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An Analysis of the Factors Determining Crime in England and Wales: A Quantile Regression Approach

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  • Siddhartha Bandyopadhyay

    (University of Birmingham,Birmingham U.K.)

  • Samrat Bhattacharya

    (FTAM, USA)

  • Rudra Sensarma

    (Indian Institute of Management Kozhikode)

Abstract

We analyze the impact of policing and socio-economic variables on crime in England and Wales during 1992-2007 using the quantile regression model which enables us to analyze different points of the crime distribution. The quantile regression model allows us to analyze whether or not the factors that affect crime do so in the same way for high and low crime areas. By using data from 43 police force areas, we examine how the effect of real earnings, unemployment, crime detection rate, income inequality and proportion of young people varies across high and low crime areas. Six crime categories are examined – burglary, theft and handling, fraud and forgery, violence against the person, robbery, and sexual assault. We find statistically significant differences in the impact of explanatory variables on various types of crime for low and high crime areas. For example, higher detection rate reduces crime but the effect is stronger in low crime areas. Further, we find opposing effects of earnings and unemployment across high and low crime areas which may explain why recessions may have no impact on crime or even lower it.

Suggested Citation

  • Siddhartha Bandyopadhyay & Samrat Bhattacharya & Rudra Sensarma, 2015. "An Analysis of the Factors Determining Crime in England and Wales: A Quantile Regression Approach," Working papers 178, Indian Institute of Management Kozhikode.
    • Handle: RePEc:iik:wpaper:178
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    References listed on IDEAS

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    Cited by:

    1. Brosnan, Stephen, 2016. "The Socioeconomic Determinants of Crime in Ireland from 2003-2012," MPRA Paper 74118, University Library of Munich, Germany.
    2. Brosnan, Stephen, 2017. "The Impact of Sports Participation on Crime in England between 2012 and 2015," MPRA Paper 78596, University Library of Munich, Germany.
    3. Udo Broll & Soumyatanu Mukherjee & Rudra Sensarma, 2020. "Risk preferences estimation of exporting firms under exchange rate uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 67(1), pages 126-136, February.
    4. Sarah A. Frederick & James J. Jozefowicz & Zackary T. Nelson, 2016. "A dynamic panel data study of the unemployment-crime relationship: the case of Pennsylvania," Economics Bulletin, AccessEcon, vol. 36(3), pages 1497-1507.
    5. Stephen Brosnan, 2018. "The Socioeconomic Determinants of Crime in Ireland from 2003-2012," The Economic and Social Review, Economic and Social Studies, vol. 49(2), pages 127-143.
    6. Entorf, Horst & Sieger, Philip, 2014. "Does the Link between Unemployment and Crime Depend on the Crime Level? A Quantile Regression Approach," IZA Discussion Papers 8334, IZA Network @ LISER.
    7. Siddhartha Bandyopadhyay & Samrat Bhattacharya & Marianna Koli & Rudra Sensarma, 2012. "Acquisitive Crime, Sentencing and Detection: An Analysis of England and Wales," Discussion Papers 12-09, Department of Economics, University of Birmingham.
    8. Jonathan Torres‐Tellez & Alberto Montero Soler, 2023. "After the economic crisis of 2008: Economic conditions and crime in the last decade for the case of Spain," American Journal of Economics and Sociology, Wiley Blackwell, vol. 82(3), pages 223-239, May.

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    Keywords

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    JEL classification:

    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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