Fulltext01

Bachelor Thesis Xiaoxue Li

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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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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

1 Jane Stewart, 11 March 2005,

. 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

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

http://www.ilo.org/public/english/employment/yett/download/g8statem.pdf�


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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

1.1 Purpose

. 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.

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


6

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

1.4 Data

. 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.

The data comes from the investigation from the China University of Mining and

Technology6

2. Keywords

. 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.

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/


7

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

total 7

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 cent8

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 unemployed9

Educational Levels of Employed Population in 2002

. 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.

Age Illiterate Primary

School

Middle

School

High

School

College University Postgraduate

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

9 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,

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 training10


May 2005

, there is still


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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


14

Employment Report, May 2005)11

4.2.5 Policy System in China

.

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.

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


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

.

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.


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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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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 China13

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

, 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.


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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

Member

4283 55.58 55.58

Party Member 1909 24.77 80.35

Public Member 515 6.68 87.03

Urban or

Rural

Rural 3381 43.87 43.87

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

Level

Bachelor

Degree

4139 53.71 53.71

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( ) =ɑ +β1X1+β2X2+β3X3+β4X4+β5X5+β6X6 (1.7)

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 -

1i

i

PP−


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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

ation

Dummy Variables

(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

Status

Party Member 1909 1.00 0.00

League

Member

4283 0.00 1.00

Public

Member

515 0.00 0.00

Urban or

Rural

Urban 4325 1.00

Rural 3381 0.00

Gender Male 4342 1.00

Female 3364 0.00

Educatio

nal Level

Bachelor 4139 1.00 0.00

Master 243 0.00 1.00

Vocational 2932 0.00 0.00


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5.6 Result of the Regression

vocational 6.868131 .758914 17.44 0.000 5.530732 8.52893 bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857 science 1.172379 .2554862 0.73 0.466 .7648449 1.797062 education 1.327837 .3747409 1.00 0.315 .7636942 2.308713 management 1.015614 .1827812 0.09 0.931 .7137346 1.445174 art .8466544 .157213 -0.90 0.370 .5883676 1.218326 engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584 economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454 urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635 league 1.93781 .1988308 6.45 0.000 1.584795 2.369461 party 2.546481 .3381768 7.04 0.000 1.962906 3.303554 age 1.136488 .0210599 6.90 0.000 1.095952 1.178524 gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431 employment Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

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

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

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 index14

. I divided the data into 10 groups.

(1.8)

14 Kohler. Ulrich, Data analysis using Stata, 2005

Prob > chi2 = 0 . 1 0 1 6 Hosmer-Lemeshow chi2( 8 ) = 2 4 . 8 6 number of groups = 1 0 number of observations = 7 6 2 3

(Table collapsed on quantiles of estimated probabilities)

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

. lfit, group(10)

1 (1 )

Gg g g

g g g g

y n pHL

n p p=

−=

−∑


22

where G is the number of group, G≤10; is the number of the case in group g;

is the number of observations in the group g; is the probability of the

group g.

b) Significance Test

I did a Stepwise Regression.

Every Iterative Step is significant.

5.8 Establish Model

_cons -3.724826 .504099 -7.39 0.000 -4.712842 -2.736811 vocational 1.926892 .1104979 17.44 0.000 1.71032 2.143464 bachelor 2.846155 .1209535 23.53 0.000 2.609091 3.08322 science .1590353 .2179211 0.73 0.466 -.2680823 .5861529 education .2835513 .282219 1.00 0.315 -.2695878 .8366904 management .015493 .1799712 0.09 0.931 -.337244 .3682301 art -.1664627 .1856874 -0.90 0.370 -.5304034 .1974779 engineering .0333415 .1385886 0.24 0.810 -.2382871 .30497 economics .0331622 .138631 0.24 0.811 -.2385496 .304874 urban .1895388 .0860115 2.20 0.028 .0209594 .3581182 league .6615586 .1026059 6.45 0.000 .4604548 .8626625 party .9347123 .1328016 7.04 0.000 .6744259 1.194999 age .127943 .0185307 6.90 0.000 .0916236 .1642625 gender .3068515 .0868745 3.53 0.000 .1365807 .4771224 employment Coef. Std. Err. z P>|z| [95% Conf. Interval]


Iteration 5: log likelihood = -1966.6261Iteration 4: log likelihood = -1966.6261Iteration 3: log likelihood = -1966.839Iteration 2: log likelihood = -1991.548Iteration 1: log likelihood = -2208.3534Iteration 0: log likelihood = -2336.028

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.

gy

gp g gn p


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The final Model is,

L=ln( )= -3.725+0.307X1+0.127X2+0.935X31+0.662X32+0.189X41+2.846X61

+1.927X62

Then we replace the variable with their name, as

L=ln( )= -3.725+0.307*gender+0.127*age+0.935*party+0.662*league+0.1893

*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.



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

1i

i

PP−

1i

i

PP−


24

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

Political Status

.

c) Political Status

Number

Party Member16 74.153 million

League Member17 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

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

.

d) Urban or Rural


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

http://cpc.people.com.cn/�

http://www.gqt.org.cn/�


25

and Linda Yueh, 2008)19

Methods for the economic active young population to find a job

. 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.

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 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,

May 2005


26

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

The necessary education level to find a desirable job

. 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. 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

http://news.xinhuanet.com/employment/2008-07/11/content_8527585.htm�


27

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


28

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.


29

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

http://www.ilo.org/public/english/employment/yett/download/g8statem.pdf�

http://www.ilo.org/public/english/region/asro/tokyo/conf/2004youth/downloads/js.pdf�

http://iple.cass.cn/�

http://www.mohrss.gov.cn/mohrss/Desktop.aspx?PATH=rsbww/sy�


30

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")

http://cpc.people.com.cn/�

http://www.gqt.org.cn/�


31

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

_cons -3.724826 .504099 -7.39 0.000 -4.712842 -2.736811 vocational 1.926892 .1104979 17.44 0.000 1.71032 2.143464 bachelor 2.846155 .1209535 23.53 0.000 2.609091 3.08322 science .1590353 .2179211 0.73 0.466 -.2680823 .5861529 education .2835513 .282219 1.00 0.315 -.2695878 .8366904 management .015493 .1799712 0.09 0.931 -.337244 .3682301 art -.1664627 .1856874 -0.90 0.370 -.5304034 .1974779 engineering .0333415 .1385886 0.24 0.810 -.2382871 .30497 economics .0331622 .138631 0.24 0.811 -.2385496 .304874 urban .1895388 .0860115 2.20 0.028 .0209594 .3581182 league .6615586 .1026059 6.45 0.000 .4604548 .8626625 party .9347123 .1328016 7.04 0.000 .6744259 1.194999 age .127943 .0185307 6.90 0.000 .0916236 .1642625 gender .3068515 .0868745 3.53 0.000 .1365807 .4771224 employment Coef. Std. Err. z P>|z| [95% Conf. Interval]


Iteration 5: log likelihood = -1966.6261Iteration 4: log likelihood = -1966.6261Iteration 3: log likelihood = -1966.839Iteration 2: log likelihood = -1991.548Iteration 1: log likelihood = -2208.3534Iteration 0: log likelihood = -2336.028

logistic employment gender age party league urban economics engineering art

management education science bachelor vocational


32



lfit, group(10)

Prob > chi2 = 0 . 1 0 1 6 Hosmer-Lemeshow chi2( 8 ) = 2 4 . 8 6 number of groups = 1 0 number of observations = 7 6 2 3

(Table collapsed on quantiles of estimated probabilities)

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

. lfit, group(10)

Fulltext01

Education

youth employment

employment system

employment ofyouth people

world andin china

bachelor thesisxiaoxue

chinas situation

todays young people

main factors