1. What Determines Financial Inclusion in China? An empirical investigation on households Danying Li Supervised by Prof. Alessandra Guariglia and Mr. Nicholas Horsewood Department of Economics University of Birmingham Friday, 26 June 2015

2. Outline Introduction Literature review Data and summary statistics Methodology Regression results Future research

3. Introduction Figure 1. The decreasing economic growth rate GDP growth rate of China is decreasing The gap is decreasing

4. Introduction The private consumption measured as percentage of GDP in China is low (38% in 2011) The structure of China economy is unbalanced

5. Introduction Chinese government is endeavouring to address: How to sustain the economic growth (in short run) How to rebalance the structure of the economy (in long run) Developing the consumer financial market is a lasting solution for both problems mentioned above (Orlik and Chen, 2015) Immediate boost household consumption Easier access to credit market helps change consumers’ behaviour How to measure the efficiency of financial market Financial inclusion: the use of financial services Financial exclusion: unable to use financial services the two sides of one coin

6. Research Question and Contribution Research question Since financial inclusion contributes to economic growth (Beck et al.,2007), I want to investigate what determines financial inclusion in China? Contribution 1. Little evidence of financial inclusion in China 2. The China Household Finance Survey (CHFS) Data 3. Taking into account the regional effects 4. The role of informal credit

7. Literature Review Studies on U.S: In 1980s, Mishkin and Hall (1982), Flavin (1984), Zeldes (1989) find that basic Life-cycle Hypothesis is highly inconsistent with real data Financial exclusion is a leading explanation Sample splitting criteria based on households’ assets, e.g.: Total wealth> 2 months average income  financial inclusion Total wealth≤ 2 months average income  financial exclusion The misclassification issue

8. Literature Review Jappelli (1990), Garcia et al. (1997), Jappelli et al.(2002) test the determinants of financial exclusion in U.S. Social and demographic characteristics, e.g. age, income, education etc. do have impacts on the probability of being excluded from financial markets. The criteria: denied loans/credit, financial exclusion otherwise, financial inclusion The criterion is observable and eliminate misclassification

9. Literature Review Studies on China Zhang and Wan( 2004), Ma and Tian (2006), Li and Zhu (2010) study financial exclusion combined with uncertainty. By applying aggregate data, they do not consider the effects of households’ heterogeneities Fungacova and Weill (2015) studies financial inclusion and make comparison with other BRICS countries No more previous representative studies on this topic because there is only limited micro level data available in China. This research will fill this gap

10. Data General introduction The 2011 China household Finance Survey (CHFS) 8,438 household level observations from 25 provinces (7,797 observations after deleting extreme values) Comprehensive information on social and demographic characteristics, and households’ financial situations Two proxies for financial inclusion The ownership of credit cards The availability of bank loans Based on the total sample, 14.2% of Chinese households currently have credit cards, 20.6% of them got bank loans.

11. Summary statistics: Age Age of household’s head No. of observationsCredit cardsBank loans <25236 25.5% 18.6% 26-30408 33.8% 24.8% 31-35626 29.7% 32.1% 36-40911 22.3% 26.6% 41-451132 15.6% 26.3% 46-501119 12.2% 20.9% 51-55864 10.1% 20.8% 56-60976 7.0% 14.9% 61-65695 4.0% 13.2% 66-70477 3.2% 10.1% 71-75353 2.8% 6.8%

12. Summary statistics: Income Household’s income(RMB) No. of observations Credit cardsBank loans <10% (0-3315) 675 9.3%14.7% 10%-25% (3315-12000) 1166 3.8%17.7% 25%-50% (12000-28422.4) 1990 5.9%16.1% 50%-75% (28422.4-54200) 2011 11.9%19.0% 75%-90% (54200-100000) 1207 22.5%25.1% >90% (100000-440000) 746 49.7%39.7%

13. Summary statistics: education level Education level of household head No. of observations Credit cardsBank loans Less than primary school 562 2.0%13.9% Primary school1760 4.0%16.1% Middle school2659 7.7%17.6% High school1618 17.0%21.9% Undergraduate1111 51.5%39.7% Postgraduate and over 87 83.4%32.0%

14. Summary statistics: regional factors Regional factorsNo. of observations Credit cardsBank loans Households from urban areas 2981 20.1%20.5% Households from rural areas 4816 4.8%20.8% Households from low-developed provinces 3286 7.6%22.6% Households from high-developed provinces 4511 19.1%19.2%

15. Summary statistics: other resource of credit No. observationsCredit cardsBank loans Households with informal loans 26639.9%27.3% Households do not own informal loans 513416.4%17.2%

16. Methodology

17. Regression results VARIABLES credit cardsBank loans Credit cards and/or bank loans (1)(2)(3) age-0.00233***-0.00203***-0.00351*** (0.000350)(0.000449)(0.000484) income0.0529***0.0315***0.0686*** (0.00414)(0.00517)(0.00555) education0.0155***0.00886***0.0210*** (0.00107)(0.00139)(0.00148) Observations7,439 7,441 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

18. Regression results Regional factors credit cardsBank loans Credit cards and/or bank loans (1)(2)(3) LOWDEV-0.0623***0.0527***0.00320 (0.00753)(0.00931)(0.0102) RURAL-0.0400***0.0309***0.0186 (0.00940)(0.0112)(0.0124)

19. Regression results Financially related factors Credit cardsBank loans Credit cards and/or bank loans (1)(2)(3) Risk attitude -0.0208***-0.0154***-0.0318*** (0.00286)(0.00376)(0.00406) INFLOAN -0.0295***0.0863***0.0585*** (0.00788)(0.00938)(0.0106) FLOAN 0.0664*** (0.00773) CREDIT 0.132*** (0.0129) ACCOUNT 0.0355***-0.0165*0.00378 (0.00783)(0.00973)(0.0106) SAVING -0.0902***-0.0527***-0.127*** (0.0100)(0.0131)(0.0141)

20. Regression results Other social characteristics credit cardsBank loans Credit cards and/or bank loans (1)(2)(3) MALE-0.00400-0.0189*-0.0120 (0.00806)(0.0108)(0.0119) Family size0.003650.00638*0.00438 (0.00283)(0.00329)(0.00365) HOME0.007020.145***0.101*** (0.00963)(0.0149)(0.0150) MARITAL0.000477-0.00494-0.0204 (0.0119)(0.0159)(0.0172) JOB0.006770.0582***0.0701*** (0.00934)(0.0125)(0.0134)

21. Empirical results Based on the sample means, only14.4%, 20.8%, 29.5% respectively of households are included in the financial market income, education level have positive effects on the probability of being included in financial market while age has negative effect. Substitution effect between informal loans and credit cards, but complementary effect between informal loans and formal/bank loans Regional differences are significant. Other characteristics, namely risk attitude, ownership of other kinds of credit have effects to some extend.

22. Future research possibilities More work on effects of informal credit markets and on the link between formal and informal credit markets. Testing possible policy implications Other financial development indicators Other year(s) dataset Other criteria for identifying financial inclusion Other explanatory variables

23. Thank you!