Over the last years, uncertainty has become a major challenge that private and public decision makers had to deal with. The most recent event of global (or aggregate) uncertainty is the spread of the COVID-19, which results in a peak of uncertainty similar to the Great Depression of 1929 (see Baker et al., 2020). This exceptional event that we are currently experiencing features uncertainty on several dimensions, like the duration of the spread of the virus, the transmission of the crisis to financial markets, the evolution of unemployment and inequalities during the lockdown or the ability of the economies to recover or to adapt to a new regime. The COVID-crisis disturbs linkages between economic players since it generates a collapse in both supply and demand and it is very likely that it be to one of the strongest recessions that the worldwide economy has experienced over the last decades. Uncertainty happens when people are unable to predict the likelihood of events they will face in the future. It is usually acknowledged that global uncertain events have large adverse effects on the economic activity through its impacts on individual decisions. For instance, firms tend to postpone investment and hiring decisions (see Bloom, 2014, for a survey). One striking example is the outcome of the Brexit referendum in 2016: It has kept firms doubtful for more than three years regarding the future economic environment and in turn has led to a significant UK output reduction and more dispersed expectations about future prospects (see Born et al., 2019; Tripier, 2019). Uncertainty also generates income insecurity for households, who are more willing to adopt precautionary saving and labour behaviours. Consequently, the analysis of aggregate uncertainty cannot be dissociated from studying heterogeneous responses of agents. This research proposal seeks to analyse decision making under aggregate uncertainty, with a special focus on labour market outcomes. Albeit, “decision making” refers to individual behaviours, aggregate uncertainty events have by definition macroeconomics consequences as they disturb a large set of decision makers. Following Ghironi’s guidance “Macro needs Micro” (see Ghironi, 2018), we will therefore resort to large set of empirical micro (or disaggregated) databases to highlight the macroeconomic consequences of aggregate uncertainty through the lens of the heterogeneous behaviour of agents. To reach this objective, this research proposal focuses on three transmission channels of uncertainty. The first task focuses on labour market adjustments (Task 1), since firms and households usually have incentives to adjust labour market decisions in order to self-ensure against uncertainty. Motivated by the growing trend of worldwide interconnectedness over the last decades, the second task looks at the international market dimension of uncertainty (Task 2). Finally, the third task is interested in some unexplored links between uncertainty and financial markets (Task 3), the latter being strong amplification channels of uncertainty. Through these three markets, this research proposal aims at providing a comprehensive overview of the direct and indirect transmission channels of global uncertainty to the labour market and the primary economic linkages between individual agents.

We aim to set up a strong research network built around the three collaborators towards a very ambitious and very interdisciplinary project that will be financed by the Horizon Europe program. The project involves approaches from economics, management, administrative sciences, psychology, computer science and mechanical engineering. We will explore how citizens can and should be involved in the design and implementation of smart systems for public services (co-engineering) and policy making (co-governance). We will theoretically and empirically assess some viable methods of aggregating public opinions on the ethical and moral aspects of automated systems and robots in public services. We propose a new methodology for integrating citizens' contribution into the design and governance of smart public services, which will improve on existing approaches that are too technocratic and hypothetical. We will identify the dimensions that explain which utilities could benefit from automation using intelligent systems. These dimensions will allow us to measure whether the citizen approach that we propose is really useful and leads to better designed public services and policies.

In this project we propose to focus on the two main business models of crowdfunding platforms: loan-based crowdfunding and equity crowdfunding. Born in the wake of the global financial crisis, these platforms are seen by some as an opportunity to increase competition in the banking industry that suffers from too big to fail problems, opacity and inefficiency. At the same time, these new entrants represent problems similar to banks, such as adverse selection and agency problems. The objective of our project is to explore differences between traditional banks and crowdfunding platforms to understand under what conditions platform-based financial intermediation could improve efficiency and stability of the financial sector. The French market merits our attention for two main reasons. First, France is home to the largest crowdfunding industry in the continental Europe. Second, Bank of France has recently opened access to the French credit registry which is a rich source of data that will allow researchers in our team to address exciting research questions that cannot be investigated in larger markets, such as the UK and the US, where credit bureau data is private and closed to researchers. In the theoretical part of our project, we propose to model a loan-based crowdfunding platform and then to study the interaction between a large incumbent banks and a small platform. We assume that platforms have a better intermediation technology to solve adverse selection problems and, hence, are able to lend to riskier firms and projects than banks. Importantly, this assumption will be tested in the empirical part. We would like to understand the conditions under which the emergence of loan-based crowdfunding platforms improves the welfare of borrowers and investors and how the competition between banks and platforms impacts the interest rates that are charged to borrowers and the rates of return to investors. Furthermore, we will analyze how the regulatory framework impacts the interest rates charged to SMEs. In the empirical part of the project, we will explore how platforms and banks compete between each other and how platforms compete with each other. First, we will attempt to explore adverse selection problems faced by platforms that are likely to receive borrowers that were rejected by banks. To do so, we will model the decision of businesses to switch to online platforms. This will allow us to understand whether banks and platforms compete for the same borrowers and/or whether platforms are filling the funding gap by financing SMEs that are underserved by banks. Second, we will evaluate the intermediation technology of crowdfunding platforms by testing if they are able to relieve borrowers’ credit constraints and lead to faster firm growth and higher profitability. We will also compare interest rates and credit conditions between banks and loan-based crowdfunding platforms, while controlling for differences in business characteristics. We will also explore the interest rate formation on loan-based crowdfunding platforms with different interest rate setting mechanisms (fixed or auction) to understand which business model leads to lower interest rate. The ultimate objective of our project is to propose guidelines for the regulatory framework for new Fintech players. The first step would be to do a review of the existing regulatory practices in the OECD countries. In cooperation with the Economics Department of the OECD, Olena Havrylchyk has already created a questionnaire that has been sent to regulators in all OECD countries. Second step would be to identify ‘best practices’ for the regulation of online platforms.

URBANE brings together a multidisciplinary group of 41 partners from 12 different EU Member States, including 12 cities, industry leaders in logistics services, novel solution providers for shared, connected and automated operations and research experts to co-develop novel last mile delivery solutions combining green automated vehicles and shared space utilisation models. The project will support the transition path towards effective, resilient, safe and sustainable last mile transport, through four Lighthouse Living Labs (Helsinki, Bologna, Valladolid and Thessaloniki, that will demonstrate TRL7/8 efficient, replicable and socially acceptable innovative last mile delivery solutions (Wave 1 LLs), building on existing TRL>5 assets. Hands-on lesson learning at European level will be primarily facilitated by an Innovation Transferability Platform comprising Digital Twinning Tools, open models, smart contracts governed by blockchain technology and a data-driven Impact Assessment Radar that will enable the adaptation and replication of Wave 1 solutions in two Twinning LLs in Barcelona and Karlsruhe (Wave 2 LLs), demonstrating their own solutions within the course of the project. URBANE’s commitment to upscaling is further strengthened by the engagement of six early adopters (Follower Cities) in innovations’ adoption feasibility studies, thus stimulating the formulation of new LL communities across Europe. The approach employs social simulation tools to experiment with possible strategies. URBANE will provide evidence of the real introduction of the Physical Internet in urban deliveries for the first time.

The main causes of overcrowding of Emergency Operating Rooms (EORs) are the growing demand for non-elective surgeries and the unprecedented medical staff shortage. The effects of overcrowding can unfortunately ripple across the hospital, resulting in increasing in-hospital mortality, longer length of stays and higher costs. DOSE project is motivated by this context, where it becomes crucial to rethink the operations engineering and optimization of EORs. The objective of DOSE is to develop a multidisciplinary data-driven approach for optimizing EOR patient care pathways. More specifically, we aim at 1) developing data-driven approaches to provide accurate and comprehensive modeling of EOR patient care pathways, including predictions for resource requirements and activity delays; 2) developing health outcome Key Performance Indicators (KPIs) with machine learning techniques and medical expertise, a Quality of Working Life model for the EOR staff with human and social sciences, and a health-economic approach for cost-effectiveness analyses; 3) deriving optimal scheduling policies for care pathway activities by accounting for the above KPIs, and integrating the results into a digital twin to support real-time decision making. DOSE will improve EOR-related care delivery by exploiting tools from Industry 4.0 such as big data, data-driven optimization, and digital twins. It will be carried out in close collaboration with the hospital La Pitié Salpêtrière, and using detailed data on EOR patients from Entrepôt de Données de Santé. The project is challenging given the high dimension and the inherent stochastic and dynamic nature of the related problems, and our ambition to center on benefits and impacts for patients and medical staff. DOSE aims at bringing multidisciplinary contributions to the literature of data analytics, stochastic optimization, simulation, medicine, health economy, and human and social sciences.