Transcript 5550_l9_2014-Dem-Info

More Demand / Begin
Information
ECO 5550/6550
Fundamental Problems with Demand
Estimation for Health Care
• Measuring quantity, price, income.
• Quantity first. It is typically very difficult
to define quantity.
• We usually look at the stuff that is
easiest to measure. Things like visits,
days of service, and the like.
Problems w/ Demand Estimation
• The problem here is that the measures
may not be meaningful.
• 5 days of inpatient care for observation
is obviously not the same as 5 days of
inpatient care for brain surgery.
• We could argue that 5 visits reflects
more treatment than 4 visits, but it could
simply indicate that the first 4 visits were
not effective.
Episodes
• Episodes represent what may be a more
theoretically desirable measure of output in a
number of ways.
• An episode starts when someone starts to
need treatment, and ends when they no
longer need it.
• For example, an episode may include a few
visits to the doctor, some inpatient
hospitalization, and maybe some follow-up
clinic visits.
Episodes
• It is usually defined chronologically. In
principle, this is the best way to measure both
instances of demand, and the costs of
treatment.
• Particularly useful, for example, if the makeup of treatment has changed. If, over time,
we have substituted outpatient for inpatient
care, and we have a few more tests, but they
are cheaper, then what is really important is
not the number of visits, or the number of
days, but the cost of the episode.
Episodes
• These seem great. What are the problems?
– They are necessarily arbitrary. We must determine
when the episode starts, and when it ends. Does a
certain visit represents more of the same episode, or
the beginning of another episode.
– We must look much more carefully into the process
that defines the episode, and at behavior within the
episode.
– We need complete data on individuals. If individuals
go to several providers, or take considerable out-ofplan coverage, it may be very difficult to create
episodes with any real confidence.
Example
•
•
•
•
Normal delivery of a child.
Length has changed
Less days in hospital
More care at home.
Income
• Most elementally, it is often difficult to find incomes.
If we are looking at insurance claims, they often don't
have people’s incomes on them.
• Hard to get wage rate to evaluate valuation of time.
• Given that you have income, there are other
concerns. Many economists, myself included, feel
that many types of expenditures are more
appropriately related to long-term, or permanent
income, than to measured, or current income. If we
try to estimate demand with current income, we get
some problems with the demand elasticity.
Price
40
Effective demand 40
30
Money Price
30
20
Effective Price
If we treat coinsurance as
simply a fraction,
econometrics should not
be too difficult.
Rather than measuring price
P, we are measuring net
price rP. A 10 % change
in coinsurance rate is
simply the same as a 10
% change in net price.
Insurance is only important
IF price is important.
20
Money price demand
10
10
Visits
Kinks from Insurance
Students tend to
fixate on the kinks.
May not necessarily
be at a kink.
Composite
3 sections
1. Deductible - same
as before
2. Coinsurance Other Goods trade
off for more health
care.
3. Limit - Insurer
won't pay more. Back
to previous slope.
Budget constraint is
now decidedly nonlinear, and nonconvex
Health Care
Rand Experiment
• The Rand experimental data randomly
assigned people to insurance
coverages, thus addressing at least
some of the problem.
• Generally these estimates gave
coinsurance elasticities of about -0.2.
What does this mean?
The Effects of Time and Money
Prices on Treatment Attendance
for Methadone Maintenance
Clients
Natalia N. Borisova
Procter and Gamble Pharmaceuticals, Cincinnati, Ohio
Allen C. Goodman
Wayne State University, Detroit, Michigan
Journal of Substance Abuse Treatment 2004
Methadone treatment
• Methadone maintenance is an unusual and
possibly unique health care model.
• First, clients are required to visit a clinic very
often (it used to be every day), so treatment
attendance becomes essential for clients’
compliance and treatment effectiveness.
• Second, treatment attendance has
implications for waste of resources in terms
of staff time and the underutilization of
equipment.
Barriers to Treatment
• Out-of-pocket treatment fees are modest due to
extensive private and public insurance coverage,
but …
• Out-of-pocket transportation costs, and, more
importantly, daily travel and waiting time costs
may be substantial, and possibly prohibitive.
• Clients who face higher treatment fees, related
transportation and childcare costs, and longer
travel and waiting times may be less likely to
attend treatment regularly.
Estimating the Model
A = β0 + β1PM + β2PT + β3Y + β4Z + ε
(1)
where:
PM is the average daily money price;
PT is the average daily time price;
Y is gross household income;
Z is a vector of variables that may influence treatment
attendance including socioeconomic and demographic
attributes; and
ε is an error term
Demand v. Willingness to Pay
• Demand
– Call out price
– Determine quantity
• Willingness to Pay (WTP)
– Call out quantity
– Determine maximum amount people would
pay.
Time Price
The travel time price measured by WTP was based on
a contingent valuation analysis (CVA) in which clients
were offered two hypothetical choices:
(1) spend twice as long as the actual travel time to the treatment
program and (24 - 2T travel – T clinic) amount of time at either work or
leisure, where T travel is travel time and T clinic is time spent at the
treatment program; or
(2) spend no time on travel to the treatment program
and (24 - T clinic ) amount of time at either work or leisure.
WTP
TT = 40
Questions
$10
If you had to pay here for each visit, what is the MOST
money you would be willing to pay?
$8
40 min  $2
If it took you twice as long as$3/hr.
usual to travel to this clinic
TT = 80
and if you had to pay, what is the MOST money you would
be willing to pay for each visit?
40 min  $3
$4.50/hr.
TT = 0
$13
If this clinic were moved right NEXT DOOR to where you
live for your convenience and if you had to pay, what is the
MOST money you would be willing to pay for each visit?
WTP, but
Also consistency
WTAccept
Table 1 - Treatment Attendance, and Mean Values of
Money and Time Prices per Treatment Day
Attendance Rate
Range
Percent ofClients
Money Price (dollars)
Time Price (dollars)
TREATMENT
FEES
TRAVEL
COST
CHILD
CARE
COST
WTP
WAGE
A = 1.00
40.6
5.04
2.80
0.16
5.45
-9.98
1.00 > A ≥ 0.99
15.8
3.61
3.24
0.75
5.55
-9.97
0.99 > A ≥ 0.98
14.2
4.56
3.31
0.81
5.69
11.77
0.98 > A ≥ 0.95
12.5
3.61
3.90
1.39
5.56
13.37
0.95 > A ≥ 0.85
10.6
4.13
3.92
1.31
6.45
18.06
0.85 > A ≥ 0.58
16.3
4.21
5.32
3.82
6.87
22.19
Total mean
-
4.42
3.36
0.85
5.71
12.27
Variables
Table 2 –
Variable
Definitions and
Sample Means
Mean* (A |A<1)
Mean (A |A=1)
ATTENDANCE RATE
0.97----
0.95------
1.00-------
AFRICAN-AMERICAN
0.33----
0.44------
0.17-------
WOMEN
0.47----
0.48------
0.45-------
EMPLOYED
0.45----
0.41------
0.52-------
MARRIED
0.24----
0.24------
0.24-------
AGE
41.80----
42.05------
41.43-------
AGE SQUARED
1807.82-
1828.49-----
1777.57-----
CLINIC IN MACOMB COUNTY
0.32----
0.19------
0.50-------
CLINIC IN OAKLAND COUNTY
0.33----
0.31------
0.36-------
FAMILY INCOME (yearly)
WEEKS IN TREATMENT
18065.
17853.
18375.
80.51----
83.17------
76.67-------
NUMBER OF PREVIOUS TREATMENTS
1.00----
1.19------
0.72-------
BUS
0.18----
0.21------
0.15-------
OTHER TRANSPORTATION
0.02----
0.02------
0.01-------
MONEY PRICE ($) per day
8.63----
9.05------
8.00-------
12.27----
13.85------
9.98-------
5.71----
5.88------
5.45-------
TRAVEL TIME (in minutes)
81.37----
91.64------
66.34-------
WAITING TIME (in minutes)
30.99----
33.92------
26.71-------
TIME PRICE ($) per day, measured by WAGE
TIME PRICE ($) per day, measured by WTP
*A is a treatment attendance rate
Mean
OBSERVATIONS
303----
180
123
Variables
Table 3 Money price
Tobit
and time
Estimates price are
Using WTPBOTH
INTERCEPT
Parameter
η†
T-Ratio
A|X
Latent
A* | X
A|
0.58 < A < 1, X
0.9586-----
12.25***
-
-
-
AFRICAN-AMERICAN
-0.0347-----
-3.13***
-0.0179
-0.0358
-0.0144
WOMEN
-0.0071-----
-0.77+++
-0.0036
-0.0073
-0.0029
EMPLOYED
0.0181-----
1.81*++
0.0093
0.0187
0.0075
MARRIED
0.0082-----
0.77+++
0.0042
0.0085
0.0034
-6.97E-04----
-0.19+++
0.0184
0.0367
0.0148
01.84E-05----
0.41+++
-
-
-
CLINIC IN MACOMB COUNTY
(OUTSIDE CENTRAL CITY)
0.1118-----
7.82***
0.0577
0.1152
0.0464
CLINIC IN OAKLAND COUNTY
(OUTSIDE CENTRAL CITY)
0.0896-----
6.32***
0.0462
0.0924
0.0372
-0.0061
-0.0122
AGE
important
AGE SQUARED
FAMILY INCOME (per week)
-3.4E-05-----
-2.06**+
-0.0049
WEEKS IN TREATMENT
-5.5E-05-----
-1.04+++
-0.0023
-0.0046
-0.0018
PREVIOUS TREATMENT
0.0065-----
1.65*++
0.0033
0.0067
0.0027
BUS
0.0109-----
0.89+++
0.0056
0.0112
0.0045
OTHER TRANSPORTATION
0.0354-----
1.03+++
0.0183
0.0365
0.0147
MONEY PRICE (per week)
-1.9E-04-----
-1.68*++
-0.0051
-0.0103
-0.0041
TIME PRICE - WTP (per week)
-4.2E-04-----
-2.84***
-0.0044
-0.0087
-0.0035
OBSERVATIONS
303
Pr (UNCENSORED)
0.5004
E (A* | X)
0.9974
E (A | X)
0.9634
E (A | 0.58 < A < 1, X)
0.9396
Information
Information
• Why do we care?
• Problem is asymmetric information.
• In many parts of the health care sector,
there are information gaps.
• Sometimes the patient knows more than
the provider. Examples? Discuss.
• Sometimes the provider knows more
than the patient. Examples? Discuss.
The Lemons Principle
• Shows the problem when we have
incomplete information. We will apply
this principle DIRECTLY to the
purchase of health insurance.
• Key feature these days.
Lemons and Cars
Probability
Probability
0.15
0.1
0.05
Quality Level
2
1.
5
1.
75
1.
25
1
0.
5
0.
75
0.
25
0
0
• We have incomplete
information on the
quality of cars.
• Some may be
creampuffs
• Others may be lemons.
• What does that do to
the market.
• Assume we have 9
cars, with quality levels
varying from 0 to 2.
Idea
• Owners know how much their cars are worth but
potential buyers DON’T.
• Owners know that cars are worth $10,000*Q, where
Q is quality.
• Potential buyers are willing to pay $15,000 per unit of
quality (they need cars)
• But they only know that the AVERAGE car is worth
how much?
• A> $10,000. Why?
Equilibrium price?
• Suppose an auctioneer calls out a price of $20,000
per car. All 9 cars will be offered. Why?
• How many will be bid on?
• A> None. Because buyers only know that the mean
quality level is 1 @ a price of $15,000. So you have
9 sellers, no buyers.
Equilibrium price? (2)
• $20,000 doesn’t work.
• Suppose auctioneer calls out a price of $15,000 per
car. 7 cars will be offered. Why?
• The BEST ones are withdrawn since they are worth
more than $15,000.
• Average quality falls from 1 to 0.750. Why?
• Potential buyers use price as an indicator of quality,
and recognize that the cars being offered are lower
quality. They would only pay $15,000*0.750 =
$11,250. Still no bidders. There never will be.
WHY?
• When potential buyers know only the
average quality of used cars, then the
market prices will tend to be lower than
the true value of top quality cars.
• High quality cars are driven out of the
market by lemons.
• KEY -- Sellers have information. Buyers
DON’T. This is NOT symmetric.
What does information do?
• If both sides have perfect information …
all is good. We’ve done enough micro
to understand.
• But what if neither side has information?
What if NEITHER has Info?
Probability
Probability
0.15
0.1
0.05
Quality Level
2
1.
5
1.
75
1.
25
1
0.
5
0.
75
0.
25
0
0
• Auctioneer starts at
$20,000.
• Owners guess their cars
have quality level Q = 1.
• All 9 cars are offered, but
none are bid.
• If price is dropped to
$15,000, all 9 cars are
still offered.
• Buyers buy them.
• KEY is that the (lack of)
information is symmetric.
More kinks
Price is correlated with the error
term. Since individuals with
large values of the error term
are likely to exceed a
deductible, and conversely,
V will be negative.
That is, a large positive (+) error
is correlated with a low price,
because after the deductible,
we're thrown into a low
copayment (and vice versa).
This is noted by error terms in
graph. This suggests that
the demand curve is more
elastic (more negative).