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This is to certify that the
dissertation entitled

ESSAYS ON RETIREMENT AND THE RESIDENTIAL
CHOICE OF THE ELDERLY

presented by
PAULA MEHBOOB KAZI

has been accepted towards fulfillment
of the requirements for the

degree In _E—conomics

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5/08 K:IProj/Acc&Pres/CIRCIDateDue indd

ESSAYS ON RETIREMENT
AND THE RESIDENTIAL CHOICE OF THE ELDERLY

By

Paula Mehboob Kazi

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Economics

2008

ABSTRACT
ESSAYS ON RETIREMENT
AND THE RESIDENTIAL CHOICE OF THE ELDERLY
By

Paula Mehboob Kazi

The dissertation consists of three chapters concerning the well being of the older adult
population in the United States. All three essays use data from the Health and Retirement
Study (HRS), a longitudinal survey on health, retirement, and aging.

The first chapter studies the relationship between retirement and time transfers within
the family. We use cross—sectional variation in the need for caregiving to assess whether
caregiving affects the retirement decision. For instance, parents’ inability to take care of
certain tasks for themselves, or accumulated and new health problems of spouses could lead
to a demand for care from working individuals. We do not find evidence that potential
parental care-need accelerates retirement transitions. Also, it does not seem that people retire
early to provide care for sick spouses. However, post-retirement health insurance coverage is
important in early retirement, in that when people have access to such coverage, they retire
sooner if a family member happens to be in ill health.

The second chapter assesses the potential usefulness of subjective expectation
information in micro data by documenting the relationship between moving expectations
and subsequent moving realizations among the United States population ages sixty-five and
older. We find that the subjective probabilities of moving are very important in predicting
future moving, even once demographic information known to be associated with the
propensity to move is added to the analysis. Motivated by the observed relationship between

the reported subjective probabilities and actual moving propensities, we hypothesize that

when people are asked for a subjective probability they report the true probability
conditional on available information, plus some random noise. \We look at the proposed
model's implications regarding which population groups are better at predicting future
residential moving. However, we fail to substantiate the hypothesis, and therefore, cannot
conclusively identify individual characteristics associated with better forecasting.

The third chapter examines the long contested issue of whether the elderly draw down
their housing wealth during retirement. In examining whether housing wealth declines
during retirement, we emphasize exploring heterogeneity across population groups in
housing wealth adjustments. Our analysis demonstrates that for non-mover retirees there is
no systematic decline in housing equity. But for retiree-movers there is a decline in the
median housing equity starting at age 71, and a decline in the mean housing equity from age
76. We find evidence of significant heterogeneity in housing equity adjustments at
retirement. Nearly a quarter of the retiree-movers report that they are moving to downsize,
and they do. Those with low non-housing wealth and with low income reduce housing
equity significantly more than their respective counterparts. Retiree-movers experiencing
widowing or divorce reduce housing equity substantially more than those without similar
experience. Our findings are, in fact, largely consistent with the existing evidence in the
literature regarding downsizing in later life. This study does not disprove or bolster either
side of the debate on the role of housing wealth in financing retirement needs. But it
highlights that the choice of emphasis regarding which side of the debate holds is often

reliant on how one chooses to interpret what is in the data.

For/17mm and Bapu

iv

ACKN OW’LEDGEMENTS

I would like to express my deepest gratitude and appreciation for my advisor, Professor
Steven J. Haider, for his guidance throughout my doctoral research years. I am immensely
grateful to Professors john H. Goddeeris and jeff E. Biddle for their invaluable guidance
and advice. I especially thank Professor Goddeeris for the experience of working with him as

his research assistant. He has significantly contributed to my learning.

Among friends and peers at Michigan State, I particularly want to thank Vandana Yadav,
Lebohang Lijane, Linda Bailey, Olena Nizalova, and Lenisa Vangjel for their encouragement,
moral support, and many other gestures of kindness. Outside of Economics, I owe a special
thanks to Shahriar Hossain; without his help the first few years of my stay in East Lansing

would have been much more difficult.

I thank with the whole of my being my mother for her unyielding and unconditional love
and support in every step of my life. I dedicate this dissertation to her and to the memory of
my father — a girl could not have asked for a more loving, nurturing, and supportive father.
My brother has been a pillar of strength in my life, and I am always proud of him. The love
and kindness of many other family and friends have touched my life. I especially would like
to mention my Mejo-Mama and Marni. Finally, I have the most pleasure in thanking my
husband, Khan W. Mahmud; his constant love, encouragement, and faith in me gave me the

strength to complete this work.

TABLE OF CONTENTS

LISTOFTABLES ............................
LIST OF FIGURES ............................................................................

CHAPTER 1
TIMING OF RETIREMENT AND FAMILY OBLIGATIONS

1. Introduction ...........................................................................

2. The Data ................................................................................

3. The Descriptive Importance of Family and Caregiving on Retirement
Behavior .................................................................................

4. Empirical Framework for Analyzing the Role of Potential Caregiving on
Retirement Tinting ......................................................................
Econometric Specification ...................................................

5. Estimation Results .....................................................................
5.1 Results for Non-Coupled Individuals ...................................

5.2 Results for Married or Partnered Individuals ...........................

6. Summary and Concluding Remarks ..................................................
APPENDIX .....................................................................................
LIST OF REFERENCES .....................................................................

CHAPTER 2
PREDICTABILITY OF RESIDENTIAL MOBILITY: EVIDENCE FROM THE
HEALTH AND RETIREMENT STUDY
1. Introduction ............................................................................
2. The Data ................................................................................
3. Moving Expectation and Its Correlates ..............................................
4. The Predictive Power of the Subjective Probabilities of Moving ..................
4.1 Relationship between Expectations and Realizations .................
4.2 Economic Models of Residential Mobility .............................
4.3 Item Non-responses in the Subjective Moving Probabilities. . . . .
5. The Heterogeneity in the Accuracy of Prediction Across Population Groups...

5.1 What do People Report as Subjective Probabilities of Moving? .......
6. Summary and Concluding Remarks ..................................................
APPENDIX .....................................................................................
LIST OF REFERENCES .....................................................................
CHAPTER 3
DO THE ELDERLY SPEND DOWN THEIR HOUSING \WEALTH?
1. Introduction ............................................................................
2. The Data ................................................................................
3. Do the Elderly Reduce their Housing W'ealth as they Age? ...................................

vi

viii

NI—l

13
13
14
16
29
34

36
38
40
42
42
43
45
45
47
55
74
76

79
83
84

3.1 Changes in Housing Wealth through Residential Moving ............ 84

3.2 Changes in Housing Wealth through Equity Extraction .............. 86
4. Why is there Relatively Little Reduction in Housing Wealth? ................................ 87
4.1 Do the Retirees Reduce their Non-Housing Wealth as they Age?... 87
4.2 The Heterogeneity in Housing Equity Reduction ...................... 88
4.2.1 Changes in Housing Equity by Reported Reasons for
Moving .............................................................. 88
4.2.2 Do People with Low Wealth Reduce Housing Equity
More? ................................................................................................ 89
4.2.3 Adverse Events, Alternative Insurance Availability, and
Housing Equity Reduction ....................................... 89
5. Summary and Concluding Remarks .................................................. 92
APPENDIX ..................................................................................... 102
LIST OF REFERENCES ..................................................................... 106

vii

Table 1.1:

Table 1.2:

Table 1.3:

Table 1.4:

Table 1.5:

Table 1.6:

Table 1.7:

Table A.1.1:

Table A12:

Table 2.1:

Table 2.2:

Table 2.3:

Table 2.4:

Table 2.5:

Table 2.6.1:

Table 2.6.2:

Table 2.7:

LIST OF TABLES

Reported Reasons for Retirement ................ . ..............................
Hours of Care Provided to Parents and Grandchildren .....................
Retirement Hazards With Respect To Living and Care-Needing Parents.
Retirement Hazards With Respect To Spousal Health ......................

Effects of Potential Parental Care Needs on Retirement Transitions of
Non-Coupled Individuals ......................................................

Effects of Potential Care Needs by Family Members on Retirement
Transitions of Coupled Individuals ............................................

Effects of Health Insurance Coverage and Spousal Employment Status
in the Context of Potential Care Needs by Family Members on
Retirement Transitions of Coupled Individuals ...............................
Sample Selection Criteria .......................................................

Summary Statistics of Variables ................................................

Observable Determinants of Moving Expectations and Relationship
Between Expectations About Future Moving and Other Events ..........

Predictiveness of the Subjective Moving Probabilities ......................

Predictiveness of the Subjective Moving Probabilities By Individual
Transition Wave .................................................................

Moving Propensity By Reported Subjective Moving Probability ...........

Subjective Moving Probabilities, Moving Propensities, and Mean
Squared Forecast Errors Among Different Groups of Population. . . . .

Exploring the Mean Squared Forecast Errors in Moving Expectations
(For Women) ......................................................................

Exploring the Mean Squared Forecast Errors in Moving Expectations

(For M en) .........................................................................

Testing for Equality of Coefficients on Variables Explaining Subjective
Probabilities of Moving and Moving Outcome ..............................

viii

18

19

20

21

22

24

27

29

31

58

61

63

65

66

68

70

72

Table A.2.1:

Table A.2.2:

Table 3.1:

Table 3.2:

Table A.3.1:

Table A.3.2:

Sample Selection Criteria ....................................................... 74
Summary Statistics of Variables ................................................ 75
Movers’ Reported Reasons for Residential Moving ......................... 94
The Heterogeneity in Housing Equity Extraction ........................... 100
Sample Selection Criteria ....................................................... 102

Housing Wealth, Net Worth, and Demographic Features In 1998 and
2004 .............................................................................. 103

ix

Figure 2.1:
Figure 2.2:
Figure 3.1:
Figure 3.2:
Figure 3.3:

Figure 3.4:

Figure 3.5:

Figure 3.6:

Figure 3.7:

Figure A.3.1:
Figure A.3.2:

Figure A.3.2:

LIST OF FIGURES

Probability Distribution of Moving Expectations ......................... 57
Moving Expectations and Actual Mobility ................................. 60
Mean and Median Changes in Housing Equity ...................................... 94
Changes in Housing Equity for Retirees by Age ............................ 95
Rate of Home Equity Line of Credit Access by Age ...................... 96
Initial Housing Equity and Change in Housing Equity For Non-

Movers by Home Equity Line Of Credit (HELOC) Access .............. 96
Changes in Non-Housing \X’ealth by Age .................................. 97
Changes in Housing Equity among Retiree Movers By Reported

Reason of Moving and By Age .............................................. 98
Changes in Housing Equity among Retiree Movers By Non-Housing

Wealth Quartiles and By Age ................................................ 99
Mean Change in Housing Equity for Retirees by Age 104
Rate of Home-Ownership in 2004 for 1998-HomeownersBy Age 104

Changes in Housing Equity among Retiree Movers By Total
Household Income Quartiles and By Age .................................. 105

Chapter 1

TIMING OF RETIREMENT
AND FAMILY OBLIGATIONS

1 Introduction

Empirical studies of retirement behavior have generally focused on the influence of financial
variables such as pensions, Social Security, employer-provided health insurance, wealth and
wages. In a broader framework, however, the decision to retire involves weighing the utility
of income against the utility from leisure and other competing time-demands. In an overview
of the economic analysis of retirement, Lumsdaine and Mitchell (1999) note that the decision
to retire is becoming less a consequence of concerns about one’s own health or need for care
and plausibly more related to the provision of care to other family members. The United
States General Accounting Office estimates that by 2040 there could be as many as 12 million
disabled elderly (Walker, 2002). Based on current trends in care provision, the vast majority
of these elderly are likely to receive care through informal networks, typically from a spouse
or an adult child (Department of Health and Human Services, 1998). Given the potential
importance of informal caregiving, this chapter explores whether there is a direct effect of
caregiving on retirement behavior.

The relationship between retirement and time transfer within the family has been the
focus of a growing body of research in sociology and gerontology. For example, studies
suggest that caregiving women are more likely than non-caregiving women to quit employ-
ment (Gibeau and Anastas, 1989; Gorey et al., 1992; Ettner, 1995; Pavalko and Artis, 1997;
Dentinger and Clarkberg, 2002; Pavalko and Henderson, 2006). There is also evidence that

husbands tend to leave the labor force when their wives are ill (Hayward, Friedman, and
Chen, 1998; Szinovacz and DeViney, 2000). On the other hand, some studies suggest that
married individuals are less likely to stop working if their spouses report work limitations than
when spouses are healthy (Pienta, 1997), and that care for parents or ill spouses or other
disabled family members does not relate to retirement decisions (Johnson and Favreault,
2001; Szinovacz, DeViney and Davey, 2001; Pienta, 2003).1 In a paper studying the effects
of own and spousal health shocks on couples’ labor supply decisions, Coile (2004) looks at
the influences of new health events and injuries on people’s retirement decisions and finds no
significant relationship between spouses’ recent health shocks and respondents’ labor force
exit behavior.

In this paper, we use cross-sectional variation in the need for caregiving to assess whether
caregiving affects the retirement decision. For instance, parents’ inability to take care of
certain tasks for themselves, or accumulated and new health problems of spouses could lead
to a demand for care from working individuals.2 To preview the results, we do not find
evidence that potential parental care-need accelerates retirement transitions. Also, it does
not seem that people retire early to provide care for sick spouses. However, post-retirement
health insurance coverage is important in early retirement, in that when people have access
to such coverage, they retire sooner if a family member happens to be in ill health.

The organization of the chapter is as follows: The next section describes the data used
in this study. Section 3 provides descriptive statistics relating retirement and potential
family time demand. Section 4 lays out the empirical framework, and Section 5 presents the

estimation results. The paper closes with concluding remarks in Section 6.

2 The Data

The data for this paper is drawn from The Health and Retirement Study (HRS). The HRS

is a longitudinal biennial survey of US. population that had its first wave of interviews in

 

1Another strand of literature in sociology looks into the relationship between retirement
preferences and perceived levels of work-family conflict. See Raymo and Sweeney (2006) for
a review of this literature.

2Changes in prices of formal care also can impact the demand for caregiving time, which
we do not account for in this study.

1992. The paper exploits seven waves of the HRS from 1992 through 2004. The initial HRS
sample consisted of some 7,700 households, in which at least one person was HRS age-eligible
in that he or she was born between 1931 and 1941 (ages 51 — 61 in the 1992 wave). The age-
eligible individuals and their spouses, irrespective of birth year, were interviewed resulting
in approximately 12,500 initial respondents. The HRS collects information on respondents’
demographics, health status, physical limitations, health care use, labor force activity, and
expectations about retirement, income, and assets. In addition, it provides detailed data on
sharing, or “transfers”, of time and help, money, and dwellings across generations within
families. It also includes some basic demographic characteristics of parents and children of
the respondents.

This study makes use of data on the HRS age-eligible individuals (the HRS-cohort, as
identified by the Health and Retirement Study).3 Even though the spouses/partners are
interviewed in all coupled households, unless the spouses are themselves HRS age-eligible
—— and thus representative of the cohort — they have not been retained for analysis. The
Health and Retirement Study over-sampled blacks, Hispanics, and Floridians, and therefore,
throughout the analysis of this paper we use respondent-level sampling weights.

We define retirement using two separate survey questions: (a) current labor force status,
and (b) self-defined retirement status. The current labor force status question asks whether
individuals at present are engaged in one of a number of activities, including working, un-
employed, retired, disabled, and homemaker. We define retirement as those individuals who
report being retired.4 The second question asks individuals to report their current retire-
ment status as being fully retired, partly retired, or not retired at all. We retain a slightly
smaller sample size when we define retirement using the self-report question. This question
is not asked to individuals who report not working for pay currently, including those who

are unemployed, or those who are homemakers.5

3The HRS introduced the AHEAD cohort (born between 1890-1923) in 1993 and two
other cohorts —— the CODA (born between 1924-1930) and the War Babies (born between
1942-1947) —— in 1998.

4Individuals are allowed multiple responses on the current labor force status question; we
do not consider an individual to be retired if the person reports being retired but simulta-
neously also reports being either unemployed or working.

5The skip pattern for the self-report question is such that the question is not asked to
some individuals. Some of these skip patterns have to do with the interviewer’s perception
about the respondent’s employment status, and others include the respondent’s self/ proxy
report status, and nursing home stay status.

 

An important aspect of our sample design is that we require individuals to be at risk
of retirement at the initial wave. In other words, the respondents had to report working
or being unemployed so that they could potentially enter into retirement during the survey
period. Thus, in order to contribute an observation to the sample, an individual has to be
observed at least in two successive interviews during the seven survey waves. An example of
how the sample design works is if a person is working (or, unemployed) in 1992, in 1994, and
in 1996, and is reported to be retired in 1998, the person will contribute three observations
to the sample. We consider retirement as an absorbing event, i.e., after the first transition
into retirement, we ignore all subsequent movements in and out of retirement.

In the Appendix Table A.1.1, we discuss the sample selection criteria and the sample
sizes for the definition of retirement based on the current labor force status question. We
also report the sample sizes for the alternative definition of retirement at the endnotes of
Table 1.A.1. We drop all the same-sex couples from the sample. When we define retirement
using the current labor force status question, there are certain individuals who conditional
on not having retired yet, report being disabled by the next survey wave. We drop these
observations when we use this definition of retirement.

Before the sample is restricted to non-missing responses on parental and spousal care-
need variables, we retain 25020 observations when we define retirement using the labor force
status question. The two-year retirement hazard rate for this sample is 20.02%. For men,
the hazard rate is 19.13%, and for women the hazard rate is 20.80%. In the sample based on
the self-defined retirement status question, the two-year retirement hazard rates are slightly
higher —— 20.76% and 23.04% for men and women, respectively. The samples used in the
regression analysis are conditional on non-missing information on parental and spousal care-
need along with demographic and other characteristics of the respondents. These sample
sizes and the two-year retirement hazard rates for coupled and non-coupled men and women
are reported in Appendix Table A.1.2 for both definitions of retirement. Table A.1.2 also
gives the summary statistics for the other variables that we use in the regression analysis.

We describe these variables in detail in Section 4.

3 The Descriptive Importance of Family and Caregiv-
ing on Retirement Behavior

This section presents some descriptive statistics that capture the potential importance of
family in people’s retirement behavior.6

Table 1.1 tabulates the relative importance of reasons that retirees report mattered in
their retirement decision. The relevant question in the HRS offers four reasons to choose
from: poor health, wanting to do other things, disliking the work, and wanting to spend
more time with family. Respondents can pick more than one category as the reason for
retirement. Each category is considered as the reason for retirement if the respondent deems
it very, moderately, or somewhat important in retirement as opposed to not important at
all. An additional category of whether the retirees felt that they were forced into retirement
is also reported in Table 1.1.7 We compute the relative importance of different factors in
retirement based on the information from 2,418 retiree observations after they make their
first transition into retirement during the sample period. Except for the age group of 52—58,
family concerns appear to be the predominant factor associated with people’s retirement.
For the youngest age group, family is only second in importance to health concerns. Thus,
a substantial segment of recent retirees report spending time with family as an important
reason for retirement.

Table 1.2 shows the amount of caregiving by the recipient and by characteristics of the

caregiver.8 Care hours to parents include time spent helping parents (or parents-in-law or

 

6We display the results based on the definition of retirement that uses the current labor
force status question. The main conclusions remain the same if we use the alternative
retirement definition.

7It draws on the question that reads: “ Thinking back to the time you (partly/completely)
retired, was that something you wanted to do or something you felt you were forced into .9”.

8We use the data on hours of care provision from 1998—2004 HRS surveys. The ear-
lier surveys (1992, 1994, and 1996) have a slightly different set of questions on care hours
to parents. For consistency, we focus on the last four survey waves in our data. Ptom
1998—2004, of the core sample in the paper, we have a total of 5,544 observations where
at least a parent or a parent-in-law is living. 4,711 of these observations are for married or
partnered individuals. In 1998 and 2000, questions on care hours to parents are asked only
to the family respondent; so for these two survey years we have data on care hours for one
respondent per household. In 2002 and 2004 we have responses on care hours to parents
from all respondents. In total — of the 5,446 at risk observations — we have non-missing
responses on care hours to parents from 3,848 observations. For the 1998 and 2000 surveys,
majority of these observations are for women, since women overwhelmingly tend to be the
family respondent in the HRS. For care hours to grandchildren we have responses from the
family respondent (one response per household). From 1998—2004, with one response per
household, we have information on whether the respondent has any grandchildren for 8,215

both for couples) with basic personal activities like dressing, eating, and bathing as well as
with other things such as household chores, errands, and transportation. Caregiving hours
are reported for a period of approximately two years. The upper panel of Table 1.2 reports
the mean care hours for all non-missing responses on care hours (including zero care hours);
the lower panel reports the mean hours for those providing positive care hours. Apparently,
women spend more care time than men both with parents and grandchildren. With respect
to different age groups, there does not appear to be any distinct pattern in the average
intensity of caregiving either for men or women.

We look into the bivariate relationship between retirement hazards and parental care-
need in Table 1.3. We consider parents in need of care when they are reported to be needing
assistance with daily activities of bathing, dressing, and eating, or when they cannot be
left on their own even for an hour. For married and partnered couples, care-need by both
parents and parents-in-law is taken into account.9 We calculate the retirement hazard rates
separately for men and women. Column (1) shows the retirement hazards for everyone
in the sample with or without a living parent (or a parent-in-law). Columns (2) and (3)
Show that conditional on not having retired yet, men and women without any living parent
are noticeably more likely to retire by the next period than those with at least one living
parent. Part of the difference in the retirement hazards is likely due to the age difference
— a difference, on average, of more than two years. Looking into the retirement hazards
of people with and without care-needing parents (Columns (4) and (5)), we find that those
with healthier parents have a smaller retirement hazard rate than those with care-needing
parents. Not surprisingly, the care—needing parents are quite older than the non—care—needing
parents of adult children.10

Finally, Table 1.4 presents the retirement hazards with respect to spousal health status.
We consider a respondent at risk of providing care to the spouse if the spouse is experiencing

any kind of adverse health condition. We take into account several dimensions of spousal

 

observations. Of that we have 6,927 observations with at least one grandchild. Data on care
hours to grandchildren is available for all 6,927 observations.

9Table 1.3 retains an observation for couples whenever non-missing care—need information
is available either for a parent or a parent-in-law. In Table 1.A.1 (Sample Selection Criteria),
this corresponds to the sample sizes in the row “If Parent or Parent-in-Law Care-need
Information Non-missing”.

10The parental age for each group reported in the table is the average of the ages of the
older (oldest) or the only living parent of the respondents in that group.

sickness. The first column in Table 1.4 gives the retirement hazards for the entire samples
of married/partnered men and women. Columns (2) and (3) use spouses’ self-reported sub-
jective health status; Columns (4) and (5) use the information on doctor-diagnosed severe
or chronic health conditions; Columns (6) and (7) use spouses’ recent hospital and nursing
home stay; and finally, Columns (8) and (9) draw on the number of limitations in activities
of daily living (ADL) that the spouse has. Irrespective of the spousal health variable con-
sidered, the retirement hazard rate is larger for those with spouses in relatively worse health
than for their counterparts with healthy spouses. Both the respondents and the spouses are
older when spouses have any kind of health condition compared to when spouses do not
report any health problems. In the multivariate analysis, we control for the respondents’
and the spouses’ ages along with other variables to find out if similar patterns of retirement

responses are still observed with respect to spousal health.

4 Empirical Framework for Analyzing the Role of P0-
tential Caregiving on Retirement Timing

The paper does not delve into developing a formal utility maximization framework, and
we cannot a priori predict the direction of causality from family time demand to retirement
patterns. Nonetheless, we make a presumption, shaped by the descriptive statistics presented
earlier, that labor force participants who assume an informal caregiving role may derive dif-
ferent utility from continued work than non-caregivers. Consequently, the former group may
retire from the labor force at a higher rate than the latter group. A health shock may alter
the value of the time shared between a couple or between a parent and an adult child. This
may be so because the affected spouse or parent may need more assistance with activities of
daily living, or because the sick family member has a shortened lifespan. At times, however,
financial considerations — and thus, the need for continuing employment — can become
predominant over the need for caregiving in determining people’s retirement behavior. This
may be particularly important in the context of financing out-of—pocket health care costs for
family members. Sources of spousal health insurance coverage as well as the potential for

accessing care from alternate caregivers may also influence individual retirement decision.

All in all, the response of retirement timing to adverse health events or care-need in the

family is theoretically ambiguous and may differ across families.

Econometric Specification

The empirical strategy of this paper is to specify and estimate a reduced form retirement
model to examine the potential role of caregiving. We consider a discrete time hazard model,
where the binary variable Bit equals 1 if a person not having retired in the previous time
period t—l retires by the current time period t. Also, let the binary variable Cit equal 1 if at
time t an individual is at risk of caregiving to a parent or the spouse/partner. Consider the

probit model,
PTlRit=1l= N30 + (31th + 3222',“ + 53021: + 541%) (1)

where f is the normal density. X denotes a set of demographic characteristics pertaining
to the respondent, and Z represents variables that capture information related to the job
held in the previous period. Since the retirees no longer have job attachments at the current
period, information like earnings from employment are used from the last period’s jobs“. C
captures the potential care-need variables, and F represents additional information about
family members and family composition.

We estimate the retirement model separately for the non-coupled group and the coupled
group. Retirement responses of these two groups have been found to be somewhat different”,
and since married or partnered individuals have an additional set of family members who can
potentially create time demands, it seems a reasonable approach. When there is only one
respondent from a household, and the respondent identifies him/herself as never married,
widowed, or separated / divorced, we include that person in the non-coupled sample. Married
or partnered respondents contribute to the coupled sample. For the non-coupled individuals
(singles), the only source of family obligation we consider is parents; whereas, for the coupled

individuals (couples), potential care-need may arise from parents, parents-in-law”, as well

 

11For individuals who are at risk of retirement but are unemployed, earnings can be zero
if no job is held during the survey year.

12For instance, Lumsdaine, Stock, and Wise (1996) find that married men are significantly
more likely to retire at age 65 than single men.

13The parents of cohabiting partners are considered as parents-in-law, and their siblings as

8

as the spouse/ partner. We estimate the probit model separately for men and women.

Recall that parental care-need is a dummy variable equal to 1 if the respondent reports
that at least one parent requires assistance with basic daily activities, such as, dressing,
bathing, and eating, or that the parent cannot be left alone even for an hour. If the respon-
dent answers either of these two parental care-need related questions in the HRS for at least
one parent, we have a non-missing observation for the parental care-need variable. A similar
care-need variable for parents-in-law is defined for married and partnered individuals.

We consider two different specifications, with the difference stemming solely from the
set of parental care-need variables that we use. In one specification, the parental variables
included are — (1) all living parents healthy, (2) Mom and Dad married and both of them in
need of care, 0R Mom/Dad unmarried and in need of care, (3) Mom and Dad married and
one of them in need of care, and (4) Mom/Dad married to stepparent and in need of care
—— with no living parent as the omitted category. The category all living parents healthy is
equal to 1 if none of the living parents needs care. The rest of the categories account for
parental marital status. After all, the spouse of a care-needing parent may be capable of
taking care of the ailing parent. Thus, the adult child’s retirement response may be impacted
by whether the care-needing parent has a potentially caregiving spouse or not. In the HRS,
questions are not asked regarding the health of the stepparent. Therefore, M om/Dad married
to stepparent and in need of care is considered as a separate category from the categories
reflecting the health status of biological parents. Mom and Dad married and both of them in
need of care and Mom/Dad unmarried and in need of care are combined into one variable,
as in both cases there is no healthy care-providing partner for the ailing parent.

In the other specification, the parental variables included are — (1) Mom or Dad or both
living, and (2) Mom or Dad or both in need of care. The second variable does not distinguish
potential parental care-need by parental marital status. We have a more parsimonious
estimation with this set of parental variables. Since only small fractions of the sample have
parents meeting the criteria Mom and Dad married and one of them in need of care and
Mom/Dad married to stepparent and in need of care (see Table 1.A.2 for summary statistics

of the parental care-need variables), this seems a useful alternative specification. When we

 

siblings-in-law.

estimate the retirement model for couples, the two specifications include dummy variables for
parents-in-law corresponding to all the variables for parents. We prefer including separate
variables for own and spousal parents, as people might respond differently to the health
needs of parents and parents-in-law.l4

As proxies for potential care-need arising from ailing spouses, we consider several vari-
ables: (1) whether the spouse has ever had any chronic health condition“, (2) whether the
spouse has ever had any severe health condition”, (3) whether the spouse has any difl‘icul—
ties with activities of daily living (ADL)”, and (4) whether the spouse has developed any
new chronic or severe health condition in the past two years.18 The first three variables are
expected to capture the overall level of spousal health. The fourth one is included in the
specifications to account for any additional effects of new or worsening health events on re-
tirement hazards. When a spouse gets sick, the unaffected spouse may not choose to retire
in the immediate next period. But it is plausible that when the sick spouse does not recover,
his/ her sickness still affects the healthy spouse’s decision to retire in the following periods,
although there may have been no further deterioration in the sick spouse’s health condition
in the recent past. As such, in addition to recent occurrences of health shocks, we account
for the level of spousal health status.

Ideally, we would have wanted to include in the specifications one set of dummy vari-
ables reflecting simultaneously potential care-need from any and all family members, instead
of including separate sets of variables for parents, parents-in-law, and the spouse/partner.
However, for certain combinations of health status for different family members we either
have no observations, or have too few observations with no variation in the outcome variable
of interest.

Siblings could represent an alternate source of informal care for parents. With this in

mind, the estimations include a variable indicating whether the individual has any siblings.

 

14We lose some observations when we use the longer list of parental variables, due to some
non-responses on parental care-need when both parents are married to each other (see Table
1.A.1 endnotes 6 and 8).

15Chronic health conditions include high blood pressure, diabetes, lung disease, arthritis,
and psychological problems.

16Severe health conditions include cancer, heart problem (heart attack or heart surgery),
and stroke.

17In addition to the doctor-diagnosed medical conditions, we include the variable for any
limitation in ADL, because activity limitations may aggravate the health effects of retirement.

18Summary statistics for these variables are given in Table 1.A.2.

10

We also include an interaction tern between this sibling indicator variable and Mom or Dad
or both in need of care. The variable Mom or Dad or both in need of care is captured by four
separate variables when we use the longer list of parental variables, but in that specification
it is econometrically feasible to use this one variable in the interaction term. The sum of
the estimated coefficients on Any sibling and Any sibling*Mom or Dad or both in need of
care tells us whether someone with a sibling and a care-needing parent has a different re-
tirement response than someone without a sibling but with a parent in need of care. In the
estimation of the coupled sample, a similar set of variables — Spouse has any sibling and
Spouse has any sibling*Mom-in-law or Dad-in-law or both in need of care — are included
to account for the role of siblings-in-law in providing care for parents-in—law. We include
another interaction variable representing the importance of siblings-in—law in providing care
for the respondents’ spouses — Spouse has any sibling*Sp0use has new chronic or severe
health condition. Conditional on overall health status and ADL limitations, spousal new
health events may influence retirement response. As such, we use Spouse has new chronic or
severe health condition in the interaction variables involving spousal health.19 The estima-
tions on couples include two additional interaction terms: Spouse has new chronic or severe
health condition*M0m or Dad or both in need of care and Spouse has new chronic or severe
health condition*Mom-in-law or Dad-in-law or both in need of care. These interactions, in
conjunction with their level variables, capture the retirement responses of individuals with
different number and combination of care-needing family members.

We include four health variables for the respondents, similar to the ones described for
the spouses, in all regressions. In couples’ regressions, we also add an indicator variable for
Spouse not working at present. Hurd (1990) and Blau (1998) find that about one-third of
couples in which both spouses are in the labor force at age 50 retire within one year of each
other. Thus, if a respondent’s spouse is not working and also happens to be in need of care,
the individual’s retirement response might be determined by the spousal labor force status
and not spousal illness. The interaction variable between Spouse not working at present and
Spouse has new chronic or severe health condition along with the spousal sickness variable

then tells us whether retirement response differs with respect to potential spousal care-need

 

19It should be emphasized that the estimations have been rerun with interaction terms
using the alternative spousal health variables, and the results do not differ qualitatively.

11

when the spouse is not working.

The variable Retiree health insurance, Medicare or other health coverage is a dummy vari-
able equal to 1 if the individual is eligible for retiree health insurance20 from the job held last
period, or is covered by a long-term care insurance, or is enrolled in any type of federal gov-
ernment health insurance program, e.g., Medicare, Medicaid, CHAMPUS/VA/TRICARE,
or any other government health insurance.21 We include in the regressions an interaction
term between Retiree health insurance, Medicare or other health coverage (RHI-M, for short)
and Mom or Dad or both in need of care to capture if access to post-retirement health
coverage makes people respond differently to parental care-need. Similarly, we include the
interaction term Retiree health insurance, Medicare or other health coverage*Spouse has new
chronic or severe health condition in couples’ regressions to account for possible differential
retirement responses to spousal care-need with respect to eligibility for RHI-M.

Often people have health insurance coverage through the employers of their spouses.
When the respondents’ employer-sponsored health insurances cover the spouses, it might
slow retirement transition for the individuals. This seems more likely if the spouse’s health
condition involves potentially large health care costs. The slowing effect on retirement transi-
tion could be offset if the spouse is Medicare eligible, or has retiree health insurance, or some
other federal health coverage. Even with spousal access to Medicare or other insurances, it
might happen that the respondents’ employer-provided health plans are simply better, and
therefore, respondents might still defer retirement. To understand these effects of health
insurances, we add in the regressions a dummy variable Spouse has retiree health insurance,
Medicare or other health coverage (SRHI-M), and a dummy variable Spouse is covered by
respondent’s employer-provided health insurance, as well as an interaction term of these two
variables.

The other control variables in the specifications are dummy variables for respondent age,

dummy variables for education, the total yearly earnings from the job held, the total house-

 

20Employer-provided retiree health insurance permits individuals to remain in the health
insurance plan of their career employer after retirement at a lower cost than they would
face purchasing similar coverage in the market. The role of retiree health insurance can be
particularly important when retirement occurs before the Medicare eligibility age of 65.

21All the regressions — both for the samples of singles and couples — have also been
estimated with an alternate post-retirement health insurance variable that includes, instead
of all government health programs, only Medicare. There is no important difference in the
results using either of the two variables.

12

hold income, and the total non-housing assets22 (all in year-2000 dollars and re-scaled)”.
As crude proxies for pension wealth, we include three indicator variables for defined bene—
fit pension, defined contribution pension, and a combination of defined benefit and defined
contribution pension (with no pension enrollment as the omitted category). Dummy vari-
ables for the HRS survey years are also included. Estimations using the sample of couples
include a quadratic in spousal age. In the estimations with the non-coupled sample, since
the individuals are never married, widowed, or separated/ divorced, two dummy variables ——
widowed and separated/divorced — are included in the regressions. Similarly, two dummy
variables — married with spouse absent and partnered — enter the regressions using the

couples’ sample.

5 Estimation Results

Table 1.5 presents the estimation results for the non-coupled individuals, and Tables 1.6
and 1.7 present the results for the couples. The results in these tables are from the probit

estimations using the retirement variable based on the current labor force status question.24

5.1 Results for Non-Coupled Individuals

From Table 1.5 there does not appear to be any statistically significant positive association
between potential parental care-need and the retirement transition of non-coupled men and
women. If anything, we find evidence of statistically significant delaying effect on retirement
for certain combinations of parental care-need and marital status (Columns 5 and 7 for men

and Columns 6 and 8 for women).25 With respect to the potential role of siblings as alternate

 

22Non-housing assets defined as the sum of the net value of real estate (not primary resi-
dence), the net value of vehicles and businesses, IRA, Keogh accounts, stocks, mutual funds,
and investment trusts, the value of checking, savings, or money market accounts, CD, gov-
ernment bonds, and T-bills, the net value of bonds and bond funds, and the net value of all
other savings, less the value of other debts. The values of primary residence, mortgages, and
other home loans are not included.

23Data on earnings, household income, and non-housing assets are taken from the RAND
HRS files (See St. Clair et al., 2006).

24We also estimate the retirement model using the alternative retirement variable based
on the self-defined retirement status question. The results are basically identical.

25For women, the negative effect of Mom or Dad or both in need of care in Column (4) and
that of Mom and Dad married and both of them in need of care, OR Mom/Dad unmarried
and in need of care in Column (8) of Table 1.5 are statistically significant when we estimate
the model using the self-defined retirement hazard variable.

13

caregivers, we find that single men with sick parents are likely to delay retirement when they
have siblings than when they do not have siblings.”

Access to post-retirement health insurance makes both single men and women retire
sooner. Moreover, the sum of the estimated coefficients on the health insurance variable
and the interaction term of this variable with the parental care-need variable is always
statistically significant and positive for men and women. This suggests that when parents
are sick, having access to post-retirement health insurance facilitates earlier retirement.

In results not displayed in Table 1.5, not surprisingly, retirement behavior is influenced
by respondent age as well as health status. Transition into retirement is delayed for single
men with larger household income. Single women retire earlier if net non-housing assets are
larger. Men tend to retire sooner if they have a combination of defined benefit and defined
contribution pension plans compared to when they do not have any pension enrollment.

Defined benefit pension plans delay single women’s retirement.”

5.2 Results for Married or Partnered Individuals

For couples’ estimations, Table 1.6 displays the coefficient estimates for the potential familial
care-need variables and the sibling related variables. Table 1.7 presents the estimates for the
health insurance and spousal employment variables.

Estimation of the retirement model for couples considers potential family obligations
from parents, parents-in-law, and the spouse/ partner. There appears to be no statistically
significant evidence that married men and women retire sooner when a parent or a parent-
in-law potentially needs care.28 Neither does it appear that married men and women retire

sooner if their spouses are in need of care. On the contrary, individuals seem to delay

 

26Estimation of the model with the alternative retirement variable shows that when parents
need care, women with siblings are likely to retire sooner than women without any siblings.

27We also estimate the retirement model defining the at risk sample to consist only of
those working in the previous period, i.e., excluding those who were unemployed. In those
specifications we additionally include an indicator for whether the individual was reported
to be self-employed in the job held last period. We do this since it is probable that self-
employment allows individuals more flexibility with their time allocation to competing needs.
We do not find any evidence that self-employed non-coupled individuals retire any differently
than non—self-employed individuals when faced with potential caregiving obligations.

28In fact, there is some evidence that for certain parental care-need and marital status,
women might delay retirement; these delaying effects are significant for more parental vari-
ables if we use the self—defined retirement variable than the retirement variable based on the
current labor force status question.

14

retirement for particular spousal health events. For instance, wives whose husbands have
activity limitations, and husbands whose wives have had experienced a severe or a chronic
health condition, are likely to postpone retirement. For women, the result is consistent with
Pozzebon and Mitchell’s (1989) finding that working women with a spouse in poor health
tend to delay retirement. There is no significant effect of spouse’s recent health shocks on
individual’s retirement timing, which is in line with Coile’s (2004) results.

Like single men and women, married men and women retire earlier if they have Retiree
health insurance, Medicare or other health coverage (RHI-M). Taking into account the esti—
mated coefficients on the interaction variables with RHI-M, we find that married men and
women with sick parents who have RHI—M are significantly more likely to retire early than
those who do not have RHI—M. Similarly, people with sick spouses who have RHI-M are
significantly likely to retire sooner than those who do not have RHI—M. Married men ap-
pear more likely to retire early when their spouses are Medicare or retiree health insurance
(SRHI-M) eligible. Also, there is some evidence that married men may be delaying retire-
ment if their wives are covered by the men’s employer-provided health plans. Women delay
retirement if the husbands are covered by women’s employer-provided health insurance, even
when the husbands have their own retiree health insurance coverage. Conversely, if wives
are eligible for retiree health insurance coverage (SRHI—M), men retire early even if spouses
are covered by the men’s employer-provided health insurances.

We find men and women retiring sooner if the spouse is not working. Thus, there is evi-
dence for complementarity of leisure for married couples.29 But the sum of the coefficients on
the interaction term Spouse not working at present*Spouse has new chronic or severe health
condition and the spousal sickness variable does not reveal that spousal complementarity of
leisure is stronger when a spouse is sick. In other words, individuals do not retire sooner
when non-working spouses are ill compared to when they are healthy. Siblings or siblings-
in-law do not affect retirement behavior in general. When husbands potentially need care,
married women with siblings~in-law retire sooner than women without any sibling-in-law ——
a result that contradicts the role of siblings as alternate caregivers.

Among a few of the other findings, the higher the level of total household income, the

 

29Coile (2004) also reports evidence for a significant complementarity of leisure effect for
men.

15

more likely married men and women are to delay retirement. Higher non-housing assets
make men retire sooner. Individuals are likely to delay retirement if they have defined

benefit pension enrollment. Own medical conditions precipitate retirement.30

6 Summary and Concluding Remarks

In this chapter we look at the influence of people’s potential caregiving roles on their retire-
ment timing. The general conclusion from this study is that familial caregiving obligation
is not a strong determinant of people’s early retirement behavior. While we do not find
any major evidence that parental care-need is associated with individuals’ retirement tim-
ing, it appears that potential spousal care-need has a delaying effect on people’s retirement.
The finding that retiree health insurance has a substantial effect on increasing the early
retirement probability corresponds to the previous literature on the effects of retiree health
insurance on retirement timing.31 What we additionally find is that individuals who have
parents or spouses in need of care are more likely to retire early if they have access to retiree
health insurances. Therefore, potentially losing employer-sponsored current or retiree health
insurance coverage in the instance of early retirement, particularly for those who are not yet
Medicare age-eligible, possibly plays a significant role in deterring individuals from retiring
sooner when faced with caregiving obligations.

We should note a few caveats of this study. It is possible that the variables needed for
defining potential care-need by family members could be improved in future data collec-
tion, thus enabling a closer examination of this issue. Particularly, it would be interesting
to have better indication of the degree of severity of various health conditions to obtain

further conclusive evidence of the possible causality between caregiving and retirement tim-

 

30As in the non-coupled sample, we estimate the retirement model for couples defining
the at risk sample to consist only of those working in the previous period, i.e., excluding
those who were unemployed. The results are not affected in any notable way. In those
specifications we additionally include an indicator for whether the individual was reported
to be self-employed in the job held last period. Self-employed coupled individuals do not
appear to retire any differently than non-self-employed individuals when faced with care-need
by family members.

31For example, Karoly and Rogowski (1994) note that the availability of retiree health
benefits increases the baseline probability of retiring by nearly 50 percent, while Gruber
and Madrian (1995) find that a single year of continuation of retiree coverage increases the
retirement hazards among persons aged 55—64 by about 20 percent.

16

ing. Moreover, it would be useful to have information on potential care-need arising from
children.

Finally, it should be emphasized that the literature on the determinants of retirement
has emphasized the importance of future retirement income accumulation that comes from
Social Security benefit formulas and pension structures. A central behavioral assumption
in an economic model of retirement is that individuals decide whether to retire by assessing
the financial benefit from delaying retirement against the loss of utility from forgone leisure.
In that regard a major deficiency of our analysis is that we do not explicitly account for
the financial incentives to retirement in the framework of a forward-looking economic model
of retirement. Consequently, our findings in this paper should be considered as suggestive

effects of potential familial obligations on the timing of retirement.

17

Table 1.1: Reported Reasons for Retirement

 

Spend
Forced Poor Time Want to Did Not
Health with Do Other Like
Family Thirhgs ' Work

 

 

 

Age Percent in Each Age Group Citing Reason as Number Of
Category Important in Retirement Observations
52-58 55.08 61.19 55.33 27.03 21.30 219
59-61 48.96 45.36 57.91 38.46 19.87 413
62-64 38.29 41.73 63.40 50.07 21.87 899
65-67 35.32 39.47 63.23 60.05 21.33 610
68-73 34.71 38.64 59.14 57.92 18.47 277

1. We observe 5009 retirement transitions in the sample out of 25020 observations (the sample

before conditioning on non-missing parental and spousal careneed variables). Of that, we have
responses on reasons for retirement from 2418 retirees after they make their first transition into
retirement.

In the HRS, each retiree is asked whether they felt that they were forced into retirement. In
another set of questions respondents are asked whether any of the four criteria — poor health,
spending time with family, wanting to do other things, not liking work — has been a reason for
retirement. Each criterion is considered as a reason for retirement if it is deemed very,
moderately or somewhat important in retirement by the respondent. Each retiree can report
multiple reasons as important in retirement.

Data Source: 1992, 1994, 1996, 1998, 2000, 2002 and 2004 waves of the Health and Retirement
Study.

W'eighted tabulations.

l8

Table 2: Hours of Care Provided to Parents and Grandchildren

 

 

 

(In Past Two Years)
Care Hours to Parents Care Hours to
Grandchildren
Sample Conditional on At Risk of Providing Care and Non-Missing Reports of Care Hours
All 185.63 (10.56) 303.18 (14.42)
[n=3848] [n=6927]
Male 86.94 (8.95) 94.00 (21.59)
[n=1412] [n=968]
Female 242.84 (15.74) 337.16 (16.34)
[n=2436] [n=5959]
Age 52-58 214.01 (31.38) 475.87 (63.55)
[n=494] [n=619]
Age 59-61 180.08 (17.83) 284.09 (30.05)
[n=943] [n=1431]
Age 62-64 175.64 (16.54) 307.06 (26.18)
[n=1271] [n=2032]
Age 65-67 175.85 (19.47) 298.36 (27.78)
[n=792] [n=1795]
Age 68-73 219.18 (60.90) 228.15 (33.54)
[n=348] [n=1050]

 

Sample Conditional on At Risk of Providing Care and Reporting Positive Care Hours

 

All 600.27 (30.94) 1023.46 (44.83)
[n=1190] [n=2052]
Male 449.66 (39.34) 575.88 (125.78)
[n=273] [n=158]
Female 645.11 (38.29) 1060.80 (47.33)
[n=917] [n=1894]
Age 52-58 597.28 (79.97) 1146.16 (143.10)
[n=177] [n=257]
Age 59-61 520.91 (46.05) 998.84 (97.07)
[n=326] [n=407]
Age 62-64 584.39 (49.10) 998.30 (78.38)
[n=382] [n=625]
Age 65-67 624.55 (59.47) 1046.01 (89.27)
(112223] [n=512]
Age 68-73 930.18 (243.45) 954.43 (130.27)
[n=82] [n=251]

 

1. Standard error of the mean reported in parentheses.

2. Sample size reported in brackets.

3. Care hours to parents include hours provided to either parents or parents-in-law for

married/ partnered couples.

4. Care hours to parents include hours spent in helping parents with household chores, errands and

basic personal activities.
Data Source: 1998, 2000, 2002 and 2004 waves of the Health and Retirement Study.

5"

6. See page 5 footnote 8 for further discussion on the sample sizes used in this table.

19

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[4] Dentinger, Emma and Martin E. Clarkberg. 2002. “Informal Caregiving and Retire-
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[5] Ettner, Susan L. 1995. “The Impact of ‘Parent Care’ on Female Labor Supply Deci-
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[6] Gibeau, Janice R. N. and Jean W. Anastas. 1989. “Breadwinners and Caregivers: In-
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[8] Gruber, Jonathan and Brigitte C. Madrian. 1995. “Health-Insurance Availability and
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[9] Hayward, Mark D, Samantha Friedman, and Hsinmu Chen. 1998. “Career Trajectories
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[10] Hurd, Michael D. 1990. “The Joint Retirement Decision of Husbands and Wives.” In
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[11] Johnson, Richard W. and Melissa Favreault. 2001. “Retiring Together or Working Alone:
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[12] Karoly, Lynn A. and Jeannette A. Rogowski. 1994. “The Effects of Access to Post—
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[13] Lumsdaine, Robin L. and Olivia S. Mitchell. 1999. “New Developments in the Economic
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[14] Lumsdaine, Robin L., James H. Stock, and David A. Wise. 1990. “Efficient Windows
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34

[15] Pavalko, Eliza K. and Julie E. Artis. 1997. “Women’s Caregiving and Paid Work: Causal
Relationships in Late Midlife.” Journals of Gerontology; Vol. 52B: pp. 8170-8179.

[16] Pavalko, Eliza K. and Kathryn A. Henderson. 2006. “Combining Care Work and Paid
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359-374.

[17] Pienta, Amy M. 1997. “Older Couples: An Examination of Health and Retirement
within the Context of the Family.” Penn State University Population Program Working
Paper No. 97-03. University Park, PA: Pennsylvania State University.

[18] Pienta, Amy. M. 2003. “Partners in Marriage: An Analysis of Husbands’ and Wives’
Retirement Behavior.” Journal of Applied Gerontology; Vol. 22: pp. 340-358.

[19] Pozzebon, Silvana and Olivia S. Mitchell. 1989. “Married Women’s Retirement Behav-
ior.” Joutnal of Population Economics; Vol. 2(1): pp. 39-53.

[20] Raymo, James M. and Megan M. Sweeney. 2006. “Work-Family Conflict and Retirement
Preferences.” Journal of Gerontology; Vol. 61B, No. 3: pp. 8161-8169.

[21] St.Clair, Patricia, Darlene Blake, Delia Bugliari, Sandy Chien, Orla Hayden, Michael
Hurd, Serhii Ilchuk, Fuan-Yue Kung, Angela Miu, Constantijn Panis, Philip Pantoja,
Afshin Rastegar, Susann Rohwedder, Elizabeth Roth, Joanna Wedell, and Julie Zissi-
mopoulos. 2006. RAND HRS Data Documentation: Version F. Labor and Population
Program: RAND Center for the Study of Aging.

[22] Szinovacz, Maximiliane E. and Stanley DeViney. 2000. “Marital Characteristics and
Retirement Decisions.” Research on Aging: Vol. 22: pp. 470-498.

[23] Szinovacz, Maximiliane E., Stanley DeViney, and Adam Davey. 2001. “Influences of
Family Obligations and Relationships on Retirement: Variations by Gender, Race, and
Marital Status.” Journal of Gerontology: Social Sciences: Vol. 56B: pp. 820-827.

[24] Walker, David. 2002. “Long-Term Care: Aging Baby Boom Generation will Increase
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Office; Testimony before the Special Committee on Aging. United States Senate. March
21,2002.

35

Chapter 2

PREDICTABILITY OF RESIDENTIAL MOBILITY:
EVIDENCE FROM THE HEALTH AND RETIREMENT STUDY

1 Introduction

Expectations of future events play a prominent role in economic models of decision-making
under uncertainty. Hurd and McGarry (1995) note that subjective expectations may deter-
mine behavior, even if incorrect. This paper adds to the growing literature that assesses the
potential usefulness of subjective expectation information in micro data. The key research
question in this paper is: What do subjective expectations about moving tell us? Drawing on
the longitudinal data from The Health and Retirement Study, we document the relationship
between moving expectations and subsequent moving realizations among the United States
population ages sixty-five and older.

There has been an upsurge of interest in the policy debate with respect to the well being
of the elderly population in the recent decades. Residential moving represents an important
economic outcome variable that can involve a change of living arrangement as an independent
household, with adult children or other unrelated persons, or in an institution. Engelhardt
and Gruber (2006) note that changes in living arrangements are likely to be associated with
changes in the level of care and assistance received by the elderly. Living arrangements
additionally affect the elderly’s eligibility and transfer level for certain types of government
assistance, such as, food stamps and supplemental Social Security (since these are determined
by the income of the household, not of the individual). As such, it is important to know
if the elderly are making mistakes in their predictions for future moving, which could have

adverse consequences.

36

Some household surveys ask about subjective probabilities, and studies analyzing the
validity of responses to these questions have found encouraging results.1 The Health and
Retirement Study (HRS) asks respondents a number of expectational questions on matters
such as survival to a target age, working beyond the normal retirement age, residential
mobility, nursing home entry, job stability, receiving inheritances, and making bequests.
Hurd and McGarry (1995, 2002) study survival expectations and conclude that the subjective
survival probabilities are not simply an alternative measure to health status and that they
predict mortality. Loughran, Panis, Hurd, and Reti (2001) and Haider and Stephens (2007)
find that retirement expectations are strong predictors of retirement. Maestas (2007) studies
the expectations of work during retirement and shows that unretirement is anticipated for
the vast majority of those returning to work. Finally, Rohwedder and Kleinjans (2006) find
that at the population level expectations about Social Security earnings are very consistent
with realizations. Besides studies that use the HRS expectational variables, there have been
a number of important studies on individual expectations about different events that exploit
data sources other than the HRS, both from the United States and elsewhere.2 The results
of these papers lend confidence that respondents on average understand the probability
questions, and by and large the papers conclude that the expectations are fairly accurate
predictors of the future event / outcome that they are supposed to characterize.

As in studies on other expectations, we also find that the subjective probabilities of
moving are very important in predicting future moving, even once demographic information
known to be associated with the propensity to move is added to the analysis. Although this
relationship is positive and monotone, the probabilities of moving rise much less than one-to-
one with subjective probabilities. Still, information on expectations improves the accuracy

of models of moving behavior most likely because it includes information about unobserved

 

1See Dominitz and Manski (1997) for a discussion of a history of subjective probability
questions in survey data.

2Most notably, Bernheim extensively analyzes the responses from the Retirement History
Survey (RHS) on age of expected retirement (1990) and expected Social Security benefits
(1988; 1989). Dominitz and Manski (1997) analyze data on expected future income from the
Survey of Economic Expectations (SEE), and Manski and Straub (2000) use workers’ subjec-
tive expectations about job security from the same source. Das and Van Soest (11999, 2001)
explore income expectations using data from the Dutch Socio-Economic Panel; appelli and
Pistaferri (2000) look at expectations of nominal income growth using the Bank of Italy
Survey of Household Income and Wealth; and Souleles (2002) examines expectations about
respondents’ future financial position from the Michigan Consumer Sentiment Surveys.

37

tastes and individual circumstances.

We find that moving is subjectively a low probability event, even for most movers. For
a sizable fraction of movers — about 43% —— moving apparently represents an entirely
unanticipated shock / event, because they report a subjective probability of zero. Individual
responses of subjective probabilities contain considerable noise in the form of inordinate
number of focal responses of “0”, “0.5”, and “1”. There is some indication that the response
of “don’t know” is similar to a response of “0.5” in the subjective probabilities.

Motivated by the observed relationship between the reported subjective probabilities and
actual moving propensities, we put forward the hypothesis that when people are asked for a
subjective probability they report the true probability conditional on available information,
plus some random noise. We look at the proposed model’s implications regarding which
pOpulation groups are better at predicting future residential moving. However, we fail to
substantiate the hypothesis, and therefore, cannot conclusively identify individual charac-
teristics associated with better forecasting.

The outline of the remainder of the chapter is as follows. The next section describes
the data used in this study. Section 3 characterizes the subjective moving probabilities and
their correlates. Section 4 presents the descriptive relationship between moving expectations
and subsequent residential moves. ,The same section also explores~ the role of subjective
probabilities in economic models of residential mobility. Section 5 examines what people
report when asked for the subjective probabilities, and also explores which groups are better

at predicting. The paper closes with a conclusion in Section 6.

2 The Data

We use data from The Health and Retirement Study (HRS). The HRS is a longitudinal
biennial survey of the American population that had its first wave of interviews in 1992.
The analysis draws on data from five survey years from 1998 to 2006 on individuals from
four cohorts in the HRS: AHEAD (born between 1890—1923), CODA (born between 1924-
1930), HRS (born between 1931-1941), and War Babies (born between 1942-1947).

The key question concerning moving expectation in the HRS is:

38

Now using the same scale as before where “0” is absolutely no chance and “100” means that
it is absolutely certain, please tell me what you think are the chances that you will move in

the next two years?
(00-10—20——30—40—50—60—70—~80—90—100)

Normalizing the responses to [0, 1] allows treating them as subjective probabilities of resi-
dential mobility.3

The moving expectation question is asked to the respondents if either of the following
conditions is met:

(1) The respondent is at least 65 years of age, or

(2) The respondent is giving a new interview (to the new interviewees the question is asked
irrespective of age).

In other words, if an individual is under age 65 and is a reinterview respondent, the moving
expectation question is not asked.

We investigate the predictability of moving for those ages 65 and older. The Health and
Retirement Study over-sampled blacks, Hispanics, and Floridians, and therefore, throughout
the analysis of this paper we use respondent-level sampling weights. Our sample design is
motivated by the idea of studying moving behavior in relation to the subjective probabilities
of moving elicited in the immediate prior survey wave. Therefore, to contribute an observa-
tion to the sample, the respondent has to be part of at least two consecutive survey waves.
For the number of years of data we consider, an individual can provide a maximum of four
observations to the sample. Table A21 in the Appendix describes the sample construction
steps. For the cohort and age-eligible respondents, we retain 61,688 observations. where we
observe an individual in any two successive interview waves. Of this, 35,720 respondents
are of 65 years of age or older. We have non-missing subjective probability responses for
31,541 of the 35,720 observations. We additionally have don’t know and refusal responses
to the moving expectation question for another 762 observations not included in this 31,541

sample size. We observe moving transitions conditional on non-missing subjective moving

 

3The questions on both moving expectation and realization in the HRS are such that any
changes in residences irrespective of the distance involved is taken into account; in other
words, a residential move can be within the same city, or the same state, as well as out of
the current city, or state, of residence.

39

probabilities for 7,725 individuals in 1998—2000, and for 7,536, 7,876, and 8,404 individuals,
respectively, for the years 2000—2002, 2002—2004, and 2004—2006. Restricting the sample
to non-missing data on other respondent and household variables we finally retain a sample
of 29,584 observations, excluding 513 observations that have don’t know or refusal responses
to the moving expectation question. Of the final sample, the number of moving transitions
observed in the four transition years are 7,494, 6,987, 7,292, and 7,811. Table A22 in the

Appendix gives the summary statistics for all the variables used in the analysis of this paper.

3 Moving Expectation and Its Correlates

The distribution of individual subjective moving probabilities for all observations is presented
in Figure 2.1 — Panel A. The figure indicates a heaping of responses most notably at 0, and
also at 0.5 and 14, a pattern found previously for other HRS subjective probability measures
(Lillard and Willis, 2001). It has been suggested previously in the literature that some of
the bunching of responses could be due to cognition error or misunderstanding (Hurd and
McGarry, 1995; Hurd, McFadden and Gan, 1998), or due to imprecision in beliefs about
these probabilities (Lillard and Willis, 2001).

Figure 2.1 also clearly displays that most individuals have a very low subjective probabil-
ity of moving, with more than 60 percent reporting zero. The figure also indicates significant
heterogeneity across individuals. To understand how much of the variation in subjective
moving probabilities can be explained by observable characteristics, Ordinary Least Squares
regressions of the probability variable on observable characteristics typically used in studies
of residential move5 are reported in Table 2.1. In Column (1), the analysis controls for race,
gender, education groups (with the group of high school graduates omitted), marital status, a
quadratic in age, health status captured by two dummy variables for the numbers of doctor-
diagnosed medical conditions (with no condition as the omitted category), ownership of

home, labor force status represented by three dummy variables (with complete retirement as

 

4The response of “1” is contributed largely by those who actually move, as can be seen in
Panel B, which plots the distributions of the subjective moving probabilities separately for
movers and non-movers.

5To mention a few of the papers on moving behavior: Venti and Wise (1989, 1990, 2004 ;
Feinstein (1996); Borch-Supan, McFadden and Schnabel (1996); Clark and Wolf (1992 ;
Borch-Supan (1989, 1990).

40

the omitted category), number of children, household size, individual earnings, total house-
hold income, net housing value, net non-housing financial wealth, and dummy variables for
survey waves. While the F-test for overall significance of the model is significant at the 0.001
level, the R2 from this regression is only 0.03. Marriedand widowed people report lower
probabilities of making a residential move compared to never married, separated, and di-
vorced people. Subjective probabilities of moving increase with education. Home-ownership
is negatively associated with moving expectations. Adverse health conditions increase sub-
jective moving probabilities. While undoubtedly more observable characteristics could be
added to explain more of the variation in moving probabilities, these results suggest that
subjective probability reports are considerably idiosyncratic.

In the next three columns, in addition to the variables that enter the regression presented
in Column (1), we add expectations about other future events to explore what future expec-
tations make moving subjectively a more probable event. For instance, we take into account
the expectation about whether income would keep up with inflation in the next five years,
the subjective probabilities of survival for another 5 to 15 years, and the expectations about
leaving a bequest and receiving an inheritance. All these expectation variables are measured
as subjective probabilities, and range between 0 and 1, inclusive. Life expectancy and sub-
jective probabilities of moving are negatively correlated. It could be that because of the
consumption value of housing, the longer people expect to live, the less they expect to make
a residential move to potentially tap into housing equity. The expectation that income would
keep up with inflation is also negatively associated with subjective probabilities of moving. It
could imply that unless the elderly are threatened that their purchasing power is going to be
impacted, they are unlikely to expect to move and alter housing consumption. The expecta-
tion of receiving inheritances is positively associated with subjective probabilities of moving.
Incidentally, the expectations about longer life, receiving inheritances, and income keeping
up with inflation can all be considered as positive things in an individual’s life. Bassett and
Lumsdaine (2001) find evidence of a common “systematic” component across an individual’s
subjective responses for different questions that is unrelated to whether the specific question
of interest bears a positive or a negative connotation. In contrast, we find that subjective

moving probabilities are both positively and negatively associated with apparently positive

41

expectations in life.

4 The Predictive Power of the Subjective Probabilities
of Moving ‘

4.1 Relationship between Expectations and Realizations

An initial examination of the data suggests that the subjective probability measure might
have predictive power for forecasting residential mobility — on average, individuals who have
not moved report a moving probability of about 12.8%; movers report 36.1%. Figure 2.1
— Panel B indicates that the subjective probability distribution for non-movers is heavily
concentrated at very low probabilities and is highly skewed. In fact, the median subjective
probability for non-movers is 0%, the 75th percentile is 10%, and the 90th percentile is 50%.
For movers, the distribution is relatively more dispersed. Their median subjective probability
is 20%, 75th percentile is 80%, and their 90th percentile is 100%. However, it is noteworthy
that approximately 43% of the movers reported a 0% chance of future moving possibility.
Thus, for a sizable fraction of the movers, moving apparently represents an unforeseen event.

The direct relationship between the subjective moving probability and subsequent moves
is shown in Figure 2.2 — Panels A and B. Panel A shows the relationship for all the survey
waves pooled together; Panel B presents the relationship separately for the four waves of
moving transitions. The dashed and dotted lines in the two panels give the fractions of
individuals actually moving by their reported subjective probability. The plots in Figure
2.2 indicate that there is a positive relationship between the subjective probabilities and the
incidence of moving. Most notably, there is at least a doubling of realized moving between
those with a 80% subjective probability and those with a 100% subjective probability. These
results are indicative of subjective probability’s predictive power. It is notable that the
non-solid lines, representing the fraction of individuals actually moving by their subjective
probability, look very similar across waves. However, the fact that the realization lines
fall below the 45-degree line (except for those who report a zero probability) suggests that a

substantial number of individuals in the sample tend to overstate their mobility probabilities.

42

For the entire sample population, the mean of subjective moving probabilities is 15.60% and
the mean moving propensity is 12.12%. In other words, the sample average of the forecast
errors (deviation between the actual moving outcome and the reported moving probability)

is non-zero: overall, there is about a 29% over-prediction in the subjective probabilities.6

4.2 Economic Models of Residential Mobility

Even though the predictions about residential mobility remain noticeably unfulfilled for
almost the entire range of reported subjective probabilities, we do observe that the realization
rate is increasing in the subjective probabilities. Naturally, if people can tell us something
about their future moving through the subjective probabilities, then the predictions have
the potential for improving modeling of moving behavior. As such, we can expect to find
additional covariation between actual and expected moving propensities beyond what is
present through common covariation with the factors that would typically be included in a
model of mobility behavior. To assess this point, we regress individuals’ moving outcomes
on individuals’ subjective mobility probabilities, and subsequently include the full set of
economic and demographic variables that we have been using in our previous regressions.

The predictive power of the subjective moving probabilities is shown in Column (1) of
Table 2.2. When only the expectation variable is included in the regression, the coeflicient
on this variable is highly statistically significant. Since the variable used in the regression
ranges from 0 to 1, the coefficient can be interpreted as stating that a 10-percentage point
increase in moving expectation increases the probability of a residential relocation by 3.3
percentage points.

Figure 2.2 reveals that the relationship between the moving expectations and realiza-
tions is nonlinear. Column (2) of Table 2.2 includes a series of dummy variables for the
non-zero probability categories to capture the nonlinearity in a nonparametric fashion. The

regression coefficients tend to increase in magnitude as the subjective moving probabilities

 

6The result of the general over-prediction in residential mobility is consistent with the
finding of Duncan and Newman (1976). They study the fulfillment rates of job- and housing-
related mobility expectations for various demographic, housing, job-related and community
characteristics using four waves of the Panel Study of Income Dynamics (PSID) and find
that irrespective of the type of the move, fewer than half of those expecting to move fulfilled
these expectations.

43

increase, and are statistically significant for all of the probability categories from 40%. The
relationship between the subjective moving probability and the probability that a residen-
tial move actually takes place is precisely similar to the pattern found in Figure 2.2. The
probability of a move is slightly increasing in the expectation up to 80% category and then
jumps rather sharply between 80% and 100% subjective probability categories. In fact, all
that the estimates in Column (2) add to Figure 2.2 are the standard errors. The probability
variables are jointly significant at the 0.001 level of significance.

Including the demographic characteristics in the regressions only has a slight qualitative
effect on the relationship between individuals’ subjective moving probabilities and future
moves. The expectations variable remains highly significant. To highlight a few of the coef-
ficients on the demographic and other variables, those with more than high school education
are significantly more likely to make a residential move than their high school graduate coun-
terparts. Medical conditions raise the likelihood of moving. Those who own their residences
are substantially less likely to move, a finding well documented in the literature. Those with
greater housing equity move with lesser probabilities, and current employment diminishes
the probability of future moving.7 The estimated coefficients for the observable characteris-
tics are almost unchanged in regressions with or without the subjective probability variable.
More importantly, the subjective measure remains a strong predictor of future residential
mobility, even conditional upon numerous observable characteristics. This means that the
predictive power of the subjective probability variable is nearly orthogonal to the predictive
ability of the demographic variables. In spite of the apparent overstatement of the expec-
tational probabilities, this subjective variable contains very important private information
that is otherwise unseen by the econometrician.8

Table 2.3 presents the regression results for the four moving transition waves separately.

As suggested visually in Figure 2.2 — Panel B, the estimated coefficients on the dummy

 

7These results in conjunction with the results in Table 2.1 allow us to explore the extent to
which expectations and outcomes of residential mobility qualitatively vary with observables
in the same way. For instance, we see that homeowners are less likely to expect to move,
just as they are less likely to make a move; those with more than high school education
appear more likely to move, and the same group of peOple also reveals larger expectations
of moving; and so on.

8All these regressions have been estimated by maximum likelihood probit also. The esti-
mated marginal effects from the probit regressions are nearly identical to the reported OLS
estimates.

44

variables for the ten non-zero probability categories are very similar for the pooled sample
and the individual transition waves. Some of the demographic and other variables, such
as, Age, Age-squared, and Home-ownership, are statistically significant both in the pooled
and the wave by wave regressions. In fact, almost all the variables that are significant in
the pooled regression — for example, White, Less than High School Graduate, More than
High School Graduate, At Least Five to Eight Health Conditions, Working for Pay, Number
of Children, and Earnings — tend to have the same sign and are significant in at least
one of the individual wave regression. The only exception is Net Housing Value, which is
statistically significant in the pooled regression, but not significantly estimated in any of the

individual wave regressions.

4.3 Item Non-responses in the Subjective Moving Probabilities

In the HRS, besides the exact responses that range between 0 and 1 (more specifically, 0
and 100; see Section 2), there are two additional responses that are allowed in the moving
expectation question: “don’t know” and “refuse”. We have omitted these observations in
the analysis thus far. For the sample population under consideration, the groups that say
“don’t know” or “refuse” are extremely small fractions of the population, 1.6% and 0.1%,
respectively. The analysis in Table 2.4 depicts that the respondents who either say “don’t
know” or give a subjective moving probability of “0.5” are both approximately 5-percentage
points more likely to make a residential move than the entire population. In other words, the
moving propensities of the two groups with these two probability responses are remarkably
close. A “don’t know” response may mean the same as a response of “0.5”, reflecting a belief

in a 50-50 chance of future moving.

5 The Heterogeneity in the Accuracy of Prediction
Across Population Groups

A relevant and important question is whether there are individual characteristics that are
associated with people making better predictions about future events. The ability to make

more precise forecasts, for instance, might be expected to vary with education and cognitive

45

ability. As a first step in capturing the variations in predictive accuracy, we group people
by characteristics and compare the mean subjective probabilities of moving with the mean
realized moving prOpensities. The averages of the subjective probabilities and actual moving
propensities for various groups are listed in Table 2.5. Mean subjective probabilities exceed
actual probabilities for almost every sub—group of population. One notable exception is the
group of peOple ages 90—105; it appears that moving is more of a surprise for the oldest adult
population than for other demographics. We also find that moving is somewhat of a surprise
(under-predicted event) for the Hispanic population, and for peOple without home-ownership.

A drawback of this straightforward comparison is that it does not say anything about
whether, within a group, those who say they are more likely to move are in fact more likely to
do so. It is possible that the group could do very well on average while individual members
are doing quite poorly. Therefore, as an alternative we look at the mean squared forecast
errors within groups. Let mt denote the binary moving indicator (a 0/1 dummy variable)
for whether one moves between periods t-1 and t, and mi] denote the reported subjective
probability of moving. Then the sample mean squared forecast errors is simply the average
of (mt — mil)“ The mean of squared errors has the advantage that it looks at individual
forecast errors and does not allow negative errors to offset positive ones. We report these
means for different groups in Column (3) of Table 2.5.

In order to interpret these mean squared errors across groups, it is useful to introduce
the true (unobserved) probability of moving, pt_ 1, conditional on QM — the information
available at the time of the forecast. A difficulty with the mean squared errors is that even
if people are predicting as well as they can, if moving is not perfectly predictable given all
available information, the mean squared errors will be larger for those whose true probability
of moving is closest to 2' To elaborate this point, let us suppose we could form groups that
are homogeneous in the conditional probability of moving, pt-1, and everyone reports pm as
their subjective probability. By definition, pH is the true expectation of moving outcome,
rm, and therefore, the term E(mt — pt_ 1)2 is the variance of mt. Since mt is a binary 0/1
outcome variable, the variance of mt can be written as: pt_1(1 — pt_1). This shows that if

in each group everyone reports pm as their subjective probability, then for the group the

mean squared error will be pt_1(1 —- pt_1). For different pt_1’s in different groups, the means

46

of squared errors will vary across groups and will be largest for groups with pt_1 closest to
%. However. we may not wish to say that groups are better at predicting just because they
are less likely to move.

One way to address this difficulty in interpreting the mean squared errors across groups
is to use a standardized distribution of subjective moving. probabilities (the overall distribu-
tion of subjective probabilities) to calculate weighted mean squared errors across groups.9
Weighting the mean-squared errors allows us to account for the differential subjective proba-
bility reports by observable characteristics. These weighted mean squared errors are reported
in the last column of Table 2.5. Weighting the mean squared errors does seem to reduce the
variation across groups compared to the unweighted mean squared errors. This appears to
be the case. for instance, for education, marital status, home-ownership, activity limitations,
and financial wealth quartiles. The relative subgroup comparisons in weighted means are
often very similar to that implied by the basic mean squared errors reported in Column (3),
but for certain groups they are quite different. For instance, the usual mean of squared
forecast errors is smallest for the least educated of the three education groups that we con-
sider, whereas the weighted mean squared errors for this group is the largest among the three
groups. Similarly, individuals in the highest quartile of financial wealth have the smallest
weighted mean squared errors, though in the usual calculation thosein the second and third
quartiles of financial wealth have smaller means of squared errors than people in the highest
quartile. Taken as a whole, there does not appear to be a straightforward interpretation of

the mean squared errors in assessing group variation in predictive accuracy.

5.1 What do PeOple Report as Subjective Probabilities of Mov-
ing?

In view of the difficulty in interpreting the mean squared errors, in this section we try

to make some sense of what people report as subjective probabilities and also try to say

something more about which groups are better at predicting. We assume that individuals

are rational based on all available information (9) when they form expectations about the

 

9In short, we calculate the average squared forecast errors for each group for each reported
value of subjective moving probability, and then use the overall distribution of subjective
probabilities to calculate the weighted mean squared errors for each group.

47

probability of moving. Then:

mtzpt-1+vt ..... (I)

where, by definition of pt-1, E(vt|Qt_1) = 0.
We hypothesize that when individuals are asked at period t-l about the probability that
they will move by period t, what they report is the true probability conditional on the

available information, pt, 1, plus some noise that is mean zero for all pt_ 1. Thus,

mi; = pi; +6 ..... (2)

where, 5 is the noise term that can be positive, negative, or zero, and in the population
E(§|Qt_1) = 0. In the data some individuals report a zero probability of moving (i.e.,
mf.1=0), and nonetheless move, and others report a probability, mi 1, of 1 and do not
move. Such behavior can be consistent with the model. The model implies that for such in-
dividuals Pt-I is greater than 0 and less than 1, but noise, 6, in their subjective probabilities
leads many of them to report the extreme values.

If pH varies across peOple, the model suggests that the subjective moving probabilities
are informative but noisy. One motivation for this hypothesis is the pattern observed in
Figure 2.2 (showing the relationship between moving propensities and subjective moving
probabilities), in which the subjective probabilities might be interpreted as reflecting moving
probabilities plus something akin to classical measurement error.

We assume that the two components of mi] in (2) — pH and 6 —— are uncorrelated
(the classical-errors—in-variables (CEV) assumption). We also maintain the assumption that

vt is uncorrelated with mg: 1. Horn (1) and (2) we can write:

If we estimate this equation by OLS with the inclusion of a constant termlo, it can be shown

 

10To be specific, we estimate the following by OLS:

mt = 50 + 51mg] + ("Ut - 516)-

48

that the plim of B] —— the coeflicient estimate on the subjective mobility measure, mi] ——

can be characterized as“:
02
p 2
2
Up + 05

plimBI = ..... (4)

where, 03 is the variance of pt_ 1, and of is the variance of the random noise term. 61 would

be 1 if we could plug in pH directly instead of the observed mil in the OLS estimation,
but plimBI < 1 if mg] is Pt-I plus measurement error.

Recall that the estimated coefficients from this OLS regression are what we present in
Column (1) of Table 2.2. This regression estimates the curve in Figure 2.2 — Panel A as a
straight line. As we can see in Table 2.2, the estimated coefficient BI is 0.334.12 Thus, the
pattern in Figure 2.2 and the estimated B] in Table 2.2 appear consistent with our model of
what people report as the subjective probabilities of moving.

The hypothesis in Equation (2) implies that the observed forecast errors (the deviation
between the binary moving outcome and the subjective moving probability) are made up of
two components: one component is due to the fact that moving is uncertain —— even given
all the available information; and the other component is the random noise element in the
subjective probability:

mg] 2 pt-) +5 ..... (2)
=> mt—mf_1 = mt-pH +6.

=> (mt - 771451)“ = (mt - PM)“ + £2 + 2(mt — Pt-Ilé-
If vt and 5 are independent it follows that:
=> E(mt - 77151)“ = E(mt - Pt-1)2+ 5&2-
Since, E(£)=0,
E(rnt — mil)? = E(mt — pt_1)2 + Var(§) ..... (5)

The left hand side in (5) is the mean squared forecast error. The first term on the right hand

side is the variance of mt”, which depends on the true probability of moving, pt_ 1, and for a
11See Wooldridge (2002); p. 75.
l2Incidentally, since B1 8: 0.33, it follows from (4) that for the sample population a? z

202.
1 pt_1 is, by definition, the ‘true’ expectation of mt.

 

49

binary (0, 1) variable mt, is largest when pt_1 is closest to g The second term is a measure
of the random noise component in the reported subjective probability. One implication of
the model is that the mean of squared forecast errors is larger the closer the average of the
true probabilities is to %.

Because the hypothesis of what people report as subjective probabilities incorporates a
random noise element, if true, the model has the potential to tell us something about which
groups predict better. If, for example, it can be shown that the variance of the random
noise component is smaller for some groups than others, it could be said that those groups
predict better. However, there are some problems with testing and applying this model.
One of the problems relates to the possibility of new information arriving between t-1 and t
that affects moving. If everyone gets the same shock (that is, all probabilities are affected in
the same way) then in a cross section forecast errors will not have zero mean even in large
samples. Some years the mean should be positive, some years negative.l4 More critically,
following Chamberlain’s (1984) argument, it is possible that new information can affect the
moving outcome in ways that could be correlated across individuals with differences in pt_ 1.
That is, shocks could affect different people differently, in a way that is correlated with the
observable characteristics in a. cross-section. For instance, in some years homeowners may be
particularly affected. or low income people.15 This implies that vt in Equation (1) may not
be orthogonal to pH across individuals in a particular cross-section. Therefore, in a cross-
section or in a short panel like ours, new information can increase forecast errors and can
influence the ex-post prediction accuracies of certain groups more than those of the others.
The important point to acknowledge is that just because a shock affects a particular group
more than the others, and thereby influences the moving outcome of that group in ways not
predicted, it should not be concluded that the affected group will consistently make worse
predictions than others. To clarify, if, for example, an unexpected downward shock to home
values makes it harder for homeowners to move, and they move less than they expected,
homeowners may not be consistently poor forecasters. Instead, if our model is correct, we

would prefer to say that one group predicts better than another when the former group has

 

l4Econometrically, this issue can be dealt with with time dummies.

15A long enough panel should be able to deal with this econometrically, because the same
groups should not get shocks in the same direction repeatedly, but we do not have a long
paneL

50

a smaller variance of the noise component than the latter group.

There is also a more basic problem with applying this model in assessing group differ-
ences in predictive abilities. We observe whether one moves and the subjective probability
reported, but we do not observe the true probability. If we did, we could calculate for dif-
ferent groups the first term on the right of Equation (5); since we observe for every group
the term on the left of Equation (5), we could then infer the second term on the right, and
see how it varies across groups. We can, however, consider an approximation of the true
probability. We regress the indicator for moving outcome, mt, on a subset of information
that the individuals have access to at the time of the forecasts, and take the fitted values
from the regression. We calculate the first term on the right of Equation (5) by plugging in
these fitted values in place of the pt-1’s.

We look into the group variation in the variances of the random noise component in the
subjective moving probabilities by considering many different groups segregated by different
individual characteristics. We use more than one criterion to disaggregate groups —- the
general one in all cases being a classification by gender — in order to retain more homogeneity
in true probabilities within groups. Tables 2.6.1 (for women) and 2.6.2 (for men) report the
averages of the subjective probabilities, mi 1, and the (approximated) true probabilities, pt, 1,
in the first two columns. The mean squared forecast errors for the various groups are reported
in Column (3). The next column of Tables 2.6.1 and 2.6.2 reports the noise variances, i.e.,
the estimates of of, which we derive using Equation (5) with the fitted values replacing
the pt_1’s in the calculation. The next two columns report the 31’s and the estimates of
05.16 It appears that the variances of the random noise component differ with individual
characteristics. Higher education does not appear to make people better forecasters, whereas

better health seems associated with less noisy reports of subjective probabilities.l7

 

l6The B1 for each group is the estimated coefficient of the subjective probability variable
from the regression of moving outcome on subjective moving probability along with a con-

stant. The estimates of a? are calculated using Equation (4). Please see Footnote 19 for

additional notes.

17Among some other findings, homeowners make better predictions than non-homeowners
(Rows 12—13). We find no distinct association between financial assets and the ability to
make less noisy forecasts (Rows 14-15). Single people, in general, are more prone to offering
noisier predictions than married adults (Rows 16-19). Finally, we do not find evidence that
the older adult population are more or less likely to give noisier predictions based on their
employment status.

51

It is useful to compare the group differences in noise variances to the group differences in
weighted and unweighted mean squared errors that we looked at in Table 2.5. After all, these
are two different approaches to trying to answer the same question of whether some groups
forecast better than others. For the most part, with respect to any particular individual
characteristic(s) we use in grouping people, the subgroup of people that has the largest
mean squared errors also happens to give the noisiest forecasts. Of course, larger mean
squared errors and larger noise variances are both indicative of relatively worse predictions.
For instance, we find that the most educated group has the highest mean of squared errors as
well as the largest noise variance among the various education groups. However, the weighted
mean squared errors is largest for the least educated group in the sample and smallest for
those with high school graduation. Married and partnered people have better forecasts both
by the measure of mean squared errors (weighted and unweighted) and that of the noise
variances relative to never married and other single people; so does the group of homeowners
compared to those without ownership. Those in worse health status — in terms of either
activity limitations or subjective health status — have the largest weighted and unweighted
mean squared errors and also the largest noise variance relative to those in better physical
health. However, we also observe some deviations to this more common pattern of association
between the mean squared errors and the noise variances across different subgroups of people.
For instance, among the various age groups, the oldest age group of people has the largest
mean squared errors. Although the oldest group of women also happens to have the largest
noise variance, it is the youngest group of men that makes the noisiest predictions. Also,
individuals in the lowest financial wealth quartile have the largest mean squared errors;
whereas, women in the highest financial wealth quartile have the largest noise variance.

These results regarding the variance of the noise term should be interpreted with caution.
The problem is that, as we have mentioned earlier, the expectations may contain additional
information not available in observable variables. In fact, if the fitted values were good
approximations of the pt_1’s, adding the explanatory variables to an equation for predicting
moving should substantially reduce toward zero the coefficient on the subjective probability.
We find in Table 2.2 that it only falls a little. The fact that the decline in the estimated

coefficient is so small suggests that most of the information in the subjective probabilities

52

is not adequately captured by the set of variables that we are using as regressors. As
such, we think that the fitted values from moving regressions do not do a very good job of
approximating the pt_1’s.18 Therefore, we should be cautious about putting too much stock
in the implied values of the variance of the noise term, and consequently about what we can
say about which groups are better at predicting.19 ’

Besides considering the model’s applicability in telling us something about which groups
predict better, the other important matter we are concerned about is validating the model
itself. In order to investigate whether the model is consistent with the data, we consider a
testable implication of the model. The procedure involves estimating Equations (1) and (2),
replacing pt_ 1 with a set of explanatory variables — a subset of the information available to
the individuals at the time of the forecast. If we maintain the hypothesis that when asked
for a subjective probability of moving, peOple respond with the true probability conditional
on available information, plus noise of mean zero, then the coefficients on the various ex-
planatory variables in the regressions of Equations (1) and (2) should be the same whether
the dependent variable is mt or mi 1'

Table 2.7 presents the estimated coefficients in the two regressions along with the test
statistics for the equality of coefficient for the pooled sample. Failure to reject the hypothesis
of equal coefficients for the explanatory variables would be consistent with the hypothesis

that reported subjective probabilities are true conditional probabilities plus noise.

 

1‘v‘We also see in Table 2.1 that the regressor variables have fairly small explanatory power

in explaining the variation in the subjective moving probabilities, as captured in the R2
statistics.

19Another issue regarding the estimates of the variance of the noise component makes us
concerned about the reliability of the magnitudes of these estimates. We calculate the noise
variances reported in Tables 2.6.1 and 2.6.2 using Equation (5), which does not require us

to have estimates of 05. However, we can obtain the estimates of 03 directly from the fitted
values that approximate the pt,1’s. If we plug in these estimated a]? in Equation (4) along
with the 61’s, we obtain another set of estimates for the variances of the noise term. These
estimates of 02 are smaller compared to the estimates that we derive using Equation (5)

and report in Tables 2.6.1 and 2.6.2. We get the approximations for pt-1’s using a subset
of the information available to individuals. As such, for a group, the variance of the true
probabilities is likely to be larger than that of the fitted values. If that is the case, then the

estimates of 03 we obtain from the fitted values of pH would give lower bound estimates
of the variances of the true probabilities. If we plug in these alternative estimates of 03 in
Equation (4), we are likely to get lower bound estimates also for of, the variance of the noise
element in the subjective moving probability.

53

As apparent in Table 2.7, we reject the hypothesis that the coefficients are jointly equal
in the two regressions.2O Thus, if we focus on the joint tests of equality of all coefficients, it
seems that the main hypothesis about what people report is wrong. However, it is possible
that the main hypothesis is not inaccurate, because the test we are conducting is not entirely
a clean test. Note that for the test to be valid, we need to assume that vt in Equation (1) is
uncorrelated with pt- 1. But as we have discussed earlier, this assumption may be violated in
a cross section even in large samples, because of new information that would affect mt in ways
that could be correlated with pH (Chamberlain, 1984). In the absence of this assumption
of orthogonality in Equation (1), the rejection of the hypothesis of equal coefficients in the
test described above may be due to the deficiency in the test, rather than the inadequacy of
the model that we have hypothesized.

When we conduct the test of equality of coefficients for one explanatory variable at a
time, we fail to reject equality of coefficients across the two regressions for majority of the
variables. The variables for which we reject the equality of coeflicients are Age, White, More
than High School Graduate, Homeowner, Number of Children, Earnings, Total Household
Income, and Working for Pay.21

If our model is correct, it is likely that there exists heterogeneity in the noise in reported
moving expectations across population groups. But it is important to acknowledge that
a group may predict badly in one year because of unanticipated shocks but this does not
imply it will consistently predict badly. Unfortunately, in the absence of being able to
account for the Chamberlain critique, and in the absence of a conclusive testable implication
to corroborate our hypothesis, we cannot be certain if the group variations that we have
shown in Tables 2.6.1 and 2.6.2 accurately capture the extent or direction in which people

differ in their noisiness in reported forecasts.

 

20We also reject the equality of coefficients in every individual transition wave.

21Incidentally, if we reject the equality of coefficients in the two regressions for any variable
in any individual transition wave, we often reject the equality of coefficients for that variable
in the pooled sample also. Moreover, except for the dummy variable Home-ownership, we
do not find the estimated coefficients of the same variables to differ repeatedly in several
transition waves. That we reject equality of coeflicients for different observable characteristics
in different survey waves (except for home-ownership) gives credence to the hypothesis that if
peOple give noisy forecasts, they are random noise, and that individuals are not systematically
wrong in reporting their moving expectations.

54

6 Summary and Concluding Remarks

The availability of subjective expectations information offers an exciting opportunity to val-
idate the importance of expectations in decision-making. However, the results in this paper
suggest the need for careful research to assess the empirical relationship between expectations
and economic behaviors. The paper provides a comprehensive account of the relationship
between the expectation and realization of a binary decision variable for people ages 65 and
older in the United States. The results show that the subjective moving expectations are
highly significant predictors of subsequent moving. Moving expectations contain additional
information beyond that found in demographic variables known to be related to residential
mobility. However, the older population appear to over-predict their mobility probabilities
during the sample period examined here. At the same time, for approximately 43% of the
movers, residential mobility is a completely unforeseen event, and as such, this 43% did not
overpredict future moving. Moving appears to be a shock, in particular, for the elderly in
their nineties and beyond, and also for a wider age group of older population without home-
ownership. There is some evidence that the responses of “don’t know” and “0.5” are similar,
that a “don’t know” response instead of representing a lazy response possibly reveals a belief
in a 50-50 chance of moving. I

In order to understand what people report when they are asked for a subjective probabil-
ity of moving, and also to say something about which groups make better moving forecasts,
we propose the hypothesis that when asked for the subjective probability of moving, people
respond with the true probability conditional on available information plus random noise.
This appears to be largely consistent with the pattern of association between the subjective
probabilities and the moving propensities. One of our main objectives has been to explore
the model’s implications regarding which groups predict future moving better. For instance,
if the variance of the random noise component is smaller for some groups than others, then it
might be interpreted that those groups make relatively better predictions. However, we have
encountered some problems in validating and applying the model. For one thing, because
of our reservations about the approximations to the true conditional probabilities that we
use in calculating the noise variances, we cannot be confident about the magnitudes of the

estimates of these variances for different population groups. In order to validate our model

55

we test whether the coefficients on variables explaining subjective probabilities are the same
as when those variables are used to explain moving. The model does not pass, but we argue
that the failure might be attributable to the Chamberlain critique. Chamberlain’s argument
implies that in the wake of new information, the true conditional probability may no longer
reflect the true probability ex-post for certain groups. In a single cross section or in a short
panel like the one in this paper, new information can increase forecast errors and can do
so differentially for groups with different characteristics. Since we have relied on the mean
squared forecast errors and the true conditional probabilities to calculate the measure of
noisiness in the subjective probabilities for various groups, we ought to be cautious about

the implied values of the variance of the noise term across different groups.

56

Probability Distribution

P robability Distribution

.9

Figure 2.1: Probability Distribution of Moving Expectations

Panel A: All Observations
1 J

 

I I I I I I I
.1 .2 .3 .4 .5 .6 .7
Subjective Probability of Moving

Probability Distribution of Moving Expectations

O—

m-i
b—
#—

Panel B: By Mobility Outcome
—— — Probability: Movers -- — -- -— -Probability: Non-Movers

1—

2_
. \'\ \ I
.1- &k\ -. . //4"'\\.. /\\//

 

 

 

I I I I I I I I I
.3 4 5 6 7 .8 .9

Subjective Probability of Moving .
Probability Distribution of Movmg Expectations

0—
L.
N

57

Table 2.1: Observable Determinants of Moving Expectations and
Relationship between Expectations about
Future Moving and Other Events

 

 

 

Dependent Variable:
Independent Variables Subjective Probability of Moving
(1) (2) (3) (4)
Age -0.013* -0.017** -0.012* -0.030**
(0.005) (0.005) (0.005) (0.008)
Age2/100 0.017** 0.010** 0.007+ 0.018**
(0.003) (0.004) (0.003) (0.005)
Female 0.004 0.006+ 0.005 0.007
(0.004) (0.004) (0.004) (0.004)
\thite 0.015** 0.017** 0.012* 0.010
(0.005) (0.006) (0.006) (0.007)
Hispanic -0.021** -0.012 -0.020** -0.013
(0.007) (0.008) (0.007) (0.009)
Married -0.034** -0.037** -0.037** -0.035**
(0.007) (0.008) (0.008) (0.009)
Widowed -0.031** -0.033** -0.033** -0.025**
(0.007) (0.008) (0.008) (0.009)
Less than High School Graduate -0.036** -0.034** -0.035** -0.035**
(0.004) (0.004) (0.004) (0.005)
More than High School Graduate 0.040** 0.040** 0.037** 0.039**
(0.004) (0.004) (0.004) (0.005)
At Least 1—4 Health Conditions 0.010+ 0.010+ 0.011* 0.013+
(0.005) (0.006) (0.005) (0.007)
At Least 5—8 Health Conditions 0.013 0.014 0.018+ 0.015
(0.010) (0.011) (0.010) (0.012)
Owns Home / Residence -0.079** -0.080** -0.081** -0.074**
(0.005) (0.005) (0.006) (0.006)
Works for Pay Full/Part-Time -0.006 -0.006 -0.005 -0.001
(0.007) (0.007) (0.007) (0.008)
Partly Retired -0.009 -0.009 -0.008 -0.012+
(0.006) (0.006) (0.006) (0.007)
Disabled or Unemployed -0.005 -0.002 —0.007 -0.003
(0.017) (0.019) (0.017) (0.022)
Number of Children -0.001 -0.001 -0.001 -0.001
(0.001) (0.001) (0.001) (0.001)
Number of Household Residents -0.006** -0.006** -0.006** -0.008**
(0.002) (0.002) (0.002) (0.003)
Earnings “06 0.027 0.033 0.028 -0.125
(0.135) (0.136) (0.135) (0.126)
Total Household Income/106 0'1“,” 0°124** “108’” 0°109**
(0.038) (0.039) (0.039) (0.041)

 

58

Table 2.1 Continued

 

 

 

 

 

Independent Variables: Dependent Variable:
Other Subjective Probabilities about Subjective Probability of Moving
the Future (1) (a (3) (4)
Net Value of Housing “06 0.004 0.005 0.004 0.006
(0.004) (0.004) (0.004) (0.004)
Net Value of Financial Wealth no" 0-006 0-006 0-006 0005
(0.004) (0.004) (0.004) (0.004)
Income Keep Up With Inflation
Next 5 Years -0.013*
(0.006)
Live Next 5 to 15 Yearsf '0°003+
(0.002)
Leave Bequests of At Least $10,000 0.008
(0.005)
Receive Any Inheritance in Next 10
Years 0.022*
(0.009)
Constant 0.788** 0.947** 0.745** 1.418**
(0.189) (0.205) (0.197) (0.295)
Observations 29584 27903 28492 20676
R—squared 0.03 0.03 0.03 0.03
1. The dependent variable — subjective probability of moving — ranges between 0 and 1,
inclusive.
2. Robust standard errors in parentheses. + Significant at 10%; * Significant at 5%; **
Significant at 1%.
3. All estimations include dummy variables for survey waves.
4. Weighted regression results reported.
5. For the set of dummy variables Married and Widowed, the excluded category is Divorced,
Separated, or Never Married.
6. The omitted education category is High School Graduation.
7. The omitted health condition category is No Doctor-Diagnosed Health Condition.
8. The excluded labor force status variable is Completely Retired (which also includes those
identified as Out of Labor Force).
9. fData from the last three transitions waves. Not asked if individuals are 90 or older.
10. Sample sizes in Columns (2) — (4) are conditional on non-missing observations on the

additional subjective probabilities that are included in the regression estimations.

59

Fraction M oving

F raction Moving

Figure 2.2: Moving Expectations and Actual Mobility

Panel A: All Transition Waves
45-Degree Line —— Moving Propensity

 

 

 

I I I I I I
4 5 6 7 .8

Subjective Probability of .Moving .
Expectation and Realization of Residential Mobility

o
...
b4
0:
b—
...—

Panel B: By Individual Transition Wave

—— Moving Propensity: 1998-2000 — — Moving Propensity: 2000-2002
-- - -- - - Moving Propemity: 2002-2004 - - - — Moving Propensity: 2004-2006

 

 

 

L0-
#—

.4 .5 .6 .7 .8
Subjective Probability of Moving

Expectation and Realization of Residential Mobility

o
____.
i0
02

60

Table 2.2: Predictiveness of the Subjective Moving Probabilities
Dependent Variable: Moving Outcome

 

 

Independent Variables (1) (2) (3) (4)
Subjective Probability of Moving 0.334** 0.317**
(0.010) (0.010)
Subjective Mobility Probability 10% 0.005 0.004
(0.006) (0.006)
Subjective Mobility Probability 20% 0.017 0.013
(0.011) (0.010)
Subjective Mobility Probability 30% 0.013 0.006
(0.011) (0.010)
Subjective Mobility Probability 40% 0.093** 0082'"
(0.023) (0.023)
Subjective Mobility Probability 50% 0.089** 0.079**
(0.007) (0.007)
Subjective Mobility Probability 60% 0.138** 0.123**
(0.032) (0.031)
Subjective Mobility Probability 70% 0.154** 0.153**
(0.035) (0.035)
Subjective Mobility Probability 80% 0.208** 0.198**
(0.018) (0.018)
Subjective Mobility Probability 90% 0.316** 0.310**
(0.035) (0.035)
Subjective Mobility Probability 100% 0.490** 0.467**
(0.017) (0.017)
Age -0.032** -0.032**
' (0.007) (0.006)
Agez/ 100 0.021** 0.021**
(0.004) (0.004)
Female -0.004 -0.003
(0.004) (0.004)
White 0.029** 0.032**
(0.006) (0.006)
Hispanic -0.002 -0.002
(0.009) (0.009)
Married -0.013 -0.012
(0.009) (0.009)
Widowed -0.010 -0.011
(0.009) (0.009)
Less than High School Graduate -0.007 -0.010*
(0.005) (0.005)
More than High School Graduate 0.009+ 0.013**
(0.005) (0.005)
At Least 1—4 Health Conditions -0.003 -0.002

 

61

Table 2.2 Continued

Dependent Variable: Moving Outcome

 

 

 

Independent Variables (1) (2) i3) (4y
At Least 5-8 Health Conditions 0.022+ 0.0234-
(0.013) (0.013)
Owns Home/ Residence -0.097** -0.095**
(0.006) (0.006)
Works for Pay Full-Time or Part-Time -0.029** -0.032**
(0.007) (0.007)
Partly Retired -0.010 -0.011+
(0.006) (0.006)
Disabled or Unemployed 0.013 0.012
(0.022) (0.022)
Number of Children 0.004** 0.004**
(0.001) (0.001)
Number of Household Residents 0.002 0.000
(0.003) (0.003)
Earnings / 1 06 0380* 0348*
(0.163) (0.160)
Total Household Income/106 ‘0-046 "0'043
(0.030) (0.030)
Net Value of Housing/106 '0°011* '0°008+
(0.005) (0.004)
Net Value of Financial Wealth/106 0001 0001
(0.003) (0.003)
Constant 0.067** 0.080** 1 1.287** 1292'”
(0.004) (0.004) (0.246) (0.243)
Observations 29584 29584 29584 29584
Ifil-scplared 0.08 0.10 0.10 0.12
1. The dependent variable — Moving Outcome — is a dummy variable equal to 1 if a residential
move has occurred between survey waves.
2. Ordinary least squares estimates are reported. Robust standard errors in parentheses. +
Significant at 10%; * Significant at 5%; ** Significant at 1%.
3. All estimations include dummy variables for survey waves.
4. Weighted regression results reported.
5. For the set of dummy variables Married and Widowed, the excluded category is Divorced,
Separated, or Never Married.
6. The omitted education category is High School Graduation.
7. The omitted health condition category is No Doctor-Diagnosed Health Condition.
8. The excluded labor force status variable is Completely Retired (which also includes those

 

 

identified as Out of Labor Force).

62

Table 2.3: Predictiveness of the Subjective Moving Probabilities
By Individual Transition Wave

Dependent Variable: Moving Outcome

 

 

Independent Variables 1998- 2000- 2002- 2004-
2000 2002 2004 2006
Subjective Mobility Probability 10% 0.031* 0.000 -0.011 0.004
(0.014) (0.014) (0.012) (0.011)
Subjective Mobility Probability 20% 0.027 0.011 0.013 0.002
(0.021) (0.022) (0.022) (0.019)
Subjective Mobility Probability 30% -0.006 0.011 -0.003 0.017
(0.016) (0.023) (0.021) (0.020)
Subjective Mobility Probability 40% 0.090+ 0.043 0.101* 0.092+
(0.050) (0.042) (0.045) (0.048)
Subjective Mobility Probability 50% 0.076** 0.089** 0.081** 0.072**
(0.013) (0.016) (0.015) (0.014)
Subjective Mobility Probability 60% 0.190* 0.166** 0.025 0.088+
(0.075) (0.058) (0.061) (0.050)
Subjective Mobility Probability 70% 0.047 0.239** 0201* 0.120*
(0.054) (0.078) (0.081) (0.061)
Subjective Mobility Probability 80% 0.210** 0.226** 0.154** 0.198**
(0.037) (0.039) (0.035) (0.032)
Subjective Mobility Probability 90% 0.304** 0.290** 0.294** 0.330**
(0.066) (0.073) (0.073) (0.064)
Subjective Mobility Probability 100% 0.433** 0.426** 0.455** 0.540**
(0.036) (0.035) (0.034) (0.031)
Age -0.021+ -0.024+ -0.031* -0.045**
(0.013) (0.013) ‘ (0.013) (0.013)
Age2/100 0.015+ 0.016+ 0.020* 0.031**
(0.008) (0.009) (0.008) (0.008)
Female 0.010 -0.009 -0.011 -0.000
(0.008) (0.009) (0.009) (0.008)
White 0.029** 0.044** 0.008 0.049**
(0.010) (0.013) (0.014) (0.011)
Hispanic 0.013 0.014 -0.037* 0.009
(0.018) (0.021) (0.017) (0.017)
Married -0.001 0.018 -0.019 -0.042*
(0.016) (0.019) (0.018) (0.017)
Widowed -0.010 0.019 -0.009 -0.038*
(0.016) (0.019) (0.018) (0.018)
Less than High School Graduate -0.019* -0.012 -0.009 -0.001
(0.009) (0.011) (0.011) (0.010)
More than High School Graduate 0.005 0.018+ 0.009 0.023*
(0.009) (0.011) (0.010) (0.009)
At Least 1—4 Health Conditions -0.001 -0.010 0.010 -0.007
(0.009) (0.013) (0.012) (0.013)

 

63

Table 2.3 Continued

Dependent Variable: Moving Outcome

 

 

 

Independent Variables 1998- 2000- 2002- 2004-
2000 2002 2004 2006
At Least 5—8 Health Conditions -0.017 , -0.001 0.030 0.047*
(0.022) (0.030) (0.024) (0.023)
Owns Home / Residence -0.085** -0.138** -0.071** -0.089**
(0.012) (0.014) (0.013) (0.012)
Works for Pay Full-Time or Part-Time -0.003 -0.062** -0.038* -0.024+
(0.014) (0.014) (0.017) (0.012)
Partly Retired -0.001 -0.033* -0.017 0.008
(0.012) (0.013) (0.013) (0.012)
Disabled or Unemployed 0.019 0.060 -0.033 0.003
(0.033) (0.055) (0.039) (0.047)
Number of Children 0.005** 0.003 0.006** 0.002
(0.002) (0.002) (0.002) (0.002)
Number of Household Residents -0.000 -0.001 -0.003 0.003
(0.005) (0.006) (0.005) (0.005)
Earnings / 106 0.354 0.146 0.516+ 0.108
(0.323) (0.309) (0.290) (0.210)
Total Household Income/106 -0.035 -0.099 0.051 -0.049
(0.086) (0.082) (0.066) (0.039)
Net Value of Housing/10" -0.010 -0.025 -0.034 -0.004
(0.006) (0.051) (0.022) (0.004)
Net Value of Financial Wealth / 106 -0.022** 0.025 -0.001 0.002
(0.008) (0.018) (0.005) (0.004)
Constant 0.820+ 1.064* . 1.322** 1.763**
(0.476) (0.503) (0.475) (0.478)
Observations 7494 6987 7292 7811
R-squared 0.12 0.11 0.10 0.15

 

1. The dependent variable — Moving Outcome — is a dummy variable equal to 1 if a
residential move has occurred between survey waves.

2. Ordinary least squares estimates are reported. Robust standard errors in parentheses. +
Significant at 10%; * Significant at 5%; ** Significant at 1%.

S”

Weighted regression results reported.

4. For the set of dummy variables Married and Widowed, the excluded category is Divorced,

Separated, or Never Married.

5. The omitted education category is High School Graduation.

F"

The omitted health condition category is No Doctor-Diagnosed Health Condition.

7. The excluded labor force status variable is Completely Retired (which also includes those

identified as Out of Labor Force).

64

Table 2.4: Moving Propensity by Reported
Subjective Moving Probability

 

 

Subjective Moving Probability Moving Propensity
Exact Non-Focal and Focal Responses (n=29584) 12.12
Probability Response 2 0 (n=19308) 8.13
Probability Response = 0.5 (n=3181) 17.08
Probability Response = 1 (n=1037) 57.16
Probability Response 2 Don’t Know (490) 16.86
Probability Response 2 Refusal (n=23) 20.31

 

Note: Weighted tabulations are reported.

65

Table 2.5: Subjective Moving Probabilities, Moving Propensities, and

 

Mean Squared Forecast Errors amoniDifferent Groups of Population

 

Subjective Mean Weighted
Moving Moving Squared Mean
Groups of Population Probabilities Propensity Errors Squared
Errors?
Full Sample of Population 15.60 12.12 0.1321 --
Gender
Men 15.27 11.77 0.1276 0.1301
Women 15.83 12.36 0.1352 0.1334
Education
Less than HS Graduate 11.41 11.03 0.1201 0.1353
High School Graduate 15.06 1 1.77 0.1294 0.1304
More than HS Graduate 19.01 13.21 0.1429 0.1343
Age
65 — 69 17.42 12.55 0.1371 0.1306
70 — 74 14.92 10.62 0.1200 0.1231
75 — 79 14.48 11.34 0.1233 0.1273
80 - 89 15.13 14.08 0.1480 0.1481
90 — 105 12.87 17.70 0.1827 0.1902
Race 8: Ethnicity
White 15.79 12.31 0.1324 0.1320
Black and Other 13.82 10.31 0.1295 0.1334
Hispanic 11.70 11.98 0.1411 0.1555
Non~Hispanic 15.79 12.13 0.1317 0.1311
Marital Status
Married or Partnered 14.96 10.82 0.1195 0.1228
Divorced / Widowed 16.38 14.02 0.1504 0.1467
Never Married 21.76 13.57 0.1551 0.1345
Ownership
Homeowners 14.18 9.55 0.1142 0.1200
Non-Homeowners 21.02 21.93 0.2006 0.1871
Employment
Working for Pay 16.81 10.30 0.1258 0.1198
Partly Retired 15.79 1 1.23 0.1226 0.1227
Completely Retired 15.43 12.39 0.1337 0.1342

 

66

Table 2.5 Continued

 

 

Subjective Mean Weighted
Groups of Population Moving Moving Squared Mean
Probabilities Propensity Errors Squared
Errorst
Limitations in Activities
No ADL 15.58 11.75 0.1276 0.1279
At Least One ADL 15.75 14.37 0.1596 0.1578
Self-Rated Health Status
Excellent or Very Good 15.95 11.73 0.1276 0.1270
Good 15.61 11.73 0.1277 0.1279
Fair or Poor 15.04 13.21 0.1447 0.1458
Net Non-Housing Financial Wealth
Lowest Quartile 14.05 13.29 0.1425 0.1455
Second Quartile 14.85 12.11 0.1286 0.1313
Third Quartile 15.53 11.53 0.1280 0.1285
Highest Quartile 17.74 11.69 0.1305 0.1253

 

1. 1' In the last column, the means of squared forecast errors are weighted by the overall population
distribution of the subjective probability reports. See page 12 for details.

2. Weighted tabulations are reported.

67

 

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71

Table 2.7: Testing for Equality of Coefficients on Variables
Explaining Subjective Probabilities of Moving and Moving Outcome
SUbiCCthC p-valuc for

Probability Chi-Squared
0f Movmg Test Statistic

Moving
Outcome

 

 

 

 

for Equality of
Coefficients
Testing for Egality of Coefficients on All Explanatory Variables
-- -- 0.0000
Testing for Equality of Coefficients for Each Explanatory Variable

Age -0.036** -0.013** 0.0004
(0.006) (0.005)

Age2/100 0.021** 0.017* 0.3047
(0.004) (0.003)

Female -0.002 0.004 0.1549
(0.004) (0.003)

White 0.034** 0.015** 0.0096
(0.007) (0.006)

Hispanic -0.008 -0.021** 0.2135
(0.009) (0.008)

Married -0.023** -0.034** 0.2057
(0.007) (0.006)

Widowed -0.020* -0.031** 0.1717
(0.007) (0.006)

Less than High School Graduate -0.021** -0.036** 0.3358
(0.005) (0.004)

More than High School Graduate 0.021** 0.040** 0.0002
(0.004) (0.004)

Homeowner -0.122** -0.079** 0.0000
(0.005) (0.004)

At Least 1—4 Health Conditions -0.001 0.010* 0.1268
(0.006) (0.005)

At Least 5—8 Health Conditions 0026* 0.013 0.2882
(0.011) (0.009)

Number of Children 0.004** -0.001 0.0000
(0.001) (0.001)

Number of Household Residents -0.002 -0.006** 0.1802
(0.002) (0.002)

Earnings/106 0.389** 0.027 0.0116
(0.128) (0.107)

Total Household Income/106 '0'009 0116” 0'00”
(0.036) (0.030)

Net Value of Housing/106 ‘0-009 0004 02638
(0.007) (0.006)

 

72

Table 2.7 Continued

Moving Subjective p-value for
Outcome Probability Chi-Squared

of Moving Test Statistic

 

for Equality of
Coefficients

Net Value of Financial Wealth/106 0002 0006* 0'25 11
(0.003) (0.002)

Work for Pay -0.031** -0.006 0.0017
(0.007) (0.006)

Partly Retired -0.012* -0.009 0.5855
(0.006) (0.005)

Disabled or Unemployed 0.011 -0.005 0.4599
(0.019) (0.016)

 

The estimated coefficients from OLS regressions on the pooled sample are reported in the first
two columns for the two dependent variables — Moving Outcome and Subjective Probability of
Moving.

Standard errors are in parentheses.

73

Appendix

Table A.2.1: Sample Selection Criteria

 

 

Number of
Observations
Total Number of Core Interview Obtained1 97729
If Cohort and Age Eligiblez’ 3 84421 86-380/0
If At Least Two Successive Interviews Obtained4 61688 63°120/ 0
If At Least Age 65 in the First of Two Successive 35720 36.55%
Interviews
If Subjective Probability of Moving Non-Missing 31541 32.27%
If Moving Outcome in the Subsequent Interview 31541 32.27%
Non-missing
If Non-missing Respondent and Household
Characteristics
If Race/ Ethnicity Non-missing 31522 32.26%
If Home-Ownership Non-missing 31339 32.07%
If Number of Resident Children Non-missirg 29584 30.27%

 

21384, 19580, 18167, 20129, and 18469 observations, respectively, from the 1998,
2000, 2002, 2004, and 2006 HRS Surveys.

Observations belong to the four cohorts in the Health and Retirement Study:
AHEAD (1890-1923), CODA (1924-1930), HRS (1931-1941), and War Babies
(1942-1947). Note that in 2004 another cohort — the Early Baby Boomer (EBB)
cohort (1948-1953) — was introduced in the 5M4}, respondents from which are not
part of our analyses.

20002, 18139, 16685, 15237, and 14358 observations, respectively, from the 1998,
2000, 2002, 2004, and 2006 HRS Surveys.

26260 of these observations — 42.57% - are male.

74

Table A.2.2: Summa

Statistics of Variables

 

Variables MearflStandard Deviation)
Moving Propensity 12.12
Subjective Probability 15.60 (0.27)
Age 73.87 (6.42)
Female 0.59

White 0.90
Hispanic 0.05
Married or Partnered 0.60
Widowed 0.31
Separated or Divorced or Never Married 0.09

Less than High School Graduation 0.26

High School Graduation 0.36

More than High School Graduation 0.38
Home-Ownership 0.79

1-4 Medically Diagnosed Conditions 0.84

5-8 Medically Diagnosed Conditions 0.04

No ADL 0.86

At Least One ADL 0.14
Number of Children 3.36 (2.11)
Household Size 1.95 (0.92)
Labor Market Earnings (in 2000 dollars) 3,405.63 (16911.7)
Total Household Income (in 2000 dollars) 42,450.70 (61943.3)

Net Value of Housing (in 2000 dollars)
Net Value of Financial Wealth (in 2000 dollars)

115,851 (279968)
140,256 (661678)

 

Works for Pay Full-Time or Part-Time 0.09
Partly Retired 0.11
Disabled or Unemployed 0.01
Completely Retired 0.79
Number of Observations 29584

 

Note: Weighted means are reported.

75

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78

Chapter 3

Do THE ELDERLY SPEND DOWN THEIR
HOUSING WEALTH?

1 Introduction

Many papers have examined the role housing wealth plays in the life-cycle consumption and
saving decisions of individuals. The cornerstone of the life-cycle theory that guides most
of the economic research on the adequacy of retirement savings is the idea of consumption
smoothing. In its basic formulation, the life-cycle model posits that saving behavior is
forward-looking and is driven by the desire to maintain consumption during low-income
periods. Thus, an important implication of the life-cycle model is that individuals will
spend down their savings during retirement (Modigliani and Brumberg, 1954; Modigliani,
1986). Refinement of the standard model, allowing for uncertainty, precautionary saving
and accidental bequests, may affect the age after which one should start observing wealth
decumulation. It does not, however, change the basic implication that individual wealth
should eventually tend to fall with age.

The relative size of housing equity in the asset portfolios of older American households
suggests its potential importance for post-retirement consumption. According to the 2001
Survey of Consumer Financesl, over 80 percent of all households with heads aged 65 and
older owned a home, and these homes were valued at nearly $3.17 trillion. Including the $781

billion of other residential real estate (largely second homes), the total value of residential

 

lSource: Apgar and Di (2006). Evidence on the assets of the elderly is also reported
in Diamond and Hausman (1984); Hurd and Wise (1989); Kennickell and Shack-Marquez
(1992); Poterba and Samwick (2001); and Sinai and Souleles (2007).

79

real estate owned by the older households increased to $3.95 trillion. Consequently, in 2001
residential real estate accounted for some 30 percent of the nearly $13.2 trillion in aggregate
asset holding of seniors.2 Given such significance of housing wealth in the asset portfolio of
the elderly, this paper examines the long contested issue of whether the elderly draw down
their housing wealth during retirement. 7

The evidence in the literature on downsizing of housing wealth in later life is fairly mixed.
Sheiner and Weil (1992), Skinner (1996), and Heiss, Hurd, and B6rch-Supan (2005) suggest
that individuals at sufficiently older ages tend to tap into housing wealth. Sheiner and Weil
(1992), using data from the Panel Study of Income Dynamics (PSID), estimate that among
households entering very old ages owning a home, just 41% still own when the surviving
spouse dies. Skinner (1996) also uses the PSID data and shows that if the elderly downsize,
69 cents of each dollar of housing equity is spent. This evidence is taken to interpret that
the elderly consume housing wealth. More recently, Heiss et a1. (2005) have used data on
the AHEAD cohort from the Health and Retirement Study (HRS) to explore if the elderly
downsize housing by focusing on homeownership rates. They find that ownership is reduced
with age. Other studies (Merrill; 1984, Feinstein and McFadden; 1989, and Venti and Wise;
1989, 1990, 2004), contrary to the ones noted above, suggest that homeowners typically do
not use their housing wealth to support non-housing consumption in later life. Feinstein
and McFadden (1989) look into the probability of residential mobility among the elderly
relying on data from the PSID. Several of the other papers focus on changes in home equity
exploiting different data sources; for instance, Merrill (1984) and Venti and Wise (1989;
1990) use data from the Retirement History Survey (RHS), and Venti and Wise (2004) use
the first four waves of the HRS as well as data from eight panels of the Survey of Income
and Program Participation (SIPP). All of these papers reach the general conclusion that,
unless there is a change in family composition, there is little evidence that families reduce

their housing wealth with age.

 

2In contrast, only 21.1 percent of all households with heads aged 65 and older owned
publicly traded stocks. Expanding the concept of stock ownership to combine the direct
ownership of publicly traded stocks plus stocks owned indirectly through mutual funds,
retirement accounts and other managed assets, the share of seniors owning stocks increased
to just 36.8 percent. Under this expanded definition, seniors owned nearly $3.4 trillion in
stocks, an amount that represented 25.8 percent of their aggregate asset holdings (Source:
Aizcorbe, Kennickell, and Moore; 2003. Tabulations done by the Joint Center for Political
and Economic Studies based on 2001 Survey of Consumer Finances).

80

In this paper, we investigate changes in housing wealth with data from the 1998 and
2004 surveys of the Health and Retirement Study (HRS). The HRS is a national longitudinal
biennial survey offering a rich source of information on the lives of older Americans, their
health and economic status. We use data on individuals from the 1998 survey and follow
them into the 2004 survey. Venti and Wise (2004) also use data from the HRS (1992—1998)
for the HRS and AHEAD cohorts. The pOpulation representation in the 1998 HRS survey
allows us to take advantage of a larger sample encompassing a broader age range.

In its simplest version, the life-cycle theory posits the systematic accumulation of assets
during the working life and gradual decumulation during retirement. Therefore, our focus
is on the already-retired individuals to determine if retirees spend down their home equity.
Previous studies have examined individuals of a particular age range irrespective of their
labor force status. Our sample design is more consistent with exploring possible wealth
draw down in later life. In examining whether housing wealth declines during retirement, we
emphasize exploring heterogeneity across population groups in housing wealth adjustments.

In the past, short of selling their homes there was virtually no other alternative for the
elderly to extract housing wealth. In recent years, markets for home equity lines of credit and
reverse mortgages have deveIOped for tapping into home equity. Although these markets still
remain small, they are gradually expanding. Thus, these financial instruments potentially
offer increasing opportunities for the elderly to extract home equity. In view of this, we
briefly examine, to the extent allowed by the HRS data, the evidence on housing equity
adjustments through a type of such instruments.

Our analysis demonstrates that for non-mover retirees there is no systematic decline
in housing equity. But for retiree—movers there is a decline in the median housing equity
starting at age 71, and a decline in the mean housing equity from age 76. The mean change
in housing equity is $9,866.2 (-$1,769.5, adjusting for two outliers) and the median change is
—$8,112.3 for all retiree-movers. The decline in housing equity stems from homeowners who
give up ownership altogether. Typically, housing equity increases for retiree-movers who buy
new houses. We find evidence of significant heterogeneity in housing equity adjustments at
retirement. Nearly a quarter of the retiree-movers report that they are moving to downsize,

and they do. Those with low non—housing wealth and with low income reduce housing equity

81

significantly more than their respective counterparts. Retiree-movers experiencing widowing
or divorce reduce housing equity substantially more than those without similar experience.

It is important to put the findings in this study in the context of the results reported in
the literature. Our results are similar to the findings in some of the papers in both strands
of the literature that conclude in support of and against evidence for housing wealth draw
down in later life. To a large extent, the different conclusions in the two sets of papers are
the product of differences in interpretation.

Using data from the Health and Retirement Study, Venti and Wise (2004), for instance,
find evidence of a decline in movers’ housing equity in both one-person and two-person house-
holds. Their reported median decline in housing equity is very similar in magnitude to that
of ours. As in their analysis, we also find that the median housing equity declines beginning
at age 75. Venti and Wise (2004) also note that the large reductions in equity are typically
observed only for homeowners who move and discontinue homeownership. Nonetheless,
they conclude that housing wealth is not spent down because most of the older households
continue to own housing, and overall housing equity appears to increase in every two-year
interval.3 Feinstein and McFadden’s (1989) conclusion that the elderly households do not
reduce housing equity is largely grounded on the finding that wealthier households are less
likely to move and to downsize. This also is consistent with our findings. On the other side
of the literature, when Skinner (1996) concludes that there is evidence of downsizing he is
basing that conclusion on a mere 8.3% of his sample that moved and reduced their housing
equity. Sheiner and Weil (1992) and Heiss, Hurd and B6rch-Supan (2005) find evidence of
average levels of home-ownership declining significantly with age. Heiss et 81. note that for
older two-person households home—ownership begins to fall from age 71, which is in line with
the finding in our study. To a large extent indeed, our results are consistent with the existing

evidence in both sides of the literature, and the different emphases of these papers mostly

 

3Venti and Wise (2004) additionally look into overall changes in housing equity in the
SIPP data and find no evidence of downsizing, which is consistent with what we find. Venti
and Wise (1989) report a decline in median housing equity for movers during the 1973—1975
interval by $6,044.3 (in 2004 dollars). But they also find that housing equity increased for
movers during three of the five intervals they look into, and that in four of the five intervals
more than half of the movers increased housing equity post-move. Thus, they conclude that
those who move, on average, do not withdraw wealth from housing equity. Merrill (1984)
retains initial non-homeowners in the sample of movers and she has a relatively younger
sample, both of which may explain why she fails to find any indication of a decline in
housing equity for movers.

82

are reflective of differences in interpretation of what is in the data.

The outline of the chapter is as follows. The next section describes the data used in this
study. Section 3 narrates the extent to which the retirees reduce their housing equity as they
age. Section 4 examines whether the elderly spend down non-housing wealth as they age,
and how this compares to individuals’ extraction of housing equity. Subsequently in this
section, we investigate whether certain individuals for whom we might expect to see housing

draw down tap into home equity more. Section 5 concludes.

2 The Data

We use data on individuals from the four cohorts in the Health and Retirement Study (HRS):
AHEAD (born between 1890—1923), CODA (born between 1924-1930), HRS (born between
1931—1941), and War Babies (born between 1942-1947). The population of inference is
adults born prior to 1948. We explore the change in housing wealth of individuals, and
not that of the household as a unit. Since our objective is to investigate housing wealth
draw down during retirement, we restrict our sample to homeowners in 1998. Table A31
in the Appendix describes the sample construction steps. We follow the cohort and age-
eligible respondents from the 1998 survey to the 2004 survey, and retain a sample of 11,957
observations on retirees and non-retirees. The HRS over-sampled blacks, Hispanics, and
Floridians. For this, we use respondent-level sampling weights in our analysis. Table A.3.2
describes some key features about the sample in 1998 and 2004 and presents the descriptive
statistics for the variables used in this paper. As expected, individuals are less likely to
be married or partnered (primarily due to widowhood) and more likely to be retired at
the end of the six-year interval. Household income appears to decline from 1998 to 2004,
whereas, there is no sign at the outset that overall housing wealth tends to decrease over
time.‘1 Out-of-pocket medical expenses increase from 1998 to 2004, and so does the fraction
of individuals with long-term care insurance.

Our intent is understanding the housing wealth adjustments primarily of retirees. For

analytical purposes, the sample of retirees we consider consists of the respondents in one-

 

4We convert all dollar amounts to 2004-dollars using the CPI-U deflator.

83

person households that are identified as partly or completely retired in both 1998 and 2004,
and the respondents in two-person households where both spouses are partly or completely
retired in both 1998 and 2004. This way, we have a sample size of 5,503 retirees.5

Of the total sample of initial homeowners (n=11,957), we observe 2,782 residential moves
representing a 23.27% moving rate over the six-year period6. For the sample of retirees
(n=5,503), there are 1,290 instances of a residential move representing a 23.44% moving

rate.

3 Do the Elderly Reduce their Housing Wealth as they
Age?

We begin our analysis with Figure 3.1, which presents the mean and median changes
in housing equity between 1998 and 2004 for (initial) 1998-homeowners. According to the
life-cycle model, saving should be positive for individuals in their working years and negative
when retired. Thus, we might expect wealth decumulation for the retirees, but not necessarily
for the non-retirees. As such, we look into the changes in housing equity for the retirees and
the non-retirees separately. The figure reveals that overall housing wealth is not spent down
over the six-year period, not even for the retirees. However, the increase in housing equity
is smaller for the retirees than for the non-retirees.

As noted in the introduction, there are two ways to extract housing equity: moving
and using a financial instrument. In what follows we first examine housing draw down via

residential moving, as it has been the focus of the literature and is empirically more relevant.

3.1 Changes in Housing Wealth through Residential Moving

The HRS asks movers about the reasons behind their residential moving. Respondents can

report up to six reasons, though typically none reports more than two. Retiree-movers most

 

5Using the classification of complete retirement instead of both partial and complete re-
tirement generates a sample of 4,147 observations. Results do not appear to be sensitive to
the choice of the definition of retiree.

6The rate of moving reflects whether there has been any residential move for an individual.
In other words, the moving rate does not capture multiple residential moves by an individual
over this period.

84

prominently cite a reason that sounds something akin to downsizing: the intent of moving
to less expensive or smaller homes. Consistent with the implication of the life—cycle model,
this reason is much less important for the non-retirees in residential moving. For this group,
employment related matters are more crucial in moving decisions. The reported reasons
for moving, therefore, provide evidence that people move to downsize. They also indicate
significant heterogeneity across individuals in moving behavior.

The housing market experienced an overall surge during much of the period between
1998 and 2004, particularly from year-2000 (Sinai and Souleles, 2007). For this, we exam-
ine the change in housing equity for non-movers along with that for the movers between
1998 to 2004. The group of non-movers provides us with a comparison group to contrast
the movers’ housing adjustments with. After all, housing equity can be impacted for all
homeowners — movers and non-movers — by housing price changes, mortgage repayments,
reverse mortgages, home equity line of credits, as well as cutting back on maintenance.

We plot the change in housing equity for retirees between 1998 and 2004 by age (age
in 2004) in Figure 3.2. Panel A depicts the mean change in housing equity and Panel B
displays the median change. Housing equity for retiree-movers declines from people’s mid-
seventies if we consider the mean change and from their early-seventies if we consider the
median change. Comparison of the changes in equity by age for all movers and movers who
remain homeowners reveals that the decrease in housing equity among retiree-movers stems
from initial homeowners that give up ownership altogether.7 Housing equity increases even
among the oldest retirees in our sample that continue owning homes, regardless of whether
they have made a residential move or not.

Among movers there are some individuals who perhaps move because they feel forced to
do so. For instance, certain individuals, particularly among those who do not have a spouse
or a partner, may feel compelled to move into a nursing home or a retirement facility for
requiring assistance with certain aspects in their daily living. It is useful to know if changes
in housing equity might be different for those who feel forced to move compared to those who

do not feel forced in the same way. Unfortunately, in our sample of retiree-movers between

 

7Since Figure 3.2 suggests that homeownership tends to decline with age, we present the
tabulations for ownership rates by age in Appendix Figure A.3.2. As expected, the rate of
owning gradually decreases with age, a finding that Sheiner and Weil (1992) and Heiss et al.
(2005) report as the evidence for downsizing in later life.

85

years 1998—2004, there are very few cases where at least one of the spouses in a two—person
household or the individual in a one-person household enters a nursing home. All these
nursing home entrant observations are for ages 76 and older. On average, these respondents
reduce their housing equity substantially more than the non-institutionalized retirees in the
corresponding age groups. But the number of observations experiencing nursing home entry
in each of these age groups is too small for us to infer much from it. Separating out these
observations from the sample of retiree-movers does not alter the pattern of declining housing

equity for the relatively older movers.

3.2 Changes in Housing Wealth through Equity Extraction

In recent years, homeowners can tap into housing equity without having to sell their homes
by taking advantage of financial instruments like home equity lines or credit (HELOC) or
reverse mortgages. However, these markets still remain small, and during the sample period
investigated in this paper (1998—2004), the use of these financial innovations were even
narrower than in the last two-three years. The Health and Retirement Study (HRS) does
not collect data on reverse mortgage use. But some information is available on the use of
HELOC by homeowners. An increasing rate of HELOC access with age would be in line with
the implication of the life-cycle model. Figure 3.3 shows the rate of HELOC access for retiree
homeowners (that own in both 1998 and 2004) by age group. The access rate is relatively
low in the sample and is gradually decreasing in age. In other words, the older homeowners
— particularly the oldest ones in our sample — do not appear to make much use of this
specific financial instrument.8 Moreover, in Figure 3.4 we see that those accessing HELOC,
on average, have slightly higher housing equity in 1998 and experience a larger increase in
housing equity between 1998 and 2004 than those not accessing HELOC.9 Thus, we do not
find evidence that the elderly extract housing equity by making use of home equity lines of

credit.

 

8The overall rate of home equity line of credit access among all retiree homeowners is
12.13%.

9The results are identical if we examine the median initial housing equity and the median
change in housing equity instead of their respective means.

86

4 Why is there Relatively Little Reduction in Housing
Wealth?

In Section 3, we find little evidence of housing equity being drawn down overall. In this
section, we examine why this appears to be the case. Do other types of wealth show any de-
cumulation? Do certain individuals for whom we might expect to see declining wealth spend
down housing equity? These questions are important in understanding whether the elderly

regard housing wealth as a potential means of financing consumption during retirement.

4.1 Do the Retirees Reduce their Non-Housing Wealth as they
Age?

It is possible that the retirees view housing wealth differently from the rest of their asset hold-
ings. After all, housing has a consumption value unlike any other types of wealth. If retirees
spend down housing and non-housing wealth differently, then it might be indicative that
the elderly generally do not want to withdraw wealth from housing equity for consumption
smoothing at retirement.

We look into changes in non-housing wealth between 1998 and 2004 for retirees by fo—
cusing on net non-housing assets, which is the sum of the net value of secondary real estate
(excluding primary residence), the net value of vehicles and businesses, IRA, Keogh accounts,
stocks, mutual funds, and investment trusts, the value of checking, savings, or money market
accounts, CD, government bonds, and T-bills, the net value of bonds and bond funds, and
the net value of all other savings, less the value of other debts. The values of primary resi-
dence, mortgages, and other home loans are not included. In the two panels of Figure 3.5,
we present the mean (Panel A) and the median (Panel B) changes in net non-housing assets
by age group. On average, non-housing wealth starts to decline from age 65. The median
non-housing wealth falls throughout all ages. Housing wealth, therefore, appears different
from non-housing wealth in that the retirees seem more likely to spend down non-housing

than housing wealth.

87

4.2 The Heterogeneity in Housing Equity Reduction

Even with little evidence of downsizing on average, we expect that certain individuals might
have a greater need to extract housing wealth than others. In this section, we examine

several potential sources of heterogeneity in housing equity adjustments during retirement.

4.2.1 Changes in Housing Equity by Reported Reasons for Moving

We have seen in Table 3.1 that the intent to move to a smaller or less expensive home
—— downsizing — is the leading reason for retirees in their moving decision. For these
downsizing-movers, housing equity changes by an average of —$23,950.7; the median equity
also changes by a similar magnitude of —$23,259.5.10 For the retirees moving for any other
reason but downsizing, the mean and median changes in housing equity are $9,089.4 and
—$19,701.2. In fact, there are two extreme outlier values in the change in housing equity
among this group of retiree-movers (we mention these two observations in the context of
Figure 3.2). Setting these two outlier values equal to the next highest value of change
in housing equity in the data, we find the mean housing equity for these retiree-movers
declining by —$13,319.0.11 In spite of the adjustments in the outliers, it appears that the
retiree-movers who say they are moving to downsize reduce housing equity substantially more
than the retiree-movers for whom the intent of downsizing is not important. To examine
how the change in housing equity differs by reported reason for moving and by age, we
plot in Figure 3.6 the mean and median changes in housing equity for downsizing-movers
and non-downsizing-movers. The retiree-movers who move to downsize spend down housing
equity nearly at all ages.12 The mean and median housing equity for the retiree-movers

who move for other reasons also begin to decline at relatively older ages. Interestingly, once

 

10Of the retiree-movers (n=1,290), we have data on reasons for moving from 845 observa-
tions (mentioned earlier in relation to Table 3.1). The mean and median changes in housing
equity for whom we do not have data on reasons for moving are $25,834.4 and $6,953.4,
respectively.

11For all retiree-movers (those that give up home-ownership and those that buy new
houses), the mean change in housing equity is $9,866.2 (—$1,769.5, adjusting for the two
outliers) and the median change in housing equity is —$8,112.3. (Note that in Figure 3.1 we
consider the mean and median changes in housing equity for both movers and non-movers.)
Retiree-movers for whom we have non-missing data on reasons for moving, the mean and
median changes in housing equity are $956.5 (—$15,936.0, adjusting for the two outliers) and
—$20,860. 1, respectively.

12Except that the mean and median housing equity increase very slightly for these movers
at ages 76-80 and ages 66-70, respectively.

88

these movers begin extracting housing wealth their equity reductions are more sizable than
the equity reductions observed for the downsizing-movers. But then the ages when housing
equity declines for non-downsizing-movers are the same ages when housing wealth declines
overall for the retiree-movers (Figure 3.2). All in all, retiree-movers moving for downsizing
certainly reduce housing equity more and sooner if life than their counterparts who move for

other reasons.

4.2.2 Do People with Low Wealth Reduce Housing Equity More?

It might be that due to the consumption value of housing, housing wealth is spent down
last. Thus, we might observe declining housing equity for those with low wealth — especially,
non-housing wealth — but not necessarily for those who have relatively abundant wealth.
To assess this point, we plot the changes in housing equity for retiree-movers by non-housing
wealth quartiles and by age in Figure 3.7 — Panel A (for mean changes) and Panel B (for
median changes). Indeed, retiree-movers in the highest non-housing wealth quartile tend to
extract housing equity only at the oldest ages. Retiree-movers who are in the lowest quartile
and in the second and third quartiles of non-housing wealth reduce their housing equity

much sooner in life.13

4.2.3 Adverse Events, Alternative Insurance Availability, and Housing Equity
Reduction

Life events, such as, widowhood, divorce, nursing home entry, and prolonged or expensive

medical treatments involving substantial out-of-pocket expenses, can influence housing draw

down in retirement. Change in household structure leading to a smaller family size at the

very least can make the existing housing appear too large. Illness or declining health may

signal reduced life expectancy and accelerate decumulation of wealth. 1" Homeowners can self-

 

13Also, people with less household income (mostly, non-labor income, except for where
the individuals are partly retired) may find it necessary to draw down housing equity. The
relationship between household income and changes in housing equity by age is examined
in Appendix Figure A.3.3. Results appear to closely mirror those reported with respect to
non-housing assets.

l4Certain health conditions might require individuals to migrate to regions with suitable
climate or better availability of health care amenities. House moves from such considerations
might result in increasing housing wealth.

89

insure against adverse health events in old age by saving in the house. In that case, having
alternative forms of insurance, such as, children, or simply a long-term care insurance, may
reduce the need to spend down housing wealth. Having children can potentially provide
the elderly with alternative sources of monetary and non-monetary support. Parents with
children may also be more willing to preserve housing equity out of bequest intentions. Either
way, retirees without children may spend down housing equity more than their counterparts
with children.

To examine these many sources of heterogeneity in housing equity adjustments of retirees,
we estimate a descriptive-type regression equation with the change in housing equity between
1998 and 2004 as the dependent variable. We estimate the equation for the retirees who are
initially homeowners (who may or may not be homeowners in 2004), and also for retirees
who are homeowners in both 1998 and 2004. We present the OLS estimates as well as the
median regression estimates. The median regression estimates are likely to be less affected by
the presence of outliers in the data. As regressors in the specification, we include a dummy
variable, Moved, indicating whether the retiree has made any residential move between 1998
and 2004. The coefficient on this variable should indicate by how much more retiree-movers
change their housing equity relative to non-movers over this period. Of course, if moving
takes place to tap into housing equity, the estimated coefficient should be negative. In order
to capture the importance of age in housing equity reductions, we include in the regression
dummy variables for age, with the age group of 56—60 as the omitted category. We add
a dummy variable for female to control for possible gender differences in housing wealth
adjustments in retirement. Two dummy variables for education are included to examine if
housing equity adjustments vary in education. We also add dummy variables representing
household income and non—housing wealth quartiles.

We include in the regression specification a dummy variable indicating spousal death
or divorce and a dummy variable indicating if the retiree (respondent or the spouse) has
been in relatively poor health condition from the beginning of the six-year period.15 More—
over, we add an interaction term between whether the individual has moved and whether

the individual has experienced spousal death/divorce. Similarly, we include an interac-

 

15We consider a composite health measure incorporating doctor-diagnosed serious medical
conditions and limitations in activities of daily living (ADLs).

90

tion term Moved*Health Condition/ADL. The sum of the estimated coefficients on Spousal
Death/Divorce and Moved*Spousal Death/Divorce gives the change in housing equity of
movers who experience divorce or widowing in relation to movers without similar change
in household structure. The sum of the coeflicients on Moved, Spousal Death/Divorce, and
the interaction term Moved *Spousal Death/Divorce tells us the change in housing equity of
movers who experience widowing or divorce in relation to non-movers without such experi-
ence. Similarly, with respect to health conditions, the sum of the coefficients on Health Condi-
tion/A DL and Moved *Health Condition/ADL gives the change in housing equity of movers
who have had poor health in relation to movers without similar health conditions. And,
the sum of the estimated coefficients on Moved, Health Condition/ADL, and Moved*Health
Condition/ADL gives the change in housing equity of movers in poorer self/ family health in
relation to non-movers without any major illness in the family.

With respect to housing equity adjustments between movers and non-movers, the regres-
sion results in Table 3.2 tell us the similar story as in Figure 3.2. Movers appear to reduce
housing equity significantly more than the non-movers. Besides, comparison of the coeffi-
cient estimates on Moved in the first two and the last two columns of Table 3.2 reveals that
the significantly greater housing equity reduction of the retiree-movers is due to those who
discontinue homeownership altogether. Continuing homeowners do not seem to reduce their
housing equity post-move. In relation to the youngest age group of retirees in our sample,
individuals ages 71 and older reduce housing equity significantly more. Retirees with more
than high school graduate education increase housing equity substantially more than retirees
with high school graduation. Those with relatively low household income and other wealth
reduce housing equity substantially more compared to those who have the largest amounts
of household income and other wealth, respectively.

Retiree-movers experiencing spousal death or divorce reduce housing equity significantly
more than movers not experiencing similar change in family composition. Moreover, com—
pared to non-movers without any change in household structure, retiree-movers experiencing
widowing or divorce reduce housing equity substantially and significantly more. These results
hold whether we take into account the initial homeowners or continuing homeowners.

The median housing equity declines more for retirees in poorer health than for retirees

91

in good health. The median reduction in equity of movers in poorer health is more sizable
than the median reduction for movers without reported severe illnesses. On average, however,
movers in poorer health do not reduce housing equity any more than movers without reported
severe illnesses. Finally, movers in poor health reduce housing equity significantly more than
non-movers in good health. None of these health effects on changing housing equity, however,
are significant in the regressions for continuing homeowners.16

We also estimate the same regressions including dummy variables for whether the retiree
has any children, any long-term care insurance, and a life insurance. Having long-term
care insurance is negatively correlated with changes in housing equity. It might be that
the retirees who perceive the need to have a long-term care insurance also need to extract
housing equity more to deal with adverse health conditions. We do not find any statistically
significant influence on housing equity adjustments with respect to having children, or a life

insurance.

5 Summary and Concluding Remarks

In this paper we examine whether retirees extract housing equity as part of a gradual decu-
mulation of assets accumulated in preparation for retirement. For the period investigated,
non-movers do not appear to systematically reduce housing equity by taking advantage of
home equity lines of credit or reverse mortgages. But the median housing equity for movers
declines starting at age 71, and the mean housing equity declines from age 76. The overall
mean change in housing equity among retiree-movers is $9,866.2 (—$l,769.5, adjusting for
two outliers) and the median change is —$8,112.3. Large reductions in housing equity, how-
ever, are observed for homeowners who move and discontinue ownership. Typically, housing
equity increases for retiree-movers who continue homeownership.

Retirees are more likely to spend down their non-housing instead of housing wealth, which
suggests that overall retirees might perceive housing equity differently from the rest of their
asset holdings. But there is certainly evidence of heterogeneity in housing equity adjustments

at retirement. Nearly a quarter of the retiree-movers report that they are moving to downsize,

 

16Except that the median equity reduction for movers in poor health is larger than the
median reduction for non-movers in good health.

92

and they do. We examine whether certain individuals are more likely to extract housing
equity because they may have a greater need to use that source of wealth. We find that
retirees with low wealth as well as retirees with low income reduce housing equity significantly
more than their respective counterparts. Additionally, retiree-movers experiencing widowing
or divorce reduce housing equity substantially more than those without similar experience.

The heterogeneity in housing equity draw down among the retirees offers evidence that
at least certain groups of people treat housing as a fungible source of wealth that can be
used to finance general consumption needs during retirement. But at the same time, we have
seen that housing appears to serve predominantly as a consumption good rather than as a
consumption-smoothing saving option for a large segment of the retirees. Our findings are,
in fact, largely consistent with the existing evidence in the literature regarding downsizing
in later life. This study does not disprove or bolster either side of the debate on the role of
housing wealth in financing retirement needs. But it highlights that the choice of emphasis
regarding which side of the debate holds is often reliant on how one chooses to interpret

what is in the data.

93

Figure 3.1: Mean and Median Changes in Housing Equity
Between 1998 and 2004 for 1998—Homeowners (in 2004—dollars)

 

70000

 

] 60000-

] 50000 ,1

1 40000

] 30000-
20000

10000 <

 

 

 

0-

Retirees Non-Retirees

 

[ [IMean Change in Housing Equity .Median Change in Housing Equity 1

 

Table 3.1: Movers’ Reported Reasons for Residential Moving

 

 

 

% Reporting
Reasons for Residential Moving Retirees N on-Retirees
(n=845) (n=977)
To Move to Less Expensive or Smaller Home 24.70 14.44
To be Near or With Child 21.96 13.69
Health Problems or Services Availability 20.58 4.30
Climate or Weather/ Leisure Activities 11 .17 9.30
To be Near or With Other Relatives/ Friends 8.97 6.87
Change in Marital Status 6.83 9.47
Work or Retirement Related Move 6.71 20.84
To Move to a Larger Home 4.84 10.16
Other 31.65 36.58

 

1. Sample consists of non-missing responses from 1,822 movers of the total 2,782 moving
observations in the whole sample of the paper.

2. “Other” includes reasons such as: old neighborhood/ location bad; old home too expensive;
natural disaster; new neighborhood/ location better; moved to retirement housing or complex;
financial reasons; family problems; could not live by self; negative change in economic status of
respondent or spouse/ partner (e.g., respondent or spouse/partner laid off or unemployed);
positive change in economic status (e.g., received inheritance); to care for relative/ family
member, to own instead of rent, etc. Clearly, some of these reasons are already implicit in the
main reasons reported by the movers.

94

Figure 3.2: Changes in Housing Equity for Retirees by Age
Panel A: Mean Change in Housing Equity for Retirees by Age1

 

120000

1mw04—————__-_«.s//\; -m_fi__L_ 2. -LL___
80000 / \
60000 \ A

 

 

 

 

 

 

 

 

 

 

 

] a» 40000 4-

[ 20000 «

1 0 ,

] _20000 56-60 61-65 66:70 71-75 76- 81-85 86-107
] -40000

[ Age in 2004

:;———Non-Movei's—All Movers ""'—Movers Buying A New House]

 

 

 

]

 

Panel B: Median Change in Housing Equity for Retirees by Age

 

 

 

] 50000»

40000-t-——-—ur-—---~-r— - -— n . .- --
[ 30000.-
] 20000 +-

10000 —
: “’ 0
f -10000
. -20000
1 -30000
N -40000 ---v-

-50000 '

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Agein2004

 

1 l'_—N

on-Movers _All Movers ...-Movers Buying a New House [

—_’ _J

 

 

 

 

1' Among retirees, there are two extreme outlier observations in changes in housing equity, both of
which belong to the age group 71-75, producing the huge spikes in Figure 3.2 Panel A. These change
amounts seem consistent with the education, household income, and other assets that these
individuals report. Still, if we consider the mean change in equity for movers excluding those two
observations, the mean change in equity for all movers is $7,187.1 and the mean change in equity for
movers buying a new house is $27,890.9. We present in the Appendix Figure A.3.1 an alternative to
Figure 3.2 Panel A, where the two extreme outlier values have been replaced by the next highest
value for the change in housing equity (instead of excluding the observations).

95

Figure 3.3: Rate of Home Equity Line of Credit Access by Age
(For Retiree Homeowners Throughout 1998—2004)

0.3

0.25

0.2

.\° 0.15
0.1

0.05

 

56-60 61 -65 66-70 71—75 76—80 81-85 86-107
Age in 2004

 

 

 

]—Rate of Home Equity Line of Credit Access (For Retiree Homeowners Throughout 1998- 1'
2004)

Figure 3.4: Initial Housing Equity and Change in Housing Equity
For Non-Movers by Home Equity Line Of Credit (HELOC) Access

 

160,000.00
140,000.00
120,000.00 -
, 100,000.00 -
u 80,000.00 <
60,000.00 -

] 40,000.00
] 20,000.00 .
0.00
[ Accesses HELOC Does Not Access HELOC

 

 

 

 

 

fl—Inifial Housing Equity for Redree Non-Movers ]

 

l ,IChange in Housing Equity for Retiree Non-Movers]

 

96

-10000

-20000

-30000

Figure 3.5: Changes in Non-Housing Wealth by Age

Panel A: Mean Change in Non-Housing Wealth by Age

 

 

 

    

56-60 61-65

S mm]
1

 

 

 

 

 

 

Agein2004

44L '1

 

 

 

5 61-6 66-70 71-75 76:80 81—85, 86-107

 

-1000
—2000

-3000 ~

as -4000

 

 

-5000
-6000

-7000 ~

 

 

 

 

 

 

—8000

Agein2004

 

[—Median Chang? 18,1998599288 Y‘ieakh .1

97

 

Figure 3.6: Changes in Housing Equity among Retiree Movers
By Reported Reason of Moving and By Age

Panel A: Mean Change in Housing Equity among Retiree Movers
By Reported Reason of Moving and By Age:

Movers who say they are moving to downsize versus movers who report other reasons

r. ,__.__ _._-

 

 

.... _._1

100000»

 

50000

 

 

 

-50000

 

—100000 .

 

 

 

 

-150000

 

Agein 2004

 

 

!
[ F365;; for—Moving is Downsizing —Reason for Moving is NotDzwnEI; gj
, .. -L L .H, ”mm ,__ ._ ______-L _ .__L_. ______L.

 

Panel B: Median Change in Housing Equity among Retiree Movers
By Reported Reason of Moving and By Age:

Movers who say they are moving to downsize versus movers who report other reasons

 

 

 

[ 10000
[ 0 ——M r T l I
-10000 56—60 61.19/6625\71—75 J6-80 81-85 86-107

1

| -20000 - ,-___ . —
] 09 -30000
] —40000 -——— —

] 50000 -,__,

] -600001 —
l

]

 

 

 

 

 

 

 

 

 

 

 

 

 

-70000
Agein2004

 

 

1 [”Reasonfor Moving is Down—sizing —Reason for Moving is Not Downsizing l

 

 

 

 

98

 

Figure 3.7: Changes in Housing Equity among Retiree Movers
By Non-Housing Wealth Quartiles and By Age

Panel A: Mean Change in Housing Equity among Retiree Movers
By Non-Housing Wealth Quartiles and By Age

 

 

150000

 

100000 «m _ __ .-__ __ _-_ __.__.__ __
50000 — m4-

0 __

56-60 66-70 71-75 -
-50000 ~-— ———— - —

 

 

 
 

 

    
 

 

 

 

-100000

 

Age in 2004

 

 

Non-Housing Wealth: Lowest Quartile
""""-Non-Housing Wealth: 2nd and 3rd Quartiles
— Non-Housing Wealth: Highest'Quartile _ _g_.

 

 

 

 

k ,2h. 7 “MM -_ ___ _——’_. . . -._—__.__fi.fi__4

Panel B: Median Change in Housing Equity among Retiree Movers
By Non—Housing Wealth Quartiles and By Age

 

140000
120000 ~
100000
80000 v- -—
60000 5~ -- ~
40000 —
20000 .
0 _
-20000
-40000 ~
-60000

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Agein2004

 

f
l

 

Non-Housing Wealth: Lowesnt Quartile
”Non-Housing Wealth: 2nd and 3rd Quartiles,
[— Non-Housing Wealth: Highest Quartile [

 

 

99

 

Table 3.2: The Heteroggneity in HousinLEJruity Extraction

Dependent Variable: Change in Housing Equity Between 1998 and 2004
Initial Homeowners:

Always Homeowners:

 

 

 

Retirees Retirees
Independent Variables OLS Median OLS Median
Regression Regression
(1) (2) (3) (4)
Moved -26,600.4* -20,870.7** - 1 4,5 89.5 -2,456.4
(10,766.7) (4,272.7) (11,352.8) (4,875.0)
Ages 61—65 -25,782.6+ -19,335.6** -28,072.4+ -18,778.9*
(14,239.6) (7,275.4) (14,689.6) (8,057.1)
Ages 66—70 -16,027.5 -13,991.9* -17,320.6 -13,615.6+
(13,3298) (6,817.2) (13,747.0) (7,579.1)
Ages 71 —75 -31,378.1* -19,849.7** -30,164.8* -17,806.1*
(13,003.0) (6,738.9) (13,426.4) (7,497.5)
Ages 76—80 -32,043.2* -20,457.3** -27,020.5* -17,109.3*
(13,023.9) (6,751.6) (13,469.6) (7,518.1)
Ages 71—85 41,080.0'" -21,551.7** -30,494.7* -16,839.7*
(13,144.3) (6,800.2) (13,637.8) (7,587.4)
Ages 86—107 47,707.2’" -28,193.0** -29,079.4* -20,135.6**
(13,607.5) (6,936.7) (14,247.0) (7,784.9)
Female -4,234.4 -287.4 -3,575.2 30.1
(3,784.0) (1,558.5) (3,935.7) (1,725.1)
Education<HSG -6,041.2 -3,925.0* -7,859.2 4,053.9+
(4,833.3) (1,987.8) (5,112.2) (2,235.8)
Education>HSG 12,833.2** 6,887.1** 15,255.8** 8,706.9**
(4,271.5) (1,769.5) ' (4,441.9) (1,961.9)
Household Income
Lowest Quartile -23,769.3** -13,038.0** -31,435.3** -16,101.7**
(7,198.2) (2,960.3) (7,471.1) (3,286.9)
Second Quartile -20,191.3** -12,401.9** -24,940.8** -15,389.5**
(6,369.7) (2,601.6) (6,587.4) (2,872.5)
Third Quartile -15,367.9* -9,636.6** -22,093.1** -10,288.2**
(6,048.1) (2,474.7) (6,199.8) (2,711.7)
Non-Housing Wealth
Lowest Quartile -19,217.6** -15,233.1** -15,745.3* -11,624.1**
(6,535.9) (2,681.5) (6,877.8) (3,016.5)
Second Quartile -18,808.1** -12,701.2** -15,023.1* -10,354.5**
(5,718.5) (2,328.1) (5,969.4) (2,589.6)
Third Quartile -3,003.6 -4,458.1* -3,665.7 4,362.74-
(5,207.1) (2,142.7) (5,342.7) (2,349.3)
Death / Divorce -1,870.1 1,569.5 1,070.6 4,208.0
(6,677.8) (2,671.4) (6,831.3) (2,916.1)
Moved*Death / Divorce —52,999.0** -3l,871.7** -44,666.8** -22,415.9**
(12,347.7) (4,881.5) (15,922.0) (6,646.0)

 

100

Table 3.2 Continued

 

 

 

 

OLS Median OLS Median
Regression Regression
(1) (2) (3) (4)
Health Condition / ADL -2,148.6 -7,327.6** -2,974.5 -7,379.9**
(5,565.6) (2,264.7) (5,610.2) (2,437.7)
Moved*Health
Condition / ADL -8,021.5 -1,045.3 9,123.8 2,734.0
(1 1,637.0) (4,651.0) (12,510.5) (5,405.6)
High Out-of-Pocket -8,763.6 187.7 -6,737.1 1,009.7
(5,920.9) (1,509.8) (6,420.1) (1,684.8)
Living Children4 -1,749.4 -213.1 -2,441.0 1,042.2
(6,820.3) (2,958.2) (7,095.3) (2,732.6)
Long-Term Care Insurance4 -12,l37.3* -5,764.2** -13,087.9** -6,199.5**
(4,882.9) (2,035.8) (5,030.8) (1,884.2)
Life Insurance4 -4,697.1 1,738.6 -7,229.8+ 1,587.9
(4,026.7) (1,703.1) (4,253.5) (1,611.1)
Constant 98,343.8** 59,109.5** 100,603.3** 57,024.5**
(14,491 .9) (7,242.0) (14,930.2) (8,036.1)
Observations 5503 5503 4956 4956
R-squared 0.11 0.10

 

1. Standard errors in parentheses. + Significant at 10%; * Significant at 5%; ** Significant at 1%.
2. Omitted age group 56—60; omitted education group high school graduation.
3. Also omitted are the dummy variables for the highest quartile of household income and the

highest quartile of net non-housing wealth.

4. The estimates for these variables are from a regression using a sample of 5445 observations in
Columns (1) and (2), and a sample of 4913 observations in Columns (3) and (4). We lose
several observations from the original samples due to missing data on these variables. The
estimated coefficients for the other covariates are virtually unchanged in the regression using

the somewhat smaller samples.

101

Appendzbc

Table A.3.1: Sample Selection Criteria

 

Number of
Observations
Total Number of Core Interview Obtained in 1998 21384
If Cohort and Age Eligible1 20002 93'540/0
If 2004 Interview Non-missingz 14380 67-25%
If Homeowners in 1998 11983 56.04%
If Race, Ethnicity, and Education Non-missing 11967 55.96%

If Ownership in 2004 and Marital Status in 1998 a 2004 Non-missing3 11957 559%

I -
1. Observations belong to the four cohorts in the Health and Retirement Study: AHEAD (1890-
1923), CODA (1924-1930), HRS (1931-1941), and War Babies (1942-1947). Note that in 2004
another cohort — the Early Baby Boomer (EBB) cohort (1948—1953) — was introduced in the
Study, respondents from which are not part of our analyses.
The 2004 survey in total interviews 15237 age-eligible respondents from the four cohorts.
3. 6,750 of these observations — 56.45% — are female.

l"

102

Table A.3.2: Housing Wealth, Net Worth, and Demographic Features

 

 

 

 

 

In 1998 and 2004
1998 2004 1998 2004
All Initial Homeowners Homeowners Both Periods
n=11957 n=11083
Age of the Respondent 62.83 68.84 62.47 68.47
(9.16) (9.22) (8.96) (9.01)
Less Than High School Graduation 20.58% 19.97%
High School Graduation 35.54% 35.28%
More Than High School Graduation 43.88% 44.75%
Married or Partnered 74.11% 67.59% 75.73% 70.31%
Respondent Partially or Completely
Retired 53.82% 70.95% 52.83% 70.31%
Respondent Completely Retired 45.52% 61.42% 44.46% 60.43%
Respondent/ At Least One Spouse
Partially or Completely Retired 64.18% 79.31% 63.49% 79.12%
Respondent/ Both Spouses Partially
or Completely Retired 43.96% 61.31% 42.60% 60.13%
Respondent/ Both Spouses
Completely Retired 34.69% 49.59% 33.31% 48.03%
Home-Ownership 100% 92.84% 100% 100%
Net Worth 433,777.8 548,661.6 445,916.7 576,501.2
(833,523.9) (1 ,941 ,638.0) (844,031 .6) (2,000,244.0)
Household Income 72,627.9 63,811.6 74,786.0 65,888.3
(97,775.7) (102,415.6) (99,872.08) (102,079.9)
Gross Housing Wealth 159,711.0 205,433.2 162,932.8 221 ,285.3
(360,086.3) (530,005.4) ' (371 ,899.6) (546,877.9)
Housing Equity 125,190.1 172,827.3 127,912.5 186,164.2
(352,335.8) (510,094.3) (364,526.8) (527,060.4)
Out of Pocket Medical Expenses 3,669.9 6,888.9 3,674.6 6,953.9
(6,900.1) (20,505.52) (6829.7) (20,9325)
Any Child 93.10% 93.07%
Has Life Insurance 83.97% 78.41% 84.72% 79.57%
Has Lon -Term Care Insurance 13.92% 16.33% 14.00% 16.82% _

 

1. Weighted tabulations are reported.
2. Standard deviations in parentheses.
3. Dollar amounts in year-2004 3 (CPI-U deflator).

103

Figure A.3.1: Mean Change in Housing Equity for Retirees by Age
(Adjusting for the Two Extreme Outlier Values for Change in Housing Equity)

 

 

]
l

[

 

100000

80000 1 -

60000 .

40000
20000 <

O ,

-20000 <

 

—40000

 

 

 

_56—60

 

 

61-65%! 66-70 _71-75 76-.. 81-85 86.107

 

 

 

Agein2004

 

— Non-Movers

 

 

All Movers """""Movers Buying A New HOLE

 

Note: The two extreme outlier values of change in housing equip for movers are replaced by the
next highest value of housing equity change in the sample of retirees.

Figure A.3.2: Rate of Home-Ownership in 2004 for 1998-Homeowners

By Age

 

96

 

 

100

95

 

90

 

 

85

 

 

 

80 4»

75

 

 

 

 

70

 

 

 

56-60

66—70 71-75 76-80

Agein2004

61-65 81-85 86-107 ]
]

 

P-Rate of Hpmeownership in 2004 forBetirees

104

Figure A.3.3: Changes in Housing Equity among Retiree Movers
By Total Household Income Quartiles and By Age

Panel A: Mean Change in Housing Equity among Retiree Movers
By Total Household Income Quartiles and By Age

 

80000

60000
40000 , , \

fi

20000

-20000
-40000 w
-60000
-80000

 

 

 

 

Agein2004

 

—Total Household Income: Lowest Quartile ]
"'"'""Total Household Income: 2nd and 3rd Quartiles
_ Total Household Income: Highest Quartile

 

 

 

 

Panel B: Median Change in Housing Equity among Retiree Movers

 

By Total Household Income Quartiles and By Age

80000
60000
40000 ~~
20000

-20000
-40000
-60000

 

Age in 2004

 

l

 

Total Household Income: Lowest Quartile '
Total Household Income: 2nd and 3rd Quartiles;
.— Total Household Income: Highest Quartile [

 

 

105

 

 

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