Predicting Bankruptcy in Tourist Accommodations: A Survival Analysis of Macroeconomics Indicators and Managerial Characteristics

Abstract

Introduction Survival analysis has evolved significantly, enhancing techniques and methods. Business survival is critical due to its economic and societal impacts. Insolvency prediction models act as valuable early warning systems (Branch, 2022).

Literature Research has traditionally focused on financial ratios as predictors of failure, with recent studies considering external factors (Gemar et al., 2019). Despite progress, studies on tourism sector bankruptcy remain limited. Most focus on individual countries like Spain or Portugal, while joint analyses are rare (Vivel-Búa et al., 2019).

Purpose This study examines time to failure and survival probabilities over fifteen years, predicting insolvency in tourist accommodation on the Iberian Peninsula. It identifies macroeconomic and management indicators that forecast bankruptcy.

Methods The sample consists of 2,013 tourist accommodations (1,540 in Spain, 473 in Portugal) from 2008–2022, using ORBIS database data. The methodology combines Kaplan-Meier survival analysis with logistic regression and a complementary log-log link (Hosmer & Lemeshow, 1999).

Results Survival depends on factors such as manager gender, senior executives, corporate group membership, GDP, and CPI. Female management increases survival, while more executives and business group membership decrease it. Higher GDP enhances survival, whereas higher CPI reduces it. Financial factors, such as employee costs, payment periods, and solvency ratios, also play key roles.

Implications This research provides governance tools to enhance business longevity. Monitoring these indicators acts as an early warning system to prevent insolvency. While focused on the Iberian Peninsula, findings are applicable to similar tourism contexts.

Originality Few studies jointly explore accommodation survival in Spain and Portugal. This research uniquely examines underexplored management and macroeconomic variables.