The status of health in demand estimation : beyond excellent, good, fair, and poor / Willard G. Manning, Joseph P. Newhouse, John E. Ware, Jr.

By:

Manning, Willard G [author.]

Contributor(s):

Series: Publisher: Santa Monica, CA : RAND, 1981Description: xi, 62 pages ; 28 cmContent type:

text

Media type:

computer
unmediated

Carrier type:

online resource
volume

ISBN:

0833002929

Subject(s):

LOC classification:

RA408.5 .M3 1981

Online resources:

Online Access

Available additional physical forms:

Also available on the internet via WWW in PDF format.

Summary: This study addresses two issues. (1) What can one gain by using more comprehensive measures of health status in demand estimation than a common single item measure? Would you rate your health as excellent, good, fair, or poor? The authors find that by using multidimensional and less-coarse health status measures they achieve an increase in precision approximately equivalent to a 10 percent increase in sample size. (2) What is the consequence of employing postdiction (i.e., predicting utilization from health status measured after the fact) rather than prediction? Using a simple, but plausible, model, the authors show that such measures cause the estimates to be inconsistent; the direction of the inconsistency generally cannot be signed a priori. Empirically the direction is generally away from zero.

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"August 1981."

Includes bibliographical references (p. 59-62).

This study addresses two issues. (1) What can one gain by using more comprehensive measures of health status in demand estimation than a common single item measure? Would you rate your health as excellent, good, fair, or poor? The authors find that by using multidimensional and less-coarse health status measures they achieve an increase in precision approximately equivalent to a 10 percent increase in sample size. (2) What is the consequence of employing postdiction (i.e., predicting utilization from health status measured after the fact) rather than prediction? Using a simple, but plausible, model, the authors show that such measures cause the estimates to be inconsistent; the direction of the inconsistency generally cannot be signed a priori. Empirically the direction is generally away from zero.

Also available on the internet via WWW in PDF format.

Description based on print version record.