Hospital Advertising in California,

Transcription

1 Robert J. Town Imran Currim Hospital Advertising in California, This paper examines the advertising behavior of California hospitals from 1991 to Using highly detailed hospital-level information, we found that hospital advertising in California increased dramatically: annual spending on advertising grew (inflation adjusted) more than sixfold over the period. In addition, advertising expenditures varied significantly across hospitals. We found that hospital advertising increased with market concentration; with the number of nearby potential patients; with the percentage of nearby patients insured through Medicare, health maintenance organizations (HMOs), and indemnity insurance; and with chain affiliation. For-profit hospitals were not found to advertise more than their not-for-profit counterparts. There is great interest among researchers and policymakers in the ways that hospitals compete and the consequences of such competition for hospital and patient welfare. Until quite recently, hospitals competed against each other using tactics that distinguished the hospital sector from most other industries. Prior to the rise of managed care, hospitals competitive efforts were directed primarily toward convincing physicians to admit their patients to a particular hospital. Potential patients were not viewed as actively influencing their own choice of hospital admission except through their choice of physician. The rise of managed care insurance has changed the health care environment in such a way that competition among hospitals now looks more like competition observed in other industries. For example, price is now an important element of hospital competition. Another feature of the new market-oriented environment is the attitude that patients are now customers, and like other industries, hospitals directly compete for these patients using advertising. Hospital competition recently has received a significant amount of attention from health care economists (see reviews by Dranove and Satterthwaite 2000, and Gaynor and Vogt 2000). This literature has focused almost exclusively on the effects of competition on hospital prices. Economic theory suggests that hospital advertising has the potential to generate significant consequences on market performance. However, little is known about the basic advertising behavior of hospitals (or other health care providers for that matter). In this paper, we focus on the advertising behavior of hospitals. We use a rather comprehensive data set on hospital advertising expenditures to document the time-series and cross-sectional patterns in advertising across hospitals in California from 1991 to Over this period, Robert J. Town, Ph.D., is an assistant professor in the Division of Health Services Research and Policy, School of Public Health, University of Minnesota. Imran Currim, Ph.D., is a professor in the Graduate School of Management, University of California-Irvine. Address correspondence to Prof. Town at Division of Health Services Research and Policy, School of Public Health, University of Minnesota, Mayo Mail Code 729, 420 Delaware St., S.E., Minneapolis, MN Inquiry 39: (Fall 2002) Excellus Health Plan, Inc /02/ $1.25

2 Hospital Advertising the role of market forces in the California hospital industry increased dramatically. Thus, our data can give insight into how hospitals marketing efforts responded to changes in the competitive environment. We find that hospital advertising in California increased dramatically during the period. The annual aggregate hospital spending in California on advertising across all hospitals in 1991 was $3,300,000; it grew (inflation adjusted) more than sixfold to $20,500,000 by During this period, more hospitals advertised, and, conditional upon advertising, hospitals advertised more intensely in 1997 than in In 1991, approximately 16% of the hospitals in our sample advertised. By 1997, the percentage of hospitals that advertised increased to 45%. While the trend was for hospitals to dramatically increase their advertising expenditures, there was significant heterogeneity across hospitals both in the level of advertising and the change in advertising expenditures during the period. This heterogeneity, in part, is linked to the location of the hospital. For example, in 1991, hospitals in San Diego advertised more heavily than their counterparts in Los Angeles. However, by 1997, Los Angeles hospitals were advertising more aggressively than those in San Diego. Our data also contain information on the media outlets hospitals used to advertise; the data indicate that like other industries the primary vehicles for the hospitals marketing message were television and newspapers. This heterogeneity in advertising expenditures across hospitals can reveal insights into the underlying reasons for hospitals to advertise. In order to better characterize the correlates of hospital advertising, we perform regression analysis that accounts for the censored nature of hospital advertising. That is, many hospitals do not advertise and our statistical analysis must account for that fact. The parameters from the regression analysis indicate that hospital advertising is positively correlated with market concentration, the number of nearby potential patients, the percentage of those patients insured through Medicare, health maintenance organizations (HMOs), and indemnity insurance, and chain affiliation. We find little difference in the advertising behavior between not-for-profit and for-profit hospitals. There is a literature devoted to the analysis of the behavior and consequences of advertising by health care providers, with most of the research focusing on the effect of advertising of optometrists and the provision of eyeglasses. 1 Benham (1972), Benham and Benham (1975), Begun (1979), Feldman and Begun (1978, 1980, 1985), Bond et al. (1980), Kwoka (1984), Haas- Wilson (1986) and Haas-Wilson and Savoca (1990) all studied the effects of advertising (or advertising bans) in the market for optometric services. In general, these studies found that advertising bans raise price and lower quality, and that there is an inverse relationship between price levels and advertising intensity. We are aware of only three studies that directly examined the effects of advertising on the market for physician services: Hibbard and Weeks (1989) and Rizzo and Zeckhauser (1990, 1992). Hibbard and Weeks conducted an experiment on the effect of physician fee information on the patient s choice of physician. They found that information about the fees did not impact the average cost of a physician visit. Rizzo and Zeckhauser (1990) examined the effects of advertising on physician income during the mid- 1980s; they found that their coefficient estimates were consistent with the notion that advertising tends to inhibit entry. In their 1992 study, Rizzo and Zeckhauser found that physicians advertise to obtain more desirable patients. We are unaware of work that has looked at advertising behavior at the hospital level to get insights into explanations of both cross-sectional and timeseries variation in advertising expenditures. The Economics of Advertising In the industrial organization branch of economics, there is a long tradition of analyzing the causes and consequences of advertising (see Scherer and Ross 1990, chapter 16 for a survey). Many economists have long held a suspicious view of noninformative advertising as creating wants, and changing and distorting tastes (Galbraith 1969; Chamberlin 1962). In addition to these effects, advertising also may result in a decline in social welfare by bestowing market power to firms by increasing perceived product differentiation or reducing the likelihood of entry (Bain 1956). However, not all theories of advertising predict that marketing efforts worsen market outcomes. Economists also have identified mechanisms by which ad- 299

3 Inquiry/Volume 39, Fall 2002 vertising can provide useful information to consumers and that such additional information promotes a more desirable market outcome. Advertising that provides truthful information about a product s existence, location, quality, or prices can reduce search costs or the costs of entry with both effects lowering prices (Stigler 1961; Nelson 1970; Benham 1972). Studies by Feldman and Begun (1980, 1985), Kwoka (1984), and Haas-Wilson (1986) of the effects of advertising by optometrists provide evidence supporting this view of the role of advertising. One of the special features of health care markets is the acute nature of the information asymmetry that exists between providers and patients (Arrow 1963). Because patients rely on providers for both the diagnoses and the cure for any potential ailment, health care consumers face substantial challenges in evaluating the quality and the necessity of their care. The severity of the information asymmetry in health care markets suggests that advertising has the potential to provide useful information to health care consumers. Conversely, the severity of the information asymmetry also implies that advertising either by providing misinformation or bestowing additional market power to the advertiser can have a large, deleterious effect on social welfare. Several institutional features of hospitals further complicate analysis of the welfare effects of advertising. First, multiple agents play a role in the selection of a hospital for a given individual. The choice of health plan (which is often selected by the employer) and the preferences of the individual s physician interact with the patient s own preferences to determine the admitting hospital. Second, individuals are exposed to information on hospital quality from numerous sources, including: advertising, physicians, health plans, media, family, friends, and regulatory and standard-setting agencies. Recently, government agencies and other neutral third parties increasingly have sought to provide health care consumers more objective information on provider quality (e.g., New York State Department of Health 2001; U.S. News and World Report 2001). How health care consumers incorporate all this information into their decision-making process is unclear (Bernstein and Gauthier 1999 and the references cited therein). In assessing the welfare impact of hospital advertising, it is necessary to understand whether hospital advertising is a complement to this information or whether it crowds out other information sources. Distinguishing whether advertising is welfare enhancing or reducing is often difficult. Rarely are the effects of advertising identified with the available data without a natural experiment (e.g., a change in regulatory environment). Given the complexity of determining the consequences of hospital advertising, we focus on characterizing the patterns of advertising in the data that is a necessary first step. We leave the study of the welfare consequences of hospital advertising for future research. Optimal Advertising and the Dorfman-Steiner Theorem A useful organizing principle through which we can view the advertising decision of the hospital is the Dorfman-Steiner (1954) theorem. Dorfman and Steiner derive the optimal, profit-maximizing advertising expenditure for a monopoly without concern for the strategic or dynamic consequences of advertising. In this simple framework, the logarithm of the advertising to sales ratio, a, is given by p mc ln(a) ln(e a) ln (1) p where e a is the elasticity of advertising (i.e., the percentage change in sales divided by the percentage change in advertising expenditure) and (p mc)/p is the markup of price, p, above marginal cost, mc. The Dorfman-Steiner theorem says that the more effective advertising is in increasing sales as measured by e a and/or the higher the profit margins the more intensively the firm advertises. In a more general framework, the relationship between advertising and price-cost margins may be more complicated than strictly implied by the Dorfman-Steiner condition, although the basic insight that there is an important relationship between the price-cost margins and advertising intensity still holds. For example, in a more realistic framework the causal link between advertising and price-cost margins is likely bi-directional. The Dorfman-Steiner insight that firms earning high margins, all else equal, have a greater incentive to advertise is preserved. However, advertising may directly differentiate 300

4 Hospital Advertising the product, and this differentiation may allow the hospital to charge higher prices to health plans as the health plan s enrollees place greater value on having the hospital in the network (Grossman and Shapiro 1984; Town and Vistnes 2001). That is, advertising may cause the pricecost margins to increase. There also may be a second strategic effect in which advertising influences the price-cost margins. Advertising may affect the pricing, product quality, entry or advertising decision of other hospitals. By altering the strategies of rivals (actual or potential), hospitals may enhance their market power. Thus, advertising not only could waste resources as it artificially enhances the image of a hospital, it also could reduce the ability of the market to allocate resources efficiently. One potentially important strategic reason for a hospital to advertise is to indirectly signal quality. The logic for signaling is that just by advertising, potential patients may infer something about the quality of care they are likely to receive at that hospital. Nelson (1974) first proposed this idea, which has been formally modeled by Milgrom and Roberts (1986). Given that the quality of hospital care is often difficult to judge prior to admission (or even after discharge) and that differences in quality of care may translate into large differences in medical outcomes, this possibility appears germane. In Milgrom and Robert s framework, a firm advertises to signal it anticipates earning significant profits from its high-quality product. The nature of the advertising does not matter. In fact, the firm does not have to spend the money on advertising; it just needs to spend the money on nonproductive activities to signal it anticipates future wealth. In actuality, hospitals must be careful about how they advertise and how those advertisements are perceived. While advertising can directly impart information about the quality of a hospital and indirectly signal quality, hospitals resorting to advertising to boost sagging profits may tell the market that they are in financial difficulties. If potential patients believe the hospital is financially strapped, they likely will become concerned that quality is on the decline. Furthermore, poorly conceived advertising campaigns may provide indirect information about the hospital that can negatively influence patient choice. In particular, a bad advertising campaign may tell the public that the hospital is poorly managed, and a poor management team may translate into poor care. Our empirical strategy is to use equation 1 as a framework for formulating our estimation strategy and to guide the choice of explanatory variables to account for variation in advertising expenditures across hospitals and time. Importantly, equation 1 will hold only for profit-maximizing hospitals. Insofar as some hospitals may not maximize profits, variables characterizing the for-profit status of the hospital should be included in the set of regressors. The Data The data used in this study come from three different databases. The advertising data come from VoiceTrak, a corporation that constructs and maintains a detailed database on the quarterly advertising expenditures of hospitals in most major metropolitan areas in the United State. VoiceTrak conducts rather extensive quarterly media spending surveys of approximately 10,000 advertising outlets. 2 The data are collected, analyzed, and then sold to hospitals. VoiceTrak is successful in its marketing efforts, indicating that its data are, at a minimum, accurate enough for hospitals to use to make business decisions. Respondents are asked to report spending activity by month and by advertiser. Radio stations, television stations, cable systems, and outdoor companies typically report gross dollars. Newspapers generally report column inches and insert activity, which then are converted to dollars by using estimated advertising rates. The response rate to the VoiceTrak survey is quite high 83% nationally and over 85% among larger media outlets. For California, the firm tracks hospitals in six broad regions: Los Angeles/Orange/Riverside, Sacramento, San Diego, San Francisco, San Bernardino, and Santa Barbara counties. VoiceTrak follows the advertising in the major media outlets in those areas. Insofar as small and rural hospitals in these areas limit their advertising to local and smaller outlets not tracked by VoiceTrak, their advertising behavior is underrepresented. To create a data set that contains hospital characteristics as well as hospital advertising expenditures, we linked the VoiceTrak data to data from the California Office of Statewide Health Planning and Development (OSHPD). The 301

5 Inquiry/Volume 39, Fall 2002 Figure 1. Annual aggregate advertising expenditure of sample of California short-term, acute care hospitals, (Source: VoiceTrak data and authors calculations) OSHPD Annual Hospital Financial database provides information on licensed bed size and for-profit and teaching status. VoiceTrak does not list hospitals if they do not advertise in any given quarter. The matching of data was done by comparing the metropolitan area and name of the hospital in the VoiceTrak data to the county and name in the OSHPD data. If a hospital was in the OSHPD data but not listed in the VoiceTrak data in any given quarter, we assigned that hospital an advertising expenditure of zero for that quarter. Also, to limit our attention to short-term, acute care hospitals, we removed drug rehabilitation centers and psychiatric hospitals from the sample. A few hospitals closed over this period, but they had no advertising expenditures and we dropped them from the data set in order to create a balanced panel. The process of linking hospitals across the two data sources left us with data on 337 hospitals for the years 1991 to For some chain hospitals, VoiceTrak did not keep track of expenditures for the individual hospitals in a given region but rather kept advertisement information for the entire chain in the region. In those cases, we allocated the advertising expenditures according to size of the hospital as measured by bed size across the region. We deflated our measure of advertising expenditures by the Producer Price Index and all of the figures are in 1992 dollars. In addition to the VoiceTrak and OSHPD financial data, we also used OSHPD s patient discharge database from corresponding years to construct measures of the relevant insured populations for each hospital for each year. This database, in principle, contains information on every inpatient hospital discharge in California, including information regarding the type of insurance and the home zip code. Zip code information was matched with the U.S. Census TI- GER database that gives the latitude and longitude of the geographic center for every zip code. We used the latitudes and longitudes to construct distance measures. Patterns in Hospital Advertising, In Figure 1, we display the aggregate, annual expenditures for all hospitals in our sample from 1991 to In the first quarter of 1991, the aggregate advertising expenditure was approximately $3.3 million. At its peak in 1997, total expenditures had grown 670% to $20.3 million. Not only did advertising grow dramatically for 302

6 Hospital Advertising Figure 2. Annual percentage of sample hospitals advertising, (Source: Voice- Trak data and authors calculations) hospitals, at its high point the level of advertising was economically significant. In 1997, the average hospital in our sample was spending $55,000 per year on advertising. To give a sense of the relative increase in advertising, it is useful to compare advertising expenditures to net revenue. Over the same period, the total net revenue (adjusted for inflation) fell slightly at an annual rate of approximately.8% per year. 3 Figure 2 displays the percentage of hospitals that had positive advertising expenditures in each month. We can see that not only did the absolute magnitude of advertising expenditures increase, but the percentage of hospitals engaged in advertising also grew dramatically. In the first quarter of 1991, only 16% of hospitals had advertising expenditures. Over the ensuing seven years, the percentage of hospitals devoting resources to advertising increased steadily, and by 1997, 45% of hospitals were advertising. The fact that hospital advertising dramatically increased is noteworthy for several reasons. It indicates hospitals responded to changes in the hospital marketplace by taking strategic actions to influence the patients hospital choice. That is, advertising has become an instrument hospitals use to affect their market environment. In order to understand the market for hospital services, researchers now have to pay more attention to the marketing arm of the process. This raises the specter that advertising may have adverse public health consequences because hospitals that have the most effective marketing departments, as opposed to hospitals that provide the most effective care, are going to attract a disproportionate share of patients. Of course, it is also possible that the information provided by the hospitals enhances the efficient operation of the market and increases the likelihood that the best hospitals will attract more patients. In Figure 3, we present the annual advertising expenditure per staffed bed by metropolitan area. While advertising expenditures increased dramatically over this period, those expenditures did not grow uniformly across the different areas. As noted already, there was significant heterogeneity across geographic areas in their advertising intensity and, quite interestingly, divergence in time-series advertising behavior across hospitals in the different metropolitan areas. For example, the average hospital in Los Angeles increased its advertising intensity 1,600% (from trough to peak), while the average hospital in San Francisco experienced ad- 303

7 Inquiry/Volume 39, Fall 2002 Figure 3. Average annual hospital advertising per bed by region (Source: VoiceTrak data and authors calculations) vertising growth at roughly half the rate of Los Angeles area hospitals. In San Diego, a different pattern emerged. Hospital advertising intensity in San Diego rose dramatically and then declined significantly, with a small net positive increase over the entire period. Unless changes in the cost of advertising varies dramatically across metropolitan regions (we have no evidence that this is the case), the heterogeneity in the timeseries advertising behavior of hospitals may reveal insights into the rationale for such behavior. The media that hospitals use to advertise themselves also says something about the goal of advertising. Table 1 presents a summary of the media outlets by metropolitan area. Newspapers were the most popular outlet (43%) for hospital advertisements, and over-the-air television (33%) was second in popularity. During the study period, these two outlets accounted for more than 75% of the advertising expenditures, suggesting that hospitals tend to advertise in media that reach a broad swath of society. Using traditional modes to transmit their message indicates that hospitals marketing strategies are not radically different from many other types of business. VoiceTrak also catalogs the primary message of each newspaper ad. In Table 2, we present the aggregate advertising expenditure by message category for four large cities in Sev- Table 1. Media summary for hospital advertising expenditures, Media Newspaper Spot TV Cable TV Radio Out of home Magazine Los Angeles (%) San Francisco (%) San Diego (%) Sacramento (%) Total (%)

8 Hospital Advertising Table 2. Message summary for 1998 hospital advertising expenditures Message Los Angeles San Francisco San Diego Sacramento Total Image/general Maternity Medicare/senior services Merger/acquisition Physician quality/affiliation Research studies Screening Seminars/workshops Treatment specialty Wellness/prevention Miscellaneous , , , Total ($000s) 3, , ,989.6 eral important patterns are apparent. First, there is heterogeneity across areas in the messages hospitals send; hospitals appear to disagree on the kind of information they wish to convey. They are new to advertising, and diffusion of expenditures across different types of messages may be a consequence of hospitals working their way up a learning curve. This brings us to the second observation about the advertising message. Hospitals spend a significant percentage of advertising budgets promoting an image of quality. Approximately 50% of the expenditures fall into three categories that can be classified broadly as quality factors: image, physician quality, and treatment specialty. We have examined a large sample of newspaper ads (VoiceTrak also runs a clipping service of newspaper ads for the large metropolitan cities), and quality claims often are based on an objective fact about the hospital. For example, a hospital may list the number of board-certified physicians on its staff. However, it would be difficult for most potential patients to translate this fact into a statement about 305

9 Inquiry/Volume 39, Fall 2002 the likelihood of successful treatment at that hospital versus a competitor. Only one specific health condition received significant advertising attention: maternity care. This is logical because prospective parents can anticipate their hospital usage far in advance, thereby allowing them to choose the hospital in which they will receive care. The time lag between the revelation of a pregnancy and the anticipated hospital admittance allows parents the option to research which hospital is best for them given location, hospital characteristics, and insurance. Obviously, hospitals attempt to influence this choice by marketing their obstetric services specifically. A large percentage of hospital patients are Medicare enrollees. However, according to the figures presented in Table 2, hospitals have not sought to target their messages to them. Given that the elderly make up a disproportionate share of the inpatient days within the hospital, it suggests that either hospitals do not earn significant margins treating these patients or that they are not successful in influencing the aged population s choice of hospital via advertising. The final observation we make from Table 2 is that hospitals do not advertise their price. This is noteworthy, because economists long have favored price advertising as likely to result in an increase in welfare. The logic behind this belief is that price advertising increases price transparency thereby increasing consumers price sensitivity. For optometrists, price advertising was found to lower prices (Bond et al. 1980). The finding that hospitals do not advertise prices is not surprising, as patients generally do not pay directly for the cost of their hospital care. Econometric Analysis of Hospital Advertising Expenditures We are interested in understanding which hospital characteristics explain the heterogeneity across hospitals in their advertising expenditures, both within the cross section and over time. As we observed in the previous section, a large number of hospitals do not advertise in any given year. Therefore, our analysis must use methods that account for this censoring of the data. We model the logarithm of one plus the advertising expenditure, A it, as given by the following ln(1 A it ) max(0, t X it u i ijt ) (2) where t is the intercept that varies over time, and X it is a vector of market and hospital characteristics described subsequently. 4 The hospital-specific random disturbance, u i, is assumed to be constant over time and is uncorrelated with both X it and the independent and identically distributed error term, it. In the estimation, u i is assumed to be distributed N(0, u2 ) and it is distributed N(0, 2 ). It is well known that simple, linear regression approaches to censored models generate biased coefficients. We estimate the parameters of interest, (, u2, 2 ), using censored dependent variable (tobit), panel data techniques in a maximum-likelihood framework as suggested by Maddala (1987). 5 As mentioned, many hospitals do not advertise. Therefore, in addition to estimating the tobit model, we also estimate the parameters of a model characterizing the dichotomous decision of whether to advertise. Here the dependent variable is binary, taking the value of one if the hospital has positive advertising expenditures in the year and zero otherwise. The parameters are estimated using random-effects, logit methodology in a maximum-likelihood framework. The Dorfman-Steiner theorem suggests that the set of X variables should be those that affect either the price-cost margin of the hospital or those that impact advertising elasticity. Of course, some variables may impact both pricecost margins and advertising elasticities. Explanatory Variables Measure of competitive intensity. The most obvious variable to include is a measure of the competitive environment that the hospital faces. Competition impacts both profit margins and the advertising elasticity. Most theories of oligopoly behavior imply that increases in competition reduce profit margins. Increases in competition likely increase advertising elasticity. The logic behind this assertion is seen by comparing a monopoly hospital market to a pair of duopoly hospitals. As patients in the monopoly hospital market have little choice, increases in advertising by the hospital only will increase patient inflow by those who would not have gone to a hospital, but now after having been exposed to the advertisement would seek care at the hospital. In contrast, in the duopoly market, a hospital that 306

10 Hospital Advertising increases its advertising expenditures may steal business from its competitor, implying that advertising has a greater effect on patient flows. The industrial organization branch of economics has traditionally used the Herfindahl- Hirschmann index (HHI) as the preferred summary measure of market power. Here we use a hospital-specific measure of the HHI. Our implementation is designed to capture the fact that hospitals are geographically differentiated and this factor matters even in urban areas (Town and Vistnes 2001). Our geographic market definition is hospital-specific and is the 15 kilometer (km) circle about a given hospital. Our measure of market share is based on the number of licensed beds. We chose the number of beds as the measure of size because, unlike patient flow measures, it is not likely to be influenced by advertising expenditures and thus can be taken as exogenous. To calculate the market shares for the market about hospital j, denoted MS jk (j indexes the reference hospital, k references the hospitals in the reference hospital s market), we use the following formula: BEDS MS j jk J. (3) 1(DIST 15) BEDS jl l 1 l Here 1( ) is an indicator function taking on the value of one if the condition in the parentheses is true and zero otherwise, and DIST jk is the distance between hospital j and k in kilometers and is calculated using the great circle formula. The HHI is the sum of the squared market shares given our market share measures and is indicated by: J 2 HHIi MS jk. (4) k 1 Hospital characteristics. Hospital characteristics likely will affect both the price-cost margin of the hospital and advertising elasticity. We include the number of licensed beds in the list of X variables to control for the effect of the size of the hospital on advertising expenditures. The Dorfman-Steiner theorem applies only for profit-maximizing hospitals. A large percentage of California hospitals are not-for-profit institutions and this may have two impacts on the incentive to advertise. The first effect is that these hospitals may not choose their marketing strategies in order to maximize profits. Second, notfor-profit institutions may have different profit margins than their for-profit counterparts. According to the Dorfman-Steiner theorem, that would lead to differences in advertising intensity. Thus, we include dummy variables for the profit status of the hospital (for-profit and public the omitted category is not-for-profit hospitals). The quality of the hospital may impact both the profit margins and the elasticity of advertising. In particular, the work of Milgrom and Roberts (1986) indicates that advertising may signal quality of care. If the correlation between quality of care and the teaching status is uncertain in the populous, and teaching hospitals do, in fact, provide higher quality care for some diagnoses, then teaching hospitals are predicted to advertise more intensively, according to this theory. Thus, we include a dummy variable for the teaching status of the hospital. Hospitals that are part of a chain may advertise more than those that are not. Hospitals that belong to a system or chain may have lower average costs of marketing since they can spread the fixed costs associated with marketing efforts across multiple hospitals. Also, the elasticity of advertising may be greater as they will have multiple hospitals that can benefit from one message. We include a dummy variable indicating whether the hospital is part of a chain of hospitals as an explanatory variable. Patient population. The profit margins and the patient responsiveness to advertising for a given hospital may be related to the type of health insurance plan that potential patients are enrolled in. Thus, we formulate measures of the size of the patient populations near the hospital by type of insurance. We do this by drawing a 15 km circle around the hospital and counting up the number of patients for five different insurance categories that are admitted to any hospital. The choice of a 15 km circle was selected because patients, on average, travel about 10 km (6.2 miles) to their chosen hospital in our data with a standard deviation of 6 km. The 15 km circle thus represents approximately the area from which the typical hospital draws 70% of its patients. We use data from the OSPHD patient discharge database to calculate the number of patients of each category. The five insurance categories are: Medicare, HMO, Medicaid (Cal- 307

11 Inquiry/Volume 39, Fall 2002 ifornia s MediCal program), indemnity and selfpay (self-pay/charity care). Of course, the total number of potential patients in an area influences the elasticity of advertising. Thus, we include a variable that measures the total number of individuals admitted to any hospital within a 15 km circle around the hospital according to the OSHPD information. In choosing our explanatory variables, we sought characteristics of hospitals and markets that were difficult for hospitals to modify once advertising became a common strategic tool in the post-1991 period and thus were exogenous to the choice of advertising expenditure. For example, our measure of market competitiveness was based on 1991 bed size, which was unlikely to be related to future advertising outcomes. However, if we formulated our measure of hospital size using actual patient flows that variable likely would be endogenous because hospitals that advertise more may attract more patients via their advertising efforts and the coefficient estimates would be biased. Since bed size was determined, in large part, prior to 1991 when advertising was inconsequential it should be largely exogenous. We limit our sample for the tobit estimation to nonrural hospitals with 50 or more beds for which we had complete information on the explanatory variables in any given year. 6 We removed small hospitals as they likely were offering a different product than the larger hospitals we were interested in studying. As mentioned earlier, VoiceTrak likely underestimates the advertising purchased by rural hospitals that are located in the rural parts of a county. We removed 87 hospitals (74 were rural hospitals) from the data set, which left us with 1,744 observations on 250 hospitals. Table 3 presents summary statistics for the variables used in the regression analysis. We divided the variables into those that vary across both time and the hospital dimension and those that vary only across hospitals. In the regression sample, as in our broader sample of hospitals analyzed in the previous section, the percentage of hospitals advertising increased from 21% in 1991 to 53% by 1997, with the average hospitals spending approximately $72,000 per year in 1997 a 600% increase over the period. This sample of urban, larger hospitals advertised more intensely and frequently than the entire sample analyzed earlier. The average hospital was moderately large with 200 beds, and faced moderate levels of competition within the 15 km circle (mean HHI.15). However, there was significant variation in the HHI across hospitals, indicating that some hospitals face substantial competition nearby while others operate in markets with few significant competitors. 7 There was a significant decline (9%) in the number of hospital admissions, reflecting the overall decline in inpatient hospital usage over the 1990s. The Medicare and Medicaid patients made up a little over half of the admitted patients, with Medicare experiencing a five-percentage point gain over the time frame. The HMO variable grew dramatically over the period. In 1991, HMO patients accounted for 23% of the patients; by the end of our sample period, they accounted for 33% of the admissions for the typical hospital. Conversely, indemnity insurance dramatically declined from 18% in 1991 to 3% in Competition, For-Profit Status, Payers, and Hospital Advertising The parameter estimates for the random-effects, tobit regression are presented in column 1 of Table 4. The dependent variable in this analysis is the logarithm of one plus advertising expenditures. In addition to the explanatory variables discussed in the previous section, we include annual fixed effects. The key findings are that hospitals with more market power and those likely to draw in Medicare and HMO patients have a larger propensity to advertise. In Table 4, column 2, we also present the parameter estimates from the random-effects, logit model. In general, the parameter estimates from the logit model yield essentially the same economic implications as from the tobit model; thus, to conserve space we focus exclusively on the estimates from the tobit framework in the following discussion. The coefficient for the logarithm of HHI is positive, large in magnitude and statistically significant. This implies that as hospitals face less competition they tend to advertise more. Conditional on doing any advertising, an increase of one standard deviation in the HHI from the mean is expected to increase advertising expenditures 72%. According to the Dorfman-Steiner condition, decreases in com- 308

12 Hospital Advertising Table 3. Summary hospital statistics of tobit regression variables Mean Variable Factors varying over time Advertising expenditure ($) 12,946 (50,144) of hospitals advertising 21.1 (40.1) Medicare patients 24.0 (8.0) Medicaid patients 24.0 (9.0) HMO patients 23.0 (9.0) indemnity patients 18.0 (7.0) charity care/self-pay patients 5.0 (2.0) Number of hospital admissions in 15 km radius 43,280 (41,172) Factors not varying over time Beds (149.5) HHI.147 (.184) not-for-profits 57.0 (49.6) for-profits 31.1 (46.3) teaching hospital 9.1 (28.1) in system 66.5 (47.3) Number of hospitals 250 Note: Standard deviations are in parentheses ,978 (182,432) 53.4 (5) 29.0 (5.0) 25.0 (7.0) 33.0 (8.0) 3.0 (2.0) 6.0 (2.0) 39,272 (36,906) petition as measured by the HHI can have two opposing effects on advertising expenditures: 1) increasing market power increases the profit margins and increasing margins lead to higher advertising expenditures; and 2) decreasing competition likely decreases the elasticity of advertising and patients have fewer hospitals from which to choose reducing the elasticity of advertising reduces the optimal advertising expenditure. The finding that the coefficient on HHI is positive suggests the profit-margin effect of competition dominates the elasticity effect. Grossman and Shapiro s (1984) model of advertising in an oligopoly setting predicts that advertising is increasing in market concentration and our results are consistent with their theory. Advertising expenditures do not influence our measure of competition, thus we can isolate the causal mechanisms of competition on advertising. The insurance mix of the potential patients in the 15 km area around a hospital is strongly correlated with advertising intensity. The patterns of the coefficients are consistent with hospitals seeking to target populations that are profitable and whose hospital choice can be affected by marketing efforts. In particular, the larger the HMO, Medicare and indemnity populations, the more a hospital is likely to advertise. The estimated coefficients on these variables are large and significantly different from zero at the 5% level. Hospitals that are likely to attract a disproportionate share of charity patients may advertise less to avoid attracting these low/nega- 309

13 Inquiry/Volume 39, Fall 2002 Table 4. Estimates from the randomeffects tobit model of the logarithm of advertising on hospital and market characteristics Randomeffects Variable tobit a Logarithm of HHI 3.20** (.81) Logarithm of beds 1.17* (.60) For-profit 1.44 (.78) Public 4.51 (1.04) Teaching hospital 1.77 (1.07) Hospital in system 2.56** (.80) Logarithm of number of 5.60** hospital admissions in 15 (.88) km radius Logarithm of percentage 6.27** Medicare (2.72) Logarithm of percentage 2.17 Medicaid (1.72) Logarithm of percentage 6.84** HMO (2.18) Logarithm of percentage 2.00* indemnity (.96) Logarithm of percentage 1.29 self-pay/charity care Constant Log likelihood u e H 0 : u 0 (p-value) Observations Number of hospitals Model and dependent variable (1.24) 51.30** (13.24) 2, (0) 1, Randomeffects logit b 1.31** (.44) 1.24** (.28).15 (.31) 1.13** (.41).23 (.40).23 (.27) 1.91** (.47) 2.67** (1.05).10 (.83) 2.04** (.78).47 (.46).22 (.58) 18.47** (4.64) (0) 1, Notes: The dependent variable is advertising expenditure per staffed bed. Annual dummies are included in the list of explanatory variables but are not reported. The null hypotheses that u 0 is distributed 2 (1). Standard deviations are in parentheses. a Logarithm of 1 Advertising Expenditure. b 1(Advertising Expenditure 1). * Significant at the 5% level. ** Significant at the 1% level. tive margin patients to their hospital. An alternative interpretation is that hospitals located in areas with a disproportionate number of charity patients are likely to have lower profits and thus have fewer resources at their disposal for marketing purposes. For-profit hospitals do not purchase advertising differently than not-for-profit hospitals. The coefficient on for-profit status is insignificant at traditional levels of confidence (the omitted category is not-for-profit hospitals). Marketing efforts are surely one of the quintessential markers of profit-seeking behavior in our quasi-capitalistic system. Thus, the fact that there does not appear to be any difference between for-profit and not-for-profit hospitals in this dimension of competition suggests that the profit motive is operating equally strong at both types of institutions. This finding is also consistent with the growing body of literature that finds little difference in the pricing and other dimensions of behavior between for-profit and not-for-profit hospitals (Sloan 2000). In contrast, public hospitals advertise significantly less than both for-profit and not-forprofit hospitals. This finding suggests, not surprisingly, that public hospitals possess other objectives than profit maximization. Furthermore, the fact that public hospitals behave differently than private hospitals re-emphasizes our previously discussed result that private, not-for-profit hospitals do not advertise differently than for-profits, ceteris paribus. Public hospitals, for a number of reasons, are not likely to maximize profits and this likelihood of differential objectives is well known. Importantly, our results are consistent with this presumed differential behavior. Because we can differentiate between public hospitals and private hospitals, it is noteworthy that we found little difference in the marketing behavior of for-profit and not-for-profit hospitals. If there were truly significant differences between the two types of hospitals, we likely would have uncovered them. The coefficient estimate for the logarithm of beds indicates that larger hospitals advertise more, although the magnitude of this effect does not indicate that there are any increasing returns to size. The parameter is not significantly different from one indicating that, controlling for other characteristics, an increase in the size of the hospital generates a proportionate increase in the amount of advertising. Hospitals in a chain or system advertise more than nonaffiliated hospitals. This effect is 310

14 Hospital Advertising significant and large in magnitude. The coefficient estimate implies that hospitals in a system spend more than twice as much as other comparable nonchain hospitals. Marketing effects cannot be targeted perfectly to a potential patient population. Depending on the advertising medium, many individuals will be exposed to advertisements that, for numerous reasons, are not likely to affect their hospital choice. Likewise some individuals who might have their hospital choice affected by advertisements might not receive the advertising message. Hospital chains are likely to have economies in advertising expenditures because they are geographically dispersed and can benefit from geographically broad, less pinpointed messages. In addition, hospital chains are likely to have larger marketing departments and can spread the fixed cost of that operation over more hospitals. The results also indicate that teaching hospitals do not advertise significantly more than nonacademic hospitals. Insofar as teaching hospitals are of higher quality, this result suggests that the signaling effects of advertising are not important. We explored several different alternative specifications of the empirical model. We estimated the coefficients using different sample selection rules, and adding nonlinear and interaction terms to the list of explanatory variables. We also tried different cut-off points for the creation of the HHI dummy variables. In all cases, the qualitative conclusions we drew were unaffected by these robustness checks. Conclusions The rise of managed care and related trends has changed the way in which hospitals compete. Hospitals are increasingly using mass media such as newspapers and spot television ads to influence potential patients hospital choices. While there is literature on the effects of competition on hospital prices, very little is known about the effects of hospital advertising, how much is being spent on advertising, how such expenditures have been changing over time, how pervasive advertising is across hospitals and how this number may be changing over time, and why some hospitals advertise more than others. These basic questions motivated our search for data and modeling efforts. Determining why certain hospitals advertise more than others provides insights into hospitals motivations to advertise. Using highly detailed data on advertising at the hospital level, we found that hospital advertising in California increased dramatically. Annual spending on advertising grew (inflation adjusted) more than sixfold between 1991 and During this time period, the percentage of hospitals advertising more than doubled from 21% to 53%. In addition, hospital advertising was found to increase with market concentration; the number of nearby potential patients; the percentage of nearby patients insured through Medicare, HMOs, and indemnity insurance; chain affiliation; and hospital size. For-profit hospitals were not found to advertise more than their not-for-profit counterparts. Our results suggest that overall, hospitals rationally target populations that are profitable and whose hospital choice may be affected by marketing efforts. We view this paper as the first step in the analysis of hospital advertising, with many remaining questions. It would be useful to study the effect of advertising on hospital choice. Does advertising by a hospital influence patients choices of hospitals? Health care consumers can get information about hospitals from a number of sources. Does hospital advertising crowd out other information? Are patients likely to go to hospitals that provide higher quality care or lower quality care? Is the overall quality of care affected by advertising? Work by Town and Vistnes (2001) suggests that increasing the desirability of a hospital from the potential patients perspective increases its value to a managed care network and thus increases its payments from the managed care organization. This finding suggests the following questions. Does hospital advertising affect managed care plans decisions to include hospitals in their provider networks? Does advertising by hospitals affect the choices made by patients about other health care providers such as physicians and health insurance plans? Are some patients more likely as a result of advertising by hospitals to first make a hospital choice followed by a choice of physician and health care plan? Does such sequential choice restrict physician and health plan options? We hope our work will motivate efforts to address these questions. 311

15 Inquiry/Volume 39, Fall 2002 Notes The authors thank Tom Buchmueller, the editor and an anonymous referee for their helpful comments; Rick Denos of VoiceTrak for graciously providing access to the data; and Jooseop Lim for research assistance. 1 There is a large literature studying the role of advertising on behaviors and its significant health consequences. For example, the pharmaceutical industry spends considerable resources marketing its products and there is a significant body of work devoted to analyzing the consequences of these marketing efforts. There is also a large body of work on the effects of smoking and alcohol advertisements on the demand for these products. 2 These outlets include local television stations, cable systems and interconnects, radio stations, outdoor advertising companies, magazines, newspapers, and business journals. 3 Source: Office of Statewide Health Planning. 4 The results are robust to adding different values to advertising expenditures. 5 If the u i s are correlated with the X s then the random-effects model will generate biased coefficients. Estimating a fixed-effects model can mitigate this bias. However, in our case the fixed-effects model is not practical given the large number of fixed effects we would have to estimate (250). Also, the fixed-effects model still generates biased coefficients although the bias tends to disappear as the time dimension increases. Importantly, given our choice of X variables that are largely exogenous under the most plausible stories of hospital behavior, it is unlikely that the correlation between the u i s and the X s is a serious problem. 6 The conclusions we draw in this paper are not sensitive to this sample selection rule. 7 The Department of Justice and the Federal Trade Commission (1997) consider a market with an HHI greater than.18 to be significantly concentrated (Horizontal Merger Guidelines). References Arrow, K Uncertainty and the Welfare Economics of Medical Care. American Economic Review 53: Bain, J. S Barriers to New Competition. Cambridge, Mass.: Harvard University Press. Begun, J The Consequences of Professionalization for Health Services Delivery: Evidence from Optometry. Journal of Health and Social Behavior 20(4): Benham, L The Effects of Advertising on the Price of Eyeglasses. Journal of Law and Economics 15(2): Benham, L., and A. Benham Regulating Through the Professions: A Perspective on Information Control. Journal of Law and Economics 18(2): Bernstein, A.B., and A.K. Gauthier Choices in Health Care: What Are They and What Are They Worth? Medical Care Research and Review 56(Supp1):5 23. Bond, R., J. Kwoka, Jr., J. Phelan, and I. Whitten Effects of Restrictions on Advertising and Commercial Practice in the Professions. Washington, D.C.: Federal Trade Commission. Chamberlain, E The Theory of Monopolistic Competition: A Re-Orientation of the Theory of Value. 8 th edition. Cambridge, Mass.: Harvard University Press. Comanor, W. S., and T.A. Wilson Advertising Market Structure and Performance. Review of Economics and Statistics 49: The Effect of Advertising on Competition: A Survey. Journal of Economic Literature 17: Dorfman, R., and P.O. Steiner Optimal Advertising and Optimal Quality. American Economic Review 44: Dranove, D., and M. Satterthwaite Industrial Organization. In Handbook of Health Economics, A. Culyer and J. Newhouse, eds. Amsterdam: Elsevier Science. Feldman, R., and J. Begun The Effects of Advertising Restrictions Lessons from Optometry. Journal of Human Resources 13(Supp): Does Advertising Reduce the Mean and Variance of Prices? Economic Inquiry 18(3): The Welfare Costs of Quality Changes Due to Professional Regulation. The Journal of Industrial Economics 34(1): Galbraith, J.K The Affluent Society. 2nd edition. Boston, Mass.: Houghton Mifflin. Gaynor, M., and W. Vogt Antitrust and Competition in Health Care Markets. In Handbook of Health Economics, A. Culyer and J. Newhouse, eds. Amsterdam: Elsevier Science. Grossman, G., and C. Shapiro Informative Advertising with Differentiated Products. Review of Economic Studies 51: Haas-Wilson, D The Effect of Commercial Practice Restrictions: The Case of Optometry. Journal of Law and Economics 29(1): Haas-Wilson, D., and E. Savoca Quality and Provider Choice: A Multinomial Logit-Least- Squares Model with Selectivity. Health Services Research 24(6): Hibbard, J.H., and E.C. Weeks Does the Dis- 312

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