We exploit migration patterns from the UK to Australia and the US to investigate whether a person’s decision to smoke is determined by culture. the country of destination and origin respectively is usually a binary variable that takes value one if individual who belongs to five-year cohort smokes at age and zero otherwise. is usually a vector of time-varying variables that potentially determine smoking behavior and as a separate covariate from the other controls in Finally we model the error term as consisting of a permanent individual-specific component and an individual- and age-varying term we follow the epidemiological literature by using data from the country of immigrant origin but we also advance the literature by refining our measure further. Specifically we proxy British smoking culture by cohort-specific smoking prevalence rates in the UK net of persistence effects and the effect of any causal contextual factors common with the host countries.1 To do this we estimate a model of smoking participation of British natives for each of the host countries. In each model we control for the average smoking prevalence of the corresponding native-born cohort Rabbit Polyclonal to OGFR. in the host country if is usually native-born which we treat as an endogenous variable. Similar to the model in equation (1) we allow for persistence effects (across all members of each five-year birth cohort. The resulting time series of the cohort to which her parents belong assuming (for now) that parents and offspring are given birth to twenty five years apart. For example individuals who are 10-15 years old have parents who are 35-40 years old. Similarly individuals who are 15-20 years old have parents who are 40-45 years old and so on. Finally we lag this value by twenty five years. That is we use the 12 months when the parents’ cohort in the UK was the same age as their children currently are.2 Formally we set: to be zero for individuals who never smoke and nonzero for individuals who do smoke for some period in their lives. We also expect these deviations to be correlated with the factors that our fixed effects capture e.g. completed education. We use the difference GMM estimator because it is not sensitive to this correlation. In estimating the models we have paid special attention to the choice of devices for our endogenous variables. The literature warns that depending on the time PMPA (NAALADase inhibitor) dimension (= 3 the method produces one instrument per endogenous variable but as grows the instrument count can explode. When one uses too many devices it is easier to overfit the endogenous variable and thereby fail to account for its endogeneity. Moreover the Hansen assessments of overidentifying restrictions are vulnerable to instrument proliferation. In this situation they more often fail to detect overfitting. To avoid PMPA (NAALADase inhibitor) this problem we significantly restrict the number of lags that we use as devices. In doing so we aim to choose lags that capture on average the time of smoking initiation of the individuals in our sample which is usually where most of the variation in our dependent variable is concentrated. Because women typically initiate smoking later and over a longer time period than men we generally use smaller lags for women than men. The exact number of lags we use differs by sub-sample and is guided by the Hansen assessments PMPA (NAALADase inhibitor) for instrument validity.8 Finally to avoid spurious precision from implausibly small standard errors we apply the Windmeijer (2005) correction in all our estimations. One can also estimate our model with the dymanic random effects probit because it controls for both persistence and individual heterogeneity. An advantage of that estimator is usually that it produces predictions strictly within the 0-1 range. However we do not use it because it relies on assumptions that are more difficult to defend. In particular this estimator requires the assumption that the initial observations in (3) so that = + and of that cohort in the year they were 15-20 PMPA (NAALADase inhibitor) years old (t-25). 3 an alternative to this specification one could estimate a reduced-form of equation (1) using as the culture proxy while controlling for (persistence) and (common context). We prefer to use the two-step approach because of its intuitive appeal because it allows us to address the potential endogeneity between and