The rate of crime across the globe has been increasing. Many people attribute this to the rise in the gap between the rich and the poor. Taylor (2006) carried out research to establish a correlation between poverty and crime in the United States of America. Also, he developed a regression equation connecting the two variables. To get a consistent data, he used data from metropolitan areas. In the study, he obtained data from 1997-1998 state and metropolitan area data book. The data covered around three hundred and twenty-two metropolitan areas. In the data, a family was classified as poor if the total family income fell below the poverty threshold provided by the state (Taylor, 2006).
In the study, he came up with the following simple regression equation Total Crime = β0 + β1 (poverty) + µ. He then plotted the data in a scatter and inserted a line of best fit. Using the line of best fit, he was able to establish the β0 (intercept of the line with the vertical axis). The slope of the line formed β1 (Taylor, 2006).
The analysis established that there is a correlation between the rate of poverty and crime in the society. This correlation is positive. Thus, increase in the poverty rate in any metropolitan area is likely to lead to an increase in the level of crime. The regression model can predict the level of crime if there is adequate data on the existing level of poverty. This is done by inserting the level of poverty in the equation. The µ in the model gives the standard error that may arise through the use of the model (Taylor, 2006). This error allowance is given because there are cases where rich people contribute to crime (Dale, & Kotz, 2011).
References
Taylor, B. (2006, January 09). Economics.fundamentalfinance.com. Retrieved January
23, 2016, from http://economics.fundamentalfinance.com/povertycrime.php
Dale, A. I., & Kotz, S. (2011). Arthur L Bowley: A pioneer in modern statistics and economics.
Hackensack, NJ: World Scientific.
