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To What Extent are Law Enforcement and Socioeconomic Improvements Significant to Deter Violent and Property Crime Chowdhury Khalid Farabee and Papa Loum.

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Presentation on theme: "To What Extent are Law Enforcement and Socioeconomic Improvements Significant to Deter Violent and Property Crime Chowdhury Khalid Farabee and Papa Loum."— Presentation transcript:

1 To What Extent are Law Enforcement and Socioeconomic Improvements Significant to Deter Violent and Property Crime Chowdhury Khalid Farabee and Papa Loum Professor LaFave Econometrics Spring 2014 Abstract According to a government survey released in October 2013, violent crime in the United States increased by 15 percent last year, and property crime was up by 12 percent. If violent crime keeps increasing at this rate, it will approximately double in just six years. To effectively allocate resources (money, police, courts, prisons), specialists in the economics of crime should fully understand the most pivotal determinants of each type of crime. Our study finds the extent to which some socioeconomic factors and regulatory measures (high school dropout rate, median income, unemployment rates, imprisonment rate, number of police officers, and the political party in precedency) relate to violent and property crime rates. Our data analysis suggests that in comparison to law enforcement variables, socioeconomic factors exert a higher influence on both violent crime and property crime rates. In other words, socioeconomic improvements would be much more effective in deterring crime as compared to tougher law enforcement. The effects of our law enforcement variables are uncertain and inconsistent. Policy makers must therefore understand they must allocate more resources in improving general economic conditions. Data Analysis and Results Sources: The crime rates below are per 100,000 people and are obtained from the Uniform Crime Reporting Statistics from the FBI. The control variables are obtained from various other reliable data sources such as the National Centre for Education Statistics, the United States Census Bureau, the Bureau of Justice Statistics etc. Conclusion Our results suggest that socioeconomic improvements would lead to a much larger drop in property crime rates in comparison to law enforcement methods. The effects of our law enforcement variables are inconsistent because of the opposing influences that factors like lagged imprisonment rates exert on crime. Since our instrument of lagged values fails to address the issue of potential feedback effects, the impact of tougher law enforcement on crime is uncertain. Even though improvements in socioeconomic factors would exert a strong negative influence on both violent crime rates and property crime rates, they tend to be much more effective in deterring property crime rates in comparison to violent crime rates. Policy makers must therefore understand that they must allocate more resources in improving socio-economic factors to deter the violent and property crime rates. Interpretation of the Regression Model: Our regressions suggest that rises in high school dropout rates relate to both higher predicted property crime rates, as well as higher predicted violent crime rates, holding all other variables in the model fixed. However, predicted violent crime rates seem to increase more than predicted property crimes, for the same rise in high school dropout rates. Rises in unemployment rate relate to an increase in the predicted property crime rates, however, a decrease in the violent crime rates. Again, a rise in median income relates to a decrease in predicted property crime rates, but an increase in violent crime rates. One interpretation of the positive effect of imprisonment rate on both crime rates is that there is a positive feedback from crime rate to prison population. Our instrument of lagged values of prison population fails to adequately address this issue for violent crime rates; however, it succeeds in addressing it for property crimes. A rise in imprisonment rates relates to a drop in the predicted property property crime rates for the following year, holding all else in the model fixed. Again, an interpretation of the positive effect of the number of police on the crime rates could be that there is a positive feedback from crime rate to number of police. In this case, our instrument of lagged values of number of police officials fails to adequately address this issue for both crime rates.


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