Does a Bear Market Delay Retirement? An Analysis of the Burst of the 1990s Bubble

Scott Joseph Klein Northwestern University

Mathematical Methods in the Social Sciences Senior Thesis

Advisor: Professor Luojia Hu

May 12, 2006

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Abstract

This paper discusses whether the timing of retirement changes in response to adverse wealth shocks caused by stock market downturns. Past research has shown that the boom of the 1990s tech bubble caused workers to retire earlier than planned, but little has been done to see if the opposite is true due to a bear market. The burst of the bubble in 2001 and the subsequent rebound in 2003 allow for such an examination. Using waves of the Health and Retirement Study from 1998 to 2004, I estimate deviations in planned retirement age following the bear market, providing evidence of its short- and long-run implications. This analysis finds that, immediately after an adverse wealth shock in the stock market, male workers delay retirement by two-and-a-half months for every 10% of assets allocated to risky assets. For example, if a man has half of his assets in stocks, then he would have increased his planned retirement timing by over a year. However, once major American indices rose again in 2003, market exposure was no longer a significant predictor of retirement age, even though risky equity was still well below where it would have been if 1990s performance had continued. This suggests that a bear market affects the actual retirement age of a worker if he initially expects to retire during or soon after the downturn, whereas long-term plans fluctuate with the market, regardless of the magnitude of any individual fluctuation. Acknowledgements I would like to thank Professor Hu for her invaluable insight on the methodology for estimating my model. Without our endless email exchanges and meetings, this paper would have a minute fraction of the validity that it current has. I would also like to thank Professor Mortensen for setting aside time to discuss the theoretical aspects included here. Special thanks to Allison, my parents, and my grandparents for being supportive for the entire year, especially with providing real-life insight into retirement planning. Finally, I am indebted to my friends, especially MMSS brethren Kevin and J.P. There is no way I could have achieved all that I have in the last three years without the unending support and motivation they have been given me.

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I. Introduction

Between 1990 and 1999, Americans oversaw a tremendous period of growth and

economic expansion. Over these years, the S&P stock index grew at a rate of nearly 15

percent per year, which was more than twice its average return for the previous forty

years (Coronado 1). After experiencing such a period of prosperity, commonly entitled

the “bubble” of the 1990s, the bubble had to burst. This burst, a sharply declining bear

market from late 2000 through 2002, substantially reduced the stock values that had

increased wealth by so much in the prior decade. Any wealth invested in the market in

2000 “would have declined in value by one-third” (Kezdi 3) if it performed as the S&P

index did over the next two years. These market fluctuations are of particular interest in

looking at retirement behavior, since those approaching retirement are dependent on the

level of their assets in deciding when to leave the work force.

In the 1990s, the planned retirement wealth of those invested in stocks increased

at a rate faster than could have been expected, while the subsequent bear market had the

opposite effect. As such, the last fifteen years have provided a useful window into the

effects of wealth shocks on retirement behavior. While some empirical work has been

done regarding the effect of the 1990s positive wealth shock on retirement behavior, the

2001-2002 bear market provides insight into the effects of an adverse wealth shock that

have not been thoroughly researched yet.

In 2003, the stock market rebounded from its freefall. While not returning to the

prosperity of the 1990s, the major indices did return to their pre-bear market values by

the end of 2003. As a result, it can be concluded that the bearish period had effectively

ended by the late 2002. In isolating the bear market, I can measure its short- and long-

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term effects on retirement behavior, which is of particular importance to retirement

policy. In recent years, employers have shifted increasingly from defined benefit plans,

which guarantee income after retirement, to defined contribution plans, which shift the

burden of funding one’s retirement to the employee. Therefore, as an increasing

percentage of the working population must rely on their own investments to support their

later lives, stock market exposure is also increasing, so that the effects of the adverse

wealth shock on retirement plans will be felt by more and more workers and employers.

In this paper, I ask whether an adverse wealth shock such as the 2001-2002 bear

market delays retirement, in terms of workers retiring or planning to retire earlier than

they expected before the market downturn. Additionally, I answer this question at two

times, 2002 and 2004. In 2002, the short-term effects of the wealth shock can be seen,

while analysis in 2004 can answer the question of whether or not these effects linger into

the future or are simply offset when the shock is reversed.

This paper’s methodology differs from the few others in the same research area in

that I measure exposure to the stock market by the percentage of one’s assets allocated to

risky assets instead of using a dummy variable for stock ownership or trying to calculate

the exact dollar value of wealth shocks. Section II portrays the manner in which this

method fits into the larger literature of wealth shocks. Section III explains the theoretical

effects of a wealth shock on planned retirement behavior. Using data from the HRS (as

explained in section IV), section V presents the econometric framework for measuring

the model described in section III and the manner in which I separate between immediate

and lingering effects of the bear market. Section VI provides the results of these

empirical measurements, and section VII concludes.

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II. Previous Literature

Quite a bit of literature exists on wealth shocks, especially regarding their effects

on consumption. For example, Maki and Palumbo (2001) predicted a consumption

change of 3-5% of wealth shocks and Kedzi and Sevak (2004) produced an updated

model that predicted a consumption adjustment of 5-7% of a wealth decline after an

adverse wealth shock. However, few papers have been written regarding the effects of

wealth shocks on retirement behavior.

Until recently, most of the research regarding wealth shocks was unrelated to the

stock market. Anderson, Burkhauser, and Quinn (1986) found that unexpected increases

in social security wealth increase the probability of early retirement. Holtz-Eakin,

Joulfaian, and Rosen (1993) showed that large inheritances reduce labor supply while

Imbens, Rubin, and Sacerdote (2001) concluded that recipients of large lottery prizes

reduce labor supply. In general, all past research showed a negative relationship between

wealth shocks and labor supply.

The 1990s stock market bubble and subsequent burst provided prime material

with which to analyze wealth shocks that occur across large proportions of the

population. Several papers have shown that the positive wealth shock associated with the

bubble’s growth added to the slowly increasing trend of early retirement. After receiving

a positive wealth shock of $100,000 from the market, stockholders retired seven months

earlier than nonstockholders and were 3.8% more likely to retire between ages 55 and 60

according to Coronado and Perozek (2003) and Sevak (2002), respectively.

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While the effects of unexpected market run-ups are largely agreed upon, there has

been very little research done regarding the plummeting of the market after 2000. Hurd,

Redi, and Rohwedder (2005) did find results symmetric to those reflected in the 1990s in

that the bear market in 2001-2002 reduced the probably of retiring by age 62. However,

this analysis failed to answer the question of how the wealth shock affects retirement ages

for the entire population. Also, Sevak (2002) performed an analysis suggesting that stock

ownership through the bear market was unrelated to the probability of retiring at a certain

time, once controlling for year effects. She stated, however, that her results were

“somewhat surprising in light of [other cross-sectional] analysis [and that] more research

is needed to reconcile these results” (18). I believe that her failure to find a meaningful

connection between market exposure and changes in retirement behavior after the 1990s

bubble burst was caused by her proxying of the market exposure. Her independent

variable of note was “stock ownership,” equaling 1 if the individual owned stock and 0

otherwise. However, this variable is ineffective in two way: first, it does not control for

total assets, even though stockowners with very few assets react very differently to the

wealth shock than those with a lot of assets. Secondly, she does not account for variation

in stock ownership; namely, differences in behavior caused by the wealth shock should

have greater magnitude as the percentage of one’s assets affected by the shock increase.

Therefore, as Sevak does not control for the biases caused by these aspects of stock

ownership, I attempt to include them in my model and reap more success than she had in

estimating the changes in retirement behavior caused by adverse wealth shocks.

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IIIa. Retirement Decisions in an Intertemporal Budget Constraint Model with

Uncertainty

In order to decide when to retire, an individual needs to first determine what his

financial needs are. Upon retirement, a worker has accumulated AR, his total assets at

retirement. He will then be drawing upon these assets for the rest of his life. In planning

how much is needed to retire, the individual needs to plan his consumption in this period.

At any given time t, the planned annual level of consumption during retirement is:

Ct = f(E(Ht), Zt, E(xt), xt)

where f is a function that maximizes the utility of consumption based on expected future

conditions at time t, knowing that future consumption infers that current consumption

must be put off. These conditions include E(Ht), which is an individual’s expected health

status during retirement, and Zt is a vector of personal and household characteristics

including marital status and demographic information such as race and sex. Both of these

variables include necessary expenses like health care and dependents’ education. Also, xt

is a composite of situations that would drastically affect Ct. One example of this is forced

retirement, which lowers Ct since you no longer have earned income with which to

increase assets to draw on. Even if such events do not occur, an individual may provide

for a risk premium in case they do. This risk premium is shown by the factor E(xt),

which augments retiree consumption.

One additional item to consider is that retirement may consist of some unearned

income. Annual unearned income (UEt) is the sum of social security payments and any

defined benefit or other annuity payments that may be expected. Altogether, the

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worker’s planned amount of assets at retirement (ARt) at time t is the sum of his annual

dissaving:

T

ARt = ∫ (Ct – UEt) · e –r(t-R) dt

R

where R and T are the expected ages of retirement and death and r is the individual’s

personal discount factor. Note that the integral ends at time T because an individual

needs to plan how long he is going to live for in order to not run out of assets. This time,

T, is a function of the expected health of an individual leading up to his mortality.

Before a worker can retire, he needs to save up to ARt. Every year, a working

individual earns an income yt. This income includes wages, income from assets, and any

other known source of cash inflow. According to Keynesian theory, the worker

consumes a certain proportion of this annual income. At any given time t, this proportion

is his marginal propensity for consumption, MPCt. The portion of income that is not

consumed is saved, so that the worker’s marginal propensity for saving, MPSt, equals 1 –

MPCt. Using this value, annual saving (St) can be calculated as:

St = MPSt · yt

and is between 0 and yt. Then, when making retirement plans at time t, the total amount

saved at retirement equals:

R T AR

t = ( At · ei(R-t) + ∫ St · ei(R-t) dt ) =( ∫ (Ct – UEt) · e –r(t-R) dt )

t R

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where i is the expected rate of interest earned on assets until retirement and r is the

personal discount factor. In determining St, the individual needs to maximize his utility

of consumption before retirement (taking into account the disutility of not saving), again

being aware of the tradeoff between pre- and post-retirement consumption. Since yt is

known, St changes based on MPSt at any given time so that:

MPSt = g(Ht, Zt, wt)

where g is a function that maximizes the utility of pre-retirement consumption based on

expected conditions at t. Ht is the worker’s health status at time t and Zt is the same as in

function f above. Also, wt is a composite of situations that would drastically affect MPSt

(similar to xt), and in fact the variables wt and xt could be the same. However, they need

not be identical, such as a situation that affects consumption temporarily like such as a

short-term mortgage purchased long before retirement.

All else equal, a worker decreases the amount needed to reach ARt by St · ei(R-t)

each year. However, St is not constant until retirement, since MPS and income most

likely change over time. At any given time, a worker’s planned annual savings should

take into account the fact that wages should increase and it is common for those

approaching retirement to save a higher percentage of income for their post-working

years than they did at a young age. Therefore, instead of assuming that St is fixed until

retirement, I will suppose that each individual takes the uncertainty into account at t and

has an expected value of annual savings until retirement, Et(S).

After determining his financial needs, a worker needs to decide his total utility of

reaching different levels of wealth at retirement. An individual’s total utility of retiring at

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a given age is a function of the level of retirement consumption that would result and the

retirement period T – R that would ensue:

U(retirement behavior) = U(Ct, T – R)

Clearly, his marginal utility is increasing in each of these factors, since greater spending

infers an improved quality of living and, presumptuously, leisure is preferred to work. If

the individual wants to consume at a higher rate after retirement, then his expected total

assets at retirement must also increase, causing his expected retirement age to increase as

well. Therefore, assets at retirement (a proxy for retiree consumption) and household

time have a negative marginal rate of substitution. Additionally, this rate of substitution

is diminishing as household time increases, since average wealth and average retirement

should be preferred to extreme amounts of wealth accompanying strongly rushed or

delayed retirement.

Figure 2 in the appendix portrays the relationships explained above and the

manner in which they provide for the optimal retirement age at time t for a given

individual.1 The budget line for the individual is the expected discounted value of total

assets at retirement at t. The vertical part represents the worker’s current assets, and

hence assets at retirement if he chooses to retire immediately. Beyond that, the budget

line is set at expected assets when retiring at a given age. The difference between assets

at time R versus t is the sum of the discounted values of Et(S) saved for the next R – t

years. When discounting the savings, note that the worker predicts the rate at which his

assets will grow until retirement, iEt. This rate of asset growth may or may not differ

1 Figure 2 is loosely based on Ehrenberg and Smith’s “Choice of Optimum Retirement Age for Hypothetical Worker” (227), which determines the optimal retirement age of a worker based on his discounted value of lifetime income.

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from actual asset growth over this time, based on market exposure and conditions. The

indifference curve shown is all combinations of predicted assets at retirement and

retirement age that produce the same utility for the individual. According to

microeconomic theory, the worker should plan to retire at the age when his marginal rate

of substitution between added assets at retirement (which determines retiree

consumption) and continued work equals the slope of his budget line, which is shown at

point M = (Rm, Am). Therefore, at time t, the worker’s utility-maximizing strategy is to

retire at age Rm, which, with the current plans for asset accumulation, will leave him with

Am (supplemented by the unearned income) to draw upon during his retirement.

I can now characterize the formation of retirement expectations in a general sense.

This model is based on the model used by Coronado and Perozek in their 2003 paper in

determining retirement expectations. Given an individual’s utility function of predicted

assets at retirement and leisure time and his expected savings over his working life, figure

2 shows his expected retirement age. The utility of a certain value of assets at retirement

is determined by the individual’s desired consumption until death, which is a function of

Zt, Ht, and xt, and his predicted remaining life after retirement, which is a forward-

looking function of Ht. In order to reach the predicted assets at retirement, an individual

takes into account his current assets, At, the expected rate of adding to his assets, Et(S),

and the expected rate at which these assets will grow, iEt. The predicted rate of saving

until retirement, Et(S), is a function of yt, Zt, Ht, and wt. Using the method shown in

figure 2, an individual’s planned retirement age at t (REt) can be characterized as:

(1) RE

t = E(R | At, iEt, yt, Zt, Ht, wt, xt)

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While current assets and personal factors (Zt) are known at t, the remaining

variables are uncertain over time. As stated earlier, the individual should take into

account this uncertainty and hence maximize the expected utility of retiree consumption

and expected savings until retirement when making retirement expectations. According

to Coronado and Perozek, “as time goes by, some uncertainty about investment returns,

health status, and [the shocks w and x] is resolved” (3). Therefore, after the passing of

some time, workers can see if the realized conditions of these variables differ from initial

expectations, and, if so, recalculate their expected retirement age.

At time t+∆, suppose the worker reconsiders his retirement plans. The difference

between expectations and reality at t+∆ can cause changes in the utility function of retiree

consumption versus leisure and/or the accumulation of assets until retirement. As such,

the indifference curves and budget line for the individual may shift, so that the optimal

retirement age at t+∆ may not equal that at t. While differences between expectations and

realizations of these variables affect retirement behavior, the manner in which they do so

depends on the initial conditions. For example, a male worker with a wife and two

dependent children may react to unexpectedly poor returns on assets differently than a

single woman would. Additionally, someone who experiences a large reduction in asset

return will be more affected the greater his assets are to begin with. Therefore, change in

retirement plans, after updating at t+∆, is:

(2) RE

t+∆ = E(R | REt, ∆A, ∆iE, ∆y, ∆Z, ∆H, ∆w, ∆x, At, Zt)

where each “∆” variable is the difference between the expected and realized values at

t+∆. In general, the fewer changes there are in wealth, health, and other variables, the

less retirement plans should vary between times t and t+∆.

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IIIb. Changing Wealth and Retirement Behavior

In late 2000, the major United States market indices took turns for the worst after

a decade of prosperity. As stated earlier, changes in retirement expectations should result

only from unanticipated changes in the factors determining retirement. When dealing

with the accumulation of assets, I propose that differences in expected retirement age at

two time periods are produced when the actual amount of assets and/or expected future

return on assets at the latter time do not equal the expected amount of assets and/or

initially expected future return. If an individual is highly dependent on the stock market,

then his expected assets and future return before the bubble should differ than those

afterwards much more than one whose assets are focused in fixed-income investment

vehicles. In fact, if an individual has all non-risky assets (which have a fixed return such

as those invested in a certificate of deposit), then there should be no deviations from

financial expectations. Therefore, in analyzing this paper’s model with respects to the

2001-2002 bear market, it is necessary to break the population down into those who have

risky assets and those who do not.

Between t and t+∆, if a worker has all of his current assets invested in non-risky

assets such as certificates of deposit and government T-bills, then his expected asset total

should equal the actual amount at time t+∆. Therefore, the budget line of discounted

value of total assets predicted at t is correct up to t+∆. At t+∆, the worker has to

reconsider his future stream of savings until retirement. This reconsideration involves a

reallocation of current assets between risky and non-risky investment vehicles and an

adjustment of expected savings until retirement. First, since the worker had no risky

assets at t, it can be assumed that he is risk averse to some degree. As such, he should

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increase the risk of his portfolio at t+∆ only if the investment outlook is improved from

that at t. Since risky assets are expected to have a higher rate-of-return than non-risky

assets (due to respective risk premia), a pessimistic investment outlook would continue to

keep this worker’s assets away from the stock market and hence keep iE fairly constant.

On the other hand, positive expectations for the market would increase allocation to it, so

iE would increase. Therefore, ∂iE/∂w = 0 for a bear market and ∂iE/∂w > 0 for a bull

market for those with no risky assets at t, where w represents macroeconomic shocks

between t and t+∆.

The total marginal effects of a bear market on an individual with no risky assets is

portrayed in figure 3. After a downturn in the market between t and t+∆, this consumer

has no reason to increase his market dependence, so iE remains fairly constant. Also,

since his planned investments have not changed, E(S) also remains constant, ceteris

paribus. The result is an unchanged line of discounted values of total assets predicted at t

and t+∆. Other factors constant, the bear market should therefore have no effect on this

individual’s retirement plans.

While there is no predicted effect on expected retirement age for a worker with no

risky assets, the same cannot be said when an investor has some degree of exposure to the

stock market. When the market spikes in some manner, the actual return on investments

differs from the predicted return. Therefore, for two consumers with the same amount of

assets, the difference between their actual asset value at t+∆ and their predicted amount at

that point (∆A) is greater for the one who has a higher percentage of his assets in the

market at t. Where %Risky is the percentage of a worker’s assets that are allocated to

investments that have variable returns, ∂∆A/∂%Risky > 0. Likewise, the effect of a

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market spike on iE is greater if a consumer is planning to be more exposed on the market

in the first place, so that ∂∆i/∂%Risky > 0.

Figure 4 shows the effects of a bear market on an individual who has some of his

assets allocated to the stock market. When returns fail to live up to expectations, the

budget line for the individual shifts down. First, notice that the value of total assets at

t+∆ is lower than that predicted at t. Also, the steepness of the slope of asset

accumulation has decreased from that at t. As explained earlier, this slope is determined

by the expected savings and the assets’ expected return of the individual until retirement.

In Kedzi and Sevak’s 2004 paper, they state that “households respond to decline in

wealth by reducing their consumption by 5 to 7 percent of the wealth decline” (1). This

finding infers that the entire population of older workers is somewhat elastic in their

saving, so that a bear market will cause their annual savings rate to increase. However,

while annual saving is augmented to maintain post-retirement spending, the reduction in

expected return on assets offsets the adjustment. Figure 4 shows the case in which

greatly decreased returns slow overall asset accumulation, despite any increases in

saving. This is conferable to the 2001-2002 bear market case where returns plummeted

after being so high for a decade (presumably causing expected returns to also be very

high). In general, the more an individual is invested in the stock market, the slower (less

steep) the planned accumulation will become at t+∆ as compared to t because of the

weighted effects of E(S) and iE.

While a worker with risky assets has a predictable budget line change after a

strong bear market, expectations for his actual retirement behavior are ambiguous. The

ambiguity stems from the fact that there are wealth and substitution effects offsetting

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each other after the adjustments in E(S) and iE. According to microeconomic theory, it is

certain that the slowed asset accumulation will cause the worker to choose to retire with

less assets than initially planned , but it is not certain when he will do so. Since he needs

to work longer in order to make up for lost assets, the worker should expect to retire later

than initially planned after the bear market. However, since the opportunity cost of

working instead of retiring at a given point is reduced, the worker should hurry his

retirement. In figure 4, the worker moves from point M at t to point N at t+∆. As such,

his wealth effect dominates the substitution effect arising from the wealth shock, so that

RE increases. In the end, whichever effect dominates for the general population will tell

whether workers delay retirement or not after an adverse wealth shock and worsening of

expectations. Past literature has shown that positive wealth shocks cause workers to

retire earlier than expected, meaning that the wealth effect dominated, so I predict that the

same is true in the opposite case of the 2001-2002 bear market. The empirical results for

this paper should help in answering this conundrum.

IV. Data: The Health and Retirement Study

The Health and Retirement Study (HRS) is a panel data set provided by the

Institute for Social Research at the University of Michigan. Its purpose is to provide

researchers with data for studies in aiding the formation of policies that affect retirement,

health insurance, saving and economic well-being. The HRS therefore has very useful

data for this particular study, including detailed information on health, employment, and

household characteristics.

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Since 1998, the HRS has collected its panel data set via a phone or telephone

interview of individuals in a “nationally representative cohort of people” (Coronado 5).

In 1998, this cohort included individuals born between 1890 and 1947 and their spouses.2

Every two years, each individual is re-interviewed, in order to gain perspective of his

actions over time. I use data from the last four completed waves of the HRS (1998, 2000,

2002, and 2004), which captures the last wave that completely took place during the

1990’s stock run-up and then remaining observations up to the present. Only individuals

who were “not at all retired” in 1998 were included in sample, since those who are

partially or already retired obviously do not have valid retirement age expectations. Also,

those who retired and then returned to work were removed from the sample, since they

are assumed to have unreliable retirement expectations.

Table 1 shows the planned retirement ages of workers who hold no risky assets

versus some risky assets. As you can see, the expected retirement age was lower in 1998

for those holding risky assets, supporting the idea that a positive wealth shock causes

early retirement. While planned retirement ages are increasing from 1998 to 2004 for all

individuals, they are increasing for stockholders at near double the rate as they are for

non-stockholders. This provides evidence of two trends: first, that retirement ages are

feeling some time effect and are increasing regardless of market exposure, and second,

that those exposed to the market are responding more

Table 2 contains summary statistics for the other variables to be used in the

analysis, broken down by gender. Interestingly, while women hold less risky assets and

less overall assets than men in 1998, they actually delay retirement more after the bear

2 Spouses of age-eligible respondents, who may be born later than 1947, are also included in my sample.

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market. Women also tended to plan to retire earlier before the bear market, but then had

a lower chance of being retired between 2000 and 2004.

Va. Econometric Specification: Immediate Effects of the Bear Market

Since 1998 was the last wave of the HRS that completely took place during the

1990’s boom, 1998 is used as time t, since expectations should be very positive for the

growth of individual assets. After 2001 and 2002, a recession hit, which should have

been unexpected and therefore would have caused changes in retirement expectations

according to equation (2). Therefore, the first method for measuring this equation is the

following OLS regression:

R02 – RE98 = β0 + β1(%Risky) + β2(∆H) + β3(A98) + β4’(Z98) + B5’(Retirement) + ε

where R02 = RE02 if still working in 2002 wave, actual retirement age if not

This model, like all others in this paper, was estimated separately for men and women,

since past empirical work has shown each gender to act differently in terms of retirement

planning.3

When creating this model this from equation (2), a few changes have occurred.

The first change occurs because expectations of future assets and investment return are

not explicitly documented in the HRS. In fact, even asset information that is documented

is often incomplete or inconsistent through different waves of the HRS. Therefore,

instead of relying on actual or calculated asset variables, my model uses “%Risky” as a

3 According to Coronado and Perozek (2001), since it is likely that “women…represent that marginal worker in the family” (14), their reaction to a wealth shock differs from that of men. In Bazzoli’s (1985) structural model of retirement age, women were not even included in the regression because of their confounding effects.

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proxy in their place to determine their relationships to retirement behavior. An

individual’s total assets includes his total values of real estate, personal business or farm,

checking accounts, savings accounts, certificates of deposits, treasury bills, stocks, risky

bonds (such as municipal and corporate bonds), vehicles, and other assets. His %Risky is

the ratio of stocks plus risky bonds to his total assets. As explained earlier, as %Risky

increases, so does the absolute value of the term ∆A and ∆rE between 1998 and 2002, so

%Risky proxies the unexpected changes in wealth caused by the bear market.

While %Risky is included in the regression, its interpretation is even more

meaningful with the inclusion of total assets at t. %Risky is a measure of an individual’s

exposure to the stock market, but overall reconsiderations of retirement behavior depend

also on total assets. If a worker has a large amount of assets and a small percentage of

them are allocated in risky investments, he is still more likely to adjust his retirement

plans due to the wealth shock than an individual who has a high %Risky but a very small

initial asset total. Therefore, controlling for total assets at t allows me to gauge the

effects of market exposure on individuals at the same level of assets, which is the real

question at hand and the situation prescribed in figures 3 and 4.

One aspect of Z98 that should be important is whether or not the individual

participates in a defined benefit plan, which provides guaranteed income after retirement.

As opposed to individuals who participate in defined contribution or no retirement plans,

defined benefit plans may decrease the delay of retirement, since they “effectively dictate

the date of retirement; therefore, the labor supply of those with DB plans may be less

elastic with respect to economic…status” (Coronado 8). DB plans usually have

actuarially determined penalties for retiring at an age other than normal retirement

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(usually 65). As such, the substitution effect is greater for individuals with DB plans, as

a decrease in household time gives them less utility, so this should lead individuals to

adjust their consumption in order to offset asset losses and avoid changing their

retirement age.

The variable ∆y is omitted in the econometric specification. Changes in income

are not included for the same reason as asset deviations. Information regarding income is

very incomplete in the HRS, and even if income amounts are accurately provided, there is

no way to know what aspects of their changes are unexpected. For example, unexpected

changes would arise out of large pay raises or pay cuts, which unrelated to inflation-

indexing or other common annual raises. However, the HRS provides no such details.

Therefore, from 1998 to 2002, the effects caused by unexpected income changes are not

taken into account. Since this is a short time frame, a large percentage of the population

should not have been affected by such large income shocks, though. Since other, smaller

income shocks such as decreased dividend earnings should be taken into account by

%Risky, the omitted variable should not take away from the overall accuracy of the

model.

The variables ∆Z, ∆w, and ∆x are also omitted from the model. Between any two

time periods, demographic characteristics such as race, gender, and education do not

change. Also, household characteristics such as participation in a DB plan, marriage

status, and children should not change over this relatively small time period. Therefore,

∆Z ≈ 0 for this model, and hence the variable is not regressed on. Finally, the shocks w

and x are difficult to account for in an empirical model because they can include different

factors for different individuals. One important aspect, though, is the effect of time

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passing between 1998 and 2002, which affect both w and x. If the entire population

increases their expected consumption at retirement, then it would retire later due to

macroeconomic factors unrelated to the individual effects in the model. These time

effects should be included in the constant β0.

The other piece of the econometric model that is not included in equation (2) is

the vector “retirement,” representing two factors that complicate the analysis of changes

in retirement expectations. The first of these factors is whether or not the individual

actually retired over the observed time frame. This β value should show the difference

between actual and planned retirement ages. Also, this coefficient should give some

information regarding people who were close to retiring when the market plunged, and

how they differ from people whose planned retirement is far in the future. Finally, if an

individual intends to retire earlier than “normal,” which is typically considered age 65, he

may be more likely to delay retirement than someone who has already pushed his plans

back. Therefore, I allow for variance based on general initial retirement expectations,

namely whether or not the worker expected to retire early when he made his bullish-era

expectations.

In addition to forming an OLS model of deviations from expected retirement age

from 1998 to 2002, I use a probit regression to model whether or not an individual delays

retirement at all. The probit model is specified as:

Pr(Ret Delay) = Φ[β0 + β1(%Risky) + β2(∆H) + β3(A98) + β4’(Z98) + B5’(Retirement)]

where Ret Delay = 1 if (R02 - RE98) > 0, and

R02 = RE02 if still working in 2002 wave, actual retirement age if not

22

Notice that all of the independent variables are the same as in the OLS regression.

The difference in interpretation here is whether or not these variables, with the same

effects as predicted above, cause a delay in retirement or not, versus having effects on the

absolute deviation in years until retirement. Specifically, the sign of the marginal effect

of %Risky should portray whether or not the wealth effect dominates in the individual’s

reactions to the bear market.

Vb. Econometric Specification: Do the Downturn’s Effects Persist?

In the beginning of 2003, it appeared that the horrendous stock market downturns

had ended. For about a year between the early 2003 to early 2004, the major American

indices rebounded to the point that they had approximately reached their 2000 marks. In

2004, the markets flattened again, but the negative turns were short-lived and not nearly

as prominent as the ones in 2001 and 2002. As such, the newest wave of the HRS,

measured in 2004, provides interesting insight into the long-term effects of the 2001-

2002 bear market.

Using 2004 as time t+∆ instead of the 2002 wave, the OLS and probit regressions

were rerun:

R04 – RE98 = β0 + β1(%Risky) + β2(∆H) + β3(A98) + β4’(Z98) + B5’(Retirement) + ε

where R04 = RE04 if still working in 2004 wave, actual retirement age if not

Pr(Ret Delay) = Φ[β0 + β1(%Risky) + β2(∆H) + β3(A98) + β4’(Z98) + B5’(Retirement)]

where Ret Delay = 1 if (R04 - RE98) > 0, and

R04 = RE04 if still working in 2004 wave, actual retirement age if not

23

Notice that the dependent variables changed to compare data from 2004, instead

of 2002, to 1998 data. Also, health deterioration was updated to include the newest wave

of answers, and “retired” now includes those who retired from 2001 up through the most

recent information.

According to my model, the directional effects predicted in the previous section

should remain the same. Since returns were so strong for a decade up until 2000, the fact

that risky assets had nearly equal value at 2000 and 2004 should have produced an

unexpected adverse wealth shock. Theoretically, the only difference between the

coefficients in these regressions versus the 2002 ones is that the finance-related variables

should be of smaller magnitudes.

In reality, comparing the results from the 2002 and 2004 regressions provides a

glance into the long-term effects of a bear market. In analyzing this long-term response,

it is imperative to veer from the model a bit in order to more accurately portray individual

behavior. It is rational to suppose that workers have a range of assets that they will not

adjust retirement plans for, instead of a single optimal asset total. If their assets are

within this range, the wealth effect caused by market changes will be completely offset

by the substitution effect so that retirement age is unchanged. However, if a shock causes

assets to decline below the range’s minimum, then deviations from retirement are caused

by the wealth effect. This dichotomy works in the opposite direction as well, with the

subsitution elasticity of retirement behavior increasing once the original asset range is

reached. 4 For example, consider a situation where the 2001-2002 bear market caused a

4 Consistent with Kezdi’s 2003 findings, I assume that the wealth elasticity of retirement behavior is fairly constant for an individual. This way, when an individual has the same absolute deviation in two periods when the absolute market deviations are not equal, this should be due to changing substitution effects relative to the wealth effects.

24

worker to delay retirement. Then, the 2003-2004 rebound caused assets to increase

again, though not to the point expected before the downturn. If the rebound from an

adverse wealth shock caused assets to increase enough that they reach his allowable

range of retirement assets, then this individual may revert to his initially planned

retirement age. In doing so, he plans to have fewer expected assets than originally

expected, though, and therefore reduces his planned consumption upon retirement.

With these conditions in mind, now consider the long-term effects of the bear

market. In the long run, if the bear market affects retirement age, then %Risky should be

significant and have the same sign in each regression. Such a situation means that the

wealth shock was powerful enough to change planned retirement age, despite a

subsequent rebound. However, if %Risky is no longer significant in the 2004 regression,

then the rebound offset the bear market enough to allow the general population to retire

as they had originally planned, though with less planned assets than before. In this latter

case, the bear market only affects the retirement age of individuals in the short-run,

meaning it affects the actual retirement behavior (as opposed to planned behavior) of

those who initially planned to retire during or soon after the downturn. Meanwhile, those

who do not expect to retire for many years have plans that fluctuate with the market, but

are not significantly affected by any single fluctuation such as the 2001-2002 bear

market.

One item to note is that the 1998 %Risky is used in each regression to proxy

responsiveness to market conditions. However, most workers will have reallocated their

assets in some manner between the downturn and upturn, so it would be helpful to

produce a panel data analysis in modeling the change in retirement plans from 1998 to

25

2002 and then from 2002 to 2004. Unfortunately, a large amount of incomplete asset

data rendered the 2002-2004 regression completely ineffective. Therefore, it is assumed

that the manner in which people respond to the market fluctuations depends on their

“market exposure,” as proxied by %Risky98. This makes sense since, in general, those

with higher percentage of risky investments in 1998 will also have higher percentage of

risky investments in 2002 based on their risk aversion.

VI. Results

Table 3 presents coefficients for the OLS regression of deviation of the 2002

expected/actual retirement age from the 1998 expected retirement age for men and

women. Also, table 4 presents the marginal effects of different predictors on the

probability that an individual retires later than planned. Both of these results display the

short-term effects of the bear market.

For men, the effects of %Risky are as predicted, having positive and significant

effect on both deviations from initially planned retirement ages and the probability of

delaying retirement at all. If an individual had half of his assets in the stock market in

1998, then there is a 17% higher chance that the adverse wealth shock caused him to

retire later than expected and, on average, he delayed retirement by almost an entire year

more than someone without any risky assets. Interestingly, women felt no such wealth

shock. The coefficients for female exposure to the stock market are very insignificant in

2002. The gender difference is consistent with Coronado and Perozek’s marginal worker

theory that women hurry retirement before men do after a positive wealth shock. It

appears here that, after the 2001-2002 bear market, women are more averse to delaying

retirement than men in terms of recovering lost wealth.

26

In line with this marginal worker theory, table 3 also suggests that women are

more reliant on DB plans and working spouses than men. A woman with a DB plan will

delay retirement by nearly eight months less than one without a DB plan and a woman

with a working husband will delay retirement by almost six months less than one without,

while men show no significant effects regarding these factors. Since both a DB plan and

a working spouse guarantee future income for a woman regardless of her work status, it

makes sense that their marginal effects on retirement age deviations are negative.

However, the fact that males are not likewise affected suggests that it is the job of men to

make up the difference in lost wealth by adjusting retirement plans, while women can

depend on their husbands and their pensions.

Interesting results arise out of the “retirement” variables. First, if a man retired by

2002, then he is expected to do 18 months earlier than planned, but this retired man is

also 14% more likely than a working one to have delayed his retirement at all. The

discrepancy is even larger for retired women, who stop working 22 months before they

initially expected to, while also being 16% more likely to have retired later than planned.

The discrepancy for both men and women suggests the presence of a small proportion of

retirees who chose to greatly reduce their retirement age while the majority delayed it to

some degree after the bear market.

Both of these effects can be predicted by the model explained earlier. Obviously,

the fact that most retirees delayed retirement makes sense given the wealth effect. Also,

those who greatly decreased their retirement age likely reached a corner solution due to

27

the bear market. One example of a corner solution is forced retirement.5 If, after the

market downturn, an individual’s employer forced him to retire, then his indifference

curves become nearly vertical. The disutility of his working is extremely high, since he

may face legal action, and should exceed the disutility of reduced retiree consumption

due to early retirement. As such, the best response for an individual facing forced

retirement is to retire immediately, regardless of future plans.

Finally, individuals who initially expected to retire before age 65 were much more

likely to delay retirement. Men were 17% more likely to delay retirement if they had

planned an early retirement, and on average did so by over 14 months. Women who

planned to retire early were even more sensitive to the wealth shock, as they delayed

retirement by an average of nearly two years and were 30% more likely to delay

retirement than those who did not intend to stop working by 65. These effects are as

predicted, where workers should be much less averse to pushing their retirement ages

back to normal retirement than to pushing them back beyond 65.

Additionally, the fact that people expecting to retire by 65 were largely expected

to delay retirement gives insight to the long-term effects of the 1990s bubble. While

empirical research has shown that retirement ages decreased during the bubble, the

elasticity of retirement plans in moving the other direction is seen here. If an individual

started by expecting to retire at the normal age (65) and then reduced his expectations in

the 1990s, he is likely to revise his plans upward again to 65. This suggests that, in the

long run, the bull market may have only affected workers’ plans if they retired before the

5 Since those forced into retirement are suspected to behave differently than those who retire voluntarily, it would be helpful to regress on the reason for retirement. Unfortunately, the HRS does not provide adequate data on forced retirements, so I cannot include this in my empirical work.

28

onset of the burst. Effectively, there are definite short-term effects of the positive wealth

shocks, but those effects run out after the shock has ended.

The long-term effects of the 1990s bubble appear to be replicated by the 2001-

2002 bear market. In tables 5 and 6, the OLS and probit estimates of planned retirement

age changes between 1998 and 2004 are portrayed. The major difference between these

regressions and the prior ones is that %Risky is no longer a significant predictor of

retirement behavior. For both men and women, additional exposure to the stock market

does not infer anything about changes in planned retirement age from 1998 to 2004.

As explained earlier, this insignificance suggests that the bear market has no

significant long-term effects on retirement age. Given the tremendous returns in 1998,

E98(A04) should still exceed A04 as %Risky increases, though not as much as in 2002. As

such, if the bear market had long-term effects on retirement age, then %Risky should

have a positive and significant coefficient in 2004. However, it appears instead that the

2003 rebound was enough to negate the adverse wealth shock by causing assets to get

near enough to expected assets (in 1998) for the substitution effect to offset the overall

wealth effect. In sum, these results suggest that the long-term effects of the 2001-2002

bear market were that workers did not experience enough of a wealth shock to change

planned retirement age, but do settle for this by reducing planned consumption after

retirement.

Another interesting note from the 2004 regressions is that the fact that an

individual retired means that, on average, he or she did so three years earlier than initially

expected. While the sign on this coefficient is the same for the OLS models, the sign on

this coefficient in the probit model switched from positive to negative for both men and

29

women (although is only significant for the latter). The OLS effects are more negative

after the 2003 rebound, which makes sense because the positive wealth shock would

allow more workers to retire earlier than planned and no such corner solution exists that

is opposite of forced retirement. Likewise, there should not a discrepancy between

changes in retirement deviations and chances of delaying retirement, since past research

shown that the substitution effect is outweighed by the wealth effect during a market

upturn.

One final item to notice is that decreasing health status was never shown to be a

significant factor in retirement age, as predicted. This insignificance may have been

caused by a lack of information regarding retiree health care. When not controlling for

this, the effects of ∆H are ambiguous. First, suppose one’s health situation worsens, so

that ∆H < 0, and he has employer-paid retiree health insurance. In this case, there is no

direct medical cost to the worker, but the opportunity cost of working is higher because it

is more difficult to work, albeit for physical or mental reasons. Therefore, there should

be a reduction in RE due to the pure substitution effect. As a result, when an individual

does not pay for health insurance after retirement, ∂RE/∂H > 0, so time until retirement

decreases with a worsening health status. On the other hand, if a worker does not have

retiree health insurance, then he has offsetting income and substitution effects caused by

increased health care costs and increased difficulty of working. ∂RE/∂H is not predictable

in this case. I suspect that the lack of differentiation between those who do and do not

have retiree health care caused the insignificance of negative health shocks, which should

have had negative effects on every dependent variable being regressed.

30

VII. Conclusion

Using data from the Health and Retirement Study, this paper estimates labor

supply responses of individuals approaching retirement to the 2001-2002 bear market. I

find that, immediately after the downturn, men delayed their retirement by nearly two-

and-a-half months for every additional 10% of asset allocation to risky assets. Also, the

entire population was about 15% more likely to retire later than expected if they actually

retired during the bear market than if they worked through it. Therefore, in the short run,

the adverse wealth shock caused by the burst of the 1990s bubble did cause increases in

retirement ages. However, after the market rebounded, those increases were negated, as

the probability of delayed retirement was lower for those who retired and market

exposure was no longer a significant factor in predicting retirement age deviations from

before the adverse wealth shock.

In general, these results support the fact that retirement ages negatively correlate

with wealth shocks. In the long run, though, the 2003 rebound proved that the burst of

1990s bubble was not powerful enough to cause retirement ages to increase. Despite

short-run fluctuations, the entire economy is expected to grow at a fairly constant rate,

and the chances of experiencing an adverse wealth shock as strong as that in 2001 are

very low. Therefore, these fluctuations over the years become unimportant in predicting

actual retirement behavior. This provides some optimism for the future of retirement

expectations. If the devastating 2001-2002 bear market was an adverse fluctuation whose

effects were forgotten after a modest bull market, then retirement expectations can be

trusted throughout most imaginable financial shocks.

31

Bibliography Anderson, Kathryn H. and Richard V. Burkhauser. “The Retirement-Health Nexus: A New Measure of an Old Puzzle.” The Journal of Human Resources 20 (3): 315- 330 (1985). Anderson, Kathryn H., Richard V. Burkhauser, and Joseph F. Quinn. “Do Retirement Dreams Come True? The Effect of Unanticipated Events on Retirement Plans.” Industrial and Labor Relations Review 39 (4): 518-256 (1986). Bazzoli, Gloria J. “The Early Retirement Decision: New Empirial Evidence on the Influence of Health.” The Journal of Human Resources 20 (2): 214-234 (1985). Cheng, Ing-Haw and Eric French. “The Effect of the Run-Up in the Stock Market on Labor Supply.” Economic Perspectives – the Federal Reserve Bank of Chicago 24 (4): 48-65 (2000). Coronado, Julia Lynn and Maria Perozek. “Wealth Effects and the Consumption of Leisure: Retirement Decisions During the Stock Market Boom of the 1990s.” The Federal Reserve Board Finance and Discussion Series 2003-20, 2003. Ehrenberg, Ronald G. and Robert S. Smith. Modern Labor Economics: Theory and Public Policy. USA: Pearson Education, Inc., 2006. Fields, Gary S. and Olivia S. Mitchell. “Economic Determinants of the Optimal Retirement Age: An Empirical Investigation.” The Journal of Human Resources 19 (2): 245-262 (1984). Hall, Arden and Terry R. Johnson. “The Determinants of Planned Retirement Age.” Industrial and Labor Relations Review 33 (2): 241-254 (1980). Holtz-Eakin, Douglas, David Loufaian, and Harvey S. Rosen. “The Carnegie Conjecture: Some Empircal Evidence.” The Quarterly Journal of Economics 108 (2): 413- 435 (1993). Hurd, Michael, Monika Reti, and Susann Rohwedder. "The Effect of Large Capital Gains or Losses on Retirement.” Unpublished paper, Rand Corporation, 2005. Imbens, Guido W., Donald B. Rubin and Bruce I. Sacerdote. “Estimating the Effect of Unearned Income on Labor Supply, Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Players.” The American Economic Review 91 (4): 778-794 (2001). Ippolito, Richard A. “Toward Explaining Earlier Retirement After 1970.” Industrial and Labor Relations Review 43 (5): 556-569 (1990).

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Kezdi, Gabor and Purvi Sevak. “Economic Adjustment of Recent Retirees to Adverse Wealth Shocks.” University of Michigan Retirement Research Center, 2004. Maki, Dean M. and Michael Palumbo. “Disentangling the Wealth Effect: A Cohort Analysis of Household Saving in the 1990s.” The Federal Reserve Board Finance and Discussion Series 2001-21, 2001. Quinn, Joseph F. “Microeconomic Determinants of Early Retirement: A Cross-Sectional View of White Married Men.” The Journal of Human Resources 12 (3): 329-346 (1977). Sevak, Purvi. “Wealth Shocks and Retirement Timing: Evidence from the Nineties.” University of Michigan Retirement Research Center, 2002.

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Appendix

Figure 1- Percentage Change from 1/1/1990

Figure 2- Determination of Optimal Retirement Age

-50%0%

50%100%150%200%250%300%350%400%450%500%550%600%650%700%750%800%850%900%950%

1000%1050%1100%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

Nasdaq S&P 500

34

Figure 3- Non-Stockholder after Bear Market

Figure 4- Stockholder after Bear Market

35

Table 1: Retirement Age Plans by Asset Holdings Individuals Holding Risky Assets, n = 485

Age at 1/1/98 1998 Retirement Age 2002 Retirement Age 2004 Retirement Age Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. 55.50 6.05 63.26 4.87 64.32 4.92 65.00 5.20

Individuals Holding Risky Assets, n = 398

Age at 1/1/98 1998 Retirement Age 2002 Retirement Age 2004 Retirement Age Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. 55.00 6.66 64.16 4.67 64.69 4.74 64.90 5.37

Table 2: Summary Statistics for HRS Sample Male Female Mean Std. Dev. Mean Std. Dev. 2002 Ret. Age Deviation 0.591 3.31 0.957 3.37 2004 Ret. Age Deviation 1.165 3.97 1.366 4.14 %Risky 0.214 0.27 0.175 0.24 Health Worse by 2002* 0.218 0.41 0.228 0.42 Health Worse by 2004* 0.350 0.48 0.342 0.47 Total Initial Assets# 0.154 0.32 0.126 0.22 DB* 0.788 0.41 0.755 0.43 Spouse in Work Force* 0.541 0.50 0.574 0.49 Married* 0.855 0.35 0.743 0.44 Child Residents 0.728 1.13 0.759 1.11 White Race* 0.541 0.29 0.848 0.36 High School Graduate* 0.861 0.35 0.877 0.33 College Graduate* 0.323 0.47 0.225 0.42 Retired* 0.417 0.49 0.286 0.452 Expect Retire Before 65* 0.441 0.50 0.540 0.50 Number of Individuals 331 552 # value in millions * dummy variable

36

Table 3: Deviations from Expected Retirement Ages, 1998-2002 Male Female Coefficient SE Coefficient SE %Risky 1.937 *** 0.74 0.00592 0.62 Health Worse -0.201 0.44 0.144 0.33 Total Initial Assets# 1.102 * 0.61 0.819 0.65 DB -0.535 0.46 -0.640 ** 0.32 Spouse in the Work Force -0.236 0.38 -0.478 * 0.29 Number of Dependents -0.249 * 0.15 0.190 0.12 (= Nonworking spouse + children at home) White Race -1.119 * 0.64 0.355 0.40 High School Graduate -0.132 0.56 0.709 0.44 College Graduate -0.337 0.41 0.263 0.34 Retired -1.515 *** 0.48 -1.853 *** 0.48 Expect Retire Before 65 1.199 *** 0.36 1.943 *** 0.28 Constant 1.799 ** 0.84 -0.466 0.58 R-Squared 0.11 0.13 Number of Individuals 331 552 # value in millions * significant at 10% ** significant at 5% *** significant at 1%

37

Table 4: Probit Estimates of Delayed Retirement, 1998-2002 Male Female Marginal Effect SE Marginal Effect SE %Risky 0.338 *** 0.12 -0.0218 0.10 Health Worse 0.0239 0.70 -0.0211 0.052 Total Initial Assets# 0.177 * 0.11 -0.0317 0.10 DB -0.0449 0.074 -0.0258 0.052 Spouse in the Work Force 0.0519 0.060 -0.0633 0.046 Number of Dependents -0.00218 0.024 0.0176 0.019 (= Nonworking spouse + children at home) White Race -0.138 0.10 0.0851 0.059 High School Graduate 0.0819 0.086 0.0790 0.066 College Graduate -0.0423 0.066 0.0771 0.054 Retired 0.138 * 0.076 0.161 ** 0.075 Expect Retire Before 65 0.169 *** 0.057 0.296 *** 0.41 Pseudo R-Squared 0.07 0.08 Number of Individuals 331 552 # value in millions * significant at 10% ** significant at 5% *** significant at 1%

38

Table 5: Deviations from Expected Retirement Ages, 1998-2004 Male Female Coefficient SE Coefficient SE %Risky 1.227 0.85 0.463 0.73 Health Worse 0.658 0.43 0.00673 0.34 Total Initial Assets# 0.722 0.70 0.728 0.76 DB -1.241 ** 0.53 -0.816 ** 0.38 Spouse in the Work Force -0.358 0.43 -0.845 ** 0.34 Number of Dependents -0.125 0.17 -0.215 0.14 (= Nonworking spouse + children at home) White Race -0.84 0.74 0.0136 0.47 High School Graduate 0.700 0.65 0.522 0.516 College Graduate -0.0488 0.48 0.380 0.397 Retired -2.920 *** 0.43 -3.082 *** 0.37 Expect Retire Before 65 2.177 *** 0.43 2.614 *** 0.328 Constant 2.296 ** 1.00 1.412 ** 0.72 R-Squared 0.18 0.20 Number of Individuals 331 552 # value in millions * significant at 10% ** significant at 5% *** significant at 1%

39

Table 6: Probit Estimates of Delayed Retirement, 1998-2004 Male Female Marginal Effect SE Marginal Effect SE %Risky 0.182 0.12 0.125 0.10 Health Worse 0.0953 0.059 0.0626 0.046 Total Initial Assets# 0.166 0.10 0.0303 0.11 DB -0.143 ** 0.069 -0.115 ** 0.051 Spouse in the Work Force 0.0142 0.060 -0.133 *** 0.048 Number of Dependents -0.0396 * 0.024 -0.0434 ** 0.019 (= Nonworking spouse + children at home) White Race -0.106 0.095 0.0857 0.063 High School Graduate 0.112 0.090 0.0822 0.071 College Graduate -0.104 0.67 0.0105 0.054 Retired -0.0906 0.061 -0.166 *** 0.051 Expect Retire Before 65 0.259 *** 0.056 0.256 *** 0.042 Pseudo R-Squared 0.08 0.08 Number of Individuals 331 552 # value in millions * significant at 10% ** significant at 5% *** significant at 1%