Small business survival and sample selection bias

Abstract

Analyses of small business and the factors affecting their survival are fairly common in the research literature. The level of research interest may stem from the fact that in the US, only about half of all new small businesses survive after 4 years (Headd 2003). However, research attempting to understand the phenomenon that employs data using only information from and about surviving firms may lead to erroneous conclusions regarding the factors that influence firm survival and failure. In this paper, we provide evidence that omitted information about the firms that disappear from the research data over time leads to biased coefficient estimates. Comparing the Heckman two-step estimation approach of switching regression models to a semi-parametric Cox hazard model, the Accelerated Failure Time (AFT) model, we conclude that the Cox ATF approach is the most appropriate model for firm survival analysis.