Essays in macroeconomics: insurance, market power, and inequality

This thesis investigates a broad range of topics related to insurance, market power, and inequality, both from an empirical and a theoretical perspective. In the first chapter, I exploit the significant heterogeneity of the shocks hitting Ethiopian households and their heterogeneous response, using relatively recent data (World Bank's LSMS-ISA for households and satellite data for weather shocks). On the one hand, households seem able to insure against most idiosyncratic and mild adverse weather shocks. On the other hand, vulnerability to stronger weather shocks (especially droughts) remains elevated. In the second chapter, starting from firms' individual data, aggregate trends about industry concentration and other proxies of competition are built. This chapter is part of a larger project conducted at the OECD in the Productivity Innovation and Entrepreneurship Division of the STI Directorate The project innovates on the existing literature in its measurement of concentration, aimed at reflecting markets more accurately. On average, aggregate concentration is found to be increasing. In the third chapter, which only lays out some preliminary steps of a more extensive inquiry, I model the heterogeneous effects of aggregate technological progress on individual economic agents and show how this can affect aggregate inequality and other aggregate indicators studied in the macroeconomics literature, such as the entrepreneurship rate and the overall firm distribution. It should be noted, however, that this note is a simple exposition of a possible modelling device rather than a full explanation of these phenomena.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.