Abstract
This paper re-examines criticisms of cross-sectional methods used to test for supplier-induced demand (SID) and re-evaluates the empirical evidence using data from Australian medical services. Cross-sectional studies of SID have been criticised on two grounds. First, and most important, the inclusion of the doctor supply in the demand equation leads to an identification problem. This criticism is shown to be invalid, as the doctor supply variable is stochastic and depends upon a variety of other variables including the desirability of the location. Second, cross-sectional studies of SID fail diagnostic tests and produce artefactual findings due to model misspecification. Contrary to this, the re-evaluation of cross-sectional Australian data indicate that demand equations that do not include the doctor supply are misspecified. Empirical evidence from the re-evaluation of Australian medical services data supports the notion of SID. Demand and supply equations are well specified and have very good explanatory power. The demand equation is identified and the desirability of a location is an important predictor of the doctor supply. Results show an average price elasticity of demand of 0.22 and an average elasticity of demand with respect to the doctor supply of 0.46, with the impact of SID becoming stronger as the doctor supply rises. The conclusion we draw from this paper 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 provides a satisfactory, and robust, explanation of the empirical data on the demand for medical services in Australia.
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Notes
Greater detail on differences in the predictions of the orthodox and SID models and their implications for health policy can be found in most health economics text books.
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Acknowledgments
Stuart Peacock is a Michael Smith Foundation for Health Research Scholar. The authors would like to acknowledge the assistance of the Commonwealth Department of Health and Ageing for the provision of the data and, in particular, to Mr Ross Saunders, Director–Medicare Statistics Section, who provided very significant assistance with the collection and interpretation of the data. The authors would also like to thank Professor Max King, former head of the Department of Econometrics, Monash University, for valuable comments on an earlier draft of this paper. The research reported in this paper was supported by a NHMRC Project Grant. The views expressed in this paper are solely those of the authors, and not the funding agency.
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Appendices
Appendix A: Small area definitions
Small area data used in empirical analysis is based on the hierarchical structure of the Australian Standard Geographical Classification [25]. In non-census years the classification consists of statistical local areas (SLAs), statistical sub-divisions (SSDs), statistical divisions (SDs) and states/territories. Under the hierarchical structure SLAs are aggregated to form SSDs, SSDs are aggregated to form SDs, and SDs aggregate into States and Territories. These spatial units cover all of Australia without gaps or overlaps. As at 1999, there were 1,331 SLAs, 194 SSDs and 66 SDs covering mainland and offshore Australian States and Territories. SLAs and SSDs are based on defining regions that show social and economic homogeneity by identifiable links between inhabitants and on local government boundaries. SDs also maintain this basis, but in addition the capital city of each State/Territory is defined as a single SD.
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Peacock, S.J., Richardson, J.R.J. Supplier-induced demand: re-examining identification and misspecification in cross-sectional analysis. Eur J Health Econ 8, 267–277 (2007). https://doi.org/10.1007/s10198-007-0044-7
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DOI: https://doi.org/10.1007/s10198-007-0044-7