Slide 1: 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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Slide 2: 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
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Jane Stewart, 11 March 2005, China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,
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http://www.ilo.org/public/english/employment/yett/download/g8statem.pdf
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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
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John Knight and Linda Yueh, The role of social capital in the labor market in China
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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 System Unemployment Qualification System Inactivity Education System Employment
Policy System
Logistic Regression
Stepwise R egression
Hosmer~Lemeshow Test
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Hanzhi Zhang, Cost Analysis of Graduate’s Employment, 2006 Jian Li, Hailang Chen and Jinfang Lin, Systems Analysis of Factors Affect the Employment of China University of Mining and Technology, http://www.cumt.edu.cn/
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Graduate Student, 2005
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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 total
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.
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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.
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Global Employment Trends for Youth, October 2008, International Labor Office, Geneva
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Slide 9: Bachelor Thesis Xiaoxue Li
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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Slide 10: Bachelor Thesis Xiaoxue Li
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 School 16-19 20-24 25-29 1.8 1.8 2.3 19 15.9 20.7 30 18.7% Middle School 72 58.3 52.6 43.2 61.2% High School 6.7 17.9 15 13.1 12.9% 0.5 4.9 7 4.3 4.1% 1.3 2.4 1.6 1.0% 0.1 0.1 0.1% College University Postgraduate
Overall 7.8 Total 2.0%
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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China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,
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May 2005
Slide 12: Bachelor Thesis Xiaoxue Li
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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China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,
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May 2005
Slide 13: Bachelor Thesis Xiaoxue Li
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.
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China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition, Volunteer College Graduates to Serve Western Regions, http://xibu.youth.cn/
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May 2005
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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 Failed examinations Economic reasons Parents did not want you continue Did not enjoy schooling Wanted to start working To get married Other 3 Rural 205 193 3 104 43 Urban 86 173 4 105 90 5 58 Total 291 366 7 209 133 5 61 Percent 26.9 33.8 0.6 19.3 12.3 0.5 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 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.
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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 Unemployed Gender Male Female Age 17-21 22 23 24 25 26 27 28 29 30 Political Status League Member Party Member 1909 24.77 6.68 43.87 56.13 7.53 30.04 3.14 32.82
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7000 706 4342 3364 349 330 673 1214 1462 1284 898 594 352 467 4283
90.84 9.16 56.35 43.65 4.58 4.33 8.83 15.93 19.18 16.84 11.78 7.79 4.62 6.13 55.58
90.84 100.0 56.35 100.0 4.58 8.91 17.74 33.66 52.84 69.68 81.46 89.26 93.87 100.00 55.58
80.35 87.03 43.87 100.0 7.53 37.57 40.71 73.53
Public Member 515 Urban Rural Major or Rural Urban Art Economics Education Engineering 3381 4325 580 2315 242 2529
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Management Others Science Educational Level Bachelor Degree Master Degree Vocational
694 229 475 4139
9.01 2.97 6.16 53.71
82.54 85.51 91.67 53.71
243 2932
3.15 38.05
56.86 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( where
Pi ) =ɑ +β1X1+β2X2+β3X3+β4X4+β5X5+β6X6 (1.7) 1 − Pi
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 19
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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 Major Engineering Management Economics Science Others Education Arts Political Status Party Member League Member Public Member Urban or Urban Rural Gender Rural Male Female Educatio nal Level Bachelor Master Vocational 4325 3381 4342 3364 4139 243 2932 1.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 515 0.00 0.00 2529 694 2315 475 229 242 580 1909 4283 Dummy Variables (1) 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 (2) 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 (3) 0.00 0.00 1.00 0.00 0.00 0.00 0.00 (4) 0.00 0.00 0.00 1.00 0.00 0.00 0.00 (5) 0.00 0.00 0.00 0.00 1.00 0.00 0.00 (6) 0.00 0.00 0.00 0.00 0.00 1.00 0.00
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5.6 Result of the Regression
Logistic regression Log likelihood = -1966.6261 employment gender age party league urban economics engineering art management education science bachelor vocational Odds Ratio 1.359139 1.136488 2.546481 1.93781 1.208692 1.033718 1.033904 .8466544 1.015614 1.327837 1.172379 17.22144 6.868131 Std. Err. .1180745 .0210599 .3381768 .1988308 .1039614 .1433054 .1432872 .157213 .1827812 .3747409 .2554862 2.082994 .758914 z 3.53 6.90 7.04 6.45 2.20 0.24 0.24 -0.90 0.09 1.00 0.73 23.53 17.44 Number of obs LR chi2(13) Prob > chi2 Pseudo R2 P>|z| 0.000 0.000 0.000 0.000 0.028 0.811 0.810 0.370 0.931 0.315 0.466 0.000 0.000 = = = = 7623 738.80 0.0000 0.1581
[95% Conf. Interval] 1.146347 1.095952 1.962906 1.584795 1.021181 .7877696 .7879764 .5883676 .7137346 .7636942 .7648449 13.58669 5.530732 1.611431 1.178524 3.303554 2.369461 1.430635 1.356454 1.356584 1.218326 1.445174 2.308713 1.797062 21.82857 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 = number of groups = Hosmer-Lemeshow chi2( 8 ) = Prob > chi2 = 7623 10 24.86 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.
HL = ∑
g =1
14
G
y g − ng p g ng pg (1 − pg )
(1.8)
Kohler. Ulrich, Data analysis using Stata, 2005
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where G is the number of group, G ≤10;
pg
yg
is the number of the case in group g;
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 Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = -2336.028 = -2208.3534 = -1991.548 = -1966.839 = -1966.6261 = -1966.6261 Number of obs LR chi2(13) Prob > chi2 Pseudo R2 Std. Err. .0868745 .0185307 .1328016 .1026059 .0860115 .138631 .1385886 .1856874 .1799712 .282219 .2179211 .1209535 .1104979 .504099 z 3.53 6.90 7.04 6.45 2.20 0.24 0.24 -0.90 0.09 1.00 0.73 23.53 17.44 -7.39 P>|z| 0.000 0.000 0.000 0.000 0.028 0.811 0.810 0.370 0.931 0.315 0.466 0.000 0.000 0.000 = = = = 7623 738.80 0.0000 0.1581
Logistic regression Log likelihood = -1966.6261 employment gender age party league urban economics engineering art management education science bachelor vocational _cons Coef. .3068515 .127943 .9347123 .6615586 .1895388 .0331622 .0333415 -.1664627 .015493 .2835513 .1590353 2.846155 1.926892 -3.724826
[95% Conf. Interval] .1365807 .0916236 .6744259 .4604548 .0209594 -.2385496 -.2382871 -.5304034 -.337244 -.2695878 -.2680823 2.609091 1.71032 -4.712842 .4771224 .1642625 1.194999 .8626625 .3581182 .304874 .30497 .1974779 .3682301 .8366904 .5861529 3.08322 2.143464 -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.
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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.
Logistic regression Log likelihood = -1966.6261 employment gender age party league urban economics engineering art management education science bachelor vocational Odds Ratio 1.359139 1.136488 2.546481 1.93781 1.208692 1.033718 1.033904 .8466544 1.015614 1.327837 1.172379 17.22144 6.868131 Std. Err. .1180745 .0210599 .3381768 .1988308 .1039614 .1433054 .1432872 .157213 .1827812 .3747409 .2554862 2.082994 .758914 z 3.53 6.90 7.04 6.45 2.20 0.24 0.24 -0.90 0.09 1.00 0.73 23.53 17.44 Number of obs LR chi2(13) Prob > chi2 Pseudo R2 P>|z| 0.000 0.000 0.000 0.000 0.028 0.811 0.810 0.370 0.931 0.315 0.466 0.000 0.000 = = = = 7623 738.80 0.0000 0.1581
[95% Conf. Interval] 1.146347 1.095952 1.962906 1.584795 1.021181 .7877696 .7879764 .5883676 .7137346 .7636942 .7648449 13.58669 5.530732 1.611431 1.178524 3.303554 2.369461 1.430635 1.356454 1.356584 1.218326 1.445174 2.308713 1.797062 21.82857 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
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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 Party Member 16 League Member 17 Public Member Number 74.153 million 75.439 million 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
China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition, News of the Communist Party of China, http://cpc.people.com.cn/ Chinese Communist Youth League, http://www.gqt.org.cn/ Liu Xiaoyu and Hu Jungang, Theoretical Analysis about the Employment of Graduate Student,2008
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May 2005
16 17 18
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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 Direct application and interview Through friend or relatives Through job fairs Through education/training institution Through advertisements Through public employment service Through labour contractor Through private employment agent Other Female 57 40 22 13 13 9 4 2 4 Male 47 45 23 14 12 10 5 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 20
John Knight and Linda Yueh, The role of social capital in the labor market in China China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,
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May 2005
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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 College Vocational School Post Graduate High School Middle School Primary School Other 2522 1888 950 579 425 218 22 46 37.8 28.3 14.2 8.7 6.4 3.3 0.3 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 22
Xinhua News, 2008, http://news.xinhuanet.com/employment/2008-07/11/content_8527585.htm China Youth Employment Report, May 2005
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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 24
Guo Dong and Lu De, What’s the employer emphasis on?, 2005 Shen Jie, the Situation, Problems and Future of Graduate Employment in China, 2005
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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.
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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
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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")
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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
Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = -2336.028 = -2208.3534 = -1991.548 = -1966.839 = -1966.6261 = -1966.6261 Number of obs LR chi2(13) Prob > chi2 Pseudo R2 Std. Err. .0868745 .0185307 .1328016 .1026059 .0860115 .138631 .1385886 .1856874 .1799712 .282219 .2179211 .1209535 .1104979 .504099 z 3.53 6.90 7.04 6.45 2.20 0.24 0.24 -0.90 0.09 1.00 0.73 23.53 17.44 -7.39 P>|z| 0.000 0.000 0.000 0.000 0.028 0.811 0.810 0.370 0.931 0.315 0.466 0.000 0.000 0.000 = = = = 7623 738.80 0.0000 0.1581
Logistic regression Log likelihood = -1966.6261 employment gender age party league urban economics engineering art management education science bachelor vocational _cons Coef. .3068515 .127943 .9347123 .6615586 .1895388 .0331622 .0333415 -.1664627 .015493 .2835513 .1590353 2.846155 1.926892 -3.724826
[95% Conf. Interval] .1365807 .0916236 .6744259 .4604548 .0209594 -.2385496 -.2382871 -.5304034 -.337244 -.2695878 -.2680823 2.609091 1.71032 -4.712842 .4771224 .1642625 1.194999 .8626625 .3581182 .304874 .30497 .1974779 .3682301 .8366904 .5861529 3.08322 2.143464 -2.736811
logistic employment
gender age party league urban economics engineering art
management education science bachelor vocational
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Logistic regression Log likelihood = -1966.6261 employment gender age party league urban economics engineering art management education science bachelor vocational Odds Ratio 1.359139 1.136488 2.546481 1.93781 1.208692 1.033718 1.033904 .8466544 1.015614 1.327837 1.172379 17.22144 6.868131 Std. Err. .1180745 .0210599 .3381768 .1988308 .1039614 .1433054 .1432872 .157213 .1827812 .3747409 .2554862 2.082994 .758914 z 3.53 6.90 7.04 6.45 2.20 0.24 0.24 -0.90 0.09 1.00 0.73 23.53 17.44 Number of obs LR chi2(13) Prob > chi2 Pseudo R2 P>|z| 0.000 0.000 0.000 0.000 0.028 0.811 0.810 0.370 0.931 0.315 0.466 0.000 0.000 = = = = 7623 738.80 0.0000 0.1581
[95% Conf. Interval] 1.146347 1.095952 1.962906 1.584795 1.021181 .7877696 .7879764 .5883676 .7137346 .7636942 .7648449 13.58669 5.530732 1.611431 1.178524 3.303554 2.369461 1.430635 1.356454 1.356584 1.218326 1.445174 2.308713 1.797062 21.82857 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 = number of groups = Hosmer-Lemeshow chi2( 8 ) = Prob > chi2 = 7623 10 24.86 0.1016
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