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  1. 1. Bachelor ThesisXiaoxue Li2009-06-05 Factors Affect the Employment of Youth in China Växjö University School of Management and Economics Bachelor Thesis Advisor: Mats Hammarstedt Examinator: Dominique AnxoXiaoxue Li 871126-0000 1
  2. 2. Bachelor ThesisXiaoxue LiSummaryTitle: Factors affect the Employment of Youth in ChinaData: 2009-06-05Course: NA3083, Thesis in Economics, 15 ECTSAuthor: Xiaoxue LiAdvisor: Prof. Mats HammarstedtKey words: Youth Employment, Logistic Regression, Hosmer~Lemeshow TestAbstract: Today’s young people are well-educated ever but in a poor employmentsituation. At the beginning of this paper, I first state the situation both in the world andin China, revealing the poor employment situation of youth. Then I introduce systemsrelated to youth employment in China and measures the government taken to helpgraduate students to find a job. The purpose of this paper is to analyze employment ofyouth people in China especially among the medium and highly educated people andfind which and how the factors contribute to it. By using the Logistic Regression bySTATA, 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 gapbetween rural and urban area are severe issues in China. Last but not least, I givesome suggestions both to the society and the individual to improve the youthemployment. 2
  3. 3. Bachelor ThesisXiaoxue LiContent 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 3
  4. 4. Bachelor ThesisXiaoxue Li 7. Conclusion ................................................................................................. 27 8. Reference ................................................................................................... 29 9.Appendix STATA Program ......................................................................... 301. IntroductionIt’s no doubt that today’s young people have being well-educated never before andhave clearly ideas about their career and life. They have a strongly willingness toachieve their ambitious in their career and an active attitude to seek opportunities inthe society. However, their energy and talent have been “wasted”. They are not theburden of the society but the wealth. “Young people bring energy, talent and creativityto economies and create the foundations for future development” (Jane Stewart) 1.In this article, I mainly state the situation of employment and unemployment of youthrefers to both the global and China. I emphasized on the education system andemployment system in China. There is a lot of problems vis-à-vis China labor marketespecially for the young people. China is suffering an aging process while thepopulation of young people is decreased leading to a decrease of labor supply in termsof the long-term sustainable development. Apart from that, the education in Chinadoesn’t meet the demand of the labor market. People are getting more and moregeneral skills in college of university level while the labor market need is the specificskilled people (China Youth Employment Report, May 2005) 2. When a graduate getsinto the labor market, the first job or the first step is really important for his or herdevelopment in the future. It is influenced by many factors, such as the educationlevel, working experience, personal abilities, family background, economic and socio1 Jane Stewart, 11 March 2005,http://www.ilo.org/public/english/employment/yett/download/g8statem.pdf2 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,May 2005 4
  5. 5. Bachelor ThesisXiaoxue Liconditions, 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’sinfluence 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 influenceas well as which are not important, according to the result we can analyze the reasonof that. I used Logistic Regression to analysis the most important factors affect one’semployment based on the random sampling survey and found the most importantfactors 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 PurposeThrough the recent employment situation of young people in China, I want to find thefactors influenced the young people to find a job. Then through the EconometricsMethod to analyses these factors systematically. At last try to explain the result withthe fact now in China as well as propose some suggestions.1.2 Research QuestionsI want to discuss in this paper “What factors affect the employment of the graduatestudent in China?” “What is the contribution of these factors?” and “Why thesefactors are affecting the youth employment in China?” “How can we solve theseissues?”1.3 LimitationsThere are some limitations of the data. In common sense there are a lot of factorsaffect the employment of people such as the house price and cost of mobility in termsof the objective condition and the personality and quality in terms of one’s subjective3 John Knight and Linda Yueh, The role of social capital in the labor market in China 5
  6. 6. Bachelor ThesisXiaoxue Licondition (Hanzhi Zhang, 2006) 4. But it is hard to measure all the factors; I justchoose the most important factors according to the “Systems Analysis of FactorsAffect the Employment of Graduate Student” by Jian Li. In this article, they find themainly factors by ISM (Interpretive Structural Modeling) and AHP (AnalyticHierarchy Process) 5. The mainly factors are one’s ability, social relationship, gender,major, society demand, educational level, living area, age, political status, one’sexpectancy, certification and health condition. Due to the handling, I just choose thegender, age, political status, live area, educational level and major to measure theinfluence.1.4 DataThe data comes from the investigation from the China University of Mining andTechnology 6. In the data, it includes the gender, age, political status, employmentcondition, birth place, living area, educational level, graduate time, major, employedtime, educational level, and company, property of company, wage and reason forunemployed and so on. I choose the most important variables due to Jian Li’s article.2. KeywordsEmployment Unemployment Inactivity Education System EmploymentSystem Qualification System Policy System Logistic RegressionStepwise Regression Hosmer~Lemeshow Test4 Hanzhi Zhang, Cost Analysis of Graduate’s Employment, 20065 Jian Li, Hailang Chen and Jinfang Lin, Systems Analysis of Factors Affect the Employment ofGraduate Student, 20056 China University of Mining and Technology, http://www.cumt.edu.cn/ 6
  7. 7. Bachelor ThesisXiaoxue Li3. MethodIn this paper, I use the Logistic Regression to find the factors affect the employmentof youth and their contribution to the influence. Because of the gender, major,educational level, living area and political status are dummy variables; I transformedit into the particular way to compare with each other. Apart from that, I use StepwiseRegression to find the factors contribute mostly and pick the ones have significantinfluence on the employment of youth.4. Situation4.1. Situation in the globalFrom 1997 to 2004, there is an increasing number of unemployed youth (aged from15 to 24 years). From 63 million in 1997 to 71 million in 2007, it increased 13.6 percent. It reached its peak in 2004 of the unemployment rate was 12.6. However, thisnumber declined in recent years. Youth occupy as much as 40.2 per cent of the totalnumber of world’s unemployed people while they only occupy 24.7 per cent of the 7total .7 Global Employment Trends for Youth, October 2008, International Labor Office, Geneva 7
  8. 8. Bachelor ThesisXiaoxue Li Source: global employment trends for youth2008As this table shows, from 1997 to 2007, the total youth labor force grew from 577 to602 million. However, the youth labor force participation rate decreased between1997 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 from44.8 to 49.5 per cent 8.8 Global Employment Trends for Youth, October 2008, International Labor Office, Geneva 8
  9. 9. Bachelor ThesisXiaoxue LiComparing with 5.7 per cent overall global unemployment rate and 4.2 per cent adultunemployment rate, the youth unemployment rate much higher reached 11.9 per centin 2007. The ratio of the youth-to-adult unemployment rate was 2.8 in 2007, showingthat 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 bettereducational condition. Today’s young people are well-educated ever. Both secondaryenrolment ratios and tertiary attainment have increased distinctly. However, theunemployment rate among youth is still high and increasing recent years. Apart fromSouth Asia and South-East Asia & the Pacific region, every region has an increasedyouth unemployment rates between 1997 and 2007. 9
  10. 10. Bachelor ThesisXiaoxue Li4.2. China’s situationChina is transiting from a planned-economy to a market-oriented economy includingthe 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 ruralpopulation. People will get better education, welfare and also enjoy the high level oflife in the urban area. That explains why people would like to develop in the urbanarea. Every year there are huge amount of people move from rural area to the urbanarea to find job in the urban area. 10
  11. 11. Bachelor ThesisXiaoxue Li4.2.1 Youth in ChinaThe total number of young people aged from 15 to 29 is 283 million taking up 23.3per cent in the total population 1.259 billion in China 2002. Among the youngpopulation, about 61.3 per cent of the total lived in the rural area while 38.7 per centof all lived in the urban area in 2002. In the total population of young people, 13 percent 37.145 million of that are enrolled in school, 70.8 per cent 200.574 million areemployed and 1.9 per cent 5.427 million is unemployed 9. Only taking considerationof the people who are educated, we can divides people into seven parts – illiterates,people of primary, middle school, senior secondary education and higher educationallevel.Educational Levels of Employed Population in 2002Age Illiterate Primary Middle High College University Postgraduate School School School16-19 1.8 19 72 6.7 0.520-24 1.8 15.9 58.3 17.9 4.9 1.325-29 2.3 20.7 52.6 15 7 2.4 0.1Overall 7.8 30 43.2 13.1 4.3 1.6 0.1Total 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 takethe biggest position. It’s like a normal distribution that people both under middleschool and above that is getting less and less. The explanation is that China has aproject that the tuition including primary and middle school are free to students. It’sno doubt that it solves a lot of parents’ economic burden. However, when people go tohigh school, they have to pay tuition by themselves. There is an investigation shows9 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,May 2005 11
  12. 12. Bachelor ThesisXiaoxue Lithat the economic reasons is the most important factors to effect people to attend ahigher education. I will describe it later. Meanwhile, the average marriage age isabove 25 in China.4.2.2 Education System in ChinaIn general, there are four parts of education level in China – primary school lasts sixyears, middle school lasts three years, high school lasts three years and universitylasts 4 years. Both the primary school and middle school are compulsory and tuitionfee is expended by the government or the state. After graduated from the middleschool, one can choose whether to go to a high school or the vocational school bothlast three years. The vocational school teaches specific subject such as engineering,nursing, designing and so on. After one graduated from the high school or thevocational school, they can chosen by the exam to decide go to a university or acollege as well as working. After that students can also pursue a higher education tothe post-graduate for three years and PHD as well.In terms of the vocational training, it is provided during the whole employmentprocess. Before one’s employed, they can receive professional vocational training bythe vocational skill training institution. Once they employed, they can acquire on-jobtraining to develop the specific skill fitting for their specific work. There also atraining especially for the people laid-off and unemployed to help them find job in thefuture. However most of the pre-employment training fee is paid by the studentthemselves 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 thantraining played a negative role in that. Although the government state that thecompany should pay 1.5 per cent of their total profit to the training 10, there is still10 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,May 2005 12
  13. 13. Bachelor ThesisXiaoxue Liinsufficient. The pre-employment training provided by vocational school is chargedby 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. Theresponsibility of every part of vocational training is decentralized restricted to theoverall planning and a waste of resource.4.2.3 Qualification System in ChinaWhen people getting into the particular industry they have to have the particularcertification demonstrate the person has the ability to competence for the job. Thesecertifications are held by the government, state, industry or some famous company. Asfor 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’sskill accurately obtained.4.2.4 Employment System in ChinaIn general, there are three mainly types of employees. The first type is the employeeswho worked in governmental institutions. It is included the officials, teachers,professors and so on. They have a stable income, welfare, insurance as well asholidays. People in these positions also called they have an “iron rice bowl”. It vividlydescribes the security and profitable of the job in the governmental institutions. Thesecond is the employees who have a permanent/fixed contract of their job in thestate-owned enterprises or other enterprises. These jobs are also relatively stable. Thelast type is other employees have temporarily contract or self-employed. They aremore flexible and not stable. The young people with a high education level are moredesire to work in the public sector due to its good welfare and salary (China Youth 13
  14. 14. Bachelor ThesisXiaoxue LiEmployment Report, May 2005) 11.The more stable a job is, the more competitive it is as well. Meanwhile, the peoplewho get into the “iron rice bowl” is extremely small compared with the enormousamount of labor force.4.2.5 Policy System in ChinaThere are many policies to help people get a job in China. I just mention some of thatwhich helps the young people.First, graduates are encouraged to work in some basic level in the society such as therural areas where the condition is tougher than that in the urban areas. There is aproject called “Volunteer College Graduates to Serve Western Regions”. Due to thisproject graduates work in the western regions 2 years and get some subsidy and after2 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 startrunning their own firms, they can have a reduced taxation for the revenue of the firmand also they can acquire loans from bank easier than others.Third, companies are encouraged to employ graduates while they will get subsidy tohire a graduate by the government or state.11 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,May 200512 Volunteer College Graduates to Serve Western Regions, http://xibu.youth.cn/ 14
  15. 15. Bachelor ThesisXiaoxue Li4.2.6 ProblemsIn terms of young people, the degree of mobility is still low in China Labor Market isthe most severe issues. Due to the division between the city and suburb, there is still abig gap in both the economy and socio development. People live in the rural area havea lower life level. They earn less and spend less. Young people have less opportunityto get into school in the rural area, especially the high school and university, becausethey have to pay tuition by their own. Also the cost of living in city is much higherthan that in suburb. As a result, it’s much difficult for rural people both to study orwork in the city.Reasons for young people with middle school or below education to stop theireducationReason for leaving school Rural Urban Total PercentFailed examinations 205 86 291 26.9Economic reasons 193 173 366 33.8Parents did not want you continue 3 4 7 0.6Did not enjoy schooling 104 105 209 19.3Wanted to start working 43 90 133 12.3To get married 5 5 0.5 Other 3 58 61 5.6As it is showed in the chart, there are 33.8 per cent of young people stop theireducation because of the economic reasons. While 26.9 per cent of young people stoptheir education because of the failed in examinations. The examination is providedbecause the insufficient of education resources so that a limit number of young peoplecan attend a higher education. In a word, the economic hardship and insufficientsupply of education resources are the main factors to stop young people attend ahigher education. 15
  16. 16. Bachelor ThesisXiaoxue Li5. Analysis by the RegressionThe main method to analysis the factors effecting graduates to find a job is LogisticRegression. I found the data from a sampling survey mainly organized by the ChinaUniversity of Mining and Technology (www.cumt.edu.cn). This investigation is morecomprehensive including twenty-three provinces, five autonomous regions and fourcities. 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’semployed.5.1 Introduction of the dataThis data is a sampling survey. It includes 7623 observations. The sample selectionsonly take the medium and highly educated people into consideration. The contentincludes 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 birthplace includes almost every province in China. The educational level include thepeople have a bachelor degree, the people have a master degree and the peoplegraduate from vocational school. The political status consists of party member, leaguemember and public member. The company of employed people includesgovernmental institutions, enterprises owned by the state, private or foreign ownedcompany.5.2 Explanation of each variablesI just explain every variable’s definition, the effect whether it will do about 16
  17. 17. Bachelor ThesisXiaoxue Liemployment is based on the common sense. We will test whether it is true later bycomputing the coefficient and see whether it is significant.● Employment: It’s an optimistic situation that in total 7706 observations, mostpeople are employed which means that, in China, medium and highly educated peoplehave comparatively high employment rate. The amount of people employed is 7000while unemployed is 706.● Gender: If male the value equals 1; female is 0. There is 3364 female taking up43.65 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 to27 years old when it is the peak time to find job for people with bachelor degree andmaster degree.● Political Status: It divides into three parts – Party member, League member andPublic people. The governmental institutions or state-owned enterprises tend to hirethe 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 aperson lives in urban then the urban equals 1 otherwise 0. The amount of people livein the urban is 4325 occupied 56.13 per cent.● Educational Level: In the data we divided it into three parts – the people have abachelor degree, the people have a master degree and the people graduate fromvocational school.● Major: The demand and supply of one’s particular major decide whether the peoplein the particular major can find a job easier. The major varies an enormous range. Idivided these majors into seven parts, according to the classification of major by theMinistry 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 issmaller than 100.00 because of the missing values.13 Ministry of Education of the People’s Republic of China, http://www.moe.edu.cn/ 17
  18. 18. Bachelor ThesisXiaoxue LiTable 1.Variable Observation Population Percentage Cumulative PercentageEmployment Employed 7000 90.84 90.84 Unemployed 706 9.16 100.0Gender Male 4342 56.35 56.35 Female 3364 43.65 100.0Age 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 6.13 100.00Political Status League 4283 55.58 55.58 Member Party Member 1909 24.77 80.35 Public Member 515 6.68 87.03Urban or Rural 3381 43.87 43.87Rural Urban 4325 56.13 100.0Major Art 580 7.53 7.53 Economics 2315 30.04 37.57 Education 242 3.14 40.71 Engineering 2529 32.82 73.53 18
  19. 19. Bachelor ThesisXiaoxue Li Management 694 9.01 82.54 Others 229 2.97 85.51 Science 475 6.16 91.67 Educational Bachelor 4139 53.71 53.71 Level Degree Master Degree 243 3.15 56.86 Vocational 2932 38.05 94.915.4 ProcessAt the beginning, I used SPSS to analysis the Logistic Regression and omit themissing value, reducing the data amount to 1674 observations. Obviously I got biasedand wrong result with higher employment in female than male.Then I do the regression again included all the missing value by STATA. The result ismore accurate than the former one.5.5 Estimation MethodLogistic Regression ModelIn my model, I used dummy variables. The response variable Y is the employmentcondition, it can take only two values (binary variable), that is, 1 if the peopleemployed and 0 if he or she is not. The probability of employed is P while theprobability of unemployed is (1-P). The explanatory variables are gender, age,political status, urban or rural, educational level and major.I wrote the Logistic Model as,L = ln( Pi ) =ɑ +β1X1+β2X2+β3X3+β4X4+β5X5+β6X6 (1.7) 1 − PiwhereX1 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
  20. 20. Bachelor ThesisXiaoxue LiParty member, League member and Public people.X4 is the urban or rural a binary variable, 1 if urban, 0 if rural.X5 is the major a trichotomous variable.X6 is the educational level a trichotomous variable.Table 2.Variable Observation Popul Dummy Variables ation (1) (2) (3) (4) (5) (6)Major Engineering 2529 1.00 0.00 0.00 0.00 0.00 0.00 Management 694 0.00 1.00 0.00 0.00 0.00 0.00 Economics 2315 0.00 0.00 1.00 0.00 0.00 0.00 Science 475 0.00 0.00 0.00 1.00 0.00 0.00 Others 229 0.00 0.00 0.00 0.00 1.00 0.00 Education 242 0.00 0.00 0.00 0.00 0.00 1.00 Arts 580 0.00 0.00 0.00 0.00 0.00 0.00Political Party Member 1909 1.00 0.00Status League 4283 0.00 1.00 Member Public 515 0.00 0.00 MemberUrban or Urban 4325 1.00Rural Rural 3381 0.00Gender Male 4342 1.00 Female 3364 0.00Educatio Bachelor 4139 1.00 0.00nal Level Master 243 0.00 1.00 Vocational 2932 0.00 0.00 20
  21. 21. Bachelor ThesisXiaoxue Li5.6 Result of the RegressionLogistic regression Number of obs = 7623 LR chi2(13) = 738.80 Prob > chi2 = 0.0000Log likelihood = -1966.6261 Pseudo R2 = 0.1581 employment Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431 age 1.136488 .0210599 6.90 0.000 1.095952 1.178524 party 2.546481 .3381768 7.04 0.000 1.962906 3.303554 league 1.93781 .1988308 6.45 0.000 1.584795 2.369461 urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635 economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454 engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584 art .8466544 .157213 -0.90 0.370 .5883676 1.218326 management 1.015614 .1827812 0.09 0.931 .7137346 1.445174 education 1.327837 .3747409 1.00 0.315 .7636942 2.308713 science 1.172379 .2554862 0.73 0.466 .7648449 1.797062 bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857 vocational 6.868131 .758914 17.44 0.000 5.530732 8.528935.7 Test of ModelFirst, the p-value associated the chi-square with 14 degrees of freedom. The value of0.0000 indicates that the model as a whole is statistically significant.Then, do the goodness-of-fit test. lfit, group(10)L o g i s t i c m o d e l f o r e m p l o y m e n t, g o o d n e s s - o f - f i t t e s t (Table collapsed on quantiles of estimated probabilities) number of observations = 7623 number of groups = 10 Hosmer-Lemeshow chi2( 8 ) = 24.86 Prob > chi2 = 0.1016In the Logistic Model, it includes both the continuous variable (age) and discretevariables (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 thePearson Chi-Square Test etc. Since there are a lot dummy variables, leading to a lot ofcovariance exist. I adopted the test produced by Hosmer~Lemeshow (1989) to testLogistic Regression, namely HL index 14. I divided the data into 10 groups. G y g − ng p gHL = ∑ (1.8) g =1 ng pg (1 − pg )14 Kohler. Ulrich, Data analysis using Stata, 2005 21
  22. 22. Bachelor ThesisXiaoxue Liwhere G is the number of group, G≤10; yg is the number of the case in group g;pg is the number of observations in the group g; ng pg is the probability of thegroup g.b) Significance TestI did a Stepwise Regression.Every Iterative Step is significant.5.8 Establish ModelIteration 0: log likelihood = -2336.028Iteration 1: log likelihood = -2208.3534Iteration 2: log likelihood = -1991.548Iteration 3: log likelihood = -1966.839Iteration 4: log likelihood = -1966.6261Iteration 5: log likelihood = -1966.6261Logistic regression Number of obs = 7623 LR chi2(13) = 738.80 Prob > chi2 = 0.0000Log likelihood = -1966.6261 Pseudo R2 = 0.1581 employment Coef. Std. Err. z P>|z| [95% Conf. Interval] gender .3068515 .0868745 3.53 0.000 .1365807 .4771224 age .127943 .0185307 6.90 0.000 .0916236 .1642625 party .9347123 .1328016 7.04 0.000 .6744259 1.194999 league .6615586 .1026059 6.45 0.000 .4604548 .8626625 urban .1895388 .0860115 2.20 0.028 .0209594 .3581182 economics .0331622 .138631 0.24 0.811 -.2385496 .304874 engineering .0333415 .1385886 0.24 0.810 -.2382871 .30497 art -.1664627 .1856874 -0.90 0.370 -.5304034 .1974779 management .015493 .1799712 0.09 0.931 -.337244 .3682301 education .2835513 .282219 1.00 0.315 -.2695878 .8366904 science .1590353 .2179211 0.73 0.466 -.2680823 .5861529 bachelor 2.846155 .1209535 23.53 0.000 2.609091 3.08322 vocational 1.926892 .1104979 17.44 0.000 1.71032 2.143464 _cons -3.724826 .504099 -7.39 0.000 -4.712842 -2.736811In 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, educationand science is not significant because the p-value larger than 0.05. Apart from that, wecan see the confidence interval, only when the confidence intervals not contain 0.0,can we consider this variable is significant. So we omit these variables. 22
  23. 23. Bachelor ThesisXiaoxue LiThe final Model is, PiL=ln( )= -3.725+0.307X1+0.127X2+0.935X31+0.662X32+0.189X41+2.846X61 1 − Pi+1.927X62Then we replace the variable with their name, as PL=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*vocational5.9 Interpretation and Explanation of the ResultI explain the result from the odds rations part.The odds ratio can be explained when there is a one unit change in the predictorvariable with all the other variables kept constant the amount of ration change. Whenthe odds ratio close to 1.0, it concluded the there is no change with the change ofpredictor variable.Logistic regression Number of obs = 7623 LR chi2(13) = 738.80 Prob > chi2 = 0.0000Log likelihood = -1966.6261 Pseudo R2 = 0.1581 employment Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431 age 1.136488 .0210599 6.90 0.000 1.095952 1.178524 party 2.546481 .3381768 7.04 0.000 1.962906 3.303554 league 1.93781 .1988308 6.45 0.000 1.584795 2.369461 urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635 economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454 engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584 art .8466544 .157213 -0.90 0.370 .5883676 1.218326 management 1.015614 .1827812 0.09 0.931 .7137346 1.445174 education 1.327837 .3747409 1.00 0.315 .7636942 2.308713 science 1.172379 .2554862 0.73 0.466 .7648449 1.797062 bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857 vocational 6.868131 .758914 17.44 0.000 5.530732 8.52893a) GenderAs we can see in the table, the odds ratio for gender is 1.359139. So we wouldconclude that compared to the female the male increase the probability to get a job by35.9 percent. It reflects the common discrimination between male and female not onlyin China but also in the world. Improving the equal of employment and eliminating 23
  24. 24. Bachelor ThesisXiaoxue Lithe discrimination between genders is still our prominent aim.b) AgeThe result shows that if one getting one year older the opportunity to be employedincreases by 13.65 per cent. It is accordance with the fact in China’s education andemployment system. The age ranges from 17 to 30, the older the young person is, thericher their experience is and better psychological quality they have. They willperform better in the interview and the probability to be employed is higher (ChinaYouth Employment Report, May 2005) 15.c) Political StatusPolitical Status NumberParty Member 16 74.153 millionLeague Member 17 75.439 millionPublic Member At least 1000 millionCompared to the public people, the Party Member will increase the probability to geta 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 ofthe Public People. It is reported that the Public Member and League Member in Chinahave better ability and quality in handling issues (Liu Xiaoyu &Hu Jungang, 2008) 18.d) Urban or RuralPeople lived in the urban area easier find a job than that lived in the rural area. Thepeople living in the urban area increase the possibility to be employed by 20.87 percent than the people living in the rural area. Graduates lived in the urban area havemore social relationship depend on their family and can get a job easily (John Knight15 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,May 200516 News of the Communist Party of China, http://cpc.people.com.cn/17 Chinese Communist Youth League, http://www.gqt.org.cn/18 Liu Xiaoyu and Hu Jungang, Theoretical Analysis about the Employment of Graduate Student,2008 24
  25. 25. Bachelor ThesisXiaoxue Liand Linda Yueh, 2008) 19. In the Employment Report of China Youth, it is showed that66 per cent of women and 49 per cent of men find job through this social relationshipranked second among all the methods.Methods for the economic active young population to find a job 20method Female MaleDirect application and interview 57 47Through friend or relatives 40 45Through job fairs 22 23Through education/training institution 13 14Through advertisements 13 12Through public employment service 9 10Through labour contractor 4 5Through private employment agent 2 4Other 4 6 Resource: China Youth Employment Report, May 2005e) MajorAccording to the data, all the majors are insignificant. In terms of the major, becauseparticular industry has particular demand for the employment, deciding the amount ofpeople they can absorbed.f) Educational LevelThe China Youth Employment Report states clearly that, during its survey,educational level has a direct effect on ones employment. However, it’s moreinteresting to observe the patterns that emerge when the data is examined in terms of19 John Knight and Linda Yueh, The role of social capital in the labor market in China20 China Youth Employment Report – Analysis Report of China’s Survey on School to Work Transition,May 2005 25
  26. 26. Bachelor ThesisXiaoxue Lithe separate educational level. Compared to the people have a master degree the factto have a bachelor increase the probability to get a job by 1622.14 per cent and tohave a vocational degree by 586.81 per cent. There is some survey support thisconclusion. The Survey Report of Employment described that from the year 2005 to2007, the employment rate of undergraduate student is 73.4 per cent whilepostgraduate student is 64 per cent (Xinhua News, 2008) 21. In this survey, expertspointed that the employment rate is not positive with the level of education. Specificjob position has the specific job requirement. Many employers tend to hireundergraduate students because of they are younger, have low wage expectation andmore stable than the postgraduate students. The demand of vocational education isalso large in the formal labor market in China. Young people graduate from vocationalschool can find a desirable work more easily.The necessary education level to find a desirable job 22Education level for a desirable work count percentUniversity 2522 37.8College 1888 28.3Vocational School 950 14.2Post Graduate 579 8.7High School 425 6.4Middle School 218 3.3Primary School 22 0.3Other 46 0.7Resource: China Youth Employment Report, May 20056. SuggestionsFirst, we should focus on eliminating the discrimination to the female, minority, youth21 Xinhua News, 2008, http://news.xinhuanet.com/employment/2008-07/11/content_8527585.htm22 China Youth Employment Report, May 2005 26
  27. 27. Bachelor ThesisXiaoxue Liand older people. We can find that more and more women pursue a higher educationallevel (China Youth Employment Report, May 2005). It reflects that women tend toachieve 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. Itmeans that with the increasing age, people get more experience and enhance theirability and quality to fit a job. As a result, we should increase our socialcommunication and taking part in the internship during in the school (Guo Dong andLu De, 2005) 23. Apart from that, we should improve the situation in the rural area notonly in the life condition but also in the study condition. With the improvement of lifecondition, people lived in the rural area can pursue higher education without theeconomy hardship and enhance the mobility. Last but not least, the evaluation ofpursuing a higher educational level is controversial. A postgraduate student maybecannot find a better job than the undergraduate student as a result whether to go onstudying should think considerable. As well as the government should support more toimprove the employment of youth such as establish a social support system to helpyoung people find job (Shen Jie, 2005) 24.7. ConclusionChina is a developing country. Due to the moderate economic development and23 Guo Dong and Lu De, What’s the employer emphasis on?, 200524 Shen Jie, the Situation, Problems and Future of Graduate Employment in China, 2005 27
  28. 28. Bachelor ThesisXiaoxue Lilimited 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 29years old, only 33.1 percent of this age group gets a territory education. Apart fromthat, the gap between urban and rural area is huge. Most youth in urban area graduatefrom high school or higher education while 50 per cent of youth in rural area onlygraduate from middle school or lower education. As a result, people in the rural areahave a low competitive ability compared with the urban youth. In addition, thetraining investment between urban and rural area is also different a lot. The fund oftraining provided by the government is about 15 per cent in the urban area while lessthan 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. Peoplehave a university, college or vocational degree will find job easier than who are justgraduate from high school or middle school. However, whether we should pursue ashigh education level as possible is still doubtfully. Due to the survey by present, theemployment of postgraduate student is not as we common thought that better than theundergraduate 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 shouldcontribute more to reduce the discrimination between genders. There is also a lot ofproblem even though one can get a job such as the employed young people get lessemployee benefits (they only get 4 per cent to 42 per cent of the total employeebenefits) and many young people are working in irregular labor market lacking of thesocial 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 andrural areas. In terms of the individual, young people should improve theircompetitiveness to the labor market not pursue higher education level blindfold. 28
  29. 29. Bachelor ThesisXiaoxue Li8. ReferenceJane 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.pdfJohn Knight and Linda Yueh, The role of social capital in the labor market in China,Economics of Transition, Volume 16(3) 2008, 389-414Jane Stewart, 3 December 2004, the importance of youth employment in a globalizingworld: the International Labor Organization viewpoint, International LaborOrganization,http://www.ilo.org/public/english/region/asro/tokyo/conf/2004youth/downloads/js.pdfInstitute 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/syFausto Miguélez and Albert Recio, The life course in SpainHanzhi Zhang, Cost Analysis of Graduate’s Employment, 2006Jian Li, Hailang Chen and Jinfang Lin, Systems Analysis of Factors Affect theEmployment of Graduate Student, 2005Alexis M. Herman, Report on the Youth Labor Force, U.S. Department of Labor,November 2000Kathy Nargi Toth, China’s Labor Pains, Printed Circuit Design, January 2008Commission on Youth, Continuing Development and Employment Opportunities forYouth (Concise Report), March 2003Country Report about China’s Youth EmploymentGlobalization and its effects on youth employment trends in Asia, International LaborOrganization, 28-30 March 2006Labor Markets in Brazil, China, India and Russia, OECD,2007Baum. Christopher F, An Introduction to modern econometrics using STATA, 2006,College Station 29
  30. 30. Bachelor ThesisXiaoxue LiLong. J. Scott, Regression models for categorical dependent variables using Stata,2006, College StationKohler. Ulrich, Data analysis using Stata, 2005, College StationNews 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, 2005Guo Dong and Lu De, What’s the employer emphasis on?, 2005, Tianjin Institute ofSocio and Technology PressWang Hui, Labor Market and Employment of Graduate Student, 2005, TianjinInstitute of Socio and Technology PressTang Jijun, Institution Economic Analysis of Employment, 2001, ContemporaryResearch of EconomicsWang Cheng, Theory and Policy about Employment of Graduate Student, 2004,Graduate Student Employment in ChinaFu Yongchang, Analysis on the Elements and Study about the Countermeasures ofInfluence of College Students Employment, 2005Zeng Yanbo, Current Issues in China, 2005Liu Xiaoyu and Hu Jungang, Theoretical Analysis about the Employment of GraduateStudent, Journal of Jiangxi University of Finance and Economics, No2, 2008, SerialNo.569.AppendixSTATA Programinsheet using d:employment.csvgen gender=(v1=="male")gen age=v2gen party=(v3=="Party Member")gen league=(v3=="League Member") 30
  31. 31. Bachelor ThesisXiaoxue Ligen 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 artmanagement education science bachelor vocationalIteration 0: log likelihood = -2336.028Iteration 1: log likelihood = -2208.3534Iteration 2: log likelihood = -1991.548Iteration 3: log likelihood = -1966.839Iteration 4: log likelihood = -1966.6261Iteration 5: log likelihood = -1966.6261Logistic regression Number of obs = 7623 LR chi2(13) = 738.80 Prob > chi2 = 0.0000Log likelihood = -1966.6261 Pseudo R2 = 0.1581 employment Coef. Std. Err. z P>|z| [95% Conf. Interval] gender .3068515 .0868745 3.53 0.000 .1365807 .4771224 age .127943 .0185307 6.90 0.000 .0916236 .1642625 party .9347123 .1328016 7.04 0.000 .6744259 1.194999 league .6615586 .1026059 6.45 0.000 .4604548 .8626625 urban .1895388 .0860115 2.20 0.028 .0209594 .3581182 economics .0331622 .138631 0.24 0.811 -.2385496 .304874 engineering .0333415 .1385886 0.24 0.810 -.2382871 .30497 art -.1664627 .1856874 -0.90 0.370 -.5304034 .1974779 management .015493 .1799712 0.09 0.931 -.337244 .3682301 education .2835513 .282219 1.00 0.315 -.2695878 .8366904 science .1590353 .2179211 0.73 0.466 -.2680823 .5861529 bachelor 2.846155 .1209535 23.53 0.000 2.609091 3.08322 vocational 1.926892 .1104979 17.44 0.000 1.71032 2.143464 _cons -3.724826 .504099 -7.39 0.000 -4.712842 -2.736811logistic employment gender age party league urban economics engineering artmanagement education science bachelor vocational 31
  32. 32. Bachelor ThesisXiaoxue LiLogistic regression Number of obs = 7623 LR chi2(13) = 738.80 Prob > chi2 = 0.0000Log likelihood = -1966.6261 Pseudo R2 = 0.1581 employment Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] gender 1.359139 .1180745 3.53 0.000 1.146347 1.611431 age 1.136488 .0210599 6.90 0.000 1.095952 1.178524 party 2.546481 .3381768 7.04 0.000 1.962906 3.303554 league 1.93781 .1988308 6.45 0.000 1.584795 2.369461 urban 1.208692 .1039614 2.20 0.028 1.021181 1.430635 economics 1.033718 .1433054 0.24 0.811 .7877696 1.356454 engineering 1.033904 .1432872 0.24 0.810 .7879764 1.356584 art .8466544 .157213 -0.90 0.370 .5883676 1.218326 management 1.015614 .1827812 0.09 0.931 .7137346 1.445174 education 1.327837 .3747409 1.00 0.315 .7636942 2.308713 science 1.172379 .2554862 0.73 0.466 .7648449 1.797062 bachelor 17.22144 2.082994 23.53 0.000 13.58669 21.82857 vocational 6.868131 .758914 17.44 0.000 5.530732 8.52893lfit, group(10). lfit, group(10)L o g i s t i c m o d e l f o r e m p l o y m e n t, g o o d n e s s - o f - f i t t e s t (Table collapsed on quantiles of estimated probabilities) number of observations = 7623 number of groups = 10 Hosmer-Lemeshow chi2( 8 ) = 24.86 Prob > chi2 = 0.1016 32

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