2 household saving behavior, portfolio choice and children evidence from the survey of consumer...
TRANSCRIPT
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Copyright
by
Tansel Yilmazer
2002
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The Dissertation Committee for Tansel Yilmazercertifies that this is the approved version of the following dissertation:
Household Saving Behavior, Portfolio Choice and
Children: Evidence from the Survey of Consumer
Finances
Committee:
Daniel T. Slesnick, Supervisor
Don Fullerton
Maxwell B. Stinchcombe
Peter J. Wilcoxen
Jacqueline Angel
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Household Saving Behavior, Portfolio Choice and
Children: Evidence from the Survey of Consumer
Finances
by
Tansel Yilmazer, B.S., M.A.
DISSERTATION
Presented to the Faculty of the Graduate School of
The University of Texas at Austinin Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
THE UNIVERSITY OF TEXAS AT AUSTIN
December 2002
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UMI Number: 3110711
________________________________________________________
UMI Microform 3110711
Copyright 2004 by ProQuest Information and Learning Company.
All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.
____________________________________________________________
ProQuest Information and Learning Company300 North Zeeb Road
PO Box 1346Ann Arbor, MI 48106-1346
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Acknowledgments
I am grateful to many people who shared the best and worst moments
of my dissertation years. First, I would like to thank my advisor, Daniel
Slesnick, for his support, patience, guidance and encouragement. I would also
like to thank my committee members Don Fullerton, Maxwell Stinchcombe,
Peter Wilcoxen and Jacqueline Angel for their valuable feedback and com-ments. Special thanks go to Asli Kes, Anne Golla, Angela Lyons, Anne Gorney,
Gorkem Celik, Adam Winship, Matias Fontenla, Mala Velamuri, Steve Trejo,
and Vivian Goldman-Leffler for their stimulating conversations and friendship.
I am indebted to my family for their love and believing in me over these years.
Finally, I wish to thank Fikret for always being there for me, in spite of the
thousands of miles between us.
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Household Saving Behavior, Portfolio Choice and
Children: Evidence from the Survey of ConsumerFinances
Publication No.
Tansel Yilmazer, Ph.D.
The University of Texas at Austin, 2002
Supervisor: Daniel T. Slesnick
Using the Survey of Consumer Finances (SCF), this dissertation ex-
amines the relationship between having children and the motives of saving: (i)
to hold assets because of the return they provide, (ii) to build up reserves as
a precaution for a rainy day, and (iii) to accumulate for anticipated future
needs, such as educational expenses.
The first chapter examines how the number of children living in the
household affects the way households allocate their wealth across different
assets, such as owner-occupied housing, risky assets and interest-bearing ac-
counts. The portfolio allocation of homeowners is compared to that of renters
by taking into account the portfolio constraint imposed by the consumption
demand for housing. The results show that the number of children increasesthe housing consumption of homeowners and the share of the portfolio al-
located to owner-occupied housing. As a result of the portfolio constraint,
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Table of Contents
Acknowledgments iv
Abstract v
List of Tables ix
List of Figures xi
Chapter 1. Introduction 1
Chapter 2. Do Children Affect Household Portfolio Allocation? 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Empirical Model . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Estimation and Results . . . . . . . . . . . . . . . . . . . . . . 242.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Chapter 3. The Effect of Precautionary Motives on HouseholdSaving and Fertility 44
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2 The Relationship between Fertility and Saving . . . . . . . . . 48
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4 Estimation and Results . . . . . . . . . . . . . . . . . . . . . . 58
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
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Chapter 4. Saving for Childrens College Education 73
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 A Model of Saving for College . . . . . . . . . . . . . . . . . . 80
4.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.4 Empirical Specification . . . . . . . . . . . . . . . . . . . . . . 86
4.5 Estimation and Results . . . . . . . . . . . . . . . . . . . . . . 88
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Appendices 103
Appendix A. Appendix for Chapter 2 104
A.1 Estimating Marginal Tax Rates . . . . . . . . . . . . . . . . . 104A.2 Definition of Variables . . . . . . . . . . . . . . . . . . . . . . 105
A.3 Estimating Permanent Income . . . . . . . . . . . . . . . . . . 106
Appendix B. Appendix for Chapter 3 109
B.1 Definition of Variables . . . . . . . . . . . . . . . . . . . . . . 109
Appendix C. Appendix for Chapter 4 111
C.1 Definition of Variables . . . . . . . . . . . . . . . . . . . . . . 111
Bibliography 113
Vita 121
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List of Figures
4.1 The Importance of Educational Expenses on Savings . . . . . 102
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Chapter 1
Introduction
Raising children is costly with their housing, educational and other ex-
penses. To meet the costs of raising their children, parents use both current
income and intertemporal transfers. Children living in the household, there-
fore, are likely to affect the level of household savings, portfolio composition
and the life-cycle profile of savings. Using data from the Survey of Consumer
Finances (SCF), this dissertation examines the relationship between children
and the motives of saving: (i) to hold assets because of the return they pro-
vide, (ii) to build up reserves as a precaution for a rainy day, and (iii) to
accumulate for anticipated future needs, such as educational expenses.
Most U.S. households hold a large portion of their wealth in the form of
owner-occupied housing. According to the 1995 SCF, 65 percent of households
are homeowners, and the value of an average homeowners property is 60
percent of its total assets. Owner-occupied housing differs from other types of
wealth in its dual role as both a consumption good and an investment good.
Since households cannot separate the level of consumption of housing services
from investment in housing as an asset, the optimal level of owner-occupied
housing may be higher than the optimal level for households only interested
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in long run returns. The demand for housing services is likely to increase with
the number of children living in the household. Therefore, the consumptionconstraint can be even more binding for households with children.
Chapter 2 uses the 1989, 1992, 1995 and 1998 SCF to investigate how
the number of children living in the household affect the portfolio choice be-
tween housing and other assets. The portfolio allocation of homeowners is
compared to that of renters by taking into account the portfolio constraint
imposed by the consumption demand for housing. The empirical model also
examines the effect of children on the demand for housing services and home-
ownership decision. The results show that the number of children increases
the housing consumption of homeowners as well as the share of the portfolio
allocated to owner-occupied housing. As a result of the portfolio constraint,
homeowners decrease the portfolio share of retirement assets as the number of
children increases.
Low levels of retirement savings of U.S. households have generated sig-
nificant concern in the last twenty years. The findings of Chapter 2 show that
households with children decrease the portfolio share for retirement savings
considerably while they increase the portfolio share for housing. If the return
on housing is less than the return on retirement accounts, there is a hidden
cost of children. Explaining the size of the portfolio effect allows a better un-
derstanding of the cost of children. Also, changes in housing programs or tax
deduction rules for mortgage interest payments influence the portfolio alloca-
tion of households with children considerably by increasing or decreasing the
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cost of homeownership.
The data on U.S. household saving show that saving rates are higher
for married couples with no children and lower for those with children. Using a
life cycle model that incorporates precautionary motives for saving, Chapter 3
investigates the relation between household saving and fertility decisions. Pre-
cautionary saving models predict that uncertainty about future income may
cause households to reduce their current consumption in order to raise their
stock of precautionary saving. By examining the implications of uncertainty
on the fertility decisions of households and incorporating fertility decisions as a
motive for household saving behavior, this chapter extends the empirical work
on precautionary saving. The 1983-89 panel of the SCF is used to examine
the interaction of income uncertainty and changes in the number of children
on the saving behavior of households at different stages of the life cycle.
The results of the empirical model in Chapter 3 show that households
with higher income uncertainty are less likely to have a child at a point in
time. The findings, however, are not consistent with the predictions of the
precautionary saving model that suggests agents faced with uncertainty about
future income increase their savings. Income uncertainty actually reduces
savings of the households with low or very high wealth holdings and does not
affect the saving behavior of other households. Also, having an additional child
decreases savings of households with young heads and increases savings of those
with older heads. This finding is consistent with the life-cycle theory of saving
and consumption and shows that household composition is an important factor
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of life-cycle savings.
Chapter 4 examines the effect of financing childrens college education
on household savings. Understanding the effect of financing childrens college
education on household saving behavior is important for at least three rea-
sons. First, parents contribute a significant amount to their childrens college
expenses. According to the 1996 National Postsecondary Student Aid Survey,
90 percent of dependent undergraduates parents contributed financially to the
costs of their childrens education. Of those contributing to their childrens
college costs in 1987, about 65 percent reported using some previous savings.
Second, families who save for college reduce their eligibility for financial aid.
The college financial aid system imposes an implicit tax on the savings of
households that are potentially eligible for financial assistance. Third, the
quality-quantity model of fertility behavior assumes that parents have pref-
erences both for the expenditure per child and the number of children. This
chapter uses the amount of parental expenditure on childrens college educa-
tion as a measure for child quality. Given the rapidly rising cost of college
tuition, an analysis of financing college education and family size highlights
an important aspect of the quality-quantity model.
Using the actual college expenditures reported in the 1983-86 SCF,
Chapter 4 estimates the households expected expenditures on childrens col-
lege education and investigates the effect of expected college expenses on
household savings. The results show that parents save for college expenses
of their children. Also, savings for college education increases with the age of
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the household head. These results are consistent with the predictions of the
life-cycle theory of saving and consumption that households save in advancefor expected expenses to smooth their consumption.
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Chapter 2
Do Children Affect Household Portfolio
Allocation?
2.1 Introduction
Empirical studies of household portfolio composition have identified
large differences in portfolio allocation choices of different demographic groups.
So far, the literature has focused on the impact of demographic variables such
as the effect of age, race and gender of the household head on the portfolio
composition.1 The influence of children living in the household on the portfolio
composition has not been yet discussed.
It is likely that children living in the household affect the way a house-
hold allocates its wealth across different assets such as owner-occupied hous-
ing, risky assets, and interest-bearing accounts. For example, households with
children may purchase more housing than households with no children or they
may have a higher probability of owning a home. Parents may choose to invest
part of their household portfolio in stocks to meet the rising costs of a college
education. Conversely, they may hold most of their financial assets in riskless
1See Poterba and Samwick [46], King and Leape [41], and Ioannides [34] for age effect;Chiteji and Stafford [13] for race; Jianakoplas and Bernasek [35], and Sunden and Surette [52]for gender effects.
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form to decrease their families exposure to risk.
Understanding the size of the impact of children on household portfolio
allocation is intrinsically interesting. It has also important policy implications.
If households with children allocate a larger share of their portfolio to owner-
occupied housing, then changes in housing programs or tax deduction rules for
mortgage interest payments influence their portfolio allocation by increasing or
decreasing the cost of homeownership. Also, as the result of higher consump-
tion demand for housing, households with children may decrease the portfolio
share for other assets considerably while they increase the portfolio share for
housing. Low levels of retirement savings of U.S. households have generated
significant concern in the last twenty years. The failure of households with
children to invest sufficient assets in retirement accounts may lead to a lower
retirement wealth.
Using data from the 1989, 1992, 1995 and 1998 SCF, this chapter in-
vestigates the effect of children on household portfolio composition, paying
particular attention to the impact of children on the demand for housing ser-
vices and homeownership decision. Specifically, I analyze a model in which
households decide on portfolio shares for different assets jointly with the tenure
choice (the decision of owning or renting) and the consumption demand for
housing services. I focus on how the number and age of children living in the
household affect (i) the homeownership decision, (ii) the portfolio shares for
housing and the other assets that homeowners and renters hold, and (iii) the
housing expenditure of homeowners and renters.
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Most U.S. households hold a large portion of their wealth in the form
of owner-occupied housing. Wolff [56] uses the 1983, 1989, 1992 1995 SCF,and King and Leape [41] examine the 1960-62 Michigan Surveys of Consumer
Finances, and both report that owner-occupied housing accounts for about 30
percent of household assets. According to the 1995 SCF, 65 percent of house-
holds are homeowners, and the value of an average homeowners property is 60
percent of its total assets. Owner-occupied housing differs from other types of
wealth in its dual role as both a consumption good and an investment good.2
In the presence of tax distortions and transaction costs, households cannot
separate the level of consumption of housing services from investment in hous-
ing as an asset, and the ownership of their principal residence determines the
level of consumption of housing services. The optimal level of owner-occupied
housing for households may be higher than the optimal level for households
that are only interested in long run returns. Households with children are
likely to have a higher demand for housing services and the consumption con-straint can be even more binding. Explaining the size of the portfolio effect
allows a better understanding of the cost of children.
While the dual role of housing has been recognized, its impact on the
portfolio choice between housing and other assets has not been discussed much.
Exceptions are the theoretical model of Brueckner [7], the general equilibrium
model of Berkovec and Fullerton [4] and the numerical analysis of Flavin and
Yamashita [20]. Brueckner analyzes the behavior of homeowners. In his model,
2See Henderson and Ioannides [27] and Berkovec and Fullerton [4]
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an investment constraint requires that the quantity of housing owned is at
least as large as the quantity of housing consumed. His model analyzes theresulting distortion of the effect of this investment constraint on the portfolio
choice of homeowners. The results of his model show that when the constraint
imposed by housing is binding, the homeowners optimal portfolio is inefficient
in a mean-variance framework. In Berkovec and Fullerton, households decide
on tenure and quantity of housing taking both consumption and investment
motives into account. Their simulation concentrates on the effect of taxes
on the tenure choice and owner-occupied housing. Flavin and Yamashita use
numerical methods to calculate the mean-variance efficient frontier. Their
results show that the portfolio constraint imposed by the consumption demand
for housing causes a life-cycle pattern in the portfolio shares for stocks and
bonds such that the ratio of stocks to net worth increases as the household
head gets older. Neither of these studies explicitly analyzes the determinants
of the consumption demand for housing and the portfolio share for housing.This chapter extends the previous studies of portfolio choice by examining the
effect of both consumption and investment motives on the portfolio share for
housing and other assets.
The literature on housing demand has recognized the role of children
on the tenure choice and the demand for housing services. For example, Harun
et al. [26] treat the presence of children in the household as endogenous and
find that a 10 percent increase in the probability of having a child raises
the likelihood of homeownership by 2.5 percent. Robst et al. [36] show that
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an additional child increases the probability of owning a home by around 8
percent. Goodman and Kawai [25] find that larger households prefer morehousing. After controlling for the household size, however, their results show
that the presence of children in school has either an insignificant or a negative
effect on the demand for housing. Ihlanfeldt [33] reports housing demand
estimates obtained separately from two samples-recent movers and nonmovers.
Among recent movers, the importance of the current and expected family size
differs between owners and renters: while renters demand more housing with
an increase in family size and expectation of an additional child within the next
nine months, these variables do not affect the housing demand of homeowners.
The results of the previous studies show that dependent children have some
impact on the demand for housing. However, as noted in Goodman [24], little
systematic treatment of children has appeared in the estimation of tenure
choice and housing demand.
Besides housing, U.S. households typically invest in only a few of the
assets available in the economy. For example, according to the 1995 SCF, only
41 percent of households held stocks directly or indirectly in IRAs, 401(k)s,
defined benefit pensions and mutual funds. Many studies have investigated
the reasons that most households choose to hold incomplete portfolios. The
information cost of monitoring and managing a portfolio is suggested as an im-
portant reason for holding riskless assets. Demographic characteristics such as
age, marital status, and race of the household head are shown to be significant
factors that reduce the level of information cost that would be sufficient to
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discourage households from investing in risky assets. For example, Bertaut [5]
uses the 1983-89 SCF to analyze the effect of household characteristics on port-folio allocation. His results show that household characteristics such as age and
education of the household head are significant in explaining the probability of
owning stocks. King and Leape [41] analyze a model in which investors choose
to hold incomplete portfolios, and they estimate equations for both the prob-
ability of owning an asset and its demand conditional upon ownership. Their
findings show that age and marital status of the household head significantly
affect the probability of asset ownership. In the conditional demand equations,
however, the effect of age and marital status appears to be significant only for
some of the assets. Using the Panel Study of Income Dynamics, Chiteji and
Stafford [13] link independent young African-American adults back to their
parents. Their finding is that parents who held stocks are more likely to have
children who hold stocks as young adults.
Children living in the household have not been the focus of any study
examining the portfolio choice of households. This chapter aims to do so by
examining the effect of the number and the age of children on household port-
folio choice. The theoretical model developed in the chapter shows how the
portfolio constraint imposed by the consumption demand for housing affects
the portfolio shares for housing and other assets. The empirical model com-
pares the portfolio allocation of homeowners to that of renters, taking into
account the effect of children on the consumption demand for housing. The
results show that the number of children has a positive and significant effect
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on the probability of owning a home. The number of children also increases
the housing demand of homeowners. As a result of the portfolio constraint im-posed by the housing demand of children, homeowners decrease the portfolio
share in retirement accounts while they increase the portfolio share in hous-
ing. Controlling for the number of children and other variables, homeowners
with all children older than age 13 invest a greater share of their portfolio in
vehicles and other real estate and a smaller share of their portfolio in housing.
Children living in the household also affects the portfolio choice of renters.
Renters invest a smaller share of their portfolio in interest-bearing accounts
with an increase in the number of children. The main conclusion of the chapter
is that homeowners shift their resources from retirement accounts to housing
with an increase in the number of children.
The remainder of this chapter is organized as follows. Section 2.2 in-
troduces the theoretical model and discusses the empirical specification of the
model. Section 2.3 describes the data set and the variables used in the empir-
ical work. The estimation results are reported in Section 2.4. A summary of
the findings and concluding remarks are presented in Section 2.5.
2.2 The Model
2.2.1 Theory
This section examines the behavior of a consumer deciding whether to
rent or own a home, and how much to allocate to other risky assets. The con-
sumer maximizes a multiperiod utility function. Following Brueckner [7] and
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Henderson and Ioannides [27], I assume that third and subsequent periods are
buried in the indirect utility function given remaining wealth at the beginningof the second period. A consumer in this economy is assumed to obtain utility
from the current consumption of a single nondurable good (c), housing services
(hc), and consumption in future periods that depends on the random total re-
turn R from the investment portfolio. The consumers objective function can
be written as follows:
U(c, hc) + E[V(R + y)], (2.1)
where y is future labor income, U gives the utility from the current consump-
tion, V is an indirect utility function, E gives the expected utility, and is
the discount factor.
The dollar amount of asset j purchased is denoted aj , j = 0, 1,..,J,
with a0 being the riskless asset. The only source of uncertainty is assumed to
be from returns on J+ 1 assets and owner-occupied housing (h). Short selling
is ruled out for all assets including housing, so that aj 0, j = 0, 1,..,J, and
h 0. The jth asset earns a gross return of rj , and owner-occupied housing
earns rh.
If the consumer purchases a house, then she holds owner-occupied hous-
ing (h > 0) and is constrained to consume the same amount of owner-occupied
housing in her portfolio (hc = h). The first period budget constraint is given
by
c = w pohhc J
j=0
aj , (2.2)
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where w is her initial wealth and poh is the current price of a unit of housing.
The total return of the portfolio is given by
R = rhh +J
j=0
rjaj . (2.3)
If the consumer rents a house, then the first period budget constraint
is given by
c = w porhc J
j=0
aj , (2.4)
where por
is the price of a unit of housing for renters. The total return of the
portfolio is given by
R =J
j=0
rjaj, (2.5)
since h is equal to zero for renters.
In the model, the return on housing and the return on other assets
are assumed to be normal variables with the expected values rh and rj, j =
1, 2,..,J, respectively. For homeowners, the total portfolio return R is a normalrandom variable with the expected value
R = rhh + r0a0 +
Jj=1
rjaj (2.6)
and the standard deviation
= (hhh2 + 2
Jj=1
hajhj +J
j=1
Kk=1
ajakjk)1/2, (2.7)
where hh and jj , j = 1,...J, are the variances of rh and rk, jk is the co-variance of returns between asset j and k, and jh is the covariance of returns
between asset j and housing. For renters, h = 0 in equations (2.6) and (2.7).
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with the probability of owning a house, 1 is a parameter vector, and 1 is an
error term. Second, the household decides on the share of portfolio allocatedto each asset and housing, and also the housing expenditure:
If owner,
sj = Xoj + oj j = 0, 1,...,Jsh = Xh + hlog Eh = Xcoc + oc.
(2.10)
If renter, s
j = Xrj + rj j = 0, 1,...,Jsh = 0log Eh = Xcrc + rc,
(2.11)
where X and Xc are vectors of household characteristics and year dummies;
oj , rj , j = 0, 1,...,J, h, oc and rc are the parameter vectors to be esti-
mated; and oj , rj , j = 0, 1,...,J, h, oc, and rc are the error terms.
Separate equations are specified for homeowners and renters, and the
error terms in equations (2.9) - (2.11) are assumed to have a joint normal
distribution. The two stage method described in Lee and Trost [42] is used to
estimate the model. In the first stage, a probit model of the tenure choice
in equation (2.9) provides an estimate of 1. In the second stage, I use
(X1)/(X1), as a regressor in estimating (2.10) for homeowners, where
and are probability density and cumulative distribution of the standard
normal distribution, respectively. Similarly, (X1)/(1 (X1)) is used as
a regressor for renters in estimating (2.11).
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2.3 Data
The data for this study are taken from the 1989, 1992, 1995 and 1998
SCF, a triennial survey conducted by the Federal Reserve Board. The sur-
vey contains detailed information on household portfolios, income, and de-
mographic characteristics. Each survey consists of a representative sample of
the U.S. population and a supplement of high-wealth households drawn from
Internal Revenue Service file of high-income returns.3
Total assets are grouped into six categories: 1) ACCOUNT includesall holdings of checking accounts, saving accounts, certificates of deposit, call
accounts, money market deposit accounts; 2) STOCK includes all assets held
in stocks, all types of bonds, and mutual funds; 3) RETIRE includes IRAs,
Keogh, 401(k)s, and other defined contribution plans; 4) HOUSE is the market
value of owner-occupied housing; 5) VEHICLE is the value of all the vehicles
the household owns; 6) RESTATE includes the market value of seasonal resi-
dences and other property; and 7) OTHER includes trusts, cash value of life
insurance, and other assets like arts and precious metals. Investments in busi-
nesses are not included in total assets because they generate an income that
is difficult to separate from earnings.
The consumption demand for housing is computed for renters and
homeowners as follows. For owners, the cost of housing services depends on
3In the 1989 SCF, the supplement consists of 866 out of 3,143 households; in 1992, 1,480out of 3,906; in 1995, 1,519 out of 4,299; and in 1998, 1,409 out of 4,309 households. TheSCF constructs sample weights to blend the supplements with the area-probability sampleto get a more representative sample of the U.S. population.
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the gross value of the residence (G), maintenance and depreciation costs (d),
the property tax rate (p), the mortgage interest payment (m), the interestrate (r), the income tax rate (), the rate of increase in the nominal price of
housing () and the overall inflation rate (). The housing expenditures (Eh)
of homeowners are then defined as
Eh = [(1 )r + d + (1 )p ( )]G m. (2.12)
This formulation assumes that homeowners claim tax deductions for property
taxes and mortgage interest payments. For renters, the annual rental expen-
diture reported in the SCF is used as the consumption demand for housing.
To calculate the housing expenditure by using equation (2.12), I make
several assumptions. Following Henderson and Ioannides [28], I assume an
annual rate of depreciation of d=0.015 for each of the sample years. Property
tax rates and mortgage interest payments are reported in the SCF. The interest
rate, r, is assumed to be the interest rate on treasury bills, and the rate of
increase in house prices, , is the rate of increase in the median sale price
of houses in that year. The inflation rate, , is the annual inflation rate
calculated using the CPI-U deflator. Since marginal tax rates are not reported
in the SCF, I impute them using detailed account information on the sources
of income and demographics for each household. The calculation of marginal
tax rates is described in Appendix A.1.
A few restrictions are imposed on the sample. First, households that
neither rent nor own their homes are excluded for lack of information to cal-
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culate housing expenditure.4 Second, households with the highest 0.1 percent
weighted wealth holdings in each wave of the SCF are dropped, to avoid theinfluence of extreme outliers on the regression.5 The final sample consists of
13,989 observations; 2,900, 3,509, 3,773 and 3,807 households in 1989, 1992,
1995 and 1998, respectively.
Table 2.1 shows the summary statistics for all the variables used in
the estimation. The variables are described in detail in Appendix A.2. Sam-
ple demographics show the age of the household head (AGE), marital status
(MARRIED) and gender (FEMALE) of the household head and the fraction
of homeowners (HOMEOWN), most of which have not changed much over
time.6 However, both mean and median wealth (ASSET) have risen since
1992. The same pattern is true for permanent income (INCOME). As a proxy
for permanent income, I take the estimated earnings of the household head and
the spouse at the age of 45 and an individual-specific effect. The calculation
of permanent income follows King and Dicks-Mireaux [40] and is described
in Appendix A.3. The calculated expenditure of housing consumption (Eh)
4A household is assumed to be a homeowner if (i) it owns the house/apartment thatit lives in or owns it as a part of a condo, a co-op or a townhouse association; (ii) itowns both the mobile house and the site; or (iii) it owns part or all of the farm/ranchon which it lives on. A household is assumed to be a renter if it rents all or part of thefarm/ranch/apartment/house/mobile home in which it lives. In 1989, 1992, 1995 and 1998,respectively, 116, 183, 317 and 309 households were neither renters nor owners and weredropped from the sample.
5Of the remaining households, 127, 214, 209 and 193 were in the 0.1 percentile of the
weighted wealth distribution in the 1989, 1992, 1995, and 1998 SCF, respectively.6The SCF defines the head of the household to be the husband for all married households.
Therefore, households with female heads are headed by single females.
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was higher for homeowners in 1992 than in other years due to the decline in
house prices in that year. The average number of children (NCHILD) livingin the household declined from 0.83 in 1989 to 0.75 in 1995 and stayed the
same in 1998. The percentage of households with all children older than age
13 (CHAGE13) has stayed the same since 1992.
As shown in Table 2.2, the composition of households portfolios reveals
the importance of housing as an asset. HOUSE is the most important asset,
representing 39.4 percent of total assets in 1998. The second largest asset
in the households portfolios is VEHICLE (18.6 percent in 1998), followed by
ACCOUNT. The portfolio share for ACCOUNT declined from 14.3 percent
in 1989 to 11.2 percent in 1995, but it rose to 13.2 percent in 1998 due to an
increase in the portfolio share for saving accounts.
Table 2.2 presents interesting changes in household portfolio structures
over time. First, the share for RETIRE increases sharply. Assets in these
accounts increased from 5.7 percent of total assets in 1989 to 10.5 percent in
1998. Second, there is a steady growth in the portfolio share for STOCK and
a steady decline in the portfolio share for RESTATE since 1989. The increases
in ACCOUNT, STOCK and RETIRE in 1998 offset the decline in HOUSE,
VEHICLE and RESTATE. This suggests that households have substituted
financial assets for nonfinancial assets.
Table 2.3 presents housing expenditures of homeowners and renters in1998. The first column shows the share of households in different income, age,
wealth and children (the number of children living in the household) groups.
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The second column indicates the percentage of each of these groups that are
homeowners. Average housing expenditures for homeowners and renters arepresented, respectively, in the remaining two columns of the table. The per-
centage of households who are homeowners increases with income, wealth and
the age of the household head. It also increases with the number of children,
reaching a peak among households with two children. The average housing
expenditure is $7,042 for homeowners and $6,030 for renters. The housing ex-
penditures of renters and homeowners also increase with income, wealth and
the number of children in the household. For homeowners, the expenditure
on housing declines after the age of 65. For renters, it declines after age 50.
Among households with wealth below $250,000 and income below $50,000,
renters spend more on housing than owners. This is due to an increase in the
value of residences and also to the tax deduction for property taxes and mort-
gage interest payments that decrease the opportunity cost of homeownership.
Tables 2.4 and 2.5 show the household portfolio composition in 1998
by household permanent income, age of the household head, wealth, and the
number of children. The first row of Table 2.4 shows the portfolio shares
of assets that homeowners and renters hold. Since the primary residence is
the largest part of homeowners wealth, accounting for 57.9 percent, there
are marked differences in household portfolios of renters and owners. First,
for renters, VEHICLE is the most important asset held (41.5 percent of total
assets) followed by ACCOUNT (26.0 percent). For homeowners, however,
VEHICLE is the third largest asset (7.8 percent of total assets) following
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RETIRE (10.2 percent). The portfolio shares for other assets such as STOCK,
RETIRE and RESTATE are almost equal for renters and owners.
For higher levels of income, as shown in Tables 2.3 and 2.4, the frac-
tion of households who are homeowners increases, while the housing share of
portfolio declines. For example, of the households with income below $15,000,
42.7 percent are homeowners holding 75.9 percent of their total assets in hous-
ing. Of the households with income above $100,000, in contrast, 86.7 percent
are homeowners, but they hold only 42.6 percent of total assets in housing.
Another noteworthy finding is that the portfolio shares for STOCK and RE-
TIRE for both homeowners and renters rise with income. Also, we observe
striking differences in the composition of portfolios by the level of wealth.
For homeowners, the share of the portfolio allocated to RESTATE and for all
households, the share of the portfolio allocated to STOCK rise at a rapid rate
with wealth. For example, among homeowners that have wealth exceeding $1
million, STOCK is the most important asset category with a share equal to
25.2 percent of total assets while housing accounted for only 22.6 percent.
Table 2.4 also presents the life cycle patterns in household portfolios.
Not surprisingly, portfolio composition of households with heads over the age
of 65 differs considerably from other age groups portfolios. Several findings
are worth noting. First, the portfolio share for ACCOUNT almost doubles
both for homeowners and renters over the age of 65 compared to 50-64 year
old group. Also, accumulation in STOCK relative to other assets increases
over age 65. This suggests that households with heads over age 65 substitute
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liquid assets for nonfinancial assets. Second, the portfolio share for HOUSE
declines with age among the households headed by persons below age 65, butit stays steady after age 65.
Finally, Table 2.5 shows the portfolio shares by the number of children
living in the household. The table indicates a strong relation between chil-
dren and the share of portfolio allocated to housing. The portfolio share for
owner-occupied housing increases with the number of children. For example,
housing accounts for 56.0 percent of the wealth for households with no chil-
dren, 60.9 percent for households with 2 children, and 65.3 percent for those
with three or more children. Homeowners invest a smaller share of their port-
folio in interest-bearing accounts and stocks with an increase in the number
of children. Also, the presence of children increases the share of the portfolio
allocated to vehicles.
Tables 2.4 and 2.5 reveal striking differences in portfolio structures
across income, wealth, and age groups. While portfolio composition differs
considerably between renters and homeowners, the relative changes in portfolio
shares of assets by income, age and wealth are similar. Children are likely to
affect the portfolio structures in two ways. The first is their effect on the choice
of tenure, and the second is their effect on asset shares of portfolios conditional
upon ownership. Table 2.3 investigates the effect of children on the tenure
choice, and table 2.5 looks at the link between children and shares of assets in
both renters and homeowners portfolios. The results indicate that the number
of children living in the household affects the portfolio shares for assets and
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the probability that a household owns a home. The empirical model below
investigates the effect of children on both asset shares and homeownershipdecision.
2.4 Estimation and Results
The resulting set of equations constitutes an endogenous switching
model in the form of a multivariate regression model. Portfolio shares of the
J+ 1 assets and housing sum to one, and the disturbance covariance matrix is
singular. Thus, I drop one group of assets, OTHER, and include ACCOUNT,
STOCK, RETIRE, HOUSE, VEHICLE, and RESTATE in the estimation of
the model. Then I solve for the parameters of OTHER from the other equa-
tions. Of 13,898 households, 410 report zero wealth holding.7 I exclude those
households from the sample and correct for sample selection.
Dummy variables indicating the number and the age of children living
in the household are included in X. The other variables in X are chosen to be
consistent with previous empirical studies. Portfolio choice theory has shown
the importance of age, permanent income and wealth in determining the asset
shares in household portfolios. Age and age-squared of the household head are
included to capture a possible change in portfolio behavior related to the life
cycle. Previous research also indicates that a households marginal tax rate
(MRT) has an effect on its asset allocation decisions. Moreover, the marital
793 households in 1989, 111 in 1992, 100 in 1995, and 106 in 1998 had zero wealth holding.
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status and the gender of the household head and willingness to undertake risky
investments (RISKY) may also affect the households asset allocation.
All variables that enter X are also included in Xc and Xh, with two
exceptions. First, the marginal tax rate affects the tenure choice and home-
owners expenditure on housing since homeowners can claim tax deductions
for mortgage interest payments and property taxes. However, the marginal
tax rate is not expected to affect the housing expenditure of renters. Thus,
marginal tax rate is not included in Xc. Second, willingness to undertake risky
investment does not enter Xc because it has an effect on the tenure choice re-
garding the investment motive but not on the expenditures on rental housing.
In addition, the vector Xh includes the race of the household head.
Table 2.6 presents the estimates of the probit model of equation (2.9).
The estimates of the homeownership equation are consistent with previous
studies. As a households permanent income rises, the probability of home-
ownership increases. Age of the household head increases the probability of
ownership until age 74. The coefficients for WHITE and MARRIED are signif-
icant and positive, indicating that at the sample mean, households with white
heads are 10.2 percent more likely to own than households with non-white
heads, and those that are married are 26.1 percent more likely to own than
those that are not. The coefficients on the variables showing the number of
children are positive and significant. Households with one child are 6.3 per-
cent, and those with two children are 10.8 percent, more likely to own relative
to households with no children. The probability of owning starts to decrease
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after the second child, household with three or more children are only 9.6 per-
cent more likely to own relative to households with no child. The probabilityof being a homeowner also increases with the households marginal tax rate,
suggesting that the tax-deductibility of property taxes and mortgage interest
is more valuable at a higher marginal tax rate.
Tables 2.7- 2.10 show the coefficients and the standard errors for each
of the seven asset equations and the housing expenditure equation for home-
owners. Permanent income has significant but small marginal effects on the
structure of homeowners portfolio. The share of the portfolio allocated to
RETIRE, HOUSE and VEHICLE increase with income, while the share allo-
cated to ACCOUNT, STOCK and RESTATE decreases with income. Higher
levels of wealth are associated with higher shares in ACCOUNT, STOCK, RE-
STATE, OTHER, and lower shares in HOUSE and VEHICLE. The marginal
effect of wealth on the share allocated to STOCKS, HOUSE and RESTATE is
large. A 10 percent increase in assets would increase the share of the average
portfolio allocated to STOCK by 0.62 percentage point. A similar increase
in assets would induce 1.25 percentage point decrease in HOUSE and 0.66
percentage point increase in RESTATE.
Age is an important determinant of portfolio shares in a homeowners
portfolio, and the results in Table 2.7 and 2.8 reveal a quadratic relationship in
terms of age. Portfolio shares for RETIRE, HOUSE and RESTATE increase
with age, reaching a peak at the age of 50, 63 and 50, respectively. Portfolio
shares for ACCOUNT and STOCK, however, decrease with age until the age of
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50 and 43, respectively. This relation between age and portfolio shares suggests
that the structure of a households portfolio changes when the household headreaches middle age. For example, households headed by persons above the age
of 45 start substituting liquid assets for nonfinancial assets such as HOUSE
and RESTATE.
The coefficients on the number and age of children suggest that the
presence of children plays a significant role on the portfolio structure of home-
owners. Several results are of particular interest. First, relative to households
with no children, households with one child have a 5.6 percent higher portfolio
share of HOUSE, controlling for age and permanent income. Similarly, house-
holds with two and three or more children have 8.9 and 9.2 percent greater
portfolio shares in HOUSE. Second, the portfolio shares for ACCOUNT, RE-
TIRE, and VEHICLE decrease with an increase in the number of children.
Controlling for the number of children, households with all the children older
than age 13 hold a smaller portfolio share in HOUSE and a greater share in
VEHICLE and RESTATE.
Finally, homeowners that are willing to undertake risky investments
hold a greater share of risky financial assets, such as STOCKS and RETIRE,
and a smaller share of less risky assets, such as ACCOUNT and HOUSE.
All other things held constant, the portfolio shares allocated to ACCOUNT
and RESTATE have declined in 1998. Households have substituted STOCK,
RETIRE and VEHICLE for the other asset categories since 1995. An increase
in the marginal tax rates leads to an increase in the portfolio share allocated
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to HOUSE and VEHICLE. It leads to a decrease in the share allocated to
ACCOUNT, RESTATE and OTHER.
Tables 2.9 and 2.10 present the estimates of the equations (2.11) for
renters. The effect of children is less pronounced for renters than for homeown-
ers. As renters have two or more children, the share for ACCOUNT decreases,
and the share for VEHICLE is significantly higher for households with three or
more children. More permanent income is associated with a higher share for
ACCOUNT, STOCK, RETIRE and RESTATE, and a lower share for VEHI-
CLE. An increase in total assets leads to an increase in the share for STOCK,
RESTATE and OTHER and a decrease in the share for ACCOUNT and VE-
HICLE. The quadratic relationship observed between the shares of assets in
homeowners portfolio and the age of the head holds true for the financial
assets in a renters portfolio. The portfolio share for RETIRE increases with
age until the age of 58, while the portfolio share for ACCOUNT and STOCK
decreases until the age 40 and 43, respectively. Since 1995, renters have shifted
toward RETIRE in their portfolio. Compared to 1989, for example, the 1998
portfolio share for RETIRE is 5.0 percent higher in renters portfolio.
Tables 2.7-2.10 report coefficients of the selectivity variables. Self-
selection occurred in households tenure choice. The coefficients on the se-
lection terms in equations for ACCOUNT, RETIRE and HOUSE for home-
owners are all statistically significant. For these assets, homeownership would
not have the same effect on renters, should they choose to buy homes. The
estimates of the Mills ratios for renters are significantly different from zero
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for ACCOUNT, RETIRE, and RESTATE. This implies that other than in re-
gards to these three assets, there were no significant differences in the averagebehavior of the two groups prior to home purchase.
The last two columns in Tables 2.8 and 2.10 present the estimates of the
housing expenditure equation. For homeowners, the expenditure on housing
increases with the number of children, but the number of children has no effect
on renters expenditure. Homeowners with one child have 11.9 percent higher
housing expenditure than homeowners with no child. The housing expenditure
of homeowners increases 8.3 percent with the second child. After the second
child, having more children increases the housing expenditures of homeowners
by only 3.2 percent. The age of the children in the household has no effect
on the housing expenditure of renters nor homeowners. For both renters and
owners, the significance and the same sign of the selection terms indicate
that self-selection occurred in a hierarchical sorting: the positive selectivity
bias indicates that those who own a house spend less compared to average
household had it chosen to own. On the other hand, the negative selectivity
bias for renters implies the reverse: renters spend less on housing compared
to average household of the sample had it chosen to rent.
I use the estimated coefficients and the variables of the model to calcu-
late the portfolio share for each asset by the number of children and the age
of the household head. Table 2.11 presents the estimates of shares for assets
that a typical homeowner holds. By a typical household, I mean a household
headed by a white married, all of the children in the household are younger
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than age 13. The household head is willing to take risky investments and holds
mean wealth ($188,160) and permanent income ($46,690) and has a 15 per-cent marginal tax rate. As mentioned above, children have two effects on the
portfolio structure of households. First, an increase in the number of children
increases the probability that a household owns a home. Second, conditional
on the tenure choice, children change the demand for each asset. The portfolio
shares of assets calculated in Table 2.11 include both of these effects. At all
ages, HOUSE is the most important asset, and its importance in the portfolio
increase with the number of children living in the household. VEHICLE is
the second most important asset in the portfolio when the household head is
30 years old. As the household head reaches middle age, more is invested in
RETIRE, and the share allocated to RETIRE becomes the second largest in
the portfolio. The number of children has a negative effect on the portfolio
share for RETIRE.
2.5 Conclusion
Using the 1989, 1992, 1995 and 1998 SCF, this chapter investigates
how the number and the age of children living in the household influence
the portfolio composition of households. One contribution of this chapter is
to study the effect of the portfolio constraint imposed by the consumption
demand for housing on the portfolio shares in housing and other assets. The
chapter examines the impact of children on the homeownership decision and
the constraint of consumption demand for owner-occupied housing. Using a
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switching regression model that takes into account the consumption demand
for housing, the chapter compares the determinants of portfolio allocation ofhomeowners to that of renters.
The results show that the number of children living in the household has
a significant effect on the tenure choice and on the housing demand of home-
owners. As homeowners have more children, the portfolio share for financial
assets such as interest-bearing accounts and retirement accounts decreases,
and the portfolio share for housing increases. However, the ratio of retirement
accounts to total assets in renters portfolios does not significantly decrease
with the number of children. This result suggests that, for households with
children, the consumption demand for housing is higher than the investment
demand. Since households cannot separate the level of consumption of housing
services from their investment in housing as an asset, the ratio of housing to
total assets increases as the number of children increases.
Considerable research has focused on whether U.S. households are sav-
ing enough for retirement. An important implication of the findings of this
chapter is that the constraint imposed by the consumption demand for housing
decreases the share of portfolio allocated to retirement wealth as the number
of children in a household increases. Therefore, the policies that change the
cost of housing and affect ownership decision influence not only the portfolio
share for owner-occupied housing but also the portfolio share for retirement
assets.
One direction for further research is to include the liabilities and bor-
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rowing constraints of households into the model of portfolio choice. Most
households finance their home purchases with mortgage debt. The impact ofchildren on the portfolio share for housing may be an important determinant
of household mortgage debt.
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Table 2.1: Descriptive Statistics by Year
1989 1992 1995 1998
Income and Assets
INCOME 47,968 46,054 49,319 50,658ASSETS (Mean) 222,151 203,328 206,815 258,191ASSETS (Median) 92,684 92,525 101,829 116,750MRT 0.158 0.164 0.154 0.131Eh 5,660 12,985 6,664 6,695
Demographics
AGE 47.8 48.5 48.3 48.9MARRIED 0.59 0.58 0.59 0.59FEMALE 0.28 0.27 0.28 0.28NCHILD 0.83 0.80 0.75 0.75CHAGE13 0.14 0.12 0.11 0.12HOMEOWN 0.64 0.65 0.65 0.66RISKY 0.51 0.50 0.55 0.61
Number of observations 2,900 3,509 3,773 3,807% with positive wealth 0.97 0.97 0.97 0.97
Source: Survey of Consumer Finances, 1989-1998.Notes: 1) Tabulations are weighted using sample weights. 2) All dollar values are reportedin 1998 dollars. The text defines total assets, permanent income and net worth. All variablesare defined in Appendix A.2.
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Table 2.2: Mean Asset Shares by Year
1989 1992 1995 1998
Portfolio Shares
ACCOUNT 0.143 0.130 0.112 0.132STOCK 0.043 0.043 0.047 0.059RETIRE 0.057 0.072 0.094 0.105HOUSE 0.415 0.432 0.410 0.394VEHICLE 0.197 0.196 0.208 0.186RESTATE 0.068 0.059 0.053 0.050OTHER 0.076 0.067 0.076 0.072
Source: Survey of Consumer Finances, 1989-1998.Notes: 1) Tabulations are weighted using sample weights. 2) The text defines the assetscalled ACCOUNT, STOCK, RETIRE, HOUSE, VEHICLE, RESTATE, and OTHER.
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Table 2.3: Expenditure on Housing, 1998Eh1998 dollars
%HH %HO HO RRAll households 100 66.78 7,042 6,030IncomeBelow $15K 10.35 42.69 3,866 4,293$15-30K 22.50 51.69 4,400 5,081$30-50K 29.79 64.54 5,764 6,378$50-100K 29.12 80.22 7,741 7,883Above $100K 8.24 86.72 14,456 11,748
AgeUnder 35 22.40 36.29 6,183 6,02435-49 34.08 68.72 7,078 6,47550-64 22.03 78.42 7,931 5,564Above 65 21.49 78.89 6,496 5,489
WealthBelow $50K 32.90 12.93 1,263 5,546$50K-100K 12.38 80.72 3,065 7,555
$100K-250K 29.26 93.04 5,438 8,587$250-1000K 21.29 95.43 9,843 9,486Above 1000K 4.16 95.90 19,847 15,645
ChildrenCHILD0 61.17 64.22 6,677 5,973CHILD1 15.46 64.46 6,976 6,081CHILD2 14.28 72.77 7,803 6,002CHILD3 9.09 67.55 8,195 6,391
Source: Survey of Consumer Finances, 1998.Notes: 1) Tabulations are weighted using sample weights. 2) HH represents all households,HO represents homeowners and RR represents renters.
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Table2.4:MeanAssetShares,199
8
ACCOUNT
STO
CK
RETIRE
HOU
SE
VEHICLE
RESTATE
HO
RR
HO
RR
HO
RR
HO
RR
HO
RR
HO
RR
Allhouseholds
0.071
0.260
0.064
0.049
0.102
0.112
0.579
0
0.078
0.415
0.054
0.043
Income
Below$15K
0.093
0.389
0.022
0.019
0.020
0.061
0.759
0
0.061
0.287
0.015
0.039
$15-30K
0.090
0.295
0.046
0.032
0.064
0.080
0.630
0
0.091
0.453
0.040
0.014
$30-50K
0.066
0.205
0.056
0.051
0.091
0.127
0.608
0
0.083
0.490
0.047
0.050
$50-100K
0.062
0.201
0.069
0.086
0.132
0.157
0.541
0
0.080
0.359
0.062
0.071
Above$100K
0.075
0.256
0.128
0.112
0.147
0.221
0.436
0
0.049
0.181
0.091
0.111
Age
Under35
0.040
0.238
0.028
0.049
0.074
0.092
0.694
0
0.107
0.489
0.018
0.021
35-49
0.048
0.213
0.055
0.041
0.122
0.149
0.587
0
0.088
0.413
0.047
0.068
50-64
0.062
0.251
0.077
0.031
0.135
0.137
0.535
0
0.072
0.401
0.068
0.052
Above65
0.129
0.454
0.083
0.090
0.055
0.059
0.559
0
0.056
0.190
0.065
0.046
Wealth
Below$50K
0.042
0.281
0.004
0.027
0.025
0.089
0.730
0
0.172
0.485
0.002
0.011
$50K-100K
0.054
0.135
0.007
0.122
0.047
0.212
0.750
0
0.101
0.143
0.017
0.183
$100K-250K
0.073
0.199
0.033
0.149
0.089
0.234
0.645
0
0.087
0.086
0.028
0.162
$250-100
0K
0.082
0.166
0.111
0.165
0.151
0.192
0.445
0
0.047
0.022
0.097
0.293
Above1000K
0.075
0.078
0.252
0.374
0.162
0.270
0.226
0
0.019
0.010
0.158
0.109
continuedo
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Table2.5:
MeanAssetShares,1998:Continued
ACCOUNT
STOCK
RETIRE
HOUSE
VEHICLE
RESTATE
HO
RR
HO
RR
HO
RR
HOR
R
HO
RR
HO
R
R
Child
ren
CHIL
D0
0.089
0.288
0.0740.061
0.098
0.117
0.560
0
0.070
0.373
0.062
0.0
40
CHIL
D1
0.052
0.222
0.0550.026
0.122
0.102
0.577
0
0.098
0.495
0.038
0.0
44
CHIL
D2
0.044
0.195
0.0490.019
0.110
0.115
0.609
0
0.087
0.471
0.044
0.0
69
CHIL
D3
0.038
0.200
0.0480.040
0.087
0.088
0.653
0
0.085
0.512
0.045
0.0
32
Source:SurveyofConsumerFinances,1998.
Notes:1)Tabulationsareweightedusingsam
pleweights.2)Alldollarvaluesa
rereportedin1998dollars.3)HO
repre-
sentshome
ownersandRRrepresentsrenters.
Thetextdefinestheassetscalled
ACCOUNT,STOCK,RETIRE,H
OUSE,
VEHICLE,
andRESTATE.
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Table 2.6: Results from Probit Estimation
HOMEOWNCoefficient Standard Errors Marginal Effects
CONSTANT -4.404 0.137 **AGE 0.118 0.005 ** 0.014AGE2/100 -0.080 0.005 **MARRIED 0.747 0.042 ** 0.261FEMALE 0.137 0.044 ** 0.046CHILD1 0.193 0.049 ** 0.063CHILD2 0.341 0.047 ** 0.108CHILD3 0.304 0.052 ** 0.096
CHAGE13 0.000 0.053INCOME/10,000 0.030 0.004 ** 0.010MTR 2.174 0.153 ** 0.007RISKY 0.210 0.030 ** 0.072WHITE 0.287 0.034 ** 0.102YEAR92 -0.085 0.041 * -0.029YEAR95 -0.052 0.040YEAR98 -0.143 0.040 ** -0.050
Notes: 1) ** indicates significance at 1 percent level, and * indicates significance at 5 percentlevel. 2) Variables are defined in Appendix A.2. The number of observations N=13,579.
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Table2.7:Results:Asse
tSharesandHousingExpend
itureofHomeowners
ACCOUNT
STOCK
RETIRE
HOUSE
VEHICLE
Coef
SE
Coef
SE
Coef
SE
Coef
SE
Coef
SE
CONSTANT
0.432
0.054**
-0.4
55
0.069**
-0.190
0.063**
1.515
0.087**
0.398
0.029**
AGE
-0.009
0.001**
-0.0
06
0.001**
0.007
0.001**
0.005
0.002**
0.000
0.001
AGE2/100
0.009
0.001**
0.0
07
0.001**
-0.007
0.001**
-0.004
0.001**
-0.001
0.000
MARRIED
-0.033
0.006**
-0.0
26
0.007**
0.001
0.007
0.059
0.010**
0.019
0.003**
FEMALE
0.008
0.005
-0.0
13
0.007
0.005
0.006
0.061
0.009**
-0.022
0.003**
CHILD1
-0.016
0.006*
-0.0
06
0.007
-0.013
0.006*
0.056
0.009**
-0.008
0.003**
CHILD2
-0.026
0.006**
-0.0
06
0.007
-0.024
0.006**
0.089
0.008**
-0.010
0.003**
CHILD3
-0.021
0.007**
-0.0
13
0.007
-0.032
0.006**
0.092
0.009**
-0.008
0.003**
CHAGE
13
-0.001
0.006
-0.0
10
0.006
0.003
0.005
-0.020
0.008**
0.013
0.002**
LINCOME
-0.007
0.002**
-0.0
09
0.003**
0.012
0.003**
0.021
0.005**
0.005
0.002**
LASSET
0.004
0.001*
0.0
62
0.002**
-0.001
0.002
-0.125
0.003**
-0.031
0.001**
MTR
-0.067
0.018**
-0.0
16
0.021
0.005
0.020
0.307
0.020**
0.024
0.011*
RISKY
-0.010
0.003**
0.0
24
0.004**
0.023
0.004**
-0.027
0.003**
-0.003
0.002
YEAR92
-0.010
0.003**
0.0
06
0.005
0.007
0.004
-0.002
0.006
-0.003
0.002
YEAR95
-0.027
0.003**
0.0
08
0.004
0.023
0.004**
-0.012
0.006*
0.008
0.002**
YEAR98
-0.027
0.004**
0.0
21
0.005**
0.037
0.004**
0.005
0.006
0.004
0.002*
MR:hom
e
-0.115
0.018**
-0.0
29
0.023
-0.049
0.020*
0.198
0.021**
0.011
0.008
MR:+w
ealth
-0.077
0.031*
0.1
12
0.054*
-0.063
0.049
0.056
0.039
-0.063
0.010**
continuedo
nthenextpage.
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Table2.8:Homeowners:Continued
RE
STATE
OTHER
logEh
Coef
SE
Coef
SE
Coef
SE
CONSTANT
-0.584
0.080**
-0.116
0.049*
-0.246
0.220
AGE
0.004
0.002**
-0.002
0.001
0.019
0.004**
AGE2/100
-0.004
0.001**
0.001
0.001
-0.015
0.003**
MARRIED
-0.010
0.009
-0.012
0.006
0.181
0.024**
FEMALE
-0.036
0.009**
-0.004
0.006
0.177
0.025**
CHILD1
-0.014
0.008
0.001
0.005
0.119
0.025**
CHILD2
-0.021
0.008**
-0.002
0.005
0.202
0.024**
CHILD3
-0.010
0.008
-0.007
0.005
0.234
0.025**
CHAGE13
0.017
0.007*
-0.002
0.004
-0.023
0.023
LINCOME
-0.018
0.003**
-0.004
0.002
0.068
0.011**
LASSET
0.066
0.002**
0.025
0.002**
0.568
0.007**
MTR
-0.158
0.025**
-0.094
0.018**
RISKY
-0.004
0.005
-0.003
0.003
YEAR92
0.010
0.005*
-0.008
0.004*
1.204
0.018**
YEAR95
0.000
0.005
0.001
0.004
0.307
0.017**
YEAR98
-0.032
0.005**
-0.008
0.004*
0.189
0.017**
MR:home
-0.001
0.025
-0.015
0.015
0.393
0.052**
MR:+wealth
0.043
0.053
-0.008
0.036
-0.503
0.120**
Notes:1)**indicatessignificanceat1perce
ntlevel,and*indicatessignifican
ceat5percentlevel.2)Thetext
defines
theassetsc
alledACCOUNT,STOCK,RETIR
E,HOUSE,VEHICLE,RESTATE
,andOTHER.Allvariablesarede
finedin
AppendixA.2.MRrepresentsMillsRatio.Th
enumberofobservationsN=10,002.
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Table2.9:Results:AssetSharesandHousingExpe
nditureofRenters
ACCOUNT
STOCK
RETIRE
VEHICLE
Coef
SE
Coef
SE
Coef
SE
Coef
SE
C
ONSTANT
0.243
0.173
-0.285
0.097**
-0.793
0.154**
2.059
0.243**
A
GE
-0.014
0.003*
*
-0.004
0.001**
0.015
0.002**
-0.001
0.004
A
GE2/100
0.017
0.002*
*
0.005
0.001**
-0.013
0.002**
-0.004
0.003
M
ARRIED
-0.086
0.021*
*
-0.035
0.011**
0.021
0.017
0.130
0.026**
F
EMALE
0.026
0.013*
0.010
0.007
0.027
0.010**
-0.059
0.018**
C
HILD1
-0.032
0.017
0.005
0.010
0.002
0.012
0.025
0.020
C
HILD2
-0.054
0.019*
*
0.000
0.011
0.019
0.014
0.034
0.023
C
HILD3
-0.077
0.021*
*
-0.005
0.015
0.010
0.015
0.061
0.024*
C
HAGE13
-0.003
0.022
-0.023
0.016
0.001
0.014
0.029
0.026
L
INCOME
0.080
0.014*
*
0.022
0.007**
0.033
0.011**
-0.109
0.020**
L
ASSET
-0.072
0.004*
*
0.017
0.002**
0.014
0.003**
-0.027
0.006**
M
TR
0.204
0.079*
0.000
0.038
0.319
0.064**
-0.120
0.103
R
ISKY
0.011
0.012
0.019
0.007**
0.028
0.009**
-0.068
0.015**
Y
EAR92
-0.015
0.014
0.001
0.008
0.018
0.014
-0.006
0.019
Y
EAR95
-0.033
0.015*
-0.001
0.008
0.051
0.012**
0.015
0.019
Y
EAR98
0.014
0.014
0.008
0.008
0.050
0.013**
-0.030
0.019
M
R:home
-0.089
0.038*
-0.029
0.019
0.079
0.031*
0.074
0.048
M
R:+wealth
0.070
0.039
0.039
0.037
0.052
0.035
-0.381
0.053**
continuedo
nthenextpage.
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Ta
ble2.10:Renters:Continued
RE
STATE
OTHER
logEh
Coef
SE
Coef
SE
Coef
SE
CONSTANT
-0.334
0.130*
0.109
0.173
4.580
0.365**
AGE
0.004
0.002
0.001
0.003
-0.004
0.004
AGE2/100
-0.004
0.002*
-0.001
0.002
-0.002
0.003
MARRIED
0.000
0.015
-0.030
0.020
-0.064
0.036
FEMALE
-0.017
0.010
0.014
0.014
0.110
0.033**
CHILD1
0.011
0.011
-0.012
0.016
-0.061
0.035
CHILD2
-0.001
0.012
0.001
0.017
-0.067
0.037
CHILD3
-0.005
0.014
0.017
0.018
0.027
0.038
CHAGE13
-0.005
0.010
0.001
0.020
0.083
0.044
LINCOME
0.038
0.003**
-0.022
0.014
0.315
0.030**
LASSET
-0.038
0.054
0.030
0.005**
0.062
0.006**
MTR
-0.001
0.008
-0.365
0.073**
RISKY
0.007
0.009
0.010
0.011
YEAR92
-0.021
0.009*
-0.005
0.014
0.069
0.030*
YEAR95
-0.022
0.010*
-0.011
0.014
0.045
0.032
YEAR98
-0.014
0.024
-0.020
0.014
0.125
0.029**
MR:home
0.102
0.033**
-0.020
0.035
-0.285
0.042**
MR:+wealth
-0.004
0.012
0.118
0.038**
-0.189
0.073**
Notes:1)**indicatessignificanceat1percentleveland*indicatessignificanceat5percentlevel.2)Thetext
defines
theassetsc
alledACCOUNT,STOCK,RETIR
E,HOUSE,VEHICLE,RESTATE
,andOTHER.Allvariablesarede
finedin
AppendixA.2.MRrepresentsMillsRatio.Th
enumberofobservationsN=3,577.
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Table 2.11: Portfolio Shares for Assets by the Number of Children and Age
CHILD0 CHILD1 CHILD2 CHILD3AGE=30ACCOUNT 0.038 0.036 0.036 0.038STOCK 0.064 0.061 0.064 0.056RETIRE 0.093 0.086 0.079 0.070HOUSE 0.594 0.626 0.642 0.650VEHICLE 0.112 0.102 0.100 0.101RESTATE 0.043 0.030 0.023 0.033
AGE=40ACCOUNT 0.049 0.043 0.040 0.043STOCK 0.056 0.053 0.055 0.047RETIRE 0.128 0.120 0.111 0.103HOUSE 0.552 0.590 0.611 0.617VEHICLE 0.105 0.096 0.094 0.095RESTATE 0.057 0.044 0.037 0.047
AGE=50ACCOUNT 0.058 0.049 0.044 0.048STOCK 0.058 0.054 0.055 0.047RETIRE 0.141 0.132 0.122 0.114HOUSE 0.534 0.577 0.602 0.607VEHICLE 0.099 0.090 0.088 0.089RESTATE 0.063 0.049 0.043 0.053
Notes: The text defines the assets called ACCOUNT, STOCK, RETIRE, HOUSE, VEHI-CLE, and RESTATE.
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Chapter 3
The Effect of Precautionary Motives on
Household Saving and Fertility
3.1 Introduction
Many recent studies have recognized the role of precautionary motiveson household saving behavior.1 Precautionary saving models predict that un-
certainty about future income may cause households to reduce their current
consumption in order to raise their stock of precautionary saving. As an exten-
sion to the traditional life-cycle model, these models are able to explain some
of the empirical consumption puzzles.2 For example, the standard life-cycle
model suggests that households smooth consumption and spread resources
across periods of high and low income. In many household-level data sets,
however, consumption profiles over age are hump-shaped, tracking the age-
earnings profile. Carroll [8] shows that this kind of consumption profile is
consistent with a precautionary saving model in which individuals face uncer-
tainty about their future earnings.
Yet, empirical work on the strength of precautionary saving has pro-
vided mixed evidence. Skinner [48], Dynan [18] and Starr-McCluer [50] find lit-
1See Zeldes [58], Kimball [39], Hubbard et al. [32] and Carroll [9].2Deaton [15] and Browning and Lusardi [6] give a list of empirical puzzles.
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tle or no evidence for precautionary motive, whereas Carroll and Samwick [11],
[12] and Lusardi [44] find more support for the precautionary motive. Brown-ing and Lusardi [6] and Carroll et al. [10] suggest that the mixed results might
be due to the difficulties in empirically testing for precautionary saving.3 One
problem that has not been mentioned in the literature is that all of these
empirical models try to explain the effect of income uncertainty on house-
hold savings, ignoring the effect of uncertainty on household composition. By
examining the implications of uncertainty on the fertility decisions of house-
holds and incorporating fertility decisions into household saving decisions, this
chapter extends the empirical work on precautionary saving.
Most of a households saving motives can be grouped into one of three
categories: life-cycle motives, precautionary motives, and bequest motives. It
seems reasonable that these motives are affected by the presence of children.
For example, the life-cycle motive includes saving for childrens education,
the precautionary motive includes saving to protect the well-being of children
against income fluctuations, and, finally, the bequest motive includes saving to
leave assets to children. Yet the causal effect might go in the opposite direction;
that is, fertility might be affected by uncertainty or income fluctuations, given
precautionary and other motives. Furthermore, household income or the age
of the head might affect household saving and fertility simultaneously. This
chapter takes account of the fact that children are endogenous along with the
3The problems include proxying certainty, finding an appropriate instrument, and incor-porating the restrictions of the theoretical model. See Browning and Lusardi [6] and Carrollet al. [10] for the details.
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saving behavior when estimating the effect of children on savings.
Table 3.1 presents the proportion of households citing the following
motives -rainy days, retirement, buying a home and education of children-
as the most important reasons for saving in the 1983 SCF (data come from
the panel of 1983-89 SCF and is discussed at length in section 3.3). The most
frequently reported reason for saving was to increase resources for rainy days
such as unemployment and unexpected needs. More than 32 percent reported
that rainy days were an important motivation for saving. The second most
frequent reason was saving for retirement, with 18 percent. The proportion of
households citing saving for childrens educational expenses and home purchase
were 5.7 and 4.1 percent, respectively. When disaggregated into age groups, all
of the four reasons reveal a hump shape: saving for rainy days peaking in the
41-50 age group, saving for retirement peaking between age 51 and 60, saving
for a home purchase peaking below age 31, and saving for the education of
children peaking between age 31-40. This suggests that the relative importance
of saving for each motive depends highly on the composition and the life-cycle
stage of the household.
This chapter also addresses a neglected topic in the childbearing liter-
ature, namely, the effect of income uncertainty on fertility over the life-cycle.
Most life-cycle fertility models incorporate some types of uncertainty.4 For
example, Wolpin [57] estimates a dynamic stochastic model of fertility within
4See Hotz et al. [30] for a survey of life-cycle fertility models.
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an environment where infant survival is uncertain.5 Hotz and Miller [31] in-
tegrate the life cycle fertility and labor supply, and consider a number ofuncertainties such as the outcome of the contraceptive effort, the time path
of the husbands income, and transitory shocks to the wifes wage. None of
these studies, however, have specifically analyzed whether uncertainty about
earnings is a significant factor on the choice of whether or not to have a child. 6
This chapter examines whether income uncertainty is associated with
lower fertility and higher savings. Using the data from the panel of 1983-89
SCF, I find that households with higher income uncertainty are less likely
to have a child. Yet the prediction of the precautionary view of savings is
not validated: income uncertainty actually reduces savings of households with
either high or low wealth holdings, and does not affect savings of the rest of
the population, even after controlling for the fact that saving is endogenous
to the fertility behavior. The finding is consistent with previous studies that
found little or no effect of precautionary motive on savings. However, there
is evidence that income uncertainty has a direct effect on fertility and family
size. This chapter also examines whether havin