This paper shows how data science can contribute to improving empirical research in economics by leveraging on large datasets and extracting information otherwise unsuitable for a traditional econometric approach. As a test-bed for our framework, machine learning algorithms allow to create a new holistic measure of innovation following a 2012 Italian Law aimed at boosting new high-tech firms. We adopt this measure to analyse the impact of innovativeness on a large population of Italian firms which entered the market at the beginning of the 2008 global crisis. The methodological contribution is organised in different steps. First, we train seven supervised learning algorithms to recognise innovative firms on 2013 firmographics data and select a combination of those models with the best prediction power. Second, we apply the latter on the 2008 dataset and predict which firms would have been labelled as innovative according to the definition of the 2012 law. Finally, we adopt this new indicator as the regressor in a survival model to explain firms' ability to remain in the market after 2008. The results suggest that innovative firms are more likely to survive than the rest of the sample, but the survival premium is likely to depend on location.

In this paper, we consider homogeneous normalized random measures with independent increments (hNRMI), a class of nonparametric priors recently introduced in the literature. Many of their distributional properties are known by now but their stick-breaking representation is missing. Here we display such a representation, which will feature dependent stick-breaking weights, and then derive explicit versions for noteworthy special cases of hNRMI. Posterior characterizations are also discussed. Finally, we devise an algorithm for slice sampling mixture models based on hNRMIs, which relies on the representation we have obtained, and implement it to analyze real data.

Even if the epidemic of malignant pleural mesothelioma (MPM) is still far from being over worldwide, the health effects of regulations banning asbestos can be evaluated in the countries that implemented them early. Estimates of MPM future burden can be useful to inform and support the implementation of anti-asbestos health policies all around the world. With this aim we described the trends of MPM deaths in Italy (1970–2014) and predicted the future number of cases in both sexes (2015–2039), with consideration of the national asbestos ban that was issued in 1992. The Italian National Statistical Institute (ISTAT) provided MPM mortality figures. Cases ranging from 25 to 89 years of age were included in the analysis. For each five-year period from 1970 to 2014, mortality rates were calculated and age–period–cohort Poisson models were used to predict future burden of MPM cases until 2039. During the period 1970–2014 a total number of 28,907 MPM deaths were observed. MPM deaths increased constantly over the study period, ranging from 1356 cases in 1970–1974 to 5844 cases in 2010–2014. The peak of MPM cases is expected to be reached in the period 2020–2024 (about 7000 cases). The decrease will be slow: about 26,000 MPM cases are expected to occur in Italy during the next 20 years (2020–2039). The MPM epidemic in Italy is far from being concluded despite the national ban implemented in 1992, and the peak is expected in 2020–2024, in both sexes. Our results are consistent with international literature.