Experience Goods and Expectational Traps: Bounded

Rationality and Consumer Behavior in Markets for Medical Care

Presented by: Brian Elbel, MPHPhD Candidate

Yale School of Public Health

With: David Stuckler, MPH

Mark Schlesinger, PhD

At: AcademyHealth Health Economics Interest Group Meeting

Outline• Background

– Physician Services as Experience Good– Dyadic v. Generalized Expectations

• Consumers’ Evaluation of Experience– Bayes Rule– Representativeness Heuristic– Expectational Traps

• Data—Consumer Experiences Survey• Estimation Strategy—Selection Model• Results• Conclusions

Experience Goods

• Nelson first recognized in the 1970’s

• In order to evaluate a good, you must “experience” or try it out

• Switch/Exit if dissatisfied– Provides incentives to improve quality

Experience Goods Literature

• Largely focused on how consumers evaluate purchased goods

• No focus on how consumers make inferences about the distribution of goods in the market

• Generally assumed:– Consumers know the distribution– They then act as Bayesians

Physician Services as Experience Good/Service

• Not many other means to assess physicians– Few quality measures– Those that do exist aren’t very good

(small n)– Some learning through social networks;

tastes very heterogeneous

Model of Evaluating Experience Goods

• When consumers are evaluating their physician/considering switching

– Assessment of Individual Physician— Dyadic Expectations

– Assessment of Physicians as a Class—Generalized Expectations

Consumers Compare Expectations

• Consumers compare Dyadic Expectations to Generalized Expectations

• If expectations are sufficiently divergent, they switch

• Problems arise when both expectations closely track each other

Expectations in Response to Problem

• Problems relatively common

• Could use information gained from problematic experience in two ways:– They act as Bayesians– They rely on the Representativeness

Heuristic

Bayesian Learning

Pr(MDbad | problem) = Pr(problem | MDbad) x Pr(MDbad)

Pr(problem)

• Expectations should diverge as long as consumer believe “bad” physician have more observable problems– Can’t say for certain what that ratio is

• Consumers likely have few “draws” by which to evaluate physicians– Generalized expectations may largely reflect

dyadic

Representativeness Heuristic

• Representativeness: assumption of correspondence, generally between an individual and a population

• Taking knowledge of one physician, and assuming it is representative of all physicians

• After experiencing a problem, generalized expectations equal to dyadic

Equal Revision of Expectations?

• Representativeness Heuristic would lead to equal revision of expectations

• Bayes rule maybe could

• Leaving little incentive to switch physicians

• Expectational Traps

• Market Doesn’t Punish Poor Physicians

We Find:• On average, following a problematic

experience dyadic expectations are revised downward as much as generalized expectations

• This matters: Divergent expectations predicts switching physicians in response to a problem

• Some evidence due to Representativeness Heuristic

Data

• Consumer Experiences Survey• N=5,000• We initially restrict sample to:

– Those with ≤ 1 problem (79.8%)– Those who saw MD in last year

(88.6%)– Those that didn’t switch physicians

(only 7.7% switched)

• Final N =3,071

Measures of Expectations

• Measured on 5 dimension for both Dyadic and Generalized Expectations– LEARN: take the time to learn about up to date

treatments– TIME: take enough time with their patients– INSURANCE: speak up for their patients in

disputes with their health plan – ERRORS: make too many mistakes in taking

care of the patients– FAIRNESS: treat all patients fairly regardless of

race• Standardized as a Z-score• Sum them then divide by 5

Measures of Problematic Experiences

• Asked if they experienced any of 15 problems in the last year

• Asked who was responsible for problem

• Three Groups– No Problem (55.0%)– Problem Blamed on Physician (13.2%)– Problem Not Blamed on Physician

(31.8%)

Model Specification I

• Where– PROB_BL = problem blamed on MD– PROB_NO= problem not blamed on

MD– X = vector of controls– λ = selection term– β = terms to be estimated

4321 __ XNOPROBBLPROBExpec

Model Specification II• Outcome = Aggregate Expectations

• Outcome = Individual Expacations– 5 Generalized Expectations– 5 Dyadic Expectations

• Specification same for each

• Controlling for:– SES, Health Status, Recency of Problem,

HC Knowledge, Severity of Problem, Social Support, Managed Care

Identification

• Potential Endogeneity of Problem Identification

• Control Function/Treatment Effects/Heckman without Truncation

• Need “instruments”– Otherwise identifying off functional form

• Our instruments:– Presence of Mental Illness, COPD, and an

index of “Don’t Know” responses

Results

Dyadic Generalized Difference

Blame NoBlame

Blame NoBlame

Blame NoBlame

AggregateExpectation

(n=3028)

-1.49(0.19)

**

-1.09(0.19)

**

-1.40(0.22)

**

-1.27(0.21)

**

0.09(0.24)

-0.19(0.24)

Influence of Problems on Expectations

Dyadic Generalized Difference

Blame NoBlame

Blame NoBlame

Blame NoBlame

AggregateExpectation

(n=3028)

-1.49(0.19)

**

-1.09(0.19)

**

-1.40(0.22)

**

-1.27(0.21)

**

0.09(0.24)

-0.19(0.24)

Influence of Problems on Expectations

Dyadic Generalized Difference

Blame NoBlame

Blame NoBlame

Blame NoBlame

AggregateExpectation

(n=3028)

-1.49(0.19)

**

-1.09(0.19)

**

-1.40(0.22)

**

-1.27(0.21)

**

0.09(0.24)

-0.19(0.24)

Influence of Problems on Expectations

Influence of Problems on Dyadic and General Expectations of Physicians

Dyadic GeneralizedDifference

Expec. Blame No Blame Blame No Blame Blame No Blame

Learn (n=3047)

-1.69 (.32)**

-1.28 (.32)**

-1.56 (.36)**

-1.51 (.35)**

0.12 (.41)

-0.23 (.41)

Time (n=3099)

-0.86(.34)*

-0.61(.32)

-1.16(.38)**

-1.09(.38)**

-0.31(.42)

-0.48(.41)

Insurance (n=2824)

-1.51(.32)**

-0.98(.34)**

-1.90(.39)**

-1.73(.37)

-0.39(.41)

-0.75(.41)

Errors (n=3034)

-1.24(.25)**

-0.80(.35)**

-0.99(.35)**

-1.01(.34)**

0.26(.50)

-0.21(.49)

Fairness (n=3071)

-1.90(.28)**

-1.37(.30)**

-1.28(.34)**

-1.11(.32)**

0.62(.46)

0.26(.47)

* - denotes p-value significant at 0.05 level; ** - denotes p-value significant at 0.01 level.

Both Expectations Revise Equally

• On the whole, expectations tend to revise equally

• But, does this really matter?

Switching• DV = Switch MD in response to problems• Selection Model

– Only those with a problem– This time, with truncation

• Made new variable to capture divergence of expectations=Generalized – Dyadic Expectations

• Three categories– >0 and ≤ 2 (34.0%)– >2 and ≤ 3 (4.2%)– > 3 (2.1%)– Excluded Category—Dyadic higher than

Generalized (59.7%)

Probability of Switch on Z-Score Difference across Generalized and Dyadic Expectations

=Generalized - Dyadic

>0 ≤ 2 > 2 and ≤ 3 > 3

Aggregate Expectation

0.02 (0.01)** 0.05 (0.01)** 0.09 (0.02)**

* - P-value significant at 0.05 level; ** - at 0.01 level

Probability of Switch on Z-Score Difference across Generalized and Dyadic Expectations

Difference between Expectations

Expectation >0 ≤ 2 > 2 and ≤ 3 > 3

Learn 0.05 (0.02)** 0.10 (0.03)** 0.63 (0.11)**

Time -0.01 (0.02) 0.03 (0.04) -0.01 (0.04)

Insurance 0.02 (0.02) 0.06 (0.06) 0.04 (0.13)

Errors 0.03 (0.02) 0.10 (0.04)** 0.14 (0.05)**

Fairness 0.05 (0.02)** -0.21 (0.29) 0.18 (0.05)**

* - P-value significant at 0.05 level; ** - at 0.01 level

Switching Responds to Expectations

• More divergent expectations leads to more switching for 3 of 5 expectations

• Divergent Expectations Matter

Bayesian Learning or Representativeness Heuristic?• Some Differences in Categories of

Expectations

• Responses of those with a greater sense of the base rate—Long-Term Medical Condition

• Blame v. No_Blame Results

Limitations

• Cross-Sectional Data

• Omitted Variables

• Noisy Measures

Conclusions

• Consumers revise Generalized Expectations essentially as much as Dyadic Expectations

• Expectational Traps: This likely does explain some of the low-switching rates

• Physician’s lack market incentive to improve quality

• Some consumers likely not acting as Bayesians