1. Violent Crime in America ECON 240A Group 4 Thursday 3 December 2009

2. Table of Contents Introduction Data Descriptive Statistics Statistical Analysis

3. What are the causes of crime? Our team hypothesized that there may be five factors contributing to the prevalence of violent crime in a specific jurisdiction  Public Expenditures on Law Enforcement and Public Safety  Public Firearm Ownership  Education  Income  Ethnicity

4. Violent crimes per 100,000 people. Our Measures of Violence

5. Data From the 50 States and DC Education: Percentage of public high school freshman going on to graduate high school. Poverty: Per capita income. Public Spending: per capita expenditures on state and local law enforcement and corrections. Ethnicity: percent of population that is non-white. Firearms percent of households that own guns.

6. Freshmen that Graduate HS Cost of State and Local Law Enforcement Guns Per Household Income Per Capita Percent Non-White (Minorities) Freshmen that Graduate HS Cost of State and Local Law Enforcement Guns Per Household Income Per Capita Percent Non-White (Minorities)

7. Negative Correlations Freshmen that Graduate HS and Percent Non-White (Minorities). Expenditures on State and Local Law Enforcement and Guns Per Household. Guns Per Household and Income Per Capita. Guns Per Household and Percent Non-White (Minorities).

8. Positive Correlations Cost of State and Local Law Enforcement and Income Per Capita Cost of State and Local Law Enforcement and Percent Non-White (Minorities)

9. Correlations to Violence Positive Percent Non-White (Minorities) Income Per Capita State and Local Law Enforcement Expenditures Negative Guns Per Household Freshmen that Graduate HS  All negative and positive correlations are statistically significant

10. Violent Crimes vs. Ethnicity Dependent Variable: VIOLENTCRIMEPER Method: Least Squares Date: 12/02/09 Time: 13:25 Sample(adjusted): 1 51 Included observations: 51 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. PERCENTNONWHITE9.2504461.5475375.9775300.0000 C191.050947.715864.0039290.0002 R-squared0.421698 Mean dependent var431.6078 Adjusted R-squared0.409896 S.D. dependent var238.3361 S.E. of regression183.0855 Akaike info criterion13.29621 Sum squared resid1642495. Schwarz criterion13.37197 Log likelihood-337.0533 F-statistic35.73086 Durbin-Watson stat1.972195 Prob(F-statistic)0.000000  H(0): t0.025,50 =2.009 n=51 H(1): t0.025,50 ≠ 2.009 α=5  Percent of population that is non white is a significant explanatory variable for violent crimes per 100,000 capita

11. Violent Crimes Regressed Against Possible Factors Data Possible FactorsR-squared t-Statistic Non-whites 0.421698 0.0000 Income per capita 0.098054 0.0253 Guns per household 0.108723 0.0181 Expend. on public security 0.333322 0.0000 Freshmen to graduate HS 0.322653 0.0000

12. Multiple Regressions - Average freshman grad and expenditure per capita are significant. - Households with guns are no longer significant. -R-squared = 53,5%

13. Regression Diagnostic The Jarque-Bera statistic suggests that the residuals plot are normally distributed.

14. Multiple Regressions Minority group is still significant in explaining violence per capita. Income per capita is not a significant explanatory variable. Regression is significant. Prob(F- statistic) = 0.000 Dependent Variable: VIOLENTCRIMEPER Method: Least Squares Date: 12/02/09 Time: 14:10 Sample(adjusted): 1 51 Included observations: 51 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. PERCENTNONWHITE8.6796041.5970965.4346160.0000 PERCAPITAINCOME0.0061280.0046821.3088960.1968 C-1.632381154.6450-0.0105560.9916 R-squared0.441628 Mean dependent var431.6078 Adjusted R-squared0.418362 S.D. dependent var238.3361 S.E. of regression181.7674 Akaike info criterion13.30036 Sum squared resid1585891. Schwarz criterion13.41399 Log likelihood-336.1591 F-statistic18.98207 Durbin-Watson stat2.082096 Prob(F-statistic)0.000001

15. Regression Diagnostic The Jarque-Bera p-statistic suggests that the residuals are not normally distributed.

16. Data Issues

17. Residuals Regressions Residuals plotted against the fitted violent crime per capita. White Heteroskedasticity Test: F-statistic20.93164 Probability0.000000 Obs*R-squared23.75862 Probability0.000007 Results from the White Heteroskedasticity for violent crime per capita regressed against expenditures on state and local law enforcement per capita.

18. Crime vs. Expenditures, DC Dummy Dependent Variable: VIOLENTCRIMEPER Method: Least Squares Date: 12/02/09 Time: 23:17 Sample(adjusted): 1 51 Included observations: 51 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. DCEXPENDITUREDUM619.4809185.58773.3379410.0016 EXPEDITURECAPITA0.7706150.04686816.442270.0000 R-squared0.456163 Mean dependent var431.6078 Adjusted R-squared0.445064 S.D. dependent var238.3361 S.E. of regression177.5461 Akaike info criterion13.23476 Sum squared resid1544608. Schwarz criterion13.31052 Log likelihood-335.4865 F-statistic41.10051 Durbin-Watson stat2.256603 Prob(F-statistic)0.000000

19. Dummied out District of Columbia Residuals plotted against the fitted violent crime per capita when District of Columbia is dummied out. White Heteroskedasticity Test: F-statistic1.429467 Probability0.246037 Obs*R-squared4.264286 Probability0.234304 Results from the White Heteroskedasticity Test for violent crime per capita regressed against expenditures on state and local law enforcement per capita when District of Columbia is dummied.

20. Conclusions Income per capita, education, and ethnicity explain violent crimes more significantly than the other explanatory variables explained. Complications:  There are strong correlations between independent variables.  Heteroskedascity was revealed within the regressions.  Hawaii and D.C. skewed the regressions  Residuals were non-normal Crime is more prevalent in concentrated areas of high income per capita, low education, and diverse ethnicities.

21. Works Cited Crime: US Justice Department, Federal Bureau of Investigation, Uniform Crime Report Income: InfoPlease Firearm: Education: Ethnicity: US Commerce Department, Bureau of the Census Public Safety Expenditures: US Justice Department, Office of Justice Statistics, Expenditures and Employment Statistics