The purpose of this contribution is to provide novel evidence about the main determinants of the short-run survival of pharmaceutical and medical device manufacturing start-up firms in Italy. In order to assess both the firm-specific de- terminants and the observed and unobserved regional and contextual characteris- tics, we model the three-year firm survival probability by means of a multilevel lo- gistic framework. The empirical analysis focuses on an internationally comparable database of the population of firms built up and managed by the Italian National In- stitute of Statistics (ISTAT), in accordance with the procedures suggested by OECD and EUROSTAT, which guarantee that data are not affected by the typical inconsis- tencies of the National Business Registers and hence provide the true information about firm entries and exits. The size of this dataset and the high number of regional random effects, however, makes the standard estimation techniques of the multi- level logistic model computationally unfeasible. The estimation is then performed by means of the cross-entropy method for noisy optimization suggested by [2].
Three-Year Survival Probability of Italian Start-up Businesses in Healthcare Industry: an Empirical Investigation through Logistic Multilevel Modelling
Santi, Flavio
2016-01-01
Abstract
The purpose of this contribution is to provide novel evidence about the main determinants of the short-run survival of pharmaceutical and medical device manufacturing start-up firms in Italy. In order to assess both the firm-specific de- terminants and the observed and unobserved regional and contextual characteris- tics, we model the three-year firm survival probability by means of a multilevel lo- gistic framework. The empirical analysis focuses on an internationally comparable database of the population of firms built up and managed by the Italian National In- stitute of Statistics (ISTAT), in accordance with the procedures suggested by OECD and EUROSTAT, which guarantee that data are not affected by the typical inconsis- tencies of the National Business Registers and hence provide the true information about firm entries and exits. The size of this dataset and the high number of regional random effects, however, makes the standard estimation techniques of the multi- level logistic model computationally unfeasible. The estimation is then performed by means of the cross-entropy method for noisy optimization suggested by [2].I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.