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Purpose Starting from a series of financial ratios analysis, this paper aims to build up two indices which take into account both the firm’s debt level and its sustainability to investigate if and to what extent the proposed indices are able to correctly predict firms’ financial bankruptcy probabilities. Design/methodology/approach The research implements a statistical approach (tandem analysis) based on both an original use of principal component analysis (PCA) and logit model. Findings The econometric results are compared with those of the popular Altman Z-score for different lengths of the reference period and with more recent classifiers. The empirical evidence would suggest a good performance of the proposed indices which, therefore, could be used as early warning signals of bankruptcy. Practical implications The potential application of the model is in the spirit of predicting bankruptcy and aiding companies’ evaluation with respect to going-concern considerations, among others, as the early detection of financial distress facilitates the use of rehabilitation measures. Originality/value The construction of the indebtedness indices is based on an original use of Robust PCA for skewed data.

BackgroundCOVID-19, like all pandemics, has territorial specificities that need to be considered: the impact of the COVID-19 crisis strongly differs not only across countries, but also across regions, districts and municipalities within countries. There are several factors that, potentially, can contribute to the differentiated impact of COVID-19, and explain the disparities seen among areas. This study aims to contribute to this debate by analyzing the role of health system and social trust in lessening the impact of the COVID-19 pandemic in French ‘départements’.MethodsThe data used in this study have been provided by the INSEE and the French Ministry of Health. Database is made up of the 96 ‘départements’ of metropolitan France. We use spatial analysis techniques to identify the groups of areas that are particularly affected, and to test the influence of local socio-economic factors on the spread of the epidemic.ResultsOur exploratory spatial analysis reveals the heterogeneity and spatial autocorrelation of the disease. The use of spatial econometric models, then, allows us to highlight the impact of emergency services, and social capital in reducing the exposition to COVID-19. Our results also report on the role of spillover effects between neighbouring areas.ConclusionsThis research shows that, although individual characteristics are important factors in explaining the probability of contracting COVID-19 disease, health care services and social trust factors also play a significant role in curbing the epidemic’s outbreak. These findings should have an interest for policy makers in the prevention of future waves of COVID-19 pandemic.

This paper investigates the role of local context, with regard to the effect of local financial development and banking concentration, on a firm’s probability of bankruptcy at the post-creation stage. Our empirical setting is based on the logit multilevel model that better allows the treatment of data referring to different levels of aggregation (firm and local variables) applied to companies located in Italy. We find that a higher level of financial development in a province decreases the likelihood of corporate bankruptcy. This result is robust considering a 2SLS regression in which we use instruments for the local financial development and for the concentration of bank branches. In addition, our estimations suggest that the effect of local financial development and bank concentration is shaped by size. Local financial development is particularly significant for small firms, which traditionally suffer from great difficulty in accessing credit, whereas local banking concentration reduces the probability of bankruptcy for medium-sized firms.https://doi.org/10.1007/s11187-018-0042-4

The purpose of this research is to study the role of spatial agglomeration economies as drivers of firm exit in France over the period 2009–2013 by focusing on two regional variables (local financial development and local specialization). The spatial autocorrelation detected in the data leads us to apply spatial econometric techniques (Spatial Dynamic Panel models and spatial GMM) that permit us to control the estimation of spatial spillover effects. Our results show that firm exit is significantly influenced by positive spatial autocorrelation. Therefore, locations with high exit rates tend to be surrounded by similar ones. However, this phenomenon differs according to the period. In addition, we find that greater local financial development reduces the exit rate in a department, whereas local specialization seems not to exert any effect.