Fulltext01

Bachelor Thesis
Xiaoxue Li

2009-06-05

Factors Affect the Employment of Youth
in China

Växjö University

School of Management and Economics

Bachelor Thesis

Advisor: Mats Hammarstedt

Examinator: Dominique Anxo

Xiaoxue Li 871126-0000
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Bachelor Thesis
Xiaoxue Li

Summary

Title: Factors affect the Employment of Youth in China

Data: 2009-06-05

Course: NA3083, Thesis in Economics, 15 ECTS

Author: Xiaoxue Li

Advisor: Prof. Mats Hammarstedt

Key words: Youth Employment, Logistic Regression, Hosmer~Lemeshow Test

Abstract: Today’s young people are well-educated ever but in a poor employment
situation. At the beginning of this paper, I first state the situation both in the world and
in China, revealing the poor employment situation of youth. Then I introduce systems
related to youth employment in China and measures the government taken to help
graduate students to find a job. The purpose of this paper is to analyze employment of
youth people in China especially among the medium and highly educated people and
find which and how the factors contribute to it. By using the Logistic Regression by
STATA, I find that the main factors are gender, age, living area, and political status,
major and educational level. The result reveals that the discrimination and gap
between rural and urban area are severe issues in China. Last but not least, I give
some suggestions both to the society and the individual to improve the youth
employment.

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Content
Summary .......................................................................................................... 2
Content ............................................................................................................. 3
1. Introduction .................................................................................................. 4
1.1 Purpose....................................................................................................... 5
1.2 Research Questions .................................................................................... 5
1.3 Limitations ................................................................................................. 5
1.4 Data ............................................................................................................ 6
2. Keywords ..................................................................................................... 6
3. Method ......................................................................................................... 7
4. Situation ....................................................................................................... 7
4.1. Situation in the global ............................................................................... 7
4.2. China’s situation...................................................................................... 10
4.2.1 Youth in China ...................................................................................... 11
4.2.2 Education System in China ................................................................... 12
4.2.3 Qualification System in China .............................................................. 13
4.2.4 Employment System in China .............................................................. 13
4.2.5 Policy System in China ......................................................................... 14
4.2.6 Problems ............................................................................................... 15
5. Analysis by the Regression ........................................................................ 16
5.1 Introduction of the data ............................................................................ 16
5.2 Explanation of each variables .................................................................. 16
5.4 Process ..................................................................................................... 19
5.5 Estimation Method ................................................................................... 19
5.6 Result of the Regression .......................................................................... 21
5.7 Test of Model ........................................................................................... 21
5.8 Establish Model ....................................................................................... 22
5.9 Interpretation and Explanation of the Result ........................................... 23
6. Suggestions ................................................................................................ 26
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7. Conclusion ................................................................................................. 27
8. Reference ................................................................................................... 29
9.Appendix STATA Program ......................................................................... 30

1. Introduction

It’s no doubt that today’s young people have being well-educated never before and
have clearly ideas about their career and life. They have a strongly willingness to
achieve their ambitious in their career and an active attitude to seek opportunities in
the society. However, their energy and talent have been “wasted”. They are not the
burden of the society but the wealth. “Young people bring energy, talent and creativity
to economies and create the foundations for future development” (Jane Stewart) 1.

In this article, I mainly state the situation of employment and unemployment of youth
refers to both the global and China. I emphasized on the education system and
employment system in China. There is a lot of problems vis-à-vis China labor market
especially for the young people. China is suffering an aging process while the
population of young people is decreased leading to a decrease of labor supply in terms
of the long-term sustainable development. Apart from that, the education in China
doesn’t meet the demand of the labor market. People are getting more and more
general skills in college of university level while the labor market need is the specific
skilled people (China Youth Employment Report, May 2005) 2. When a graduate gets
into the labor market, the first job or the first step is really important for his or her
development in the future. It is influenced by many factors, such as the education
level, working experience, personal abilities, family background, economic and socio

1
Jane Stewart, 11 March 2005,
http://www.ilo.org/public/english/employment/yett/download/g8statem.pdf
2
China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,
May 2005
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conditions, political status, major and so on. Knight and Yueh, in their research,
discovered that the social capital affects the urban labor market in China, but it’s
influence among the young people is not significant as in the middle age people
(2008) 3. Among these factors, which are important and the degree of their influence
as well as which are not important, according to the result we can analyze the reason
of that. I used Logistic Regression to analysis the most important factors affect one’s
employment based on the random sampling survey and found the most important
factors are gender, age, political status, urban or rural, educational level and major.
According to the recent situation of youth in China, there are some suggestions.

1.1 Purpose

Through the recent employment situation of young people in China, I want to find the
factors influenced the young people to find a job. Then through the Econometrics
Method to analyses these factors systematically. At last try to explain the result with
the fact now in China as well as propose some suggestions.

1.2 Research Questions

I want to discuss in this paper “What factors affect the employment of the graduate
student in China?” “What is the contribution of these factors?” and “Why these
factors are affecting the youth employment in China?” “How can we solve these
issues?”

1.3 Limitations

There are some limitations of the data. In common sense there are a lot of factors
affect the employment of people such as the house price and cost of mobility in terms
of the objective condition and the personality and quality in terms of one’s subjective

3
John Knight and Linda Yueh, The role of social capital in the labor market in China
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Xiaoxue Li

condition (Hanzhi Zhang, 2006) 4. But it is hard to measure all the factors; I just
choose the most important factors according to the “Systems Analysis of Factors
Affect the Employment of Graduate Student” by Jian Li. In this article, they find the
mainly factors by ISM (Interpretive Structural Modeling) and AHP (Analytic
Hierarchy Process) 5. The mainly factors are one’s ability, social relationship, gender,
major, society demand, educational level, living area, age, political status, one’s
expectancy, certification and health condition. Due to the handling, I just choose the
gender, age, political status, live area, educational level and major to measure the
influence.

1.4 Data

The data comes from the investigation from the China University of Mining and
Technology 6. In the data, it includes the gender, age, political status, employment
condition, birth place, living area, educational level, graduate time, major, employed
time, educational level, and company, property of company, wage and reason for
unemployed and so on. I choose the most important variables due to Jian Li’s article.

2. Keywords

Employment Unemployment Inactivity Education System Employment
System Qualification System Policy System Logistic Regression
Stepwise Regression Hosmer~Lemeshow Test

4
Hanzhi Zhang, Cost Analysis of Graduate’s Employment, 2006
5
Jian Li, Hailang Chen and Jinfang Lin, Systems Analysis of Factors Affect the Employment of
Graduate Student, 2005
6
China University of Mining and Technology, http://www.cumt.edu.cn/
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3. Method

In this paper, I use the Logistic Regression to find the factors affect the employment
of youth and their contribution to the influence. Because of the gender, major,
educational level, living area and political status are dummy variables; I transformed
it into the particular way to compare with each other. Apart from that, I use Stepwise
Regression to find the factors contribute mostly and pick the ones have significant
influence on the employment of youth.

4. Situation

4.1. Situation in the global

From 1997 to 2004, there is an increasing number of unemployed youth (aged from
15 to 24 years). From 63 million in 1997 to 71 million in 2007, it increased 13.6 per
cent. It reached its peak in 2004 of the unemployment rate was 12.6. However, this
number declined in recent years. Youth occupy as much as 40.2 per cent of the total
number of world’s unemployed people while they only occupy 24.7 per cent of the
7
total .

7
Global Employment Trends for Youth, October 2008, International Labor Office, Geneva
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Source: global employment trends for youth2008
As this table shows, from 1997 to 2007, the total youth labor force grew from 577 to
602 million. However, the youth labor force participation rate decreased between
1997 and 2007 from 55.2 to 50.5 per cent. In the same time, the youth inactivity rate
(youth who are inactivity means those who are outside the labor force) increased from
44.8 to 49.5 per cent 8.

8
Global Employment Trends for Youth, October 2008, International Labor Office, Geneva
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Comparing with 5.7 per cent overall global unemployment rate and 4.2 per cent adult
unemployment rate, the youth unemployment rate much higher reached 11.9 per cent
in 2007. The ratio of the youth-to-adult unemployment rate was 2.8 in 2007, showing
that the number of youth unemployed is nearly three times as that of adult.

It’s strange that youth in a poor condition in terms of employment, have a much better
educational condition. Today’s young people are well-educated ever. Both secondary
enrolment ratios and tertiary attainment have increased distinctly. However, the
unemployment rate among youth is still high and increasing recent years. Apart from
South Asia and South-East Asia & the Pacific region, every region has an increased
youth unemployment rates between 1997 and 2007.

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4.2. China’s situation

China is transiting from a planned-economy to a market-oriented economy including
the employment system since 1990s. Before that, people’s job arranged by the state,
everything is planned. Now people are free to choose their job. People’s ability,
education level etc. decide whether they can be employed.

In China, we divided the population into two parts: urban population and rural
population. People will get better education, welfare and also enjoy the high level of
life in the urban area. That explains why people would like to develop in the urban
area. Every year there are huge amount of people move from rural area to the urban
area to find job in the urban area.

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4.2.1 Youth in China

The total number of young people aged from 15 to 29 is 283 million taking up 23.3
per cent in the total population 1.259 billion in China 2002. Among the young
population, about 61.3 per cent of the total lived in the rural area while 38.7 per cent
of all lived in the urban area in 2002. In the total population of young people, 13 per
cent 37.145 million of that are enrolled in school, 70.8 per cent 200.574 million are
employed and 1.9 per cent 5.427 million is unemployed 9. Only taking consideration
of the people who are educated, we can divides people into seven parts – illiterates,
people of primary, middle school, senior secondary education and higher educational
level.

Educational Levels of Employed Population in 2002

Age Illiterate Primary Middle High College University Postgraduate
School School School

16-19 1.8 19 72 6.7 0.5

20-24 1.8 15.9 58.3 17.9 4.9 1.3

25-29 2.3 20.7 52.6 15 7 2.4 0.1

Overall 7.8 30 43.2 13.1 4.3 1.6 0.1

Total 2.0% 18.7% 61.2% 12.9% 4.1% 1.0% 0.1%

Above the chart, we can see clearly that among young people in middle school take
the biggest position. It’s like a normal distribution that people both under middle
school and above that is getting less and less. The explanation is that China has a
project that the tuition including primary and middle school are free to students. It’s
no doubt that it solves a lot of parents’ economic burden. However, when people go to
high school, they have to pay tuition by themselves. There is an investigation shows

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May 2005
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that the economic reasons is the most important factors to effect people to attend a
higher education. I will describe it later. Meanwhile, the average marriage age is
above 25 in China.

4.2.2 Education System in China

In general, there are four parts of education level in China – primary school lasts six
years, middle school lasts three years, high school lasts three years and university
lasts 4 years. Both the primary school and middle school are compulsory and tuition
fee is expended by the government or the state. After graduated from the middle
school, one can choose whether to go to a high school or the vocational school both
last three years. The vocational school teaches specific subject such as engineering,
nursing, designing and so on. After one graduated from the high school or the
vocational school, they can chosen by the exam to decide go to a university or a
college as well as working. After that students can also pursue a higher education to
the post-graduate for three years and PHD as well.

In terms of the vocational training, it is provided during the whole employment
process. Before one’s employed, they can receive professional vocational training by
the vocational skill training institution. Once they employed, they can acquire on-job
training to develop the specific skill fitting for their specific work. There also a
training especially for the people laid-off and unemployed to help them find job in the
future. However most of the pre-employment training fee is paid by the student
themselves or their family and the on-job training is paid by the employer. As a result,
the employers are not willing to pay it and they are stress more on working than
training played a negative role in that. Although the government state that the
company should pay 1.5 per cent of their total profit to the training 10, there is still

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May 2005
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insufficient. The pre-employment training provided by vocational school is charged
by the Ministry of Education while the Ministry of Agriculture is for the rural area.
On-job training is charged by the Ministry of Labor and Social Security. The
responsibility of every part of vocational training is decentralized restricted to the
overall planning and a waste of resource.

4.2.3 Qualification System in China

When people getting into the particular industry they have to have the particular
certification demonstrate the person has the ability to competence for the job. These
certifications are held by the government, state, industry or some famous company. As
for some specific industry, this is a continual process such as the medical science.
Certification in these industries will overdue one or two years to make sure people’s
skill accurately obtained.

4.2.4 Employment System in China

In general, there are three mainly types of employees. The first type is the employees
who worked in governmental institutions. It is included the officials, teachers,
professors and so on. They have a stable income, welfare, insurance as well as
holidays. People in these positions also called they have an “iron rice bowl”. It vividly
describes the security and profitable of the job in the governmental institutions. The
second is the employees who have a permanent/fixed contract of their job in the
state-owned enterprises or other enterprises. These jobs are also relatively stable. The
last type is other employees have temporarily contract or self-employed. They are
more flexible and not stable. The young people with a high education level are more
desire to work in the public sector due to its good welfare and salary (China Youth

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Employment Report, May 2005) 11.

The more stable a job is, the more competitive it is as well. Meanwhile, the people
who get into the “iron rice bowl” is extremely small compared with the enormous
amount of labor force.

4.2.5 Policy System in China

There are many policies to help people get a job in China. I just mention some of that
which helps the young people.

First, graduates are encouraged to work in some basic level in the society such as the
rural areas where the condition is tougher than that in the urban areas. There is a
project called “Volunteer College Graduates to Serve Western Regions”. Due to this
project graduates work in the western regions 2 years and get some subsidy and after
2 years volunteer work they will distribute to the governmental institutions to get an
“iron rice bowl” 12.

Second, graduates are also encouraged to start their own business. If graduates start
running their own firms, they can have a reduced taxation for the revenue of the firm
and also they can acquire loans from bank easier than others.

Third, companies are encouraged to employ graduates while they will get subsidy to
hire a graduate by the government or state.

11
May 2005
12
Volunteer College Graduates to Serve Western Regions, http://xibu.youth.cn/
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4.2.6 Problems

In terms of young people, the degree of mobility is still low in China Labor Market is
the most severe issues. Due to the division between the city and suburb, there is still a
big gap in both the economy and socio development. People live in the rural area have
a lower life level. They earn less and spend less. Young people have less opportunity
to get into school in the rural area, especially the high school and university, because
they have to pay tuition by their own. Also the cost of living in city is much higher
than that in suburb. As a result, it’s much difficult for rural people both to study or
work in the city.

Reasons for young people with middle school or below education to stop their
education

Reason for leaving school Rural Urban Total Percent

Failed examinations 205 86 291 26.9

Economic reasons 193 173 366 33.8

Parents did not want you continue 3 4 7 0.6

Did not enjoy schooling 104 105 209 19.3

Wanted to start working 43 90 133 12.3

To get married 5 5 0.5

Other 3 58 61 5.6

As it is showed in the chart, there are 33.8 per cent of young people stop their
education because of the economic reasons. While 26.9 per cent of young people stop
their education because of the failed in examinations. The examination is provided
because the insufficient of education resources so that a limit number of young people
can attend a higher education. In a word, the economic hardship and insufficient
supply of education resources are the main factors to stop young people attend a
higher education.

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5. Analysis by the Regression

The main method to analysis the factors effecting graduates to find a job is Logistic
Regression. I found the data from a sampling survey mainly organized by the China
University of Mining and Technology (www.cumt.edu.cn). This investigation is more
comprehensive including twenty-three provinces, five autonomous regions and four
cities. I did a quantitative analysis in terms of the gender, age, political status,
educational level, urban or rural and major, whether or how that effect one’s
employed.

5.1 Introduction of the data

This data is a sampling survey. It includes 7623 observations. The sample selections
only take the medium and highly educated people into consideration. The content
includes gender, age, political status, employment situation, birth place, urban or rural,
educational level, graduate time, major, employment time, company, employment city,
educational level, company ownership, employed people’s position in the company,
monthly salary, how to get this job and so on. The age ranges from 17 to 30. The birth
place includes almost every province in China. The educational level include the
people have a bachelor degree, the people have a master degree and the people
graduate from vocational school. The political status consists of party member, league
member and public member. The company of employed people includes
governmental institutions, enterprises owned by the state, private or foreign owned
company.

5.2 Explanation of each variables

I just explain every variable’s definition, the effect whether it will do about

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Bachelor Thesis
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employment is based on the common sense. We will test whether it is true later by
computing the coefficient and see whether it is significant.
● Employment: It’s an optimistic situation that in total 7706 observations, most
people are employed which means that, in China, medium and highly educated people
have comparatively high employment rate. The amount of people employed is 7000
while unemployed is 706.
● Gender: If male the value equals 1; female is 0. There is 3364 female taking up
43.65 per cent of total while the amount of male is 4342.
● Age: According to the data, it ranges from 17 to 30. The data gathered during 23 to
27 years old when it is the peak time to find job for people with bachelor degree and
master degree.
● Political Status: It divides into three parts – Party member, League member and
Public people. The governmental institutions or state-owned enterprises tend to hire
the person who is a Party member or a League member.
● Urban or Rural: As I discussed before, it is easier for urban people find a job. If a
person lives in urban then the urban equals 1 otherwise 0. The amount of people live
in the urban is 4325 occupied 56.13 per cent.
● Educational Level: In the data we divided it into three parts – the people have a
bachelor degree, the people have a master degree and the people graduate from
vocational school.
● Major: The demand and supply of one’s particular major decide whether the people
in the particular major can find a job easier. The major varies an enormous range. I
divided these majors into seven parts, according to the classification of major by the
Ministry of Education of the People’s Republic of China 13, which is engineering,
management, economics, education, science, arts and others.

Table 1. is the description of all the variables. Some of the cumulative percentage is
smaller than 100.00 because of the missing values.

13
Ministry of Education of the People’s Republic of China, http://www.moe.edu.cn/
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Table 1.

Variable Observation Population Percentage Cumulative
Percentage

Employment Employed 7000 90.84 90.84

Unemployed 706 9.16 100.0

Gender Male 4342 56.35 56.35

Female 3364 43.65 100.0

Age 17-21 349 4.58 4.58

22 330 4.33 8.91

23 673 8.83 17.74

24 1214 15.93 33.66

25 1462 19.18 52.84

26 1284 16.84 69.68

27 898 11.78 81.46

28 594 7.79 89.26

29 352 4.62 93.87

30 467 6.13 100.00

Political Status League 4283 55.58 55.58
Member

Party Member 1909 24.77 80.35

Public Member 515 6.68 87.03

Urban or Rural 3381 43.87 43.87
Rural Urban 4325 56.13 100.0

Major Art 580 7.53 7.53

Economics 2315 30.04 37.57

Education 242 3.14 40.71

Engineering 2529 32.82 73.53

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Management 694 9.01 82.54

Others 229 2.97 85.51

Science 475 6.16 91.67

Educational Bachelor 4139 53.71 53.71
Level Degree
Master Degree 243 3.15 56.86

Vocational 2932 38.05 94.91

5.4 Process

At the beginning, I used SPSS to analysis the Logistic Regression and omit the
missing value, reducing the data amount to 1674 observations. Obviously I got biased
and wrong result with higher employment in female than male.

Then I do the regression again included all the missing value by STATA. The result is
more accurate than the former one.

5.5 Estimation Method

Logistic Regression Model
In my model, I used dummy variables. The response variable Y is the employment
condition, it can take only two values (binary variable), that is, 1 if the people
employed and 0 if he or she is not. The probability of employed is P while the
probability of unemployed is (1-P). The explanatory variables are gender, age,
political status, urban or rural, educational level and major.
I wrote the Logistic Model as,
L = ln( Pi ) =ɑ +β1X1+β2X2+β3X3+β4X4+β5X5+β6X6 (1.7)
1 − Pi
where
X1 is the gender, also a binary variable, 1 if male, 0 if female.
X2 is the age, ranges from 17 to 30.
X3 is the political status. It is a multiple-category (trichotomous), having three parts -

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Party member, League member and Public people.
X4 is the urban or rural a binary variable, 1 if urban, 0 if rural.
X5 is the major a trichotomous variable.
X6 is the educational level a trichotomous variable.

Table 2.

Variable Observation Popul Dummy Variables
ation (1) (2) (3) (4) (5) (6)

Major Engineering 2529 1.00 0.00 0.00 0.00 0.00 0.00

Management 694 0.00 1.00 0.00 0.00 0.00 0.00

Economics 2315 0.00 0.00 1.00 0.00 0.00 0.00

Science 475 0.00 0.00 0.00 1.00 0.00 0.00

Others 229 0.00 0.00 0.00 0.00 1.00 0.00

Education 242 0.00 0.00 0.00 0.00 0.00 1.00

Arts 580 0.00 0.00 0.00 0.00 0.00 0.00

Political Party Member 1909 1.00 0.00
Status League 4283 0.00 1.00
Member

Public 515 0.00 0.00
Member

Urban or Urban 4325 1.00
Rural Rural 3381 0.00

Gender Male 4342 1.00

Female 3364 0.00

Educatio Bachelor 4139 1.00 0.00
nal Level Master 243 0.00 1.00

Vocational 2932 0.00 0.00

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5.6 Result of the Regression

Logistic regression Number of obs = 7623
LR chi2(13) = 738.80
Prob > chi2 = 0.0000
Log likelihood = -1966.6261 Pseudo R2 = 0.1581

employment Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431
age 1.136488 .0210599 6.90 0.000 1.095952 1.178524
party 2.546481 .3381768 7.04 0.000 1.962906 3.303554
league 1.93781 .1988308 6.45 0.000 1.584795 2.369461
urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635
economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454
engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584
art .8466544 .157213 -0.90 0.370 .5883676 1.218326
management 1.015614 .1827812 0.09 0.931 .7137346 1.445174
education 1.327837 .3747409 1.00 0.315 .7636942 2.308713
science 1.172379 .2554862 0.73 0.466 .7648449 1.797062
bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857
vocational 6.868131 .758914 17.44 0.000 5.530732 8.52893

5.7 Test of Model

First, the p-value associated the chi-square with 14 degrees of freedom. The value of
0.0000 indicates that the model as a whole is statistically significant.
Then, do the goodness-of-fit test
. lfit, group(10)

L o g i s t i c m o d e l f o r e m p l o y m e n t, g o o d n e s s - o f - f i t t e s t

(Table collapsed on quantiles of estimated probabilities)

number of observations = 7623
number of groups = 10
Hosmer-Lemeshow chi2( 8 ) = 24.86
Prob > chi2 = 0.1016

In the Logistic Model, it includes both the continuous variable (age) and discrete
variables (gender, political status, birth place, urban or rural, educational level,
education level and major). As a result, we cannot use the common test such as the
Pearson Chi-Square Test etc. Since there are a lot dummy variables, leading to a lot of
covariance exist. I adopted the test produced by Hosmer~Lemeshow (1989) to test
Logistic Regression, namely HL index 14. I divided the data into 10 groups.

G y g − ng p g
HL = ∑ (1.8)
g =1 ng pg (1 − pg )
14
Kohler. Ulrich, Data analysis using Stata, 2005
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where G is the number of group, G≤10; yg is the number of the case in group g;
pg is the number of observations in the group g; ng pg is the probability of the
group g.

b) Significance Test
I did a Stepwise Regression.
Every Iterative Step is significant.

5.8 Establish Model

Iteration 0: log likelihood = -2336.028

LR chi2(13) = 738.80
Prob > chi2 = 0.0000

employment Coef. Std. Err. z P>|z| [95% Conf. Interval]

gender .3068515 .0868745 3.53 0.000 .1365807 .4771224
age .127943 .0185307 6.90 0.000 .0916236 .1642625
party .9347123 .1328016 7.04 0.000 .6744259 1.194999
league .6615586 .1026059 6.45 0.000 .4604548 .8626625
urban .1895388 .0860115 2.20 0.028 .0209594 .3581182
economics .0331622 .138631 0.24 0.811 -.2385496 .304874
engineering .0333415 .1385886 0.24 0.810 -.2382871 .30497
art -.1664627 .1856874 -0.90 0.370 -.5304034 .1974779
management .015493 .1799712 0.09 0.931 -.337244 .3682301
education .2835513 .282219 1.00 0.315 -.2695878 .8366904
science .1590353 .2179211 0.73 0.466 -.2680823 .5861529
bachelor 2.846155 .1209535 23.53 0.000 2.609091 3.08322
vocational 1.926892 .1104979 17.44 0.000 1.71032 2.143464
_cons -3.724826 .504099 -7.39 0.000 -4.712842 -2.736811

In final, we got the model with the independent variables are X1 (Gender), X2 (Age),
X3 (Political Status), X4 (Urban or Rural) and X6 (Educational Level).

From the result, we found that the party, engineering, others, management, education
and science is not significant because the p-value larger than 0.05. Apart from that, we
can see the confidence interval, only when the confidence intervals not contain 0.0,
can we consider this variable is significant. So we omit these variables.
22

Bachelor Thesis
Xiaoxue Li

The final Model is,
Pi
L=ln( )= -3.725+0.307X1+0.127X2+0.935X31+0.662X32+0.189X41+2.846X61
1 − Pi
+1.927X62

Then we replace the variable with their name, as
P
L=ln( i )= -3.725+0.307*gender+0.127*age+0.935*party+0.662*league+0.1893
1 − Pi
*urban+2.846* bachelor+1.927*vocational

5.9 Interpretation and Explanation of the Result

I explain the result from the odds rations part.
The odds ratio can be explained when there is a one unit change in the predictor
variable with all the other variables kept constant the amount of ration change. When
the odds ratio close to 1.0, it concluded the there is no change with the change of
predictor variable.
LR chi2(13) = 738.80
Prob > chi2 = 0.0000


gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431
age 1.136488 .0210599 6.90 0.000 1.095952 1.178524
party 2.546481 .3381768 7.04 0.000 1.962906 3.303554
league 1.93781 .1988308 6.45 0.000 1.584795 2.369461
urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635
economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454
engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584
art .8466544 .157213 -0.90 0.370 .5883676 1.218326
management 1.015614 .1827812 0.09 0.931 .7137346 1.445174
education 1.327837 .3747409 1.00 0.315 .7636942 2.308713
science 1.172379 .2554862 0.73 0.466 .7648449 1.797062
bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857
vocational 6.868131 .758914 17.44 0.000 5.530732 8.52893

a) Gender
As we can see in the table, the odds ratio for gender is 1.359139. So we would
conclude that compared to the female the male increase the probability to get a job by
35.9 percent. It reflects the common discrimination between male and female not only
in China but also in the world. Improving the equal of employment and eliminating

23

Bachelor Thesis
Xiaoxue Li

the discrimination between genders is still our prominent aim.
b) Age
The result shows that if one getting one year older the opportunity to be employed
increases by 13.65 per cent. It is accordance with the fact in China’s education and
employment system. The age ranges from 17 to 30, the older the young person is, the
richer their experience is and better psychological quality they have. They will
perform better in the interview and the probability to be employed is higher (China
Youth Employment Report, May 2005) 15.

c) Political Status

Political Status Number

Party Member 16 74.153 million

League Member 17 75.439 million

Public Member At least 1000 million

Compared to the public people, the Party Member will increase the probability to get
a job by 154.65 per cent and the League Member will increase that by 93.78 per cent.
It reveals that employers tend to hire the Party Member or League Member instead of
the Public People. It is reported that the Public Member and League Member in China
have better ability and quality in handling issues (Liu Xiaoyu &Hu Jungang, 2008) 18.

d) Urban or Rural
People lived in the urban area easier find a job than that lived in the rural area. The
people living in the urban area increase the possibility to be employed by 20.87 per
cent than the people living in the rural area. Graduates lived in the urban area have
more social relationship depend on their family and can get a job easily (John Knight

15
May 2005
16
News of the Communist Party of China, http://cpc.people.com.cn/
17
Chinese Communist Youth League, http://www.gqt.org.cn/
18
Liu Xiaoyu and Hu Jungang, Theoretical Analysis about the Employment of Graduate Student,2008
24

Bachelor Thesis
Xiaoxue Li

and Linda Yueh, 2008) 19. In the Employment Report of China Youth, it is showed that
66 per cent of women and 49 per cent of men find job through this social relationship
ranked second among all the methods.

Methods for the economic active young population to find a job 20

method Female Male

Direct application and interview 57 47

Through friend or relatives 40 45

Through job fairs 22 23

Through education/training institution 13 14

Through advertisements 13 12

Through public employment service 9 10

Through labour contractor 4 5

Through private employment agent 2 4

Other 4 6

Resource: China Youth Employment Report, May 2005

e) Major
According to the data, all the majors are insignificant. In terms of the major, because
particular industry has particular demand for the employment, deciding the amount of
people they can absorbed.

f) Educational Level
The China Youth Employment Report states clearly that, during its survey,
educational level has a direct effect on ones employment. However, it’s more
interesting to observe the patterns that emerge when the data is examined in terms of

19
John Knight and Linda Yueh, The role of social capital in the labor market in China
20
May 2005
25

Bachelor Thesis
Xiaoxue Li

the separate educational level. Compared to the people have a master degree the fact
to have a bachelor increase the probability to get a job by 1622.14 per cent and to
have a vocational degree by 586.81 per cent. There is some survey support this
conclusion. The Survey Report of Employment described that from the year 2005 to
2007, the employment rate of undergraduate student is 73.4 per cent while
postgraduate student is 64 per cent (Xinhua News, 2008) 21. In this survey, experts
pointed that the employment rate is not positive with the level of education. Specific
job position has the specific job requirement. Many employers tend to hire
undergraduate students because of they are younger, have low wage expectation and
more stable than the postgraduate students. The demand of vocational education is
also large in the formal labor market in China. Young people graduate from vocational
school can find a desirable work more easily.

The necessary education level to find a desirable job 22

Education level for a desirable work count percent

University 2522 37.8

College 1888 28.3

Vocational School 950 14.2

Post Graduate 579 8.7

High School 425 6.4

Middle School 218 3.3

Primary School 22 0.3

Other 46 0.7

Resource: China Youth Employment Report, May 2005

6. Suggestions

First, we should focus on eliminating the discrimination to the female, minority, youth

21
Xinhua News, 2008, http://news.xinhuanet.com/employment/2008-07/11/content_8527585.htm
22
China Youth Employment Report, May 2005
26

Bachelor Thesis
Xiaoxue Li

and older people. We can find that more and more women pursue a higher educational
level (China Youth Employment Report, May 2005). It reflects that women tend to
achieve a higher education to make them more competitive in the labor market.

In the model, we can see that with the increasing age, people will find job easier. It
means that with the increasing age, people get more experience and enhance their
ability and quality to fit a job. As a result, we should increase our social
communication and taking part in the internship during in the school (Guo Dong and
Lu De, 2005) 23. Apart from that, we should improve the situation in the rural area not
only in the life condition but also in the study condition. With the improvement of life
condition, people lived in the rural area can pursue higher education without the
economy hardship and enhance the mobility. Last but not least, the evaluation of
pursuing a higher educational level is controversial. A postgraduate student maybe
cannot find a better job than the undergraduate student as a result whether to go on
studying should think considerable. As well as the government should support more to
improve the employment of youth such as establish a social support system to help
young people find job (Shen Jie, 2005) 24.

7. Conclusion

China is a developing country. Due to the moderate economic development and

23
Guo Dong and Lu De, What’s the employer emphasis on?, 2005
24
Shen Jie, the Situation, Problems and Future of Graduate Employment in China, 2005
27

Bachelor Thesis
Xiaoxue Li

limited financial market, the supply of educational resource is insufficient. As a result,
it cannot meet the demand of youth education. During the age between 15 and 29
years old, only 33.1 percent of this age group gets a territory education. Apart from
that, the gap between urban and rural area is huge. Most youth in urban area graduate
from high school or higher education while 50 per cent of youth in rural area only
graduate from middle school or lower education. As a result, people in the rural area
have a low competitive ability compared with the urban youth. In addition, the
training investment between urban and rural area is also different a lot. The fund of
training provided by the government is about 15 per cent in the urban area while less
than 7 per cent in the rural area (China Youth Employment Report, May 2005).
Educational level dose have a directly influence on the employment of youth. People
have a university, college or vocational degree will find job easier than who are just
graduate from high school or middle school. However, whether we should pursue as
high education level as possible is still doubtfully. Due to the survey by present, the
employment of postgraduate student is not as we common thought that better than the
undergraduate student. In terms of the gender, male will get job easier than female.
It’s not only in China but an issue all over the world. Nevertheless we still should
contribute more to reduce the discrimination between genders. There is also a lot of
problem even though one can get a job such as the employed young people get less
employee benefits (they only get 4 per cent to 42 per cent of the total employee
benefits) and many young people are working in irregular labor market lacking of the
social security and so on.

China still should contribute more to reduce the gap between urban and rural area,
increasing investment in rural area and improving the mobility between urban and
rural areas. In terms of the individual, young people should improve their
competitiveness to the labor market not pursue higher education level blindfold.

28

Bachelor Thesis
Xiaoxue Li

8. Reference

Jane Stewart, 11 March 2005, Statement in G8 Labor and Employment Ministers’
Conference, International Labor Organization,
http://www.ilo.org/public/english/employment/yett/download/g8statem.pdf
John Knight and Linda Yueh, The role of social capital in the labor market in China,
Economics of Transition, Volume 16(3) 2008, 389-414
Jane Stewart, 3 December 2004, the importance of youth employment in a globalizing
world: the International Labor Organization viewpoint, International Labor
Organization,
http://www.ilo.org/public/english/region/asro/tokyo/conf/2004youth/downloads/js.pdf
Institute of Population and Labor Economics, CASS, http://iple.cass.cn/
Ministry of Human Resources and Social Security of the People’s Republic of China,
http://www.mohrss.gov.cn/mohrss/Desktop.aspx?PATH=rsbww/sy
Fausto Miguélez and Albert Recio, The life course in Spain
Hanzhi Zhang, Cost Analysis of Graduate’s Employment, 2006
Jian Li, Hailang Chen and Jinfang Lin, Systems Analysis of Factors Affect the
Employment of Graduate Student, 2005
Alexis M. Herman, Report on the Youth Labor Force, U.S. Department of Labor,
November 2000
Kathy Nargi Toth, China’s Labor Pains, Printed Circuit Design, January 2008
Commission on Youth, Continuing Development and Employment Opportunities for
Youth (Concise Report), March 2003
Country Report about China’s Youth Employment
Globalization and its effects on youth employment trends in Asia, International Labor
Organization, 28-30 March 2006
Labor Markets in Brazil, China, India and Russia, OECD,2007
Baum. Christopher F, An Introduction to modern econometrics using STATA, 2006,
College Station

29

Bachelor Thesis
Xiaoxue Li

Long. J. Scott, Regression models for categorical dependent variables using Stata,
2006, College Station
Kohler. Ulrich, Data analysis using Stata, 2005, College Station
News of the Communist Party of China, http://cpc.people.com.cn/
Chinese Communist Youth League, http://www.gqt.org.cn/
Shen Jie, the Situation, Problems and Future of Graduate Employment in China, 2005
Guo Dong and Lu De, What’s the employer emphasis on?, 2005, Tianjin Institute of
Socio and Technology Press
Wang Hui, Labor Market and Employment of Graduate Student, 2005, Tianjin
Institute of Socio and Technology Press
Tang Jijun, Institution Economic Analysis of Employment, 2001, Contemporary
Research of Economics
Wang Cheng, Theory and Policy about Employment of Graduate Student, 2004,
Graduate Student Employment in China
Fu Yongchang, Analysis on the Elements and Study about the Countermeasures of
Influence of College Students' Employment, 2005
Zeng Yanbo, Current Issues in China, 2005
Liu Xiaoyu and Hu Jungang, Theoretical Analysis about the Employment of Graduate
Student, Journal of Jiangxi University of Finance and Economics, No2, 2008, Serial
No.56

9.Appendix
STATA Program

insheet using d:employment.csv
gen gender=(v1=="male")
gen age=v2
gen party=(v3=="Party Member")
gen league=(v3=="League Member")

30

Bachelor Thesis
Xiaoxue Li

gen public=(v3=="Public People")
gen employment=(v4=="Employed")
gen urban=(v6=="Urban")
gen economics=(v27=="Economics")
gen engineering=(v27=="Engineering")
gen art=(v27=="Arts")
gen others=(v27=="Others")
gen management=(v27=="Management")
gen education=(v27=="Education")
gen science=(v27=="Science")
gen bachelor=(v13=="Bachelor")
gen master=(v13=="Master")
gen vocational=(v13=="Vocational")
logit employment gender age party league urban economics engineering art
management education science bachelor vocational

LR chi2(13) = 738.80
Prob > chi2 = 0.0000

employment Coef. Std. Err. z P>|z| [95% Conf. Interval]

gender .3068515 .0868745 3.53 0.000 .1365807 .4771224
age .127943 .0185307 6.90 0.000 .0916236 .1642625
party .9347123 .1328016 7.04 0.000 .6744259 1.194999
league .6615586 .1026059 6.45 0.000 .4604548 .8626625
urban .1895388 .0860115 2.20 0.028 .0209594 .3581182
economics .0331622 .138631 0.24 0.811 -.2385496 .304874
engineering .0333415 .1385886 0.24 0.810 -.2382871 .30497
art -.1664627 .1856874 -0.90 0.370 -.5304034 .1974779
management .015493 .1799712 0.09 0.931 -.337244 .3682301
education .2835513 .282219 1.00 0.315 -.2695878 .8366904
science .1590353 .2179211 0.73 0.466 -.2680823 .5861529
bachelor 2.846155 .1209535 23.53 0.000 2.609091 3.08322
vocational 1.926892 .1104979 17.44 0.000 1.71032 2.143464
_cons -3.724826 .504099 -7.39 0.000 -4.712842 -2.736811

logistic employment gender age party league urban economics engineering art
management education science bachelor vocational

31

Bachelor Thesis
Xiaoxue Li
LR chi2(13) = 738.80
Prob > chi2 = 0.0000


gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431
age 1.136488 .0210599 6.90 0.000 1.095952 1.178524
party 2.546481 .3381768 7.04 0.000 1.962906 3.303554
league 1.93781 .1988308 6.45 0.000 1.584795 2.369461
urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635
economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454
engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584
art .8466544 .157213 -0.90 0.370 .5883676 1.218326
management 1.015614 .1827812 0.09 0.931 .7137346 1.445174
education 1.327837 .3747409 1.00 0.315 .7636942 2.308713
science 1.172379 .2554862 0.73 0.466 .7648449 1.797062
bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857
vocational 6.868131 .758914 17.44 0.000 5.530732 8.52893

lfit, group(10)
. lfit, group(10)

L o g i s t i c m o d e l f o r e m p l o y m e n t, g o o d n e s s - o f - f i t t e s t

(Table collapsed on quantiles of estimated probabilities)

number of observations = 7623
number of groups = 10
Hosmer-Lemeshow chi2( 8 ) = 24.86
Prob > chi2 = 0.1016

32

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