1. Determining what factors affect violent crime arrests in California Zhengying Cao, Chad Nassif, Corinna Traumueller, Ryan Sturtevant, Jeong-Jun Lee & Liz Montano

2. Introduction What –Want to estimate what factors affect violent crimes arrests in the state of California. Why –We hope to find what particular characteristics of certain counties cause changes in violent crime arrests throughout the state.

3. Introduction How –Collect data on each of the 58 counties in California for the year 1998. –Run a cross sectional multiple regression analysis.

4. Executive Summary Rather than gathering data across time, we will run a cross sectional analysis across counties. This will help us determine what particular aspects about counties in California affect violent crime arrests. Did violent crime arrests in 1998 depend on unemployment, education, population, expenditures and % minority population

5. Executive Summary Dependent variable –Violent crime arrests Independent variables –Unemployment rate –Weapons arrests –Alcohol arrests –County population –County personal income –Government expenditures on crime and justice –% minorities in county population –education

6. What We Expect Positive Correlation –Unemployment rate –Weapons arrests –Alcohol arrests –Population –% Minorities in county Negative Correlation –Median years in school –Personal income –Crime and Justice expenditures

7. Initial Test

8. The big peak is due to LA county, which is large in comparison to the other California counties.

9. Results Inconsistency of t-stat and f-stat may be due to multicollinearity. By using backward stepwise regression we were able to form a second regression.

10. Second Regression

11. Next step Run violent crimes against population alone to see how well it explains it.

12. Next step It seems logical that the towns with higher populations also have higher violent crime.

13. Major Problem! Population seems to be collinear with almost every variable. Higher populations are correlated with higher levels of personal income, crime expenditures, weapons arrests and alcohol arrests. That is why our initial regression was such a good model.

15. Fix our errors! We must hold population constant by using rates, percentages and per capita variables. Adjusted Variables –Violent crimes per capita –Per capita personal income –Weapons arrests per capita –Alcohol arrests per capita –Expenditures per capita

16. Fix Our Errors Wald test proves that personal income = unemployment = education. –Therefore will only use one, education. Secondly, crime & justice expenditures are dependent on violent crime arrests and violent crime arrests are dependent on crime & justice expenditures. –Therefore we need to either run a two-stage least squares analysis or eliminate it from the model.

17. Final Regression

18. Descriptive Statistics

19. Statistical Analysis When we adjust for population we see that education/per capita personal income/unemployment rate, alcohol arrests per capita and weapons arrests per capita all have an impact on violent crime arrests in the state of California.

20. Statistical Analysis Disregarding multicollinearity, the only insignificant variable seems to be the % of minorities in county population. However minorities are correlated with unemployment, education and personal income. –It is usually minorities within a county that are less educated, unemployed and have less personal income.

21. Conclusion Less employment and less education leads to more miscellaneous & misdemeanor crimes such as alcohol arrests and weapons arrests. The more crime in general per county leads to more violent crimes per county.