The determinants of transitions between different states of financial distress are analyzed using two versions of Markov chain models: a multinomial logit model without random effects and a multinomial logit model capturing such unobservable factors. The empirical analysis is based on a panel...

The determinants of transitions between different states of financial distress are analyzed using two versions of Markov chain models: a multinomial logit model without random effects and a multinomial logit model capturing such unobservable factors. The empirical analysis is based on a panel...

We investigate the workforce heterogeneity of startups with respect to education, age and wages. Our explorative study uses data on the population of 1,614 Danish firms founded in 1998. We track these firms until 2001 which enables us to analyze changes in workforce composition over time. Such a...

We investigate the workforce heterogeneity of startups with respect to education, age and wages. Our explorative study uses data on the population of 1,614 Danish firms founded in 1998. We track these firms until 2001 which enables us to analyze changes in workforce composition over time. Such a...

We investigate the workforce heterogeneity of startups with respect to education, age and wages. Our explorative study uses data on the population of 1,614 Danish firms founded in 1998. We track these firms until 2001 which enables us to analyze changes in workforce composition over time. Such a...

The determinants of transitions between different states of financial distress are analyzed using two versions of Markov chain models: a multinomial logit model without random effects and a multinomial logit model capturing such unobservable factors. The empirical analysis is based on a panel...

We investigate the workforce heterogeneity of startups with respect to education, age and wages. Our explorative study uses data on the population of 1,614 Danish firms founded in 1998. We track these firms until 2001 which enables us to analyze changes in workforce composition over time. Such a...

Can publicly available, web-scraped data be used to identify promising business startups at an early stage? To answer this question, we use such textual and non-textual information about the names of Danish firms and their addresses as well as their business purpose statements (BPSs)...

We compare individuals presently employed either at an university or at a firm from a R&D intensive sector and analyze which of their personal-specific and employer-specific characteristics determine their choice of subsequently founding a startup. Our data set is unusually rich and combines the...