Education Inequality between Rural and Urban
Areas of the People’s Republic of China,
Migrants’ Children Education,
and Some Implications
∗
DANDAN ZHANG, XIN LI, AND JINJUN XUE

Education inequality between the rural and urban areas of the People’s Republic
of China (RPC)—a potential bottleneck for human capital accumulation—has
long been of interest to researchers and policymakers. This paper uses data
from the China Family Panel Survey (CFPS) and the Rural–Urban Migration
in China (RUMiC) survey to compare the education performance of rural chil-
les enfants, children of rural-to-urban migrants, and urban children over the period
2009–2010. Results show that education performance of rural children and
migrants’ children is significantly lower than that of their urban counterparts
even after accounting for differences in personal attributes such as nutrition
and parenting style. This provides useful insights for policymaking to reduce
rural–urban education inequality and assist human capital accumulation in the
RPC.

Mots clés: education inequality, rural-to-urban migration, human capital accu-
mulation
Codes JEL: I24, O18, P36

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je. Introduction

Over the past 3 decades, the People’s Republic of China (RPC) has experi-
enced dramatic economic growth. Entre 1978 et 2013, gross domestic product
(PIB) grew by more than 10% a year on average in the country, which is about
three times the growth of Organisation for Economic Co-operation and Develop-
ment (OECD) des pays. As a consequence, the PRC has become the second largest
economy in the world since 2011, second only to the United States. Cependant, le
period of “miraculous” economic growth appears to be nearing its end. Since 2011,
the annual GDP growth rate has declined from 12.0% à 7.7% in the PRC, et le

∗Dandan Zhang (corresponding author, ddzhang@nsd.pku.edu.cn): Assistant Professor at the National School of
Développement, Peking University, 5 Yiheyuan Road, Haidian District, Beijing. Xin Li is a PhD student at the National
School of Development, Peking University. Jinjun Xue is a professor at the Nagoya University, Japan. The authors
are grateful for the support of the Nagoya University and all workshop participants at the Asian Development Bank
Institute–National School of Development joint workshop on the New Phase of Chinese Reform and Growth and the
Asian Development Review Conference 2014: The PRC’s Future, Reforms, and Challenges.

Revue du développement en Asie, vol. 32, Non. 1, pp. 196–224

C(cid:3) 2015 Banque asiatique de développement
et Institut de la Banque asiatique de développement
Publié sous Creative Commons
Attribution 3.0 IGO (CC PAR 3.0 IGO) Licence

EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 197

declining trend seems to be continuing over time. This has aroused concern that the
country would fall into the “middle-income trap.”

Depuis des décennies, the PRC’s economic growth has been criticized for its reliance
on raising input usage with relatively low productivity improvement (Wu, Ma,
and Guo 2014). Substantial rural-to-urban migration provides a large amount of
unskilled labor and a relatively high rate of return (because of relatively low wages),
encouraging public and private investment. When there is strong demand from
the international market, labor-intensive manufacturing production can be easily
duplicated, which in turn drives economic growth. Cependant, this kind of growth is not
sustainable from an economic perspective. As the population dividend diminishes
and environmental concerns and international competition intensify, increasing input
usage through low wages and rising investment can no longer fuel economic growth
in the PRC as they did in the 1980s and 1990s.

In neoclassical economic growth theory, long-term development relies on
productivity improvements driven primarily by human capital accumulation. Ce
implies that, to maintain rapid growth and escape the so-called “middle-income”
trap, the PRC needs to increase production efficiency and upgrade industries in
order to make them high-valued, service-based, and innovation-based. Cependant,
recent statistics show that total factor productivity (TFP) in the PRC’s industrial
sector has been extremely low, while manufacturing production has been dominated
by labor-intensive production techniques (Wu, Ma, and Guo 2014). A shortage of
skilled labor supply serves as a major bottleneck for productivity improvement and
economic transformation in the country.

Compared to other developing countries, human capital accumulation cannot
meet the requirements of economic development in the PRC. Based on the 2005 1%
Population Sampling Survey (conducted by National Bureau of Statistics, NBS for
short), the number of years of schooling of the country’s labor force was 8.6 années
on average, while only 25% of the labor force (aged between 15 et 65) had an
education level of junior high school or above. This implies that there is a large gap in
human capital endowment between the PRC and Asian countries that have escaped
the middle-income trap such as Japan and the Republic of Korea (Rozelle 2013).
Among others, significant disparities in education between rural and urban areas
could be an important factor in the PRC, affecting human capital accumulation at
the national level. Cependant, little is known about how education inequality between
rural and urban areas has changed over time and only a few studies have been carried
out to examine the education of migrants’ children.

This paper uses data from the 2010 China Family Panel Survey (CFPS) et
le 2009 Rural–Urban Migration in China (RUMiC) survey to compare education
performance of rural children and urban children between 2009 et 2010. In the
analyse, we distinguish rural-to-urban migrants’ children from those of rural non-
migrants and urban residents. The results show that education performance of rural
enfants (including those of rural non-migrants and rural-to-urban migrants) est

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198 ASIAN DEVELOPMENT REVIEW

significantly lower than their urban counterparts. Although attributes of different
groups such as nutrition, parenting style, and education quality have played important
roles in explaining inequality between rural and urban children, the remaining
unexplained education disparity is still substantial.

Compared to previous research, this paper is the first to consider migrants’
children separately when analyzing education inequality between rural and urban
areas of the PRC. To reduce measurement errors associated with self-assessment, nous
use test scores in a unique dataset, namely CFPS, to measure education performance
of children of different groups. En outre, the analysis to some extent also accounts
for personal attributes and their effects in identifying education inequality across
groupes. The findings obtained from this study not only provide useful insights on
potential education reforms in the PRC but also help to inform other developing
countries with similar experiences.

II. Education Inequality in Rural and Urban Areas

of the People’s Republic of China

Since 1978, the PRC has exerted great effort to improve education level of
the labor force in both rural and urban areas by increasing public investment in
éducation. Cependant, like most other developing countries experiencing economic
transition, education inequality is still widely observed between rural and urban
domaines. Income disparity, various institutional barriers, and different parenting styles,
entre autres, are regarded as potential causes of education inequality. With the
increased migration of rural labor into cities in recent years, education inequality
between rural and urban areas of the PRC has started to negatively affect human
capital accumulation in the urban labor market. This section briefly summarizes
education inequality between rural and urban areas in the country and the education
performance of rural-to-urban migrants’ children.

UN.

Education Disparity between Rural and Urban Areas

Although the 9-year compulsory education policy was implemented simulta-
neously throughout the whole country in 1985, the effect of this policy on education
attainment in rural and urban areas of the PRC significantly differed. Entre 1985
et 2005, average education levels of rural and urban populations both increased,
but the latter grew more quickly than the former. As a consequence, the gap in
education levels of the labor force in rural and urban areas of the country widened
over the period.

There is a substantial gap in the average number of years of schooling between
the rural and urban labor force, and this has not diminished over time. Chiffre 1
compares the average years of schooling of various birth cohorts of the rural and

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 199

Chiffre 1. Average Years of Schooling for Each Birth Year of the Rural and
Urban Labor Force in 2005

Source: Authors’ calculations based on the 2005 1% Population Sampling Survey of the PRC (from the National
Bureau of Statistics).

urban labor force using the 2005 1% Population Sampling Survey data. For those
aged 15–65 years old, the average years of schooling increased from 4 to nearly 8
for the rural labor force and from 7 à 12 for the urban labor force. Cependant, the gap
in the average years of schooling between the rural and urban labor force does not
decrease as cohort age declines. This implies that the urban labor force has more
years of schooling than the rural labor force in the PRC, and that the gap has not
narrowed over time.

There is also a significant gap in the average enrollment and graduation
rates1 of children (of school age) between the rural and urban population of the
RPC, which implies that the educational disparity between rural and urban areas
of the country has widened, especially in recent years, after the implementation of
the compulsory education policy. Chiffre 2 compares the average enrollment and
graduation rates of three grades (junior high, senior high, and college/university)
of students living in rural and urban areas who enrolled in 2010 and graduated
dans 2012 based on statistics taken from the China Education Statistical Yearbook
(2010–2013). Compared to Figure 1, Chiffre 2 provides more information on the
effects of the 9-year compulsory education policy on school attendance of the rural
population in the PRC, as it includes the birth cohorts after 1983.

As shown in Figure 2, among 100 rural children, 88% completed primary
education and entered junior high schools, while the rest (12%) dropped out from
primary schools. De plus, only 70% of those who entered junior high schools
completed their study. This means that around 38% of rural children were not able
to fulfill the 9-year compulsory education. The finding is consistent with some

1Graduation rate is defined as the number of graduated students divided by the number of enrolled students.

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200 ASIAN DEVELOPMENT REVIEW

Chiffre 2. Average Enrollment and Graduation Rates of Junior High, Senior High, et
College/University Students in 2010–2012, Rural and Urban

Note: See Table A1.1 of Appendix 1 for detailed data on the generation of dropout rates for junior and senior high
school students.
Source: Ministry of Education. 2009–2013. China Education Statistical Yearbook. Department of Development and
Planning, Ministry of Education, RPC. Beijing: People’s Education Press.

calculations using survey data, which show a high proportion of dropouts from
primary and junior high schools (40%–50%). Enfin, only 6 de 100 rural children
can enter senior high schools, among whom 3 can finally graduate from senior high
schools. Around 1–2 rural children had a chance to obtain tertiary education.

In contrast, almost all urban children finished junior high school education, de
whom 63% entered senior high schools. Among the urban children who graduated
from junior high schools, more than half (54%) entered college for tertiary education.
Of those not enrolled in senior high schools, a majority were able to study in
vocational or technical schools.

Bien sûr, one should note that the above analysis of education inequality
between rural and urban populations of the PRC is subject to two limitations. D'abord,
formal education levels do not necessarily link to education performance of rural
and urban children. Deuxième, the data used to estimate the enrollment ratio of rural
children is likely to suffer from a selection problem.2 It is therefore necessary to carry

2Since the data collected from the China Educational Statistical Yearbook (Ministry of Education 2009–2013)
are compiled using school location rather than hukou registration place of students, there may be miscalculations
regarding the enrollment of rural children. Par exemple, rural children may have been given the chance to enter urban
schools, in turn increasing enrollment in urban schools while reducing enrollment in rural schools. To reduce this
bias, we add the increase in urban enrollment compared to the first grade to rural statistics. Cependant, we still cannot
adjust the bias for first grade enrollment rate because of the absence of hukou information for admitted students
at each school level. Par conséquent, the statistics illustrated in Figure 2 may to some extent exaggerate the rural and
urban education disparity in terms of enrollment and graduation rates. Cependant, given the substantial difference in
the statistics, such measurement bias is unlikely to change the fact that education attainment has not been equally
achieved in rural and urban areas of the PRC.

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 201

out more thorough comparisons before a strong conclusion on education inequality
between rural and urban areas in the PRC can be reached.

B.

Rural-to-Urban Migration and Education of Migrants’ Children

Rural-to-urban migration is a feature specific to the PRC’s economic trans-
formation, one that has played an important role in shaping the structure of rural and
urban labor markets in the country over the past 2 decades. Entre 1990 et 2010,
there had been 164 million workers moving from rural to urban areas, accounting for
a third of total urban unskilled labor supply. As more and more rural migrants move
into cities, their children’s education and the implications for education inequality
between rural and urban populations become important issues.

Due to restricted institutional arrangements and discrimination by urban res-
idents, rural-to-urban migrants generally fail to gain access to the urban social
welfare system in the PRC (Meng and Zhang 2013). As a consequence, migrants’
children are unable to obtain the same opportunities as their urban counterparts in
entering the formal education system. The children of migrants mostly receive their
education through rural schools, urban informal education institutions, or a mixture
of the two. The hybrid education experience, plus an unstable life, reduces the edu-
cation performance of migrants’ children. As rural migrants’ children account for a
large proportion of rural children, this exacerbates the education inequality between
rural and urban populations of the country.

Migrants’ children usually have two choices (get left behind or migrate with
their parents), and education opportunities faced by various groups of children
generally differ. When rural migrants work in urban areas, they can choose to leave
their children in their rural hometowns or bring their children into the city for
éducation. In the former case, migrants’ parents play a role as the guardian of the
enfants, while in the latter case, migrants have to pay an additional sponsorship fee
to send their children to local schools (where most of these schools are for migrants’
enfants). It should be noted that since migrants often need to work for long hours,
they cannot spend much time on their children even if they live together.

Compared with children of non-migrants in rural areas, children of migrants
seem to have better opportunities to attend schools in urban areas. Par exemple,
many migrants’ children may have spent a period of time for education in cities,
especially at a young age. Given the difference in the quality of education institutions
in rural and urban areas, this would be a benefit. Cependant, due to unfair treatment
faced by migrants in urban areas and economic concerns, most migrants’ children
also spend a significant amount of time in rural areas for their education.

As an example, the RUMiC survey shows that economic and discrimination
concerns are the two most important reasons why migrants leave their children

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202 ASIAN DEVELOPMENT REVIEW

in their hometowns. Roughly 36% of the migrants make the choice to leave their
children at home because of economic concerns, alors que 26.5% make the choice
because they have no time to take care of their children in urban areas. This may
lead to the inconsistent education outcomes of migrants’ children in rural and urban
domaines. En outre, since migrants face a lot of pressures when working in cities, their
enfants (including both those left behind and those migrating) are usually unable
to get adequate parental care.

There is no serious empirical evidence regarding the education performance
of migrants’ children. Cependant, there are those who believe that the performance
of migrants’ children is likely inferior to their rural/urban counterparts. A primary
reason is that mixing rural and urban education leads to inconsistency. Entre-temps,
lack of care from parents also makes it hard for migrants’ children (especially the
left-behind rural children) to obtain good education outcomes. In extreme cases,
migrants’ children may be prone to commit crimes due to lack of discipline received
from their parents (Cameron, Meng, and Zhang 2014).

C.

Education Inequality, Rural-to-Urban Migration, and Related Literature

There have been many studies carried out in recent years that explore educa-
tion inequality between rural and urban areas of the PRC and education performance
of migrants’ children. A common feature of these studies is the analysis of why ed-
ucation levels of rural and urban residents differ. Two interesting arguments are
summarized below.

D'abord, unlike successful neighbors in Asia, the PRC’s central government has
traditionally spent less on education, particularly of rural residents. Par exemple,
Heckman and Li (2003) show that the PRC spent about 2.5% of GDP on education
in the 2000s, which was much lower than the amount spent by other developing
countries in Asia (about 4%–5%) and the world average (5.2%). Most of the spending
had been used to support compulsory education of urban residents.

Deuxième, the relatively low private and social rates of return associated with
rural education usually discourage private investment. Rates of return to education
in the rural areas of the PRC have on average been perceived to be no more than
5% in the late 1980s and 1990s (Meng 1996, Zhao 1999). This differs from the
average for other Asian countries and the world (10%). Given the relatively low
return to education, it is not surprising to see rural residents drop from high schools.
As an example, well over 90% of students in large cities of the PRC attend senior
high school, in contrast to only half of all junior high graduates in poor rural areas
(Loyalka et al. 2014, Shi et al. 2014).

As for attempts to examine the education level of migrants’ children, most
studies have focused on cross-country migration. Edwards and Ureta (2003), Hanson

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 203

and Woodruff (2003), and Mansuri (2006) find that international migration can
impose positive effects on the performance of migrants’ children in education.
Cependant, Long (1975), Pribesh and Downey (1999), and de Brauw and Giles (2006)
find that international migration generates negative effects on family life and in terms
of the continuity of education of migrants’ children.

Although significant progress has been made in related data collection, only
a few studies have been carried out to examine rural-to-urban migration and its
effects on the performance of migrants’ children in education in the PRC. These
include Ye, Murray, and Yihuan (2005); Han (2003); Liang and Chen (2005); Feng
and Chen (2012); and Meng and Yamauchi (2013). These studies generally find an
association between the lack of parental care and the mental pressure and the sense
of insecurity felt by left-behind children. They also find a lower enrollment rate for
children migrating to the cities compared to their urban counterparts as well as to
non-migrant children in the migrant-sending communities. En outre, éducation
outcomes of migrant children in cities seem to be significantly worse than those of
their local counterparts.

In sum, previous literature provides useful information on education inequal-
ity between rural and urban areas in the PRC, and the migration behavior and its
effects on education performance of migrants’ children. Cependant, few attempts have
been made to combine these two issues. This leaves room for this paper to re-examine
the effects of education inequality in the country from a migration perspective.

III. Data Sources

The data used in this study comes from two surveys: 2009 RUMiC and 2010
CFPS. Both datasets have their advantages and shortcomings and serve different
purposes in our analysis.

The RUMiC survey started to collect information from households in 2008,
et 2 years of data have been made available to the public. About 18,000 households
were surveyed each year, with the sample split into three groups representing rural
residents (8,000 households), rural-to-urban migrants (5,000 households), and urban
residents (5,000 households). The sampled households in 2009 were traced from
2008 whenever possible. The random sampling technique and the sample rotation
technique were used to fill the gap and to ensure representativeness of the population
across regions and over time.

The survey was carried out in both rural and urban areas. In rural areas, nine
provinces/municipal cities were selected, including Henan, Jiangsu, Sichuan, Hubei,
Anhui, Zhejiang, Guangdong, Hebei, and Chongqing. These provinces/municipal
cities accounted for around 47% of emigrants from rural areas in 2000 according
to the China Population Census for that year. In urban areas, 15 large and medium-
sized cities were selected, namely Shanghai, Hangzhou, Ningbo, Nanjing, Wuxi,

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204 ASIAN DEVELOPMENT REVIEW

Guangzhou, Shenzhen, Dongguan, Zhengzhou, Luoyang, Hefei, Bengbu, Wuhan,
Chongqing, and Chengdu. These cities accounted for around 66% of immigrants
into the urban areas in 2000 according to the national census.

The main advantage of RUMiC is that the data collected from the survey
provide a good representation of migrants, as they simultaneously provide sam-
ples among the three population groups (rural residents, rural-to-urban migrants,
and urban residents).3 Donc, one can easily distinguish between migrating and
left-behind children. De plus, the survey also provides detailed and consistent
information on social and economic behavior of rural residents, rural-to-urban mi-
subventions, and urban residents at the individual, household, and regional levels.4 The
information enables us to compare education performance, expenditure, and school
choices of migrants’ children (including both left-behind children and migrating
enfants) and their rural and urban counterparts.

Cependant, the RUMiC survey has two shortcomings. D'abord, it measures edu-
cation performance of migrants’ children using their math and word test scores in
the latest final exam. The measurement could be biased since it involves subjective
assessments of performance and differences in test questions. Deuxième, the survey
does not provide information on children’s mental health, which can affect their
education performance. To overcome these two problems, we also use CFPS data.
The CFPS is a nationwide, biannual, and longitudinal survey of communities,
families, and individuals launched in 2010. The survey was conducted by the Institute
of Social Science Survey (ISSS) of Peking University and covered 25 provinces
(except Xinjiang; Xizang; Qinghai; Inner Mongolia; Ningxia; Hainan; Hong Kong,
Chine; Macau, Chine; and Taipei,Chine), representing 95% of the total population. Dans
2010, the baseline survey successfully interviewed 14,789 families, covering 33,600
adults and 8,990 enfants. The second wave in 2012 surveyed 13,319 families or
à propos 36,062 adults and 8,627 children.5

Compared to other surveys on the Chinese family, the CFPS provides much
more basic information on each family member as well as various indicators mea-
suring children’s education performance and psychological well-being. En particulier,
unified math, word, and psychological tests were carried out for children aged 10 à
15. The scores obtained from the tests were used to construct a measure of children’s
education performance and psychological well-being. Specifically, each child partic-
ipating in the survey needed to answer 24 arithmetic questions in sequence, arranged
from easy to hard. The number of correctly answered questions was treated as the
child’s math test score. The word test scores were obtained in a similar way with

3For detailed sampling strategy of the migrant samples, please refer to the following survey website:

http://rse.anu.edu.au/research-projects/rural-urban-migration-in-china-and-indonesia/.

4The survey collects a large amount of information on migrants (including their hometown, destination,
occupation and skills, working experience, and income) and information on their children (under school age),
including education level, school choice, and education performance.

5The differences between the 2 years mainly come from the combination and split of communities and

families as well as the birth and death of individuals.

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 205

34 different characters. Psychological well-being status is measured by depression
level with six selected psychological questions.

Similar to the RUMiC survey, the CFPS allows for a comparison of children
coming from different groups. Using information on individuals’ working history
in both rural and urban areas, we distinguished the sample into migrants’ children
(including left-behind and migrating children) and rural/urban residents’ children
as in the RUMiC data. Specifically, a left-behind child is defined as one living in
the rural area with an agriculture hukou and at least one parent going out for work,
while a rural non-migrant child is defined as one with an agriculture hukou but with
both parents staying at home. Using this definition may cause bias, cependant. Si le
surveyed child lives with a single mother or single father, par exemple, he or she will
be wrongly categorized as a left-behind child. In this sense, the revealed proportion
of left-behind children is likely to be lower than the real proportion.

Due to data constraints, the definitions for migrating children and urban
residents’ children when using CFPS data are complex and require multifaceted
criteria. We define migrating children as those residing in urban areas and born
in rural areas with an agriculture hukou. Accordingly, urban residents’ children are
defined as those residing in urban areas and born in urban areas with non-agriculture
hukou. These definitions, though useful, may cause concern, as they exclude two
types of children residing in urban areas. One type comprises those born in urban
areas but with agriculture hukou, while the other type comprises those born in rural
areas but with non-agriculture hukou.6

Enfin, the RUMiC survey and the CFPS each has its own advantages and
disadvantages. RUMiC data define rural and urban children in an explicit way and
thus provide more reliable information for cross-group comparison. CFPS data
meanwhile provide an objective measure of the education performance of children.
In this paper, we will use both datasets to examine education inequality between
rural and urban populations in the country.

IV. Education Inequality between Rural and Urban Areas of the People’s

Republic of China: Comparison Analysis

By comparing two measures of education performance (c'est à dire., self-reported and
test scores) across sample groups from different datasets, we examined education
inequality between rural and urban areas of the PRC. De plus, left-behind and
migrating children are split from rural and urban residents’ children, respectivement,
and their education performances are separately examined. The discussion on the

6These children are apparently hard to categorize. An urban-born child with an agriculture hukou could
be an urban local child or a migrants’ child born secretly (without birth certification). A rural-born child with a
non-agriculture hukou could be a migrating child with hukou alteration or an urban child born in a rural area.

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206 ASIAN DEVELOPMENT REVIEW

Tableau 1. Children’s School Performance
(2009 RUMiC)

Rural Household Survey

Urban Migrant
Survey

Urbain
Household
Survey

Rural

Gauche-
behind Migrated

Gauche-
behind Migrated

Urbain

Self-reported school
performance (%)

Very good/Above average
Average
Below average

Observations
Self-reported score in
Chinese exam during
the last semester
(full score = 100)
Observations
Self-reported score in
math exam during the
last semester
(full score = 100)
Observations
Average study time outside
school (hours per week)
Observations

41.87
54.68
3.44
1,655

38.72
57.43
3.85
1,144

65.00
30.00
5.00
80

82.10
(11.39)
1,453

82.49
(10.86)
1,025

85.40
(12.46)
70

84.23
(10.89)
404

84.93
(11.03)
517

83.54
(12.01)
1,450
8.68
(6.90)
1,247

83.49
(11.83)
1,024
7.98
(6.56)

722

87.01
(13.32)
70
11.32
(7.23)
56

84.67
(12.68)
399

7.63
(8.40)

423

85.48
(12.21)
515

7.41
(6.22)

652

60.97
36.82
2.21

994

87.63
(10.68)
906

89.36
(10.35)
908
12.48
(7.62)

887

Note: Standard deviations are in parentheses.
Source: 2009 Rural–Urban Migration in China Survey.

attributes of different sample groups, such as personal characteristics, individual
living and social environments, and institutional arrangements, provides some po-
tential explanation on education inequality.

UN.

Comparing Education Performance of Children
in Rural and Urban Areas

Using data obtained from 2009 RUMiC survey, we construct measures of
education performance based on self-reported school performance and test scores
and compare these measures for rural and urban children (Tableau 1).

Education performance of urban children generally exceeds that of rural
enfants. Par exemple, 61% of urban residents believe their children have obtained
good or very good school performance (in terms of scores), alors que 37% believe their
children have obtained common school performance. In contrast, only around 40%
of rural residents believe their children have obtained good or very good school
performance, while about 55% believe their children have obtained common school
performance. We find similar results for self-reported scores in individual subjects

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 207

Tableau 2. Scores on Word and Math Tests of Rural and Urban Children
(2010 CFPS)

Word test score (full score = 10)

Primary school

Junior high school

Math test score (full score = 10)

Primary school

Junior high school

Group difference
with t-test results
−0.751∗∗∗

−0.708∗∗∗

−0.384∗∗∗

−0.536∗∗∗

−0.356∗∗∗

−0.219∗∗∗

Rural
6.144
(2.166)
5.417
(2.115)
7.526
(1.479)
4.489
(1.886)
3.535
(1.495)
6.298
(1.030)
1,719

Urbain
6.895
(1.930)
6.125
(1.946)
7.910
(1.354)
5.025
(1.790)
3.891
(1.405)
6.518
(0.948)
848

Observations
∗∗∗ = significant at 1%, ∗∗ = significant at 5%, ∗ = significant at 10% level.
Note: Standard deviations are in parentheses.
Source: 2010 China Family Panel Survey.

(c'est à dire., in word and math). The average self-reported scores of urban children are
87.6 et 89.4, respectively for word and math (total score is 100), which are higher
than those of rural children. This implies that there are significant differences in
education performance of children in rural and urban areas of the country, si
it is measured using self-reported school performance or exam scores.

Since self-reported school performance and scores are likely to be affected
by subjective judgment and differences in test quality, the comparison analysis
using these measures could be biased. To overcome this problem, we also use the
objective (word and math) test scores obtained from 2010 CFPS to construct a
measure of education performance. Since the two objective tests are only carried
out for students from 10 à 15 years of age, inferences from this exercise can only be
made for children falling into specific age groups. En outre, to capture the change
in education performance over time, we split the sample into two groups: primaire
school and junior high school.

As shown in Table 2, urban children, on average, perform better in objective
test scores than rural children. The gaps in objective test scores between the two
groups of children are a standard deviation (SD) de 0.75 for word tests and 0.54
SD for math tests, with both gaps being statistically significant. The finding is
consistent with that previously obtained using self-reported school performance and
scores, suggesting there is indeed education inequality between rural and urban
areas in the country. En outre, the gap in objective test scores between rural
and urban children does not change significantly for those enrolled in primary
and junior high schools. This implies that education inequality between rural and

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208 ASIAN DEVELOPMENT REVIEW

Tableau 3. Summary Statistics for Rural and Urban Children
(2010 CFPS)

Observations

%
Gender

% Male

Age

average

Health

Weight(kg)

Height(cm)

Degree of depression

Family characteristics

Mother’s years of schooling

Father’s years of schooling

Annual education expense(Yuan)

School attendance

% Junior high school

School type

% Key School

Class type

% Key class in a school

Rural

1,719
66.965

49.971
(0.500)

12.554
(1.724)

36.709
(10.342)
144.156
(17.101)
1.275
(1.659)

4.599
(3.870)
6.365
(3.706)
844.188
(1,283.733)

34.497
(0.475)

2.618
(0.160)

6.399
(0.245)

Urbain

848
33.035

51.297
(0.500)

12.456
(1.732)

40.870
(11.875)
150.708
(14.967)
1.143
(1.510)

8.579
(4.444)
9.533
(3.903)
2232.514
(3,619.970)

43.160
(0.496)

8.962
(0.286)

12.618
(0.332)

Group difference
with t-test results

—
—

−0.013

0.097

−4.161∗∗∗

−6.552∗∗∗

0.132∗

−3.980∗∗∗

−3.168∗∗∗

−1,388.326∗∗∗

−8.664∗∗∗

−6.344∗∗∗

−6.219∗∗∗

∗∗∗ = significant at 1%, ∗∗ = significant at 5%, ∗ = significant at 10% level.
Note: Standard deviations are in parentheses. Migrating children are included in the urban sample as they were
sampled in the cities.
Source: 2010 China Family Panel Survey.

urban areas may not diminish as these children grow older and obtain more formal
éducation.

Previous literature has cited plenty of possible factors based on developed
countries’ experience to explain education inequality between rural and urban pop-
ulations in the PRC. These include differences in nutrition, parenting style, genetics,
and living environments (Edwards and Ureta 2003; Meng and Yamauchi 2013; Feng
and Chen 2012). Although it is hard for us to establish a causal relationship between
differences in personal attributes and education inequality because of identification
problems, it is still worth reporting the differences in these attributes (Tableau 3).

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 209

As shown in Table 3, weight and height of rural children are generally lower
than those of their urban counterparts. En moyenne, rural kids were 4.2 kilograms
lighter and 6.5 centimeters shorter than their urban counterparts. As there are no sig-
nificant differences in sex–age distributions of rural and urban children, substantial
differences in weight and height may imply poor nutrition of rural children, lequel
may lead to even worse education outcomes. En outre, there exist significant dif-
ferences between rural and urban children in terms of their parents’ education level.
Mothers of rural children spent 4.6 years on average in school, presque 4 years less
than the time spent by mothers of urban children. De la même manière, the average number of
years of schooling of fathers of rural children was 6.4 années (c'est à dire., primary school),
which is 3.2 years less than the average of their urban counterparts (9.5 years of
schooling). The extremely low education outcomes of parents of rural children may
negatively affect the education performance of rural children. Enfin, there exists a
significant gap in education investment at the household level between rural and ur-
ban populations. En moyenne, the family of rural children spend just CNY844 a year
on schooling, or CNY1,388 less than their urban counterparts (up to CNY2,233). Dans
sum, all these differences highlight the education disparity between rural and urban
areas in the PRC.

B.

Comparing Education Performance of Migrants’ Children
with Rural and Urban Counterparts

What is the level of education performance of migrating children and how
does the migration behavior affect education inequality between rural and urban
areas of the PRC? To answer these questions, we compare the education performance
of migrating children (measured using self-reported and objective test scores) avec
that of rural and urban children.

Education performance of migrating children is generally lower than that of
urban residents’ children but higher than that of rural non-migrants’ children. Le
average objective scores of migrating children for word and math tests are 6.3 et
4.6, respectivement, which are higher than the scores of rural non-migrants’ children
(6.1 et 4.5) but lower than those of urban residents’ children (7.2 et 5.2) (Tableau 4).
This is consistent with the findings obtained from subjective assessments of school
performance. De plus, when we split migrants’ children into the left-behind group
and the migrating group, we find that: (je) there is no strong evidence to show that
migrating children’s education performance is better than the performance of left-
behind children, et (ii) education performance of migrating children is significantly
weaker than that of urban residents’ children.

The findings above generate important insights on the potential effects of
rural-to-urban migration on education inequality between rural and urban areas
of the PRC. On one hand, there is no strong evidence to show that migrants’
children will be better off if they migrate with their parents to cities and enter

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210 ASIAN DEVELOPMENT REVIEW

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 211

urban schools. On the other hand, the significant gap in education performance of
migrating children and urban residents’ children suggests that education inequality
exists between the two groups, working specifically against migrating children in
urban areas. Both findings suggest that rural-to-urban migration cannot help mitigate
education inequality between rural and urban populations in the country under the
current institutional environment.

V. Education Inequality between Rural and Urban Areas of the People’s

Republic of China: Regression Analysis

Although the descriptive analysis is informative, it does not provide solid
evidence on education inequality between rural and urban populations of the PRC or
between migrants’ and non-migrants’ children. In practice, education performance
of children is not only affected by the training that they receive from schools but on
many other factors such as children’s health status, parents’ income and education
levels, parenting styles, and school characteristics. If these factors are not taken
into account, one could overestimate education inequality between rural and urban
children and between migrants’ and non-migrants’ children. To bolster the findings
in the comparison analysis, we use the regression analysis that controls for individual
attributes to test education inequality between children of different groups.7

Three regression scenarios are employed to analyze the objective test scores.
In the first scenario, both rural and urban samples are used to examine if there is
a gap between the education performance of rural and urban children (with urban
children as the base group). In the second scenario, the urban sample is used to
examine if there is a gap between the education performance of migrating children
and urban residents’ children (urban residents’ children as the base group). In the
third scenario, the migrating children sample is compared with the rural sample to
examine whether there is a gap between the education performance of migrating
children and rural children (rural children as the base group). The corresponding
results for word and math test scores are reported in Tables 5 et 6, respectivement.
The major findings are summarized below.

D'abord, even after accounting for various personal attributes, family character-
istics, and school quality, there are still significant differences in word test scores
between rural and urban children. The coefficient of the rural children dummy (depuis
first scenario regressions, column 1 of Table 5) is –0.242 and significant at the
5% level, which suggests that when other conditions are the same, rural children’s
word test score is 0.242 SD less than that of urban counterparts. Compared with the
raw test score gap shown in Table 2, 32% of the raw test score gap between rural
and urban children (6.144–6.895 = −0.751) can be explained by the difference in

7See Appendix 2 for a detailed discussion of the model specification.

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212 ASIAN DEVELOPMENT REVIEW

Tableau 5. Regression Analysis of Word Test Scores of Chinese Children

(1)

(3)

(6)

(4)

Rural vs.
Urbain

(2)
Entre
Urbain:
Migrating
vs. Urban Migrating Rural vs.
vs. Rural

(5)
Entre
Urbain:
Migrating
vs. Urban Migrating
vs. Rural
Children Children Children Children Children Children
−0.242∗∗
(0.100)
—
—

−0.450∗∗∗
(0.149)
—
—

—
—
−0.392∗∗∗
(0.151)

—
—
−0.623∗∗
(0.254)

—
—
—
—

—
—
—
—

Urbain

Local

Local

—
—

—
—

0.275∗∗∗
(0.039)
—
—

0.311∗∗∗
(0.067)
—
—

0.086
(0.110)

0.307∗∗∗
(0.040)
—
—

—
—

—
—

0.099
(0.221)

0.205∗∗∗
(0.051)
0.088∗∗
(0.044)

0.280∗∗∗
(0.071)
0.087
(0.072)

0.308∗∗∗
(0.041)
−0.005
(0.065)

Dependent
Variable:
(standardized
word test
score)

Group dummy

(rural = 1, urban = 0)

Group dummy

(migrating = 1,
urban local = 0)

Group dummy

(migrating = 1,
rural = 0)
Child’s age

Interaction between
child age and the
group dummy
Dummy for male

Height

Weight

Mother’s years of

Dummy for school

Degree of depression

level (junior high = 1)

schooling
Father’s years of
schooling

−0.386∗∗∗ −0.266∗∗ −0.341∗∗∗ −0.388∗∗∗ −0.268∗∗ −0.341∗∗∗
(0.071)
−0.001
(0.005)
0.023∗∗∗
(0.004)
−0.026
(0.022)
0.039∗∗∗
(0.011)
0.051∗∗∗
(0.012)
0.680∗∗∗
(0.119)
0.030∗∗∗
(0.011)
0.243∗
(0.125)
0.278∗∗
(0.111)
1.929∗∗∗
(0.700)
Oui
Control for province
2,271
Number of observations
0.406
R-squared
∗∗∗ = significant at 1%, ∗∗ = significant at 5%, ∗ = significant at 10% level.
Note: Robust standard errors are in parentheses.
Source: 2010 China Family Panel Survey.

(0.107)
0.004
(0.007)
0.024∗∗∗
(0.007)
−0.019
(0.035)
0.053∗∗∗
(0.019)
0.014
(0.020)
0.244
(0.205)
0.030∗∗∗
(0.011)
0.265
(0.177)
0.305∗∗
(0.141)
1.922∗∗
(0.957)
Oui
848
0.407

(0.107)
0.004
(0.007)
0.023∗∗∗
(0.007)
−0.017
(0.034)
0.052∗∗∗
(0.019)
0.015
(0.020)
0.257
(0.205)
0.030∗∗∗
(0.012)
0.267
(0.178)
0.317∗∗
(0.139)
2.101∗∗
(0.960)
Oui
848
0.408

(0.078)
0.006
(0.006)
0.019∗∗∗
(0.004)
−0.032
(0.025)
0.052∗∗∗
(0.012)
0.048∗∗∗
(0.012)
0.610∗∗∗
(0.123)
0.047
(0.030)
0.184
(0.188)
0.436∗∗∗
(0.148)
1.971∗∗∗
(0.516)
Oui
2,015
0.383

(0.070)
−0.000
(0.005)
0.022∗∗∗
(0.004)
−0.024
(0.022)
0.039∗∗∗
(0.011)
0.049∗∗∗
(0.012)
0.701∗∗∗
(0.119)
0.029∗∗
(0.011)
0.249∗∗
(0.126)
0.292∗∗∗
(0.110)
2.206∗∗∗
(0.695)
Oui
2,271
0.407

(0.078)
0.006
(0.006)
0.019∗∗∗
(0.004)
−0.033
(0.024)
0.052∗∗∗
(0.012)
0.048∗∗∗
(0.012)
0.609∗∗∗
(0.123)
0.047
(0.030)
0.183
(0.189)
0.436∗∗∗
(0.148)
1.979∗∗∗
(0.500)
Oui
2,015
0.383

Dummy for being in a
key class of a school

Dummy for being in

Annual education

Constant term

a key school

expense

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 213

Tableau 6. Regression Analysis of Math Test Scores of Chinese Children

Dependent
Variable:
(standardized
math test
score)

Group dummy

(rural = 1, urban = 0)

Group dummy

(migrating = 1,
urban local = 0)

Group dummy

(migrating = 1,
rural = 0)
Child’s age

Interaction between
child age and the
group dummy
Dummy for male

Weight

Height

Degree of depression

Mother’s years of

schooling
Father’s years of
schooling

Dummy for school level
(junior high = 1)

Annual education

expense

Dummy for being in

a key school

Dummy for being in a
key class of a school

Constant term

(3)

(1)

(4)

(6)

Rural vs.
Urbain

(2)
Entre
Urbain:
Migrating
vs. Urban Migrating Rural vs.
vs. Rural

(5)
Entre
Urbain:
Migrating
vs. Urban Migrating
vs. Rural
Children Children Children Children Children Children
−0.196∗∗∗
(0.073)
—
—

—
—
−0.443∗∗∗
(0.105)

—
—
−0.432∗∗
(0.174)

−0.178
(0.109)
—
—

—
—
—
—

—
—
—
—

Urbain

Local

Local

—
—

—
—

−0.069
(0.077)

—
—

—
—

−0.081
(0.144)

0.317∗∗∗
(0.030)
—
—

0.387∗∗∗
(0.047)
—
—

0.292∗∗∗
(0.031)
—
—

0.323∗∗∗
(0.037)
−0.008
(0.030)

0.388∗∗∗
(0.049)
−0.004
(0.049)

0.025
0.002
(0.080)
(0.052)
0.003
0.005
(0.005)
(0.004)
0.014∗∗∗
0.009∗∗∗
(0.003)
(0.004)
−0.046∗∗∗ −0.026
(0.026)
(0.016)
0.033∗∗∗
0.004
(0.014)
(0.008)
0.024∗
0.017∗
(0.014)
(0.009)
1.206∗∗∗
1.569∗∗∗
(0.150)
(0.096)
0.019∗
0.017
(0.011)
(0.010)
0.100
0.106
(0.124)
(0.098)
0.028
0.118
(0.116)
(0.085)
1.534∗∗∗
0.841
(0.587)
(0.403)
Oui
Oui
848
2,271
0.641
0.589

0.025
0.002
−0.012
(0.080)
(0.052)
(0.057)
0.007∗
0.003
0.005
(0.005)
(0.004)
(0.004)
0.014∗∗∗
0.009∗∗∗
0.007∗∗
(0.003)
(0.004)
(0.003)
−0.046∗∗∗ −0.046∗∗∗ −0.026
(0.026)
(0.016)
(0.017)
0.033∗∗∗
0.039∗∗∗
0.004
(0.014)
(0.008)
(0.008)
0.024∗
0.017∗∗
0.011
(0.014)
(0.009)
(0.009)
1.206∗∗∗
1.567∗∗∗
1.696∗∗∗
(0.150)
(0.096)
(0.099)
0.019∗
0.017∗
0.032
(0.011)
(0.010)
(0.021)
0.100
0.106
0.098
(0.124)
(0.098)
(0.123)
0.202∗
0.028
0.117
(0.115)
(0.085)
(0.110)
1.509∗∗∗
1.092∗∗∗
0.832
(0.596)
(0.413)
(0.402)
Oui
Oui
Oui
848
2,271
2,015
0.641
0.589
0.574

0.291∗∗∗
(0.031)
0.004
(0.044)

−0.012
(0.057)
0.007∗
(0.004)
0.007∗∗
(0.003)
−0.046∗∗∗
(0.017)
0.039∗∗∗
(0.008)
0.011
(0.009)
1.695∗∗∗
(0.099)
0.032
(0.021)
0.098
(0.123)
0.202∗
(0.110)
1.099∗∗∗
(0.412)
Oui
2,015
0.574

Control for province
Number of observations
R-squared
∗∗∗ = significant at 1%, ∗∗ = significant at 5%, ∗ = significant at 10% level.
Note: Robust standard errors are in parentheses.
Source: 2010 China Family Panel Survey.

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214 ASIAN DEVELOPMENT REVIEW

observed attributes included in the regressions. Entre-temps, for the math test score
résultats, the rural–urban education gap is –0.196 with 1% level of significance. Le
controlled attributes can help explain 26% of the raw test score gap in math between
rural and urban local children.

Deuxième, when focusing on the urban sample, we find that urban residents’
children have much better education performance than migrating children. Le
coefficient of the dummy for migrating children (from second scenario regressions,
column 2 of Table 5) is –0.392 and significant at the 1% level. Given that the dif-
ference in the raw word test scores of the two groups of children is 0.912 (Tableau 4),
the regression results show that 43% of the test score gap can be explained by the
regression controls. For the math test regression in column 2 of Table 6, the coeffi-
cient of the dummy for migrating children (–0.443) is also negative and significant
à la 1% level. Up to 71% of the raw math test score gap (–0.624) can be explained
by the controlled characteristics.

Troisième, the difference in test scores between migrating children and rural resi-
dents’ children (including both left-behind and rural non-migrants’ children) is small
and statistically insignificant and likely due to different personal attributes. This sug-
gests that migrating with parents cannot really improve the education performance
of migrants’ children.

Fourth, the major contributors to the rural–urban education disparity in-
clude demographic features, physical health measures, parental education levels,
and household education spending. Basically, a child’s age positively affects the
word test score, with girls often exhibiting better performance. Physical health can
generate better education outcomes, while parental education positively correlates
with test scores. Other attributes that impact on word and math test scores also
include the depression level of the child, which has a strong negative effect on math
test scores though little effect on word test scores, and being in a key school, which is
positively associated with word test scores but with no significant effect on math test
scores.

Fifth, to gauge how the education disparity changes over time, we include an
interaction term of the child’s age and the group dummy into the regressions. Le
coefficients of the interaction terms show that the group difference in test scores
varies with the age of the children. The results are shown in the last three columns
of Tables 5 et 6. As seen in column 4 of Table 5, the coefficient of the interaction
term between the rural dummy and age is positive and significant at the 5% level.
This suggests that, holding other things constant, the gap in word test scores between
rural and urban children is wider for the younger age group. The other interactions
are insignificant, which implies that those group differences in test scores would not
change with a child’s age.

The above findings suggest the following: (je) there exists a substantial dispar-
ity in education outcomes of rural and urban children even after controlling for many
attributes, (ii) there is no significant difference in education outcomes between rural

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 215

children who stay in rural areas and those who migrate with parents and receive their
education in urban areas, (iii) migrating children generally have significantly poorer
education performance than their urban counterparts although they are educated
in the same cities, et (iv) there is evidence to show that the education disparity
between rural and urban children tends to be widened for younger age cohorts.

VI. Policy Implications

Improving education performance of children has long been regarded as one
of the most important targets of national policy in the PRC because it affects human
capital accumulation of the country. Cependant, how to efficiently use limited public
resources to achieve this target is still under question. In this analysis, we find that
there is still significant inequality in the education performance of children between
rural and urban areas of the PRC. En particulier, rural-to-urban migration, lequel
had been expected to play an active role in reducing education inequality, could not
contribute much to narrow the gap. À ce jour, there is still a significant difference in
the education performance of migrating children and urban residents’ children. Ce
provides some useful insights for policymaking.

D'abord, it is essential to reduce education inequality between rural and urban
populations of the PRC in order to improve average education performance at the
national level. Although there are many personal attributes that affect education
performance, providing equal rights and access to quality schools is important to
improve education inequality between rural and urban areas of the country.

Deuxième, it is important to reduce institutional barriers and discriminative poli-
cies against migrating children in urban areas in order to improve their education
performance. In our analysis, migrating children do not exhibit better performance
in education than left-behind children and rural residents’ children, and are un-
able to catch up with the performance of urban residents’ children. En outre
to non-education-related factors, such as parenting styles and family characteris-
tics, existing institutional barriers and discriminative policies that restrict migrating
children from accessing the urban education system may be a reason. From this per-
spective, reducing these restrictions may allow more migrating children to improve
their education performance and thus contribute to reducing education inequality
between rural and urban populations of the PRC.

Troisième, in addition to reforming the education system, public policies should
pay more attention to factors such as family income, children’s nutrition, parenting
style, and mental health, as these factors can also affect migrant children’s educa-
tion performance. This paper has shown that personal attributes, such as mental
health status, play an important role in explaining the difference in math test perfor-
mance of rural and urban children. Although it is hard to quantify the real effects
due to potential identification problems, improving the living conditions of rural

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216 ASIAN DEVELOPMENT REVIEW

left-behind children and paying more attention to their living environment will
definitely help to increase their education performance.

VII. Conclusion

This paper uses CFPS and RUMiC survey data to examine the differences
in education performance of children between rural and urban areas of the PRC. Dans
particular, we separately examine the education performance of migrants’ children
(including both left-behind and migrating children) and compare this to the education
performance of their rural and urban counterparts. Results show that there exists a
substantial disparity between rural and urban children, with rural-to-urban migration
playing a weak role in terms of narrowing the gap. En particulier, our analysis shows
that education performance of migrating children is significantly worse than that of
urban residents’ children, which causes some concerns.

As urban birth rate declines and more rural migrants move into cities, ru-
ral children are becoming an important part of the urban labor supply. Improv-
ing education performance of migrants’ children, especially those migrating into
cities with their parents, is not only in the interest of migrants but also crucial
for human capital accumulation and the long-term economic growth of the PRC.
Since there is a large gap in education performance of children in rural and ur-
ban areas of the country, further reforms need to be implemented to address the
problem.

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de Brauw, Alan, and John Giles. 2006. Migrant Opportunity and the Educational Attainment of
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Edwards, Alexandra, and Manuelita Ureta. 2003. International Migration, Remittances, et
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Feng, Shuaizhang, and Yuanyuan Chen. 2012. School Type and Education of Migrant Children:
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218 ASIAN DEVELOPMENT REVIEW

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EDUCATION INEQUALITY BETWEEN RURAL AND URBAN AREAS 219

Table A1.2. CFPS Summary Statistics—Education-related Features

Total no. of observations

%

Non. of observations by groups

%
Gender

% Male

Age

Average years

Health

Weight (kg)

Height (cm)

Depression

Family characteristics

Mother’s years of schooling

Father’s years of schooling

Annual education expense (CNY)

School attendance

% Junior high school

School type

% Key School

Class type

% Key class in a school

Rural

1,719
66.965

Rural

Urbain

848
33.035

Left-behind

non-migrants Migrating

Urbain

549
31.937

1170

68.063

296
34.906

48.998
(0.500)

12.607
(1.721)

36.526
(9.939)
144.501
(17.152)
1.392
(1.721)

50.427
(0.500)

12.529
(1.725)

36.794
(10.529)
143.994
(17.082)
1.220
(1.626)

52.027
(0.500)

12.689
(1.685)

38.590
(11.900)
146.547
(17.201)
1.150
(1.419)

552
65.094

50.906
(0.500)

12.332
(1.745)

42.092
(11.690)
152.938
(13.101)
1.140
(1.558)

4.224
(3.790)
6.434
(6.332)
773.244
(1,183.061)

4.774
(3.896)
3.493
(3.803)
877.477
(1,327.525)

5.280
(3.975)
7.057
(3.762)
1,098.980
(1,674.712)

10.348
(3.594)
10.861
(3.283)
2,840.351
(4,193.070)

33.698
(0.473)

2.550
(0.158)

6.560
(0.248)

34.872
(0.477)

2.650
(0.161)

6.320
(0.244)

40.541
(0.492)

2.360
(0.152)

8.450
(0.279)

44.565
(0.497)

12.500
(0.331)

14.860
(0.356)

Note: Standard deviations are in parentheses.
Source: 2010 China Family Panel Survey.

CFPS Data

Table A1.2 shows the basic summary statistics for each child category that

we have defined.

Similar to the RUMiC data, boys are more likely to number among the
migrating children, while girls are more likely to be among the left-behind children.
Cependant, the finding that migrating children tend to be relatively younger than left-
behind children no longer holds. This probably occurs because of a province-specific
effect, as the RUMiC and the CFPS draw their samples from different regions. Le

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220 ASIAN DEVELOPMENT REVIEW

migration of children from different regions starts in different years, giving rise to
differences in the distributions based on children’s age when RUMiC and CFPS data
are used. Entre-temps, Chinese families’ preference for taking care of boys is the
same for different regions.