The model provides a dynamic description of the crime rate in a given time period by using a multiplicative combination of three independently derived factors: the average rate at which individual offenders commit crime, the proportion of the criminal population which is free at any given time, and the proportion of the population choosing to engage in crime at a given time. The proportion who commit crimes is viewed as a function of the probability of imprisonment for a convicted offender and the average sentence length. The model can be used to determine the sentencing strategy which will have the greatest effectiveness in controlling crime. The model can also separate the incapacitative and deterrent effects of a sentencing policy. Application of the model to data from Missouri, Texas, and Georgia supports the hypothesis that career criminals tend to commit crimes in a nonseasonal fashion and that the seasonality in crime data results from the effects of marginal and transient elements within the total criminal population. Findings also forecast that crime rates will rise in the three States over the next two decades as a result of an increasing level of deviance within a fairly fixed criminal population size. The model also implies that Texas and Missouri should be able to partially offset the expected increase in crime through adjustment of imprisonment policy. Tables, an appendix discussing methodological background, and a list of 33 references are provided.