![]() Classification error refers to the mistakes in UCR statistics caused by the misclassification of criminal offenses, for example recording a crime as aggravated assault when it should have been simple assault. This paper offers a methodological approach for estimating classification error in police records then determining the statistical accuracy of official crime statistics reported to the Uniform Crime Reporting (UCR) program. Although we find some evidence supporting inequality and culture as a channel, results based on data for the year 2000 suggest that ethnic fractionalization/segregation is the most important mediator. We consider three channels of transmission between slavery and violent crime: inequality, a culture of violence, and ethnic fractionalization/segregation. This relationship is robust to including state fixed effects, controlling for numerous historical and contemporary factors, as well as to instrumenting for slavery using environmental conditions. Using county-level data for the USA, we find that the proportion of slaves in the population in 1860 is associated with significantly higher rates of violent crime in all census years for the period 1970-2000. Although considerable qualitative evidence suggests that slavery has been a key factor behind the prevalence of violence, especially in Southern USA, there has been no large-N study supporting this claim so far. This study investigates the long-term relationship between slavery and violence in the USA. Some tentative implications of these findings are discussed, as are areas for further research." ![]() When National data were disaggregated by offense type we found varying degrees of conformity, with murder, rape, and robbery indicating less conformity than other offense types. We examined crime statistics at the National, State, and local level in order to test for conformity to the Benford distribution, and found that National- and Statelevel summary UCR data conform to Benford’s law. This study explored whether crime statistics are Benford distributed. Forensic auditors have successfully used digital analysis vis-a`-vis the Benford distribution to detect financial fraud, while government investigators have used a corollary of the distribution (focused on trailing digits) to detect scientific fraud in medical research. ![]() Benford’s law suggests that the distribution of leading (leftmost) digits in data of an anomalous nature (i.e., without relationship) will conform to a formula of logarithmic intervals known as the Benford distribution.
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