2 household saving behavior, portfolio choice and children evidence from the survey of consumer...

8/7/2019 2 Household saving behavior, portfolio choice and children Evidence from the survey of consumer finances

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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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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

________________________________________________________

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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.

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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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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

nthenextpage.

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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.

39


52/133

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.

40


53/133

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.

41


54/133

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.

42


55/133

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.

43


56/133

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.

44


57/133

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.

45


58/133

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.

46


59/133

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

2 household saving behavior, portfolio choice and children evidence from the survey of consumer...

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