Appl Health Econ Health olicy 2006; 5 (2): 87-98 REVIEW ARTICLE 1175-5652/06/0002-0087/$39.95/0 2006 Adis Data Information BV. All rights reserved. Supplier-Induced Demand Reconsidering the Theories and New Australian Evidence Jeffrey R.J. Richardson 1 and Stuart J. eacock 2,3 1 Centre for Health Economics, Monash University, Clayton, Victoria, Australia 2 British Columbia Cancer Agency, Vancouver, British Columbia, Canada 3 Department of Health Care and Epidemiology, University of British Columbia, Vancouver, British Columbia, Canada Contents Abstract...87 1. The Theory of Supplier-Induced Demand (SID)...88 2. Empirical Evidence of SID Using Australian Data...91 2.1 Cross-Sectional atterns of Utilisation...91 2.2 Time-Series atterns of Utilisation...93 2.3 Econometric Analysis of SID Using Cross-sectional Data...94 2.4 rice and SID Effects on Demand...95 3. Conclusion...96 Abstract This paper reconsiders the evidence and several of the key arguments associated with the theory of supplier-induced demand (SID). It proposes a new theory to explain how ethical behaviour is consistent with SID. The purpose of a theory of demand and one criterion for the evaluation of a theory is the provision of a plausible explanation for the observed variability in service use. We argue that Australian data are not easily explained by orthodox possible explanation. We also argue that, having revisited the theory of SID, the agency relationship between doctors and patients arises not simply because of asymmetrical information but from an asymmetrical ability and willingness to exercise judgement in the face of uncertainty. It is also argued that the incomplete demand shift that must occur following an increase in the doctor supply is readily explained by the dynamics of market adjustment when market information is incomplete and there is non-collusive professional (and ethical) behaviour by doctors. Empirical evidence of SID from six Australian data sets is presented and discussed. It is argued that these are more easily explained by SID than by conventional demand side variables. We conclude that once the uncertainty of medical decision making and the complexity of medical judgements are taken into account, SID is a more plausible theory of patient and doctor behaviour than the orthodox model of demand and supply. More importantly, SID provides a satisfactory explanation of the observed pattern and change in the demand for Australian medical services, which are not easily explained in the absence of SID.
88 Richardson & eacock [2-4] The suggestion that doctors are imperfect agents in the medical market. Section 2.3 critiques the and can induce demand for healthcare directly conmarises methodology of cross-sectional studies and sumflicts with the full information and consumer sovermand a recent Australian re-evaluation of the de- eignty assumptions of the orthodox model of deusing function for general practitioner (G) services mand and supply. [1] As a result, supplier-induced correct cross-sectional methods. Section 2.4 demand (SID) has been one of the most researched then describes a natural experiment that examines topics in health economics. Volumes of empirical the relative importance of price and SID effects on evidence addressing the possibility of SID have patient demand, created by the idiosyncratic funding been presented from a range of different health of hospital services in Australia. On the basis of these discussions, some conclusions are drawn in systems, which have been reviewed elsewhere. Many of these studies have used cross-sectional data section 3. sets, and examined the effect of the doctor supply on the demand for healthcare by including doctor sup- 1. The Theory of Supplier-Induced ply as an independent variable when estimating the Demand (SID) demand equation. [5-9] As discussed in section 2.3, these studies have been criticised on methodological While the postulate of SID may have evolved grounds. In order to explain why doctors do not shift from a pragmatic explanation of observed variation demand as far as possible, Evans [10] has suggested in services use, it has also been necessary to provide that doctors have a target income or target number a satisfactory theoretical explanation of both patient of working hours in mind that they seek to achieve. and doctor behaviours that is consistent with the rima facie, this conflicts with the profit maximisasary to explain why patients allow their preferences theory of SID. More specifically, it has been necestion assumption of the orthodox model of the supply of healthcare. In order to reconcile SID and utility to be determined, at least in part, by doctors and why maximising behaviour it has been suggested that doctors don t shift demand to its technical limit, SID implies unethical behaviour and that doctors controlling excess (induced) demand by raising fees therefore derive disutility from inducing demand. [11] and thereby maximising profit and personal utility? With respect to the first question, it is generally This review critiques and provides additional inacknowledged that patients face asymmetrical inforsights into some of the theoretical arguments behind mation in the agency relationship with the doctor SID, and presents new empirical evidence from patients rarely possess the technical knowledge nec- Australian data that is relevant to the debate. essary to determine the expected benefits from their The implications of SID for patient and doctor healthcare consumption choices. However, we sugbehaviour have been discussed at length in the liter- gest that this explanation is not complete or necessaature (see McGuire [12] for a summary). In this article rily the most important element in the abdication of we argue that the most common theoretical explana- decision making by patients. As discussed in this tions of SID are, in important respects, unconvinc- section, medical decision making is characterised by ing and that there are a number of different explana- uncertainty and, particularly at the level of the inditions for patient and doctor behaviour that are more vidual, depends upon judgement. Even with well consistent with empirical evidence (section 1). The informed patients (e.g. those with chronic illnesses) most persuasive support for the theory of SID re- there is an asymmetrical ability to pass judgement as mains that SID provides the most satisfactory and, doctors have clinical experiences that patients do possibly the only, plausible explanation of observed not. Doctors have accepted the role as decision behaviour in the medical market. We present empir- maker, whereas patients, recognising their inexperiical evidence of SID from six Australian data sets ence, will rely upon the doctor s judgement. In sum, (section 2) as well as cross-sectional (section 2.1) even in the absence of an asymmetry in the technical and times-series (section 2.2) evidence of behaviour information available, a well educated and normally
Supplier-Induced Demand in Australia 89 Based on numerous studies of variation in health- care patterns, Wennberg s main conclusion is per- suasive: The evidence from small area analysis, from the critical appraisal of strengths and weak- nesses of the scientific basis of medicine and the failure of expert panels to reach consensus on appro- priate practice build a consistent and strong case against the rational agency hypothesis and the asso- ciated assumptions about the nature of demand in medical markets. [21] This suggests a more complex theory of the demand and supply of medical services than proposed in the orthodox model. First, patients face asymmetric information and an asymmetrical capacity to evaluate this information in the face of uncertainty. However, secondly, and following Wennberg, [21] uncertainty may characterise not just patient decision making but also the decisions made by doctors. Under these circumstances doctors may understandably believe that more care is better than empowered individual is likely to ask for the doctor s judgement and accept that the doctor s experi- ence makes this judgement better than the patient s. This is consistent with the psychologists paternalistic model of the doctor-patient relationship, where the patient and doctor form an information-based alliance in which the patient seeks medical help, provides active input and then places him- or herself in the care of the doctor. [13] At the level of the patient, SID is little more than the theory that patients generally trust their doctor s judgement more than their own. Discussion of doctor behaviour in the context of SID has been clouded by the debate over what is the medically, and ethically, correct level of service provision in healthcare. SID has often been characterised as a form of unethical behaviour, and it is a recognition of their own unethical behaviour that restrains doctors from fully exploiting SID. However, unless SID is quantitatively small, this assumption implies the almost universal unethical behaviour of doctors, a conclusion that appears improbable and for which there is no independent evidence. The theory of SID need not suggest that doctors are behaving in an unethical manner. However, as noted by Dranove, [1] under certain conditions a doctor will have an incentive to recommend treatments whose costs outweigh their medical benefits. Furthermore, psychologists have argued that the economic model of medical decision making often fails to mirror decision making in the real world. [14] They propose alternative models based on behavioural decision-making theory. [15] Departures from the rational economic model have been demonstrated to be caused by a range of sources of cognitive bias, including variations in the decision frame in which a particular choice occurs and in what information the patient and doctor consider important to the decision. [16] For example, oncologists have been shown to place greater value on small improvements in survival than their patients. [17] These types of behaviour may be characterised as being socially inefficient, rather than unethical. In the last 2 decades it has become increasingly apparent that there has been no well defined level of service that has been accepted as medically and ethically correct. [18-20] The evidence-based medicine movement is a reaction to this. Wennberg [21] sum- marised this characteristic of medical care when he noted that many of the specific theories physicians hold as being valid regarding appropriate practices are now recognised as problematic, and professional uncertainty rather than consensus about the scientific basis of clinical practice is emerging as the domi- nating reality. In part, this uncertainty is a function of the small number of services that have been evaluated even for clinical efficacy. One Organisation for Economic Co-operation and Development (OECD) study suggests that only 20% of procedures in common use have been evaluated. [22] Rather than reflecting an established set of responses to well defined indicators, practice patterns appear to vary with the myriad of variables that influence clinical decision making: training, peer behaviour, confer- ence attendance, personal temperament, personal experience, financial rewards and, most importantly here, time and infrastructural capacity to undertake more or less intensive investigative and therapeutic work.
90 Richardson & eacock less. Such a belief would justify the creation of demand to the point where doctors reached their leisure constraint, while simultaneously believing that the induced demand represented better quality and ethically commendable care. If demand inducement is not unethical, then there is the unexplained question of why doctors do not shift the demand curve to its limit and control excess demand by increasing their fees. Not doing this conflicts with the profit maximisation assumption of the orthodox model of the supply of healthcare by doctors. However, to suggest that doctors sole objective is the pursuit of income or profit seems implausible. Evans [10] first suggested that doctors do not induce demand to its technical limit because they have some target income or target number of working hours in mind that they seek to achieve. That is, doctors, like all other economic actors, face an income-leisure trade-off decision. If the profit maximisation assumption is relaxed, so that doctors are allowed to have a target number of working hours, and three further weak (and plausible) assumptions are added to the orthodox model of utility maximisation, then doctors behaviour and motivation associated with SID are easily explained. These four assumptions are that: 1. medical prices are sticky in the sense that doctors are reluctant to significantly change them in the short-run; 2. individual doctors face relatively elastic demand curves; 3. doctors have limited knowledge of overall industry conditions; and 4. there is a target number of working hours that the doctor, for personal and professional reasons, seeks to achieve. With these assumptions, incomplete SID is a likely consequence of the dynamics of market adjustment following an increase in the supply of doctors. This is illustrated in figure 1. In the initial equilibrium (figure 1ia) the medical industry faces a relatively inelastic demand curve and a completely inelastic supply curve set by the number of doctors and their target working week. With either a competitive or monopolistic model the equilibrium, i ii a 1 0 1 2 0 iii 1 3 2 0 iv 1 3 2 0 S 1 D 1 MR 1 Q S 1 S 2 S 1 S 2 S 2 D 1 Q D 1 Q D 3 D 1 Q b s 1 d 1 mr 1 0 q s 1 d 1 d 2 0 q s 1 d 2 0 q q 2 q 1 s 1 d 3 d 2 0 q q 2 q 1 Fig. 1. Adjustment to a change in the supply of medical services (a) in the medical industry and (b) by an individual doctor. Illustrated are the: (i) initial equilibrium; (ii) final equilibrium in a marked with no demand shift following an increase in supply; (iii) dynamics of the adjustment process; and (iv) new equilibrium created by the doctor shifting demand (courtesy of Richardson and eacock [23] ). D/d = demand; MR/mr = marginal reserve; = price; Q/q = quantity demanded; S/s = supply. profit maximising price 1 will clear the market. The individual doctor faces a price-elastic demand curve as patients can switch relatively easily be- tween doctors. The curve is not completely elastic because of patient loyalty to their doctor. The individual doctor is also in equilibrium at 1 (figure 1ib).
Supplier-Induced Demand in Australia 91 Figure 1ii shows the final equilibrium in a market with no demand shift following an increase in sup- ply from S1 to S2. Industry supply and demand result in market clearing at price 2. For the individual doctor, the increase in supply results in a reduction in demand from d 1 to d2 (figure 1iib). However, this second equilibrium is never reached. Figure 1iii illustrates the dynamics of the adjustment process. In the short-run sticky prices fall only to 3 (figure 1iiia), and at this price there is excess supply. The individual doctor will experience a significant re- duction in demand from q1 to q2 (figure 1iiib). In response to this the doctor can shift demand from d 2 to d3 (figure 1iv), at which point both industry and doctor are in a new equilibrium. At this new equilibrium, limited demand shift has occurred and the doctor is at a new profit-maximising equilibrium consistent with the target working week. Additional demand shift at this price would encounter the leisure constraint. If the doctor were to experimentally increase price, demand would fall significantly. Unlike the previous decrease in demand, the doctor could attribute this directly to his or her own pricing decision which would appear to be unprofitable. Because of poor market information and noncollusive behaviour, this would induce the doctor to accept 3 as a final equilibrium price. The behaviour postulated here would be less likely to occur if the doctor s only motivation was the maximisation of profit. Repeated experimentation with incremental increases in price and further demand shift might increase profit. However, this behaviour might well be regarded as unethical. The experimental increases in price would intentionally reduce the demand of less well off patients and appear unprofessional in a way that increasing the amount of attention given to a patient does not. In sum, the behaviour postulated here is that the major- ity of doctors have some interest in short-run profit maximisation, but they are also motivated by the achievement of professional objectives through the provision of what they judge to be useful services. The behaviour described in figure 1 is consistent with weak profit maximisation and demand shift. However, this is only one possible outcome from the exploration of the market dynamics. Demand shift with rising prices could similarly be explained if falling demand per doctor resulted in an increased price to maintain income and a corresponding de- mand shift to achieve the target working week. The general point here is that there are a number of explanations for doctor behaviour that are consis- tent with demand shift if the dynamics of the market are explored. We have demonstrated one such model using the plausible assumptions of weak profit maximisation, belief in the efficacy of the services provided and a professional commitment to ethical behaviour. 2. Empirical Evidence of SID Using Australian Data A theoretical model of the demand and supply of medical services should be capable of providing an explanation of observed patterns of service use, both cross-sectionally and through time. Empirical evi- dence from six sets of Australian data is discussed in the following sections. This evidence indicates the utilisation patterns that must be explained by our theory. Section 2.1 summarises a previous study of variation in the use of 15 hospital procedures con- ducted by Richardson, [24] and updates and early study by Richardson and Deeble [25] of G supply and use with new cross-sectional evidence. New evidence from time-series data on the supply and use of Gs and specialists is presented in section 2.2. A critique of the methodology of cross-sectional studies and new evidence on the demand function for G services are summarised in section 2.3, and a new analysis of price and SID effects using data on variation in hospital procedures is described in sec- tion 2.4. A final choice between SID and orthodox theory should, ideally, be based upon the explanato- ry power of the two theories in each of these contexts. 2.1 Cross-Sectional atterns of Utilisation The first data set (relating to hospital procedures) was used to plot differences in the 2-year utilisation rates per 1000 population in the statistical local areas in the state of Victoria, Australia. [24] The re-
92 Richardson & eacock rocedure Variance Ex(variance) Coronary angiography 13.4 Coronay revascularistion procedure 5.4 Cataract extraction 15.4 Tonsils and adenoids 7.5 Myringotomy 11.7 Carpal tunnel release 8.4 Vertabral discetomy 2.1 Laminectomy 1.9 Total hip replacement 3.8 Hysterectomy 6.4 rostatectomy 3.9 Colonoscopy 45.3 Cholecystectomy 5.3 Exploratory laparotomy 1.7 Appendectomy 5.9 0 50 100 150 200 250 300 350 400 Fig. 2. Standardised rate ratios for various operations in the statistical local areas in Victoria, Australia, compared with the rate ratios for all Victoria. Bold lines indicate the median value. Rectangles represent the 25th and 75th percentiles for statistical local areas, standardised to the Victorian state ratio for age and sex and normalised so that the expected use per 1000 population is set equal to 100. Extreme values >3 times 50th to 75th and 25th to 50th percentile intervals are recorded as separate points. See the Appendix for an explanation of small area units of analysis (reproduced from Richardson, [24] with permission from Allen & Unwin; www.allenandunwin.com.au). Gs across Australia and the use of their services. As there is significant border crossing, especially between statistical subdivisions (the unit of analysis in figure 4), the relationship is not a necessary one (see the Appendix for small area definitions). In principle, local supply could be unrelated to local demand, with border crossing and variable work- loads accounting for the discrepancy. The two figures do not, of course, demonstrate a causal rela- tionship between G supply and the demand for their services for at least two reasons: increased border crossing would be associated with increased time costs and, consequently, poorly supplied re- gions would have lower demand because of these costs. However, from the perspective of a govern- ment interested in the creation of equal access or in the allocation of a health budget, the distinction between SID (as envisaged by academic economists) and a supplier-induced variation in time costs, which explains demand, is of very little inter- est. As a minimum, the close correlation suggests that SID could be accepted by government economists as an instrumental theory; that is, a theory that is useful for prediction and policy, if not for the description of individual behaviour and welfare. Im- portantly, however, border crossing was not a signif- sults shown in figure 2 are standardised for age and sex and normalised so that the expected use per 1000 population is set equal to 100. oints in the box plots show actual utilisation relative to this norm. The figure identifies a 4- to 6-fold variation in the use of different procedures. This cannot be attributed to random variation. When the variance in the utilisation rates for each procedure is divided by the variance predicted from the age/sex composition of each area (assuming a oisson distribution for the use of health services in each age cohort), the ratio shown to the left of each plot varies from 1.7 for exploratory laparotomy to 45.3 for colonoscopy. The conclusion appears to be that the dominant factor in service use is the clinical judgement of doctors. It is implausible to suggest that, with the removal of significant income and price barriers, such variation could arise from differences in individual patient preferences. The results indicate both the magnitude of the differences that exist in the use of similar procedures and the importance of the agency relationship between doctor and patient. Figure 3 and figure 4 are constructed from data sets from 1976 and 1996/7, respectively, and indicate that there has been, and remains, a very close relationship between the geographic availability of
Supplier-Induced Demand in Australia 93 icant determinant of demand in either the econometric analysis presented in section 2.3 or an earlier study by Richardson. [7] The significance of these two figures is 2-fold. First, they demonstrate the possibility of SID: such a correlation between the geographic availability of Gs and the use of their services is a necessary, but not sufficient, condition for the existence of SID. Secondly, it is possible that Gs locate their practices in areas of high autonomous demand. It is for this reason that statistical analysis of cross-sectional data must attempt to take account of reverse causation and endogenise the G supply. However, the importance of reverse causation could be overstated. Age/sex standardisation does not reduce the variation in either the 1976 or 1996/7 data significantly. rice, income and socioeconomic variation are also insufficient to explain a significant part of the variation. G services per capita 5.60 4.80 4.00 3.20 2.40 1.60 0.80 2.00 Q(G) = 0.5 + 0.64 (0.09) G R 2 = 0.50 4.00 6.00 8.00 10.00 Full-time equivalent Gs per 10 000 population Fig. 3. General practitioner (G) supply vs use by statistical division, Australia, 1976. [25]. See the Appendix for an explanation of small area units of analysis (reproduced from Richardson, [26] with permission). G services per capita 10 8 6 4 2 0 0 2 4 6 8 10 12 14 Full-time equivalent Gs per 10 000 population Fig. 4. General practitioner (G) supply and use by statistical subdivision, 1996/7. See the Appendix for an explanation of small area units of analysis [25] (reproduced from Richardson, [26] with permission). 2.2 Time-Series atterns of Utilisation The most persuasive data are those presented in figure 5 and figure 6 for Gs and specialists, respectively. They indicate that over time there has been a nearly perfect correlation between the growth in numbers of Gs and specialists, and the use of their services, both in term of consultations (figure 5 and figure 6) and specialist diagnostic services (figure 7). The apparent impact of supply upon demand is most clearly illustrated in figure 5, which shows the growth in the G supply (from 1985 to 2002) and the corresponding cessation in the growth of service use. Observations below the trend line represent years in which the G supply was expanding. The seven observations above 1.2 Gs per 1000 population represent years in which the supply was contracting. The difference suggests that the later years may have experienced excess demand, possibly as a result of an upward ratcheting of expectations. However, over the full time period the magnitude of the change cannot be attributed to increases in per capita income or to the small changes in patient net payments that occurred. The remaining demand side variable, time cost, may have contributed to the correlation during the contractionary years but is unlikely to have been the chief causal agent overall,
94 Richardson & eacock G attendances per capita 5.60 5.40 5.20 5.00 4.80 4.60 4.40 4.20 y = 5.797x 2.2421 R 2 = 0.8419 4.00 1.00 1.08 1.16 1.24 1.32 1.40 Full-time equivalent Gs per 1000 population Fig. 5. General practitioner (G) supply and use, 1985 2002 (data provided by the Commonwealth Department of Health and Family Services [27] ). particularly during 1985 95, the years of growing G supply. Correlational evidence is weak when the direc- tion of the causation is in doubt, and when one or more other variables contaminate the correlation because they are also correlated with the variables that are being studied. However, correlational evidence is far more powerful when reverse causation is improbable, and when other variables that confound the interpretation of the correlation cannot be identified. In the present case, reverse causation requires that there has been a permanent excess demand for G services, and that utilisation is therefore determined by supply. Whilst there is evidence that excess demand exists in rural and remote areas, and especially in indigenous communities, [28] this does not appear to have been the case in cities, where the bulk of the Australian population lives. The G : population ratio in Australian cities is almost double that of remote areas, and over twice the OECD average. [28] Yet summary measures of population health suggest Australian metropolitan populations are relatively healthy. Until the G supply per 1000 population fell, general belief, based upon casual evidence, was that there was an excess supply of Gs which, inter alia, was responsible for the very low rates of patient charges above the rebate. Reduction in the G supply was the result of exogenous factors, namely government policy and the relativities in government rebates for medical services that have led medical trainees away from general practice to specialties that have more profitable procedures as part of their practice, particularly some surgical subspecialties. [28] erhaps more importantly, the doctor supply in Australia is largely determined by exogenous national and state policies relating to medical school places. The number of doctors per 10 000 population has roughly doubled over the last 30 years, almost double the rate of population growth, largely as a result of these policies. [28,29] However, arrangements to undertake formalised projections of future workforce requirements were only introduced in 1996. [28] No other variable has changed to such an extent that it appears capable of effecting such a large change in the use of G services, which have remained relatively homogeneous despite the technological advances in specialist services. 2.3 Econometric Analysis of SID Using Cross-sectional Data Some of the more important arguments to defend the orthodox model have been summarised by Does- sel, [30] who also presents an analysis of Australian Specialist attendances per capita 1.05 1.00 0.95 0.90 0.85 0.80 0.75 y = 0.9544x + 0.2016 R 2 = 0.8977 0.70 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 Full-time equivalent specialists per 1000 population Fig. 6. Specialist supply and use, 1985 2002 (data provided by the Commonwealth Department of Health and Family Services [27] ). Note: specialist supply excludes pathology and radiology.
Supplier-Induced Demand in Australia 95 Diagnostic services (pathology, radiology) per capita 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 y = 7.5757x 2.7385 R 2 = 0.9462 0.0 0.50 0.60 0.70 0.80 0.90 1.00 Full-time equivalent specialists per 1000 population Fig. 7. Specialist supply and use of diagnostic services (pathology and radiology), 1985 2002 (data provided by the Commonwealth Department of Health and Family Services [27] ). commonly been interpreted in the literature as the inclusion of the doctor supply in the demand equation, leading to an identification problem. This interpretation is invalid. In two-stage least squares regression the reduced form equation for each of the endogenous variables has the same exogenous variables. There is no reason that a change in the structural equations would necessarily change the reduced form equations. Ramsey and Wassow s [31] criticism did not apply to our analysis. Importantly, our findings showed that demand equations that did not include the doctor supply were mis-specified. New empirical evidence on the demand function for G services from Australian data supported the notion of SID (full results are available in a working paper by Richardson and eacock [23] ). Demand and supply equations were well specified and had very good explanatory power. The demand equation was identified, and the desirability of a location was an important predictor of the doctor supply. Results showed an average price elasticity of demand of 0.22, and an average elasticity of demand with re- spect to the doctor supply of 0.46, with the impact of SID becoming stronger as the doctor supply rose. The conclusion we can draw from this re-evaluation is that two of the main criticisms of the empirical evidence supporting the SID hypothesis have been inappropriately levelled at the methods used. More importantly, SID provided a satisfactory, and robust, explanation of the empirical data on the demand for medical services in Australia. G data to support those arguments. In particular, cross-sectional studies that have included the doctor supply as an independent variable in the demand equation to test for the presence of SID have been criticised on two grounds. The first and most important criticism made by Auster and Oaxaca, [11] is that under the orthodox specification of demand and supply equations the SID effect cannot be econometrically identified if the supply of healthcare services is added into the demand equation. This has commonly been interpreted in the literature to mean that the inclusion of the doctor supply in the demand equation leads to an identification problem. The second criticism, by Ramsey and Wassow, [31] is that early empirical studies of SID fail more recently developed diagnostic tests for the performance of econometric analyses, and produce artefactual findings due to model mis-specification. [31] The original criticism by Auster and Oaxaca [11] remains valid. However, in our (and others ) empirical work, including the original econometric modelling by Fuchs, [5,6] this does not lead to an identification problem because the doctor supply enters the demand equation as an independent variable, not as the supply of services. The doctor supply variable is stochastic and depends upon a variety of variables, including the desirability of the location. However, the original criticism by Auster and Oaxaca [11] has 2.4 rice and SID Effects on Demand Australia provides a unique laboratory for the examination of the relative importance of price and SID effects on patient demand. The peculiar financ- ing of Australian health services results in a public sector in which hospital patients are treated without cost, but a private sector in which, following the purchase of private health insurance, the patient is still left with significant out-of-pocket expenses. In a simple market equilibrium public demand per cap- ita would be expected to exceed private demand per capita. However, incentives facing doctors also dif- fer between public and private sectors. In the public
96 Richardson & eacock Table I. Ratio of the likelihood of a procedure for private to public patients in private and public hospitals, 1995/7 [32] Time from admittance to rivate hospital patients : private patients in public rivate patients in public hospitals : public patients in procedure hospitals public hospitals angiography revascularisation angiography revascularisation Within 14 days Men 2.20 3.43 1.77 1.53 Women 2.27 3.86 1.57 1.81 Within 3 months Men 2.24 3.43 1.53 1.23 Women 2.28 3.34 1.49 1.32 Within 12 months Men 2.16 2.89 1.42 0.97 Women 2.22 2.84 1.48 1.10 sector there is no financial benefit from the treat- vate hospital the discrepancy remained at over 100% ment of additional patients. In the private sector a for all patients. The inescapable conclusion appears full fee is earned. Therefore, there is no financial to be that these patterns were driven by physician incentive for doctors to increase demand in the judgement rather than patient preference; that is, public sector, but there is a strong financial incentive that doctors, not patients, determined the use of to increase demand in the private sector. services. In a recent study, Richardson and Robertson [32] examined the treatment of patients after an emergency admission with an acute myocardial infarction 3. Conclusion (AMI; heart attack). Various treatments are possible for AMI. The most expensive and recent of these This paper has reconsidered the theoretical arguinclude angiography (a diagnostic test) and the protional Australian data that are relevant to the debate. ments and evidence behind SID, and presented addi- cedures collectively known as revascularisation ; that is, coronary artery bypass surgery, balloon angipartially The most common explanations of SID are only oplasty and stenting. Each of these four procedures convincing. It has been argued here that attracts a significant fee in the private sector. patients suffer not only from an asymmetry of infor- For the analysis of price and SID effects, 100% of mation, but also from an asymmetrical capacity to the procedures delivered in the state of Victoria exercise judgement in the face of uncertainty and in were analysed over a 2-year period. The likelihood the absence of clinical experience. of a private patient receiving a procedure in both Uncertainty is also of pivotal importance in unprivate and public hospitals was divided by the derstanding the behaviour of doctors. In the absence likelihood of these procedures occurring for public of established practice norms incorporating the prinpatients in a public hospital. The results, reported in ciples of evidence-based medicine it is plausible and table I, indicate a significantly greater likelihood of reasonable to believe that more care means better a procedure in the 14 days following admission for care; that is, that the use of spare capacity is benefiall categories of private patients. The discrepancy cial to the patient as well as profitable for the doctor. varied from 57% in the case of female private pa- The explanation for doctors not fully exploiting SID tients in public hospitals receiving angiography to can be found in the dynamics of market adjustment 286% for female private patients in private hospitals when doctors have limited market information, there receiving a revascularisation procedure. In the sub- is no collusion and there is satisfaction from the sequent 12 months these discrepancies decreased achievement of the professional objective of providslightly, but for patients initially admitted to a pri- ing high-quality care.
Supplier-Induced Demand in Australia 97 The most persuasive support for the theory of planation of the observed pattern and change in the SID is that it provides the best and possibly only demand for Australian medical services. Variations explanation of the variation in service use across the in other observed variables appear to be incapable of country and through time. Several sets of Australian providing this explanation within the orthodox data have been employed in this paper. Australian framework. small area data on the geographic availability of Gs and the use of their services reveal a very close Acknowledgements relationship between supply and demand. Such a The authors would like to thank the Commonwealth Department correlation is a necessary, but not sufficient, condiof of Health and Ageing and the Victorian Department Human Services for the data used in the empirical studies, tion for the existence of SID. This evidence becomes and two anonymous referees for their comments on an earlier more persuasive when Australian medical market draft of the paper. The research was supported by a National conditions are considered. These make reverse cau- Health and Medical Research Council (NHMRC) roject sation improbable and there are few, if any, con- Grant. The views expressed in this paper are those of the founding variables that could explain the data. authors, and not the funding agency. This paper is a revised version of a working paper (Rich- Two of the main criticisms of the empirical evi- ardson J, eacock S. Supplier induced demand reconsidered. dence supporting the SID hypothesis have also been Working paper no. 81. Melbourne (VIC): Centre for Health inappropriately levelled at the cross-sectional methods rogram Evaluation, Monash University, 1999). used to test and quantify SID. When these methods are applied in Australia SID provides a Appendix satisfactory, and robust, explanation of the observed variation in services per capita. Importantly, omis- 1. Small Area Definitions sion of the stochastic variable for the doctor supply results in the mis-specification of the demand equabased on the hierarchical structure of the Australian Small area data used in empirical analysis are tion. These findings support our correlational evidence and confirm the direction of causality. Standard Geographical Classification. [33] In non- census years the classification consists of statistical Australia provides a unique laboratory for the local areas (SLAs), statistical subdivisions (SSDs), examination of the relative importance of price and statistical divisions (SDs) and states/territories. SID effects on patient demand. Small area data show Under the hierarchical structure, SLAs are aggregatthat private sector patients are between two and ed to form SSDs, SSDs are aggregated to form SDs, three times more likely to receive certain cardiovasand SDs aggregate into states and territories. These cular procedures than public sector patients. Under spatial units cover all of Australia without gaps or the orthodox model we would expect to see the overlaps. As at 1999, there were 1331 SLAs, 194 opposite, i.e. much greater use of services or signifi- SSDs and 66 SDs covering mainland and offshore cant queues in the public system where care is free at Australian states and territories. SLAs and SSDs are the point of consumption. This is not the case. It based on defining regions that show social and ecoseems an inescapable conclusion that these utilisanomic homogeneity through identifiable links betion patterns are driven primarily by physicians and tween inhabitants, and on local government boundanot by patients. ries. SDs also maintain this basis but, in addition, the Therefore, we conclude that, once the uncertainty capital city of each state/territory is defined as a of medical decision making, the complexity of med- single SD. ical judgements and dynamic adjustment with imperfect market information are taken into account, References SID is based upon a more plausible theory of patient 1. Dranove D. Demand inducement and the physician/patient relationship. Econ Inq 1988; 26 (2): 281-98 and doctor behaviour than the orthodox model. 2. Folland S, Goodman AC, Stano M. The economics of health and More importantly, SID provides a satisfactory ex- health care. New York: MacMillan, 1993
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