A pooled cross-section analysis of the health care expenditures of the OECD countries

Dev Health Econ Public Policy. 1992;1:287-310. doi: 10.1007/978-94-011-2392-1_14.

Abstract

This paper has two purposes. The first, empirical purpose is to estimate and evaluate the effects of aggregate income, institutional and socio-demographic factors on health care expenditures in the OECD countries. The second purpose is methodological, and comprises assessment of temporal instability, the choice of functional form, and misspecification of the estimated relationships. Data compiled over three years (1974, 1980 and 1987) from 19 OECD countries are used in a pooled cross-section regression analysis. Like previous studies, this one concludes that aggregate income measured by Gross Domestic Product per capita is the statistically most important factor in cross-national variation in health care expenditures, and that the aggregate income elasticity exceeds one. However, the data analyzed in this study also show some evidence that public financing of health care services is associated with lower expenditures per capita, and that countries with fee for service as the dominant form of remuneration have higher expenditures. The examined relationships appear to be temporally stable over the three years except for upward shifts, and there is no indication of statistical misspecification. This does not necessarily imply a correct specification, and we do note the presence of measurement errors in some of the variables. Moreover, the selected log-linear functional form appears to be non-optimal according to a likelihood criterion, and is rejected against a quadratic form. Based on the analyses from this study the results do not appear to be sensitive to use of the quadratic form specification.

Publication types

  • Review

MeSH terms

  • Europe
  • European Union
  • Financing, Government
  • Health Expenditures / statistics & numerical data*
  • Health Services Needs and Demand
  • Health Services Research / methods
  • Humans
  • Infant, Newborn
  • Models, Econometric*
  • Private Sector
  • Public Sector
  • Regression Analysis
  • Research Design
  • Socioeconomic Factors