Impact of Long-term Care Insurance on
Setting and Use of Formal and Informal
Care
Christine Bishop, Ph.D.
Schneider Institute for Health Policy, Heller School for Social
Policy and Management, Brandeis University
Boryana Dimitrova, Ph.D.
Deloitte & Touche
Marc Cohen, Ph.D.
LifePlans, Inc.
AcademyHealth Annual Research Meeting
Boston
June 27, 2005
Research Support
• Health Care Financing and Organization, an
initiative of Robert Wood Johnson Foundation
• Data developed in previous projects supported
by
 ASPE/ Office of the Assistant Secretary for Planning
and Evaluation, Department of Health and Human
Services
 Robert Wood Johnson Foundation/HCRI
2
The LTCI Market Has Experienced
Steady Growth
Policies Sold (In Thousands)
10000
9,000
9000
8,261
8000
7,529
6,831
7000
6,080
6000
5,542
4,960
5000
4,351
3,837
4000
3,417
2,930
3000
2,430
2000
1000
1,550
815
1,930
1,130
0
1987
1992
1997
Source: LifePlans computations; Number of Long-Term Care Insurance Policies Sold, Cumulatively
1987-2002 estimates AAHP-HIAA LTC Market Survey 2003. Includes preliminary numbers for 2002.
3
2002
The Fastest Growing Segment of the
LTCI Market is Employer-Sponsored
Average Annual Growth Rates in the LTC Insurance Marketplace
(1991-2001)
25
Percent
20
22%
18%
15
12%
10
6%
5
0
Total
Individual
Source: HIAA LTC Market Survey 2003: Lifeplans Computations
4
Employer
Life Riders
Estimated Market Penetration 2002
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
9%
6%
4%
Age 45+
LifePlans Inc., 2003
5
Ages 45 to
64
Age 65+
Long-Term Care Insurance
 A “contingent claim”
 Provides holder with more resources when the
insured “event” (disability) occurs
 If the event occurs, resources have high value
for consumption
6
Research Questions
•
Does insurance affect choice of setting of care


at home
vs.
residential setting
• For community residents, does insurance affect


7
Use of paid care
Use of unpaid care (informal care)
Impact of insurance on choice of
setting
•
•
•
8
Higher income is associated in cross-section with
greater use of residential care given disability, perhaps
because of living arrangement and caregiver patterns
by socioeconomic status
“Extra” resources contingent on disability could
support earlier entry into residential care
OR
Could support care at home, other things (marital
status, age, disability, living arrangement, caregivers)
constant.
Choice of Setting by Income for Elders
with Disabilities—NLTCS 1999
Income <$20K
>$20K
All
Care at Home
36.6%
17.2%
30.1%
Residential Care
63.4%
82.8%
69.9%
100.0%
9
100.0% 100.0%
Modeling Selection Effect for Holding
LTC Insurance
• LTC insurance market is new; age will differ
• Income: Medicaid programs, premium costs 
•
•
•
10
higher income for purchasers
Education
Marital status at time of purchase: more likely
to be married
Race: marketing to whites?
Unmeasured Effects
• Purchasers have a higher self-assessed
probability of needing LTC in next five years
• Purchasers are more risk averse than
nonpurchasers
 See Finkelstein and McGarry (2004)
11
Data Sources
• All insured persons claiming benefits from 8
private LTC insurance companies in 1999
 Disabilities in 2+ ADLs
OR
 Cognitive impairments
• 1999 National Long-term Care Survey
comparison group – same functional level
 Disabilities in 2+ ADLs
OR
 Cognitive impairments
12
Comparing the two groups
• Insured group with physical disability is
 Younger
 More likely to be married
 Higher education
 Higher income
 More likely to be white
13
Elders with disabilities: Insured elders
differ from general population
Age
Married
Male
Education
HS, Not College
College Graduate
Income $20K+
Proportion White
14
Insured
Not Insured with
Disability
85.723
79.843 ***
0.142
0.487 ***
0.216
0.346 ***
0.350
0.074
0.327
0.898
0.531
0.349
0.715
0.974
***
***
***
***
Proportion in nursing home
100%
90%
38.0%
80%
70%
65.2%
60%
50%
40%
62.0%
30%
20%
34.8%
10%
Nursing Home
Receiving Care at Home
0%
Not Insured with Disability
15
Insured with Disability
Bivariate Probit for Insured State and
Setting of Care
•
•
Sample: restrict to nonpoor (couples or singles)
Insured status
 Insurance as the endogenous variable of interest
 Education and never married as instruments: affect insured
•
state
Setting of care
 Insurance
 Level of disability
 Income
16
Bivariate Probit Results
Estimates for Insured Status Insured = 1
Variable
Coefficient
se
t
Pr > |t|
Intercept
-2.287
0.542
-4.22 <.0001
Never married
-0.659
0.253
-2.60 0.0092
HS graduate
0.558
0.132
4.23 <.0001
Technical school
1.682
0.235
7.17 <.0001
Some college
1.213
0.158
7.68 <.0001
College graduate
1.470
0.168
8.77 <.0001
Graduate school
2.167
0.261
8.30 <.0001
White
1.443
0.215
6.70 <.0001
Age:Years 65-59
0.227
0.116
1.96 0.0497
Age: Years 70-79
-0.052
0.022
-2.35 0.0185
Age: Years 80+
-0.096
0.012
-8.14 <.0001
17
Bivariate Probit Results (2)
Estimates for Setting of Care: Community = 1
Variable
Coefficient
se
t
Pr > |t|
Intercept
3.023
0.621
4.87 <.0001
Insured
1.427
0.220
6.49 <.0001
Married
0.212
0.129
1.64 0.1008
Never married
-0.394
0.270
-1.46 0.1443
Male
-0.205
0.116
-1.77 0.0761
Log of Income (000)
-0.113
0.074
-1.53 0.1249
White
-1.819
0.190
-9.56 <.0001
Age:Years 65-59
0.115
0.114
1.01 0.3112
Age: Years 70-79
-0.036
0.022
-1.65 0.0996
Age: Years 80+
-0.032
0.013
-2.42 0.0156
Disabled in 1 ADL
-1.331
0.344
-3.87 0.0001
- 2 ADLs
-1.203
0.313
-3.84 0.0001
- 3 ADLs
-1.426
0.308
-4.64 <.0001
- 4 ADLs
-1.685
0.304
-5.54 <.0001
- 5 ADLs
-1.793
0.295
-6.07 <.0001
- 6 ADLs
-2.187
0.302
-7.24 <.0001
Cognitive impairment
-0.647
0.118
-5.49 <.0001
18
Marginal Effects on Probability of Community
Residence
Variable
Insured
Married
Never married
Male
Log of Income (000)
White
Age:Years 65-59
Age: Years 70-79
Age: Years 80+
Disabled in 1 ADL
- 2 ADLs
- 3 ADLs
- 4 ADLs
- 5 ADLs
- 6 ADLs
Cognitive impairment
19
Mean Marginal
Effect for Sample
0.390
0.058
-0.108
-0.056
-0.031
-0.497
0.032
-0.010
-0.009
-0.364
-0.329
-0.390
-0.460
-0.490
-0.598
-0.177
Insurance is Associated with Higher
Probability of Care at Home
• Care at home is not associated with higher
•
•
20
income -- does not appear to be a “superior
good”
But perhaps additional resources contingent on
disability are used differently from ordinary
income?
Or people preferring care at home are more
likely to purchase insurance?
For Community Residents: Effect of
Insurance on Paid Care and/or
Unpaid Care?
• Conditional on community residence: selection
effect
 Because insurance affects who remains at home, we
are observing a different group than would occur
otherwise
• Should be modeled as simultaneous with choice
•
•
21
of setting (choose based on care available)
Paid and unpaid care: substitutes or
complements?
Zeros
Bivariate Probit for Joint Distribution of ANY
Paid and ANY Unpaid Help
Any Paid Care = 1 Any Unpaid Care = 1
Variable
Coefficient
Sig t Coefficient
Sig t
Intercept
-1.398
0.298
-0.748 ns
Insurance
2.659 <.0001
0.090 ns
Log of income (000)
0.166 ns
-0.092 ns
Male
0.284
0.260
0.168 ns
Years 65 to 69
0.079 ns
0.087 ns
Years 70 to 79
-0.066
0.131
-0.004 ns
Years Over 80
0.010 ns
0.055
0.019
3 ADLs
0.415
0.202
0.508
0.035
4 ADLs
0.159 ns
0.726
0.002
5 ADLs
0.917
0.002
0.595
0.004
6 ADLs
1.098
0.001
0.430
0.107
Cognitive Impairment
-0.425
0.059
0.547
0.002
Child within 25 miles
0.088 ns
0.497
0.001
Married
-0.554
0.044
0.893 <.0001
Rho
0.108 ns
22
If assume unpaid hours given -Variable
Intercept
Insured
Log of Income (000)
Male
Age:Years 65-59
Age: Years 70-79
Age: Years 80+
- 3 ADLs
- 4 ADLs
- 5 ADLs
- 6 ADLs
Cognitive impairment
Child within 25 mi
Married
Informal Care Hours
23
Hours of Paid Care
Coefficient
Sig t
-37.087
0.0328
56.282 <.0001
3.405
0.1739
2.115 ns
2.355 ns
-0.946
0.1129
-0.300 ns
12.113
0.0191
19.405 <.0001
25.537 <.0001
46.469 <.0001
-3.863
0.2509
-5.146
0.1052
-18.489 <.0001
-0.211 <.0001
Tobit Analysis Paid and Informal Hours
Variable
Intercept
Insured
Log of Income (000)
Male
Age:Years 65-59
Age: Years 70-79
Age: Years 80+
- 3 ADLs
- 4 ADLs
- 5 ADLs
- 6 ADLs
Cognitive impairment
Child within 25 mi
Married
24
Hours of Paid Care
Hours of Informal Care
Coefficient
Sig t
Coefficient
Sig t
-40.750
0.025
-27.074
0.185
59.843 <.0001
-11.338
0.022
3.982
0.124
-3.248
0.296
1.096 ns
5.980
0.162
2.045 ns
4.737
0.241
-0.856
0.166
-0.090 ns
-0.492 ns
0.999
0.071
10.046
0.059
21.834
0.000
15.996
0.001
27.174 <.0001
21.361 <.0001
29.646 <.0001
41.828 <.0001
32.861 <.0001
-5.375
0.121
12.469
0.002
-6.485
0.047
10.448
0.006
-23.536 <.0001
30.554 <.0001
Insurance has a strong effect on paid
care for community-resident elders
• Bivariate probit shows large significant effect
•
on any paid (of course)
Estimate given paid hours are nonzero implies
56 hours a week more for insured; paid hours
are reduced by about 12 minutes for every hour
of informal care
• Tobit shows large effect – 60 hours
25
Insurance affects informal care
through effect on amount of paid care
•
•
•
26
No significant effect on the probability of any informal
care
Tobit shows reduction of about 11 hours for insured
(Of course) insured must be buying more care for
themselves than they would with an equal income
increase – (about an hour a week for a $10000 income
increase)
Compute Inverse Mills Analog Based
on Bivariate Probit Results


Y  X 

 ( X )
1


2 2
 (1   ) 
commres 
F ( X , Y ,  )
27
Paid Hours (2SLS)
Variable
Intercept
Insurance
Hours of Unpaid Help
ln Income (est)
Number ADL Disabilities
Cognitive Impairment
R-squared =.2439
N=414
28
Parameter Standard
Estimate
Error
-5.263
20.717
-0.702
1.401
8.779
4.695
7.465
3.866
0.138
2.279
1.312
3.227
t Value
Pr > |t|
-0.71
0.4812
5.36 <.0001
-5.09 <.0001
0.61
0.539
6.69 <.0001
1.45
0.1465
Unpaid Hours (2SLS)
Variable
Intercept
Hours of Paid Help
Child within 25 miles
Married
Number ADL Disabilities
Cognitive Impairment
R-squared =.25684
N=414
29
Parameter Standard
Estimate
Error
1.326
-0.451
3.089
15.510
8.285
7.627
4.272
0.114
2.926
3.171
1.027
3.156
t Value
Pr > |t|
0.31
0.7564
-3.96 <.0001
1.06
0.2917
4.89 <.0001
8.07 <.0001
2.42
0.0161