The time until closure of small business enterprises in Umbria, Italy and the factors that influence it, has been previously analyzed by using Cox regression with time-varying covariates. We considered only the event of failure (closure), from any cause. However, different routes to inactivity exist: court-orderedwinding-up(790ofthe8999firmsinourdata,65.9% of the 1199 failures);bankruptcy(199 firms,16.6%);andclosurewithout action by creditors or courts (210 firms, 17.5%). These are competing risks - as if the various causes are racing to be the first to cause failure. The earlier analysis provides a valuable overall picture, but it is also interesting to examine the separate causes, the rates at which they operate and which factors influence them separately. Data for 2008-2013 provided by the Chamber of Commerce of Perugia included the firm’s year of foundation, location, legal form and sector of activity. Financial indexes were constructed from annual balance sheets. Macroeconomic variables were obtained from the National Statistical Service. If the firm ceased activity, the date of, and reason for, closure were recorded. We carry out competing risk analysis using both the main regression methods, constructing cause-specific hazards and sub-distribution hazards.
Failure of small business enterprises: A competing risks analysis
Francesca Pierri;Elena Stanghellini
2018
Abstract
The time until closure of small business enterprises in Umbria, Italy and the factors that influence it, has been previously analyzed by using Cox regression with time-varying covariates. We considered only the event of failure (closure), from any cause. However, different routes to inactivity exist: court-orderedwinding-up(790ofthe8999firmsinourdata,65.9% of the 1199 failures);bankruptcy(199 firms,16.6%);andclosurewithout action by creditors or courts (210 firms, 17.5%). These are competing risks - as if the various causes are racing to be the first to cause failure. The earlier analysis provides a valuable overall picture, but it is also interesting to examine the separate causes, the rates at which they operate and which factors influence them separately. Data for 2008-2013 provided by the Chamber of Commerce of Perugia included the firm’s year of foundation, location, legal form and sector of activity. Financial indexes were constructed from annual balance sheets. Macroeconomic variables were obtained from the National Statistical Service. If the firm ceased activity, the date of, and reason for, closure were recorded. We carry out competing risk analysis using both the main regression methods, constructing cause-specific hazards and sub-distribution hazards.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
