arXiv subjects

Francesco Angelini

Publications and source records attributed to Francesco Angelini.

4 recordsLinked to original sources

arXiv2026

Normative boundaries of AI in scientific work: Evidence from PhD researchers

Artificial intelligence (AI) is increasingly embedded in scientific work, but researchers may not evaluate its use uniformly across research tasks. This study examines task-specific attitudes towards AI among an international, self-selected sample of 3,785 PhD students in STEM and medical and health sciences who participated in Nature's Graduate Survey 2025. We analyse respondents' comfort with using AI for writing a research article, collecting and analysing data, designing experiments, tracking scientific literature, and summarising it. Latent class analysis identifies four distinct attitudinal profiles. The dominant profile reflects a "division of labour," in which AI is widely accepted for literature-related tasks but resisted in activities closely associated with intellectual contribution, such as writing, data analysis, and experimental design. A "status quo" profile is broadly uncomfortable across tasks, an "all-purpose" profile is broadly comfortable, and an "undecided" profile expresses substantial uncertainty. These patterns suggest that attitudes towards AI in research are organised less around a simple acceptance-rejection divide than around task-specific boundaries, likely concerning delegation, authorship, and responsibility. Because the survey measures comfort rather than legitimacy, the profiles are best interpreted as attitudinal configurations with a normative dimension. The findings highlight the importance of task-specific approaches to AI governance, doctoral training, disclosure, and research evaluation.

econ.GN↗

arXiv2025

Strategic Play and Home Advantage: Coaches' Tactical Impact in Serie A

We analyze how coaching strategies affect goal difference and home win probabilities using hand-coded Serie A match commentary (2011/12--2013/14). Our dataset captures in-game dynamics, referee actions, and team behavior. Applying generalized linear, logit, and proportional-odds models with robust and bootstrap standard errors, we uncover stable effects across model averaging. Aggressive opening tactics consistently boost performance, while technical actions like crosses and goal-kicks show distinct patterns. Home advantage remains significant after full control. Our approach reveals the economic logic of real-time coaching, offering a novel, data-driven method to study decision-making under uncertainty in competitive environments.

stat.AP↗

arXiv2023

Testing for Threshold Effects in Presence of Heteroskedasticity and Measurement Error with an application to Italian Strikes

Many macroeconomic time series are characterised by nonlinearity both in the conditional mean and in the conditional variance and, in practice, it is important to investigate separately these two aspects. Here we address the issue of testing for threshold nonlinearity in the conditional mean, in the presence of conditional heteroskedasticity. We propose a supremum Lagrange Multiplier approach to test a linear ARMA-GARCH model against the alternative of a TARMA-GARCH model. We derive the asymptotic null distribution of the test statistic and this requires novel results since the difficulties of working with nuisance parameters, absent under the null hypothesis, are amplified by the non-linear moving average, combined with GARCH-type innovations. We show that tests that do not account for heteroskedasticity fail to achieve the correct size even for large sample sizes. Moreover, we show that the TARMA specification naturally accounts for the ubiquitous presence of measurement error that affects macroeconomic data. We apply the results to analyse the time series of Italian strikes and we show that the TARMA-GARCH specification is consistent with the relevant macroeconomic theory while capturing the main features of the Italian strikes dynamics, such as asymmetric cycles and regime-switching.

econ.EM↗

arXiv2022

Digital leisure and the gig economy: a two-sector model of growth

The process of market digitization at the world level and the increasing and extended usage of digital devices reshaped the way consumers employ their leisure time, with the emergence of what can be called digital leisure. This new type of leisure produces data that firms can use, with no explicit cost paid by consumers. At the same time, the global digitalization process has allowed workers to allocate part of (or their whole) working time to the Gig Economy sector, which strongly relies on data as a production factor. In this paper, we develop a two-sector growth model to study how the above mechanism can shape the dynamics of growth, also assessing how shocks in either the traditional or the Gig Economy sector can modify the equilibrium of the overall economy. We find that shocks in the TFP can crowd out working time from a sector to the other, while shocks on the elasticity of production to data determines a change in the time allocated to digital leisure.

econ.TH↗