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The unemployment rate, unemployment volatility, and crime

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Author Info

  • Firouz Fallahi
  • Hamed Pourtaghi
  • Gabriel Rodríguez

Abstract

Purpose – The paper aims to study the effect of the unemployment rate and its volatility on crime in the USA. It proposes that not only the unemployment rate, but also its volatility affect the crime. Design/methodology/approach – First, the volatility of the unemployment rate is calculated using ARCH models. Next, using the results from the first stage the ARDL approach to cointegration is used to examine the link between the unemployment rate and its volatility on the crime. Findings – The cointegrated or long-run relationships are found only for burglary and motor-vehicle theft. The results indicate that the unemployment rate has a significant effect on burglary and motor-vehicle theft only in the short run and the unemployment volatility has a negative effect on motor-vehicle theft regardless of time span. However, it has a positive effect on burglary in the short run and no effect in the long run. Originality/value – The effect of unemployment rate on crime is documented in the literature. However, to the best of our knowledge, this is the first paper that emphasizes the importance of unemployment rate volatility on the crime. JEL classification: C22, E24

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Bibliographic Info

Article provided by Emerald Group Publishing in its journal International Journal of Social Economics.

Volume (Year): 39 (2012)
Issue (Month): 6 (May)
Pages: 440-448

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Handle: RePEc:eme:ijsepp:v:39:y:2012:i:6:p:440-448

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Related research

Keywords: ARCH; Crimes; Unemployment; Unemployment volatility;

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References

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  1. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July.
  2. Raphael, Steven & WINTER-EBMER, RUDOLF, 1998. "Identifying the Effect of Unemployment on Crime," University of California at San Diego, Economics Working Paper Series qt5hb4h56g, Department of Economics, UC San Diego.
  3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  5. Firouz Fallahi & Gabriel Rodríguez, 2007. "Using Markov-Switching Models to Identify the Link between Unemployment and Criminality," Working Papers 0701E, University of Ottawa, Department of Economics.
  6. Grogger, Jeff, 1998. "Market Wages and Youth Crime," Journal of Labor Economics, University of Chicago Press, vol. 16(4), pages 756-91, October.
  7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  8. Corman, Hope & Mocan, Naci, 2005. "Carrots, Sticks, and Broken Windows," Journal of Law and Economics, University of Chicago Press, vol. 48(1), pages 235-66, April.
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