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1.
J Drug Educ ; 28(2): 147-57, 1998.
Article in English | MEDLINE | ID: mdl-9673074

ABSTRACT

Identifying groups of young people that might be especially susceptible to marijuana use would aid in the design and implementation of drug policy programs. This article examines whether those who participated in high school athletics have a different pattern of marijuana use than comparable non-athletes. The results indicate that male athletes have a higher incidence of marijuana use than non-athletes. The same is not true for female athletes, who actually engage in less marijuana use than their non-athlete counterparts. However, female athletes are more likely than non-athletes to wait until their post-high school years to try the drug for the first time.


Subject(s)
Doping in Sports/prevention & control , Health Policy , Marijuana Abuse/prevention & control , Adolescent , Adult , Cross-Sectional Studies , Curriculum , Doping in Sports/statistics & numerical data , Female , Health Education , Humans , Longitudinal Studies , Male , Marijuana Abuse/epidemiology , United States/epidemiology
2.
Appl Econ Lett ; 4(11): 665-9, 1997 Nov.
Article in English | MEDLINE | ID: mdl-12348722

ABSTRACT

PIP: The authors examine the temporal relationship between population growth and economic growth in Nepal, India, Ghana, Sri Lanka, Bolivia, Philippines, Guatemala, Syria, Peru, Thailand, Chile, Argentina, and Mexico, conducting Granger-causality tests in the context of error correction models when cointegration is present. Their goal is to provide additional time series econometric evidence on the short-run and long-run time series behavior of population growth and the growth of real per capita gross domestic product for a sample of low to middle income developing countries. Cointegration was found in only 3 of the 13 countries examined. Even though 10 countries in this study exhibited no properties of cointegration, researchers conducting time series studies of the relationship between population growth and economic growth using differenced data should nonetheless evaluate the possible long-term relationship. Capturing the short- and long-run behaviors of the respective time series may give the researcher a more robust test of Granger-causality.^ieng


Subject(s)
Developing Countries , Economics , Models, Theoretical , Population Growth , Time Factors , Demography , Population , Population Dynamics
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