ERC FUNDED PROJECTS

Antibiotics have contributed to a tremendous increase in human well-being, saving many millions of lives. However, antibiotics become obsolete the more they are used as selection pressure promotes the development of resistant bacteria. The World Health Organization has proclaimed antibiotic resistance as a major global threat to public health. Today, 700,000 deaths per year are due to untreatable infections. To win the battle against antibiotic resistance, new policies affecting the supply and demand of existing and new drugs must be designed. I propose new research to identify and evaluate feasible and effective demand-side policy interventions targeting the relevant decision makers: physicians and patients. ABRSEIST will make use of a broad econometric toolset to identify mechanisms linking antibiotic resistance and consumption exploiting a unique combination of physician-patient-level antibiotic resistance, treatment, and socio-economic data. Using machine learning methods adapted for causal inference, theory-driven structural econometric analysis, and randomization in the field it will provide rigorous evidence on effective intervention designs. This research will improve our understanding of how prescribing, resistance, and the effect of antibiotic use on resistance, are distributed in the general population which has important implications for the design of targeted interventions. It will then estimate a structural model of general practitioners’ acquisition and use of information under uncertainty about resistance in prescription choice, allowing counterfactual analysis of information-improving policies such as mandatory diagnostic testing. The large-scale and structural econometric analyses allow flexible identification of physician heterogeneity, which ABRSEIST will exploit to design and evaluate targeted, randomized information nudges in the field. The result will be improved rational use and a toolset applicable in contexts of antibiotic prescribing.

1 498 920 €

Start date: 2019-01-01, End date: 2023-12-31

This project aims to develop a universal approach to angular momentum in quantum many-body systems based on the angulon quasiparticle recently discovered by the PI. We will establish a general theory of angulons in and out of equilibrium, and apply it to a variety of experimentally studied problems, ranging from chemical dynamics in solvents to solid-state systems (e.g. angular momentum transfer in the Einstein-de Haas effect and ultrafast magnetism). The concept of angular momentum is ubiquitous across physics, whether one deals with nuclear collisions, chemical reactions, or formation of galaxies. In the microscopic world, quantum rotations are described by non-commuting operators. This makes the angular momentum theory extremely involved, even for systems consisting of only a few interacting particles, such as gas-phase atoms or molecules. Furthermore, in most experiments the behavior of quantum particles is inevitably altered by a many-body environment of some kind. For example, molecular rotation – and therefore reactivity – depends on the presence of a solvent, electronic angular momentum in solids is coupled to lattice phonons, highly excited atomic levels can be perturbed by a surrounding ultracold gas. If approached in a brute-force fashion, understanding angular momentum in such systems is an impossible task, since a macroscopic number of particles is involved. Recently, the PI and his team have shown that this challenge can be met by introducing a new quasiparticle – the angulon. In 2017, the PI has demonstrated the existence of angulons by comparing his theory with 20 years of measurements on molecules rotating in superfluids. Most importantly, the angulon concept allows one to gain analytical insights inaccessible to the state-of-the-art techniques of condensed matter and chemical physics. The angulon approach holds the promise of opening up a new interdisciplinary research area with applications reaching far beyond what is proposed here.

1 499 588 €

Start date: 2019-02-01, End date: 2024-01-31

This project enters the experimental investigation of anyonic quantum gases. We will study anyons – conjectured particles with a statistical exchange phase anywhere between 0 and π – in different many-body systems. This progress will be enabled by a unique approach of bringing together artificial gauge fields and quantum gas microscopes for ultracold atoms. Specifically, we will implement the 1D anyon Hubbard model via a lattice shaking protocol that imprints density-dependent Peierls phases. By engineering the statistical exchange phase, we can continuously tune between bosons and fermions and explore a statistically-induced quantum phase transition. We will monitor the continuous fermionization via the build-up of Friedel oscillations. Using state-of-the-art cold atom technology, we will thus open the physics of anyons to experimental research and address open questions related to their fractional exclusion statistics. Secondly, we will create fractional quantum Hall systems in rapidly rotating microtraps. Using the quantum gas microscope, we will i) control the optical potentials at a level which allows approaching the centrifugal limit and ii) use small atom numbers equal to the inserted angular momentum quantum number. The strongly-correlated ground states such as the Laughlin state can be identified via their characteristic density correlations. Of particular interest are the quasihole excitations, whose predicted anyonic exchange statistics have not been directly observed to date. We will probe and test their statistics via the characteristic counting sequence in the excitation spectrum. Furthermore, we will test ideas to transfer anyonic properties of the excitations to a second tracer species. This approach will enable us to both probe the fractional exclusion statistics of the excitations and to create a 2D anyonic quantum gas. In the long run, these techniques open a path to also study non-Abelian anyons with ultracold atoms.

1 497 500 €

Start date: 2019-01-01, End date: 2023-12-31

Since the invention of the spinning frame, automation has been one of the drivers of economic growth. Yet, workers, economist or the general public have been concerned that automation may destroy jobs or create inequality. This concern is particularly prevalent today with the sustained rise in economic inequality and fast technological progress in IT, robotics or self-driving cars. The empirical literature has showed the impact of automation on income distribution. Yet, the level of wages itself should also affect the incentives to undertake automation innovations. Understanding this feedback is key to assess the long-term effect of policies. My project aims to provide the first quantitative account of the two-way relationship between automation and the income distribution. It is articulated around three parts. First, I will use patent data to study empirically the causal effect of wages on automation innovations. To do so, I will build firm-level variation in the wages of the customers of innovating firms by exploiting variations in firms’ exposure to international markets. Second, I will study empirically the causal effect of automation innovations on wages. There, I will focus on local labour market and use the patent data to build exogenous variations in local knowledge. Third, I will calibrate an endogenous growth model with firm dynamics and automation using Danish firm-level data. The model will replicate stylized facts on the labour share distribution across firms. It will be used to compute the contribution of automation to economic growth or the decline of the labour share. Moreover, as a whole, the project will use two different methods (regression analysis and calibrated model) and two different types of data, to answer questions of crucial policy importance such as: Taking into account the response of automation, what are the long-term effects on wages of an increase in the minimum wage, a reduction in labour costs, or a robot tax?

1 295 890 €

Start date: 2018-11-01, End date: 2023-10-31