Why Do the Insured Use More Health Care?

The Role of Insurance-Induced Unhealthy Behaviors

Yingying Dong

Boston College

10/16/2008

1/29

A Real Life Story

From the New York Times (Nov. 25, 2002):

Mr. & Ms. Brooks dropped their health insurance because of increased premiums.

Then…

“Mr. Brooks, 50, has stopped taking Lipitor to control high cholesterol and has

started taking over-the-counter herbal supplements. Ms. Brooks no longer takes

Singulair for asthma and has adopted an exercise program intended to regulate

her breathing. Ms. Brooks estimates they are saving $150 a month by not using

prescription drugs. ‘We changed our diets a lot to help the effectiveness of the

supplements, and maybe that’s a good thing,’ she said.”

--Broder, John. “Problem of Lost Health Benefits is Reaching into the Middle Class.” Also cited by Dave & Kaestner (2006)

2/29

Motivation – (1)

Health insurance is associated with an increased use of health care. (Yelin et al, 2001; Meer and Rosen, 2003; Pauly, 2005; Bajari, Hong and Khwaja, 2006;

Deb and Trivedi, 2006)

Usually assumed: Individuals who potentially have a greater need of heath care are

more motivated to get insured (selection effect).

Insurance reduces the effective price of health care and hence induces individuals to use more.

★ What’s missing: Insurance encourages unhealthy behaviors, which may cause

increased use of health care. Unhealthy behaviors: Drinking, Smoking, Insufficient Exercise, and

unhealthy diet

3/29

Motivation- (2)

Intuitions: Insurance lowers the offsetting cost for the negative health

consequences of unhealthy behaviors. disincentive effect

Car insurance reduces precaution and stimulates car accidents; Workplace injury compensations increase injuries.

Individuals with health problems substitute medication for behavioral improvement. substitution effect

Easily accessible health care may distort the perceived risk of unhealthy behaviors. distorted image

4/29

Illustration of Causalities

True Moral Hazard: the disincentive effect of health insurance on individuals’ healthy behaviors, which may generate additional medical care demand.

Pure Price Effect: the effect that health insurance lowers the effective price of health care and hence induces individuals to use more care ceteris paribus.

Health Insurance

Use of Health Care

Selection Effect

(Ex post) Moral Hazard

Health Insurance

Use of Health Care

Selection Effect

Pure Price Effect

Health Related Behaviors

True Moral Hazard

(1) Causalities traditionally studied (2) A fuller View of Causalities

Selection Effect

5/29

Literature Review

Most existing literature studies the insurance effect on medical utilization; a small strand examines the insurance effect on health behaviors. They do not look at the structural causal relationships among the three.

Literature examining the insurance effect on medical utilization: Reviews: Zweifel and Manning (2000), Buchmueller et al (2005)

Studies examining the insurance effect on prevention, including behaviors (discrete outcomes):

Kenkel (2000), Courbage and Coulon (2004)

A few studies that do consider all three focus on discrete outcomes

Khwaja (2002, 2006) and Card et al. (2004)

6/29

Research Questions & Approaches

(1) What are the effects of health insurance on individuals’ health behaviors?

(2) What is the (total) effect of health insurance on health care utilization?

(3) Within the total effect of health insurance on health care utilization, how much is caused by the pure price effect, and how much is caused by individuals’ behavior change when they have insurance?

Start from a theoretical model to derive the structural model for the insurance decision, health behaviors, and health care utilization

Solve the structural model to obtain the semi-reduced form equations determining behaviors and care utilization as functions of endogenous health insurance

Derive the structural parameters of interest: the direct and indirect effects of health insurance on health care utilization

Empirical analysis adopts the generalized Tobit specification with transformations on the dependent and lagged dependent variables.

7/29

Contributions

Theoretically,

Set up a two-period dynamic forward-looking model and derive the structural causal relationship among the insurance, behaviors and health care utilization;

Model continuous choices instead of discrete choices.

Empirically,

Distinguish between the extensive margin (changes in the proportion of the unhealthy behavior participants) and the intensive margin (changes in the quantity of unhealthy behaviors given participation) of the insurance effect;

Accommodate the distribution characteristics of the data and adopt flexible transformations;

Analyze the insurance effects on heavy drinking, and how the insurance-induced drinking affects medical utilization.

8/29

Basic Theoretical Model – (1) A two-period dynamic forward-looking model;

The individual draws utility from composite good consumption (Ct), unhealthy behaviors (Bt), and health (Ht ); Assume rational addiction of unhealthy behaviors.

Utility function:

Health evolution equation:

Budget constraint:

Ht = Initial health status/Health stock; Mt = medical care utilization; Bt = (Bt

1, Bt2, Bt

3)': drinking, smoking and exercise; st = health shock, which may depend on Ht, Bt; = permanent taste parameter; = taste shifter; Wt = present value of total wealth; It = insurance dummy; dt = the insurer co-payment rate.

1 ( , , )t t t t tH H H M s B1 1 1 1 1 1( , , , , , ) [ ( , , , , , )]t t t t t t t t t t t tC U H s C U H s B B B B

1 1( (1 ) ) ( ) .t It t B t t t t B t tC P I dI M C W P B P B

t

9/29

Basic Theoretical Model – (2) The individual invests in health in 1st period (t), and bears the addiction and

negative health consequences of unhealthy behaviors in 2nd period (t+1).

C: choice set: {It} {Ct, Bt, Mt} {Ct+1, Bt+1} F: information set: {Bt,Ht, ,t} {Bt,Ht,It, ,t} {Bt,Ht+1,It,,t+1, st+1} S: shock set: {st , st+1, t+1} {st, st+1, t+1} {st+1}

Expected Utility maximization

s.t.

At the beginning of 1st period

In 1st period In 2nd period

1 1

1 1

*, , 1 1 1 1 1 1; , ,

,

max ( , , , , , ) ( , , , , , )t t t

t t t t

t t

s s t t t t t t t t t t t tI C M

C

E C U H s C U H s

B

B

B B B BF

1 1( (1 ) ) ( ) .t It t B t t t t B t tC P I dI M C W P B P B

10/29

Structural & Semi-reduced Form Equations

Maximizing expected utility by backward induction

Structural equations (FOC’s) for Mt, Bt and the insurance decision It

Bt-1, PIt : exclusion restrictions

fI() = PIt*: willingness-to-pay for insurance

Intuitions

Solve (1),(2) for Bt and Mt semi-reduced form equations

Assume U() is quadratic and health production function is linear

linear functions for Bt and Mt and the It index

1

( , , , , ) (1)

( , , , , , ), for 1,2,3 (

(

2)

1 0 , , , ) (3)l

t M t t

II t t

t tl

t

t t t t tB

t t

M f I H

B f I M H l

PI f H

t-1

B

B

B

1

1

( , , , , ) (4)

( , , , , ), for (1,2,3) (5)l

t M t t t tlt t t t tB

M g I H

B g I H l

B

B

11/29

Direct and Indirect Insurance Effects on Care Utilization - (1)

Eqs. (1), (4) and (5) imply the following decomposition

t t t t

t t t t

dM M M

dI I I

B

B

1

1 1

t t t

t t t

M M

B

B B B

31 33 31

Total effect = direct price effect + indirect effect

Obtained from Eq. (4)

Obtained from Eq. (5)

To be backed out

13 31

(I)

(II)

Obtained from the Eq. (5)

Obtained from Eq. (4)

12/29

Direct and Indirect Insurance Effects on Care Utilization– (2)

(I) is a vector form of

(II) is the solution of a linear equation system

t t t t

t t t t

dM M M

dI I I

B

B

1 2 3

1 2 3.t t t t t t t t

t t t t tt t t

dM M M B M B M B

dI I I I IB B B

1

1 1

t t t

t t t

M M

B

B B B

1 2 3

1 1 1 1 2 1 31 1 1 1

1 2 3

2 2 1 2 2 2 31 1 1 1

1 2 3

3 3 1 3 2 3 31 1 1 1

,

,

.

t t t t t t t

t t t t t t t

t t t t t t t

t t t t t t t

t t t t t t t

t t t t t t t

M B M B M B M

B B B B B B B

M B M B M B M

B B B B B B B

M B M B M B M

B B B B B B B

Three unknowns

13/29

General Data Problems

A significant fraction of zeros (68% zero drinking, 83% zero smoking)

Discrete change between zero & non-zero consumptionextensive margin of the insurance effect

Continuous change in the positive level of consumptionintensive margin of the insurance effect

Two part specification: Insurance effect on the probability of non-zero consumption Insurance effect on the level of consumption, given participation

Generalized Tobit model (sample selection model)

A skewed distribution of positive observations (nonnormality)

Transformation on the dependent and lagged dependent variables IHS (Inverse hyperbolic sine), Box-Cox, log

14/29

Empirical Model & Identification

Generalized Tobit (sample selection) model with transformations on the dependent and lagged dependent variables

where Yt = Bt or Mt ;

Insurance is endogenous joint estimate It and Bt or Mt .

PIt is an exclusion restriction (Instrumental Variable): Age≥65 dummy (Card, Dobkin and Maestas, 2004) Self-employment status(Meer and Rosen, 2003; Deb and Trivedi,

2006)

1 0 ,t tt It tI P X α

11 0 1 0 ,t t t tY b I X b

1( , ) 1 0 ( ),t Y t t t tT Y Y b I X b

1(1, ( , ), , ) .t t tT H BX B

15/29

IV Validity-(1)

Relevance insurance holding rate:

Age<65 (88.1%), Age≥65 (99.1%) Self-employed(78.7% ), Non Self-employed (92.6%)

Exogeneity Measures of medical care utilization and health behaviors would have evolved

smoothly with age in the absence of the discrete change in insurance coverage at age 65.

Include in all equations a smooth age profile function, tentatively include age dummy in the outcome equations, coefficients are not significant

Sargan’s and Basmann’s over-identification tests Hansen’s J test Using panel data check the transition into/out of self-employment on behaviors

16/29

IV Validity-(2)

Treatment 1 Control 1 Diff.-In-Diff. P-value

Change in drinking (level) .271(.300) -.087(.197) .358(.359) .319

Change in drinking (prob.) -.028(.021) -.029(.014) .001(.025) .965

Change in smoking (prob.) -.009(.008) -.014(.007) .005(.010) .637

Change in exercising (prob.) -.050(.033) -.027(.018) -.023(.038) .536

Treatment 1 Control 1 Diff.-In-Diff. P-value

Change in drinking (level) -.509(.419) -.207(.041) .301(.421) .476

Change in drinking (prob.) -.022(.028) -.030(.004) .008(.028) .787

Change in smoking (prob.) -.029(.015) -.012(.002) -.017(.015) .267

Change in exercising (prob.) .029(.041) -.014(.005) .043(.041) .300

Table 2-(a) The impact of transition out of self-employment on health behaviors

Table 2-(b) The impact of transition into self-employment on health behaviors

17/29

Sample Description

RAND HRS : 3rd, 4th and 5th (Year 96, 98, 00) waves of data

Not include: Age>70, On social security disability insurance, Deceased within 2 years since being observed in the sample

N=14,289

Dependent variables : Dummies: smoking, exercising (3 or more times per week), drinking,

visiting a doctor/hospital (at least once for the past two years)

Levels: # of alcoholic drinks consumed per week

# of doctor/hospital visits per year

18/29

Summary Statistics-(1)

Insured (n=13,016) Uninsured (n=1,273)

Mean Std. Dev. Mean Std. Dev.

Visiting a doctor/hospital .941 .235 .811 .391

# of visits 4.35 7.74 3.02 4.98

Current period: Smoking .167 .373 .262 .440

Exercising .504 .500 .485 .500

Drinking .330 .470 .265 .441

Positive # of alcoholic drinks 7.14 8.50 10.17 12.61

The insured are more likely to visit a doctor or hospital, and they also have more visits on average than the uninsured.

Insurance is associated with healthier behaviors; e.g., The insured are less likely to smoke; more likely to drink, but on average drink much less.

★ These may not be causal: confounding factors or selection effects.

19/29

Summary Statistics-(2)

Insured (n=13,016) Uninsured (n=1,273)

Mean Std. Dev. Mean Std. Dev.

Last period: Smoking .184 .387 .274 .446

Exercising .523 .499 .516 .500

Drinking .353 .478 .295 .456

Positive # of alcoholic drinks 7.01 8.46 10.58 15.29

Last period diagnosed disease*: Cancer

.065 .246 .048 .214

Hypertension .367 .482 .342 .475

Heart disease .119 .324 .072 .259

Lung disease .050 .218 .038 .191

Age 61.65 5.19 59.48 4.97

The insured have healthier behaviors ex ante (last period); The insured are in general older, tend to have chronic diseases;

* This list leaves out some chronic diseases due to space limit.

20/29

Summary Statistics-(3)

Insured (n=13,016) Uninsured (n=1,273)

Mean Std. Dev. Mean Std. Dev.

Male .406 .491 .341 .474

Hispanic .067 .251 .235 .424

Last period health status: Fair/ Poor .166 .372 .269 .444

Good/Very good .652 .476 .571 .495

Excellent .182 .386 .159 .366

Education: Less than high-school .192 .394 .437 .496

High-school or GED .386 .487 .311 .463

College or above .422 .494 .252 .434

Income($1000) 62.80 86.19 36.27 65.93

Number of children 3.45 2.05 4.01 2.54

The insured also tend to have higher income, higher education…

21/29

Estimated Insurance EffectsTable 1 Insurance effects on the probabilities of using health care and health behaviors

Dependent variable Coeff. Of Insurance (SE) Marginal Effect

Visiting a doctor/hospital (0/1) .037 (.146) .008

Exercising(0/1) -.112 (.127) -.045

Smoking(0/1) .166 (.192) .017

Drinking(0/1) -.187 (.145) -.065

Model (1): Two-part Model ; Model (2): Sample selection Model; ***Significant at .01 level;

Table 2 Insurance effects on the levels of health care utilization and drinking

HIS Box-Cox Log

% change (SE) #% change (SE)

#% change (SE)

#

Model (1)

# of visits / year .367(.069)*** 1.67 .365(.069) *** 1.66 .368(.056) *** 1.68

# of alcoholic drinks/week .122(.100) .900 .120(.098) .886 .108(.095) .794

Model (2)

# of visits / year .347(.069)*** 1.58 .359(.068) *** 1.63 .348(.057) *** 1.58

# of alcoholic drinks/week .043(.097) .318 .041(.095) .302 .029(.093) .214

22/29

Further Investigation on Drinking

Puzzle 1: Insurance may decrease the probability of drinking, although it may increase the amount of drinking by the drinkers.

Puzzle 2: The insurance effect on the probability of drinking and that on smoking have opposite signs.

Unlike smoking, a low level of drinking is generally considered as

healthy.

In the data, the insured have a smaller proportion of smokers, but a larger proportion of drinkers; whereas the insured drinkers tend to be light drinkers.

Distinguish between light drinking and heavy drinking

Disincentive effect of insurance on healthy behaviors: Does insurance holding encourage heavy drinking?

23/29

Further Investigation on Heavy Drinking

Table 3 Insurance effects on heavy drinking

Heavy drinking Probability Weekly # of drinks (IHS model)

Cutoff percentile

# of drinks

Sample mean

Coeff.(SE)Marginal

effect Marginal effect (SE)

Change in level

50th >4 12.8 .093(.175) .011 .131(.066) ** 1.67

60th >6 14.9 .190(.165) .013 .155(.071) ** 2.31

70th (mean) >7 16.8 .127(.168) .007 .120(.070) * 2.02

80th >12 21.4 .229(.188) .005 .177(.074) ** 3.79

85th >14 27.2 .307(.211) .003 .232(.100) ** 6.31

90th >15 28.5 .330(.278) .002 .255(.099) ** 6.98

* Significant at .1 level; **Significant at .05 level; ***Significant at .01 level

24/29

Increased Number of Visits due to Insurance-Induced Drinking

Increased medical utilization due to insurance-induced drinking:1 2 3

1 2 3 (I) t t t t t t t t t t t t

t t t t t t t t tt t t

dM M M B M B M B dM M M

dI I I I I dI I IB B B

B

B

11 1 1

1 1 11 1

t t t t t

t tt t t

M B B M B

I IB B B

Among the above-the-median drinkers, the increased number of visits caused by insurance-induced drinking is at most 0.3%.

Averaging it over the whole population will make it even smaller.

If the number of visits caused by the insurance-induced changes in smoking and exercise is of the same magnitude, then the increased number of visits caused by insurance-induced unhealthy behaviors as summarized by drinking, smoking and insufficient exercise is less than 1%.

11

1 1 11 11 1

(II) t t t t t t

t t tt t t

B M M M M

B B B

B

B B B

25/29

Conclusions

Health insurance encourages individuals’ unhealthy behaviors, in particular, heavy drinking, but this does not induce an immediate perceivable increase in their use of health care.

The insurance effects at the extensive margin are in general less significant than those at the intensive margin.

Eg. The effects on the probabilities of smoking, drinking/heavy drinking, and visiting a doctor/hospital are small and insignificant; The effects on the quantity of heavy drinking and on the number of visits are more considerable and statistically significant.

Within the total effect of insurance on health care utilization, the pure price effect is dominant.

Policy implications

Kept Back to Get Ahead?

Kindergarten Retention and Academic Performance

26/29

Kept Back to get ahead? – Kindergarten Retention and Academic Performance

Basic facts from the Early Childhood Longitudinal Study –- Kindergarten Cohort (ECLS-K):

Not every school allows children to be held back in kindergarten;

Children in retention schools (schools that permit repeating kindergarten) and non-retention schools are significantly different in terms of parents and family characteristics.

We observe children being held back in K if and only if 1) they attend a school that allows for repeating K, and 2) receive the treatment of repeating K.

The two decisions are jointly determined: common observed and unobserved parental/family characteristics may affect both.

27/29

Kept Back to get ahead?— Repeating K Decision

School choice (S):

S = I(XS' + s > 0) (1)

S = 1 if attending a retention school; S = 0 otherwise; XS = vector of observables; s = unobservable; XS' + s =a child’s propensity of attending a retention school.

Decision of repeating kindergarten (D*):

D* = I(XD' + D > 0) (2)

XD = vector of observables; D = unobservable; XD' + D = a child’s propensity of repeating kindergarten.

Observed repeating kindergarten status (D);

D = SD* = SI(XD + D > 0) (3)

D=1 if repeating kindergarten; D=0 otherwise

28/29

Kept Back to get ahead?— Performance Outcome Equation

Suppose for each child, we can observe his test scores when he repeats kindergarten and when he does not. Denote the two potential outcomes as Y1 and Y0:

Y1 = XY'1 + Y1 Y0 = XY'0 + Y0

The observed test score is

Y = DY1 + (1 D)Y0 = D(XY'1 + Y1) + (1 D)(XY'0 + Y0)

For simplicity, assume 1 and 0 are different only in constant terms, but not slopes

Y = XY' + D + DY1 + (1 D)Y0 (4)

29/29

Kept Back to get ahead?– The Full Model

A double hurdle model:

S = I(XS+ s > 0) (1)

D*= I(XD+ D > 0) (2) D = SD* (3) Y = XY+ D + DY1 + (1 D)Y0 (4)

Parameter of interest: ATT = E(Y1 – Y0 | D=1)= + E(Y1 Y0 | D = 1)

Develop a control function estimator

Main results: Repeating K has positive but diminishing effects on academic performance

over time;

Controlling for unobservable makes a difference, simple matching method underestimates these effects.

^_^

Thank you !!!