WHO VOLUNTEERS IN JAPAN AND HOW?
– BASIC FINDINGS FROM MICRO DATA

Satoshi Nakano

May 2005

Keio Economic Observatory Discussion Paper No. 96WHO VOLUNTEERS IN JAPAN AND HOW?
– BASIC FINDINGS FROM MICRO DATASATOSHI NAKANOInstitute for Economic and Industrial Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japannakano@sanken.keio.ac.jp

Abstract
Previous studies (especially in Japan) of volunteer labor supply have the following three
weaknesses. (1) Most rely on extemporary questionnaire surveys of hundreds or thousands ofindividuals about volunteer activities. This approach induces sample-selection bias to the extent
that respondents are those who are most interested in those activities. (2) They neglect qualitative
aspects of volunteer activities, which make it difficult to explain how free-rider problems occur andhow they might be solved. (3) In married couples, husbands’ activities are influenced by wives’
activities and vice versa, but previous studies have not examined these mutual relationships. We
focus on these three issues by utilizing micro data from the Survey on Time Use and Leisure
Activities, which is a general survey of approximately 200,000 household members about theallocation of time to various kinds of activities, including volunteering. We find the following. (1)Most previous studies have overestimated individual participation rates in volunteer activities. (2)Married couples tend to participate jointly in volunteer activities. (3) Free-rider problems are more
serious in urban areas than in rural areas, especially in activities related to social services. These
results suggest that a policy framework for promoting volunteering should be flexibly designed bycarefully considering location, the attributes of households and individuals, and the qualitative
aspects of activities.
JEL Keywords: H41; J22; J49.

1.

Introduction

As Japanese society matures, peoples’ demands diversify. However, the public services
provided by the Japanese government are standardized and thus unable to satisfy thevaried desires of consumers. At the same time, because of the government’s cumulative
deficit, an expansion of services is impractical. The services provided by volunteers andnot-for-profit organizations have filled the gap between demand and supply.
In this paper, we focus on volunteer labor supply and econometrically analyze
volunteer activities. Most previous studies rely on extemporary questionnaire surveys ofhundreds or thousands of individuals. Thus, they may suffer from sample-selection bias
because respondents tend to be those who are most interested in those activities, and they
may be based on small samples. To solve these problems, we use micro data from the1

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Survey on Time Use and Leisure Activities (STULA) conducted by the Statistical Bureau
of Japan. We aim to answer the question “Who volunteers what and how?”, which is an
original perspective on this area. Is volunteering motivated by altruism or individualism?1
If it is altruism, the free-rider problem may occur. However, we suggest that whether thisproblem arises depends on the type of volunteer activity. In other words, if volunteer
work has the characteristics of a public good, even if others do the work, then volunteers
motivated by altruism may experience an increase in utility.
If service providers expect a non-monetary reward, such as gratitude from recipients,
voluntary contributions are motivated by individualism, and the free-rider problem may
not be the main concern. With this in mind, we classify volunteer activities into two
categories: social activities and individual activities. The former covers voluntary workthat generates a public good. One feature of this paper is an analysis of these activities.
A second feature of the paper is that we consider the voluntary work of married
couples. Whether people volunteer as individuals or as couples is important. If couples
volunteer, policies that encourage spouses to volunteer jointly are needed because,otherwise, neither participates.
Third, we focus on the effect of the residential area on volunteer labor supply.
Individuals who have close relationships with their communities may be more motivatedto volunteer.
This paper is organized as follows: In the next section, we present an overview ofprevious studies on volunteer labor supply. In section 3, we explain the model and thedata used. In section 4, we use the results to discuss the relationship between individual
attributes and participation in volunteer activities.
2.

Literature Survey

Previous empirical analysis on volunteer labor supply has two main focuses: oneconcerns the circumstances under which the free-rider problem occurs and the otherconcerns why and how people volunteer.
If volunteer work generates public goods, the standard framework for collective goods
production of Becker (1974) implies that free-rider problems might arise. As Sugden(1982) has illustrated, the more participants join a community, the lower the number ofpublic goods per capita that are supplied through voluntary donation. This theoretical
implication, however, contradicts reality. Sugden’s (1984) “reciprocity” model, Cornes
and Sandler’s (1984) joint production system, Andreoni’s (1990) “warm glow”
hypothesis and Anderson et al.’s (1998) decision error model were developed to fill thegap between theory and reality.
Because of limited data availability, most feasibility tests have been conducted by
performing experiments. Marwell and Ames (1979a, 1979b), Mark and Shetland (1983),
Kim and Walker (1984), Andreoni (1993), Andreoni (1995) and Chan et al. (2002) have1

The objective of altruism is to increase another’s utility. On the other hand, the objective of individualism is
to increase one’s own utility by increasing another’s utility through one’s own volunteer activities.

3

designed games that theoretically induced free-rider situations and examined theiroutcomes. They confirmed that the games elicited participants’ cooperative behavior anddid not necessarily support the theoretical predictions.
However, whether volunteering is motivated by altruism or individualism depends on
the type of volunteer activities undertaken. When volunteering is motivated by altruism,
there can be a free-rider problem. The models of Sugden (1984), Andreoni (1990) andothers are thus inadequate for empirical study of the relationship between volunteer
activities and the free-rider problem. Experiments are unlikely to clarify the motivation
behind volunteering. We, therefore, divided volunteer activities into those that have thecharacteristics of public goods and those that do not.
In the context of volunteer labor supply, the free-rider problem has not been the mainconcern. More research has sought to explain why and how people volunteer.
Theoretically, volunteering can be treated in the same way as donating money, althoughtime, rather than money, is given. In terms of resource allocation, while volunteers donate
time to directly supply labor to produce services, money can be spent on any useful
resource. In this sense, the latter has more flexibility and may cause less resource
misallocation.
Clotfelter (1985) (Chapter 4) focused on the relationship between donating time anddonating money from the viewpoint of the tax system. The estimated volunteer supply
functions imply a positive (marginal) tax effect on volunteering. This suggests that giving
money and giving time are complementary. Menchik and Weisbrod’s (1987) and Brown
and Lankford’s (1992) estimated cross-price elasticities between time giving and money
giving also suggest a strong complementary relationship.
Menchik and Weisbrod (1987) developed a model that explains volunteer labor
supply from the perspective of both consumption and investment. While volunteer
activities in the form of consumption merely increase temporal utility levels, activities
that constitute investment raise future expected incomes as volunteers accumulate humancapital. From empirical studies based on a survey of U.S. citizens, Menchik andWeisbrod (1987) found that volunteers’ working hours are negatively correlated withafter-tax wage rates, complementary with monetary donations, and have a positive but
diminishing correlation with total income.2
Day and Devlin (1997, 1998) examined the effect of volunteering on earnings by
estimating income functions. Their results imply significant effects of volunteering on
earnings and support the investment model. Segal and Weisbrod (2002), using a surveyof volunteers in the U.S., estimated hours supply functions in the health, education andchurch sectors. Their results did not support the homogeneity hypothesis for volunteer

2

Freeman (1997), based on the Supplement on Volunteer Activity as a supplement of The Current Population
Survey in the U.S., empirically analyzed the determinants of volunteer activity hours. According to empiricalresults showing that volunteer workers have high skills and opportunity costs, he concluded that volunteer
activities are hardly explained by the “standard theory of labor supply” but by the ‘F-Connection’ (seeBen-Porath, 1980), which is based on human relationships within families, friends and firms.

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activities. Therefore, the marginal effects of changing tax rates and individual attributes
and the motives behind volunteering – to provide consumption or investment goods –differ between sectors.
The focus of volunteer labor supply analyses has shifted from the properties ofvolunteer activities themselves to the specific people who undertake volunteer activities.
Carlin (2001) considered the volunteer labor supply of married women in the U.S. andestimated participation functions and hours supply functions. The results imply that the
number of children has a positive influence on married women’s participation involunteering but a negative effect on the number of hours volunteered. Monetary
donations and volunteering are complements at the participation stage but are substitutes
in terms of hours supplied. The coefficient of the woman’s wage rate was not statisticallysignificant.
Garcia and Marcuello (2002) suggested the family model of labor supply using
Spanish data. The family decides the labor supply of both spouses. Their empirical results
show that family size and pre-school children have negative influence on the time given
over to voluntary work by the wife, but family size has positive on the time given over tovoluntary work by the husband. Also, monetary donations by the family arecomplementary relationship with time donations by the spouses.
Bryant et al. (2003) also estimated the effect of the number of children, as did Carlin(2001), but they did not focus only on women. They found that as marriage helps toincrease social capital through the development of social networks, spouses have apositive effect on the decision to participate in volunteer work.
Schady (2001), focusing on rural areas in Peru, and Smith (2004), focusing on
middle-aged workers in Missouri in the U.S., estimated volunteer participation functions.
Schady (2001) found that in rural areas, volunteer activities that have the characteristics
of public goods develop social capital for people in the area, including the volunteers.
Hence, regional characteristics must be considered when econometrically analyzing
volunteering.
Cappellari and Turati (2004) considered workers’ intrinsic and extrinsic motivations.
Based on the extemporary questionnaire surveys of Italian employees and themultivariate probit model for endogeneity, they estimated volunteering probability
functions in social services, political associations and trade union. The results show
extrinsic motivations to lower voluntary labor supply in social services and politicalassociations, whereas the opposite holds for intrinsic motivations in trade union.
In this paper, we adopt ideas from previous studies, such as the relationship between
volunteer activities that have the characteristics of consumption goods and those thathave the characteristics of investment goods. We also consider the relationship between
monetary donations and volunteer activities, and the effect of the number of children on
volunteering. We also consider new issues, such as the effects of residential area andmarriage. The survey used in previous studies is not sufficient for robust results becauseof its limited sample size. We estimated volunteer labor supply functions on a large

5sample by using micro data from STULA. Because STULA includes a wide range ofquestions on daily life, including volunteering work, it is unlikely that respondents are
only those who are most interested in volunteer activities.
In Japanese research, Yamauchi (1997, 2001), Sanwa Sogo Kenkyusho (2000) and the
Osaka School of International Public Policy and the Center for Non-profit Research andInformation (2004) applied Becker’s (1965) household production activity model toderive a volunteer labor supply function from a household’s utility-maximization
problem. They used aggregate data from STULA and original questionnaires as theirmain data sources. They found that volunteer activities have the characteristics ofconsumption goods, volunteer activities and monetary donations are complementary, andthat living in urban areas has a negative effect on participating in volunteer activities.
Fukushige (1999) estimated volunteer participation functions by using macro datafrom STULA as the main data source, as did Yamauchi (1997). The coefficients ofmonetary asset balances, proportion of elderly people and proportion of young people
were significantly positive.
Atoda and Fukushige (2000) estimated volunteer participation functions and hours
supplied functions by using a survey of middle-aged people in the Tokyo metropolitan
area and the Oita and Nagano prefectures. They found positive effects for the number ofchildren and educational attainment in all areas. However, in the Tokyo metropolitan area,
the wage rate had a negative effect, which suggests that effects on the decision toparticipate in volunteer work may differ between regions.
The few studies of volunteer activities in Japan may suffer from sample-selection bias
because of the surveys on which they are based. In addition, it may be difficult to obtainrobust results from surveys that are targeted at particular people and have small samples.
In some studies, although macro data from STULA is used, sample sizes are still small.
Although Segal and Weisbrod (2002) recommended distinguishing between types ofvolunteer activities, all are treated equally in these studies. As stated above, Japanese
econometric analysis on volunteer work is inadequate.
In this paper, by using STULA, we avoid the sample-selection bias problem. In
addition, micro data from STULA ensures a large sample size. STULA’s detailed
information on the type of volunteer activity enables a detailed analysis of volunteer
work. It also enables us to distinguish volunteer activities provided to society, which havethe characteristics of public goods, from those that are provided to individuals. Moreover,data on variables hardly used in previous empirical work, such as marital status andregion, are used in our analysis. Hence, our econometric analysis is comprehensive, noveland free of data problems.
3.

The Model and the Data

To answer the question “Who works as a volunteer and how?”, we need a model dealing
with the discrete choice of whether an individual is involved in volunteer activities or not.
A large amount of data that is free of selection bias and has a positive perspective on

6volunteer activities is also desirable. We used STULA, a general survey of national life
and the economy, which is unlikely to induce sample-selection bias. The micro data fromSTULA allows large samples and has information on different categories of volunteer
activity.
3.1.

The Model

A standard model of volunteer labor supply derives a volunteer supply function from anindividual’s utility-maximization problem, given a time endowment. The model assumes
that an individual is given a certain amount of time and decides how to allocate this time
between work, leisure (including sleep) and volunteer activities. At the same time, theindividual allocates earned income between consumption and monetary donations. As
Menchik and Weisbrod (1987) show, whether volunteering takes the form of
consumption or investment can be ascertained from the coefficients of the independent
variables (as explained in the next subsection.) If the motivation to volunteer has thecharacteristics of consumption goods, the total income, including the opportunity cost of
spending time on volunteer activities, has a positive effect on the time spent on volunteer
activities. Wage after tax, which is the opportunity cost of volunteer activities, has anegative effect on time spent volunteering.
This paper investigates the decision-making process relating to volunteering in twostages. (1) What kinds of people volunteer for what kinds of activities? (2) What kinds ofindividual attributes affect the frequency and patterns of participation?3 To model the firstdecision, whether to volunteer and what for, we use a multinomial logit model. For the
regression analysis, the sample is divided into five groups: unemployed males;
unemployed females; employed males; employed females; and the self-employed.4
3.2.

The Data

The data used in this paper are micro data from the Survey on Time Use and Leisure
Activities (STULA), 1981, 1986, 1991, 1996 and 2001. The dependent variable isinvolvement in volunteering by type of activity. The independent variables are: age (age);marital status (marry); educational attainment (study); residential status (house); total
income (fincome); presence of child under six (child); presence of person over 65 (old);presence of person needing care (care); weekly rest system (holiday); living area (area);and spouse’s involvement in volunteer activities (couple).5
Dependent variables for involvement in volunteering by kind of activities are as
follows: not engaged in volunteer activities = 0; involved in local community = 1;
3

The second decision is not the subject of this paper.
We tried to estimate some regressions by using the multivariate probit model. However, the results arealmost the same as those from the multinomial logit model. Therefore, we suggest that the residual terms of
the regressions are not correlated with each other and used the multinomial logit model.
5
When we used the spouse’s involvement in volunteer activities as an independent variable, we included
only married couples in the sample.
4

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involved in social welfare provision = 2; provide activities for children, the elderly, andthe handicapped = 3; involved in certain districts = 4; provision to general public = 5;
involved in social services = 6; involved in social activities = 7. In 1981 and 2001, thedata classification is different from that of the other years; the data were modified asfollows: (1) for 1981, involved in local community = 1; involved in social welfare
provision = 2; involved in districts affected by disaster = 3; provision to certain groups =4; other activities = 5; involved in public activities = 6. (2) for 2001, health- and medical
care-related activities = 1; services to the elderly = 2; provision for people withdisabilities = 3; provision for children = 4; involved in sports and culture = 5; involved in
activities related to town planning = 6; involved in safe-living planning = 7; involved in
activities related to nature and the environment related = 8; involved in disaster relief = 9;other activities = 10.6
In this paper, we estimated the model relating to the decision to choose a type ofvolunteer activity by dividing activities into those provided for society, which have thecharacteristics of public goods, and those provided for individuals. For this model: notengaged in volunteer activities = 0; involved in activities related to public goods = 1;others = 2.7
For independent variables, the original data were modified as shown in Table 1. Total
income (fincome) includes not only labor income but also the opportunity cost of
volunteer activities. Although one should consider unearned income additional to thoseincomes, STULA does not have this information. Since labor income, for which annual
household income is used, ranges from 1 million Japanese yen (JPY) to 199 million JPY,
from 200 million JPY to 299 million JPY and so on, we used the median of each range.8
The opportunity cost of volunteer activities is calculated by multiplying the hourly wage
(annual household income divided by annual working hours) by the number of hours
spent volunteering.9 The cost of making a monetary donation of one JPY (crossp)
6

Volunteer activities are defined as social services and social activities in STULA. Social activities weresurveyed in 1991 and 1996. They are activities for the whole of society including the respondent: for example,
women’s activities and citizens’ activities.
7
We classified volunteer activities as follows: (1) for 1981, “in local community”, “others” and “public
activities” as volunteer activities for society and “in social welfare facilities”, “in districts afflicted bydisasters” and “to certain groups” as activities for individuals. (2) for 1986, “in local community”, “to generalpublic” and “social service” as activities for society and “in social welfare facilities”, “to children, the elderly
and the handicapped” and “in certain districts” as activities for individuals. (3) for 1991 and 1996, “in local
community”, “to general public”, “social service” and “social activities” as activities for society and “insocial welfare facilities”, “to children, the elderly, and the handicapped” and “in certain districts” as activities
for individuals. (4) for 2001, “town planning related”, “safe living planning related”, “nature andenvironment related” and “others” as activities for society and “health and medical care related”, “to theelderly”, “to people with disabilities”, “to children”, “sports and culture related” and “disaster relief” asactivities for individuals.
8
We should not use household income but individual income. However, because of limited data availability,
we used household income.
9
Annual working hours were calculated by multiplying weekly working hours (median) by 52 (1 year = 52
weeks). Additionally, we obtained information about the frequency of volunteer activities from STULA.

8

represents the cross price of volunteer activity, which is defined as 1 – the tax rate. If thecoefficient of the cross price is positive, volunteer activity and monetary donations aresubstitutes; otherwise, they are complements. The tax rate used in these calculations is
the progressive tax rate on gross income, and we included neither employment income
deductions nor personal deductions. To allow for interactions between the living area andmonetary donations, the cross product of these variables (crdum2) is included as anindependent variable.
4.

Empirical Results and Discussion

We estimated the volunteer labor supply functions for each of the five years, 1981, 1986,
1991, 1996 and 2001. However, due to limitations on space, we only report results for2001 (in Tables 2-6).10
4.1.

Children and Volunteer Activities

There have been several empirical studies of the relationship between the number ofchildren and volunteering. Carlin’s (2001) study for the U.S. showed that a higher
number of children encourages married women to participate in volunteer activities but
reduces the amount of time they spend doing those activities. Segal and Weisbrod (2002)
suggested that the number of children has a positive effect on the amount of time spent on
volunteer activities in education and church-related activities. For Japan, Atoda andFukushige (2000) found that the number of children has a positive effect on participationin volunteer activities in the Tokyo metropolitan area and the Oita prefecture.
Our estimation results indicate that children under six tend to reduce participation involunteer activities by employed and unemployed women. Children under six reduce
participation by married and unmarried women. These results contradict those ofprevious studies. However, the burden of bringing up children falls unduly on women in
Japan. The number of children under six hardly affects participation in volunteer
activities by unemployed men. The differences between our results and those of Atoda
and Fukushige (2000) could be due to sample-selection bias, a small sample size and theomission of a separate analysis of women.
For employed men, who have not been analyzed in previous studies, children under
six tend to increase participation in volunteer activities provided for society but reduce
participation in activities provided for individuals. This also applies to married men.
Employed men seem to connect with their communities by engaging in volunteer
activities for society in their spare time.

Therefore, annual volunteering hours were calculated by multiplying annual volunteering days (median) by
eight (we assumed an individual worked an 8-hour day on volunteer activities).
10
Please refer to Nakajima et al. (2005) for more detailed information.

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4.2.

The Aged and Volunteer Activities

The effect of the elderly over 65 on volunteering depends on whether volunteers havejobs. For unemployed men and women, the elderly has a negative effect on participationin volunteer activities. For employed men and women, the elderly has a positive effect.
According to the results of Fukushige (1999) and Sanwa Sogo Kenkyusho (2000), thegreater the number of aged persons, the higher is the rate of participation in volunteer
activities. However, if the decision to participate depends on whether volunteers are
employed, this finding may not be complete.
Employed persons with no spare time need community support or help from welfare
and nursing facilities. Because elderly people have close links with their communities,
employed people with the elderly build relations with society. Furthermore, given that theaged encourage participation in volunteer activities geared towards not only society but
also to specific individuals, such as the aged-care and welfare and nursing facilities,
households with elderly people use services provided for the elderly.
4.3.

Those Who Need Nursing and Volunteer ActivitiesThose who need nursing have a positive effect on participation in volunteer activities
geared towards both society and specific individuals. This is consistent with Atoda andFukushige’s (2000) finding for the Oita prefecture that those who need nursing have apositive effect on participation.
Those who need nursing raise participation in volunteer activities for the same reasons
as children and the elderly raise participation in volunteer activities. When the time andmoney spent on nursing is considered, people with those who need nursing motivate
volunteers more than do people with children or the elderly.
4.4.

Living Areas and Volunteer Activities

Living in one of the three major cities, Tokyo, Osaka or Aichi, has a negative effect onparticipation in volunteer activities for the unemployed, both men and women. Thissuggests that unemployed individuals in rural areas tend to participate more actively involunteer activities.
City dwelling negatively affects participation in all volunteer activities by employed
men and women. In many cases, there is a negative effect on the decision to participate involunteer activities geared towards society. This means that those in employment andthose with little leisure time try to link with their communities. This is particularly true
for those living in rural areas where more support can be obtained from the community.
For self-employed people living in either rural or urban areas, relations with thecommunity are important, and where people live rarely affects participation in volunteer
activities.
Sanwa Sogo Kenkyusho (2000) concluded that living in urban areas has a negative
effect on the decision of a householder and a spouse to participate in volunteer activities.

10

However, as our estimated results show, living in urban areas does not necessarily
discourage volunteering.
4.5.

Monetary Donation and Volunteer Activities

Many studies deal with the relationship between monetary donations and volunteer
activities. Notably, in the U.S., Clotfelter (1985), Menchik and Weisbrod (1987), Brown
and Lankford (1992), Carlin (2001), Segal and Weisbrod (2002) and others haveanalyzed the degree of complementarity between monetary donations and volunteer
activities. However, Segal and Weisbrod (2002) found that while there iscomplementarity between monetary donations and time spent on volunteer activities inthe education sector, donating and volunteering are substitutes in the church sector. In
Japan, Sanwa Sogo Kenkyusho (2000) found that volunteer activities by a householder
and a spouse and monetary donations are complementary.
Our results show that complementarity does not always prevail. Relationships varyover time. For employed men and women, when the price of donations increases, there isless participation in volunteer activities. However, over time, the trend shifts towards
more participation in volunteer activities. Therefore, monetary donations and volunteer
activities have changed from being complements to substitutes.
For the self-employed, the coefficient for volunteer activities geared towards
individuals went from positive in 1996 to negative in 2001. Since this is only a change
between two points, the finding is not necessarily robust. Nevertheless, monetary
donations and volunteer activities have changed from substitutes to complements.
To determine whether preferences between monetary donations and volunteer
activities are affected by living areas, which has not been addressed by previous studies,interaction terms in the price of monetary donations and living areas are included. Its
coefficient is positive for employed men and women and negative for the self-employed.
4.6.

Married Couples and Volunteer Activities

In all samples, spouses in general have a positive effect on the decision to participate inall kinds of volunteer activities. Spouses, as opposed to single individuals, tend to deepen
relations with society and, at the same time, use time more efficiently to gain leisure time.
Consequently, participation in volunteer activities increases.
The negative effect on participation in volunteer activities of the absence of a spouse
was found by Bryant et al. (2003). The effect of participation by one spouse in volunteer
activities on the participation of the other spouse has not been analyzed. For individuals
in the sample with spouses, participation by their spouse in volunteer activities raises
their own participation for all volunteer activities. Since this applies to all years, thisfinding is robust.
The decision to participate in volunteer activities is not necessarily madeindependently by each individual, but, rather, relates to that of the spouse in the same
household. It may be that when one spouse participates in volunteer activities, the other11

spouse either extends collaboration or shows an interest to enhance satisfaction within thehousehold. Therefore, an increase in volunteer activities where married couples canparticipate together could encourage volunteering.
4.7.

Other Characteristics and Volunteer Activities

Having a high educational attainment, owning one’s own home, having a high income(for those in employment) and having two or more days’ holiday per week (for those inemployment) are characteristics that have a generally positive effect on the decision toparticipate in all volunteer activities.
Hence, the effect of high educational attainment suggests that education about
volunteer activities is often provided, which stimulates interest in volunteer activities.
Furthermore, having plenty of time to spare and money to spend promotes volunteering.
Menchik and Weisbrod (1987) and Yamauchi (1997) showed that volunteer activities
have the characteristics of consumption goods on the basis of a positive coefficient for
total income and a negative coefficient for the wage rate with respect to participation involunteer activities. Our results not only confirm this, but also show that thesecharacteristics do not depend on volunteer activities or other characteristics.
4.8.

For Promotion of Volunteer Activities

As Nakajima et al. (2004) report, it is difficult to implement volunteer activities in
welfare and nursing facilities. Additional surveys by interview found that these facilities
rely heavily on part-time workers, not volunteers, to maintain the quality of services
under the budgetary constraints. Since there are such constraints on the demand side that
hamper the use of volunteers, unplanned attempts to promote volunteering could be
ineffective.
Given that high educational attainment can raise participation in all volunteer
activities, providing willing volunteers with opportunities to learn and acquire expertiseand knowledge would be an effective way of promoting volunteer activities. In addition,
trained volunteers need to be linked to those requiring volunteers.
However, Brudney (1990) and Brudney and Duncombe (1992) report that the costs oftraining, education and introduction would not only exceed the savings in fiscal payments
generated by the use of volunteers, they would increase the fiscal burden. Consequently,
when quality of service is important, willing volunteers could contribute to the cost of
training themselves.
5.

Conclusion

In this paper, by using micro data from the Survey on Time Use and Leisure Activities(STULA), which uses a large sample and is free of bias, we have estimated supply
functions for volunteer labor. We analyzed the data by grouping volunteer activities into
activities geared towards society, which have the characteristics of public goods, andthose geared towards specific individuals. We found that participation in activities geared

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towards society, as opposed to those geared towards specific individuals, is negatively
affected by living in urban areas. This means that volunteer activities geared towards
society are heavily influenced by where people live and that free riding is likely to arise
in urban areas. However, for the self-employed, the decision to participate in volunteer
activities is hardly affected by where they live.
Measures to promote volunteer activities may be divided into two categories: those topromote overall volunteer activities and those to promote activities geared towards
specific targets. In the context of overall activities, we recommend reducing theopportunity costs of volunteer activities and applying preferential taxation for monetary
donations.
However, individual decisions to participate in volunteer activities may not be
synonymous with the characteristics of consumption goods. Thus, reducing theopportunity costs of volunteering would not necessarily promote volunteer activities.
Moreover, volunteer activities and monetary donations may change from being
complements to substitutes. Therefore, preferential taxation for donations may notnecessarily promote volunteer activities.
The fact that the decisions of husbands and wives to participate in volunteer activities
are related is useful for considering volunteer activities geared towards specific targets.
This suggests that an increase in volunteer activities in which married couples canparticipate together, rather than as individuals, will promote volunteer activities.
In urban areas, it is better to promote volunteer activities geared towards specificindividuals rather than those geared towards society, which are affected by the free-rider
problem. Expertise and knowledge are required to provide activities geared towards
specific individuals. In this context, it is important to provide opportunities to train andeducate volunteers and to match trained volunteers with those in need of their services.
Above all, flexibility is needed in mapping out measures to promote volunteer activities.
The estimation results of this paper should be reviewed and the frequency andpatterns of participation should be analyzed for all volunteer activities or for specificallytargeted volunteer activities. In addition, it must be decided whether the promotion ofvolunteer activities is necessary. For this, it may be necessary to determine whether thecurrent level of volunteering is appropriate. We recommend that these issues be
addressed.

Acknowledgments
This study is part of the “Economic Analysis of Not-for-Profit Organizations andVolunteers” project at the Policy Research Institute, Ministry of Finance. I would like tothank Prof. Takanobu Nakajima (Faculty of Business and Commerce, Keio University)and Mr. Syunsuke Imada (Policy Research Institute, Ministry of Finance) for helpfulcomments.

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15

Table 1. Independent variables.
Age
Marital status
Educational attainment
Residential status
Presence of child under 6
Presence of person aged over 65
Presence of person needing care
Weekly rest system
Living areas
Spouse’s participation in volunteer
activities

10s=1, 20s=2, 30s=3, 40s=4, 50s=5,
60s=6, Above 70=7
Married=1, Single=0
In college and above=1, High-school
graduate and below=0
Homeowner=1, Renter=0
Yes=1, No=0
Yes=1, No=0
Yes=1, No=0
Above two days=1, Under two days=0
Tokyo, Osaka or Aichi=1, Elsewhere=0
Yes=1, No=0

16

Table 2. Volunteer labor supply functions of unemployed men in 2001.

ONE
(
AGE
(
MARRY
(
STUDY
(
HOUSE
(

All individuals(1) Society(2) Individual–2.103***–2.086***–34.401 )( –31.245 )
0.051
***
–0.0124.654)( –0.890
)
0.724
***
0.509
***
13.629)( 7.665
)
0.203
***
0.572
***
4.914
)
( 12.378
)
0.537
***
0.284
***
9.942
)
( 4.745)

Married(1) Society(2) Individual–2.289 ***–3.334***( –9.645 )( –10.899 )
0.068
*
0.094
**
( 1.921)( 2.086
)

(
(

0.313
5.419
0.683
7.689

***
)
***
)

(

)

(

)

(

)

(

(

0.586
8.620
0.722
6.011

***
)
***
)

FINCOME
CHILD(–0.351–1.2760.131
1.276–0.064–0.681(–0.479–8.031

(
OLD
(
CARE

)

(

)
*
)

(

(

–0.387–1.159–0.067–0.488
0.195
1.764

(

–0.330–4.733

***
)

)

(

)

(

)

***
)

(

–0.306–0.849–0.091–0.726
0.086
0.699

0.268
0.653
–0.184–1.158
0.288
1.938

)
*
)

–0.260–.596
1.912
33.032

***
)
***
)

)

HOLIDAY
CROSSPAREA(

COUPLE(–0.393–4.8751.903
41.340

***
)
***
)

(
(

CRDUM2
Number24161
12275
Log likelihood–21354.280–10851.400Note) Values in parentheses are t-statistics.
*** (**,*) denotes rejection of the hypothesis at 1% (5%, 10%) significance level.

17

Table 3. Volunteer labor supply functions of unemployed women in 2001.

ONE
(
AGE
(
MARRY
(
STUDY
(
HOUSE
(

All individuals(1) Society(2) Individual–1.280***–0.964***–31.528 )( –23.657 )–0.063***–0.176***–10.340 )( –27.405 )
0.677
***
0.502
***
27.437)( 18.161
)
0.385
***
0.765
***
13.339)( 26.156
)
0.353
***
0.353
***
11.360)( 10.558
)

(
(

(
(

Married(1) Society(2) Individual–0.753***–1.167***–9.475)( –13.306 )–0.171***–0.153***–11.954 )( –9.695
)

0.386
9.974
0.410
9.355

***
)
***
)

–0.590–10.824
0.076
1.221
0.078
1.078

***
)

(

)

(

)

(

–0.118–2.3581.791
57.000

**
)
***
)

(
(

0.717
17.471
0.385
7.931

***
)
***
)

–0.724–11.958
0.114
1.695
0.206
2.673

***
)
*
)
***
)

–0.041–0.7601.606
46.153)***)

FINCOME
CHILD(–0.418–10.130
0.278
5.834–0.158–2.859

***
)
***
)
***
)

(

–0.277–7.409

***
)

(
OLD
(
CARE

***
)
***
)

(

(

–0.862–18.395
0.262
5.024
0.024
0.407

)

(

(

–0.218–5.440

***
)

(

(
(

(

HOLIDAY
CROSSPAREA
COUPLE(((CRDUM2
Number52333
Log likelihood–49670.180Note) Values in parentheses are t-statistics.
*** (**,*) denotes rejection of the hypothesis at 1% (5%, 10%) significance level.

27977–25923.650

18

Table 4. Volunteer labor supply functions of employed men in 2001.

ONE
(
AGE
(
MARRY
(
STUDY
(
HOUSE
(
FINCOME
(
CHILD
(
OLD
(
CARE
(
HOLIDAY(
CROSSP(
AREA(

All samples(1) Society(2) Individual–2.967 ***–3.347 ***–9.182 )( –8.682 )
0.132
***
–0.051 ***13.365 )( –4.270 )
0.713
***
0.684
***
22.103 )( 18.134 )
0.140
***
0.448
***
6.371
)
( 17.652 )
0.482
***
0.318
***
17.242 )( 9.921
)
0.001
***
0.001
***
8.624
)
( 8.658)0.088**–0.083 **2.576)( –2.109 )
0.339
***
0.360
***
11.817 )( 10.499 )
0.354
***
0.382
***
5.349
)
( 4.916
)
0.300
***
0.279
***
13.504 )( 10.462 )
0.358
1.093
**
0.987
)
( 2.528)–2.415 ***–2.376 ***–4.940 )( –4.259 )

(
(

(
(
(
(
(
(
(
(
(

COUPLE(CRDUM2

2.244
3.568

***
)

2.385
3.327

***
)
56654
–53772.500

Married(1) Society(2) Individual–2.584 ***–2.694 ***–6.136 )( –5.292 )
0.062
***
–0.086 ***4.328)( –4.982 )

0.009
0.298
0.571
15.592
0.001
6.991
0.103
2.456
0.296
7.981
0.300
3.482
0.238
8.200
0.326
0.689
–2.543–4.0281.504
57.490
2.460
3.014

)
***
)
***
)
**
)
***
)
***
)
***
)
)
***
)
***
)
***
)

(
(
(
(
(
(
(
(
(
(

(
(
(
(
NumberLog likelihoodNote) Values in parentheses are t-statistics.
*** (**,*) denotes rejection of the hypothesis at 1% (5%, 10%) significance level.

0.285
8.511
0.383
9.028
0.001
6.409
–0.032–0.676
0.233
5.209
0.312
3.029
0.220
6.196
0.580
1.013
–2.237–2.972
1.484
47.086
2.211
2.269

***
)
***
)
***
)
)
***
)
***
)
***
)

)
***
)
***
)
**
)
35714
–33373.890

19Table 5. Volunteer labor supply functions of employed women in 2001.

ONE
(
AGE
(
MARRY
(
STUDY
(
HOUSE
(
FINCOME
(
CHILD
(
OLD
(
CARE
(
HOLIDAY(
CROSSP(
AREA(

All samples(1) Society(2) Individual–2.781 ***–2.476 ***–7.464 )( –6.237 )
0.128
***
–0.016
11.486 )( –1.324 )
0.865
***
0.493
***
27.120 )( 14.884 )
0.384
***
0.649
***
14.226 )( 23.213 )
0.133
***
0.072
**
4.080
)
( 2.123
)
0.000
***
0.000
***
5.255
)
( 4.474)–0.009–0.151 ***–0.194 )( –2.878 )
0.185
***
0.193
***
5.432
)
( 5.019
)
0.279
***
0.503
***
4.037
)
( 7.118)
0.109***–0.0354.472)( –1.363 )
0.581
0.994
**
1.400
)
( 2.248)–1.869 ***–0.292–3.676 )( –0.561 )

(
(

(
(
(
(
(
(
(
(
(

COUPLE(CRDUM2

1.921
2.979

***
)

0.037
0.056

Married(1) Society(2) Individual–1.713 ***–2.137 ***–3.310 )( –3.674 )–0.135 ***–0.159 ***–7.231 )( –7.528 )

0.287
7.572
0.289
6.328
0.000
5.239
–0.334–5.511
0.019
0.420
0.273
2.851
0.015
0.438
0.800
1.376
–1.586–2.1331.555
47.058
1.814
1.904

***
)
***
)
***
)
***
)

(
(
(
(

)
***
)

(

)

(

)
**
)
***
)
*
)

(

(

(
(

(
(
)
(
(
Number43291
Log likelihood–42516.790
Note) Values in parentheses are t-statistics.
*** (**,*) denotes rejection of the hypothesis at 1% (5%, 10%) significance level.

0.553
13.443
0.226
4.461
0.000
4.399
–0.305–4.594
0.013
0.258
0.433
4.196
–0.072–1.940
1.054
1.612
0.757
0.932
1.425
38.359–1.039–0.990

***
)
***
)
***
)
***
)
)
***
)
*
)
)
)
***
)

)
23392
–22702.340

20

Table 6. Volunteer labor supply function of the self-employed in 2001.

ONE
(
AGE
(
MARRY
(
STUDY
(
HOUSE
(
FINCOME
(
CHILD
(
OLD
(
CARE
(
HOLIDAY(
CROSSP(
AREA(

All individuals(1) Society(2) Individual–1.146 ***–0.132–2.601 )( –0.252 )0.033**–0.026 *2.557)( –1.707 )
0.432
***
0.373
***
9.581
)
( 6.920
)
0.371
***
0.726
***
9.253
)
( 16.504 )
0.645
***
0.318
***
10.916 )( 4.956
)
0.000
**
0.000
2.381
)
( 0.902)–0.047–0.080–0.660 )( –0.989 )
0.275
***
0.170
***
6.482
)
( 3.376
)
0.141
**
0.385
***
2.002
)
( 4.875)–0.124 ***0.000–2.816 )( –0.008 )–0.565–1.765 ***–1.169 )( –3.056 )
0.534
0.945
0.755
)
( 1.214
)

(
(

(
(
(
(
(
(
(
(
(

COUPLE(CRDUM2–1.375–1.555–1.608–1.643

Married(1) Society(2) Individual–1.965 ***–0.579–3.594 )( –0.911 )0.033*–0.044 **1.734)( –2.038 )

0.280
5.406
0.543
7.315
0.000
2.498
0.013
0.136
0.215
3.868
0.031
0.336
–0.067–1.212
0.048
0.079
0.181
0.197
1.892
49.543–0.915–0.797

***
)
***
)
**
)

(
(
(

)
***
)

(
(

)

(

)

(

)

(

)
***
)

(
(

(
)
(
)
(
)
(
Number24263
Log likelihood–24738.500Note) Values in parentheses are t-statistics.
*** (**,*) denotes rejection of the hypothesis at 1% (5%, 10%) significance level.

0.672
12.062
0.287
3.563
0.000
0.562
–0.040–0.387
0.102
1.580
0.353
3.484
0.069
1.123
–1.512–2.158
1.361
1.342
1.768
39.451–2.171–1.694

***
)
***
)
)
)
)
***
)
)
**
)

)
***
)
*
)
17704
–16581.530