title: The pervasiveness of pharmaceutical expenditure inertia in the OECD countries.
abstract: This paper constructs and estimates an economic model for testing statistically the strength of possible 'expenditure inertia' as a plausible reason for rising drug expenditures of the Organization for Economic Cooperation and Development (OECD) countries. The ethical drugs sector in the OECD health care systems is increasingly targeted as the major culprit in the rising cost. Using multiple regression analysis, and the maximum likelihood estimation method, the data of each country (taken from OECD Health Data, 1997) were first tested for functional form optimality with the Box-Cox power family transformations model. Drug expenditure elasticities, at data means, were computed using each country's optimal regression model estimates. The results indicate that the traditionally fitted a priori limited functional form models (e.g., linear, log-log) are not globally consistent with data across countries. The effect of a one-period lagged real per-capita drug expenditure (capturing inertia or habit persistence) on current period real per-capita prescription expenditure is statistically significant in most countries. Pharmaceutical demands are inelastic, and tend to behave like a necessity, as expected. Since the significant effects of economic, demographic, and other drivers of high drug spending differ across countries, country-specific implications and policy suggestions for cost controls ought to differ.
authors.affiliation: Department of Economics, Rm. 450BB, FCoBE, The University of Memphis, Memphis, TN 38152, USA. aokunade@memphis.edu
authors.forename: Albert A
authors.initials: AA
authors.lastname: Okunade
authors.forename: Chutima
authors.initials: C
authors.lastname: Suraratdecha
mesh_terms: D000368:Aged; D019049:Developed Countries; D016527:Drug Costs; D005260:Female; D005102:Health Expenditures; D006291:Health Policy; D006801:Humans; D016013:Likelihood Functions; D017059:Models, Econometric; D015999:Multivariate Analysis; D012044:Regression Analysis