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Determinants of International Differences in Health Expenditures

Determinants of International Differences in Health Expenditures. Economics 737.01 1/18/11. Outline. I. Introduction II. Theory III. Cross-Sectional Analyses IV. Panel Analyses V. Conclusion. I. Introduction. Why does the US spend so much more on health care than anyone else?

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Determinants of International Differences in Health Expenditures

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  1. Determinants of International Differences in Health Expenditures Economics 737.01 1/18/11

  2. Outline • I. Introduction • II. Theory • III. Cross-Sectional Analyses • IV. Panel Analyses • V. Conclusion

  3. I. Introduction • Why does the US spend so much more on health care than anyone else? • Are international differences in health care spending all about income … or do institutions matter? • More or less … but institutions can matter on the margin

  4. I. Introduction • Table 1 (1998): • Per capita health spending in OECD countries ranges from $391 (Mexico) to $4090 (US). • Positive relationship between GDP and health spending is evident • Effect of public financing is less obvious

  5. I. Introduction • Numerous studies have employed country-level regression analysis in an attempt to explain these differences. • Advantage of international approach (relative to, for instance, a county- or state-level approach using US data): • Can study impacts of variables that vary only at the national level (i.e. institutional characteristics of health care system) • Disadvantages of international approach: • Lack of data comparability across countries • Small sample size

  6. II. Theory • There is a lack of formal theoretical modeling of the factors that could influence a nation’s health care spending • Would be very complicated • Factors that could play a role • Income: increases demand => increases price and quantity

  7. II. Theory • Larger government role in health care system • Could increase costs because of bureaucratic inefficiency • Could increase (or decrease) costs because of lack of competition • Could decrease costs through economies of scale • Could decrease costs by increasing buyer’s bargaining power (monopsony) • How does this tie into debate about public option?

  8. II. Theory • Physician payment method • Fee-for-service: pay based on services provided • Capitation: paid a set amount per patient • Salary • Which of these would we expect to lead to the highest expenditures? • Organization of health care system • Reimbursement: service provided then bill sent; typically private providers and private payers (US fee-for-service) • Contract: prearranged agreement between payers and providers; typically private providers and public payer (Canada’s single-payer system) • Integrated: same agency (usually government) controls provision and payment (UK’s government-run system)

  9. II. Theory • Public insurance budget requirements • No budget ceiling: If go over budget, adjust reimbursement rates later (U.S. Medicare) • Budget ceiling: Stay in budget by rationing (Canada) • Budget ceiling should lead to lower spending • Gatekeeper: general practitioner must refer to specialists • Could decrease costs by reducing specialist visits • Could increase costs by increasing g.p. visits

  10. II. Theory • Number of doctors: • Could lower costs through increased competition • Could increase costs through demand inducement • Expanded insurance coverage: should increase costs by lowering price faced by consumers • Increased provision of high-cost procedures: should increase costs • More in-patient as opposed to outpatient care: should increase costs

  11. III. Cross-Sectional Analyses • Early studies used a cross-section of countries and estimated models of the form • Where HEi =exchange-rate-adjusted health expenditures in country i and there are k explanatory variables (GDP and maybe others). • What do you think of this approach? • Key debate: Is the income elasticity of HE >1? • If so, HE would naturally be a larger share of GDP as GDP rises

  12. III. Cross-Sectional Analyses • Newhouse (1977) • 13 countries, 1971 data • GDP was the only explanatory variable and explained 92% of the variation in HE • Income elasticity>1 => health care is luxury good • Leu (1986) • 19 countries, 1974 data • Added variables reflecting age, urbanization, government structure, and extent of public sector involvement (proportion of beds public; proportion of payment public) • GDP dominant variable; income elasticity still > 1 • Public provision increased costs, but this has been disputed in later work

  13. III. Cross-Sectional Analyses • Gerdtham et al. (1992a, 1992b) • 19 countries, 1987 data (1992a) or pooled data from 1974, 1980, and 1987 (1992b) • Combining the results from the two papers yields the following set of results:

  14. IV. Panel Analyses • How would panel data help to overcome the identification problems present with cross-sectional data? • Gerdtham (1992) uses both random and fixed effects models. A priori, what are the benefits of each approach in this context?

  15. IV. Panel Analyses • Gerdtham (1992) • 22 countries, panel spanning 1972-1987 • Data favors two-way (country and year) fixed effects • Independent variables: • GDP • Inflation • Fraction of Public Financing • Fraction 65+ • Results • Income elasticity 0.74 • Other variables insignificant

  16. IV. Panel Analyses • Hitiris and Posnett (1992) • 20 countries, panel spanning 1960-1987 • County but not year fixed effects • Income elasticity around 1 • Some small effects of non-income variables

  17. IV. Panel Analyses • Gerdtham et al. (1998) • 22 countries, panel spanning 1970-1991 • Independent variables: • GDP, % 75 years and older % 4 years and under • female l.f.p. rate unemployment rate • alcohol intake tobacco consumption • % health bills public % health bills inpatient • renal dialysis per capita gatekeeping system • physicians per capita physicians*fee for service • reimbursement system integrated system • budget ceilings wage/salary • direct payment by patient capitation • fee for service overbilling (no set price schedules) • country and year fixed effects

  18. IV. Panel Analyses • Barros (1998) • 24 countries; panel data from 1960-90 • Dependent variable in main analysis: 10-year growth rate of HE • Independent variables: • Initital HE and HE squared • Gatekeeper • Reimbursement and integrated dummies • GDP growth rate • % over 65 years old • Decade dummies • Results: • Income elasticity less than but around 1 • Unable to conclude institutional variables mattered for growth rates

  19. V. Conclusion • Literature has evolved from cross-sectional to panel data models but still leaves room for improvement regarding theory and empirical identification • Income appears to be the dominant variable in explaining international differences in health spending • In best models, income elasticity slightly <1 • Some weak evidence that incentives from institutions matter

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