Asset allocation of only children: expectations and tests based on trust and risk attitudes
【Abstract】China once implemented the one-child policy for more than 30 years. Therefore, it is of great practical significance to study the behavior of only children after they grew up. Against the social background of China’s “one-child generation” becoming adults, entering the labor market and becoming the backbone of all sectors of society, the long-term economic impact of the one-child policy may be the key to China’s economic development. Using the data of the 2010 China Family Panel Studies and the 2011 China Household Finance Survey, the paper empirically assessed the asset allocation behavior of only children to reveal the far-reaching impact of China’s one-child policy on China’s financial market. The study found the following. (1) Compared with non-only children, only children are more risk-averse in asset allocation activities: the households whose heads grew up as an only child have a significantly lower probability of investing in stocks than those whose heads grew up with siblings and the market value of stocks by them is also smaller. (2) The reason why only children are more risk-averse than non-only children may be due to the lack of social interaction with and resource dilution of siblings, making only children less trusting and more risk-averse than non-only children. (3) The mechanism test confirms that the trust and risk preferences of household heads significantly increase the probability of the households investing in stocks and the market value of the stocks held by such households, and only children are more distrustful than non-only children. However, there is no evidence showing that there are significant differences in the risk attitudes between only children and non-only children. The above conclusions mean that the behavioral characteristics of only children being averse to risk assets will have a significant impact on the market structure of financial products. Therefore, relevant government departments need to pay close attention to the potential risks and economic impacts that may be hidden.
【Keywords】 only child; asset allocation; trust; risk attitude;
. ① Source: http://finance.people.com.cn/GB/1045/3964145.html [^Back]
. ① In order to increase the sample size, this variable has a value of 0 for households that do not have a stock account. [^Back]
. ① Among them, age is the age of a resident at the time of interviewing; educational attainment is assigned integers of 1−8, which correspond to “no schooling,” “primary school,” “middle school,” “high school,” “bachelor’s degree,” “master’s degree” and “doctor’s degree” from low to high; marital status includes two dummy variables: married and divorced; in the CHFS2011 survey, there is no household income survey, and we use the total household expenditure as a proxy. And in the same way as the total household income in the CFPS2010 survey, it enters the regression equation in a logarithmic form; household assets are measured by household assets deducted by stocks, and their natural logarithms are also taken in regression. [^Back]
. ② Due to space limitations, this paper does not give the difference in the mean of each variable between only children and non-only children in the form of a data sheet, which is available upon request. [^Back]
. ① Due to space limitations, the results of regression Equation (2) are not reported in this paper and are available upon request. [^Back]
. ② In the first-stage regression by the instrumental variable method, the instrumental variable increases the probability that the individual is an only child, and passes the 1% significance test, indicating that the instrumental variable is not a weak instrumental variable for only-child identity. Due to space limitations, this article does not report the first-stage regression results. In addition, Lewbel et al. (2012) pointed out that when the endogenous explanatory variable in the probit regression equation is a dummy variable, using the instrumental variable method to estimate the probit model cannot obtain a consistent and effective estimator of the regression coefficients; an alternative option is to use a linear probability model with instrumental variables to estimate the equation. Therefore, in accordance with the recommendations of Lewbel et al. (2012), this paper uses a linear probability model with instrumental variables to estimate the impact of household head’s only-child identity on the probability of household’s investment in stocks. [^Back]
. ① Data on trust in the majority come from respondents’ answers to the question: “Generally, do you think most people can be trusted, or it is better to more carefully deal with others.” If the respondent chooses the answer that “most people can be trusted,” the trust in the majority is assigned a value of 1. On the contrary, if the respondent chooses the answer that “it is better to more carefully deal with others,” the trust in the majority is assigned a value of 0. Data on trust in strangers come from respondents’ answers to the question: “How much do you trust in strangers?” Integers of 0−10 are assigned to the answers, with 0 meaning very distrustful and 10 meaning very trusting. [^Back]
. ① Data on financial risk come from respondents’ answers to the question: “If you have an asset, which investment project are you willing to choose.” Integers of 1−5 are assigned to the answers, corresponding to “unwilling to take any risk” and “slightly low risk and slightly small return,” “average risk and average return,” “slightly high risk and slightly large return” and “high risk and high return” from low to high; the data on wearing a seatbelt are from respondents’ answers to the question: “Do you always wear the seatbelt when you take a car or drive.” Integers from 1 to 3 are assigned, corresponding to the answers such as “yes,” “it depends” and “no.” The data on running a red light come respondents’ answers to the question: “Do you strictly observe the rules of traffic lights when crossing the road. Integers of 1−4 are assigned, which correspond to the answers: “always observe,” “occasionally not observe,” “it depends” and “occasionally observe.” [^Back]
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