Quantitative criminologists often use temporally lagged variables to estimate the structural forces contributing to variation in crime rates. We elucidate the relevance of temporal lags for cross-national research by looking specifically at the lagged longitudinal relationship between urbanization and homicide rates. Using cross-national time-series data for (n = 83) nations, we run a series of 10 separate panel models, in which we incrementally increase the time lag between the dependent variable homicide rate and two independent measures of urbanization, controlling for changes in GDP and age-structure as well as fixed effects for time and unit. Results from these panel models confirm that the two measures of urbanization are oppositely associated with homicide rates. Moreover, while the magnitudes of the associations for both predictors decline as lag time increases, they continue to be statistically significant. These results provide evidence that urbanization has countervailing and persistent consequences for homicide rates that ripple through time. These results also lead us to conclude that a more systematic approach to lag time in longitudinal research is needed.