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Beyond the Market: Designing Nonmarket Accounts for the United States
culty is in decomposing medical spending increases into price and real output. To put it another way, it can be difficult to differentiate among price, quantity, and quality changes. For example, a change in the observed price of treating a disease may reflect a change in the price of unchanged treatment inputs, a change in the amount of inputs (e.g., a surgeon’s time) required, the development and use of new drugs or procedures that alter outcomes (e.g., survival rates, patient quality of life), or simultaneous changes in more than one of these factors.
To measure changes in real output, changing quality must be taken into account. For indexes constructed from quantity data, quality adjustments can be made directly. If a new drug is introduced that is equally effective as the old one but only has to be taken once a week instead of every day, the quality (and, in a real sense, the per unit quantity) has increased. If the account is built from price and expenditure data, as in most NIPA components, quality adjustment is made by deflating prices. In the drug example, if the observed unit price increases by anything less than seven-fold, the real price (as dictated by how much has to be spent on the drug per week) actually decreases. Whether a direct quantity-based index or a price-deflated quantity index can be constructed depends on data availability.
This quality measurement problem is prevalent in many industries, not just medical care. When a car costs more because the brakes are better, or when airline tickets cost less because the quality of service is poorer, the change in cost should be attributed to the change in real output, not a change in price. For some manufactured goods, it is possible to estimate the portion of an observed price change attributable to a change in quality in a relatively straightforward way—using direct measurement or hedonic adjustment techniques. Indeed, the Bureau of Labor Statistics (BLS) does just this for a number of goods in construction of its price indexes.1 The hedonic approach can be problematic for the medical care case. Because most people have health insurance, and because patients are not completely in charge of what they buy, it is not clear that observed prices reflect willingness to pay for particular medical sevices. Since there is not as yet a direct measure of health, or of the contribution of medical care to health, BLS is not able to estimate the productivity of medical care in the usual way. As a result, it is widely believed that some of what is captured as growth in the price of medical care in fact reflects improvements in the quality of care, and that medical care inflation is therefore overstated (see Berndt et al., 2000; Boskin et al., 1996; U.S. Bureau of Labor Statistics, 1997; Shapiro and Wilcox, 1996). Better estimates
Hedonic models are regression equations designed to capture the relationship between a good’s characteristics and its price. The estimated coefficients are used to decompose observed changes in price into a portion attributable to changes in items’ characteristics and a portion that represents true price change. See National Research Council (2002a: Ch. 4) for a discussion of hedonic methods and National Research Council (2002a: Ch. 6) for recommendations specifically on the pricing of medical care.