Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2022 | 56 | nr 5 | 101--113
Tytuł artykułu

Importance of the Size of Local Government in Avoiding the Fiscal Distress - Empirical Evidence on Communes in Poland

Warianty tytułu
Języki publikacji
Theoretical background: In the literature on finance there are findings which examine reasons for the fiscal distress of units of the public sector, including local governments. However, this distress might be differently defined. Therefore, it determines both the approach to identify this phenomenon and the types of explanatory variables. Nevertheless, in the field of the business sector in the econometric models concerning the financial distress the size of the unit is considered. In this case there are also some possibilities to apply the correct proxy variable. This results from the fact that the size of local government might determine its fiscal capability as well as the level and structure of expenditures, which affect fiscal distress.

Purpose of the article: The aim of this paper is to examine the influence of the size of the local government on the probability of the decrease of the exposure to the fiscal distress.

Research methods: The author reviewed the literature in the field of the fiscal distress and introduced a multi-criteria decision analysis as well as a logistic regression modelling to examine this. The research procedure also required the use of the linear ordering to construct the dependent variable of the fiscal distress in order to analyse the "size effect" on the fiscal distress.

Main findings: Fiscal distress of local governments is a core issue, which should be constantly analysed. It depends on the financial, economic, social and even political aspects. To identify exposure to this distress the TOPSIS method can be used. However, the fiscal distress can be affected by the size of the unit, which influences lots of budgetary categories. Due to the specificity of dependent and independent variables in the econometric models the "size effect" might be represented through the level of the population or the assets. Using the ordinal logistic regression in the research, the authors should consider that this effect can differ between the units with the disparate exposure. So, the partial proportional odds models can be required. Thus, the growth of the size of the unit, measured by the population, increases the odds of reaching very low exposure to fiscal distress. Simultaneously, there are some other important issues which should be included in this type of research.(original abstract)
Opis fizyczny
  • University of Gdansk, Poland
  • Alaminos, D., Fernandez, S.M., Garcia, F., & Fernandez, M.A. (2018). Data mining for municipal financial distress prediction. Lecture Notes in Artificial Intelligence, 10933, 296-308.
  • Ansori, A., Nasir, N., Diantimala, Y., & Abdullah, S. (2021). The role of revenues in reducing local government fiscal distress: An empirical study in Indonesia. Journal of Asian Finance, Economics and Business, 8(6), 597-607. doi:10.13106/jafeb.2021.vol8.no6.0597
  • Arnett, S.B. (2012). Fiscal Stress in the U.S. States: An Analysis of Measures and Responses. Dissertation. Atlanta: Georgia State University. doi:10.57709/2852426
  • Bąk, A. (2016). Porządkowanie liniowe obiektów metodą Hellwiga i TOPSIS - analiza porównawcza. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 426, 22-31.
  • Bruns, M., & Poghosyan, T. (2018). Leading indicators of fiscal distress: Evidence from extreme bounds analysis. Applied Economics, 50(13), 1454-1478. doi:10.1080/00036846.2017.1366639
  • Bumgarner, M., Martinez-Vazquez, J., & Sjoquist, D.L. (1991). Municipal capital maintenance and fiscal distress. The Review of Economics and Statistics, 73(1), 33-39.
  • Dang, C., Li, Z., & Yang, C. (2018). Measuring firm size in empirical corporate finance. Journal of Banking and Finance, 86, 159-176. doi:10.1016/j.jbankfin.2017.09.006
  • Deb, P., Norton, E.C., & Manning, W.G. (2017). Health Econometrics Using Stata. College Station: Stata Press.
  • Diaz, D., & Green, G.P. (2002). Fiscal stress and growth management effort in Wisconsin cities, villages, and towns. State and Local Government Review, 33(1), 7-22.
  • Dudek, A., & Jefmański, B. (2015). The fuzzy TOPSIS method and its implementation in the R Programme. Business Informatics. Informatyka Ekonomiczna, 1(35), 19-27.
  • Ehrhardt, M.C., & Brigham, E.F. (2008). Financial Management. Theory and Practice. Mason: Thomson South-Western.
  • Fagerland, M.W., & Hosmer, D.W. (2017). How to test for goodness of fit in ordinal logistic regression models. The Stata Journal, 17(3), 668-686. doi:10.1177/1536867X1701700308
  • Fahami, N.A., Azhar, F.W., Rahim, Z.H.A., Karim, H.A., & Rahim, Z.S.K.N.A. (2019). Application of TOPSIS analysis method in financial performance evaluation: A case study of construction sector in Malaysia. Journal of Computational and Theoretical Nanoscience, 16, 1-5.
  • Galiński, P. (2021). Zagrożenie fiskalne jednostek samorządu terytorialnego. Uwarunkowania, pomiar, ograniczanie. Gdańsk: Wyd. UG.
  • Gorina, E., Maher, C., & Joffe, M. (2018). Local fiscal distress: Measurement and prediction. Public Budgeting & Finance, 38(1), 72-94.
  • Goryl, A., Jędrzejczyk, Z., Kukuła, K., Osiewalski, J., & Walkosz, A. (2009). Wprowadzenie do ekonometrii. Warszawa: Wyd. Naukowe PWN.
  • Gruszczyński, M. (2020). Financial Microeconometrics. A Research Methodology in Corporate Finance and Accounting. Cham: Springer.
  • Henshaw, S.A. (2022). Auditor of Public Accounts, Monitoring for Local Government Fiscal Distress 2020 and 2021 Report. Richmond: Auditor of Public Accounts Commonwealth of Virginia.
  • Hosmer, D.W., & Lemeshow, S. (2000). Applied Logistic Regression. Second Edition. New York: John Wiley & Sons.
  • Jones, S., & Walker, R.G. (2007). Explanators of local government distress. ABACUS, 43(3), 396-418. doi:10.1111/j.1467-6281.2007.00238.x
  • Kloha, P., Weissert, C.S., & Kleine, R. (2005). Developing and testing a composite model to predict local fiscal distress. Public Administration Review, 65(3), 313-323.
  • Liu, X. (2016). Applied Ordinal Logistic Regression Using Stata. Los Angeles: Sage Publications.
  • Long, J.S. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage Publications.
  • Long, S.S., & Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata. Third Edition. College Station: Stata Press.
  • Maher, C.S., Oh, J.W., & Liao W.-J. (2020). Assessing fiscal distress in small county governments. Journal of Public Budgeting, Accounting and Financial Management, 32(4), 691-711. doi:10.1108/JPBAFM-02-2020-0016
  • Maria, E., Halim, A., & Suwardi, E. (2021). Financial distress, regional independence and corruption: An empirical study in Indonesian local governments. Journal of Accounting and Strategic Finance, 4(1), 54-70. doi:10.33005/jasf.v4i1.159
  • Muñoz, E., & Olaberria, E. (2019). Are budget rigidities a source of fiscal distress and a constraint for fiscal consolidation? Policy Research Working Paper, 8957.
  • Navarro-Galera, A., Lara-Rubio, J., Buendía-Carrillo, D., & Rayo-Cantón, S. (2017). What can increase the default risk in local governments? International Review of Administrative Sciences, 83(2), 397-419. doi:10.1177/0020852315586308
  • Perło, D., & Roszkowska, E. (2017). The application of soft modelling and TOPSIS method for the analysis of competitiveness of companies in urban functional areas in Poland. Optimum. Studia Ekonomiczne, 5(89), 67-84.
  • Soon, J.-J. (2010). The determinants of students' return intentions: A partial proportional odds model. Journal of Choice Modelling, 3(2), 89-112. doi:10.1016/S1755-5345(13)70037-X
  • Trussel, J.M., & Patrick, P.A. (2014). The socio-demographic, economic and financial profiles of municipalities at risk of financial distress in Pennsylvania. Austin Journal of Accounting, Audit and Finance Management, 1(1), 1-9.
  • Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal, 6(1), 58-82. doi:10.1177/1536867X0600600104
  • Williams, R. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, 40(1), 7-20. doi:10.1080/0022250X.2015.1112384
  • Winarna, J., Widagdo, A.K., & Setiawan, D. (2017). Financial distress of local government: A study on local government characteristics, infrastructure, and financial condition. Global Business & Finance Review, 22(2), 34-47. doi:10.17549/gbfr.2017.22.2.34
  • Ziolo, M. (2015). Diagnosing fiscal distress: Regional evidence from Polish municipalities. Romanian Journal of Fiscal Policy, 6(2), 14-33.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.