Multiple Regression

Investigating the determinants of property crime in the United States

Case #49 – Property Crimes

Introduction

This report is an investigation of the determinants of property crimes in the United States. By property crimes, typically the reference is to burglary, larceny, theft and motor vehicle theft. We seek to gain an understanding of what socio-economic characteristics of a particular state make it more susceptible to a larger number of property crimes.

Data and methodology

By using multiple regression analysis, the report is in pursuit of obtaining statistical evidence for or against commonly held beliefs regarding causality of various factors and crime. We use a State-wide data set that includes a record of property crimes rates (CRIMES) as well as a record on per capita income (PINCOME), school dropout rates (DROPOUT), precipitation amounts (PRECIP), percentage of public aid recipients (PUBAID), population density (DENSITY), public aid for families with kids in terms of dollars received (KIDS), percentage of unemployed workers (UNEMPLOY), percentage of population living in urban areas.

The methodology that we use is that of multiple regression analysis to obtain the magnitude and signs of the coefficients and t and F-tests obtain whether the respective coefficients are significant, individually, or jointly. The regression equation we estimate is the following:

(1) CRIMES= β0+β1PINCOME+β2DROPOUT+β3PUBAID+β4DENSITY++β5KIDS+β6PRECIP+β7UNEMPLOY+β8URBAN+ε

Results

In this section we present the results of the analysis. Table 1 presents the results of the estimation of equation (1).

Table [ 1 ]: Results of simple OLS regression, all variables included

Before interpreting the coefficients we look at the individual and joint significances of the estimated coefficients. From the upper right hand panel we find that F(8, 41) = 11.43 and prob>F =0.000. Recall that the null hypothesis of the f-test is that all coefficients are jointly zero. From the...