- Email: [email protected]
social interaction, and household finance
Economics Letters 136 (2015) 194–196
Contents lists available at ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Demographics, family/social interaction, and household finance Ming Gao a , Robert (Chi-Wing) Fok b,∗ a
Peking University, 5 Yiheyuan Road, Haidian District, Beijing, 100871, China
b
Department of Business, University of Wisconsin-Parkside, 900 Wood Road, Kenosha, WI 53141-2000, USA
highlights • • • • •
Demographic factors affect household saving, investing, and financing decisions. Large households are less likely to save and invest in risky assets. The age of household head and different financial activities are nonlinearly related. Family/social interaction increases the odd of saving, risky investing and borrowing. Family interaction increases the odd of using informal sources of financing.
article
info
Article history: Received 2 July 2015 Received in revised form 16 August 2015 Accepted 22 September 2015 Available online 3 October 2015
abstract We examine the role of demographics and family/social interaction in Chinese household finance. The impacts of demographic characteristics are not limited to stock market participation, but extend to other financial activities. Households with strong family and social interaction are more likely to save, invest in risky assets and borrow. Family interaction is positively related to informal financing. © 2015 Elsevier B.V. All rights reserved.
JEL classification: G11 Keywords: Household financial decisions Demographics Family/social interaction
1. Introduction Previous studies on household finance mainly focus on one financial activity once at a time. Some studies examine the role of demographic factors in stock market participation (e.g. Campbell, 2006); others investigate household saving with an emphasis on macroeconomics factors (e.g. Karlan et al., forthcoming). Most studies on financing decision are about corporations rather than households (Allen et al., 2013). Recently, researchers pay attention to the impacts of social interaction on household finance, but the focus point is whether to invest in the stock market (Liang and Guo, forthcoming). We know very little about how demographic factors and social interaction affect the other aspects of household finance besides stock market participation. We fill the gap by examining the roles of demographics and social interaction in Chinese household decisions of investing
∗
Corresponding author. Tel.: +1 262 595 2460; fax: +1 262 595 2680. E-mail addresses: [email protected] (M. Gao), [email protected] (R. Fok).
http://dx.doi.org/10.1016/j.econlet.2015.09.027 0165-1765/© 2015 Elsevier B.V. All rights reserved.
in risky assets, saving, and financing. As family is a cornerstone of Chinese society, we also consider the impacts of family interaction. 2. Data and methodology We use the data from the 2010 Chinese Family Panel Studies (CFPS), which surveys detailed financial information at the community, household and individual level across 25 provinces in China. The study covers 14,960 households and 42,590 individuals. To analyze the impacts of demographic factors on different aspects of household finance, we estimate probit models in which the dependent variables are the dummies for each type of financial activities. Risky assets include stocks and mutual funds. We divide household financing into formal and informal financing. Formal financing is borrowing from financial institutions such as commercial banks and credit unions. Informal financing is borrowing from family, friends, or other sources. Independent variables are the following demographic characteristics: household size, education level, gender, age and its squared term, occupation,
M. Gao, R. Fok / Economics Letters 136 (2015) 194–196
195
Table 1 Demographics and household finance (probit models). The dependent variable is a dummy variable that equals 1 when a household is participating in a particular financial activity. Household size is the number of family members in a household. Three dummy variables are used to capture the impact of education level: Primary, Secondary, and Tertiary. Male equals 1 if the household head is a male. Age and Age-squared are the household head’s age and its square divided by 100. Majority equals 1 if the household head is a Han, the largest Chinese ethnic group. Own House equals 1 if the household owns a house. Household net income is measured by the log of household net income. Urban equals 1 for urban areas and Log (Education) is the average years of education of the county. All models are controlled for province-specific fixed effects. Variables
Probit models Deposit
Risky assets
Formal financing
Household size Education: primary Education: secondary Education: tertiary Male Age Age-squared Occupation: agriculture Occupation: waged Occupation: self-employ Agricultural hukou Majority Own house Household net income Urban Log (Education)
−0.059***
−0.043** −0.045 0.367*** 0.881*** −0.115** 0.079*** −0.086*** −0.334** 0.061 0.022 −0.306*** 0.094 0.185** 0.348*** 0.444*** 1.987***
0.038*** 0.030 0.105** 0.535*** 0.151*** 0.038*** −0.055*** 0.121*** −0.126** 0.199*** 0.228*** −0.036 0.020 0.135*** −0.188*** 0.161
0.015 0.034*** −0.053*** 0.022 −0.041 0.011 0.113*** 0.012 −0.057 −0.103*** −0.149*** −0.363***
Constant
−3.891***
−12.525***
−4.956***
0.416
12,478 0.128
12,365 0.372
12,503 0.125
12,503 0.082
Observations Pseudo R-squared * ** ***
0.116*** 0.269*** 0.546*** 0.052* −0.042*** 0.040*** 0.108*** 0.179*** 0.238*** 0.012 0.044 0.073 0.320*** 0.227*** 0.560***
Informal financing 0.091***
−0.048 −0.103*** −0.189***
10% level of significance. 5% level of significance. 1% level of significance.
hukou,1 majority, home ownership, household net income, urban areas and county’s average years of education. We control for province fixed effects in all models. Family is an essential element in Chinese society; it affects a wide range of household behaviors, including financial ones. Family and social interaction would likely affect household financial decisions through the word-of-mouth communication or observational learning (Ellison and Fudenberg, 1995). The CFPS data allow us to construct unique variables to measure family and social interaction. These variables are dummy variables for tombsweeping, keeping a family tree, and household head’s having a management job respectively, number of gifts a household gives in a year, and the frequency of paying attention to economic news. Tomb-sweeping is a family active playing respect and honoring ancestors and is a signal of strong family connection. Similarly, maintaining a family tree gives a sense of coherence and indicates a strong family network. Household heads holding a management position with discretion over resource allocation are likely to have stronger social connections. Number of gifts increases with the degree of social interaction, and thus can be a proxy for household sociability. Paying attention to economics news demonstrates awareness of financial and economic environments, which is likely to influence a household’s financial decisions. We describe the measurement of variables in Tables 1 and 2. 3. Empirical results 3.1. The roles of demographic factors Table 1 shows that household size is negatively related to the propensity to save and the probability of investing in risky assets. As financial burden increases with household size, the
1 Hukou is a household registration system in China. Every resident are classified as agricultural or non-agricultural and are assigned to a location. A resident who holds an agriculture hukou does not necessarily has a job in agriculture. The type of government benefits and services residents received is based on their type and location of registration.
ability to save and invest in risky assets declines. Financing needs increase with household size. The coefficients of household size are significantly positive in formal and informal financing, indicating that large households are more likely to borrow. The coefficients of primary, secondary, and tertiary education are all positively significant in the saving equation and increase with the level of education. A similar pattern emerges for risky investments except that the coefficient of primary education is insignificant. The coefficients of secondary and tertiary education are positively significant in formal financing but negatively significant in informal financing. Household heads with higher education have better access to formal financing and are less likely to resort to informal financing. Male is more likely to save but less likely to invest in risky assets; it contradicts to the general belief that women are more risk averse than men. Households with a male household head are more likely to borrow through formal channels, possibly because of the higher economic status of male in Chinese society. The coefficient of age (age square) is negatively (positively) significant in the saving equation. At first the propensity to save decreases with the age of household head due to increasing financial burden (e.g. education expenses) and relatively low income. As financial burden declines and income increases over time, it starts to decrease at a declining rate and then increases with age at some point. The coefficients of age and age square for all other financial decisions illustrate an inverted U-shaped relationship. Household heads are more likely to invest in risky assets and borrow when they are young. However, this behavior reverses as household heads accumulate wealth and become more risk averse at the higher age. Self-employed and waged workers have a higher chance to save. Household heads working in agriculture are more likely to save and use formal means of financing, but less likely to invest in risky assets. Households who hold agricultural hukou are less likely to invest in risky assets and have higher chance to borrow through both formal and informal sources. High income households are more likely to save and invest in risky assets. Home ownership has a strong association with risky investments only. Households resident in urban areas are more likely to save and invest in risky assets possibly
196
M. Gao, R. Fok / Economics Letters 136 (2015) 194–196
Table 2 Family/social interaction and household finance (probit models). Manager equals 1 if the household head holds a management position; Tomb-sweeping equals 1 if the household engages in tomb-sweeping activity; Family Tree equals 1 if the household maintains family tree. Economic News measures how often a household head pays attention to economic news; it equals 3 if the household frequently pays attention to economic news; 2 for occasionally; 1 for rarely; and 0 for not at all. Log (Gifts) is the natural log of number of gifts a household gives in a year. The dependent variables, other independent variables, and asterisks are as defined in Table 1. All models are controlled for province-specific fixed effects. Variables
Probit models Deposit
Risky assets
Formal financing
Informal financing
Manager Tomb-sweeping Family tree Economic news Log (Gifts) Urban Log (Education)
0.153** 0.060** 0.113*** 0.025** 0.044*** 0.210*** 0.631***
−0.010
0.204** −0.156*** 0.058 0.057*** 0.116*** −0.181*** 0.297*
−0.112
Demographic factors Constant
Yes −3.895***
Yes
Yes
−12.594***
−4.924***
Yes 0.533
Observations Pseudo R-squared
12,058 0.130
11,942 0.387
12,077 0.135
12,077 0.084
0.092* 0.002 0.146*** 0.046* 0.445*** 2.064***
due to the availability of financial services, but they are less likely to have formal and informal financing. Average education level, which is a control for social and economic disparities among counties, has a positive impact on saving and risky investments but is negatively related to informal financing. Table 1 shows that demographics are important in explaining various household financial activities, not just stock market participation. 3.2. The roles of family/social interaction Table 2 shows that households engaging in tomb-sweeping are more likely to save and invest in risky assets but less likely to have formal financing. Similarly, those with a family tree have a higher propensity to save and are more likely to borrow through informal mechanisms, indicating that a strong family network enhances saving and reduces the reliance of formal financing. In Chinese society, borrowing from family members is a popular substitute for formal financing. A strong family tie may have an impact on household head’s risk aversion, sense of financial responsibility, and the bequest motive to save more. Household heads holding a management position is more likely to have deposits and borrow from financial institutions. Paying attention to economic news is positively associated with the probability of saving, investing in risky assets and using formal means of financing. Households that are more aware of economic environment may be less anxious in making risky investment, realize the importance of saving, and are more informed about formal financing channels. The probability of savings, making risky investment, having formal and informal financing all increase with the frequency of gift giving, indicating that household heads with strong sociality are more informed and engage in more financial activities.
0.018 0.061* 0.013 0.041*** −0.150*** −0.327***
4. Conclusion Demographic characteristics have impacts on a wide range of household financial activities. Household size, education level, agricultural hukou, and household net income affect the decisions of investing in risky assets and saving in a similar way. Gender, age, square of age exhibit opposite effects on saving and risky investment. Household heads with higher education are more likely to rely on formal financing than informal financing. The propensity to borrow first increases then decreases with age. Households with strong family and social interaction are more likely to save, invest in risky assets, and borrow. Family interaction is positively related to informal financing. Acknowledgment The China Family Panel Studies (CFPS) data is funded by the 985 Program of Peking University and is constructed through the Institute of Social Science Survey of Peking University. References Allen, F., Carletti, E., Qian, J., Valenzuela, P., 2013. Financial intermediation, markets, and alternative financial sectors. In: Constantinides, G., Harris, M., Stulz, R. (Eds.), Handbook of the Economics of Finance, Volume 2A. North-Holland, Amsterdam, Netherlands, pp. 759–798. Campbell, J., 2006. Household finance. J. Finance 61, 1553–1604. Ellison, G., Fudenberg, D., 1995. Word-of-mouth communication and social learning. Quart. J. Econ. 110, 93–125. Karlan, D., Ratan, A., Zinman, J., 2014. Savings by and for the poor: A research review and agenda. Rev. Income Wealth forthcoming. Liang, P.H., Guo, S.Q., 2015. Social interaction, Internet access and stock market participation: An empirical study in China. J. Comp. Econ. forthcoming.
Contents lists available at ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Demographics, family/social interaction, and household finance Ming Gao a , Robert (Chi-Wing) Fok b,∗ a
Peking University, 5 Yiheyuan Road, Haidian District, Beijing, 100871, China
b
Department of Business, University of Wisconsin-Parkside, 900 Wood Road, Kenosha, WI 53141-2000, USA
highlights • • • • •
Demographic factors affect household saving, investing, and financing decisions. Large households are less likely to save and invest in risky assets. The age of household head and different financial activities are nonlinearly related. Family/social interaction increases the odd of saving, risky investing and borrowing. Family interaction increases the odd of using informal sources of financing.
article
info
Article history: Received 2 July 2015 Received in revised form 16 August 2015 Accepted 22 September 2015 Available online 3 October 2015
abstract We examine the role of demographics and family/social interaction in Chinese household finance. The impacts of demographic characteristics are not limited to stock market participation, but extend to other financial activities. Households with strong family and social interaction are more likely to save, invest in risky assets and borrow. Family interaction is positively related to informal financing. © 2015 Elsevier B.V. All rights reserved.
JEL classification: G11 Keywords: Household financial decisions Demographics Family/social interaction
1. Introduction Previous studies on household finance mainly focus on one financial activity once at a time. Some studies examine the role of demographic factors in stock market participation (e.g. Campbell, 2006); others investigate household saving with an emphasis on macroeconomics factors (e.g. Karlan et al., forthcoming). Most studies on financing decision are about corporations rather than households (Allen et al., 2013). Recently, researchers pay attention to the impacts of social interaction on household finance, but the focus point is whether to invest in the stock market (Liang and Guo, forthcoming). We know very little about how demographic factors and social interaction affect the other aspects of household finance besides stock market participation. We fill the gap by examining the roles of demographics and social interaction in Chinese household decisions of investing
∗
Corresponding author. Tel.: +1 262 595 2460; fax: +1 262 595 2680. E-mail addresses: [email protected] (M. Gao), [email protected] (R. Fok).
http://dx.doi.org/10.1016/j.econlet.2015.09.027 0165-1765/© 2015 Elsevier B.V. All rights reserved.
in risky assets, saving, and financing. As family is a cornerstone of Chinese society, we also consider the impacts of family interaction. 2. Data and methodology We use the data from the 2010 Chinese Family Panel Studies (CFPS), which surveys detailed financial information at the community, household and individual level across 25 provinces in China. The study covers 14,960 households and 42,590 individuals. To analyze the impacts of demographic factors on different aspects of household finance, we estimate probit models in which the dependent variables are the dummies for each type of financial activities. Risky assets include stocks and mutual funds. We divide household financing into formal and informal financing. Formal financing is borrowing from financial institutions such as commercial banks and credit unions. Informal financing is borrowing from family, friends, or other sources. Independent variables are the following demographic characteristics: household size, education level, gender, age and its squared term, occupation,
M. Gao, R. Fok / Economics Letters 136 (2015) 194–196
195
Table 1 Demographics and household finance (probit models). The dependent variable is a dummy variable that equals 1 when a household is participating in a particular financial activity. Household size is the number of family members in a household. Three dummy variables are used to capture the impact of education level: Primary, Secondary, and Tertiary. Male equals 1 if the household head is a male. Age and Age-squared are the household head’s age and its square divided by 100. Majority equals 1 if the household head is a Han, the largest Chinese ethnic group. Own House equals 1 if the household owns a house. Household net income is measured by the log of household net income. Urban equals 1 for urban areas and Log (Education) is the average years of education of the county. All models are controlled for province-specific fixed effects. Variables
Probit models Deposit
Risky assets
Formal financing
Household size Education: primary Education: secondary Education: tertiary Male Age Age-squared Occupation: agriculture Occupation: waged Occupation: self-employ Agricultural hukou Majority Own house Household net income Urban Log (Education)
−0.059***
−0.043** −0.045 0.367*** 0.881*** −0.115** 0.079*** −0.086*** −0.334** 0.061 0.022 −0.306*** 0.094 0.185** 0.348*** 0.444*** 1.987***
0.038*** 0.030 0.105** 0.535*** 0.151*** 0.038*** −0.055*** 0.121*** −0.126** 0.199*** 0.228*** −0.036 0.020 0.135*** −0.188*** 0.161
0.015 0.034*** −0.053*** 0.022 −0.041 0.011 0.113*** 0.012 −0.057 −0.103*** −0.149*** −0.363***
Constant
−3.891***
−12.525***
−4.956***
0.416
12,478 0.128
12,365 0.372
12,503 0.125
12,503 0.082
Observations Pseudo R-squared * ** ***
0.116*** 0.269*** 0.546*** 0.052* −0.042*** 0.040*** 0.108*** 0.179*** 0.238*** 0.012 0.044 0.073 0.320*** 0.227*** 0.560***
Informal financing 0.091***
−0.048 −0.103*** −0.189***
10% level of significance. 5% level of significance. 1% level of significance.
hukou,1 majority, home ownership, household net income, urban areas and county’s average years of education. We control for province fixed effects in all models. Family is an essential element in Chinese society; it affects a wide range of household behaviors, including financial ones. Family and social interaction would likely affect household financial decisions through the word-of-mouth communication or observational learning (Ellison and Fudenberg, 1995). The CFPS data allow us to construct unique variables to measure family and social interaction. These variables are dummy variables for tombsweeping, keeping a family tree, and household head’s having a management job respectively, number of gifts a household gives in a year, and the frequency of paying attention to economic news. Tomb-sweeping is a family active playing respect and honoring ancestors and is a signal of strong family connection. Similarly, maintaining a family tree gives a sense of coherence and indicates a strong family network. Household heads holding a management position with discretion over resource allocation are likely to have stronger social connections. Number of gifts increases with the degree of social interaction, and thus can be a proxy for household sociability. Paying attention to economics news demonstrates awareness of financial and economic environments, which is likely to influence a household’s financial decisions. We describe the measurement of variables in Tables 1 and 2. 3. Empirical results 3.1. The roles of demographic factors Table 1 shows that household size is negatively related to the propensity to save and the probability of investing in risky assets. As financial burden increases with household size, the
1 Hukou is a household registration system in China. Every resident are classified as agricultural or non-agricultural and are assigned to a location. A resident who holds an agriculture hukou does not necessarily has a job in agriculture. The type of government benefits and services residents received is based on their type and location of registration.
ability to save and invest in risky assets declines. Financing needs increase with household size. The coefficients of household size are significantly positive in formal and informal financing, indicating that large households are more likely to borrow. The coefficients of primary, secondary, and tertiary education are all positively significant in the saving equation and increase with the level of education. A similar pattern emerges for risky investments except that the coefficient of primary education is insignificant. The coefficients of secondary and tertiary education are positively significant in formal financing but negatively significant in informal financing. Household heads with higher education have better access to formal financing and are less likely to resort to informal financing. Male is more likely to save but less likely to invest in risky assets; it contradicts to the general belief that women are more risk averse than men. Households with a male household head are more likely to borrow through formal channels, possibly because of the higher economic status of male in Chinese society. The coefficient of age (age square) is negatively (positively) significant in the saving equation. At first the propensity to save decreases with the age of household head due to increasing financial burden (e.g. education expenses) and relatively low income. As financial burden declines and income increases over time, it starts to decrease at a declining rate and then increases with age at some point. The coefficients of age and age square for all other financial decisions illustrate an inverted U-shaped relationship. Household heads are more likely to invest in risky assets and borrow when they are young. However, this behavior reverses as household heads accumulate wealth and become more risk averse at the higher age. Self-employed and waged workers have a higher chance to save. Household heads working in agriculture are more likely to save and use formal means of financing, but less likely to invest in risky assets. Households who hold agricultural hukou are less likely to invest in risky assets and have higher chance to borrow through both formal and informal sources. High income households are more likely to save and invest in risky assets. Home ownership has a strong association with risky investments only. Households resident in urban areas are more likely to save and invest in risky assets possibly
196
M. Gao, R. Fok / Economics Letters 136 (2015) 194–196
Table 2 Family/social interaction and household finance (probit models). Manager equals 1 if the household head holds a management position; Tomb-sweeping equals 1 if the household engages in tomb-sweeping activity; Family Tree equals 1 if the household maintains family tree. Economic News measures how often a household head pays attention to economic news; it equals 3 if the household frequently pays attention to economic news; 2 for occasionally; 1 for rarely; and 0 for not at all. Log (Gifts) is the natural log of number of gifts a household gives in a year. The dependent variables, other independent variables, and asterisks are as defined in Table 1. All models are controlled for province-specific fixed effects. Variables
Probit models Deposit
Risky assets
Formal financing
Informal financing
Manager Tomb-sweeping Family tree Economic news Log (Gifts) Urban Log (Education)
0.153** 0.060** 0.113*** 0.025** 0.044*** 0.210*** 0.631***
−0.010
0.204** −0.156*** 0.058 0.057*** 0.116*** −0.181*** 0.297*
−0.112
Demographic factors Constant
Yes −3.895***
Yes
Yes
−12.594***
−4.924***
Yes 0.533
Observations Pseudo R-squared
12,058 0.130
11,942 0.387
12,077 0.135
12,077 0.084
0.092* 0.002 0.146*** 0.046* 0.445*** 2.064***
due to the availability of financial services, but they are less likely to have formal and informal financing. Average education level, which is a control for social and economic disparities among counties, has a positive impact on saving and risky investments but is negatively related to informal financing. Table 1 shows that demographics are important in explaining various household financial activities, not just stock market participation. 3.2. The roles of family/social interaction Table 2 shows that households engaging in tomb-sweeping are more likely to save and invest in risky assets but less likely to have formal financing. Similarly, those with a family tree have a higher propensity to save and are more likely to borrow through informal mechanisms, indicating that a strong family network enhances saving and reduces the reliance of formal financing. In Chinese society, borrowing from family members is a popular substitute for formal financing. A strong family tie may have an impact on household head’s risk aversion, sense of financial responsibility, and the bequest motive to save more. Household heads holding a management position is more likely to have deposits and borrow from financial institutions. Paying attention to economic news is positively associated with the probability of saving, investing in risky assets and using formal means of financing. Households that are more aware of economic environment may be less anxious in making risky investment, realize the importance of saving, and are more informed about formal financing channels. The probability of savings, making risky investment, having formal and informal financing all increase with the frequency of gift giving, indicating that household heads with strong sociality are more informed and engage in more financial activities.
0.018 0.061* 0.013 0.041*** −0.150*** −0.327***
4. Conclusion Demographic characteristics have impacts on a wide range of household financial activities. Household size, education level, agricultural hukou, and household net income affect the decisions of investing in risky assets and saving in a similar way. Gender, age, square of age exhibit opposite effects on saving and risky investment. Household heads with higher education are more likely to rely on formal financing than informal financing. The propensity to borrow first increases then decreases with age. Households with strong family and social interaction are more likely to save, invest in risky assets, and borrow. Family interaction is positively related to informal financing. Acknowledgment The China Family Panel Studies (CFPS) data is funded by the 985 Program of Peking University and is constructed through the Institute of Social Science Survey of Peking University. References Allen, F., Carletti, E., Qian, J., Valenzuela, P., 2013. Financial intermediation, markets, and alternative financial sectors. In: Constantinides, G., Harris, M., Stulz, R. (Eds.), Handbook of the Economics of Finance, Volume 2A. North-Holland, Amsterdam, Netherlands, pp. 759–798. Campbell, J., 2006. Household finance. J. Finance 61, 1553–1604. Ellison, G., Fudenberg, D., 1995. Word-of-mouth communication and social learning. Quart. J. Econ. 110, 93–125. Karlan, D., Ratan, A., Zinman, J., 2014. Savings by and for the poor: A research review and agenda. Rev. Income Wealth forthcoming. Liang, P.H., Guo, S.Q., 2015. Social interaction, Internet access and stock market participation: An empirical study in China. J. Comp. Econ. forthcoming.