Figurea shows temporal changes of companies in the plateau between 2016 and 2017. Companies in the horizontal shaded area are in the 2017 plateau, but showed less ESR in 2016. Companies in the vertical shaded area are in the 2016 plateau, but not in 2017, i.e. reduced their ESRI. 20 out of 32 2016-plateau-firms remain in the 2017-plateau . Of the 12 companies leaving the plateau from 2016 to 2017 some remain very risky others become less risky . 12 companies enter the plateau in 2017 .
a we show three different economic shock scenarios that affect the NACE4 class 2611 . All three initial shocks have the same size on the sector level, but affect different companies within the sector. Theis exogenously applied and triggers a spreading of shocks in the production network. Theis the shock each sector receives as a result of the propagation of the initial shock.
), affected by the initial shock. The first shock scenario serves as a reference scenario. It applies a homogeneous initial shock of 18% to all of the 69 firms in sector 2611. The received shock of all 568 NACE 4 sectors present in the data marks the reference response. The received shocks are shown on the y-axis as a fraction of this reference response, and by construction, the received shock for the homogeneous scenario is 1 .
We find that the 32 top risky companies contribute to 45 % of the entire systemic risk, the top 100 companies contribute to 74 %. Only 165 companies have more than 1% risk . The 32 top risky companies show extremely high systemic risk of about 23%. They are connected by a network of highly critical supply relations . Of those, only a fraction appears to be inherently risky .
CSHVienna SciReports This is VERY cool! 'unique dataset of value-added tax (VAT) information', you are lucky to have this. I am working on a similar thing for flow-of-funds in the financial sector, as I get time and can find data. Anyone got data!
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