Using survey data from a representative sample of Irish Small and Medium Enterprises (SMEs), we study how firms are likely to recover under macroeconomic forecasts of the pandemic recovery. The rate of financial distress among firms is expected to fall under baseline forecasts from a peak of 12 per cent in 2020 to 7 per cent by 2024. We find that those firms that struggle to recover by the end of our scenario window were mostly unprofitable or distressed prior to the pandemic. Beyond our baseline case, we further model three alternative recovery scenarios to study the effect of fiscal support tapering, a partial recovery due to structural change in sectoral demand, and a financing gap driven by credit risk retrenchment by lenders. Our findings highlight the continued importance of "bridging" liquidity finance provision to ensure the longterm solvency of viable firms. 1 We thank Sean Fitzpatrick, Sarah Holton, Vasileios Madouros, Paul Reddan, and seminar participants for helpful comments. The views in this paper are our own and do not necessarily represent the views of the Central Bank of Ireland.
On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial reordering of the housing market in the city. We find a negative correlation between insurance payouts and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) -indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority's building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.
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