- Email: [email protected]
Financial literacy and formal credit accessibility: Evidence from informal businesses in China
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Financial Literacy and Formal Credit Accessibility: Evidence from Informal Businesses in China Nana Xu PhD candidateVisiting Scholar , Jingye Shi Associate professor , Zhao Rong Professor , Yan Yuan Professor PII: DOI: Reference:
S1544-6123(19)30472-6 https://doi.org/10.1016/j.frl.2019.101327 FRL 101327
To appear in:
Finance Research Letters
Received date: Revised date: Accepted date:
15 May 2019 25 September 2019 11 October 2019
Please cite this article as: Nana Xu PhD candidateVisiting Scholar , Jingye Shi Associate professor , Zhao Rong Professor , Yan Yuan Professor , Financial Literacy and Formal Credit Accessibility: Evidence from Informal Businesses in China, Finance Research Letters (2019), doi: https://doi.org/10.1016/j.frl.2019.101327
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Highlights Investigate the role of financial literacy in businesses’ access to bank loans Use the 2015 China Household Finance Survey and apply the IV Probit method Find owners’ financial literacy positively related to informal businesses’ access It only exists among informal businesses for owners with rural hukou It is more pronounced in areas where formal-credit accessibility is lower
1
Financial Literacy and Formal Credit Accessibility: Evidence from Informal Businesses in China Nana Xu, PhD candidate at Research Institute of Economics and Management (RIEM), Southwestern University of Finance and Economics, China and Visiting Scholar at University of Illinois Urbana-Champaign, United States. Address: 555 Liutai Boulevard, Wenjiang District, Chengdu 611130, China Email: [email protected] Jingye Shi, Associate professor, Research Institute of Economics and Management (RIEM), Southwestern University of Finance and Economics, China Address: 55 Guanghuacun Street, Chengdu 610074, China. Email: [email protected] Zhao Rong, Corresponding author. Professor, Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, China Address: 182 Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, P.R. China Email: [email protected] Yan Yuan, Professor, Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, China Address: 182 Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, P.R. China Email: [email protected] Declarations of interest: none
Abstract: There is a sizable informal business sector in emerging markets. This paper investigates informal businesses’ accessibility to formal credit in China and their owners’ 2
financial literacy as a potential determinant. By examining informal businesses in urban areas from the 2015 China Household Finance Survey, we find that business owners’ financial literacy is positively associated with the likelihood of their businesses holding bank loans. This positive relationship only exists among businesses whose owners have rural hukou, and it is more pronounced in areas where formal-credit accessibility is lower.
JEL Classifications: G21, O17, O16, L26
Key words: Formal credit; Financial literacy; Informal businesses; China
1. Introduction One important characteristic of emerging markets is their sizable informal business sectors, which represent, on average, 36% of the GDP in 76 emerging market countries (Buehn and Schneider, 2009). However, informal businesses’ access to formal credit is generally impeded by their opaque financial information (Stiglitz and Weiss, 1981). The World Bank has proposed that financial education could enhance informal businesses’ financial inclusion, thereby boosting developing countries’ economies (World Bank, 2014). Though apparently reasonable, the validity of this suggestion relies on answering the following question: does the lack of financial knowledge among their owners result in informal businesses’ poor financial inclusion? Unfortunately, direct evidence is so far scarce. This paper attempts to fill the gap by investigating informal businesses’ accessibility to formal credit, an important measure of financial inclusion, in the largest emerging market, China.
3
In China, informal businesses are not required to employ financial experts. Consequently, financial decisions are made solely by business owners. It is thus reasonable to expect that the financial literacy of business owners substantially influence informal businesses’ financial status, particularly accessibility to formal credit. First, financial literacy allows business owners to better understand the importance of banking services. In other words, a lack of financial knowledge may discourage a business owner from pursuing bank credits (Cole et al., 2011) or may further result in a qualified borrower failing to apply for a loan (Kon and Storey, 2003). Second, the approval of a bank loan requires a complex process, which is time-consuming and financial knowledge demanding as well (Kon and Storey, 2003). We thus postulate that financial literacy of owners positively influences informal businesses’ accessibility to formal credit. We also conjecture that such an effect is less pronounced among formal businesses since they are required by law to employ financial specialists, making their owners’ financial literacy less important. Our data come from the 2015 China Household Finance Survey (CHFS), conducted by Southwestern University of Finance and Economics (SWUFE).1 We investigate small businesses owned by surveyed households in urban areas. These businesses are informal and mainly are geti gongshanghu (sole industrial and commercial proprietorship). The 2015 CHFS included several questions on financial literacy, which we use to measure a business owner’s financial literacy. Our estimation results show that a business is more likely to obtain bank loans when the owner’s financial literacy is higher. By contrast, the relationship is insignificant when formal businesses are examined. 1
See Gan et al. (2013) and https://chfs.swufe.edu.cn for details. 4
However, an owner’s financial literacy may be improved through participation in formal financial services, so as to result in the positive relationship. To address the potential endogeneity, we follow Lusardi and Mitchell (2014) and use the average level of financial literacy in the community as an instrument. Our IV estimation results confirm that the positive relationship is causal. More interestingly, this positive relationship only exists among businesses whose owners hold rural hukou. For these businesses, we further find that this positive relationship is more pronounced in regions with lower formal credit accessibility. This paper is closely related to the literature on the determinants of small businesses’ formal credit accessibility. Previous studies have focused on the supply side’s efforts of better serving small businesses. Through relationship lending or bank competition, it becomes easier for small businesses to obtain formal credit (Berger and Udell, 1995; Egli et al., 2006). Recently, a growing literature shows that entrepreneurs’ demographic characteristics (e.g., gender, race, ethnicity, and education) and behavioral attributes (e.g., optimism and overconfidence) are important drivers of small businesses’ credit accessibility (e.g., Asiedu et al., 2012). Our paper extends this body of literature by providing evidence that owners’ financial literacy is another important determinant for informal businesses. This paper is also related to the studies on how financial literacy influences individual financial inclusion. Financial literacy has been found to influence credit accessibility (Lyons et al., 2017), stock market participation (Kimball and Shumway, 2006), and insurance participation (Zhang et al., 2016). Among the few studies that associate financial literacy with firm performance, Drexler et al. (2014) find that entrepreneurs’ financial literacy enhances their
5
firms’ financial practices. Our paper enriches the literature by relating entrepreneurs’ financial literacy to their businesses’ financial inclusion. 2. Variables and Model Specification 2.1. Financial literacy The 2015 CHFS included three questions on financial literacy. These questions are related to interest rates, inflation, and risk diversification (see the Online Appendix for details). Following van Rooij et al. (2011), we generate two measures of financial literacy based on how these three questions were answered. The first measure is FL Score, which equals the number of questions correctly answered. One limitation of this measure is that answering incorrectly may reveal information on the extent of financial literacy differently from answering “I do not know.” We thus construct our second measure, FL Index to take into account such differences. Specifically, for each question, two binary variables are generated based on “whether answering the question correctly or not” or “whether understanding the question or not.” As a result, we have six variables. The index is generated through iterated principal component analysis on these six variables, which takes full advantage of all information in these three questions.
2.2. Model specification Our dependent variable is formal credit access, a binary variable equal to one if the business has bank loans and zero otherwise. Accordingly, we set up a probit model as follows. (
where
)
(
)
is the formal credit access dummy.
(1)
is one of these two measures of financial
literacy of the business owner. Controli represents a vector of characteristics at the business, 6
owner, and household level. The business characteristics include age of the business and annual sales (in log value). The owner characteristics include the business owner’s age, gender, education level, marriage status, risk attitude, and type of hukou. The household characteristics include number of family members, owning a local property or not, log of net wealth, and local family network. Additionally, we include province dummies to control for regional differences and industry dummies to control for industrial differences. of a normal distribution.
is the cumulative density function
is the error term.
3. Estimation results 3.1. Data and summary statistics Our data come from the 2015 CHFS. We restrict our sample to urban households with informal businesses, most of which are registered as geti gongshanghu. After the screening process, we end up with 3,243 sampled businesses (see the Online Appendix for details). To mitigate the effects of outliers, we winsorize all continuous variables at both the top and bottom 1% percentiles. Table 1 presents the summary statistics of these sampled businesses. As shown, only 9% of businesses had bank loans, suggesting that their access to formal credit was somewhat limited. In terms of financial literacy, on average 1.21 of the three questions were answered correctly. Table 1 Summary statistics of household and business characteristics Full sample Mean
Rural hukou S.D
Mean
Dependent variable: 7
Urban hukou S.D
Mean
S.D
Bank loan dummy
0.09
0.28
0.09
0.28
0.09
0.29
FL score
1.21
0.90
1.11
0.89
1.34
0.89
FL index
0.27
0.63
0.18
0.66
0.38
0.58
Firm age
9.54
7.84
9.43
7.75
9.68
7.95
Sales (in 10,000 yuan)
24.19
64.95
22.84
61.67
25.98
69.01
Age
43.71
12.37
43.53
12.11
43.95
12.69
Male
0.53
0.50
0.53
0.50
0.52
0.50
Elementary (reference group)
0.17
0.38
0.23
0.42
0.09
0.29
Junior high
0.38
0.49
0.45
0.50
0.28
0.45
Senior high
0.27
0.44
0.23
0.42
0.33
0.47
College
0.18
0.38
0.09
0.29
0.30
0.46
Married
0.88
0.32
0.90
0.30
0.85
0.35
Urban hukou
0.43
0.50
0.00
0.00
1.00
0.00
Risk loving (reference group)
0.39
0.49
0.36
0.48
0.42
0.49
Risk averse
0.56
0.50
0.58
0.49
0.54
0.50
Risk unsure
0.05
0.22
0.06
0.23
0.04
0.19
Family size
3.75
1.44
3.93
1.48
3.53
1.34
Owning a local property
0.82
0.39
0.78
0.41
0.87
0.34
Net wealth (in 10,000 yuan)
101.38
142.03
89.25
132.73
117.35
151.99
Local family network
0.38
0.48
0.37
0.48
0.39
0.49
Observations
3243
Variables of interest:
Business characteristics:
Owner characteristics:
Household characteristics:
1841
8
1402
3.2. Baseline regressions Table 2 presents the estimation results of Equation (1). Estimated average marginal effects are reported, and robust standard errors are estimated. In column 1, FL Score is used to measure financial literacy. Its coefficient is positive and significant at the 1% level. Its magnitude indicates that with one more question correctly answered, the business’s probability of access to formal credit will increase by 1.4 percentage points. Therefore, this effect is also economically significant. We find similar results when FL Index is used in column 2. As to control variables, the coefficient on ln(Sales) is significantly positive, which is consistent with previous findings that larger and established businesses are more likely to obtain bank loans. In contrast to previous findings (e.g., Vos et al., 2007), the effect of firm age tends to be negative. It may be justified by counting in the nature of informal businesses: keeping an aged business but remaining as informal simply implies a lack of growth potential. Owners’ risk attitude has been found important to credit use for businesses (e.g., González et al., 2013). We find that the owner being risk averse results in lower access to bank loans. Last, when family size is larger, the informal business is more likely to have bank loans. For formal businesses, we expect such a positive relationship to be less pronounced. Unfortunately, we are not able to test this hypothesis by using the CHFS data because formal businesses only represent a small portion of the businesses in the 2015 CHFS survey and detailed questions on businesses are asked only if the business is informal. To do so, we turn to another data set, the 2015 China Micro and Small Enterprise Survey data (2015 CMSE), in which small and micro enterprises (mostly in the form of sole proprietorship or limited liability) are surveyed,
9
and conduct similar regressions.2 As shown in Online Appendix Table 6, such a relationship among formal businesses is insignificant. However, financial literacy may be endogenous; that is, it is possible that the level of financial literacy is improved through financial practices. For example, business owners accumulate financial knowledge through participating in financial market transactions. Additionally, financial literacy and financial market participation may be correlated to some unobservable factors. To address the above endogenous issues, we use the average financial literacy at the community level as an instrument for a business owner’s financial literacy. As suggested by Lusardi and Mitchell (2014) and Peng et al. (2018), an individual’s financial literacy can be influenced by interacting with others. Given that the financial literacy of others can be regarded as exogenous to his or her financial decisions in addition to the studies above, it is qualified to act as a valid instrument. We thus perform the IV-probit estimation in columns 3 and 4. As shown in Online Appendix Table 7, in the first-stage estimations the coefficient on our instrument is positive and significant at the 1% level, and the F-statistic is way above the critical value (10) to avoid the weak IV problem. The second-stage estimations show that the coefficient is positive and significant at 10% and 5% level, respectively. Therefore, we confirm a causal relationship from owners’ financial literacy to businesses’ formal credit access.
2
The related variable definitions, sample screening process, and summary statistics are presented in Online Appendix Tables 3 to 5, respectively. 10
Table 2 The effect of financial literacy on formal credit access (1)
(2)
(3)
Specification
Probit
IV probit
FL score
0.014***
0.059*
(2.59)
(1.88)
FL index
Firm age
Ln(Sales)
Age
Male
Junior high
Senior high
College
Married
Urban hukou
Risk averse
(4)
0.019**
0.104**
(2.08)
(2.03)
-0.001*
-0.001*
-0.001*
-0.001*
(-1.83)
(-1.81)
(-1.72)
(-1.68)
0.027***
0.027***
0.027***
0.027***
(7.43)
(7.33)
(7.20)
(6.88)
-0.000
-0.000
-0.000
0.000
(-1.07)
(-1.06)
(-0.06)
(0.41)
-0.012
-0.012
-0.016
-0.016
(-1.31)
(-1.22)
(-1.55)
(-1.47)
-0.011
-0.012
-0.020
-0.031
(-0.67)
(-0.75)
(-1.14)
(-1.50)
0.022
0.020
0.008
-0.009
(1.32)
(1.18)
(0.39)
(-0.38)
0.004
0.004
-0.017
-0.029
(0.23)
(0.19)
(-0.66)
(-1.02)
0.021
0.020
0.021
0.014
(1.35)
(1.28)
(1.28)
(0.81)
-0.001
-0.001
-0.005
-0.009
(-0.07)
(-0.09)
(-0.47)
(-0.71)
-0.032***
-0.032***
-0.024**
-0.022*
(-3.24)
(-3.26)
(-2.05)
(-1.89)
11
Risk unsure
-0.078**
-0.079**
-0.057
-0.048
(-2.46)
(-2.46)
(-1.59)
(-1.27)
0.013***
0.013***
0.013***
0.014***
(3.45)
(3.50)
(3.35)
(3.42)
0.010
0.010
0.011
0.013
(0.65)
(0.66)
(0.69)
(0.79)
0.007
0.007
0.005
0.003
(1.44)
(1.45)
(0.88)
(0.60)
0.003
0.003
0.004
0.002
(0.36)
(0.30)
(0.36)
(0.20)
Industry dummies
Yes
Yes
Yes
Yes
Province dummies
Yes
Yes
Yes
Yes
16.66
28.29
3243
3243
Family size
Owning a local property
Ln(Net wealth)
Local family network
First stage F statistic Observations
3243
3243
Pseudo R2
0.144
0.143
Notes: Estimated marginal effects are reported. Standard errors are robust. T-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
3.3. Cross-sectional regressions To identify underlying mechanisms, in this subsection we explore whether the positive relationship between financial literacy and formal credit access exhibits heterogeneity in the cross-section along several characteristics. Hukou type. In China, due to the hukou system, when migrating to cities, households with rural hukou, compared to local residents with urban hukou, are discriminated in terms of employment and access to public services such as social welfares and children’s school enrollment. It is likely that households with rural hukou are also discriminated by banks when applying for loans. We thus rerun the baseline regression by hukou types, rural and urban hukou 12
in Table 3. The positive relationship is significant only among businesses owners who have rural hukou. In contrast, such a relationship is insignificant among businesses with urban hukou, indicating the effect of financial literacy is negligible among these businesses. As shown in Table 1, the average FL score for urban-hukou owners is substantially higher than that for rural-hukou owners (1.34 vs. 1.11), so is the FL index (0.38 vs. 0.18). It partially explains why financial literacy only matters for business owners with rural hukou. For this reason, in the following subsections we only discuss the sub-sample of businesses with rural hukou.3 Table 3 Financial literacy and formal credit access, by hukou type (1)
(2)
(3)
Owners’ hukou type
Rural
Urban
FL score
0.029***
-0.007
(4.05)
(-0.90)
FL index
0.028**
0.003
(2.51)
(0.18)
Controls
Yes
Industry dummies
Yes
Province dummies
Yes
Observations
1818
1818
3
(4)
1395
1395
Thanks for the suggestion by an anonymous reviewer. One potential concern is that rural-hukou owners with lower financial literacy deliberately choose to run their businesses in smaller cities, where financial development is generally lower. If so, our results might merely reflect that lower financial literacy is associated with lower regional financial development. To alleviate this concern, we first investigate the regional distribution of rural-hukou business owners and that of urban-hukou owners. It turns out that these two distributions are comparable, suggesting that the selection issue among rural-hukou owners is minor. Second, we rerun the baseline regressions by including city dummies to better capture the potential influence from regional financial development. Our major results remain unchanged. Therefore, it is unlikely that our major results are driven by the selection issue. 13
Pseudo R2
0.185
0.176
0.139
0.138
Notes: Estimated marginal effects are reported. Standard errors are robust. T-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
Regional financial development. In addition, it is interesting to consider how financial literacy (demand side) and financial development (supply side) interact with each other; that is, are they substitutes or complements? Based on whether a household was holding any bank loans, we construct the proportion of households having access to formal credit at the community level.4 We then divide the rural-hukou businesses into two groups by the median of the proportion and rerun the baseline regressions in Table 4. The coefficient on financial literacy is significantly positive only in areas with low financial development, while it turns insignificant and its magnitude becomes smaller when areas with high development are examined. The results hold regardless which measure of financial literacy is used.5 Our finding thus suggests that financial literacy and regional financial development are substitutes rather than complements. It echoes Grohmann et al. (2018), who find that financial literacy is positively related to access to bank accounts at the country level, and this relationship is more pronounced in those countries whose financial sectors are less developed.6
4
The households include both the households with or without business. The household itself is excluded when calculating the proportion. 5
We rerun the regression of Table 4 among informal businesses for urban-hukou owners, and the coefficients on the financial literacy indicators remain insignificant. It further confirms our argument that the effect of financial literacy on formal credit accessibility only exists among informal businesses for rural-hukou owners. 6
Similar to Table 2, the coefficient on owning a local property remains insignificant in Tables 3 and 4. One possible explanation is that home ownership was very common among the sampled households (82% as reported in Table 1), so collateral was readily available for most of these businesses when applying for bank loans. 14
Table 4 Financial literacy and formal credit access among rural-hukou owners, by local financial development (1)
(2)
(3)
Financial development
Low
High
FL score
0.039***
0.019
(4.52)
(1.11)
FL index
(4)
0.037***
0.016
(2.69)
(0.63)
Controls
Yes
Industry dummies
Yes
Province dummies
Yes
Observations
1099
1099
504
504
Pseudo R2
0.220
0.199
0.167
0.165
Notes: Estimated marginal effects are reported. Standard errors are robust. T-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
4. Conclusions and implications By investigating two nationally representative datasets in China, we find that owners’ financial literacy contributes only to informal businesses’ formal credit accessibility. This paper thus supports World Bank’s suggestion of promoting financial literacy to improve economic growth in developing countries and extends the idea in two ways. First, we find that this effect among informal businesses differs substantially by owners’ hukou types. It suggests that informal businesses’ financial inclusion can be improved by educating owners in financial knowledge and it is more effective to target disadvantaged groups. Second, we also find that this effect is more pronounced in areas where financial development is lower, suggesting that personal financial literacy and institutional financial development (so called “institutional”
15
financial literacy) are substitutes. Thus, improving “institutional” financial literacy can also be considered. References Asiedu, E., Freeman, J. A., Nti-Addae, A., 2012. Access to credit by small businesses: How relevant are race, ethnicity, and gender? Am. Econ. Rev. 102(3), 532-37. Berger, A. N., Udell, G. F., 1995. Relationship lending and lines of credit in small firm finance. J. Bus. 68(3), 351-381. Buehn, A., Friedrich, S., 2009. Shadow economies and corruption all over the world: Revised estimates for 120 countries, Economics: The Open-Access, Open-Assessment E-Journal. Cole, S., Sampson, T., Zia, B. 2011. Prices or knowledge? What drives demand for financial services in emerging markets? J. Fin. 66(6), 1933-1967. Drexler, A., Fischer, G., Schoar, A., 2014. Keeping it simple: Financial literacy and rules of thumb. Appl. Econ. 6(2), 1-31. Egli, D., Ongena, S., Smith, D.C., 2006. On the sequencing of projects, reputation building, and relationship finance. Financ. Res. Lett. 3(1), 23-39. Gan, L., Yin, Z., Jia, N., Xu, S., Ma, S., Zheng, L., 2013. Data you need to know about China: Research Report of China Household Finance Survey, 2012. Springer Science & Business Media. González, M., Guzmán, A., Pombo, C., Trujillo, M.A., 2013. Family firms and debt: Risk aversion versus risk of losing control. J. Bus. Research 66(11), 2308-2320. Grohmann, A., Klühs, T., Menkhoff, L., 2018. Does financial literacy improve financial inclusion? Cross country evidence. World Dev. 111, 84-96. Kimball, M., Shumway, T., 2006. Investor sophistication, and the participation, home bias, diversification, and employer stock puzzles. University of Michigan. Kon, Y., Storey, D. J., 2003. A theory of discouraged borrowers. Small Bus. Econ. 21(1), 37-49. Lusardi, A., Mitchell, O. S., 2014. The economic importance of financial literacy: Theory and evidence. J. Econ. Lit. 52(1), 5-44. 16
Lyons, A., Grable, J., Zeng, T., 2017. Impacts of financial literacy on loan demand of financially excluded households in China. Working Paper. Peng, G., Liu, F., Lu, W., Liao, K., Tang, C., Zhu, L., 2018. A spatial-temporal analysis of financial literacy in United States of America. Financ. Res. Lett. 26, 56-62. Stiglitz, J. E., Weiss, A., 1981. Credit rationing in markets with imperfect information. Am. Econ. Rev. 71(3), 393-410. Van Rooij, M., Lusardi, A., Alessie, R., 2011. Financial literacy and stock market participation. J. Financ. Econ. 101(2), 449-472. Vos, E., Yeh, A.J.Y., Carter, S., Tagg, S., 2007. The happy story of small business financing. J. Bank Fin. 31(9), 2648-2672. World Bank Group, 2014. Global Financial Development Report: Financial Inclusion. Zhang, D., Wang, H. Y., Guariglia, A., Fan, G., 2016. Growing out of the growing pain: The role of financial literacy on the demand for life insurance in China. Working Paper.
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Financial Literacy and Formal Credit Accessibility: Evidence from Informal Businesses in China Nana Xu PhD candidateVisiting Scholar , Jingye Shi Associate professor , Zhao Rong Professor , Yan Yuan Professor PII: DOI: Reference:
S1544-6123(19)30472-6 https://doi.org/10.1016/j.frl.2019.101327 FRL 101327
To appear in:
Finance Research Letters
Received date: Revised date: Accepted date:
15 May 2019 25 September 2019 11 October 2019
Please cite this article as: Nana Xu PhD candidateVisiting Scholar , Jingye Shi Associate professor , Zhao Rong Professor , Yan Yuan Professor , Financial Literacy and Formal Credit Accessibility: Evidence from Informal Businesses in China, Finance Research Letters (2019), doi: https://doi.org/10.1016/j.frl.2019.101327
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Highlights Investigate the role of financial literacy in businesses’ access to bank loans Use the 2015 China Household Finance Survey and apply the IV Probit method Find owners’ financial literacy positively related to informal businesses’ access It only exists among informal businesses for owners with rural hukou It is more pronounced in areas where formal-credit accessibility is lower
1
Financial Literacy and Formal Credit Accessibility: Evidence from Informal Businesses in China Nana Xu, PhD candidate at Research Institute of Economics and Management (RIEM), Southwestern University of Finance and Economics, China and Visiting Scholar at University of Illinois Urbana-Champaign, United States. Address: 555 Liutai Boulevard, Wenjiang District, Chengdu 611130, China Email: [email protected] Jingye Shi, Associate professor, Research Institute of Economics and Management (RIEM), Southwestern University of Finance and Economics, China Address: 55 Guanghuacun Street, Chengdu 610074, China. Email: [email protected] Zhao Rong, Corresponding author. Professor, Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, China Address: 182 Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, P.R. China Email: [email protected] Yan Yuan, Professor, Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, China Address: 182 Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, P.R. China Email: [email protected] Declarations of interest: none
Abstract: There is a sizable informal business sector in emerging markets. This paper investigates informal businesses’ accessibility to formal credit in China and their owners’ 2
financial literacy as a potential determinant. By examining informal businesses in urban areas from the 2015 China Household Finance Survey, we find that business owners’ financial literacy is positively associated with the likelihood of their businesses holding bank loans. This positive relationship only exists among businesses whose owners have rural hukou, and it is more pronounced in areas where formal-credit accessibility is lower.
JEL Classifications: G21, O17, O16, L26
Key words: Formal credit; Financial literacy; Informal businesses; China
1. Introduction One important characteristic of emerging markets is their sizable informal business sectors, which represent, on average, 36% of the GDP in 76 emerging market countries (Buehn and Schneider, 2009). However, informal businesses’ access to formal credit is generally impeded by their opaque financial information (Stiglitz and Weiss, 1981). The World Bank has proposed that financial education could enhance informal businesses’ financial inclusion, thereby boosting developing countries’ economies (World Bank, 2014). Though apparently reasonable, the validity of this suggestion relies on answering the following question: does the lack of financial knowledge among their owners result in informal businesses’ poor financial inclusion? Unfortunately, direct evidence is so far scarce. This paper attempts to fill the gap by investigating informal businesses’ accessibility to formal credit, an important measure of financial inclusion, in the largest emerging market, China.
3
In China, informal businesses are not required to employ financial experts. Consequently, financial decisions are made solely by business owners. It is thus reasonable to expect that the financial literacy of business owners substantially influence informal businesses’ financial status, particularly accessibility to formal credit. First, financial literacy allows business owners to better understand the importance of banking services. In other words, a lack of financial knowledge may discourage a business owner from pursuing bank credits (Cole et al., 2011) or may further result in a qualified borrower failing to apply for a loan (Kon and Storey, 2003). Second, the approval of a bank loan requires a complex process, which is time-consuming and financial knowledge demanding as well (Kon and Storey, 2003). We thus postulate that financial literacy of owners positively influences informal businesses’ accessibility to formal credit. We also conjecture that such an effect is less pronounced among formal businesses since they are required by law to employ financial specialists, making their owners’ financial literacy less important. Our data come from the 2015 China Household Finance Survey (CHFS), conducted by Southwestern University of Finance and Economics (SWUFE).1 We investigate small businesses owned by surveyed households in urban areas. These businesses are informal and mainly are geti gongshanghu (sole industrial and commercial proprietorship). The 2015 CHFS included several questions on financial literacy, which we use to measure a business owner’s financial literacy. Our estimation results show that a business is more likely to obtain bank loans when the owner’s financial literacy is higher. By contrast, the relationship is insignificant when formal businesses are examined. 1
See Gan et al. (2013) and https://chfs.swufe.edu.cn for details. 4
However, an owner’s financial literacy may be improved through participation in formal financial services, so as to result in the positive relationship. To address the potential endogeneity, we follow Lusardi and Mitchell (2014) and use the average level of financial literacy in the community as an instrument. Our IV estimation results confirm that the positive relationship is causal. More interestingly, this positive relationship only exists among businesses whose owners hold rural hukou. For these businesses, we further find that this positive relationship is more pronounced in regions with lower formal credit accessibility. This paper is closely related to the literature on the determinants of small businesses’ formal credit accessibility. Previous studies have focused on the supply side’s efforts of better serving small businesses. Through relationship lending or bank competition, it becomes easier for small businesses to obtain formal credit (Berger and Udell, 1995; Egli et al., 2006). Recently, a growing literature shows that entrepreneurs’ demographic characteristics (e.g., gender, race, ethnicity, and education) and behavioral attributes (e.g., optimism and overconfidence) are important drivers of small businesses’ credit accessibility (e.g., Asiedu et al., 2012). Our paper extends this body of literature by providing evidence that owners’ financial literacy is another important determinant for informal businesses. This paper is also related to the studies on how financial literacy influences individual financial inclusion. Financial literacy has been found to influence credit accessibility (Lyons et al., 2017), stock market participation (Kimball and Shumway, 2006), and insurance participation (Zhang et al., 2016). Among the few studies that associate financial literacy with firm performance, Drexler et al. (2014) find that entrepreneurs’ financial literacy enhances their
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firms’ financial practices. Our paper enriches the literature by relating entrepreneurs’ financial literacy to their businesses’ financial inclusion. 2. Variables and Model Specification 2.1. Financial literacy The 2015 CHFS included three questions on financial literacy. These questions are related to interest rates, inflation, and risk diversification (see the Online Appendix for details). Following van Rooij et al. (2011), we generate two measures of financial literacy based on how these three questions were answered. The first measure is FL Score, which equals the number of questions correctly answered. One limitation of this measure is that answering incorrectly may reveal information on the extent of financial literacy differently from answering “I do not know.” We thus construct our second measure, FL Index to take into account such differences. Specifically, for each question, two binary variables are generated based on “whether answering the question correctly or not” or “whether understanding the question or not.” As a result, we have six variables. The index is generated through iterated principal component analysis on these six variables, which takes full advantage of all information in these three questions.
2.2. Model specification Our dependent variable is formal credit access, a binary variable equal to one if the business has bank loans and zero otherwise. Accordingly, we set up a probit model as follows. (
where
)
(
)
is the formal credit access dummy.
(1)
is one of these two measures of financial
literacy of the business owner. Controli represents a vector of characteristics at the business, 6
owner, and household level. The business characteristics include age of the business and annual sales (in log value). The owner characteristics include the business owner’s age, gender, education level, marriage status, risk attitude, and type of hukou. The household characteristics include number of family members, owning a local property or not, log of net wealth, and local family network. Additionally, we include province dummies to control for regional differences and industry dummies to control for industrial differences. of a normal distribution.
is the cumulative density function
is the error term.
3. Estimation results 3.1. Data and summary statistics Our data come from the 2015 CHFS. We restrict our sample to urban households with informal businesses, most of which are registered as geti gongshanghu. After the screening process, we end up with 3,243 sampled businesses (see the Online Appendix for details). To mitigate the effects of outliers, we winsorize all continuous variables at both the top and bottom 1% percentiles. Table 1 presents the summary statistics of these sampled businesses. As shown, only 9% of businesses had bank loans, suggesting that their access to formal credit was somewhat limited. In terms of financial literacy, on average 1.21 of the three questions were answered correctly. Table 1 Summary statistics of household and business characteristics Full sample Mean
Rural hukou S.D
Mean
Dependent variable: 7
Urban hukou S.D
Mean
S.D
Bank loan dummy
0.09
0.28
0.09
0.28
0.09
0.29
FL score
1.21
0.90
1.11
0.89
1.34
0.89
FL index
0.27
0.63
0.18
0.66
0.38
0.58
Firm age
9.54
7.84
9.43
7.75
9.68
7.95
Sales (in 10,000 yuan)
24.19
64.95
22.84
61.67
25.98
69.01
Age
43.71
12.37
43.53
12.11
43.95
12.69
Male
0.53
0.50
0.53
0.50
0.52
0.50
Elementary (reference group)
0.17
0.38
0.23
0.42
0.09
0.29
Junior high
0.38
0.49
0.45
0.50
0.28
0.45
Senior high
0.27
0.44
0.23
0.42
0.33
0.47
College
0.18
0.38
0.09
0.29
0.30
0.46
Married
0.88
0.32
0.90
0.30
0.85
0.35
Urban hukou
0.43
0.50
0.00
0.00
1.00
0.00
Risk loving (reference group)
0.39
0.49
0.36
0.48
0.42
0.49
Risk averse
0.56
0.50
0.58
0.49
0.54
0.50
Risk unsure
0.05
0.22
0.06
0.23
0.04
0.19
Family size
3.75
1.44
3.93
1.48
3.53
1.34
Owning a local property
0.82
0.39
0.78
0.41
0.87
0.34
Net wealth (in 10,000 yuan)
101.38
142.03
89.25
132.73
117.35
151.99
Local family network
0.38
0.48
0.37
0.48
0.39
0.49
Observations
3243
Variables of interest:
Business characteristics:
Owner characteristics:
Household characteristics:
1841
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1402
3.2. Baseline regressions Table 2 presents the estimation results of Equation (1). Estimated average marginal effects are reported, and robust standard errors are estimated. In column 1, FL Score is used to measure financial literacy. Its coefficient is positive and significant at the 1% level. Its magnitude indicates that with one more question correctly answered, the business’s probability of access to formal credit will increase by 1.4 percentage points. Therefore, this effect is also economically significant. We find similar results when FL Index is used in column 2. As to control variables, the coefficient on ln(Sales) is significantly positive, which is consistent with previous findings that larger and established businesses are more likely to obtain bank loans. In contrast to previous findings (e.g., Vos et al., 2007), the effect of firm age tends to be negative. It may be justified by counting in the nature of informal businesses: keeping an aged business but remaining as informal simply implies a lack of growth potential. Owners’ risk attitude has been found important to credit use for businesses (e.g., González et al., 2013). We find that the owner being risk averse results in lower access to bank loans. Last, when family size is larger, the informal business is more likely to have bank loans. For formal businesses, we expect such a positive relationship to be less pronounced. Unfortunately, we are not able to test this hypothesis by using the CHFS data because formal businesses only represent a small portion of the businesses in the 2015 CHFS survey and detailed questions on businesses are asked only if the business is informal. To do so, we turn to another data set, the 2015 China Micro and Small Enterprise Survey data (2015 CMSE), in which small and micro enterprises (mostly in the form of sole proprietorship or limited liability) are surveyed,
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and conduct similar regressions.2 As shown in Online Appendix Table 6, such a relationship among formal businesses is insignificant. However, financial literacy may be endogenous; that is, it is possible that the level of financial literacy is improved through financial practices. For example, business owners accumulate financial knowledge through participating in financial market transactions. Additionally, financial literacy and financial market participation may be correlated to some unobservable factors. To address the above endogenous issues, we use the average financial literacy at the community level as an instrument for a business owner’s financial literacy. As suggested by Lusardi and Mitchell (2014) and Peng et al. (2018), an individual’s financial literacy can be influenced by interacting with others. Given that the financial literacy of others can be regarded as exogenous to his or her financial decisions in addition to the studies above, it is qualified to act as a valid instrument. We thus perform the IV-probit estimation in columns 3 and 4. As shown in Online Appendix Table 7, in the first-stage estimations the coefficient on our instrument is positive and significant at the 1% level, and the F-statistic is way above the critical value (10) to avoid the weak IV problem. The second-stage estimations show that the coefficient is positive and significant at 10% and 5% level, respectively. Therefore, we confirm a causal relationship from owners’ financial literacy to businesses’ formal credit access.
2
The related variable definitions, sample screening process, and summary statistics are presented in Online Appendix Tables 3 to 5, respectively. 10
Table 2 The effect of financial literacy on formal credit access (1)
(2)
(3)
Specification
Probit
IV probit
FL score
0.014***
0.059*
(2.59)
(1.88)
FL index
Firm age
Ln(Sales)
Age
Male
Junior high
Senior high
College
Married
Urban hukou
Risk averse
(4)
0.019**
0.104**
(2.08)
(2.03)
-0.001*
-0.001*
-0.001*
-0.001*
(-1.83)
(-1.81)
(-1.72)
(-1.68)
0.027***
0.027***
0.027***
0.027***
(7.43)
(7.33)
(7.20)
(6.88)
-0.000
-0.000
-0.000
0.000
(-1.07)
(-1.06)
(-0.06)
(0.41)
-0.012
-0.012
-0.016
-0.016
(-1.31)
(-1.22)
(-1.55)
(-1.47)
-0.011
-0.012
-0.020
-0.031
(-0.67)
(-0.75)
(-1.14)
(-1.50)
0.022
0.020
0.008
-0.009
(1.32)
(1.18)
(0.39)
(-0.38)
0.004
0.004
-0.017
-0.029
(0.23)
(0.19)
(-0.66)
(-1.02)
0.021
0.020
0.021
0.014
(1.35)
(1.28)
(1.28)
(0.81)
-0.001
-0.001
-0.005
-0.009
(-0.07)
(-0.09)
(-0.47)
(-0.71)
-0.032***
-0.032***
-0.024**
-0.022*
(-3.24)
(-3.26)
(-2.05)
(-1.89)
11
Risk unsure
-0.078**
-0.079**
-0.057
-0.048
(-2.46)
(-2.46)
(-1.59)
(-1.27)
0.013***
0.013***
0.013***
0.014***
(3.45)
(3.50)
(3.35)
(3.42)
0.010
0.010
0.011
0.013
(0.65)
(0.66)
(0.69)
(0.79)
0.007
0.007
0.005
0.003
(1.44)
(1.45)
(0.88)
(0.60)
0.003
0.003
0.004
0.002
(0.36)
(0.30)
(0.36)
(0.20)
Industry dummies
Yes
Yes
Yes
Yes
Province dummies
Yes
Yes
Yes
Yes
16.66
28.29
3243
3243
Family size
Owning a local property
Ln(Net wealth)
Local family network
First stage F statistic Observations
3243
3243
Pseudo R2
0.144
0.143
Notes: Estimated marginal effects are reported. Standard errors are robust. T-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
3.3. Cross-sectional regressions To identify underlying mechanisms, in this subsection we explore whether the positive relationship between financial literacy and formal credit access exhibits heterogeneity in the cross-section along several characteristics. Hukou type. In China, due to the hukou system, when migrating to cities, households with rural hukou, compared to local residents with urban hukou, are discriminated in terms of employment and access to public services such as social welfares and children’s school enrollment. It is likely that households with rural hukou are also discriminated by banks when applying for loans. We thus rerun the baseline regression by hukou types, rural and urban hukou 12
in Table 3. The positive relationship is significant only among businesses owners who have rural hukou. In contrast, such a relationship is insignificant among businesses with urban hukou, indicating the effect of financial literacy is negligible among these businesses. As shown in Table 1, the average FL score for urban-hukou owners is substantially higher than that for rural-hukou owners (1.34 vs. 1.11), so is the FL index (0.38 vs. 0.18). It partially explains why financial literacy only matters for business owners with rural hukou. For this reason, in the following subsections we only discuss the sub-sample of businesses with rural hukou.3 Table 3 Financial literacy and formal credit access, by hukou type (1)
(2)
(3)
Owners’ hukou type
Rural
Urban
FL score
0.029***
-0.007
(4.05)
(-0.90)
FL index
0.028**
0.003
(2.51)
(0.18)
Controls
Yes
Industry dummies
Yes
Province dummies
Yes
Observations
1818
1818
3
(4)
1395
1395
Thanks for the suggestion by an anonymous reviewer. One potential concern is that rural-hukou owners with lower financial literacy deliberately choose to run their businesses in smaller cities, where financial development is generally lower. If so, our results might merely reflect that lower financial literacy is associated with lower regional financial development. To alleviate this concern, we first investigate the regional distribution of rural-hukou business owners and that of urban-hukou owners. It turns out that these two distributions are comparable, suggesting that the selection issue among rural-hukou owners is minor. Second, we rerun the baseline regressions by including city dummies to better capture the potential influence from regional financial development. Our major results remain unchanged. Therefore, it is unlikely that our major results are driven by the selection issue. 13
Pseudo R2
0.185
0.176
0.139
0.138
Notes: Estimated marginal effects are reported. Standard errors are robust. T-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
Regional financial development. In addition, it is interesting to consider how financial literacy (demand side) and financial development (supply side) interact with each other; that is, are they substitutes or complements? Based on whether a household was holding any bank loans, we construct the proportion of households having access to formal credit at the community level.4 We then divide the rural-hukou businesses into two groups by the median of the proportion and rerun the baseline regressions in Table 4. The coefficient on financial literacy is significantly positive only in areas with low financial development, while it turns insignificant and its magnitude becomes smaller when areas with high development are examined. The results hold regardless which measure of financial literacy is used.5 Our finding thus suggests that financial literacy and regional financial development are substitutes rather than complements. It echoes Grohmann et al. (2018), who find that financial literacy is positively related to access to bank accounts at the country level, and this relationship is more pronounced in those countries whose financial sectors are less developed.6
4
The households include both the households with or without business. The household itself is excluded when calculating the proportion. 5
We rerun the regression of Table 4 among informal businesses for urban-hukou owners, and the coefficients on the financial literacy indicators remain insignificant. It further confirms our argument that the effect of financial literacy on formal credit accessibility only exists among informal businesses for rural-hukou owners. 6
Similar to Table 2, the coefficient on owning a local property remains insignificant in Tables 3 and 4. One possible explanation is that home ownership was very common among the sampled households (82% as reported in Table 1), so collateral was readily available for most of these businesses when applying for bank loans. 14
Table 4 Financial literacy and formal credit access among rural-hukou owners, by local financial development (1)
(2)
(3)
Financial development
Low
High
FL score
0.039***
0.019
(4.52)
(1.11)
FL index
(4)
0.037***
0.016
(2.69)
(0.63)
Controls
Yes
Industry dummies
Yes
Province dummies
Yes
Observations
1099
1099
504
504
Pseudo R2
0.220
0.199
0.167
0.165
Notes: Estimated marginal effects are reported. Standard errors are robust. T-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
4. Conclusions and implications By investigating two nationally representative datasets in China, we find that owners’ financial literacy contributes only to informal businesses’ formal credit accessibility. This paper thus supports World Bank’s suggestion of promoting financial literacy to improve economic growth in developing countries and extends the idea in two ways. First, we find that this effect among informal businesses differs substantially by owners’ hukou types. It suggests that informal businesses’ financial inclusion can be improved by educating owners in financial knowledge and it is more effective to target disadvantaged groups. Second, we also find that this effect is more pronounced in areas where financial development is lower, suggesting that personal financial literacy and institutional financial development (so called “institutional”
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financial literacy) are substitutes. Thus, improving “institutional” financial literacy can also be considered. References Asiedu, E., Freeman, J. A., Nti-Addae, A., 2012. Access to credit by small businesses: How relevant are race, ethnicity, and gender? Am. Econ. Rev. 102(3), 532-37. Berger, A. N., Udell, G. F., 1995. Relationship lending and lines of credit in small firm finance. J. Bus. 68(3), 351-381. Buehn, A., Friedrich, S., 2009. Shadow economies and corruption all over the world: Revised estimates for 120 countries, Economics: The Open-Access, Open-Assessment E-Journal. Cole, S., Sampson, T., Zia, B. 2011. Prices or knowledge? What drives demand for financial services in emerging markets? J. Fin. 66(6), 1933-1967. Drexler, A., Fischer, G., Schoar, A., 2014. Keeping it simple: Financial literacy and rules of thumb. Appl. Econ. 6(2), 1-31. Egli, D., Ongena, S., Smith, D.C., 2006. On the sequencing of projects, reputation building, and relationship finance. Financ. Res. Lett. 3(1), 23-39. Gan, L., Yin, Z., Jia, N., Xu, S., Ma, S., Zheng, L., 2013. Data you need to know about China: Research Report of China Household Finance Survey, 2012. Springer Science & Business Media. González, M., Guzmán, A., Pombo, C., Trujillo, M.A., 2013. Family firms and debt: Risk aversion versus risk of losing control. J. Bus. Research 66(11), 2308-2320. Grohmann, A., Klühs, T., Menkhoff, L., 2018. Does financial literacy improve financial inclusion? Cross country evidence. World Dev. 111, 84-96. Kimball, M., Shumway, T., 2006. Investor sophistication, and the participation, home bias, diversification, and employer stock puzzles. University of Michigan. Kon, Y., Storey, D. J., 2003. A theory of discouraged borrowers. Small Bus. Econ. 21(1), 37-49. Lusardi, A., Mitchell, O. S., 2014. The economic importance of financial literacy: Theory and evidence. J. Econ. Lit. 52(1), 5-44. 16
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