Research

My research areas include behavioral and experimental economics, as well as applied microeconomics, with a particular focus on causal inference for policy evaluation. I have been working with NLP algorithms and LLMs prompts to extract quantitative informtion from qualitative text data.

My interests cover behavioral biases and market frictions that prevent market efficiency and influence household decision-making. I aim to focus more on empirical policy evaluation research, particularly in the environmental sector. 

My current research, which is part of my PhD thesis, examines behavioral dynamics related to decision-making  under asymmetric information at both the household and corporate levels.
The first project involves an online experiment based on a one-shot sender-receiver game that mimics the housing rental market and examines voluntary disclosure dynamics. It aims to provide policy recommendations to address the friction of bounded rationality when real world decision making falls apart from full unraveling prediction (Milgrom, 1981). This study lays the behavioral ground for a follow-up field intervention.

The second chapter presents a novel instrument to measure greenwashing in the energy sector, involving the analysis of web scraped material and observational data. It aims to address  heterogeneity across companies who adopt this practice and the relation between greenwashing and economic performance.

The third project measures the elasticity of energy demand to price fluctuation in the residential market across peak and off-peak hours.
My PhD scholarship is co-financed by the Italian energy company Estra S.p.A., addressing the economic value of sustainability in the energy sector.

In addition, before starting the PhD, I have worked on randomized survey experiments on a variety of topics, including COVID-19 vaccination hesitancy, cognitive biases, trust measurement, and pro-environmental decision-making.