Nursing Home Share of Total
Health Care Expenditures
Moral Hazard in Nursing Home Use
9
8
7
6
%
David C. Grabowski
Harvard Medical School
5
4
3
Jonathan Gruber
Massachusetts Institute of Technology
2
1
0
1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000
Who Pays?
Private Other
insurance 6%
8%
Medicaid
47%
Out of pocket
27%
Research Question
• Do more generous state Medicaid rules
encourage greater nursing home use,
• Or, is nursing home care an option of “last
resort” that does not respond to public
policy?
Medicare
12%
Background
• Medicaid eligibility for nursing home care
determined by income and asset tests
• Some states allow medical and nursing
home expenditures to count against
income test
– Medically needy rules
– 209(b) status
• Spousal impoverishment rules
– 1990 Medicare Catastrophic Care Act
Theory
• Std market clearing model. Individuals
desire nursing home care and also want to
leave bequests for their heirs
• Loosening Medicaid eligibility standards is
thought to have two effects:
– Mechanical. More individuals eligible for
services
– Behavioral. Allowing individuals to qualify for
Medicaid while retaining more assets will
raise total demand for NH care
1
Previous Research
• Cutler & Sheiner, 1994; Hoerger, Picone &
Sloan 1996; Norton, 1995; Norton &
Kumar, 2000; Reschovsky, 1996
• At most, previous studies find modest
effect of Medicaid rules on utilization
• Concerns
– Unobserved heterogeneity
– Selectively control for eligibility standards
– Somewhat outdated
Data
• National Long-Term Care Survey
– 1982, 1984, 1989, 1994, 1999
– Surveys community and institutional
populations
– Data are longitudinal with replacement
• Survey of Medicaid program rules
• Medicaid payment rate and nursing home
beds from Harrington et al state data
books
Variables
General Empirical Approach
NHist = ßPOLICYst + γXist +νi + ηs + λt + (λt × νi) + εist
Where:
NHist is nursing home use for person i in state s of year t,
POLICYst is a vector of state motor vehicle laws
Xist is a set of individual level control variables
νi = marriage fixed effects
• Policies
–
–
–
–
–
–
• Covariates
Spend-down provision
Income test
Asset test
Spousal asset test
Medicaid payment rate
Beds per 1,000 elderly
–
–
–
–
–
–
Age
Race/ethnicity
Gender
Number of children
Income (endogenous?)
Health (endogenous?)
ηs = state fixed effects
λt = year fixed effects
εist is a randomly distributed error term
Methods
• Estimate probit models
– Present marginal probability effects
• Use NLTCS weights
• May be autocorrelation in outcomes and
policies
– Cluster standard errors within states
– Present results that include state-specific
linear time trends
Descriptive Statistics
Variable
Nursing home use
Spend-down provision
Income test (1999$)
Asset test (1999$)
Spousal asset test (1999$)
Medicaid per diem (1999$)
Beds per 1,000 elderly
N
Mean (SD)
0.046 (0.210)
0.66 (0.47)
619 (1,026)
3,234 (1,323)
12,928 (26,372)
84.13 (25.79)
53.30 (15.49)
25,697
2
Results
Robustness Checks
Basic Model w/ Time Trends
Spend-down
0.002 (0.004)
0.006 (0.007)
Income test ($1,000s)
0.001 (0.001)
0.001 (0.001)
Asset test ($10,000s)
-0.013 (0.010)
-0.012 (0.008)
0.000029 (0.005)
0.004 (0.004)
Medicaid rate ($100s)
-0.007 (0.014)
-0.009 (0.020)
Beds per elderly
0.330 (0.187)
0.079 (0.438)
24,417
24,417
Spousal assets ($100,000s)
N
• Synthetic panel approach
– NLTCS includes lifetime history of NH use
– Age everyone in NLTCS back to 65
• Transitions model
– Conditional on individuals in community at previous
NLTCS wave
• Differences-in-differences-in-differences model
with spousal impoverishment rules
– Pre vs post MCCA; single vs married; high vs low
asset states
Conclusions
• Results consistent with inelastic demand for
nursing home care
– No statistically significant effects
– Standard errors reject relatively small effects
• Changes in Medicaid eligibility standards may
potentially increase Medicaid share, but little
evidence of significant behavioral effects
– From a policy perspective, contraction of Medicaid
eligibility will not decrease total utilization of services
3