Network Study of Rural-urban migration in China Evidence from Shenzhen Xiaoyi Jin 1,2 and Haifeng Du 1,2 in Collaboration with Shuzhuo Li 1, 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: Xiaoyi Jin, xiaoyij@stanford.edu; Haifeng Du, haifengd@stanford.edu Shuzhuo Li, shzhli@mail.xjtu.edu.cn;
CONTENTS 1. Background 2. Survey & Data 3. Classical Social Network Analysis 4. Complexity Properties 5. Detecting Network Community Structure 6. Conclusion
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: Facts: Evolution of attitudes and behaviors 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.
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 1.4 Objectives and methods Objectives: Migrant s social networks in urban areas and social cohesion; Evolution of attitudes and behaviors and its socio-demographic implications; Complex network models; Policy suggestions to improve social cohesion and sustainable development. Methods: Statistical analysis Social network analysis Simulation Public policy analysis
1. BACKGROUND 1.5 Main contents: Social network analysis Classical Social Network Analysis Ego networks Whole networks Complexity properties Small-world phenomenon Scale-free properties Detecting network community structure Concept Results
2. SURVEY & DATA 2.1 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
2 SURVEY & DATA 2.2 Survey Components and Contents Survey components Random street interviews Sampling survey Community investigation Individual interviews
2. SURVEY & DATA 2.3 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
2.3 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
2.3 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
2.3 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 Children Children Children Children Children His/her attitude towards Desired number of children ( Boys Girls no ( Boys Girls no ( Boys Girls no ( Boys Girls no ( Boys Girls no preference) preference) preference) preference) preference) His/her children Boys Girls Boys Girls Boys Girls Boys Girls Boys Girls 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) * 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
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
Interviewer training Questionnaire check
2.4 Data: Characteristics of respondents Demographics of the samples: Sampling survey Total No. of Samples: 1739 Percent (%) Percent (%) Gender Nationality Male (888 samples) 51.1 Han 97.5 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 7.78(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
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.1 Ego 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
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.1 Ego 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 7.41 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
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.1 Ego network 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.
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.2 Whole network 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.
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.2 Whole network 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
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.2 Whole network Example A: Instrumental Support Network Site HM AMT XYX CZ SZ Density.11.86.49.22.21 Degree centrality 2.225 7.36 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 7.63 2.885 3.617 5.24 1.641 Clustering coefficient.121.296.235.243.446
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.2 Whole network 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
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.2 Whole network 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.
3. CLASSICAL SOCIAL NETWORK ANALYSIS 3.3 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
4. COMPLEXITY PROPERTIES 4.1 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
4. COMPLEXITY PROPERTIES 4.1 Introduction Four types of networks: Regular Network Random Network Small World Network Scale Free Network Average path length Big Small Small Small Clustering coefficient Big Small Big Big Degree distribution \ Poisson distribution \ Power law distribution
4. COMPLEXITY PROPERTIES 4.2 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.
4. COMPLEXITY PROPERTIES 4.2 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
4. COMPLEXITY PROPERTIES 4.2 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
4. COMPLEXITY PROPERTIES 4.2 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
4. COMPLEXITY PROPERTIES 4.2 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
4. COMPLEXITY PROPERTIES 4.2 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
4. COMPLEXITY PROPERTIES 4.2 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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
4. COMPLEXITY PROPERTIES 4.3 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.
4. COMPLEXITY PROPERTIES 4.4 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.
5. DETECTING NETWORK COMMUNITY STRUCTURE 5.1 Concept 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
5.1 Concept Cohesive subgroup, social group, clique Detection Computer science approaches Sociological approaches Newman s algorithms Evaluation Sociological measure (Wasserman & Faust ) Modularity (Newman)
5.1 Concept Evaluation : modularity Consider a particular division of a network in to m communities, define 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
5.1 Concept 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 5.1 Concept
5.1 Concept Evaluation : modularity Modularity m Q= ecc e c= 1 i ci 2 The bigger the value of Q, the stronger is community structure of the network. Values of Q typically fall in the range from about.3 to.7.
5.2 Results: Abbreviations: ISN ESN SCN MDN CDN CoDN ADN Instrumental support network Emotional support network Social contact network Discussion networks about marriage Discussion networks about childbearing Discussion networks about contraception Discussion networks about ageing life
I. The modularity I. The modularity 5.2 Results:.794 /.775.76.725.683.691 SZ.787.783.742.76.72.717.711 CZ.589.65.591.526.422.498.462 XYX.519.685.6117.523.378.455.376 AMT.676.74.673.643.545.578.547 HM ADN CoDN CDN MDN SCN ESN ISN Survey sites
5.2 Results: II. 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.
5.2 Results: III. Examples of community structure (a): HM-Instrumental support network
5.2 Results: III. Examples of community structure (b): AMT: Instrumental support network
5.2 Results: IV. Examples of community structure (c): XYX: Instrumental support network
5.2 Results: IV. Examples of community structure (d): CZ: Instrumental support network
5.2 Results: III. Examples of community structure (e): SZ: Instrumental support network
6. CONCLUSION Social networks of the migrants become smaller and strong ties are dominant, the level of social cohesion is low; Social networks have effects on attitudes and behavior evolution, rural-urban migration is the way of urbanization in China; The small-world phenomena and scale-free property are common for most of the China rural-urban migration social network; The rural-urban migrations use different networks to live in the city. Especially, the social discussion networks are different from the social support network; The male-dominate networks are different from the femaledominate networks; Rural-urban migration social networks have strong community structure. And the female-dominate networks are stronger than the male-dominate networks.
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