While several studies have documented the expansion of the informal sector and its detrimental impact on development, few have noted that informality and wage inequality tend to move together. Using Mexico as a case study, I show that between 1987 and 2002 wage inequality within informal workers accounted for over 60 % of total wage inequality and that the Mexican financial crisis of the mid-1990s increased the share of informal workers and, via this, wage inequality. The results provide supportive evidence that in Mexico higher wage dispersion is one of the channels through which informality negatively affects development.

A significant proportion of the US population does not believe that climate change is a serious problem and immediate action is necessary. We ask whether individuals’ experiences with long-run changes in their local climate can override the power of partisanship that appears to dominate this opinion process. We merge individual-level data on climate change perceptions and the main determinants previously identified by the literature with county-level data on an exogenous measure of local climate change. While we find that local climate change significantly affects perceptions and in the expected direction, partisanship and political ideology maintain the strongest effect. We then field a randomized online experiment to test whether partisanship also drives support for pro-climate policies and the willingness to make environmentally friendly individual choices.

Artificial intelligence (AI) is widely expected to transform healthcare, yet its adoption in clinical practice remains limited. This paper examines the perspectives of Italian clinicians and medical physicists on the drivers of and barriers to AI use. Using an online survey of healthcare professionals across different domains, we find that efficiency gains—such as reducing repetitive tasks and accelerating diagnostics—are the strongest incentives for adoption. However, trust in AI systems, explainability, and the limited availability of AI tools are major obstacles. Respondents emphasized that AI should augment, not replace, medical expertise, calling for participatory development processes where clinicians are actively involved in the design and validation of decision support tools. At the organizational level, the adoption of AI tools is facilitated by innovation-oriented leadership and sufficient resources, while conservative management and economic constraints hinder implementation. The awareness of regulatory frameworks, including the EU AI Act, is moderate, and many clinicians express the need for targeted training to support safe integration. Our findings suggest that the successful adoption of AI in healthcare will depend on building trust through transparency, clarifying legal responsibilities, and fostering organizational cultures that support collaboration between humans and AI. The role of AI in medicine is therefore best understood as a complement to clinical judgment, rather than a replacement.

We study the impact of the COVID-19 pandemic and its side effects on the academic achievement of students in a large university located in a northern Italian region severely affected by the pandemic. Thanks to the richness of our data, we can investigate for the first time the role of two specific channels: the increase in study time due to the exceptionally strict confinement measures adopted and the availability of class recordings. We use administrative data on four cohorts of students merged with original survey data. We adopt a fixed-effect difference-in-differences approach, where we compare the outcomes of students from different enrolment cohorts observed in the same semester of their academic career before and after the COVID-19 outbreak. We find a generalized positive effect of the COVID-19 pandemic on students’ academic achievement in terms of both earned credits and GPA. We provide evidence that both increased study time and the availability of class recordings contributed to mediating this positive effect.