Abstract
This research empirically explores whether the intensity of social networks affects the wages of workers or not using survey data from the Chinese households’ income project in 2002. After controlling for gender, work experience, political affiliation, marital status, years of education and other factors, we show that the social networks are useful for the job hunter to find jobs, but they have little contributions to higher wages. Further studies indicate that the social networks with “strong ties” for job seekers are not conducive to enhancing the wages of workers. However, the social networks with “weak ties” have a lower job hunting probability and heterogeneity benefit for improving wage levels. Furthermore, we employ family dynamic survey data from China in 2006 to make robustness check, and results are still robust.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
In CHIP2002 survey data, work experience was described as follows, “by the end of 2002, the time since you started to work or employment time (years)”.
In the CHIP2002 questionnaire, the types of job hunting approach are as follows: government arrangement (including normal work transfer), introduced by employment department, public examinations, introduced by other individuals, self-employed entrepreneur, privately owned enterprise, hunting job by oneself, newspaper recruitment, free from work.
It should be noted that in job hunting through social networks, job hunters may job hunt through “strong ties”, and also can find jobs through “weak ties”. Individual characteristics (gender, marriage and social activities, etc.) also have an important impact on job searching approaches in social networks. Whether job hunters find jobs through “strong ties” may be the result of self-selection. Thus, both the dependent variable (individuals job hunt through social networks) and the independent variable (finding jobs through “strong ties” or not) are limited variables, both of them have the problem of self-selection. Therefore, the estimates in the Heckman second-stage requires the use of the Treatment Effects Model for regression. In summary, the econometric model needs to use the Treatment Effects Model and Heckman sample selection model for joint estimation. Of course, the Treatment Effects Model is not common in empirical papers. On the issue of selecting “strong ties” in finding jobs as the independent variable, most domestic and foreign literature did not take it into account, so this paper puts the established method of Treatment Effects Model and its regression results in Appendix 1 as a robustness test.
The 12 provinces (municipalities and autonomous regions) in the survey are Anhui, Beijing, Gansu, Guangdong, Henan, Hubei province, Jiangsu, Liaoning, Shanxi, Sichuan, Yunnan and Chongqing.
Men’s retirement age is generally 60 and women’s retirement age is generally 55.
In the survey data, there were 16 industries. Specifically: agriculture, forestry, animal husbandry and fishery; mining industry; manufacturing; power, gas and water production and supply industry; construction; geological prospecting and water conservancy; transportation, warehousing and postal and telecommunication services; wholesale, retail and catering trade; The financial and insurance; the real estate industry; social services; health, sports and social welfare; education, arts and culture, radio, film and television industry; scientific research and comprehensive technology services; state organisations, party and government organizations and social groups, and; other industries.
Introducing virtual variables such as industry variables, province variables, enterprise ownership variables and occupational category variables have the similar basic principles with the panel data fixed effects model. The panel data model fixed effects either through poor conversion from way realization, also can realize by introducing virtual variable, therefore, the panel data model of the fixed effects if flexion–extension (FE) radiographs can also be called least squares virtual variable model (does not make sense). Panel data fixed effects model can be achieved by deviation conversion or by introducing a virtual variable (Cameron and Trivedi 2005). So, panel data fixed effects can also be called the least squares virtual variable model. The least square virtual variable model provides a guideline for establishing a fixed effects model. Using stata software commands, this paper establishes the fixed effects models of industry, province, the ownership of enterprises and occupational category by introducing virtual variables. Thus we can control the impacts of resource endowments and policy differences among provinces, different industry features, different enterprise ownership types, and occupational category differences upon the estimated results. Taking province fixed effects as an example, the survey contains data for about 12 provinces in all. So, in order to decrease the impacts of the differences among provinces upon the estimated results, 11 virtual variables are introduced into the estimating equations to estimate. The method to set the fixed effects of industry and enterprise ownership occupational category is all the same and so are the effects.
May 2008, National Bureau of Statistics carried out the first resident time-use survey in 10 provinces of China (Beijing, Hebei, Heilongjiang, Zhejiang, Anhui, Henan, Guangdong, Sichuan, Yunnan and Gansu). The survey objects are the people aged from 15 to 74 and the survey households are the entire urban and town state sample and some of the rural state sample in the current income and expenditure survey points of 10 provinces. In total 16,661 households and 37,142 people were investigated, including 19,621 urban people, 17 521 rural people, 18,215 males and 18,927 females. By recording daily activities of the objects in detail, the Chinese resident time-use survey reflects the living patterns and behaviors of all kinds of people and further reflects the different responsibilities and roles of people in daily life. Specifically, the Chinese resident time-use survey measures and shows female unpaid labor.
For example, something heard from one friend or relative may have already been heard from another friend who they have talked with beforehand. There is no shortage of such examples in daily life.
CHIP2002 survey on the employment unit: “your employment unit is: 1, enterprise; 2, party and government organs; 3, institutions; 4, the others.”
References
Allen WD (2000) Social networks and self-employment. J Soc Econ 29(5):487–501
Antoninis M (2006) The wage effects from the use of personal contacts as hiring channels. J Econ Behav Organ 59(1):133–146
Appleton PR, Hall HK, Hill AP (2009) Relations between multidimensional perfectionism and burnout in juniorelitemale athletes. Psychol Sport Exerc 10(4):457–465
Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, Chichester
Banfield E (2017) Political influence. Routledge
Blau FD, Devaro J (2007) New evidence on gender differences in promotion rates: an empirical analysis of a sample of new hires. Ind Relat 46(3):511–550
Bridges WP, Villemez WJ (1986) Informal hiring and income in the labor market. Am Sociol Rev 51:574–582
Breslin J, Decker S (2007) The future of social networks on the internet: the need for semantics. IEEE Internet Comput. https://doi.org/10.1109/MIC.2007.138
Cameron AC, Trivedi PK (2005) Microeconometrics. Cambridge University Press, Cambridge
Chen Y, Sylvie D, Martin F (2005) Earnings differentials and ownership structure in Chinese enterprises. Econ Dev Cult Change 53(4):933–958
Cheung C, Yong G (2006) Job referral in China: the advantages of strong ties. Hum Relat 59:847–872
Chin KH (2016) Organized Crime, Business, and Politics in Taiwan: Organized Crime, Business, and Politics in Taiwan. Routledge
De Graaf ND, Flap HD (1988) With a little help from my friends: social resources as an explanation of occupational status and income in West Germany and the United States. Soc Forces 67:452–472
Flap H, Boxmann E (2001) Getting started: the influence of social capital on the start of the occupational career. Aldine de Gruyter, New York
Gilbert E, Karahalios K (2009) Predicting tie strength with social media[C]//Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp. 211–220
Ginther DK, Hayes KJ (2003) Gender differences in salary and promotion or faculty in the humanities 1977–1995. J Human Resour 38(1):34–73
Grant AM (2007) Relational job design and the motivation to make a prosocial difference. Acad Manag Rev 32(2):393–417
Granovetter MS (1973) The strength of weak ties. Am J Sociol 78:1360–1380
Granovetter MS (1974) Getting a job. Harvard University Press, Cambridge
Harvey P (2008) The presence and absence of speech in the communication of gender. Intercultural Discourse and Communication: The Essential Readings 8:164
Heckman JJ (1979) Sample selection bias as a specification error. Econometrica 47:153–161
Jack SL (2005) The role, use and activation of strong and weak network ties: A qualitative analysis. J Manage Stud 42(6):1233–1259
Laura H, Sandra P (2010) Market access and individual wages: evidence from China. Rev Econ Stat 92(1):145–159
Li H (2003) Economic transition and returns to education in China. Econ Educ Rev 22(3):17–328
Lin N (1999) Social networks and status attainment. Ann Rev Sociol 25:467–487
Lin N, Ensel WM, Vaughn JC (1981a) Social resources and strength of ties: structural factors in occupational status attainment. Am Sociol Rev 46:393–405
Lin N, Vaughn JC, Ensel WM (1981b) Social resources and occupational status attainment. Soc Forces 59:1163–1181
Loury LD (2006) Some contacts are more equal than others: Informal networks, job tenure, and wages. J Labor Econ 24(2):299–318
Mar D (2005) Individual characteristics vs. city structural characteristics: explaining self-employment differences among Chinese, Japanese, and Filipinos in the United States. J Soc Econ 34(3):341–359
Marsden PV, Groman EH (2001) Social networks, job changes, and recruitment. Kluwer Academic/Plenum Publishers, New York
Marsden PV, Hurlbert JS (1988) Social resources and mobility outcomes: a replication and extension. Soc Forces 66:1038–1059
Mincer J (1974) Schooling, experience and earnings. Columbia University Press, New York
Munch AA, McPherson JM, Smith-Lovin L (1997) Gender, children, and social contact: the effects of childrearing for men and women. Am Sociol Rev 62(4):509–520
Ng TWH, Eby LT, Sorensen KL et al (2005) Predictors of objective and subjective career success: A meta analysis. Pers Psychol 58(2):367–408
Oaxaca R (1973) Male–female wage differentials in urban labor markets. Int Econ Rev 14:693–709
Obukhova E, Lan G (2013) Do job seekers benefit from contacts? A direct test with contemporaneous searches. J Manage Sci 59(10):2204–2216
Pellizzari M (2010) Do friends and relatives really help in getting a good job?. ILR Review 63(3):494–510
Ransom M, Oaxaca RL (2005) Intrafirm mobility and sex differences in pay. Ind Labor Relat Rev 58(2):219–237
Rosenbaum JE, DeLuca S, Miller SR, Roy K (1999) Pathways not work: short and long term effects of personal and institutional ties. Sociol Educ 72:179–196
Ryan TP (2011) Statistical methods for quality improvement. Wiley
Skeels MM, Grudin J (2009) When social networks cross boundaries: a case study of workplace use of facebook and linkedin. In: Proceedings of the ACM 2009 international conference on Supporting group work. ACM, pp. 95–104
Tassier T (2006) Labor market implications of weak ties. South Econ J 72:704–719
Tsutsui Y, Benzion U, Shahrabani S (2012) Economic and behavioral factors in an individual’s decision to take the influenza vaccination in Japan. J Soc Econ 41(5):594–602
Wegener B (1991) Job mobility and social ties: social resources, prior job, and status attainment. Am Sociol Rev 56:60–71
Wanberg CR, Zhu J, Kanfer R et al (2012) After the pink slip: Applying dynamic motivation frameworks to the job search experience. Acad Manag J 55(2):261–284
Yu R (2008) Social networks and labor market performance - the case of Taiwan. The Sociological Association of Taiwan in 2008
Acknowledgements
I want to take this chance to thank my co-authors: Bin Liu and Lei Li. Special thanks to them for their help in fixing the English.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Shi, Y., Zhu, X., Liu, B. et al. Social networks and the wages of job seekers: the case of China. Soc. Netw. Anal. Min. 8, 51 (2018). https://doi.org/10.1007/s13278-018-0529-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-018-0529-7