Michael Beenstock, Daniel Felsenstein (p), Dai Xieer

Karima Kourtit

We suggest the use of outdegrees from graph theory to rank locations in terms of their contagiousness. We use this approach for measuring the contagiousness of locations for operating 'traffic light' policy as an alternative to national mitigation policy. This measure is applicable to any contagious disease and not just Covid-19. The outdegree for locations measure their contribution to national morbidity. Outdegree justly penalizes locations for the contagion that they propagate. By contrast the practice of calculating R for locations unfairly exonerates locations, which export contagion, and penalizes locations, which import contagion. We show that outdegrees are equal to the column sums of spatial autoregressive matrices, which may be estimated using econometric methods for spatial panel data. Simulation methods are used to illustrate the concept of outdegrees and its structural determinants in terms of centrality, indigenous contagion and spatial contagion. A secondary criteria for traffic light shading involves the stochastic structure of morbidity shocks, which induce 'spiking' through their autoregressive persistence, conditional heteroscedasticity and diffusion jump parameters

Simone Grabner, Alexandra Tsvetkova (p)

Daniel Felsenstein

The emergence of the Covid-19 pandemic caused immense turbulences in the labor market and provoked a large-scale turn towards teleworking. This paper contributes to the understanding of how teleworking shapes regional economic outcomes by focusing on labor market resilience in U.S. cities during the 2020 COVID-19 emergency. The paper examines employment and labor demand, proxied by online job vacancy data, and finds that the pre-pandemic share of teleworkable jobs in a city is linked to stronger employment resilience in general and labor demand resilience during the onset of the pandemic and in smaller cities. The paper discusses possible mechanisms behind the link and policies that can help leverage the promise of teleworking for resilient labor markets.

Annekatrin Niebhur, Silke Hamann, Georg Sieglen (p), Duncan Roth

Alexandra Tsvetkova

The COVID-19 crisis has a far-reaching and possibly persistent economic impact at a global scale. Notwithstanding its global character, the crisis has an important spatial dimension in many respects. There is a current debate on whether cities are hit more severely than other regions by the COVID-19 crisis because the pandemic might reduce agglomeration economies that rely on proximity and spatial interaction. However, findings on this issue are still scarce. This paper investigates the effects of the COVID-19 pandemic on regional labour markets in Germany between March 2020 and October 2021. We combine a shift-share model with a difference-in-difference specification in a two-stage regression approach to examine regional differences in entries to and exits from unemployment that are caused by the crisis. More precisely, we investigate whether the size of the local labour market shock and the recovery after the shock differ significantly between large agglomerations, urbanized regions and small rural labour markets. The results indicate that, in fact, the impact of the crisis differs significantly across region types. While the initial shock in April-May 2020 was largest in rural labour markets, the adverse effects seem to be more persistent in large urban labour markets. We also provide some tentative evidence on different channels that might give rise to the strong medium-term impact of the pandemic on large cities.

Umut Turk, John Osth, Karima Kourkit (p) and Peter Nijkamp

Georg Sieglen