Does the Average Payment Period Play a Relevant Role in Explaining the Portuguese Municipalities’ Financial Distress?

• Autor(a) principal: Gomes dos Santos, Paula
• Data de Publicação: 2023
• Outros Autores: Albuquerque, Fábio
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.5/30410
• topic: Average Payment Period; Financial Distress; Portuguese Municipalities; Over-Indebtedness; Panel Data Analysis

The average payment period (APP) is an important measure to monitor public sector liquidity and commercial debt sustainability. In the European Union, the relevance of APP as an indicator to mitigate those concerns was settled by the issuance of a Directive on this topic. Then, considering the likely relationship between the APP and financial distress (FD), this paper aims to assess if the current and historical APP plays a relevant role in explaining the data for 308 Portuguese municipalities’ FD. Regression analysis, including panel data and simple linear regression models, is used for the period between 2011 and 2019 (the last year available for APP). The findings show that the number of municipalities that exceed the debt limit has been reducing. Some of them comply with the 30 days threshold being, nonetheless, in a financial distress situation and subject to recovery mechanisms. By APP levels, the findings identify that the APP significantly predicts FD for those entities that do not comply with the threshold. By size, the FD seems to be better explained by historical APP within the smaller entities. Finally, the robustness test and further analyses performed indicate the likely relevance of macroeconomic factors for predicting FD using APP as an explanatory variable. This paper contributes to previous studies on the municipalities’ financial condition, namely by considering the APP in the analysis of the financial situation of the municipalities. As a novelty, this study considers a new proxy for municipal FD and panel data analysis.