An Economic Instrument to Reduce the Crime Rate in Sweden
DOI:
https://doi.org/10.14738/abr.810.9282Keywords:
Crime prevention; Economic instrument; Incentives; Sweden.Abstract
Objectives
Crime is one of the most important social problems, affecting public safety, children’s development, and adult socioeconomic status. The Naturally Optimised Revenue Demand in Communities, the NORDIC model was applied to reduce the crime rate.
Methods
The NORDIC model that formed the basis of my study, was adapted to crime prevention. The approach was tested in a realistic case study on the crime rates in Sweden.
Results
Governments obtained a tool to monitor, manage, and evaluate criminality. End-users including law enforcement authorities and politicians could also use the tool to redesign the crime policy. The launched model produced constructed shadow costs to induce economic incentives to reduce the crime rate. The model considered the public’s awareness of the crime.
Conclusions
This paper introduced a practical, economic instrument for improved management of the crime rates. The NORDIC model could be used to reduce the Swedish crime rates and its danger to health as well as to raise the public’s awareness of crime issues.
References
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[30]. Moore, S., and J. P. Shepherd. The cost of fear: Shadow pricing the intangible costs of crime. Applied Economics, 2006. 38: p. 293-300. https://doi.org/10.1080/00036840500367781
[31]. Stetson, B., A. Beacham, S. Frommelt, K. Boutelle, J. Cole, C. Ziegler, and S. Looney. Exercise slips in high-risk situations and activity patterns in long-term exercisers: An application of the relapse prevention model. Annals of Behavioral Medicine, 2005. 30: p. 25-35. https://doi.org/10.1207/s15324796abm3001_4
[32]. Wilcox, P., N. Quisenberg, D. T. Carbrera, and S. Jones. Busy places and broken windows? Toward defining the role of physical structure and process in community crime models. The Sociological Quarterly, 2004. 45: p. 185-207. https://doi.org/10.1111/j.1533-8525.2004.tb00009.x
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[35]. SPPS. Prison and probation service and statistics (KOS) 2018, in Swedish, 2020. Retrieved from https://www.kriminalvarden.se/globalassets/publikationer/kartlaggningar-och-utvarderingar/kos-2018.pdf
[2]. Swedish Prison and Probation Service (SPPS). Annual report 2018, in Swedish, 2019. Retrieved from https://www.kriminalvarden.se/globalassets/publikationer/ekonomi/kriminalvardens-arsredovisning-2018.pdf
[3]. Martinsson, J., and U. Andersson (Ed.). Swedish trends 1986-2018, SOM-Institute, Gothenburg University, in Swedish, 2020. Retrieved from https://som.gu.se/digitalAssets/1736/1736224_6.-svenska-trender-1986-2018_v2.pdf
[4]. Ljungwald, C., and R. Elias. The emergence of crime victims as a target group in the Swedish social services act. Journal of Scandinavian Studies in Criminology and Crime Prevention, 2010. 11: p. 170-188. https://doi.org/10.1080/14043858.2010.523556
[5]. Stenis, J. An economic instrument to improve migration. Applied Economics and Finance, 2020a. 7(1): p. 15-20. http://dx.doi.org/10.11114/aef.v7i1.4630
[6]. Stenis, J. Application of the naturally optimised revenue demand in communities NORDIC model to improve the workforce in Sweden. Applied Economics and Finance 2020b. 7(2): p. 18-23. https://doi.org/10.11114/aef.v7i2.4711
[7]. Stenis, J. Application of the naturally optimised revenue demand in communities NORDIC model to improve the education in Sweden. Theoretical Economics Letters, 2020c. 10: p. 523-534. https://doi.org/10.4236/tel.2020.103033
[8]. Stenis, J. An economic instrument to improve the climate in Sweden. Theoretical Economics Letters, 2020d. 10: p. 758-769. https://doi.org/10.4236/tel.2020.104047
[9]. Stenis, J., and W. Hogland. An economic instrument to improve communities. Theoretical Economics Letters, 2019. 9: p. 1804-1816. https://doi.org/10.4236/tel.2019.96115
[10]. Hajela, G., M. Chawla, and A. Rasool. A clustering-based hotspot identification approach for crime prediction. Procedia Computer Science, 2020. 167: p. 1462-1470. http://doi.org/10.1016/j.procs.2020.03.357
[11]. Hazra, D. What does (and does not) affect crime in India. International Journal of Social Economics, 2020. 47: p. 503-521. https://doi.org/10.1108/IJSE-03-2019-0206
[12]. Berg, N., and K. Jeong-Yoo. Economics of the broken window theory. Asian Journal of Law and Economics, 2019. 10: p. 9. https://doi.org/10.1515/ajle-2019-0026
[13]. Taylor, R. B., C. P. Haberman and E. R. Groff. Urban park crime: Neighborhood context and park features. Journal of Criminal Justice, 2019. 64. https://doi.org/10.1016/j.jcrimjus.2019.101622
[14]. Wang, Y., W. Yu, S. Liu, and S. D. Young. The relationship between social media data and crime rates in the United States. Social Media + Society, 2019. 5: p. 1-9. https://doi.org/10.1177/2056305119834585
[15]. Zaman, K., B. Usman, S. M. Sheikh, Iswan, A. Khan, A. B. M. Kosnin, A. S. B. Rosman, … S. S. Hishan. Managing crime through quality education: A model of justice. Science & Justice, 2019. 59: p. 597-605. https://doi.org/10.1016/j.scijus.2019.08.004
[16]. Ingilevich, V., and S. Ivanov. Crime rate prediction in the urban environment using social factors. Procedia Computer Science, 2018. 136: p. 472-478. https://doi.org/10.1016/j.procs.2018.08.261
[17]. Quick, M., G. Li, and I. Brunton-Smith. Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale. Journal of Criminal Justice, 2018. 58: p. 22-32. https://doi.org/10.1016/j.jcrimjus.2018.06.003
[18]. Gu, Y., Q. Wang, and G. Yi. Stationary patterns and their selection mechanism of urban crime models with heterogeneous near-repeat victimization effect. European Journal of Applied Mathematics, 2017. 28: p. 141-178. https://doi.org/10.1017/S0956792516000206
[19]. Loughran, T. A., D. S. Nagin, and H. Nguyen. Crime and legal work: A Markovian model of the desistance process. Social Problems, 2017. 64: p. 30-52. https://doi.org/10.1093/socpro/spw027
[20]. Meehan, B., and B. L. Benson. Does private security affect crime? A test using state regulations as instruments. Applied Economics, 2017. 49: p. 4911-4924. https://doi.org/10.1080/00036846.2017.1296551
[21]. Van Graan, J. Multi-sector cooperation in preventing crime: The case of a South African Neighborhood Watch as an effective crime prevention model. Police Practice and Research, 2016. 17: p. 136-148. https://doi.org/10.1080/15614263.2015.1128159
[22]. Bun, M. J. G. Identifying the impact of deterrence on crime: Internal versus external instruments. Applied Economics Letters, 2015. 22: p. 204-208. https://doi.org/10.1080/13504851.2014.934422
[23]. Grifin, P. A., and D. Abreu. The Sequential Intercept Model and criminal justice: Promoting community alternatives for individuals with serious mental illness. 2015, New York: Oxford University Press, (USA).
[24]. He, L., A. Páez, D. Liu, and S. Jiang. Temporal stability of model parameters in crime rate analysis: An empirical examination. Applied Geography, 2015. 58: p. 141-152. https://doi.org/10.1016/j.apgeog.2015.02.002
[25]. Shoesmith, G. Space-time auto regressive models and forecasting national, regional and state crime rates. International Journal of Forecasting, 2013. 29: p. 191-201. https://doi.org/10.1016/j.ijforecast.2012.08.002
[26]. Surette, R. Cause or catalyst: The interaction of real world and media crime models. American Journal of Criminal Justice, 2013. 38: p. 392-409. https://doi.org/10.1007/s12103-012-9177-z
[27]. Arnio, A. N., and E. P. Baumer. Demography, foreclosure, and crime: Assessing spatial heterogeneity in contemporary models of neighborhood crime rates. Demographic Research, 2012. 26: p. 449-488. https://doi.org/10.454/DemRes.2012.26.18
[28]. Halicioglu, F., A. R. Andrés, and E. Yamamura. Modelling crime in Japan. Economic Modelling, 2012. 29: p. 1640-1645. https://doi.org/10.1016/j.econmod.2012.05.026
[29]. Andrews, D. A., J. Bonta, and J. S. Wormith. The Risk-Need-Responsivity (RNR) Model: Does adding the Good Lives Model contribute to effective crime prevention? Criminal Justice and Behaviour, 2011. 38: p. 735-755. https://doi.org/10.1177/0093854811406356
[30]. Moore, S., and J. P. Shepherd. The cost of fear: Shadow pricing the intangible costs of crime. Applied Economics, 2006. 38: p. 293-300. https://doi.org/10.1080/00036840500367781
[31]. Stetson, B., A. Beacham, S. Frommelt, K. Boutelle, J. Cole, C. Ziegler, and S. Looney. Exercise slips in high-risk situations and activity patterns in long-term exercisers: An application of the relapse prevention model. Annals of Behavioral Medicine, 2005. 30: p. 25-35. https://doi.org/10.1207/s15324796abm3001_4
[32]. Wilcox, P., N. Quisenberg, D. T. Carbrera, and S. Jones. Busy places and broken windows? Toward defining the role of physical structure and process in community crime models. The Sociological Quarterly, 2004. 45: p. 185-207. https://doi.org/10.1111/j.1533-8525.2004.tb00009.x
[33]. Shadow Price. In Wikipedia, the free encyclopedia, 2020. Retrieved May 15, 2020, from https://en.wikipedia.org/wiki/Shadow_price
[34]. Ekonomifakta. National accounts, in Swedish, 2020. Retrieved from https://www.ekonomifakta.se
[35]. SPPS. Prison and probation service and statistics (KOS) 2018, in Swedish, 2020. Retrieved from https://www.kriminalvarden.se/globalassets/publikationer/kartlaggningar-och-utvarderingar/kos-2018.pdf
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Published
2020-11-08
How to Cite
Stenis, J. (2020). An Economic Instrument to Reduce the Crime Rate in Sweden. Archives of Business Research, 8(10), 121–130. https://doi.org/10.14738/abr.810.9282
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