Working Paper Series 

Congressional Budget Office 

Washington, D.C. 

PRIVATE SAVINGS, MEDICAID AND UNCERTAIN NURSING HOME EXPENSES

Lina Walker 

Congressional Budget Office 

Washington, D.C. 

(E-mail: lina.walker@cbo.gov) 

August 2006 

2006-10 

Working papers in this series are preliminary and are circulated to stimulate discussion and 

critical comment. These papers are not subject to CBO’s formal review and editing 

processes. The analysis and conclusions expressed in them are those of the author and 

should not be interpreted as those of the Congressional Budget Office. References in 

publications should be cleared with the author. Papers in this series can be obtained at 

www.cbo.gov (select Publications and then Working and Technical Papers). 
Abstract 

The Medicaid program is the largest payer of nursing home services in the nation. The 

magnitude of program spending and the anticipated program growth as the baby boom 

cohort ages have raised questions about how Medicaid affects private savings. This 

paper, which extends previous research, examines how high but uncertain nursing home 

expenses interact with Medicaid assistance to affect the savings decisions of working-age 

households. The analysis uses data from the Asset and Health Dynamics of the Oldest 

Old, and relies on variation in Medicaid eligibility standards over time and across states 

to estimate the effect of Medicaid on retirement wealth. Using a difference in difference 

methodology, this analysis finds that retirement wealth is about 15 percent lower for 

households living in states with easier access to Medicaid. This amounts to a difference 

of about $24,000, which is about one-third of median household net worth in the sample. 

The results are generally consistent with previous findings. 
1. Introduction1

In 2001, the average annual cost of nursing home services was about $60,000 

(Metlife, 2004). Most nursing home costs are paid out-of-pocket or by the Medicaid program 

because private insurance for nursing home expenses is limited and the Medicare program 

does not cover long-term nursing home care.2

 To put these costs in perspective, in 2001, the 

median elderly household held only $40,000 in liquid assets and about $180,000 in 

household net worth.3

 As a consequence, a nursing home stay has the potential to exhaust 

the financial resources of an elderly household in a relatively short period of time. 

One policy concern is that insufficient private resources for nursing home expenses 

will compromise the care the elderly receive.4 This issue is mitigated by the Medicaid 

program, which assists with nursing home payments for individuals whose income and assets 

are low enough. There is, however, an additional concern that Medicaid’s assistance 

program creates a disincentive to save for retirement and nursing home expenses. In order to 

qualify for Medicaid assistance, an elderly person must have few assets and low income. 

The extent to which individuals adjust their resources to that low level in order to qualify for 

Medicaid assistance is unclear. To address this question, this paper examines whether 

private savings is lower for households with access to Medicaid assistance. The evidence is 

then used to infer the extent to which working-aged households plan ahead to prepare 

financially for future nursing home expenses. 

 1

 I would like to thank Charles Brown, Kerwin Charles, John Laitner, Lucie Schmidt, Purvi Sevak, 

Dan Silverman, and participants at the University of Michigan public finance seminar for helpful comments. I 

also acknowledge support from the Michigan Retirement Research Center and the National Institute for Health 

for financial support. I am responsible for the content of the paper. 2

 In 2003, Medicaid spending accounted for 46 percent of total nursing home spending. Private 

payments made up 28 percent of total costs. Medicare’s share was about 12 percent and private insurance and 

other sources accounted for 14 percent of total costs (http://ltc.georgetown.edu/pdfs/nursinghomecosts.pdf). 3

 Estimates from Gouskova and Stafford (2002) using data from the Panel Study of Income Dynamics. 

4

 See Congressional Budget Office (2004) for policy issues relating to the financing of long-term care 

for the elderly. 

1
The premise of the paper is that in the absence of private or public insurance for outof-pocket nursing home expenses, households will self-insure (that is, save) for nursing home 

expenses. The availability of Medicaid nursing home assistance mitigates some of the outof-pocket payment risk, which may then reduce the level of savings. Therefore, variation in 

access to Medicaid nursing home assistance should lead to variation in the accumulation of 

retirement wealth. 

For working-aged adults, the need for nursing home care will likely manifest in their 

retirement years, potentially 20 or 30 years in the future. The underlying issue for this paper 

is whether households are forward-looking and make saving decisions for nursing home 

expenses years before an actual nursing home stay is likely to occur. Kimball (1994) notes 

that for uncertain medical expenses to have a quantitatively large impact on retirement 

wealth accumulation, those expenses must be large relative to lifetime resources. Given the 

magnitude of nursing home expenses, it is plausible that anticipated nursing home costs in 

retirement could influence the savings decisions of working-aged adults.5

 

In simulation studies, working-aged households facing uncertain medical expenses in 

retirement accumulate sizeable private savings in the form of precautionary wealth (Dynan, 

Skinner, and Zeldes, 2002; Palumbo, 1999; and Kotlikoff, 1989). The empirical evidence 

using micro-level survey data, however, is mixed. Levin (1995) finds indirect evidence to 

support planning for uncertain future health expenses whereas Webb (2001) finds little 

support.6

 Potentially, the mixed empirical finding might suggest incomplete planning. 

 5

 After discounting for 30 years at a rate of 4 percent, the present value cost of nursing home care to a 

working-aged adult is about $23,000. If evaluated over a 20 year period, the capitalized cost of a nursing home 

stay is about $35,000. The calculations are based on the following sources: the average length of stay in a 

nursing home is 456 days (Liu and Manton, 1984); annual nursing home cost is $60,000 in 2001 (Metlife 

survey, 2004). 6

 Levin (1995) finds a positive relationship between illiquid assets and health insurance premiums 

using the Retirement History Survey. He argues that the ability to self-insure will vary negatively with the 

2
There is some evidence that households adjust their savings in response to social 

insurance programs, such as the Medicaid program. This evidence comes from Hubbard, 

Skinner, and Zeldes (1995), whose simulations show lower savings among households 

eligible for public insurance; from Gardner and Gilleskie (2006) who observe that individuals 

hold more wealth in states with higher Medicaid asset qualifying standards; and from Gruber 

and Yelowitz (1999) who observe a fall in private savings when Medicaid coverage was 

extended to young households in the Aid to Families with Dependent Children program.7 

The work of Gruber and Yelowitz (1999), however, focuses on the savings response of 

working-aged households around the time when they qualify for Medicaid assistance. Little 

is known about the savings response of working-aged households when potential 

participation in the Medicaid program is well into the future. 

I use data from the Assets and Health Dynamics of the Oldest Old (AHEAD) survey. 

The identification strategy relies on variation in Medicaid eligibility standards over time and 

across states. The AHEAD data include individuals who attained retirement age several 

years before the implementation of the Medicaid program in 1965 and several years after. 

Individuals who retired before the availability of Medicaid faced different saving incentives 

from those who retired later, enabling a pre- and post-Medicaid comparison. A second 

source of variation comes from differences in states’ eligibility standards for nursing home 

assistance. Specifically, the difference comes from whether the state allows individuals to 

use income net of medical expenses to meet the Medicaid income qualifying standard (this 

criterion is termed the “medically-needy” standard). This second source of variation enables 

 proportion of illiquid assets in the household's portfolio; suggesting households are self-insuring against 

uninsured medical expenses. Using data from AHEAD, Webb (2001) finds no difference in the dis-saving rate 

between households that anticipate nursing home admission and households that do not. 7

 Aid to Families with Dependent Children provided health insurance coverage through Medicaid to 

young mothers with children. 

3
a difference-in-difference estimation method. 

Use of this quasi-randomization of public insurance coverage at the state level should at 

least reduce the bias associated with who selects into receiving Medicaid assistance. 

Potentially, however, individuals with low retirement wealth might move to states with easier 

access to Medicaid and this selection may be driving the empirical results. Results from 

several robustness checks that account for possible migration bias indicate this is not the case.8

 

I find that households living in states with easier access to Medicaid (states that cover 

the medically-needy population) accumulate lower retirement wealth than households in 

states that do not cover the medically-needy population. The retirement wealth of 

households living in medically-needy states is, on average, about 15 percent lower than the 

retirement wealth of similarly-situated households in other states, amounting to a difference 

of about $24,000. I find that the Medicaid medically-needy standard has the largest effect on 

the savings of households in the middle of the wealth distribution. Households in the upper 

end of the wealth distribution are less likely to seek Medicaid assistance probably because of 

the stringent resource standard for eligibility. These findings suggest that Medicaid does 

affect private savings which, in turn, suggests some planning for nursing home expenses. 

The magnitude of the estimate, however, is too small relative to actual nursing home costs to 

interpret as complete forward-planning for anticipated out-of-pocket nursing home costs. 

2. Background 

Evolution of Public Assistance with Nursing Home Expenses

Prior to 1960, federal assistance for medical care was available only to recipients who 

 8

 Potentially, states that tend to have households with lower savings might be more likely to provide 

easier access to Medicaid assistance; however, the more generous Medicaid states include California and New 

York, which tend to have wealthier households. 

4
were also receiving cash-assistance through the Old Age Assistance (OAA) program.9

 This 

circumstance meant that elderly persons whose income and assets were too high to qualify 

for cash assistance could not receive publicly-funded medical care. Individuals in need of 

nursing home care at that time had to rely on their own resources or on charity care. 

Beginning in 1960, states were given the authority to extend health insurance 

coverage to the elderly who were not financially eligible for cash assistance but whose 

income was insufficient to meet their medical expenses (the “medically-needy”). This 

coverage was available through the Medical Assistance for the Aged (MAA) program. 

Under this program, individuals who met the asset test could deduct medical expenses from 

income to bring their net income down to the income threshold.10 

As a requirement for receiving federal funds under MAA, states had to provide both 

institutional and non-institutional care. As a consequence, public assistance with nursing 

home expenses, by and large, began with the passage of the MAA program. By August 

1963, 20 states had implemented a MAA program that assisted with nursing home payments. 

(These states are highlighted with an `X' in column 1 of Table 1.11) 

The Medicaid program was established in 1965, at the same time that Medicare was 

enacted. Medicare pays for hospital and physician services while Medicaid pays for services 

that have limited or no coverage under the Medicare program. One such service is nursing 

home care. The Medicaid program also pays the Medicare part B insurance premium and 

other Medicare cost-sharing for cash assistance recipients. This assistance means that for the 

 9

 Medical assistance provided under OAA, however, was very limited and most states did not cover 

nursing home expenses (Health Law Project, 1972). 10 For instance, if the income threshold for qualifying is $400 a month, a person with a monthly income 

of $600 can qualify if her medical expenses are more than $200 a month; and that person’s assets do not exceed 

the asset threshold. 11 Data for Table 1 are compiled from: Bruen, et al. (1999), Burwell and Rymer (1987), Congressional 

Research Service (1988, 1993), Health Law Project (1972), Health Care Financing Administration (1970, 1980, 

1984, 1986), Special Committee on Aging (1963), Spitz and Holahan (1996). 

5
elderly who qualify for cash assistance, Medicaid provides in-kind transfers even when the 

recipient does not incur medical expenses. 

States are required to provide Medicaid coverage to all cash-assistance recipients. 

Because the financial eligibility standards for cash assistance is fairly uniform across states, 

there is limited cross-state variation in eligibility standards for cash assistance recipients.12 

In addition to covering cash-assistance recipients, some states chose to cover the medically 

needy (as they could under the MAA program). Because individuals in medically-needy 

states can deduct medical expenses from income to qualify, this raises the effective income 

threshold in these states above the cash assistance limit. In contrast, in non-medically-needy 

states, only individuals who meet the cash assistance income standard can qualify. 

Therefore, the variation in access to Medicaid substantially arises from the variation in 

coverage of the medically-needy population.13 Table 1 shows the states that covered the 

medically-needy through Medicaid in selected years between 1966 and 1992.14 The data 

shows very little variation in medically-needy coverage over time. 

Legislation in 1974 gave states without a medically-needy program the option to 

cover institutionalized people whose income was no more than 300 percent of the SSI benefit 

for that year. By the 1980s, all states without medically-needy programs had implemented a 

special income standard for the institutionalized, and most states set that standard at 300 

 12 Prior to 1974, each state independently established its financial eligibility standards for cash-assistance 

under OAA so there was some variation but the variation was small. In 1974, cash-assistance payments were 

federalized under the Supplemental Security Income (SSI) program. From that time on, eligibility for cashassistance was based on the SSI financial standards regardless of state residence. States do provide Medicaid 

assistance to elderly persons who are not on SSI but who are receiving State Supplemental Payments (SSP). The 

number of individuals who qualify only for SSP and not SSI, however, is very small. 13 For example, suppose the income standard is $400. Then, in a medically-needy state, the individual 

qualifies if (income-medical expenses) ≤ $400; whereas in a non medically-needy state, she qualifies if 

(income ≤ $400). Given that nursing home expenses can reach $3,000 a month, the medically-needy standard 

substantially increases the number of individuals who can qualify in medically-needy states. 14 Since 209(b) states are allowed to deduct medical expenses from income to qualify, in spirit, these 

states are similar to medically-needy states. As such, I categorize 209(b) states as medically-needy states in 

Table 1. 

6
percent of SSI. With the implementation of the 300 percent rule, the income standard for 

Medicaid nursing home assistance was, effectively, no lower than 300 percent of SSI, 

regardless of the state in which the recipient lived. Therefore, beginning in the 1980s, living 

in a medically-needy state only benefited households whose income was above 300 percent 

of SSI. 

Figure 1 shows public spending for nursing home services from 1950 through 1977. 

In the 10 year period between 1960 and 1970, public payments rose from $260 million to 

$2,610 million -- a ten-fold increase (HCFA, 1980). Most of that growth occurred between 

1965 and 1970, coinciding with the passage of Medicaid.15 The accelerated pace of growth 

and the magnitude of program spending suggest an increasing awareness by the public of the 

role of the Medicaid program in financing nursing home expenses.16 

3. The Theoretical Framework 

The Basic Model

I assume the agent lives for two periods, where period one corresponds to the agent's 

working years and period two corresponds to retirement years. Earnings in period one for 

agent i are denoted as and the agent has no earnings in retirement. Earnings are assumed 

to be exogenous in this model. The subscript i on indicates that agents differ by income. 

Income is the only dimension along which agents differ. Suppose the agent expects with 

probability, p, a good health outcome in period two and with probability, 1-p, a bad health 

outcome. When the agent receives a bad health draw, she incurs positive medical expenses, 

Yi1

Y1

 15 The increase between 1970 and 1975 coincides with the inclusion of the disabled in the 

Supplemental Security Income program, which meant their inclusion in the Medicaid program. 16 The growth in nursing home spending during this period was largely driven by growth in public 

spending. In 1960, private expenditure on nursing home expenses accounted for 2.46 percent of total health 

spending compared to 0.48 percent in public spending. In 1970, private spending accounted for 3.07 percent 

whereas public spending for nursing home care had increased to 2.31 percent of total health care spending. 

7
M>0; otherwise, she has no medical expenses. 

The agent cares only about consumption in both periods and health does not enter the 

agent's utility function directly. Instead, health affects consumption through the effect of 

medical expenses on the budget constraint. The only uncertainty in the second period is 

whether the agent draws good or bad health. It is assumed that the agent faces imperfect 

capital markets, so she is unable to completely insure against possible medical expenses. Her 

second-period consumption can be expressed as: 

health bad if ))(1(

health good if ))(1(

2, 11

2, 11

MCYrC

CYrC

bi ii

gi ii

−−+=

+= − 

where is first-period savings and r is the real rate of interest. − CY ii 11

In this basic model, there is no Medicaid program. The agent's maximization problem 

can be written as: 

 )]()1()([ 1

1

1 )()(max 2, 2, , 22,1i gi bi CCC

CUtEU CpU CUp bgi−+

+

+=

ρ

 (1) 

 s.t. 

 

⎪

⎪

⎩

⎪

⎪

⎨

⎧

+

+

+

+

+

+

=

health bad if 

r1

M

1

health good if 1

2,

1

2,

1

1

r

C

C

r

C

C

Y

gi

i

gi

i

i (2) 

≤ YC 11 (3) 

where E is the expectations operator, ρ is the subjective discount rate, and subscripts 2g and 

2b in (1) denote good and bad health in period two, respectively. Utility, , is concave in 

C and it displays the usual properties: . In addition, I assume that 

preferences are such that if . Since agents can choose such that savings 

is less than M, this latter assumption ensures this outcome is never an optimal choice and 

U ⋅)(

 ,0 0 ' '' UU <>

U ' ∞→ 0 U ' → C1

8
assures positive consumption in each period.17 Expression (2) describes the budget 

constraint with good and bad health outcomes, and the inequality constraint reflects the 

borrowing constraint. 

Substituting the expressions for and into (1) and taking the derivative with 

respect to gives the following first order condition (FOC): 

C 2, gi C 2, bi

Ci1

[ ] 2, 1

'

2,

'

1

' )()1()( 1

1 )( λ

δ

−+ +

+

+

i = CpU gi CUp bi

r CU (4) 

where λ1 is the shadow price of the borrowing constraint. 

Because the agent has no second period income, she must save; therefore, < YC 11

and λ1=0. The agent's FOC reduces to [ )()1()( ] 1

1 )( 2,

'

2,

'

1

'

i CpU gi CUp bi

r CU −+

+

+ = δ

. The 

agent’s optimal consumption path, , equates the marginal utility of period one 

consumption with the expected marginal utility of period two consumption. 

* Ci

From (4), we see that the optimal choice of , and savings, will vary with p. 

Removing subscripts (for ease of exposition) and taking the total derivative of (4) with 

respect to p gives: 

* Ci1

0

)1(

)(

1

'' 2

2

1

'' 2

2

''

1

'

2

'

1 2

>

− −−

− =

dC

dC Up dC

dC

pKUU

UUK

dp

dC

b

b

g

g

bg (5) 

where 

+δ

+ =

1

1 r K . This condition implies that the greater the likelihood of poor health, the 

higher the level of savings. In the absence of insurance markets, the agent will self-insure 

against uncertain medical expenses. 

 17 I assume the Inada condition because it accommodates a constant relative risk aversion (CRRA) 

utility function. 

9
A Model with Medicaid

In this section, I modify the basic model to include Medicaid program characteristics 

that will affect the agent’s consumption choices.18 The setup of the model follows Hubbard, 

Skinner, and Zeldes (1995). 

As described in the previous section, the Medicaid program provides two types of 

transfers: assistance with Medicare cost-sharing for agents with very low resources and 

assistance with nursing home expenses. Since Medicare out-of-pocket payments are very 

small relative to nursing home expenses, I denote good health as the event where the agent 

only incurs Medicare cost-sharing and bad health as the event where she incurs out-of-pocket 

nursing home expenses. 

Eligibility for Medicaid assistance depends on second-period resources.19 If the 

agent's second-period resources meet Medicaid's resource standard, then she qualifies for the 

transfer. The resource standard differs for transfers in good health and transfers in bad 

health. These are expressed in (6) and (7). If D denotes eligibility, then: 

Good Health: 

⎩

⎨

⎧ + ≤ = (not 0 qualify) otherwise if 

(qualify) 1 )C-r)(Y(1 if 2 i1i1 C D (6) 

Bad Health: 

 18 In this model, I abstract from concerns about differences in quality between Medicaid-financed and 

privately-financed nursing homes. 

19 For simplicity, I do not separate the effects of the income and asset tests on savings. This 

simplification is justifiable because there is limited variation in both the income and the asset-standard across 

states. In 1992, the income standard ranged from $100 to $760, with the average being $400; while the asset 

limit ranged from $1,600 to $5,000, with the majority of states adopting $2,000. The substantive variation 

comes from whether medical expenses are deducted from gross income when assessing eligibility; that is, the 

medically-needy coverage. 

10
⎩

⎨

⎧ + ≤ = (not 0 qualify) otherwise if 

(qualify) 1 -)C-r)(Y(1 if 2 1i1i1 CM D θ (7) 

where θ1 reflects the state Medicaid eligibility rules for nursing home assistance and C is 

some minimum consumption level. 

From above, we see that in order to receive Medicaid assistance with Medicare 

premiums (transfers in good health), the agent's second period resources, denoted by 

))(1( , can be no higher than −+ CYr ii 11 C2 and this condition does not vary across states.20 

Eligibility for Medicaid nursing home assistance (transfers in bad health), on the other hand, 

depends on θ1 . In medically-needy states, agents can deduct medical expenses from second 

period resources to qualify, therefore, θ1 =1. In non medically-needy states, agents only 

qualify if they meet the cash-assistance standard, soθ1=0. This condition means that if the 

agent lives in a medically-needy state, she can hold up to C2 + M and still qualify for 

Medicaid. 

Once qualified, Medicaid recipients must apply all resources towards the cost of 

medical care (this is sometimes referred to as the Medicaid tax). Medicaid will cover any 

excess nursing home payments and provide a minimum level of consumption level C2 . The 

size of the Medicaid transfer is: 

Good health: TR2g = { 2 −+− CYrC ii 11 ))(1(,0max }

Bad health: TR2g = { 2 −+−+ CYrMC ii 11 ))(1(,0max } (8) 

From above, we see that the agent is able to affect whether she qualifies for Medicaid, and 

the size of the transfer payment, through her choice of . Ci1

 20 Although the eligibility standard under OAA varied across states, the variation was relatively small 

and unlikely to produce quantitatively significant differences in savings. 

11
With transfers, the agent's second period consumption is: 

, where j=2g, 2b and j ii +−−+= TRMCYrC jj ))(1( 11 M 2 g = 0 . Then, the FOC can be 

rewritten as: 

1

2, 2

'

2, 2

'

1

'

 

)1)(()1( 1

1 

)1)(( 1

1 )(

λ

δ

δ

+

⎟ − − ⎠

⎞ ⎜

⎝

⎛

+

+

+

⎟ − ⎠

⎞ ⎜

⎝

⎛

+

+ =

bi b

i gi g

DCUp r

DCpU r CU

 (9) 

If the agent knows with certainty that she will not qualify for Medicaid in period two, then 

 and equation (9) reduces to (5). This is the FOC for the basic model without 

Medicaid transfers. 

2 g = DD 2b

Because of the non-convexity of the budget set, there are multiple local maxima to 

this maximization problem. The agent in a medically-needy state has three possible 

outcomes: the agent can choose transfers in good health and bad health; can receive transfers 

only in bad health; or can choose not to take-up the program at all. In non medically-needy 

states, because there is only one type of transfer, there are only two local maxima: one with 

transfers in good and bad health and one with no transfers. To obtain the global maximum, 

the agent compares the utility from each local maximum and chooses the outcome that gives 

the highest utility. 

Local Optima 1

In order to qualify in both health events, the agent must meet the eligibility standard 

in the good health draw. Therefore, her second period resources must 

be 211 ))(1( CCYr ii <−+ . If 2 g = 1 = DD 2b , she consumes exactly C2 in either health draw. 

12
Since saving $1 from period one will only reduce her transfer and it does not increase her 

second period consumption in either health outcome, the agent will choose not to save. 

Then, , = YC ii 11 0 λ1 > , and her FOC is . 11

' CU i )( = λ

Local Optima 2

Agents in medically-needy states have the possibility of only taking the transfer in 

bad health. Then, 2 g = DD 2b = 1 ,0 and the agent consumes C2 in bad health. From (7), we 

see that an agent can hold as much as C2 + M in resources and still qualify for the transfer. 

Since saving an extra $1 can increase her second period consumption in good health above 

C without disqualifying the agent from transfers in bad health; therefore, this agent will 

save. Then, 0 λ1 = and her FOC becomes )( 1

1 )( 2,

'

1

'

i CU gi

r pCU ⎟

⎠

⎞ ⎜

⎝

⎛

+

+ = δ . The agent’s savings 

will depend on p, the probability of good health. In the extreme case where p=0 and the 

agent expects bad health with certainty, the agent will not save. In the other extreme where 

good health is expected with certainty, p=1, the agent saves the certainty equivalent amount. 

Local Optima 3

In the third scenario, the agent chooses not to take-up Medicaid. Then, 

 and the FOC is D2g 0 == D2b )}()1()({ 1

1 )( 2,

'

2,

'

1

'

i CpU gi CUp bi

r CU ⎟ −+ ⎠

⎞ ⎜

⎝

⎛

+

+ = δ

. This is the 

FOC of the basic model with no Medicaid. Here, the agent self-insures against uncertain 

nursing home expenses. The agent chooses such that second-period consumption is 

higher than 

C1

C2 in both good and bad health. This is the solution of the basic model. 

13
Solution 2 will lead to lower savings than solution 3 because in 2, the extra $1 in 

savings will only increase consumption in good health and not in bad health. The agent's 

opportunity cost of consuming an extra $1 in period one is the discounted marginal utility of 

consumption in good health multiplied by p, the probability of good health. Since p is less 

than unity, it is not optimal for the agent to save the amount that corresponds to her interior 

solution. 

This result means that as long as solutions 1 or 2 are optimal choices for some agents, 

average savings will be lower in a model with Medicaid than in the basic model. This gives 

the first testable prediction: it is expected that private savings fell when Medicaid was 

implemented in 1965. However, households that were at, or close to, retirement age in 1965 

would have had less time to adjust their pre-retirement consumption than households that 

were younger and several years from retirement. Therefore, retirement wealth of the younger 

(the post-Medicaid regime) household is expected to be lower than the retirement wealth of 

the older (pre-Medicaid regime) household.21

The choice of the optimal solution will vary by income. In non medically-needy 

states, the agent only has the choice of solution 1 or solution 3. Given the concavity of the 

utility function, receiving the transfer in good health will be optimal only for agents with low 

lifetime income whereas agents with high lifetime income will not choose Medicaid 

assistance.22 In medically-needy states, agents have the additional option of only receiving 

the transfer in bad health. In that circumstance, consumption in good health can be as high as 

 21 In a dynamic model, the agent can adjust consumption in retirement in response to the new 

information. In that event, an optimizing agent will annuitize the gain from the anticipated transfer by 

increasing consumption over her remaining lifetime. This behavior will reduce the measured difference in 

retirement wealth between the pre- and post-Medicaid households, and this difference will diminish over time. I 

return to this point in the empirical section. 22 If the agent cares about equalizing marginal utilities, then she will avoid having very large 

consumption in one outcome and very low consumption in another. This preference rules out high income 

households consuming all their resources in period one to qualify for Medicaid in period two. 

14
C2 + M. Agents with moderate lifetime income will choose solution 2. Given that C2 is 

very low, agents that choose solution 2 will save less than those that choose solution 3. 

This result provides the second testable prediction: the retirement wealth of 

households with moderate lifetime income will be lower in medically-needy states than in 

non medically-needy states; whereas there should be no cross-state variation in the retirement 

wealth of households with very low or high lifetime income.23

4. The Econometric Analysis 

The Identification Strategy

I use data from the 1993 Asset and Health Dynamics of the Oldest Old. The AHEAD 

surveyed individuals, and their spouses, who were born in 1923 or earlier. In 1993, survey 

respondents were 70 years of age and older. The advanced age of the sample enables the 

identification strategy. Specifically, the younger cohort in the sample were in their prime 

earning years around the time the Medicaid program was implemented in 1965, whereas the 

older cohort was at or close to retirement during that same period. The age distribution of the 

sample therefore allows me to use the event of Medicaid’s implementation, and the 

consequent difference in the risk of out-of-pocket nursing home expenses facing the two 

cohorts, as a source of variation. 

It is unlikely that near-retirement aged workers anticipated nursing home assistance 

through either the MAA program or the Medicaid program and adjusted their consumption 

prior to the enactment of these programs. Potentially, households with low savings may have 

moved to states with easier access to Medicaid in order to qualify for coverage in their 

 23 The effect of the Special Income Limit (the 300 percent rule) on savings is not described because the 

sample of households in this paper is too old to have been affected by these rules during their earning years. 

This rule, however, potentially could effect dis-saving behavior in retirement. I return to this point later in the 

paper. 

15
retirement years. This issue, which is addressed later in the paper, does not appear to be 

driving the results. 

As noted in the previous section, after Medicaid was implemented, there were 

substantial differences in eligibility standards between states. This difference was largely 

coming from whether the state chose to cover the medically-needy population. 

The variation in eligibility over time together with the variation in eligibility 

standards across states provides clear predictions in a difference in difference (DD) 

estimation. Specifically, the difference in retirement wealth between pre- and post-Medicaid 

households living in medically needy states should be larger than the difference in retirement 

wealth between pre- and post-Medicaid households living in non medically-needy states. 

That is, the DD estimate should be unambiguously negative.24

Theory predicts that the younger (post-Medicaid) cohort would accumulate lower 

retirement wealth than the older (pre-Medicaid) cohort because of the availability of 

Medicaid assistance. However, even if the older, pre-Medicaid workers did not anticipate 

Medicaid assistance when they retired, once Medicaid was implemented, it is expected they 

would revise their assessment of risk and annuitize the gain from the lower risk by increasing 

consumption over her remaining lifetime.25 This adjustment in retirement means that the 

measured difference in wealth will be smaller when observed at more advanced ages than 

around retirement age. Given that the average age of my sample is 80 years, the reported 

estimates should be interpreted as a lower bound of the true effect. 

 24 The DD approach is widely used in studies that evaluate the impact of a program or treatment over a 

population of individuals. The appeal of the DD method is that it reduces the likelihood that time-invariant 

omitted variables (such as, changes in state laws and labor market conditions) and time trends (such as, capital 

accumulation or technological progress) will bias the estimated effect of the program since these effects are 

“differenced-out''. See Meyer (1995) and Angrist and Krueger (2000) for more detailed discussion. 25 With concave utility, she prefers a smooth consumption path; therefore she spreads the gain over her 

remaining lifetime rather than consume the gain in one lump-sum. 

16
The identification strategy relies on two assumptions. The first is that individuals are 

aware of Medicaid program rules. Conceivably, the elderly might have learned about the 

rules from the publicity surrounding the program in the years soon after implementation. 

Working-aged households may have learned about Medicaid’s coverage when assisting 

elderly family members with their long-term care choices. A second assumption is that 

households were saving for nursing home expenses prior to the implementation of Medicaid. 

Data from the Centers of Medicare and Medicaid indicate that private spending for nursing 

home expenses has hovered between 2.5 percent and 3.5 percent of national health care 

spending from 1960 to 2004.26 The historical trend lends validity to this assumption. 

Data

I use the 1993 AHEAD data linked to administrative data from the Social Security 

Administration. That linkage allows me to construct a measure for household lifetime 

earnings. I use the husband's lifetime earnings as a proxy for household lifetime earnings since 

the husband was usually the primary and single earner in the household for this cohort.27 

Since lifetime income is constructed from earnings that were subject to Social 

Security tax, lifetime income will be understated for individuals whose earnings were 

consistently above the Social Security earnings maximum, or who had earnings that were not 

subject to the Social Security tax (“uncovered employment”). Therefore, instead of relying 

directly on the value of constructed lifetime earnings, I sort households into lifetime earning 

 26 Private spending for nursing home expenditure accounted for 2.46 percent of national health care 

expenditure in 1960. In 2003, it was 2.48 percent of national health care expenditure. Private spending on 

nursing home expenditure as a share of national health spending peaked in 1990 at 3.59 percent. Source: 

http://www.cms.hhs.gov/NationalHealthExpendData/02_NationalHealthAccountsHistorical.asp 27 Details of the construction of husband's lifetime income can be obtained from the author. 

17
deciles and use their relative ranking instead.28

Tables 2a and 2b present the median and means of wealth and income variables by 

lifetime earnings decile. Wealth and pension income are generally monotonically increasing 

with lifetime income up to the median decile group. Beyond that, wealth appears to 

fluctuate. This variation is probably due to the understatement of lifetime earnings for higher 

earning households as a result of the Social Security earnings cap. Median household net 

worth is $25,500 in the lowest decile group and median wealth is about $77,000.29 

I assign households to the pre-Medicaid regime if the husband was 58 years or older 

in 1965; all other households are assigned to the post-Medicaid regime.30 For comparability, 

I focus on households where the husband was 49 or older in 1965. After deleting all cases 

with incomplete information, my sample includes 1,502 households. Table 3 provides some 

descriptive statistics for the sample. Median household net worth is about $75,000. Total 

wealth for the median household is about $163,000. About 10 percent of households in the 

sample had less than $407 in Social Security and pension income in 1992.31 About threequarters of the sample lived in a medically-needy state, of which about 50 percent lived in a 

state that previously implemented the MAA program. The average household is about 80 

years old and only 40 percent were married in 1993.32 Seventy percent of these households 

own a home and 11 percent are black households. The advanced age of this sample explains 

the low percent of married households. 

 28 This is similar to the procedure used by Venti and Wise (1999). The authors argue that the ranking 

by Social Security earnings is a good approximation to the ranking by true lifetime earnings, and so the Social 

Security earnings decile are a good approximation to true lifetime earnings decile.

29 Household net worth is the sum of stocks, bonds, certificate of deposits, checking and savings, 

business and real estate, automobile, home equity, less debt. 30 Where the husband's age is missing, I use the wife’s age and adjust it upwards by three. Three years 

is the mean age difference for married households in the AHEAD sample. 31 $407 was the Supplemental Security Income benefit amount in 1992. 

32 I assume the younger spouse will be the longer-lived spouse in the household, so I take that age as 

the age of the household. 

18
As noted in the background section, states began covering the medically-needy as 

early as 1960, through the MAA program. Presumably, household savings would have 

adjusted to the MAA program. However, as Table 1 shows, not all MAA states continued to 

cover the medically-needy through Medicaid. For the cleanest measure, I focus only on those 

states that covered the medically-needy for the first time through Medicaid. I denote this 

group as the medically-needy (MN) group. I denote the group that covered the medicallyneedy through the MAA program as the MAA group. 

In the theoretical framework, it is the Medicaid eligibility standards that were in place 

during the household's earning years that affected the household's consumption and saving 

decisions. Therefore, I use the Medicaid standards in 1977 in this paper. By 1977, Medicaid 

had been operational in some states for almost 11 years and all but Arizona had implemented 

a Medicaid program. In addition, in 1977, the 300 percent rule hadn’t yet been implemented 

so there were still large differences in eligibility standards between states. Figure 2 shows 

the geographic distribution of MAA and MN states. 

Validity of Difference in Difference Estimator

Before proceeding with the estimation, I address a potential shortcoming of the DD 

estimation. The DD identification strategy is valid as long as there are no omitted variables 

that vary with time and across states, and where the variation is correlated with the 

interaction term of interest. For instance, it would be problematic if agents with low wealth 

are more likely to move to states that cover the medically-needy and this propensity 

increased over time.33 Then, it would be difficult to identify whether the negative correlation 

 33 Wealth in the AHEAD sample might overstate average wealth of the cohort because the data include 

only households that survived to age 70 or older. When comparing differences over time, however, this 

19
between retirement wealth and medically-needy status is due to this selection effect or to the 

effect of lower exposure to out-of-pocket nursing home expenses.34 

As a check on the validity of the identifying assumption, I compare state-level and 

household-level characteristics of the treatment groups to those of the control group. This 

comparison will highlight whether there are observable variables related to wealth that are 

correlated with the interaction of interest and provide an indication of the likelihood that 

unobserved variables are driving the results. The comparison tables are presented in Tables 

4a, 4b, and 4c. As hypothesized, wealth is lower in medically-needy states but a comparison 

of state and household characteristics do not reveal any obvious factors that could be driving 

down wealth. In fact, households in medically-needy states have higher lifetime income. In 

addition, medically-needy states experienced better economic outcomes over time that non 

medically-needy states. 

Regression Results

I estimate the effect of the Medicaid program on retirement wealth using Ordinary 

Least Squares (OLS). 

i i i i ii

i i i ii

MAA MN ZY

W MAA MN

εβββββ

β β β

++++++

⋅+= + ⋅

3 4 5 6 7

10 2

 1965

log 1965( 1965() ) (10) 

where is total retirement wealth and the variable is set to 1 for the post-Medicaid 

regime household. Retirement wealth is the sum of Social Security wealth, pension wealth, 

Wi 1965i

 

selection will either bias the DD estimate downwards (if the pre-Medicaid group has a higher proportion of 

healthier households) or be absorbed by the time dummy. 34 I would argue that this particular example is less of an issue since the main destination states for the 

elderly (Florida, Arizona, Texas) are states that do not cover the medically-needy. Nevertheless, I conduct 

several robustness checks to examine the extent to which elderly migration affects the estimated results: these 

are reported later in the paper. 

20
veteran benefits, and household net worth.35 As noted earlier, it is predicted that the DD 

estimate will be negative. However, since households in MAA states had less access to 

nursing home assistance, on average, than households in medically-needy states, it is 

expected that the DD estimate for the MAA group will be less negative than the DD estimate 

for the MN group. That is, 1 2 12 β < β < ,0 ,0 β < β 36. 

The term is a vector of dummies for lifetime earning decile group and Yi i ε is an 

independent normal random term with mean zero and variance, . The control vector 

includes variables expected to affect the level retirement wealth, such as age of the 

household, race, number of children, marital transitions (separation, divorce, or death), 

whether a homeowner, whether the household lived on a farm, and state-specific 

characteristics (the change in real housing prices between 1960 and 1990, median housing 

price in 1990, and the number of nursing home beds per 1000 elderly persons). 

2 σ ε Zi

The coefficient β 5 absorbs the effect of time trends that are common to households in 

every state (such as, the impact of Medicare insurance). The coefficients β 3 and β 4 absorb 

the permanent average differences in MAA and MN states, respectively. Observations are 

weighted by sample weights for the AHEAD data to reflect the elderly non-institutionalized 

population in the United States. Standard errors are clustered by state to correct for possible 

correlations between observations within each state. 

Table 5 presents the results for total wealth. As expected, the coefficients on the 

 35 Social Security and pension wealth are the expected present discounted value of the flow of benefits. 

Because virtually all households had either Social Security or pension benefits, only a handful of households in 

the data had zero wealth. These observations were dropped with the logarithmic transformation. 36 I also estimated the above equation using the log of non-housing wealth as the dependent variable 

since there is some evidence that households regard housing wealth as non-fungible wealth (Levin, 1998; Venti 

and Wise, 1989, 2001). The point estimates were very similar (-0.03 and -0.15, respectively) although they 

were both imprecisely estimated. These results are not reported in this paper. 

21
interaction term, is negative and significant. Access to Medicaid’s medicallyneedy coverage reduced total retirement wealth by 15 percent; that is, households in 

medically-needy states held $24,400 less in retirement wealth than households in non 

medically-needy states. 

1965( ) MNii ⋅

The point estimate for MAA states, however, is essentially zero and it is imprecisely 

estimated. Potentially, this may be because there were a number of MAA states that dropped 

medically-needy coverage soon after the implementation of Medicaid.37 As a result, there is 

insufficient variation in eligibility standards between MAA states and non medically-needy states. 

The coefficients on the remaining variables are as expected. Households appear to be 

drawing down wealth with age. Households with fewer children have higher retirement 

wealth: households with 1-3 children have 9 percent less wealth and childless households 

have 12 percent less wealth than households with four or more children. Black households 

and households where a spouse died, or where there was a separation or divorce, held lower 

retirement wealth. Home-owners and households that live on a farm held higher wealth. 

The Savings Effect by Lifetime Income Group

The theoretical model predicts that only households with moderate lifetime income will 

respond to the Medicaid medically-needy incentive. Since the regression specification above 

includes households in the top and bottom lifetime income distribution, the DD estimates 

understates the effect for this middle group. Ideally, one would focus on this middle group to 

ascertain the true effect of the medically-needy standard on retirement wealth. 

In practice, however, it is difficult to identify households that fall into this “moderate 

lifetime income” category accurately because we do not observe preferences and cannot 

 37 These states are such as Arkansas, Idaho, Louisiana, Oregon, and South Carolina. 

22
identify the thresholds that determine membership in this income group. Simply using current 

household income is inappropriate because current income includes income from assets and the 

level of assets in retirement is affected by the availability of medically-needy coverage. 

As an approximation, I use quantile regression analysis to examine whether the effect 

of the medically-needy coverage varies across the wealth distribution. Table 6 reports results 

at the tenth percentile of retirement wealth and at selected points through to the 90th 

percentile of retirement wealth. From Table 6, we see that retirement wealth is lower for 

households in MN states throughout the entire wealth distribution but it is significantly 

different from zero only from the 20th percentile through the 60th percentile. Over that 

range, retirement wealth is between 14 percent and 24 percent lower for households in 

medically-needy states. This result is consistent with the OLS estimates. Beyond the 60th 

percentile, the standard errors on the DD estimator increase and the estimate loses precision. 

The quantile regression estimates for MAA states differ from the OLS estimates in 

that the point estimate for MAA states is negative and precisely estimated from the 25th 

percentile to the 35th percentile. Beyond the 35th percentile, the point estimates fall by half 

and remain close to zero throughout the rest of the distribution. This suggests that the earlier 

OLS results for the MAA states may have been partly driven by outliers. Between the 25th 

and 35th percentile, retirement wealth of households in MAA states is between 12 percent 

and 16 percent lower than for similar households in low access states. As hypothesized, the 

point estimate is smaller for households in MAA states than for households in MN states. 

Taken together, the results from Tables 5 and 6 suggest that households are 

responding to the Medicaid medically-needy coverage by reducing retirement wealth. This 

response indicates that access to Medicaid does affect retirement savings, and it also suggests 

that some component of retirement saving can be attributable to saving for medical expenses. 

23
Robustness Checks

Earlier, I raised the concern that the DD estimates may be biased by elderly 

migration. I had assumed throughout that households remained in the same state throughout 

their lifetime. Given that over 70 percent of the sample lived in their house for over 10 years, 

this assumption seems reasonable. However, three of the top five states with the largest 

proportion of elderly who migrated from other states do not cover the medically-needy. 

These states are Florida, Arizona, and Texas.38 If an increasing number of wealthy 

households moved to these, then this migration could account for higher average retirement 

wealth among households in non medically-needy states relative to medically-needy states. 

For a cleaner test, it would be ideal to exclude all elderly households whose current state of 

residence is not the state in which they spent their working years. Given the limitation of the 

data, I instead constrain the sample to households that are less likely to have moved. 

In the first instance, I exclude the top three destination states listed above and reestimate (10). The results are reported in column 1 of Table 7. In the second specification, I 

control for whether the elderly person has lived in their home for over 10 years. The results 

of this specification are reported in column 2. In the final specification, I exclude the top five 

net migration states for the elderly over the period 1985-1990 in the hopes of selecting only 

households that are less likely to have moved at all.39 These results are reported in column 3. 

Looking across all three columns in Table 7, the results are robust across each 

specification. In fact, when states with high elderly migration are excluded from the sample, 

it improves the point estimate and the precision for MN states, although it does not 

 38 The other two are California and North Carolina. California is a MAA state and North Carolina is a 

medically-needy state. These states represent the top 5 elderly immigration states for the period 1985-1990 and 

1995-2000 (Longino, et.al, 2003). 

39 These states are Florida, Arizona, North Carolina, Nevada, and Oregon. 

24
significantly change the results for the MAA group. In specifications 1 and 3, the retirement 

wealth of households living in medically-needy states is 27 percent lower than the retirement 

wealth of comparable households in non medically-needy states. 

Finally, the model makes predictions about the relative level of wealth at a point in the 

life-cycle when the household is at risk for nursing home expenses. Ideally, I want to observe 

households just when they need nursing home care but before they begin to draw-down their 

assets to pay for medical expenses. The current sample, however, includes widowed 

households, who may already have incurred out-of-pocket nursing home expenses for the 

deceased spouse. The inclusion of widowed households may understate the estimates of β1

and β 2 . 

To adjust this bias, I re-estimate (10) for married households only. I assume that 

intact married households are less likely to have incurred substantial nursing home expenses 

and, therefore, their retirement wealth will better reflect their full accumulation for nursing 

home expenses. 

There is a second reason for limiting the sample to only married households. 

Potentially, households may transfers assets to relatives in order to qualify for Medicaid 

nursing home assistance (also known as “spend-down”). If spend-down behavior is more 

prevalent among the younger cohort of households living in medically-needy states, this 

behavior potentially could bias the DD estimate downwards and confound the interpretation 

of the coefficient of interests. 

Evidence on spend-down behavior suggests that widowed households are more likely 

to engage in transfers to avoid the Medicaid tax than married households because the 

treatment of assets subject to the Medicaid tax is more favorable for married households with 

a non-institutionalized spouse. If the earlier estimates were mainly attributable to spend25
down behavior, then constraining the sample to married households, who are less likely to 

engage in spend-down, should produce a smaller point estimate. If, on the other hand, the 

point estimates do not change substantively, then spend-down is less of a concern. 

The estimates for married households are presented in column 4 of Table 7. 

Retirement wealth is 33 percent lower for households in high access states and it is highly 

significant. The difference amounts to about $54,000 in retirement wealth between household 

in medically-needy states and households in non medically-needy states. This estimate is 

more than twice the estimate for the full sample, suggesting that the earlier estimate may 

have been understated because some part of wealth had already been spent on medical 

expenses. In addition, the new point estimates indicate earlier results were not driven by 

asset transfer behavior. One should, however, regard the results for married households with 

caution because the estimates are based on small cell sizes.40

5. Conclusion 

The Medicaid program is the largest payer of nursing home services in the nation. 

Medicaid’s nursing home expenditure in 2003 was $56 billion and spending is projected to 

increase sharply as the baby boom cohort reaches retirement age. The magnitude of program 

spending and anticipated program growth has raised questions about how Medicaid 

influences private savings for nursing home care. Although access to Medicaid nursing 

home assistance may reduce the incentive to save, the asset standard for qualifying may also 

deter individuals from seeking Medicaid assistance. This paper evaluates how high but 

uncertain nursing home expenses interact with Medicaid assistance to affect the level of 

 40 Of the pre-Medicaid cohort, there were only 53 households from MAA states and 22 households 

from MN states. 

26
retirement wealth accumulation. 

The empirical results indicate that households that anticipated lower out-of-pocket 

nursing home expenses accumulated lower retirement wealth than households that 

anticipated higher out-of-pocket nursing home expenses. On average, retirement wealth is 

about 15 percent lower for households living in states that allowed individuals to use income 

net of medical expenses to reach the income qualifying standard (that is, households in 

medically-needy states). The difference in retirement wealth is about $24,000, which is one 

third the median household net worth in the sample. 

There is some evidence, albeit weak, that the savings of households in the middle of the 

wealth distribution are more responsive to the medically-needy standard than are the savings of 

households at the lower and the upper end of the wealth distribution. Households in the upper 

end of the distribution are less likely to seek Medicaid assistance, probably because of the 

stringent resource standards, and it is households in the middle of the distribution who are 

more likely to benefit from the state’s adoption of the medically-needy provision. 

The results in this paper support the notion that households incorporate information 

about Medicaid access into their saving decisions. Although the magnitude of the point 

estimate is substantial relative to household net worth, the level is far below the actual cost of 

nursing home care. With nursing home costs averaging $60,000 a year, and average nursing 

home stays lasting one and a half years, a household would need about $90,000 to fund their 

entire nursing home stay with private resources. 

There are reasons to believe that the estimated effect of the Medicaid coverage is 

understated. For instance, some households may already have incurred out-of-pocket nursing 

home expenses or the control group in the experiment (the pre-Medicaid households) may 

have responded to the treatment by adjusting their consumption in retirement. Yet, even after 

27
accounting for these adjustments, it is unlikely that the estimated effect would come close to 

the anticipated cost of nursing home care. This result can be interpreted in two ways. 

One interpretation is that individuals save for nursing home expenses while young but 

they cannot perfectly anticipate their need for nursing home care when they are older; or they 

incorrectly assume Medicare will cover these costs. As a consequence, they save too little. 

If so, the estimates reflect actual, although incomplete, savings for anticipated out-of-pocket 

nursing home expenses. 

An alternative interpretation is that most of the adjustment to consumption occurs in 

retirement. Potentially, working-age households may not have saved for nursing home 

expenses. When they reach retirement age, they obtain new information about their risk for 

out-of-pocket nursing home expenses and only then make adjustments to consumption. This 

behavior would be consistent with the well-documented discrete fall in consumption around 

retirement, which has been interpreted, in part, as response to new information (Banks, 

Blundell, and Tanner, 1998). 

In order to distinguish between these two explanations, it is necessary to identify the 

timing of savings, that is, whether adjustments to consumption occurs before or after 

retirement. Doing so would require information about the level of household wealth 

immediately after retirement. Given the limitations of the current data, this question is 

deferred to a later date. 

28
References 

American Association of Retired Persons (2002). Across the States: Profiles of LongTerm Care. Washington, D.C. 

Agency for Health Care Research (2000). Expenses and Sources of Payment for 

Nursing Home Residents, 1996: MEPS Research Findings, No. 13. U.S. 

Department of Health and Human Services, Washington, D.C. 

Angrist, J. and Krueger, A. (2000). “Empirical Strategies in Labor Economics.” 

Handbook of Labor Economics, eds. O. Ashenfelter & D. Card. Elsevier, North 

Holland, pp. 1277–1366. 

Banks, J., Blundell, R. and Tanner, S. (1998). “Is there a Retirement-Savings Puzzle?” 

American Economic Review, Vol. 88(4), pp. 769–788. 

Bruen, B. K., Weiner, J. M., Kim, J. and Miazad, O. (1999). “State Usage of Medicaid 

Coverage Options for Aged, Blind, and Disabled People.” The Urban Institute 

Discussion Paper 99-09, Washington, D.C. 

Burwell, B. and Rymer, M. P. (1987). “Trends in Medicaid Eligibility: 1975-1985.” 

Health Affairs, pp. 30–45. 

Congressional Budget Office (April 2004). Financing Long-Term Care for the 

Elderly. Washington, D.C. 

Congressional Budget Office (1999). Projections of Expenditures for Long-Term Care 

Services for the Elderly. CBO Memorandum, Washington, D.C. 

Congressional Research Service (1988). Medicaid Source Book: Background Data 

and Analysis. U.S. Government Printing Office, Washington, D.C. 

Congressional Research Service (1993). Medicaid Source Book: Background Data 

and Analysis (a 1993 Update). U.S. Government Printing Office, Washington, 

D.C. 

Coughlin, T., Ku, L. and Holahan, J. (1994). Medicaid since 1980. The Urban 

Institute, Washington, D.C. 

Crown, W. H., Burwell, B. and Alecxih, L. (1994). “An Analysis of Asset Testing for 

Nursing Home Benefits.” AARP Public Policy Institute Publication, No. 9409, 

Washington, D.C. 

Dynan, K. E., Skinner, J. and Zeldes, S. P. (2002). “The Importance of Bequests and 

Life-Cycle Saving in Capital Accumulation: A New Answer.” The American 

Economic Review, Vol. 92(2), pp. 274–278. 

Fisher, J. (1953). “Trends in Insitutional Care of the Aged.” Social Security Bulletin, 

Vol. 16(10), Washington, D.C. 

Gale, W. G. et al. (2004). Retirement Saving and Long-Term Care Needs: An 

Overview. The Retirement Security Project, George Washington University. 

29
Gardner, Lara and Donna Gilleskie. (2006). “The Effects of State Medicaid Policies 

on the Dynamic Savings Patterns of the Elderly.” National Bureau of Economic 

Research Working Paper, No. 12208. 

Gouskova, E. and Stafford, F. (2002). “Trends in Household Wealth Dynamics, 1999-

2001.” Panel Study of Income Dynamics Working Paper Series, University of 

Michigan. 

Greenfield, M. (1968). Medicare and Medicaid: The 1965 and 1967 Social Security 

Amendment. University of California, Berkeley. 

Gruber, J. (2000). “Medicaid.” National Bureau of Economic Research Working 

Paper, No. 7829. 

Gruber, J. and Yelowitz, A. (1999). “Public Health Insurance and Private Savings.” 

The Journal of Political Economy, Vol. 107(6), pp. 1249–1274. 

Health Care Financing Administration (1970). Characteristics of State Medical 

Assistance Program under Title XIX of the Social Security Act. U.S. Department 

of Health, Education, and Welfare, Washington, D.C. 

Health Care Financing Administration (1980). National Annual Medicaid Statistics, 

FY 1973 to 1979. U.S. Department of Health, Education, and Welfare, 

Washington, D.C. 

Health Care Financing Administration (1984, 1986). Analysis of State Medicaid 

Program Characteristics. U.S. Department of Health, Education, and Welfare, 

Washington, D.C. 

Health Law Project (1972). Medicaid: Volume 5. University of Pennsylvania Law 

School. 

Hubbard, R. G., Skinner, J. and Zeldes, S. P. (1995). “Precautionary Saving and Social 

Insurance.” Journal of Political Economy, Vol. 103, pp. 360–399. 

Kaiser Commission on Medicaid and the Uninsured (2002). The Medicaid Resource 

Book. The Kaiser Commission, Washington, D.C. 

Kimball, M. (1994). “The Importance of Precautionary Motives in Explaining 

Individual and Aggregate Saving: A Comment.” Carnegie-Rochester Conference 

Series on Public Policy, Vol. 40(0), pp. 127–32. 

Kotlikoff, L. J. (1989). What Determines Savings. MIT Press, Massachusetts. 

Lansdale, R., Long, E. and Leisy, A. (1939). The Administration of Old Age 

Assistance. Public Administration Service, Washington, D.C. 

Levin, L. (1995). “Demand for Health Insurance and Precautionary Motives for 

Savings among the Elderly.” Journal of Public Economics, Vol. 57, pp. 337–367. 

Levin, L. (1998). “Are Assets Fungible? Testing the Behavioral Theory of Life-Cycle 

Savings.” Journal of Economic Behavior and Organization, Vol. 36, pp. 59-83. 

30
Liu, K. and Manton, G. (1984). “The Characteristics and Utilization Pattern of An 

Admission Cohort of Nursing Home Patients.” The Gerontologist, Vol. 24(1), pp. 

70–76. 

Longino, Jr., C. F. and Bradley, D. E. (2003). “A First Look at Retirement Migration 

Trends in 2000.” The Gerontologist, Vol. 43(6), pp. 904–907. 

Metropolitan Life Insurance Company (2004). MetLife Market Survey on Nursing 

Home and Home Care Costs 2002. MetLife Mature Market Institute, Westport, 

CT. 

Meyer, B. (1995). “Natural and Quasi-Experiments in Economics.” The Journal of 

Business and Economic Statistics, Vol. 13(2), pp. 151-161. 

Palumbo, M. G. (1999). “Uncertain Medical Expenses and Precautionary Saving Near 

the End of the Life Cycle.” The Review of Economic Studies, Vol. 66, pp. 395–

421. 

Perkins, E. (1951). “Old Age Assistance and Aid to Dependent Children, 1940-1950.” 

Social Security Bulletin, Vol. 14(11). 

Rhoades, J. A. and Sommers, J. P. (2001). Nursing Home Expenses, 1987 and 1996: 

Meps Chartbook, No. 6, Agency for Healthcare Research and Quality, 

Washington, D.C. 

Special Committee on Aging (1963). Medical Assistance for the Aged: The Kerr-Mills 

Program, 1960-1963. U.S. Government Printing Office, Washington, D.C. 

Spitz, B. and Holahan, J. (1996). Modifying Medicaid Eligibility and Benefits. Urban 

Institute, Washington, D.C. 

Venti, S. F. and Wise, D. A. (1989). “Aging, Moving, and Housing Wealth.” The 

Economics of Aging, ed. D. Wise. University of Chicago Press, Chicago. 

Venti, S. F. and Wise, D. A. (2001), “Aging and Housing Equity: Another Look.” 

National Bureau of Economic Research Working Paper, No. 8608. 

Venti, S. and Wise, D. (1999). “Lifetime Earnings, Saving Choices, and Wealth at 

Retirement.” Wealth, Work, and Health: Innovations in Measurement in the 

Social Sciences, eds. James P. Smith and Robert J. Willis. University of Michigan, 

Ann Arbor. 

Webb, A. (2001). “The Impact of the Cost of Long-Term Care on the Saving of The 

Elderly.” Unpublished Manuscript, International Longevity Center, New York. 

White, R. (1952). “Medical Services in the Old Age Assistance Program.” Social 

Security Bulletin, Vol. 15(6). 

Yelowitz, A. (1996). “The Medicaid Notch, Labor Supply, and Welfare Participation: 

Evidence from Eligibility Expansion.” Institute for Research on Poverty 

Discussion Paper, No. 1084-96. 

31
Figure 1: Federal and State Nursing Home Expenditures

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1950 1955 1960 1965 1970 1975 1980

Year

1977 Dollars (millions)

Source: Health Care Financing Adminstration (1980), Special Committee on Aging (1963)

33
Figure 2: Medically-Needy and 

Medical Assistance for the Aged (MAA) States

Non Medically-Needy States

Medically-Needy States

MAA States Source: Health Care Financing Administration (1980)

Notes: Medically-needy and MAA states allow individuals to deduct medical expenses from income to qualify for Medicaid.

34
pre-Medicaid Year

MAA States* Medicaid

State 1963 Implemented 1966 1970 1976 1980 1987 1992

ALABAMA 1970

ALASKA 1972 ---

ARIZONA 1982 --- --- --- ---

ARKANSAS X 1970

CALIFORNIA X 1966 XXXXXX

COLORADO 1969 ---

CONNECTICUT X 1966 XXXXXX

DELAWARE 1966

DISTRICT OF COLUMBIA X 1968 --- XXXXX

FLORIDA 1970 ---

GEORGIA 1967 --- X

HAWAII X 1966 XXXXXX

IDAHO X 1966

ILLINOIS 1966 XXXXXX

INDIANA 1970 --- XXXXX

IOWA 1967 ---

KANSAS 1967 --- XXXXX

KENTUCKY X 1966 XXXXXX

LOUISIANA X 1966 X X X

MAINE 1966 XXXX

MARYLAND 1966 XXXXXX

MASSACHUSETTS X 1966 XXXXXX

MICHIGAN X 1966 XXXXXX

MINNESOTA 1966 XXXXXX

MISSISSIPPI 1970 --- XXXXX

MISSOURI 1967 --- XXXXX

MONTANA 1967 --- XXXX

NEBRASKA 1966 XXXXX

NEVADA 1967 ---

NEW HAMPSHIRE 1967 --- XXXXX

NEW JERSEY 1970

NEW MEXICO 1966

NEW YORK X 1966 XXXXXX

NORTH CAROLINA 1970 XXXXX

NORTH DAKOTA X 1966 XXXXXX

OHIO 1966 X X

OKLAHOMA X 1966 XXXXXX

OREGON X 1967 --- X X

PENNSYLVANIA X 1966 XXXXXX

RHODE ISLAND 1966 XXXXXX

SOUTH CAROLINA X 1968 --- X

SOUTH DAKOTA 1967 ---

TENNESSEE X 1969 --- XXXX

TEXAS 1967 ---

UTAH X 1966 XXXXXX

VERMONT 1966 XXXXXX

VIRGINIA 1969 --- XXXXX

WASHINGTON X 1966 XXXXX

WEST VIRGINIA X 1966 XXXX

WISCONSIN 1966 XXXXXX

WYOMING 1967 --- X

Notes: 

Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid program ("spend-down").

"X" indicates the state covers the medically-needy. Dashes indicate the state had no Medicaid program in that year.

Medically-needy states include 209(b) states.

The MAA program is the Medical Assistance for the Aged program. MAA states allowed spend-down prior to Medicaid. 

"*" denotes MAA states that covered nursing home care.

Data compiled from: Bruen, et al. (1999), Burwell and Rymer (1987), Congressional Research Service (1988, 1993), Health Law Project (1972)

Health Care Financing Administraion (1970, 1980, 1984, 1986), Special Committee on Aging (1963), Spitz and Holahan (1996)

Medicaid States

Table 1 : States that Cover the Medically-Needy in Selected Years 

35
Non-Housing Household Investment

Decile Group Total Wealth Wealth Net Worth Home Equity Pension Income Income1=Lowest Decile Group 77,157 51,726 25,450 11,500 42,422 02 107,244 68,131 40,250 22,000 51,973 03 140,237 87,720 68,586 40,000 60,039 04 160,798 102,639 65,000 38,000 71,970 05 191,449 125,126 100,000 42,000 80,771 3006 177,447 118,701 89,000 50,000 76,771 2007 146,094 110,805 69,000 35,000 67,748 38 207,125 136,841 97,898 50,000 85,970 9009 253,972 178,472 147,500 65,000 97,861 98010=Highest Decile Group 232,466 168,811 117,000 52,000 89,518 800Notes:Data are from the 1993 Asset and Health Dymanics of the Oldest Old (AHEAD)The lifetime earnings measure is the husband's Primary Insurance Amount.Wealth is the sum of Social Security, pension wealth and household net worthNet worth includes stocks, bonds, CDs, checking and savings, business and real estate, automobile, home equity, less debt.Investment income includes income from stocks, bonds, checking accounts, and CDs. 

Non-Housing Household Investment

Decile Group Total Wealth Wealth Net Worth Home Equity Pension Income Income1=Lowest Decile Group 154,586 125,394 82,378 29,192 72,208 1,5072 244,487 166,390 169,330 78,097 75,157 3,2973 210,424 154,969 130,449 55,455 79,975 3,6394 219,719 169,915 126,354 49,942 93,884 2,3485 243,736 178,188 146,503 65,339 96,403 2,8176 250,469 171,094 156,800 79,374 93,668 3,7447 213,229 165,657 129,228 47,572 84,001 3,8458 238,251 177,285 146,349 60,966 91,903 6,5479 357,991 273,689 236,931 84,301 121,060 6,15210=Highest Decile Group 307,230 234,718 196,694 74,570 115,695 6,111Notes:Data are from the 1993 Asset and Health Dymanics of the Oldest Old (AHEAD).The lifetime earnings measure is the husband's Primary Insurance Amount.Wealth is the sum of Social Security, pension wealth and household net worth.Net worth includes stocks, bonds, CDs, checking and savings, business and real estate, automobile, home equity, less debt.Investment income includes income from stocks, bonds, checking accounts, and CDs. 

Tabel 2a : Table of Median Values by Lifetime Earnings Decile Group

Tabel 2b : Table of Mean Values by Lifetime Earnings Decile Group

36
Variables Mean S

Total Wealth 242,589 3Non-Housing Wealth 180,537 2Average Lifetime Earnings 1,628 Household Net Worth 150,483 2Home Equity 62,052 1

92,107 

Investment Income 3,948 Distribution of Households by Pension Income*: Less than SSI 0.11 

Between SSI and 300% SSI 0.70 Above 300% SSI 0.19 

Households Living in Medically-Needy States: Previously had an MAA program 0.47 

Previously had no MAA program 0.27 

Households Living in the Medicaid Regime 0.70 Age 80Number of Children 2.54 Separated, Divorced, or Widowed Household 0.57 Own Home 0.70 Black Household 0.11 n=1502

Notes:Data are from the 1993 Asset and Health Dymanics of the Oldest Old (AHEAD).Wealth is the sum of Social Security, pension wealth and household net worth.Net worth includes stocks, bonds, CDs, checking and savings, business and real estate, automobile, home equity, less debt.Pension income includes Social Security, pension, and veteran benefits. Investment income includes income from stocks, bonds, checkiHouseholds in the Medicaid regime are households where the head (or, husband if married) was between ages 49 and 57 in 1965.Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid program ("spend-down").The MAA program is the Medical Assistance for the Aged program. MAA states allowed spend-down prior to Medicaid.The lifetime earnings measure is the husband's Primary Insurance Amount.Dollar amounts are in 1993$. The 1992 SSI benefit level was $407 a month.*Figures may not sum to 1 due to rounding.

Table 3: Summary Statistics for the Sample

Pension Income 

37
Variables Mean Std. Dev. Median Mean Std. D

Total Wealth 242,258 304,159 165,640 243,228 327,Non-Housing Wealth 173,121 203,219 112,751 194,835 308,5Household Net Worth 149,843 279,907 74,000 151,715 304,5Housing Equity 69,137 187,558 40,000 48,393 55,8Household-Level CharacteristicsPension Income 92,415 75,154 73,728 91,513 74,2Non Pension Investment Income 4,078 12,370 5 3,695 9,92Average Lifetime Earnings 1,670 937 1,568 1,546 90Percent with Income below 300%of SSI 0.81 0.38 0.82 0.3Percent with Income below SSI 0.09 0.29 0.14 0.3Age 80 80State-Level CharacteristicsMedian House Price in 1990 100,840 52,129 74,848 84,500 32,9Change in Housing Prices, 1960-1990 0.71 0.59 0.56 0.57 0.3No. of Nursing Home Beds per 1000 Elderly, 1973 63.91 19.17 62.60 50.35 20.Median Household Income in 1994 40,380 4,757 42,643 38,725 5,0Percent of Elderly Receiving OASDI Benefits 92.54 4.06 94.30 90.77 3.3number of observations 989 513

Notes:Wealth and other demographic variables are from 1993 AHEAD data.Wealth is the sum of Social Security and pension wealth and household net worth.Net Worth includes stocks, bonds, CDs, checking and savings, business and real estate, automobile, hom

e equity, less debt.

Pension income includes Social Security, pension, and veteran benefits.Investment income includes income from stocks, bonds, checking accounts, and CDs.Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid program ("spend-downThe 1992 SSI benefit level was $407 per month.Dollar amounts are in 1993$.

Medically-Needy States Non Medically-

Table 4a: Selected Characteristics by Medically-Needy Status

38
State-Type Variables Mean Std. Dev. Median Mean 

Non Medically-Needy Pension Income 56,198 39,177 46,932 108,407 States Investment Income 3,355 10,979 125 3,857 (Control group) Lifetime Earnings 1,759 910 1,840 1,444 

Percent with Income below SSI 0.16 0.37 0.13 Percent with Income below 300%of SSI 0.92 0.27 0.77 Age 85 6.64 78 Live on Farm 0.06 0.17 0.04 Own Home 0.64 0.48 0.75 Black Household 0.15 0.36 0.09 Median House Price in 1990 80,600 28,261 76,566 86,365 Change in Housing Prices, 1960-1990 0.53 0.29 0.48 0.58 No. of Nursing Home Beds per 1000 Elderly, 1973 51 22 46 50 Median Household Income in 1994 38,278 4,384 37,392 38,939 number of observations 166 347

Medically-Needy Pension Income 57,776 43,486 47,173 108,096 States Investment Income 4,313 11,694 187 3,963 (Treatment Group) Lifetime Earnings 1,886 811 1,969 1,561 

Percent with Income below SSI 0.12 0.33 0.09 Percent with Income below 300%of SSI 0.96 0.19 0.78 Age 86 4.74 77 Live on Farm 0.05 0.21 0.08 Own Home 0.56 0.50 0.76 Black Household 0.08 0.28 0.12 Median House Price in 1990 78,182 22,490 70,660 77,153 Change in Housing Prices, 1960-1990 0.44 0.46 0.25 0.43 No. of Nursing Home Beds per 1000 Elderly, 1973 71 18 70 73 Median Household Income in 1994 41,384 3,390 4,300 41,560 number of observations 105 256

Notes:Wealth and other demographic variables are from 1993 AHEAD data.Pension income includes Social Security, pension, and veteran benefits.Investment income includes income from stocks, bonds, checking accounts, and CDs.The lifetime earnings measure is the husband's Primary Insurance Amount.Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid program ("spend-down").The 1992 SSI benefit level was $407 per month.Dollar amounts are in 1993$.

Pre-Medicaid Regime Pos

Tabel 4b : Selected Characteristics for Non Medically-Needy and Medically-Needy States, Pre- and 

39
State-Type Variables Mean Std. Dev. Median Mean 

Non Medically-Needy Pension Income 56,198 39,177 46,932 108,407 States Investment Income 3,355 10,979 125 3,857 (Control group) Lifetime Earnings 1,759 910 1,840 1,444 

Percent with Income below SSI 0.16 0.37 0.13 Percent with Income below 300%of SSI 0.92 0.27 0.77 Age 85 6.64 78 Live on Farm 0.06 0.17 0.04 Own Home 0.64 0.48 0.75 Black Household 0.15 0.36 0.09 Median House Price in 1990 80,600 28,261 76,566 86,365 Change in Housing Prices, 1960-1990 0.53 0.29 0.48 0.58 No. of Nursing Home Beds per 1000 Elderly, 1973 51 22 46 50 Median Household Income in 1994 38,278 4,384 37,392 38,939 number of observations 166 347

MAA Pension Income 63,835 49,850 46,760 103,348 States Investment Income 3,964 11,053 0 3,876 (Treatment Group) Lifetime Earnings 1,915 856 1,957 1,526 

Percent with Income below SSI 0.13 0.34 0.08 Percent with Income below 300%of SSI 0.86 0.35 0.79 Age 85 6.72 79 Live on Farm 0.05 0.22 0.06 Own Home 0.58 0.50 0.73 Black Household 0.13 0.33 0.12 Median House Price in 1990 108,693 56,823 74,848 111,817 Change in Housing Prices, 1960-1990 0.82 0.58 0.59 0.85 No. of Nursing Home Beds per 1000 Elderly, 1973 59 16 61 60 Median Household Income in 1994 39,255 5,002 39,869 39,594 number of observations 198 475

Notes:Wealth and other demographic variables are from 1993 AHEAD data.Pension income includes Social Security, pension, and veteran benefits.Investment income includes income from stocks, bonds, checking accounts, and CDs.The lifetime earnings measure is the husband's Primary Insurance Amount.Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid program ("spend-down")The MAA program is the Medical Assistance for the Aged program. MAA states allowed spend-down prior to Medicaid. The 1992 SSI benefit level was $407 per month.Dollar amounts are in 1993$.

Pre-Medicaid Regime Pos

Tabel 4c : Selected Characteristics for Non Medically-Needy and MAA States, Pre- and Post-M

40
Dependent Variable: Log Retirement Wealth Coef

Medicaid * MAA States (1965*MAA) -0.002

(.089)

Medicaid * Medically-Needy States (1965*MN) -0.158 *

(.088)

Medicaid Regime (1965) 0.204 ***

(.051)

MAA States (MAA) -0.070

(.077)

Medically-Needy States (MN) 0.029

(.095)

Age 0.292 ***

(.036)

No Children 0.124 *

(.071)

1-3 children 0.095 ***

(.035)

Black -0.420 ***

(.075)

Marital Disruption -0.318 ***

(.043)

Own Home 0.832 ***

(.062)

Live on Farm 0.140 *

(.079)

Constant 13.360 ***

(.328)

Controls for Lifetime Earning Deciles yes

State Controls yes

n 1502

R-square 0.5

Notes:

Medically-needy states allow individuals to deduct medical expenses from income to qualify for the

Medicaid program ("spend-down"). The MAA program is the Medical Assistance for the Aged program. 

MAA states allowed spend-down prior to Medicaid. Omitted group includes non medically-needy states.

Medicaid regime means the head (or, husband if married) was between ages 49 and 57 in 1965.

The pre-Medicaid regime are households where the head, or husband, was age 58 or older in 1965.

Wealth and other demographic variables are from 1993 AHEAD data.

Total wealth is the sum of Social Security, pension wealth and household net worth.

Household net worth includes stocks, bonds, CDs, checking and savings, business and real estate, 

automobile, home equity, less debt.

Age is the age of the younger person in the household divided by 10. 

The lifetime earnings measure is the husband's Primary Insurance Amount.

Marital disruption means separation, divorce, or widowhood.

State controls are: median household income in 1994, nursing home bed per 1000 elderly, real housing 

value in 1990, and change in real housing prices between 1960 and 1990.

Estimates are weighted to reflect the non-institutionalized population in the United States.

Standard errors are clustered at the state level. Significance denoted as: *** - 1%, ** - 5%, * -10%.

Table 5 : Ordinary Least Squares Results

(Robust Standard Errors in Parenthesis)

Effect of Medicaid Medically-Needy Coverage on Retirement Wealth

41
Dependent Variable: Log Retirement Wealth Coef Coef Coef Coef Coef Coef 

Medicaid * MAA States (1965*MAA) -0.104 -0.122 -0.133 * -0.155 * -0.078 -0.007 

(.107) (.076) (.076) (.082) (.066) (.088) 

Medicaid * MN States (1965*MN) -0.096 -0.187 ** -0.193 ** -0.167 * -0.138 * -0.238 ** 

(.133) (0.093) (.093) (.099) (.080) (.108) 

Medicaid Regime (1965) 0.229 ** 0.317 *** 0.342 *** 0.298 *** 0.216 *** 0.187 *** 

(.093) (.065) (.064) (.067) (.053) (.071) 

MAA States (MAA) 0.047 0.076 0.071 0.063 -0.023 -0.116 

(.090) (.063) (.064) (.068) (.055) (.074) 

Medically-Needy States (MN) 0.055 0.123 0.112 0.100 0.066 0.104 

(.115) (.079) (.079) (.084) (.068) (.093) 

Age -0.314 *** -0.319 *** -0.317 *** -0.317 *** -0.294 *** -0.310 

(.050) (.036) (.035) (.036) (.029) (.039) 

No Children -0.142 * -0.068 -0.019 0.018 0.107 ** 0.135 

(.075) (.053) (.053) (.057) (.046) (.062) 

1-3 children 0.075 0.105 *** 0.098 ** 0.090 ** 0.159 *** 0.175 

(.054) (.038) (.038) (.041) (.033) (.044) 

Black -0.215 *** -0.226 *** -0.256 *** -0.275 *** -0.312 *** -0.383 

(.072) (.051) (.050) (.053) (.043) (.058) 

Marital Disruption -0.214 *** -0.244 *** -0.220 *** -0.263 *** -0.300 *** -0.320 

(.051) (.034) (.034) (.037) (.030) (.041) 

Own Home 0.755 *** 0.761 *** 0.819 *** 0.863 *** 0.890 *** 0.905 

(.051) (.035) (.034) (.038) (.031) (.042) 

Live on Farm 0.260 ** 0.177 ** 0.154 ** 0.091 0.154 ** 0.143 

(.102) (.071) (.069) (.076) (.061) (.082) 

Constant 12.981 *** 13.048 *** 13.064 *** 13.177 *** 13.146 *** 13.493 

(.442) (.323) (.310) (.319) (.255) (.339) 

Controls for Lifetime Earning Deciles yes yes yes yes yes yes 

and selected state characteristicsn 1502 1502 1502 1502 1502 1502 R-square 0.40 0.40 0.40 0.38 0.34 0.31 

Notes:Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid program ("spend-down").The MAA program is the Medical Assistance for the Aged program. MAA states allowed spend-down prior to Medicaid. Omitted group includes non medically-needy states.Medicaid regime means the head (or, husband if married) was between ages 49 and 57 in 1965. The pre-Medicaid regime are households where the head, or husband, was age 58 or Wealth and other demographic variables are from 1993 AHEAD data. Total wealth is the sum of Social Security, pension wealth and household net worth.Household net worth includes stocks, bonds, CDs, checking and savings, business and real estate, automobile, home equity, less debt. The lifetime earnings measure is the husband's Age is the age of the younger person in the household divided by 10. Marital disruption means separation, divorce, or widowhood.State controls are: nursing home bed per 1000 elderly, real housing value in 1990, and change in real housing prices between 1960 and 1990.Estimates are weighted to reflect the non-institutionalized population in the United States. Standard errors are clustered at the state level. Significance denoted as: *** - 1%, ** - 5%, * 

Table 6 : Quantile Regression Results

Effect of Medicaid Medically-Needy Coverage on Retirement Wealth

(Robust Standard Errors in Parenthesis)

10th percentile 20th percentile 25th percentile 35th percentile 50th percentile 60th percentile 

42
sample selection

Coef Coef Coef 

Medicaid * MAA (R*MAA) -0.120 -0.002 -0.172 * 

(.088) (.094) (.089) 

Medicaid * Medically-Needy (R*MN) -0.274 *** -0.141 * -0.269 *** 

(.079) (.084) (.080) 

N 1229 1492 1093 R-squared 0.63 0.64 0.61 

Notes:Medically-needy states allow individuals to deduct medical expenses from income to qualify for the Medicaid progrThe MAA program is the Medical Assistance for the Aged program. MAA states allowed spend-down prior to MediOmitted group includes non medically-needy states. Data are from the Asset and Health Dynamics of the Oldest OMedicaid regime means the head (or, husband if married) was between ages 49 and 57 in 1965.The pre-Medicaid regime are households where the head, or husband, was age 58 or older in 1965.Excluded sunbelt states are Florida, Arizona, and Texas.The top 5 net migration states are Florida, Arizona, North Carolina, Nevada, and Oregon.Estimates are weighted to reflect the non-institutionalized population in the United States. Standard errors are clusSignificance denoted as: *** - 1%, ** - 5%, * -10%.

Exclude Top 5

Table 7: Robustness Checks

Dependent Variable is Log of Retirement Wealth

(1) (2) 

States

(Robust Standard Errors in Parenthesis)

Dependent Variable: Log Retirement Wealth

over 10 years Migration States 

Exclude Sunbelt 

Live in Home

(3)

43