ERC FUNDED PROJECTS

In most European countries social insurance programs, like welfare, unemployment insurance and disability insurance are characterized by low reemployment rates. Therefore, governments spend huge amounts of money on active labour market programs, which should help individuals in finding work. Recent surveys indicate that programs which aim at intensifying job search behaviour are much more effective than schooling programs for improving human capital. A second conclusion from these surveys is that despite the size of the spendings on these programs, evidence on its effectiveness is limited. This research proposal aims at developing an economic framework that will be used to evaluate the effectiveness of popular programs like offering reemployment bonuses, fraud detection, workfare and job search monitoring. The main innovation is that I will combine economic theory with recently developed econometric techniques and detailed administrative data sets, which have not been explored before. While most of the literature only focuses on short-term outcomes, the available data allow me to also consider the long-term effectiveness of programs. The key advantage of an economic model is that I can compare the effectiveness of the different programs, consider modifications of programs and combinations of programs. Furthermore, using an economic model I can construct profiling measures to improve the targeting of programs to subsamples of the population. This is particularly relevant if the effectiveness of programs differs between individuals or depends on the moment in time the program is offered. Therefore, the results from this research will not only be of scientific interest, but will also be of great value to policymakers.

550 000 €

Start date: 2008-07-01, End date: 2013-06-30

Subjective data play an increasing role in modern economics. For instance, new welfare measurements are based on people’s subjective assessments of their happiness or their life satisfaction. A problem of such measurements is that people have no incentives to tell the truth. To solve this problem and make those measurements incentive compatible, I will introduce a new market institution, called Bayesian markets. Imagine we ask people whether they are happy with their life. On Bayesian markets, they will trade an asset whose value is the proportion of people answering Yes. Only those answering Yes will have the right to buy the asset and those answering No the right to sell it. Bayesian updating implies that “Yes” agents predict a higher value of the asset than “No” agents do and, consequently, “Yes” agents want to buy it while “No” agents want to sell it. I will show that truth-telling is then the optimal strategy. Bayesian markets reward truth-telling the same way as prediction markets (betting markets) reward people for reporting their true subjective probabilities about observable events. Yet, unlike prediction markets, they do not require events to be objectively observable. Bayesian markets apply to any type of unverifiable truths, from one’s own happiness to beliefs about events that will never be observed. The present research program will first establish the theoretical foundations of Bayesian markets. It will then develop the proper methodology to implement them. Finally, it will disseminate the use of Bayesian markets via applications. The first application will demonstrate how degrees of expertise can be measured and will apply it to risks related to climate change and nuclear power plants. It will contribute to the political debate by shedding new light on what true experts think about these risks. The second application will provide the first incentivized measures of life satisfaction and happiness.

1 500 000 €

Start date: 2016-01-01, End date: 2020-12-31

Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making. The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.

1 405 652 €

Start date: 2015-09-01, End date: 2020-08-31

Systemic sclerosis (SSc) is an autoimmune disease that culminates in excessive extra-cellular matrix deposition (fibrosis) in skin and internal organs. SSc is a severe disease in which fibrotic events lead to organ failure such as renal failure, deterioration of lung function and development of pulmonary arterial hypertension (PAH). Together, these disease hallmarks culminate in profound disability and premature death. Over the past three years several crucial observations by my group changed the landscape of our thinking about the ethiopathogenesis of this disease. First, plasmacytoid dendritic (pDCs) cells were found to be extremely frequent in the circulation of SSc patients (1000-fold) compared with healthy individuals. In addition, we observed that pDCs from SSc patients are largely dedicated to synthesize CXCL4 that was proven to be directly implicated in fibroblast biology and endothelial cell activation, two events recapitulating SSc. Finally, research aimed to decipher the underlying cause of this increased pDCs frequency led to the observation that Runx3, a transcription factor that controls the differentiation of DC subsets, was almost not expressed in pDC of SSc patients. Together, these observations led me to pose the “SSc immune postulate” in which the pathogenesis of SSc is explained by a multi-step process in which Runx3 and CXCL4 play a central role. The project CIRCUMVENT is designed to provide proof of concept for the role of CXCL4 and RUNX3 in SSc. For this aim we will exploit a unique set of patient material (cell subsets, protein and DNA bank), various recently developed in vitro techniques (siRNA for pDCs, viral over expression of CXCL4/RUNX3) and apply three recently optimised experimental models (CXCL4 subcutaneous pump model, DC specific RUNX3 KO and the SCID/NOD/rag2 KO mice). The project CIRCUMVENT aims to proof the direct role for Runx3 and CXCL4 that could provide the final step towards the development of novel therapeutic targets

1 500 000 €

Start date: 2012-08-01, End date: 2017-07-31