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This paper shows how data science can contribute to improving empiricalresearch in economics by leveraging on large datasets and extractinginformation otherwise unsuitable for a traditional econometric approach. As atest-bed for our framework, machine learning algorithms allow us to create anew holistic measure of innovation built on a 2012 Italian Law aimed atboosting new high-tech firms. We adopt this measure to analyse the impact ofinnovativeness on a large population of Italian firms which entered the marketat the beginning of the 2008 global crisis. The methodological contribution isorganised in different steps. First, we train seven supervised learningalgorithms to recognise innovative firms on 2013 firmographics data and selecta combination of those with best predicting power. Second, we apply the formeron the 2008 dataset and predict which firms would have been labelled asinnovative according to the definition of the law. Finally, we adopt this newindicator as regressor in a survival model to explain firms' ability to remainin the market after 2008. Results suggest that the group of innovative firmsare more likely to survive than the rest of the sample, but the survivalpremium is likely to depend on location.

The survival of start-ups in time of crisis. A machine learning approach to measure innovation