Last in, first out? Estimating the effect of seniority rules in Sweden

In this paper we investigate whether a relaxation in seniority rules (the ‘last-in-first-out’ principle) had any effect on firms’ employment behaviour. Seniority rules exist in several countries and, like Sweden, most European countries have a more lenient employment protection for firms below a certain size. Despite the fact that small firms represent a large share of all firms and stand for a substantial share of total employment, there is limited knowledge of how such exemption rules affect firms’ employment behaviour — the consequences of seniority rules on firms’ employment behaviour have not been examined at all. Using data including the population of firms matched with the population of workers for the period 1999–2002, we do not find any general effects on worker flows or on hires and separations. The only exception is a tendency of an increase in the share of separations for older workers and workers with longer seniority. The result points to the importance of considering in detail how legislation is formulated and how it works in practice.Employment protection; employment change; hires; separations; regression discontinuity

Uncertainty, Climate Change and the Global Economy

The paper illustrates how one may assess our comprehensive uncertainty about the various relations in the entire chain from human activity to climate change. Using a modified version of the RICE model of the global economy and climate, we perform Monte Carlo simulations, where full sets of parameters in the model’s most important equations are drawn randomly from pre-specified distributions, and present results in the forms of fan charts and histograms. Our results suggest that under a Business-As-Usual scenario, the median increase of global mean temperature in 2105 relative to 1900 will be around 4.5 C. The 99 percent confidence interval ranges from 3.0 C to 6.9 C. Uncertainty about socio-economic drivers of climate change lie behind a non-trivial part of this uncertainty about global warming.Climate-economy models; Global warming; Monte Carlo study

Uncertainty, Climate Change and the Global Economy

The paper illustrates how one may assess our comprehensive uncertainty about the various relations in the entire chain from human activity to climate change. Using a modified version of the RICE model of the global economy and climate, we perform Monte Carlo simulations, where full sets of parameters in the model's most important equations are drawn randomly from pre-specified distributions, and present results in the forms of fan charts and histograms. Our results suggest that under a Business-As-Usual scenario, the median increase of global mean temperature in 2105 relative to 1900 will be around 4.5 °C. The 99 percent confidence interval ranges from 3.0 °C to 6.9 °C. Uncertainty about socio-economic drivers of climate change lie behind a non-trivial part of this uncertainty about global warming.

More than energy savings : quantifying the multiple impacts of energy efficiency in Europe

Energy efficiency improvements have numerous benefits/impacts additional to energy and greenhouse gas savings, as has been shown and analysed e.g. in the 2014 IEA Report on "Multiple Benefits of Energy Efficiency". This paper presents the Horizon 2020-project COMBI ("Calculating and Operationalising the Multiple Benefits of Energy Efficiency in Europe"), aiming at calculating the energy and non-energy impacts that a realisation of the EU energy efficiency potential would have in 2030. The project covers the most relevant technical energy efficiency improvement actions and estimates impacts of reduced air pollution (and its effects on human health, eco-systems/crops, buildings), improved social welfare (incl. disposable income, comfort, health, productivity), saved biotic and abiotic resources, and energy system, energy security, and the macroeconomy (employment, economic growth and public budget). This paper explains how the COMBI energy savings potential in the EU 2030 is being modelled and how multiple impacts are assessed. We outline main challenges with the quantification (choice of baseline scenario, additionality of savings and impacts, context dependency and distributional issues) as well as with the aggregation of impacts (e.g. interactions and overlaps) and how the project deals with them. As research is still ongoing, this paper only gives a first impression of the order of magnitude for additional multiple impacts of energy efficiency improvements may have in Europe, where this is available to date. The paper is intended to stimulate discussion and receive feedback from the academic community on quantification approaches followed by the project

Overview of the MOSAiC expedition: Snow and sea ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Uncertainty, Climate Change and the Global Economy

The paper illustrates how one may assess our comprehensive uncertainty about the various relations in the entire chain from human activity to climate change. Using a modified version of the RICE model of the global economy and climate, we perform Monte Carlo simulations, where full sets of parameters in the model's most important equations are drawn randomly from pre-specified distributions, and present results in the forms of fan charts and histograms. Our results suggest that under a Business-As-Usual scenario, the median increase of global mean temperature in 2105 relative to 1900 will be around 4.5 °C. The 99 percent confidence interval ranges from 3.0 °C to 6.9 °C. Uncertainty about socio-economic drivers of climate change lie behind a non-trivial part of this uncertainty about global warming.Climate-economy models; Global warming; Monte Carlo study