"Hen-pecked" or "Not Hen-pecked": The Research on Factors Affecting Chinese Household Financial Decision-making Mode

会计学院 2016 级硕士研究生 ***

【Abstract】In this paper, we use the database of CHFS to study the factors in the decisions of the allocation of family financial rights. We focus on exploring the effects of a husband and wife financial discourse of their income gap. The results show that when the husband is a party member, or the wife is a party member as well as financial practitioner, the husband is more likely to make family financial decisions. And the more income a husband earns the greater likelihood of his making decisions in the family financial affairs. In terms of the income gap between spouses, we find that a husband has more tendencies to make family financial decisions when he earns more compared to his wife’s income. So, we reach the conclusion that hen-pecked husbands can have more financial discourse power when they earn more money. And most hen-pecked husbands do not give up their financial rights without protest, but have to due to their lack of financial resources.【Key words】Financial Decision; Income gap; Age Gap; Regional Distribution

I. Introduction

Equality between men and women are always the hot spot of world educational research, which embodies the social civilization and progress. One of the fundamental points of equality between men and women is to achieve the gender equality in rights and responsibilities.The article attempts to analyze the equality between men and women of the family in the angle of

economics ， which is reflected in the equality of distribution in financial decision-making power.Combined with the reality of life, the article is to research financial decision-making of husband and wife in Chinese family based on the “hen-pecked”1 phenomenon. We define the “hen-pecked” as the husband who volunteer to assign the financial decision-making to his wife, without affecting by his individual talents.The opposite side is defined as the “not hen-pecked”.

Western researches in household finance account for four aspects(Cheryl Doss, 2011): 1.the household utility model; 2. the effectiveness of financial decision; 3.the factors of allocation of resources within family; 4.design behavior experiment to study the family internal decision-making processes. Western countries have conducted comprehensive researches in household finance, while Chinese researches in household finance are in infancy. Owe to the limit of data, there is almost no empirical literature on the factors affecting financial decision-making distribution. Thus the articleprovides a new perspective for academic circles.

The both sides of husband and wife family status reflect as the power of financial decision-making. The article discusses the factors affecting the power of financial decision making in family. The research emphases that: 1.by building income gap index, to research the effect of income gap on stocks decision in family and to verify whether economic strength is affecting the family financial decisions; 2.devide into three groups by ages, which are young, middle aged, and

1 In Chinese culture, the husband who is afraid of his wife is always called “hen-pecked”. For such those husbands, no matter how successful they are in enterprises and how social status they have, they would be reverent and respectful in front of wife and they volunteer to be managed by their wife, which includes the assignment of financial decision-making right. Thus we define the husbands who volunteer to assign financial decision-making right without considering their economic advantage and social status as “hen-pecked”. Otherwise, they are defined as “not hen-pecked”.

the elderly group, so as to explore the effect of life cycle on the financial decision-making in family. In this paper, we found that: income gap, education years, industry are the important factors affecting the financial decision-making distribution between husband and wife in family. Besides, the income gap between couples and the education years of husband are both significantly positive-related to the decision power of husband. It means the more obvious economic advantages and the longer education years the husband has, the higher probability the husband charges the decisions in family. Then the wife’s industry is significantly negative-related to the decision power of husband. It means when the wife’s industry is fiancé, the probability that the wife charges the decisions in family is higher. Meanwhile, the different time preference has certain influence on “hen-pecked”. Chinese men have larger utility discount factor compared to the Chinese women. It means men pay more attention to short-run benefits and are inclined to own the financial decision-making rights at the very beginning. Thus the levels of “hen-pecked” are increasing as time passes. Finally we explore whether the gender of decision makers would affect the investment income, and we find that the decision makers’ gender would not be significant factors affecting investment income.

II. Literature Review

Family internal resource allocation is an important subject of the western social science research on the equality of husband and wife. Most studies abroad on couples’internal bargaining power and the allocation of resources are mostly based on the female perspective. They hold the opinion that women bargaining ability would influence some key conditions of family, such as the health and education levelof next generation, the female’s benefits and rights, and the distribution of decision-making rights, etc. In addition, the bargaining power of the female can affect household production, such as labor supplies, household, agricultural production and work and so on.For policy makers, therefore, the research on the bargaining power between husband and wife is of great significance.

For the simulation in the family decisions, economists adopted three methods oftheory, namely common preference model, cooperative game model and non-cooperative game model (Pollak, 1994). Common preference model keeps the single form of family the utility function, whose essence is the same with traditional new classical model. Cooperative game model and can be divided intodivorce threat model and separate-spheres models, whose common featureis to use Nash bargaining theory to get Nash solutions corresponding to their respective of threat points.Non-cooperative model inherited non-cooperative bargaining theory established by Rubinstein using strategic method, and discussed the both sides of the reservation utility in determining the role of repeated game solution.The model simulated the bargaining process by dynamic method and considered the status and ability of both bargaining parties. The mode of non-cooperative game model is used by the divorce threat model. The dynamic game models developed by the mode provide a plausible explanation for the phenomenon that the traditional theory failed to explain.

(I) The effect of income on financial decision-making rightA large number of foreign literature show that although the west married women employment

increase year by year, but the family finance decision-making right is still less than her husband. The existing theoretical literature research perspectives choose labor economics as a starting point, analyze its impact on family decisions, and argue that inequality of financial decision-making would derive from sexes’ inequality. Whether in the family or in society, women's position is

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lower than her husband, so they have economic dependence on their husbands to different level. And this dependence makes his wife at a disadvantage in the "negotiations" with her husband, and has to assign the financial decision-making right to her husband (Brines, 1994; Lennon reclaimed, Rosenfield, 1994).In addition, husband is important "maintenance" position in the home, more extensive social networks and strong social skills make the husband easier to achieve the family decision-making right. While the wife is usually limited in the range of family, has less economic resources, so family decision-making power is less than the husband (Blood & Wolfe, 1960; Steil.1997).In the empirical literature, Chery Doss (2013) found that wife's degree of education, income and assets could measure the bargaining power, and the family that the wife has higher bargaining power would have better welfare results. MartinBrownings et al (2013) argues that both sides of couples can get more consumer goods through marriage, including private consumption and public consumption. The couple's bargaining power not only affects the allocation of resources but also affects the number of consumption and spending. Marcos a. Rangel (2006), using the data from Brazil, found that the higher bargaining power the wife grasp, the less timeshe workevery day, and then the more spending for the next generation education. Anandi Mani (2011), using a set of experimental data in the UK, found that the family investment effectiveness has nothing to do with the couple to master information, on the contrary, couples tend to sacrifice the effectiveness of the investment to grasp more family economic resources. SoniaOreffice (2011) found that between the same-sex couples, the higher the age and the higher the non-labor income they have, the behavior individual have higher bargaining ability and they provide less labor. It can be seen that foreign literatures mostly are how the bargaining power between the couples affect the family welfare, including the validity of the allocation of resources, investment, etc. However the literatures barely involve how the bargaining power of husband and wife influence on the distribution of financial decision-making right.Therefore, the study of this paper is innovation to some degree.

For the domestic literature, it can be seen from the literature review of household decision-making theory (Wu guiying, 2002) that past Chinese literatures account for how the financial situation affect the female status and family relationship. In addition, there are literatures about the economic background and the status of rural women(Gong, Zhong, and Sun, 2009), Children’s gender preference and the female’s status (Wu and Li, 2011), and the factors of income gap between the couple (Han xiulan, 2012). The article of Shi daiming (2005) that research the decision of financial assets is a little related to my article, exploring how the household risk-bearing ability, financial situation, and the talent of the household personal endowment affect the decision of household financial assets. However the data of that article comes from the questionnaire survey of local area of Sichuan province, which is not representative enough. The article use the CHFS data in 2013 to investigate the factors of couple’s bargaining power in the angle of household finance, explore how the income affect the distribution of decision-making right, providing a new angle for the household finance area.

(II) The effect of time preference on financial decision-making rightIn the classical finance theory, the objective function of decision model across the period is a

utility function that is time addictive and state separable, and the single-period utility can be discounted by exogenous and exponential declined discount rate.Partricia K. Smith (1995) pointed out that the level of satisfaction for individual has different "time preference"1. Similar to the

1 Time preference means the phenomenon that behavior individual has more preference in current than future. The time preference ratio equals the marginal rate of substitution of current utility and future utility, which is represented as discount rate.

discount rate of the economics, the utility also has a discount rate -- utility discountfactor.Alan R. Rogers (1994) show that affected by the nature selection, different individuals have

different utility coefficient of time preference. Subject in the western study of a lot of time preference of literature, but the conclusions verifies.Alan R. Rogers (1994) and Patricia K. Smith et al (2005) has been proved that the utility discount factors of men and women are significantly different. The utility discount factor of men is higher than women, which means that men pay more attention to long-run benefits. However, Sunghan Kim et al. (2002) found that the sex of children has no effect on their time preference coefficient; on the contrary, the age and race are significantly affecting the discount factor. The domestic researches on time preference concentrate on the consumption behavior, investment behavior, and the design of financial product1. This article use CHFS data in 2011 to explore the factors affecting Chinese household financial decision-making mode, and discovery some new founding.

III. Theoretical Assumption

(I) The effect of income on financial decision-making rightForeign empirical literature 2shows income is the important factor affecting the household

decision-making right between the husband and wife. NavaAshraf (2009) adopted the daily wages to measure the income gap of the both sides of husband and wife, and found that the parties that have higher average daily wages would have higher decision-making power. Some literature used the non-labor income to measure the bargaining power between the couples3, similarly finding that the non-labor income has a significant influence on bargaining power.In short, many empirical literatures achieve that the income is the important factor affecting the household decision-making right between the husband and wife.

In theory, the separate spheres models are built to conduct the relationship of income and financial decision-making right. In the separate spheres models, the husband and wife use Nash bargaining to resolve differences, but the deal in the marriage is inefficient and non-cooperative equilibrium.In non-cooperative equilibrium, given the partners’ behavior,the other party spontaneously provides his own discretionary economic resources, and the nondiscretionary economic researches falling in his “hemisphere”, and chooses the utility-maximizationbehavior. By the control of economic resources, select the utility maximization behavior.The noncooperation marriage may be better than the divorce situation for bother parties of couples. When they fail to reach an agreement, divorce may be eventually threat to spouse, but because of the consumption related goods and services, the interests of both sides can get marriage may be repeated non-cooperative bargaining threat.Ball bargaining produces household demand, in some cases, this demand is nonrelated to who obtain income after divorce, but is related to who obtain and control the income before the divorce.

The Neumann-Morgenstern utility function is established: Uh(xh , q1 , q2)and

Uw (xw , q1 , q2). xhandxw represent the discretionary economic resources of husband and wife,

which directly indicating their financial decision-making right. q1∧q2represent the nondiscretionary economic resources of husband and wife. We suppose that both of them are not altruist, and both q1∧q2 are provided voluntarily. Simultaneously, we define “threat point” as the

utility achieved by the game parties after the marriage bargaining fails. T h( p1 , p2 , I h , Iw) and

1Such as Ye Dezhu (2004), Ye and Lu (2009).2 Such as Hoddinott and Haddad(1995), Phipps and Burton(1993)3 Thomas(1993), Thomas and Chen(1993) and Scheultz(1990)

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T w ( p1 , p2 , I h , Iw) represent the divorce threat function of husband and wife. p1∧p2 represent

the price of related goods and services and we suppose the price of xhand xw both equal 1. I h∧Iw represent the income of husband and wife. Nash equilibrium solution is obtained when xh , xw , q1 , q2 , reach to the maximization, which is shown as figure 1.

Figure1 Nash equilibrium solution

We contribute a symmetrical Cobb-Douglas function—“Nash marriage benefits function”:

N= [U h (xh , q1, q2)−T h ( p1 , p2 , I h , Iw ) ][Uw (xw ,q1 , q2 )−T w ( p1 , p2 , I h , Iw )]The function is limited by the household consumption and income:

xh+xw+ p1∗q1+ p2∗q2=I h+ IwThe demand function is obtained:

x i=gxi ( p1 , p2 , I h , Iw ) i=h ,w

qk=gq k ( p1 , p2 , I h, Iw )k=1,2In the non-cooperative marriage, q1 is provided voluntarily by husband,q2 is provided

voluntarily by wife. The number of q1 and q2 provided is determined by the husband and wife

simultaneously.The husband should obtain the number of xh∧q1to maximize the U h (xh , q1 , q2' )

in the limitation of xh+p1∗q1=I h, and the q2' is determined by the wife. We suppose the

response function as xh=f x k ( p1 , I h , q2' ). At the same time the wife should obtain the xw∧q2 in

the same way. To simplify the analysis, considering the Klein-Rubin-Stone-Geary utility function below:

U h=ah log (xh−xh' )+βh log (q1−q1h

' )+(1−ah−βh ) log ¿)

Uw=aw log (xw−xw' )+ βw log (q2−q2h

' )+(1−aw−βw) log ¿)

Owe to the separation of the utility function of couples and the response function is independent of the nondiscretionary economic resources, we can simplify the demand functions as:

xh=xh' +ah¿)

xw=xw' +aw¿)

q1=q1h' +

βh

p1¿)

q2=q2h' +

βw

p1¿)

Based on the analysis above, it can be seen that the discretionary financial decision-making right in a family is positively related to the individual income of husband or wife. Therefore, the distribution of financial decision-making right is directly related to the income of couples, the party who have higher income would grasp more financial decision-making right. In a word, based on the existing literature or theory conduction, we could suppose that the income indeed affect the financial decision-making right. Therefore, hypothesis 1 is raised.

Hypothesis 1：The income gap between the husband and wife would affect the distribution of financial decision-making right, and the party who have higher income would grasp more financial decision-making right.

(II) The effect of time preference on financial decision-making rightIn a marriage, would the time preference difference aroused by sex gap affect the distribution

of financial decision-making right in different life cycle period? We try to conduct the theory. Combined with the discount factor utility models raised by Samuelson (1937), we conduct a time preference decision-making right game model to analyze how the life cycle affect the distribution of household financial decision-making right. We suppose that the utility obtained by the party who make the decision is “a”, and the other party obtain b (a >b). When both parties make the decision or do not make the decision simultaneously, the utility they obtain are both 0. It can be illustrated by the matrix below:

Female

Male L RT ¿

a ,b¿B ¿b , a¿0,0¿

TL represent both parties do not make decisions, TR represent the husband make decisions, BL represent the wife make decisions, and BR represent both parties make the decisions. We suppose the husband’s discount factor of future utility isδ 1, and the wife’s discount factor of future

utility is δ 2. The utility function is as follows:

Husband: π1=b (1−δ 1T )+a∗δ1

T

Wife π2=a (1−δ 2T )+b∗δ2

T

Household utility: π2=b (1−δ 1T )+a∗δ1

T+a (1−δ2T )+b∗δ 2

T

=(a+b )+(a−b)(δ1T−δ2

T)Then we conduct the derivation of household utility to T, and obtain the optimal solution of

T:

Optimal choice: dπ

dT=(a−b)¿*ln δ 2¿=0

Foreign literature (Alan R. Rogers, 1994) shows that the discount factor of the men is higher than the women. If we suppose a=3, b=1, δ 1=0.8, δ 2=0.6, the T ¿=¿2.8716. The figure is as follows:

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Figure2 the optimal decision-making period

It can be seen that, the household utility of decision-making is increasing firstly and decreasing secondly as the time passes. And the household utility of decision-making reaches to the peak when T equals T ¿. Nash equilibrium is : before T ¿, wife makes the decision, after T ¿, husband makes the decision. The discount factor of husband is higher than the wife, which means the husband pays more attention to the long-run benefits while the wife pays more attention to the short-run benefits. Based on the theory analysis above, hypothesis 2 is raised.

Hypothesis 2：The household decision-making utility discount factor of husband is higher than the wife’s. The husband is inclined to assign the decision-making right to his wife in the early stage of marriage, but to grasp the right in the later stage of marriage.

IV. Data and variable

(I) Data The article uses CHFS data (2011) to do the analysis, which is one of the most representative

databases of household finance in China. The data comes from the household data and individual data in 2011. Owing the article chooses the decision-making of stocks as the household financial decision-making, we would:

(1) Delete the household that do not buy in stocks and delete the household whose data is incomplete.

(2) Delete the household that any parties of husband’s or wife’s income data are incomplete.

(3) Screen out the household that the stocks decision-making is by the husband or wife. Then delete the household whose other information is incomplete.

(II) Variable1. Dependent variable:The article focuses on the factors affecting household financial assets decision-making right,

so it is important to describe the financial assets decision-making right. Based on the CHFS data, stocks is the most important financial asset. The household who own stocks account for 8.84%, which is much higher than the household who own bonds (0.07%), funds (4.24%), derivatives (0.05%) and financial products (1.10%). In china, stocks are the most popular investment channel for the household. Therefore, it is representative for using the decision-making right of stocks to

describe the household financial assets decision-making right.In the CHFS data, the selections of stocks decision-making are particular. The single

decision-making data is chosen, and divided into two groups that are the husband makes decision and the wife makes decision. And we suppose the group that is the husband makes decision as 1; the group that is the wife makes decision as 0.

2. Independent variable (1) Household economic structure a. Income gap between couplesBased on the related theory of economic decision-making (Brines, 1994; Lennon

&Rosenfield, 1994), the distribution of financial decision-making right is essentially the game of economic resources owned by bother parties between the couples. Therefore, the income gap between couples have important effect on financial decision-making right. Considering the income gap might be related to the number of income and different household income would produce different income gap, we choose the income gap (husband’s income minus wife’s income) divided by the whole income of husband and wife to describe the income gap between couples.

b. Household net assets The household net assets represent the household discretionary economic resources, which

has significant effect on the investment decision-making. The household wealth is the net assets accumulated by all the family members in the long-run. Income is the flow-oriented concept while the household wealth is the stock-oriented concept. The household net assets affect the preference of investment products and the level of risk-bearing ability, then affect the household investment decision-making.

(2) Competence effect Competence effect means that people's uncertainty aversion depends on the person's

subjective ability level, when people think that they have related ability, they tend to make decisions based on their own judgment rather than choose an event with high probability. When people think their ability isinsufficient,however, tend to choose an event with high probability.

Many empirical researches show that competence effect has significant effect on explaining the investors’ behavior in stock market. Combined with the past research achievement, we choose the education degree and industry to describe the competence effect.

a. Education degree As intuitive measure of human capital, education degree is a good way to depict competence

effect.Those who have higher education degree can quickly understand the characteristics of financial products, participate in the relatively low cost, easy to grasp investment opportunities, tend to think their own ability is stronger. Thus education degree would affect the investment decisions of the financial products.

b. Industry Industry's influence on the distribution of the financial decision-making power is mainly

manifested in the information cognition and the professional level. Intuitively, individual in the financial industry is easier to grasp the speaking right on stocks investment, and would have the more advantages on the allocation of financial decisions. Also, we will do the 0-1 variable

processing for industry variable， recording industry for the financial sector is 1, otherwise 0.(3) Social-demographic characteristicsa. Age gapBased on the theory of life cycle, the risk preference in different life cycle varies. Generally

speaking, the age is negatively related to the risk preference. Therefore, it is necessary to put the

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age gap between husband and wife into the right of the equation. We use husband’s age minus wife’s age as the age gap.

b. Average age In the same way, the average age of husband and wife would have effect on the distribution

of financial decision-making right.c. Politics status In combination with Chinese national conditions, we would include the politics status in the

independent variables. The political status reflects the political resources (relationships) and social status that investors can grasp to a certain extent. And it would have certain influence on investment decisions. We suppose the communist as 1, non-communist is 0.

d. Census register Similar to the political landscape, we incorporate census register in independent variables.

Census register is characteristic of the system in China. For a long time it not only has a significantly influence on China's macro economy, but also affect the characteristics and risk recognition of the individuals. We suppose the urban as 1, rural is 0.

e. The number of children The number of children is an important factor of family structure. Foreign literature has

shown that the number of children and sex will have an impact on the mother's family status (Steil 2005).Accordingly, in our opinion, the number of children will also affect the couple's bargaining power, therefore also incorporated in the independent variables

Table 1 Variables and definitionsVariable Symbol Definition

Financial decision-making mode maldec the husband makes the decision=1，the wife makes decision =0

The coefficient of income gap incdis(husband’s yearly income –wife’s yearly

income ) / (husband’s yearly income +wife’s yearly income )

Husband’s industry malefin Finance industry=1， other =0Wife’s industry femfin Finance industry=1， other =0

Husband’s education degree maledu The year of educationWife’s education degree femedu The year of education

Husband’s census register malpla Urban =1， rural =0Wife’s census register fempla Urban =1， rural =0

The age gap agedis Husband’s age –wife’s ageThe average age agemea (husband’s age +wife’s age ) / 2

The husband’s politics status malpar Communist =1， other =0The wife’s politics status fempar Communist =1， other =0The number of children chinum The number of children in the householdThe household net assets log(netcap) Log(assets-liabilities)

3. The descriptive statistics

Table 2 The descriptive statistics of variables

Variables Observations Mean Std.dev. Min Max

Financial decision-making mode 270 0.60 0.49 0.00 1.00The coefficient of income gap 270 0.14 0.35 -1.00 1.00

Husband’s industry 270 0.05 0.22 0.00 1.00Wife’s industry 270 0.06 0.23 0.00 1.00

Husband’s education degree 270 14.01 2.79 9.00 22.00Wife’s education degree 270 13.43 3.15 6.00 22.00

The average age 270 45.09 11.97 24.00 76.50The age gap 270 1.90 2.87 -8.00 13.00

Husband’s census register 270 0.96 0.21 0.00 1.00Wife’s census register 270 0.93 0.26 0.00 1.00

The husband’s politics status 270 0.47 0.50 0.00 1.00The wife’s politics status 270 0.31 0.46 0.00 1.00The number of children 270 0.82 0.54 0.00 3.00The household net assets 270 141.68 158.23 14.07 1110.00

It can be seen from the above descriptive statistics of variables that the stock decisions are mostly make by the husband. Overall, her husband's income is more than his wife, and different family situation varies greatly. The mean age of selected samples is at about 45 years old, and the variance is relatively high. The regression analysis will be focused on the income gap and age.

V. Model construction

The above analysis shows that income may be the key factors influencing the distribution of financial decision-making. The article will choose income as key variable. In the literature there are different ways to measure the income of both sides of husband and wife, and the most common method is to use both sides of husband and wife's wage income (Hoddinott and Haddad, (1995) and Phipps and Burton (1993), Ganesh Seshan and Dean Yang 13 Nava Ashraf 1).Measured by wage income, however, it would lead to two problems in the bargaining power of husband and wife. One of them is the bargaining power of the party who has no wage income cannot be measured accurately, the other of them is the wage income would affect the shadow price of marriage public products because of the existence of opportunity costs. Therefore, wage income cannot accurately measure the economic resources that the couple grasp, and also cannot accurately reflect the discussion ring bargaining power of both sides of husband and wife.In order to make up for the defect of wage income, literature use income that is not from wage (Thomas and Chen, 1993 and Sheultz1990), wage rates (Robert a. Pollak 2006), and other indicators to complete the measurement of income.Considering the availability of data as well as China's national conditions, this paper uses the disposable income of husband and wife as income after repeatedly weigh, and adopts the given algorithm in CHFS "questionnaire training manual". Disposable income is obtain by all the annual income of the wage income, operating income and property income, transfer income and sale of property income, after minus the personal income, which is the main factor to explore its effect on the distribution of financial decision-making. At the same time, there is little literature choosing income as the only measure of bargaining power, and also think the education degree (Thomas, 1994), age, race, the first children age, gender, family size, working hours, personal savings and household production (Robert a. Pollak 2005) affects the bargaining power of both sides of husband and wife. Because most of these literatures use the bargaining power as the independent variable to study the problems such as household consumption, investment effectiveness, which is different from the research idea of this article to a certain degree. Therefore in this paper, we delete some related factors in the model of setting. Finally, we draw lessons from Chery Doss (2013) research. Model (1) is constructed.

maldec=β0+β1incdis+β2malfin+β3 femfin+β4maledu+β5 femedu+ β6agemea+β7agedis+μ (1)

maldec represent the financial decision-making mode. Incdis represent the coefficient of

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income gap. Malefin and femafin represent the industry of husband and wife. Maleduand femedurepresent the education degree(education years) of husband and wife. Agedis represent the age gap between the husband and wife. Agemean represent the average age of couples.

Considering the China's national conditions, we think the census register and the politics status also affect the bargaining power of both sides of husband and wife. So on the basis of the model (1), we add the census register and the politics status of both parties of the household as control variable. Model (2) is constructed.

maldec=β0+β1incdis+β2malfin+β3 femfin+β4maledu+β5 femedu+ β6agemea+agedis+β8malpla+ β9 fempla+β10malpar+β11 fempar+μ (2)

malpla and fempla represent the consensus register of husband and wife. Malpar and fempar represent the politics status of husband and wife. Other definitions are the same with model (2).

In addition, foreign literature also found that the fertility rate (Robert a. Pollak, 2005) and assets (Cheryl R.Doss 1996) difference would lead to the differenceof bargaining power of both sides of husband and wife in different household. Thuswe add the number of children and household net assets as control variables. Model (3) is constructed.

maldec=β0+β1incdis+β2malfin+β3 femfin+β4maledu+β5 femedu+ β6agemea+β7agedis+β8malpla+β9 fempla+ β10malpar+β11 fempar+β12chinum+ β13 log (netcap )+μ

(3)

chinum represent the number of children. Log(netcap) represent the logarithm of household net assets.

We first adopt linear probability model (LPM) regression. However because of two shortcomings of the linear probability model (fitting the probability may be less than zero or greater than 1, partial effect with any of the variables are all the same), we increase the probitmodel for research.

In order to explore how the couples’ time preference affects financial decision-making right distribution, we made a further study.Based on Franco Modigliani's life cycle theory, we divided the average age of the couples. According to the average age of couples, we add two dummy variables, one is whether couples’ average age is less than or equal to 35 years old or not the variable equals 1 when the condition is satisfied, otherwise 0;The second is whether couples’ average age is more than 50 years old or not , the variable equals 1 when the condition is satisfied, otherwise 0. By the two dummy variables, we divided the whole sample into three groups according to age, young couples under the age of 35, respectively, 35 to 50 years old of middle-aged couples and over the age of 50 middle-aged couples. And we add two interaction variable that are(income difference *dummy variable that is less than or equal to 35 years old or not) and (the income difference* dummy variable that is more than 50 years old or not). After adding those two virtual variables and interaction in the above three model, structure model (4), (5), (6), exploring the characteristics of different groups.

We add two dummy variables that are age_35 and age_50. When the average age of couple is

less than or equal to 35, age _35=1 ， otherwise age_35=0. When the average age of couple is

more than 50，age_50=1，otherwise age_50=0. We contribute two interaction variable that are age_35*incdis and age_50*incdis to explore how the income gap of the young couple, middle-age couple, and old couple affect the financial decision-making right distribution.

VI. The empirical regression result and analysis

We use LPM and Probit to do the regression on model (1), (2), (3) to explore the effect of income gap on decision-making right distribution. The regression result is as table 3 shows.

Table 3 Regression result (the dependent variable: decision-making mode)Model(1) Model (2) Model (3)

Variable LPM Probit-AME LPM Probit-AME LPM Probit-AMEincdis 0.180** 0.185** 0.178** 0.184** 0.175** 0.182**

(0.087) (0.085) (0.089) (0.085) (0.089) (0.086)malfin 0.058 0.091 0.044 0.074 0.051 0.079

(0.135) (0.149) (0.140) (0.154) (0.141) (0.154)femfin -0.354*** -0.343*** -0.358*** -0.344*** -0.350*** -0.335***

(0.129) (0.122) (0.130) (0.121) (0.131) (0.121)maledu 0.040*** 0.039*** 0.041*** 0.040*** 0.041*** 0.040***

(0.014) (0.013) (0.015) (0.014) (0.015) (0.014)femedu -0.019 -0.019 -0.016 -0.015 -0.015 -0.014

(0.014) (0.014) (0.015) (0.015) (0.015) (0.015)agemea -0.008*** -0.007*** -0.007** -0.007** -0.006** -0.006**

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)agedis 0.004 0.004 0.003 0.003 0.002 0.002

(0.010) (0.010) (0.010) (0.010) (0.010) (0.010)malpla -0.165 -0.183 -0.167 -0.184

(0.173) (0.179) (0.174) (0.180)fempla -0.009 -0.023 -0.009 -0.022

(0.144) (0.142) (0.145) (0.142)

malpar 0.002 -0.001 -0.003 -0.005

(0.064) (0.062) (0.064) (0.062)

fempar -0.032 -0.029 -0.040 -0.037(0.074) (0.072) (0.075) (0.073)

chinum 0.031 0.024(0.057) (0.055)

log(netcap) -0.006 -0.007(0.029) (0.028)

cons 0.633** 0.643** 0.720** 0.721** 0.737* 0.741*(0.248) (0.239) (0.289) (0.287) (0.435) (0.431)

N 270 270 270 270 270 270

As can be seen from the regression results, the income gap between husband and wife has significant effect on household financial decision-making right distribution. LPM and Probit estimates of three models are around 0.18, which means that when the degree of income gap increase 1%, the probability of family financial decision madeby the husband would increase by 0.18%.After adding more control variables for two times, the significance of impact of income gap on family financial decision-making right distribution has not been changed significantly, held at 5% significant level. And its coefficient is similar, which also shows that the results have certain robustness. According to the results, in general husband is likely to grasp the financial decision-making right when the income gap increases. Those who assign their financial decision-making right to his wife no matter how their income and seniority are high is not widely throughout the country. On the contrary, most husbands should belong to the“Not hen-pecked”, and they also hope to grasp the household financial decision-making right. The reason of the household that the wife make decisions may be the income of husband does not have obvious advantages compared with his wife. "Economic base determines the superstructure", this also applies to a certain extent in the family.

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Besides, whether the wife is in the financial industry has significant effect on the household financial decision-making right distribution. The probability that the wife make decisions when the wife is in financial industry is raised by 35%. This is understandable, women who worked in the financial industry tend to be more familiar with the financial field, and have the professional knowledge. Therefore, in the family, the wife is more likely to make financial decisions.The education degree of husband has also significant effect on financial decision-making right distribution. Husband who has higher education degree is more likely to make the financial decisions. In particular, when the education year of husband increase by 1 year, the probability that husband make decision increases by 4%.

Another significant factor affectinghousehold financial decision-making is the average age of the both sides of husband and wife. With the increase of the average age of husband and wife, the whole family financial decisions tend to be made by his wife.Specifically, when the average age of husband and wife increases by one year old, the possibility that the wife make decisions would increase but 0.8%. We think there are two possible explanations, one kind is with the increase of age, people become more conservative, but men's conservative degree increase more quickly than women, and as a result, the possibility of women make decisions increases.Another kind is older couples may be more likely to be “hen-pecked”. We will do the further inquiry in the latter.For exploring how the time preference affects household financial decision-making right distribution, we regress model (4), (5), (6). The result is as table 4 shows.

Table 4 Regression result of time preference model (independent variable: decision-making mode)

Model (4) Model(5) Model(6)

Variables LPM Probit-AME LPM Probit-AME LPM Probit-

AMEincdis 0.008 -0.001 -0.003 -0.013 -0.001 -0.014

(0.143) (0.140) (0.145) (0.141) (0.146) (0.142)

femfin -0.348*** -0.346*** -

0.353*** -0.343*** -0.353*** -0.347***

(0.128) (0.119) (0.129) (0.117) (0.130) (0.118)maledu 0.044*** 0.043*** 0.045*** 0.045*** 0.044*** 0.044***

(0.014) (0.013) (0.015) (0.014) (0.015) (0.014)age_35 -0.119 -0.115 -0.129 -0.123 -0.136 -0.135

(0.101) (0.098) (0.103) (0.098) (0.107) (0.102)age50_ -0.181 -0.154 -0.190 -0.163 -0.181 -0.156

(0.129) (0.121) (0.130) (0.121) (0.133) (0.124)age_35∙incdis 0.421** 0.507** 0.442** 0.529** 0.444** 0.547**

(0.200) (0.216) (0.201) (0.215) (0.204) (0.221)age50_∙incdis -0.0003 -0.007 -0.003 0.003 -0.007 0.004

(0.208) (0.196) (0.209) (0.195) (0.210) (0.196)cons 0.576* 0.579* 0.672* 0.673* 0.733 0.723

(0.326) (0.328) (0.352) (0.351) (0.487) (0.479)N 270 270 270 270 268 268

As can be seen from the table, for couples with an average age of less than or equal to 35 young husband and wife, income gap of couples has significant effect on the household financial decision-making right distribution. In the family of young couple, when the income gap increases by 1%, the probability that the husband make decisions increases by 0.5%. But, by contrast, in the 35 to 50 years old of middle-aged families and the elderly over the age of 50 families, husband and wife's income gap do not have significant influence on household financial decision-making right distribution.In these two types of families, there exist more common “hen-

pecked”phenomenon, that is the increase of husband’s income cannot help them grasp the right. Investigating its reason, we believe that the couple that is under the age of 35 is a young couple, their family status was more obviously affected by income level, mainly because young people are more independent in terms of economy, those who have higher income may be more likely to make decisions. For middle-aged and old couples, economic independence is not strongas young man, everyone become a part of the total family income, and the goal of decisions are always maximizing the whole welfare.So the relationship between personal income and decision-making mode is relatively lower. This finding is contrary to hypothesis 2. It may mainly because that the theoretical part mainly reflect the situation of the western countries, and western individual own relatively higher economic independence. And the Chinese culture makes economic independence between husband and wife decrease as the ages increase. Therefore, the result presents contrary to hypothesis 2.

VII. Further study –would the financial decision-making mode affect the investment income.

We use the investment income of stocks as a dummy dependent variable, and we suppose it equals 1 when gains profits, otherwise 0. Based on the literature (Anandi Mani, 2011), we set the model as follows:

profit=β0+β1maldec+β2marlen+β3chiunm+ β4maledu+β5 femedu+β6agemean+μ (7)

Table 5 The regression result of model (7)Variable LPM Probit-AMEmaldec 0.015 0.019

(0.051) (0.050)marlen 0.000 0.000

(0.000) (0.001)chinum -0.051 -0.046

(0.048) (0.047)log(netcap) 0.055** 0.063**

(0.024) (0.025)maledu 0.018 0.018

(0.012) (0.011)femedu -0.009 -0.011

(0.011) (0.011)agemea 0.006** 0.005**

(0.003) (0.003)cons -0.890*** -0.871***

(0.343) (0.310)N 268 268

As can be seen from the regression results, household net assets have significant influence on the probability of investment. The higher the net assets are, the probability of stocks gaining profits is higher. This may be because that in the household that has high net assets, investors’ ability is higher and own more related information. Besides the age also has significant influence on the investment income, which owe to the experience in investment. It is important that the sex of investors do not have significant influence on investment income. Therefore, it makes no sense on investment income whether the decision is made by men origin women.

VIII. Conclusion

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Based on the research on the factors of household financial decision-making right distribution between both sides of husband and wife, we found that the income have significant influence on the distribution of financial decision-making right. The party who has higher income would be inclined to make financial decisions. Nationally, Chinese men are widely “Not hen-pecked”, namely they would assign the decision-making right based on the individual ability factor. And the assignment is because of their bargaining power is relatively lower than their wives. At the same time, we found that the degree of education and the industry have significant influence on distribution of financial decision-making right. When the education degree is higher and the industry is finance, the individual would own higher bargaining power on financial decision-making right distribution. Meanwhile, the age gap between couples, the consensus register, politics status, and the number of children do not have significant influence on the household financial decision-making right distribution. In addition, the time preference difference would affect “hen-pecked” phenomenon significantly. Chinese men's utility discount factor is higher than women’s, which means that men would pay more attention to the short-run profits and be inclined to grasp the decision-making right in the early period of marriage. Therefore, the “hen-pecked” phenomenon increases as the time passes by. Finally we simply explore how decision-making mode affects the investment income. But we find that sex is not the significant factor of investment income. And the household that have higher education degree, higher net assets, and higher average age would tend to be easier to gain profits.

The article analyzes the factors of household financial decision-making right distribution in the angle of household finance. There exist some shortcomings in the empirical analysis, which need to be explored in the future.

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中国家庭转移支付动机探究：资产跨期配置或是“关系”投资

——基于 CHFS 的实证分析中国金融研究中心 2016 级硕士研究生 ***

【内容摘要】本文旨在探究中国“人情支出”的动机以及“人情”往来的期望回报。为此，本文考虑了利他假说和交换假说，利用 CHFS 的数据，本文发现家庭获得转移支付的概率与其转移支付前收入负相关，而获得转移支付的金额与其转移支付前收入正相关，这两点特征与利他动机不符而更多支持了交换动机。其次，本文发现，在交换动机的前提下，人们进行转移支付时，更多地期望的是“关系”回报而非金钱回报。【关键词】家庭转移性支付；利他动机；交换动机；金钱回报；关系回报

一、引言在中国社会中，“礼尚往来”已经成为社交中不言而喻的一项规则，作为一个典型的

关系型社会(Yang,1994；Bian,1997)［2］，人们之间互相赠送礼金或礼物是维系和发展社会网络的重要渠道，中国家庭其收入当中的很大一部分用于赠送他人(主要是亲戚、朋友) 礼金或礼物，这一支出又通常被称为人情支出。近年来，人情支出的增长速度远远超过居民收入的增长速度，人情支出已经成为一些家庭的重要经济负担，社会上不禁发出了“人情支出猛于虎”的感慨，根据周广肃、马光荣（2015）［1］的研究，人情支出占用家庭收入的平均比重达到 15.53%。那么，人们究竟为什么在感到经济负担沉重时，仍然愿意进行人情往来呢？从经济学的角度，我们对这种社会现象可以进行两方面的探讨：首先，人们进行人情

支出，即学界所说“转移性支付”时，是出于怎样的动机，完全是出于关爱他人的目的，还是由于这样的人情往来实际上对自身也有利可图呢？其次，当这种行为表现为“有利可图”的时候，“利”的实质内容指的是什么？

在国外，对家庭转移支付进行探讨的文献非常丰富，文献中最主要存在的两个动机即为利他动机和交换动机。为什么讨论家庭转移支付的动机对我们来说是有意义的呢？这不仅仅是因为社会中几乎每一个人都自愿或非自愿地进行过转移支付，还因为私人的转移支付实际上是一种收入的再分配，转移支付的发出动机将决定其最终流向，进而影响到社会的整体福利。

本文的目的即是通过实证研究的方法，对以上的两个问题进行回答。在国内，对家庭转移支付的研究还大多集中在代际间财富的流动上，对于家庭和家庭之间的转移性支付进行的讨论还很少。本文的意义就在于采用了较新的微观数据，验证了在中国这样的关系型社会中转移支付的动机，确定“人情”交易的实质内容。

二、文献综述根据已存在的文献，家庭转移支付作为以资金、劳务、实物为形式的非正规的福利分

18

配形式，在几乎所有发展中国家的家庭收入和支出中具有重要的作用。家庭间转让中有一个值得分析的很有趣的部分，那就是动机。正如Hochguertel(2009)［3］的研究所说，转让动机因其收入再分配的效应因而非常重要。做出转移性支付决定的潜在动机可能包含了利他动机或者交换动机。

Becker(1974)［4］提出一个个体关心另一个个体的幸福包括在利他动机的框架内。父母对子女的教育投资、子女对父母的赡养行为、收入高的家庭成员支持收入低的家庭成员都是出于利他动机(Lucas and Robert, 1985)［5］。在利他动机下，一个家庭获得转移支付的机会将随家庭收入的增加而减少 (Cox,1987)［6］。例如，当父母的养老金增多时，将导致利他动机下子女对他们的父母的转移支付的减少(Cox and Jimenez,1989)［7］。Barro(1974)［8］， Lucas(1985)［9］，Cox(1987) 则认为，转移支付的馈赠方如果具有完全信息，他在发现接受方收入提高时，就会减少自己对接受方馈赠的金额。与之相关的是，一些研究认为 ,利他动机下家庭之间的转移支付会影响公共收入再分配政策,如果家庭内部的转移支付是无私的且能够内部协调,那么这种私人转移支付会使社会保障、教育、医疗等公共政策失效或抵消公共收入再分配的部分效果(Barro,1974)。例如,在上文中提到的养老金增加是由于税收提高来支 持 的 话 ， 子 女 们 就 会 减 少 对 父 母 进 行 馈 赠 的 金 额 (Cox and

Jimenez,1989)。Barro（1974），Lucas（1985），Cox（1987)等认为,完全信息下的转移支付馈赠方在察觉接受方由于公共转移支付而收入增加时,会降低自己的转移支付金额。

交换动机是由 Cox(1987)提出的，他认为交换动机的产生是由于资本市场不完全，人们无法跨期配置自己的财富，转移性支付成为克服市场不健全的重要方式。可以理解为，际遇好的家庭通过向经济条件不如自身的家庭进行转移支付，以预防将来家庭际遇发生改变或反转的情况，相当于为自己的家庭买了一份保险。例如， Blenheim，Shleifer and

Summers (1985) ［10］，Hoddinott(1994)［11］等认为，私人转移支付数量会随着个人收入的增加而增加。私人转移的支付方具有在将来得到经济回报的倾向。另外，转移支付的未来回报不一定是金钱，而有可能是“关系”。一些学者研究了个

人的社会网络背景是否有可能带来转移支付。Marcel and Bart(2002)［12］分析了企业层面的价值转移，认为有“关系”的员工能够创造更多价值，一方面这些“关系”是一种信任，可以减少交易成本；另一方面可以减少业务中的搜索成本，使之受益。Andrew(2005)［13］

将社会关系网络分为策略性的结盟、认知和关系三个维度，认为社会网络可促进知识之间的流动和转移，这也一定程度上解释了关系网络在转移支付上的演变机制。那么，根据以上这些理论以及现存的证据，我们能了解到什么呢？目前，家庭转移支

付 的 动 机被研 究最多 的 国 家 是美国 ，但结 论并不 统 一 。 Cox （ 1987 ） ， Cox and

Rank（1992）发现转移支付金额和接受者的收入之间成正相关的关系，因此否定了利他动机存在的可能性。但更多的研究恰好与之相对，Mcgarry，Schoeni（1995a；1995b）［14］［15］分析了数个调查中的数据，并且发现转移支付金额和接受者收入之间呈现显著的负相关，他们还发现孩子提供给父母的关心和帮助与父母给予孩子的转移支付规模之间没有明显的联系，总的来说， 他们的研究更多地指出了一种利他动机的倾向而不是交换动机。与

之相同的是，Altonji，Hayashi and Kotlikoff（1995；1996）［16］［17］发现更富有的接受者会收到更少的转移支付。除了美国，在其它国家中发现的证据也不能为转移支付的动机给出一个统一的答案。Hayashi（1995）［18］检验了日本两代人组成的家庭，发现家庭内资源的分配会影响接受者对转移支付的要求，因此与纯粹的利他动机相冲突。在肯尼发现亚，一项研究转移支付的金额与接受者的收入之间没有显著的关系，因此无法断定动机，与之相似的是 Lucas and Stark（1985）年对博茨瓦纳的研究发现尽管两者间存在正相关，但系数在统计意义上并不显著。最后，在 1994 年 Lee，Paris and Willis［19］对台湾的代际支持的研究中，他们发现尽管利他因素显然是动机的一部分，交换动机也促成了一部分金钱的代际流动。

对于中国家庭转移支付的动机研究还较少，具有有代表性的是 2007 年 Secondi［20］基于 1988 年 CHIP 的调查数据进行的研究，他发现更富有的家庭容易得到更多的转移支付，也就是支持了交换动机的假说。目前，国内家庭间转移支付的研究主要集中于转移支付的福利效应，例如：转移支付对于减少贫困所起的作用、公共转移支付与私人转移支付之间的关系等等。除此而外，在国内鲜有讨论家庭转移支付这一研究话题。

三、理论模型要分辨出家庭转移性支付的动机，需要找到家庭背景与其接受转移支付数额之间的关

系。根据 Becker and Barro(1974)提出的利他动机，这两者之间始终存在负相关关系。Cox

在 1978 年的研究则表明，转移支付可能来自交换动机，转移支付和家庭背景之间可能还存在着正相关的关系。接下来，本文将概述利他动机和交换动机的理论模型，以进一步进行实证分析。

（一）转移支付的决定首先，分析转移支付的决定是如何做出的。转移支付对于馈赠方和接收方来说，是一

个愿打一个愿挨的过程，两方都有一个金钱和“人情”的边际替代率，当馈赠方的边际替代率大于接收方时，转移支付就会发生，假设在初始点（未进行转移支付，互不相欠“人情”的时候），馈赠者的边际替代率为，接受者的边际替代率为，决定转移支付是否发生的变量可以表示为：

t=Pd0−Pr

0(3.1)

Cox(1987)证明了这个变量与馈赠者和接受者收入存在以下关系： ∂ t∂ I r

<0 , ∂ t∂ I d

>0 (3.2)

这一结果能够被直观地推导得出：馈赠者的收入提高，就会导致他的边际消费效用降低，作为“人情”的需求方，此时他会愿意用更多的消费去交换第一单位的“人情”；同理，当接受者的收入提高时，作为“人情”的提供方，他的边际消费效用降低，他要求更多的消费来补偿提供出去的“人情”。其价格可以一路提升，直到转移支付不会发生的程度。下面分析转移支付收入和动机之间的具体关系。

20

（二）利他动机 对家庭间转移支付的利他动机首先进行定义的是 Becker(1974)。本文在此考虑 Becker

的利他动机模型的一个严格利他情况：1.馈赠者与接受者之间是相互关爱的，即希望对方的收入提高、生活环境改善；2.资本市场是不完全的，即人们无法在市场上进行跨期资产配置，那么当接受者的收入足够低时，馈赠者1就会将自己收入的一部分让渡给接受者，馈赠者的效用函数为：

U=U (Cd ,V ) (3.3)

这里U 代表馈赠者的效用，代表馈赠者的消费，则代表接受者的效用。由于假设利他行为是相互的，接受者实际上也关心着馈赠者，那么类似地有，接受者效用为：

V=V (Cr ,U ) (3.4)

这里代表接受者的消费。资本市场不完全可以被表示为： C r=I r+T

Cd=I d−T (3.5)

表示转移支付的金额，表示转移前接受者的收入，为转移前馈赠者的收入。在考虑多阶段生命周期的情况下，利他动机将会促使家庭克服无法跨期配置资本的问题，这也很可能是早期人们解决此问题的方法。仅考虑第一个周期的情况，从馈赠者的角度看，当接受者收入越高时，两者的收入差距越小，转移支付也就越小。即：

(3.6)

上式右端第一个常数项说明，当每增加一元钱，就将减少一元钱。但根据不同的效用函数形式，右端的值将 0到-1 之间变化，例如采用柯布道格拉斯函数，并且基于馈赠者和接受者相同权重时， 每增加一元就会引起减少 0.5元。2

（三）交换动机交换动机由 Cox(1987)提出，比起利他动机，交换动机更为复杂：接受者和馈赠者之

间不再是完全出于关爱他人的目的进行转移支付行为，而是都意识到转移支付行为的发生对两方来说都是有利可图的。

1.基于金钱的交换动机Cox(1998)［21］仍然假设了两阶段的生命周期和不完美的资本市场，对于馈赠者和接受

者来说，效用方程为：U=U 1 ( I d1−T )+

U2 ( I d2+R )1+ ρ

+ βV (3.7)

V=V 1 ( I r1+T )+V 2 ( I r2−R )

1+ ρ+γU (3.8)

其中为贴现率，下标数字表示发生转移支付的时间。为第二期逆向转移支付的金额。1 此处所说的“馈赠者”是指第一时期收入高，并对他人进行转移支付的个体。2 关于此模型的详细推导可参见 Cox, D. Motives for Private Income Transfers[J].Journal of Political Economy, 1987 (3):508-546.

/

T

假设他们都是由纳什讨价还价模型决定的，Cox 提出的交换动机并没有排除利他的因素，由于利他心理的效用乘数为和。如果转移支付并未发生，两者的效用分别为：

U 0=U 10 ( Id 1−T )+

U 20 ( I d2+R )1+ρ

+βV 0 (3.9)

V 0=V 10 ( I r1+T )+

V 20 ( I r2−R )

1+ρ+γ U 0 (3.10)

模型的解为：maxTR

N=(U−U0)×(V−V 0) (3.11)

一阶求导后：∂N∂T

=(V−V 0 ) ∂U∂T

+(U−U 0 ) ∂V∂T (3.12)

∂N∂R

=(V−V 0 ) ∂U∂R

+(U−U0) ∂V∂ R (3.13)

对等式 3.5-3.8 进行数值模拟后，Cox(1998)发现，与利他动机相反，和的符号是先为正后为负的，图形如下所示：

图 1 家庭获得转移支付收入与其收入之间的关系也可将它理解为，当接受者收入增高时，对他进行转移支付的人预期在未来能获得的

回报也会相应增多，这种转移的价格随之增高。当其增高到一定程度之后，所能带来的人情收益相对不那么“划算”，此时的转移支付反而会下降。

2.基于关系的交换动机在生命周期第一阶段进行转移支付的人们所预期的回报不仅仅局限于金钱的回报，还

有可能是希望从社会网络中获益，即“关系”回报。而“关系”一说又往往同权势联系在一起。在此我们考虑官员的权势对获得转移支付的影响，从获得转移支付的概率看，定义为官员获得转移支付的概率，为家里有官职1的人数，则为至少有一人获得转移支付的概率。应有：

1− (1−r )n>1−(1−r )n−1 (3.14)

即如果家庭的官员人数越多，获得转移支付的概率越大。从转移支付的金额看，可以将转移支付看作对于“权势”及其带来的社会关系的报酬，

1 官职在此定义为企业负责人或有职务（科长及以上）的政府工作人员。22

假若“权势”也可以按单位标价，那么转移支付可以表示为：T=pf (3.15)

其中，为人情的价格，则为“权势”的量。其定义方法如下：应为的增函数，z 为影响权势的其它因素，即随着家庭中官员人数的增加，家庭社会网络越发达，带来的权势越多，转移支付的金额也会越多。

四、计量模型、变量与数据来源（一）实证框架本文旨在分析中国社会中家庭性转移性支付，即俗称“人情往来”的支出发生的动机

及影响其金额的因素。如前文所述，本文将验证两个假设的动机：利他动机和交换动机。为验证动机，首先要构建一个如下的 Probit模型：

(4.1)

其中 表示家庭 是否接受净转移支付（ 表示家庭接受了净转移支付，反之则反然）， 表示一系列解释变量的向量， 则为一系列未知参数的向量， 为标准正态分布的累计分布函数形式。根据模型推断，利他动机和交换动机都预测一个家庭转移支付前收入与接受转移支付的概率成反比，Probit模型估计了家庭接受转移支付的概率：

(4.2 )

其中 代表家庭， 为是否接受转移支付的虚拟变量， 为作为接受者的家庭 的转移支付前收入， 为向家庭 提供转移支付的给予者的转移前收入， 则表示一系列控制变量， 则为误差项。本文假设若转移性支付是由利他动机驱动的，则 符号为负，符号为正。即接受者的收入越低，而馈赠者的收入越高，这种家庭间的转移支付就有更高的机率发生。假设转移性支付是由交换动机驱动的，则 符号为负， 符号不确定。由于数据中无法观测到馈赠者的收入，当忽略了馈赠者收入后，可能会产生有偏估计，但根据 Cox and Rank (1992)［22］和 Harounan Kazianga (2004)［23］的研究发现即使发生偏误，也是十分微小，在可接受范围内的，所以本文选择将方程简化为下式：

(4.3)

转移支付本身包含两个部分：家庭是否进行转移支付的决定和家庭转移支付的金额，以上的 Probit模型仅仅预测了第一个部分，而仅从第一个部分我们无法分辨是出于利他动机或是交换动机的，因此，我们需要对影响家庭转移支付的金额的因素进行分析，本文借鉴 Luigi Aldieri, Damiano，Fiorillo(2015)［24］的研究方式，以 Tobit模型估计以下方程：

(4.4)

根据 Cox（1987）的研究，家庭转移支付前的收入对其所接受的转移支付金额的影响

有可能是非线性的，本文加入收入的平方项。在利他动机下，只接受 的值为负，而在交换动机下， 的值可以为正。

其次，本文在 4.3式和 4.4式的基础上，加入新的关注变量——家庭的官员数量，根据上文的模型推导，我们预测在交换动机下 Probit模型估计和 Tobit模型估计的系数应为正。但家庭的官员数是内生于获得的转移支付的：越善于经营社会网络的家庭越有机会获得转移支付，而且也越容易取得高的职位，所以遗漏了经营社会网络的能力这一变量是会引起有偏估计。解决的方法可以是找到这一遗漏变量的代理变量加入方程进行回归，但本文并没有找到一个合适的代理变量，因此只能采取第二种方法，即采用工具变量来减小内生性的影响。关于工具变量的选取，本文选用了家庭主要关注的信息是否为政治和经济类这一哑变量，此变量与家庭的官员数直接相关，而与获得的转移支付收入在直觉上并不相关，因此可以作为尝试。

（二）变量与数据来源本文采用了中国家庭金融调查(CHFS)2011 年的调查数据，此次调查在全国除西藏、新

疆、内蒙和港澳台地区外的 2585 个县/区中，随机抽取了 80 个县/区，共获得了 8438户家庭的抽样调查数据，个人样本总量为 29324。在 CHFS 的数据库中，包含了以下三点本文研究所需要的信息：（1）家庭层面的变量：例如家庭资产、收入等（2）家庭规模以及户主信息（3）所有家庭成员的个人信息：例如工作、年龄、健康、收入等。最主要的是，CHFS 以家庭层面收集了家庭转移性支付和收入的金额，给予方和馈赠

方均为非家庭成员。其中家庭转移性支出包括：春节、中秋节等节假日支出、红白喜事（包括做寿、庆生等）、教育、医疗、生活费支出以及其它支出；家庭转移性收入除上述各项外还包括遗产继承，由于收入高的家庭比起收入低的家庭，需要教育、医疗、生活费资助的可能性小，所以本文接受的转移性支付只包含节假日支出、红白喜事和其它支出。家庭的公共转移性收入则包括各类补贴、救济金、赈灾款、失业保险等等。

本文首先剔除缺失以下任何数据的户主和家庭信息删除(年龄，性别，教育程度，收入水平，子女数量，家庭规模，户籍，转移支付和子女特征信息等)，使样本减少到 7152户。将户主年龄小于十八岁的样本剔除，使样本减少到 6298户，在对家庭的转移支付前总收入的观察之中，发现了收入两极数据歧高的现象，为了提高整体解释能力，文章将位于最低和最高收入水平的 1%数据删除，使得最终样本减少到 6172户。所有文章中所用到的变量都将在表 1 中进行简单的描述性统计，所有变量的定义可参

见附录一。通过描述性统计容易发现，此次调查结果的一个特征是：获得转移支付的家庭在所有家庭中占少数，只有 1151户家庭获得了净转移性支付收入，占总体样本的 18.6%，这是符合现实生活的规律的，由于在过去一年内发生婚丧嫁娶等特殊社会事件的家庭更有可能获得净转移性支付收入，而这些特殊社会事件发生的概率是较小的，所以不难理解在过去一年内获得净转移支付的家庭是少数。参与转移性支付的家庭占到样本的 79.5%，这一比例符合发展中国家家庭转移性支付盛行的基本情况。

24

表 1 描述统计—三类人群净转移支付接受者

净转移支付为零者

净转移支付给予者

变量 均值 标准差 均值 标准

差 均值 标准差

家庭层面家庭净转移支付收入 6319 16248 0 0 -7012 15492

家庭转移支付前收入 26482 80731 24886 59439 61915 334887家庭官员数 0.080 0.294 0.048 0.227 0.051 0.239

公共转移支付 595.0 2719 564.9 4310 680.4 10438家庭是否拥有住房 0.886 0.318 0.886 0.318 0.920 0.272家庭拥有住房套数 1.007 0.522 1.010 0.689 1.060 0.496家庭是否拥有土地 0.514 0.500 0.534 0.499 0.483 0.501

家庭规模 3.292 1.644 3.522 1.617 3.541 1.469是否为农村 0.476 0.500 0.451 0.498 0.388 0.487省份虚拟变量户主层面

女性 0.476 0.500 0.449 0.498 0.451 0.498已婚 0.825 0.381 0.844 0.363 0.896 0.305

离婚/分居 0.0313 0.174 0.0325 0.177 0.0253 0.157丧偶 0.115 0.319 0.0642 0.245 0.0364 0.187

国家机关党群组织、企事业单位负责人 0.0324 0.106 0.0167 0.128 0.0113 0.177

专业技术人员 0.0617 0.241 0.0619 0.241 0.0973 0.296办事人员和有关人员 0.0226 0.149 0.0278 0.164 0.0394 0.194商业、服务业人员 0.0495 0.217 0.0817 0.274 0.0726 0.260

农、林、牧、渔水利生产人员 0.00608 0.0778 0.00952 0.0971 0.00638 0.0796

生产、运输设备操作人员及有关人员 0.0321 0.176 0.0381 0.191 0.0535 0.225

年龄 53.56 15.98 50.04 13.90 48.32 13.65年龄的平方 3124 1715 2697 1445 2521 1390初等教育 0.596 0.491 0.630 0.483 0.578 0.494中等教育 0.169 0.375 0.145 0.352 0.210 0.407高等教育 0.0765 0.266 0.0912 0.288 0.137 0.344

家庭拥有孩子数量 0.794 0.852 1.048 0.931 1.007 0.850非常健康 0.0782 0.269 0.0912 0.288 0.102 0.303比较健康 0.209 0.407 0.212 0.409 0.283 0.450一般健康 0.563 0.496 0.520 0.500 0.495 0.500比较不健康 0.121 0.326 0.151 0.358 0.102 0.302团员 0.0426 0.202 0.0484 0.215 0.0407 0.198党员 0.108 0.310 0.0896 0.286 0.156 0.363

民主党派 0.00348 0.0589 0.00159 0.0398 0.00346 0.0587样本量 1151 1261 3760

从上表可以看出，转移支付前的家庭总收入在净转移支付的接受者家庭中表现得比给予者家庭更低，分别为 26482元和 61915元，而不参与转移支付的家庭收入均值与接受者家庭相近，为 24886元，因此可以发现，转移支付往往是从收入较高的家庭流向了收入较低的家庭。在净转移支付的接受者中，家庭平均拥有的官员数量高于净转移支付的给予者，分别为 0.08 和 0.05。而公共转移支付的金额则在净转移支付的给予者家庭中表现得更高，为 680.4。净转移支付的接受者家庭中有 47.6%为农村家庭，而给予者中这一数字为38.8%。

在户主个人信息方面，净转移支付接受者家庭中，在国家机关党群组织、企事业单位工作的户主多于净转移支付给予者的家庭，前者户主的年龄平均高于后者。在净转移支付接受者家庭中，7.65%的户主受到过高等教育，而在给予者中，13.7%的户主接受了高等教育。在拥有孩子数量上，净转移支付接受者的子女数低于给予者，平均分别为 0.794 和1.007。在自评健康状况上，家庭转移支付的给予者比接受者认为自己更健康，其中“非常健康”，“很健康”的比例都高于后者，而“一般健康”“很不健康”的比例低于后者。在政治面貌上，净转移支付的接受者中，党员所占的比例较小，为 10.8%，而给予者中，所占比例较高，为 15.6%。

五、实证结果（一）基于金钱关系的动机表 2 报告了家庭接受转移性支付概率的估计结果。在 Probit模型估计下，家庭转移前

收入的系数为负，并且在 1%的显著性水平下显著，这与利他动机和交换动机的预期都是相符合的。结果显示户主为女性的家庭并没有显著地增加其获得转移性支付的概率，这与Cox (1987)研究中的利他假设不符，他认为若家庭转移支付发生的动机为利他，则女性会获得作为劳动力市场上性别歧视的补偿的转移支付。户主结婚、丧偶、离婚的家庭均在1%的显著性水平下比户主未结婚的家庭更有可能获得转移支付，根据交换动机的解释，结婚的家庭被认为在未来有更稳定的现金流，人们选择对结婚的家庭进行转移性支付是为了更有把握在未来获得他们的回报。

在年龄上，户主年龄与家庭接受转移支付的概率呈现倒U 型结构（见图 2），这一结果与生命周期理论相符合（见图 2）中虚线，根据生命周期，青年人处于独立生活的初期，收入在一生中处于低谷时期，更需要他人的支持和帮助，自身也不具备向他人进行转移支付的能力。而青壮年和中老年人已经有了一些储蓄，承担起了抚养孩子和赡养老人的责任，更可能是转移支付的给予者而非接受者。到了老年时期，由于逐步丧失劳动能力和退休等原因，收入再一次回到低谷，此时接受转移支付的概率上升。

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概率或收入

10 20 30 40 50 60 70 80 年龄

获得概率家庭收入

图 2 家庭获得转移支付的概率/家庭收入与户主年龄的关系户主每多一名子女，得到转移支付的概率在平均意义上将下降 4.74%，这也与利他动

机的假说相悖，而与交换动机的假说相符合。由于要抚养的子女数增多，这一家庭将被认为在未来的现金流会紧缩，那么对此家庭进行转移支付的给予者在未来获得回报的概率就将降低，所以理智的选择是减少对这些家庭的转移支付。户主为团员、民主党派成员对其获得转移支付的概率均没有显著影响，但户主为共产党员时，其获得转移支付的概率将显著减少 3.65%。除上述变量而外，户主的教育程度和健康程度并没有对家庭转移支付接受的概率产生

预期中的显著影响，这可能是由于在中国社会，无论任何教育程度的家庭都将“人情”的“礼尚往来”视为必要的支出，因此在不同的教育程度之间没有明显差异。而健康程度一方面是自评的，并没有反映出个体的真实健康水平，另一方面只有当个体患病到达一定严重程度之后才会被家庭外的成员关注到，所以这一变量对接受转移性支付的概率也不存在显著影响。

表 2 Probit 模型估计：家庭转移支付收入与家庭总收入的关系(1) （2）

变量 系数 Z值 边际效应 标准差家庭层面

家庭转移支付前收入（千元） -0.000459*** (-3.86) -0.000116 0.0000301

公共转移支付收入（千元） -0.00146 (-0.52) -0.000369 0.00707

家庭是否拥有住房 0.185** (2.27) 0.0467 0.0206住房套数 0.0250 (0.60) 0.00631 0.0106

家庭是否拥有土地 -0.0134 (-0.25) -0.00339 0.0137家庭规模 0.0210 (1.18) 0.00529 0.00447

是否是农村 0.112** (2.04) 0.0282 0.0138省份虚拟变量

户主层面女性 0.0258 (0.61) 0.00650 0.0107结婚 0.433*** (3.58) 0.109 0.0304

离婚/分局 0.598*** (3.66) 0.151 0.0412丧偶 0.789*** (5.45) 0.199 0.0363年龄 -0.0416*** (-4.67) -0.0105 0.00224

年龄的平方 0.000450*** (5.39) 0.000114 2.10e-05国家机关党群组织、企事业单位负

责人-0.236 (-1.49) -0.0595 0.0400

专业技术人员 -0.0867 (-1.08) -0.0219 0.0203办事人员和有关人

员 -0.114 (-0.94) -0.0288 0.0305

商业、服务业人员 -0.159* (-1.85) -0.0401 0.0216农、林、牧、渔水

利生产人员 -0.0670 (-0.30) -0.0169 0.0567

生产、运输设备操作人员及有关人员 -0.195* (-1.89) -0.0491 0.0260

初等教育 -0.0639 (-0.98) -0.0161 0.0165中等教育 -0.0481 (-0.59) -0.0121 0.0208高等教育 -0.178* (-1.71) -0.0448 0.0262

户主拥有孩子数量 -0.188*** (-5.51) -0.0474 0.00856非常健康 -0.0959 (-0.68) -0.0242 0.0354比较健康 -0.147 (-1.14) -0.0371 0.0326

一般健康 -0.0313 (-0.25) -0.00791 0.0316比较不健康 -0.130 (-0.97) -0.0328 0.0337团员 0.0702 (0.69) 0.0177 0.0258党员 -0.145** (-2.16) -0.0365 0.0168

民主党派 0.0524 (0.16) 0.0132 0.0850常数项 -0.575* (-1.74)样本量 6172

Probit模型的估计只能为是否是利他动机或是交换动机提供证据，却不足以让我们确切地分辨出是其中的哪一个。为了进一步探究家庭转移支付的动机，以及影响家庭转移支付数目的因素，我们对式 4-4 进行 Tobit估计，结果见下表：

表 3 Tobit 模型估计：接受者(1) （2）

变量 系数 T值 边际效应 标准差家庭层面

家庭转移支付前收入（千元） 6.96** (2.11) 1.38 0.650家庭转移支付前收入的平方 -9.22e-10* （-1.66） -3.88e-9 2.34e-9公共转移支付收入(千元) -2.70 (-0.65) -5.36 8.20

家庭是否拥有住房 2431.0 (1.61) 483.1 298.8住房套数 224.9 (0.27) 44.68 165.7

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家庭是否拥有土地 -627.2 (-0.56) -124.6 222.2家庭规模 523.9 (1.45) 104.1 71.31

是否是农村 1310.4 (1.15) 260.4 225.3省份虚拟变量

女性 94.73 (0.11) 18.82 165.1结婚 9498.1*** (3.30) 1887 568.2

离婚/分局 12635.4*** (3.38) 2511 738.1丧偶 14336.1*** (4.18) 2849 675.3年龄 -633.1*** (-3.56) -125.8 35.19

年龄的平方 6.348*** (3.85) 1.261 0.326国家机关党群组织、企事业单位负责人 -6122.8* (-1.93) -1217 628.1

专业技术人员 -535.5 (-0.29) -106.4 366.7办事人员和有关人员 -2570.5 (-0.92) -510.8 553.9商业、服务业人员 -4308.9** (-2.42) -856.2 353.0

农、林、牧、渔水利生产人员 -2187.2 (-0.50) -434.6 862.0

生产、运输设备操作人员及有关人员 -5890.3*** (-2.88) -1170 404.3

初等教育 -828.3 (-0.72) -164.6 230.0中等教育 860.0 (0.48) 170.9 354.1高等教育 -2725.0 (-1.34) -541.5 405.0

家庭拥有孩子数量 -3828.7*** (-4.23) -760.8 177.2非常健康 235.5 (0.09) 46.80 506.6比较健康 -1694.6 (-0.80) -336.7 420.4一般健康 354.4 (0.18) 70.41 399.7比较不健康 -1459.9 (-0.67) -290.1 433.6团员 3242.2 (1.41) 644.3 457.1党员 -2266.7 (-1.64) -450.4 273.1

民主党派 534.6 (0.10) 106.2 1093_常数项 -15232.3** (-2.23)

_cons 20422.0*** (7.84)样本量 6172t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

首先关注家庭收入对接受转移支付的金额的影响，本文发现，家庭转移支付前收入对接受转移支付的金额起到正向的影响，家庭每增加 1元钱收入，就将在平均意义上引致0.00138元的转移支付收入，而随着收入的增加，收入的平方项系数暗示了这一正向的影响将不断减小甚至可能为负，即呈现倒U形的结构，这一结果支持了转移性支付的交换动机假说。

其它变量的系数符号均与接受概率估计的系数符号相同，婚、丧偶、离异的户主所在家庭比未结婚的户主所在家庭每年的转移性支付收入平均多 1887元、2511元和 2849元。户主年龄与接受转移支付金额之间依然存在 U形关系，即在家庭收入低时获得更多的转移支付收入，在家庭收入高时获得较少的转移支付收入。家庭每增加一个子女将减少 3828.7

元转移性支付收入。其余变量如户主的教育水平、健康水平、家庭是否拥有土地等，他们的影响依然不显

著。（二）基于权势的动机表 4 和表 5 分别报告了将关注变量换为家庭官员数后的两个模型回归结果，此时工具

变量的选取为“家庭主要关注的信息是否为政治或经济”，通过了弱工具变量检验，F值大于 10（具体见附录二），经过Hausman检验，发现与原估计没有系统性差异，这可能是由于工具变量的选取并不好。以下的结果只报告了部分的变量，整体的回归结果见附录三和四。根据 IVProbit 的回归，本文发现家庭官员数增加将显著增加家庭获得转移支付收入的

概率，家庭每增加一名官员，将在平均意义上增加 5.57%的获得转移支付收入的概率。通过与表 5-1 的对比，我们可以发现，在加入家庭官员数作为关注变量后，家庭转移支付前收入对获得转移支付的概率影响不显著，从一定程度上来说，人们在交易动机下行动，尽管仍然有期望在未来获得金钱的回报，但更多地是期望能得到“关系”的回报，人们是以一个家庭的“权势”而非经济状况主要的标准来决定是否对这个家庭进行转移支付的。

表 4 IVProbit 模型估计：家庭转移支付收入与家庭官员数的关系(1) （2）

变量 系数 Z值 边际效应 标准差家庭层面

家庭官员数 0.148** (2.17) 0.0557 0.0256家庭转移支付前收入(千元) 6.09e-05 (0.90) 2.30e-05 2.55e-05

家庭规模 0.0670*** (4.25) 0.0252 0.00591是否是农村 0.0176 (0.38) 0.00663 0.0174户主层面

结婚 0.732*** (7.32) 0.276 0.0371离婚/分局 0.747*** (5.33) 0.281 0.0524丧偶 0.977*** (7.75) 0.368 0.0468年龄 -0.0645*** (-7.86) -0.0243 0.00304

年龄的平方 0.000599*** (7.58) 0.000226 2.93e-05家庭拥有孩子数量 -0.108*** (-3.73) -0.0406 0.0109

_cons 0.373 (1.28)N 6172

而根据 IVTobit 的结果，本文发现家庭官员数增加将显著增加家庭获得转移支付收入的金额，家庭每增加一名官员，将在平均意义上增加 289.2元的转移支付收入。通过与表 3

的对比，我们可以发现，在加入家庭官员数作为关注变量后，家庭转移支付前收入对获得转移支付的影响减小，从一定程度上来说，这反映出家庭的转移支付决定更多地出自于交换“关系”的动机，而非交换金钱的动机。

表 5 IVTobit 模型估计：家庭转移支付收入与家庭官员数的关系30

(1) （2）变量 系数 T值 边际效应 标准差

家庭层面家庭官员数 993.7** (2.11) 289.2 137.0

家庭转移支付前收入（千元） 0.810* (1.90) 0.236 0.124家庭规模 424.0*** (4.41) 123.4 27.92

是否是农村 -275.3 (-0.85) -80.12 94.21户主层面

结婚 5646.2*** (4.08) 1643 389.7离婚/分局 5813.7*** (3.47) 1692 476.9丧偶 6454.2*** (4.29) 1879 423.9年龄 -387.1*** (-4.19) -112.7 26.02

年龄的平方 3.291*** (4.07) 0.958 0.228家庭拥有孩子数量 -868.3*** (-3.80) -252.7 65.63

_cons 8028.6*** (7.96)样本量 6172t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

为了进一步阐释以上结论的合理性，本文进行了一些检验，如下表：表 6 Probit 估计：家庭在外就餐的概率与家庭官员数的关系

变量 系数Z值 边际效应

家庭官员数 0.210*** (2.86) 0.0615家庭总收入 0.000305** (2.08) 8.94e-08

t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

表 7 Probit 估计：家庭从非亲属处获得转移支付的概率与家庭官员数的关系变量 系数 Z值

家庭官员数 0.3599*** (2.65)家庭总收入 0.000000103 (0.69)

t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

我们发现家庭官员数越多，每月在外就餐的次数就越多，能一定程度上说明官员具有更广泛的社交生活。其次，家庭官员数越多，家庭从非亲属那里获得的净转移支付收入就越多，也能在一定程度上说明人们往往针对更有权势的家庭进行转移支付。

六、结论本文探寻了中国社会“人情支出”的动机，考虑了两个假说，即利他动机和交换动机。

Probit模型回归的结果显示，家庭获得转移性支付的概率与其转移支付前收入成反比，Tobit模型对获得净转移支付的家庭进行回归，结果显示，家庭获得转移性支付的金额与其转移前收入之间呈非单调关系，首先随收入增加而增加，然后随收入增加而降低，这两个结果都支持了家庭转移支付的交换动机。虽然我们不能否认亲朋好友之间存在无可取代的自然的关爱之情，但我们确实看到了“人情味”的另一面是人们默契地进行着对彼此都有

利可图的行动。此外，本文还发现，在我国，交换动机往往表现为金钱对权势的交换而非金钱与金钱

的交换。家庭里的官员数增加将显著增加家庭获得转移支付的概率和金额，且在控制官员数后，家庭转移支付前收入的系数不再显著，这说明收入是间接通过权势对家庭获得转移支付造成影响的，直接对家庭获得转移支付造成影响的是权势或者说地位。有官员的家庭更容易成为净转移支付的接受者这一事实暗示，许多私人转移支付的最终目的地为官员所在的家庭，而非那些收入处于底层并且没有权势的弱势群体，私人转移支付并没有起到良好的缩小社会贫富差距和缓解社会不公平的作用。最后，由于本文作者能力所限，文章还存在以下的不足之处：1、由于采用的微观数据

库只有一期数据，因此难以避免转移支付发生的偶然性，这会对估计转移支付和家庭收入之间的关系产生偏误；2、忽略转移支付的馈赠方的收入而引起的内生性问题，尽管存在文献说明内生性影响较小，但始终不是一个完美的结果，可以考虑采用地区人均收入作为馈赠方收入的代理变量；3、家庭官员数的内生性问题，以关注时事为工具变量也许并不是最好的选择。主要参考文献：[1]周广肃,马光荣.人情支出挤出了正常消费吗?——来自中国家户数据的证据[J].浙江社会科学,2015,03:15-26

[2]Bian,Yang．“ Bringing Strong Ties Back in: Indirect Ties ， Network Bridges ， a-nd Job Searches in

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编号 MAPE 编号 MAPE 编号 MAPE 编号 MAPE1 51. 59% 56 11. 66% 111 49. 88% 166 10. 59%2 11. 74% 57 27. 22% 112 54. 33% 167 29. 84%3 28. 12% 58 37. 84% 113 7. 10% 168 24. 81%4 77. 44% 59 50. 98% 114 94. 67% 169 95. 54%5 39. 68% 60 58. 98% 115 36. 74% 170 95. 90%6 14. 68% 61 50. 77% 116 97. 84% 171 17. 62%7 15. 82% 62 47. 21% 117 71. 85% 172 63. 87%8 70. 75% 63 41. 46% 118 47. 53% 173 70. 77%9 17. 38% 64 25. 01% 119 82. 24% 174 20. 41%10 18. 17% 65 7. 77% 120 3. 18% 175 48. 22%11 76. 20% 66 28. 22% 121 70. 41% 176 4. 96%12 60. 07% 67 65. 81% 122 34. 63% 177 1. 92%13 55. 10% 68 71. 80% 123 9. 70% 178 21. 08%14 31. 26% 69 64. 02% 124 90. 69% 179 3. 68%15 22. 75% 70 34. 52% 125 40. 72% 180 50. 72%16 23. 18% 71 57. 67% 126 98. 87% 181 25. 13%17 25. 40% 72 31. 58% 127 5. 95% 182 59. 62%18 25. 85% 73 97. 00% 128 16. 12% 183 10. 15%19 26. 31% 74 12. 21% 129 40. 02% 184 25. 90%20 26. 77% 75 75. 52% 130 73. 77% 185 43. 99%21 74. 37% 76 32. 95% 131 66. 28% 186 26. 63%22 43. 04% 77 2. 51% 132 27. 75% 187 7. 31%23 56. 64% 78 10. 31% 133 51. 79% 188 3. 78%24 30. 57% 79 7. 73% 134 6. 80% 189 0. 33%25 94. 46% 80 47. 02% 135 6. 14% 190 25. 89%26 33. 58% 81 15. 06% 136 52. 78% 191 7. 20%27 85. 75% 82 29. 07% 137 27. 85% 192 45. 32%28 36. 20% 83 81. 10% 138 53. 86% 193 35. 62%29 38. 36% 84 73. 56% 139 35. 30% 194 69. 00%30 38. 92% 85 24. 92% 140 0. 13% 195 35. 90%31 65. 13% 86 46. 72% 141 16. 25% 196 31. 23%32 52. 78% 87 9. 62% 142 29. 06% 197 19. 95%33 40. 60% 88 50. 60% 143 18. 46% 198 4. 47%34 52. 94% 89 55. 66% 144 24. 08% 199 26. 51%35 53. 59% 90 42. 11% 145 21. 36% 200 0. 33%36 81. 84% 91 45. 64% 146 45. 54% 201 10. 24%37 45. 92% 92 35. 54% 147 22. 38% 202 2. 00%38 26. 73% 93 10. 19% 148 15. 45% 203 59. 87%39 50. 53% 94 63. 56% 149 51. 03% 204 18. 85%40 52. 33% 95 15. 87% 150 18. 59% 205 28. 89%41 46. 63% 96 75. 02% 151 19. 01% 206 39. 09%42 13. 09% 97 94. 04% 152 37. 93% 207 16. 38%43 25. 43% 98 36. 49% 153 19. 73% 208 9. 11%44 27. 09% 99 81. 07% 154 19. 33% 209 19. 38%45 95. 31% 100 39. 68% 155 89. 29% 210 42. 38%46 42. 00% 101 3. 67% 156 50. 37% 211 25. 94%47 1. 13% 102 39. 92% 157 62. 66% 212 47. 27%48 46. 89% 103 63. 77% 158 54. 82% 213 26. 79%49 22. 82% 104 19. 68% 159 69. 07% 214 29. 05%50 85. 64% 105 75. 33% 160 46. 47% 215 33. 18%51 58. 19% 106 31. 48% 161 68. 41% 216 57. 74%52 80. 00% 107 36. 85% 162 98. 00% 217 12. 32%53 44. 50% 108 56. 92% 163 71. 29% 218 37. 17%54 32. 09% 109 37. 82% 164 72. 77% 219 80. 02%55 76. 78% 110 61. 05% 165 4. 75% 220 8. 92%

34

附表 实际值与预测值比较计算的MAPE值