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We develop a novel empirical approach to identify the effectiveness of policies against a pandemic. The essence of our approach is the insight that epidemic dynamics are best tracked over stages, rather than over time. We use a normalization procedure that makes the pre-policy paths of the epidemic identical across regions. The procedure uncovers regional variation in the stage of the epidemic at the time of policy implementation. This variation delivers clean identification of the policy effect based on the epidemic path of a leading region that serves as a counterfactual for other regions. We apply our method to evaluate the effectiveness of the nationwide stay-home policy enacted in Spain against the Covid-19 pandemic. We find that the policy saved 15.9% of lives relative to the number of deaths that would have occurred had it not been for the policy intervention. Its effectiveness evolves with the epidemic and is larger when implemented at earlier stages.
The Pantanal is a wetland biome in the interior of Brazil. It is known for its rich macrofauna. Botanically, it is relatively species poor, although the marshes have trees and shrubs throughout and there are occasional forested, even somewhat rocky hills. Lichens have received only scant attention so far, but the area is not very species rich (Canêz et al. 2020). We visited the Pantanal several times and collected in different areas. Here we describe four new species, one of which is locally the most common macrolichen, which was found on places elsewhere in the state and in the bordering state of Mato Grosso as well.
Nine species of Graphidaceae are described as new to science from South and Central Brazil, in 7 different genera: Acanthothecis normuralis, A. psoromica, Acanthotrema minus, Aggregatorygma submuriforme, Allographa medioinspersa, Diorygma isidiolichexanthonicum, Fissurina excavatisorediosa, Graphis norsorediata, and Graphis tricolor.
Eleven species of lichens are described as new from the Serra do Bodoquena in Mato Grosso do Sul (Brazil): Alyxoria cyanea, Astrothelium ochraceum, Chiodecton xanthonosorediatum, Gyalecta perithecioidea, Gyalecta uniseptata, Pyrenula rubroacutispora, Ramonia xylophila, Synarthonia xanthosarcographoides, Trypethelium aureornatum, Trypethelium endoflavum, and Trypethelium xanthostiolornatum. Around 400 further species are reported, of which 27 are first records for Brazil and 265 are first records for the state.
Four new Astrothelium species and a Mazaediothecium from Várzea areas in Mato Grosso do Sul, Brazil
(2020)
Five species of lichens are described as new from Várzea areas in Mato Grosso do Sul (Brazil): Astrothelium fernandae, A. pseudodermatodes, A. septoconicum, A. xanthopseudocyphellatum, and Mazaediothecium serendipiticum, the latter being deviating from all other species in its order by the at least morphologically chlorococcoid photobiont. Further, we found 226 identifiable species in the Várzea reserve near Jateí and 47 on a farm near Naviraí. Of these, 15 are new records for Brazil and a further 88 are first reports from the state.
The case for corona bonds
(2020)
Corona bonds are feasible and important to preserve the European project. We set out a number of principles that might serve as a blueprint for the European institutions. Importantly, Corona bonds could be issued through a new public law entity and include all the safeguards required for the protection of the fundamental values of the EU. This proposal is pragmatic in the sense that it facilitates the choice European leaders have to make now; necessary to secure the resilience of the European Union. The political risks are significantly higher now than in 2010. The gargantuan challenge of tackling the combined impact of climate change, migration, digitalization, geopolitical shifts, and the spread of autocracy, requires leadership and joint action by the Council and the Eurogroup.
The paper compares provision of public infrastructure via public-private partnerships (PPPs) with provision under government management. Due to soft budget constraints of government management, PPPs exert more effort and therefore have a cost advantage in building infrastructure. At the same time, hard budget constraints for PPPs introduce a bankruptcy risk and bankruptcy costs. Consequently, if bankruptcy costs are high, PPPs may be less efficient than public management, although this does not result from PPPs’ higher interest costs.
Using a novel experimental design, I test how the exposure to information about a group’s relative performance causally affects the members’ level of identification and thereby their propensity to harm affiliates of comparison groups. I find that both, being informed about a high and poor relative performance of the ingroup similarly fosters identification. Stronger ingroup identification creates increased hostility against the group of comparison. In cases where participants learn about poor relative performance, there appears to be a direct level effect additionally elevating hostile discrimination. My findings shed light on a specific channel through which social media may contribute to intergroup fragmentation and polarization.
Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.
We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated to fundamentals, typically observed during a flash crash. Slow traders provide liquidity instead of HFTs, taking advantage of the discounted price. We thus uncover a trade-o↵ between the greater liquidity and efficiency provided by HFTs in normal times, and the disruptive consequences of their trading activity during distressed times.