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compare pre- and post-treatment periods (where the “treatment” is a change in price) and control

for any time trend as well as any permanent average difference between the treatment and

control group (e.g., whether a firm offers coverage at all). Difference-in-difference estimation

assumes that a parallel trend would have occurred for the treatment and control groups in the

absence of the treatment, all else being equal. It yields a biased estimate of demand elasticity if

this assumption fails.

Observational studies—those that rely on secondary data from household or employer

surveys—typically suffer most from endogeneity. Most observational studies capture price

variation based on existing differences in plans or coverage, making it very difficult to establish

how price changes may have affected demand. Researchers have developed complex statistical

models to deal with endogeneity, many relying on instrumental variables. An instrumental

variable must be correlated with the endogeneous variable itself (in this case, price), but

uncorrelated with the outcome variable (i.e., demand), except through the endogeneous variable.

A variable that would meet both criteria is extremely hard to find.

C. OMITTED VARIABLES

Many statistical models, using a variety of data sources, have been developed to control for

factors other than price or income in estimating demand elasticity. Nevertheless, the likelihood

that an estimation model would miss some determinants of demand—and that the resulting

elasticity estimate is biased—is very high. However, the number of potential omitted variables

probably has declined, as data and information about health insurance and health care markets

have improved. Many variables that were unavailable in earlier studies now are often available

and widely used—for example, individual health status, plan benefit design, and whether

coverage is obtained through a spouse or other source.

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