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Vol. 9 No. 2 (2023)
June 2023

Articles

Financial risk in the activity of voivodships in Poland: Synthetic measure as an element of risk assessment in the activities of local government units in the years 2010-2020

https://doi.org/10.15678/IER.2023.0902.06

Published 2023-06-30

  • Andrzej Pawlik
    • ,
  • Paweł Dziekański

Andrzej Pawlik
Jan Kochanowski University in Kielce

Paweł Dziekański
Jan Kochanowski University in Kielce

Abstract

Objective: The article aims to analyse the spatial diversity of financial risk in the activity of voivodships in Poland and to use a synthetic measure to present selected factors that have a direct impact on the risk assessment.

Research Design & Methods: The authors used literature and statistical analysis for the research. The technique for order preference by similarity to ideal solution (TOPSIS) was used to create synthetic measures. Empirical data were collected by voivodeships in Poland for the years 2010-2020.

Findings: The synthetic rate of financial risk in 2020 ranged from 0.40 (Lubelskie) to 0.77 (Mazowieckie), and in 2010 from 0.37 (Opolskie) to 0.61 (Śląskie). Comparing 2020 to 2010, the voivodships Śląskie, Podlaskie, Warmińsko-Mazurskie, and Lubelskie showed a decrease in the value of the synthetic measure. The measure of financial risk was correlated, among others, with own revenues, operating surplus, income from participation in taxes constituting state budget revenues, level of current transfers, liabilities, number of entities and natural persons conducting business activity, and number of employees.

Implications & Recommendations: The detected correlations in the area of financial risk of the voivodships show that the local authorities in their actions should take into account the risk assessment system. The voivodships should define probabilities and impacts in terms of risks, criteria for assessment, and risk analysis in the organization. Finance and economy are interlinked. The actions taken in this aspect must be based on analyses that facilitate comparisons and on current information necessary for effective ac.

Contribution & Value Added: The value of the article is the indicated set of variables allowing us to assess the financial risk of the voivodships, the years of the presented analysis 2010-2020, and the synthetic measure as a basis for the assessment of financial risk.

Keywords

risk, financial risk, voivodship, synthetic measure, sustainable finance

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Author Biography

Andrzej Pawlik

Department of Economics and Finance; Jan Kochanowski University in Kielce; Kielce; Poland. Department of Economics and Finance; Jan Kochanowski University in Kielce; Kielce; Poland; Academic and research interests: local development, development strategy, local and regional innovation.

Paweł Dziekański

Department of Economics and Finance; Jan Kochanowski University in Kielce; Kielce; Poland; Academic and research interests: public finance, local government finance, public sector economics, local government efficiency, financial health assessment, localization of economic activity, development finance, green economy, green infrastructure, green capital, local/regional development.

References

  1. Ahmeti, R., & Vladi, B. (2017). Risk Management in Public Sector: A Literature Review¬, 2(5), 190-196. https://doi.org/10.26417/ejms.v5i1.p323-329
  2. Akintoye, A.S., & MacLeod, M.J. (1997). Risk analysis and management in construction. International Journal of Project Management, 15(1), 31-38.
  3. Allegrini, M., D'Onza, G., Paape, L., Melville, R., & Sarens, G. (2006). The European literature review on internal auditing. Managerial Auditing Journal, 21, 8, 845-853. https://doi.org/10.1108/02686900610703787
  4. Almeida, R., Teixeira, J.M., da Silva, M.M., & Faroleiro, P. (2019). A conceptual model for enterprise risk management. Journal of Enterprise Information Management, 32, 5, 843-868. https://doi.org/10.1108/JEIM-05-2018-0097
  5. Anselin, L., & Bera, A. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics (pp. 237-289) [In:] A. Ullah, D.E.A. Giles (ed.), Handbook of Applied Economic Statistics. New York: Marcel Dekker.
  6. Anselin, L. (1995). Local Indicators of Spatial Association – LISA. Geographical Analysis 1995, 27(2), 93-115.
  7. Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069.
  8. Bernstein, P. (1998). Against the Gods: The Remarkable Story of Risk. New York: John Wiley & Sons.
  9. Damodaran, A. (2002). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. New York: Wiley Finance.
  10. De Siano, R., & D’Uva, M. (2013). Italian regional specialization: a spatial analysis. Retrieved from https://ideas.repec.org/p/crj/dpaper/7_2012.html on March 9, 2023.
  11. Dziekanski, P., Pawlik, A., Wrońska, M., & Karpińska, U. (2020). Demographic Potential as the Basis for Spatial Differentiation of the Financial Situation Communes of Eastern Poland in 2009-2018. European Research Studies Journal, 23(2), 872-892. https://doi.org/10.35808/ersj/1903
  12. Dziekański, P., & Prus, P. (2020). Financial Diversity and the Development Process: Case study of Rural Communes of Eastern Poland in 2009-2018. Sustainability, 12(16), 6446. https://doi.org/10.3390/su12166446
  13. Filipiak, B. (2013). Przesłanki dokonania oceny samorządowego długu publicznego na tle podejścia badawczego [In:] E. Denek, M. Dylewski (ed.), Szacowanie poziomu zadłużenia jednostek samorządu terytorialnego w warunkach zwiększonego ryzyka utraty płynności finansowej. Warszawa: Difin.
  14. Filipiak, B. (2017). Ocena wydatków inwestycyjnych jednostek samorządu terytorialnego szczebla wojewódzkiego w świetle ryzyka realizacji zadań. Annales Universitatis Mariae Curie-Skłodowska. Sectio H. Oeconomia, 51, 4, 95-105.
  15. Getis A., & Ord, J.K. (1992). The analysis of spatial association by distance statistics. Geographical Analysis, 24(3), 189-206.
  16. Getis, A. (2007). Reflections on spatial autocorrelation. Regional Science and Urban Economics, 37(4), 491-496.
  17. Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), https://doi.org/10.1016/j.socec.2004.09.033
  18. Griffith, D.A. (2003). Spatial Autocorrelation and Spatial Filtering. Berlin-Heidelberg: Springer.
  19. Jahanshahloo, G.R., Lotfi, F.H., & Izadikhah, M. (2006). An Algorithmic Method to Extend {TOPSIS} for Decision-Making Problems with Interval Data Applied Mathematics and Computation, 2(175), 1375-1384.
  20. Jajuga, K., & Jajuga, T. (1998). Inwestycje. Instrumenty finansowe, ryzyko finansowe, inżynieria finansowa: Warszawa: PWN.
  21. Jastrzębska, M., Janowicz-Lomott, M., & Łyskawa, K. (2014). Zarządzanie ryzykiem w działalności jednostek samorządu terytorialnego. Warszawa: Wolters Kluwer.
  22. Kata, R. (2012). Ryzyko finansowe w działalności jednostek samorządu terytorialnego – metody oceny. Zeszyty Naukowe SGGW - Ekonomika i Organizacja Gospodarki Żywnościowe, (96), 129-141.
  23. Kukuła, K. (1999). Metoda unitaryzacji zerowanej na tle wybranych metod normowania cech diagnostycznych. Acta Scientifica Academiae Ostroviensis, 4, 5-31.
  24. Łuczak, A., & Wysocki, F. (2005). Wykorzystanie metod taksonometrycznych i analitycznego procesu hierarchicznego do programowania rozwoju obszarów wiejskich. Poznań: Wyd. Akademii Rolniczej im. Augusta Cieszkowskiego w Poznaniu.
  25. Malina, A. (2006). Analiza czynnikowa jako metoda klasyfikacji regionów Polski. Przegląd Statystyczny, 53(1), 33-48.
  26. Marshall, C. (2001). Measuring and Managing Operational Risk in Financial Institutions. John Wiley & Sons, Singapure.
  27. Merna, T., & Al-Thani, F.F. (2008). Corporate Risk Management, 2nd ed..Chichester: John Wiley and Sons.
  28. Poniatowicz, M. (2010). Transfer ryzyka jako instrument zarządzania ryzykiem w jednostkach samorządu terytorialnego. Zeszyty Naukowe Uniwersytetu Szczecińskiego, 620 (Ekonomiczne Problemy Usług 61).
  29. Rampini, A.A, Sufi, A., & Viswanathan, S. (2014). Dynamic risk management. Journal of Financial Economics, 111, 2, 271-296, https://doi.org/10.1016/j.jfineco.2013.10.003
  30. Schmidt, P. (2020). Econometrics. CRC Press.
  31. Trussel, J.M., & Patrick, P.A. (2009). A predictive model of fiscal distress in local governments. Journal of Public Budgeting, Accounting & Financial Management, 21, 4, 578-616, https://doi.org/10.1108/JPBAFM-21-04-2009-B004
  32. Upton, G., & Fingleton, B. (1985). Spatial Data Analysis by Example. New York: Wiley.
  33. Velasquez, M., & Hester, P.T. (2013). An Analysis of Multi-Criteria Decision Making Methods. International Journal of Operations Research, 2(10), 56-66.
  34. Wang, H., Liang, P., Li, H. & Yang, R. (2016). Financing Sources, R&D Investment and Enterprise Risk. Procedia Computer Science, 91, 122-130, https://doi.org/10.1016/j.procs.2016.07.049
  35. Wysocki, F. (2010). Metody taksonomiczne w rozpoznawaniu typów ekonomicznych rolnictwa i obszarów wiejskich. Poznań: Wyd. Uniwersytetu Przyrodniczego w Poznaniu.
  36. Wysocki, F., & Lira, J. (2005). Statystyka opisowa. Poznań: Wyd. AR w Poznaniu.
  37. Zeliaś, A., & Malina, A. (1997). O budowie taksonomicznej miary jakości życia. Syntetyczna miara rozwoju jest narzędziem statystycznej analizy porównawczej, Taksonomia, z. 4.

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