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Responding to high crime rates: what is the mix of prevention, insurance and mitigation individuals choose and its results?

Author

Listed:
  • Melek Cigdem-Bayram

    () (RMIT)

  • David Prentice

    () (Infrastructure Victoria)

Abstract

In this paper we take first steps in providing parameters capturing some wider impacts of crime on individuals for the cost benefit analysis of investments in justice infrastructure. Statistical matching methods are applied to the HILDA dataset in the first broad economic analysis of how individuals respond to living in an acutely high crime environment and the consequences. Compared with individuals living in a postcode with a moderately high crime rate, the matching analysis shows individuals living in postcodes with acutely high crime rates are more likely to be a victim of a violent crime and spend less on insurance. They are also more likely to have a family member incarcerated even if they are no more likely to be incarcerated themselves. There are no significant differences in household incomes or full-time employment rates though those living in an acutely high crime rate postcode are more likely to be unemployed. Finally, although there are no significant differences in measures of mental health, individuals in acutely high crime rate areas spend less on health. This could be because they are less likely to have a long term health condition but could also reflect underinvesting in health care which may have negative consequences for health in the long term.

Suggested Citation

  • Melek Cigdem-Bayram & David Prentice, 2018. "Responding to high crime rates: what is the mix of prevention, insurance and mitigation individuals choose and its results?," Technical papers 201803, Infrastructure Victoria.
  • Handle: RePEc:inv:tpaper:201803
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    File URL: http://repec.infrastructurevictoria.com.au/RePEc/inv/tpaper/IVT201803.pdf
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    References listed on IDEAS

    as
    1. Christian Dustmann & Francesco Fasani, 2016. "The Effect of Local Area Crime on Mental Health," Economic Journal, Royal Economic Society, vol. 126(593), pages 978-1017, June.
    2. Cheng, Zhiming & Smyth, Russell, 2015. "Crime victimization, neighborhood safety and happiness in China," Economic Modelling, Elsevier, vol. 51(C), pages 424-435.
    3. Melek Cigdem‐Bayram & David Prentice, 2019. "How Do Crime Rates Affect Property Prices?," The Economic Record, The Economic Society of Australia, vol. 95(S1), pages 30-38, June.
    4. Francesca Cornaglia & Naomi E. Feldman & Andrew Leigh, 2014. "Crime and Mental Well-Being," Journal of Human Resources, University of Wisconsin Press, vol. 49(1), pages 110-140.
    5. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    6. Janke, Katharina & Propper, Carol & Shields, Michael A., 2016. "Assaults, murders and walkers: The impact of violent crime on physical activity," Journal of Health Economics, Elsevier, vol. 47(C), pages 34-49.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Crime; Matching Methods; Cost-Benefit Analysis; Employment; Education; Health; Insurance; Australia; Victoria;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General

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