Classification Analysis
The generation and use of data in the crime area is extremely important in that much of policy turns on conflicting interpretations of the data. As in many ill structured policy areas, the facts often do not speak for themselves. For this reason, the analyst needs to get the facts straight before the interpretation war begins. In the case of prison crowding, there are several significant data trends and data categories that need to be considered to analyze policy, perhaps forcing redefinition of the problem.
First, as indicated in this chapter's case study, since 1980 the prison population has grown ten times faster than the rate of violent crime. While the overall rate of crime has fallen, the rate of violent crime has risen by about 33%. But also since 1980, although the number of inmates has tripled, the proportion of violent criminals in the total prison population has decreased, according to the Cato Institute (2003). The data lead to the obvious question of causation. Some argue that the increased rate of incarceration has led to reduced crime rates. But in New York crime has fallen along with the incarceration rate (The Economist 1999b, 30). In general, increasing incarceration can be associated with decreased crime rates. But disaggregated by state and using different time periods, the proportions vary widely-in many states below-average incarceration rates could also be associated with decreases in crime rates. King, Mauer, and Young (2004) estimate that only about 25% of the decrease in crime rata is attributable to increases in incarceration rates (cited in Rushefsky 2008, 262).Thus, causal relationships need to be examined further.
Second, a useful distinction could be made between the categories of prisoners and drug users to get a clearer picture of the problem. What is the profile of incarcerated offenders? One study explains that 67% of the increase in