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THE ANALYSIS OF INEQUALITY BASED ON

CEPS:EDUCATIONAL ACHIEVEMENT,PHYSICAL STATUS AND

FINANCIAL STATUS

BY

HAO ZHENXU

STUDENT NO. 13252291

A PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

BACHELOR OF SOCIAL SECIENCES(HONOURS) DEGREE

IN CHINA STUDIES

ECONOMICS CONCENTRATION

HONG KONG BAPTIST UNIVERSITY

APRIL 2018

1

HONG KONG BAPTIST UNIVERSITY

April 2018

We hereby recommend that the Project by Mr. HAO ZHENXU entitled “THE

ANALYSIS OF INEQUALITY BASED ON CEPS: EDUCATIONAL ACHIEVEMENT,

PHYSICAL STATUS AND FINANCIAL STATUS”

Be accepted in partial fulfillment of the requirements for the Bachelor of Social Sciences

(Honours) Degree in China Studies in Economics.

_________________ __________________

Dr. Wang Ruixin Dr.

Project Supervisor Second Examiner

2

Acknowledgements

I would like to thank my supervisor Dr. Wang Ruixin for suggesting the research topic and

guiding me through the entire study. Thanks also due to Professor Ruan Danching for her

assistance in knowledge about sociology of education.

_____________________

Student’s signature

China Studies Degree Course

(Economics Concentration)

Hong Kong Baptist University

Date : ________________

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Table of Contents:

Introduction----------------------------------------------------------------------------------4

Literature Review---------------------------------------------------------------------------6

Methodology---------------------------------------------------------------------------------11

Data Source-----------------------------------------------------------------------------------14

Empirical Result and Interpretation----------------------------------------------------16

Conclusion------------------------------------------------------------------------------------56

References-------------------------------------------------------------------------------------58

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Introduction

Since the first version law of compulsory education was announced in 1986, the

compulsory education has developed for decades. The law of compulsory education was

edited in 2006. The main addition is that education should be made sure to be developed in

an equal way. In other ways, government tries to place actions to the inequality among

compulsory education. Also many scholars like (Jane and Sherry, 2016) show that the

achievements of expanding educational system produce significant educational inequality

form varied dimensions. The educational inequality plays an important role in shaping the

society. For example of economics perspective, Nannan Yu , Bo Yu , Martin de Jong and

Servaas Storm(2015) point out that educational inequality is relative to China’s economic

growth, especially in some far developed areas of China. It’s shared that there are many

successful works about educational inequality of compulsory education. However, this

paper still wants to expand the studies in this field. To author’s knowledge, some works

study in educational attainment (like years of schooling or admission rate), few studies talk

about inequality of educational achievement (like academic results). Even there are many

studies about the government expenditure in China and higher education inequality

(university) in China, few studies have talked about the inequality of education

achievement in lower level education with county level data. This paper tries to present the

education inequality via a new perspective. The object of this paper is to talk about the

inequality of compulsory education within a selected period but with a wide range of

considerations. As showed in the title, there are 3 main parts to analyze. And this paper

uses a county level data in a relatively new period. Besides, the main method of analysis is

to show the inequality decomposition based varied groups from classification. The

classification is related to some polices from government .So another purpose is to provide

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government a new look of inequality in compulsory education. Finally, this paper holds an

ambition to offer some experiments and ways of logic to analyze the education inequality in

the future.

The hypothesis is that inequality exists and the contributions of group-group divides to

overall inequality are at a certain degree. From the results, the paper proves the inequality

exists and also presents the contributions. From the inequality decomposition, readers can

easily find out the inequality form groups with advantage and disadvantage. From the

difference, it’s also easy to find the effect from polices.

This paper consists of several parts. In literature review part, this paper will quote some

studies that deeply influence author to write this paper and do the research. In methodology

part, this part will illustrate the ideas about how to find inequality, how to decompose the

inequality and some sociology theories about education. Besides, this part illustrates how to

do the classification. In data sources part, the origin and brief introduction of the dataset

and variables are included. In empirical results and interpretation part, this paper depicts the

result of inequality, inequality decomposition. Also this part includes analysis about the

result by using comparison. Within the empirical results and interpretation part, the content

is divided into three parts. First part is the core part of the whole paper. The second and

third parts play complementary roles in this paper to show a more comprehensive look of

the students in CEPS. First part includes all results of this paper about educational

achievement and cognitive achievement of students survived by CEPS. Second part talks

about the physical status of the students via same methods to do analysis. Third part

contains financial status of the students and is analyzed in the same way. Finally, the

conclusion part will summarize the observation and analysis.

6

Literature Review

Educational inequality is always a hot topic to study. This paper learns the method and

logic form the paper (Kanbur and Zhang,1999). Kanbur and Zhang (1999) points out the

serious income inequality of urban-rural and inland –coastal. Also, Kanbur and Zhang

(1999) use inequality decomposition to find out contribution of different groups. They

find very high contribution of urban-rural divide and inland-coastal to overall inequality.

This paper learns how to measure inequality and how to do inequality decomposition from

their paper (Kanbur and Zhang, 1999). Compulsory education in China is always important

for Chinese society.

Other papers of studies about education inequality in China also provide this paper a lot of

help. These papers use different method to describe and demonstrate the educational

inequality of China form different perspective. To author’s knowledge, Wang’ paper seems

to be the oldest paper to present the education in rural region. Wang (1991) shows a case

study about the beginning of compulsory education system in China. The paper (1991)

recognizes the compulsory education system as a result of modernization of China. The

research paper ( Wang, 1991) tries to examine and evaluate the development of the

compulsory education via documentation review , questionnaire , observation and interview.

Wang (1991) also calls for more attention to the rural compulsory education system which

is a huge burden for Chinese peasants. Also, another paper about rural education in China

brings this paper a closer look to rural education in China. Hannum and Kong (2007)

describe the real education situation in rural area of Gansu province in China. The paper

describes and presents the situation of education resource in rural and poor area in China.

This paper describes many type types of resources, including basic facilities, financial

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arrangement, situation about teachers and classroom environment. Hannum and Kong

(2007) noted some barriers for the education in rural area, like economic factor and

teacher labor resource. These works use their ways to show the author a shocked fact of

rural education. It’s one of the reasons that urban-rural inequality is the point that this paper

focuses on.

As mentioned in title and introduction, this paper uses three categories through the content:

educational achievement, financial status and physical status. The idea is from these

published papers showed in the literature review part. Also, the logic of classification is

learnt from these published papers.

For example, the papers of Ding and Wang play important roles in the part of financial

status in this paper. Ding (2005) talks about the financial inequality of compulsory

education system. The book (Ding, 2005) shows the history and development of

compulsory education in China. Ding (2005) also points out that grants from the

government to the disadvantaged students are too small to solve the financial inequality

problem. The paper (Ding, 2005) also illustrates that the fiscal-transfer projects are

increasing the grants and will target more disadvantaged students. Wang (2014) also pays

attention to the inequality within the compulsory education from a financial perspective.

Based on the data form 1998 to 2008, the paper (Wang, 2014) presents the inequality in

compulsory education finance via the method of factor decomposition and GINI coefficient

decomposition. Also, the analysis in the paper (Wang, 2014) demonstrates that some

factors may deteriorate the inequality situation in this period of 10 years. For example,

GDP per capita presents a show power to drive up inequality.

8

For the logic of classification that will be mentioned in methodology part, these published

works offer a clear direction. For example, different household system(hukou) and mobile

status(local or migrant) are main classifications in this paper. The ideas are from works of

Wu, Hao and Yu. Wu (2011) talks about the urban-rural inequality in contemporary China

related to household registration system (hukou). The paper (Wu, 2011) obviously shows

that there is a significant gap between people’s education attainment due to different

hukou status. Wu (2011) notes that people with agriculture hukou have disadvantages in

education attainment. The paper (Wu, 2011) recognizes that it is hard to find many

countries like China to use a strong policy intervention( hukou system) to create

stratification. Hao and Yu (2015) analyze the education inequality in China from the

viewpoints of national policies. The paper (Hao and Yu, 2015) also uses CEPS as data

resource. Hao and Yu (2015) point out the education inequality in compulsory system and

call for the new policies or polices change to correct the inequality issue. Since policy

issues is the main factor for the paper (Hao and Yu, 2015), Hao and Yu (2015) illustrate

that hukou system (household registration system) is a huge obstacle for EFA (education

for all) goal in China. Migrated students are vital factors in CEPS. Hao and Yu (2015) also

summarize the policies change about migrant children. From the policy changes in past

decades, the government also tries to alleviate the serious inequality in education.

When considering how to classify the urban and rural, some papers about consolidation of

school are vital factors. Consolidation of schools applied for over 10 years in rural area

plays very important role in the development of rural education. The action of consolidation

is started in 2001 and officially banned in2012. Zhang (2014) presents the benefits and

problems duo to the consolidation in China. The paper (Zhang. 2014) demonstrates that

9

consolidation school policy tries to balance the inequality of education between urban and

rural .However, Zhang (2014) also reveals that consolidation school policy never increases

the education quality of rural education but increases the drop-out rate of rural students.

Shan and Wang (2015) hold the similar point that the purpose of consolidation of rural

school is to lower the education coast for their rural area. However, inequality of education

among regions is never alleviated by the consolidation.

In terms of the part about physical status, some papers also give this paper a hint to find a

suitable variable. Xu (2014) talks about the physical growth, mental health of children (age

7-18) in China. The paper (Xu, 2014) mentions the trend of poor eyesight for children in

China and calls for more attention to face the risk and challenge of children’s health

development in China.

From some papers, the attitudes to compulsory education are also found. Many papers

believe that the compulsory education is the right action for government to keep even the

inequality among compulsory education exists. Liang and Li (2012) use their paper to

illustrate that compulsory education brings huge benefits to China. For example,

compulsory education equips a large amount of population with basic education. The

population with basic education level is an important resource of cheap and skilled labor for

China’s rapid economic growth. The paper (Liang and Li, 2012) recognize compulsory

education expansion and establishment of national examination for college entrance as

speechless revolution for China. However, the education inequality is still serious. For

example, form the admission data of Peking University and Suzhou University, rural

students are less likely admitted by First Tier University in China. Their paper also reflects

the EMI theory to show the high level education inequality in China. Yang, Huang and

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Liu(2014) also hold positive attitudes to compulsory education. Yang, Huang and Liu

(2014) illustrate the inequality in education and the formation mechanism of the

educational inequality. The paper (Yang, Huang and Liu, 2014) points out that even the

education expansion policy alleviates the inequality, the situation of education inequality is

still serious and educational development gap among regions is still deep. Yang, Huang and

Liu (2014) also call for attention from government to balance the situation and help the

people in disadvantaged categories.

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Methodology:

Getting inspired by the paper about income inequality written by Kanbur and Zhang(1999),

this paper also uses GE (1) or Theil index and Gini coefficient to measure the inequality of

educational achievement, financial support from government and students’ physical

development. Higher Theil index and Gini coefficient are, higher level inequality

exists .The more important analysis is about the inequality decomposition .Due to that

generalized entropy (GE) class of inequality measure is adopted and GE measure is

addictively decomposable, this paper uses formula showed below.

11, I(y)= ∑ ( )

(

) (

) , c=1 μ=total sample mean ,yi= ith individual’s grade ,

(yi)= the population share of yi in total population, n = total population

2, For K exogenously given groups indexed by g ,

I(y) = ∑ ( ) ,

(μ μ )=between-group inequality, ∑ = within-group inequality

μg= mean of gth group ,Ig =inequality in gth group ,eg is a vector of one of length ng,,

ng is population of gth group .

3, = g(μg/μ) [gth group population share * relative mean of gth group], c=1

gth group ‘s contribution to total inequality =

( )

Inter- group contribution to total inequality is 100% minus sum of all groups ’contributions.

1 Ravi, K. & Xiaobo , Z.(1999) . Which Regional Inequality? The Evolution of Rural–Urban

and Inland–Coastal Inequality in China from 1983 to 19951 . Journal of Comparative Economics , 27, 691

12

As mentioned before, inequality decomposition plays a crucial role in this paper. So the

method to classify the variables must be very rational and has its social meaning. It is

generally shared that there are some main factors playing important roles in education

achievement and cognitive ability. This paper considers family influence, social influence

and school influence as principles to do classification. This paper uses parents’ educational

levels to measure family influence. Institutional factors include social arrangements, like

government policies, laws, regulations, institutions and so on. This paper uses hukou

system (household registration system), government support to school, policies for migrant

students and location of school to measure institutional influence.

In this paper, these 3 directions are measured by different variables. (Table 2-12) For

family influence, parents’ education level is used to measure the family influence. The

criteria is that whether the student’s mother or father has a higher education than their

children (middle school) .For school influence , school’s ownership and location are

classified and measure . All types of schools are divided into group based on whether the

school can get government support .For schools defined as school with government support,

they include public school and private school with government support (“minbangongzhu”).

For schools defined as schools without government support, they are ordinary private

school, private migrant children’s school and other types. All types of school location are

reorganized into two categories: urban or rural. For urban locations, they include central

district of city or county, marginal district of city or county, rural-urban continuum of city

or county and town of city or county. Rural location means countryside area. In terms of

social influence, HUKOU system is the main factor that this paper focuses on. In this paper,

HUKOU system divided students into different groups. On the one hand, this paper makes

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research on the inequality of students with agricultural HUKOU and non-agricultural

HUKOU .On the other hand, this paper studies local students and immigrated students

(whether the school location they enrolled in is same to the residence showed on their

HUKOU). Besides the first-step classification, this paper continues a second-step

classification. Among all schools with government support, there is a new classification up

to the schools’ location: urban or rural. (Table 13) Also, this paper divides all urban schools

into two groups based on whether the schools get government support (Table 14).

Effectively Maintained Inequality theory will be used in analysis about the result and may

explain the origin of the inequality. EMI theory is useful to explain the inequality issue.

EMI theory is also used for divide students into advantaged group and disadvantaged group.

EMI theory shows (Liu, 2016) that the effects of social origin on educational attainment

would not decrease, even if a given level of education became universal. It also reveals that

(Raftery and Hout, 1993) more advantaged social classes would seek to secure qualitatively

better types of education in the face of expansion of educational opportunities for other

groups.

14

Data Source

This paper uses CEPS dataset to do the research. CEPS is China Education Panel Survey

organized by RENMIN University of China .CEPS is started on 2013-2014 semester for

students of grade 7 and grade 9 .Due to the average education level and population mobility

ratio ,CEPS randomly select 28 county level units. Firstly, CEPS selects 15 county level

units from overall 2870 county level units in mainland of China. Secondly, CEPS selects 3

county level units from 18 units of Shanghai city. Because Shanghai is a very special super

city for its economic performance, large amount of migrants and big population .Thirdly, it

selects 10 units from 120 counties of china with big mobilized population. Among these 28

county level units, CEPS randomly select 4 schools per unit and gets 112 schools. When

selecting classes, CEPS will contain all classes in Grade 7or Grade 9 of the school if there

is only one or two classes in this school .For school with more than 2 classes in grade

7/Grade 9, CEPS will randomly select 2 classes. After the selection, there are 438 classes

and nearly 20,000 students in CEPS.

CEPS is operated by questionnaire .The questions in the survey are so comprehensive that

the results can offer accurate and detailed data for the scholars , policy makers or

administrators in school. CEPS 2013 only includes data and results from Grade7 and Grade

9 students. This paper uses midterm results in three subjects (Chinese, mathematics,

English), original and standardized scores of cognitive test to measure educational

achievement. And this paper uses appropriation and eyesight situation to measure financial

status and physical status respectively. This paper also utilizes the characteristics of the

school (location, ownership), hukou system of students, education level of students’ parents

and mobility status of students from CEPS.

15

For an overall view of the educational development in middle school, this paper also uses

Chinese education reports (2007-2015) from Chinese department of education .These

reports reveal basic information about middle school of China .For example, the number of

middle school, the number of freshman and graduate, the ratio of graduate and admission to

high school and the number of teachers.

16

Empirical Result and Interpretation

Middle school condition in China (unit: 10,000)2

From the statistics of China official record ,the number of middle school , the number of

freshman and graduate decrease during the period .The trend is in line with the low

fertility rate and follow-up effect of one –child policy .For graduation and admission to

high school ratio , the ratios follow an increasing trend ,even there was a little bit decrease

2 Ministry of education of People’s Republic of China

http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/moe_1485/tnull_34073.html

http://www.moe.gov.cn/s78/A03/ghs_left/s182/moe_633/201002/t20100205_88488.html

http://www.moe.gov.cn/srcsite/A03/s180/moe_633/201008/t20100803_93763.html





http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/201507/t20150730_196698.html


Number of

middle

school

Number

of

freshman

Number of

graduate

Graduate and

admission to high

school ratio

Number of

Middle school

teacher

2007 5.94 1868.5 1963.71 80.48% 347.3

2008 5.79 1859.6 1867.95 83.4% 347.55

2009 5.63 1788.45 1797.7 85.6% 351.8

2010 5.49 1716.58 1750.35 87.5% 352.54

2011 5.41 1634.73 1736.68 88.62% 352.45

2012 5.32 1570.77 1660.78 88.4% 350.44

2013 5.28 1496.09 1561.55 91.2% 348.1

2014 5.26 1447.82 1413.51 95.1% 348.84

2015 5.24 1411.02 1417.59 94.1% 347.56

http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/moe_1485/tnull_34073.html

http://www.moe.gov.cn/s78/A03/ghs_left/s182/moe_633/201002/t20100205_88488.html






http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/201507/t20150730_196698.html

17

in 2012. This increasing trend seems exciting .Because it suggests more percentage of

students can attain high school education .However, it also reflects more fierce competition

in job market .For the number of teachers working in middle school, it increased from 2007

to 2010 with the largest amount and gradually decreased to the same amount in 2007 .This

trend is related to the dropping number of middle school.

18

Empirical Result and Interpretation

Table 1

Inequality Measure

Table 1 shows GE(1)index/ Theil index and GINI coefficient of all the dependent

variables that the paper focus on .Higher theil index and higher Gini coefficient reveal

higher level of inequality . Educational achievement and cognitive ability of grade 9

students have higher Theil index and GINI coefficient than those of grade 7 students.

Appropriation for students also has a high Theil index and GINI coefficient. Among all

the categories, standardized scores of students’ cognitive ability has the highest GE (1)

index and GINI coefficient, regardless of different grades. As showed in many paper about

financial inequality of compulsory education in China, the second highest inequality shows

in the category of appropriation per student. The inequality of three main subjects in middle

school in China(Chinese ,mathematics ,English) are also revealed in the table 1.Among

Category GE(1)/Theil index GINI

Original Chinese mid-term grade7 0.03050 0.12500

Original Math mid-term grade7 0.07682 0.20265

Original English mid-term grade7 0.06424 0.19090

Standardized scores of students'

cognitive ability (grade 7)

0.27144 0.40470

Original scores of students'

cognitive ability

(grade 7)

0.05473 0.18169

Original Chinese mid-term grade

9

0.03632 0.14191

Original Math mid-term grade 9 0.11113 0.25430

Original English mid-term grade 9 0.09746 0.24528


cognitive ability(grade 9)

0.27359 0.40772


cognitive ability

(grade 9)

0.09288 0.23994

19

these 3 subjects , mathematics test scores express highest level of inequality. Compared to

scores of these 3 subjects for grade 7 students, scores of grade 9 students attain higher

inequality. The same result of comparison also shows in the cognitive ability test,

regardless the original scores or standardized scores .Scores of Grade 9 students have

higher inequalities than those of Grade 7 students.

20

Table 2

Mean and Ratio

In Table 2, this paper divides students into two groups based on their school’s ownership

(whether get support from government).The hypothesis of the comparison is mostly in line

with the real mean comparison in Table 2. Generally ,schools with government support are

very likely offer better quality education .So the scores of students in schools with

government support will be higher than those of students in schools without

government .For the categories of this paper, most of them don’t reject the

hypothesis ,except scores of Original Chinese mid-term in grade7, Original Chinese and

English mid-term grade 9.From the column of the ratio, the appropriation per student

category has the highest ratio ,which is consistent with the hypothesis that students in

school with government support get more financial support than students in school without

Category With government

support

Without

government support

With/without

Original Chinese mid-term grade7 79.01161 80.82759 0.97753

Original Math mid-term grade7 77.32462 69.91154 1.10604

Original English mid-term grade7 83.01426 74.66692 1.11179



0.71014 0.70600 1.00586


cognitive ability

(grade 7)

10.81030 9.76955 1.10653

Original Chinese mid-term grade 9 86.90387 92.48430 0.93966

Original Math mid-term grade 9 79.59742 79.05785 1.00683

Original English mid-term grade 9 75.43179 79.55620 0.94816



0.72143 0.62796 1.14885


cognitive ability

(grade 9)

9.12773 8.49109 1.07498

21

government support .To sum up ,inequality of education achievement can be observed

between different school ownership in Table 2.

22

Table 3

Mean and Ratio

In Table 3, this paper divides students into 3 groups up to different institutional

characteristic (hukou system in China) :agriculture ,non-agriculture and other(in some

region ,there is no agriculture or non-agriculture).From the table ,all scores of students with

non-agriculture hukou system are higher than those of students with agriculture . From the

column of ratio, the largest is appropriation per students. In China, hukou system always

plays a crucial role in people’s life. Non-agriculture hukou in first cities of

China(Beijing ,shanghai ,Guangzhou ,Shenzhen ) provides owners privilege in property

and car purchasing and other social welfare including education .So the inequality of

education achievement can be found in students with different HUKOU systems in Table 3.

Category Agricultural

Hukou

Non-

agriculture

Hukou

others Non/ag

Original Chinese mid-term

grade7

77.43469 85.29963 77.59645 1.10157

Original Math mid-term

grade7

73.16931 86.60632 76.19185 1.18364

Original English mid-term

grade7

78.36368 93.46482 81.51763 1.19271

Standardized scores of

students' cognitive ability

(grade 7)

0.63874 0.78629 0.74508 1.231


cognitive ability

(grade 7)

10.24013 11.85108 10.84861 1.15732


grade 9

84.72320 91.79776 89.99486 1.0835


grade 9

75.66242 87.54916 81.84714 1.1571


grade 9

69.91393 85.31738 81.15376 1.22032


students' cognitive

ability(grade 9)

0.62140 0.80775 0.79209 1.29989


cognitive ability

(grade 9)

8.33857 10.32587 9.73110 1.23833

23

Table 4

Mean and Ratio

In Table 4, this paper divides students into 2 groups (urban and rural) based on their

schools’ locations. The hypothesis is that urban schools can offer higher quality

education .Therefore students in urban schools get higher scores than those in rural schools.

Consistent with hypothesis, the scores of all subjects in urban category are higher than

those in rural category. From the ratio column, the largest ratios are from standardized

scores of students’ cognitive ability in Grade 7 and Grade 9. Ratios of students’ scores in

Grade 9 are higher than those of students’ scores in Grade 7 in the subject of Chinese,

English and original scores of students’ cognitive ability. Generally, Table 4 shows

inequality of educational achievement exits in categories of urban and rural.

Category Urban Rural Urban/Rural






0.73640 0.53493 1.37663


cognitive ability

(grade 7)

10.9622 9.68386 1.132






0.74294 0.54185 1.37112


cognitive ability

(grade 9)

9.38580 7.76467 1.20878

24

Table 5

Mean and Ratio (mother’s education)

In Table 5, the comparison of scores depends on student mothers’ different education level.

Higher education means the mother has a higher education level (higher than middle school)

than the students. Generally, parents with higher education level have a more positive

family influence in students’ educational achievement. For example, parents with higher

education level are more likely to help students in study, pay more attention to students’

education and have higher expectations about students’ further study. Consistent with the

hypothesis, students with higher education level mothers get higher scores in all categories.

From the column of ratio, scores of students in Grade 9 have high ratios than those of

students in Grade 7. For scores of all categories in Grade 7 students, standardized scores of

Category Lower education Higher education Higher/lower






0.64602 0.78773 1.21936


cognitive ability

(grade 7)

10.21070 11.71337 1.14717






0.64791 0.81034 1.2507


cognitive ability

(grade 9)

8.38404 10.58270 1.26224

25

students’ cognitive ability has a highest ratio .Fro scores of all categories in Grade 9

students , scores of English test ,standardized and original scores of cognitive test have

high ratios. Generally, inequality of education achievement based on groups of different

education level of students ‘mothers is obvious in table 5.And the inequality of Grade 9 is

higher than that of Grade 7.

26

Table 6

Mean and Ratio (father’s education)

In Table 6, the comparison of scores depends on student fathers’ different education level.

Higher education means the father has a higher education level (higher than middle school)

than the students. Generally, parents with higher education level have a more positive

family influence in students’ educational achievement. For example, parents with higher

education level are more likely to help students in study, pay more attention to students’

education and have higher expectations about students’ further study. Consistent with the

hypothesis, students with higher education level father get higher scores in all categories.

From the column of ratio, scores of students in Grade 9 have high ratios than those of

students in Grade 7. For scores of all categories in Grade 7 students and Grade 9 students,

standardized score of students’ cognitive ability has a highest ratio. Generally, inequality of

Category Lower education Higher education Higher/lower






0.63993 0.77129 1.20527


cognitive ability

(grade 7)

10.12996 11.57992 1.14314






0.62415 0.81511 1.30595


cognitive ability

(grade 9)

8.28828 10.38246 1.25267

27

education achievement based on groups of different education level of students ‘mothers is

obvious in table 5.And the inequality of Grade 9 is higher than that of Grade

From table 5 and table 6, the result shows that students with higher education level parents

get higher scores than those with lower education level parents in all the categories this

paper talks about. Even the result is just mean value and not accurate enough, the result also

follow the EMI theory as mentioned in methodology part .Family influence is not only

correlated to the investment of education, but also related to education climate like parents’

expectation to their children’s further education.

28

Table 7

Mean and ratio (immigration)

Category Local Migrant Local/migrant

Original Chinese mid-

term grade7

78.02013 76.92695 1.01421

Original Math mid-

term grade7

73.58874 71.97732 1.02239

Original English mid-

term grade7

80.30987 77.27560 1.03927


students' cognitive

ability (grade 7)

0.66808 0.63066 1.05933

Original scores of

students' cognitive

ability

(grade 7)

10.26800 10.08637 1.01801


term grade 9

83.33516 84.81443 0.98256

Original Math mid-

term grade 9

74.23293 73.50165 1.00995


term grade 9

68.69417 70.33926 0.97661


students' cognitive

ability(grade 9)

0.60940 0.63906 0.95359

Original scores of

students' cognitive

ability

(grade 9)

8.16073 8.24547 0.98972

Table 7 divides students into 2 groups based on the mobility status: local, migrant. The

admission to middle school in China is related to the HUKOU system. Basically, migrant

students cannot be admitted to the public school of the city they migrate to. However, in

some big cities like Shanghai or Beijing, there are some policies for the migrant students

due to large amount migrant population .Sometimes, some migrated parents pay extra fee

for their children to study in the middle school of migrate city without normal student

29

status .So all migrant students in CEPS show that these students are allowed to take courses

in middle school like local students. For these migrant students studying in the same class

with local students, their education achievement may not vary with local students’ too

much. From the column of ratio, the results are mostly consistent with hypothesis. For

scores of students in Grade 7, the scores of all categories in local is little higher than those

of migrant group .For scores of students in Grade 9, the scores of most categories in local

are little lower than those of Grade 9 students, except scores of mathematics test .For

comparison in appropriation per student, the surprising result is that mean value of local is

lower than that of migrant students. Generally, the Chinese society places bad treatment to

migrant group, due to the competition of social welfare between local citizens and migrants.

From the table 7, inequality about education achievement of two groups are not large .As

mentioned before, the migrant students who are allowed to study in middle school of

migrated city even without normal student status are much luckier than those who are

rejected by middle school of migrated city. The dilemma of migrant students without

normal student status occurs in higher education like high school education with more

serious consequences. For example, students without normal student status are not allowed

to attend national college entrance examination (“gaokao”).In other words, compulsory

education alleviates degree of inequality between local students and migrant students.

30

Table 8

Inequality within group : GE (1) index

Category Urban Rural Agriculture Non -

agriculture

With Without

Original Chinese

mid-term grade7

0.03091 0.02458 0.03085 0.02172 0.02956 0.03343

Original Math mid-

term grade7

0.07643 0.07810 0.08363 0.05056 0.06991 0.10512

Original English

mid-term grade7

0.06329 0.07375

0.06821 0.04469 0.06096 0.07546

Standardized scores

of students' cognitive

ability (grade 7)

0.27236 0.25315 0.29451 0.24648 0.26359 0.29284

Original scores of

students' cognitive

ability

(grade 7)

0.05357 0.06700 0.05569 0.03939 0.05345 0.05492

Original Chinese

mid-term grade 9

0.03663 0.03204 0.03704 0.03370 0.03741 0.02039

Original Math mid-

term grade 9

0.10903 0.11685 0.11591 0.09199 0.11262 0.09045

Original English

mid-term grade 9

0.09254 0.10633 0.10201 0.08175 0.09809 0.08795

Standardized scores


ability(grade 9)

0.26720 0.28191 0.28906 0.24109

0.27329 0.26977

Original scores of

students' cognitive

ability

(grade 9)

0.09028 0.09091 0.09136

0.07959

0.09292 0.08985

31

Table 8 presents results on GE inequality within each group as mentioned before, i .e. ,

urban, rural, agriculture ,non-agriculture, with government support and without

government support ,. A higher GE (1) index means a higher degree of inequality. From

all categories of these 6 groups, standardized scores of cognitive test of students in Grade

7 and Grade 9 have the highest GE index, which means highest within group inequality.

From comparison among different groups in every category, this paper finds more detailed

contents. Firstly, when comparing GE (1) index of urban and rural groups, there are no big

gaps between these two groups in most categories, except appropriation per students for

these 2 groups. For the comparison between students in Grade 7 and students in Grade 9,

scores of Grade 9 students in every category and every group have higher level within

group inequality than those of Grade 7 students.

32

Table 9

Inequality within group : GE (1) index

Category Lower

education(mo

m)

Higher

education

(mom)

Lower

education(fat

her)

Higher

education

(father)

Local Migrant

Original

Chinese mid-

term grade7

0.03294 0.02301 0.03432 0.02249 0.03011 0.03803

Original Math

mid-term

grade7

0.08715 0.05252 0.09056 0.05381 0.08553 0.09306

Original

English mid-

term grade7

0.06999

0.04700 0.07224 0.04800 0.06490 0.07497

Standardized

scores of

students'

cognitive ability

(grade 7)

0.28721

0.24591 0.28665 0.25214 0.29311 0.29816

Original scores

of students'

cognitive ability

(grade 7)

0.05753 0.04345 0.05880 0.04410 0.058

0.05995

Original

Chinese mid-

term grade 9

0.03940 0.02541 0.03992 0.02604 0.03596 0.04055

Original Math

mid-term grade

9

0.12317 0.07734 0.12525 0.08222 0.11874 0.12106

Original

English mid-

term grade 9

0.10193 0.07165 0.10306 0.07549 0.10161 0.09418

Standardized

scores of

students'

cognitive

ability(grade 9)

0.28745 0.24448 0.28729 0.24605 0.29009 0.27797

Original scores

of students'

cognitive ability

(grade 9)

0.09535 0.07179 0.09504 0.07547 0.09353

0.10109

33

Table 9 presents results on GE inequality within each group as mentioned before, i .e. ,

lower and higher education level of parents, mobility status of students: local, migrated .A

higher GE (1) index means a higher degree of inequality. From all categories of these 6

groups, standardized scores of cognitive test of students in Grade 7 and Grade 9 have

highest GE index, which means highest within group inequality. When comparing

different groups in every category, this paper finds more detailed contents. Firstly, when

comparing GE (1) index of lower and higher education level of parents, this paper finds

scores of students with lower education level parents have higher GE(1)

index ,representing higher level of within group inequality . Following this logic, the

paper also finds scores from migrated students have higher within group inequality than

those of local students in every category. For the comparison between students in Grade 7

and students in Grade 9, scores of Grade 9 students in every category and every group

have higher level within group inequality than those of Grade 7 students.

34

Table 10

GE Inequality Decomposition: Contributions to Overall Inequality

Category Urban Rural Urban-rural

Original Chinese

mid-term grade7

80.77%

18.26% 0.97%

Original Math mid-

term grade7

76.80% 21.36% 1.84%

Original English

mid-term grade7

79.76%

18.74%

1.50%

Standardized

scores of students'

cognitive ability

(grade 7)

87.5% 10.67% 1.83%

Original scores of

students' cognitive

ability

(grade 7)

82.2% 15.89% 1.91%

Original Chinese

mid-term grade 9

83.39% 15.28% 1.33%

Original Math mid-

term grade 9

81.41% 17.9% 0.69%

Original English

mid-term grade 9

80.04% 17.14% 2.82%

Standardized

scores of students'

cognitive

ability(grade 9)

87.83% 10.38% 1.79%

Original scores of

students' cognitive

ability

(grade 9)

81.78% 15.52% 2.70%

To get a more accurate analysis of the inequality, this paper uses the inequality

decomposition method mentioned in the methodology part. Starting from table 10, this

paper will use some tables to reveal the decomposition analyses for overall inequality under

different groups discussed before. Table 10 shows the inequality is decomposed into 3

components: within-urban inequality, within-rural inequality and inequality between urban

35

and urban. In the formula of inequality decomposition, the contribution to overall inequality

is highly correlated with population share. From the table, this paper shows some

characteristics of the data from CEPS. For example, students in urban schools occupy the

largest proportion of all students CEPS studies in.

The content of the column on the right side is the main result that this paper focuses on.

Generally, compared to the income inequality decomposition mentioned in previous studies

(Kanbur and Zhang, 1999), there is a relatively low level contribution of urban-rural

inequality from the third column. The highest urban-rural inequality occurring in scores of

English test for Grade 9 students just occupies 2.82% of overall inequality.

When comparing the urban-rural inequality’s contribution between Scores form Grade 7

students and Grade 9 students , this paper finds scores of Grade 9 students have higher

urban-rural inequality contribution than those of Grade 7 students in most categories like

Chinese, English, original scores and standardized scores of cognitive test.

From the table and analysis above, this paper concludes that the urban-rural inequality of

educational achievement of different subjects, financial support from government and BMI

index is relatively at a low level .From this result, this paper illustrates that compulsory

education plays an important role in alleviating the inequality between the division of urban

and rural schools.

36

Table 11


Category Agriculture Non -

agriculture

Others Agriculture -

Non-agriculture-others

Original Chinese

mid-term grade7

53.62% 20.47% 22.78%

3.13%

Original Math

mid-term grade7

54.97% 19.38% 22.22% 3.43%

Original English

mid-term grade7

53.18% 20.45% 21.9% 4.47%

Standardized

scores of

students'

cognitive ability

(grade 7)

44.34% 32.15% 21.88% 1.63%

Original scores

of students'

cognitive ability

(grade 7)

51.81% 20.63% 24.02% 3.54%

Original Chinese

mid-term grade

9

55.87% 26.39% 15.87% 1.87%

Original Math

mid-term grade

9

55.43% 24.43% 18.29% 1.85%

Original English

mid-term grade

9

53.96% 25.22% 16.52% 4.3%

Standardized

scores of

students'

cognitive

ability(grade 9)

43.13% 33% 20.94%

2.93%

Original scores

of students'

cognitive ability

(grade 9)

50.37% 25.9% 18.62% 5.11%

37

Table 11 presents the inequality decomposition based on students’ different hukou systems.

The overall inequality is decomposed into 4 parts: within-agriculture inequality, within

non-agriculture, within-others inequality and inequality among agriculture---non-

agriculture—others. In the formula of inequality decomposition, the contribution to overall

inequality is highly correlated with population share. From the table, this paper shows some

characteristics of the data from CEPS. As the logic of analysis mentioned before, this

paper pays more attention to the right column. From the column of agriculture---non-

agriculture—others gap, original scores of cognitive ability test has the highest proportion

to overall inequality at around 5%. From the result of column, scores of Chinese,

Mathematics and English for Grade 7 students have higher inequality among agriculture,

non-agriculture and others than those for Grade 9 students. However, inequality among

groups in original scores and standardized scores of cognitive test for Grade 9 students

contributes more to the overall inequality than those for Grade 7 students.

From the table and analysis above, the inequality among different hukou systems is much

higher than that inequality between urban and rural mentioned in table10 .However, the

contribution of inequality among different hukou systems to overall inequality is around or

less than 5%, which is still a low percentage .Hukou system is the most typical institutional

factor for education In China. The gap between agriculture and non-agriculture hukou

exists in the whole lives of all people in China.

Since hukou system was extended to both urban and rural areas of China in 1955, it has

been influencing people’s lives and causing inequality until now. The hukou system can

cause opportunity inequality .For example, school admission may not open to students with

different types of household registration systems. Also, the hukou system can cause

38

opportunity inequality. This inequality will convert to the inequality of educational

achievement. From the table 11, the result indeed reveals the inequality between varied

groups up to hukou systems. However, the inequality between groups is not as large as

expected one .Because the inequality between different hukou systems in economic

perspective is considerable. In other words, the compulsory may play such an important

role in alleviating the inequality of education, which is also a reliable explanation of the

relatively low inequality among groups in table 11.

39

Table 12


Category Lower

education(mo

m)

Higher

education

(mom)

Lower-

higher

education(m

om)

Lower

education(fath

er)

Higher

education

(father)

Lower-

higher

education

(father)

Original

Chinese mid-

term grade7

68.84%

28.95%

2.21% 63.78% 33.4% 2.82%

Original Math

mid-term

grade7

69.55% 27.12% 3.33%

63.49% 32.52% 3.99%

Original

English mid-

term grade7

66.8%

29.05%

4.15%

60.9%

34.64%

4.46%

Standardized

scores of

students'

cognitive ability

(grade 7)

54.14%

44.06%

1.80%

47.12%

51.29%

1.59%

Original scores

of students'

cognitive ability

(grade 7)

64.91%

31.03%

4.06%

59.07%

36.86%

4.07%

Original

Chinese mid-

term grade 9

71.72%

24.41%

3.87%

66.22%

29.81%

3.97%

Original Math

mid-term grade

9

70.66%

25.42%

3.92%

64..95%

31.57%

3.48%

Original

English mid-

term grade 9

66.27%

27.56%

6.17%

60.26%

33.87%

5.87%

Standardized

scores of

students'

cognitive

ability(grade 9)

54.91%

42.82%

2.27%

47.44%

49.32%

3.24%

Original scores

of students'

cognitive ability

(grade 9)

64.43%

28.88%

6.69%

57.76%

35.58%

6.66%

40

Table 12 represents the inequality decomposition based on the classification about

education level of students’ parents. Generally, table 12 combines the education levels of

students’ parents to analyze the impact of family factor on students’ educational

achievement. In table 12, inequality based on mother’s education level and father’s

education level is decomposed into 3 parts: within-lower education level inequality, within-

higher education level, inequality between lower-higher respectively. From the contribution

of lower education and higher education categories to the overall inequality, the result also

reflects the population share of varied categories .So the students with lower education

parents take more proportion than those with higher education parents in the dataset this

paper uses. Generally, this paper focuses more on the contributions of lower-higher

education inequality to overall inequality. For the family influence form mother’s education,

the inequalities of lower-higher education in original scores in English for Grade 9 students

and original scores of cognitive test for Grade 9 students contribute (6% ~ 7%) more than

other categories .Lower-higher inequality about standardized scores of cognitive test

reveals lowest contribution(1.8%) to overall inequality .The situation of father’s education

level also express same feature .Inequalities between low-education and high-education of

original scores of cognitive test for Grade 9 students and standardized scores of cognitive

test for Grade 7 students contribute the highest and lowest proportions

respectively .Another same feature between mother’s education influence and father’s

education influence is that inequalities of lower-higher education part for Grade 9 students

contribute more than those Grade 7 students in every category .The comparison between

the results of mother’s and father’s education is meaningful .The contributions of lower-

higher education about father influence are higher than those about mother influence in

41

most categories ,except standardized score of cognitive test for Grade 7 students and

original scores of mathematics, English, cognitive test for Grade 9 students.

Family influence plays a crucial role in students’ educational achievements. Also this paper

shows that inequality between low education and high education from the classification

based on parents’ education level contributes relatively high inequality to overall inequality

than other types of classification. Difference in parents’ education level will influence the

income level, investment of education and parents’ expectation about their children. Also,

table 12 shows that most of the students’ parents in the survey get lower education (lower

than middle school junior high school). However, it is very hard for their children to get

good jobs or just similar jobs as their parents with lower education (lower than junior high

school) in fierce job market in China.

42

Table 13


Category Local Migrant Local-migrant


term grade7

44.82%

55.11%

0.07%


grade7 48.76% 51.17% 0.07%


term grade7 47.6% 52.13% 0.27%


students' cognitive

ability (grade 7)

53.14% 46.72% 0.14%

Original scores of

students' cognitive

ability

(grade 7)

49.85% 50.08% 0.07%


term grade 9 57.51% 42.4% 0.09%


grade 9 60.66% 39.34% 0.00%


term grade 9 62.12% 37.81% 0.07%


students' cognitive

ability(grade 9)

59.31% 40.59% 0.10%

Original scores of

students' cognitive

ability

(grade 9)

58.51% 41.48% 0.01%

43

Table 13 shows the inequality is decomposed into 3 parts: within-local students’ inequality,

within-migrated students’ inequality and inequality between local and migrant. From the

contributions of local and migrated students, the population share is also clear. The

population share of local students is a little bit higher than that of migrated students. For

local-migrant part, the contribution to overall inequality is very low. Contributions of

inequality between local and migrant to overall inequality in other categories are less than

1%. The results seem to present a low level inequality between local and migrant. However,

the competition even the conflict between local citizens and migrant in China is always

fierce and serious, especially in big cities like Beijing or Shanghai. The conflicts come

from limited resource .This paper focus more on the education resource .From the table 13 ,

it seems like very low level of inequality between these two groups exists .The result is

consistent with the analysis mentioned in table 7. In some big cities , government uses

control of admission for migrated students to control and migrated population and protect

local students ‘ education resource .

44

Table 14


Category With Without With-Without

Original Chinese

mid-term grade7 94.48% 5.46% 0.06%

Original Math mid-

term grade7 93.49% 6.14% 0.37%


term grade7 92.62% 6.87% 0.51%

Standardized scores


ability (grade 7)

95.55% 4.45% 0.00%

Original scores of

students' cognitive

ability

(grade 7)

91.67% 7.77% 0.56%

Original Chinese

mid-term grade 9 95.62% 4.02% 0.36%

Original Math mid-

term grade 9 94.52% 5.48% 0.00%


term grade 9 93.49% 6.42% 0.09%

Standardized scores


ability(grade 9)

94.97% 4.86% 0.17%

Original scores of

students' cognitive

ability

(grade 9)

93.76% 6.08% 0.16%

Table 14 shows the inequality decomposition of classification about school characteristics.

The overall inequality is decomposed to 3 parts: within-with government support inequality,

within-without government support inequality, inequality between with and without .The

contribution to overall inequality is also related to population share. So from the results in

table 14, the population share in with government support is much higher than that without

government support. Also, the inequality between 2 groups is the core part this paper

45

focuses on .From the column about contribution of inequality between groups, the results

are mostly lower than 1 %.The result of inequality decomposition is in line with the

analysis mentioned in table 2. Due to the low percentages, it is clear that inequality between

with-without contributes a little to the overall inequality. That means inequality of scores

for students studying in schools with varied feature is small.

From table 10-14, this paper decomposes inequality of education achievement, BMI index

and financial support based on different classification mentioned in methodology part. The

inequality contribution between groups to the overall inequality is the main point that this

paper wants to talk about .Generally ,the contribution of inequality based on different

classification method just accounts for lower than or around 5 % of overall inequality .It

means that inequality between groups doesn’t play an important role in the overall

inequality .To get a more accurate and detailed ,this paper also make a second-level

decomposition .In the deeper decomposition ,this paper controls the school type of students

(with government support) and decomposes the inequality based on urban-rural division in

table 15. Meanwhile, this paper also controls the school location (urban school) and

decomposes the inequality based on with-without government support divide .The reason

that this paper only uses fixed urban school and school with government support is that

there are nearly no observation on the opposite side, which is consistent with the population

share of different groups as mentioned before. Besides, the analysis about table 10 and table

14 shows that the inter-group inequality‘s contribution to overall inequality is low .So this

paper tries to use the second level decomposition to explain the puzzle.

46

Table 15

Urban –rural inequality decomposition under school with government support

Table 15 presents the similar result as table 10 .For example, the similar feature of

population share, comparison about the urban-rural inequality contribution among all

categories. Compared to the analysis result in table 1, the urban-rural inequality‘s

contributions of almost every category in table 15 are higher than those in table

10.However, the urban-rural group still accounts for a low percentage.



grade7 79.71% 19.33% 0.96%


grade7 74.92% 22.84% 2.24%


grade7 77.89% 20.23% 1.88%



(grade 7)

86.9% 11.17% 1.93%


cognitive ability

(grade 7)

80.24% 17.34% 2.42%


grade 9 82.8% 15.98% 1.22%

Original Math mid-term grade

9 80.31% 18.94% 0.75%


grade 9 78.73% 18.33% 2.94%


students' cognitive

ability(grade 9)

87.06% 10.93% 2.01%


cognitive ability

(grade 9)

80.34% 16.56% 3.10%

47

Table 16

With-without inequality decomposition under school in urban region

The results showed in table 16 almost shared similar characteristics with the results in table

14, like the comparison in population share, with-without group inequality’s contribution to

overall inequality among all categories. Compared to table 14, contribution of with-without

inequality in table 16 gets a higher proportion.

After the second level decomposition, the urban-rural inequality and with-without

inequality contribute more to overall inequality. However, the proportions are still limited.

Category With government

support

Without government

support

With-without


grade7 93.24%

6.76% 0%


grade7 91.2% 7.99% 0.81%


grade7 90.46% 8.62% 0.92%



(grade 7)

94.9% 5.08% 0.02%


cognitive ability

(grade 7)

89.49% 9.45% 1.06%


grade 9 94.95% 4.83% 0.22%

Original Math mid-term grade

9 93.25% 6.73% 0.02%


grade 9 91.96% 8.02% 0.02%


students' cognitive

ability(grade 9)

94.14% 5.54% 0.32%


cognitive ability

(grade 9)

92.1% 7.43% 0.47%

48

Table 17



Original Chinese

mid-term grade7

39.26%

57.56%

3.18%

Original Math mid-

term grade7

31.24%

63.81%

4.95%

Original English

mid-term grade7

32.97%

60.70%

6.33%

Standardized

scores of students'

cognitive ability

(grade 7)

47.28%

50.42%

2.30%

Original scores of

students' cognitive

ability

(grade 7)

34.16%

60.83%

5.01%

Original Chinese

mid-term grade 9

32.03%

63%

4.97%

Original Math mid-

term grade 9

38.47%

58.78%

2.75%

Original English

mid-term grade 9

35.94%

57.96%

6.10%

Standardized

scores of students'

cognitive

ability(grade 9)

48.78%

47.87%

3.35%

Original scores of

students' cognitive

ability

(grade 9)

37.03%

57.05%

5.92%

From table 10 and table 15, the contribution of urban-rural divide inequality is so limited

that conflicts with the generally shared statement .In line with the consolidation of school

policies, this paper tries a new and more practical method to classify the urban and rural

schools. The revised “urban group” only includes schools located in central district of

county or city. The revised “rural group” contains the rest: marginal district of city or

county, rural-urban continuum of city or county, town of city or county and countryside

49

area. The revised results are presented in table 17. The result of table 17 is much better than

pervious tables and is in company with the generally shared statement. Compared to the

table 10 and table 15, the contribution of urban-rural divide inequality to overall inequality

increases a lot. From the column of urban-rural divide, the highest contribution to overall

inequality is found in category of appropriation per student at 6.1%. When comparing the

contributions of different categories between Grade 7 and Grade 9, contributions of urban-

rural to overall inequality for Grade 7 students in math and English are higher than those

for Grade 9 students. In other words, the contributions of Grade 9 students urban-rural to

overall inequality in other categories are higher than those of Grade 7 students,

After new classification, the result of urban-rural contribution to overall inequality is

much closer to the hypothesis. However, compared to other paper about inequality

decomposition for urban and rural area in China, the result is still not serious.

According to the results in table 11, table 12, table13 and table 17, the contribution of

group-group divide to overall inequality is highest in most categories based on the

classification of urban and rural showed in table 17. The contributions based on

classification local/migrant (table13) and with/without government support (table 14,

table16) are very small. The contribution based on hukou system (table11) seems to be

larger than that based on parents’ education levels (table12).

50

Physical health (eyesight)

Inequality Measure

Mean and Ratio

Eyesight=1: myopia

Eyesight=3: no myopia

Category Agriculture Non -agriculture Others

eyesight 1.91755 1.63215 1.77595

Category Local Migrant

eyesight 1.92527 1.91105

Category(new version) Urban Rural

eyesight 1.67099 1.91093

Category With Without

eyesight 1.81072 1.92928

GE (1) index

Category Agriculture Non -agriculture Others

eyesight 0.13748 0.14836 0.14567


eyesight 0.13691 0.13796

Category GE(1)/ Theil index GINI

eyesight 0.14369 0.26588

51

Category(new version) Urban Rural

eyesight 0.14831 0.13796

Category With government support Without government support

eyesight 0.1441 0.13661



agriculture


Non-agriculture-

others

eyesight 54.40%

24.1%

19.93%

1.57%

Location of

school(new version)

Urban Rural Urban-rural

eyesight 36.41%

62.14%

1.45%


eyesight 55.24%

44.76%

0

Category With Without With-without

eyesight 93.04%

6.87%

0.09%

This paper introduces eyesight as the variable to measure the students’ physical situation.

Good eyesight is essential for the study. CEPS includes a survey about students’ eyesight

issue. It mainly talks about the myopia problem of students by “yes” or “no” question. This

paper converts the result via quantitative way. For students with myopia problem, their

results of eyesight will be 1. For students without myopia problem, their results of eyesight

will be 3. Based on the transformation, this paper uses the method mentioned in

52

methodology part to measure the inequality and do the inequality decomposition. The

classification in inequality decomposition is important. The logic of classification part is in

line with the idea used in educational achievement. The classification is form hukou system,

migrated situation, school location and school’s relationship with government.

Form the inequality measure part, the GE (1) index and GINI coefficient show a degree of

inequality existing in eyesight of students.

From the statistic results of mean, mean of eyesight for students with agriculture hukou

system is highest at 1.91775. Means for students with non-agriculture hukou system and

other hukou system are 1.63215 and 1.77595 respectively. Means of students in different

migrated situations are similar. Mean of students in urban school is lower than that in rural

school. Mean of students in school with close relationship with government is lower than

that of students in school with relatively low level relationship with government. From the

mean of eyesight, this paper finds that myopia seems to be a very widespread situation for

the researched students.

GE (1) index is provided above. From the results, there are similar within group inequality

in different groups.

Inequality decomposition part is very important for the analysis in this paper. The

contributions of local-migrant divide and with-without divide are very limited to overall

inequality. The contributions of urban-rural divide and agriculture-non-agriculture-other

divide are much higher at 1.5%. The inequality due to different groups is not very serious.

53

Financial situation (appropriation per student)

Inequality Measure

Financial inequality of education is also the hot topic. Many research papers use province

data or county level data. This paper uses data form the result of CEPS, which is provided

by the administrative staffs of studied school. Since the appropriation for the students in

same school is same, this paper doesn’t analyze the situation based on different grades.

From the result of GE (1) index and GINI coefficient, there is a certain degree of inequality

existing.

Mean and Ratio


agriculture

Others Agriculture/non-

agriculture

Appropriation per

student

864.58140 1135.18395 1121.01770 1.31299

Category Local Migrant Local/migrant

Appropriation per

student

806.26728 957.86094 0.841737

Location of school(new) Urban Rural Urban/rural

Appropriation per

student

1184.91662 867.00583 1.36668

Category With Without With/Without

Appropriation per

student

1006.53875 498.43478 2.0194

Category GE(1)/ Theil index GINI

Appropriation per student 0.17860 0.30925

54

The paper divides the students into different groups for inequality decomposition .The logic

of classification in showed in methodology part. Some basic statistic results about mean of

appropriation are provided above. Most results are similar to the hypothesis or the results of

pervious papers. The appropriation per student of students in disadvantaged group (like

agricultural hukou, rural school and school with weak relationship with government) is less.

One exception is that appropriation of migrated students is higher than that of local student.

The highest mean of appropriation among different groups is from students with non-

agriculture hukou. The smallest mean of appropriation is from students in school keeping

weak relationship with government. From the ratio part, the highest ratio appears in

classification of school’s relationship with government.

GE(1) index

Category Agriculture Non -agriculture

Appropriation per

student

0.09930 0.23807

GE(1) indexes of varied groups are provided in this paper. The results show the within

group inequality level. The highest within-group inequality of appropriation is found in

Location of school(new) Urban Rural

Appropriation per student 0.2427

0.1008

Category With government support Without government support




55

urban school, which means a relatively high level of inequality of appropriation existing in

different urban schools. The lowest within-group inequality is found in rural schools.



agriculture


Non-agriculture-

others

Appropriation

per student

25.87% 41.67% 27.45% 5.01%

Location of

school(new version)

Urban Rural Urban-rural

Appropriation per

student

63.03%

30.25%

6.72%

In inequality decomposition part, the paper focuses more on the group-divide contribution

to the overall inequality. Form the results, all inequalities due to group-divide contribute to

overall inequality. According to the tables above, the urban-rural divide holds a highest

contribution to overall inequality at 6.72%, which reflects that part of financial inequality

comes from the different school location. The inequality of with-without divide shows a

comparatively low contribution to overall inequality at 2.8%. The result is not as serious as

the assumption.

Category With Without With-without

Appropriation per

student

96.74% 0.46% 2.80%


Appropriation per student 38% 58.91% 3.09%

56

Conclusion

To sum up, this paper uses data from CEPS to describe inequality situation of students

through three main concerns: educational and cognitive achievement, physical status and

financial status. The logic to analyze the inequality is measuring the overall inequality,

measuring the inequality after meaningful classification and dong inequality decomposition

based on the classification. For analysis of educational and cognitive achievement, this

paper finds out the inequality and decomposes the inequality through different

classification ways. The results from the inequality decomposition show that the inequality

from group-group divide (like urban-rural divide, local –migrant divide and so on) indeed

contribute to the overall inequality. The analysis of physical status and financial status also

presents the same pattern. However, the contributions of inequality form group-group

divide are not high as the hypothesis. If the comparison is hold among these three

parts(educational achievement, financial status and physical status), the contributions of

group-group divide to overall inequality form educational achievement part and financial

status part are much higher than those from physical status part.

Something special in the results from GE(1) index and inequality decomposition is that

the inequality within the group is high. From the methodology part, one explanation is that

contribution to overall inequality is related to population share. However, the GE (1)

indexes in different groups indeed depict certain levels of inequality. The explanation of

within group inequality is not the object of this paper. However, the explanation of the

similar result from Jun Yang , Xiao Huang, Xin Liu (2014) seems like reliable. For

example, the relatively high within inequality is found in urban and rural group. However,

57

when the region inequality (like Gansu Province and Zhejiang Province) is taken into

account, the within group inequality seems convincing and understandable.

Limitation of this paper is about the measurement of educational achievement of the

student. Since this paper uses mid-term test scores, the difference of the examination paper

may influence the analysis result. However, the cognitive test is uniform for every student

in CEPS. This measure also guarantees the credibility of the analysis result.

There is also another point to be improved in this paper. Even CEPS contains a wide range

of survey about students, it doesn’t have a long time series data which may constrain the

research about the education situation. In the future, other papers can use the new CEPS

results to do research by using this paper’s logic and add more comparison parts among

different time periods.

58

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