Social structure, crime, and imprisonment : a causal analysis

Abstract

The major objective of this research was the development and analysis of an integrated causal model for predicting crime rates, prison admissions, and prison releases at the macro (state) level of analysis. These dependent variables were predicted in a sequential fashion from structural attributes which included percent urban, percent Negro, age distribution, educational level, per capita income, and population size. Standardized regression coefficients were computed for a static model, which utilized data from the year 1970, and a dynamic model, which employed data reflecting changes in all relevant variables from 1960 to 1970. General systems theory was utilized as a broad, underlying framework. Several theoretical perspectives were utilized for the construction of the causal model. They included social structural theory, conflict theory, labeling theory, and demographic theory. The static model was strongly supported. Crime rates were effectively predicted from social structural characteristics, with percent urban being the primary determinant. Prison admissions were predicted from structural attributes and crime rate, with percent Negro having the strongest effect on prison admissions. Prison releases were predicted from all causally prior variables, with prison admissions being the primary determinant. With regard to the dynamic model, changes in the structural attributes were found to account for a large amount of the variation in changes in crime rate. The states which experienced the greatest increases in crime rate were those which had gained in per capita income..