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Presented by: Brian Elbel, MPH PhD Candidate Yale School of Public Health With:

Experience Goods and Expectational Traps: Bounded Rationality and Consumer Behavior in Markets for Medical Care. Presented by: Brian Elbel, MPH PhD Candidate Yale School of Public Health With: David Stuckler, MPH Mark Schlesinger, PhD

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Presented by: Brian Elbel, MPH PhD Candidate Yale School of Public Health With:

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  1. Experience Goods and Expectational Traps: Bounded Rationality and Consumer Behavior in Markets for Medical Care Presented by: Brian Elbel, MPH PhD Candidate Yale School of Public Health With: David Stuckler, MPH Mark Schlesinger, PhD At: AcademyHealth Health Economics Interest Group Meeting

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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%)

  16. 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

  17. 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

  18. 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

  19. Results

  20. Influence of Problems on Expectations

  21. Influence of Problems on Expectations

  22. Influence of Problems on Expectations

  23. Both Expectations Revise Equally • On the whole, expectations tend to revise equally • But, does this really matter?

  24. 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%)

  25. Switching Responds to Expectations • More divergent expectations leads to more switching for 3 of 5 expectations • Divergent Expectations Matter

  26. 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

  27. Limitations • Cross-Sectional Data • Omitted Variables • Noisy Measures

  28. 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

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