Marijuana is the most common illicit drug with vocal advocates for legalization. Among other things, legalization would increase access and remove the stigma of illegality. Our model disentangles the role of access from preferences and shows that selection into access is not random. We find that traditional demand estimates are biased resulting in incorrect policy conclusions. If marijuana were legalized, those under 30 would see modest increases in use of 28 percent, while on average use would increase by 48 percent (to 19.4 percent). Tax policies are effective at curbing use, where Australia could raise AU$1 billion (and the United States US$12 billion). (JEL D12, H25, K14, K42)
This paper is concerned with the use of a Bayesian approach to fuzzy regression discontinuity (RD) designs for understanding the returns to education. The discussion is motivated by the change in government policy in the United Kingdom (UK) in April of 1947 when the minimum school leaving age was raised from 14 to 15, a change that had a discontinuous impact on the probability of leaving school at age 14 for cohorts who turned 14 around the time of the policy change. We develop a Bayesian fuzzy RD framework that allows us to take advantage of this discontinuity to calculate the effect of an additional year of education on subsequent log earnings for the (latent) class of subjects that complied with the policy change. We illustrate this approach with a new data set composed from the UK General Household Surveys.
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AbstractChild birth leads to a break in a woman's employment history and is considered one reason for the relatively poor labor market outcomes observed for women compared to men. However, the time spent at home after child birth varies significantly across mothers and is likely driven by observed and, more importantly, unobserved factors that also affect labor market outcomes directly. In this paper we propose 1 We are grateful to J.J. Heckman, Remi Piatek, Siddharta Chib and seminar participants at the University of Melbourne, Monash University, University of Innsbruck, as well as at the ESOBE 2012 meeting, the 5th Rimini Bayesian Workshop, and the Melbourne Bayesian Econometrics Workshop 2013 for helpful comments and suggestions. We gratefully acknowledge funding from the Austrian Science Fund (FWF): S10309-G16 and University of Melbourne.2 Corresponding author. email: Sylvia.Fruehwirth-Schnatter@wu.ac.at 1 two alternative Bayesian treatment modeling and inferential frameworks for panel outcomes to estimate dynamic earnings effects of a long maternity leave on mothers' earnings in the years following the return to the labor market. The frameworks differ in their modeling of the endogeneity of the treatment and the panel structure of the earnings, with the first framework based on the modeling tradition of the Roy switching regression model, and the second based on the shared factor approach. We show how stochastic variable selection can be implemented within both frameworks and can be used, for example, to test for the heterogeneity of the treatment effects. Our analysis is based on a large sample of mothers from the Austrian Social Security Register (ASSD) and exploits a recent change in the maternity leave policy to help identify the causal earnings effects. We find substantial negative earnings effects from long leave over a 5 years period after mothers' return to the labor market, with the earnings gap between short and long leave mothers steadily narrowing over time.
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