Rural-urban Migration in China

Rural-urban Migration in China Some Findings Shuzhuo Li 1, Xiaoyi Jin 1,2 and Haifeng Du 1,2 in Collaboration with Marcus W. Feldman 2 1 Institute for Population and Development Studies Xi an Jiaotong University 2 Morrison Institute for Population and Resource Studies Stanford University E-mail: Shuzhuo Li, shzhli@mail.xjtu.edu.cn; Xiaoyi Jin, xiaoyij@stanford.edu; Haifeng Du, haifengd@stanford.edu

CONTENTS 1. Background 2. Study Design 3. Survey & Data 4. Statistical Analysis 5. Classical Social Network Analysis 6. Complexity Properties 7. Detecting Network Community Structure 8. Current and Future Work

1. BACKGROUND 1.1 Introduction: Rural-urban migration Household registration system (hukou) before 1978, confined most Chinese citizens to their place of birth; Economic reforms since 1978 caused a significant rural labor surplus (The real unemployment rate is 34.8% for rural areas); Urban-biased and pro-coastal development policy enabled cities to achieve rapid economic growth and attracted labor migration from rural to urban areas since the mid-198s;.14 billion rural migrants residing in cities without required permanent legal status, 3% of rural labor force; Circular migrants, moving back and forth frequently. Increasingly important in Chinese demographic change and social development.

Floating direction: The main inter-provincial migration flows in China, 1995-2 Source: DING Jinhong, et al. Areal differentiation of inter-provincial migration in China and characteristics of the world. ACTA Geographical Sinica. 25, 6(1): 16-114

Distribution of inter-provincial migration rates of China, 1995-2 (a) In-migration (b) Out-migration (c) Net-migration Source: DING Jinhong, et al. Areal differentiation of inter-provincial migration in China and characteristics of the world. ACTA Geographical Sinica. 25, 6(1): 16-114

1. BACKGROUND 1.2 Features Urban population grows much faster than total population, especially in the first 1 years; Rural-urban migration turns out to be the dominant source of Chinese urban growth in 1978-1999; Most migration takes place across provinces, from inland rural areas to coastal urban areas; Distances matter in the migration; 1. Provinces having the most emigration: Sichuan (19%), Henan (14%), Anhui (11%), Hunan (8%), and Jianxi (6%); 2. Provinces having the most immigration: Guangdong (31%), Zhejiang (1%), and Fujian (6%).

1. BACKGROUND 1.3 Socio-demographic Implications: Evolution of attitudes and behaviors Facts: Rural areas: Strict patrilineal family system, low economic development level & strong son preference; Urban areas: Son preference has been weakened by the process of modernization and improvement of the social security system; Rural-urban migrants: Dramatic change of lifestyle and formation of new social networks have influenced their attitudes and behaviors.

Evolution

Rural-urban migrants at a city railway station http://news.tom.com http://news.tom.com Strange environment: Eager eyes

Walking on the downtown street http://bbs.people.com.cn/bbs/readfile http://bbs.people.com.cn/bbs/readfile Rural-urban migrants and a permanent urban resident

1. BACKGROUND 1.3 Socio-demographic Implications: Evolution of attitudes and behaviors Phenomena: The original, strongly male-biased culture and behaviors are influenced by the modern culture in cities Later marriage; Later childbearing; Weakened son preference but still with high SRB in short term; Aging & old-age support Increased problems for elderly non-migrants; Improved financial well-being of elderly relatives; Reduced provision of daily care and emotional well-being; Excess burden of rearing grandchildren.

1. BACKGROUND 1.3 Socio-demographic Implications: Evolution of attitudes and behaviors Consequences: Weakened son preference; New pattern of family formation, i.e. timing of marriage and childbearing; New pattern of family support for the elderly; Social cohesion and urbanization; Cultural transition of the whole population.

2. STUDY DESIGN 2.1 Objectives Migrant s social networks in urban areas and social cohesion; Evolution of attitudes and behaviors and its sociodemographic implications; Complex network models; Policy suggestions to improve social cohesion and sustainable development.

2. STUDY DESIGN 2.2 Methodology & Methods Methodology: Combining methods of sociology, demography, statistics, and complexity science, etc. Quantitative methods Statistical analysis Social network analysis Simulation Public policy analysis

Promoting sustainable socio-demographic development in rural and urban areas Background Theories Models &Methods Process &Phenomenon 2.3 Framework

3. SURVEY & DATA 3.1 Selection of Survey Sites Shenzhen, Guangdong province Location: South of Guangdong; History: Set up in 1979, established as special economic region in 198; Features: Representative of coastal and well-developed cities in China; Region: Six districts--luohu, Futian, Nanshan, Yantian, Bao an and Longgang. Economy: High-tech, advanced manufacturing and service industries; the 4th highest GDP among cities of China in 23. Population (2 census) Total number: 7,8,8 Average age: 3.8 Ratio of migrants to permanent urban residents: 4.3:1 Features: High density, Rapid increase, Low education level of labor force

Shenzhen

3. SURVEY & DATA 3.2 Survey Components and Contents Survey components Random street interviews Sampling survey Community investigation Individual interviews

3. SURVEY & DATA 3.2.1 Random Street Interviews Respondents Above 15 years old, not including foreigners, tourists and short-term visitors Contents of the questionnaire Basic individual information Attitudes towards marriage, childbearing, aging and future plan Life satisfaction in Shenzhen Attitudes towards rural-urban migrants etc. Sites: 4 places Bagualing (high proportion of rural-urban migrants) Huaqiangbei, Book city, East gate walking street (high proportion of permanent residents) Implementation Time: April 18, 25 Duration: 1 day Planned survey size: 1, people Number of qualified questionnaires: 1,11

Bagualing East gate walking street Book city Huaqiangbei

3. SURVEY & DATA 3.2.2 Sampling Survey Respondents Above 15 years old, rural-urban migrants, not including permanent urban residents; Scattered residence: Rural-urban migrants living in the communities with high or medium proportion of permanent urban residents; Concentrated residence: Rural-urban migrants living together within a relatively concentrated community, with few permanent urban residents.

Survey sites(1): Concentrated residence Entrance of Airmate Co. Dormitory of Airmate company, most of the workers living together Respondents of Airmate are from one of the buildings, they live in the same floor and undertake the same kind of work.

Survey sites(1): Concentrated residence Interviews Dormitory

Survey sites(2): Scattered residence

3.2.2 Sampling Survey Contents of the questionnaire Basic information Social network information Whole network of concentrated residence (companies) Individual information Attitudes and behaviors towards marriage and family Attitudes and behaviors towards childbearing Attitudes and behaviors towards old-age support Social support network: Job hunting, instrumental, emotional and social contact Social discussion network: Marriage & Family, Childbearing & Education, Contraception, Old-age support Individual information Relation with other respondents working in the same company

3.2.2 Sampling Survey Sampling: methods and principles Scattered residence Stratified simple random sampling in four townships of three districts, Luohu, Yantian, and Nanshan Concentrated residence 2 construction companies and 3 manufacture companies in three districts, Nanshan, Longgang and Bao an

3.2.2 Sampling Survey Network data collection Ego Network -Data collected from Scattered & Concentrated Residence Respondents from Scattered Residence live dispersedly among various communities, most of them have no contact with each othersociomatrix cannot be structured; Data are mainly analyzed by statistical methods. Whole Network -Data collected from Concentrated Residence Respondents from Concentrated Residence live in the same community or dormitory (such as factories or construction sites), they are likely to know each other- sociomatrix can be structured; Data are mainly analyzed by methods of social network analysis.

Ⅰ Social Support Network Job hunting: 11.How did you get your first job after arriving in Shenzhen? 13.How did you get your current job? Instrumental support: 15.If you want to borrow some stuff (like money, sugar or pliers), or do some housework (like moving furniture, buying daily necessities, etc.), whom will you find for help? Emotional support: 16.If you feel depressed because of having conflicts with others, facing difficulties in work or life, whom will you find to confide in? Social companionship: 17.If you have social activities, such as shopping, attending party or dinner, playing cards, chatting, etc., whom will you find? The number of network members in hometown before the migration: Neighbor Relatives Others (Please note: ) The number of network members in Shenzhen: Ⅱ Discussion Network 21.If you want to discuss something about marriage and family, whom will you find? They are: 23.If you want to discuss something about childbearing and child education, whom will you find? They are: 25.If you want to discuss something about contraceptive use, whom will you find? They are: 26.If you want to discuss something about old age support, whom will you find? They are: Except the above persons, are there anybody discussed with you, or you can find to discuss? Please provide the number: persons, among them:

Social Network Note: In the following forms, when the one who appears repeatedly, only write down his or her name or code name. The answers please see the page Coding for Social Network. 23.If you want to discuss something about childbearing and child education, whom will you find? They are: Network member Whether has an urban household registration in Shenzhen (Shenzhenese)? Relation Sex Age Marital status Occupation Education Intimacy When did you know him/her? Frequency of Face to face contacts Calling or writing letters His/her attitude towards Desired number of children His/her children His/her attitude towards when first child is a girl * His/her attitude towards Boys should be educated more than girls * Did he/she actually discuss with you? (1.Yes,2.No) Children Children ( Boys ( Boys Girls Girls no no preference) preference) Boys Girls Boys Girls Children ( Boys Girls no preference) Boys Girls Children ( Boys Girls no preference) Boys Girls * Attitude towards when first child is a girl : 1.Stop childbearing 2.Have one more, regardless of sex 3.Not stop childbearing until have a boy * Attitude towards Boys should be educated more than girls : 1 Extremely disagree 2 Disagree 3 Indifferent 4 Agree 5 Extremely agree Children ( Boys Girls no preference) Boys Girls

Coding for Social Network Relation: 1.Spouse/Partner 2.Parents or parents in law 3.Child 4.Sibling 5.Other relatives 6.Fellow-villager 7.Permanent resident in Shenzhen you work together 8.Rural-urban migrants from other places you work together 9.Employer 1.Schoolmate 11.Friend 12.Landlord in Shenzhen 13.Neighbor in Shenzhen 14.Acquaintance in Shenzhen 15.Acquaintance in other places 16.No relation 17.Others (Please note: ) Whether has an urban household registration in Shenzhen (Shenzhenese)? 1.Yes 2.No Sex: 1.Male 2.Female Marital Status: 1.Never married 2.Firstly married 3.Remarried 4.Widowed 5.Divorced Occupation: 1 Manager 2 Owner of private enterprise 3 Professional and technical personnel 4 Clerk 5 Self-employed worker 6 Workers in commerce and service industries 7 Industrial workers 8 Unemployed 9 Cadre in local labor union 1 Cadre in local woman union 11 Cadre in local family planning committee 12 Cadre in local government 13 Cadre of your homeland government 14 Farmer or peasant 15 Others (Please note: ) Education: 1.Illiterate 2.Primary school 3.Junior high school 4.Senior high school (Technical secondary school, etc.) 5.Junior college 6.Undergraduate 7.Graduate and above Intimacy: 1.Extremely intimate 2.Relatively intimate 3.commonly 4.Not intimate 5.Extremely not intimate When did you know him/her? Year Month Frequency of seeing each other: 1.Everyday 2.Several times every week 3.Several times every month 4.Once a month 5.Several times every year 6.Once several years Frequency of calling or writing to each other: 1.Everyday 2.Several times every week 3.Several times every month 4.Once a month 5.Several times every year 6.Once several years

Concentrated residence networks (Whole Network) Note:Scattered residents needn t answer this page! Network members should be chosen from the name list of the factory/company/construction site, in which all the persons are the respondents in this survey. Name Code Coding is same with the above Relation Intimacy When did you know each other Whether turn to him/her for daily trivia help Whether turn to him/her seeking emotional support Whether find him/her as a partner in social activities Yes:1 No:2 Whether discuss with Whether discuss him/her with him/her about about marriag childbearing e and family Whether discuss with him/her about contraceptive use Whether discuss with him/her about old age support

3.3 Implementation Duration of survey: April 2-27, 25 interview Composition Sampling survey In-depth interviews Focus group discussion Community investigation Survey sites Nanshan Luohu Yantian Longgang Bao an Sample types Scattered residence in a village Concentrated residence in 2 construction companies Scattered residence Scattered residence Scattered residence Scattered residence Concentrated residence in 1 company Concentrated residence in 2 companies Nanshan, Luohu, Longgang, Yantian Longgang community in Qingshuihe street, Luohu District Hongming company in Buji township, Longgang District Nanshan, Luohu, Longgang, Yantian Size 29 136 47 224 252 25 253 2 76 92 6 1 6 9 Remarks Survey sites of concentrated residence: 3 Survey sites of scattered residence: 5 Total samples: 1739 6 people Total: 16 9

3.4 Quality Control Interviewer training Sampling survey Random street interviews Sampling Instruction at the beginning of the survey Formal survey ` Questionnaires check Follow-up Interviews Data input Consistency check Error correction

Interviewer training Questionnaire check

4. STATISTICAL ANALYSIS 4.1 Brief characteristics of respondents Demographics of the samples (1): Street random interviews Total No. of Samples: 111 Gender Age 25-34 Male Female Household registration Yes No 24-35+ Marriage Never-married Ever-married Percent (%) 52.8 47.2 38.2 45.5 16.3 16.3 63.6 36.4 23.8 76.2 Education 8-9-12 13+ Average monthly income (yuan) 1-799 8~999 1~1499 15~1999 2~2999 3+ Percent (%) 15.2 39.7 45 4.7 8.7 4.4 18.9 11.4 21.2 33.5

Occupation of the samples(1): Street random interviews 3 25 2 15 1 5 manager private enterprise professional and technical personel clerk selfemployed workers workers in commerce and service industries industrial workers unemployed others

4.1 Brief characteristics of respondents Demographics of the samples (2): Sampling survey Total No. of Samples: 1739 Percent (%) Gender Nationality Male (888 samples) 51.1 Han Percent (%) 96.5 Female (851 samples) 48.9 Minority 3.5 Age Marriage 15-24 27. Never-married 32. 25-34 4. Ever-married 68. 35+ 32.9 Average monthly income (yuan) Situation before entering Shenzhen 4. Temporary laborers in other cities 19. 6 1-799 2.2 Farmers 5.8 8~999 15. Rural students 25.9 1~1499 32.5 No. of jobs ever have after entering Shenzhen 15~1999 2~2999 11.8 7.4 4.1 3+ 9.2 1 55.4 Average years in Shenzhen 6.78(years) 2 2.6 Way of entering Shenzhen 3 1.6 With fellow-villagers, family members or others 62.9 4+ 9.3 Alone 37.1

Occupation of the samples(2): Sampling survey 45 4 35 3 25 2 15 1 5 Commerce and service Manufacture Transportation Construction Others

4. STATISTICAL ANALYSIS 4.2 Characteristics of social network Sizes of social support networks before and after migration; Characteristics of social network members: Social support networks; Discussion networks; Attitudes and behaviors of discussion network members

4. STATISTICAL ANALYSIS 4.2 Characteristics of social network Sizes of social support networks before and after migration Size shrinking after migration: Compared with social support network in hometown, the size of the three support networks has all been reduced: Network Instrumental support Emotional support Social contact Before migration 7.79 3.83 6.41 After migration 2.6 1.7 2.51

Size of Support Networks (Samples: 1739) (%) Size 1 2 3 4 5 First job finding support 29.1 6. 9. 1.4.3.2 Current job finding support 75.9 2. 3.2.6..3 Instrumental support 7.8 39. 3.6 12. 4.3 6.2 Emotional support 9.9 47.6 26.2 9.7 3.3 3.2 Social contact 6.8 33.6 29. 16.2 7.2 7.1 Basic information of members in support networks (%) (The whole number of network members appears in parentheses) First job Current Instrumental Emotional finding support job finding support support support Urban household registration in Social contact (1464) (516) (329) (2761) (3415) Shenzhen: Yes 6.6 12.3 4.5 3.2 4. No 93.4 87.7 95.5 96.8 96. Relation (whether belongs to relatives (1464) (515) (3212) (3212) (3415) or countrymen) Strong ties 75.6 65.7 58.3 58.7 47.2 Weak ties 24.4 34.3 41.7 41.3 52.8 Gender (1469) (515) (326) (326) (342) Male 66.5 68.9 59.8 51.7 55.1 Female 33.5 31.1 4.2 48.3 44.9 Education (1436) (468) (316) (2737) (3368) Elementary school and lower 6. 6.3 6.3 8.1 6. Junior high school 54.2 48.8 58.8 57.3 61. Senior high school and above 39.8 44.9 34.9 34.6 33. Intimacy (147) (514) (324) (2761) (3413) Extremely intimate 26.8 29.5 26. 38.3 22.9 Relatively intimate 43.4 41.2 45.8 4.8 45.9 Commonly 28.6 29.1 27.6 2.5 3.9 Not intimate.8.2.4.2.2 Extremely not intimate.4..2.2.1 Frequency of get together (1466) (515) (3214) (2769) (342) Everyday 38.5 48.2 6.7 62.8 68.4 Several times every week 13. 14.3 15.3 13.5 14.8 Several times every month 12.6 13.3 1.3 9.2 9.6 Once a month 8. 8.1 5.1 3.4 3.3 Several times every year 16.4 8.9 6.6 7.6 2.7 Once several years 11.5 7.2 2. 3.5 1.2 Frequency of calling or writing (1466) (51) (3195) (2769) (3412) Everyday 15.6 16.8 18.8 21.3 2.6 Several times every week 16.9 17.3 19.3 21.3 18.3 Several times every month 19.3 21.1 18.1 16.9 17. Once a month 11.7 11.5 8.5 8.7 9. Several times every year 1.8 9.2 7. 5.2 4.8 Once several years 25.7 24.1 28.3 26.6 3.3 Samples 1233 419 163 1566 162

Size of Discussion Networks (Samples: 1739) (%) Size 1 2 3 4 5 Marriage and Family Discussion 9.3 49. 26.6 9.7 2.8 2.7 Childbearing Discussion 9.5 56.7 21.4 8.1 2.3 2. Contraceptive Use Discussion 21.9 6.7 11.4 4.2.9.9 Aging-life Discussion 13.8 58.3 15.4 8.6 1.8 2.1 Basic information of members in discussion networks (%) (The whole number of network members appears in parentheses) Marriage and Family Discussion Childbearing Discussion Contraceptive Use Discussion Urban household registration in Shenzhen: Aging-life Discussion (315) (2482) (182) (235) Yes 3. 2.8 2.4 3. No 97. 97.2 97.6 97. Relation (whether belongs to relatives or countrymen) (281) (2479) (1817) (2311) Strong ties 2.5 5.6 46.5 53.7 Weak ties 79.5 49.4 53.5 46.3 Gender (313) (2477) (1817) (237) Male 47.1 47.1 62.4 5.9 Female 52.9 53. 37.6 49.1 Marital Status (313) (2468) (194) (2299) Unmarried 23.8 18.9 24.1 22. Married 76.2 81.1 75.9 78. Education (313) (2482) (186) (2298) Elementary school and lower 23.8 13.2 1.6 12.3 Junior high school 76.2 55. 53.7 52.8 Senior high school and above. 31.9 35.7 34.9 Intimacy (326) (2488) (1814) (232) Extremely intimate 46.2 52.7 49. 55.5 Relatively intimate 35.2 31.3 34.7 29.1 Commonly 17.9 15.6 15.9 15. Not intimate.5.3.3.3 Extremely not intimate.2.1.1.1 Frequency of get together (327) (2492) (182) (2316) Everyday 56.4 59.5 64.8 6.2 Several times every week 12.8 12.1 13.8 11.3 Several times every month 7.8 7.8 6.6 8. Once a month 3.7 2.8 3. 3.5 Several times every year 13. 12.6 8.1 11.8 Once several years 6.3 5.2 3.7 5.2 Frequency of calling or writing (32) (2482) (1818) (2311) Everyday 2.6 23.1 24.9 22.4 Several times every week 26. 24.3 24.2 23.8 Several times every month 17.9 17.3 15.8 18.9 Once a month 6.9 7.4 7.4 7.5 Several times every year 5.1 4.4 3.7 4.6 Once several years 23.5 23.6 24. 22.8 Samples 1577 1573 1359 1499

Attitudes and behaviors of individuals discussion network members (%) Marriage and family % Childbearing % Contraceptive use Attitude toward 3368 Desired number of 2443 Contraceptive Rural women children use working independently in cities % Aging life % 1651 Plan for future aging life Extremely disagree 1.4. Sterilization 19.1 Social insurance, shared with affiliated organization Disagree 4.7 1 23.1 IUD 32.4 Commercial 5.1 insurance Indifferent 14.5 2 7.4 Condom, Pills 34.9 Saving money 5.1 Agree 63.4 3+ 6.5 Natural contraception in periods or lactation 1. Depending on children Extremely agree 15.9 None 12.6 No plan 4.8 Attitude toward Premarital pregnancy 2938 Children ever born 249 Whether recommended to adopt his/her method(s)? 183 Whom are you willing to live with when you are old? Extremely disagree 18.3 24.7 Yes 5.7 Son and 45.9 daughter-in-law Disagree 62. 1 31.1 No 49.3 Daughter and son 3. -in-law Indifferent 17.4 2 3.1 Son or daughter 7.5 Agree 2.2 3+ 14.1 Live alone or with 41.6 spouse Extremely agree.1 If first child is a 24 Others 1.9 girl, you will attitude towards extramarital love affairs 2941 Stop childbearing 25.6 Extremely disagree 36.9 Have one more, regardless of sex Disagree 55.4 Not stop childbearing until have a boy Indifferent 6.4 Attitude toward Boys should be educated more than girls 61.5 12.9 2446 Agree 1.2 Extremely disagree 29.3 Extremely agree.1 Disagree 41.9 Indifferent 16.5 Agree 6.4 Extremely agree 5.9 Sample 1577 Sample 1573 Sample 1359 Sample 1499 2253 1.4 29.6 2265

4. STATISTICAL ANALYSIS 4.3 Social network and its implications Job seeking networks Determinants- Seeking a job by mass media, public sections or social networks; Effects- Having a better job? Basic social support networks Determinants; Socio-economic outcomes Effects of social network on attitude and behavior evolution Marriage pattern and family formation; Son preference, fertility and contraception; Old-age support; Socio-demographic consequences. Gender analysis Public policy studies

4. STATISTICAL ANALYSIS 4.3 Social network and its implications -One example: Discussion network members and individuals attitudes Analysis Framework Overall effect of network members = I A Ii: degree of intimacy of network member i, Ai: attitude of network member i; i i i Weak ties: Network members are Managers, Owners of private enterprise, Professional and technical personnel, and Officers.

Estimates of Ordinal regression for current degree of son preference Dependent Variable: What will you do when the first child is a girl? <Stop childbearing (-1); Have a second child and stop (); Have more children until have a boy (1)> Variables Model 1 Model 2 Model 3 Model 4 Factors of social networks Overall effect of network members (No effect) Positive effect (with son preference) 1.44*** 1.34*** Negative effect (without son preference) -1.47*** -1.3*** Weak ties (No) Yes -.46** -.4* Migration experience Living in cities before entering Shenzhen: (No) Yes.2.2 Years of living in Shenzhen: (-1) 1-5 -.31 -.48+ 5-7 -.4 -.6* 7 +.16 -.27 Times back home per year: () 1.3*.23+ 2+.39*.37* Individual characteristic Gender: (Female) Male.32**.1 Age: (24 - ) 25-34..6 35 +.23.2 Education: (Elementary school and lower) Junior high school -.35+ -.22 Senior high school and above -.88*** -.58** Income: (8-) 81-1.21.2 11-15 -.26+ -.17 151-2.11.1 21+.6.27 Children ever born: No child Only girl(s).43*.45* Only boy(s).2.2 Boy(s) and girl(s) 1.9***.86*** -2LL 131.95*** 183.12*** 873.74*** 1956.74*** Samples 1447

Estimates of Logistic regression for the likelihood of current attitude towards old-age living arrangements(whether willing to live apart from adult children) Variables Model 1 Model 2 Model 3 Model 4 Factors of social networks Overall effect of network members (No effect) Positive effect (willing to live apart from children) 2.74** 2.92** Negative effect (willing to live with children).29**.3** Weak ties (No) Yes 1.7 1.6 Migration experience Living in cities before entering Shenzhen: (No) Yes 1.6** 1.65** Years of living in Shenzhen: (-1) 1-5.76.49* 5-7.93.54* 7 +.88.62 Times back home per year: () 1.85.94 2+.82.84 Individual characteristic Gender: (Male) Female.96.96 Age: (24 - ) 25-34 1.48* 1.36 35 + 1.8.86 Education: (Elementary school and lower) Junior high school 1.34 1.2 Senior high school and above 1.73** 1.16 Income: (8-) 81-1 1.14 1.1 11-15 1.18 1.1 151-2 1.16 1.4 21+ 1..92 Having children: (No) Yes.81.8-2LL 1534.29*** 1865.78* 1854.25** 1498.98*** Samples 1359

4. STATISTICAL ANALYSIS 4.4 Conclusion- Tentative Results Social networks are reconstructed, but smaller; blood and geographical relations (strong ties) are still the main social connections. Rural migrants have not integrated well into the city. Respondents attitudes towards marriage, childbearing, old-age life have been changed by social discussion networks, migration experiences and Individual characteristics. Rural-urban migration helps shrinking the difference of urban residents and farmers in terms of attitudes and behaviors.

5. CLASSICAL SOCIAL NETWORK ANALYSIS 5.1 Introduction Hongming company (HM) Located in Longgang district, northwest of Shenzhen Produces electronic equipments Most of the workers are women aged from 2 to 3. Airmate company (AMT) Produces electronic appliance and equipment About 8% of the workers are very young women Xin Yongxing company (XYX) A spraying workshop Most workers are younger than 4 years old Half of the whole 5 workers are women Chuangzhu company (CZ) A construction company; Most of the workers are men; Many of them change their jobs annually because they have to transfer to another construction company when one project is completed. Shizheng company (SZ) A smaller construction company; Most of the workers are men.

5. CLASSICAL SOCIAL NETWORK ANALYSIS 5.1 Introduction All data of whole network are from the survey of the 5 survey sites located in Longgang, Bao an and Nanshan Districts; The whole network from the 5 settlements arises from 7 networks, a total of 35 matrixes, including: Instrumental support network; Emotional support network; Social contact network; Marriage, childbearing, contraceptive use, and aging life discussion networks.

Basic Characteristics of the Respondents in Whole Networks Basic Characteristics of the Respondents in Whole Networks (%) Site HM AMT XYX CZ SZ Total Size 2 75 9 135 47 547 Gender Male 5. 1 91.5 4.8 Female 1 1 5. 8.5 59.2 Age 19-9.5 31.5 13.3 1.5 6.4 1.8 2-34 88 65.6 67.8 46.4 23.4 65.63 35+ 2.5 2.6 18.9 54.6 72.8 23.58 Education 6-3.9 2. 11. 25.5 8.8 7-9 51.5 71.1 72.2 78.7 68.1 65.6 1+ 48.5 25 7.8 1.3 6.4 25.5 Migration experience Ever migration 8.5 23.7 27.8 38.2 31.9 23.3 Never migration 91.5 76.3 72.2 61.8 68.1 76.97 Migration characteristic Alone 16. 13.2 34.4 36.8 17. 23.8 Spouse 3. 18.4 17.8 3.7 8.5 5.7 Other family members 34. 61.8 17.8 13.2 27.7 23.4 Fellow-villager 39.5 6.6 3. 46.3 46.8 43.5 Others 7.5 3.7 Living environment Citizen's community 5. 6.7 2.9 Rural migrants community 76. 1 63.3 99.3 1 85. Mixed living areas 19. 3. 11.9 Others.7.2 Marriage Never-married 59. 89.5 44.4 16.9 21.3 47.2 Ever-married 41. 1.5 55.6 83.1 78.7 52.9

5. CLASSICAL SOCIAL NETWORK ANALYSIS 5.2 General properties of whole networks Example A: Instrumental Support Network Site HM AMT XYX CZ SZ Density.11.86.49.22.21 Degree centrality 2.225 6.36 4.356 2.926.957 Out-degree centralization.125.324.28.53.9 In-degree centralization.54.118.11.16.68 Betweenness centralization.142.116.93.75.3 Transitivity.191.377.32.33.489 Reciprocity.129.19.14.176.125 Average distance 6.63 2.885 3.617 5.24 1.641 Clustering coefficient.121.296.235.243.446

5. CLASSICAL SOCIAL NETWORK ANALYSIS 5.2 General properties of whole networks Example B: Childbearing Discussion Network Site HM AMT XYX CZ SZ Density.6.2.18.1.21 Degree centrality 2.11 1.57 1.589 1.296.957 Out-degree centralization.116.157.175.5.9 In-degree centralization.8.48.39.65.68 Betweenness centralization.3.25.51.4.3 Transitivity.121.215.267.289.489 Reciprocity.9.76.44.61.125 Average distance 3.686 2.69 4.348 2.354 1.338 Clustering coefficient.96.15.236.178.36

5. CLASSICAL SOCIAL NETWORK ANALYSIS 5.3 Conclusion Density and degree centrality of AMT, XYX and HM network are larger, which indicates there are more connections in these three networks; Out-degree centralization is bigger than in-degree centralization among all the networks, distribution of the number of members seeking for help is more dispersive than that of sought for help; Connections of discussion network are fewer compared with those in social support networks, indicating there is less communication on topics of marriage, childbearing, contraceptive use and aging life among rural-urban migrants.

5. CLASSICAL SOCIAL NETWORK ANALYSIS 5.4 Summary These networks are complex; Traditional statistical methods are not capable of analyzing these networks, especially the whole networks; Two famous complexity networks Small-world network Scale-free network

6. COMPLEXITY PROPERTIES 6.1 Introduction Three important parameters Clustering coefficient c c = Number of links between neighbors Total number of possible links Average path length l l = Sum of minimal length between each pair of nodes Size of the network Degree distribution in real networks

6. COMPLEXITY PROPERTIES 6.1 Introduction Four types of networks: Average path length Regular Network Random Network Small World Network Scale Free Network Big Small Small Small Clustering coefficient Big Small Big Big Degree distribution \ Poisson distribution \ Power law distribution

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Remarks: In the following 5 tables: Data Source: the Sample Survey in Shenzhen in 25 <k> is represent the average degree, l is the average path length C is the clustering coefficient of our investigate networks. And l rand is the average path length and C rand is the clustering coefficient of the corresponding random networks.

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Networks of Hongming company size <k> l l rand C C rand Instrumental support network 2 2.225 7.63 7.6.121.8 Emotional support network 2 2.14 7.436 7.15.122.4 Social contact network 2 2.52 5.861 5.382.125.13 Discussion networks about marriage 2 1.75 5.343 8.35.117.11 Discussion networks about childbearing 2 1.15 3.686 4.142.96.3 Discussion networks about contraception 2.675 2.21 3.779.138. Discussion networks about ageing life 2.935 2.565 4.62.129.3

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Networks of Airmate company size <k> l l rand C C rand Instrumental support network 75 7.36 2.885 2.516.296.75 Emotional support network 75 3.8933 3.791 3.338.214.57 Social contact network 75 5.5467 2.943 2.711.269.7 Discussion networks about marriage 75 2.44 3.934 4.314.185.37 Discussion networks about childbearing 75 1.567 2.69 7.613.15.25 Discussion networks about contraception 75.8267 1.569 4.349.44.9 Discussion networks about ageing life 75 1.84 3.799 5.38.162.13

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Networks of Xin Yongxin company size <k> l l rand C C rand Instrumental support network 9 4.3556 3.617 3.138.235.55 Emotional support network 9 3.3889 4.12 3.68.229.38 Social contact network 9 4.5111 3.493 3.18.23.44 Discussion networks about marriage 9 2.222 2.473 5.329.16.2 Discussion networks about childbearing 9 1.5889 4.348 7.643.236.23 Discussion networks about contraception 9.7222 1.366 2.344.157.42 Discussion networks about ageing life 9 1.9111 2.518 7.15.194.12

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Networks of Chuangzhu company size <k> l l rand C C rand Instrumental support network 135 2.9259 5.24 4.359.243.16 Emotional support network 135 2.5852 3.629 5.417.24.2 Social contact network 135 3.547 5.361 3.894.254.3 Discussion networks about marriage 135 1.2889 2.63 5.897.24.15 Discussion networks about childbearing 135 1.2963 2.354 8.277.178.7 Discussion networks about contraception 135.4296 1.2 1.734.239. Discussion networks about ageing life 135 1.337 1.998 7.951.187.16

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Networks of Shizheng company size <k> l l rand C C rand Instrumental support network 47 2.426 1.641 4.196.446.18 Emotional support network 47 1.721 1.722 4.56.4.8 Social contact network 47 1.9362 2.1 7.581.374.51 Discussion networks about marriage 47.7234 1.34 1.91.18.3 Discussion networks about childbearing 47.9574 1.338 2.561.36.14 Discussion networks about contraception 47.638 / / / / Discussion networks about ageing life 47.7234 1.29 1.86.322.14

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Results: The average length of the rural-urban migrants networks is almost equal to the random networks; The clustering coefficient of the rural-urban migrants networks is much bigger than the random; It has been proved that Small-world Phenomena exist in most of these 35 networks

6. COMPLEXITY PROPERTIES 6.2 Properties of Small World Network Conclusion: Comparing the seven networks of the same survey site: Average degree <k> of social support networks is bigger than that of discussion networks; Average path length <l>of social support networks and marriage discussion network is bigger than that of the other discussion networks; Average clustering coefficient <C> of social support networks is bigger than that of discussion networks. Comparing the same network among five survey sites: the male-dominate networks (CZ and SZ) have smaller average degree than the female-dominate networks (HM, AMT and XYX)

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 1. Instrumental support network Data Source: HM, 25 Sample Survey in Shenzhen

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 2. Emotional support network Data Source: HM, 25 Sample Survey in Shenzhen

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 3. Social companionship network Data Source: HM, 25 Sample Survey in Shenzhen

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 4. Discussion networks about marriage Data Source: HM, 25 Sample Survey in Shenzhen

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 5. Discussion networks about childbearing Data Source: HM, 25 Sample Survey in Shenzhen

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 7. Discussion networks about contraceptive use Data Source: the Sample Survey in Shenzhen in 25

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Degree distribution in-degree distribution Out-degree distribution Network 7. Discussion networks about ageing life Data Source: HM, 25 Sample Survey in Shenzhen

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Regression for logp(k)~logk: Remarks: Degree distribution of the Scale Free Network obeys power law distribution, namely, P(k)~ k r, where k is the degree and r is the degree exponent. If we have the logarithmic distribution, there should be linear relation between log P(k) and log k. The 35 matrices are directed, so the in-degree and out-degree distribution may be not the same. The following tables are shown the linear regression results between log P(k) and log k for most of the 35 matrices. Notes: ***P<.1,**P<.1, *P<.5, +P<.1

Linear regression results -A Hongming Company r constant adjust-r 2 Instrumental support network: In-degree -1.865*** -.15.86*** Instrumental support network: Out-degree -1.51*** -.348.842*** Emotional support network: In-degree -1.513*** -.268.82** Emotional support network: Out-degree -1.481*** -.355*.875*** Social companionship network: In-degree -1.715*** -.182.862*** Social companionship network: Out-degree -1.473*** -.3*.916*** Discussion networks about marriage: In-degree -2.51*** -.13.86*** Discussion networks about marriage: Out-degree -1.496*** -.324 +.868*** Discussion networks about childbearing: In-degree -2.65*** -.156.894*** Discussion networks about childbearing: Out-degree -1.445*** -.376 +.819*** Discussion networks about contraception: In-degree -2.162* -.219.841* Discussion networks about contraception: Out-degree -1.212* -.386.741* Discussion networks about ageing life: In-degree -1.985*** -.182.91*** Discussion networks about ageing life: Out-degree -1.212*** -.421*.87***

Linear regression results -B Airmate Company r constant adjust-r 2 Instrumental support network: In-degree -.119-1.29* -.73 Instrumental support network: Out-degree -.653* -.758***.52* Emotional support network: In-degree -1.59* -.471+.568* Emotional support network: Out-degree -1.78*** -.462*.824*** Social companionship network: In-degree.318-1.339***.11 Social companionship network: Out-degree -.75* -.677*.542* Discussion networks about marriage: In-degree -1.336* -.317.76* Discussion networks about marriage: Out-degree -1.158* -.426*.724* Discussion networks about childbearing: In-degree -1.64* -.211.799* Discussion networks about childbearing: Out-degree -.931* -.457+.579* Discussion networks about contraception: In-degree -1.36 -.233.734 Discussion networks about contraception: Out-degree -.935 -.382.47 Discussion networks about ageing life: In-degree -1.113* -.361.698* Discussion networks about ageing life: Out-degree -.94* -.55*.598*

Linear regression results -C Xin Yongxin Company r constant adjust-r 2 Instrumental support network: In-degree -1.68* -.52+.537* Instrumental support network: Out-degree -.87*** -67*.574*** Emotional support network: In-degree -1.148* -.411.579* Emotional support network: Out-degree -.957*** -.55*.647*** Social companionship network: In-degree -.835* -.625*.469* Social companionship network: Out-degree -.768*** -.683*.571*** Discussion networks about marriage: In-degree -1.558* -.24.74* Discussion networks about marriage: Out-degree -.89* -.526*.719* Discussion networks about childbearing: In-degree -1.467 -.246.422 Discussion networks about childbearing: Out-degree -.838*** -.532***.922*** Discussion networks about contraception: In-degree -2.159* -.135.911* Discussion networks about contraception: Out-degree -.237 -.745*.48 Discussion networks about ageing life: In-degree -1.791* -.166.736* Discussion networks about ageing life: Out-degree -.76*** -.698***.651***

Linear regression results -D Chuangzhu Company r constant adjust-r 2 Instrumental support network: In-degree -1.448* -.292+.827*** Instrumental support network: Out-degree -1.26* -.399.46* Emotional support network: In-degree -1.582*** -.246.789*** Emotional support network: Out-degree -1.38* -.338.595* Social companionship network: In-degree -1.282*** -.363*.784*** Social companionship network: Out-degree -.991+ -.52.331+ Discussion networks about marriage: In-degree -1.779* -.22.836* Discussion networks about marriage: Out-degree -.979* -.434*.85* Discussion networks about childbearing: In-degree -1.83* -.26.816* Discussion networks about childbearing: Out-degree -1.194* -.386.691* Discussion networks about contraception: In-degree -2.44 -.46.823 Discussion networks about contraception: Out-degree -.784+ -.491+.443+ Discussion networks about ageing life: In-degree -1.994* -.128.79* Discussion networks about ageing life: Out-degree -1.266* -.338.64*

Linear regression results -E Shi Zheng Company r constant adjust-r 2 Instrumental support network: In-degree -.961* -.454.77* Instrumental support network: Out-degree -1.145* -.356*.882* Emotional support network: In-degree -.924* -.439+.611* Emotional support network: Out-degree -1.13* -.366.657* Social companionship network: In-degree -.984* -.389*.98* Social companionship network: Out-degree -1.118* -.351*.919* Discussion networks about marriage: In-degree -2.227* -.13.919* Discussion networks about marriage: Out-degree -.452 -.476.63 Discussion networks about childbearing: In-degree -1.685+ -.179.737+ Discussion networks about childbearing: Out-degree -1.199 -.336.47 Discussion networks about contraception: In-degree / / / Discussion networks about contraception: Out-degree / / / Discussion networks about ageing life: In-degree -1.842 -.141.934 Discussion networks about ageing life: Out-degree -.922 -.356.492

6. COMPLEXITY PROPERTIES 6.3 Fit to Scale Free Network Conclusion: The degree distributions of the 35 networks are more like power law distribution (namely, P(k)~ )~k r ) than Poisson distribution; The degree exponent r is different with the traditional scale-free network (BA model, r =-3, Barabàsi et al, 1999); Distributions do not have simple power law properties Scale free network model needs modification. The out-degree and in-degree distributions are different in social discussion networks, but almost equal in social support networks. Rural-urban urban migrations are not willing to discuss private issues.

6. COMPLEXITY PROPERTIES 6.4 Discussion Small-World Phenomenon indicates a network with high clustering subnets, including local contacts nodes and some random long-range shortcuts; Scale-Free network reveals a few hubs exist in a network as the key nodes, playing an important role in diffusion processes; The high clustering subnets or the hubs both indicate the existence of cliques or parties in social networks of Chinese rural-urban migrants.

6. COMPLEXITY PROPERTIES 6.4 Discussion A property that seems to be common to many networks is community structure, the division of network nodes into groups within which the network connections are dense, but between which they are sparser -----M. E. J. Newman

7. ALGORITHM DETECTING NETWORK COMMUNITY STRUCTURE Introduction Concept The algorithm Application Conclusion

7.1 Introduction: Example Instrumental support network of Shizheng company (random topology) Data Source: Sample Survey in Shenzhen in 25

7.1 Introduction: Example Instrumental support network of Shizheng company (community structure topology) Data Source: Sample Survey in Shenzhen in 25

7.2 Concept Cohesive subgroup, subgroup, social group, clique. Community structure is another topological description for a network based on the community Community structure detection is the process of classifying the nodes in the network into different subgroups, called communities

7.2 Concept Community structure Detection Computer science approaches Sociological approaches Newman s algorithms Evaluation Sociological measure (Wasserman & Faust ) Modularity (Newman)

7.2 Concept Evaluation : modularity Consider a particular division of a network in to m communities, define a evaluation matrix E: Evaluation Matrix e E pq m m Where e pq is the fraction of edges in the original network that connect nodes in community p to those in community q

1 2 3 4 5 6 7 8 9 1 1 1 1 1 9 1 1 1 8 1 1 7 1 1 1 6 1 1 5 1 1 1 1 4 1 1 1 3 1 1 2 1 1 1 9 8 7 6 5 4 3 2 1 Evaluation : modularity

Evaluation : modularity 3 1 4 2 5 Community 1: 1,2,3,4; Community 2: 5,6,7 Community 3: 8, 9, 1 6 7 e 11 e 12 e 13 8 e 21 e 22 e 23 1 9 e 31 e 32 e 33

Evaluation : modularity 1 1 1 9 1 1 1 8 1 1 7 1 1 1 6 1 1 5 1 1 1 1 4 1 1 1 3 1 1 2 1 1 1 9 8 7 6 5 4 3 2 1 2/11 1/11 3/11 1/11 1/11 1/11 4/11 e 33 e 32 e 31 e 23 e 22 e 21 e 13 e 12 e 11 4/11 1/11

Evaluation : modularity Modularity m Q= ecc e c= 1 i ci 2 The bigger the value of Q, the more the community structure of the network. Modularity is not only used to evaluate community structure, but can also give some hints for detection.

7.3 Algorithm Main ideas Community structure detection is an optimization process for modularity Q; The characteristics of the social network may give prior knowledge: Degree: the nodes with highest degree could be centers of communities; Distance

7.3 Algorithm Main steps Based on prior knowledge of node degrees, an original community structure can be found; According to increasing Q values, communities are combined;

7.3 Algorithm Find an original community structure 1 1 14 2 14 3 4 1 5 25 31 8 12 6 7 25 3 1 9 3

7.3 Algorithm Combine the community 1 1 4 2 4 3 4 1 5 5 3 8 2 6 7 5 3 1 9 3

7.3 Algorithm Combining communities 1 2 3 4 1 2 e 11 e 12 e 13 e 14 e ' 11 e ' 12 e ' 13 e 21 e 22 e 23 e 24 e e ' 31 21e 32 e 33 e 34 e e ' 41 31 e 42 e 43 e 44 3 4 m Q= ecc e c= 1 i m m 2 = ecc eci c= 1 c= 1 i ci 2 If community p combines with community q, we have

7.3 Algorithm Combining communities m m 2 m m Q = e + e + e e 2 e e cc pq qp ci qj pj c= 1 c= 1 i j j Q Q 2e 2 e e = + m m pq qj pj j j m m if 2e 2 e e then Q Q φ = > > pq pq qj pj j j Choosing the biggest φ pq, the efficiency of the combination can be guaranteed.

7.3 Algorithm Description of the algorithm Step1: Calculate the degree for each node in the network; Step2: Find the node with maximum degree, classify this node and all the nodes directly connected to it into one community; Step3: Remove all connections of the node with maximum degree Step4: If there are no connections in the network, go to step 5, else go to step 1; Step5: Calculate the change in modularity upon joining two communities; Step6: Combine the two communities which have the maximum change in modularity upon joining; Step7: Repeat step 5 and 6 until only one community remains

7.4 Applications (1) Computer-generated Network: I. Random topology

7.4 Applications (1) Computer-generated Network: II. Community structure

7.4 Applications (1) Computer-generated Network: III. Comparison of two algorithms.67.67

7.4 Applications (2) Ucinet Network (Zachary s karate club network ) I. Random topology

7.4 Applications (2) Ucinet Network (Zachary s karate club network ) II. Community structure

7.4 Applications (2) Ucinet Network (Zachary s karate club network ) III. Comparison of two algorithms.412.383

7.4 Applications (3) Ucinet Network (Drugnet) I. Random topology

7.4 Applications (3) Ucinet Network (Drugnet) II. Community structure

7.4 Applications (3) Ucinet Network (Drugnet) III. Comparison of two algorithms.751.745

7.4 Applications (4) Migrant social network: Data from Shenzhen, China Abbreviations: ISN Instrumental support network ESN Emotional support network SCN Social contact network MDN Discussion networks about marriage CDN Discussion networks about childbearing CoDN Discussion networks about contraception ADN Discussion networks about ageing life

(4) Migrant social network: Data from Shenzhen, China I. Comparison of Q (modularity) calculated by three algorithms ISN ESN SCN MDN CDN CoDN ADN HM N-G.437.48.463.553.544.389.465 A-N.539.578.546.638.68.74.678 New.547.578.545.643.673.74.676 AMT N-G.327.418.364.497.543.512.476 A-N.385.473.39.499.613.685.517 New.376.455.378.523.6117.685.519 XYX N-G.421.455.351.474.556.371.556 Q A-N.464.57.413.496.63.69.587 New.462.498.422.526.591.65.589 CZ N-G.692.696.696.658.66.612.761 A-N.728.713.71.762.744.783.789 New.711.717.72.76.742.783.787 SZ N-G.583.599.656 /.579 /.73 A-N.691.683.725.76.774 /.791 New.691.683.725.76.775 /.794

(4) Migrant social network: Data from Shenzhen, China II. Number of iterations from two algorithms ISN ESN SCN MDN CDN CoDN ADN HM A-N 176 181 187 17 139 11 114 New 88 84 1 88 87 29 43 AMT A-N 7 69 7 64 59 45 56 New 47 43 46 32 22 14 24 T XYX A-N New 85 47 82 43 82 48 76 33 73 26 45 8 74 26 CZ A-N 122 119 125 92 98 44 93 New 68 62 72 36 39 8 41 SZ A-N 35 31 36 26 27 / 24 New 17 15 18 6 9 / 6

(4) Migrant social network: Data from Shenzhen, China III. Some findings: Social networks of the rural-urban migrants have clear community structures; The community structures are much stronger for social discussion networks than for social support networks; Community structures are stronger for men s network than for women s.

(4) Migrant social network: Data from Shenzhen, China IV. Examples of community structure (a): HM-Instrumental support network

(4) Migrant social network: Data from Shenzhen, China IV. Examples of community structure (b): AMT: Instrumental support network

(4) Migrant social network: Data from Shenzhen, China IV. Examples of community structure (c): XYX: Instrumental support network

(4) Migrant social network: Data from Shenzhen, China IV. Examples of community structure (d): CZ: Instrumental support network

(4) Migrant social network: Data from Shenzhen, China IV. Examples of community structure (e): SZ: Instrumental support network

7.5 Conclusion Prior knowledge of network structure (e.g. degree of the nodes) helps to detect social community structure; Modularity provides some hints for construct detection; Using prior knowledge of network structure, our algorithm shortens computing time and produces clear social community structure. Our algorithm is feasible and effective to detect community structure.

8. CURRENT AND FUTURE WORKS 8.1 General analysis Statistical analysis: Determinants and outcomes of job-seeking networks and other social support networks; Determinants of attitudes and behaviors about marriage, childbearing, contraceptive use, old-age life etc. Socio-demographic implications of rural-urban migration Social cohesion, public policy analysis and promoting strategies Social network analysis: Structure of the social network: P* model (Wasserman, S. & P. Pattison, 1996; Anderson, J., S. Wasserman, & B. Crouch, 1999 ) The weighted social network analysis New classing or clustering algorithms

8.2 The evolution of the social network model For example: A modified scale-free model The rural-urban migrants in china always immigrate by groups. So we construct the network like this: Preferential attachment The original network New comer s network at t 1 New comer s network t n