Welfare economics | EPFL Graph Search

Welfare economics is a field of economics that applies microeconomic techniques to evaluate the overall well-being (welfare) of a society. This evaluation is typically done at the economy-wide level, and attempts to assess the distribution of resources and opportunities among members of society. The principles of welfare economics are often used to inform public economics, which focuses on the ways in which government intervention can improve social welfare. Additionally, welfare economics serves as the theoretical foundation for several instruments of public economics, such as cost–benefit analysis. The intersection of welfare economics and behavioral economics has given rise to the subfield of behavioral welfare economics. Two fundamental theorems are associated with welfare economics. The first states that competitive markets, under certain assumptions, lead to Pareto efficient outcomes. This idea is sometimes referred to as Adam Smith's invisible hand. The

Sustainable Food Supply

Simone Lydia Voldrich

In the context of a growing world population, the fundamental need for foodstuff makes a sustainable management of the food supply an imperative. Sustainability issues in the food supply chain range from responsible use of limited resources, safe and secure food supply, minimizing food waste, while maintaining economic interests of each player. A novel ap- proach is presented integrating Processing Time, Cost and Operational Risk (PTCORk1) in one quantitative model. It allows for designing Pareto optimal monitoring systems. In a com- parative study, exact and (meta) heuristic methods are compared for best performance. The study with real-world data from an international food company is based on a common and widely consumed foodstuff with typical sustainable food supply chain challenges. Results are thus generalizable to other value chains in the food industry. It provides novel understanding of the seeming contradiction between Processing Time & Cost (PTC) and Operational Risk (ORk). On the system level, the efficient frontier reveals a quantified non-linear relationship. Within the monitoring system the relative contribution of each monitoring activity to the objective function shows a saturation effect enabling practicioners to identify critical monitor- ing activities and engage in step-by-step optimization. Evidence is presented that favorable solutions reveal potential for equidistribution of operational risks and time- and cost-efficient risk allocation. The advantages of a multi-objective approach are compared to either single objective approach. Away from conflicting formulations of minimal operational risk, process- ing time and cost, the approach fosters a more differentiated and quantifiable understanding of the relationship of processing time, cost and operational risk enabeling decision makes to formulate advantageous trade-offs. Optimized scenarios are tested for robustness and potential multiplicatory effects by numerical simulation of a connected dynamic supply chain environment. The approach provides valuable insights for practitioners and enables specific actions for a sustainable and competitive food supply.

EPFL2017

Institutions and Incentives in Knowledge Production and Diffusion: From Science to Innovation

Charles Chadi Ayoubi

This thesis presents four essays providing novel empirical and theoretical insights on the incentives and institutional structures that favor knowledge production and diffusion. The first two studies analyze these processes in the realm of scientific research, while the last two essays evaluate broader applications with vast social welfare implications. The first essay (chapter 2) of this dissertation, in collaboration with Michele Pezzoni and Fabiana Visentin, exploits a dataset on all applicants to a prestigious Swiss grant to explore a central process in academic life: the application for funds. The results suggest that scientists applying to a grant significantly increase their publicationsâ quality and quantity, learn more, and extend their collaboration network. Beyond the effect of applying, receiving the research funds increases the probability of co-authoring with co-applicants, but it does not have any additional effect on other scientific outcomes. These results justified the title of the chapter since, as it is the case in the Olympics, in research grants, âthe important thing is not to win, it is to take part.â The second essay (chapter 3), also in collaboration with Michele Pezzoni and Fabiana Visentin, uses the same empirical context to explore the determinants of knowledge flows among collaborating scientists. The chapter proposes a novel methodology based on journal references to track knowledge flows among researchers working together. The results suggest that geographical distance does not significantly affect the knowledge flows between team members, but the cognitive distance separating two members does. More specifically, there is an inverted U-curve effect of cognitive distance on the learning among team members: the higher the distance between two scientists in terms of subjects studied, the more they exchange knowledge, up to the point when the distance becomes detrimental because they have too little common ground to communicate. The third essay (chapter 4), in collaboration with Boris Thurm, goes beyond the exploration of the determinants for scientistsâ knowledge production and diffusion to delve into the incentives of all individuals to exchange knowledge. The chapter has two major contributions. First, it acts as a literature review of the empirical evidence on non-financial incentives for knowledge diffusion, such as social recognition, career prospects, and moral considerations. Second, the chapter proposes a simple economic model with heterogeneous agents holding both selfish and moral motives to derive some novel policy implications. The last essay (chapter 5), in collaboration with Dominique Foray, delves into a specific case of knowledge diffusion, the integration of machine learning technologies in healthcare. The analysis suggests that machine learning has the potential for spurring innovation in healthcare but faces several institutional levers. Collecting quantitative data on patents and publications, and qualitative data on hospitals, the results show that machine learning affects healthcare in different ways than older information and communication technologies. The apparition of new business models encourages tech giants to enter the healthcare sector. These patterns have the potential to increase social welfare by reducing externalities in terms of innovation complementarities, but they pose new challenges such as competition policy and human capital formation.

EPFL2020

Fair Welfare Maximization

Ashish Goel, Thomas Alois Weber

We consider the general problem of finding fair constrained resource allocations. As a criterion for fairness we propose an inequality index, termed “fairness ratio,” the maximization of which produces Lorenz-undominated, Pareto-optimal allocations. The fairness ratio does not depend on the choice of any particular social welfare function, and hence it can be used for an a priori evaluation of any given feasible resource allocation. The fairness ratio for an allocation provides a bound on the discrepancy between this allocation and any other feasible allocation with respect to a large class of social welfare functions. We provide a simple representation of the fairness ratio as well as a general method that can be used to directly determine optimal fair allocations. For general convex environments, we provide a fundamental lower bound for the optimal fairness ratio and show that as the population size increases, the optimal fairness ratio decreases at most logarithmically in what we call the “inhomo-geneity” of the problem. Our method yields a unique and “balanced” fair optimum for an important class of problems with linear budget constraints.