The basic structure of our regression model is given by: Yist=α+b1X1ist+….+bnXnist+bmplaMLDAst+εi where Yist refers to an outcome of interest for individuals i living in state s and born in year t. In the case of a logit model, Y is Ln(R/1−R)i, where R may be the probability of a past-year alcohol or substance use disorder, or the probability of a cross-state move for individuals with a vector of characteristics Xi …Xn; in the case of a probit model, Y is the inverse of the cumulative normal distribution function for R. The vector of covariates always includes survey, gender, ethnicity, and unordered indicators of state of residence, age group, and year of birth. Some models also included interaction terms between MLDA exposure and demographic subgroups (e.g., gender or race), and others included an interaction between state and a linear measure of birth year, to further control for state-specific time trends. Some “long” models included other control variables, including family history of alcohol problems, state beer taxes when the respondent was 18, and state per capita alcohol consumption when the respondent was
