Transmitting fiscal Covid-19 counterstrikes effectively: Mind the banks!
Bibliographic record
Abstract
The German government launched an unprecedented range of support programmes to mitigate the economic fallout from the Covid-19 pandemic for employees, self-employed, and firms. Fiscal transfers and guarantees amount to approximately €1.2 billion by now and are supplemented by similarly impressive measures taken at the European level. We argue in this note that the pandemic poses, however, also important challenges to financial stability in general and bank resilience in particular. A stable banking system is, in turn, crucial to ensure that support measures are transmitted to the real economy and that credit markets function seamlessly. Our analysis shows that banks are exposed rather differently to deteriorated business outlooks due to marked differences in their lending specialisation to different economic sectors. Moreover, a number of the banks that were hit hardest by bleak growth prospects of their borrowers were already relatively thinly capitalised at the outset of the pandemic. This coincidence can impair the ability and willingness of selected banks to continue lending to their mostly small and medium sized entrepreneurial customers. Therefore, ensuring financial stability is an important pre-requisite to also ensure the effectiveness of fiscal support measures. We estimate that contracting business prospects during the first quarter of 2020 could lead to an additional volume of non-performing loans (NPL) among the 40 most stressed banks ‒ mostly small, regional relationship lenders ‒ on the order of around €200 million. Given an initial stock of NPL of €650 million, this estimate thus suggests a potential level of NPL at year-end of €1.45 billion for this fairly small group of banks already. We further show that 17 regional banking markets are particularly exposed to an undesirable coincidence of starkly deteriorating borrower prospects and weakly capitalised local banks. Since these regions are home to around 6.8% of total employment in Germany, we argue that ensuring financial stability in the form of healthy bank balance sheets should be an important element of the policy strategy to contain the adverse real economic effects of the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".