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Social networks and informal financial inclusion in China

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Abstract

Using the 2011 China Household Finance Survey (CHFS) database, we explore the heterogeneous impacts of social networks on informal financial inclusion for Chinese urban and rural households. We find that social networks significantly increase the probability of households’ participation in the informal financial market, augment the size of informal financial transaction, and raise the ratio of informal lending over the total household assets. We also identify the mechanisms through which the social networks affect households’ participation in the informal financial market. By reducing information cost, perceived risk and precautionary saving, social networks play a larger role for urban households than rural households. Notably, the effects of social networks on informal finance are strengthened with the development of formal financial market.

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Notes

  1. Social networks in the Chinese context quite often termed as social ties or guanxi serve as powerful instruments and operational code for how best to get things done, for example, to acquire and allocate scarce economic resources.

  2. For example, rational agents tend to trade within their social networks when the market transaction cost or the contract enforcement cost is higher. Similarly, when people have no or limited access to the formal financial market, social network plays a role in allocating finance resources.

  3. Financial inclusion means that “individuals and businesses have access to useful and affordable financial products and services that meet their needs—transactions, payments, savings, credit and insurance–delivered in a responsible and sustainable way.” The definition comes from http://www.worldbank.org/en/topic/financialinclusion/overview#1.

  4. A detailed description of the gift expenses and total daily expenditures is given in the data section and the summary statistics are provided in Table 1.

  5. See, for example, Glaeser, Laibson, and Sacerdote (2002), Guiso, Sapienza, and Zingales (2004), and Tabellini (2010), among others.

  6. The China Household Finance Survey (CHFS) database is provided by the Survey and Research Center of China Household Finance in Southwestern University of Finance and Economics. For more details about the dataset, please see Gan et al. (2014).

  7. In CHFS, there is a question regarding “whether a household has lent money to others who are not living together with the household head.” If the answer is yes, we use 1 to denote that there is informal lending; if the answer is no, we use 0 to denote that there is no informal lending. There are 1016 households who answer yes.

  8. The household’s current assets include banking deposits, cash, informal lending, stocks, funding, bonds, financial products, foreign exchanges and financial derivatives.

  9. In CHFS, there are questions regarding “whether a household has borrowed from non-banking channels for business startups, house purchases, vehicle purchases, stocks and funding purchases, education expenses and other expenses on medical services, marriage ceremonies and funerals.” If the answer is yes, we use 1 to denote that there is informal borrowing and use 0 to denote that there is no informal borrowing. There are in total 3026 households who answer yes.

  10. We find that financial lendings between parents and children are very limited. The flow of funding could be explained as gifts rather than loans.

  11. We thank one anonymous referee to point out that each guanxi or renqing expense (gift) is associated with a household’s richness in his or her social loop regardless of the size of the social network. For example, a wealthy family may involve a larger amount of gift expenses than a poor family though they may have the same degree of social network. Similarly, a poor household can have a larger network with smaller renqing expenses. We are inspired by the referee to use the ratio rather than the level form of gift expenses to measure the strength of social network.

  12. A household’s total daily expenses include money spent on food, clothes, entertainment, books, utilities, public and private transportation, gifts and the like, which excludes the occasional lump-sum spending on housing construction, renovation and durable goods.

  13. The CHFS defines non-family members as mothers-in-law, sons-in-law, daughters-in-law, grandchildren, grandchildren-in-laws, nephews/nieces and other friends or relatives who are not living close to each other.

  14. We do not take the number of registered family members as an accurate measure of household size because labor relocation and migration leads to biased estimates.

  15. There is a survey question in CHFS regarding a householder’s perceived risk, asking “which type of project are you willing to invest if you have an asset.” The choices given are respectively: (1) projects with the highest risks and the highest returns; (2) projects with above-average and below-the-highest risks and the corresponding returns; (3) projects with average risks and average returns; (4) projects with below-average risks and the corresponding returns; and (5) projects without risks. We use discrete variables ranging from 1 to 5 to denote a householder’s perceived risk.

  16. The F statistics of Cragg-Donald tests for the whole sample, the rural subsample and the urban subsample are respectively 190.83, 211.58 and 26.91, which are larger than the critical value of 16.38 at the 10% significance level.

  17. The estimation results are not reported due to space constraints but available upon request.

  18. We thank one anonymous referee to make the constructive comments on how to construct the information variable. The referee points out that the information is not any other kind of information, but the information about the lenders or borrowers within the network.

  19. The access to varied public information regarding macro social-economic conditions is also vital. For example, timely access to such information as economic growth, official rate of loan, stock market fluctuations, business opportunities from varied public channels helps borrowers (lenders) understand the opportunity cost of informal finance and impact on their incentives in informal lending and borrowing. We create an alternative discrete variable to measure the number of information sources owned by a household to proxy the information diversity. A higher value represents more information channels available to a household. We first estimate the relationship between social network and information diversity with Poisson regression, and examine that how information diversity affects households’ informal lending and borrowing. The results, which are available but not reported due to space limits, confirm that social networks facilitate households’ participation in informal finance through access to diversified information.

  20. Alternatively, we add the interactive term of social network and information in the regression model. The results confirm our findings and are available upon request.

  21. Alternatively, we add the interactive term of social network and precautionary saving incentive in the regression model. The findings are similar. Results are available upon request.

  22. The 2011 China Household Financial Survey discloses which province a households is living in while does not provide more disaggregate geographical information at the county or district level. We make the attempt to proxy the development of formal financial market in each province using the NERI Index (Fan & Wang, 2011) as suggested by the referee and find that results are similar. As expected, the market index which measures the development of market intermediary and law and regulations reduces probability to participate in informal finance. And as the market intermediary and law and regulations are more developed, the positive role of social network played in informal finance is strengthened. Results are available upon request. As compared to the provincial-level market index, using the time measure accounts for heterogeneity within households’ place of residence.

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Acknowledgements

We are grateful to the editor and two anonymous reviewers for their invaluable comments and suggestions. We also acknowledge the comments provided by participants in the 5th seminar on Asia and Pacific Economies, the two-day paper development workshop held in Xi’an Jiaotong University. Dr. Shijun Chai thanks the financial support from Henan Province Philosophy and Social Science General Project with grant number 2017BJJ054, Henan Province government decision-making research tendering project 2017B274, and the Nanhu Scholars Award Scheme Youth Project (XYSY18076) funded by Xinyang Normal University. Dr. Dezhu Ye acknowledges the financial support from Chinese Ministry of Education Liberal Arts and Social Sciences Foundation (Project No. 13YJA790139), and National Science Foundation of China (Project No. 71473102).

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  1. Business School, Xinyang Normal University, Xinyang, China

    Shijun Chai

  2. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yang Chen

  3. Asian Development Bank Institute, Tokyo, Japan

    Bihong Huang

  4. School of Economics, Jinan University, Guangzhou, China

    Dezhu Ye

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Chai, S., Chen, Y., Huang, B. et al. Social networks and informal financial inclusion in China. Asia Pac J Manag 36, 529–563 (2019). https://doi.org/10.1007/s10490-017-9557-5

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