Using Willingness to Pay to
Evaluate Hospital Mergers:
Results from 16 Mergers
Presented by Rich Lindrooth
Co-authors: David Dranove
Mark Satterthwaite
Northwestern University
This research was funded by Robert
Wood Johnson’s HCFO Initiative
Using WTP to Evaluate Mergers
• Technique was refined in Capps Dranove
and Satterthwaite (Rand 2002)
• WTP is a measure based on the value
health plan enrollees place on inclusion
of a hospital in a managed care network.
• Direct theoretical link between WTP
estimates and hospital prices
Key Institutional Details
• Health plans assemble networks
• Health plans negotiate with local hospitals
• Traditional competition models do not apply
• Must instead invoke bargaining models
• Health plans market networks to local employers
• Must provide geographic coverage coincident with where
employees live
• Services that employee are likely to need
• Hospitals that are most attractive to networks bargain
for the highest prices
Computing WTP
• Hypothetical WTP calculation
• MCO considering adding hospital X to
network
• Hospital X is a leader in CABG, but is not
conveniently located
• What is a typical enrollees WTP to have
access to this hospital?
Example continued
• Typical enrollee considers future medical needs
• Most likely (e.g., 90%) they will remain healthy: WTP = 0
• Small chance (e.g. 9.9%) of requiring hospital for routine
needs
• Local hospital will do just fine
• WTP to have access to X if routine problem arises = $1000
• Or for our purposes 9.9%*1000=$99
• Very small chance (e.g. 0.1%) of requiring CABG
• Hospital X is best in town
• WTP to access X if CABG required = $20,000
• Or for our purposes 0.1%*20000=$200
- Overall WTP to have access to X = $299.
- This is the maximum amount that X can “squeeze” out of the
negotations (on a per patient basis)
- This will take the form of higher overall prices.
Three possible health states for person i: Torn Knee ligament, Need a CABG, Healthy
Interim
Conditions
Torn Ligament (A)
Interim Choice
Probabilities:
Healthy
Pˆi1 , Pˆi 2 ,..., PˆiJ
CABG (B)
Pi1 , Pi 2 ,..., PiJ
WTPij = 0
WTPi ,Interim
(Diagnosis  Torn Ligament)
k
WTPi ,Interim
(Diagnosis  Need CABG)
k
 EU Interim G  1,..., k ,..., J  | Torn Ligament 
 EU Interim G  1,..., k ,..., J  | CABG 
 EU Interim G / k  | Torn Ligament 
 EU Interim G / k  | CABG 
PR(Torn Ligament | Demographicsi )
Probabilities of Each
Condition
PR(CABG | Demographicsi )
WTPi ,k  EU i ({G})  EU i ({G / k })
D
  PR(Diagnosis  d | Demographicsi ) WTPi ,Interim
(Diagnosis  d )
k
d 1
Aggregate Over
The Population
WTPk  N  WTPik f ( X i )dX
Mathematically:
V (G )  V (G / k ) 
W  G   N  EY , Z , 





1 
1
N
ln 
f (Yi , Z i , i ) dYi dZi d i ,

Y , Z , 
1  sk  G, Yi , Z i , i  
EA
k
Key limitations
• Do not observe prices
• Do not observe MD information
• Omitting MD does not bias the model
• MD choice of hospital reflects patient preferences
• Analyst could infer that the MD “ran the show” when in
fact the MD was deferring to the power of the hospital
• Assumes network is formed to fully reflect
employee preferences
A look at 16 mergers
• We use a sample of 16 mergers occurring in
1995-2000:
•
•
•
•
•
•
•
Bakersfield, CA (2)
Buffalo, NY (3)
Daytona, FL (4)
Denver, CO (2 total; use 1)
Jacksonville, FL (2, one de-merger)
Rochester, NY (4)
Seattle (Use as a control. 1 hospital switched
systems in 2000)
• Milwaukee (Market too tumultuous over time to
model)
Example: Daytona Market
• Ownership in many of the markets was
tumultuous during this period
• Many mergers, some de-mergers, and
some exits.
• The following maps trace merger activity
in Daytona from 1995 until 2000.
• Each color represents a system.
• A white dot is a independent hospital
Data
• Patient-level data
• the Healthcare Cost and Utilization Project
State Inpatient Database (HCUP-SID)
• Hospital Characteristics
• American Hospital Association Annual Survey
(AHA)
• Hospital Financial Data
• CMS Medicare Cost Reports (MCR)
Estimating the Effect of the
Mergers on Net Inpatient Revenue
• Step 1: Calculate WTP
• Value a hospital/system brings to a network
• Step 2: Estimate effect of change in WTP on
Net Inpatient Revenue
• Step 3: Measure Percent change in Net revenue:
Change in Net Revenue/Pre-merger Net Revenue
Calculating WTP
• Estimate WTP for independent hospitals
and system combinations one year prior to
the first merger in the market.
• Prior to system consolidation calculate the
sum of the independent hospital’s WTP
• After system consolidation use the system
WTP
• The difference reflects the change in WTP is
solely a result of the merger
WTP Estimates
Bakersfield
Merger 1
Merger 2
Buffalo
Merger 1a
Merger 1b
Merger 2
Denver
Merger 1
Daytona
Merger 1a
Merger 1b
Merger 2
Merger 3
Jacksonville
Merger 1
Demerger 1
Merger 2
Rochester
Merger 1
Merger 2
Merger 3
Merger 4
Year
1997
2000
# of hospitals Pre-WTP Post-WTP % Change
3+1
4,468
5,127
14.7%
1+1
1,591
1,724
8.3%
1996
1998
1997
2+1
3+2
1+1+1+1
13,871
14,573
11,720
14,573
17,623
14,170
5.1%
20.9%
20.9%
1997
1+1
13,805
14,555
5.4%
1999
2000
1998
1996
3+1
4+1
1+1
1+1
8,696
9,885
10,390
1,012
9,885
11,595
11,118
1,053
13.7%
17.3%
7.0%
4.1%
1997
1999
1999
3+1
3
1+1
21,959
27,164
3,849
27,164
21,959
4,093
23.7%
-19.2%
6.3%
1998
1996
1999
2000
3+1
1+1
1+1
1+1
35,284
3,014
6,930
1,964
36,153
3,101
7,213
2,463
2.5%
2.9%
4.1%
25.4%
HHI (Just for reference)
Bakersfield
Merger 1
Merger 2
Buffalo
Merger 1a
Merger 1b
Merger 2
Denver
Merger 1
Daytona
Merger 1a
Merger 1b
Merger 2
Merger 3
Jacksonville
Merger 1
Demerger 1
Merger 2
Rochester
Merger 1
Merger 2
Merger 3
Merger 4
Year
1997
2000
# of hospitals
in merger
3+1
1+1
1996
1998
1997
2+1
3+2
1+1+1+1
1,488
2,146
1,694
1,694
3,201
2,146
206
1,055
452
1997
1+1
2,047
2,463
416
1999
2000
1998
1996
3+1
4+1
1+1
1+1
3,920
4,230
3,762
3,739
4,230
4,794
3,920
3,762
310
564
158
23
1997
1999
1999
3+1
3
1+1
2,831
4,269
2,831
2,840
1,438
0
9
1998
1996
1999
2000
3+1
1+1
1+1
1+1
2,545
2,530
2,611
2,614
2,611
2,545
2,614
3,250
66
15
3
636
Pre-HHI
4,796
4,853
Post-HHI
4,853
4,853
Change
57
0
Do changes in WTP due to consolidation 
increased net inpatient revenue?
• Regress net inpatient revenue:
•
•
•
•
•
•
WTP, WTP*Yrs since merger indicator
Hospital fixed effect
Hospital payer mix
Bedsize,
Total admissions, and
Control for whether the facilities were
combined.
Details of regression
• Unit of observation: Entity
• Independent hospital or system
• 6 years of data
• Unbalanced, some hospitals don’t report
in some years
• If one hospital in the system doesn’t report
• Drop the system observation for the year
System Fixed Effect Results
WTP
3909.923**
(1937.421)
WTP*Second Year of Merger
WTP*Third Year of Merger
WTP*Forth Year of Merger
6649.468***
(2383.316)
4606.421**
(2143.295)
155.476
(341.553)
1110.533*
(660.681)
-78.326
(933.719)
234.834
(303.028)
324.111
(625.334)
-1096.700
(829.123)
340.953
(299.064)
657.558
(499.414)
-255.871
(606.881)
-4024.480***
(990.239)
-376.645
(1269.217)
WTP*First Year of Second Merger
WTP*Second Year of Second Merger
WTP*First Year of De-merger
WTP*Second Year of De-merger
R-squared
Controls
8800.893***
(2654.161)
0.36
None
409.136
(1058.589)
1244.982
(1303.386)
0.37
Year FE
-2732.502*** -2642.907***
(905.473)
(894.974)
1370.700
403.522
(1141.233)
(1009.329)
974.265
(983.494)
1359.085
(1143.716)
0.61
All Controls
0.65
All Controls
Results (in words)
• WTP coefficient is statistically different
than zero with 95-99% confidence
• Though not significant if only time trends
in the model without WTP*Merger year
interactions
• Magnitude of the coefficient varies from
about $3900 to $8800.
• Favored specification indicates an
increase of about $6,650 per unit of WTP
Effect of mergers on net inpatient revenues
MSA
Bakersfield
Merger 1
Merger 2
Buffalo
Merger 1a
Merger 1b
Combination
Merger 2
Denver
Merger 1
Daytona
Merger 1a
Merger 1b
Combination
Merger 2
Merger 3
Jacksonville
Merger 1
Merger 2
Rochester
Merger 1
Merger 2
Merger 3
Merger 4
Total
Increase in Net
Change in WTP inpatient Revenue
Premerger net
Percent
inpatient revenue Increase
658.6
132.8
$4,379,723
$883,080
$178,039,547
$18,509,967
2.46%
4.77%
702.3
3049.6
$353,153,430
$335,892,953
2450.5
$4,670,295
$20,280,040
$24,950,335
$16,295,958
$511,717,061
1.32%
6.04%
7.07%
3.18%
749.9
$4,987,035
$311,633,985
1.60%
1189.0
1709.9
$230,319,971
$228,534,594
728.1
41.7
$7,906,963
$11,371,128
$19,278,091
$4,841,599
$277,145
$208,648,796
$63,230,045
3.43%
4.98%
8.37%
2.32%
0.44%
5204.3
243.8
$34,608,462
$1,621,071
$499,638,496
$306,650,559
6.93%
0.53%
868.6
87.4
283.3
499.1
$5,776,257
$581,163
$1,884,231
$3,318,776
$416,377,613
$172,894,372
$505,253,995
$40,754,389
1.39%
0.34%
0.37%
8.14%
18,599
$123,682,924
$4,381,249,771
2.82%
Conclusions
• Only four mergers led to an increase in net
inpatient revenue greater than 5%.
• However, this is a very conservative estimate
• The denominator revenue number includes all
payers.
• If the increase was due to solely private payers then
the denominator should include only private payer’s
revenue.
• For example if 50% of the net revenues were
private then the average % increase would be 5.6%
Conclusions
• Shows promise for prospective merger
analyses
• R-squared was much lower than what is
observed in a single market.
• Not surprising given unmeasured variation
across markets
Caveats
• Would prefer to have data on private profits
rather net inpatient revenues.
• We ignore any efficiencies (or inefficiencies) that
result from mergers.
• The prospective WTP estimates do not always
coincide with the realized post-merger estimates
(a problem with any prospective measure).
• The second mergers during the period perform
worse (may be due to small sample of second
mergers). There are other factors at work that
need to be addressed in future research.