Topic 8: Welfare Analysis of Public Health Insurance...Topic 8: Welfare Analysis of Public Health...
Transcript of Topic 8: Welfare Analysis of Public Health Insurance...Topic 8: Welfare Analysis of Public Health...
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Topic 8: Welfare Analysis of Public Health Insurance
Nathaniel Hendren
Harvard and NBER
Fall, 2018
Thanks to Raj Chetty and Amy Finkelstein for generously providing their lecturenotes, some of which are reproduced here
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Public Health Insurance
Medicaid is the largest transfer program in the US ($550B in 2015)
Potential rationales for gov’t provision:Adverse selectionSamaritan’s dilemma (uncompensated care)Externalities on othersProductive impacts on children
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Goals of This Lecture
Impact of Public Health Insurance on AdultsConsumption smoothing / reducing high out of pocket spendingIncreases in healthcare utilization (i.e. “moral hazard”)Labor supply
Conduct/discuss welfare analysisStructural assumptions vs. revealed preferenceRole of uncompensated care
Impact of health on ChildrenLarge evidence of health impacts
GE E�ectsInsurance increases hospital expansion/innovation/etcLeads to increased costs...
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Key Themes: It’s all about the kids...
Insurance limits out of pocket payments and decreases financial stressDoes not have (measurable) health impacts on adults
Large crowd-out of uncompensated care in recent expansionsThe uninsured aren’t fully “uninsured”
In contrast, lots of evidence suggesting insurance improves health forchildren
Similar to MTO / neighborhoods?Strong evidence insurance increases costs through GE e�ects
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1 Impact of Medicaid on Adults
2 Welfare Analysis of Medicaid
3 Impact Medicaid on Children
4 Impact of Medicare: Health and GE E�ects
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Oregon Health Insurance Experiment
In 2008, Oregon ran a lottery for its Medicaid program for low-incomeadults
Was previously closed to new enrollment
Approximately 90,000 people signed up.Budget for 10,000 people
Lotteried 30,000 with ~30% takeup
Finkelstein et al. (2012, QJE “The Oregon Health InsuranceExperiment: Evidence from the First Year”)
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Oregon Health Insurance Experiment
Intention to treat specification
yi = b0 + b1LOTTERYi + b2Xi + ei
where Xi are covariates correlated with probability of winning thelottery (e.g. household size)
LATE specification
yi = p0 + p1INSURANCEi + p2Xi + ni
where first stage is
INSURANCEi = g0 + g1LOTTERYi + g2Xi + hi
Compliers are those induced to get insurance through the lottery
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Hospitalization Utilization Increases (QJE, 2012)
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Emergency Department Use (Science, 2014)
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Emergency Department Use (Science, 2014)
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Emergency Department Use (Science, 2014)
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Emergency Department Use (NEJM, 2016)
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Preventative Care (NEJM, 2013)
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Diabetes Diagnosis (NEJM, 2013)
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Utilization Summary
Increases in healthcare utilization across the boardED use goes up (contrary to some theories)Preventative care increasesIncreased diagnosis of diabetes
What about financial strain?
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Oregon Health Insurance Experiment
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Oregon Health Insurance Experiment
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Oregon Health Insurance Experiment
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Reduction in Collections (QJE, 2012)
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Reduction in Self-Reported Hardship (NEJM, 2013)
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Financial Strain Summary
Robust evidence that Medicaid reduces financial strainLower OOP spendingFewer bankruptciesFewer collectionsetc...
What about health outcomes?
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No change in blood pressure (NEJM, 2013)
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Some reductions in depression (NEJM, 2013)
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Health Impacts Summary
Some evidence of increased subjective well-being and reduceddepression
But, no statistically significant change in medical conditions
Lack of power?
Also can’t reject clinical trial estimated impact on outcomes
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Medicaid and Fiscal Externalities
What about impacts on labor supply and other program participation?
Why do we care?
Fiscal externality...
Is the LATE what we want?
What about ex-ante responses?
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Impacts on Earnings (AER, P&P 2014)
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Medicaid on Labor Supply
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Medicaid on Labor Supply
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Welfare Relevance of Program Participation E�ects?
Medicaid Lottery increases Food Stamp enrollment
What is the welfare impact?
Information versus price e�ects
If people learned from their doctor or other program o�cer that theywere eligible for other benefits beyond Medicaid, can generate firstorder welfare benefit from changing behavior in response to thisinformation
Labor supply didn’t change -> eligibility didn’t change; onlyenrollment?
Was it information?
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Beyond Oregon: Impacts on Financial Strain
MA health insurance expansion required everyone to obtain insurance
Impact on financial strain: Mazumder and Meyer (2016)
Study county-level credit records in MA
Look at heterogeneity as function of %uninsured prior to MA reform
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Mazumder and Meyer (2016)
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Mazumder and Meyer (2016)
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Uncompensated Care
Health insurance expansions reduce bankruptcy and unpaid billsImplies beneficiaries of public health insurance are not necessarily thebeneficiaries themselves
Garthwaite, Gross, and Notowidigdo (2015): “Hospitals as Insurers ofLast Resort”
Document significant impact of public health insurance on reductionsin other forms of charity care and uncompensated care
Use two empirical strategies:Panel regression of uncompensated care cost on %uninsured
Control for state and year e�ectsLarge dis-enrollment in Tennessee and Missouri Medicaid program fromfunding reduction
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Garthwaite, Gross, and Notowidigdo (2015)
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Garthwaite, Gross, and Notowidigdo (2015)
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Garthwaite, Gross, and Notowidigdo (2015)
Greater uninsured lead to greater uncompensated care paid byhospitals
Implies beneficiaries of public health insurance are not necessarily thebeneficiaries themselves
Estimates suggest each additional uninsured person costs localhospitals $900 each year in uncompensated care
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1 Impact of Medicaid on Adults
2 Welfare Analysis of Medicaid
3 Impact Medicaid on Children
4 Impact of Medicare: Health and GE E�ects
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Informal Welfare Analysis
Media:“Medicaid Makes ’Big Di�erence’ in Lives, Study Finds”
National Public Radio (2011)
“Spending on Medicaid Doesn’t Actually Help the Poor”Washington Post (2013)
Public policy centers:“Oregon’s lesson to the nation: Medicaid Works”
Oregon Center for Public Policy (2013)
“Oregon Medicaid Study Shows Michigan Medicaid Expansion NotWorth the Cost”
MacKinac Center for Public Policy (2013)
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Welfare Analysis
Present results from two recent approaches:“Model-based” approach in Finkelstein, Hendren, and Luttmer (2016)
Conduct welfare analysis of Oregon Health Insurance Experiment“Revealed-preference” approach in Finkelstein, Hendren, and Shepard(2017)
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Model-Based Approach: Two Frameworks
1 Complete-information approachCompletely specify normative utility function and estimate causal e�ectof Medicaid on distribution of all utility-relevant arguments
Here: Consumption and Health
Don’t need to assume consumer optimization or need to model howMedicaid a�ects budget set
2 Optimization approachesAssume consumer optimizationModel how Medicaid a�ects the budget set (in each state of the world)Only specify marginal utility function over one argumentImplement three versions:
Consumption-Based Optimization Approach using “consumption proxy”Consumption-Based Optimization Approach using “CEX data”Health-Based Optimization Approach
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Setup (common to both frameworks)
Individuals derive utility from health, h, and consumption ofnon-medical goods and services, c
u = u (c , h)
Health h produced according to h = h̃(m; q)
Medical spending, mq denotes underlying state variable
medical conditions, other factors a�ecting health, etc.
Assume each Medicaid recipient faces same distribution of q
Conceptually: welfare analysis behind behind veil of ignoranceEmpirically: cross-sectional distribution of outcomes capture di�erentpotential qPresence of Medicaid denoted by q 2 {0, 1}
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Complete-Information Approach
Define c (q; q), h (q; q), and m (q; q) to be distributions ofconsumption, health, and medical spending conditional on insurance q
Define welfare impact of Medicaid on recipient, g(1):
E [u (c (0; q) , h (0; q))] = E [u (c (1; q)� g(1), h (1; q))]
Expectations taken over all possible states of world q
To recover g(1) from above equation requires:Estimates of distribution of c and h at q = 1 and q = 0Specification of normative utility function over all its arguments (in ourapplication: c , h)
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Complete-Information Approach: Implementation
Assumption 1: Full specification of utility function:
u (c , h) = c1�s
1 � s+ fh
g(1) solves:
E"
c (0; q)1�s
1 � s+ fh (0; q)
#= E
"(c (1; q)� g (1))1�s
1 � s+ fh (1; q)
#
Requirements:Causal e�ects on distribution of c and mean h for q = 0 and q = 1Full specification of normative utility function
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Optimization Approaches: Model Program Structure
Reduce information requirements through additional assumptions
Assumption 2: (Program structure) Medicaid’s only direct e�ect ison the out-of-pocket price for medical care, p(q)
Rules out other ways Medicaid might a�ect consumption or healthE.g., impacts on provider behavior
Implementation: define out-of-pocket spending, x , for medical careby:
x(q,m) ⌘ p(q)m
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Assumption 3: Individual Optimization
Assumption 3: Individuals choose m and c optimally, subject totheir budget constraint
maxc,m
u�c , h̃ (m; q)
�subject to c = y (q)� x (q,m) 8m, q, q.
y(q) denotes (potentially state-contingent) income plus any(potentially state-contingent) changes in assets (savings or borrowings)
Not an innocuous assumption in health care context!Decisions are taken jointly with other agents (e.g., doctors) who mayhave di�erent objectives (e.g., Arrow 1963)Complex nature of decision may generate individually sub-optimalbehavior
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Thought Experiment: Marginal Expansion of Medicaid
Let q 2 [0, 1] trace a “marginal” expansion in Medicaid:
x(q,m) = (1 � q)p(0)m+qp(1)m
Marginal expansion of Medicaid (marginal increase in q), relaxes theindividuals budget constraint by � ∂x
∂q :
�∂x(q,m(q; q))∂q = (p(0)� p(1))m(q; q)
Note: this is program parameter (i.e., holding behavior, m, constant)
Value to recipient of getting fraction q of Medicaid is given by g(q):
E [u (c (0; q) , h (0; q))] = E [u (c (q; q)� g(q), h (q; q))]
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Consumption-Based Optimization ApproachUse envelope theorem to derive value of marginal expansion of insurance:
dg
dq = E
2
6666666664
ucE [uc ]| {z }
Relativemarginal utility
⇥ (p(0)� p(1))m(q; q)| {z }Marginal relaxation of
budget constraint: �∂x/∂q
3
7777777775
Value budget constraint relaxation by ucE [uc ]
in each state, q
Because derivation is based on envelope theorem, we do not requirefirst-order condition to hold everywhere (i.e., medical spending can be“lumpy”)
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Consumption-Based Optimization Approach
Decompose dg(q)dq into a transfer term and a pure-insurance term
Implementation will be based on estimating each term separately
dg (q)dq = (p(0)� p(1))E [m(q; q)]| {z }
Transfer Term
+Cov
ucE [uc ]
, (p(0)� p(1))m(q; q)
�
| {z }Pure-Insurance Term
(consumption valuation)
Transfer term: Value to beneficiary of expected resource transfersfrom rest of economy
Medical spending times change in out-of-pocket pricePure-insurance term: Value of reallocating resources (by relaxingbudget constraint) across di�erent states of world
Medicaid adds value if it moves resources from states of the world withlower marginal utility of consumption into states of the world withhigher marginal utility
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Consumption-Based Optimization Approach
To arrive at non-marginal estimate, integrate over q :
g (1) =Z
1
0
dg (q)dq dq =
(p(0)� p(1))Z
1
0
E [m (q; q)]dq| {z }
Transfer Term
+Z
1
0
Cov⇣ uc
E [uc ], (p(0)� p(1))m(q; q)
⌘dq
| {z }Pure-Insurance Term
(consumption valuation)
The transfer term does not depend on the utility functiontherefore relatively straightforward to implementsame for all optimization approaches (whether consumption based orhealth based)
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Implementation: Pure-Insurance Term
Requires partial specification of utility function: only marginal utilityof consumptionAssumption 4: Utility function has the form:
u(c , h, .. , ..) = c1�s
1 � s+ v(h, .. , ..)
where v(.) is unspecified subutility function over health and any otherarguments of the utility function
As a result, can write the pure-insurance term as:
Cov uc
E [uc ], (p(0)� p(1))m(q; q)
�
| {z }Pure-Insurance Term
(consumption valuation)
= Cov
c (q; q)�s
E [c (q; q)�s], (p(0)� p(1))m(q; q)
!
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Implementation: Interpolation
Only observe q at 0 and at 1.Need additional assumption to obtain g(1)
Baseline: statistical assumption: dgdq linear in q
Explore sensitivity to alternatives (e.g., m linear in q, or m as anyincreasing function of q, bounds on transfer term)
Assumption 5: Linear approximation:
g (1) ⇡ 12
dg (0)
dq +dg (1)
dq
�
Compare to complete-information approach which can delivernon-marginal welfare estimates directly
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Baseline Estimates, g(1)
I II III IV
Consumption-Based
(Consumption Proxy)
Consumption-Based
(CEX Cons. Measure)
Health-Based
A. Welfare Effect on Recipients, γ(1) 1675 1421 793 690 (standard error) (60) (180) (417) (420)
Transfer component, T 699 661 661 661Pure-insurance component, I 976 760 133 30
Notes: Estimates of welfare effects and moral hazard costs are expressed in dollars per year per Medicaid recipient. Standard errors are bootstrappedwith 500 repetitions.
Complete-InformationApproach
(Consumption Proxy)
Table 2: Welfare Benefit Per Recipient
Optimization Approaches
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Benchmarks
G is cost to Government of providing Medicaid:
G = E [m (1; q)] = $3, 600
N is monetary transfer by Medicaid to external parties:
N = E [m (0; q)]� E [x (0,m(0; q))] = $2, 721 � $569 = $2, 152
C is net resource cost of Medicaid = G � N = increase in m plusdecrease in x :
C = G � N = $3, 600 � $2, 152 = $1, 448
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Comparisons
I II III IV
Cons.-Based (Consumption
Proxy)
Cons.-Based (CEX Cons.
Measure)
Health-Based
A. Welfare Effect on Recipients, γ(1) 1675 1421 793 690
B. Transfer to External Parties, � 2152 2152 2152 2152
C. Efficiency Pure-insurance component, I 976 760 133 30 Moral hazard cost, G-N-T = C - T 749 787 787 787
D. Ratios of γ(1) relative to: monetary transfer to external parties, γ(1)/N 0.78 0.66 0.37 0.32 net costs, γ(1)/C 1.16 0.98 0.55 0.48 gross costs, γ(1)/G 0.47 0.39 0.22 0.19
Complete-InformationApproach
(Consumption Proxy)
Table 2B: Comparisons
Optimization Approaches
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Key Findings From Baseline Specifications, I
First two key findings:1 Recipients’ value from Medicaid is 1/3 to 3/4 of transfers to external
parties, g (1) < N2 Cash vs. In-kind: Recipients would rather give up Medicaid than pay
G , g(1) < G
Driven by substantial transfers to external parties (N/G = 0.6).Uninsured pay only about $0.20 on the dollar for medical spendingConsistent with other estimates of share of medical expenses paid byuninsured (e.g., Coughlin et al., 2014; estimates in MEPS)Consistent with other evidence of implicit insurance
Medicaid substantially reduces provision of uncompensated care byhospitals (Garthwaite et al. 2015)Impact of health shocks on access to credit similar for insured anduninsured (Dobkin et al. 2015)
Key question: economic incidence of transfers to external parties
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Summary
Key limitation for welfare analysis of public health insurance /Medicaid: Do not observe choices
FHL2016 impose a utility function (or coe�. of risk aversion)
Maybe people really are WTP more for health insurance than isgenerated from CRRA=3?
Alternative: Exploit setting where we do see prices
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Revealed Preference Approach
Finkelstein, Hendren, and Shepard (2016) exploit subsidized healthinsurance exchange in Massachusetts (pre ACA)
Charged premiums that were discontinuous functions of income
Estimate demand and cost curves for insuranceIdea: Enrollment on exchange reveals willingness to pay (demand)Key variation: Premium discontinuities by income group
E.g., Cheapest plan is $0 for 100-150% pov.; $39 for 150-200% pov.RD Strategy: Compare 149% poverty vs. 151% poverty to measurehow much higher premium reduces demand, a�ects avg. costs
No evidence of income manipulation across thresholds (why is thisimportant?)
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010
020
030
040
0
$ pe
r mon
th
135 150 200 250 300Income, % of FPL
Subsidy and Premium Discontinuities (2011)
Subsidies
Insurer Price
Enrollee Premium
“Affordable Amt.” (cheapest plan)
Four Other Plans
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0.2
.4.6
.81
135 150 200 250 300Income, % of FPL
Share of Eligible Population Insured94%
70%
76%
56%
44%
58%
%Δ = -26% %Δ = -27%
%Δ = -24% Pmin = $39 Pmin = $77 Pmin = $116
Pmin = $0
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RD = 46.3 (17.3) RD = 31.9
(17.9)RD = 9.4 (22.1)
300
340
380
420
Avg.
cos
t ($/
mon
th)
133 150 200 250 300Income as % of FPL
InsurerCosts
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Average Cost (H)
Marginal Cost (H)
Value_H
0 10
0 20
0 30
0 40
0 $
per m
onth
.2 .3 .4 .5 .6 .7 .8 .9 1 Fraction in H Plan
Value vs. Cost Curves (adj. to 150% FPL)
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Average Cost (H)
Marginal Cost (H)
Value_H
0 10
0 20
0 30
0 40
0 $
per m
onth
.2 .3 .4 .5 .6 .7 .8 .9 1 Fraction in H Plan
Result #1: Substantial Adverse Selection
Downward sloping MC and AC
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Average Cost (H)
Marginal Cost (H)
Value_H
0 10
0 20
0 30
0 40
0 $
per m
onth
.2 .3 .4 .5 .6 .7 .8 .9 1 Fraction in H Plan
Result #2: Little Take-up w/out Large Subsidies
Demand well below average cost
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Average Cost (H)
Marginal Cost (H)
Value_H
0 10
0 20
0 30
0 40
0 $
per m
onth
.2 .3 .4 .5 .6 .7 .8 .9 1 Fraction in H Plan
Result #3: Adverse selection alone cannot explain low covg. Suggests most enrollees would prefer cash to coverage
Demand also well below marginal cost
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Average Cost (H)
Marginal Cost (H)
Value_H
0 10
0 20
0 30
0 40
0 $
per m
onth
.2 .3 .4 .5 .6 .7 .8 .9 1 Fraction in H Plan
Normative Conclusions Not Immediate
Private WTP = Social WTP?
Private MC = Social MC? (e.g. Charity Care?)
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Ex-Ante WTP?
What about ex-ante WTP we discussed last class?Apply approach from Hendren (2018)
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010
0020
0030
0040
0050
0060
00
0 .2 .4 .6 .8 1
Uncompensated Care Estimate
C(s)
AC(s)
D(s)sCE =0
WTP and Cost Curves: Net Resource Cost
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010
0020
0030
00
0 .2 .4 .6 .8 1
Market Surplus Maximizing Allocation
C(s)
D(s)
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010
0020
0030
00
0 .2 .4 .6 .8 1
Market Surplus Maximizing Allocation
sms =41%
pms =$1581
C(s)
D(s)
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010
0020
0030
00
0 .2 .4 .6 .8 1
$182
sms =41%
pms =$1581
C(s)
D(s)
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From Observed to Ex-Ante WTP
§ Whataboutex-anteWTP?
§ Requiresmeasureofriskaversion
§ Baselinecase:!=5x10-4 (Handel,Hendel,andWhinston 2016)
– Estimatedforricherpopulation->lowerboundunderDARApreferences
§ But,impliesCRRAof8ifconsumptionis150%FPL
– ConsideralternativescenarioofCRRAof3(CARAof1.8x10-4)
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010
0020
0030
00
0 .2 .4 .6 .8 1
From Observed to Ex-Ante Demand
D(s)+EA(s)
C(s)
D(s)
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010
0020
0030
00
0 .2 .4 .6 .8 1
From Observed to Ex-Ante Demand
D(s)+EA(s)
C(s)
D(s)
sea =55%
pea =$1089
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010
0020
0030
00
0 .2 .4 .6 .8 1
From Observed to Ex-Ante Demand
C(s)
D(s)
sea =55%
pea =$1089
Result#1:Ex-anteoptimalinsurancepricesare$1089not$1581
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010
0020
0030
00
0 .2 .4 .6 .8 1
From Observed to Ex-Ante Demand
C(s)
D(s)
sea =55%
pea =$1089
Result#1:Ex-anteoptimalinsurancepricesare$1089not$1581
Ex-AnteOptimalAllocationinvolves“deadweightloss”
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010
0020
0030
00
0 .2 .4 .6 .8 1
$228
sea =55%
pea =$1089
D(s)+EA(s)
C(s)
D(s)
From Observed to Ex-Ante Demand
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010
0020
0030
00
0 .2 .4 .6 .8 1
$228
sea =55%
pea =$1089 C(s)
D(s)
Result#2:EveryoneisWTP$228toliveinaworldwithmarginalpriceof$1089forinsurancepriortolearnings
From Observed to Ex-Ante Demand
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010
0020
0030
00
0 .2 .4 .6 .8 1
$182
$227
Mandatelowersmarketsurplusby$45
Welfare Cost of Mandates: Market Surplus
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010
0020
0030
00
0 .2 .4 .6 .8 1
$158
$228Mandateincreasesex-antewelfareby$70
Welfare Cost of Mandates: Ex-Ante Welfare
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010
0020
0030
00
0 .2 .4 .6 .8 1
$158
$228
Welfare Cost of Mandates: Ex-Ante Welfare
Result#3:Mandatesareex-anteoptimal,butresultinlowermarketsurplus
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Summary
Modest premiums deter coverage substantially and raise costsAdverse selection!
Low-income WTP for insurance far below costConsistent with Finkelstein, Hendren, and Luttmer (2016) and factthat uninsured pay 20-30% of their costs
Contrasts with health insurance for high-income peopleConsistent with model in which uncompensated care only provided tolow-income peopleOpen question: Implications for optimal tax/transfers?!
Ex-ante welfare perspective can matter in this instance for whetherWTP exceeds resource costs
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1 Impact of Medicaid on Adults
2 Welfare Analysis of Medicaid
3 Impact Medicaid on Children
4 Impact of Medicare: Health and GE E�ects
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Impacts on Children
Substantial evidence that public health insurance improves health forchildren
But, contrasts with minimal estimated impacts on adults
Currie and Gruber (1996, QJE): Health Insurance Eligibility,Utilization of Medical Care, and Child Health
Exploits state variation in expansion of Medicaid to children andpregnant mothers
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Currie and Gruber (1996)
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Currie and Gruber (1996): Simulated Instruments
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Currie and Gruber (1996)
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Currie and Gruber (1996)
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Currie and Gruber (1996)
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Evidence of Medicaid Impacts using Birthdate RD
Further evidence exploiting Medicaid expansion that o�ered Medicaidto children born after September 30, 1983
Amazing source of identification...Regression discontinuity!
Wherry, Miller, Kaestner, and Meyer: “Childhood Medicaid Coverageand Later Life Health Outcomes”
Wherry and Meyer (2015): “Saving Teens: Using a PolicyDiscontinuity to Estimate the E�ects of Medicaid Eligibility”
Builds on Card and Shore-Sheppard (2004, RESTAT)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Medicaid Impacts on Children
Evidence Medicaid reduces mortality of children
What about other health impactsDirect health impactsImpacts on costs later in life
Wherry, Miller, Kaestner, and Meyer: “Childhood Medicaid Coverageand Later Life Health Outcomes”
Look at impacts on later-life hospitalization, ED visitsFocus on visits for chronic conditions
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Wherry and Meyer (2015)
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Medicaid Impacts on Children
Medicaid reduced mortality ratesInfant mortality (Currie and Gruber 2006)Child mortality (Wherry and Meyer 2015)
Medicaid reduced later-life chronic conditions and hospitalizationReduced later life costs on the systemReduces cost of medicaid expansion by 2-5%
But, less impact of Medicaid on adults (e.g. Oregon...)Similar to impact of place via MTO: Significant impacts on children,but not on adults?
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1 Impact of Medicaid on Adults
2 Welfare Analysis of Medicaid
3 Impact Medicaid on Children
4 Impact of Medicare: Health and GE E�ects
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Impact of Medicare
Focus on two papers looking at impact of Medicare
Exploit:
Age 65 discontinuity (Card, Dobkin, and Maestas, 2009)
Look at health e�ects
Pre-Medicare variation in coverage rates (Finkelstein, 2007)
Look at “GE” e�ects
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Card, Dobkin, and Maestas (2009)
Card, Dobkin, and Maestas (2009) exploits discontinuity in eligibilityfor Medicare at age 65
Document increase in medical care provided
Document reduction in mortality
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Card, Dobkin, and Maestas (2009)
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Card, Dobkin, and Maestas (2009)
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GE E�ects of Health Insurance: Finkelstein (2007)
Health insurance can have e�ects on providers
Health expenditures are growing dramaticallyCould health insurance cause this growth?
Increases incentive to innovate by creating excess demandIs this bad from a welfare perspective?
Finkelstein (2007): Studies impact of Medicare introduction
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Finkelstein (2007): Empirical Strategy
Empirical analysis of Medicare is di�cultMedicare is a national program!
Enacted in 1965Finkelstein (2007): exploit variation in pre-1965 insurance rates
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Finkelstein (2007): Pre-1965 Insurance
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Finkelstein (2007): Estimating Equation
log (yijt) = aj + dt +1975
Ât=1948
ltMcareimpactz ⇤ yeart + Xst b + eijt
where:yijt is outcome in hospital i in county j at time taj is county fixed e�ectdt is year fixed e�ectXst is outcomes in state s at time tMcareimpactz = % elderly in region z without Blue Cross hospitalinsurance in 1963Does lpost � lpre capture GE e�ects? What might be missing?
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Finkelstein (2007): Main Results
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Finkelstein (2007): Main Results
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Finkelstein (2007): Main Results
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Finkelstein (2007): Entry and Exit
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Finkelstein (2007): Adoption of new technologies
Paper also looks at impact of adoption of new technologies byhospitals:
Newtechis = lMcareimpactz + Xs b + eis
Newtech indicates adoption of technology in hospital i in state s
NOTE: Only cross-sectional data available...
Potential bias?
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Finkelstein (2007): Results
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Summary
Public health insurance for adults leads to:Reductions in OOP spendingReductions in financial strain
And reductions in uncompensated careBut, beneficiaries generally not willing to pay full cost
Perhaps because incidence is on third parties
Public health insurance for children leads to:Reductions in infant and child mortalityReductions in future medical costs and chronic conditions
Evidence of GE e�ects of health insurance on hospital entry and newtechnologies
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