Financial analysis of rural tourism in Catalonia and Galicia pre- and post COVID-19
This article analyses the financial health of Catalan and Galician rural tourism businesses between 2019 and 2021 by examining their profitability and solvency, in order to assess their survivability over the period affected by the COVID-19 pandemic. To this end, we classify firms into groups with s...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/25279 |
| Acceso en línea: | http://hdl.handle.net/10256/25279 |
| Access Level: | acceso embargado |
| Palabra clave: | Turisme rural -- Aspectes econòmics -- Catalunya Rural tourism -- Economic aspects -- Catalonia Turisme rural -- Aspectes econòmics -- Galícia Rural tourism -- Economic aspects -- Galicia Anàlisi de conglomerats Cluster analysis Allotjament turístic -- Aspectes econòmics -- Catalunya Tourist camps, hostels, etc. -- Economic aspects -- Catalonia Allotjament turístic -- Aspectes econòmics -- Galícia Tourist camps, hostels, etc. -- Economic aspects -- Galicia Anàlisi de ratios Ratio analysis |
| Sumario: | This article analyses the financial health of Catalan and Galician rural tourism businesses between 2019 and 2021 by examining their profitability and solvency, in order to assess their survivability over the period affected by the COVID-19 pandemic. To this end, we classify firms into groups with similar financial health. The article uses accounting data from the SABI (Iberian Balance Sheet Analysis System) database and the Compositional Data (CoDa) methodology, which offers a reliable analysis of sectoral financial statements, and is immune to the statistical problems of classical ratios (skewness, non-normality, non-linearity, and outliers, among others). The classification with the k-means method reveals three groups with distinct financial profiles. None have a positive return on equity. One cluster is under special financial distress, with high indebtedness, low liquidity and an extremely negative margin. The size of this cluster increases during the 2020 lockdown but returns to normal in 2021 |
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