Have mitigating measures helped prevent insolvencies in Austria amid the COVID-19 pandemic?

We employ a novel modeling approach to capture the impact of the COVID-19 pandemic on sectoral insolvency rates in Austria. Turnover shocks derived from a macroeconomic scenario generate stress to firms’ profits and cash flows. Over time, both the equity and the liquidity (cash and bank) positions deteriorate, which causes insolvencies if firms fall under certain thresholds. Our model builds on data for nonfinancial incorporated Austrian enterprises available from the BACH and SABINA databases. Since only two firm-level variables (equity ratio, cash and bank) are available at sufficient coverage, we generate a hypothetical firmlevel dataset for 17 NACE 1 sectors by using a Monte Carlo simulation. The granularity of our model allows us to assess the impact of mitigating measures implemented in light of the COVID-19 shock. Such measures serve to cushion the loss of companies’ revenue and households’ income triggered by the COVID-19 containment measures. Put differently, they are meant to minimize the damage resulting from the deliberate temporary reduction in economic activity. In our analysis, we only investigate measures aimed at firms. These measures include equity injections via grants and subsidies (e.g. short-time work), longterm payment deferrals (e.g. credit guarantees) and short-term payment deferrals (e.g. social security contributions). We used all available data sources to calibrate the mitigating measures, with August 31, 2020, as cutoff date. The model indicates a marked increase of COVID-19-induced insolvency rates, but mitigating measures reduce such insolvencies substantially. Without mitigating measures, the insolvency rate would rise to 5.8% by the end of 2020, more than quintupling its pre-crisis average (2017–2019: 1.0%). By end-2022, 9.9% of all Austrian firms would fail, which corresponds to an annual insolvency rate of 3.3%. With mitigating measures in place, the insolvency rate is significantly lower, reaching 2.1% by end-2020, and 6.9% by end-2022. Projected insolvency rates should be interpreted with caution. The merit of this novel approach, however, lies less in the calculated sectoral insolvency rates themselves, but in the model’s capacity to compare and rank the efficiency and efficacy of various mitigating measures. As to the current measures, we, for instance, find that credit guarantees appear most effective, followed by fixed cost support and short-time work. In the short term, delayed filing for insolvency is most efficient, but is set to mostly reverse itself in 2021, once public institutions recommence their usual practice. At the OeNB, the model has also been used to assess implementation delays and the extension of mitigating measures. We intend to continuously extend the model, both in terms of its core functionality and the calibration of mitigating measures to address questions from (1) a macroeconomic perspective, in particular the loss of productive capacities (potential output), (2) a fiscal policy perspective, to estimate the costs of mitigating measures, and (3) a macro- and microprudential banking supervisory perspective, to provide a basis for estimating credit default probabilities for the banking system.