An Economic Instrument to Reduce the Crime Rate in Sweden

Authors

DOI:

https://doi.org/10.14738/abr.810.9282

Keywords:

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.

Author Biography

Jan Stenis, Kristianstad, Sweden

Private researcher.

References

Wang, H., H. Yao, D. Kifer, C. Graif, and Z. Li. Non-stationary model for crime rate inference using modern urban data. IEEE Transactions of Big Data, 2019. 5: p. 180-194. http://doi.org/10.1109/TBDATA.2017.2786405

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.2018.06.003 129 Stenis, J. (2020). An Economic Instrument to Reduce the Crime Rate in Sweden. Archives of Business Research, 8(10). 121-130.

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

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

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

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

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

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).

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

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

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

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

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

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

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

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

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

Shadow Price. In Wikipedia, the free encyclopedia, 2020. Retrieved May 15, 2020, from https://en.wikipedia.org/wiki/Shadow_price

Ekonomifakta. National accounts, in Swedish, 2020. Retrieved from https://www.ekonomifakta.se

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

Downloads

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