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Assessing the degree of competitiveness in the market for outpatient hospital services
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Assessing the degree of competitiveness in the market for outpatient hospital services ⁎
Iryna Topolyana, , David Brasingtona, Xu Xub a b
Department of Economics, University of Cincinnati, 2925 Campus Green Dr., Cincinnati, OH 45221, USA Trustmark National Bank, 248 E. Capitol Street, Jackson, MS 39201, USA
A R T IC LE I N F O
ABS TRA CT
JEL classification: D40 I11 L10
We assess the level of competition in the market for outpatient hospital services in the State of California using popular new empirical industrial organization measures, such as the BresnahanLau method, Lerner index, and the Boone indicator. We match county-level data from Area Health Resources Files (AHRF) with hospital-level data from the California’s Office of Statewide Health Planning and Development (OSHPD). All three methods suggest that hospitals enjoy substantial market power. The relationship between the degree of spatial competition and the average markups is rather weak.
Keywords: Market structure Outpatient services Bresnahan-Lau model Lerner index Boone indicator
1. Introduction Health care is an important sector of the U.S. economy. The Bureau of Labor Statistics predicts health care will have the fastest employment growth and add the most jobs of any sector between 2014 and 2024.1 Rising prices in health care in general, and hospital prices in particular, are a major concern to policymakers. Assessing the nature of competition in this sector is critical to understanding its impact on social welfare. Several studies investigate the degree of competitiveness among hospitals as well as physicians. The early literature consists mostly of structure-conduct-performance studies, which use various measures of concentration (typically Herfindahl-Hirschman index) as proxies for the degree of competitiveness (see Dunn and Shapiro (2014) and Gaynor and Town (2011) for surveys). Subsequently, new empirical industrial organization (NEIO) methods were developed; the main idea behind those methods is inferring the degree of competition by observing firms' behavior, such as prices and quantities.Town and Vistness (2001), Gaynor and Vogt (2003), among others, estimate the demand equation using patient-level data, with some information from the demand equation as well as hospital-level data in the supply equation. Studies like Tay (2003) and Gunning and Sickles (2013) estimate a demandsupply system in the markets for hospital services and physician private practices, respectively, using the theoretical framework developed by Bresnahan (1989). Wong (1996) estimates a reduced-form equation derived from profit-maximizing conditions in a long-run equilibrium, in the spirit of Panzar and Rosse (1987). Some studies focus on the impact of the competitive environment on hospitals' or physicians' pricing behavior while others
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Corresponding Author. E-mail addresses: [email protected] (I. Topolyan), [email protected] (D. Brasington), [email protected] (X. Xu). 1 Source: Bureau of Labor Statistics, Economic News Release, Employment Projections: 2014–24 Summary, available at https://www.bls.gov/ news.release/ecopro.nr0.htm. https://doi.org/10.1016/j.jeconbus.2019.03.002 Received 12 April 2018; Received in revised form 23 March 2019; Accepted 27 March 2019 0148-6195/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Iryna Topolyan, David Brasington and XuXu , Journal of Economics and Business, https://doi.org/10.1016/j.jeconbus.2019.03.002
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investigate the impact on quality.2 While it is generally found that markups are lower in more competitive environments, the impact of competition on quality is ambiguous. On the one hand, competition puts pressure on firms (be it hospitals of private physicianfirms) to offer a higher-quality service, see for example Kessler and McClellan (2015); Bloom, Propper, Seiler, and Van Reenen, (2015), and Beckert, Christensen, and Collyer, (2012). On the other hand, competitive pressure may encourage firms to invest into characteristics easily observable by consumers which may not necessarily improve quality of care (such as marketing campaigns, deluxe buildings and furniture, etc.) at the expense of more subtle attributes that are key to quality (such as the number of personnel per bed, equipment, etc.). As a result, the mortality rates or the number of complications may be higher in more competitive markets (Propper, Burgess, & Green, 2004; Propper, Burgess, & Gossage, 2008). In the spirit of the NEIO measures, we assess competition by observing pricing behavior of hospitals. Specifically, we employ the Bresnahan-Lau method (Bresnahan, 1982 and Lau, 1982) and the Lerner index, both of which are based on the oligopoly model. We also apply a non-structural approach that was recently developed by Boone (2000, 2008). Related to Bresnahan-Lau, Panzar-Rosse method is another popular NEIO technique. Both methods are derived from a firm's profit-maximization problem, and examine, from somewhat different angles, firms' ability to leverage higher markups above their marginal costs. Both methods have been widely used in the empirical industrial organization literature to assess competition in various markets, notably the banking sector (see for example Bikker, Shaffer, & Spierdijk, 2012; Shaffer, 1993; Qin & Shaffer, 2014). However, the Panzar-Rosse method has been criticized recently in its ability to identify market structures (Bikker et al., 2012; Shaffer & Spierdijk, 2015). Therefore, between the two methods, we focus on the Bresnahan-Lau. In addition, we use the Lerner index and the Boone indicator as alternative measures of market power. The former assesses the degree of divergence between the price and the marginal cost, while the latter is capable of capturing the reallocation effect from more to less efficient firms, based on the premise that inefficient firms are punished more harshly by the market forces in markets with higher levels of competition. Our study is focused on the market for outpatient hospital services. Although the cost per incident tends to be significantly higher for inpatient compared to outpatient services, the industry exhibits a steady trend of shifting from inpatient to outpatient services. This is due to incentives provided by the value-based reimbursement system, and advances in medical technology that enable more procedures to be done on an outpatient basis. According to the American Hospital Association, the share of gross outpatient revenue in the total revenue exhibited a steady increase over the past 20 years and reached 48% in 2016.3 All three methods provide qualitatively similar results. We find that in the market for hospital outpatient services, the suppliers (hospitals) exercise a substantial degree of market power. This paper is organized as follows. Section 2 describes the data while Section 3 summarizes the theoretical methodology and describes our estimation strategy. The estimation results are presented in Section 4. The last section concludes. 2. Data Our methodology requires data on both the demand and supply sides of the market. We match county-level data from Area Health Resources Files (AHRF) with hospital-level data from the California’s Office of Statewide Health Planning and Development (OSHPD). The data is cross-sectional for the fiscal year 2013–14 for the State of California. AHRF, funded by the National Center for Health Workforce Analysis Workforce, the Bureau of Health Workforce and several other agencies within the Department of Health and Human Services, provides health care access and health care provider information at the county level, the state, and the national level. The extensive county-level database contains data from more than 50 sources and contains approximately 6000 variables for each county in the United States. The data set provides information on health facilities, health professions, measures of resource scarcity, health status, economic activity, health training programs, and socioeconomic and environmental characteristics, as well as geographical codes and descriptors. Hospital-level information comes from OSHPD, which collects and publicly discloses timely and accurate healthcare quality, outcome, financial, utilization, patient characteristics, and services information about California's healthcare. More than 100 data products are available to the public. The paper uses the 39th year OSHPD annual data (hospital fiscal years spanning 2013 and 2014), which is based on the Hospital Annual Disclosure Report submitted by about 450 hospitals in California. The data set contains detailed information for each hospital, including the type of ownership, spectrum of provided services; number of beds and corresponding utilization patient statistics by payer; balance sheet and income statement with a detailed breakdown of revenues and costs, and productive hours and average hourly rates of employees. We match information from the two data sets by county, as the OSHPD data contains the city and zip code where each hospital is located and AHRF provides geographical codes and descriptors. A zip code database4 is used in the matching process. For some hospitals, city and zip code lead to different county names. Additional research identifies the appropriate counties for these cases. The description of variables and summary statistics are presented in Appendix A in Table A1. The Bresnahan-Lau method requires information on cost and quantity. The additional variables contain unacceptable numbers of missing values. We impute missing values for the model using standard methods in SAS. The Markov chain Monte Carlo method imputes all missing values by drawing simulations from a Bayesian distribution for normally distributed data. The imputations use a single chain rather than a separate chain for each imputation to achieve more precise posterior mode estimates (Schafer, 1997, p. 2
The only study we are aware of that investigates the effect of competition on both pricing behavior and quality is Kessler and McClellan (2015). Source: American Hospital Association, https://www.aha.org/system/files/2018-05/2018-chartbook-table-4-2.pdf. 4 Data source: https://www.unitedstateszipcodes.org/zip-code-database/ 3
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138). The posterior mode estimates come from an EM algorithm (Dempster, Laird, & Rubin, 1977) with an uninformative prior (Jeffreys starting value) with 200 burn-in iterations and 100 draws. 3. Empirical methodology 3.1. Bresnahan-lau test of market structure The Bresnahan-Lau method is derived from a firm's profit-maximization problem; it examines firms' ability to leverage higher markups above their marginal costs. The method requires estimating a system of demand and supply equations and teases out the markup (the difference between the market price and the marginal cost) directly, as a measure of market power. Bresnahan (1982) and Lau (1982) show that the index of the degree of competitiveness, λ, is identified in the system of demand-supply equations if and only if the (inverse) demand equation contains exogenous variables that rotate the demand function rather than cause a vertical shift, i.e., the inverse demand function is not additively separable in exogenous variables that affect the demand but not the marginal cost. In our model the variables Income, Insured, and Old serve this function. We estimate the following system using full information maximum likelihood method:
Q = a0 + a1 P + a2 Income + a3 Insured + a4 Old + a5 P Insured + a6 P Old + a7 P Income + a8 Income Insured + a9 Income Old
P=
+ a10 Old Insured
(1)
C −λ Q + (b1 + b2lnQ + b3 lnW1 + b4 lnW2) a1 + a5 Insured + a6 Old + a7 Income Q
(2)
Here Q is the total number of outpatient visits, a measure of the quantity of outpatient services provided. Income is county median household income in dollars; Insured is the proportion of population under 65 years old with health insurance; Old is the proportion of population over 65 years old. W1 and W2 are average hourly wages of registered nurses and management/supervision in dollars, which represent factor input prices; these variables are found in OSHPD. The price of outpatient services, P is the ratio of gross outpatient revenue to the total number of outpatient visits, and C is total outpatient cost. The data set does not distinguish between inpatient and outpatient costs (unlike revenue). We use the following proxy for gross outpatient costs (3)
C = Total cost × (GROP) / ( GROP + GRIP)
where GROP is the gross outpatient revenue and GRIP is the gross inpatient revenue. The coefficient λ in Eq. (2) measures the degree of divergence between the average cost and price, and could range between 0 (in case of perfect competition) and 1 (in case of monopoly or perfectly colluding oligopoly). Labor is a major factor of production that enters a hospital's production function. Labor can be disaggregated into several categories of workers that hospitals employ. We capture the price of labor using the average hourly rate of registered nurses (W1) and the average hourly rate of management/supervision (W2 ). Due to missing values problem, we had to exclude the average hourly rate of salaried physicians (420 missing values out of 446 observations) and the average hourly rate of non-physician medical practitioners (363 missing values). The OSHPD data set contains a detailed breakdown of cost (by units of service within a hospital; operating versus non-operating expenses, etc.), but has no information about the number of units of capital. We doubt such information is available from any publicly available data set. The inability to account for the price of capital constitutes a limitation of our study. The main advantages of the Bresnahan-Lau method over Panzar-Rosse are that the former neither assumes firms are in long-run equilibrium, nor does it impose additional assumptions about the cost structure. On the other hand, this methodology requires somewhat sophisticated estimation techniques, and it requires data on both the demand and supply sides of the market. The method relies on the assumption that hospitals are profit-maximizing. While this assumption is appropriate for hospitals owned by investors (for-profit hospitals), there are doubts about whether profit-maximization applies to not-for-profit hospitals. Such hospitals have charitable obligations to the communities they serve and may develop outreach and educational programs, offer health screenings, etc. However, as Tay (2003) notes, not-for-profit hospitals care about their profits, because any surplus funds must be reinvested in the hospital, helping a hospital better achieve its mission and secure its mere existence. Thus, following Tay (2003), we assume profit-maximizing behavior in our study. Another limitation of this method is related to whether the patients select their hospitals based on price. Patients who are on a Medicare or a Medicaid plan pay a small fraction of a hospital bill or do not pay at all. However, patients who are insured via a third party pay a fraction which varies significantly depending on the insurance plan and procedures performed. At the same time, uninsured patients are solely responsible for their bill. Moreover, quality (Tay, 2003), waiting time and distance may play an important role in hospital choice. Therefore, although it appears that overall patients are sensitive to prices to some extent, the importance of price in hospital choice may be weakened by the aforementioned factors, which presents a limitation of this study. 3.2. Lerner index The Lerner index is a popular method of measuring the degree of market power in empirical industrial organization. The method has been known for a long time and has recently seen a resurgence in popularity. Like the Bresnahan-Lau method, the Lerner index is derived from equilibrium in oligopoly model. The Lerner index, however, identifies the degree of market power by measuring the 3
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divergence between the price and the marginal cost (price-cost margin). For each hospital, we calculate its Lerner index as follows:
L=
P − MC P
(4)
where, as before, P is the ratio of gross outpatient revenue to the total number of outpatient visits and measures the hospital price, and MC is the marginal cost. Since the marginal costs are unobservable, we use the average variable cost as a proxy for the marginal cost, which is a convention in empirical literature:
MC =
C Q
(5)
where C is the total cost of outpatient services described by Eq. (3), and Q is the total number of outpatient visits. Markets for hospital services are known to be highly localized. Baker (2001) and Mobley, Frech, and Anselin, (2009), among others, note that identification of the market boundaries is highly important. To assess the effects of spatial competition among hospitals, we examine whether markups tend to be higher in regions that are horizontally more competitive. The degree of spatial competition is assessed by counting the number of hospitals within a hospital referral region (HRR).5 Following De Silva, Jung, and Kosmopoulou, (2018), we use HRR as a definition of geographic market. The boundaries of HRRs are identified by the Dartmouth Atlas of Health Care by determining where patients were referred for major cardiovascular surgical procedures and for neurosurgery (see http://www.dartmouthatlas.org/data/region/ for details about HRR methodology). Following De Silva et al. (2018), we average the individual Lerner indices over hospitals within each HRR. We use gross outpatient revenues as weights and calculate the weighted average Lerner index as follows.
Lj =
∑ Li wi (6)
i∈Ij
where I j is the index set of hospitals in HRR j , Li is hospital i 's Lerner index, and wi is hospital i 's weight, calculated as
wi = GROPi/ ∑ GROPk (7)
k∈Ij
where GROPi is the gross outpatient revenue of hospital i. 3.3. Boone Indicator The Boone indicator (Boone, 2000; Hay & Liu, 1997, and Boone, 2008), gauges competition by measuring the extent to which efficiency translates into performance (in terms of market shares or profits). This approach is based on the premise that firms are punished more harshly for being inefficient in more competitive markets, as opposed to less competitive markets. Boone's original methodology (Boone, 2008) calls for analyzing performance related to primary business activities, since it is the efficiency of operation that we are trying to gauge. Our dataset is well-suited for this methodology, since we have information on total direct expenses, which is a good proxy for variable cost, from primary business activities. Following van Leuvensteijn, Bikker, van Rixtel, and Sørensen, (2011), we estimate the relation between hospitals' market share within its HRR (based on the number of outpatient visits) and efficiency in the log-linear form. Ideally we would use the marginal costs as a measure of efficiency. While the marginal costs are almost never observed directly, some authors, including van Leuvensteijn et al. (2011), are able to calculate it by estimating a firm's production function. As our dataset does not allow for such an estimation, we use average variable costs as a proxy for efficiency, which is common in the literature (see, for example, Schaek & Cihák, 2010). We estimate the following simple equation by ordinary least squares:
ln(sj ) = a1 + a2ln(Average variable costj ),
(8)
where
sj = Qj / ∑ Qi (9)
iεI j
Average variable costj = (Total direct expensesj /Qj ) ×
GROPj GROPj + GRIPj
(10)
As before, we use HRR to define the scope of a hospital's market, and compute hospital j's market share as the ratio of the number of outpatient visits of hospital j to the total number of outpatient visits within j's HRR (recall that I j denotes the index set of hospitals in HRR j ). We approximate average variable cost for outpatient visits by taking the ratio of total direct expenses to the total number of outpatient visits (Q) and correcting for the share of gross outpatient revenue in gross revenue. 5 Several other studies, including Propper et al. (2004) and Propper et al. (2008), use the number of hospitals within a geographic market ('catchment area') as a measure of spatial competition. Herfindahl-Hirshman index is an alternative measure popular in empirical research.
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Fig. 1. The average Lerner index across HRRs.
Coefficient a2 in Eq. (8) is the Boone indicator, which is expected to be negative in competitive markets, based on the premise that more efficient firms (i.e., those with lower cost) are able to lower its prices and achieve a higher market share. The more competitive the market is, the stronger the relation between efficiency and performance, which is reflected in a greater absolute value of the Boone indicator. 4. Results Table A2 summarizes the estimation results for the Bresnahan-Lau model. The index of the degree of competitiveness, λ, is statistically significant and has an intermediate value of about 0.47, which suggests that hospitals enjoy significant market power. Linear homogeneity in input prices is a regularity condition imposed on a translog cost function. We are not able to reject the null hypothesis b3 + b4 = 0 , therefore we can conclude that linear homogeneity in input prices is satisfied. The estimate of hourly wages of registered nurses (b3 ) is unexpectedly negative and statistically significant. Multicollinearity between factor prices does not seem to be driving the result. A VIF test of the explanatory variables shows no cause for concern, as no estimate exceeds 3.4. The direct correlation between ln(W1) and ln(W2) is 0.60, also not particularly concerning. Omitted variable bias may play a role, as we were unable to include the average hourly rate of salaried physicians and the average hourly rate of nonphysician medical practitioners, due to missing values problem. Fig. 1 shows the scatter plot of average Lerner indices and the number of hospitals within each HRR. The Pearson correlation coefficient between the two variables is about -0.07, which suggests a negative (albeit weak) relationship between the degree of spatial competition and the average markup, similar to De Silva et al. (2018).6 There are twenty-eight HRRs in our dataset; their sizes vary drastically, ranging from a single hospital to 116 hospitals. The weighted average Lerner index varies across HRRs, ranging from 0.37 to 0.83. Notably, the lowest value of 0.37 is achieved in an HRR comprised of only two hospitals, while the highest value of 0.83 is achieved with just five hospitals. This suggests that the nature of regional competition is not homogenous across HRRs, so we observe a rather fierce competition in an HRR with just two competitors, and at the same time, one might suspect that in another region, a small number of competitors may lead to collusive behavior. Overall, however, higher markups tend to prevail in HRRs with a relatively low number of hospitals, although the relation is rather weak. Along with this observation, Lerner indices in the 80th percentile (0.724 and above) are observed in HRRs comprised of up to twelve hospitals. The weighted average of Lerner index in our sample is about 0.54, which suggests that the degree of market power is quite high. Note that the numerical values of the Lerner index are not directly comparable to those reported by De Silva et al. (2018), since we use the gross outpatient revenue per visit as a proxy for average price while De Silva et al. (2018) estimate Lerner index as the reciprocal of the price elasticity of demand. The latter is obtained by estimating the demand equation in the healthcare industry, 6 Our sample contains an outlier - a very large HRR with 116 hospitals - which may drive the observed negative relationship. When this outlier is dropped, the correlation coefficient becomes positive, about 0.078. All in all, we conclude that the relationship between the degree of spatial competition and markups is rather weak.
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using detailed information on prices per medical procedure. However, our findings (based on both the Bresnahan-Lau method and Lerner index) are qualitatively similar to the results by De Silva et al. (2018), namely, we find that in the market for outpatient services, hospitals enjoy substantial market power. Estimation results for the Boone indicator are presented in Table A3. The log-log specification facilitates interpretation of the Boone indicator, which is the elasticity of the market share with respect to efficiency, as measured by the average variable costs. The estimate of the Boone indicator is −0.334, which is in line with the broader literature on other industries (Schaek & Cihák, 2010; van Leuvensteijn et al., 2011). It indicates that lower average variable costs indeed translate into a higher market share due to competition. However, this relationship is not that strong, as witnessed by the low absolute value of the Boone indicator. This suggests that the level of competition among hospitals is fairly low, which is in line with our findings according to the Bresnahan-Lau methodology and Lerner index. 5. Conclusion To examine the degree of competitiveness in the market for hospital outpatient services, we apply three popular new empirical industrial organization methodologies, the Bresnahan-Lau method, Lerner index, and the Boone indicator. All three methods tell a qualitatively similar story, suggesting that hospitals enjoy substantial market power. Based on Lerner index, we find that the relationship between the degree of spatial competition and the average markups is rather weak. One of the earlier studies on competition in health care, Wong (1996), applies the Panzar-Rosse test to assess the degree of competitiveness in the physician services industry. Wong (1996) finds the markets for primary care, general practice, and family practice to be monopolistically competitive. It also finds limited evidence that the market for surgeons is monopolistically competitive while the market for internal medicine physicians is characterized by consumer informational confusion (Satterthwaite, 1979). Wong (1996) also uses the method of Pauly and Satterthwaite (1981) to study the role of consumer information on pricing in the market of primary care physicians, and finds that availability of consumer information about physicians is indispensable in explaining market pricing. Our results are not directly comparable to those by Wong (1996) since we are not attempting to test among different competitive regimes. Rather, we assess the degree of market power as a one-dimensional variable and find that it quite high. Among recent studies, De Silva et al. (2018) reports a lack of competition in the U.S. healthcare industry on the basis of Lerner index. Although the values of Lerner index found in their study are not directly comparable to those in our study, due to the differences in the way Lerner index is estimated empirically, their results are qualitatively similar to ours, suggesting low levels of competition. It is important to point out the limitations of our study. Although our dataset provides a detailed breakdown on revenues and costs, due to the missing value problem we were unable to include some of the important variables measuring factor prices (such as the average hourly rates of certain personnel). Moreover, we were unable to account for the price of capital. Both issues might have an effect on the Bresnahan-Lau estimation. Our theoretical models rely on the assumption that the patients select their hospitals based on price. As discussed earlier, price may not be a prominent factor in the patients' hospital choice due to availability of insurance, as well as other important factors such as hospital quality, waiting time and distance. Thus, our results should be interpreted with caution and compared to those in other studies. Acknowledgement We would like to thank the Associate Editor and two anonymous referees for their detailed and insightful comments that significantly improved the paper. Appendix A
Table A1 Description of Variables and Summary Statistics. Variable
Mean (standard deviation)
Source
Total number of outpatient visits (Q) Price (P) = GROP / Q County median household income, dollars Proportion of population under 65 years old with health insurance Proportion of population over 65 years old Total cost, dollars Average hourly rate of registered nurses Average hourly rate of management/supervision Average variable cost, outpatient (Eq. (10))
125,574 (150,652) 2,749.1 (2626.7) 59,150 (12,986) 0.79 (0.04) 0.13 (0.03) 73,921,427 (101,191,934) 54.7 (13.1) 53.5 (10.0) 708.5 (656.7)
OSHPD OSHPD AHRF AHRF AHRF OSHPD OSHPD OSHPD OSHPD
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Table A2 Estimates of the Bresnahan-Lau Model in Eqs. (2) and (3). Model Parameter
Estimate
Quantity equation Price Income Insured Old Price× Insured Price× Old Price× Income Income× Insured Income× Old Insured× Old Price equation λ Average cost Average cost × ln(Q) Average cost × ln(W1) Average cost × ln(W2) Log-likelihood H0: λ = 0 H0: λ = 1 H0: b3 + b4 = 0
−193.359 (−1.77)* −31.23 (−0.75) −7841703 (−1.39) −20180000 (−0.66) 443.074 (2.4)** 563.473 (1.71)* −0.002 (−3.78)*** 55.191 (1.12) −88.123 (−0.94) 30957944 (0.75) 0.453 (2.26)** 0.317 (0.31) 0.274 (5.42)*** −2.455 (−10.57)*** 2.789 14.01)*** −8786 [0.024] [0.01] [0.15]
Number of observations = 398. t-ratios in parentheses, p-values in brackets. Intercept from Eq. (1) not reported. Table A3 Estimates of the Boone indicator. Model Parameter
Estimate
Boone indicator F-statistics R-squared
−0.337** (−2.40) 5.75 [0.017] 0.016
Number of observations = 354. t-ratios in parentheses, p-values in brackets. Intercept not reported. Dependent variable is the natural log of a hospital’s market share within its referral region.
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Assessing the degree of competitiveness in the market for outpatient hospital services ⁎
Iryna Topolyana, , David Brasingtona, Xu Xub a b
Department of Economics, University of Cincinnati, 2925 Campus Green Dr., Cincinnati, OH 45221, USA Trustmark National Bank, 248 E. Capitol Street, Jackson, MS 39201, USA
A R T IC LE I N F O
ABS TRA CT
JEL classification: D40 I11 L10
We assess the level of competition in the market for outpatient hospital services in the State of California using popular new empirical industrial organization measures, such as the BresnahanLau method, Lerner index, and the Boone indicator. We match county-level data from Area Health Resources Files (AHRF) with hospital-level data from the California’s Office of Statewide Health Planning and Development (OSHPD). All three methods suggest that hospitals enjoy substantial market power. The relationship between the degree of spatial competition and the average markups is rather weak.
Keywords: Market structure Outpatient services Bresnahan-Lau model Lerner index Boone indicator
1. Introduction Health care is an important sector of the U.S. economy. The Bureau of Labor Statistics predicts health care will have the fastest employment growth and add the most jobs of any sector between 2014 and 2024.1 Rising prices in health care in general, and hospital prices in particular, are a major concern to policymakers. Assessing the nature of competition in this sector is critical to understanding its impact on social welfare. Several studies investigate the degree of competitiveness among hospitals as well as physicians. The early literature consists mostly of structure-conduct-performance studies, which use various measures of concentration (typically Herfindahl-Hirschman index) as proxies for the degree of competitiveness (see Dunn and Shapiro (2014) and Gaynor and Town (2011) for surveys). Subsequently, new empirical industrial organization (NEIO) methods were developed; the main idea behind those methods is inferring the degree of competition by observing firms' behavior, such as prices and quantities.Town and Vistness (2001), Gaynor and Vogt (2003), among others, estimate the demand equation using patient-level data, with some information from the demand equation as well as hospital-level data in the supply equation. Studies like Tay (2003) and Gunning and Sickles (2013) estimate a demandsupply system in the markets for hospital services and physician private practices, respectively, using the theoretical framework developed by Bresnahan (1989). Wong (1996) estimates a reduced-form equation derived from profit-maximizing conditions in a long-run equilibrium, in the spirit of Panzar and Rosse (1987). Some studies focus on the impact of the competitive environment on hospitals' or physicians' pricing behavior while others
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Corresponding Author. E-mail addresses: [email protected] (I. Topolyan), [email protected] (D. Brasington), [email protected] (X. Xu). 1 Source: Bureau of Labor Statistics, Economic News Release, Employment Projections: 2014–24 Summary, available at https://www.bls.gov/ news.release/ecopro.nr0.htm. https://doi.org/10.1016/j.jeconbus.2019.03.002 Received 12 April 2018; Received in revised form 23 March 2019; Accepted 27 March 2019 0148-6195/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Iryna Topolyan, David Brasington and XuXu , Journal of Economics and Business, https://doi.org/10.1016/j.jeconbus.2019.03.002
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investigate the impact on quality.2 While it is generally found that markups are lower in more competitive environments, the impact of competition on quality is ambiguous. On the one hand, competition puts pressure on firms (be it hospitals of private physicianfirms) to offer a higher-quality service, see for example Kessler and McClellan (2015); Bloom, Propper, Seiler, and Van Reenen, (2015), and Beckert, Christensen, and Collyer, (2012). On the other hand, competitive pressure may encourage firms to invest into characteristics easily observable by consumers which may not necessarily improve quality of care (such as marketing campaigns, deluxe buildings and furniture, etc.) at the expense of more subtle attributes that are key to quality (such as the number of personnel per bed, equipment, etc.). As a result, the mortality rates or the number of complications may be higher in more competitive markets (Propper, Burgess, & Green, 2004; Propper, Burgess, & Gossage, 2008). In the spirit of the NEIO measures, we assess competition by observing pricing behavior of hospitals. Specifically, we employ the Bresnahan-Lau method (Bresnahan, 1982 and Lau, 1982) and the Lerner index, both of which are based on the oligopoly model. We also apply a non-structural approach that was recently developed by Boone (2000, 2008). Related to Bresnahan-Lau, Panzar-Rosse method is another popular NEIO technique. Both methods are derived from a firm's profit-maximization problem, and examine, from somewhat different angles, firms' ability to leverage higher markups above their marginal costs. Both methods have been widely used in the empirical industrial organization literature to assess competition in various markets, notably the banking sector (see for example Bikker, Shaffer, & Spierdijk, 2012; Shaffer, 1993; Qin & Shaffer, 2014). However, the Panzar-Rosse method has been criticized recently in its ability to identify market structures (Bikker et al., 2012; Shaffer & Spierdijk, 2015). Therefore, between the two methods, we focus on the Bresnahan-Lau. In addition, we use the Lerner index and the Boone indicator as alternative measures of market power. The former assesses the degree of divergence between the price and the marginal cost, while the latter is capable of capturing the reallocation effect from more to less efficient firms, based on the premise that inefficient firms are punished more harshly by the market forces in markets with higher levels of competition. Our study is focused on the market for outpatient hospital services. Although the cost per incident tends to be significantly higher for inpatient compared to outpatient services, the industry exhibits a steady trend of shifting from inpatient to outpatient services. This is due to incentives provided by the value-based reimbursement system, and advances in medical technology that enable more procedures to be done on an outpatient basis. According to the American Hospital Association, the share of gross outpatient revenue in the total revenue exhibited a steady increase over the past 20 years and reached 48% in 2016.3 All three methods provide qualitatively similar results. We find that in the market for hospital outpatient services, the suppliers (hospitals) exercise a substantial degree of market power. This paper is organized as follows. Section 2 describes the data while Section 3 summarizes the theoretical methodology and describes our estimation strategy. The estimation results are presented in Section 4. The last section concludes. 2. Data Our methodology requires data on both the demand and supply sides of the market. We match county-level data from Area Health Resources Files (AHRF) with hospital-level data from the California’s Office of Statewide Health Planning and Development (OSHPD). The data is cross-sectional for the fiscal year 2013–14 for the State of California. AHRF, funded by the National Center for Health Workforce Analysis Workforce, the Bureau of Health Workforce and several other agencies within the Department of Health and Human Services, provides health care access and health care provider information at the county level, the state, and the national level. The extensive county-level database contains data from more than 50 sources and contains approximately 6000 variables for each county in the United States. The data set provides information on health facilities, health professions, measures of resource scarcity, health status, economic activity, health training programs, and socioeconomic and environmental characteristics, as well as geographical codes and descriptors. Hospital-level information comes from OSHPD, which collects and publicly discloses timely and accurate healthcare quality, outcome, financial, utilization, patient characteristics, and services information about California's healthcare. More than 100 data products are available to the public. The paper uses the 39th year OSHPD annual data (hospital fiscal years spanning 2013 and 2014), which is based on the Hospital Annual Disclosure Report submitted by about 450 hospitals in California. The data set contains detailed information for each hospital, including the type of ownership, spectrum of provided services; number of beds and corresponding utilization patient statistics by payer; balance sheet and income statement with a detailed breakdown of revenues and costs, and productive hours and average hourly rates of employees. We match information from the two data sets by county, as the OSHPD data contains the city and zip code where each hospital is located and AHRF provides geographical codes and descriptors. A zip code database4 is used in the matching process. For some hospitals, city and zip code lead to different county names. Additional research identifies the appropriate counties for these cases. The description of variables and summary statistics are presented in Appendix A in Table A1. The Bresnahan-Lau method requires information on cost and quantity. The additional variables contain unacceptable numbers of missing values. We impute missing values for the model using standard methods in SAS. The Markov chain Monte Carlo method imputes all missing values by drawing simulations from a Bayesian distribution for normally distributed data. The imputations use a single chain rather than a separate chain for each imputation to achieve more precise posterior mode estimates (Schafer, 1997, p. 2
The only study we are aware of that investigates the effect of competition on both pricing behavior and quality is Kessler and McClellan (2015). Source: American Hospital Association, https://www.aha.org/system/files/2018-05/2018-chartbook-table-4-2.pdf. 4 Data source: https://www.unitedstateszipcodes.org/zip-code-database/ 3
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138). The posterior mode estimates come from an EM algorithm (Dempster, Laird, & Rubin, 1977) with an uninformative prior (Jeffreys starting value) with 200 burn-in iterations and 100 draws. 3. Empirical methodology 3.1. Bresnahan-lau test of market structure The Bresnahan-Lau method is derived from a firm's profit-maximization problem; it examines firms' ability to leverage higher markups above their marginal costs. The method requires estimating a system of demand and supply equations and teases out the markup (the difference between the market price and the marginal cost) directly, as a measure of market power. Bresnahan (1982) and Lau (1982) show that the index of the degree of competitiveness, λ, is identified in the system of demand-supply equations if and only if the (inverse) demand equation contains exogenous variables that rotate the demand function rather than cause a vertical shift, i.e., the inverse demand function is not additively separable in exogenous variables that affect the demand but not the marginal cost. In our model the variables Income, Insured, and Old serve this function. We estimate the following system using full information maximum likelihood method:
Q = a0 + a1 P + a2 Income + a3 Insured + a4 Old + a5 P Insured + a6 P Old + a7 P Income + a8 Income Insured + a9 Income Old
P=
+ a10 Old Insured
(1)
C −λ Q + (b1 + b2lnQ + b3 lnW1 + b4 lnW2) a1 + a5 Insured + a6 Old + a7 Income Q
(2)
Here Q is the total number of outpatient visits, a measure of the quantity of outpatient services provided. Income is county median household income in dollars; Insured is the proportion of population under 65 years old with health insurance; Old is the proportion of population over 65 years old. W1 and W2 are average hourly wages of registered nurses and management/supervision in dollars, which represent factor input prices; these variables are found in OSHPD. The price of outpatient services, P is the ratio of gross outpatient revenue to the total number of outpatient visits, and C is total outpatient cost. The data set does not distinguish between inpatient and outpatient costs (unlike revenue). We use the following proxy for gross outpatient costs (3)
C = Total cost × (GROP) / ( GROP + GRIP)
where GROP is the gross outpatient revenue and GRIP is the gross inpatient revenue. The coefficient λ in Eq. (2) measures the degree of divergence between the average cost and price, and could range between 0 (in case of perfect competition) and 1 (in case of monopoly or perfectly colluding oligopoly). Labor is a major factor of production that enters a hospital's production function. Labor can be disaggregated into several categories of workers that hospitals employ. We capture the price of labor using the average hourly rate of registered nurses (W1) and the average hourly rate of management/supervision (W2 ). Due to missing values problem, we had to exclude the average hourly rate of salaried physicians (420 missing values out of 446 observations) and the average hourly rate of non-physician medical practitioners (363 missing values). The OSHPD data set contains a detailed breakdown of cost (by units of service within a hospital; operating versus non-operating expenses, etc.), but has no information about the number of units of capital. We doubt such information is available from any publicly available data set. The inability to account for the price of capital constitutes a limitation of our study. The main advantages of the Bresnahan-Lau method over Panzar-Rosse are that the former neither assumes firms are in long-run equilibrium, nor does it impose additional assumptions about the cost structure. On the other hand, this methodology requires somewhat sophisticated estimation techniques, and it requires data on both the demand and supply sides of the market. The method relies on the assumption that hospitals are profit-maximizing. While this assumption is appropriate for hospitals owned by investors (for-profit hospitals), there are doubts about whether profit-maximization applies to not-for-profit hospitals. Such hospitals have charitable obligations to the communities they serve and may develop outreach and educational programs, offer health screenings, etc. However, as Tay (2003) notes, not-for-profit hospitals care about their profits, because any surplus funds must be reinvested in the hospital, helping a hospital better achieve its mission and secure its mere existence. Thus, following Tay (2003), we assume profit-maximizing behavior in our study. Another limitation of this method is related to whether the patients select their hospitals based on price. Patients who are on a Medicare or a Medicaid plan pay a small fraction of a hospital bill or do not pay at all. However, patients who are insured via a third party pay a fraction which varies significantly depending on the insurance plan and procedures performed. At the same time, uninsured patients are solely responsible for their bill. Moreover, quality (Tay, 2003), waiting time and distance may play an important role in hospital choice. Therefore, although it appears that overall patients are sensitive to prices to some extent, the importance of price in hospital choice may be weakened by the aforementioned factors, which presents a limitation of this study. 3.2. Lerner index The Lerner index is a popular method of measuring the degree of market power in empirical industrial organization. The method has been known for a long time and has recently seen a resurgence in popularity. Like the Bresnahan-Lau method, the Lerner index is derived from equilibrium in oligopoly model. The Lerner index, however, identifies the degree of market power by measuring the 3
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divergence between the price and the marginal cost (price-cost margin). For each hospital, we calculate its Lerner index as follows:
L=
P − MC P
(4)
where, as before, P is the ratio of gross outpatient revenue to the total number of outpatient visits and measures the hospital price, and MC is the marginal cost. Since the marginal costs are unobservable, we use the average variable cost as a proxy for the marginal cost, which is a convention in empirical literature:
MC =
C Q
(5)
where C is the total cost of outpatient services described by Eq. (3), and Q is the total number of outpatient visits. Markets for hospital services are known to be highly localized. Baker (2001) and Mobley, Frech, and Anselin, (2009), among others, note that identification of the market boundaries is highly important. To assess the effects of spatial competition among hospitals, we examine whether markups tend to be higher in regions that are horizontally more competitive. The degree of spatial competition is assessed by counting the number of hospitals within a hospital referral region (HRR).5 Following De Silva, Jung, and Kosmopoulou, (2018), we use HRR as a definition of geographic market. The boundaries of HRRs are identified by the Dartmouth Atlas of Health Care by determining where patients were referred for major cardiovascular surgical procedures and for neurosurgery (see http://www.dartmouthatlas.org/data/region/ for details about HRR methodology). Following De Silva et al. (2018), we average the individual Lerner indices over hospitals within each HRR. We use gross outpatient revenues as weights and calculate the weighted average Lerner index as follows.
Lj =
∑ Li wi (6)
i∈Ij
where I j is the index set of hospitals in HRR j , Li is hospital i 's Lerner index, and wi is hospital i 's weight, calculated as
wi = GROPi/ ∑ GROPk (7)
k∈Ij
where GROPi is the gross outpatient revenue of hospital i. 3.3. Boone Indicator The Boone indicator (Boone, 2000; Hay & Liu, 1997, and Boone, 2008), gauges competition by measuring the extent to which efficiency translates into performance (in terms of market shares or profits). This approach is based on the premise that firms are punished more harshly for being inefficient in more competitive markets, as opposed to less competitive markets. Boone's original methodology (Boone, 2008) calls for analyzing performance related to primary business activities, since it is the efficiency of operation that we are trying to gauge. Our dataset is well-suited for this methodology, since we have information on total direct expenses, which is a good proxy for variable cost, from primary business activities. Following van Leuvensteijn, Bikker, van Rixtel, and Sørensen, (2011), we estimate the relation between hospitals' market share within its HRR (based on the number of outpatient visits) and efficiency in the log-linear form. Ideally we would use the marginal costs as a measure of efficiency. While the marginal costs are almost never observed directly, some authors, including van Leuvensteijn et al. (2011), are able to calculate it by estimating a firm's production function. As our dataset does not allow for such an estimation, we use average variable costs as a proxy for efficiency, which is common in the literature (see, for example, Schaek & Cihák, 2010). We estimate the following simple equation by ordinary least squares:
ln(sj ) = a1 + a2ln(Average variable costj ),
(8)
where
sj = Qj / ∑ Qi (9)
iεI j
Average variable costj = (Total direct expensesj /Qj ) ×
GROPj GROPj + GRIPj
(10)
As before, we use HRR to define the scope of a hospital's market, and compute hospital j's market share as the ratio of the number of outpatient visits of hospital j to the total number of outpatient visits within j's HRR (recall that I j denotes the index set of hospitals in HRR j ). We approximate average variable cost for outpatient visits by taking the ratio of total direct expenses to the total number of outpatient visits (Q) and correcting for the share of gross outpatient revenue in gross revenue. 5 Several other studies, including Propper et al. (2004) and Propper et al. (2008), use the number of hospitals within a geographic market ('catchment area') as a measure of spatial competition. Herfindahl-Hirshman index is an alternative measure popular in empirical research.
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Fig. 1. The average Lerner index across HRRs.
Coefficient a2 in Eq. (8) is the Boone indicator, which is expected to be negative in competitive markets, based on the premise that more efficient firms (i.e., those with lower cost) are able to lower its prices and achieve a higher market share. The more competitive the market is, the stronger the relation between efficiency and performance, which is reflected in a greater absolute value of the Boone indicator. 4. Results Table A2 summarizes the estimation results for the Bresnahan-Lau model. The index of the degree of competitiveness, λ, is statistically significant and has an intermediate value of about 0.47, which suggests that hospitals enjoy significant market power. Linear homogeneity in input prices is a regularity condition imposed on a translog cost function. We are not able to reject the null hypothesis b3 + b4 = 0 , therefore we can conclude that linear homogeneity in input prices is satisfied. The estimate of hourly wages of registered nurses (b3 ) is unexpectedly negative and statistically significant. Multicollinearity between factor prices does not seem to be driving the result. A VIF test of the explanatory variables shows no cause for concern, as no estimate exceeds 3.4. The direct correlation between ln(W1) and ln(W2) is 0.60, also not particularly concerning. Omitted variable bias may play a role, as we were unable to include the average hourly rate of salaried physicians and the average hourly rate of nonphysician medical practitioners, due to missing values problem. Fig. 1 shows the scatter plot of average Lerner indices and the number of hospitals within each HRR. The Pearson correlation coefficient between the two variables is about -0.07, which suggests a negative (albeit weak) relationship between the degree of spatial competition and the average markup, similar to De Silva et al. (2018).6 There are twenty-eight HRRs in our dataset; their sizes vary drastically, ranging from a single hospital to 116 hospitals. The weighted average Lerner index varies across HRRs, ranging from 0.37 to 0.83. Notably, the lowest value of 0.37 is achieved in an HRR comprised of only two hospitals, while the highest value of 0.83 is achieved with just five hospitals. This suggests that the nature of regional competition is not homogenous across HRRs, so we observe a rather fierce competition in an HRR with just two competitors, and at the same time, one might suspect that in another region, a small number of competitors may lead to collusive behavior. Overall, however, higher markups tend to prevail in HRRs with a relatively low number of hospitals, although the relation is rather weak. Along with this observation, Lerner indices in the 80th percentile (0.724 and above) are observed in HRRs comprised of up to twelve hospitals. The weighted average of Lerner index in our sample is about 0.54, which suggests that the degree of market power is quite high. Note that the numerical values of the Lerner index are not directly comparable to those reported by De Silva et al. (2018), since we use the gross outpatient revenue per visit as a proxy for average price while De Silva et al. (2018) estimate Lerner index as the reciprocal of the price elasticity of demand. The latter is obtained by estimating the demand equation in the healthcare industry, 6 Our sample contains an outlier - a very large HRR with 116 hospitals - which may drive the observed negative relationship. When this outlier is dropped, the correlation coefficient becomes positive, about 0.078. All in all, we conclude that the relationship between the degree of spatial competition and markups is rather weak.
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using detailed information on prices per medical procedure. However, our findings (based on both the Bresnahan-Lau method and Lerner index) are qualitatively similar to the results by De Silva et al. (2018), namely, we find that in the market for outpatient services, hospitals enjoy substantial market power. Estimation results for the Boone indicator are presented in Table A3. The log-log specification facilitates interpretation of the Boone indicator, which is the elasticity of the market share with respect to efficiency, as measured by the average variable costs. The estimate of the Boone indicator is −0.334, which is in line with the broader literature on other industries (Schaek & Cihák, 2010; van Leuvensteijn et al., 2011). It indicates that lower average variable costs indeed translate into a higher market share due to competition. However, this relationship is not that strong, as witnessed by the low absolute value of the Boone indicator. This suggests that the level of competition among hospitals is fairly low, which is in line with our findings according to the Bresnahan-Lau methodology and Lerner index. 5. Conclusion To examine the degree of competitiveness in the market for hospital outpatient services, we apply three popular new empirical industrial organization methodologies, the Bresnahan-Lau method, Lerner index, and the Boone indicator. All three methods tell a qualitatively similar story, suggesting that hospitals enjoy substantial market power. Based on Lerner index, we find that the relationship between the degree of spatial competition and the average markups is rather weak. One of the earlier studies on competition in health care, Wong (1996), applies the Panzar-Rosse test to assess the degree of competitiveness in the physician services industry. Wong (1996) finds the markets for primary care, general practice, and family practice to be monopolistically competitive. It also finds limited evidence that the market for surgeons is monopolistically competitive while the market for internal medicine physicians is characterized by consumer informational confusion (Satterthwaite, 1979). Wong (1996) also uses the method of Pauly and Satterthwaite (1981) to study the role of consumer information on pricing in the market of primary care physicians, and finds that availability of consumer information about physicians is indispensable in explaining market pricing. Our results are not directly comparable to those by Wong (1996) since we are not attempting to test among different competitive regimes. Rather, we assess the degree of market power as a one-dimensional variable and find that it quite high. Among recent studies, De Silva et al. (2018) reports a lack of competition in the U.S. healthcare industry on the basis of Lerner index. Although the values of Lerner index found in their study are not directly comparable to those in our study, due to the differences in the way Lerner index is estimated empirically, their results are qualitatively similar to ours, suggesting low levels of competition. It is important to point out the limitations of our study. Although our dataset provides a detailed breakdown on revenues and costs, due to the missing value problem we were unable to include some of the important variables measuring factor prices (such as the average hourly rates of certain personnel). Moreover, we were unable to account for the price of capital. Both issues might have an effect on the Bresnahan-Lau estimation. Our theoretical models rely on the assumption that the patients select their hospitals based on price. As discussed earlier, price may not be a prominent factor in the patients' hospital choice due to availability of insurance, as well as other important factors such as hospital quality, waiting time and distance. Thus, our results should be interpreted with caution and compared to those in other studies. Acknowledgement We would like to thank the Associate Editor and two anonymous referees for their detailed and insightful comments that significantly improved the paper. Appendix A
Table A1 Description of Variables and Summary Statistics. Variable
Mean (standard deviation)
Source
Total number of outpatient visits (Q) Price (P) = GROP / Q County median household income, dollars Proportion of population under 65 years old with health insurance Proportion of population over 65 years old Total cost, dollars Average hourly rate of registered nurses Average hourly rate of management/supervision Average variable cost, outpatient (Eq. (10))
125,574 (150,652) 2,749.1 (2626.7) 59,150 (12,986) 0.79 (0.04) 0.13 (0.03) 73,921,427 (101,191,934) 54.7 (13.1) 53.5 (10.0) 708.5 (656.7)
OSHPD OSHPD AHRF AHRF AHRF OSHPD OSHPD OSHPD OSHPD
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Table A2 Estimates of the Bresnahan-Lau Model in Eqs. (2) and (3). Model Parameter
Estimate
Quantity equation Price Income Insured Old Price× Insured Price× Old Price× Income Income× Insured Income× Old Insured× Old Price equation λ Average cost Average cost × ln(Q) Average cost × ln(W1) Average cost × ln(W2) Log-likelihood H0: λ = 0 H0: λ = 1 H0: b3 + b4 = 0
−193.359 (−1.77)* −31.23 (−0.75) −7841703 (−1.39) −20180000 (−0.66) 443.074 (2.4)** 563.473 (1.71)* −0.002 (−3.78)*** 55.191 (1.12) −88.123 (−0.94) 30957944 (0.75) 0.453 (2.26)** 0.317 (0.31) 0.274 (5.42)*** −2.455 (−10.57)*** 2.789 14.01)*** −8786 [0.024] [0.01] [0.15]
Number of observations = 398. t-ratios in parentheses, p-values in brackets. Intercept from Eq. (1) not reported. Table A3 Estimates of the Boone indicator. Model Parameter
Estimate
Boone indicator F-statistics R-squared
−0.337** (−2.40) 5.75 [0.017] 0.016
Number of observations = 354. t-ratios in parentheses, p-values in brackets. Intercept not reported. Dependent variable is the natural log of a hospital’s market share within its referral region.
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