Quantifying of objective poverty in the districts of the Banská Bystrica Region (Slovak Republic)
-
Received April 1, 2023;Accepted May 18, 2023;Published June 23, 2023
-
Author(s)Link to ORCID Index: https://orcid.org/0000-0001-9826-8213Link to ORCID Index: https://orcid.org/0000-0002-1687-8067Link to ORCID Index: https://orcid.org/0000-0002-5220-2857Link to ORCID Index: https://orcid.org/0000-0003-1161-0384Link to ORCID Index: https://orcid.org/0000-0002-5266-7750
-
DOIhttp://dx.doi.org/10.21511/ppm.21(2).2023.57
-
Article InfoVolume 21 2023, Issue #2, pp. 630-641
- TO CITE АНОТАЦІЯ
- 382 Views
-
147 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Poverty, as a multispectral phenomenon caused by severe material depriving of the population, is now becoming one of the most watched socio-economic phenomena. Its scale and severity continue to increase in social consequences. The aim of the paper is to quantify and visualize the extent and level of poverty risk in the selected districts in Slovakia based on selected indicators and to compare their rates in 2015, 2019, and 2021.
The methodology of the pilot case study is based on a multi-criteria assessment of the poverty rate in a statistically unprocessed territorial unit (district) through 19 objective indicators in three domains: socio-demographic, economic performance, and infrastructure. Metfessel allocation, Fuller, and Saaty methods were used for its evaluation.
It is gratifying that the at-risk-of-poverty rate expressed by the synthesis of 19 indicators has decreased in all districts. In 2019, the poverty level decreased by 2.61 points (21.17%) compared to 2015 (23.78%). In 2021, the problem worsened again, and the poverty rate increased by 2.03 points to 23.2%. The Banská Bystrica region is characterized by low economic activity, reflected in the second lowest employment and the second highest unemployment rates, where up to 48.5% of the unemployed are under 35. The paper contributes to the growing debates on the inequality in living conditions, poverty, and marginalization, the scale and severity of which continue to increase in social consequences.
Acknowledgments
The article is supported by financial resources within the framework of Project no. 033SPU-4/2022 entitled “Functional, innovative and digital education of the subject Tourism Marketing.”
- Keywords
-
JEL Classification (Paper profile tab)R12, R23, I32
-
References58
-
Tables0
-
Figures4
-
- Figure 1. At-risk-of-poverty rate after social transfers, total and by regions, 2019 and 2021
- Figure 2. Indicator sheets of economic, infrastructural, socio-demographic dimensions
- Figure 3. At-risk-of-poverty rate in the districts of the Banská Bystrica region in 2015, 2019 and 2021
- Figure 4. Development of the growth rate of the economic, infrastructural, and socio-demographic dimensions in the districts of the Banská Bystrica region in 2015 and 2021
-
- Bartsch, H. J. (1987). Matematické vzorce. Praha: SNTL. (In Czech).
- Beyers, J. (2014). The effect of religion on poverty. HTS Theological Studies, 70(1), 1-8.
- Bolečeková, M. (2013). Chudoba a nástroje boja proti chudobe. Rozvojové vzdelávanie témy a metódy. Bratislava: Nadácia Pontis. (In Slovak).
- Borys, T. (2000). Wezlowe problemy statystyki transgranicznej. Wroclaw: AE we Wroclawiu. (In Polish).
- Boujelbene, Y., & Derbel, A. (2015). The performance analysis of public transport operators in Tunisia using AHP method. Procedia Computer Science, 73, 498-508.
- Cobbinah, P. B., Black, R., & Thwaites, R. (2013). Dynamics of poverty in developing countries: Review of poverty reduction approaches. Journal of Sustainable Development, 6(9), 25-35.
- Crettaz, E. (2011). Fighting working poverty in post-industrial economies. Cheltenham: Edward Elgar Publishing Limited.
- Dekkers, G. J. M. (2008). Are you unhappy? Then you are poor! Multidimensional poverty in Belgium. The International Journal of Sociology and Social Policy, 28(11/12), 502-515.
- Dewilde, C. (2004). The multidimensional measurement of poverty in Belgium and Britain: A categorical approach. Social Indicators Research, 68(3), 331-369.
- Domfeh, K. A., & Bawole, J. N. (2009). Localising and sustaining poverty reduction: Experiences from Ghana. Management of Environmental Quality, 20(5), 490-505.
- EU SILC. (n.d.). European Union statistics on income and living.
- European Parliament. (2020). Boj proti chudobe, sociálnemu vylúčeniu a diskriminácii [The fight against poverty, social exclusion and discrimination]. (In Slovak).
- Eurostat. (2020, October 16). Over 20% of EU population at risk of poverty or social exclusion in 2019.
- Eurostat. (2021, October 15). One in five people in the EU at risk of poverty or social exclusion.
- Eurostat. (n.d.). At-risk-of-poverty thresholds – EU-SILC and ECHP surveys.
- Gerbery, D., & Džambazovič, R. (2017). Urbánna chudoba na Slovensku [Urban poverty in Slovakia]. Geografický Časopis – Geographical Journal, 69(3), 263-280. (In Slovak).
- Guhathakurta, S. (2013). Integrated land use and environmental models: A survey of current applications and research. Chamonix: Springer Science & Business Media.
- Gwariro, S., Haruzivishe, C., Kasu, Ch., Mhlanga, M., Dzimiri, T., Ncube, F., Ncube, B., Kanyaruma, D., Longwe, R., Musvipa, M., Njanje, A., Chihava, I., Dube, A., Chimbetete, S., Sibindi, T., Munangaidzwa, S., Dzvinyayi, E., & Ndaimani, A. (2017). Poverty: What is it and what is it not? A concept analysis. International Journal of Health Sciences and Research, 7(5), 346-353.
- Hajdúchová, S. (2015). Rozhodovací proces v modeli hodnotenia nákladov zariadenia staveniska [Decision making process for cost assessment model of the construction equipment]. Mladá Veda – Young Science, 3(2), 72-81. (In Slovak).
- Hanzl, J. (2020). General application of multiple criteria decision making methods for finding the optimal solution in city logistics. Open Engineering, 10(1), 147-153.
- Inštitút Zamestnanosti (Employment Institute). (n.d.). Least developed regions.
- Jenčová, S., & Litavcová, E. (2015). Analytický pohľad na chudobu Slovenska [Analytical view on poverty in Slovakia]. Finančné Trhy, 4(1). (In Slovak).
- Kallio, J., & Niemelä, M. (2014). Who blames the poor? European Societies, 16(1), 112-135.
- Knowles, J. C. (2000). A look at poverty in the developing countries of Asia. Asia-Pacific Population and Policy, 52, 1-4.
- Křovák, J. (1981). Možnosti víceaspektního hodnocení podniků. Statistika, 6, 264-282. (In Czech).
- Labudová, V., Vojtková, M., & Linda, B. (2010). Aplikácia viacrozmerných metód pri meraní chudoby [Application of multidimensional methods to measure poverty]. E+M. Ekonomie a Management – Economics and Management, 1, 6-22.
- Laca, S. (2011). Súčasne aspekty chudoby v Slovenskej spoločnosti [Actual aspects of poverty in a Slovak society]. Prohuman. (In Slovak).
- Lehning, A., Vu, C. M., & Pintak, I. (2007). Theories of poverty. Journal of Human Behavior in the Social Environment, 16(1-2), 5-19.
- Lizbetin, J. (2018). Decision-making processes in introducing RFID technology in manufacturing company. Naše More, 65(4), 289-292.
- Michálek, A., & Madajová, M. S. (2019). Identifying regional poverty types in Slovakia. GeoJournal, 84, 85-99.
- Michálek, A., & Podolák, P. (2016). Regióny chudoby na Slovensku. Bratislava: Geografický ústav SAV. (In Slovak).
- Michálek, A., Podolák, P., Výbošťok, J., & Bilková, K. (2020). Príjmové nerovnosti a ich prejavy v regiónoch Slovenska. Bratislava: Geografický ústav SAV. (In Slovak).
- Minár, J., & Tremboš, P. (1998). Porovnanie jednotlivých variantov diaľnice D2 Bratislava a výber optimálneho variantu. Geographical Spectrum, 1, 113-117. (In Slovak).
- Minnitti, M. (2013). The dynamics of entrepreneurship: Evidence from global entrepreneurship monitor data. Oxford: Oxford University Press.
- Misturelli, F., & Heffernan, C. (2008). What is poverty? A diachronic exploration of the discourse on poverty from the 1970s to the 2000s. The European Journal of Development Research, 20(4), 666-684.
- Nándori, E. S. (2011). Subjective poverty and its relation to objective poverty concepts in Hungary. Social Indicators Research, 102(3), 537-556.
- Nándori, E. S. (2021a). Evolution of the interpretation of poverty in Hungary between 2007 and 2019. Corvinus Journal of Sociology and Social Policy, 12(2), 23-45.
- Nandori, E. S. (2021b). Perception of the reasons for living in poverty in Hungary. Hungarian Studies, 35(1), 80-86.
- Nandori, E. S. (2021c). Individualism or structuralism-differences in the public perception of poverty between the United States and East-Central Europe. Journal of Poverty, 26(4), 337-359.
- Narayan, D., Patel, R., Schafft, K., Rademacher, A., & Koch-Schulte, S. (2000). Voices of the poor: Can anyone hear us? Oxford: The World Bank and Oxford University Press.
- Niemietz, K. (2010). Measuring poverty: Context-specific but not relative. Journal of Public Policy, 30(3), 241-262.
- Noble, M., Ratcliffe, A., & Wright, G. (2004). Conceptualizing, defining and measuring poverty in South Africa: An argument for a consensual approach.
- Nowak, E. (1990). Metody taksonomiczne w klasifikacji obiektów spoleczno- gospodarczych. Warszava: PWE Warszawa. (In Polish).
- Nyasulu, G. (2010). Revisiting the definition of poverty. Journal of Sustainable Development in Africa, 12(7), 147-158.
- Rakoczyová, M., & Mareš, P. (2005). Social exclusion and poverty in the Czech Republic in comparison with EU countries, the direction of Czech social policy with emphasis on the Lisbon Strategy agenda.
- Rochovská, A., & Horňák, M. (2008). Chudoba a jej percepcia v marginálnych regiónoch Slovenska. Geographia Cassoviensis, 2(1), 152-156. (In Slovak).
- Rogalewicz, V., & Juřičková, I. (2014). Multiple-criteria decision making: Application to medical devices. Proceedings 2nd International Work-Conference on Bioinformatics and Biomedical Engineering (pp. 1359-1372).
- Sen, A. (1999). Development as freedom. New York: Knopf.
- Sharp, A., Grimes, P. W., & Register, C. A. (2009). Economics of social issues. New York: McGraw-Hill/Irwin Education.
- Siekelova, A., Podhorska, I., & Imppola, J. (2021). Analytic hierarchy process in multiple-criteria decision-making: A model example. SHS Web of Conferences, 90, 1-10.
- Singer, A. E. (2006). Business strategy and poverty alleviation. Journal of Business Ethics, 66(2-3), 225-231.
- Spicker, P. (2010). Definitions of poverty: Twelve clusters of meaning.
- Stankovičová, I. (2010). Regionálne aspekty monetárnej chudoby na Slovensku. In I. Pauhofová, O. Hudec, & T. Želinský (Eds.), Sociálny kapitál, ľudský kapitál a chudoba v regiónoch Slovenska: Zborník statí (pp. 67-75). Košice: Ekonomická fakulta TUKE. (In Slovak).
- Todaro, P. M., & Smith, S. C. (2006). Economic development (9th ed.). Washington D.C.: Pearson Education, Harlow.
- Tremboš, P. (1998). Multikriteriálne hodnotenie ako metóda optimalizácie socioekonomických aktivít – Niektoré metódy stanovenia váh kritérií. In Z. Izakovičová, M. Kozová, & E. Pauditšová (Eds.), Implementácia trvalo udržateľného rozvoja. Smolenice: SAV. (In Slovak).
- Tremboš, P., & Minár, J. (1996). Využitie metódy multikriteriálneho hodnotenia v procese posudzovania vplyvov na životné prostredie. Acta Facultatis Rerum Naturalium Universitatis Commenianae, Geographica, 39, 145-156. (In Slovak).
- World Bank. (2018). Poverty and shared prosperity 2018. Piecing together the poverty puzzle.
- World Bank. (2020). Poverty and shared prosperity 2020: Reversals of fortune.
-
-
Conceptualization
Miroslava Trembošová, Ľudmila Nagyova
-
Data curation
Miroslava Trembošová, Janka Beresecká, Alena Dubcová
-
Formal Analysis
Miroslava Trembošová, Jan Kramoliš
-
Funding acquisition
Miroslava Trembošová, Ľudmila Nagyova
-
Methodology
Miroslava Trembošová, Jan Kramoliš, Ľudmila Nagyova
-
Project administration
Miroslava Trembošová
-
Resources
Miroslava Trembošová
-
Software
Miroslava Trembošová
-
Supervision
Miroslava Trembošová, Jan Kramoliš
-
Validation
Miroslava Trembošová, Jan Kramoliš
-
Visualization
Miroslava Trembošová, Jan Kramoliš
-
Writing – original draft
Miroslava Trembošová, Ľudmila Nagyova, Janka Beresecká, Alena Dubcová
-
Writing – review & editing
Miroslava Trembošová, Jan Kramoliš, Ľudmila Nagyova
-
Investigation
Janka Beresecká, Alena Dubcová
-
Conceptualization
-
Impact of macroeconomic factors and interaction with institutional performance on Vietnamese bank share prices
Banks and Bank Systems Volume 16, 2021 Issue #1 pp. 127-137 Views: 1288 Downloads: 721 TO CITE АНОТАЦІЯShares of listed banks in Vietnam gain a lot of interest from investors and regulators. It is important to study the primary drivers of the banks’ share prices. In this context, Gross Domestic Product (GDP), Gold Price (GP), Ninety-day Interbank Interest Rate (R), and USD/VND Exchange Rate (FX) are selected as representatives for macroeconomic variables. A new contribution of this study is the application of interactive factors between macroeconomics and bank performance (i.e., Equity Capital (E), Deposit Аmounts (D), Loan Amounts (L), Non-performing Loans (NPLs), Leverage (LEV), Capital Adequacy Ratio (CAR), Return on Assets (ROA), and Stock Beta (Beta)) in evaluating their impact on bank share prices. Applying the econometric method of Two-Stage Least Square (2SLS) and the quarterly financial data of 13 listed banks from Q1/2009 to Q3/2020, the regression results show that GDP improvements can foster an increase in bank share prices, and this impact is strengthened if banks have good performance of ROA, CAR, and with strict control of NPLs. The R also has a positive impact on bank share prices, and the price level increases if NPLs, LEV, and Beta are controlled at optimal levels. However, empirical evidence drawn from the study also suggests that an increase in FX and GP is not a significant contributor to bank share prices, especially if the bank does not manage NPLs and LEV. Moreover, the impact of E, D, and L on the movements of bank share prices is not significant.
-
Impact of internal and external factors on the net interest margin of banks in Indonesia
Banks and Bank Systems Volume 15, 2020 Issue #4 pp. 99-107 Views: 1191 Downloads: 1194 TO CITE АНОТАЦІЯThis study aims to assess the impact of bank-specific factors and macroeconomic indicators on the net interest margin (NIM) of commercial banks in Indonesia. Data from Indonesian commercial banks are used. Data are collected from the banks’ annual reports and the Financial Services Authority (OJK) for the period 2008 to 2018. A panel data regression model is used to estimate the effect of bank-specific and macroeconomic factors. The results prove that the variables of Non-Performing Loans (NPL), Loan to Deposit Ratio (LDR), Return on Assets (ROA), Interest Rate (SBI), and Exchange Rate (FOREX) affect NIM. The exchange rate variable has a predominant effect, while the NPL factor has a less strong influence on NIM. The empirical evidence from this research is important for commercial banks in Indonesia to improve operational efficiency through NIM performance. Internal and external factors of a bank should be subject of attention of bank managers.
-
Management priorities of tax reform in Ukraine: implementation of international experience
Yuriy Turyanskyy , Irena Svydruk , Orystlava Sydorchuk , Nataliіa Mitsenko , Olga Klepanchuk doi: http://dx.doi.org/10.21511/imfi.17(2).2020.25Investment Management and Financial Innovations Volume 17, 2020 Issue #2 pp. 320-333 Views: 816 Downloads: 134 TO CITE АНОТАЦІЯThe paper proves that the Ukrainian economy’s systematic structural crises stipulated the necessity of choosing the effective forms of tax mechanism for its regulation. Systemic and institutional methods have been used to study the peculiarities of Ukrainian tax regulation. The methods of coefficient and relative values have been used to assess certain parameters of the tax burden. The dynamics of statistical data have been studied by the method of trend analysis. To determine the impact of the current tax system of Ukraine on economic growth, the authors tested several hypotheses about the dependence of the tax system and: GDP (1), industrial production (2), exports (3), investment dynamics (4), and unemployment rate (5) using econometric analysis with the package-statistical module EViews. The existence of a directly proportional relationship between the growth of tax revenues to the budget of Ukraine and the change of certain macroeconomic indicators is substantiated. It was found that the total tax burden on business in Ukraine reaches 41.5% of corporate profits, which exceeds similar indicators in most European countries. A tax regulation mechanism to stabilize the Ukrainian economy is proposed, in particular: proposals to revise tax rates, implement macroeconomic risk management tools, customs post-audit while providing transparency of tax legislation and its harmonization with the EU Customs Code, digitalization of the service component of tax administration.