Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth

Evaluations of support measures for companies often require a good assessment of the viability of firms or the probability that a firm will exit the market. On March 17, 2020, a lockdown and associated social-restriction measures were announced, which hit specific in the economy severely. To compensate companies and the self-employed for the loss of income, an extensive package of support measures has been designed. These support measures had hardly any restrictions, because they had to be paid out quickly. This raises the question whether unhealthy companies have made disproportionate use of support and to what extent these support measures have kept viable or non-viable companies afloat. In this paper, we use machine learning techniques to predict whether a company would have left the market in a world without corona. These predictions show that unhealthy companies applied for support less often than healthy companies. But we also show that the COVID-19 support has prevented most exits among unhealthy companies. This indicates that the corona support measures have had a negative impact on productivity growth.