Start-ups survival through a crisis. Combining machine learning with econometrics to measure innovation

Abstract

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 hightech 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.

Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research

“…This is strongly connected to their performance in terms of mortality. It has been proven that innovative firms are more likely to survive than those that are not innovative (Guerzoni et al. , 2020).…”

Risk perception and determinants in small‐ and medium‐sized agri‐food enterprises amidst the COVID‐19 pandemic: Evidence from Egypt

“…The HHZ approach has been extensively used for conditional inference within several disciplines. Recent applications within business research include consumer branding (Schivinski, 2019) as well as innovation and technology transfer (Guerzoni et al, 2020).…”

Multi Criteria Decision Analysis, Hesitant Fuzzy Methodology towards to Expert Systems for analysing financial markets dynamics

“…The latter represents one of the major machine learning issues even if in the presence of a rapid growing of machine learning theories and applications in the last decade. The selection of a suitable machine learning classifier is challenging and allow to overcome performance of econometric models [112,113]. In this field, the need of robust classification techniques is urgent [114][115][116] especially given the presence of not well-defined, vague or imbalanced data [117][118][119].…”