This paper estimates the effect of alcohol use on consumption of hard drugs using the exogenous decrease in the cost of accessing alcohol that occurs when individuals reach the minimum legal drinking age. By using a regression discontinuity design and the National Longitudinal Study of Youth 1997, I find that all measures of alcohol consumption, even alcohol initiation increase discontinuously at age 21 years. I also find evidence that consumption of hard drugs decreased by 1.5 to 2 percentage points and the probability of initiating the use of hard drugs decreased by 1 percentage point at the age of 21 years, while the intensity of use among users remained unchanged. These estimates are robust to a variety of specifications and also remain robust across different subsamples.

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Abstract:Empirically, teenagers who use alcohol or marijuana in one period are more likely to use hard drugs in the future. This pattern can be explained by a causal effect of soft drug consumption on future consumption of hard drugs (i.e., state dependence between drugs or stepping-stone effects) or by unobserved characteristics that make people more likely to use both soft and hard drugs (i.e., correlated unobserved heterogeneity). I estimate a dynamic discrete choice model of alcohol, marijuana and hard drug use over multiple years, and separately identify the contributions of state dependence and unobserved heterogeneity. I find modest-sized but statistically significant "stepping-stone" effects from softer to harder drugs that are largest among the youngest individuals in my sample. This study also suggests that alcohol, marijuana and hard drugs are complements in utility.JEL: I10, C10, C33.

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for helpful comments and suggestions. Dave acknowledges funding support from the Agency for Healthcare Research and Quality (AHRQ) (1 R03 HS025014-01). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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We thank Ross Epstein of SafeGraph for providing us with the data and helping us understand it. This research was supported by the Integrative Biosciences Center (IBio) which has received grant funding from the Michigan Health Endowment Foundation The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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