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

Table of Contents Introduction Data Descriptive Statistics Statistical Analysis

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

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

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.

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)

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).

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)

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

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. PERCENTNONWHITE C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)  H(0): t0.025,50 =2.009 n=51 H(1): t0.025,50 ≠ α=5  Percent of population that is non white is a significant explanatory variable for violent crimes per 100,000 capita

Violent Crimes Regressed Against Possible Factors Data Possible FactorsR-squared t-Statistic Non-whites Income per capita Guns per household Expend. on public security Freshmen to graduate HS

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

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

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) = 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. PERCENTNONWHITE PERCAPITAINCOME C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

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

Data Issues

Residuals Regressions Residuals plotted against the fitted violent crime per capita. White Heteroskedasticity Test: F-statistic Probability Obs*R-squared Probability Results from the White Heteroskedasticity for violent crime per capita regressed against expenditures on state and local law enforcement per capita.

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. DCEXPENDITUREDUM EXPEDITURECAPITA R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

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-statistic Probability Obs*R-squared Probability 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.

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.

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