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 determinants and the observed and unobserved regional and contextual characteristics, we model the three-year firm survival probability by means of a multilevel logistic framework. The empirical analysis focuses on an internationally comparable database of the population of firms built up and managed by the Italian National Institute of Statistics (ISTAT), in accordance with the procedures suggested by OECD and EUROSTAT, which guarantee that data are not affected by the typical inconsistencies 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 multilevel 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
Dickson, Maria Michela;
2016
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 determinants and the observed and unobserved regional and contextual characteristics, we model the three-year firm survival probability by means of a multilevel logistic framework. The empirical analysis focuses on an internationally comparable database of the population of firms built up and managed by the Italian National Institute of Statistics (ISTAT), in accordance with the procedures suggested by OECD and EUROSTAT, which guarantee that data are not affected by the typical inconsistencies 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 multilevel logistic model computationally unfeasible. The estimation is then performed by means of the cross-entropy method for noisy optimization suggested by [2].
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