Working Paper: NBER ID: w16953
Authors: Amitabh Chandra; Jonathan S. Skinner
Abstract: In the United States, health care technology has contributed to rising survival rates, yet health care spending relative to GDP has also grown more rapidly than in any other country. We develop a model of patient demand and supplier behavior to explain these parallel trends in technology growth and cost growth. We show that health care productivity depends on the heterogeneity of treatment effects across patients, the shape of the health production function, and the cost structure of procedures such as MRIs with high fixed costs and low marginal costs. The model implies a typology of medical technology productivity: (I) highly cost-effective "home run" innovations with little chance of overuse, such as anti-retroviral therapy for HIV, (II) treatments highly effective for some but not for all (e.g. stents), and (III) "gray area" treatments with uncertain clinical value such as ICU days among chronically ill patients. Not surprisingly, countries adopting Category I and effective Category II treatments gain the greatest health improvements, while countries adopting ineffective Category II and Category III treatments experience the most rapid cost growth. Ultimately, economic and political resistance in the U.S. to ever-rising tax rates will likely slow cost growth, with uncertain effects on technology growth.
Keywords: No keywords provided
JEL Codes: D24; I1; I12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Technological growth (O49) | Rising health care expenditures (H51) |
| Technological growth (O49) | Improved health outcomes (I14) |
| Rising income levels (F61) | Increased health care costs (H51) |
| Insurance structures (G22) | Expenditure growth (E20) |
| Economic and political resistance to tax increases (H26) | Slowed cost growth (E31) |