In 2021, the digital economy appears more than ever a wealth of opportunities for economic agents. However, behind the scenes also lie major environmental concerns. Digital devices are fueled with data which are processed, transferred and stored by data centers, which need tremendous electricity and cooling. We aim to better understand the environmental impact of the digital economy and to propose a set of adequate regulatory responses to reduce it. The topic is attracting increasing attention among civil society and politicians. A legislative draft was introduced by the French Senate in October 2020 and the French government released last February a roadmap intended to bring together the ecological and digital transitions. However, many experts urge for more stringent action against digital platforms rather than adopting soft law instruments such as codes of conduct drafted in partnership with digital platforms, and the launch of public campaigns on sober digital practices. Moreover, France, Sweden and the Czech Republic (which together form the trio of presidency of the Council of the European Union from January 2022 to June 2023) have announced that digital governance will be one of their priorities. The project aims to contribute to this public debate and could allow to look afresh at other debates on digital technology (such as the deployment of 5G or the regulation of cryptocurrencies and data privacy policy) and their environmental consequences. Specific features of the digital economy need to be taken into account to design an adequate regulatory response. First, the legal framework is still under construction, which complicates the design of regulation. Second, most online services are freely provided to users. In a market without explicit price signals, it appears insufficient to introduce taxes and price incentives to change users’ behaviors. Third, some digital platforms have considerable power to influence legislative processes and also use technology to push digital users to consume more data. The objectives of DIGIPOLL are twofold: understanding how users behave online (objective 1) is key to design an adequate regulatory response (objective 2). With objective 1, we aim to identify the factors influencing these behaviors. Can the choice of digital users be explained exclusively by a lack of information? Is this choice rather motivated by a lack of attention or the existence of habits rooted in the user’s life? Do other cognitive biases or a lack of concern for the environment enter into play? . We are planning on using a representative panel of French households and implement three surveys on this panel. Altogether, the three waves of the survey will allow us to describe behaviors of digital users and assess the impact of different interventions. This survey will be particularly important to validate and select the right theoretical framework to study the optimal design of policies. The second objective of DIGIPOLL is to examine what regulatory tools may be used to reduce the environmental footprint of the digital economy. It appears in this regard essential to identify not only the singularities of the phenomenon but also its similarities with other sectors of activities. We also propose to address whether the introduction of these environmental concerns would challenge the business models of digital platforms. Furthermore, we will assess whether these environmental concerns should be integrated into existing laws and regulations applicable to digital platforms and whether new regulatory instruments should be introduced to tackle this phenomenon. Finally, based on the results of the survey we will determine how to design instruments to change users' behaviour. The project adopts a multidisciplinary approach and brings together economists from different specialties (microeconometrics, digital economics, behavioral economics, and environmental economics), a lawyer and a psychologist.

Risk is an important component of decision making. Risk attitudes play a crucial role in a variety of settings, from insurance, financial decision making, education and career choices, to development and public policy. The accurate measure of risk preferences is of large importance in both theoretical and applied work. Increasing experimental evidence points to the fact that the behavioral measures we use to elicit risk attitudes fail us. Risk elicitation tasks (RETs), at least in the way they are typically implemented in behavioral economics and psychology, 1. correlate poorly with self-reported risk attitudes, real-world risk behaviors, and among themselves; 2. introduce distinct measurement errors and behavioral biases, and 3. are not robust to sit-resit exercises. This project is an ambitious and comprehensive attempt at rethinking risk elicitation. The project addresses the question of the low predictive power of RETs by means of a meta-analysis and two large experiments. The meta-analysis will focus on the predictive validity of RETs, and will both document exhaustively the extent of the problems faced by RETs and give a detailed map of which features of a RET are more conducive to predictive validity. Experiment 1 will focus on the role of noise in explaining the disappointing results of RETs. Firmly rooted in utility theory, the experiment will make use of standard pairwise lottery choices and structural estimation techniques. The experiment will be a conscious effort aimed directly at maximising the external validity of the estimates. We will use a mixture of meta-analysis, simulations, on-line and lab experiments, machine learning techniques, to find the set of lotteries that can obtain the largest possible external validity while keeping high internal validity. The estimates obtained will then be correlated with a risk factor extracted from self-reported measures. This experiment will allow us to assess the predictive gain (if any) obtained by ‘going big’ and taking into account noise. Experiment 2 will be a step outside of utility theory and will focus on risk perception. Econmists usually (tacitly) assume that the riskiness of a lottery is an objective metrics. In Experiment 2 we will allow subjects to have differing perceptions of risk. We will ask subjects to directly assess their risk perception of lotteries before we ask them to choose. This Experiment will a) develop an empirical way to measure the risk perception of different lotteries; b) use this to further optimize the set of lottery choices to be given to subjects to best identify their risk attitudes and c) check if behavior, once cleaned from the effects of risk perception, is more consistent than previously thought across RETs and between RETs and self-reported measures. The project will bring about several improvements in the field of risk elicitation: a deeper understanding of the state of the art in the external validity of risk elicitation measures (via the meta-analysis); a map of the features a RET needs to have predictive power (via preparatory work and results of Experiment 1); and an enhanced understanding of the risk perception of RETs (via methods and results of Experiment 2). If successful, the project will most importantly deliver a new, stronger, more predictive RET robust to noise and incorporating risk perceptions, that could replace the RETs used in the field and provide more reliable and predictive risk data. This is something the whole field of experimental economics has been awaiting for years. This is not just an interesting academic exercise. Fixing the low predictive power, reliability and robustness of RETs would have direct and valuable consequences in all fields involving uncertain options – as in insurance, education, medicine.

The PRIMOFOOD project aims to innovate in modelling the effect of food prices on household purchasing behaviour. It is in line with axis 1.5 of the AAPG2019 "Food and Food Systems". The proposed innovations will allow for a better assessment of the effectiveness and distributive effects of nutritional taxation policies. The identification of price effects can be based either on the econometric analysis of existing market data or on the analysis of experimental data generated in lab. Econometric methods may have limited internal validity, while experimental methods have questionable external validity. Our project therefore proposes to address the respective weaknesses of these two methods and, beyond that, to exploit their complementarities. WP1 will develop innovative experimental analyses of price effects. First, using innovative protocols, we will analyze the effect of large price changes, similar to the levels used in micro-simulations of pricing policies, but much larger than the changes observed in market data. The objective is to identify possible salience and reference price effects, which can induce non-linearities and discontinuities in consumer reactions to prices. We will also test the potential complementarities between pricing policies and nutritional labelling of the NutriScore type. We will also study the effects of social norm, cognitive load, and the existence of opportunity costs. WP2 will develop a structural econometric model of quality and quantity demand under multiple constraints. Beyond the usual budget constraint, the model will include a nutritional intake constraint, with possible extension to multiple linear constraints. The objective is to reflect that the choices of some households are constrained by the need to ensure a minimum of energy intake, or for some potentially addictive goods (alcohol and sugar). This econometric modelling work poses various theoretical and practical challenges. The empirical work will use Kantar WorldPanel scanner data on the consumption of non-alcoholic drinks by French households, and will aim in particular to identify the existence of effects of sugar habituation. WP3 will aim to show the possibility of evaluating the ex-ante evaluation of pricing policies by a micro-simulation of taxation policies based on a model that integrates the outcomes of the two work packages. We will first cross-validate the experimental and econometric methods, comparing the econometric approach with the data generated in the experiments. This will allow us to identify opportunities to improve the specifications of the econometric model, an improvement that will be implemented by a Bayesian method. Finally, we will carry out micro-simulations based on the case of the taxation of sweetened drinks, which has been in place in France since 2012. This will allow us to better characterize the effectiveness of this tax, as well as its distributive and welfare impacts on the various socio-economic segments of the population. PRIMOFOOD provides methodological contributions to the scientific communities and to important public policy issues. The team includes researchers covering the spectrum of issues addressed, from fundamental methodological issues to public policy expertise. The project proposes an international approach, with the ambition of replicating part of the work on American data, with a view to comparing food systems. Finally, the project will produce tools to better understand the links between the long-term dynamics of price changes and the sustainability of food systems.

Our research project has several objectives. The first is to create an original database to describe the characteristics and interactions between caregivers working in healthcare teams in the emergency department. These data will be extracted (or desilated) from the PREDIMED clinical data warehouse (CDW), which gathers health and administrative data from patients and healthcare professionals working at Grenoble University Hospital. Then, the analysis of social networks will allow us to identify the modes of collaboration in place between caregivers and their ability to adapt to their environment. Impact evaluation methods will allow us to estimate the impact of the organizational changes caused by the covid-19 health crisis on the quality of work and the well-being of healthcare professionals.

Diet is a key lever for preserving human health and the environment. Without rapid and drastic changes in food systems, from production to consumption patterns, the environmental limits for the sustainability of ecosystems will be reached by 2050 and the incidence of chronic diseases will continue to rise. While the awareness of stakeholders seems to be strong and sustainable diets are well characterized, "food solutions" remain global (not individualized) and prospective work is based on scenarios. Indeed, no study has quantitatively assessed recent dietary changes and whether they are consistent with a transition to a more sustainable, healthier, more accessible and more environmentally friendly diet. There is an urgent need to assess, using a multi-criteria and transdisciplinary approach, the dietary transitions underway, the associated individual characteristics and to measure their impacts on health, the environment and in socio-economic terms. A better understanding of these aspects will enable us to propose actions with regard to the specific levers and barriers of population groups. The TRANSFood project aims to respond to these different issues by using a systemic approach "from field to plate" (agriculture, accessibility, consumption, etc.) based on two epidemiological studies (one cross-sectional, the other longitudinal), an experimental study and a consumer panel. More specifically, the specific objectives of the TRANSFood project include: 1) a study of dietary change trajectories over an 8-year period (covering the COVID-19 crisis and its aftermath) at the individual level (based on repeated data from the same sample) integrating food system specificities (agro-ecological and conventional farming practices). Recent changes will be assessed over an 8-year period in the NutriNet-Santé cohort in order to generate a typology of change on which subgroups of the French population (INCA3) will be located, 2) a detailed assessment of the sustainable values (economic, nutritional, health and environmental) of diets and their changes as well as the characterization of their determinants (socio-demographic, socio-economic, geographical). The environmental and health implications of these transitions will be assessed using life cycle analysis databases, a biodiversity indicator and a risk model in order to quantify the potential improvements that could be expected in the near future, 3) identification of barriers and levers and measurement of the actual individual willingness to change. A precise analysis of the levers and barriers to change will be carried out via the administration of questionnaires on the propensity to trade and via an experiment on willingness to pay. The socio-demographic (gender, age/generation, family situation), socio-economic (cost of diet, education, income etc.) and geographical (accessibility, supply) determinants of these trajectories will be highlighted, 4) the establishment of recommendations adapted to the population groups and trajectories identified by the implementation of multi-criteria models based on optimization under multiple constraints (environmental, nutritional, health, economic) which will make it possible to propose sustainable diets, according to the various characteristics, barriers and levers previously identified. The results of this project will be widely disseminated to the academic community but also to stakeholders and in particular to consumers.