JACIII Vol.26 No.4 pp. 639-654
doi: 10.20965/jaciii.2022.p0639


The Effects of Social Network and Institutional Embeddedness on Household Consumption: Evidence from China Household Finance Survey

Chaoxun Ding* and Ruidan Zhang**,†

*School of Management, Henan University of Science and Technology
No.263 Kaiyuan Avenue, Luoyang, Henan 471023, China

**Development Planning Division, Henan University of Science and Technology
No.263 Kaiyuan Avenue, Luoyang, Henan 471023, China

Corresponding author

January 30, 2022
May 20, 2022
July 20, 2022
social network, institutional embeddedness, household consumption, CHFS

Consumer behavior is embedded in a certain social structure and social networks, and the scale and density of household social networks will be likely to affect consumption expenditure. To explore the impact of social networks and institutional embeddedness on household consumption, this study constructs a model of consumption influencing factors, and devises an empirical study using the data of China Household Finance Survey (CHFS). The results show some innovation. (1) The impact of household social networks on total household consumption is significant. A 1% increase in social networks spending boosts household consumption spending by 0.364%. (2) The institutional embeddedness will affect household consumption. Every 1% increase of social security account balance (the proxy variable of institutional embeddedness) can boost household consumption by 0.196%. This proves that the social insurance institution can enhance consumer confidence and promote current consumption growth. (3) The results of the robustness test confirmed that even after replacing the dependent variable with “the proportion of developmental consumption in total household consumption,” the influence of social networks and institutional embeddedness on consumption is still significant. Using the variable “communication expenses” instead of “gift income and expenditure” as the proxy variable of social networks, the estimation result is still robust. (4) Social networks have a significant influence on all types of household consumption except medical care consumption, but the degree of influence is different. Further discussion revealed that the estimation results are different for different regions in China, but the coefficients of core independent variables are not significantly different. This conclusion is different from people’s intuition, which holds that people in regions with low economic development rely more on social communication and spend more on social communication to maintain a certain social status. The conclusion of this paper is of great significance for formulating policies and institutions affecting residents’ consumption.

Cite this article as:
C. Ding and R. Zhang, “The Effects of Social Network and Institutional Embeddedness on Household Consumption: Evidence from China Household Finance Survey,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.4, pp. 639-654, 2022.
Data files:
  1. [1] M. Granovetter, “Economic institutions as social constructions: a framework for analysis,” Acta Sociologica, Vol.35, No.1, pp. 3-11, 1992.
  2. [2] V. Nee and P. Ingram, “Embeddedness and Beyond: Institutions, Exchange and Social Structure,” M. C. Brinton and V. Nee (Eds.), “The New Institutionalism in Sociology,” Russell Sage Foundation, pp. 19-45, 1998.
  3. [3] M. Granovetter, “Economic Action and Social Structure: The Problem of Embeddedness,” American J. of Sociology, Vol.91, No.3, 1985.
  4. [4] M. M. Ali and D. S. Dwyer, “Social network effects in alcohol consumption among adolescents,” Addictive Behaviors, Vol.35, No.4, pp. 337-342, 2010.
  5. [5] D. Acier, L. Nadeau, and M. Landry, “Influence of the social network on consumption in drug addicts exhibiting psychiatric comorbidity,” Encephale, Vol.37, No.4, pp. 249-256, 2011 (in French).
  6. [6] F. Mertens, J. Saint-Charles, and D. Mergler, “Social communication network analysis of the role of participatory research in the adoption of new fish consumption behaviors,” Social Science and Medicine, Vol.75, No.4, pp. 643-650, 2012.
  7. [7] R. S. Burt, M. Kilduff, and S. Tasselli, “Social network analysis: foundations and frontiers on advantage,” Annual Review of Psychology, Vol.64, No.1, pp. 527-547, 2013.
  8. [8] K. S. DeMartini, M. A. Prince, and K. B. Carey, “Identification of trajectories of social network composition change and the relationship to alcohol consumption and norms,” Drug and Alcohol Dependence, Vol.132, No.1-2, pp. 309-315, 2013.
  9. [9] Y. Kim, S. Yun, and J. Lee, “Can Companies Induce Sustainable Consumption? The Impact of Knowledge and Social Embeddedness on Airline Sustainability Programs in the U.S.,” Sustainability, Vol.6, No.6, pp. 3338-3356, 2014.
  10. [10] O. Mowbray, “The moderating role of social network in the relationship between alcohol consumption and treatment utilization for alcohol-related problems,” J. of Substance Abuse Treatment, Vol.46, No.5, pp. 597-601, 2014.
  11. [11] C. Sismeiro and A. Mahmood, “Competitive vs. Complementary Effects in Online Social Network and News Consumption: A Natural Experiment,” Management Science, Vol.64, No.11, pp. 5014-5037, 2018.
  12. [12] C. Duh-Leong and S. Braganza, “Social Network and Sugar-Sweetened Beverage Consumption in a Pediatric Urban Academic Practice,” Behavioral Medicine, Vol.46, No.1, pp. 1-8, 2018.
  13. [13] E. Quiroga, A. Pinto-Carral, I. García, A. J. Molina, T. Fernández-Villa, and V. Martín, “The Influence of Adolescents’ Social Networks on Alcohol Consumption: A Descriptive Study of Spanish Adolescents Using Social Network Analysis,” Int. J. of Environmental Research and Public Health, Vol.15, No.9, Article No.1795, 2018.
  14. [14] J. A. Muncy and R. Iyer, “The impact of the implicit theories of social optimism and social pessimism on macro attitudes towards consumption,” Psycology and Marketing, Vol.37, No.2, pp. 216-231, 2019.
  15. [15] D. Liu, X. Zeng, B. Su, W. Wang, K. Sun, and U. H. Sadia, “A social network analysis regarding electricity consumption and economic growth in China,” J. of Cleaner Production, Vol.274, Article No.122973, 2020.
  16. [16] A. Scalco, T. Craig, S. Whybrow, G. Horgan, and J. Macdiarmid, “Modelling the effects of social network in interventions aimed at reducing meat consumption using a social simulation,” Proc. of the Nutrition Society, Vol.79, No.OCE3, Article No.E783, 2020.
  17. [17] Y. Y. Hwang, G. Y. Jo, and M. J. Oh, “The Persuasive Effect of Competence and Warmth on Clothing Sustainable Consumption: The Moderating Role of Consumer Knowledge and Social Embeddedness,” Sustainability, Vol.12, No.7, Article No.2661, 2020.
  18. [18] M. Luo, R. Fan, and Y. Zhang, “Spatial Correlation of Electricity Consumption in China Based on Social Network Approach,” IEEE Access, Vol.8, pp. 201271-201285, 2020.
  19. [19] B. Meza, C. E. Pollack, D. M. Levine, C. A. Latkin, J. M. Clark, and K. A. Gudzune, “You are what you think your social network eats: Public housing, social networks, and fast-food consumption,” J. of Health Care for the Poor and Underserved, Vol.31, No.4, pp. 1712-1726, 2020.
  20. [20] R. Bapna and A. Umyarov, “Do your online friends make you pay? A randomized field experiment on peer influence in online social networks,” Management Science, Vol.61, No.8, pp. 1902-1920, 2015.
  21. [21] J. Yin and S. Shi, “Social interaction and the formation of residents’ low-carbon consumption behaviors: An embeddedness perspective,” Resources, Conservation and Recycling, Vol.164, Article No.105116, 2021.
  22. [22] I. Schubert, J. I. M. d. Groot, and A. C. Newton, “Challenging the status quo through social influence: changes in sustainable consumption through the influence of social network,” Sustainability, Vol.13, No.10, Article No.5513, 2021.
  23. [23] J. G. Pickard, S.-L. W. Woodson, and S. D. Johnson, “The relationship of public and private religiosity to African American women caregivers’ use of alcohol for coping with caregiving burden,” Aging and Mental Health, Vol.25, No.3, pp. 551-558, 2021.
  24. [24] S. Zukin and P. J. DiMaggio, “Introduction to Structures of Capital,” S. Zukin and P. J. DiMaggio (Eds.), “Structures of Capital: The Social Organization of the Economy,” Cambridge University Press, pp. 1-56, 1990.
  25. [25] U. Andersson, M. Forsgren, and U. Holm, “The Strategic Impact of External Networks: Subsidiary Performance and Competence Development in the Multinational Corporation,” Strategic Management J., Vol.23, No.11, pp. 979-996, 2002.
  26. [26] J. Hagedoorn, “Understanding the cross-level embeddedness of interfirm partnership formation,” The Academy of Management Review, Vol.31, No.3, pp. 670-680, 2006.
  27. [27] N. Wang, “Institutional embeddedness in Consumer Behaviors: A Research Program for Sociology of Consumption,” J. of Sun Yatsen University (Social Science Edition), No.4, pp. 140-145, 2008 (in Chinese).
  28. [28] Z. Yi, “On Consumers’ Behavior in the Network Society,” Social Sciences of Beijing, No.1, pp. 79-88, 2015 (in Chinese).
  29. [29] D. Wu and M. Zhang, “Virtual Corporation: An Invisible “Pushing Hand” Behind Users’ Information Consumption Behavior On WeChat – a Perspective for Embeddedness Theory,” J. of Zhejiang University of Media and Communications, No.2, pp. 2-8+14+151, 2017 (in Chinese).
  30. [30] M. Jin and M. Pan, “The influence of relationship embedding on consumers’ willingness to buy online from the perspective of social network,” J. of Commercial Economics, No.12, pp. 52-56, 2018 (in Chinese).
  31. [31] W. Dai and Y. Liu, “Local vs. Non-Local Institutional Embeddedness, Corporate Entrepreneurship, and Firm Performance in a Transitional Economy,” Asian J. of Technology Innovation, Vol.23, No.2, pp. 255-270, 2015.
  32. [32] E. Totin, C. Roncoli, P. S. Traoré, J. Somda, and R. Zougmoré, “How does institutional embeddedness shape innovation platforms? A diagnostic study of three districts in the Upper West Region of Ghana,” Wageningen J. of Life Sciences, Vol.84, pp. 27-40, 2018.
  33. [33] L. Song and P. J. Pettis, “Does whom you know in the status hierarchy prevent or trigger health limitation? Institutional embeddedness of social capital and social cost theories in three societies,” Social Science and Medicine, Vol.257, Article No.111959, 2020.
  34. [34] R. Long, W. Lang, and X. Li, “Does Institutional Embeddedness Promote Regional Enterprises’ Migration? An Empirical Analysis Based on the “Double Transfer” Strategy in Guangdong, China,” Sustainability, Vol.12, No.7, Article No.2908, 2020.
  35. [35] S. Hu and X. Wang, “The Origin of Proactive Environmental Corporate Social Responsibility (ECSR) of Large Firms: Institutional Embeddedness-Driven, Family Involvement-Promoted, or Resource-Dependent?,” Sustainability, Vol.13, No.3, Article No.1197, 2021.
  36. [36] M. M. Coşgel, “Consumption Institutions,” Review of Social Economy, Vol.55, No.2, pp. 153-171, 1997.
  37. [37] R. E. Dwyer, “Making a Habit of It: Positional Consumption, Conventional Action and the Standard of Living,” J. of Consumer Culture, Vol.9, No.3, pp. 328-347, 2009.
  38. [38] J. S. Woersdorfer, “When Do Social Norms Replace Status-Seeking Consumption? An Application to the Consumption of Cleanliness,” Metroeconomica, Vol.61, No.1, pp. 35-67, 2010.
  39. [39] J. Zhao and M. Lu, “The Contribution of Guanxi to Income Inequality in Rural China and a Cross-Regional Comparison: A Regression-Based Decomposition,” China Economic Quarterly, Vol.9, No.1, pp. 363-390, 2010.
  40. [40] X. Yi, B. Zhang, U. Yang, and B. Yang, “The Family Social Network and the Rural Household Saving Behavior: A Case Study Based on China’s Villages,” Management World, No.5, pp. 43-51, 2012 (in Chinese).
  41. [41] Y. Hu and K. Wei, “The impact study about social networks to the consumption expenditure in urban and rural residents: based on the analysis of survey data of China’s western socio-economic changes,” Urban Problems, No.5, pp. 16-20, 2013 (in Chinese).
  42. [42] X. Zheng, C. Wei, P. Qin, J. Guo, Y. Yu, F. Song, and Z. Chen, “Characteristics of Residential Energy Consumption in China: Findings from a Household Survey,” Energy Policy, Vol.75, pp. 126-135, 2014 (in Chinese).
  43. [43] R. Yang, J. He, S. Li, W. Su, Y. Ren, and X. Li, “Different effects of main influence factors on household energy consumption in three typical rural villages of China,” Energy Reports, Vol.4, pp. 603-618, 2018 (in Chinese).
  44. [44] A. J. Moran, N. Khandpur, M. Polacsek, and E. B. Rimm, “What factors influence ultra-processed food purchases and consumption in households with children? A comparison between participants and non-participants in the Supplemental Nutrition Assistance Program (SNAP),” Appetite, Vol.134, pp. 1-8, 2018.
  45. [45] Y. Nan, Q. Zhou, and L. Huang, “Social Network, Informal Finance, and Household Consumption Behavior: Empirical Evidence Based on Chinese Household Tracking Survey Data,” Rural Economy, Vol.6, pp. 80-86, 2018 (in Chinese).
  46. [46] Y. Yang, J. Jiang, Z. Yin, and X. Song, “Social Network and Household Consumption in Beijing-Tianjin-Hebei Areas: An Empirical Study Based on CHFS Data,” Research on Economics and Management, Vol.39, No.10, pp. 81-92, 2018 (in Chinese).
  47. [47] T. Zhang, X. Meng, and S. Wang, “The impact of social network on household consumption and real estate investment,” Research on Financial and Economic Issues, No.6, pp. 122-130, 2019 (in Chinese).
  48. [48] H. Cheng, Y. P. Zhi, Z. W. Deng, Q. Gao, and R. Jiang, “Crowding-Out or Crowding-In: Government Health Investment and Household Consumption,” Frontiers in Public Health, Vol.9, Article No.706937, 2021.
  49. [49] H. Nakamura, F. Amimo, S. Yi, S. Tuot, T. Yoshida, M. Tobe, M. M. Rahman, D. Yoneoka, A. Ishizuka, and S. Nomura, “Developing and validating regression models for predicting household consumption to introduce an equitable and sustainable health insurance system in Cambodia,” The Int. J. of Health Planning and Management, Vol.36, No.6, pp. 2094-2105, 2021.
  50. [50] J. X. Wan, C. T. Nie, and F. Zhang, “Does broadband infrastructure really affect consumption of rural households? A quasi-natural experiment evidence from China,” China Agricultural Economic Review, Vol.13, No.4, pp. 832-850, 2021.
  51. [51] L. Li, H. Ming, W. Fu, Q. Shi, and S. Yu, “Exploring household natural gas consumption patterns and their influencing factors: An integrated clustering and econometric method,” Energy, Vol.224, Article No.120194, 2021.
  52. [52] A. T. John, S. Makkar, S. Swaminathan, S. Minochaa, P. Webb, A. V. Kurpad, and T. Thomas, “Factors influencing household pulse consumption in India: A multilevel model analysis,” Global Food Security, Vol.29, Article No.100534, 2021.
  53. [53] J. Guo, M. Liao, B. He, J. Liu, X. Hu, D. Yan, and J. Wang, “Impact of the COVID-19 pandemic on household disinfectant consumption behaviour and related environmental concerns: A questionnaire-based survey in China,” J. of Environmental Chemical Engineering, Vol.9, No.5, Article No.106168, 2021.
  54. [54] L. Sabokkhiz, F. G. Lisaniler, and I. D. Nwaka, “Minimum wage and household consumption in Canada: evidence from high and low wage provinces,” Sustainability, Vol.13, No.12, Article No.6540, 2021.
  55. [55] M. D. Schiff and D. Mendez, “A decade of drinking: temporal trends in apparent household beer intake and standard drink consumption in the United States,” Substance Use and Misuse, Vol.56, pp. 1363-1373, 2021.
  56. [56] X. Zhao and X. Wang, “The Effects of the Increase in Food Prices on Urban Households’ Consumption and Welfare: Based on EASI Model,” J. of Finance and Economics, Vol.42, No.3, pp. 51-68, 2016 (in Chinese).
  57. [57] Y. Pei and W. Xu, “Chinese Household Property Wealth and Household Consumption: An Empirical Research Based on CFPS Database,” J. of Audit and Economics, Vol.32, No.4, pp. 93-104, 2017.
  58. [58] J. Wang and Y. Zhan, “Number of Children and Family Consumption Behavior: Impact and Mechanism,” Finance and Trade Research, No.1, pp. 1-13, 2021 (in Chinese).
  59. [59] B. Hang, “Gift Spending and Urban Household Consumption – an Empirical Study Based on Status Seeking,” Statistical Research, Vol.32, No.4, pp. 68-76, 2015 (in Chinese).
  60. [60] T. Li and M. Zhu, “Formal System, Informal System and Rural Households’ Consumption Spending – a Spatial Econometric Analysis Based on the Insurance and Social Network,” Insurance Studies, No.8, pp. 3-18, 2017 (in Chinese).
  61. [61] J. H. Stock and M. Yogo, “Testing for Weak Instruments in Linear IV Regression,” National Bureau of Economic Reseach Technical Working Papers, Article No.284, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Aug. 05, 2022