Donald Kenkel
;
Joseph V. Terza

the effect of physician advice on alcohol consumption: count regression with an endogenous treatment effect (replication data)

Although there are encouraging trends, alcohol abuse continues to be a significant public health problem. Econometric studies of alcohol demand have yielded a great deal of information for alcohol abuse prevention policy. These studies suggest that higher alcohol taxes and stricter drunk-driving policies can reduce heavy drinking and drunk driving. In this paper we explore the role physician advice plays in the campaign to prevent alcohol-related problems. Compared to alcohol taxation, physician advice is a more precisely targeted intervention that does not impose extra costs on responsible drinkers. Compared to the resource costs of arresting, processing, and punishing drunk drivers, physician advice may be a lower-cost intervention. To provide a basis for alcohol policy analysis, we use an alcohol demand framework to test whether physician-provided information about the adverse consequences of alcohol abuse shifts demand to more moderate levels. There are three aspects of our alcohol demand model that complicate the estimation: (1) the dependent variable is non-negative (it is a count variable-number of drinks consumed); (2) a non-trivial number of sample observations have zero values for the dependent variable; and (3) because the data we use is non-experimental, the treatment variable indicating receipt of advice from a physician may be endogenous. We implement an estimation method that is specifically designed to deal with these three complicating factors. Our results show that advice has a substantial and significant impact on alcohol consumption by males with hypertension, and that failing to account for the endogeneity of advice masks this result.

Data and Resources

Suggested Citation

Kenkel, Donald; Terza, Joseph V. (2001): The effect of physician advice on alcohol consumption: count regression with an endogenous treatment effect (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022314.0708760896