and findings from the household survey are used to
explore policy implications.
2. Household Structure, Left-Behind Elderly, and Rural Migration
2.1. Internal Migration and the Household Division of Labor in Rural China
As indicated previously, market reforms have created enormous opportunities for
rural households to diversify from own-farm agricultural production into wage
employment by the out-migration of one or more members, either to urban areas
or other, more prosperous, rural areas (De Brauw et al., 2002; Zhao, 1999). In
2009, the number of internal migrants in China had reached 150 million (Fang,
2010).
Migration has reshaped the structure of rural populations and household-
level demography, creating “divided households,” with individuals who consider
themselves to be members of the same household living in both rural and urban
areas. Migration is particularly attractive for younger adult males, whereas those
“left behind” are primarily older and younger household members, often the
parents, children, and, to a lesser extent, wives of migrants (Mu and van de Walle,
2011; Zhong and Jing, 2012). The prospects offered by urban employment
are also often sufficiently attractive to persuade many married women, even
4CHEN FENGBO ET AL.
Parents
Young
Couple
Children
Remittances
(cash and kind)
Caring
Farm or Local
Nonfarm Work
Wage Work
in City
Food Production
Housekeeping
Maintain Land Rights Cash Income
Sustained and Improved
Household Livelihoods
Study in
Rural School
Figure 1. Positive Circle of Divided Households in Rural China
those with very young children, to join the migrant flow (Roberts et al., 2004).
School-age children cannot usually accompany migrant parents. Most migrants
have to live in communal hostels, and even if they can find appropriate
accommodation, local governments often refuse to fund education for the
children of migrants, requiring substantial “out-of-pocket” payments if those
children are to be enrolled in public schools (Kwong, 2004).
Thus, a substantial proportion of rural households have adopted a livelihood
strategy that involves the out-migration to urban areas of younger married
couples, while their elderly parents remain in the rural area, maintaining
household land rights, farming that land and taking care of grandchildren. The
migrants will typically send money home and return each spring festival bringing
a range of household items and consumables (Chang, Dong, and MacPhail,
2011).
The previously described livelihood strategy (Figure 1) has been adopted by
a very substantial number of rural households in China and, in many cases,
has dramatically increased their incomes. However, it can also be seen to have
increased their vulnerability to certain types of livelihood shock or stress. For
Chinese migrants, employment can often be insecure and income flows unstable
(Chen, 2011; Mou et al., 2009), as most clearly demonstrated during the recent
relative slowdown in economic growth. Loss of migrant incomes for households
that have become dependent on those incomes can be a devastating livelihood
shock. Serious illness, afflicting either migrants themselves or one of those left
behind, is a common cause, necessitating a return home either to seek or provide
care. Typically, migrants will only have claims on anything more than basic
health services if they return to their official place of residence.
Household Structure and Rural Migration in China 5
Given that large-scale internal migration has continued for many years, a
substantial proportion of the rural adult population may be returned migrants
who have suffered injury or illness during urban employment or have simply
grown too old to undertake the long hours of manual labor that such employment
often entails (Chen et al., 2014). Given that the great majority of both the
elderly and younger adults with a physical disability in rural areas have minimal
access to pensions or social welfare, the household farm acts as their safety
net (Fan and Wang, 2008), providing at least food, a modest income, and
access to rural health services. They will typically continue to work as long
as they are able (Pang, de Brauw, and Rozelle, 2004). At one level, they might
be seen as a marginalized group, dependent on a limited agricultural income
and remittances from migrant members of their households. However, from
a household livelihoods perspective, they are very important. By maintaining
the rural base, they both allow younger members, especially those with young
children, to seek urban employment and also limit the risks to the household
in the event that the migrants lose that employment. Indeed, they can be seen
as playing an essential role in a virtuous circle that raised household living
standards, reduced rural poverty, and contributed the labor that has been central
to China’s economic growth.
2.2. Household Structure, Left Behind, and Rural Migration
Historically, rural Chinese favored “joint-stem” households in which parents
lived with one or more married sons and their families (Freedman et al., 1978).
In more recent times, partly because of the one-child policy but also reflecting
social change over the past 30 years, “three-generation” (grandparents, working-
age parents, and children) and “nuclear” households (parents and children) are
most common.
It would seem reasonable to expect that the presence of one or more young
children in a household will substantially reduce the possibility of parental
migration. In nuclear households that do decide to adopt this strategy, the
husband would typically travel to seek employment, with the wife remaining
in the rural area to take responsibility for child care, farmwork, and domestic
activities. In three-generation households, grandparents who can care for
children and maintain the rural base can facilitate migration of one or both of
the younger adults. However, the sickness or disability of an elderly household
member will both reduce the supply of labor and increase the demands on that
labor, to the extent that they themselves require care, reducing both household
productivity and incomes (Bartel and Taubman, 1979; Cropper, 1977; Schultz
and Tansel, 1997). This would seem likely to decrease the possibilities for
migration. Giles and Mu (2007) found that in general younger adults were
less likely to work as migrants if one of their parents was ill. The presence of
elderly household members with and without a serious illness could thus be used
to test their importance in terms of migration decisions.
6CHEN FENGBO ET AL.
In the next section, we will explore the extent to which the assumptions
inherent in the previous discussion are supported by data from a large-scale
sample survey in the Sichuan and Hubei Provinces of China.
3. Data and Samples
3.1. Data
The data in this article come from the POVILL (Poverty and Serious Illness)
study in 2006, which was funded by the European Union. This included a
household survey in Fushun County and Langzhong County in Sichuan Province
and Xiaogan County and Hong’an County in Hubei Province. All these counties
are officially classified as poor and have experienced the migration of their
populations to the more prosperous eastern coastal regions since the end of
the 1980s.
The household survey was conducted from July to August 2006, and the data
were collected on 12,131 households with 50,358 members. The topics addressed
in the survey included household demographic composition, economic status,
health problems over the previous year, and associated coping strategies. Note
that traditional definitions of the household, for example as a unit consisting
of “a number of kin relations bounded by a common budget and a single
cooking stove” (Croll, 1994, p. 19), have become increasingly problematic in
China as the divided households described previously have become increasingly
prevalent. The household survey adopted two criteria to determine household
membership: having a common household registration and sharing a common
budget.
3.2. Household Characteristics
Partly as a consequence of the one-child policy, family size in China has steadily
declined and the proportion of nuclear households increased (Yuesheng, 2006).
This is in line with findings from the household survey that nuclear households
accounted for some 38% of the total. Three-generation households were the
second most common group in rural areas, accounting for some 33% of the
total (see Table 1). More than 80% of three-generation households had at
least one child under the age of 16, while this was true for some 47% of
nuclear households. Couple-only and singleton households made up 17% of
the total, with the great majority of members in this kind of household being
over 50 years old. The remaining households accounted for 11%, the most
common being extended family households, those which included more distant
relations.
The high proportion of three-generation households does not imply that the
young couples in these households lived with their parents. Internal migration
has led to a situation in which the resident population in the study areas differs
radically from the population identifying themselves as household members
Household Structure and Rural Migration in China 7
Table 1. Household Demographic Composition
Total
Household Composition
With Child under
16 Years Old
Nobody under
16 Years Old Frequency Percentage
Three-generation
household
3,363 674 4,037 33.28
Nuclear family household 2,192 2,468 4,660 38.41
Couple or single person 0 2,055 2,055 16.94
Others 813 566 1,369 11.37
Total 6,369 5,761 12,131 100
Source: Estimated from household survey data.
2,200 1,650 1,100 550 0550 1,100 1,650 2,200
Frequency
020 40 60 80 100
Age
Migrated Left Behind
Figure 2. Population Age and Migration Status (source: estimated from household
survey data)
(Figure 2). This resident population is dominated by those under 16 (25%) or
over 45 (54%). Approximately 79% of men and 60% of women aged 16–35
were reported as living away from home for extended periods. The average time
spent away from home was about 10 months.
Migration patterns strongly influence the household division of labor
(Table 2). In general, those aged 16–45 were undertaking off-farm work,
primarily in urban areas, whereas those aged 45–60 identified agricultural
production as their main activity. Most men over 60 saw themselves as still
8CHEN FENGBO ET AL.
Table 2. Labor Division in Rural Households (%)
Age Groups
<16 16–45 46–60 >60
Working Categories Male Female Male Female Male Female Male Female
Agricultural work 0.32 0.43 14.08 29.85 73.10 78.45 64.80 43.76
Nonfarm work 1.88 3.27 66.76 50.70 14.19 2.49 1.57 0.47
Family enterprise 0.07 0.02 4.83 4.06 4.34 2.38 1.84 0.66
Government work 0.05 0.00 2.31 0.81 2.63 0.36 0.77 0.00
Housework 0.11 0.31 0.55 6.13 2.47 13.78 15.00 34.96
Student 70.35 71.24 8.84 6.26 0.00 0.00 0.00 0.00
Others 27.22 24.73 2.64 2.19 3.27 2.54 16.01 20.15
Total 100 100 100 100 100 100 100 100
Source: Estimated from household survey data.
Table 3. Distribution of Reported Serious Illness across Household Members
Frequency
Age Groups
No Serious
Illness
Self-Reported
Serious Illness
% Self-Reported
Serious Illness
<16 9,168 734 7.41
16–45 18,571 3,477 15.77
46–60 7,232 4,848 40.13
>60 4,324 4,099 48.66
Total 39,295 13,158 25.09
Source: Estimated from household survey data.
primarily engaged with farmwork, though almost 15% reported that they were
mainly involved in household activities. As indicated previously, the elderly
population will essentially work until they are unable to do so. More than 43%
of women over 60 also reported agricultural work, while approximately 35%
prioritized household activities.
In this article, we focus on the impact of a serious illness suffered by an
elderly household member1on migration by younger adults. From Table 3,
nearly 48% of those over 60 reported a serious illness, falling to 16% for
those aged 16–45 and 7% for children under 16. In interpreting the high rates
for those over 60, it may be useful to note that the most common conditions
related to musculoskeletal disorders, often seen by those afflicted as an inevitable
consequence of the aging process, prolonged heavy manual labor, and a relatively
high risk of work-related injuries.
1 In this study, the definition of serious illness had three mutually exclusive components: (1)
hospitalization costs during the previous year greater than 1,000 yuan; (2) outpatient expenses over
the previous year greater than 1,000 yuan; and (3) limitations on productive activities because of illness
lasting longer than 3 months.
Household Structure and Rural Migration in China 9
4. Models
4.1. A Probit Model of Factors Influencing Migration Decisions
We would generally assume that individuals will try to maximize their utility
by engaging in activities that maximize the return on their labor time. In
an efficient labor market, that return will be primarily determined by their
personal characteristics including physical health, education level, technical
skills, experience, personality, and so forth. However, migration can also be
seen as one possible component of the overall livelihood strategy adopted by
a household in response to the opportunities and limitations imposed by the
external environment (Wood, 1981). In this context, the choices made by an
individual will typically be influenced by the needs and preferences of other
household members. In particular, as discussed previously, the presence of young
children and elderly household members may influence decisions to migrate in
search of improved employment opportunities.
Probit and Tobit models could both be used to explore the relationship
between the decision to migrate and individual and household characteristics.
A possible dependent variable would be months of migration, which is discrete
between 0 and 12. From the survey data, the histogram of this variable has a peak
at 0 for nonmigrants and another at 10–11 months, with smaller percentages
migrating for 1–9 months. With that kind of distribution, it becomes difficult to
interpret the meaning of a marginal effect. For this reason, we find the Probit
model more compelling than the Tobit model.
Following Li and Zahniser (2002), we adopt a Probit model in which
it is assumed that an underlying latent variable, which in this case can be
conceptualized as the gain or loss in expected utility arising from the decision
to migrate, determines the likelihood of the choice that an individual will make.
The value of this latent variable is determined by both individual and household
characteristics. Specifically, we model the probability of choosing to migrate
(yi=1) as follows:
Pyi=1xij =βjxij .
where is the cumulative distribution function of the standard normal
distribution, and the xij’s are the values of the independent individual and
household variables for individual i.βjis vectors of independent variables. The
marginal effects of each independent variable will be estimated.
4.2. Dependent and Explanatory Variables
Given the key role played by younger adults in household livelihood strategies,
we focus here on migration decisions by younger adults, in the age group 16–45.
Some 69% of this age group had been working away from home for more than
6 months at the time of the survey. In order to simplify the research, we focus on
10 CHEN FENGBO ET AL.
Table 4. Variables Considered in the Model and Their Definitions
Variables Definitions of Variables
Independent variable
Migration 1 =Migrated; 0 =did not migrate
Explanatory variables
Individual charactertics
Sex 1 =Male; 0 =female
Age Age of the household member making migration decision (16–45)
Age square Square of the above
Marriage 1 =Married; 0 =not married
Activity 1 =Candoheavywork;0=cannot do heavy work
Educational level
Less than secondary 1 =Primary school or illiterate; 0 =otherwise
Secondary 1 =Secondary school; 0 =otherwise
More than secondary 1 =Senior, junior, or higher education; 0 =otherwise
Household characteristics
Three-generation household 1 =Three-generation household; 0 =nuclear family household
Children number Number of children <16 years in the household
Elderly number Number of elderly >60 years in the household
Serious illness elderly number Number of elderly >60 years reporting serious illness
Province 1 =Hubei Province; 0 =Sichuan Province
three-generation households with one or two children (infant to under 16) and
one or two elderly members (age 60 and older), and nuclear households with
one or two children.
The individual characteristics considered are age, sex, marriage, education,
and capacity to undertake activities. Because we expect the effects of age to
be nonlinear (migration initially increasing with age but declining among older
workers), we also include a quadratic term in the equation. The household-
level characteristics considered are household structure, number of children,
number of elderly, and number of elderly with serious illness. These variables
are chosen based on the existing literature cited previously. Finally, we also
include a dichotomous variable “Province” to allow for regional differences.
Table 4 lists all the dependent and explanatory variables, and Table 5 provides
descriptive statistics.
5. Results
5.1. Household Demographic Profile and Migration Decisions
We initially model the relationship between migration decisions and household
composition, number of children and number of elderly, controlling for
individual characteristics. Because of the significant linear correlation between
the number of elderly and number of elderly with serious illness, this model does
not include the latter variable. Table 6 shows the results of the Probit model.
Household Structure and Rural Migration in China 11
Table 5. Descriptive Statistics (N =20,659)
Variables Mean Standard Deviation
Dependent variables
Migration 0.65 0.48
Explanatory variables
Individual characteristics
Sex 0.52 0.50
Age 30.07 9.13
Age square 987.42 554.88
Marriage 0.63 0.48
Activity 0.92 0.28
Educational level
Less than secondary 0.36 0.48
Secondary 0.49 0.50
More than secondary 0.16 0.36
Household characteristics
Three-generation household 0.48 0.50
No child 0.47 0.50
One child 0.36 0.48
Two children 0.17 0.37
No elderly 0.66 0.47
One elderly 0.21 0.41
Two elderly 0.13 0.34
No serious illness elderly 0.84 0.37
One serious illness elderly 0.14 0.35
Two serious illness elderly 0.03 0.16
Province 0.59 0.49
Note: We use the variables “Children number,” “Elderly number,” and “Serious illness elderly number”
as dummy variables in this table.
As expected, these results show that a range of individual characteristics,
including age, gender, marital status, capacity for activity, and education level
have significant effects on the migration decision. The age variable has a positive
effect on the probability, but the negative coefficient on the age-squared variable
indicates that at older ages this effect reverses. As individuals reach adulthood,
the likelihood of migration for work increases but then declines as they approach
middle age. The effect of the gender variable reflects the traditional division
of labor in Chinese rural households. Women are more likely to undertake
household activities and work on the family farm, whereas men are more likely to
migrate to seek employment elsewhere. The probability to migrate is significantly
decreased for those who are married. As might be expected, given that they
might be associated with improved employment opportunities and higher wages,
superior levels of health status and education are positively associated with
migration.
For the household-level characteristics, those from three-generation
households were significantly more likely to migrate compared with those from
12 CHEN FENGBO ET AL.
Table 6. The Influence of Household Composition on the Migration Decision
Probit Model (N =20,659)
Coefficients Marginal Effects
(standard error) (standard error)
Sex 0.4557∗∗∗ 0.1612∗∗∗
(0.0207) (0.0072)
Age 0.3834∗∗∗ 0.1360∗∗∗
(0.0130) (0.0046)
Age square −0.0065∗∗∗ −0.0023∗∗∗
(0.0002) (0.0001)
Marriage −0.7917∗∗∗ −0.2608∗∗∗
(0.0541) (0.0160)
Activity 0.6978∗∗∗ 0.2679∗∗∗
(0.0396) (0.0155)
Secondary 0.3140∗∗∗ 0.1108∗∗∗
(0.0235) (0.0082)
More than secondary 0.1961∗∗∗ 0.0671∗∗∗
(0.0349) (0.0115)
Three-generation household 0.6238∗∗∗ 0.2174∗∗∗
(0.0352) (0.0118)
One child −0.4174∗∗∗ −0.1511∗∗∗
(0.0244) (0.0089)
Two children −0.5086∗∗∗ −0.1912∗∗∗
(0.0306) (0.0119)
One elderly −0.2907∗∗∗ −0.1067∗∗∗
(0.0348) (0.0131)
Two elderly 0.0238 0.0084
(0.0400) (0.0141)
Province −0.1961∗∗∗ −0.0688∗∗∗
(0.0223) (0.0077)
Log (pseudo) likelihood −10,182.4690
Pseudo R20.2361
Probability >χ
20.0000
Notes: Statistical significance is based on the robust standard error: ∗,P<0.10; ∗∗,P<0.05; ∗∗∗,P<
0.01. For individual education, the base category is “Less than secondary”; for number of children, the
base category is “No child”; and for number of elderly, the base category is “No elderly.”
a nuclear household. This supports the assumption that the elderly2may play
important roles in these households in terms of agricultural work and caring
for grandchildren. On the other hand, an increase in the number of children
significantly decreased the probability of migration. On average, the presence
of a single elderly member in the household significantly discouraged migration,
whereas in households with two elderly members there was no significant effect.
2 From the household survey data, it is common that a couple will become grandparents at around
age 50.
Household Structure and Rural Migration in China 13
Table 7. The Influence of Interaction Elderly and Child for the Migration Decision in Three-
Generation Household (children number ࣘ2 and elderly number ࣘ2)
Probit Model (N =10,000)
Coefficients Marginal Effects
(standard error) (standard error)
Sex 0.4156∗∗∗ 0.1246∗∗∗
(0.0301) (0.0089)
Age 0.4288∗∗∗ 0.1286∗∗∗
(0.0209) (0.0063)
Age square −0.0073∗∗∗ −0.0022∗∗∗
(0.0003) (0.0001)
Marriage −0.4398∗∗∗ −0.1185∗∗∗
(0.0722) (0.0171)
Activity 0.6419∗∗∗ 0.2255∗∗∗
(0.0563) (0.0218)
Secondary 0.2282∗∗∗ 0.0684∗∗∗
(0.0333) (0.0100)
More than secondary 0.1854∗∗∗ 0.0527∗∗∗
(0.0529) (0.0142)
One child −0.8422∗∗∗ −0.2571∗∗∗
(0.0870) (0.0263)
Two children −0.9085∗∗∗ −0.3148∗∗∗
(0.0973) (0.0359)
One elderly member −0.5177∗∗∗ −0.1600∗∗∗
(0.0911) (0.0286)
Two elderly members −0.1733 −0.0536
(0.0983) (0.0313)
One child and one elderly member 0.3687∗∗∗ 0.0993∗∗∗
(0.1005) (0.0238)
One child and two elderly members 0.3072∗∗∗ 0.0831∗∗∗
(0.1118) (0.0268)
Two children and one elderly member 0.3526∗∗∗ 0.0932∗∗∗
(0.1156) (0.0262)
Two children and two elderly members 0.3115∗∗∗ 0.0832∗∗∗
(0.1274) (0.0297)
Province −0.3776∗∗∗ −0.1146∗∗∗
(0.0304) (0.0093)
Log (pseudo) likelihood −4,777.2817
Pseudo R20.1653
Probability >χ
20.0000
Notes: Statistical significance is based on the robust standard error: ∗,P<0.10; ∗∗,P<0.05; ∗∗∗,P<
0.01. For individual education, the base category is “Less than secondary”; for number of children, the
base category is “No child”; and for number of elderly members, the base category is “No elderly.”
In three-generation households, we assume that the elderly play an important
role in caring for children. We, therefore, extend the model to consider
interaction effects relating to the joint presence of elderly household members
and children. The results (Table 7) show that on average the presence of one or
14 CHEN FENGBO ET AL.
two elderly members will significantly reduce the probability of migration. This
will also be the effect of having one or two children in a household. However,
where there are children and two elderly members, the probability of migration
is significantly increased, supporting the assumption that the knowledge that
children left behind will be adequately cared for removes an important barrier
for potential migrants.
5.2. The Effect of Serious Illness of the Elderly
We anticipate that there may be a complex relationship between household
composition, serious illness, and migration decisions. Illness typically reduces
labor supply and increases labor demands, which might discourage migration.
On the other hand, as discussed previously, illness also typically reduces
household income and increases outlays, for example to pay for care and possibly
to hire labor. If migration provides an opportunity to substantially increase
household income and thus offset these additional financial burdens, migration
may be seen as an appropriate response to illness. It should also be remembered
that the migration decision may have preceded the illness. The decision then
becomes one of whether to abandon what may well be the primary source of
household income in order to return home.
To simplify our exploration of these issues, we focus our attention on the
most common household type in our sample, the three-generation household
consisting of a couple with one or two children under 16 and their elderly
parents. The results in Table 8, where we explore the effects for households with
two elderly members, show that when the household has no children, the serious
illness of only one elderly member has a statistically significant positive effect on
the migration decision. However, for households with children, the relationship
between serious illness of one or both elderly members and migration are not
statistically significant.
Next, we focus on households with one elderly member. Table 9 shows that,
when there are no children, the serious illness of the only elderly member has
a significantly positive effect on the migration decision. However, when the
household includes one or two children, the serious illness of the elderly member
has a significant negative effect on the migration decision.
Combining Tables 8 and 9would suggest that the welfare of children rather
than that of elderly household members is an important factor in determining
whether to migrate for younger adults. Overall, the serious illness of an elderly
household member, contrary to our assumption and to the findings of Giles
and Mu (2007), does not decrease, and in some circumstances increases, the
likelihood of migration by the younger adult member. One possible explanation
is the expectation that migration will increase the household income and allow
the household to offset losses in productivity and meet the additional costs
involved in caring for the affected member. However, when the serious illness of
an elderly member limits his or her capacity to meet the needs of the children in
Household Structure and Rural Migration in China 15
Table 8. Impact of Serious Illness of Elderly Members for the Migration Decision in Three-
Generation Households with Two Elderly Members
Probit Model (N =2,498)
Coefficients Marginal Effects
(standard error) (standard error)
Sex 0.3154∗∗∗ 0.0884∗∗∗
(0.0596) (0.0167)
Age 0.3184∗∗∗ 0.0891∗∗∗
(0.0414) (0.0116)
Age square −0.0053∗∗∗ −0.0015∗∗∗
(0.0006) (0.0002)
Marriage −0.1285 −0.0348
(0.1559) (0.0407)
Activity 0.6395∗∗∗ 0.2150∗∗∗
(0.1070) (0.0405)
Secondary 0.2474∗∗∗ 0.0685∗∗∗
(0.0663) (0.0181)
More than secondary 0.1591 0.0424
(0.1021) (0.0258)
One child −0.4083∗∗∗ −0.1190∗∗∗
(0.1104) (0.0333)
Two children −0.4874∗∗∗ −0.1518∗∗∗
(0.1233) (0.0418)
Serious illness of one elderly member 0.2457∗∗ 0.0676∗∗
(0.1089) (0.0294)
Serious illness of two elderly members 0.2119 0.0560
(0.1298) (0.0323)
One child in household and serious illness of one elderly
member
−0.1568 −0.0458
(0.1488) (0.0452)
One child in household and serious illness of two elderly
members
−0.2101 −0.0632
(0.1844) (0.0591)
Two children in household and serious illness of one elderly
member
−0.2108 −0.0633
(0.1730) (0.0554)
Two children in household and serious illness of two elderly
members
−0.0685 −0.0197
(0.2275) (0.0671)
Province −0.2269∗∗∗ −0.0644∗∗∗
(0.0613) (0.0176)
Log (pseudo) likelihood −1,199.6229
Pseudo R20.0942
Probability >χ
20.0000
Notes: Statistical significance is based on the robust standard error: ∗,P<0.10; ∗∗,P<0.05; ∗∗∗,P<
0.01. For individual education, the base category is “Less than secondary”; for number of children, the
base category is “No child”; for number of elderly, the base category is “No elderly”; and for number of
serious illness elderly, the base category is “No serious illness elderly.”
16 CHEN FENGBO ET AL.
Table 9. Impact of Serious Illness of Elderly Member for the Migration Decision in Three-
Generation Households with One Elderly Member
Probit Model (N =4,055)
Coefficients Marginal Effects
(standard error) (standard error)
Sex 0.4565∗∗∗ 0.1601∗∗∗
(0.0458) (0.0157)
Age 0.4435∗∗∗ 0.1562∗∗∗
(0.0316) (0.0111)
Age square −0.0076∗∗∗ −0.0027∗∗∗
(0.0005) (0.0002)
Marriage −0.3832∗∗∗ −0.1286∗∗∗
(0.1242) (0.0393)
Activity 0.6342∗∗∗ 0.2422∗∗∗
(0.0828) (0.0326)
Secondary 0.2519∗∗∗ 0.0882∗∗∗
(0.0515) (0.0179)
More than secondary 0.2895∗∗∗ 0.0961∗∗∗
(0.0812) (0.0251)
One child in household −0.3817∗∗∗ −0.1372∗∗∗
(0.0663) (0.0242)
Two children in household −0.4559∗∗∗ −0.1697∗∗∗
(0.0825) (0.0318)
Serious illness of elderly member 0.2105∗∗∗ 0.0731∗∗∗
(0.0714) (0.0244)
One child in household and serious illness of elderly member −0.2729∗∗∗ −0.1001∗∗∗
(0.1019) (0.0386)
Two children in household and serious illness of one elderly
member
−0.2780∗∗ −0.1029∗∗
(0.1272) (0.0490)
Province −0.3722∗∗∗ −0.1301∗∗∗
(0.0465) (0.0160)
Log (pseudo) likelihood −2,096.3182
Pseudo R20.1919
Probability >χ
20.0000
Notes: Statistical significance is based on the robust standard error: ∗,P<0.10; ∗∗,P<0.05; ∗∗∗,P<
0.01. For individual education, the base category is “Less than secondary”; for number of children, the
base category is “No child”; for number of elderly, the base category is “No elderly”; and for number of
serious illness elderly, the base category is “No serious illness elderly.”
the household, the parents of those children are less likely to risk leaving them
behind even if this limits the income-generating potential of the household.
6. Conclusions and Policy Implications
In this article, we have considered the influence of household demographic
composition and serious illness on the allocation of labor by rural households.
Household Structure and Rural Migration in China 17
Using Probit models, we explored the influence of individual characteristics,
household demographic structure, and serious illness of an elderly family member
on one key aspect of labor allocation in China—the decision by adult workers to
migrate in search of higher incomes. We find that three-generation households
support migration by younger adults. For households with children under 16,
the presence of elderly relatives facilitates the ability of younger adults to work
away from home to both enhance the economic well-being of their households
and contribute to the rapid economic growth in China over the past decades.
In this sense, the rural elderly population can be seen as playing a key role in
that growth. However, it should be recognized that this has created enormous
numbers of rural divided households, which have specific types of vulnerability
to ill health and other shocks that need to be considered in the design of social
support mechanisms.
Contrary to our assumption, the possibility of migration by young adults is
increased, not decreased, when the elderly get a serious illness. However, this
changes when there are young children who are cared for by an elderly member
of the family. If that elderly person falls ill, the child’s parents may need to return
from their jobs.
For the rural population, migration is the most important way to benefit from
China’s rapid economic development, but the household structure influences
migration decisions. Because a child cannot live with his or her migrant parents,
the grandparents care for the child. Rural families pursue livelihood strategies
through migration to create a better life for the household unit, but these
might not be best for all members, especially for the elderly. The existing social
support systems were not designed with split rural–urban households in mind.
Among policy reforms that would make these households less vulnerable to
health-related risks would be measures to enable their children to obtain an
education either by permitting them to go to school in urban areas or by
making it possible for them to board in rural schools (at least temporarily
when a carer falls ill). Reforms would also include improvements to rural
health services to strengthen their capacity to provide medical and social
support to the elderly when they fall temporarily ill or become dependent on
outside support. This would reduce the need for migrants to return to the
countryside. Rural–urban migration has been a major source of labor needed to
fuel China’s economic growth. The joint efforts by three-generation households
have played an important role in enabling large numbers of people to move
to the cities. More could be done to protect households as they manage this
move.
References
Bartel, A., and P. Taubman. “Health and Labor Market Success: The Role of Various
Diseases.” Review of Economics and Statistics 61,1(1979):1–8.
18 CHEN FENGBO ET AL.
Chang, H., X. Dong, and F. MacPhail. “Labor Migration and Time Use Patterns of the
Left-Behind Children and Elderly in Rural China.” World Development 39,12(2011):
2199–210.
Chen, C., S. Ding, S. Cook, and M. Pong. “Coming Home: The Return of Migrant Workers
with Illness or Work-Related Injuries in China’s Hubei and Sichuan Provinces.” United
Nations Research Institute for Social Development (UNRISD) working paper 2014–5,
Geneva, Switzerland: UNRISD, 2014.
Chen, J. “Internal Migration and Health: Re-examining the Healthy Migrant Phenomenon in
China.” Social Science & Medicine 72,8(2011):1294–301.
Croll, E. From Heaven to Earth: Images and Experiences of Development in China. London:
Routledge, 1994.
Cropper, M.L. “Health, Investment in Health, and Occupational Choice.” Journal of Political
Economy 85,6(1977):1273–94.
De Brauw, A., J. Huang, S. Rozelle, L. Zhang, and Y. Zhang. “The Evolution of China’s
Rural Labor Markets during the Reforms.” Journal of Comparative Economics
30,2(2002):329–53.
Dewen, W., C. Fang, and Z. Guoqing. “Employment and Wage Determination of Rural
Migrant Workers : The Role of Education and Training.” China Economic Quarterly
4(2008):1131–48.
Fan, C.C. “Rural-Urban Migration and Gender Division of Labor in Transitional China.”
International Journal of Urban and Regional Research 27,1(2003):24–47.
Fan, C.C., and W.W. Wang. “The Household as Security: Strategies of Rural–Urban Migrants
in China.” Migration and Social Protection in China. R. Smyth and I. Nielsen, eds.
Hackensack, NJ: World Scientific, 2008, pp. 205–43.
Fang, C. “Demographic Transition, Demographic Dividend, and Lewis Turning Point in
China.” China Economic Journal 3,2(2010):107–19.
Freedman, R., B. Moots, T.-H. Sun, and M.B. Weinberger. “Household Composition and
Extended Kinship in Taiwan.” Population Studies 32,1(1978):65–80.
Giles, J., and R. Mu. “Elderly Parent Health and the Migration Decisions of Adult Children:
Evidence from Rural China.” Demography 44,2(2007):265–88.
Haberfeld, Y., R.K. Menaria, B.B. Sahoo, and R.N. Vyas. “Seasonal Migration of Rural Labor
in India.” Population Research and Policy Review 18,5(1999):471–87.
Kwong, J. “Educating Migrant Children: Negotiations between the State and Civil Society.”
China Quarterly 180(December 2004):1073–88.
Li, H., and S. Zahniser. “The Determinants of Temporary Rural-to-Urban Migration in
China.” Urban Studies 39,12(2002):2219–35.
Mou, J., J. Cheng, D. Zhang, H. Jiang, L. Lin, and S.M. Griffiths. “Health Care Utilisation
amongst Shenzhen Migrant Workers: Does Being Insured Make a Difference?” BMC
Health Services Research 9(2009):214.
Mu, R., and D. van de Walle. “Left Behind to Farm? Women’s Labor Re-allocation in Rural
China.” Labour Economics 18,S1(2011):S83–97.
Pang, L., A. de Brauw, and S. Rozelle. “Working until You Drop: The Elderly of Rural China.”
China Journal 52(July 2004):73–94.
Roberts, K., R. Connelly, Z. Xie, and Z. Zheng. “Patterns of Temporary Labor Migration of
Rural Women from Anhui and Sichuan.” China Journal 52(July 2004):49–70.
Schmink, M. “Household Economic Strategies: Review and Research Agenda.” Latin
American Research Review 19,3(1984):87–101.
Household Structure and Rural Migration in China 19
Schultz, T.P., and A. Tansel. “Wage and Labor Supply Effects of Illness in C ˆ
ote d’Ivoire and
Ghana: Instrumental Variable Estimates for Days Disabled.” Journal of Development
Economics 53,2(1997):251–86.
Scoones, I. “Sustainable Rural Livelihoods: A Framework for Analysis.” Institute of
Development Studies (IDS) working paper 72, Brighton, United Kingdom: IDS, 1998.
Tan, C. “‘Left-Behind’ in the Countryside: Rural Households in Gansu, China.” Master’s
thesis, Department of Anthropology, McGill University, Montreal, 2009.
Todaro, M.P. “A Model of Labor Migration and Urban Unemployment in Less Developed
Countries.” American Economic Review 59,1(1969):138–48.
Wood, C.H. “Structural Changes and Household Strategies: A Conceptual Framework for
the Study of Rural Migration.” Human Organization 40,4(1981):338–44.
Yang, D., and P. Zhishui. “Migration and Poverty Reduction” [In Chinese.] China Rural
Survey 5(2003):2–9.
Yuesheng, W. “The Changing Family Structure in Contemporary China: An Analysis.” [In
Chinese.] Social Sciences in China 1(2006):96–108.
Zhang, Y., and F.W. Goza. “Who Will Care for the Elderly in China? A Review of the
Problems Caused by China’s One-Child Policy and Their Potential Solutions.” Journal
of Aging Studies 20,2(2006): 151–64.
Zhao, Y. “Labor Migration and Returns to Rural Education in China.” American Journal of
Agricultural Economics 79,4(1997):1278–87.
Zhao, Y. “Leaving the Countryside: Rural-to-Urban Migration Decisions in China.” American
Economic Review 89,2(1999):281–86.
Zhong, F., and X. Jing. “Demographic Transition in Rural China.” Rebalancing and
Sustaining Growth in China. H. McKay and L. Song, eds. Canberra, Australian Capital
Territory, Australia: ANU E Press, 2012, pp. 251–63.
