Schematic Outline of the GP Market

The endogenous variables provide the nodes of Figure 4.1. The figure shows the

relationships between these variables and identifies their main exogenous determinants.

Broadly, the endogenous variables are expected to be determined as follows:

 The aggregate supply of services offered in an area depends on the GP density in the area and the number of services each GP wishes to offer;

 GP density (number of GPs per 1,000 population) in an area depends on the number of GPs in Australia, the attractiveness of an area in terms of how much GPs are able to charge, and a range of factors which influence the appeal of the area as a place for GPs to work and to live;

 The number of services the average GP will offer in an area depends again on the fee the GP can charge, and on a range of factors mostly related to the GPs own characteristics (e.g. on average female GPs offer fewer services than male GPs);

o The number of services offered is also the result of the attitudes of the GP

community overall to what are reasonable hours of work which, as discussed later, appear to be changing over time;

 The demand for services in an area depends on the price of those services, the health of the community (which in turn is influenced by the presence of hospitals and nursing homes), the GP density in the area, population characteristics and geographic characteristics reflecting on the access patients have to the GP;

 Mortality potentially depends on the GP services provided, but mainly depends on patient characteristics of age and income, and may change over time with improving technology and changing behaviour patterns such as reductions in smoking; and  Prices/fees which depend on the supply/demand balance, but also on the level of the

Relationship of GP numbers and GP activity

There are a number of variables which have strong correlations, but where there is no behavioural link. Perhaps the most important of these, as noted in Chapter 2 in relation to consideration of Richardson & Peacock (2003), is between GP density in an area and the average number of services offered by GPs in that area.

It is not plausible that GPs choose areas because of low or high average workloads, as each GP is able to set their own workload to the degree that they set their own hours. While GPs may be attracted to areas according to the degree of undersupply of GP services and hence potential work, this need not correlate with the existing GP workloads.

The number of GPs per capita in an area may influence the average workload in an arithmetic sense. While services per capita vary, at least in bigger areas they vary across a fairly narrow range, so the greater the number of GPs the lower the average services per GP. However, this is not a behavioural relationship. The GP density and number of services per GP are therefore treated as behaviourally independent.

Relationship of prices and other endogenous variables

While, for presentation purposes, prices are grouped in Figure 4.1, gross prices determine supply as these are the prices pertinent to the GP income, and net prices determine demand as these are the prices the patients face. The relationship between the price components was described in Table 3.1.

In supply and demand charts both curves relate to gross fees. The gross fee demand curve can be derived either arithmetically from the net fee curve by adding on the MBS rebate, or explicitly in an equation which includes both the gross fee and the MBS rebate. While the former version is mainly used in chapters 6 and 7, the latter equation was estimated and behaved as expected.

It is also arguable that the impact of price on demand is through the level of bulk billing rather than through the overall average net fees. Manning, Newhouse et al. (1987)

was between free services and 25 per cent gap payment services, with smaller declines at higher levels of payment. In Australia when a gap fee is charged, the patient usually pays the full amount before claiming a rebate, meaning a large up-front payment is required, potentially making the lack of bulk billing a considerable deterrent to seeking services. Demand equations based on bulk billing rates are therefore also specified and are estimated in Chapter 6.

Income and health

As discussed in Chapter 2, the relationship between income and health has been the subject of wide-ranging research examining the direction of causality, leading

researchers such as Thornton (2002) and Connelly & Doessel (2004) to treat income as endogenous in their health production equations.

Marmot (2003) outlined the socio-economic gradient in health. The question of

whether health influences income or vice versa was addressed by Case, Lubotosky et al.

(2002) and by Burgess, Propper et al. (2004) who found that the direct effect of the

income of parents on the health of their children was small. Smith (2004) concluded that the main direction was from health to income, but when caused by serious health events. As the measures of both health and income used in this study were aggregated, serious health events for an individual were not observed. Income and socio-economic status (SES) measures were treated as exogenous determinants of health in this

aggregate analysis, and formal testing for endogeneity did not contradict this assumption.

The aggregate supply equation

Aggregate supply can either be estimated from an independently specified equation (Connelly 1999 provides the only Australian example), or by multiplying the GP density by the number of services per GP. As the multiplicative relationship is an identity, in a system which includes equations for GP density and for the number of services per GP, specifying an independent equation applies severe (but very complex) constraints on the other two equations.

The multiplicative approach is taken and provides a clear view of a supply equation which can be used both to assist understanding of the market and for predictive

purposes. In Chapter 6, the two equations expressed only as functions of gross price at the mean values of all other variables are multiplied to give a quadratic relationship between supply and gross fee. This generates a backward bending supply curve, which bends above the mean so the curve is upward sloping across most of the plausible fee range. Bootstrapping was applied to calculate standard errors.

This approach permits both the estimation (at the mean of all other variables) of the supply curve for each year for which data are available, as shown in Chapter 6, and permits projection of the curve by applying the mean values of exogenous variables for later years as shown in Chapter 7.

The alternative approach of estimating a direct supply equation with a quadratic price term did not give stable results, as the price and price squared terms are extremely highly correlated. Estimating an equation with a linear price term gave a negative slope, comparable with Connelly (1999).

Figure 4. 1: Outline of the GP model

(Standard text endogenous variables, italicized text exogenous variables, Dotted lines indicate relationship between endogenous variables)

Price: gross/net, bulk billing

GP characteristic, ED

Number of GPs in Australia, population density, State

Hospital beds, ED, school, nursing homes, rurality, workers in area, neighbouring fees and GP density, population

characteristics GP characteristics, hospital beds, rurality, time trends to reflect attitude change, State

Government rebate, indemnity indicator, service mix, patient characteristics, concession card holding, socio-economic status

Patient characteristics, socio- economic status, distance to GP, ED, neighbouring prices and GP density, workers in area

Patient characteristics, nursing homes, hospital beds, economic conditions, time trend to reflect technological change, rurality, State

Number of GPs in area Demand for services Services per GP Supply of services Patient health Services provided = =