日本版 General Social Surveys 研究論文集[6] JGSS で見た日本人の意識と行動 JGSS Research Series No.3

To give, or not to give; to volunteer, or not to volunteer, that is the question
Evidence on Japanese philanthropic behavior revealed by the JGSS-2005 data set

Yoshiho MATSUNAGA
Faculty of Business Administration
Osaka University of Commerce
JGSS-2005 にみる日本人の寄付行為とボランティア活動柗永 佳甫
大阪商業大学総合経営学部
This paper employs a unique new data set (the JGSS-2005) to test the relationship between
giving and volunteering. What factors affect people’s decisions to give and/or to volunteer? Is agiver also a volunteer and vice visa? Research on philanthropic behavior to provide answers tothese questions has been overlooked because volunteering is regarded as working for nothing andgiving is regarded as a money transfer from one to the other. In short, they do not fit traditional
principles of economics. However, philanthropic behavior now receives considerable attention in
the literature because several surveys have revealed that the number of people who give time andmoney is not negligible. While this paper also searches for factors affecting people’s decisions togive and to volunteer, this paper mainly focuses on the direct effect of giving on volunteering andvice visa. The results from our estimation reveal that a volunteer is also a giver, while a giver is
not necessarily a volunteer.
Key Words: JGSS, Giving, Volunteering
本稿は、日本人はどのような要因で、寄付あるいはボランティアをするということを決めているのか」ということを、JGSS-2005 のデータを用いて明らかにしようとするものである。ボランティアは「無償の労働供給」、寄付は「人から人への単なる資金移転」という視点から、特に経済理論には馴染まないと考えられていたため、これまで慈善活動に関する研究を経済学者が盛んに行うことはなかった。ところが近年、多くの人々が寄付やボ
ランティアを行っていることが明らかとなり、慈善活動に関するより多くの研究がなされるようになった。本稿の目的は、先行研究を参考にしながら、寄付とボランティアの意思決定要因を探りつつ、寄付をする人はボランティアもするのか、ボランティアをする人は寄付もするのか、ということも明らかにしようとするものである。分析の結果、寄付をする人はボランティアもするが、その逆は成り立たないことが明らかとなった。
キーワード：JGSS、寄付、ボランティア

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日本版 General Social Surveys 研究論文集[6] JGSS で見た日本人の意識と行動 JGSS Research Series No.3

1. Introduction
Is time more valuable than money? According to USA Today (April, 2005: v133 i2719 p11),
many Americans who performed volunteer service maintained that their motivation was to act on theirmoral values and felt that it is more important to volunteer one's time than to give money to acharitable cause.
Philanthropic behavior such as giving time and money has received little attention as a research
agenda for most economists because the decisions regarding whether to volunteer or not and whether
to give or not are determined not by people’s economic values but by people’s moral values. As
Menchik and Weisbrod (1987) argued, the nature of the supply function of volunteer labor has been
overlooked because the wage of volunteer labor is zero. Likewise, giving has been simply considered
to be a money transfer from one individual or organization to the other, and from a macroeconomic
point of view, people are executing nothing but a zero-sum game.
However, philanthropic behavior now receives considerable attention in the literature because
several surveys (e.g. Hodgkinson and Weitzman 1984) have revealed that the number of people who
give time and money is not negligible. We now have a significant number of research papers whose
research agenda is to investigate the factors affecting people’s decisions to give and to volunteer.
Applying very fundamental concepts of economics to giving and volunteering, such as the opportunity
cost of volunteering and the concept of marginal tax rate of giving, several economic theory models ofgiving and/or volunteering have been developed.
Each previous work has a unique research goal, but what they essentially aim for is to find newfactors affecting either the decision to give and/or volunteer or the level of giving and/or volunteering.
These previous papers revealed that researchers basically aimed for finding socio-demographic factors
affecting either the decision to give and/or volunteer or the level of giving and/or volunteering
depending on the availability of data. Some carried out empirical examinations of theoretic models ofgiving and/or volunteering that are based on the utility maximization problem within static models(Menchik and Wiesbrod 1987; Smith, Kehoe, Cremer 1995; Freeman 1997; Garcia and Marcuello2001; Enjolras 2002; Yen 2002; Cappellari and Turati 2004) and dynamic models (Menchik andWiesbrod 1987; Auten, Sieg, and Clotfelter 2002; Hrung 2004).
Overall, in the previous papers we found inconsistent estimation results of the coefficients ofsocio-demographic variable. There are several factors that might cause such an inconsistency: (1) the
sample size was insufficient; (2) the model for estimation suffered from a specification error; (3)people’s decisions to give and to volunteer vary dramatically in accordance with the locality, because
it is likely that philanthropy is heavily dependent upon people’s customs and morality, as USA Today(2005) implied. If factor (1) or factor (3) or both is a feasible cause of the non-robustness ofsocio-demographic variables, then it is quite conceivable that we observe such inconsistencies inprevious papers. If factor (2) is a feasible cause, then it would be sensible to search for a suitableeconometric method to estimate equations for giving and/or volunteering. Therefore, we do not
explore the cause of such inconsistencies in previous papers, nor do we compare the estimation resultsin this paper to those in previous papers. Rather, we treat socio-demographic variables simply as
control variables in order to perform accurate estimations of the giving model and/or the volunteeringmodel.
Though this paper draws significantly on previous works, we also address unique issues. For
example, do people who volunteer also give and vice visa? If people think that the supply of public
services is the responsibility of the government, then do they increase or reduce the supply of giving
and/or volunteering?

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2. Models and Data
2.1 The econometric model
We consider the following simultaneous probit model (SPM) in order to examine one of ourresearch issues – the question of whether a giver is also a volunteer and vice versa.

GIVE ∗ = γ 1VOLR ∗ + β 1' X 1 + ε 1 ,'∗∗VOLR = γ 2 GIVE + β 2 X 2 + ε 2 ,

(1a )
(1b)

where GIVE = 1 if GIVE ∗ > 0, 0 otherwise
andVOLR = 1 if VOLR ∗ > 0, 0 otherwise.
This is a conventional simultaneous equations model in the latent, starred structural variables,
GIVE*and VOLR*. The observed counterparts are GIVE and VOLR. We assume a bivariate normal
distribution with zero means. The variables X1 and X2 are independent variables that are assumed toaffect the giving equation and the volunteering equation respectively. Following previous papers, we
also assume that giving and volunteering would each be functions of the socio-demographic variables
such as the level of education, age, and sex and the other variables that may affect the utility
maximization problem. Therefore, the explanatory variables X1 and X2 contain all thesocio-demographic variables available from our data set.
Brown and Lankford (1992) and the Center for Nonprofit Research and Information at Osaka
University and UFJ Institute (2005) estimated a giving equation and a volunteering equation assumingthat their error terms are correlated with each other or that a giving equation and a volunteering
equation are implicitly correlated. Thus, their model is a conventional seemingly unrelated estimator in
the latent structural variables, GIVE*and VOLR*, otherwise known as a bivariate probit (BP) model.
However, in our model, GIVE and VOLR appear explicitly in the giving equation and volunteering
equation respectively. Consequently, our system of equations (1a) and (1b) is the SPM. This system
can be justified because the household or a member of the household will generally change the optimal
mix of giving time and money based on economic principles, particularly utility maximization, and themodel based on economic principles would have the household or the member of the household
explicitly and optimally choosing giving and volunteering. In addition, estimating equation (1a) and(1b) gives us the information that could not be given if we assumed the conventional BP model. Thatis, whether people who give tend to volunteer and vice versa.
The reduced form and disturbance covariance matrix for the structural variables are

GIVE ∗ = π 1' Z + v1 ,'∗VOLR = π 2 Z + v 2 ,where( 2a )( 2b)

σ 12 σΣ =  11σ 21 σ 22 

The matrix form of explanatory variables, Z is all of the exogenous variables including those in
the structural equation and the additional instrumental variables. The identification of (2a) in the
reduced model implies that at least one exogenous variable in X1 that is not in X2 has explanatory
power. Likewise, the identification of (2b) implies that at least one exogenous variable in X2 that is notin X1 has explanatory power.

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Table1 Dependent and independent variables

Dependent variables
GIVE= 1 if the respondent made a donation
VOLR= 1 if the respondent participated in volunteer activities
Independent Variables
SEX
AGE
SPOUSEREMOTE= 1 if the respondent is male
= Age at last birthday= 1 if the respondent is married= 1 if the respondent grew up in a farming or fishing village
The level of the respondent's health condition
poor

HEALTH=

0
Atheist

good
1

2

3

Not sodevoted1

Somewhatdevoted2

Verydevoted
3

RELIG

=

EDUC
EDUC2

= Respondent's educational attainment= Square of the respondent's educational attainment

04Total pre-tax annual income of the household ( including income from stock
shares, pensions, and real estate)

HOUSINC

TPHOUSESCALE1SCALE20. None ~ Less than 700,000 yen
1. 700,000 ~ Less than 1.3 million
2. 1.3 million ~ Less than 2.5 million=
3. 2.5 million ~ Less than 4.5 million
4. 4.5 million ~ Less than 6.5 million
5. 6.5 million ~ Less than 8.5 million= 1 if the respeondent lives in a rental housing= 1 if the respondent lives in a middle-sized city
= 1 if the respondent lives in a small city

6.
7.
8.
9.

8.5 million ~ Less than 12 million
12 million ~ Less than 16 million
16 million ~ Less than 23 million
23 million yen or over

The respondent's opinion of the following statement: "Who do you think should be
responsible for dealing with the following social issues? ″Individuals and families

OPSRWFY
OPSRMDY
OPCCAREOPCCEDOPGVEQ

Governments= Livelihood of the elderly= Medical and nursing care
= Childcare= Education of children

0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
The respondent's opinion of the following statement: "It is the responsibility of thegovernment to reduce the differences in income between families with high incomes andthose with low incomes.″
DisagreeSomewhat
Neither
SomewhatAgreedisagreeagree nor
agree
0
1
2
3
4
=

COMPEDU = Number of child(ren) in compulsory school education
MEMVLN = 1 if the respondent is a member of volunteer groups
MEMIND= 1 if the respondent is a member of trade associations
MEMCIV= 1 if the respondent is a member of a citizens' movement or consumers' cooperative groups
MEMRL= 1 if the respondent is a member of religious groups
MEMSPT= 1 if the respondent is a member of sports groups
MEMPLT = 1 if the respondent is a member of political associations

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By following Maddala (1983), the two step procedure is to estimate the two reduced forms by the
probit MLE (maximum likelihood estimator) method, estimating the two dependent variables using
the linear functions, and then to use these linear functions in structural MLEs. The predicted value of
GIVE*, that can be obtained by estimating equation (2a) by probit MLEs, is added to the volunteer
equation (1b). Likewise, the predicted value of VOLR*, that can be obtained by estimating equation(2b) by probit MLEs is added as the independent variable to equation (1a).
Typical socio-demographic variables given in previous empirical works include SEX, AGE, andSPOUSE, as shown in Table 1. These socio-demographic variables have already been well examinedin a significant number of previous works and the results are inconsistent. Therefore, previous papers
have revealed that socio-demographic variables are not robust explanatory variables. Thus, this paper
regards the socio-demographic variables SEX, AGE, and SPOUSE as control variables in order tocapture the accurate effect of the specific explanatory variables corresponding to the research agendaof this paper.
2.2 The data
The data we use in this paper is provided by the JGSS (Japanese General Social Surveys Project)2005. The General Social Survey (GSS) in the United States served as a model for the JGSS. The GSS
is a well-known general social survey that has been conducted repeatedly by the University of Chicago
National Opinion Research Center. The JGSS conducts repeated social surveys to study the attitudes
and behavior of Japanese people comprehensively. In 1999, the Institute of Regional Studies at the
Osaka University of Commerce (the agency conducting the project) was designated as a “key instituteon the frontiers of academic projects” by the then Ministry of Education in Japan, recognizing it as an
excellent research organization expected to grow in the future; furthermore, it has received support for
project promotion. The Institute of Social Science at the University of Tokyo has also acted as a major
partner. Basic information about JGSS-2005 in terms of the sampling methods is as follows:(a) Sample area: Nationwide(b) Sample population: Men and women 20-89
years of age living in Japan(c) Sample size: 4,500(d) Sampling method: Two-stage stratified
random sampling; stratified by regional
block and population size(e) Number of survey points: 307(f) Number of samples at each survey point: 15(g) Number of respondents contacted: 4,500(h) Number of valid responses: 2,023(i) Number of no responses or invalid responses:
2,477(j) Response rate: 50.5％

JGSS-2005 was conducted from late August through the beginning of November 2005 and usedboth interview and placement methods for each respondent. For practical reasons the administrative
order (i.e. whether to conduct the face-to-face interview before or after the questionnaire) was
determined by the interviewer or administrator depending on the circumstances of each case. In any
case the administrative order was recorded for each respondent.
The previous works show that the prices of donating time and that of money are important factors
in people’s decisions of how much time and money to donate. In the case of Japan, the system of tax
deduction is very different from the US. There are only 48 nonprofit organizations that we can obtaintax deduction. The formula of tax deduction due to donating money to nonprofit organizations is alsoquite different from country to country. Tax deductions on donations in the case of political parties are
given by the following formula:73

日本版 General Social Surveys 研究論文集[6] JGSS で見た日本人の意識と行動 JGSS Research Series No.3

The amount of tax deduction =[min(30% of wage income, the amout of donating money) − 5000 yen]× 30%
whereas tax deductions on donations not in the case of political parties are given by the following
formula:The amount of tax deduction =min (30% of wage income, the amout of donating money) − 5000 yen.
Therefore, it is not appropriate to define the price of donating money in Japan as in the US case: (1−Marginal Tax Rate). In addition, according to Center for Nonprofit Research and Information at Osaka
University and UFJ Institute (2005), only 3.7% of Japanese people claimed a tax deduction. This tax
system does not attract tax payers to donate large amounts of money. Consequently, most charitable
donations in Japan are in the form of small contributions collected on the street. Therefore, unlike
previous papers using US data, it causes no problem when we estimate models of giving andvolunteering ignoring the effect of the price of tax on the decision of giving and volunteering. In
addition, according to Yen (2002), the price of giving is absorbed in the constant term in his theoreticaland empirical model. Yen (2002) claimed that it is unclear how prices might be distinguished among
donation activities.
As far as the price of volunteering (the opportunity cost of volunteering or the wage rate) isconcerned, it is possible to calculate it using the JGSS-2005 data. However, this could cause a serious
measurement error problem when we estimate equations (1a) and (1b). There are two reasons why this
could happen. First, in JGSS-2005 the respondent was asked to state their total hours of volunteeringin the last year. However, it is highly unlikely that the respondent would actually be able to precisely
remember the total hours of volunteering in the last year. Second, the total annual income of the
respondent before tax is categorized in the same way as the total pre-tax annual income of thehousehold (the annual income was categorized into 19 levels). As a result, we cannot obtain a precise
wage rate of the respondent or the price of volunteering. Thus, the price of volunteering is not one of
the explanatory variables. Hence, the fact that we cannot observe the cross price effects of giving andvolunteering seems to represent a shortcoming in the data.
Therefore, we cannot investigate whether the relationship between giving and volunteering is oneof substitutes or complements according to the classical economic definition of these terms.
Nonetheless, the coefficient of VOLR and that of GIVE will tell us whether people who give also tend
to volunteer and vice versa.
2.3 Our preferred model specification and results from estimations
The results from the estimation of equations (1a) and (1b) are given in Table 2.
The rank condition for identifying the volunteering equation (1a) implies that the giving equationmust contain at least one exogenous variable with a nonzero coefficient that is excluded from thevolunteering equation. The rank condition for identifying the giving equation is simply the mirror
image of that of the volunteering equation. Table 2 shows the results from estimating the SPE model.
Table 2 reveals that the rank condition for identifying equation (1a) is satisfied because COMPEDU
and HOUSINC have nonzero coefficients that are excluded from equation (1b). Likewise, the rank
condition for identifying equation (1b) is also satisfied because MEMCIV, MEMRL, MEMVLN,MEMSPT, and HEALTH and have nonzero coefficients that are excluded from equation (1a).

74日本版 General Social Surveys 研究論文集[6] JGSS で見た日本人の意識と行動 JGSS Research Series No.3

Table 2 also reveals that VOLR in equation (1a) has explanatory power (p-value = 0.0308)
whereas GIVE in equation (1b) has no explanatory power (p-value = 0.3679).
Table 2 Estimated simultaneous probit equations
Dependent variable:

GIVE

Independent variables: Coefficient
VOLR
0.172 **
Constant-3.094 ***SEX
0.392 ***
AGE
0.013 ***SPOUSE
0.299 ***
EDUC
0.296 ***
EDUC2
-0.011 **RELIG
0.302 ***OPSRWFY-0.029OPSRMDY-0.032OPCCED-0.076 *OPCCARE
0.035
OPGVEQ
0.125 ***COMPEDU
0.118 *
HOUSINC
0.111 ***SCALE1
0.107
SCALE2
0.150
Log-L
N

Standard error
0.080
0.868
0.086
0.004
0.112
0.114
0.005
0.064
0.046
0.053
0.043
0.045
0.043
0.065
0.027

Dependent variable:

VOLR

Independent variable:
GIVE
Constant
SEX
AGE
SPOUSE
EDUC
EDUC2
RELIG
OPSRWFY
OPSRMDY
OPCCED
OPCCAREOPGVEQ

Coefficient-0.356-5.590 ***-0.0240.018 ***
0.496 *
0.305
-0.0100.193
0.071-0.105-0.0930.079
0.045

Standard error
0.396
1.751
0.191
0.006
0.268
0.220
0.008
0.147
0.064
0.077
0.065
0.065
0.078

0.761 *
0.788 ***
2.221 ***
0.460 ***
-0.0500.197
0.092 *
-0.0830.393 **
0.229

0.433
0.212
0.239
0.160
0.315
0.298
0.051
0.170
0.161
0.205

MEMCIVMEMRL
MEMVLNMEMSPT
MEMPLTMEMINDHEALTHTPHOUSESCALE1SCALE2

0.113
0.145

-607.043
1124

Log-L
N

-300.597
1124

Note 1: The robust covariance matrix is used for estimates.
Note 2: *, **, and *** represent 10% significance level, 5% significance level, and 1% siginificance level respectively.
Calculated by Limdep 8.0

An insignificant coefficient of GIVE in equation (1b) suggests that we should consider the SPE
model with γ2 = 0. Thus, the expression for the SPE model given by equations (1a) and (1b) canalternatively be expressed as(Prob[GIVE = 1, VOLR = 1 | X 1 , X 2 ] = Φ BVN β1' X 1 + γ 1 , β 2' X 2 , ρ(

Prob[GIVE = 1, VOLR = 0 | X 1 , X 2 ] = Φ BVN β1' X 1 , β 2' X 2 , − ρ

)

)

(3b)

(

Prob[GIVE = 0, VOLR = 1 | X 1 , X 2 ] = Φ BVN − β1' X 1 − γ 1 , β 2' X 2 , − ρ()

Prob[VOLR = 0, GIVE = 0 | X 1 , X 2 ] = Φ BVN − β 1' X 1 , − β 2' X 2 , ρ ,

75

(3a)

)

(3c)
(3d)

日本版 General Social Surveys 研究論文集[6] JGSS で見た日本人の意識と行動 JGSS Research Series No.3

where Φ BVN is used to depict the cumulative distribution function of the bivariate normal
distribution. According to Maddala (1983) and Greene (1998), if the two dependent variables in themodified BP model are jointly determined, then the following equations can be estimated by full
information maximum likelihood ignoring the simultaneity in the system.

GIVE ∗ = γ 1VOLR ∗ + β 1' X 1 + ε 1 ,'∗VOLR = β 2 X 2 + ε 2 ,

(4a )
(4b)

where [ε 1 , ε 2 ] ~ bivariate normal[0,0,1,1, ρ ], − 1 < ρ < 1 .
In this system of equations, the correlation between the two structural disturbances (ρ) wasallowed to vary freely (The estimation results of the modified BP (MBP) model are not shown). The
estimated value was -0.2086 and the t-ratio on this coefficient was -1.149. The null hypothesis thatρ=
0 (against the alternative hypothesis:ρ≠0) cannot be rejected, and therefore, we cannot statisticallystate that the two structural disturbances are correlated. We also carry out a likelihood ratio (LR) test of
H0:ρ= 0. The test statistic, LR is distributed as chi-squared with one degree of freedom. Since LR =-2[-907.3038-(-907.9678)] = 1.3280 < 3.84, the null hypothesis cannot be rejected. This result is
consistent with our t-test result. On the basis of these results, we should re-estimate the MBP modelwithρ= 0.
Our estimation results of our preferred model shown in Table 3 reveal that it is highly likely that a
volunteer is a giver but a giver is not necessarily a volunteer (γ1 > 0, γ2 = 0, and ρ= 0). It makes
sense because the major charitable donations in Japan are contributions on the street, and being atemporal giver does not lead to being a volunteer. It is highly likely that contributions on the street
constitute a one-off form of giving and the amount of giving is relatively small, perhaps around 100
yen (about 80 cents in the US) or so. People make a charitable donation on the street because they
hesitate to pass by without giving any money when they run into volunteers who ask for a donation.
However, this type of donation is controlled by donors’ fleeting emotions. Meanwhile, in general
people who donate time or engage in volunteer activities belong to volunteer groups or NPOs. Being amember of such organizations occasionally yields a significant number of opportunities to make acharitable donation. Consequently, a volunteer tends to be also a giver. Since this model is considered
to be our most preferred model representing the features of our data set, we closely look at the
explanatory power of other variables.
Our other research aim relates to whether or not explanatory power can be attributed to thevariables representing people’s opinions regarding the responsibility of the government on social andeconomic issues, such as: livelihood of the elderly (OPSRWFY), medical and nursing care of theelderly (OPSRMDY), education of children (OPCCED), childcare (OPCCARE), and income inequality(OPGVEQ). The question is, do people increase or decrease the supply of giving and/or volunteering if
they think that the supply of these public services is more the responsibility of the government than theindividual or household. If they think that the supply of public services listed in Table 1 is more theresponsibility of the government than the individual or household, and consequently they decrease thesupply of giving and/or volunteering, then it is conceivable that demand for these public services
should be less than or equal to the supply of these public services by the government. In this case,there should be no shortage of these public services. However, if they tend to think that the supply ofthese public services is more the responsibility of the government than the individual or household andyet increase the supply of giving and/or volunteering, then it suggests that the demand for these public
services is more than the supply of these public services by the government. People may donate more76

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money and/or time to nonprofit organizations so that nonprofit organizations can take on the
responsibilities of government as a supplier of these public services. The government failure theory
implies that demand heterogeneity is one of the main factors causing the nonprofit sector to expand
because nonprofit organizations can supply a variety of public services whereas government supplies
only relatively uniform public services.
Table 3 Results from estimation of two probit equations (ρ= 0 is imposed)
Dependent variable:

GIVE

Independent variables:
Constant
VOLR
SEX
AGE
SPOUSE
EDUC
EDUC2
RELIG
OPSRWFY
OPSRMDY
OPCCED
OPCCAREOPGVEQCOMPEDU
HOUSINCSCALE1SCALE2

Coefficient-3.722 ***0.315 **
0.374 ***
0.015 ***
0.353 ***
0.319 ***
-0.011 **0.329 ***
-0.018-0.047-0.087 *0.046
0.123 ***
0.123 *
0.107 ***

Standard error
0.845
0.152
0.088
0.004
0.110
0.123
0.005
0.063
0.050
0.056
0.049
0.050
0.044
0.067
0.027

0.157
0.178

0.111
0.145

Dependent variable:

VOLR

Independent variable:
Constant
SEX
AGE
SPOUSE
EDUC
EDUC2
RELIG
OPSRWFY
OPSRMDY
OPCCED
OPCCAREGOVEQ
MEMCIVMEMRL
MEMVLNMEMSPT
MEMPLTMEMINDHEALTHTPHOUSESCALE1SCALE2

Coefficient-4.498 ***Standard error
1.373-0.1670.015 ***
0.305 *
0.187
-0.0060.080
0.075-0.084-0.067
0.066
0.006

0.133
0.005
0.163
0.185
0.007
0.093
0.076
0.081
0.078
0.074
0.064

0.670 **
0.725 ***
2.102 ***
0.388 ***
0.043
-0.001
0.098 *
0.004
0.354 *
0.195

0.314
0.223
0.183
0.140
0.277
0.196
0.054
0.160
0.197
0.264

（Disturbance correlation）
ρ (1,2)0 (Imposed)
Log-L -907.968
NOTE: *, **, and *** represent 10% significance level, 5% significance level, and 1% siginificance level respectively.
Calculated by Limdep 8.0

Table 3 shows that education of children (OPCCED) and income inequality (OPGVEQ) in thegiving equation have explanatory power. The coefficient of OPCCED is negative whereas thecoefficient of OPGVEQ is positive. Therefore, as people tend to think that the supply of education ofchildren is more the responsibility of the government than of the individual or household, then theprobability of giving decreases, thereby implying there exists no shortage of OPCCED. However, if
people tend to think that solving income inequality is more the responsibility of government than theindividual or household, then the probability of giving increases. Thus, there should be a shortage of
public services that is design for solving income inequality.
However, the probability of volunteering is independent of all types of public services listed in77

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Table 1. This implies that the shortage or the surplus of these public services does not affect people’s
decisions to volunteer or not to volunteer. An important factor affecting people’s decision to volunteer
is whether the individual is a member of a citizens’ movement or a consumers’ cooperative group(MEMCIV), religious group (MEMRL), volunteer group (MEMVLN), and sports group (MEMSPT).
The statistically significant coefficients of these variables are all positive. It is conceivable that people
who are members of these groups will have more opportunities to volunteer than those who arenonmembers.
There are typical socio-demographic variables, such as SEX, AGE, and SPOUSE, that can be seen
in much of the relevant existing literature, that are also included in our model as control variables.
Table 3 shows that the probability of giving is higher for male respondents than female respondents.
The probabilities of both giving and volunteering are higher for elder respondents than younger
respondents. This result may be a reflection of the fact that elders tend to have more time forvolunteering and more money to give than younger people.
Human capital EDUC and its square, EDUC2, have explanatory powers in the giving equation.
The probability of giving increases at a decreasing rate as the respondent accumulates more humancapital. However, the accumulation of human capital has no effect on people’s decisions to volunteer.
RELIG measures the degree of religious belief. The results from our estimation show that theprobability of donating money is positively correlated with the degree of faith of the respondent.
However, the degree of religious belief has no effect on the probability of volunteering. This may
imply that in Japan giving is considered as a way of showing faith, whereas volunteering is not.
HOUSINC is the pre-tax annual income of the household. The estimation results show that anincrease in household income has a positive effect on the probability of giving. As expected, being richincreases the probability of giving.
Health capital HEALTH has positive effect on the probability of volunteering. This result makes
sense because if the respondents are not healthy, then it is not likely that they will be able to go out tovolunteer.
The proxies for the regional effects are SCALE1 and SCALE2. Only SCALE1 in the volunteering
equation has explanatory power, indicating that living in a medium-sized city increases the probabilityof volunteering. This may imply that a medium-sized city is the preferable size for people to volunteer.
Further research is required in order to investigate this result in detail.
The number of children in compulsory school education COMPEDU increases the probability ofgiving. A possible reason for this may be that as the number of children in compulsory school
education increases, parents will have more opportunities to be asked by the school to make acharitable donation.
2.3 Marginal effects
Before closing section 2, we calculate the marginal effect of the explanatory variable, VOLR on
which we have been focusing throughout this paper. The marginal effects of the MBP model arederived by Greene (1996). The conditional mean function in the giving model and the volunteeringmodel is given by

(

) (

)

(

) (

E [GIVE | X 1 , X 2 ] = Φ β 2' X 2 Φ β 1' X 1 + γ 1 + Φ − β 2' X 2 Φ β1' X 1

)

(5a)

and that in the volunteering model is given by()

E [VOLR | X 2 ] = Φ β 2' X 2 ,

(5b)

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where Φ is used to depict the cumulative distribution function of the normal distribution. Thus,
1. For a continuous variable, k, that might appear in X1 and/or X2,∂E [GIVE | X 1 , X 2 ]= Φ β 2' X 2 Φ β1' X 1 + γ 1 + Φ − β 2' X 2 Φ β 1' X 1 β1k∂k+ φ β 2' X 2 Φ β1' X 1 + γ 1 + φ − β 2' X 2 Φ β1' X 1 β 2 k ,

[ (
[(

) (
) (

) (
) (

)]
)]

) (
) (

(6a)

where φ is used to depict the probability density of the normal distribution.
2. For a binary variable, m, that might appear in X1 and/or X2,

E [GIVE | X 1 , X 2 , m = 1] − E [GIVE | X 1 , X 2 , m = 0]

[ ( ) () () ( )]− [Φ (β X )Φ (β X + γ ) + Φ (− β X )Φ (β X )] | m = 0.
= Φ β 2' X 2 Φ β 1' X 1 + γ 1 + Φ − β 2' X 2 Φ β1' X 1 | m = 1
'
2

'
1

2

1

'
2

1

'
1

2

(6b)

1

3. For an endogenous binary variable, VOLR,

E [GIVE | X 1 , X 2 ,VOLR = 1] − E [GIVE | X 1 , X 2 ,VOLR = 0]

(

)

(

)

(6c)

= Φ β1' X 1 + γ 1 − Φ β1' X 1 .

In particular, the marginal effect of VOLR in the giving model can be obtained by applying
equation (6c) and its value is 0.0974 (its asymptotic standard error, 0.0427 is computed using the delta
method). That is, an increase in the probability of giving is approximately 9.74% for those individuals
who volunteer. Note that this marginal effect is computed at the means of the explanatory variables
excluding VOLR in the giving model.
13%

90%

11.97%

80%

11.54%
70%
61.14%

10.99%

70.69%

68.43%

72.86%

76.87%

74.92%

78.72% 80.45%

11%63.65%

10.36%

66.08%

60%

10%

50%

8.88%

9%

40%

8.09%

8%

30%

7.28%

Prob(GIVE|VOLR=1,HOUSINC=0,…,9)20%

Marginal effects

9.65%
Prob(・)

12%

7%

6.48%

Marginal effect (ME)

6%

10%

5.71%

0%
0

Figure 1

1

2

3

4

5

6

7

8

5%

9
HOUSINC

The marginal effects of volunteering depending upon household income levels

An interesting observation is that the marginal effect of VOLR in the giving model decreases as
the pre-tax annual income of the household, HOUSINC increases (See Figure 1). Each marginal effectin Figure 1 is computed at the means of the explanatory variables excluding VOLR and HOUSINC.
Note that we converted the original 19 categorizations in the JGSS-2005 dataset such that categories 0
and 1 were combined to correspond to level 0, likewise categories 2 and 3 were combined to79

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correspond to level 1, and so on up to level 9.
As Figure 1 shows the marginal effect of volunteering on giving tends to be more profound for
low income households than that for high income households.

3. Concluding remarks
Analysis of people’s philanthropic activities such as charitable giving and volunteering is in
general a challenging research agenda because people’s decisions are closely related to their customs
and morality. It is feasible that these activities vary dramatically according to people’ssocio-demographic characteristics, by the level of human capital accumulation, by the level of healthcapital accumulation, and by the locality. The level of religious faith seems also to be an importantfactor for philanthropic activities. Despite these adverse factors, quite a few researchers haveattempted to analyze the factors that determine the level of people’s philanthropic activities. For
example some researchers tried to examine the relationship between the level of people’s philanthropic
activities and the level of government spending. Abrams and Schmitz (1978) found that such a
relationship would imply a crowding out effect, however this finding is not supported by Reece (1979).
Warr (1982) and Roberts (1984) found that increases in government spending on social services
encouraged donations. The theoretical framework developed by Schiff (1985) decomposed charitable
contributions into an income effect and a substitution effect and showed that government spendingneed not crowd out charitable contributions and may even encourage giving.
Therefore, as Menchic and Wiesbrod (1987) and Schiff (1985) stated, according to the existing
literature the crowding out effects of government spending caused by giving and/or volunteering are
ambiguous. Further research is required in order to interpret and investigate this result.

[Acknowledgement]
The Japanese General Social Surveys (JGSS) are designed and carried out at the Institute of Regional
Studies at Osaka University of Commerce in collaboration with the Institute of Social Science at the Universityof Tokyo under the direction of Ichiro TANIOKA, Michio NITTA, Noriko IWAI and Tokio YASUDA. The
project is financially assisted by Gakujutsu Frontier Grant from the Japanese Ministry of Education, Culture,
Sports, Science and Technology for 1999-2008 academic years, and the datasets are compiled and distributed by
SSJ Data Archive, Information Center for Social Science Research on Japan, Institute of Social Science, theUniversity of Tokyo.

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