1. Department of Economics
Information Matters: Quality Competition and Information in a Fixed-Price Market
Shin-Yi Chou, Mary E. Deily, Suhui Li, Yi Lu
Department of Economics
Lehigh University
April 2011

2. Background
In theory, hospitals have an incentive to compete on quality because prices are frequently regulated or not observable to consumers
However, consumers do not have information about the important dimension of quality: health outcomes
Efforts to provide information about quality are based on the assumption that more information will improve outcomes

3. Research Question
Does providing information about the quality of hospitals’ services cause quality competition among them to increase?
Is this effect more intense in less concentrated markets?

4. Quality competition in fixed-price markets
Theory
firms will compete on quality if they can’t compete on price; more competition means higher quality
Empirical
Quality lower in more concentrated markets Gaynor et al (2010); Cooper et al (2010); Kessler & McClellan (2000); Kessler & Geppert (2005); Tay (2003)
Quality higher/unaffected in more concentrated markets Gowrisankaran & Town (2003); Mukamel et al (2001); Shortell & Hughes (1988)

5. Impact of Quality Information
Theory
For firms with fixed prices, if costs of acquiring quality are not too different, better-informed consumers will improve quality in competitive markets (Gravelle & Sivey, 2009; 2010)
Empirical
Firms may increase quality (Jin & Leslie, 2003; Bennear & Olmstead, 2008)
In health care markets? Providers may try to “game” the system (Dranove, 2003; Lu, 2009)

6. CABG Report Cards
Coronary Artery Bypass Graft surgery is a serious, relatively frequent, procedure
Pennsylvania’s Guide to Coronary Artery Bypass Graft Surgery--CABG report cards—provide information about risk-adjusted health outcomes for individual hospitals
First two report cards printed; the third was posted online in May 1998

7. Our goal Examine health outcomes for CABG patients in Pennsylvania
Before and after CABG report cards went online in May 1998
Across hospital markets with different levels of concentration
Hypothesis: online publication of report cards intensified quality competition, particularly in less concentrated hospital markets

8. Data
Inpatient claims from Pennsylvania Health Care Cost Containment Council (PHC4): sample of 57,039 nonrural Medicare patients, :II
Hospital CABG surgery report card grades from PHC4
Hospital data from AHA Annual Survey of Hospitals
Hospital cost-to-charge ratios data from the Centers for Medicare & Medicaid Services
County level HMO penetration from PA Department of Health

9. Empirical Strategy Baseline Specification
Outcomeikt: outcome quality realized by patient i from zip code area k in year t
Ckt: variables measuring market competition
Postt=1 for time periods after 1998:II
Mkt: predicted market characteristics variables
Pit: patient characteristics
τt denotes year dummies, ςk denotes zip code dummies

10. Measuring Market Concentration
Estimate conditional logit for each patient’s hospital choice (Kessler & McClellan, 2000)
Hospital choice: quality and distance to options; patient characteristics
Choice set: hospitals within 50 miles of patient’s zip code
Hospital characteristics: size; teaching; report card grade; NFP
Distance:
Reference hospital is closest hospital
For each hospital j in choice set, calculate difference in travel distance to hospital j vs. travel distance to closest hospital
Interact (quartile of) travel distance with information on hospital characteristics
Patient Characteristics interacted with hospital j characteristics
Result is predicted probability that patient i picks hospital j

11. Aggregate these probabilities
Over patients in zipcode for hospital j
Over zipcodes served by each hospital j
Over hospitals serving zipcode k
Result is a measure of market concentration for each zip code based on predicted patient flows that varies with changes in
Size of hospital market (consolidation, entry, exit)
Patients’ willingness to trade off travel distance vs. other hospital characteristics
Distribution of patient populations

12. We create two dummies, Most Competitive and Competitive.
“Most competitive” = 1 if HHI is in highest quartile of HHI distribution (for entire period).
“Competitive” = 1 if HHI falls in second or third quartile
Least competitive is the base group
Each dummy is interacted with “Post” which equals 1 for time periods after 1998:II

13. Outcomes In-hospital Mortality
Readmission for problems related to ischemic heart diseases within 12 months
Total cost (Charge x cost-to-charge ratio)

14. Table 1. Descriptive Statistics for Outcomes and HHI
By Admission Year
By Predicted HHIb
1995
2000
2005
Most
Competitive
Least Competitive
Patient Outcome Variables
Death
0.033
0.027
0.031
0.025
0.029
1-Year readmission
0.202
0.184
0.122
0.182
0.153
0.241
Total costs
24359
21402
21798
28124
21980
21987
[11199]
[11501]
[10673]
[75125]
[21562]
[10925]
Predicted HHIb
Most competitive
0.517
0.436
0.306 1
0.287
0.471
0.566
Most Competitive
0.196
0.093
0.129
Predicted HHI
0.369
0.344
0.404
0.183
0.443
0.774

15. Other Variables
Mkt: predicted number of patients, predicted number of beds, and predicted number of teaching hospitals Pit: age, gender, race/ethnicity, emergency admission, HMO enrollee; distance to closest hospital, severity of illness (Charlson index); admission source

16. Table 2. Information, Concentration, and Patient Outcomesa
Total Cost
Mortality
Readmissionb
(1)
(2)
(3)
Zip-code Market Characteristics
Most competitive*post
0.0253
0.0028 **
[0.0239]
[0.0052]
[0.0188]
Competitive*post
0.1225*** *
[0.0244]
[0.0055]
[0.0184]
Most competitive
0.0042
0.0076
0.0325
[0.0404]
[0.0059]
[0.0226]
Competitive
***
0.0063
0.0093
[0.0389]
[0.0053]
[0.0185]
Predicted demand
0.0004
[0.0025]
[0.0004]
[0.0010]
Predicted bed size
0.0005***
[0.0001]
[0.0000]
Predicted teaching status
***
[0.0636]
[0.0103]
[0.0254]

17. Alternative Explanations: Contemporaneous Events
HMO Penetration
CON eliminated, December 1996
Balanced Budget Act, 1997

18. Table 3. Impact of Contemporaneous Eventsa
Dependent Variable:
Log of Total Cost
1-Year Readmissionb
(1)
(2)
(3)
(4)
(9)
(10)
(11)
(12)
Most competitive*post
0.0253
0.0289
0.0643**
0.0656** ** ** * *
[0.0239]
[0.0250]
[0.0255]
[0.0257]
[0.0188]
[0.0187]
[0.0186]
Competitive*post
0.1225***
0.1273***
0.1308***
0.1309*** * ** ** **
[0.0244]
[0.0249]
[0.0237]
[0.0184]
[0.0185]
Most competitive
0.0042
0.0032
0.0325
0.0328
0.0318
0.0317
[0.0404]
[0.0391]
[0.0387]
[0.0226]
[0.0224]
Competitive
***
***
***
***
0.0093
0.0092
0.0126
0.0128
[0.0389]
[0.0369]
[0.0365]
HMO pen. rate ** *
0.0254
0.0060
0.0124
[0.0990]
[0.0969]
[0.0956]
[0.0529]
[0.0535]
[0.0536]
Share new entrants
***
*** ** **
[0.0463]
[0.0467]
[0.0282]
% Medicare patients
0.2878
0.1867
[0.2716]
[0.1427]
Observations
57039
55430

19. Table 4. Effect of Information and Competition by Severity of Patient's Conditiona
Panel A: The impact of competition on patient outcomes, by risk groups
Low Risk Patients 
Median Risk Patients  
High Risk Patients
(1)
(3)
(4)
(6)
(7)
(9)
Total Cost
Readmission
Most competitive*post
0.1033***
0.0599**
0.0496
***
[0.0386]
[0.0234]
[0.0256]
[0.0238]
[0.0389]
[0.0311]
Competitive*post
0.1538***
0.1229***
0.1217***
***
[0.0378]
[0.0226]
[0.0233]
[0.0369]
[0.0318]
Most competitive
0.0297
0.0538
[0.0442]
[0.0355]
[0.0419]
[0.0261]
[0.0522]
[0.0377]
Competitive
***
0.0039
***
0.0107 **
0.0385
[0.0397]
[0.0258]
[0.0385]
[0.0214]
[0.0475]
[0.0332]
Observations
10396
10251
30935
30120
15708
15059

20. Robustness Checks Pre-trend effect Mean-reverting effect
Most Competitive x D1997
Competitive x D1997
Mean-reverting effect
Add year dummies x “average Y”k,1995
System effects

21. Table 5. Robustness Checks: Pre-existing Trends, Mean Reversion, and System Effectsa
Dependent Variable:
Log of Total Cost
1-Year Readmissionc
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Most competitive*post
0.0581**
0.0810***
0.0502*
0.0570** ** *
[0.0271]
[0.0244]
[0.0263]
[0.0248]
[0.0206]
[0.0152]
[0.0231]
[0.0172]
Competitive*post
0.0731***
0.0994***
0.1037***
0.1124*** **
*** * **
[0.0257]
[0.0208]
[0.0227]
[0.0185]
[0.0155]
[0.0221]
[0.0163]
Most competitive
0.0036
0.0112
0.0232
0.0399**
0.0231
0.0403**
[0.0370]
[0.0299]
[0.0354]
[0.0286]
[0.0237]
[0.0179]
[0.0177]
Competitive
***
***
***
0.0185
0.0403***
0.0250
0.0399***
[0.0355]
[0.0321]
[0.0229]
[0.0146]
[0.0190]
[0.0132]
Most competitive*D1997
0.0217
0.0188
[0.0369]
[0.0328]
[0.0201]
[0.0205]
Competitive*D1997
***
***
[0.0345]
[0.0309]
[0.0210]
[0.0204]
Add Competition Dummies×D1997 Y N
Add Year Dummies×Y1995b
Use System-level HHI's
Observations
57039
56309
55430
54701

22. Does patient go to a high-quality hospitals?
Table 6. Quality Information, Competition, and Hospital & Surgeon Choices
Does patient go to a high-quality hospitals?
Does patient go to a top 10% high-quality surgeon?
Does patient go to a top 20% high-quality surgeon?
Does patient go to a top 30% high-quality surgeon?
Does patient go to a top 40% high-quality surgeon?
Most competitive*post
0.0862***
0.0042
0.0251*
0.0567***
0.0596***
[0.0326]
[0.0144]
[0.0146]
[0.0160]
[0.0184]
Competitive*post
0.1628***
0.0123
0.0429***
0.0019
[0.0343]
[0.0136]
[0.0152]
[0.0151]
[0.0193]
Most competitive **
*** *
[0.0418]
[0.0260]
[0.0261]
[0.0288]
[0.0390]
Competitive ** *
0.0385
[0.0399]
[0.0150]
[0.0181]
[0.0202]
[0.0254]
Observations
57039

23. Discussion
Given quality information, competition increases quality even with a small number of hospitals
Consistent results found using samples that include patients from rural areas
Most likely providers, particularly hospitals, responding to the quality information, rather than patients
Some of the additional expenditure of resources is wasted?—higher costs for lower-risk patients yields little improvement in health outcomes

24. Conclusion
After the report cards went online, costs were higher and readmission rates were lower in more competitive nonrural markets
Patients, doctors, or insurers became more quality-sensitive with the improved information about quality of medical care
Results are for fixed-price markets