1. Determining what factors have an impact on the burglary rate in the United States Team 7 : Adam Fletcher, Branko Djapic, Ivan Montiel, Chayaporn Lertarattanapaiboon, Jared Smith, Carson Marries

2. Introduction What? Our study is to determine which factors have an impact on the burglary rate in the US. Why? Burglary is one of major crimes in the society. It leads to many social problems such as murder and rape. It leads to inefficient and ineffective economy. This test would help us to determine what factors could explain the burglary variable.

3. Introduction How? Figure out what could be possible factors. Income distribution, Unemployment rate, Number of Illegal immigrants. Immigration rates (Removals) : INS Immigration and Naturalization Service -Department of Justice www.ins.govwww.ins.gov Labor rates (Unemployment) : Bureau of Labor Statistics www.bls.gov www.bls.gov Income per capita : www.census.gov Burglary rates : FBI database

4. Executive Summary OBJECTIVE : test which factors have an impact on the annual number of burglaries in the United States (based on the data from 1976-2000) Data Type : Interval Dependent Variable : Number of burglaries per 100,000 people per year. Independent variables : - unemployment rate - lowest income people (% of aggregate income earned by the lowest fifth of the population), - removals (number of immigrants' deportations, exclusions and removals).

5. Number of Burglaries/100,000 People (1976-2001)

6. Number of Immigrants’ Removal (1976-2000)

7. Unemployment Rate and Percentage of Income Earned by the Poorest Population

8. Descriptive statistics (Eviews) BURGLERIES REMOVALS LOWINCOME(%) UNEMPLOYMENT(%) Mean 1212.969 51996.52 3.900000 6.379423 Median 1270.000 30039.00 3.800000 6.137500 Maximum 1684.100 184775.0 4.400000 9.700000 Minimum 728.8000 15216.00 3.500000 4.050000 Std. Dev. 270.1101 52190.02 0.280000 1.449984 Skewness -0.274363 1.792831 0.382653 0.506457 Kurtosis 2.117314 4.620994 1.790920 2.862837 Jarque-Bera 1.170255 16.12978 2.218197 1.131877 Probability 0.557035 0.000314 0.329856 0.567827 Observations 26 25 26 26

9. Correlation matrix BURGLARIES REMOVALS LOWINCOME UNEMPLOYMENT BURGLARIES 1.000000 REMOVALS -0.823129 1.000000 LOWINCOME 0.850458 -0.603907 1.000000 UNEMPLOYMENT 0.669331 -0.701337 0.598846 1.000000

10. Hypothesis Testing Ho: The burglary rate is not dependent on the unemployment rate, deportations, and lowest income people ( b 1,b 2, b 3 =0) H1: The burglary rate is dependent on unemployment rate, deportations, and lowest income people (neither b 1,b 2,nor b 3 =0) The model was developed with multiple regression consisting of the three independent variables with respect to their relationship with burglary. Burglaries = c + Unemployment * b 1 +Lowincome*b 2 + Removals* b 3 + e

11. LS // Dependent Variable is BURGLERIES Date: 11/25/02 Time: 10:17 Sample(adjusted): 1976 2000 Included observations: 25 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. UNEMPLOYMENT -2.123837 20.38615 -0.104180 0.9180 LOWINCOME 527.1420 96.19844 5.479735*** 0.0000 REMOVALS -0.002436 0.000566 -4.302746*** 0.0003 C -692.0907 375.6851 -1.842210 0.0796 R-squared 0.874156 Mean dependent var 1231.856 Adjusted R-square 0.856178 S.D. dependent var 257.5627 S.E. of regression 97.67773 Akaike info criterion 9.308994 Sum squared resid 200359.7 Schwarz criterion 9.504014 Log likelihood -147.8359 F-statistic 48.62434 Durbin-Watson stat 0.791210 Prob(F-statistic) 0.000000

12. Results of Least Squares regression Burgleries = -692.09 - 2.12 Unemployment + 527.1420 Lowincome - 0.002436 Removals The overall model is significant with F-Stat of 48.62. The proportion of robberies that can be explained by the independent variables was calculated to be approximately 87 percent. (R 2 = 87%) The t-stat indicate that Lowincome and removals are significant, but not the unemployment rate. Low income variable is positive significant (the lower income the poorest people gets, the less burglaries). Removals variable negative significant (the more deportations the less robberies). Unemployment rate is not significant: people who commit crimes might not be registered as unemployed.

13. LS // Dependent Variable is BURGLERIES Date: 12/04/02 Time: 16:33 Sample(adjusted): 1976 2000 Included observations: 25 after adjusting endpoints VariableCoefficient Std. Error t-StatisticProb. LOWINCOME 428.4075 85.30810 5.021882*** 0.0001 UNEMPLOYMENT-24.40797 18.44607 -1.323207 0.2000 LOGREMOVALS-240.3018 40.17511 -5.981358*** 0.0000 C 2243.962 697.3820 3.217691 0.0041 R-squared 0.912419 Mean dependent var 1231.856 Adjusted R-squared 0.899907 S.D. dependent var 257.5627 S.E. of regression 81.48621 Akaike info criterion 8.946514 Sum squared resid 139440.0 Schwarz criterion 9.141534 Log likelihood-143.3049 F-statistic 72.92594 Durbin-Watson stat 1.050864 Prob(F-statistic) 0.000000

14. Conclusions Unemployment rate does not explain burglaries rate. A better measure could be labor participation rate because most criminals might not applying for jobs but simply leave the labor force. Although the people who earn less income are getting poorer overtime, the burglary rate doesn’t increase as we expected it would. (We had the opposite result!) Deportations have a negative impact on burglaries. The more people deported the less burglaries.