This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.

During the COVID-19 pandemic, the introduction of mandatory face mask usage triggered a heated debate. A major point of controversy is whether community use of masks creates a false sense of security that would diminish physical distancing, counteracting any potential direct benefit from masking. We conducted a randomized field experiment in Berlin, Germany, to investigate how masks affect distancing and whether the mask effect interacts with the introduction of an indoor mask mandate. Joining waiting lines in front of stores, we measured distances kept from the experimenter in two treatment conditions – the experimenter wore a mask in one and no face covering in the other – in two time spans – before and after mask use becoming mandatory in stores. We find no evidence that mandatory masking has a negative effect on distance kept toward a masked person. To the contrary, masks significantly increase distancing and the effect does not differ between the two periods. However, we show that after the mandate distances are shorter in locations where more non-essential stores, which were closed before the mandate, had reopened. We argue that the relaxations in general restrictions that coincided with the mask mandate led individuals to reduce other precautions, like keeping a safe distance.

Governments across the world have implemented restrictive policies to slow the spread of COVID-19. Recommended face mask use has been a controversially discussed policy, among others, due to potential adverse effects on physical distancing. Using a randomized field experiment (N = 300), we show that individuals kept a significantly larger distance from someone wearing a face mask than from an unmasked person during the early days of the pandemic. According to an additional survey experiment (N = 456) conducted at the time, masked individuals were not perceived as being more infectious than unmasked ones, but they were believed to prefer more distancing. This result suggests that wearing a mask served as a social signal that led others to increase the distance they kept. Our findings provide evidence against the claim that mask use creates a false sense of security that would negatively affect physical distancing. Furthermore, our results suggest that behavior has informational content that may be affected by policies.

This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.

Around the globe, the COVID-19 pandemic has turned daily live upside down since social distancing is probably the most effective means of containing the virus until herd immunity is reached. Social norms have been shown to be an important determinant of social distancing behaviors. By conducting two experiments and using the priming method to manipulate social isolation recollections, we study whether social distancing has in turn affected norms of prosociality and norm compliance. The normative expectations of what behaviors others would approve or disapprove in our experimental setting did not change. Looking at actual behavior, however, we find that persistent social distancing indeed caused a decline in prosociality – even after the relaxation of social distancing rules and in times of optimism. At the same time, our results contain some good news since subjects seem still to care for norms and become more prosocial once again after we draw their attention to the empirical norm of how others have previously behaved in a similar situation.

We study the dynamic impact of Covid-19, economic mobility, and containment policy shocks. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through sign and zero restrictions. Incidence and mobility shocks raise cases and deaths significantly for two months. Restrictive policy shocks lower mobility immediately, cases after one week, and deaths after three weeks. Non-pharmaceutical interventions explain half of the variation in mobility, cases, and deaths worldwide. These flattened the pandemic curve, while deepening the global mobility recession. The policy tradeoff is 1 p.p. less mobility per day for 9% fewer deaths after two months.

This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.

Seit der globalen Finanzkrise 2007/2008 liegt aufgrund ihrer Schlüsselrolle für ein funktionierendes Finanzsystem ein besonderer Fokus auf den so genannten systemrelevanten Finanzinstitutionen (systemically important financial institutions, SIFIs). Neben der Größe von Finanzinstitutionen ist auch das Ausmaß ihrer Vernetzung im internationalen Finanzsystem entscheidend für die Klassifikation als systemrelevant. Obwohl die Vernetzung von Banken untereinander in der Regel schwer zu messen ist, kann sie aus der Entwicklung von Prämien von Kreditausfallversicherungen (den so genannten Credit Default Swap (CDS) Spreads) und Aktienrenditen abgeleitet werden. Dieser Beitrag untersucht, inwieweit sich mit Hilfe der sich daraus ergebenden Co-Crash-Probability vor der Finanzkrise vorhersagen lässt, welche Finanzinstitutionen während der Krise Teil von staatlichen Rettungsprogrammen (bailout programmes) wurden.