“…For example, in monthly data, an increase in the police force in a given month will affect criminal activity in the same month, but an increase in crime cannot alter the size of the police force in that same month because of the much longer lag between a policy decision to increase the working police force and the actual deployment of police officers on the street. This identification strategy has been employed by Corman andMocan (2000, 2005). The third strategy is to find a natural experiment which generates a truly exogenous variation in deterrence, as in Di Tella and Schargrodsky (2004), who use the increase in police protection around Jewish institutions in Buenos Aires after a terrorist attack to identify the impact of police presence on car thefts, and Drago, Galbiati and Vertova (2009) Although these empirical strategies have permitted researchers to refine and improve upon earlier estimates, a convincing natural experiment is very difficult to find, the validity of any instrumental variable can always be questioned, and one can argue that if policy makers have foresight about future crime rates, low frequency data could also suffer from simultaneity bias.…”