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Unformatted text preview: 438 A number of econometric studies (Charles R. Hulten 1992; Byong-Hong Bahk and Michael Gort 1993; Plutarchos Sakellaris and Daniel J. Wilson 2004) have investigated the hypothesis that capital equipment employed by US manu- facturing firms embodies technological change, i.e., that each successive vintage of investment is more productive than the last. Equipment is expected to embody significant technical progress due to the relatively high research and development (R&D) intensity of equip- ment manufacturers. The method that has been used to test the equipment-embodied technical change hypothesis is to estimate manufacturing production functions, including (mean) vintage of equipment as well as quantities of capital and labor. These studies have concluded that techni- cal progress embodied in equipment is a major source of manufacturing productivity growth. Embodied technical progress may also be an important source of economic growth in healthcare. One important input in the produc- tion of health—pharmaceuticals—is even more R&D intensive than equipment. According to the National Science Foundation (NSF), the R&D intensity of drugs and medicine manufacturing is 74 percent higher than the R&D intensity of machinery and equipment manufacturing. There- fore, it is quite plausible that there is also a high rate of pharmaceutical-embodied technical progress. This study examines the effect of changes in the vintage distribution of prescription drugs on US longevity and medical expenditure during the 1990–2003 period. We will estimate the follow- ing model, using longitudinal disease-level data: (1) ln Y it 5 b X it 1 a i 1 d t 1 e it , where Y it is a measure of mortality or healthcare utilization, and X it is a measure of prescription drug vintage for medical condition (disease) i in The Impact of New Drugs on US Longevity and Medical Expenditure, 1990–2003: Evidence from Longitudinal, Disease-Level Data By Frank R. Lichtenberg* year t . Since the model includes condition and year fixed effects, it is a difference-in-differences model. Negative and significant estimates of b would indicate that conditions with above average increases in prescription drug vintage had above average declines (or below average increases) in mortality and hospitalization. Equation (1) will be estimated using weighted least squares (WLS), where the weight is equal to Y i 5 (1/ T ) g t Y it . Since the dependent variable is ln Y it , and the model includes fixed condition effects, we are in effect analyzing percentage deviations from condition means. Low-mean conditions exhibit much more volatility (noise) than high-mean conditions, so it is appropriate to give more weight to the percentage deviations from high-mean conditions....
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This note was uploaded on 04/04/2011 for the course ECON 3301 taught by Professor Staff during the Spring '08 term at UT Arlington.
- Spring '08