**4. Empirical results**

Our regression results are presented in Table 2 below.

Empirical results indicate that higher competitive pressures translate into better health outcomes as measured by decreases in excess mortality as well as decreases in risk adjusted mortality rates relative to the state average. Results are statistically significant at the 10% level for most specifications of the model. Although hospitals in the United States may not compete based on price due to high prevalence of public insurance and high prevalence of nonprofit hospitals in the industry, competition based on quality may be relatively more important, especially as public insurance coverage is being expanded after the passage of the Patient Protection and Affordable Care Act of 2010.

Other important variables include hospital ownership status. Although there is no statistically significant relationship between mortality rates in for-profit and nonprofit hospitals, the nonprofit hospitals saw the same or smaller improvements in mortality than for-profits between 2003 and 2007. Municipal hospitals (city and county hospitals) show higher risk adjusted mortality rates and Table 2 shows that they saw smaller improvements in mortality rates even after we controlled for CABG volume and area characteristics.

Although we do not see a statistically significant relationship between CABG volume and health outcomes, alternative specifications of the volume variable yield a better fit. For example, square root of volume has a statistically positive effect on mortality improvements (result not shown). This indicates that initial increases in CABG volume improve outcomes but this improvement, interestingly, increases at a decreasing rate. We also did not find a significant relationship between increases in the number of CABG surgeries performed and mortality statistics. This result is consistent with the previous research that shows that over time, the disparity in outcomes between low- and high-volume hospitals has narrowed as outcomes have improved significantly for all hospitals (Ho, 2000).

Finally, the effect of hospital competition on health outcomes can be ambiguous since higher competitive pressures may potentially decrease the volume of surgeries that each hospital performs. If higher-volume hospitals, in fact, deliver better quality of care, competition may be undesirable. In our model we measure the effect of hospital competition holding CABG volume constant. Thus, we re-estimate our model without the effect of CABG volume. We find that even in this specification of the model the relationship between the hospital competition (as measured by HHI) holds.

Can Competition Save Your Life? 161

In this study we concentrate only on clinical outcomes as measured by risk adjusted mortality rates. Previous research by Gaynor and Vogt (2000) point out that patient and physician preferences may be the driving force in hospital competition. In response to patient preferences, some hospitals may compete along both clinical and nonclinical dimensions. Some hospitals may respond to competitive pressures by offering private rooms with televisions and private phones, hotel-like lobbies and waiting rooms (Lindrooth 2008). Improvements in such amenities may be important to patients but they are not

This study estimates the effect of changes in hospital competition on risk adjusted measures of hospital outcomes as measured by risk adjusted mortality rates. Using the data from the Office of Statewide Health Planning and Development of the State of California for the period 2003-2007 we find that hospitals that saw higher competitive pressures also experienced greater improvements in health outcomes as measured by mortality statistics following Coronary Artery Bypass Graft (CABG) surgery. Although higher competition in hospital markets may not affect health care prices due to the presence of the third-party

A review of health care consolidation trends by Goldberg (1999) indicates that consolidation is likely to continue at a rapid pace. Such consolidation can have a negative effect on health outcomes if it leads to increases in market power. Results of this study show that a decrease in the number of hospitals may not necessarily decrease hospital competition index as measured by HHI. Increases in HHI (i.e. decreases in hospital competition) significantly decrease quality of care as measured by risk adjusted mortality rates. In addition, Dranove and White (1994) estimated a trend beginning in the mid-1980s in which higher hospital competition lowered prices and cost of care. Similar results were found by Gaynor and Haas-Wilson (1999) and Keeler et al. (1999). Mounting empirical evidence leads us to conclude that hospital competition improves quality of care and lowers cost of care and

Our results imply that overtime both technological improvements and antitrust policies will play a role in determining improvements in hospital quality. Antitrust analysis of the hospital industry should incorporate the potential effects of pro-competitive policies on

Dranove D, White WD. Recent Theory and Evidence on Competition in Hospital Markets.

Gaynor M, Haas-Wilson D. Change, Consolidation, and Competition in Health Care

Gaynor M, Moreno-Serra R, Propper C. Competition Could Substantially Benefit Healthcare.

*Journal of Economics and Management Strategy* 1994 3(1):169-209.

Markets*. Journal of Economic Perspectives* 1999 13(1): 141-164.

payers, it does translate into quality competition and better health outcomes.

**5. Limitations of the study** 

addressed in this study.

**6. Conclusions and policy implications** 

prices, thus improving patient welfare.

*BMJ* 2011 343:d4727.

**7. References** 

health outcomes since such policies may in fact save lives.


*Notes*: \* indicates significance at p<0.1 level, \*\* indicates significance at p< 0.05 level. All continuous variables are in the log form.

Table 2. Determinants of excess mortality differences (standard errors are in parentheses)

**Competition measures** 


**Hospital Characteristics** 

(0.000397) - 0.00170\*

(0.0000978)


0.372 (0.242)

0.696\* (0.371)




0.0885 (0.126)


0.0810 (0.126)



0.293 (0.609)

0.0228 (0.131)


**Market characteristics** 

*Notes*: \* indicates significance at p<0.1 level, \*\* indicates significance at p< 0.05 level. All continuous

Table 2. Determinants of excess mortality differences (standard errors are in parentheses)

Excess mortality differences (2003-2007) Risk adjusted mortality rates differences (2003-2007)

(0.000995)


0.915 (0.607)

1.562\* (0.950)




0.231 (0.314)


0.180 (0.314)



0.718 (1.511)

0.358 (0.389)


> 0.000361\* (0.00220)

> > -0.397 (0.345)

> > 1.114\* (0.607)

1.908\*\* (0.930)




0.235 (0.316)


0.160 (0.314)



0.756 (1.525)

0.00774 (0.329)


mortality differences (2003-2007)

0.000657\*

(0.138)

(0.243)

(0.380)

(0.304)

(0.706)

(0.452)

(0.126)

(0.426)

(0.125)


(0.829)

(0.604)

(0.155)

(8.567)

Variable Excess

CABG volume -0.160

Staffed beds 0.295

Church affiliation 0.564

Nonprofit, investor owned -0.473

Nonprofit, other -0.864

Municipal -0.993\*\*

Trauma unit 0.0868

Emergency room -0.205

Teaching 0.0887

Uninsured -0.483

Per capital income 0.275

Percent Medical 0.157

Constant -3.899

Disproportionate share

variables are in the log form.

hospital

HHI differences, HHI based on available beds

HHI differences, HHI based on CABG volume

(2003-2007)

(2003-2007)

### **5. Limitations of the study**

In this study we concentrate only on clinical outcomes as measured by risk adjusted mortality rates. Previous research by Gaynor and Vogt (2000) point out that patient and physician preferences may be the driving force in hospital competition. In response to patient preferences, some hospitals may compete along both clinical and nonclinical dimensions. Some hospitals may respond to competitive pressures by offering private rooms with televisions and private phones, hotel-like lobbies and waiting rooms (Lindrooth 2008). Improvements in such amenities may be important to patients but they are not addressed in this study.
