Very preliminary version; please do not quote or circulate.

Aging and Household Stockholdings:
Evidence from Japanese Household Survey Data

Hiroshi FUJIKI,* Naohisa HIRAKATA,** and Etsuro SHIOJI***

Abstract
Using Japanese household survey data from 2007 to 2010, our analysis examined how
household age, income, financial assets, and education affect the tendency toparticipate in the stock market. Our analysis suggests the probability of stock market
participation correlates weakly with age, given other variables such as income,financial assets, education, and a dummy variable for preference for online financialtransactions. Of total household financial assets, stocks account for a share that
correlates positively with age, given the other variables above. Our results suggest
older households tend to have greater stockholdings than younger households, but forreasons involving greater financial assets or higher income, not the effects of age perse.
Keywords: stockholding puzzle, participation, online financial transactions
JEL classification: C34, C35, D12, E21* Associate Director-General and Senior Economist, Institute for Monetary and Economic
Studies, Bank of Japan (E-mail: hiroshi.fujiki@boj.or.jp)**Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan(E-mail: naohisa.hirakata@boj.or.jp)*** Professor, Graduate School of Economics, Hitotsubashi University (E-mail:shioji@econ.hit-u.ac.jp)
The views expressed in this paper are those of the authors and do not necessarily reflect theofficial views of the Bank of Japan. The authors participated in the Research Group on theSurvey of Household Finances during work on this paper and received permission from theCentral Council for Financial Services Information (CCFSI) to use data from the Survey ofHousehold Finances. The authors wish to thank the CCFSI for providing the requisite data.

I. Introduction
Many developed economies, including Japan, are in the midst of a demographictransition that will profoundly affect national economies, including financial and realestate markets. Some economists argue, for example, that the run-up in asset prices in
the 1990s is linked to demographic change. Recent papers making this case include
Liu and Spiegel (2011) on stock prices and Takáts (2010) on real estate prices. Such
results have drawn the keen interest of policy makers.
While these papers provide interesting empirical evidence based on aggregate
data and financial market data, structural interpretations of the evidence pose boththeoretical and empirical difficulties. First, establishing clear theoretical links between
asset price and aging requires significant assumptions, as pointed out by Poterba(2004). Second, regarding stock prices, empirical evidence based on micro data showsthat the majority of households hold neither common stock nor other risky financial
securities—a tendency called the ―stockholding puzzle‖ by Mankiw and Zeldes (1991).
Based on this evidence, before asserting that the retirement of baby-boomers may leadto massive sales of stocks, one must ask whether older households tend to hold morein stocks than younger households.
This paper also revisits the second difficulty above, based on a body of Japaneseindividual household data called the Survey of Household Finances (SHF hereafter).
We begin by demonstrating the presence of the stockholding puzzle in the Japanesedata and by analyzing the relationship between stockholding and age using the SHF.
We then move on to test whether household age, financial assets, and education affectstockholding by Japanese households by running regressions, examining whether the
results are broadly consistent with studies based on micro data in other economies.1
Of various financial assets, we restrict our attention to stocks. Nevertheless, this serves
as a good approximation, since shares and other equities account for a significant
portion of risky financial assets held by Japanese households (Figure 1). We
summarize our results below.
We obtained three results regarding the stockholding puzzle and the relationshipbetween stockholding and age.
First, we found evidence for the stockholding puzzles from the SHF. Average
1

Cucuru, Heaton, Lucas and Moore (2010) and Campbell (2006) report conditions in the US
based on the Survey of Consumer Finance (SCF). Guiso, Haliassos, and Jappelli (2003) provide
international comparisons on household stockholdings. Guiso, Haliassos, and Jappelli ed. (2001)
collects both theoretical and empirical research by renowned researchers in this field. The volumecontains papers on empirical analysis of household portfolios in the US, UK, Italy, Germany, andthe Netherlands.
1participation rates, defined as the percentage of households holding stock, hoveredaround 15 percent, based SHF data for 1991 to 2010.
Second, the SHF data for 1991 to 2010 shows that the relationship between
participation rate and age is hump-shaped. However, the relationship between age andconditional share—defined as the percentage of stock in financial assets conditional on
the fact that the household holds stock—is not hump-shaped.
Third, while participation rates for those in the 20-29, 60-69, and over 70 age
groups in the 2000s are higher than in the 1990s, the participation rates for the 30-39,
40-49, and 50-59 age groups are lower than in the 1990s.2
We obtained three results from the 2007 to 2010 SHF data for regression analysis
in response to the question of whether household age, financial assets, and education
affect stockholding behavior.
First, participation rates correlate weakly with age, given other variables such as
income, financial assets, education, and a dummy variable for preference for online
financial transactions.
Second, conditional shares correlate positively with age, given the other variables
discussed above.
Third, a dummy variable for preference for online financial transactions correlates
positively with participation rates, given other variables. The result is consistent withobservations that the lower fees for online stock trading offered by many Japanese
online securities firms significantly boosted online stock trading among Japanesehouseholds after the deregulation of Japanese financial markets around 2000.
We draw the following policy implications from our results‖.
First, participation rates do not correlate with age, given other variables. If theJapanese government wants to encourage Japanese households to hold more stock, toenhance adequate risk sharing between younger generations and older generations
during current demographic transition, such efforts should target all generations, notjust older households. In particular, the rise in stock market participation in younger
households, perhaps attributable to the prevalence of cheap online stock trading in theearly 2000s, may increase the stock market participation of future older households.
Second, given other variables such as income and total financial assets, theconditional share should increase as young generations age. Clearly, this naïve
prediction may not hold if income and financial assets decline.
2

Based on the Family Income and Expenditure Survey, Iwaisako (2012) reports that the share ofstocks in total financial assets increased between 2002 and 2010 and that the share of households
whose head is aged 20-29 increased by 5% from 2002 to 2010. However, his study does notprovide data on stock market participation for this period.
2

Before moving on to the details of our analysis, we review the relevant literature
based on Japanese household data. Amemiya, Saito, and Shimono (1993), an early
study of household portfolios in Japan, analyzed demand for cash, deposits, and risky
assets using 1984 data on households in Tokyo. Tanigawa and Tachibanaki (1990)
used data for 1985. Fujiki and Shioji (2006) studied the cash and deposits demand ofhouseholds using the Public Opinion Survey on Household Financial Assets andLiabilities. 3 Iwaisako (2009, 2012) and Kitamura and Uchino (2010) examinedJapanese household portfolios using the Nikkei Radar data, which covers justhouseholds in the Tokyo metropolitan area.
Of these studies, the closest to ours are Iwaisako (2009, 2012). Regarding therelationship between age and stockholdings, Iwaisako (2009) reports results consistent
with ours. In his regression exercises, Iwaisako (2012) also uses a variable that
represents household Internet use to control for stock market participation. Our datafrom the SHF explicitly asked regarding preference for online financial transactions,while the Nikkei Radar data asks about Internet use in general.
The rest of the paper is organized as follows. Section 2 provides a brief overview
of household portfolios theories related to our studies. Section 3 explains our datafrom the SHF data in detail. Section 4 shows the results of regressions for the stock
market participation and the conditional share. Section 5 describes robustness checks.
Section 6 shows a simulation of how aging affects total household stockholdings.
Section 7 concludes the paper.
II. Determinants of household stockholdings
We are interested in the effects of age on stockholding by Japanese households.
However, besides age, many other variables may affect stockholdings, such as
financial assets or labor income. In this section, we provide a brief theoretical
overview of the effects of these variables on the stockholding of household to help
grasp the results of our regression analyses in the following sections.
A. Financial assets
We expect stock market participation to increase with the size of total financial assets
for the following reasons:In the absence of entry costs, standard portfolio theory suggests that each investor
will hold riskless assets as well as a portfolio of risky securities that yields the
maximum expected return for each level of variance of financial assets. This means
stock market participation rate should be 100%. If we assume buying and selling stock3

The data set was renamed the SHF in 2007.
3

entails transaction costs, the poor will not hold risky assets, because the utility loss
attributable to abstaining from stock market participation is too small to offset the
fixed participation cost. Thus, we would expect a strong positive correlation between
stock market participation and the investor’s financial assets.
The relationship between the conditional share and financial assets depends on theforms of utility functions. For example, if utility function is of the Constant Relative
Risk Aversion (CRRA) type, the conditional share is independent of financial assets. If
the utility function is of the Constant Absolute Risk Aversion (CARA) type, as ahousehold accumulates greater wealth, the conditional share will decrease, since thevalue of the shock holding in his portfolio remains constant, regardless of financial
assets.
B. Labor income
We would expect stock market participation and the conditional share to grow withlabor income. As future labor income grows more assured and as variations in labor
income correlate less with variations in returns from stocks, the conditional share andparticipation in the stock market grow, as the following example illustrates.
As an extreme example, consider a household with a completely predictable futurelabor income. Such a household should have a portfolio composed entirely of risky
assets. If labor income is risky but uncorrelated with risky financial assets, theportfolio should tilt less toward risky assets. No matter how idiosyncratically risky, allinvestors with labor income should tilt their portfolios towards risky financial assets.
However, as the variance of labor income increases, the tilt towards risky assets
diminishes. If labor income is sufficiently positively correlated with risky financial
assets, we would expect households to hold more safe financial assets.
Note that the discussions above assume exogenous labor income. However, as
Bodie, Merton, and Samuelson (1992) emphasize, the ability to adjust labor supply
heightens an investor’s willingness to take on financial risk. If an investor can respondto adverse circumstances by increasing work effort as well as by reducing
consumption, the investor has two margins of adjustment, not one, and is more tolerantof financial risk.
C. AgeWe would expect age to have ambiguous effects on stock market participation andconditional share, given labor income and financial assets, as the following examples
show.
Our first example is the case in which the effect of age on stock market
4

participation and conditional share is negative, given labor income and financial assets.
Suppose the amounts of annual labor income are given for all agents up to somepredetermined age. Suppose further that agents can purchase either stock or risk-freefinancial assets. In this situation, compared to older agents, younger agents have agreater amount of safe future income. This means the share of risky assets in theportfolio of financial assets tends to decline with age, up until the point at which labor
income is zero.
Our second example is the case in which the effect of age on stock market
participation and conditional share is positive, given labor income and financial assets.
Assume two households with equal current subsistence levels and equal wealth, butdifferent investment horizons. Given equal current consumption for both investors, thepresent value of future subsistence levels is higher for the investor with a longer
horizon, compelling the long-horizon investor to set aside more wealth in risklessassets to meet subsistence needs and giving the long-horizon investor a moreconservative portfolio, as underscored by Samuelson (1989).
D. Entrepreneurial risk
We would expect lower stock market participation and lower shares of stocks among
those with a stake in private businesses, since one can reasonably assume thathouseholds operating private businesses face higher and more variable businessincome than other households. Indeed, Heaton and Lucas (2000) report evidence thatprivate businesses may crowd out ownership of publicly traded equities and thathouseholds with more variable proprietary income tend to have smaller equity
allocations.
E. Preference for online financial transactions
We assume that an individual who prefers online financial transactions would havehigher stock market participation, given other variables, based on the following
development in the Japanese financial markets.
After the ―Japanese Financial Big Bang,‖ Japanese online securities firms
dramatically reduced the fees for online stock trading. According to a survey by theJapan securities dealers association in 2005, before the Big Bang, someone purchasing
one million yen in stock paid an average of 1.2% in transaction fees. After the Big
Bang, someone purchasing one million yen in stock online paid an average of 0.2%.
Moreover, online securities firms began providing unique services, including
afterhours transactions via proprietary trading systems, the ability to purchase foreign
securities, or access to information relevant to personal investment.
5Originally offered by online securities firms, these new services eventually spreadto other Japanese financial institutions. The new services reduced transaction costs forstocks and the cost of gathering information for investing in stocks. Thus, we would
assume that an individual preferring online financial transactions would exhibit higher
stock market participation, given other variables.
III. Data
This section first discusses the data from the SHF, then goes on to discuss the trends in
key variables.
A. SHF (Survey of Household Finances)
The SHF is an annual survey of household financial assets undertaken by the Central
Council for Financial Services Information (CCFSI). Beginning in 1953, thisnationwide survey canvasses households with two or more people. From 2007 to 2010,
the survey targeted nationwide 8,000 households of two or more people. For the 2010
survey, 4,032 of 8,000 households responded. The samples are selected each year; theSHF data is not panel data.
The survey asks for the amount of household financial assets and liabilities;
selection of financial products, including outstanding amounts (to the nearest ¥10,000)
of deposits (both current deposits and time deposits); postal savings (both postal
savings current deposits and postal savings time deposits); money trusts and loan
trusts; life insurance and postal life insurance; nonlife insurance; personal annuity
insurance; bonds; stocks; investment trusts; workers’ asset formation savings; andother financial products. In addition, the survey asks for various household
characteristics, including annual income, age of household head, householdcomposition, education, and self-employment status.
The CCFSI allows us to use the survey data from 1991 to 2010. Due todiscontinuities in sampling designs in 2004 and 2007, we used data from 1991 to 2010
for households with two or more people for this section’s analysis and data from 2007
to 2010 for households with at least two members in the regression analysis in Section
4.4 In the regression analysis in Section 5, we used data from 2007 to 2010 for
single-person households. This analysis is possible because the SHF has collected data
4

Before 2003, the survey was carried out nationwide and targeted 6,000 households with morethan two people; 4,158 households responded to the 2003 survey (response rate: 69.3%). The
survey used a stratified two-stage random-sampling method. From 2004 to 2004, the survey was
undertaken nationwide, targeting 10,080 individuals; 3,478 households responded to the 2006
survey (response rate: 34.5%). The response rates from 2004 to 2006 are much lower than before
2004.
6since 2007 for single-person households from a pool of individuals registered with asurvey company through the Internet. The sampling probability is assigned based on
the latest Census by age, gender, and region.
B. Trends in Key Variables
From the 1991 to 2010 SHF data, we computed three annual average variables:
unconditional share (US hereafter), participation rate (PR hereafter) and conditional
share (CS hereafter). US is the average portfolio share held in stocks across all
households. CS is the average portfolio share only for households that have stocks,
defined below:

 S ,
US 
 W
 S ,,
CS 
 W
N

i 1
T

i

i 1

i

N

PR 

i 1
N

i

i 1

i

N
,
T

where Si is amount of stockholding, Wi the amount of financial assets, i = 1,…,N is theindex of households with stocks, and i = N + 1,…,T is the index of households without
stockholdings, and T is sample size.5
Figure 2 shows PR. The stock market participation rate is around 15%, which isroughly consistent with direct stock market participation rates in the U.S. andEuropean countries. 6 This indicates the presence of the stockholding puzzle inJapanese data.
Figure 3 shows CS. Conditional share declined from 1991 to 2001, increased from2001 to 2007, then declined again after 2008. The changes appear consistent withtrends in Japanese stock price.
Figure 4 shows US, together with US computed from the aggregate Flow of Funds
data, which is the share of equity in financial assets, since the unconditional share by
SHF is conceptually comparable to the portfolio share in financial asset by Flow of
5

US is decomposed into CS and PR as follows:WN

US  CS  PR  T

W


1

 W ,W   W .
,W 
N

N

i 1

N

T

i

T

i 1

i

T

6

Guiso, Haliassos, and Jappelli (2003) report stock market participation rates in France, Germany,
Italy, the Netherlands, UK, and US of 15%, 17%, 7%,14%, 27%, and 19% respectively, as of 1998.
7

Funds. These two series show similar trends, but the equity share by Flow of Funds ismore volatile than unconditional share by SHF. Note that one source of discrepancy
between these two CSs are reporting errors in the SHF. For example, it is unclear towhat extent stockholding in the SHF data is reported in terms of the market priceconsistent with the SHF’s request to respondents. It is also unclear to what extent
households having ETF on stock indices report their outstanding amounts as stock or
as investment trusts. Moreover, it is unclear to what extent stockholdings in SHF
data overlaps with the stockholdings through employee stockholdings or defined
contribution plans.
While the conditional share shown by Figure 3 varies with stock price, theparticipation rate in Figure 2 is near-constant, except during the period from 2004 to
2006. This indicates that time series changes in unconditional share are mainly causedby fluctuations in conditional share, specifically stock price, not by fluctuations inparticipation rate.
Figure 5 shows the relationship between age and CS, US, and PR using pooled
from 1991 to 2010. US shows a hump-shaped profile, resulting from the combinationof a concave age profile for PR and a flat profile for CS. PR increases with age,peaking with the 60-69 age group. In contrast, CS is fairly constant across age groups(around 22 percent).
Figure 6 show developments in PR by age groups, estimated by residuals from
regressions of participation rates on time dummies and age dummies. PRs for the
20-29, 60-69, and over 70 age groups in the 2000s are higher than in the 1990s. PRs
for 30-39, 40-49, and 50-59 age groups in the 2000s are lower than in the 1990s.7
Figure 7 shows PR increases with the scale of financial assets held by households,
a result consistent with the theoretical prediction based on participation cost explainedin Section 2. A. At low levels of wealth, very few investors hold stock directly, while
this fraction increases rapidly with greater wealth. This pattern is also reported inprevious studies of other countries.
Figure 8 shows CS decreases with the scale of financial assets held by households.
However, the negative correlation between CS and the scale of financial assets appears
weak compared to the negative correlation between PR and scale of financial assets.8
The results are consistent with the findings of Guiso, Haliasos, and Jappelli (2001),
whose summary conclusion is that the relationship in certain countries between wealth
7

The results are consistent with Iwaisako (2012).
The findings below are consistent with findings reported in previous studies in Japan and othercountries. See Iwaisako (2012) for Japanese findings. Guiso, Haliassos, and Jappelli (2003) reportconditions in the U.S. and European countries.
88and conditional share of stock is relatively modest.9
IV. Estimation
In this section, we used annual micro data from SHF 2007 to 2010 and estimated astatistical model of the share of stocks in household financial assets and stock market
participation as a function of age, financial assets, income, and several household
characteristics that may affect portfolio decisions.
A. Statistical model
In the following analysis, we use the simplified model of household choice of financial
assets used by Fujiki and Shioji (2006).
Suppose a household has a certain wealth. We assume that every household will
allocate some of their wealth to currency, which is consistent with the SHF report thatnearly all household have currency. We further assume that the household will choose
one of two combinations of financial assets. The first combination consists of currency,
stocks, and other financial products, such as bank deposits. The second combinationconsists of currency and financial products other than stocks. The theoreticalbackground for separating stocks from other financial products is that stocks pose highprice fluctuation risks compared to deposits, which are highly liquid. Stocks also differ
qualitatively from products like insurance, which have very long-term contract periods
compared to deposits.
B. Empirical model
Armed with the statistical model above, using 2007 to 2010 SHF data, we estimated an
individual’s stock market participation and conditional share based on the sample
selection model set forth by Heckman (1979).
In participation and share of stocks regressions, we controlled for householdincome and wealth, household characteristics (family size, number of children, andregion of residence), and variables related to the household head (age, gender,
occupation).
To estimate Heckman’s model, we must include certain variables that may affectthe decision to participate in stock market, but not the equity share. In the participation
equations, assuming that variations in information and entry costs are important in thisregard, we included an education and dummy variable for preference for online
financial transactions that affects the cost of gathering information and learning about
stock markets.
9

See Table I.7 in Guiso, Haliasos, and Jappelli (2001) for details.
9C. Variables for regression
We explain the explanatory variables used in the estimation in detail.
(1) Age
We included dummy variables for the age range of the household head: under 29(omitted), 30-39, 40-49, 50-59, 60-69, 70-79, and 80 and over. We set time dummies
to indicate the survey year (omitting 2007).
In Section 2, we noted that the effects of age on stock market participation andconditional share are ambiguous, given labor income and financial assets. These age
dummies should capture two unobserved variables that evolve with age and affectstockholdings: the present value of remaining labor income for a household and thepresent value of future subsistence level for a household.
As Ameriks and Zeldes (2004) have pointed out, it is impossible to separately
identify age effects, time effects, and cohort effects on portfolio choice. Even if one
has complete panel data on portfolios of households over time, any pattern in the data
can be made to fit equally well by age and time effects, age and cohort effects, or timeand cohort effects. Accordingly, we follow most other studies in setting cohort effects
to zero. Based on this assumption, age effects can be estimated in any cross-section.10(2) Financial assets and income
For financial assets and income, we use dummies of financial assets and income
percentiles: below 5%(omitted), 5-10%, 10-25%, 50-75%, 75-90%, 90-95%, top 5%.11(3) Other household characteristics
We included the following household characteristics as conditioning variables:
household type (household headed by men or not, household size); whether ahousehold head is self-employed; the education of the household head (higher thancollege degree or not). We also included a dummy variable for occupation to capture
possible differences in income variability, whether the head is self-employed or not
and whether a household head is employed as a regular worker or not and in what
10

Identifying cohort effects requires additional assumptions or variables. For example,
Malmendier and Nagel (2011) find that individuals who have experienced low stock-market
returns throughout their lives so far report lower willingness to assume financial risk, are less likely
to participate in the stock market, invest a lower fraction of their liquid assets in stocks if they
participate, and are more pessimistic about future stock returns.
11
We adopt a dummy variable to capture nonlinearities. For robustness checks, we also estimate
specifications that adopt polynomials for age, financial asset. The results are nearly identical to thebenchmark results.
10industry the household head works (agriculture, construction, manufacturing,transportation, wholesale and retail, service, medical service, public sector, other).
(4) Preference for online financial transactions
Many studies include Internet use as a conditioning variable for stock market
participation. Thanks to the SHF, we can construct a dummy variable for preference
for online financial transactions, rather than Internet use. The distinction is importantbecause Internet use does not necessarily mean stock market participation.12
We construct a dummy variable for preference of online financial transactions asfollows. In the SHF 2010, respondents were asked ―What features are important foryou when you choose financial institutions?‖ and instructed to choose up to three of 13
possible answers. These 13 answers include: (1) Branch or ATMs located in theneighborhood (79% of respondents picked this answer); (2) financial status is soundand trustworthy (32% of respondents picked this answer); (3) a nationwide branch
network (28% of respondents picked this answer); and so forth. Of these 13 answers,
7% of respondents picked the answer ―Provides a wide range of online services andtransactions.‖ We constructed a dummy variable to assume the value of one if ahousehold picked this answer and used a dummy variable as a proxy for thehousehold’s preference for online financial transactions.
Note that even if a household indicates a preference for online financialtransactions, it is possible the household limits their activities to online banking, rather
than engaging in online stock transactions as well. However, households preferring
online financial transactions are more likely to take advantage of the benefits of cheapand quick online stock transactions and are thus more likely to participate in the stock
market in this way.
D. Results of regression
Table 1 gives the results of the participation and the conditional share equations forstocks.
The coefficients on age dummy are positive and significant in the conditional
share equation. However, the coefficients for the age dummy are negative but notstatistically different from zero in five percentage points of statistical significance inthe participation equation.
The coefficients of financial asset and income are positive and statisticallysignificant in the participation equation, while they are negative and significant in the
12

Iwaisako (2012) uses a variable representing household Internet use but does not discuss its
effects on stock market participation and estimation results.
11

conditional share equation. These results oppose the average results summarized in
Figure 5, which do not control for the other variables that could affect stockholding.
Our regression supports the idea that old households tend to hold more stock than
young households. However, this is likely due to greater financial assets or higher
income, not the effects of age per se.
We offer one interpretation of the different signs for the estimated coefficients offinancial asset and income between participation and share equation below. In thepresence of entry costs for stockholdings, households with higher financial assets arelikely to hold stocks. This implies that if households with lesser financial assets holdstocks, they are less risk averse. Households that are less risk averse hold large sharesof stocks in financial assets. So, conditioning on participation, financial assets andshare are negatively correlated.
The regression coefficients of the participation equation indicate that the
self-employed and farmers are less likely to hold stocks. These coefficients of theparticipation equation are negative and statistically significant, which is consistent
with the entrepreneurial risk hypothesis discussed in Section 2.
As for quantitative impact, at the sample means, increasing financial assets from 3
million yen (25th percentile) to 20 million yen (75th percentile) raises the predictedprobability by 25 percentage points. Increasing education from less than high school
graduate to university degree raises the predicted probability by 4 percentage points.
The coefficient for a dummy variable for a household’s preference for online
financial transactions in the participation equation is positive and statisticallysignificant. The result supports our hypothesis that the emergence of online stocktransactions reduced the cost of gathering and processing information and the fees
associated with acquiring household stockholdings.13
One may argue that the effects of online stock transactions on stock market
participation differ with household age. This is because holding other conditions
constant, younger households, who tend to be more comfortable with and more adept
at handling computers or cell phones than older households, have readier access to thebenefits of online financial transactions and other technological innovations in finance.
Figure 9 shows the evidence for our argument. The age-participation profile of13

One might wonder if households wishing to purchase stock for the first time would actually startwith online stock transactions. If so, our analysis would suffer from an endogeneity bias due to thisreverse causality. To counter this problem, we estimated probit models to obtain predicted values
for the dummy variable for preference for the online financial transaction, using the same variable
used in the benchmark estimation as explanatory variables. Next, we substituted the predictedvalues for the observed values of the dummy variable in the benchmark estimation. The results arenearly identical to the benchmark results.
12

households that picked the answer ―Provides wide range of online services andtransactions‖ appears above the age-participation profiles of households that do notselect this answer. Figure 10 shows more younger households choose the ―Provides
wide range of online services and transactions‖ response than older households.
V. Robustness check: Analysis based on single-person household data
For this section, we used data from single-person households from 2007 to 2010 froma pool of individuals registered through the Internet with a survey company. The
sampling probability is assigned based on the latest Census, by age, gender, andregion.
It is reasonable to assume that single-person households in the sample would tend
to be more accustomed to adopting new technologies like the Internet than the average
single-person household. Based on this reasoning, we proposed the three following
hypotheses:
First, we would expect single-person households to be more likely to hold stock
than households of two or more people. Second, even if we control for Internet use by
restricting our attention to our single-person household data, we would still expect
higher participation rates among those preferring online financial transactions. Third,
once we control for Internet use, we would not expect that younger people prefer
online financial transactions than those older. We will examine whether our hypotheses
prove true.
Figure 11 supports our first hypothesis. The figure illustrates the age-participation
profiles of single-person households and households of two or more people. We would
like to note three key items. First, both age-participation profiles increase with age.14
Second, participation rates for all age groups are greater in single person households
than in households with two or more people. Third, the slope of the profile of thesingle person households is steeper than that of households of two or more people.
Figure 12 supports our second hypothesis. Figure 12 illustrates theage-participation profiles for households who picked the answer ―Provides wide rangeof online services and transactions‖ on the one hand and who did not pick the answer―Provides wide range of online services and transactions‖ on the other. First, as withhouseholds of two or more people, single-person households choosing this answer are
more likely to hold stocks. Second, in contrast to households of two or more people,
14

Single-person households who are older than 70 years old are excluded from the sample, due tothe difficulty of surveying older households through Internet. This means we cannot confirm
whether the age-participation profile of single-person households is hump-shaped, like the profilefor households consisting of two or more people.
13the two profiles have nearly identical slopes.
Figure 13 supports our third hypothesis, illustrating the relationship between the
proportion of households picking the answer ―Provides wide range of online services
and transactions‖ and age group. Unlike the sample of households with two or more
people, we see no correlation between age group and fraction of households picking
the answer ―Provides wide range of online services and transactions.‖ This suggeststhat once a young person is accustomed to online financial transactions, he or she islikely to remain more likely to hold stocks at some point.
Table 2 shows the estimation results for the single-person households’ sample. As
we would expect, the coefficient for a dummy variable for a person’s preference foronline financial transactions in the participation equation is positive and statisticallysignificant. Once again, these results support our hypothesis that the emergence of
online stock transactions reduced the cost of gathering and processing information andfees associated with household stockholdings. Table 2 shows positive and significant
coefficients for both participation and conditional share for age dummies for 50-59
and 60-69 years old.
VI. Simulation of how aging affects household stockholdings
This section discusses a simulation of how aging affects household stockholdings.
Figure 14 illustrates the latest official projections for the Japanese population (overage 20) by age groups published in January 2012 by the National Institute ofPopulation and Social Security Research. These projections point to two changes thatwould affect household stockholdings. First, the population over age 20 at 2010 is105.125 million and may fall by 2050 to 84.106 million. Second, the ratio of the
population over the age of 60 appears likely to continue to rise over time.
Based on these projections, aging will affect household stockholdings though twochannels. First, the decrease in population and the number of households will reduce
household stockholdings. Second, the increase in the ratio of the population over age
60 will increase household stockholdings due to the two positive effects of financial
asset accumulation on participation and of aging on stockholding share.
The overall quantitative effects of aging on household stockholding depend on therelative size of the two channels. We undertook a simulation to gauge the overall
effect.
Figure 15 shows two projected paths for household stockholdings based on the
projected population shown in Figure 14 and the SHF 2010. We will discuss the twopaths in turn.
14

First, assume that conditional shares, participation rates, and financial assets perhousehold remain constant at 2010 levels over the projection period from 2020 to2060. Based on this assumption, we calculate the stockholdings path reflecting thefuture age structure of Japanese population (Figure 14). The blue line with marks
indicates the results of this simulation; we call this path the baseline projection. The
baseline indicates household stockholdings in 2020 and 2030 will be higher than in2010 (around 80 trillion yen) since the second channel, the increase in the ratio of oldhouseholds dominates the first channel, due to a declining total population. However,household stockholdings in 2050 and 2060 are lower than in 2010. Within thesetimeframes, the first channel dominates the second channel.
Second, we assume that participation rates for all age groups will rise by 1.4
percentage points from 2010 to 2060. The red line corresponds to the householdstockholdings based on this assumption. The red line shows that the householdstockholding in 2050 will be close to that in 2010. Namely, if one wishes to offset thedecrease in household stockholdings due to aging, given the current income and asset
profile, one measure would be to raise the participation rate of all age groups by 1.4
percentage points. Is this within our grasp?
One way to achieve this change would be to expand online stock transactions,
thereby increasing participation rates for the current young age group, who will in turnbe succeeded by younger generations. We can examine how such a mechanism works.
Figure 16 illustrates the impact of the increase in preference for online financialtransactions on stock market participation by age groups. The impact is attributable tothe marginal effects obtained from the coefficients on household’s online financialtransactions (Table 1), evaluated at current levels of stock market participation andother conditioning variables. The numbers in the figure are normalized to thedeviations from the participation rate at age 80 or over. In passing, the figure showsthat the spread of online stock transactions puts the participation rate of younger
households (age 20-29) above that of older households (age 80 or over) by about
1.4%.
Suppose that younger generations, born ten, twenty, thirty, and forty years later
exhibit the same preference levels for online financial transactions. Suppose further
that the level of preference for online financial transactions for each age group will not
decline forever. In the year 2050, the participation rates for all age groups would thenrise by 1.4 percentage points compared to levels for current age groups.
These projections make numerous restrictive assumptions. Nevertheless, taken atface value and assuming that other variables such as income and financial asset
profiles remain constant, they suggest sustaining the current growth in preference for15online transactions for younger generations will maintain current levels of Japanesehousehold stockholdings, despite the progress of an aging society.
VII.
Conclusion
This paper investigated how household age, wealth, and education affect stockholdingof Japanese households by running regressions using 2007 to 2010 SHF data. We
obtained three results.
First, participation rates correlate weakly with age, given other variables such as
income, financial assets, education, and a dummy variable for preference for online
financial transactions. Older households do tend to hold more stock than younger
households, but for reasons involving greater financial assets or higher income, not theeffects of age per se.
Second, the conditional share correlates positively with age, given the othervariables discussed above.
Third, a dummy variable for preference for online financial transactions correlates
positively with participation rates, given other variables. The result is consistent withthe observation that the lower fees for online stock trading offered by many Japanese
online securities firms significantly boosted online stock trading by Japanesehouseholds following the deregulation of Japanese financial markets around 2000.
Our estimates suggest two policy implications.
First, participation rates do not correlate with age, given other variables. To
expand stockholding by Japanese households, rather than inducing old households tohold more stock, one should encourage all generations to buy stocks. In particular, thegrowth in the stock market participation of younger households at this point,potentially attributable to the prevalence of cheap online stock trading after the early
2000s, may increase the stock market participation of future older households.
Second, given other variables such as income and total financial assets, theconditional share should increase as aging proceeds. Declining income and financial
assets would clearly affect this projection.

16

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18

Household portfolio composition(%)
25

65

20

60

15

55

10

50

5

45

0

40

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Investment trust beneficiary certificates

Trust beneficiary rights

Securities

Shares and other equities

Insurance reserves

Pension reserves

Other

Currency and deposits (right scale)Figure 1: Household portfolio composition in Japan
Source: Flow of Funds

19

18

Participation Rate

16
14
12
10
8
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Figure 2. Participation rate

20

30

Conditional Share

25

20

15

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Figure 3: Conditional share of stock

21

18

Unconditional Share16

Survey data
Flow of Funds

14
12
10

8
6
4

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Figure 4: Unconditional share of stock by SHF and unconditional share from Flow ofFunds data.
Source: Survey of Household Finances, Flow of Funds

22

Stockholding and Age

25

20

15
Conditional Share
10

Participation Rate
Unconditional Share

5

0
20-29

30-39

40-49

50-59

Figure 5: Stockholding and age

23

60-69

70-

-1
2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

40-49
4

3

3

2

0
-1

-2
-3

-3

-4

24
2006
2007
2008

2009
2010

2006
2007
2008

2009
2010

2001

2000

2005

0

2005

1

2004

1
2004

2
2003

70-

2003

-3

2002

-3

2002

-2

2001

-1

1999

4

2000

-2

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

3

1999

0

1998

0

1998

1

1997

1

1997

2

1996

2

1996

3

1995

3

1995

30-39

1994

-4

1994

-2

1993

-1

1993

1

1992

2

1991

3

1991

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

4

1992

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

20-29

1991

4

2000

4

1999

1998

1997

1996

1995

1994

1993

1991

6

1999

1998

1997

1996

1995

1994

1993

1992

1991

-1

1992

-1

1991

7

5
2

50-59

1

0

0

-2

-3
-3

-4

60-69

-2

Figure 6: Participation rates by age groups
Note: The participation rates shown in the figure are residuals of regression ofparticipation rates on time dummies and age dummies.

60
50

40
30
20

10
0
bottom 5%

5-10％

10-25％

25-50％

Figure 7: Participation rate and financial asset

25

50-75％

75-90％

90-95％

upper 5％

80
70
60
50
40
30
20

10
0
bottom 5%

5-10%

10-25%

25-50%

Figure 8: Conditional share and financial asset

26

50-75%

75-90%

90-95%

upper 5%Age-participation profile:
Households with two of more people

60

50

40

30

20

10

0
20-29

30-39

40-49

50-59

60-69

70-79

80-

households choosing “Provides wide range of online
service and transactions"households not choosing “Provides wide range of online
service and transactions"Figure 9: Age participation profiles of households by choice of ―Provides wide rangeof online services and transactions‖27

Fraction of households choosing“Provides wide range of online service and transactions” by age

18
16
14
12
10

8
6
4
2
0
20-29

30-39

40-49

50-59

60-69

70-79

80-

Figure 10: Fraction of the households choosing ―Provides wide range of online
services and transactions‖ by age

28

40

Age-participation profile

35

Single-person households

30

Households with two or more
people

25
20
15
10
5
0
20-29

30-39

40-49

50-59

60-69

70-

Figure 11: Age participation profiles of single-person households and households of
two or more people.

29

Age-participation profile:Single-person households
50

households not choosing “Provides wide range of online
services and transactions"

45
40

35

households choosing “Provides wide range of online
services and transactions"

30
25
20

15
10
5
0

20-29

30-39

40-49

50-59

Figure 12: Age-participation profile of single-person households.

30

60-69

Fraction of the households choosing“Provides wide range of online services and transactions”
60

50

40

30

20

10

0
20-29

30-39

40-49

50-59

60-69

Figure 13: Fraction of households choosing ―Provides wide range of online services
and transactions‖ by age

31

100%

120,000

80-

90%
70-79

60-69

100,000
80%

70%
80,000
60%

50-59
50%

40-49

60,00040%

40,000
30%
30-39
20%

20,000
20-29

10%

0%
Population over age
20 (right scale,thousands)

2010

0

2020

2030

2040

2050

Figure 14: Projection of Japanese age structure and population

32

2060

household stockholdings: Trillion yen

Baseline

95.00

participation rates rise by 1.4%
90.00
Stockholdings at 2010
85.00

80.00
75.00
70.00
65.00
60.00

2010

2020

2030

2040

Figure 15: Projections of household stockholdings.

33

2050

2060

Percentage point
1.6
1.4
1.2

1
0.8
0.6
0.4
0.2
0
20-29

30-39

40-49

50-59

60-69

70-79

80-

Figure 16: Impact of preference for online financial transactions on stock market
participation.
Note: The impact is attributable to marginal effects obtained from estimatedcoefficients of the preference for online transactions shown in Table 1, evaluated at thecurrent level of stock market participation and other conditioning variables. The
numbers in the figure are normalized to deviations from the participation rate at age 80
or over.

34

Financial asset
percentile dummy
bottom 5% is omitted

5-10％
10-25%
25-50%
50-75%
75-90%
90-95%
Top 5%
Income10-25%
percentile dummy
25-50%
bottom 10% is omitted
50-75%
75-90%
90-95%
Top 5%
Household size
house owner dmmuy
Educationcollege graduate or more
Preference for online financial transactions dummy
Industry dummies
Agriculture
Construction
Manufacturing
Transportation
wholesale and retailServices
MedicalPublic service
Occupation dummies
Self-employed
non full-time worker
household head, maleAge dummies
30-39
age 20-29 is omitted
40-49
50-59
60-69
70-79
80Time dummies
year 2008
year 2007 is omitted
year 2009
year 2010
constant

Coef.
-0.1982-0.3362-0.4970-0.5800-0.6286-0.6642-0.6840-0.0189-0.0541-0.0721-0.0880-0.0817-0.0399-0.00210.0023

Share
Std. Err.
0.08040.07220.07470.07960.08340.08880.09050.02940.02440.02500.02680.02880.02920.00410.0095z-2.46 **
-4.66 ***
-6.65 ***
-7.29 ***
-7.53 ***
-7.48 ***
-7.56 ***
-0.64
-2.22 **
-2.88 ***
-3.29 ***
-2.84 ***
-1.37
-0.52
0.24

0.0507-0.0048-0.0071-0.0249-0.00700.02550.01620.02970.00510.02670.01110.10340.13080.13810.15580.16560.17230.0064-0.0285-0.03630.8106

0.03120.01880.01300.02390.01680.03440.02710.01770.01580.01390.02310.03870.0386
0.03870.03940.0406
0.04370.01220.01210.01200.1117

1.63
-0.25
-0.55
-1.04
-0.42
0.74
0.60
1.67 *
0.32
1.92 *
0.48
2.67 ***
3.39 ***
3.57 ***
3.96 ***
4.08 ***
3.95 ***
0.53-2.36 **
-3.01 ***
7.26 ***

Coef.
0.9026
1.19471.59202.00252.27142.68242.80590.07800.25540.3814
0.53620.54490.6160-0.0560
0.1256
0.26290.5266-0.3736-0.1076-0.0330-0.0668
0.0379-0.0227-0.3562-0.3495-0.1385-0.08750.1453-0.1919-0.2206-0.2097-0.1660-0.1408-0.17540.01950.01520.0808-2.9640

Participation
Std. Err.
0.15460.12210.11580.11490.11710.12560.12690.09240.07580.07630.08000.08930.09090.01340.03230.03190.0532
0.09580.06390.04780.0815
0.06180.1173
0.09600.05840.05610.05030.07740.12640.12550.12490.12770.13270.14430.04420.04380.04340.1937z
5.84 ***
9.78 ***
13.74 ***
17.43 ***
19.40 ***
21.36 ***
22.12 ***
0.84
3.37 ***
5.00 ***
6.70 ***
6.10 ***
6.78 ***-4.19 ***
3.89 ***
8.25 ***
9.90 ***-3.90 ***
-1.68 *
-0.69
-0.82
0.61
-0.19
-3.71 ***
-5.99 ***
-2.47 **
-1.74 *
1.88 *-1.52-1.76 *
-1.68 *
-1.30
-1.06
-1.22
0.44
0.35
1.86 *-15.30 ***Table 1: Benchmark results of pooled regression for participation and the share ofstocksNote:***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

35

Financial asset
percentile dummy
bottom 5% is omitted

5-10％
10-25%
25-50%
50-75%
75-90%
90-95%
Top 5%
Income10-25%
percentile dummy
25-50%
bottom 10% is omitted
50-75%
75-90%
90-95%
Top 5%
house owner dummy
Education dummy
College education or more
Preference for online financial transactions dummy
Industry dummies
Agriculture
Construction
Manufacturing
Transportation
wholesale and retailServices
MedicalPublic service
Occupation dummies
Self-employed
non full-time worker
household head, maleAge dummies
30-39
age 20-29 is omitted
40-49
50-59
60-69
Time dummies
year 2008
year 2009
year 2010
constant

Coef.
-0.3832-0.5646-0.5755-0.6277-0.6806-0.6787-0.7069
0.0283-0.0188-0.0188-0.0553-0.0516-0.0524-0.0432

Share
Std. Err.
0.26080.25720.26000.26260.26550.26760.25500.04220.03320.03240.0336
0.03600.03920.0209z-1.47-2.20 **
-2.21 **
-2.39 **
-2.56 **
-2.54 **
-2.77 ***
0.67
-0.57
-0.58
-1.65
-1.43
-1.34
-2.07 **

0.02810.00370.0041-0.03090.0176
0.0340-0.0306-0.0129-0.0101
0.01930.08350.03350.01290.04510.0727-0.0369-0.0555-0.05310.90380.07190.03400.01660.02960.02100.04140.02960.02300.02150.01860.01460.01730.02130.02170.02510.01470.01470.01530.2915

0.39
0.11
0.25
-1.05
0.84
0.82
-1.03
-0.56
-0.47
1.04
5.70 ***
1.94 *
0.61
2.07 **
2.89 ***-2.51 **
-3.77 ***
-3.48 ***
3.10 ***

Coef.
1.0102
1.61942.00952.28772.61232.79701.2419-0.00410.01200.06480.39570.42450.49600.24120.14650.36440.2034-0.3334
0.15430.03350.0289-0.1764-0.3088-0.2694-0.2389
0.19000.23740.08040.05940.28850.53940.03680.0446-0.0352-3.6479

Participation
Std. Err.
z
0.35422.85 ***
0.33404.85 ***
0.33146.06 ***
0.33106.91 ***
0.33197.87 ***
0.33738.29 ***
0.33133.75 ***
0.1148-0.04
0.09160.13
0.09030.72
0.08944.43 ***
0.09834.32 ***
0.1093
4.54 ***
0.06293.83 ***
0.03584.09 ***
0.0327
11.14 ***
0.22840.89
0.0905-3.68 ***
0.0496
3.11 ***
0.08790.38
0.06140.47
0.1120-1.57
0.0763-4.05 ***
0.0648-4.16 ***
0.0628-3.80 ***
0.05133.70 ***
0.03596.62 ***
0.04871.65 *
0.06160.96
0.0587
4.91 ***
0.06178.74 ***
0.04440.83
0.04471.00
0.0457-0.77
0.3459-10.55 ***Table 2: Results of pooled regression for participation and the share of stocks using single-personhousehold data
Note:***Significant at the 1 percent level.
**Significant at the 5 percent level.
*Significant at the 10 percent level.

36