erasmus university rotterdam patient choice when prices don’t matter what do time-elasticities...
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Erasmus University Rotterdam Background Health system reform in The Netherlands –Introduction of managed competition Van de Ven and Schut (2008, HA) –How to assess Dutch hospitals’ market power? Prices for most hospital services are still fixed Out-of-pocket payments are absent Patients do not yet face restricted provider networks –Varkevisser, Capps and Schut (2008, HEPL): “As a result, in the current context, the time-elasticity approach seems to be the appropriate approach to defining hospital markets in The Netherlands.” 3TRANSCRIPT
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Patient choice when prices don’t matterWhat do time-elasticities tell about hospitals’ market power?
Academy Health Annual Research Meeting Saturday, June 7, 2008Washington, DC
Marco Varkevisser (Erasmus University Rotterdam)
Health Economics Interest Group Meeting
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Contact: [email protected]
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• Background• Model• Empirical specification• Data• Estimation results• Substitutability of Dutch hospitals?• Concluding remarks
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• Health system reform in The Netherlands– Introduction of managed competition
• Van de Ven and Schut (2008, HA)– How to assess Dutch hospitals’ market power?
• Prices for most hospital services are still fixed• Out-of-pocket payments are absent• Patients do not yet face restricted provider networks
– Varkevisser, Capps and Schut (2008, HEPL):• “As a result, in the current context, the time-elasticity
approach seems to be the appropriate approach to defining hospital markets in The Netherlands.”
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• Based on standard patient utility function– Following previous studies for US hospital choice– Utility patient i visiting hospital j is given by
– Travel time (tij) and hospital attributes (Hj) as main determinants of patient hospital choice
• Prices are not included since these are irrelevant– Interaction terms to capture patient heterogeneity
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• Conditional logit model (McFadden, 1974):
– Travel time (tij)
– Hospital attributes (Hj)• Type, size, reputation, and waiting time
– Patient characteristics (Pi)• Gender, age (adult vs. non-adult), and social status
• Probability that patient i selects hospital j5
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• Individual patient level data from large Dutch health insurer– Non-emergency first outpatient hospital visits for
neurosurgery in 2003– Patients travelling > 60 minutes are excluded
• Patient i’s choice set = all hospitals ≤ 60 minutes• On average, the choice set includes 26 hospitals
– Resulting study sample contains 5,389 visits• Mean travel time 19 minutes• For 95% of the patients travel time ≤ 45 minutes
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• Estimation results– Hausman-McFadden test:
• IIA assumption seems to hold here– Brief summary of estimated parameters
• Coefficient for travel time is negative and significant• Patients are less likely to visit academic medical centre• Overall reputation and waiting time affect choice• Several patient attributes seem to affect hospital choice
– Model predicts patients’ actual choices fairly well• 43% visited hospital with the highest probability
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Coeff. Coeff.
Travel time: Interacted with Retired:Travel time -0.1600 ** Travel time -0.0180 **
University medical centre 0.1213Hospital attributes:University medical centre -1.7820 ** Interacted with Unemployed:Total hospital beds 0.0016 ** Travel time 0.0135First hospital visits for neurosurgery 0.0018 ** University medical centre -0.1924Good overall reputation 0.1101 *Good reputation for neurosurgery 0.0161 Interacted with Incapacitated for work:Waiting time below average 0.3397 ** Travel time 0.0171 **
University medical centre 0.6456 **Interacted with Female:Travel time -0.0105 ** Interacted with Social security:University medical centre -0.0164 Travel time -0.0257 **
University medical centre 0.1249Interacted with Non-adult:Travel time 0.0532 ** Interacted with Self employed:University medical centre 4.1262 ** Travel time 0.0174
University medical centre 0.0118
Log likelihood -8951.69Degrees of freedom 21Number of included observations 142,037Note: ** denotes significance at 1%; * at 5%.
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• Time-elasticities as an attempt to indirectly estimate hospitals’ demand elasticities– Details: Capps et al. (2001, NBER) – Estimation of hospital j’s isolated time-elasticity
1. Assign all patients to hospital with highest probability 2. Artificially increase travel time to hospital j by 10%3. Predict hospital j’s “new” market share4. Divide ∆% market share by ∆% travel time
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Artificially raising travel time to hospital j by 10%
Hospital j Predicted patients (#) ∆ Patients (#) ∆ Patients (%) Time-elasticity
Hospital 46 348 -195 -56.0 -5.6 Hospital 22 178 -85 -47.8 -4.8 Hospital 59 92 -40 -43.5 -4.3 Hospital 3 297 -85 -28.6 -2.9 Hospital 37 344 -97 -28.2 -2.8 Hospital 18 80 -21 -26.3 -2.6 Hospital 48 105 -24 -22.9 -2.3 Hospital 56 975 -219 -22.5 -2.2 Hospital 63 342 -70 -20.5 -2.0 Hospital 52 792 -147 -18.6 -1.9 Hospital 32 816 -135 -16.5 -1.7 Hospital 39 309 -32 -10.4 -1.0 Hospital 64 133 -8 -6.0 -0.6
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Predictedpatients 56 32 52 46 37 63 39 3 22 64 48 59 18
Hospital 56 975 -219 40 79 11 1Hospital 32 816 53 -135 17 23 7Hospital 52 792 -147 102 20Hospital 46 348 119 -195 24 7 36 18Hospital 37 344 84 -97 23Hospital 63 342 2 67 -70 33Hospital 39 309 16 -32 10Hospital 3 297 11 65 29 -85 12Hospital 22 178 5 15 -85 14Hospital 64 133 13 13 -8Hospital 48 105 48 18 -24Hospital 59 92 8 24 5 5 -40Hospital 18 80 34 -21Hospital 45 54 8Hospital 2 46 3 7Hospital 54 32 10Hospital 35 31 1Hospital 36 24 5Hospital 47 18 4Hospital 33 16 3Hospital 60 15 1Hospital 20 14 1Hospital 21 5 2Hospital 53 4 1Hospital 14 1 1Sum 5,071 0 0 0 0 0 0 0 0 0 0 0 0 0
?Patients (#) when travel time is increased by 10%:
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Predictedpatients 56 32 52 46 37 63 39 3 22 64 48 59 18
Hospital 56 975 -219 40 79 11 1Hospital 32 816 53 -135 17 23 7Hospital 52 792 -147 102 20Hospital 46 348 119 -195 24 7 36 18Hospital 37 344 84 -97 23Hospital 63 342 2 67 -70 33Hospital 39 309 16 -32 10Hospital 3 297 11 65 29 -85 12Hospital 22 178 5 15 -85 14Hospital 64 133 13 13 -8Hospital 48 105 48 18 -24Hospital 59 92 8 24 5 5 -40Hospital 18 80 34 -21Hospital 45 54 8Hospital 2 46 3 7Hospital 54 32 10Hospital 35 31 1Hospital 36 24 5Hospital 47 18 4Hospital 33 16 3Hospital 60 15 1Hospital 20 14 1Hospital 21 5 2Hospital 53 4 1Hospital 14 1 1Sum 5,071 0 0 0 0 0 0 0 0 0 0 0 0 0
?Patients (#) when travel time is increased by 10%:
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-7.0
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• From our simulations it follows that:– Point estimates of Dutch hospitals’ isolated time-
elasticities range from -0.6 to -5.6– Estimated time-elasticities are overall fairly high,
but some hospitals may have market power– Overall, estimated time-elasticities are robust
• Time-elasticity approach has the potential to become a useful instrument for assessing Dutch hospitals’ substitutability– To health insurers as well as antitrust agencies
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