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David Dranove
1. Delivering Bad News: Market
Responses to Obstetricians’ Negligence
David Dranove, Northwestern University
Subramaniam Ramanarayanan, UCLA
Yasutora Watanabe, Northwestern University
2. Deterring Negligence
• Markets generally punish low quality sellers
• The liability system complements the market
• If markets are effective, then the liability system
may be redundant
• Anecdotal evidence of market responses to
negligence abound
– Tylenol
– Airjet
– Toyota
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3. More on Market Responses
• Surprisingly few systematic studies of market
responses to negligence
• Event studies (e.g., Prince/Rubin (2002);
Dranove/Olsen (1994)) show stock price declines
but do not decompose market and legal costs
• Garber/Adams (1998) examine auto liability
verdicts and find no effect on sales
• Fournier/McInnes (2001) find that doctors who
have malpractice claims lose FFS patients
– Our paper is related to F/M but provides a theoretical
foundation that leads to a highly nuanced analysis
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4. Malpractice
• Substantial concern about deterring negligence in
health sector
• Proposed tort reforms will reduce health
spending (Dafny et al., 2010) but may lessen
deterrence
• Does the present system deter negligence?
– Community rated premiums
– Minimal time cost
– Some emotional cost
– Suggests that market responses may be a critical
factor in deterring negligence
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5. Are Market Responses Plausible?
• In specialties such as obstetrics, lawsuits are
common but not excessively so.
– Most physicians are not sued during 10 years of
our data
• Lawsuits are not usually publicized but are
common knowledge in medical community
• Patients may learn about the associated
negligence through word of mouth
– Information “network” for expectant mothers may
be particularly strong
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6. Sorting out the Effects
• In theory, one can sort out market from litigation effects
– Statute of limitations is two years from time plaintiff “knew or
should have known” of negligence
– Could identify differential demand effect in this two year window
• Our data do not indicate when plaintiff “knew or should
have known”
– Instead, data record date of delivery (“occurrence”)
• In practice, most lawsuits are filed within 6 months of when
plaintiff “knew or should have known”
– Not clear if maternity patients would change doctors in this time
frame
• Thus, we cannot convincingly sort out the two effects though
research is ongoing)
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7. Mixed Markets
• Studying demand responses in obstetrics is
complicated by large presence of Medicaid
• Traditional economic models suggest that some
physicians may ration access to Medicaid patients
• Thus, a demand shock (i.e., a physician is sued)
may not affect the number of Medicaid patients
treated by that physician
• Formal modeling reveals these nuanced effects
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8. Model of Patient Demand in a Mixed Market
• Monopolist seller faces demand from two
types of customers
– Type φ (“private customer”) displays downward
sloping demand Pφ(Qφ)
– Type γ (“government customer”) displays perfectly
elastic demand at a price Pγ that is set by fiat
• Seller faces a limited number of potential type γ buyers,
denoted Qγmax
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13. Key Results
• After a “high quality” MD experiences negligence
– Lose privately insured patients
– Gain Medicaid patients
– Exact offset
• After a “low quality” MD experience negligence
– Lose Medicaid patients
– Ambiguous impact on quantity of privately insured
patients (though at lower price)
• Decomposing PPO and HMO effects
– Depends on relative shift of PPO and HMO demand curves
– F/M find gain in HMO, suggesting relatively small demand
reduction
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14. What is Quality?
• Quality is a latent variable
– Quality refers to whether MD is rationing access to
Medicaid patients
– We use proportion of PPO/Medicaid to proxy for
quality
• May introduce mean reversion bias in some
specifications
– Instrument for quality to eliminate mean reversion
bias
– Use counterfactual analysis to show that instrument is
effective
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15. Utilization Data
• Florida AHCA inpatient data
• Administrative claims data similar to other state data
– Diagnostic information
– Patient characteristics
– Year and quarter of discharge
– Unit of observation is MD/year/quarter
• “Operating” physician license number
– Unique and consistent over time
– Restrict attention to “high volume” (50 deliveries annually)
MDs
– 1418 high volume MDs account for 91 percent of all
deliveries
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16. Litigation Data
• Florida Department of Financial Services
• Closed claims from 1979-2003
– We begin analysis in 1994
– Continue utilization data through 2007
• Detailed information about
– Date of claim, date of occurrence, date of lawsuit filing, date of
resolution
– Lawsuit usually occurs within two years of occurrence; filing
soon after that
– Resolution of case requires another two years
– Have not yet disentangled the effect of occurrence and effect of
lawsuit
• Match to AHCA using MD license number
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17. Methods
• Estimate following model
– The subscripts p, q and y refer to the physician, quarter and year
– Phys Volpqy measures the number of patients treated by physician p in
quarter q of year y
– The primary predictor is an indicator that takes the value 1 for physician
p in quarter q of year y if a lawsuit has been filed against physician p
prior to or in the quarter q.
– Also estimate with separate indicators for each post year
– Includes full set of fixed effects for each physician, year and quarter (MD
FE imply that MDs sued prior to 1994 do not enter analysis)
– Includes FE for year in which suit is filed (lawsuits in earlier years have
greater chance of resolution, which may affect characteristics of
observed suits)
• OLS and Poisson
• Separate models for PPO, HMO, Medicaid
• Interact demand effects with “quality” 17
18. Measuring Quality
• The model shows that litigation effects depend on
whether the MD rations Medicaid
– Rationing occurs when PPO demand is high relative to MC
– In this sense, rationing is related to quality
• Empirical implementation
– Use PPO/Total patient ratio as indicator of rationing
• Statistical concern
– Could suffer from mean regression
– Do “high quality” MDs lose PPO patients due to chance?
– Demonstrate with falsification test
• Instrument for quality with hospital PPO ratio
– Instrument survives falsification test
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23. Falsification Test
• If there is mean reversion, then any MD having
“high quality” should exhibit a similar pattern
– Having high PPO/Total ratio in “pre” period should be
associated with lower ratio in post period due to
random chance
• Randomly select “pseudo” lawsuits in same
frequency as actual lawsuits
• Repeat regression on pseudo data, defining
quality as PPO/Total
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25. Instrument to Eliminate Mean
Reversion
• Require a variable that is correlated with a
physician’s PPO/Total ratio but is not subject
to mean reversion
• Use hospital’s non-maternity PPO/Total ratio
for entire time period
– Captures market area and general quality of
hospital
– Not susceptible to before/after timing
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29. Financial Implications
• Consider high quality MD seeing 100 PPO, 50 HMO and
50 Medicaid patients annually
• After litigation, these figures are 90, 54, and 56
• Using data from Physician Compensation Report and
assuming Medicaid pays half of PPO fee, MD earnings
drop from $400,000 to $389,600.
– Every year for at least 5 years
• Low quality MD with 20 PPO, 90 HMO, and 90
Medicaid patients goes from $320,000 to $300,000
– Again, impact felt for at least 5 years
• Financial impact dwarfs any direct costs of litigation
(legal fees/lost time)
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30. Discussion
• Despite community rated insurance, negligence
does cost obstetricians
• Effects are nuanced and are consistent with
economic theory
• Does this mean that deterrent effect of litigation
is redundant?
– Depends on social costs of negligence and efficiency
of courts
– Given that the latter is questionable at best, our
findings suggest that tort system may be at best a
marginal deterrent relative to the market
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