Migration, Remittances and Educational Investment
in Rural China
Mengbing ZHU#
GATE, École Normale Supérieure de Lyon
March 29, 2016
Abstract
Using rural household data from China Household Income Project 2013, this paper aims
at investigating the impacts of migration and remittances on school enrollment and
educational investment. Somewhat, we find they both play a negative role. First, both
migration and remittances adversely affect educational decision, especially for children in the
older age group. Second, we provide evidence of negative and statistically significant
associations between migration-sending households/remittance-receiving households and
educational investment. In households with one child at school, households without migrants
or remittances care more about the quality of compulsory education. Two reasons can be
given to interpret the lower investment in education in households with migrants or
remittances: One is that households with migrants or remittances are much poorer and have to
spend more at the margin on food and health care; Another is due to the relatively low return
to education in rural China.
Keywords: Migration, Remittances, Educational investment
JEL Codes: J61, R23, D12; I23
#
École Normale Supérieure de Lyon, F-69342 Lyon, France; CNRS GATE Lyon-St Etienne, 93 Chemin des
Mouilles, Ecully, F-69130, France, e-mail: [email protected].
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1
1. Introduction
Rapid demographic and economic changes have been taking place in China since 1978.
One of the most important changes in recent years is a rapid increase in the number of
rural-urban migrants. Two main reasons caused this Great Migration: one is the economic
reforms implemented in China since the late 1970s, which improved the agriculture
productivity in the rural areas and liberated a large amount of rural surplus labor (Lewis 1956;
Lin 1992; John Knight et al. 2010); the second is the release of administrative controls
(Hukou policy) on rural-urban labor mobility. Before the economic reforms, there was a
significant segregation between urban and rural residents, which restricted their mobility. As a
result, rural-urban migration in the early 1980s amounted to less than 2 million people (Sheng
2000). Then since the early 1990s, the food rationing system, which restricted the rural-urban
mobility, was abolished, and the registration system was gradually released as well. On the
other hand, along with the rapid development of urban enterprises, there was a huge income
gap between non-farm and farm employment, which attracted the rural surplus labor force to
migrate to urban areas (Cai 2007), leading to the increase of rural-urban migration in China.
Recent estimates from the National Bureau of Statistics reported that the total number of
migrant workers reaches as far as almost 274 million in 2014, indicating that there is one
migrant among every six Chinese people. Wealth of studies have been analyzing the
consequences of the Great Migration on the social and economic changes in China in recent
years, including its effects on the economic society, i.e., migration promotes the transfer of
rural surplus labor force, accelerates urban industrialization and urbanization (Cai, 2010); the
mobility of rural-urban migration helps to narrow the income gap between urban and rural
areas and to coordinate the urban-rural development (Li et al. 1999); the impact on the
natives' labor market outcomes in the destination cities (Combes et al. 2015); the effect on
rural household income as well as on the left-behinds in rural areas (Taylor et al 2003,
Démurger and Li 2013, Giulietti et al. 2013). With more and more individuals migrate to
urban areas, the number of children left-behind in rural China is dramatically increasing
(Démurger and Xu, 2015). Based on Census 2010, recent estimate reports that, the amount of
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2
left-behind children in rural China is over 61 million1, accounting for 21.88% of the total
children in China. With such a great number of children left-behind, the impact of Great
Migration on left-behind children is worth to study.
Given the importance of migrants, there is little doubt that the mobility of the rural
surplus labor contributes to the income growth in the rural areas, and this may go through two
transmission mechanisms: first, as part of the labor force in rural household, the transfer of
rural surplus labor stimulates the marginal productivity of the labor left-behind who are
engaged in agriculture, which may in turn increase the average income of rural labor force
and benefit the growth of income in rural areas; second, rural-urban migrants earn more
money than they did in rural areas, and they may allocate part of their income to remit back,
namely, remittances. Consequently, it acts as part of the transfer income and has a strong
impact on household total income in rural China. In general, migration increases household
income, which will in turn affect the household consumption patterns and investment
decisions.
As an important component of household investment on human capital, expenditures on
education play a vital role in human capital accumulation and improvement. Along with the
economic development and income growth in rural areas, the level of education increases.
However, there still exists disparities in schooling between rich and poor areas. For instance,
Wang (2003) provides evidence that children from poor households are less likely to complete
junior high school. With a particular focus on schooling, this paper studies the effects of
migration and remittances on school enrollment and educational investment in rural China.
Generally, there are three fundamental channels through which migration may affect
investment in education in rural areas: First, income, especially through remittances from
migrants, may have a direct impact on educational investment. As part of total household
income, remittances sent back home increase household income and relax the budget
constraint, therefore affecting the consumption patterns (Taylor and Mora 2006) including the
educational investment decision. Some recent literatures find a positive relationship between
remittances and educational investment, referring mostly to developing countries, such as
Ghana (Adams et al 2010; 2013), the Philippines (Yang 2008), Mexico (Taylor et al. 2006) or
1
!
<Research report on children left-behind in Rural China> (2014), published by China Women’s Federation.
3
Kenya (Hines et al 2015). However, on the other hand, since most migrants are unskilled
workers, who are less educated relative to local workers (Démurger et al., 2009; Deng et al,
2010), it is not easy for migrants to secure a stable and well-paid job in cities. Studies find
that most rural migrants work primarily in the informal sector (Cai et al., 2008; Démurger et
al., 2009) and face poor and unsafe working conditions. Thus, they may be confronted with
unemployment risk at destination. As a result, considering the long-term livelihood, the
left-behinds (mostly elderly and children) may decrease consumption including education
expenditure and accumulate their saving. Second, the lost of household labor or the absence
of parental migrants may play a negative or negligible role on educational performance, and
also educational investment. The mobility of labor may impose a social cost on left-behinds
(Démurger 2015), and since the lack of labor, children left-behind have to spend more time on
agriculture while less on school. Especially the parental absence, which is always consistent
with decrease in child care and supervision, may adversely affect household investment in
education. Third, the perceived returns to education in China may have a effect on educational
investment in opposite ways. On the positive side, Stark et al. (1997) shows that migration
can lead to higher level of human capital in the source country, so households may invest in
education of certain members, who then migrate and earn a higher wage than they would
otherwise (Lucas and Stark 1985). Thus there is a possibility that households may increase
their spending on education, depending more on the perceived returns to education. On the
negative side, because of the lower returns to education in rural China, children left-behind
may be attracted by higher income of migrant workers. Then education may be viewed as a
consumption good rather than an investment good (Song et al 2005), so households may
reduce their spending on education. As a result, children may be experiencing a negative
shock by the decreasing of household labor and have to drop off from school to do agriculture
work at home or migrate.
The study of educational investment trends in function of migration and remittances is of
key relevance and some studies provide evidence of the net negative or net positive
relationship between migration/remittances and educational expenditure in Nepal (Bansak et
al 2009), Ecuador (Chaaban et al 2011; Calero et al 2009), Salvador (Cox et al 2003) and
Mexico (Alcaraz et al 2012). However, systematic research on how investment in education is
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4
affected by migration and remittances in rural China is still missing.
As far as we know, very few studies have investigated the relationship between
migration, remittances and educational investment in China. The main contribution of this
paper refers to two aspects. First, unlike previous studies using small scale databases, this
research relies on the most recent data from China Household Income Project 2013, which
was conducted in 12 provinces and 2 province-level municipalities in China, covering around
10,000 rural households and 39,065 individuals. The data contains detailed information on
individual characteristics, household income as well as consumption components in rural
China, which allows us to measure the effect of migration and remittances on investment in
education. Second, migrants may affect educational investment in ways that remittances don’t
adequately capture (Taylor and Mora 2006). As emphasized before, the mechanisms through
which migration and remittances influence educational investment are not the same. For
instance, remittances may affect schooling mainly through its effects on total income and
budget constraint, while migration may have an impact on consumption behavior or habits of
the left-behinds, including the decision on investment in education. Most studies estimate the
effect of migration on school enrollment, school performance and educational expenditure.
However, since the two effects are different, few papers are concerned with the differential
effects of migration and remittances effects on educational investment.
This structure of this paper proceeds as follows. Section 2 reviews the available literature.
Section 3 describes the data from rural China and presents some descriptive statistics. Section
4 shows the empirical strategies used in this paper. Then the results are presented in Section 5
and Section 6 provides tentative explanation regarding our finding. Section 7 concludes.
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5
2. Literature Review
In China, as industries expanded and agricultural productivity improved, rural-urban
migration is unavoidable and becoming the largest labor flow in the world history. Despite the
contribution that rural migrants made for urban economic development, they still cannot have
the same rights as urban residents because of the institution segmentation in China. As a result,
rural migrants can’t move together with their families, and the left-behinds are usually
children and elderly. In terms of the effect of migration on rural areas, researches show
contrasting impacts on farm production (Rozelle et al. 1999; Taylor et al. 2003), labor force
participation (De Brauw et al. 2002, Démurger and Li 2013), poverty (Du et al. 2005) and
educational performance of children (Chen et al. 2009). Migration may affect left-behinds
through two main channels. On one hand, acting as an insurance of total income, remittances
may ease the budget constraint and change the living standards of the left-behinds. On the
other hand, the mobility of labor may impose a social cost on left-behinds (Démurger 2015),
especially because the absence of parental migrants on children left-behind.
More and more researchers focus on the effect of migration on education, and study two
aspects. The majority of studies investigate the tradeoff between attending school and
working. They provide evidence of a positive influence on school enrollment and children’
school performance in most developing countries, such as Nepal (Bansak et al. 2009),
Ecuador (Calero et al. 2009, Chaaban et al. 2011), El Salvador (Cox et al. 2003) and Mexico
(Alcaraz et al. 2012). By contrast, some studies argued that because of the mobility of labor in
the household, children left-behind have to do more housework and spend less time on school,
which leads to a negative effect of migration (Battistella and Conaco, 1998).
As for China, previous studies investigate the effect of parental migrants on school
performance and provide contrasting results. Some scholars report that the absence of parental
migrants may have multiple adverse effects on school enrollment and educational
performance (Li, 2004; Ye et al. 2006; Lv, 2006; Hu and Li, 2009; Tao and Zhou, 2012). Tao
and Zhou (2012) find a negative correlation between parental migrants and school
performance of left-behind children, and the adverse effect increases with parents leaving for
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6
a longer period of time. Using data from a survey of 36 primary schools in 12 townships in
Shanxi Province, Chen et al. (2009) employ difference-in-difference method to examine the
effect of migration on school performance. They find no significant negative effect of the
absence of parents on the educational achievement of children. In contrast, they provide
evidence that the educational performance improves in households with father migrated.2
Recently, some studies have investigated the relationship between remittances and
household education consumption decision and found a positive effect in most developing
countries, showing that households who receive remittances spend more on education, in
Ghana (Adams et al. 2010; 2013), the Philippines (Yang 2008), Mexico (Taylor et al. 2006)
Guatemala (Adams and Cuecuecha 2010) and Kenya (Hines and Simpson 2015). Yang (2008)
find that an increase in remittances lead to human capital accumulation, with more children
attending school and educational investment rising in the Philippines. Using the
Working-Leser Model, Adams and Cuecuecha (2010) analyzes the impact of migrants’
remittances on household consumption and investment decisions in Guatemala and they find
that households receiving international remittances spend less at the margin on food
consumption, and instead spend more at the margin on education and housing. Based on the
same model, Göbel (2013) analyzes the impact of remittances on household’s budget
allocation in Ecuador and provides evidence of a positive relationship between remittances
and spending on education, showing that households receiving remittance have a stronger
motivation to accumulate human capital. Hines and Simpson (2015) develop a theoretical
model predicting remittances as a mechanism to transfer migrants’ income, which
independently affects household consumption patterns. They find that increasing remittances
enhance educational investment in Kenya.
2
! Beside!school!achievement,!a!significant!negative!effect!is!also!found!in!terms!of!the!effect!of!migration!on!food!and!
nutrition!(Gao!et!al!2010;!Kong!et!al!2010).!
!
7
3. Data
3.1. The database
The data employed for this study come from the China Household Income Project
conducted by the China Institute of Income Distribution, with the reference year of 2013
(CHIP 2013). The households surveyed were drawn from the sampling framework of the
regular household survey annually conducted by the National Bureau of Statistics of China
(Luo and Li, 2016). The field survey includes detailed information about the demographic
characteristics, the household structure and employment, while the information about items of
income and expenditure is provided directly from the NBS’s regular survey. The survey
covers 12 provinces and 2 province-level municipalities in China, with approximately 10,000
rural households and around 39,065 individuals, scattered over eastern (Beijing, Liaoning,
Jiangsu, Shandong, Guangdong), central (Shanxi, Anhui, Henan, Hubei, Hunan), and western
(Gansu, Sichuan, Chongqing, Yunnan) China. After cleaning ourselves outlier on the
household data on expenditure, the final sample size is 9,702 households.
Particular focus in this paper is on the impact of migration and remittances on
educational investment, so the definitions of migrant-sending and remittance-receiving are
worth to be noted. The definition of migrants used in this paper is rural residents who worked
outside for at least 180 days or were working outside the county surveyed in 2013. The
migrant-sending household is defined as household with at least one migrant, while
remittance-receiving household is household has received remittances in 2013 (following
Démurger and Wang, 2016). Migrant-sending households and remittance-receiving
households do not perfectly match, showing that there are some rural households without
migrants but receive remittances3.
To investigate the differential contributions of migration and remittances on educational
investment, the household sample is divided into 4 groups: non-migrant sending and
non-remittance receiving households, migrant-sending and remittance-receiving households,
3
In this case, remittances may come from relatives or some short-term migrants who worked outside for less
than 180 days but remitted in 2013.
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8
non-migrant
sending
but
remittance-receiving
households,
migrant-sending
but
non-remittance receiving households. As Table 1 shows, 2461 rural households send migrants
and receive remittances, accounting for 25.37% of the total number of households; 1,733
households (17.86% of the total households) are migrant-sending but do not receive any
remittance; 7.94% of the households receive remittances while not send any migrant; and
4,738 households (48.84%) do not send any migrant or receive any remittance.
[Table 1 here]
The summary statistics for income and income shares are listed in Table 2. Compared to
non-remittance receiving households, the total household income and per capita income4 are
clearly smaller, either in the remittance-receiving households with migrants or not. The
average net income per capita is lowest in households with migrants and remittances (6899
Yuan), which is just 54% of that in households without migrant or remittance (12,698 Yuan).
It may indicate that since less wealthy families are more likely to send migrants and receive
remittances. To understand it better, we also compare the distribution of households among
each income per capita quartile and find evidence that the proportion of households that send
migrants and receive remittances decreases form the bottom to the top income groups. In
other words, compared to richer households, poorer families prefer to send migrants out for
remitting purpose to finance the whole household. In addition to household income, the shares
of transfer income shown in Table 2 further reflect that transfer income is an essential
composition in remittance-receiving households. The corresponding shares of remittances in
total household income are around 35.5% and 22.5% in migrant-sending and
remittance-receiving households and non-migrant sending but remittance-receiving
households. By contrast, in the households without remittances, the share of transfer income
is much smaller.
[Table 2 here]
As with expenditures, household consumption expenditure is aggregated into five
consumption categories (following Démurger and Wang 2016): 1) food (including food,
4
As for the definition of income and expenditure per capita, we impose income to be for all the members in the
household, including migrants. In contract, expenditures are just for permanent residents, since the consumption
of migrants is not counted into the total expenditure. So per capita expenditure (excluding migrants) is defined as
per capita expenditure of each permanent resident.
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9
clothing and miscellaneous goods and services); 2) durables goods (including expenditures on
facility and services, communication and transportation); 3) housing; 4) education 5
(including spending on education such as tuition, textbooks, accommodation or other
school-based fees on children, entertainment, and cultural activities); 5) health care.
[Table 3 here]
Based on these consumption items, Table 3 provides a comparison of expenditure and
average budget shares based on the four household groups. Consistent with the income results,
the consumption results also reflect that total household expenditure is much lower in
remittance-receiving households than its counterpart, i.e. households without remittances.
Household expenditure per capita listed in the table reveals that it is highest in
migrant-sending households without remittances, whereas smallest in remittance-receiving
households without migrants. As for spending on education, both total educational
expenditure and educational expenditure per child6 are significantly higher in households
without remittances while much lower in remittance-receiving households. It is reasonable
since remittance-receiving households are less wealthy or with more elderly left-behinds, and
then more likely to spend money on food or medical care rather than education. And this may
also explain that remittance-receiving households tend to spend less on education (accounting
for 8.1% and 7.62% in remittance-receiving households with migrants or without migrants
respectively) while households without remittances tend to spend more (constituting 8.19%
for households without migrant or remittances and 9.25% for households with migrants but
received no remittances).
3.2. School Enrollment
In order to capture the impact of migration and remittances on school enrollment, we
restrict samples on child-level database with children aged between 7 and 18 years old, who
should attend primary school (aged between 7~12), junior secondary school (aged between
13~15) and senior high school (aged between 16~18). The final child-level sample size is
5
Expenditures on durables and housing are treated as “consumptive investment” (de Brauw and Rozelle 2008),
while expenditures on education and health care are counted as human capital investment.
6
Education expenditure per child is defined as educational expenditure on each child who was at school in
2013.
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10
4,863.
7
[Table 4 here]
Among all the children aged 7~18, the enrollment rate in rural China in 2013 is about
88%. Table 4 compares school enrollment across different types of households. In households
without migrants or remittances, over 90% of children attend school while in
remittance-receiving households without migrants, the enrollment rate is almost 10 percent
lower. In terms of the gender difference, enrollment rates are 89.20% for girls while 87.27%
for boys, with a similar tendency in each type of households. Before discussing the age
disparities in this table, the Nine-year Compulsory Education System in China is worth to be
explained. Following most developed countries, China’s government made education
compulsory and free since 1986, stipulating 9 years of compulsory education including six
years of primary school and three years of junior secondary school. Consequently, the
enrollment rate is much higher in lower age group, with over 96% of children aged between
7~12 attending school and for the age group 13~15 over 93% of children enrolled in school.
On the other hand, the enrollment rate is only 70.91% in the upper age group 16~18,
indicating that nearly 30% of children drop off high school, which is mainly due to the high
cost or the competition for the entrance. The difference of school enrollment between
different household groups is largest in this older group. The comparison shows that 76.29%
of children aged between 16~18 attend school in households without migrants or remittances,
whereas the high school enrollment rate is only 58.82% in households with remittances but
without migrants. In addition, the comparison of three regions reveals that the total school
enrollment is lowest in households in western areas and highest in eastern areas, which is
consistent with the difference of economic growth between the three regions.
7
! Table A.1 (See Appendix) illustrates the descriptive statistics of children aged between 7~18 and their corresponding
household-level characteristics. As shown, households without migrants or remittances tend to have fewer children and fewer
old dependent people, while the average education level of household adult members and the proportion of households with
at least a member with higher education (above high school education) are much higher. By contrast, although with more
labor in migrant-sending households with remittances, there are more children and old dependent people, less households
assets as well.!
!
11
3.3. Educational Investment
Table 5 documents the differences in educational expenditures, on the household-level
database with at least one child at school in 20138. The comparison shows that relative to
non-receiving households, remittance-receiving households have more children enrolled in
school. However, expenditures on education are much lower in remittance-receiving
households. Educational investment per child is the lowest in remittance-receiving households
with non-migrant sending (2948 Yuan per child), and the highest in households without
migrants or remittances (4406 Yuan per child). When it comes to the budget share of
education, in households with remittances but no migrant, only 12.79% of total expenditure
are allocated to education. Whereas in households without migrants or remittances, the
corresponding budget share reaches 14.89%.
[Table 5 here]
4. Methodology
4.1. Measuring the impact on school enrollment
Based on the child-level database aged between 7~18, we use binary model (1) given
below to estimate the effect of migration and remittances on educational decision:
Child(school) =!" + !# $%&'(ℎ%*+ +!, -ℎ.*+ + !/ 01 + !2 Household Asset+!2 Province +
3
(1)
where the dependent variable Child(school) is 1 if the child is enrolled in school, and 0
otherwise. Household, the main variable of interest, is the household type based on
migrant-sending and remittance-receiving. Other explanatory variables include -ℎ.*+, a
vector of child-level characteristics, such as age, gender, age-group and age-gender category9;
8
! The summary statistics of household characteristics in households with at least one child at school display in Table A.2(See
Appendix). Seen from the table, beside fewer children and fewer old people, the household asset is much higher in
non-receiving households. On the contrary, households with remittances tend to have more children and elder people, and to
be less wealthy.!
9 “Age-Group” is a dummy variable, referring to children aged between 16~18. While “age-gender category’ is 1 if the child
is a boy aged 16~18, 0 otherwise.
!
12
Household characteristics, 01 , contains not only the average age of adults, the average
education of adults, but also household composition variables such as the number of children
below age 6, the number of children aged between 7 and 12, the number of children aged
between 13 and 15, the number of children aged between 16 and 18, the number of household
members aged between 19 and 55, the number of household members aged between 56 and
65, the number of elderly (over 66 years old). A dummy variable “Having at least a member
with higher education” indicates whether the educational decision may be affected by the
most educated household member10. Since the higher investment in education may be due to a
higher level of wealth, so we also include household wealth, Household Asset, measured as
the logarithm of housing value and total agricultural land. Province stands for provincial
dummies that account for unobservable variables which can affect the effects of migration and
remittances at provincial level.
4.2. Measuring the effect on educational investment
Another question relates to whether migration and remittances have differential impacts
on household educational spending on children at school. To examine this, we estimate a
model of household expenditure on education. In the database, nearly 10% of households
have a value of zero for this variable, which suggests that these households, with at least one
child at school, spend zero on education in the survey year. An OLS model assumes that the
dependent variable is normally distributed, which may be not appropriate here since the
educational investment is censored at zero. To take the censored spending on education into
account, a Tobit Model is employed11. The model is specified as follows:
45∗ =!" + !# $%&'(ℎ%*+5 + !, 057 + !/ $%&'(ℎ%*+89''(:5 + !2 Province + 3
Y=
45∗ , if845∗ > 0
0, if845∗ = 0
(2)
(3)
Where 45∗ is the latent variable and Y is the observed variable.The main dependent
variable is 45 , the logarithm of household i spending on education, is a dummy variable,
which takes the value one or zero based on migrant-sending or remittance-receiving. Right
10 Hines and Simpson (2015) provide evidence that a highly educated family member in the household has a stronger
preference for investment in education.!
11
! It should be noticed that because the dependent variable is the logarithm of educational investment, so we rescaled
expenditure on education so that the minimum value is one instead of zero.!
!
13
hand variables are almost the same as in the model for education decision (see above), except
we also control the number of children at school in this estimation.
Following the method proposed by McDonald and Moffitt (1980), we can decompose the
estimated coefficient into two marginal effects: One is the unconditional marginal effect, the
other is conditional marginal effect on the fact that the dependent variable is already over zero.
The marginal effects can be shown as follows:
IJ K
ILM
= N O !5 /Q
IJ K|KS∗ T"
ILM
=!5 1 −
Where8O =
function, and
L]
^
(4)
WX W
Y W
− N O , /Z(O),
(5)
, N is the normal density, Z is the cumulative normal distribution
Q is the standard error of the error term 3.
The endogeneity of migration and remittance decisions
The potential problem related to this research is the endogeneity of migration decision as
well as that of the receipt of remittances, which could lead to biased estimates of the impacts
of migration and remittances on educational investment in the Tobit model. To solve this
endogeneity problem, we employ instrumental variables, which are correlated with migration
or remittances decision, but not related to household spending on education. Previous studies
demonstrate the roles that social networks (Munshi 2003; de Brauw and Harigaya 2007; Tylor
and Mora 2006; Adams and Cuecuecha 2008; Hines and Simpson 2015), distance to the
railway station (Adams and Cuecuecha 2013; Hines and Simpson 2015), the fraction of
households receiving remittance (Adams and Cuecuecha 2013) play in the decision to migrate
or remit.
Following the existing literature, we constructed several instrumental variables. The
fraction of households receiving remittance in the original village excluding household i is
used as an instrumental variable for migration decision. The assumption here, as documented
before, is that a higher fraction of remittance-receiving households in the village will play a
strong role in migration decision, through stimulating more labor force to migrate. In terms of
the receipt of remittances, we also take the fraction of households receiving remittance in the
original village excluding household i as an instrumental variable to explain the remittance
decision. It is more clear that the fraction in a village may have a positive effect on remittance
!
14
decision. In addition, borrowed from Adams and Cuecucha (2013), the second instrument is
the distance to the nearest county times the age of household head. The distance to the nearest
county is a proxy for the economic development. Mainly due to the difficult transport
facilities, a village far away from the nearest countyseat may be less wealthy, which may
increase the probability (the need) for households to receive remittances.
5. Empirical Findings
5.1. The effect of migration and remittances on school enrollment
[Table 6 here]
Table 6 displays estimates of the Probit model measuring the effect of migration and
remittances on educational decision. When we use “migrant-sending households or not” as
the main explanatory variable, the result shows that children in households with migrants are
less likely to attend school than those in households without migrants. It also provides a
evidence of significantly negative correlation between remittance-receiving households and
school enrollment. The school enrollment is 1.5% lower for children in remittance-receiving
households than for those in households without remittances. Since the roles that
migrants/remittances
play
may
be
different
in
households
with
or
without
remittances/migrants, we then try to separate the differential impacts. For instance, to capture
the different impact of migration, using households without migrants or remittances and
remittance-receiving households without migrants as reference groups separately, it is clear
that the coefficients of non-remittance receiving households with migrants in Column 1
(Table 7) and remittance-receiving households with migrants in Column 3 (Table 7) can
reflect the effects of migration on educational decision in non-remittance households and
remittance-receiving households respectively. There are two fundamental findings: First, the
effect of migration is only significantly negative for children in households without
remittances, and the marginal effect is -0.029. By contrast, the effect of migration in
remittance-receiving households is positive at 10 percent level; Second, it seems that only in
households without migrants, the impact of remittances on educational decision is
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15
significantly negative and the marginal effect reaches -0.053.
[Table 7 here]
The conclusions above document the different impacts of migration and remittances on
educational decision. Although it shows a decreasing trend on school enrollment for children
both in migrant-sending households and remittance-receiving households, the effects of
migration and remittances are diverse when taking the four groups into account. The effect of
migration is significantly negative in households without remittances, which may be due to
the absence of parental migrants in the household so children left-behind are more likely to
work(migrate) rather than to attend school. At the same time remittances can act as an
insurance mechanism, which is illustrated by the statistically positive effect of migration in
households with remittances. Furthermore, only in households without migrants, the impact
of remittances on education decision is significantly negative. It is reasonable since
households with remittances but non-migrants are much poorer and have more elderly people,
consequently they have to spend more on food and medical care instead of education.
We further use sub-samples of the child-level database, which include children aged
between 7~12 (who should be in primary school), children aged between 13~15 (who should
attend secondary junior high school) and children aged between 16~18 (who should enroll in
senior high school) respectively. Using the same approach, marginal effects are listed in Table
8. Unsurprisingly, the coefficient of remittance-receiving household is only statistically
negative in the subgroup with children aged between 16~18, who should enroll in high school
while is not compulsory and costly. It indicates that receiving remittances significantly
decreases the likelihood of attending senior high school and the marginal effect is -0.088.
Also Table 8 provides evidence that in the older group, only the impact of remittances in
households without migration is significantly negative in households without migrants, the
school enrollment rate is nearly 12% less in households with remittances than households
without remittances. In the subgroup with children aged between 13~15, the impact of
remittances is significant and positive in households with migrants, whereas the impact of
migration is significantly negative in households without remittances.
[Table 8 here]
!
16
5.2. The effect of migration and remittances on educational investment
To investigate the impact of migration on educational investment, Table 9 reports the
estimation results of the Tobit Model, showing the unconditional marginal effects and the
marginal effects conditional on a positive educational investment. There is a negative
association between migration-sending households and household spending on education,
which is statistically significant. The unconditional marginal effect is -0.194 for all of the
households, and the marginal effect for households with positive educational investment is
-0.183. After using instrumental variables, both unconditional marginal effect and conditional
marginal effect increase, indicating that households with migrant spend significantly less on
children at school. The bottom of the table shows the result of Wald test of the exogeneity of
the instrumented variable. The test statistic is significant, which implies that the coefficient in
the Tobit model is underestimated and the estimated marginal effects from IV-Tobit are
consistent and unbiased.
[Table 9 here]
The estimated result reveals a negative correlation between investment in education and
migration-sending households. Then what is the role that remittances play in educational
investment? Table 10 lists the marginal effects from Tobit and IV-Tobit models for
remittance-receiving versus non-receiving households. We can see that households who
receive remittances has a lower educational expenditure, one possible explanation is that since
migrants are young labor in the household whereas the ones left-behind are elderly, who may
less educated and less value education. Then considering the endogeneity of remittances, the
marginal effects of remittance-receiving households is much larger. And the Wald test
indicates that the marginal effect from Tobit estimation is inconsistent and biased downward
by the endogeneity of remittances.
[Table 10 here]
Consistent with the different effects of migration and remittances on educational decision,
the impact of migration on educational investment may also be affected by the educational
level of children at school. Since primary and secondary junior high school are free in China,
if a household spends more on children at primary school or junior high school, it may
!
17
indicate a higher concern about the quality of the school. On the other hand, school is not
compulsory but costly at senior high school, so if households spend more on children at
senior high school or college, it may imply that they care more about higher education and
long-run return. Since the survey just provided information on total educational expenditure,
and not per child educational spending, we restrict the samples to households with only one
child at school (67.53% of our total samples) to estimate the different impacts of migration on
educational investment based on different subgroups of households (households with one
child at primary school or secondary junior high school and households with one child at
senior high school or college).
[Table 11 here]
Table 11 reports the associated unconditional marginal effects and conditional marginal
effects on households with positive educational expenditure based on migrant-sending
households or not. Wald test show that for households with one child in primary school or
junior high school, the marginal effect results from Tobit Model seems to be inconsistent and
biased. By contrast, it does not seem to be the same case for households with one child in
senior high school or college. Interestingly, households with migrants tend to spend much less
on compulsory education, indicating that households without migrants concern more about
the quality of compulsory education. On the other hand, for households with one child in
senior high school or college, the marginal effects are still significant and negative but much
smaller. Households with migrants spend almost 46 percent less on education than households
without migrants, showing that households without migrants also concern more about higher
education. In addition, the different marginal effects of remittances on educational investment
based on different subgroups of households are listed in Table 12. In households with children
in primary school or junior high school, the marginal effects from IV-Tobit model shows a
significantly negative marginal effect between remittance-receiving households and education
expenditure. However, in households with children at senior high school or college, Tobit
result shows that remittance-receiving households do not significantly affect investment on
education.
[Table 12 here]
!
18
Three findings are worth emphasizing: First of all, both households with migrants and
households with remittances are more likely to spend less on education, and the marginal
effect of migrant-sending households is much larger than that of remittance-receiving
households. Second, focusing on households with one child at school, we find that both
households with migrants or remittances tend to spend much less on compulsory education,
indicating that households without migrants or remittances value more about the quality of
compulsory education. Third, only for households with one child in senior high school or
college, the marginal effect of migrant-sending households is significantly negative, showing
that households with migrants spend almost 46 percent less on education than households
without migrants.
6. Explaining on the Low Investment in Education
6.1. Marginal Budget Shares
The results above show that both migration and remittances have a negative effect on
investment in education, and that the effect of migration is much larger than that of
remittances. To interpret this result, we need to analyze the household marginal educational
expenditure, as well as the marginal expenditure pattern of the households. That is, how
migration and remittances affect the expenditure pattern at the margin. To solve this question,
a Working-Leser Model (Working, 1943 and Leser, 1963) is employed, which relates the
budget share linearly to the logarithm of total household expenditure. The Model can be
written as follows:
_75 =
`MS
JabS
= !5 + d5 *%efgh5 + i5
(6)
where -75 represents expenditure on good j in household i, fgh5 is the total
consumption for household i , !5 and d5 are the parameters to be estimated, and i5 is the
error term. Then _75 reflects the average budget share of good i, and it requires
-75 /
fgh5 = 1. In addition to the basic Working-Leser Model, other variables which may affect the
budget share of different categories of goods should be taken into account. To investigate the
effect of migration and remittances on educational investment, we compare the marginal
!
19
budget shares of different consumption categories in different types of households. In addition,
when comparing consumption behaviors, various variables such as household composition,
household characteristics and geographic characteristics (province dummy) also need to be
taken into account. Then a specification for this paper is:
_75 = !5 + d5 *%efgh5 +
35 j5 + i5
(7)
j5 denotes the household characteristics which may influence the budget shares, for
instance, the average age of adults, the average education of adults (years), whether the
household has at least one member having higher education, household composition variables
such as household size, the number of children below age 6, the number of children aged
between 7 and 18, the number of household members aged between 56 and 65, the number of
elderly (over the age of 66)12, the logarithm of housing value and total agricultural land. Also,
we use province dummy variables to control the unobservable variables that may affect the
estimated results at the provincial level.
Taken from equation (7), the partial derivative of average budget share with respect to
the total expenditure can be derived as follows:
JabS
k_75 /kfgh5 =
lmMS
lnopS
q`MS
lnopS
lnopS
JabS r
=
lmMS
lnopS
JabS
-
`MS
JabS r
=
sS
JabS
(9)
Then the marginal budget share for good j in household i can be written as follows:
tu_75 = k-75 /kfgh5 = d5 + _75
(10)
Based on the definition of elasticity, the expenditure elasticity (v) is equivalent to:
v75 =
wxyMS
zxyMS
8=8
sS 8
8yMS
+1
(11)
In practice, the estimation technique used in the first step is an OLS Model. As
mentioned earlier, the household consumption components are aggregated into five
consumption categories: 1) food; 2) durables goods; 3) housing; 4) education; 5) health care.
Since in the samples two of the categories are censored at zero (education and health care
consumption), then a censored Tobit approach may be more appropriate. However, the sample
size censored at zero is very small (0.1% for education consumption and 5% for health care)
and there is not much difference between these two models, so we view this small size as
12
!
Using “the number of household members aged between 19 and 55” as the omitted variable.
20
omitted variables in the estimation and employ OLS model.
The objective of this section is to explain the larger effect of migration on educational
investment than that of remittances. So we use the household-level database with at least one
child at school in 2013. The average budget shares for five categories by migrant-sending and
remittance-receiving status of the household is documented (See Table A.3 in Appendix). It
reveals that in households with migrants or remittances, the average budget share of education
is relatively smaller, while they spend more on food and medical care.
The regression results based on Equation (7) for the five categories of commodities are
reported in Table A.4 and A.5 in Appendix. Then, taken these coefficients in the estimated
equations, the marginal budget share and elasticity of specific categories are listed in Table 13.
From Table 13, we can see that: compared to households without migrants or remittances,
households with migrants or remittances spend less at the margin on education, and the
difference is much larger between households with migrants or not. Specifically, at the mean,
households with migrants spend 5.82% less at the margin on education than households
without migrants. While households with remittances spend 4.59% less at the margin on
education than their counterpart. Why do they prefer to invest less on education? It may can
be explained by the marginal budget shares of other commodities. Households with migrants
spend more at the margin on food and health care, while households with remittances spend
much more at the margin on housing and health care. As mentioned before, households with
migrants or remittances are much poorer and with more elderly people in the household, so
they have to allocate more expenditure on food and health care, rather than education.
[Table 13 here]
!
21
6.2. Lower Returns to Education in rural China
Another explanation for the low investment of migrant-sending and remittance-receiving
households in children education may be related to the relatively low returns to education in
rural China. Wealth of studies estimate returns to education in China and find evidence that
both in urban areas and rural areas, returns to education have been increasing since the 1990s.
Using China Household Income Project in 1995 and 1999, Li and Ding (2003) report that the
returns to education in urban China is 2.43% in 1990 while it increased to 8.1% in 1999. Li
and Heckman (2004) investigate heterogeneous returns among individuals and find that the
returns to schooling in urban China are nearly 11% in 2000. Then the returns to education
decreases since 2004, due to the education expansion and employment difficulty. Using data
from the Urban Household Survey from 1998 to 2009, Yao et al (2013) find that returns to
education in urban areas is increasing and reaches to 10.3% in 2009. Despite the shortage of
peasant, returns to education increases for rural labor force since the 1990s. Based on China
Household Nutrition Survey, Deng and Ding (2013) find that returns to schooling in rural
China increases from 4.02% in 1988 to 8.2% in 2005. However, compared to urban China,
returns to schooling in rural China is still much lower. Based on China Household Income
Project in 1988, Li and Li (1994) report that the difference in returns between urban and rural
areas is 2 percent. The gap reaches up to 7 percent in 2001 (Li 2003), and then decreases to 2
percent in 2009 (Deng and Ding 2013).
In terms of rural migrants, mainly due to the market segmentation, a significant
difference exists between urban workers and migrant workers (Meng and Zhang, 2001; Deng,
2007; Xing, 2008). Xing et al. (2013) provide evidence that the overall returns to schooling of
migrant workers declined from 0.0751 in 2005 to 0.0466 in 2011, which is mainly due to
regional difference and moving barriers between regions and cities. The lower returns to
schooling in rural China may also be responsible for the lower investment in education.
Because lower returns, households with migrants or remittances may value education as a
consumption good, rather than an investment good. And since migrants are subject who
witness the declining returns trend, so it can also explain the larger disparity in educational
investment between households with migrants or without migrants.
!
22
7. Conclusion
This paper aims at investigating the impact of migration and remittances on school
enrollment and educational investment in rural China. Using the rural household data from
the China Household Income Project 2013, we find that in households without migrants or
remittances, over 92 percent of children attended school while in remittance-receiving
households without migrants, the proportion of children at school is almost 10 percent lower.
A huge school enrollment difference is found between four types of households with children
aged between 16 to 18. Then the comparison of the total educational expenditures or per child
education expenditure reveals that the educational investment is significantly higher in
households without remittances while much lower in remittance-receiving households.
To understand how migration and remittances affect education decisions in rural
households, a Probit model is employed. We find that both migration and remittances act as
negative roles in educational decision. And when take the age group of children into account,
the impact of migration and remittances is significantly negative and large in older age group.
As for the estimates of the impact of migration and remittances on education investment,
the censored Tobit model is used. To solve the potential problem of the endogeneity of
migration and remittances, instrumental variable strategy is employed. The result from Tobit
and IV regressions of educational expenditures on migrant-sending and remittance-receiving
households both provide evidence of significantly negative relationships. Still the influence of
migration is much larger. Then the subgroup results, households with one child at school,
show that both households with migrants or remittances tend to spend much less on
compulsory education, indicating that households without migrants or remittances value more
the quality of compulsory education. Moreover, only in households with one child in senior
high school or college, the marginal effect of migrant-sending households is significantly
negative.
To interpret the lower investment in education in households with migrants and
remittances, Working-Leser Model is employed to estimate the household marginal
expenditure patterns. Compared to households without migrants or remittances, households
!
23
with migrants or remittances spend much less at the margin on education. It is mainly due to
the fact that these households are much poorer and with more elderly people so they would
spend more at the margin on food and health care, while households with remittances spend
much more at the margin on housing and health care. Another reason is the relatively low
returns to education in rural China. As a result, households with migrants or remittances may
value education as a consumption good, rather than an investment good.
!
24
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Table 1
Distribution of households
Observations13
Share
Non-migrant and non-receiving
4,738
48.84
Migrant-sending and remittance-receiving
2,461
25.37
Migrant-sending but non-receiving
1,733
17.86
770
7.94
9,702
100
Non-migrant but receiving
Total
Source: Author’s calculation from 2013 China Household Income Project.
Table 2
Summary statistics by group-income and income share
Non-migrant
Migrant
Migrant
Non-migrant
non-receiving
receiving
Non-receiving
receiving
Total
Total net income(Yuan)
39388
29008
42191
31471
36628
Per capita net income (Yuan)
12698
6899
10990
9927
10702
3145
10248
1717
8715
5133
0
8826
0
6039
2718
Share of transfer income(%)
7.15
43.87
9.81
31.52
18.88
#Share of remittance(%)
0.00
35.54
0.00
22.48
10.80
4,738
2,461
1,733
770
9,702
Net transfer income(Yuan)
#remittance(Yuan)
Observations
Source: Author’s calculation from 2013 China Household Income Project.
Table 3
Summary statistics by household expenditure and expenditure budget shares
Total expenditure
Non-migrant
Migrant
Migrant
Non-migrant
non-receiving
receiving
Non-receiving
receiving
Total
28975
23944
31649
22966
27700
migrants)
9333
9845
14123
7286
10156
Education expenditure
2929
2224
3248
2114
2743
1460
1043
1506
966
1323
Budget share for food
47.20
48.40
45.70
49.20
47.40
Budget share for durable goods
15.40
15.20
16.80
15.30
15.60
Budget share for housing
21.80
21.00
21.40
20.10
21.40
Budget share for medical care
7.38
7.36
6.86
7.65
7.31
Budget share for education
8.19
8.10
9.25
7.62
8.31
Observations
4,738
2,461
1,733
770
9,702
Household Expenditure Per capita (excluding
#Per child education expenditure
Source: Author’s calculation from 2013 China Household Income Project.
13
If we restricted household samples to households with positive educational expenditure, the distribution of the
total samples is similar.
!
29
Table 4
Differences in school enrollment in children aged 7-18
Non-migrant
Migrant
non-receiving
receiving
90.51
86.42
Boy
89.41
Girl
At school(% of children)
Migrant
Non
receiving
Non-migrant
receiving
Total
88.41
80.82
88.16
86.33
88.41
77.99
87.27
91.75
86.52
88.41
84.92
89.20
96.98
95.08
95.85
94.65
96.04
Gender
Age
7
12
13
15
94.80
94.79
89.47
88.78
93.37
16
18
76.29
63.71
75.00
58.82
70.91
East
91.88
83.61
89.14
83.33
89.58
Centre
88.86
88.03
88.33
82.39
87.92
West
90.47
85.86
87.44
78.87
87.07
Region
Source: Author’s calculation from 2013 China Household Income Project.
Table5
The differences in education expenditures in children enrolled in school
Non-migrant
Migrant
Migrant
Non-migrant
non-receiving
receiving
non-receiving
receiving
Total
Numbers of children at school
1.35
1.41
1.31
1.43
1.36
Education expenditure
4406
3030
3981
2948
3854
Per child education expenditure
3490
2351
3284
2282
3061
Budget share of education
14.89
13.22
14.45
12.79
14.21
1932
1045
750
318
4045
Observations
Note: Household-level database with at least one child at school in 2013.
Source: Author’s calculation from 2013 China Household Income Project.
!
30
Table 6
The effects of migration and remittances on Education Decisions
Probit Model
Probit Model
Coefficient
Marginal Effect
-0.101*
-0.016
0.0602
(0.0093)
migrant-sending household
Remittance-receiving household
Coefficient
Marginal Effect
-0.158**
-0.015
0.0614
(0.0093)
Child characteristics
YES
YES
YES
YES
Households characteristics
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
Number of Observations
4386
4386
Mean of dependent variables
88.16%
88.16%
Pseudo R squared
0.2190
0.2203
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
31
Table 7
The effect of remittances on Education Decision
Effect of migration
!
Cofficient
Non-migrant non-receiving
Reference
Migrant non-receiving
Non-migrant receiving
Migrant receiving
Marginal Effect
Cofficient
Marginal Effect
0.340***
0.053
0.0967
0.0149
-0.189**
-0.029
0.151
-0.023
0.0819
0.0127
0.1090
0.0169
-0.340***
-0.053
Reference
0.0967
0.0149
-0.171*
-0.026
0.169*
-0.026
0.0741
0.0114
0.100
0.0155
Child characteristics
YES
YES
YES
YES
Households characteristics
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
Number of Observations
4386
4386
Mean of dependent variables
88.16%
88.16%
Pseudo R squared
0.2230
0.2230
Effect of remittance
Cofficient
Non-migrant non-receiving
Non-migrant receiving
Marginal Effect
Reference
Cofficient
Marginal Effect
0.189**
0.029
0.0819
0.0127
-0.340***
-0.053
-0.151
-0.023
0.0967
0.0149
0.1090
0.0169
-0.189*
-0.029
Reference
0.0819
0.0127
-0.171**
-0.026
0.019
-0.003
0.0741
0.0114
0.0866
0.0134
Child characteristics
YES
YES
YES
YES
Households characteristics
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
Number of Observations
4386
4386
Mean of dependent variables
88.16%
88.16%
Pseudo R squared
0.2230
0.2230
Migrant non-receiving
Migrant receiving
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
32
Table 8
The effect of migration/remittances on Education Decision-Subgroup
Variable
Migrant-sending
Non-migrant non-receiving
Migrant non-receiving
Non-migrant receiving
Migrant receiving
Non-migrant receiving
Migrant receiving
Non-migrant non-receiving
Migrant non-receiving
Remittance-receiving
Non-migrant non-receiving
Non-migrant receiving
Migrant non-receiving
Migrant receiving
Children between 7~12
Children between 13~15
Children between 16~18
Marginal Effect
Marginal Effect
Marginal Effect
-0.014
-0.004
-0.027
(0.0087)
(0.0154)
(0.0257)
Reference
Reference
Reference
-0.016
0.041**
-0.016
(0.0114)
(0.0187)
-0.004
-0.025
(0.0359)
(0.0160)
(0.0249)
-0.0159
0.024
(0.0114)
(0.0205)
(0.0309)
Reference
Reference
Reference
-0.01
0.049*
-0.039
(0.0159)
(0.0271)
(0.0410)
0.0040
0.0250
0.120***
(0.0160)
(0.0249)
(0.0388)
(0.1180)
(0.0160)
0.103**
(0.0170)
(0.0266)
(0.0458)
-0.006
0.025
(0.0089)
(0.0165)
(0.0255)
Reference
Reference
Reference
-0.004
-0.025
-0.120***
(0.0160)
(0.0249)
(0.0388)
-0.016
0.024
-0.016
(0.0114)
(0.0205)
(0.0359)
0.120***
(0.0388)
0.080**
0.088***
-0.0159
0.024
(0.0114)
(0.0205)
(0.0309)
Reference
Reference
Reference
0.002
0.065***
-0.064
(0.0117)
(0.0219)
(0.0378)
0.0159
0.041**
0.016
(0.0114)
(0.0187)
(0.0359)
0.0118
0.016
(0.0170)
(0.0266)
(0.0458)
Child characteristics
YES
YES
YES
Household characteristics
YES
YES
YES
Household Assets
YES
YES
YES
Provincial dummies
YES
YES
YES
Number of Observations
2065
1074
1274
96.04%
93.37%
70.91%
Migrant non-receiving
Migrant receiving
Non-migrant non-receiving
Non-migrant receiving
Mean of dependent variables
0.080**
0.103**
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
33
Table 9
The effect of migration on Educational Investment
Tobit
IV
Tobit
Marginal Effect
Marginal Effect
Marginal Effect
Marginal Effect
(Unconditional
(Conditional on being
(Unconditional
(Conditional on being
Expected Value)
uncensored)
Expected Value)
uncensored)
Migrant-sending
-0.194**
-0.183**
-2.806***
-2.578***
0.0947
0.0897
0.4763
0.4309
Household characteristics
YES
YES
YES
YES
Household Asset
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
YES
YES
3762
3762
30.58
30.58
0.000
0.000
Instrument
Fraction of receiving
remittances
Number of Observations
Pseudo R squared
Wald chi2
3762
3762
0.0288
0.0288
1
Prob > chi2
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
Table 10
The effect of remittances on Educational Investment
Tobit
Remittance-receiving
IV
Tobit
Marginal Effect
Marginal Effect
Marginal Effect
Marginal Effect
(Unconditional
(Conditional on being
(Unconditional
(Conditional on being
Expected Value)
uncensored)
Expected Value)
uncensored)
-0.442***
-0.4193***
-1.451***
-1.368***
(0.1007)
(0.0954)
(0.2238)
0.2099
Household characteristics
YES
YES
YES
YES
Household Asset
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
remittances
YES
YES
Age*distance
YES
YES
3648
3648
27.98
27.98
0.000
0.000
Instruments
Fraction of receiving
Number of Observations
Pseudo R squared
Wald chi2
3648
3648
0.0301
0.0301
1
Prob > chi2
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
34
Table 11
The effect of migration on Education Investment-Subgroup Samples
(Education group)
Tobit
IV
Tobit
Marginal Effect
Marginal Effect
Marginal Effect
Marginal Effect
(Unconditional
(Conditional on being
(Unconditional
(Conditional on being
Expected Value)
uncensored)
Expected Value)
uncensored)
Compulsory Education
Migrant-sending household
-0.196
-0.183
-3.658***
-3.228***
0.1385
0.1289
0.6413
0.5535
Household characteristics
YES
YES
YES
YES
Household Asset
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
YES
YES
1639
1639
29.95
29.95
0.000
0.000
-0.459**
-2.029
-1.913
0.2324
0.2213
0.4763
0.4309
Household characteristics
YES
YES
YES
YES
Household Asset
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
YES
YES
815
815
1.57
1.57
0.2097
0.2097
Instrument
Fraction of receiving
remittances
Number of Observations
Pseudo R squared
Wald chi2
1639
1639
0.0227
0.0227
1
Prob > chi2
Higher Education
Migrant-sending household
0.482**
Instrument
Fraction of receiving
remittances
Number of Observations
Pseudo R squared
Wald chi2
815
815
0.0204
0.0204
1
Prob > chi2
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
35
Table 12
The effect of remittances on Education Investment-Subgroup Samples
(Education group)
Tobit
IV
Tobit
Marginal Effect
Marginal Effect
Marginal Effect
Marginal Effect
(Unconditional
(Conditional on being
(Unconditional
(Conditional on being
Expected Value)
uncensored)
Expected Value)
uncensored)
Compulsory Education
Remittance-receiving
-0.644***
-0.5985***
-2.075***
-1.906***
(0.1510)
(0.1402)
(0.3192)
0.2910
Household characteristics
YES
YES
YES
YES
Household Asset
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
remittances
YES
YES
YES
YES
Age*distance
YES
YES
YES
YES
Number of Observations
1588
1588
1588
1588
0.0251
0.0301
Wald chi2
25.96
25.96
Prob > chi2
0.000
0.000
Instrument
Fraction of receiving
Pseudo R squared
Higher Education
Remittance-receiving
-0.306
-0.291
-1.119*
-1.063*
(0.2505)
(0.2385)
(0.5937)
0.5625
Household characteristics
YES
YES
YES
YES
Household Asset
YES
YES
YES
YES
Provincial dummies
YES
YES
YES
YES
remittances
YES
YES
YES
YES
Age*distance
YES
YES
YES
YES
Number of Observations
786
786
786
786
0.0202
0.0202
Wald chi2
2.29
2.29
Prob > chi2
0.1304
0.1304
Instrument
Fraction of receiving
Pseudo R squared
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
36
Table 13 The marginal budget share and elasticity
Marginal Budget Share
Percentage
Percentage Difference,
Difference,
With
Migrant VS. No
Non
With
Remittance VS.
Non Mirant
Migrant
Migrant
Remittance
Remittance
No Remittance
Education
0.1985
0.1870
-5.82
0.1973
0.1882
-4.59
Food
0.3340
0.3553
6.39
0.3472
0.3348
-3.57
Durables
0.2235
0.2141
-4.24
0.2316
0.1932
-16.59
Housing
0.1586
0.1545
-2.61
0.1457
0.1770
21.51
Health
0.0853
0.0891
4.48
0.0782
0.1068
36.47
Elasticity
Education
1.35
1.36
1.33
1.44
1.35
Food
0.76
0.78
0.80
0.72
0.76
Durables
1.35
1.44
1.40
1.34
1.35
Housing
0.84
0.79
0.76
0.93
0.84
Health
1.45
1.38
1.33
1.60
1.45
!
!
37
Table A.1
Summary statistics – Children characteristics and Household
characteristics
Non-migrant
Migrant
Migrant
Non-migrant
Non-receiving
receiving
Non-receiving
receiving
Total
Characteristics of children
Age
13.80
13.56
13.68
14.16
13.74
53
54.4
54
59.1
54.1
Older Age group(%)
28.6
27.4
28.3
34.9
28.8
boys aged 16~18(%)
15.5
14.3
14.4
21.7
15.5
Number of infants
0.174
0.209
0.168
0.171
0.182
Number of children aged between 7 and 12
0.676
0.779
0.705
0.692
0.71
Number of children aged between 13 and 15
0.393
0.378
0.358
0.349
0.379
Number of children aged between 16 and 18
0.406
0.448
0.433
0.495
0.43
Number of members aged between 19 and 55
2.085
2.398
2.326
2.123
2.216
Number of members aged between 56 and 65
0.372
0.573
0.453
0.468
0.45
Number of members aged over 66
0.231
0.363
0.227
0.299
0.272
Average age of adult members (years)
32.42
33.35
32.39
33.06
32.72
Average education of adult members (years)
7.742
7.151
7.779
6.993
7.521
68
67.3
69.2
63.5
67.6
Log of estimated housing value
11.63
11.42
11.66
11.31
11.55
Total land
7.067
6.108
6.756
7.684
6.8
Boy(%)
Household Composition
Household Characteristics
Having at least a member with higher education(%)
Household Wealth
Note: “Infants in household” reflects the number of children below the age of 6. “Older Age Group” indicates the percentage
of children aged between 16~18.
Source: Author’s calculation from 2013 China Household Income Project.
!
38
Table A.2
Summary statistics –Household characteristics
Non-migrant
Migrant
Migrant
Non-migrant
Non-receiving
receiving
Non-receiving
receiving
Total
Household Composition
Number of children at school
1.345
1.408
1.312
1.433
1.362
Number of infants
0.241
0.338
0.288
0.261
0.277
Number of children aged between 7~12
0.507
0.573
0.499
0.552
0.526
Number of children aged between 13~15
0.281
0.272
0.248
0.267
0.271
Number of children aged between 16~18
0.269
0.275
0.284
0.316
0.277
Number of members aged between 19~55
2.246
2.618
2.56
2.255
2.403
Number of members aged between 56~65
0.451
0.69
0.556
0.564
0.542
Number of members aged over 66
0.219
0.313
0.221
0.267
0.248
33.67
34.08
33.5
33.83
33.76
8.099
7.39
8.168
7.356
7.87
20.2
14.5
21.8
16.3
18.7
Log of estimated housing value
11.7
11.41
11.72
11.39
11.6
Total land
6.657
6.343
6.443
7.954
6.636
Observations
1982
1092
795
326
4195
Household Characteristics
Average age of adult members (years)
Average education of adult members
(years)
Having at least a member with higher
education(%)
Household Wealth
Source: Author’s calculation from 2013 China Household Income Project.
Table A.3 Average Budget Shares by migrant and remittance status
Non migrant-sending
Migrant-sending
Total
Budget share for food
0.441
0.454
0.447
Budget share for durable goods
0.165
0.149
0.158
Budget share for housing
0.188
0.196
0.192
Budget share for education
0.147
0.137
0.142
Budget share for medical care
0.059
0.065
0.062
Observations
2045
1724
3769
Non remittance-receiving
Remittance-receiving
Total
Budget share for food
0.435
0.468
0.447
Budget share for durable goods
0.165
0.144
0.158
Budget share for housing
0.192
0.191
0.192
Budget share for education
0.149
0.131
0.142
Budget share for medical care
0.059
0.067
0.062
Observations
2446
1323
3769
Source: Author’s calculation from 2013 China Household Income Project.
!
39
Table A.4 Regression Results (Migrant-sending vs Non migran-sending)
Non Migrant-sending Households
Education
Food
Durables
Housing
Health
0.050***
-0.098***
0.065***
-0.041***
0.024***
(0.0059)
(0.0058)
(0.0047)
(0.0050)
(0.0041)
Household Characteristics
YES
YES
YES
YES
YES
Province Dummy
YES
YES
YES
YES
YES
Constant
-0.526***
1.693***
-0.302***
0.138*
-0.002
(0.0686)
(0.0678)
(0.0546)
(0.0588)
(0.0481)
Observations
2045
2045
2045
2045
2045
Adjusted R2
0.250
0.274
0.114
0.212
0.042
Log of total expenditure
Migrant-sending Households
Education
Food
Durables
Housing
Health
0.052***
-0.107***
0.058***
-0.030***
0.026***
(0.0061)
(0.0063)
(0.0047)
(0.0056)
(0.0049)
Household Characteristics
YES
YES
YES
YES
YES
Province Dummy
YES
YES
YES
YES
YES
Constant
-0.626***
1.834***
-0.235***
0.03
-0.003
(0.0757)
(0.0777)
(0.0584)
(0.0687)
(0.0603)
Observations
1724
1724
1724
1724
1724
Adjusted R2
0.270
0.292
0.108
0.85
0.045
Log of total expenditure
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
40
Table A.5 Regression Results (Remittance-receiving vs. Non remittance-receiving)
Non Remittance-receiving Households
Education
Food
Durables
Housing
Health
0.048***
-0.088***
0.067***
-0.047***
0.019***
(0.0052)
(0.0051)
(0.0041)
(0.0045)
(0.0037)
Household Characteristics
YES
YES
YES
YES
YES
Province Dummy
YES
YES
YES
YES
YES
Adjusted R2
-0.525***
1.612***
-0.301***
0.145**
0.068
(0.0615)
(0.0604)
(0.0491)
(0.0529)
(0.0434)
2446
2446
2446
2446
2446
0.252
0.251
0.123
0.220
0.045
Log of total expenditure
Remittance-receiving Households
Education
Food
Durables
Housing
Health
0.057***
-0.133***
0.049***
-0.014*
0.040***
(0.0075)
(0.0077)
(0.0056)
(0.0068)
(0.0061)
Household Characteristics
YES
YES
YES
YES
YES
Province Dummy
YES
YES
YES
YES
YES
Constant
-0.709***
2.119***
-0.193*
0.01
-0.227*
(0.1186)
(0.1227)
(0.0888)
(0.1078)
(0.0965)
Observations
1323
1323
1323
1323
1323
Adjusted R2
0.252
0.330
0.087
0.176
0.045
Log of total expenditure
Notes: Standard errors in parentheses. *Significant at 10% level, **Significant at 5% level, ***Significant at 1% level.
Source: 2013 China Household Income Project.
!
41