WORKING 

 PAPERS

The Effect of Hospital Mergers on Inpatient Prices: 

A Case Study of the New Hanover-Cape Fear Transaction

Aileen Thompson

WORKING PAPER NO. 295

January 2009

___________________________________________________________________________

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author to protect the tentative character of these papers.

______________________________________________________________________________

BUREAU OF ECONOMICS

FEDERAL TRADE COMMISSION

WASHINGTON, DC 20580
1 

 

The Effect of Hospital Mergers on Inpatient Prices: 

A Case Study of the New Hanover-Cape Fear Transaction 

Aileen Thompson1

Federal Trade Commission 

Washington, DC 

January 2009 

Abstract 

The Federal Trade Commission initiated a Hospital Merger Retrospective Project in 2002 

to analyze the effects of consummated mergers. One of the mergers studied was the 1998 

acquisition by New Hanover Regional Medical Center (“New Hanover”) of Columbia Cape Fear 

Memorial Hospital (“Cape Fear”) in Wilmington, North Carolina. In this paper, we employ 

patient-level claims data from four different insurers to estimate the effects of this merger on 

inpatient prices. Our results provide mixed evidence. Two of the insurers experienced 

substantial post-merger price increases relative to the control group of hospitals. The postmerger price changes for another insurer, however, were comparable to those for the control 

group, while the fourth insurer actually experienced a significant price decrease following the 

merger. Thus, it is difficult to draw conclusions about the impact of this merger on inpatient 

pricing. 

 

1

 The views expressed in the paper are those of the author and not necessarily those of the 

Commission or any individual Commissioner. I am thankful to Michelle Kambara, Peter 

Newberry, and Jorge Roberts for outstanding research assistance and to Steve Tenn, Mike Vita, 

and an anonymous referee for helpful comments. All errors are my own. 
2 

 

Introduction 

The hospital industry went through substantial consolidation during the 1990s. During 

this time, the Federal Trade Commission, Department of Justice, and the California Attorney 

General challenged seven hospital mergers and lost all seven cases.2

 As a result, the Federal 

Trade Commission initiated a Hospital Merger Retrospective Project in 2002 to analyze the 

effects of consummated mergers. One of the mergers studied was the 1998 acquisition by New 

Hanover Regional Medical Center (“New Hanover”) of Columbia Cape Fear Memorial Hospital 

(“Cape Fear”) in Wilmington, North Carolina. In this paper, we evaluate the effects of this 

merger on inpatient prices. 

New Hanover is a large public non-profit hospital that offers a wide range of services, 

including tertiary care such as cardiac surgery. At the time of the acquisition, it had 546 staffed 

beds. Cape Fear was a small community hospital with 109 staffed beds that offered general 

acute care services. The two hospitals are located six miles apart from one another while the 

next closest hospital is over 20 miles away. Thus, those consumers located near New Hanover 

and Cape Fear may have viewed the two hospitals as very close substitutes for providing general 

acute care services. Thus, it is plausible that the acquisition enabled one or both of the merged 

parties to increase prices. 

 On the other hand, it is possible that competition from nearby hospitals constrained 

potential price increases. Table 1 lists the six hospitals that are located within 60 miles of New 

Hanover. The two closest hospitals, located within approximately 30 miles of New Hanover, are 

2 The seven cases were: California v. Sutter Health System (2000), FTC v Tenet Healthcare 

Corp. (1998), United States v. Long Island Jewish Medical Center, (1997), FTC v. Butterworth 

Health Corp. (1996), United States v. Mercy Health Services (1995), FTC v. Freeman Hospital, 
3 

 

very small – approximately one-half of the size of Cape Fear or smaller at the time of the 

acquisition.3

 The closest hospitals to New Hanover that are of comparable size to Cape Fear are 

45 miles away. While this appears to be a long distance to travel for hospital services, the courts 

have accepted such large geographic markets. In FTC v. Freeman, for example, the court 

considered the merging parties to compete with hospitals located 50 miles away.4

 

 Another issue is whether New Hanover’s status as a public non-profit hospital would 

reduce its incentive to exercise market power. It has been argued that the objective of non-profit 

hospitals is to serve the community rather than to maximize profits.5

 Indeed, this argument has 

been accepted by the courts as a rationale for ruling that mergers among non-profit hospitals are 

not likely to be anticompetitive.6 Recent hospital merger retrospective studies, however, have 

provided evidence of significant post-merger price increases from mergers involving non-profit 

hospitals.7

 Another consideration is whether the merger led to efficiencies that may have offset any 

potential anticompetitive effects. Following the merger, for example, New Hanover opened an 

(1995), In re Adventist Health System (2004). 

3 Pender Memorial, located 32 miles from New Hanover, is somewhat larger, but it has 

been operated by New Hanover since 1999. Thus it is not an independent competitor. 

4

 FTC v. Freeman Hospital, 911 F Supp. 1213 (W.D. MO. 1995), aff’d 69 F.3d 260 (8th

Cir. 1995). See also Capps, Dranove, Greenstein, and Satterthwaite (2002) for a discussion of 

geographic market definition in recent hospital cases. 

5

 See, for example, Lynk (1995). 

6

 See, for example, United States v. Long Island Jewish Med. Ctr., 983 F. Supp. 121, 149, 

146 (E.D.N.Y 1997) and FTC v. Butterworth Health Corp., 1997-2 Trade Cas. (CCH) (6th Cir. 

1997). This argument was recently rejected, however, in the decision of Evanston Northwestern 

Healthcare Corp., FTC Docket No. 9315, Initial Decision (October 20, 2005). 

7

 See Vita and Sacher (2001), Haas-Wilson and Garmon (2009), and Tenn (2008). 
4 

 

orthopedic specialty center at Cape Fear and consolidated orthopedic surgery at this location.8

 

As another example, obstetric services were consolidated to the New Hanover location.9

 To the 

extent that these consolidations led to cost savings that were passed on to consumers, prices may 

have fallen, other things equal. Consolidations such as these may have also increased the quality 

of care in these areas. An analysis of possible merger-related quality improvements, however, is 

beyond the scope of this paper. 

We estimate the effect of the New Hanover-Cape Fear merger on inpatient prices. Our 

analysis is based on patient-level claims data from New Hanover and four large managed care 

insurers. These data contain detailed information about the diagnosis, procedures, and payments 

relating to the claim as well as demographic information about the patient. We perform 

econometric analysis to control for factors, such as the types of illnesses treated, that are 

unrelated to the merger that may affect hospital prices. In addition, there may be unobservable 

factors that are also experienced by other hospitals, such as changes in technology. To control 

for such factors, we estimate the price changes at New Hanover relative to those at a control 

group of similar hospitals. In other words, we estimate the difference between the price changes 

for New Hanover and the price changes for the control group hospitals. This “difference-indifferences” approach is used in other merger retrospective studies.10 

 8 www.nhrmc.org, accessed on 11/20/2008. 

9 www.nhrmc.org, accessed on 11/20/2008.

10 See, for example, Vita and Sacher (2001), Taylor and Hosken (2007), Tenn (2008), 

and Haas-Wilson and Garmon (2009). 
5 

Econometric Model 

A typical difference-in-differences approach to analyzing a hospital merger would 

involve estimating an equation similar to the following: 

ln pi = Xiβ + γhi + αPost-Mergeri + θ(Post-Mergeri*Mi)+ ωi

The dependent variable, pi, is the price for a given inpatient hospital admission for individual i, Xi

is a set of patient and diagnosis characteristics, and γhi represents the hospital fixed effect for 

hospital h. The variable Post-Mergeri is a dummy variable that is equal to one if patient i is 

admitted to hospital hi during the post-merger period, and Mi is a dummy variable equal to one if 

hospital hi is one of the merging parties. The coefficient θ is the difference-in-differences 

estimator and reflects the difference between the price change for the merging parties and the 

price change for the control group, after controlling for observable characteristics X. 

 As discussed by Bertrand, Duflo, Mullainathan (2004) and Donald and Lang (2007), OLS 

estimation of difference-in-differences equations such as the one above is likely to lead to 

standard errors that are substantially biased downward. This is because the error terms are likely 

to be correlated within groups. In our case, the error terms are likely to be correlated among 

patients treated at the same hospital under the same insurance contract. Thus, there is less new 

information from additional patients at the same hospital than would be implied by the number 

of observations. In addition, due to random variation among hospitals, the price changes at the 

merging and control hospitals would not generally be the same even in the absence of the 

merger. Failure to take account of the group-error structure generates underestimates of the 

standard errors. 
Unbiased standard errors can be obtained by using a simple two-stage approach.11 

Thus, we follow Tenn (2008) and estimate the price change for New Hanover and Cape Fear 

relative to the control group in two stages. In the first stage, we estimate the price changes for 

each hospital using the following model for each insurer separately: 

ln pi = Xiβ + γh + δh Post-Mergeri + εi (1) 

The hospital-specific coefficient of the Post-Merger dummy variable, δh, measures the postmerger price change of hospital h after controlling for characteristics X. 

 In the second stage, we estimate the difference between the price changes for the merging 

hospitals and the control group hospitals: 

hh h +Μ+= υθαδ^

 (2) 

The dependent variable is estimated from equation (1), and Mh is a dummy variable equal to one 

if hospital h is one of the merging parties. As above, the coefficient θ is the difference-indifferences estimator. The fact that the dependent variable is estimated rather than observed 

raises the possibility that the error term may be heteroskedastic. The error term will be 

approximately homoskedastic, however, if the number of observations for each control group 

hospital is large, as is the case in this study.12 In contrast to the one-step procedure that is 

commonly employed, OLS estimation of (2) will yield unbiased standard error estimates, 

assuming that the error term, νh , is i.i.d. normal.13 

6 

 11 See Moulton (1990), Bertrand et al., (2004), Donald and Lang (2007), and Tenn 

(2008). 

12 See Donald and Lang (2007), 

13 See, for example, Moulton (1990), Bertrand et al., (2004), Donald and Lang (2007), 

and Tenn (2008) for a discussion of these two econometric approaches to difference-in-
7 

 

 We estimated our benchmark models using both the one-step and the two-step methods. 

Consistent with the discussion above, the estimated coefficients for the two methods are similar, 

but the standard errors estimated by the two-step approach are substantially larger. The results 

reported below are based on the two-step procedure. 

Data 

We received patient-level claims data from New Hanover as well as from four large 

managed care insurers. These data contain detailed information about the diagnosis, procedures, 

and payments relating to the claim as well as demographic information about the patient. The 

New Hanover data include claims for New Hanover and Cape Fear only, while the data that we 

received from the insurance companies include claims data for all of the hospitals in the state of 

North Carolina. In order to use the same data source for the merging hospitals and the control 

group hospitals, our difference-in-differences analysis is based on the data provided by the 

insurers. However, we also estimate equation (1) using the data provided by New Hanover to 

provide a benchmark and to test the robustness of the results based on the insurer data. 

The variables used in the econometric analysis are defined in Table 2. Our measure of 

price is the total amount paid for each inpatient stay. This includes the payments made by the 

insurance company as well as the patient’s copayment and deductible. For some claims, the total 

payments reported in the data are too low to be credible, presumably due to incomplete claims, 

difference estimation. 
8 

 

coding errors, or missing data.14 We restrict the dataset to claims for which the average payment 

per day is greater than $250. 

The claims for New Hanover and Cape Fear are not identified separately post-merger in 

the insurer datasets. Thus, we estimate the combined price changes for New Hanover and Cape 

Fear (“New Hanover/Cape Fear”) in our difference-in-differences analysis. Using the data 

provided by New Hanover, however, we are able to estimate the price changes for the two 

hospitals separately. Based on these data, the estimated price changes for New Hanover alone 

are similar to those for New Hanover and Cape Fear combined. This reflects, in part, the small 

size of Cape Fear relative to New Hanover. 

The merger was consummated in November 1998, but prices were largely determined by 

the existing pre-merger contracts until new contracts were negotiated. New Hanover negotiated 

its post-merger contracts with individual insurers at different times, with the effective dates of 

these contracts ranging from February 1999 to January 2001. In our benchmark specification, 

the pre-merger period is defined to be 1997-1998, and the post-merger period is defined to be 

2001-2002. 15 The two years, 1999 and 2000, are considered to be the transition years and are 

omitted from the estimation. 

14 The data that we received from the insurers include multiple lines for each claim, 

representing the various procedures or services performed. The data are aggregated for each 

claim based on the claim number and/or other identifying information. 

15 Some of the datasets do not include data prior to 1997. In order for the pre-merger 

period to include two full years of data, the full year of 1998 is considered to be part of the premerger period even though the merger was consummated in November of that year. To the 

extent that New Hanover adjusted its pricing immediately, our results will underestimate the full 

impact of the merger. Sensitivity analysis, however, indicates that our results are robust to a 

number of different event windows. 
9 

 

Our benchmark control group includes urban hospitals in North Carolina that are similar 

in size to New Hanover.16 In particular, this group is defined to include all urban hospitals in the 

state that have over 400 beds. One of the hospitals meeting these criteria was omitted from the 

benchmark control group because it also was involved in a merger of two hospitals located in 

close proximity to one another during the sample period.17 The resulting control group consists 

of eleven hospitals.18 One of the insurers had contracts with only nine of these hospitals during 

the sample period so the control group for this insurer consists of nine hospitals. 

To control for changes in the mix of services provided and/or the severity of illnesses, we 

assign each claim to one of fifteen categories based on the patient’s Diagnosis Related Group 

(DRG) that is identified in the claims data. Each DRG is defined to include a group of diagnoses 

that require similar resources to treat. A dummy variable for each category is included in the 

estimation equation. These categories are similar to those upon which many hospital contracts 

are based. A contract between a hospital and an insurer, for example, may include general per 

diem or per case rates for surgery and medical admissions and then specify special rates for 

categories such as cardiac surgery and obstetrics.19 For one insurer, DRG codes are not 

16 We choose hospitals that are similar to New Hanover rather than Cape Fear because 

the observed price changes are driven largely by New Hanover due, in part, to the relative sizes 

of the two hospitals. 

17 Sensitivity analysis indicates that including this hospital in the control group does not 

affect the results substantially. 

18 These hospitals are: Carolinas Medical Center, Duke University Hospital, Forsyth 

Medical Center, Moses Cone Health System, Pitt County Memorial Hospital, Presbyterian 

Healthcare, North Carolina Baptist Hospital, Northeast Medical Center, Rex Healthcare, 

University of North Carolina Hospitals, and WakeMed. 

19 See Table 2 for a list of categories included in the analysis. 
10 

 

reported in the data so we use dummy variables based on the patient’s primary ICD9 diagnosis 

code.20 

We also control for changes in the severity of illness by including the patient’s length of 

stay as an independent variable. For payers with more than one insurance plan, we employ 

dummy variables for each type of plan (e.g., HMO, PPO, etc.) to control for different pricing 

structures among these plans. In addition, when available in the particular dataset, we include 

the patient’s age and sex. To protect the identity of the insurers as well as to avoid inadvertently 

reporting information that may be competitively sensitive, we do not provide summary statistics 

of the data. 

Results 

Table 3 summarizes the post-merger changes in the price per admission based on the data 

provided by New Hanover for each of the four insurers. The unadjusted average price per 

admission charged by New Hanover/Cape Fear increased post-merger for three of the insurers. 

The price increases ranged from 24% for Insurer 3 to 106% for insurer 1. In contrast, price per 

admission decreased by 18% for Insurer 4. As mentioned above, the price changes for New 

Hanover alone are similar to those for New Hanover and Cape Fear combined. Cape Fear only 

20 ICD9 codes are very detailed diagnosis codes. We assign a dummy variable for each 

of the nineteen major groups of codes. For example, the group “Diseases of the Circulatory 

System” includes individual diagnoses such as aortic aneurysm (icd9 code 441), chronic 

rheumatic heart diseases (icd9 codes 393-398.9), and diseases of veins and lymphatics (icd9 

codes 351-359.9). Note that while the diagnoses within these groups may be similar in terms of 

the parts of the body affected, they may not be similar in terms of severity or the resources 

required for treatment. 
had contracts with two of the insurers during the pre-merger period. The Cape Fear price change 

was similar to the New Hanover price change for one of these insurers and insignificant for the 

other. In order to protect the identity of the insurers, we do not report these results in the table. 

 The econometric results of equation (1) are reported in the next section of the table. The 

results in this table are based only on the data submitted by New Hanover and therefore do not 

reflect differences from the control group hospitals. The coefficient of the Post-Merger dummy 

variable indicates the change in admission prices after controlling for the patient characteristics, 

diagnosis, and type of insurance plan. This coefficient is statistically significant at the 1% level 

for all of the insurers. When changes are relatively small, this coefficient is a good 

approximation of the estimated percent price change. For larger changes, the implied price 

change can be derived as exp ⎥

⎦

⎤ ⎢

⎣

⎡ − 2

2

δ δ se - 1, whereδ is the coefficient of the Post-Merger 

dummy variable, and δ se is its standard error.21 The estimates indicate that prices increased by 

over 26% for Insurers 1, 2, and 3, and decreased by 23% for Insurer 4. In order to protect the 

identity of the individual insurers, we do not report the weighted average price change for the 

four ins

e 

e in the length of stay 

leads to

the 

e 

 

urers. 

The estimated coefficients for the length of stay variable are statistically significant at th

1% level for all four insurer equations. They indicate that a 10% increas

 an increase in price per admission ranging from 5.7% to 7.5%. 

Table 4 reports the post-merger price changes for New Hanover/Cape Fear relative to 

control group hospitals based on the admissions data provided by the insurers. Although th

11 

21 See, for example, http://shazam.econ.ubc.ca/intro/dumlog.htm. 
12 

r/ 

s 

lati ly little 

change

ange 

among 

y 

Insurer 1 is statistically significant at the 10% level, and the coefficients for Insurers 2 and 4 are 

 

unadjusted numbers do not match exactly those from New Hanover’s admissions data, the 

implications are similar: Insurers 1, 2, and 3 experienced large price increases following the 

merger while Insurer 4 experienced a price decrease. The second row reports the price changes 

for the control group hospitals calculated over the same time period. If one views the changes in

the control group prices as reflecting changes in costs and technology that are common to large 

hospitals in North Carolina, then a rough estimate of the impact of the merger on New Hanove

Cape Fear prices would be the difference between changes in New Hanover/Cape Fear price

and changes in the control group prices. This exercise suggests that the merger led to large 

relative price increases for Insurers 1 and 2 (131% and 49.5%, respectively), re ve

 for Insurer 3 (2.7%) and a large price decrease for Insurer 4 (-29%). 

The econometric difference-in-differences estimates from equation (2) of the two-stage 

procedure are reported in the next panel of Table 4.22 The intercept reflects the average ch

in the control group price, after controlling for length of stay, diagnosis category, type of 

insurance plan and, when available, the age and sex of the patient. The pattern of coefficients 

the insurers is similar to the pattern of calculated price changes based on the raw data. 

The coefficient of the New Hanover/Cape Fear dummy variable reflects the change in 

New Hanover/Cape Fear’s price relative to the control group after controlling for diagnosis and 

patient characteristics. The coefficient for Insurer 3 is not statistically different from zero at an

conventional level of significance. This is consistent with the fact that the control group price 

changes were similar to those at New Hanover/Cape Fear for this insurer. The coefficient for 

 

22 We do not report the results of equation (1) in order to protect the identity of the 
13 

statistically significant at the 1% level. The estimated coefficients imply that Insurers 1 and 2 

both experienced substantial post-merger price increases of 56.5% and 65.3%, respectively, and 

that prices actually fell for Insurer 4 by 30%. Thus, the econometric estimates imply price 

changes that are broadly similar to those based on the unadjusted difference-in-differences 

calculations for Insurers 2 and 4. The estimated price change for Insurer 1 is smaller than the 

unadjusted difference and has a wide 95% confidence interval, but the point estimate still 

indicates a substantial price increase. 

Sensitivity Analysis 

We estimated a number of alternative specifications in order to test the robustness of our 

results. For example, one alternative uses case mix weights rather than DRG category dummy 

variables to control for changes in the mix of patients in equation (1). A case mix weight is 

calculated by the Center for Medicare and Medicaid Services (CMS) for each DRG and is 

defined as the average cost of treating a patient for that DRG relative to the average cost for all 

DRGs. The dummy variable approach used in our benchmark analysis is more flexible because 

it allows the data to determine the relative weights for patients treated at New Hanover. If some 

illnesses are treated relatively infrequently, however, there may be too few observations to 

estimate the weights with any precision. We re-estimated equation (1) using the CMS case mix 

weights for the three insurer datasets that included DRG codes. The coefficient is positive as 

expected and statistically significant for all three insurers. More importantly, the results with 

 

insurers. 
14 

e 

er the estimation equation includes the variables for the patient’s length of 

r 

 not 

 price increases following the 

erger

the 

tess 

increase, at least for this insurer. While we did not repeat this sensitivity analysis for the other 

respect to the post-merger price changes are very similar to those reported above. Our results ar

also robust to wheth

stay, age and sex. 

 We also tested the sensitivity of our results to the definition of the pre- and post-merger 

periods. For one specification, we defined the pre-merger period to be the one year prior to the 

consummation of the merger (i.e., 11/1997 – 10/1998) and the post-merger period to be one yea

following the effective date of the first post-merger contract between New Hanover/Cape Fear 

and the particular insurer. The results are broadly similar to those reported in Table 4 and do

affect our conclusions that Insurers 1 and 2 experienced large

m while Insurer 4 experienced a large price decrease. 

 Our results are also robust to whether individual control group hospitals are included or 

omitted from the control group. In other words, our results are not driven by one or two of 

control group hospitals. Another possible control group would consist of the six hospitals 

located in the counties surrounding New Hanover County. These hospitals are all relatively 

small and, thus, were not included in our initial control group. They presumably, however, face

similar local costs such as wages. We repeated our analysis for Insurer 1 using this alternative 

group of control hospitals. Prices for the control group fell by 10% between the pre- and pos

merger periods, and the estimated price change for New Hanover/Cape Fear relative to this 

group was similar to our benchmark results for this insurer. In addition to providing a robustn

check for our benchmark results, the substantial increase in New Hanover/Cape Fear’s price

relative to nearby hospitals indicates that these hospitals were not able to constrain a price 
15 

 

insurers, the decrease in prices for this control group suggests that it is unlikely that local cost 

increases could explain the large price increases experienced by Insurer 2. 

 

Conclusion 

Our results provide mixed evidence regarding the effect of the New Hanover-Cape Fear 

transaction on inpatient prices. Two of the insurers experienced substantial post-merger price 

increases relative to the control group of hospitals. The post-merger price changes for another 

insurer, however, were comparable to those for the control group, while the fourth insurer 

actually experienced a significant price decrease following the merger. 

An interesting question that arises from these results is whether differences among 

insurers may lead to different post-merger outcomes. Haas-Wilson and Garmon (2009) also find 

that estimated post-merger price changes varied across insurers in their study of two hospital 

mergers in Chicago. Possible explanations for such variations include the insurers’ bargaining 

abilities, the types of plans that they offer, and the services that they provide. 23 It may be, 

however, that some of the estimated price changes reflect factors that are unrelated to the merger. 

Thus, it is difficult to draw conclusions about the impact of the New Hanover-Cape Fear merger 

on inpatient pricing. 

23 We cannot address this issue here because we are required to protect the identity of the 

insurers. 
16 

References 

Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We 

Trust Differences-in-Differences Estimates?” Quarterly Journal of Economics 119(1): 249-75. 

Capps, Cory, Dranove, David, Greenstein, Shane, and Mark Satterthwaite. 2002. “Antitrust 

Policy and Hospital Mergers: Recommendations for a New Approach.” Antitrust Bulletin,

March 2002: 677-714. 

Donald, Stephen G. and Kevin Lang. 2007. “Inference with Difference-In-Differences and 

Other Panel Data.” The Review of Economics and Statistics 89(2): 221-233. 

Gaynor, Martin and William B. Vogt. 2003. “Competition Among Hospitals.” RAND Journal 

of Economics 34(4): 764-85. 

Haas-Wilson, Deborah and Christopher Garmon. 2009. “Two Hospital Mergers on Chicago’s 

North Shore: A Retrospective Study.” FTC Working Paper, No 294. 

Lynk, William J. 1995. “Nonprofit Hospital Mergers and the Exercise of Market Power.” 

Journal of Law and Economics 38(2): 437-61. 

Moulton, Brent R. 1990. “An Illustration of a Pitfall in Estimating the Effects of Aggregate 

Variables on Micro Units.” Review of Economics and Statistics 72(2):334-38. 

Taylor, Christopher T. and Daniel S. Hosken. 2007. “The Economic Effects of the MarathonAshland Joint Venture: The Importance of Industry Supply Shocks and Vertical Market 

Structure.” Journal of Industrial Economics 55(3): 419-51. 

Tenn, Steven. 2008. “The Price Effects of Hospital Mergers: A Case Study of the SutterSummit Transaction.” FTC Working Paper, No 293. 

Vita, Michael G. and Seth Sacher. 2001. “The Competitive Effects of Not-for-Profit Hospital 

Mergers: A Case Study.” Journal of Industrial Economics 49(1):63-84. 
17 

Table 1: Hospitals Located within 60 Miles of New Hanover

Hospital Name Location # of Staffed Beds 

in 1998 

Distance from New 

Hanover 

Merging Parties 

New Hanover Wilmington, NC 546 

Columbia Cape Fear Wilmington, NC 109 6 miles 

Closest Hospitals 

Brunswick Community Supply, NC 56 30 miles 

J. A. Dosher Memorial Southport, NC 40 31 miles 

Pender Memorial* Burgaw, NC 68 32 miles 

Columbus County Whiteville, NC 117 48 miles 

Bladen County Elizabethtown, NC 58 53 miles 

Onslow Memorial Jackonsville, NC 133 56 miles 

Duplin Kenansville, NC 80 60 miles 

* Pender Memorial has been operated by New Hanover since 1999. 

Sources: The number of staffed beds was obtained from the 1998 American Hospital 

Association Guide. Distances were calculated using www.mapquest.com.
18 

Table 2: Variable Definitions 

Variable Definition 

Dependent Variable: 

Price per Admission 

The total payments made by the insurer and patient for each 

inpatient admission. 

Post- Merger Dummy Variable A dummy variable is equal to zero for admissions during the 

pre-merger period, 1997-1998, and equal to one during the 

post-merger period, 2001-2002.

Diagnosis We control for each diagnosis by including a dummy variables 

based on the patient’s DRG code for each of the following 

categories: angioplasty, c-section (high), c-section (normal), 

cardiac stent, cardiac surgery, other cardiology, joint 

replacement, neurosurgery, nicu, normal newborn, vascular, 

obstetrics (other than c-section), high-risk obstetrics (other 

than c-section), medical (not otherwise specified), and surgical 

(not otherwise specified). For one insurer, DRG codes are not 

reported in the data so we use dummy variables based on the 

patient’s primary ICD9 diagnosis code. 

Sex A dummy variable equal to one if the patient is female. 

Age The patient’s age, measured in years 

Length of Stay The number of days that the patient was in the hospital for the 

particular admission. 

Plan Type A dummy variable equal to one if the patient’s insurance plan 

is an HMO. 
19 

Table 3: Post-Merger Price Changes for New Hanover-Cape Fear 

Based on the New Hanover’s Admissions Records 

Insurer 1 Insurer 2 Insurer 3 Insurer 4 

Unadjusted Price Change 

106% 62% 24% -18% 

Econometric Results 

Post-Merger 0.509** 

(0.014) 

0.722** 

(0.024) 

0.235** 

(0.024) 

-0.260** 

(0.012) 

Length of Stay 0.571** 

(0.013) 

0.746** 

(0.020) 

0.638** 

(0.021) 

0.677** 

(0.012) 

Age 0.006** 

(0.001) 

0.008** 

(0.001) 

0.008** 

(0.001) 

0.003** 

(0.001) 

Sex (female=1) 0.079** 

(0.016) 

0.038 

(0.024) 

0.089** 

(0.028) 

0.048** 

(0.014) 

R-squared 0.749 0.751 0.778 0.767 

Implied Post-Merger Price Change 

66% 106% 26% -23% 

Notes: The post-merger period is defined to be 2001-2002 and the pre-merger period is defined to be 1997-1998. 

The estimation equations also include dummy variables for diagnosis categories and dummy variables for the type 

of insurance plan. Standard errors are in parentheses. 

** The estimate is statistically significant at the 1% level. 
20 

Table 4: Price Changes for New Hanover-Cape Fear relative to the Control Group 

Based on the Health Insurers’ Admission Records 

Insurer 1 Insurer 2 Insurer 3 Insurer 4

Unadjusted Price Change 

New Hanover/Cape Fear 135% 46.7% 30.3% -16% 

Control Group 4% -2.8% 27.6% 13% 

Difference 131% 49.5% 2.7% -29% 

Econometric Results 

Intercept 0.065 

(0.077) 

-0.124** 

(0.047) 

0.243** 

(0.025) 

0.090** 

(0.033) 

New Hanover-Cape Fear 0.483* 

(0.266) 

0.516** 

(0.164) 

0.073 

(0.079) 

-0.350** 

(0.115) 

Adjusted R-squared 0.174 0.449 -0.018 0.4301 

Implied Price Change 

95% Confidence Interval 

56.5% 

-10% , 193% 

65.3% 

16% , 141% 

7.2% 

-11% , 29% 

-30% 

-45% , -9% 

Notes: The dependent variable is the estimated log price change of inpatient admissions of each hospital between 

the pre-merger and post-merger periods, after controlling for length of stay, diagnosis and the type of insurance plan. 

When available in the relevant dataset, the age and sex of the patient are also included in the first stage estimation. 

The pre-merger period is 1997-1998 and the post-merger period is 2001-2002 for three of the insurers. One of the 

insurers was not able to provide data for 1997 so the pre- and post-merger periods are 1998 and 2001, respectively. 

Standard errors are in parentheses. 

* The estimate is statistically significant at the 10% level. 

** The estimate is statistically significant at the 1% level.