A reversing case within trajectories of liberalisation:The revival of neo-corporatism in Austria since 2008

The overall dominating trend of liberalisation, deregulation and privatisation has accelerated since the global economic crisis in 2008. Under the paradigm of competitiveness, a major policy goal has been the implementation of ‘structural reforms’ replacing neo-corporatist practices with market coordination. However, Austria’s coordinating institutions have been strengthened since 2008, contrasting the EU-wide liberalising trend. To explain this puzzle, government members’ biographies since 1983 were analysed, seven elite interviews conducted and official government documents evaluated. Under the logic of access, social partner organisations made active use of a ‘revolving door effect’, placing their employees as ‘interlocking directorates’ in government positions to gain influence on policies. For this ‘power-policy exchange’ social partners defended political compromises of the government and supported the weakened social democratic (SPÖ) and the conservative (ÖVP) party leadership. Such a ‘tactical alliance’ is fragile, as it depends on the interest constellation of actors involved, but outlines the remaining scope for domestic politics in an age of increased liberalising pressures from globalisation and EU integration

Labour hoarding during the pandemic: assessing the impact of job retention schemes in Europe

Job retention schemes have helped Europe to avoid mass unemployment during the Covid-19 pandemic. Bernhard Ebbinghaus and Lukas Lehner write that while these schemes had an immediate impact during lockdown, the future development and long-term consequences of job retention policies remain uncertain

Beggaring thy co-worker:Labor market dualization and the wage growth slowdown in Europe

As temporary employment has become a pervasive feature of modern labor markets, reasons for wage growth have become less well understood. To determine whether these two phenomena are related, the authors investigate whether the dualized structure of labor markets affects macroeconomic developments. Specifically, they incorporate involuntary temporary workers into the standard wage Phillips curve to examine wage growth in 30 European countries for the period 2004–2017. Relying on individual-level data to adjust for a changing employment composition, their findings show, for the first time, that the incidence of involuntary temporary workers has strong negative effects on permanent workers’ wage growth, thereby dampening aggregate wage growth. This effect, which the authors name the competition effect, is particularly pronounced in countries where wage bargaining institutions are weak. The findings shed further light on the reasons for the secular slowdown of wage growth after the global financial crisis.</p

BUZZARD: A NUMA-Aware In-Memory Indexing System

With the availability of large main memory capacities, in-memory index structures have become an important component of modern data management platforms. Current research even suggests index-based query processing as an alternative or supplement for traditional tuple-at-a-time processing models. However, while simple sequential scan operations can fully exploit the high bandwidth provided by main memory, indexes are mainly latency bound and spend most of their time waiting for memory accesses. Considering current hardware trends, the problem of high memory latency is further exacerbated as modern shared-memory multiprocessors with non-uniform memory access (NUMA) become increasingly common. On those NUMA platforms, the execution time of index operations is dominated by memory access latency that increases dramatically when accessing memory on remote sockets. Therefore, good index performance can only be achieved through careful optimization of the index structure to the given topology. BUZZARD is a NUMA-aware in-memory indexing system. Using adaptive data partitioning techniques, BUZZARD distributes a prefix-tree-based index across the NUMA system and hands off incoming requests to worker threads located on each partition's respective NUMA node. This approach reduces the number of remote memory accesses to a minimum and improves cache utilization. In addition, all indexes inside BUZZARD are only accessed by their respective owner, eliminating the need for synchronization primitives like compare-and-swap

Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that utilizes the implicit clustering of the Nearest Neighbor Contrastive Learning (NNCLR) objective to induce abstraction in the topmost layers of a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Notably, MAE-CT does not rely on hand-crafted augmentations and frequently achieves its best performances while using only minimal augmentations (crop & flip). Further, MAE-CT is compute efficient as it requires at most 10% overhead compared to MAE re-training. Applied to large and huge Vision Transformer (ViT) models, MAE-CT excels over previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. With ViT-H/16 MAE-CT achieves a new state-of-the-art in linear probing of 82.2%

Welfare state support during the COVID-19 pandemic: Change and continuity in public attitudes towards social policies in Germany

Our analysis asks whether the pandemic situation affects welfare state support in Germany. The pandemic has increased the health and income risks calling for welfare state intervention. While increased needs, more deservingness, and higher state responsibility during such a crisis would suggest augmented support generally and among those at risk, this might be a short‐term effect and cost considerations could reverse this trend. We study public attitudes towards four key social policy areas based on the German Internet Panel (GIP). We use three waves prior and further three waves since the pandemic had been declared in March 2020. The analysis shows both continuity in the popularity of social policies, in particular health and pensions, and some short‐term increase in support for unemployment and family policies. The results after nearly 2 years suggest rather continuation with some thermostatic short‐term boosts in support instead of any long‐lasting change

Chromatic number is not tournament-local

Scott and Seymour conjectured the existence of a function $f \colon \mathbb{N} \to \mathbb{N}$ such that, for every graph $G$ and tournament $T$ on the same vertex set, $\chi(G) \geqslant f(k)$ implies that $\chi(G[N_T^+(v)]) \geqslant k$ for some vertex $v$. In this note we disprove this conjecture even if $v$ is replaced by a vertex set of size $\mathcal{O}(\log{\lvert V(G)\rvert})$. As a consequence, we answer in the negative a question of Harutyunyan, Le, Thomass\'{e}, and Wu concerning the corresponding statement where the graph $G$ is replaced by another tournament, and disprove a related conjecture of Nguyen, Scott, and Seymour. We also show that the setting where chromatic number is replaced by degeneracy exhibits a quite different behaviour.Comment: 7 pages; funding information adde

Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections