State policy of preventing crimes against a person: Which best practices should be used by Azerbaijan?
-
Author(s)Zamina AliyevaLink to ORCID Index: https://orcid.org/0000-0003-2693-0057
-
DOIhttp://dx.doi.org/10.21511/ppm.21(4).2023.57
-
Article InfoVolume 21 2023, Issue #4, pp. 771-789
- 118 Views
-
57 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
State policy of prevention, detection, termination, disclosure, and investigation of crimes against a person in Azerbaijan should be based on other countries’ best practices and experience. The choice of countries to be followed by Azerbaijan should be very well-founded, given that the dynamics of crimes against a person depend significantly on many social and economic determinants: income inequality, the dominance of the rule of law in the country, the level of literacy and financial literacy of citizens, or racial diversity.
50 countries are clustered according to the similarity of trends regarding the dependence of crimes against persons on these socio-economic determinants. Clustering is based on data of 2021 from the World Bank, World Population Review, UNODC, and WGI (the selection of countries is due to the availability of comparable statistical information, the choice of year – to the availability of the most up-to-date data). Clustering was carried out using two methods (DBSCAN and K-Means) to ensure the adequacy of the calculations. Clustering is performed for 3 combinations: 1) by the entire set of crimes and their determinants; 2) by the specific type of crime and all types of determinants; 3) by the entire set of crimes and a specific socio-economic determinant. Albania, Jordan, Mongolia, Romania, and Serbia were most often in the same cluster with Azerbaijan. Therefore, the best experience and best practices of these countries can be used by the state regulatory bodies of Azerbaijan in developing state policy on preventing crimes against the person.
view full abstract
- Keywords
-
JEL Classification (Paper profile tab)K14, K42, H70, B55
-
References44
-
Tables5
-
Figures3
-
- Figure 1. Scree plot
- Figure 2. Clusters built on the entire set of crimes and their determinants by the K-Means method
- Figure 3. Results of clustering by the entire set of crimes and their socio-economic determinants using the DBSCAN method
-
- Table 1. Variables of the number of crimes against the person and the levels of their socio-economic determinants for different countries as of 2021
- Table 2. Normalized input data on the number of crimes against the person and the levels of their socio-economic determinants for different countries
- Table 3. Descriptive analysis
- Table 4. The results of clustering by specific type of crime and all types of their socio-economic determinants (K-Means and DBSCAN methods)
- Table 5. Results of clustering by all types of crime and a specific socio-economic determinant (by K-Means and DBSCAN methods)
-
- Aliyeva, Z. (2022). Influence of state policy components on the rate of violence and crime against human life and health. Problems and Perspectives in Management, 20(4), 451-464.
- Awojobi, O. N. (2022). Health inequalities and social determinants of indigenous peoples’ health in Australia, Canada, and the United States: Causes and policies options. Health Economics and Management Review, 3(1), 8-18.
- Bhandari, M. P. (2023a). The fundamental principles of social sciences. Business Ethics and Leadership, 7(2), 73-86.
- Bhandari, M. P. (2023b). Women and society a major field of studies to understand the discrimination. SocioEconomic Challenges, 7(2), 78-93.
- Breiding, M. J., Smith, S. G., Basile, K. C., Walters, M. L., Chen, J., & Merrick, M. T. (2015). Prevalence and characteristics of sexual violence, stalking, and intimate partner violence victimisation – National intimate partner and sexual violence survey, United States, 2011. American Journal of Public Health (AJPH), 105(4), e11-e12.
- Cooper, A.-A., & Mujtaba, B. G. (2022). Assessment of workplace discrimination against individuals with autism spectrum disorder (ASD). SocioEconomic Challenges, 6(2), 19-28.
- Didenko, I., Petrenko, K., & Pudlo, T. (2023). The role of financial literacy in ensuring financial inclusion of the population. Financial Markets, Institutions and Risks, 7(2), 72-79.
- Dluhopolskyi, O., Pakhnenko, O., Lyeonov, S., Semenog, A., Artyukhova, N., Cholewa-Wiktor, M., & Jastrzębski, W. (2023). Digital financial inclusion: COVID-19 impacts and opportunities. Sustainability, 15(3), 2383.
- Dzwigol, H. (2022). Research methodology in management science: Triangulation. Virtual Economics, 5(1), 78-93.
- Dzwigol, H. (2023). The quality determinants of the research process in management sciences. Virtual Economics, 6(2), 35-55.
- Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (pp. 226-231).
- Fast, R. (2021). The effects of education on Alabama’s violent crime rate. SocioEconomic Challenges, 5(2), 5-14.
- Gupta, S., & Bhandari, S. (2022). Dual process ethical decision-making models: Need for empirical examination. Business Ethics and Leadership, 6(3), 47-56.
- Haque, E. (2022). Book review on Prof. Dr. Mizanur Rahman (2018). Human Rights Theory, Law and Practice in Bangladesh: Lectures and Essays. Business Ethics and Leadership, 6(1), 109-112.
- Harshad, D. (2022). The constitution of value. Financial Markets, Institutions and Risks, 6(2), 75-90.
- Hussain, A. A. (2022). Crimes against persons. In R. T. Gopalan (Ed.), Victimology (pp. 253-282). Springer.
- Isik, A. (2022). The most common behavioural biases among young adults in Bristol, UK and Istanbul. Financial Markets, Institutions and Risks, 6(1), 27-39.
- Kaufmann, D., & Kraay, A. (2023). Worldwide Governance Indicators. The World Bank.
- Kellermann, A. L., Rivara, F. P., Rushforth, N. B., Banton, J. G., Reay, D. T., Francisco, J. T., Locci, A. B., Prodzinski, J., Hackman, B. B., & Somes, G. (1993). Gun ownership is a risk factor for homicide in the home. The New England Journal of Medicine, 329(15), 1084-1091.
- Kharazishvili, Y., & Kwilinski, A. (2022). Methodology for determining the limit values of national security indicators using artificial intelligence methods. Virtual Economics, 5(4), 7-26.
- Klapper L., Lusardi A., & Oudheusden P. V. (2015). Financial literacy around the world: Insights from the Standard & Poor’s Ratings Services global financial literacy survey. The World Bank.
- Klochko, A., Kvasha, A., Zahynei, Z., Logvinenko, M., & Kurylo, M. (2020). Combating crime in the banking sector as a method for ensuring its stability (evidence from Ukraine). Banks and Bank Systems, 15(1), 143-157.
- Kuzior, A., Didenko, I., Vorontsova, A., Lyeonov, S., & Brożek, P. (2022). Managing educational determinants of financial inclusion as a key factor of sustainable development: Logit-probit modeling. Polish Journal of Management Studies, 26(2), 265-279.
- Louis, R. (2022). The global socio-economic impact of mental health. SocioEconomic Challenges, 6(2), 50-56.
- Lyeonov, S., Vasilyeva, T., Bilan, Y., & Bagmet, K. (2021). Convergence of the institutional quality of the social sector: The path to inclusive growth. International Journal of Trade and Global Markets, 14(3), 272-291.
- Moskalenko, B., Lyulyov, O., Pimonenko, T., Kwilinski, A., & Dzwigol, H. (2022). Investment attractiveness of the country: Social, ecological, economic dimension. International Journal of Environment and Pollution, 69(1-2), 80-98.
- Mujtaba, B. G., & Kaifi, B. (2023). Safety audit considerations for a healthy workplace that puts “People before Profit” and OSHA compliance. Health Economics and Management Review, 4(1), 11-25.
- Policastro, C., Gainey, R., & Payne, B. K. (2015). Conceptualising crimes against older persons: Elder abuse, domestic violence, white-collar offending, or just regular old crime. Journal of Crime and Justice, 38(1), 27-41.
- Premasundari, M., & Yamini, C. (2019). A violent crime analysis using fuzzy c-means clustering approach. ICTACT Journal on Soft Computing, 9(3), 1939-1944.
- Rasoul, K., Siamak, M., & Amin, K. (2015). Analysis and prediction of crimes by clustering and classification. International Journal of Advanced Research in Artificial Intelligence, 4(8).
- Salju, S., Junaidi, J., & Goso, G. (2023). The effect of digitalization, work-family conflict, and organizational factors on employee performance during the COVID-19 pandemic. Problems and Perspectives in Management, 21(1), 107-119.
- Surahman, Shee, H., Fitrian, Z., Adi, A. S., & Yudaruddin, R. (2023). The effect of digital transformation and innovation on SMEs’ performance in times of COVID-19. Problems and Perspectives in Management, 21(4), 84-100.
- The Global Organized Crime Index. (2023). Criminality.
- Turkebayeva, K., Bekturganova, M., Sabden, O., Dauliyeva, G., & Kenzhegulova, G. (2022). Assessment of the relationship between inequality, income and economic growth in the regions of Kazakhstan. Problems and Perspectives in Management, 20(2), 511-521.
- Uittenbogaard, A., & Ceccato, V. (2011). Space-time clusters of crime in Stockholm, Sweden. Procedia Environmental Sciences.
- United Nations Office on Drugs and Crime (UNODC). (n.d.). Violent & Sexual Crime.
- Vasilyeva, T., Kuzmenko, O., Stoyanets, N., Artyukhov, A., & Bozhenko, V. (2022a). The depiction of cybercrime victims using data mining techniques. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2022(5), 174-178.
- Vasilyeva, T., Yarovenko, H., Babenko, D., Kalicheva, N., Frolova, N., & Shylovtseva, N. (2022c). The barycentric model of determining the sustainable growth determinants. Heritage and Sustainable Development, 4(2), 145-164.
- Vasylieva, T., Vysochyna, A., & Filep, B. (2022b). Economic development and income inequality: Role in country resistance to COVID-19. Economics & Sociology, 15(4), 286-302.
- World Bank. (n.d.a). Intentional homicides (per 100,000 people).
- World Bank. (n.d.b). Gini index.
- World Population Review. (n.d.a). Most diverse countries.
- World Population Review. (n.d.b). Literacy rate by country.
- Yarovenko, H., Lopatka, A., Vasilyeva, T., & Vida, I. (2023). Socio-economic profiles of countries – cybercrime victims. Economics & Sociology, 16(2), 167-194.
Close
Problems and Perspectives in Management