The impact of new drug launches on longevity: evidence from longitudinal, disease-level data from 52 countries, 1982-2001 Frank R. Lichtenberg Columbia University and National Bureau of Economic Research United Nations Human Development Index (unweighted) average of three indexes:
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
The impact of new drug launches on longevity:evidence from longitudinal, disease-level data from 52 countries, 1982-2001
Frank R. Lichtenberg
Columbia University and
National Bureau of Economic Research
(unweighted) average of three indexes:
Nordhaus: “to a first approximation, the economic value of increases in longevity over the twentieth century is about as large as the value of measured growth in non-health goods and services”
Unlike GDP, longevity is converging
Sources of longevity increase?
improved quality of, and access to, medical care
other factors
Y = (A L) 1- K
Y = output
A = the “stock of ideas”
L = labor used to produce output
K = capital
0 < < 1
The cumulative number of drugs launched (N_DRUG) is analogous to the stock of ideas.
AGE_DEATHijt = b ln(N_DRUGij,t-k)
+ g Xijt + eijt
AGE_DEATHijt = a statistic based on the age distribution of deaths from disease i in country j in year t
N_DRUGij,t-k = the number of drugs launched to treat disease i in country j by year t-k
Xijt = a vector of other factors (e.g. education, income, nutrition, the environment, and “lifestyle”) affecting the age distribution of deaths from disease i in country j in year t
Hypothesize that many of the “other factors” affecting the age distribution of deaths from disease i in country j in year t (e.g. per capita income, public health expenditure, and environmental quality) are:
decompose Xijt as follows:
Xijt = a’it + d’jt + ’ij + ’ijt(2)
where
a’it = a fixed effect for disease i in year t
d’jt = a fixed effect for country j in year t
’ij = a fixed effect for disease i in country j
AGE_DEATHijt = b ln(N_DRUGij,t-k)
+ ait + djt + ij + uijt
Zero-lag equation (k = 0), is estimated using 4678 observations, included 496 country*year effects, 189 disease*year effects, and 502 country*disease effects. The equations are estimated via weighted least squares, using the number of deaths in that disease-country-year cell as the weight.
Launch dateCountry
6/00USA
3/01Finland
5/01UK
9/01Norway
10/01Canada
10/01South Africa
11/01Ireland
AGE_DEATHijt = bNCE ln(CUM_NCEij,t-k)
+ bNON ln(CUM_non-NCEij,t-k)
+ ait + djt + ij + uijt
CUM_NCE = the cumulative number of NCEs launched
CUM_non-NCE = the cumulative number of non-NCEs launched
Hypothesize that bNCE > bNON
bNON could be negative?
11 broad disease categories
Micro evidence from a Medicaid program
Probability
of death by
end of 2002
540,000 people
12.2 million claims
The Economics of Invention Incentives: Patents, Prizes, and Research Contracts
Brian D. Wright
American Economic Review 73,
1983, pp. 691-707.
Alternative mechanisms:
Optimal number of firms
If government can determine the optimal number of contracts (n), and firms engage in energetic research even though payments are independent of success, govt. should offer research contracts to the n lowest bidders; competition drives price down to cost
When the government has full information, patents and joint ventures are less desirable than prizes or research contracts because they distort pricing
However, if inventors have more information before they start inventing than do government officials, patents and joint ventures may be superior